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# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from typing import Optional, Union
import yaml
from .utils import get_stripped_tokens, split_max
SYMBOL_TYPES = ["intent", "object", "type", "property", "utterance", "sym", "lookup"]
def parse_pattern(pattern):
"""
Parses a pattern from the Markdown-friendly format to an internal format.
E.g. parse_pattern("show [me](user=CURRENT) the deals I've [won](deal__status)") =
(show me the deals I've {deal__status}, {'user': 'CURRENT'}).
For patters with "assignment patterns" like "show [me](user=CURRENT) the deals" we
transform it into:
"show {user=CURRENT} the deals" with the mapping:
{
"user=CURRENT": "me"
}
:param pattern: The pattern in Markdown-friendly format.
:return: A tuple (pattern, params) where pattern is a pattern containing only
text and {capture} tokens, params is a dict of 'implicit' parameter values.
"""
params = {}
for expr_param in re.findall(r"\[(.*?)\]\((.*?)\)", pattern):
expr = expr_param[0]
param = expr_param[1]
to_replace = f"[{expr}]({param})"
value = f"<CAPTURE>{expr}"
if "=" in param:
value = expr
pattern = pattern.replace(to_replace, "{" + param + "}")
params[param] = value
return pattern, params
def parse_md_lang(file_name, content=None):
"""Returns the language of the .md file.
The content can be also passed as a parameter to skip reading it.
It searches for `lang: XX` in a yaml code block.
By default it assumes the language is English.
"""
if content is None:
file = open(file_name, "r")
content = file.read()
file.close()
yaml_lines = []
in_yaml = False
# We extract meta data and identify language
for line in content.split("\n"):
if not line.strip():
continue
# parse the meta data in yaml
if line.startswith("```yaml"):
in_yaml = True
elif line.endswith("```"):
try:
yaml_data = yaml.safe_load("\n".join(yaml_lines))
except Exception as ex:
raise Exception(f"Error parsing {file_name}: {ex}")
if "lang" in yaml_data:
return yaml_data["lang"]
else:
return "en"
elif in_yaml:
yaml_lines.append(line)
return "en"
def _get_param_type(type_str):
"""Helper to return the type of a parameter.
For now we use a simple heuristic:
1. If it's a primitive type, we leave it as such.
2. If it already has some sort of prefix with ":", we leave it as such.
3. If it starts with lower case and it's not one of the primitive types
then we map it to "type:..."
2. If it starts with an upper case, then we map it to an "object:..."
:param type_str: A string representing the type.
:return: The actual type.
"""
if type_str.lower() in [
"string",
"str",
"text",
"bool",
"boolean",
"timedate",
"datetime",
"int",
"number",
"double",
"currency",
]:
return type_str
if ":" in type_str:
return type_str
if type_str[0].islower():
return f"type:{type_str}"
else:
return f"object:{type_str}"
def _get_symbol_type(sym):
"""Helper to determine if a symbol is prefixed with its type.
:param sym: The name of the symbol.
"""
for symbol_type in SYMBOL_TYPES:
if sym.startswith(f"{symbol_type}:"):
return symbol_type
return None
def _get_typed_symbol_name(sym: str, symbol_type: str):
"""Returns the symbol name prefixed with the type, if not already."""
if _get_symbol_type(sym):
return sym
return f"{symbol_type}:{sym}"
def _record_utterance(
result: dict,
sym: str,
symbol_params: list,
symbol_context: Optional[str],
symbol_meta: dict,
symbol_context_meta: dict,
data: Union[str, dict],
):
"""Helper to record an utterance in the .md parsing result.
It supports both string utterances and rich utterances.
:param result: The result to append the utterance to.
:param sym: The current symbol e.g. "utterance:welcome"
:param symbol_params: Any additional symbol parameters.
It is an array like ["$role=admin", "channel.type=messenger"]
:param symbol_context: An additional contextual expression that must be evaluated to True/False.
:param symbol_meta: Meta information for the symbol in general.
:param symbol_meta: Meta information for the symbol in this context.
:param data: The data for the utterance, either string or something "rich"
:return:
"""
utterance_id = split_max(sym, ":", 1)[1]
if isinstance(data, str):
text = data
# We replace `field` with $field
for param in re.findall(r"`(.*?)`", text):
text = text.replace("`" + param + "`", f"${param}")
utterance_data = {
"text": text,
"_context": {},
}
else:
utterance_data = {
"elements": data if isinstance(data, list) else [data],
"_context": {},
}
# if we have symbol params that start with "$", then we record them as keys
# that need to be matched in the context
for param in symbol_params:
if param.startswith("$") and "=" in param:
key, value = get_stripped_tokens(param[1:].split("="))
utterance_data["_context"][key] = value
# If we have a generic contextual expression, we add it.
# (special case for the 'None' value, which will allow us to reset the context during
# the parsing of same symbol)
if symbol_context and symbol_context.strip() != "None":
utterance_data["_context"]["_expression"] = symbol_context
meta = {}
# If we have meta information, we add it
if symbol_meta:
for k in symbol_meta.keys():
meta[k] = symbol_meta[k]
if symbol_context_meta:
for k in symbol_context_meta.keys():
meta[k] = symbol_context_meta[k]
if meta:
utterance_data["_meta"] = meta
# if we find more than one result, we make it an array
if utterance_id in result["utterances"]:
if not isinstance(result["utterances"][utterance_id], list):
result["utterances"][utterance_id] = [result["utterances"][utterance_id]]
result["utterances"][utterance_id].append(utterance_data)
else:
result["utterances"][utterance_id] = utterance_data
def parse_md_file(file_name, content=None):
"""Parse a Markdown file for patterns.
The content can be also passed as a parameter to skip reading it.
:param file_name: A markdown file
:param content: The content of the file.
:return: A list of patterns.
"""
if content is None:
file = open(file_name, "r")
content = file.read()
file.close()
sym = None
# First we extract the language
file_lang = parse_md_lang(file_name, content)
result: dict = {"patterns": [], "mappings": [], "utterances": {}}
# The supported symbol types are: "intent", "object", "utterance"
symbol_type = "intent"
symbol_params = []
symbol_context = None
symbol_meta = {}
symbol_context_meta = {}
idx = 0
lines = content.split("\n")
while idx < len(lines):
line = lines[idx].strip()
idx += 1
# Skip blank lines
if not line:
continue
if line == "### IGNORE BELOW ###":
break
if line.startswith("#") and not line.startswith("##"):
_type = line[1:].lower().strip()
if _type.startswith("intent"):
symbol_type = "intent"
elif _type.startswith("object"):
symbol_type = "object"
elif _type.startswith("utterance"):
symbol_type = "utterance"
elif _type.startswith("property") or _type.startswith("properties"):
symbol_type = "property"
elif _type.startswith("type"):
symbol_type = "type"
# Deal with intents part
if line.startswith("##") and not line.startswith("###"):
sym = line[2:].strip()
if not sym:
raise ValueError(f"sym cannot be empty at line: {idx + 1}")
symbol_type = _get_symbol_type(sym) or symbol_type
symbol_params = []
symbol_context = None
symbol_meta = {}
symbol_context_meta = {}
# TODO: remove this hack to ignore lines starting with "> "
# it was added for the quick demo
if line.startswith(">") and not line.startswith("> "):
sym = line[1:].strip()
if not sym:
raise ValueError(f"sym cannot be empty at line: {idx + 1}")
# check if we have mappings as parameters
# e.g. symbol(param1: type1, param2: type2, ...)
symbol_params = []
symbol_context = None
if "(" in sym:
sym, symbol_params = split_max(sym, "(", 1)
symbol_params = get_stripped_tokens(
symbol_params.split(")")[0].split(",")
)
# Make sure we have the type of the symbol in the name of the symbol
symbol_type = _get_symbol_type(sym) or symbol_type
sym = _get_typed_symbol_name(sym, symbol_type)
# append the mappings also
for param in symbol_params:
# It's a mapping only if it contains ":"
if ":" in param:
name, value = get_stripped_tokens(split_max(param, ":", 1))
result["mappings"].append((f"{sym}:{name}", _get_param_type(value)))
# Lines starting with "> " represent a mapping for the current symbol
# Record the mappings also
if line.startswith("> "):
parts = get_stripped_tokens(split_max(line[4:], ":", 1))
# We have a special case for the "_context" parameter, which marks the context
# of the symbol. So, we record it separately and use it further down the line.
if parts[0] == "_context":
symbol_context = parts[1]
# We also reset the symbol context meta on context change
symbol_context_meta = {}
continue
# We have another special case for "_meta_*" parameters which mark parameters
# that must be passed as meta information to the NLG and further
if parts[0].startswith("_meta_"):
var_name = parts[0][6:]
var_expr = " ".join(parts[1:])
# we put this either in the symbol meta, or symbol context meta
if symbol_context:
symbol_context_meta[var_name] = var_expr
else:
symbol_meta[var_name] = var_expr
continue
# Make sure we have the type of the symbol in the name of the symbol
sym = _get_typed_symbol_name(sym, symbol_type)
# For objects, we translate the "string" type to "kb:Object:prop|partial"
param_type = _get_param_type(parts[1])
if symbol_type == "object" and param_type in ["string", "text"]:
object_name = split_max(sym, ":", 1)[1]
param_type = f"kb:{object_name}:{parts[0]}|partial"
# TODO: figure out a cleaner way to deal with this
# For the "type:time" type, we transform it into "lookup:time"
if param_type == "type:time":
param_type = "lookup:time"
result["mappings"].append((f"{sym}:{parts[0]}", param_type))
symbol_params.append(parts[0])
elif line.startswith("-") or line.startswith("*"):
if sym is None:
raise ValueError(f"sym is none at line: {idx + 1}")
else:
kind = line[0]
pattern, params = parse_pattern(line[1:].strip())
# If we have a context for the symbol, we record it here
if symbol_context:
params["_context"] = symbol_context
# Make sure we have the type of the symbol in the name of the symbol
sym = _get_typed_symbol_name(sym, symbol_type)
# For intent, objects, properties and types, we record the pattern
if symbol_type in [
"intent",
"object",
"property",
"type",
"sym",
"lookup",
]:
# For "type" symbols, we need to make sure that the capture parameter
# (should be only one) is specified as [bla](type_name=value)
# So, we need to convert:
# - [bla](type_name) -> [bla](type_name=bla)
# - [bla](value) -> [bla](type_name=bla)
# - [bla](value=bla2) -> [bla](type_name=bla2)
#
# Also, we need to make sure we update the pattern itself
if symbol_type == "type":
symbol_name = split_max(sym, ":", 1)[1]
for k in list(params.keys()):
if (
k == "value" or k == symbol_name
) and k not in symbol_params:
value = params[k][9:]
new_k = f"{symbol_name}={value}"
params[new_k] = value
del params[k]
pattern = pattern.replace(f"{{{k}}}", f"{{{new_k}}}")
elif k.startswith("value="):
new_k = f"{symbol_name}{k[5:]}"
params[new_k] = params[k]
del params[k]
pattern = pattern.replace(f"{{{k}}}", f"{{{new_k}}}")
# if the symbol does not start with its type, we prepend it
pattern_config = dict(
lang=file_lang,
type="PATTERN" if kind == "-" else "ARG",
sym=sym,
body=pattern,
params=params,
)
result["patterns"].append(pattern_config)
# For utterances, we record them in the separate dict
elif symbol_type == "utterance":
_record_utterance(
result,
sym,
symbol_params,
symbol_context,
symbol_meta,
symbol_context_meta,
data=pattern,
)
# Here we're dealing with a YAML block
elif line.startswith("```"):
block_lines = []
# then we fetch the whole block
line = lines[idx]
idx += 1
while not line.startswith("```"):
block_lines.append(line)
line = lines[idx]
idx += 1
# we also skip the last ``` line
idx += 1
# at this point we need to parse the yaml block
d = yaml.safe_load("\n".join(block_lines))
# If we don't have an active symbol, we skip
# (maybe we're dealing with the `lang` tag)
if not sym:
continue
sym = _get_typed_symbol_name(sym, symbol_type)
# Currently we only support the YAML block for utterances
if symbol_type == "utterance":
_record_utterance(
result,
sym,
symbol_params,
symbol_context,
symbol_meta,
symbol_context_meta,
data=d,
)
else:
raise Exception(f"YAML blocks for symbol {sym} not supported.")
return result
__all__ = ["parse_md_file"]
| NeMo-Guardrails-main | nemoguardrails/language/comd_parser.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import re
from ast import literal_eval
from typing import List, Optional
import yaml
from .utils import (
char_split,
extract_main_token,
extract_topic_object,
get_first_key,
get_numbered_lines,
get_stripped_tokens,
params_tokenize,
parse_package_name,
remove_token,
split_max,
string_hash,
word_split,
ws_tokenize,
)
# The full list of valid main tokens
VALID_MAIN_TOKENS = [
# Global
"import",
"include",
"use",
"define",
# Branching
"when",
"else when",
# Elements
"user",
"bot",
"event",
"do",
"flow",
"allow",
"accept",
"disallow",
"deny",
"reject",
"goto",
"go to",
"run",
"set",
"expect",
"label",
"checkpoint",
# "check",
"if",
"else",
"else if",
"any",
"infer",
"pass",
"continue",
"break",
"stop",
"abort",
"return",
"done",
"context",
"meta",
"log",
"while",
"for",
"foreach",
]
class ColangParser:
def __init__(
self,
filename: str,
content: str,
include_source_mapping: bool = False,
snippets: Optional[dict] = None,
):
"""Parses a file in .co format to a YAML flows format
:param filename: The name of the file.
:param content: The content.
:param include_source_mapping: Whether to include source mapping into the flow elements.
:param snippets: Snippets to use when parsing the file.
"""
self.filename = filename
self.content = content
self.include_source_mapping = include_source_mapping
self.snippets = snippets
self.lines = get_numbered_lines(self.content)
self.next_line = {}
self.current_line_idx = 0
self.current_line = {}
self.current_indentation = 0
self.current_namespace = ""
self.current_namespaces = []
self.current_indentations = []
# What is the level of indentation required for params (1=next indentation, 2=second indentation)
# In general, the params are included with an indentation level, however, in certain cases
# like in a `when x`, if there are parameters required for `x`, they would need two indentation levels
# to distinguish them from what would follow naturally after the current statement e.g. a branch
self.current_params_indentation = 1
# The current element i.e. user, bot, event, if ...
self.current_element = None
# The flows that have been parsed
self.flows = {}
# The imports that were found
self.imports = []
# This is where we gather intent/utterances content
# TODO: replace this temporary solution with something more robust
self.md_content = []
# The stack of branches
self.branches = []
# The stack of ifs
self.ifs = []
# Used to keep track of the last flow interrupt/abort event
self.last_event_flow_element = None
self.symbol_name = None
self.symbol_type = ""
# Whether the current flow is an interruption flow. It influences how the
# flow events are parsed i.e. whether they have `this` set to True or False.
# For interruption flows it is set to `False`.
self.is_interruption_flow: bool = False
# If the current flow is a "response" flow then it influences how the "stop"
# keyword is parsed.
self.is_response_flow: bool = False
# The current main token
self.main_token = ""
# The current text of the line
self.text = ""
# The current mode of the parser
self.mode = "colang"
def _normalize_line_text(self):
"""Applies a series of normalizations for a line of colang."""
rules = []
if self.mode == "colang":
# First, we get rid of extra spaces
self.text = " ".join(ws_tokenize(self.text))
var_name = r"\$[\w.]+"
# The label that should be used for "..." is decided dynamically, based
# on what's on the next line
ellipsis_label = "auto_resume"
if self.next_line and (
self.next_line["text"].startswith("bot ")
or " bot " in self.next_line["text"]
):
ellipsis_label = "force_interrupt"
# Regex normalization rules
rules = [
# get rid of any ":" at end of line, for compatibility with python
(r":$", r""),
# Transform "infer x" into "infer event x" when infer is not followed by "user"/"bot"
(r"^infer (?!user )(?!bot )(?!event )", "infer event "),
# Transfer "(else )?when x" into "(else )?when event x"
(
r"^when (?!user )(?!bot )(?!event )(?!flow )(?!no )(?!any)",
"when event ",
),
(
r"^else when (?!user )(?!bot )(?!event )(?!flow )(?!no )(?!any)",
"else when event ",
),
# replace def with define
(r"^def ", r"define "),
# Get rid of "says" after user/bot
(r"^(user|bot) says(\b)", r"\1\2"),
(r"^when (user|bot) says(\b)", r"when \1\2"),
(r"^else when (user|bot) says(\b)", r"else when \1\2"),
# Turn `user/bot something` into `user/bot *`
(r"^(user|bot) something(?: else)?(\b)", r"\1 *\2"),
(r"^when (user|bot) something(?: else)?(\b)", r"when \1 *\2"),
(r"^else when (user|bot) something(?: else)?(\b)", r"else when \1 *\2"),
# Turn any other "something" into "..."
(
r'^((?:else )?(?:when )?(?:user|bot)\s+(?:[^"]*)\s+)something(?: else)?(\b)',
r"\1...\2",
),
# Turn "execute"/"exec" into "run"
(r"^execute ", r"run "),
(r"^exec ", r"run "),
# Alternative syntax for label
(r"^set ([^$]+) label (?:to )?(\$.*)", r"label \1 \2"),
# normalize running actions i.e. add the `run` in front
(r"^(\$[\w.]+) = (?:run|execute|exec) ", r"run \1 = "),
# normalize `set` instructions i.e. add the missing `set`
(r"^(\$[\w.]+) = (?!run )(?!execute )(?!exec )", r"set \1 = "),
# priority shorthand, from "priority 2" to 'meta {"priority": 2}'
(r"^\s*priority\s*([\.\d]+)\s*$", r'meta {"priority": \1}'),
# += operator
(r"^(\$[\w.]+)\s*\+=", r"set \1 = \1 +"),
# -= operator
(r"^(\$[\w.]+)\s*\-=", r"set \1 = \1 -"),
# Turn 'new' into 'infer'
(r"^new( |$)", r"infer\1"),
(r"^create( |$)", r"infer\1"),
]
elif self.mode == "markdown":
rules = [
# Turn 'in the context of' into 'context'
(r"^(?:in )?(?:the )?context(?: of)?(.*)$", r"context\1"),
# Turn "expecting user x" into 'expecting("x")
(r"expecting user (.*)$", r'expecting("\1")'),
# Turn "context user x" into 'expecting("x")
(r"context user (.*)$", r'context expecting("\1")'),
]
for rule in rules:
self.text = re.sub(rule[0], rule[1], self.text)
# We have a specific case for anonymous flows.
# We compute a hash from the content to infer the name
if self.mode == "colang" and self.text.strip() == "define flow":
# We add a hash computed from all the lines with a higher indentation level
flow_text = ""
ll = self.current_line_idx + 1
while (
ll < len(self.lines)
and self.lines[ll]["indentation"] > self.current_line["indentation"]
):
flow_text += self.lines[ll]["text"]
ll += 1
flow_hash = string_hash(flow_text)
self.text += " anonymous-" + flow_hash
# Below are some more advanced normalizations
if self.mode == "colang":
# TODO: this is a bit hackish, to think of a better way
# if we have an "else" for a when, we turn it into "else when flow resuming"
if self.main_token == "else":
if (
len(self.ifs) == 0
or self.ifs[-1]["indentation"] <= self.current_indentation
):
self.text = "else when flow resuming"
def _fetch_current_line(self):
self.current_line = self.lines[self.current_line_idx]
self.current_indentation = self.current_line["indentation"]
self.current_params_indentation = 1
self.next_line = (
self.lines[self.current_line_idx + 1]
if self.current_line_idx < len(self.lines) - 1
else None
)
# Normalize the text of the line
self.text = self.current_line["text"]
# Extract the main token
self.main_token = extract_main_token(self.text)
# Apply the normalization step
self._normalize_line_text()
# Extract the main token again, in case the normalization changed the text
self.main_token = extract_main_token(self.text)
def _create_namespace(self, namespace):
# First we need to pop all the namespaces at deeper indentation
while (
len(self.current_indentations) > 0
and self.current_indentations[-1] > self.current_line["indentation"]
):
self.current_indentations.pop()
self.current_namespaces.pop()
# Now, append the new one
self.current_namespaces.append(namespace)
self.current_namespace = ".".join(self.current_namespaces)
self.current_indentation = self.next_line["indentation"]
self.current_indentations.append(self.next_line["indentation"])
# Reset the branches and the ifs on a new flow
self.branches = []
self.ifs = []
self.current_line_idx += 1
def _ignore_block_body(self):
self.current_line_idx += 1
# We also skip all indented lines i.e. the body of the snippet
while self.current_line_idx < len(self.lines):
if self.lines[self.current_line_idx]["indentation"] > 0:
self.current_line_idx += 1
else:
break
def _include_source_mappings(self):
# Include the source mapping information if required
if self.include_source_mapping:
if self.current_element and "_source_mapping" not in self.current_element:
self.current_element["_source_mapping"] = {
"filename": self.filename,
"line_number": self.current_line["number"],
"line_text": self.current_line["text"],
"comment": self.current_line.get("comment"),
}
def _record_import(self):
self.text = remove_token(self.main_token, self.text)
package_name = parse_package_name(self.text)
if package_name not in self.imports:
self.imports.append(package_name)
self.current_line_idx += 1
def _check_flow_exists(self):
if self.main_token in [
"user",
"bot",
"event",
"if",
"while",
"for",
"when",
"any",
"run",
"label",
"set",
"goto",
"go to",
"do",
"flow",
"continue",
"break",
"stop",
"abort",
"done",
"return",
"check",
"meta",
"global",
"var",
"local",
"param",
"log",
]:
# we make sure the current flow has been created
if self.current_namespace not in self.flows:
current_flow = []
self.flows[self.current_namespace] = current_flow
self.current_element = {}
# initialize the branch also
self.branches = [
{
# TODO: replace this with the elements array when migrating the
# flow to a dict
"elements": current_flow,
"indentation": self.current_line["indentation"],
}
]
def _check_ifs_and_branches(self):
# If the current indentation is lower than the branch, we pop branches
while (
len(self.branches) > 0
and self.current_indentation < self.branches[-1]["indentation"]
):
self.branches.pop()
# If the current indentation is lower than then the if, we pop the if
while (
len(self.ifs) > 0
and self.current_indentation < self.ifs[-1]["indentation"]
and (
self.main_token != "else"
and self.main_token != "else if"
or self.current_indentation < self.ifs[-1]["keyword_indentation"]
)
):
self.ifs.pop()
def _extract_markdown(self):
"""Helper to extract markdown content.
The `self.symbol_type` and `self.symbol_name` must be set correctly before calling this.
It will start with the next line, and use it as a reference for the indentation level.
As long as the indentation is higher, it will keep parsing the lines as markdown.
"""
yaml = False
self.md_content.append(f"## {self.symbol_type}:{self.symbol_name}")
self.current_line_idx += 1
self.mode = "markdown"
md_indentation = None
# The indentation levels on which we have "if"s
if_levels = []
last_if_level = 0
# The current array of context expressions, per if level.
# all the ones up to the last one must be negated.
expressions = {}
while self.current_line_idx < len(self.lines):
self._fetch_current_line()
md_line = self.text.strip()
# record the indentation on the first line
if md_indentation is None:
md_indentation = self.current_line["indentation"]
tokens = word_split(md_line, " ")
# Check if we're dealing with a parameter definition
if tokens[0] in [
"param",
"parameter",
"entity",
"property",
"attribute",
"attr",
"prop",
]:
assert (
(len(tokens) == 4) or (len(tokens) == 5) and tokens[2] == "as"
), "Invalid parameters syntax."
# If we have 5 tokens, we join the last two with ":".
# This is for support for "define X as lookup Y"
if len(tokens) == 5:
tokens[3] += ":" + tokens[4]
tokens = tokens[0:4]
# We make sure we remove the "$" from the param name if it's used
param_name = tokens[1]
if param_name[0] == "$":
param_name = param_name[1:]
self.md_content.append(f"> {param_name}: {tokens[3]}")
elif tokens[0] == "set":
var_name = tokens[1][1:]
assert tokens[2] in ["=", "to"]
self.md_content.append(f"> _meta_{var_name}: {' '.join(tokens[3:])}")
elif tokens[0] in ["context"]:
self.md_content.append(f"> _context: {' '.join(tokens[1:])}")
elif tokens[0] in ["if", "else"] and self.symbol_type == "utterance":
# If we were in yaml mode, we stop
if yaml:
self.md_content.append("```")
yaml = False
if_level = self.current_indentation
last_if_level = if_level
if if_level not in if_levels:
if_levels.append(if_level)
# We turn if's into contexts
if tokens[0] == "if" or (
len(tokens) > 1 and tokens[0] == "else" and tokens[1] == "if"
):
if tokens[0] == "if":
expr = " ".join(tokens[1:])
# We reset the expressions at a level when we get to an if
expressions[if_level] = [expr]
else:
expr = " ".join(tokens[2:])
if len(expressions[if_level]) > 0:
# We need to negate the last one before adding the new one
expressions[if_level][
-1
] = f"not({expressions[if_level][-1]})"
expressions[if_level].append(expr)
else:
# if we're dealing with a simple else, we just negate the last expression too
expressions[if_level][-1] = f"not({expressions[if_level][-1]})"
# Extract all expressions that apply to this level
all_expressions = []
for _if_level in if_levels:
if _if_level <= if_level:
all_expressions.extend(expressions[_if_level])
self.md_content.append(f"> _context: {' and '.join(all_expressions)}")
elif tokens[0] in ["bot"]:
# We need to start a new flow that maps one on one the intent with
# what follows next. The easiest way, is to actually alter the input
# and add the missing lines.
# We create a flow with the name `{self.symbol_name}_direct`.
self.lines.insert(
self.current_line_idx,
{
"text": f"{self.symbol_name}_direct:",
# We keep the line mapping the same
"number": self.current_line["number"],
"indentation": self.current_indentation - 2,
},
)
self.lines.insert(
self.current_line_idx + 1,
{
"text": f"user {self.symbol_name}",
"number": self.current_line["number"],
"indentation": self.current_indentation,
},
)
# We stop with the markdown parsing here
self.mode = "colang"
return
else:
# If we don't have strings, there are two cases:
# 1. we have an yaml object (rich utterance)
# 2. we're dealing with multi intents and we have a reference to another intent
# To figure out if we're dealing with an yaml, we check if we have ":" in the text.
# If it's yaml, it will be a key definition for sure on the first line.
if not yaml and self.text[0] != '"':
# We use the word_split to avoid the ":" in potential strings
parts = word_split(self.text, ":")
if len(parts) > 1 or len(parts) == 1 and self.text.endswith(":"):
yaml = True
self.mode = "yaml"
self.md_content.append("```yaml")
if yaml:
# we don't add the stripped version as we need the proper indentation
self.md_content.append(
f"{' ' * self.current_indentation}{self.text}"
)
else:
# we split the line in multiple components separated by " and "
parts = word_split(md_line, " and ")
# Apply some transformations for each component:
# - remove double quotes
# - replace $xyz with [x](xyz)
# - replace references to other intents with {intent:x}
for i in range(len(parts)):
parts[i] = parts[i].strip()
# get rid of double quotes
if parts[i][0] == '"':
assert parts[i][-1] == '"', 'Invalid syntax, missing "'
parts[i] = parts[i][1:-1]
# We also transform "$xyz" into "[x](xyz)", but not for utterances
if self.symbol_type != "utterance":
replaced_params = {}
for param in re.findall(
r"\$([^ \"'!?\-,;</]*(?:\w|]))", parts[i]
):
if param not in replaced_params:
parts[i] = parts[i].replace(
f"${param}", f"[x]({param})"
)
replaced_params[param] = True
else:
# We're dealing with another intent here, so we prefix with "sym:"
# and we're also replacing spaces with "|".
parts[i] = "{intent:" + parts[i].replace(" ", "|") + "}"
# Put it all back together by joining with the intent and
md_line = " {intent:and} ".join(parts)
# If we went below the last if indentation level, we need to issue
# a new _context line.
if self.current_indentation <= last_if_level:
all_expressions = []
for _if_level in if_levels:
if _if_level < self.current_indentation:
all_expressions.extend(expressions[_if_level])
# If we're left with nothing, we just set a simple "True" expression
if len(all_expressions) == 0:
self.md_content.append(f"> _context: True")
else:
self.md_content.append(
f"> _context: {' and '.join(all_expressions)}"
)
self.md_content.append(f" - {md_line}")
self.current_line_idx += 1
if self.current_line_idx < len(self.lines):
self.next_line = self.lines[self.current_line_idx]
else:
self.next_line = None
# Get out of the markdown mode
if not self.next_line or self.next_line["indentation"] < md_indentation:
if yaml:
self.md_content.append("```")
self.md_content.append("")
self.mode = "colang"
return
def _process_define(self):
# TODO: deal with "when" after "else when"
# If there is no next line, or it is not indented, and we have a multi-intent
# definition, then we add a default line.
# i.e. if we have "define user X and Y" we add a line with "X and Y"
if (
self.next_line is None
or self.next_line["indentation"] <= self.current_line["indentation"]
and self.text.startswith("define user")
):
self.next_line = {
"text": self.text.replace("define user", ""),
# We keep the line mapping the same
"number": self.current_line["number"],
# We take the indentation of the flow elements that follow
"indentation": self.current_line["indentation"] + 2,
}
self.lines.insert(self.current_line_idx + 1, self.next_line)
assert (
self.next_line["indentation"] > self.current_line["indentation"]
), "Expected indented block after define statement."
self.text = remove_token("define", self.text)
# Extract what we define
define_token = extract_main_token(self.text)
self.symbol_name = remove_token(define_token, self.text)
allowed_tokens = [
"bot",
"user",
"flow",
"subflow",
"action",
]
# We extract the modifiers if they are present e.g. test, interruption
modifiers = {}
while define_token not in allowed_tokens:
# For interruption flows i.e interruption handlers
if define_token in ["interruption", "repair"]:
modifiers["is_interruption_flow"] = True
# For test flows
elif define_token in ["test"]:
modifiers["is_test"] = True
# For non-interruptable flows
elif define_token in [
"non-interruptable",
"noninterruptable",
"continuous",
]:
modifiers["interruptable"] = False
# For recursive flows
elif define_token in ["recursive", "parallel"]:
modifiers["allow_multiple"] = True
# For extension flows
elif define_token in ["extension"]:
modifiers["is_extension"] = True
# For sample flows
elif define_token in ["sample"]:
modifiers["is_sample"] = True
# For response flows
elif define_token in ["response"]:
modifiers["is_response"] = True
else:
raise Exception(f'Unknown token: "{define_token}"')
# Remove the modifier token
self.text = remove_token(define_token, self.text)
define_token = extract_main_token(self.text)
self.symbol_name = remove_token(define_token, self.text)
# During normal parsing, we ignore the snippets
if define_token == "snippet" or define_token == "action":
self._ignore_block_body()
return
# For the define flow syntax, we transform it into the shorthand one, and reprocess
if define_token in ["flow", "subflow"]:
# We add a ":" in front, to make sure that even if it starts with a valid main token
# e.g. "define" it will not be interpreted as such
self.lines[self.current_line_idx]["text"] = f":{self.symbol_name}:"
# if we're dealing with a subflow, we also add the meta information
if define_token == "subflow":
modifiers["subflow"] = True
# If we have modifiers, we add them as the meta information
if modifiers:
# If we don't have a meta block, we add it
if self.lines[self.current_line_idx + 1]["text"] != "meta":
self.lines.insert(
self.current_line_idx + 1,
{
"text": f"meta",
# We keep the line mapping the same
"number": self.current_line["number"],
# We take the indentation of the flow elements that follow
"indentation": self.next_line["indentation"],
},
)
meta_indentation = self.next_line["indentation"] + 2
else:
meta_indentation = self.lines[self.current_line_idx + 2][
"indentation"
]
# We add all modifier information
for modifier in modifiers.keys():
value = modifiers[modifier]
self.lines.insert(
self.current_line_idx + 2,
{
"text": f"{modifier}: {value}",
# We keep the line mapping the same
"number": self.current_line["number"],
# Increase the indentation a bit
"indentation": meta_indentation,
},
)
# Record whether this is an interruption flow or not
self.is_interruption_flow = False
if "is_interruption_flow" in modifiers:
self.is_interruption_flow = modifiers["is_interruption_flow"]
self.is_response_flow = False
if "is_response" in modifiers:
self.is_response_flow = modifiers["is_response"]
return
# If we're dealing with a topic, then we expand the flow definition
if define_token == "topic":
self._insert_topic_flow_definition()
return
# Compute the symbol type
if define_token == "user":
self.symbol_type = "intent"
# We also normalize the name and replace spaces with "|"
self.symbol_name = "|".join(word_split(self.symbol_name, " "))
elif define_token == "bot" or define_token == "template":
self.symbol_type = "utterance"
else:
# For type, lookup, token, it's the same
self.symbol_type = define_token
# Finally, we parse the markdown content
self._extract_markdown()
def _extract_indentation_levels(self):
"""Helper to extract the indentation levels higher than the current line."""
indentations = []
p = self.current_line_idx + 1
while (
p < len(self.lines)
and self.lines[p]["indentation"]
> self.lines[self.current_line_idx]["indentation"]
):
if self.lines[p]["indentation"] not in indentations:
indentations.append(self.lines[p]["indentation"])
p += 1
indentations.sort()
return indentations
def _extract_indented_lines(self):
"""Helper to extract the indented lines, relative to the current line.
It also needs to take into account if the params should be indented one level or two.
"""
initial_line_idx = self.current_line_idx
p = self.current_line_idx + 1
indented_lines = []
while (
p < len(self.lines)
and self.lines[p]["indentation"]
> self.lines[self.current_line_idx]["indentation"]
):
indented_lines.append(self.lines[p])
p += 1
# If the params should be on the second level of indentation,
# we check if there is a lower indentation than the first one
if len(indented_lines) > 0 and self.current_params_indentation == 2:
# Take the indentation of the first line, and look for one lower than that
params_indentation = indented_lines[0]["indentation"]
i = 0
while i < len(indented_lines):
if indented_lines[i]["indentation"] < params_indentation:
break
i += 1
# If we did not reach the end, then we only take the first i lines as the ones
# for the indentation
if i < len(indented_lines):
indented_lines = indented_lines[0:i]
self.current_line_idx = initial_line_idx + i - 1
else:
# in this case, we actually didn't have indented lines
indented_lines = []
self.current_line_idx = initial_line_idx
else:
# Advance to the last process lined
self.current_line_idx = p - 1
return indented_lines
def _extract_params(self, param_lines: Optional[List] = None):
"""Helper to parse additional parameters for an element.
We transform the indented lines into valid YAML format. It should end up being a dict
and not a list.
:param param_lines: If provided, these lines will be used to extract the params.
"""
# Fetch the param lines if not already provided
if param_lines is None:
param_lines = self._extract_indented_lines()
if not param_lines:
return
# TODO: figure out a better heuristic
# We need to know if advanced features are use, to skip certain transformations
raw_yaml = "\n".join([line["text"] for line in param_lines])
advanced_yaml = "{" in raw_yaml or "[" in raw_yaml
# We also apply a series of transformations
for i in range(len(param_lines)):
param_line = param_lines[i]
next_param_line = param_lines[i + 1] if i < len(param_lines) - 1 else None
if not advanced_yaml:
# First, we do some normalization using regex
rules = [
# parameters set with "set" are meta parameters and we prefix them with "_"
(r"^set \$?([\w.]+) to", r"_\1:"),
(r"^set \$?([\w.]+) =", r"_\1:"),
(r'^(".*")', r"_text: \1"),
]
line_text = param_line["text"]
for rule in rules:
line_text = re.sub(rule[0], rule[1], line_text)
tokens = params_tokenize(line_text)
# inline list e.g. `quick_replies: "good", "bad"`
if len(tokens) > 3 and tokens[1] == ":" and tokens[3] == ",":
tokens = [*tokens[0:2], "[", *tokens[2:], "]"]
elif len(tokens) > 2 and tokens[1] != ":" and tokens[2] == ",":
tokens = [tokens[0], ":", "[", *tokens[1:], "]"]
# add the missing ":"
elif len(tokens) == 2 and tokens[1] != ":" and tokens[0] != "-":
tokens = [tokens[0], ":", tokens[1]]
# turn "=" into ":"
elif len(tokens) == 3 and tokens[1] == "=":
tokens[1] = ":"
# turn single element into a key or a list element
# TODO: add support for list of dicts as this is not yet supported
elif len(tokens) == 1:
if (
next_param_line is None
or next_param_line["indentation"] <= param_line["indentation"]
):
tokens = ["-", tokens[0]]
else:
tokens = [tokens[0], ":"]
param_line["text"] = " ".join(tokens)
# Next, we process all the lines and create a valid YAML block
base_indentation = param_lines[0]["indentation"]
# yaml_lines = [" " * (line["indentation"] - base_indentation) + line["text"] for line in param_lines]
# More verbose way that transpiles correctly
yaml_lines = []
for line in param_lines:
line_indent = ""
for i in range(line["indentation"] - base_indentation):
line_indent += " "
yaml_lines.append(line_indent + line["text"])
yaml_block = "\n".join(yaml_lines)
yaml_value = yaml.safe_load(yaml_block)
# NOTE: this is needed to parse the syntax that is used for training LLM
# e.g.
# user "Set the alarm for 6am"
# request set alarm
if isinstance(yaml_value, str):
yaml_value = {"$0": yaml_value}
# self.current_element.update(yaml_value)
for k in yaml_value.keys():
# if the key tarts with $, we remove it
param_name = k
if param_name[0] == "$":
param_name = param_name[1:]
self.current_element[param_name] = yaml_value[k]
def _is_test_flow(self):
"""Returns true if the current flow is a test one.
NOTE:
This will not work correctly if the current position is nested inside another branch
like an "if". But currently, it is meant for test flows, which should be linear.
"""
branch_elements = self.branches[-1]["elements"]
if len(branch_elements) == 0 or get_first_key(branch_elements[0]) != "meta":
return False
if "is_test" in branch_elements[0]["meta"]:
return branch_elements[0]["meta"]["is_test"]
return False
def _is_sample_flow(self):
"""Returns true if the current flow is a sample one.
NOTE:
This will not work correctly if the current position is nested inside another branch
like an "if". But currently, it is meant for test flows, which should be linear.
"""
branch_elements = self.branches[-1]["elements"]
if len(branch_elements) == 0 or get_first_key(branch_elements[0]) != "meta":
return False
if "is_sample" in branch_elements[0]["meta"]:
return branch_elements[0]["meta"]["is_sample"]
return False
# PARSE METHODS FOR SPECIFIC SYMBOLS
def _parse_when(self):
# TODO: deal with "when" after "else when"
assert (
self.next_line["indentation"] > self.current_line["indentation"]
), "Expected indented block after 'when' statement."
# Create the new branch
new_branch = {"elements": [], "indentation": self.next_line["indentation"]}
# # on else, we need to pop the previous branch
# if self.main_token == "else when":
# branches.pop()
# Add the array of elements directly into the parent branch
self.branches[-1]["elements"].append(new_branch["elements"])
# And append it as the last one
self.branches.append(new_branch)
# A bit hackish, but we now change the text to get rid of the main token
# Essentially, we make the following transformation
# when user greeting
# bot "hi"
# -->
# user greeting
# bot "hi"
if self.main_token == "when":
self.text = remove_token("when", self.text)
# if we have a "when no" then we transform it
if self.text.startswith("no "):
self.text = remove_token("no", self.text)
# And we add the
# continue
# else
# ...
self.lines.insert(
self.current_line_idx + 1,
{
"text": f"continue",
# We keep the line mapping the same
"number": self.current_line["number"],
"indentation": self.next_line["indentation"],
},
)
self.lines.insert(
self.current_line_idx + 2,
{
"text": f"else",
# We keep the line mapping the same
"number": self.current_line["number"],
"indentation": self.current_indentation,
},
)
# refresh the next line as it was changed
self.next_line = self.lines[self.current_line_idx + 1]
else:
self.text = remove_token("when", remove_token("else", self.text))
# We extract all the indentation levels and set the branch at the first
branch_indentations = self._extract_indentation_levels()
self.current_indentation = branch_indentations[0]
new_branch["indentation"] = branch_indentations[0]
# Also, mark that the params should be on the second indentation level for this line
self.current_params_indentation = 2
self.main_token = extract_main_token(self.text)
def _parse_user(self):
# Check if we're dealing with a "or" of intents
# in which case we transform it into a "any"
or_intents = False
if " or " in self.text:
parts = word_split(self.text, " or ")
if len(parts) > 1:
or_intents = True
p = self.current_line_idx + 1
self.lines.insert(
p,
{
"text": f"any",
# We keep the line mapping the same
"number": self.current_line["number"],
"indentation": self.current_indentation,
},
)
p += 1
for part in parts:
self.lines.insert(
p,
{
"text": part,
# We keep the line mapping the same
"number": self.current_line["number"],
# This just needs to be bigger then the next indentation
"indentation": self.current_indentation + 8,
},
)
p += 1
# Otherwise, it's a normal intent
if not or_intents:
user_value = split_max(self.text, " ", 1)[1].strip()
# Support for "user intent_name as $var" syntax
re_as_variable = r"(?P<intent>.*?)(?: as \$(?P<var>.+)$)"
as_var_match = re.match(re_as_variable, user_value)
as_var = None
# If we have a match, we save the info and update the intent
if as_var_match:
gd = as_var_match.groupdict()
as_var = gd["var"]
user_value = gd["intent"]
# Check if the with syntax is used for parameters
re_with_params_1 = r"(?P<intent>.*?)(?: (?:with|for) (?P<vars>\$.+)$)"
re_with_params_2 = (
r"(?P<intent>.*?)(?: (?:with|for) (?P<vars>\w+\s*=\s*.+)$)"
)
match = re.match(re_with_params_1, user_value) or re.match(
re_with_params_2, user_value
)
if match:
d = match.groupdict()
# in this case we convert it to the canonical "(" ")" syntax
user_value = f"{d['intent']}({d['vars']})"
# Deal with arrays self.current_line_idx.e. multi intents
if user_value[0] == "[":
user_value = get_stripped_tokens(user_value[1:-1].split(","))
self.current_element = {"user": user_value}
# if it was a quoted text, we mark that we need to resolve the intent
if user_value[0] in ["'", '"']:
user_value = user_value[1:-1]
self.current_element["user"] = user_value
# This is the special marker that this is an example for an intent
self.current_element["_is_example"] = True
# parse additional parameters if it's the case
if (
self.next_line
and self.next_line["indentation"] > self.current_indentation
):
self._extract_params()
# Add to current branch
self.branches[-1]["elements"].append(self.current_element)
# If we have an "as $var" statement, we record the info
if as_var:
self.current_element["_as_context_variable"] = as_var
def _parse_bot(self):
"""Parser for the `bot X` syntax.
The syntax is quite flexible, see example_45.co for various ways.
A meta syntax is the following:
bot <utterance_id>? "sample_utterance"? (with/for $param=value)?
$?param (:|=) value
"sample_utterance_1"?
"sample_utterance_2"?
When id and sample utterance is included, an utterance definition is also
generated as markdown content.
"""
self.current_element = {}
# We're dealing with a rich self.current_element with parameters on the next self.lines
if self.text.strip() == "bot":
self._extract_params()
if "_type" not in self.current_element:
self.current_element["_type"] = "element"
self.current_element = {"bot": self.current_element}
else:
# We put this first, so it is the first key
self.current_element["bot"] = None
text = self.text.strip()
text = remove_token("bot", text)
# If it's rich element, we don't do anything
if text[0] == "{":
self.current_element["bot"] = literal_eval(text)
else:
utterance_text = None
utterance_id = None
# re_params_at_end = r'^.* ((?:with|for) (?:,?\s*\$?[\w.]+\s*(?:=\s*(?:"[^"]*"|\$[\w.]+|[-\d.]+))?)*)$'
re_param_def = r'\$?[\w.]+\s*(?:=\s*(?:"[^"]*"|\$[\w.]+|[-\d.]+))?'
re_first_param_def_without_marker = (
r'\$?[\w.]+\s*=\s*(?:"[^"]*"|\$[\w.]+|[-\d.]+)'
)
re_first_param_def_just_variable = r"\$[\w.]+"
re_first_param_def = rf"(?:(?:{re_first_param_def_just_variable})|(?:{re_first_param_def_without_marker}))"
# IMPORTANT! We must not mix escapes with r"" formatted strings; they don't transpile correctly to js
# Hence, why we've extracted re_comma_space separately
re_comma_space = r"\s*,\s*"
re_params_at_end = f"^.* ((?:with|for) {re_first_param_def}(?:{re_comma_space}{re_param_def})*)$"
# Recognizes a parameter assignment
re_param_value = r'\s*\$?([\w.]+)\s*[:=]\s*("[^"]*"|\$[\w.]+|[-\d.]+)'
# First, we need to determine if we have parameters at the end
if re.match(re_params_at_end, text):
# and if we do, we need to extract them
params_str = re.findall(re_params_at_end, text)
# Should be only one
assert (
len(params_str) == 1
), f"Expected only 1 parameter assignment, got {len(params_str)}."
params_str = params_str[0]
# remove the parameters from the string
text = text[0 : -1 * len(params_str)].strip()
# now, get rid of the with/for
params_str = split_max(params_str, " ", 1)[1].strip()
param_parts = word_split(params_str, ",")
idx = 0
for param_part in param_parts:
k_vs = re.findall(re_param_value, param_part)
# If it's a parameter given with name and value, we use that
if len(k_vs) > 0:
# Should be only one
for item in k_vs:
k = item[0]
v = item[1]
if v[0] == '"':
v = v[1:-1]
self.current_element[k] = v
else:
# Otherwise, we use it as the value and try to infer the name
# and if not, we just use it as a positional parameter
v = param_part
# TODO: should cross check if there is an actual variable for
# the specified utterance
if v.startswith("$") and "." not in v:
k = v[1:]
else:
k = f"${idx}"
self.current_element[k] = v
idx += 1
# Next we check if we have an utterance text
results = re.findall(r'"[^"]*"', text)
if len(results) > 0:
assert (
len(results) == 1
), f"Expected only 1 parameter assignment, got {len(results)}."
utterance_text = results[0]
# And remove it from the text
text = text.replace(utterance_text, "").strip()
# If we're left with something, it is the utterance id
if len(text) > 0:
utterance_id = text
initial_line_idx = self.current_line_idx
# Next, we look at the indented lines, to decide if there are additional
# parameters, or additional examples
indented_lines = self._extract_indented_lines()
# First, we expect to have param lines, and then example lines, so, we try
# to detect the first example line
i = 0
while i < len(indented_lines):
line_text = indented_lines[i]["text"].strip()
tokens = line_text.split(" ")
if tokens[0] == "if" or tokens[0][0] == '"':
break
i += 1
# If we have param lines, we extract the parameters
if i > 0:
self._extract_params(indented_lines[0:i])
# If we have an utterance id and at least one example, we need to parse markdown.
# However, we only do this for non-test flows
if utterance_id is not None and (
utterance_text is not None or i < len(indented_lines)
):
if not self._is_test_flow() and not self._is_sample_flow():
# We need to reposition the current line, before the first line we need to parse
self.current_line_idx = initial_line_idx + i
if utterance_text is not None:
self.lines.insert(
self.current_line_idx + 1,
{
"text": f"{utterance_text}",
# We keep the line mapping the same
"number": self.current_line["number"],
"indentation": self.current_indentation + 2
if i == len(indented_lines)
else indented_lines[i]["indentation"],
},
)
self.symbol_type = "utterance"
self.symbol_name = utterance_id
self._extract_markdown()
# The extract markdown will move the current line to the last processed one
# so we move back one position, as it will be advanced automatically in
# the main loop
self.current_line_idx -= 1
else:
# We need to skip the lines as if they were consumed by the markdown parser
self.current_line_idx = initial_line_idx + len(indented_lines)
# Finally, decide what to include in the element
if utterance_id is None:
self.current_element["bot"] = {
"_type": "element",
"text": utterance_text[1:-1],
}
# if we have quick_replies, we move them in the element
if "quick_replies" in self.current_element:
self.current_element["bot"][
"quick_replies"
] = self.current_element["quick_replies"]
del self.current_element["quick_replies"]
else:
self.current_element["bot"] = utterance_id
# Add to current branch
self.branches[-1]["elements"].append(self.current_element)
# If there was a bot message with a snippet, we also add an expect
# TODO: can this be handled better?
try:
if "snippet" in self.current_element["bot"]:
self.branches[-1]["elements"].append(
{
"expect": "snippet",
"snippet": self.current_element["bot"]["snippet"],
}
)
# noinspection PyBroadException
except:
pass
def _parse_event(self):
text = split_max(self.text, " ", 1)[1]
# Check if the with syntax is used for parameters
re_with_params_1 = r"(?P<event_name>.*?)(?: (?:with|for) (?P<vars>\$.+)$)"
re_with_params_2 = (
r"(?P<event_name>.*?)(?: (?:with|for) (?P<vars>\w+\s*=\s*.+)$)"
)
match = re.match(re_with_params_1, text) or re.match(re_with_params_2, text)
if match:
d = match.groupdict()
# in this case we convert it to the canonical "(" ")" syntax
text = f"{d['event_name']}({d['vars']})"
self.current_element = {"event": text}
# parse additional parameters if it's the case
if self.next_line and self.next_line["indentation"] > self.current_indentation:
self._extract_params()
# Add to current branch
self.branches[-1]["elements"].append(self.current_element)
def _split_inline_params(self, value):
# Check if the "with/for" syntax is used for parameters
re_with_params_1 = r"(?P<name>.*?)(?: (?:with|for) (?P<vars>\$.+)$)"
re_with_params_2 = r"(?P<name>.*?)(?: (?:with|for) (?P<vars>\w+\s*=\s*.+)$)"
match = re.match(re_with_params_1, value) or re.match(re_with_params_2, value)
if match:
d = match.groupdict()
# in this case we convert it to the canonical "(" ")" syntax
value = f"{d['name']}({d['vars']})"
parts = split_max(value, "(", 1)
if len(parts) > 1:
name = parts[0]
params = value[len(name) :]
else:
name = value
params = ""
return name, params
def _parse_do(self):
# Otherwise, it's a normal intent
do_value = split_max(self.text, " ", 1)[1].strip()
flow_name, flow_params = self._split_inline_params(do_value)
# if we need to save the return values, we store the info
if "=" in flow_name:
return_vars, flow_name = get_stripped_tokens(split_max(flow_name, "=", 1))
else:
return_vars = None
self.current_element = {"flow": f"{flow_name}{flow_params}"}
# parse additional parameters if it's the case
if self.next_line and self.next_line["indentation"] > self.current_indentation:
self._extract_params()
# record the name of the return vars, without the $ sign
if return_vars:
return_vars = get_stripped_tokens(return_vars.split(","))
return_vars = [_var[1:] if _var[0] == "$" else _var for _var in return_vars]
self.current_element["_return_vars"] = return_vars
# Add to current branch
self.branches[-1]["elements"].append(self.current_element)
def _parse_goto(self):
if self.main_token == "goto":
value = split_max(self.text, " ", 1)[1]
else:
value = split_max(self.text, " ", 2)[2]
self.current_element = {"goto": value}
# Add to current branch
self.branches[-1]["elements"].append(self.current_element)
def _parse_meta(self):
self.current_element = {}
# Remove the text and check if we've got something else
self.text = remove_token("meta", self.text)
if self.text and self.text[0] == "{":
try:
self.current_element = json.loads(self.text)
except Exception:
raise Exception(f"Bad meta value: {self.text}")
# parse additional parameters as the content of the meta
if self.next_line and self.next_line["indentation"] > self.current_indentation:
self._extract_params()
# Add the meta element if it's missing
branch_elements = self.branches[-1]["elements"]
if len(branch_elements) == 0 or get_first_key(branch_elements[0]) != "meta":
branch_elements.insert(0, {"meta": {}})
# Update the elements coming from the parameters
for k in self.current_element.keys():
branch_elements[0]["meta"][k] = self.current_element[k]
def _parse_generic(self):
value = split_max(self.text, " ", 1)[1].strip()
# if it's a quoted string, we remove the quotes
if value[0] in ["'", '"']:
value = value[1:-1]
self.current_element = {self.main_token: value}
# parse additional parameters if it's the case
if self.next_line and self.next_line["indentation"] > self.current_indentation:
self._extract_params()
# Add to current branch
self.branches[-1]["elements"].append(self.current_element)
def _parse_run(self):
value = split_max(self.text, " ", 1)[1].strip()
action_name, action_params = self._split_inline_params(value)
self.current_element = {"run": f"{action_name}{action_params}"}
# parse additional parameters if it's the case
if self.next_line and self.next_line["indentation"] > self.current_indentation:
self._extract_params()
# Add to current branch
self.branches[-1]["elements"].append(self.current_element)
def _parse_label(self):
"""Supports parsing labels, with or without a value.
e.g.
label bla
label speech hints $hints
"""
name = split_max(self.text, " ", 1)[1].strip()
# We separate the name and the value
parts = re.findall(r'([^$"]+)(\$.*|".*")?', name)
assert len(parts) == 1, "Invalid label syntax."
name = parts[0][0].strip()
value = parts[0][1] or None
self.current_element = {"label": name}
if value:
# Get rid of the quotes
if value.startswith('"'):
value = value[1:-1]
self.current_element["value"] = value
# parse additional parameters if it's the case
if self.next_line and self.next_line["indentation"] > self.current_indentation:
self._extract_params()
# Add to current branch
self.branches[-1]["elements"].append(self.current_element)
def _parse_if_branch(self, if_condition):
self.current_element = {"if": if_condition, "then": []}
self.branches[-1]["elements"].append(self.current_element)
self.ifs.append(
{
"element": self.current_element,
"indentation": self.next_line["indentation"],
# We also record this to match it with the else
"keyword_indentation": self.current_indentation,
}
)
# Add a new branch for the then part
self.branches.append(
{
"elements": self.ifs[-1]["element"]["then"],
"indentation": self.ifs[-1]["indentation"],
}
)
def _parse_if(self):
if_condition = split_max(self.text, " ", 1)[1].strip()
self._parse_if_branch(if_condition)
def _parse_else_if(self):
# Add a new branch for the then part
if_element = self.ifs[-1]["element"]
if_element["else"] = []
self.branches.append(
{
"elements": self.ifs[-1]["element"]["else"],
"indentation": self.ifs[-1]["indentation"],
}
)
# If we have a second if, we need to create a new if block
if self.main_token == "else if":
if_condition = split_max(self.text, " ", 2)[2].strip()
self._parse_if_branch(if_condition)
def _parse_while(self):
while_condition = split_max(self.text, " ", 1)[1].strip()
self.current_element = {"while": while_condition, "do": []}
self.branches[-1]["elements"].append(self.current_element)
# Add a new branch for the then part
self.branches.append(
{
"elements": self.current_element["do"],
"indentation": self.next_line["indentation"],
}
)
def _parse_any(self):
self.current_element = {
"any": [],
}
self.branches[-1]["elements"].append(self.current_element)
# Add a new branch for the then part
self.branches.append(
{
"elements": self.current_element["any"],
"indentation": self.next_line["indentation"],
}
)
def _parse_infer(self):
self.text = remove_token("infer", self.text)
# If we have the event right after the infer keyword, we move it to the next line
if self.text:
self.lines.insert(
self.current_line_idx + 1,
{
"text": self.text,
# We keep the line mapping the same
"number": self.current_line["number"],
"indentation": self.current_indentation + 1,
},
)
self.next_line = self.lines[self.current_line_idx + 1]
self.current_element = {
"infer": [],
}
self.branches[-1]["elements"].append(self.current_element)
# Add a new branch for the then part
self.branches.append(
{
"elements": self.current_element["infer"],
"indentation": self.next_line["indentation"],
}
)
def _parse_continue(self):
self.current_element = {
"continue": True,
}
self.branches[-1]["elements"].append(self.current_element)
def _parse_stop(self):
self.current_element = {
"bot": "stop",
}
self.branches[-1]["elements"].append(self.current_element)
def _parse_break(self):
self.current_element = {
"break": True,
}
self.branches[-1]["elements"].append(self.current_element)
def _parse_return(self):
parts = split_max(self.text, " ", 1)
if len(parts) > 1:
return_values = get_stripped_tokens(parts[1].split(","))
else:
return_values = []
self.current_element = {
"return": True,
}
if return_values:
self.current_element["_return_values"] = return_values
self.branches[-1]["elements"].append(self.current_element)
def parse(self):
while self.current_line_idx < len(self.lines):
self._fetch_current_line()
try:
# If we're importing another model, we just record the import
if self.main_token in ["import", "include"]:
self._record_import()
continue
# if we're dealing with a definition
elif self.main_token in ["define", "def"]:
self._process_define()
continue
# Make sure we get rid of the finished branches/ifs
self._check_ifs_and_branches()
# NAMESPACE
# if it's not a main token that makes sense and if the next
# line is indented, then it's a new namespace (which could be a flow)
if (
self.main_token not in VALID_MAIN_TOKENS
and self.next_line
and self.next_line["indentation"] > self.current_line["indentation"]
):
# We can only create a namespace if there are no elements in the current branch
# or there is no current branch
if (
len(self.branches) == 0
or len(self.branches[-1]["elements"]) == 0
):
namespace = self.text
# We make sure to remove the pre-pended ":" if it's the case
if namespace.startswith(":"):
namespace = namespace[1:]
self._create_namespace(namespace)
continue
# Make sure we have an active flow at this point
self._check_flow_exists()
# Create a new branch on "when" or "else when".
# This will alter the text of the current line to to processed further
# after the new branch is created
if self.main_token in ["when", "else when"]:
self._parse_when()
# Now we parse the main content of the line, according to the main token
if self.main_token == "user":
self._parse_user()
elif self.main_token == "bot":
self._parse_bot()
elif self.main_token == "event":
self._parse_event()
elif self.main_token in ["do"]:
self._parse_do()
elif self.main_token in ["goto", "go to"]:
self._parse_goto()
elif self.main_token in ["meta"]:
self._parse_meta()
elif self.main_token in ["set", "expect", "check"]:
self._parse_generic()
elif self.main_token in ["run"]:
self._parse_run()
elif self.main_token in ["label", "checkpoint"]:
self._parse_label()
elif self.main_token == "if":
self._parse_if()
elif self.main_token == "while":
self._parse_while()
elif self.main_token in ["else", "else if"]:
self._parse_else_if()
elif self.main_token == "any":
self._parse_any()
elif self.main_token == "infer":
self._parse_infer()
elif self.main_token in ["pass", "continue"]:
self._parse_continue()
elif self.main_token in ["stop", "abort"]:
self._parse_stop()
elif self.main_token in ["break"]:
self._parse_break()
elif self.main_token in ["return", "done"]:
self._parse_return()
else:
raise Exception(
f"Unknown main token '{self.main_token}' on line {self.current_line['number']}"
)
# Include the source mappings if needed
self._include_source_mappings()
except Exception as ex:
error = f"Error parsing line {self.current_line['number']} in {self.filename}: {ex}"
exception = Exception(error)
# Decorate the exception with where the parsing failed
exception.filename = self.filename
exception.line = self.current_line["number"]
exception.error = str(ex)
raise exception
self.current_line_idx += 1
result = {"flows": self.flows}
if self.imports:
result["imports"] = self.imports
if self.md_content:
result["markdown"] = "\n".join(self.md_content)
return result
def _extract_snippet_name(self):
"""Helper to extract the name of a snippet. Also updates self.text."""
# we need to figure out when the parameters begin, so we can extract the name
# of the snippet, which can have spaces in it
snippet_params_start_pos = 0
while snippet_params_start_pos < len(self.text):
if (
self.text[snippet_params_start_pos] == '"'
or self.text[snippet_params_start_pos] == "<"
):
break
else:
snippet_params_start_pos += 1
snippet_name = self.text[0:snippet_params_start_pos].strip()
self.text = self.text[snippet_params_start_pos:]
return snippet_name
def parse_snippets_and_imports(self):
"""Parses just the snippets and imports from the file.
The data is returned in the format
{
"snippet_name": {
"name": "snippet_name",
"params": ["T", "A"],
"lines": <numbered lines>
}
}, ["skill_1", ...]
"""
snippets = {}
imports = []
snippet = None
while self.current_line_idx < len(self.lines):
self._fetch_current_line()
# If we are in a snippet, we just record the line
if snippet:
if self.current_line["indentation"] == 0:
# this means the snippet just ended
snippet = None
else:
d = {}
for k in self.current_line.keys():
d[k] = self.current_line[k]
d["filename"] = self.filename
snippet["lines"].append(d)
self.current_line_idx += 1
continue
if self.main_token == "define":
self.text = remove_token("define", self.text)
define_token = extract_main_token(self.text)
if define_token == "snippet":
self.text = remove_token(define_token, self.text)
snippet_name = self._extract_snippet_name()
# Extract the params and get rid of the surrounding tags
param_names = re.findall("(<[^>]+>)", self.text)
param_names = [param[1:-1] for param in param_names]
snippet = {"name": snippet_name, "params": param_names, "lines": []}
snippets[snippet["name"]] = snippet
elif self.main_token in ["import", "include"]:
self.text = remove_token(self.main_token, self.text)
package_name = parse_package_name(self.text)
if package_name not in imports:
imports.append(package_name)
self.current_line_idx += 1
return snippets, imports
def parse_coflows_to_yml_flows(
filename: str,
content: str,
include_source_mapping: bool = False,
snippets: Optional[dict] = None,
):
"""Parses a file in .co format to a YAML flows format
:param filename: The name of the file.
:param content: The content.
:param include_source_mapping: Whether to include source mapping into the flow elements.
:param snippets: Snippets to use when parsing the file.
:return:
"""
parser = ColangParser(filename, content, include_source_mapping, snippets)
return parser.parse()
def parse_snippets_and_imports(filename: str, content: str):
"""Parses just the snippets and imports from the file.
The data is returned in the format
{
"snippet_name": {
"name": "snippet_name",
"params": ["T", "A"],
"lines": <numbered lines>
}
}, ["skill_1", ...]
:param filename: The name of the file
:param content: The content
:return:
"""
parser = ColangParser(filename, content)
return parser.parse_snippets_and_imports()
__all__ = ["ColangParser", "parse_coflows_to_yml_flows", "parse_snippets_and_imports"]
| NeMo-Guardrails-main | nemoguardrails/language/colang_parser.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | nemoguardrails/language/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import textwrap
from typing import List, Optional
from nemoguardrails.language.colang_parser import (
parse_coflows_to_yml_flows,
parse_snippets_and_imports,
)
from nemoguardrails.language.comd_parser import parse_md_file
from nemoguardrails.language.coyml_parser import parse_flow_elements
log = logging.getLogger(__name__)
def _extract_flow_code(file_content: str, flow_elements: List[dict]) -> Optional[str]:
"""Helper to extract the source code for a flow.
Currently, it uses a simple heuristic that copies all the lines between the minimum
and the maximum lines
"""
content_lines = file_content.split("\n")
min_line = -1
max_line = -1
for element in flow_elements:
if "_source_mapping" not in element:
continue
line_number = element["_source_mapping"]["line_number"] - 1
if min_line == -1 or line_number < min_line:
min_line = line_number
if max_line == -1 or line_number > max_line:
max_line = line_number
# If we have a range, we extract it
if min_line >= 0:
# Exclude all non-blank lines
flow_lines = [
_line
for _line in content_lines[min_line : max_line + 1]
if _line.strip() != ""
]
return textwrap.dedent("\n".join(flow_lines))
return None
def parse_colang_file(filename: str, content: str):
"""Parse the content of a .co file into the CoYML format."""
snippets, imports = parse_snippets_and_imports(filename, content)
result = parse_coflows_to_yml_flows(
filename, content, snippets=snippets, include_source_mapping=True
)
flows = []
for flow_id, items in result["flows"].items():
elements = parse_flow_elements(items)
source_code = _extract_flow_code(content, elements)
flows.append({"id": flow_id, "elements": elements, "source_code": source_code})
user_messages = {}
bot_messages = {}
if result.get("markdown"):
log.debug(f"Found markdown content in {filename}")
md_result = parse_md_file(filename, content=result["markdown"])
# Record the user messages
# The `patterns` result from Markdown parsing contains patterns of the form
# {'lang': 'en', 'type': 'PATTERN', 'sym': 'intent:express|greeting', 'body': 'hi', 'params': {}}
# We need to convert these to the CoYML format.
for pattern in md_result["patterns"]:
sym = pattern["sym"]
# Ignore non-intent symbols
if not sym.startswith("intent:"):
continue
# The "|" is an old convention made by the parser, we roll back.
intent = sym[7:].replace("|", " ")
if intent not in user_messages:
user_messages[intent] = []
user_messages[intent].append(pattern["body"])
# For the bot messages, we just copy them from the `utterances` dict.
# The elements have the structure {"text": ..., "_context": ...}
for intent, utterances in md_result["utterances"].items():
if intent not in bot_messages:
bot_messages[intent] = []
if not isinstance(utterances, list):
utterances = [utterances]
for utterance in utterances:
bot_messages[intent].append(utterance["text"])
data = {
"user_messages": user_messages,
"bot_messages": bot_messages,
"flows": flows,
}
return data
| NeMo-Guardrails-main | nemoguardrails/language/parser.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import uuid
from typing import List, Optional, Text, Tuple
def split_max(text, separator, max_instances):
"""Helper to simulate the behavior of .split(..., max_instances).
This implementation is meant to transpile correctly to the JS>
"""
parts = text.split(separator)
if len(parts) > max_instances + 1:
new_parts = parts[0:max_instances]
new_parts.append(separator.join(parts[max_instances:]))
parts = new_parts
return parts
def split_args(args_str: str) -> List[str]:
"""Split a string that represents arguments for a function.
It supports keyword arguments and also correctly handles strings and lists/dicts.
Args:
args_str: The string with the arguments e.g. 'name="John", colors=["blue", "red"]'
Returns:
The string that correspond to each individual argument value.
"""
parts = []
stack = []
current = []
closing_char = {"[": "]", "(": ")", "{": "}", "'": "'", '"': '"'}
for char in args_str:
if char in "([{":
stack.append(char)
current.append(char)
elif char in "\"'" and (len(stack) == 0 or stack[-1] != char):
stack.append(char)
current.append(char)
elif char in ")]}\"'":
if char != closing_char[stack[-1]]:
raise ValueError(
f"Invalid syntax for string: {args_str}; "
f"expecting {closing_char[stack[-1]]} and got {char}"
)
stack.pop()
current.append(char)
elif char == "," and len(stack) == 0:
parts.append("".join(current))
current = []
else:
current.append(char)
parts.append("".join(current))
return [part.strip() for part in parts]
def get_numbered_lines(content: str):
"""Helper to returned numbered lines.
Comments and empty lines are ignored.
"""
raw_lines = content.split("\n")
lines = []
i = 0
multiline_comment = False
current_comment = None
while i < len(raw_lines):
raw_line = raw_lines[i].strip()
# If we have a line comment, we record it
if raw_line.startswith("#"):
if current_comment is None:
current_comment = raw_line[1:].strip()
else:
# For line comments on consecutive lines, we gather them
current_comment += "\n" + raw_line[1:].strip()
# Get rid of empty lines and comments
if len(raw_line) == 0 or raw_line[0] == "#":
i += 1
continue
# If there is a comment at the end of the line, we first remove it
parts = word_split(raw_line, "#")
raw_line = parts[0]
if not multiline_comment and raw_line.startswith('"""'):
if raw_line == '"""' or not raw_line.endswith('"""'):
multiline_comment = True
current_comment = raw_line[3:]
else:
current_comment = raw_line[3:-3]
i += 1
continue
if multiline_comment:
if raw_line.endswith('"""'):
current_comment += "\n" + raw_line[0:-3]
multiline_comment = False
else:
current_comment += "\n" + raw_line
i += 1
continue
# Compute indentation level
ind = 0
while raw_lines[i][ind] == " ":
ind += 1
# As long as the line ends with "\", we also append the next lines
# but without the indentation.
# Also, if there's an active "operator" like "or", we also continue to the next line
text = raw_line
while i < len(raw_lines) - 1 and text[-1] == "\\" or text.endswith(" or"):
i += 1
if text[-1] == "\\":
text = text[0:-1]
if text[-1] != " ":
text = text + " "
text = text + raw_lines[i].strip()
lines.append(
{
# Get rid of any white space
"text": text,
"number": i + 1,
"indentation": ind,
"comment": current_comment,
}
)
current_comment = None
i += 1
return lines
def remove_token(token: str, line: str):
"""Helper to remove a token"""
line = line.strip()
parts = split_max(line, " ", 1)
assert parts[0] == token
return parts[1].strip() if len(parts) > 1 else ""
def extract_main_token(text: str):
"""Helper to extract the main token from a line"""
main_token = text.split(" ")[0]
# For else, we also want to catch the next keyword (if/when)
if main_token == "else" and text.strip() != "else":
main_token = "else " + split_max(text, " ", 1)[1].strip().split(" ")[0]
if main_token == "go":
main_token = "go " + split_max(text, " ", 1)[1].strip().split(" ")[0]
return main_token
def char_split(
text: str, c: str, ignore_parenthesis=False, ignore_strings=False
) -> List[str]:
"""Helper method to split a string by a given character.
:param text: The text to split.
:param c: The character to use as the separator
:param ignore_parenthesis: If set, it will now account for lists
i.e. starting with [], () or {}
:param ignore_strings: If set, it will not take into account strings.
"""
parts = []
# Edge case
if text == "":
return [""]
# The current position
i = 0
# The start of the current part
s = 0
in_string = False
parenthesis_counter = 0
while i < len(text) - 1:
if in_string:
if text[i] == '"':
in_string = False
i += 1
else:
if text[i] == '"' and not ignore_strings:
in_string = True
# Only split by character when not inside a parenthesis
if text[i] == c and parenthesis_counter == 0:
part = text[s:i].strip()
if len(part) > 0:
parts.append(part)
i += 1
s = i
else:
if text[i] in ["(", "[", "{"] and not ignore_parenthesis:
parenthesis_counter += 1
elif text[i] in [")", "]", "}"]:
parenthesis_counter -= 1
i += 1
if s < len(text):
part = text[s:].strip()
if len(part) > 0:
parts.append(part)
return parts
# This implementation must stay here as it is transpiled into JS, although a
# duplicate of the one in utils.
# noinspection DuplicatedCode
def word_split(text: str, word: str):
"""A simple logic that splits by word but takes strings into accounts."""
parts = []
# Edge case
if text == "":
return [""]
# The current position
i = 0
# The start of the current part
s = 0
in_string = False
while i < len(text) - len(word):
if in_string:
if text[i] == '"':
in_string = False
i += 1
else:
if text[i] == '"':
in_string = True
if text[i : i + len(word)] == word:
part = text[s:i].strip()
if len(part) > 0:
parts.append(part)
i += len(word)
s = i
else:
i += 1
if s < len(text):
part = text[s:].strip()
# edge case, make sure the part does not end with the actual word
if part.endswith(word):
part = part[0 : -1 * len(word)]
if len(part) > 0:
parts.append(part)
return parts
def ws_tokenize(text):
"""Tokenize a text by whitespace and taking strings into account."""
return word_split(text, " ")
def params_tokenize(text):
"""Tokenizer specific to the params parsing."""
tokens = []
# The current position
i = 0
# The start of the current part
s = 0
in_string = False
while i < len(text):
if in_string:
if text[i] == '"':
in_string = False
i += 1
else:
if text[i] == '"':
in_string = True
if text[i] in [" ", "-", ":", ",", "="]:
token = text[s:i].strip()
if len(token) > 0:
tokens.append(token)
if text[i] != " ":
tokens.append(text[i])
i += 1
s = i
else:
i += 1
if s < len(text):
token = text[s:].strip()
if len(token) > 0:
tokens.append(token)
return tokens
def get_stripped_tokens(tokens: List[str]):
return [token.strip() for token in tokens]
def get_first_key(d: dict):
"""Helper to get the first key, which transpiles correctly."""
for k in d.keys():
return k
def extract_topic_object(text: Text) -> Tuple[Text, Optional[Text]]:
"""Helper to extract the object from the definition of a topic.
Supported expressions
is_open_source
is_open_source for @roboself
is_open_source for $company
is_open_source($roboself)
is_open_source(@roboself)
"""
if " " in text:
parts = ws_tokenize(text)
assert len(parts) == 3
assert parts[1] == "for"
return parts[0], parts[2]
elif "(" in text:
parts = split_max(text[0:-1], "(", 1)
assert len(parts) == 2
return parts[0], parts[1]
else:
return text, None
def parse_package_name(text):
"""Helper to extract a normalized package name."""
# get rid of quotes
package_name = text
if package_name[0] == '"' or package_name[0] == "'":
package_name = package_name[1:-1]
# Get rid of the "bot/"
if package_name[0:4] == "bot/":
package_name = split_max(package_name, "/", 1)[1]
return package_name
def new_uuid() -> str:
"""Helper to generate new UUID v4.
In testing mode, it will generate a predictable set of UUIDs to help debugging.
"""
return str(uuid.uuid4())
def string_hash(s):
"""A simple string hash with an equivalent implementation in javascript.
module.exports.string_hash = function(s){
let hash = 0;
if (s.length === 0) return hash;
for (let i = 0; i < s.length; i++) {
let char = s.charCodeAt(i);
hash = ((hash<<5)-hash)+char;
hash = hash & hash; // Convert to 32bit integer
}
if (hash < 0) hash *= -1;
return hash.toString(16);
}
"""
_hash = 0
if len(s) == 0:
return 0
for i in range(len(s)):
_char = ord(s[i])
_hash = ((_hash << 5) - _hash) + _char
_hash = _hash & 0xFFFFFFFF
if _hash >= (1 << 31):
_hash = -1 * (_hash - (1 << 32))
return hex(_hash)[2:]
| NeMo-Guardrails-main | nemoguardrails/language/utils.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
from typing import List
import typer
import uvicorn
from nemoguardrails.actions_server import actions_server
from nemoguardrails.cli.chat import run_chat
from nemoguardrails.eval.cli import evaluate
from nemoguardrails.logging.verbose import set_verbose
from nemoguardrails.server import api
app = typer.Typer()
app.add_typer(evaluate.app, name="evaluate")
logging.getLogger().setLevel(logging.WARNING)
@app.command()
def chat(
config: List[str] = typer.Option(
default=["config"],
exists=True,
help="Path to a directory containing configuration files to use. Can also point to a single configuration file.",
),
verbose: bool = typer.Option(
default=False,
help="If the chat should be verbose and output the prompts",
),
):
"""Starts an interactive chat session."""
if verbose:
set_verbose(True)
if len(config) > 1:
typer.secho(f"Multiple configurations are not supported.", fg=typer.colors.RED)
typer.echo("Please provide a single folder.")
raise typer.Exit(1)
typer.echo("Starting the chat...")
run_chat(config_path=config[0], verbose=verbose)
@app.command()
def server(
port: int = typer.Option(
default=8000, help="The port that the server should listen on. "
),
config: List[str] = typer.Option(
default=[],
exists=True,
help="Path to a directory containing multiple configuration sub-folders.",
),
verbose: bool = typer.Option(
default=False,
help="If the server should be verbose and output detailed logs including prompts.",
),
disable_chat_ui: bool = typer.Option(
default=False,
help="Weather the ChatUI should be disabled",
),
):
"""Starts a NeMo Guardrails server."""
if config:
api.app.rails_config_path = config[0]
else:
# If we don't have a config, we try to see if there is a local config folder
local_path = os.getcwd()
local_configs_path = os.path.join(local_path, "config")
if os.path.exists(local_configs_path):
api.app.rails_config_path = local_configs_path
if verbose:
logging.getLogger().setLevel(logging.INFO)
if disable_chat_ui:
api.app.disable_chat_ui = True
uvicorn.run(api.app, port=port, log_level="info", host="0.0.0.0")
@app.command("actions-server")
def action_server(
port: int = typer.Option(
default=8001, help="The port that the server should listen on. "
),
):
"""Starts a NeMo Guardrails actions server."""
uvicorn.run(actions_server.app, port=port, log_level="info", host="0.0.0.0")
| NeMo-Guardrails-main | nemoguardrails/cli/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Optional
from nemoguardrails import LLMRails, RailsConfig
os.environ["TOKENIZERS_PARALLELISM"] = "false"
def run_chat(config_path: Optional[str] = None, verbose: bool = False):
"""Runs a chat session in the terminal."""
rails_config = RailsConfig.from_path(config_path)
# TODO: add support for loading a config directly from live playground
# rails_config = RailsConfig.from_playground(model="...")
# TODO: add support to register additional actions
# rails_app.register_action(...)
rails_app = LLMRails(rails_config, verbose=verbose)
history = []
# And go into the default listening loop.
while True:
user_message = input("> ")
history.append({"role": "user", "content": user_message})
bot_message = rails_app.generate(messages=history)
history.append(bot_message)
# We print bot messages in green.
print(f"\033[92m{bot_message['content']}\033[0m")
| NeMo-Guardrails-main | nemoguardrails/cli/chat.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import random
from typing import Optional
from langchain import LLMChain, PromptTemplate
from langchain.llms import BaseLLM
from nemoguardrails.actions.actions import ActionResult, action
from nemoguardrails.kb.kb import KnowledgeBase
log = logging.getLogger(__name__)
@action(is_system_action=True)
async def retrieve_relevant_chunks(
context: Optional[dict] = None,
kb: Optional[KnowledgeBase] = None,
):
"""Retrieve relevant chunks from the knowledge base and add them to the context."""
user_message = context.get("last_user_message")
context_updates = {}
context_updates["relevant_chunks"] = ""
if user_message and kb:
chunks = await kb.search_relevant_chunks(user_message)
relevant_chunks = "\n".join([chunk["body"] for chunk in chunks])
context_updates["relevant_chunks"] = relevant_chunks
return ActionResult(
return_value=context_updates["relevant_chunks"],
context_updates=context_updates,
)
| NeMo-Guardrails-main | nemoguardrails/actions/retrieve_relevant_chunks.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass, field
from typing import Any, List, Optional
# A decorator that sets a property on the function to indicate if it's a system action or not.
def action(is_system_action: bool = False, name: Optional[str] = None):
def decorator(fn_or_cls):
fn_or_cls.action_meta = {
"name": name or fn_or_cls.__name__,
"is_system_action": is_system_action,
}
return fn_or_cls
return decorator
@dataclass
class ActionResult:
# The value returned by the action
return_value: Optional[Any] = None
# The events that should be added to the stream
events: Optional[List[dict]] = None
# The updates made to the context by this action
context_updates: Optional[dict] = field(default_factory=dict)
| NeMo-Guardrails-main | nemoguardrails/actions/actions.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .actions import action
| NeMo-Guardrails-main | nemoguardrails/actions/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from typing import Optional
from langchain.llms.base import BaseLLM
from nemoguardrails.actions.llm.utils import llm_call
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.taskmanager import LLMTaskManager
from nemoguardrails.llm.types import Task
log = logging.getLogger(__name__)
async def check_facts(
llm_task_manager: LLMTaskManager,
context: Optional[dict] = None,
llm: Optional[BaseLLM] = None,
):
"""Checks the facts for the bot response."""
evidence = context.get("relevant_chunks", [])
response = context.get("last_bot_message")
if evidence:
prompt = llm_task_manager.render_task_prompt(
task=Task.FACT_CHECKING,
context={
"evidence": evidence,
"response": response,
},
)
with llm_params(llm, temperature=0.0):
entails = await llm_call(llm, prompt)
entails = entails.lower().strip()
log.info(f"Entailment result is {entails}.")
return "yes" in entails
# If there was no evidence, we always return true
return True
| NeMo-Guardrails-main | nemoguardrails/actions/fact_checking.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from typing import Optional
from langchain.llms.base import BaseLLM
from nemoguardrails.actions.actions import ActionResult, action
from nemoguardrails.actions.llm.utils import llm_call
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.taskmanager import LLMTaskManager
from nemoguardrails.llm.types import Task
from nemoguardrails.utils import new_event_dict
log = logging.getLogger(__name__)
@action(is_system_action=True)
async def check_jailbreak(
llm_task_manager: LLMTaskManager,
context: Optional[dict] = None,
llm: Optional[BaseLLM] = None,
):
"""Checks if the user response is malicious and should be masked."""
user_input = context.get("last_user_message")
if user_input:
prompt = llm_task_manager.render_task_prompt(
task=Task.JAILBREAK_CHECK,
context={
"user_input": user_input,
},
)
with llm_params(llm, temperature=0.0):
check = await llm_call(llm, prompt)
check = check.lower().strip()
log.info(f"Jailbreak check result is {check}.")
if "yes" in check:
return ActionResult(
return_value=False,
events=[
new_event_dict(
"mask_prev_user_message", intent="unanswerable message"
)
],
)
# If there was no user input, we always return True i.e. the user input is allowed
return True
| NeMo-Guardrails-main | nemoguardrails/actions/jailbreak_check.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from typing import Optional
from langchain.llms.base import BaseLLM
from nemoguardrails.actions import action
from nemoguardrails.actions.llm.utils import llm_call
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.taskmanager import LLMTaskManager
from nemoguardrails.llm.types import Task
log = logging.getLogger(__name__)
@action(is_system_action=True)
async def output_moderation_v2(
llm_task_manager: LLMTaskManager,
context: Optional[dict] = None,
llm: Optional[BaseLLM] = None,
):
"""Checks if the bot response is appropriate and passes moderation."""
bot_response = context.get("last_bot_message")
user_input = context.get("last_user_message")
if bot_response:
prompt = llm_task_manager.render_task_prompt(
task=Task.OUTPUT_MODERATION_V2,
context={
"user_input": user_input,
"bot_response": bot_response,
},
)
with llm_params(llm, temperature=0.0):
check = await llm_call(llm, prompt)
check = check.lower().strip()
log.info(f"Output moderation check result is {check}.")
if "yes" in check:
return False
return True
| NeMo-Guardrails-main | nemoguardrails/actions/output_moderation_v2.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from typing import Optional
from langchain.llms.base import BaseLLM
from nemoguardrails.actions import action
from nemoguardrails.actions.llm.utils import llm_call
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.taskmanager import LLMTaskManager
from nemoguardrails.llm.types import Task
log = logging.getLogger(__name__)
@action(is_system_action=True)
async def output_moderation(
llm_task_manager: LLMTaskManager,
context: Optional[dict] = None,
llm: Optional[BaseLLM] = None,
):
"""Checks if the bot response is appropriate and passes moderation."""
bot_response = context.get("last_bot_message")
if bot_response:
prompt = llm_task_manager.render_task_prompt(
task=Task.OUTPUT_MODERATION,
context={
"bot_response": bot_response,
},
)
with llm_params(llm, temperature=0.0):
check = await llm_call(llm, prompt)
check = check.lower().strip()
log.info(f"Output moderation check result is {check}.")
if "no" in check:
return False
return True
| NeMo-Guardrails-main | nemoguardrails/actions/output_moderation.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
from typing import Optional
from urllib import parse
import aiohttp
from nemoguardrails.actions import action
from nemoguardrails.actions.actions import ActionResult
from nemoguardrails.utils import new_event_dict
log = logging.getLogger(__name__)
APP_ID = os.environ.get("WOLFRAM_ALPHA_APP_ID")
API_URL_BASE = f"https://api.wolframalpha.com/v2/result?appid={APP_ID}"
@action(name="wolfram alpha request")
async def wolfram_alpha_request(
query: Optional[str] = None, context: Optional[dict] = None
):
"""Makes a request to the Wolfram Alpha API
:param context: The context for the execution of the action.
:param query: The query for Wolfram.
"""
# If we don't have an explicit query, we take the last user message
if query is None and context is not None:
query = context.get("last_user_message") or "2+3"
if query is None:
raise Exception("No query was provided to Wolfram Alpha.")
if APP_ID is None:
return ActionResult(
return_value=False,
events=[
new_event_dict(
"BotIntent", intent="inform wolfram alpha app id not set"
),
new_event_dict(
"StartUtteranceBotAction",
script="Wolfram Alpha app ID is not set. Please set the WOLFRAM_ALPHA_APP_ID environment variable.",
),
new_event_dict("BotIntent", intent="stop"),
],
)
url = API_URL_BASE + "&" + parse.urlencode({"i": query})
log.info(f"Wolfram Alpha: executing request for: {query}")
async with aiohttp.ClientSession() as session:
async with session.get(url) as resp:
if resp.status != 200:
log.info(f"Wolfram Alpha request failed : {query}")
return ActionResult(
return_value=False,
events=[
new_event_dict(
"BotIntent", intent="inform wolfram alpha not working"
),
new_event_dict(
"StartUtteranceBotAction",
script="Apologies, but I cannot answer this question at this time. I am having trouble getting the answer from Wolfram Alpha.",
),
new_event_dict("BotIntent", intent="stop"),
],
)
result = await resp.text()
log.info(f"Wolfram Alpha: the result was {result}.")
return result
| NeMo-Guardrails-main | nemoguardrails/actions/math.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module for the calling proper action endpoints based on events received at action server endpoint """
import importlib.util
import inspect
import logging
import os
from typing import Any, Dict, List, Optional, Tuple, Union
from langchain.chains.base import Chain
from nemoguardrails.logging.callbacks import logging_callbacks
log = logging.getLogger(__name__)
class ActionDispatcher:
def __init__(
self, load_all_actions: bool = True, config_path: Optional[str] = None
):
"""Initializes an actions dispatcher.
:param load_all_actions: When set, it will load all actions in the `actions` folder
both in the current working directory and in the package.
:param config_path: The path from which the configuration was loaded. If there are
actions at the specified path, we load them as well.
"""
log.info("Initializing action dispatcher")
self._registered_actions = {}
if load_all_actions:
# TODO: check for better way to find actions dir path or use constants.py
# First, we load all actions from the library
self.load_actions_from_path(os.path.join(os.path.dirname(__file__), ".."))
# Next, we load all actions from the current working directory
# TODO: add support for an explicit ACTIONS_PATH
self.load_actions_from_path(os.getcwd())
# Last, but not least, if there was a config path, we try to load actions
# from there as well.
if config_path:
self.load_actions_from_path(config_path)
log.info(f"Registered Actions: {self._registered_actions}")
log.info("Action dispatcher initialized")
@property
def registered_actions(self):
return self._registered_actions
def load_actions_from_path(self, path: str):
"""Loads all actions from the specified path.
It will load all actions in the `actions.py` file if it exists and all actions
inside the `actions` folder if it exists.
"""
actions_path = os.path.join(path, "actions")
if os.path.exists(actions_path):
self._registered_actions.update(self._find_actions(actions_path))
actions_py_path = os.path.join(path, "actions.py")
if os.path.exists(actions_py_path):
self._registered_actions.update(
self._load_actions_from_module(actions_py_path)
)
def register_action(
self, action: callable, name: Optional[str] = None, override: bool = True
):
"""Registers an action with the given name.
:param name: The name of the action.
:param action: The action function.
:param override: If an action already exists, whether it should be overriden or not.
"""
if name is None:
action_meta = getattr(action, "action_meta", None)
name = action_meta["name"] if action_meta else action.__name__
# If we're not allowed to override, we stop.
if name in self._registered_actions and not override:
return
self._registered_actions[name] = action
def register_actions(self, actions_obj: any, override: bool = True):
"""Registers all the actions from the given object."""
# Register the actions
for attr in dir(actions_obj):
val = getattr(actions_obj, attr)
if hasattr(val, "action_meta"):
self.register_action(val, override=override)
def get_action(self, name: str) -> callable:
return self._registered_actions.get(name)
async def execute_action(
self, action_name: str, params: Dict[str, Any]
) -> Tuple[Union[str, Dict[str, Any]], str]:
"""Endpoint called from action server to execute an action.
This endpoint interacts with different supported actions
"""
if action_name in self._registered_actions:
log.info(f"Executing registered action: {action_name}")
fn = self._registered_actions.get(action_name, None)
# Actions that are registered as classes are initialized lazy, when
# they are first used.
if inspect.isclass(fn):
fn = fn()
self._registered_actions[action_name] = fn
if fn is not None:
try:
# We support both functions and classes as actions
if inspect.isfunction(fn) or inspect.ismethod(fn):
result = await fn(**params)
elif isinstance(fn, Chain):
try:
chain = fn
# For chains with only one output key, we use the `arun` function
# to return directly the result.
if len(chain.output_keys) == 1:
result = await chain.arun(
**params, callbacks=logging_callbacks
)
else:
# Otherwise, we return the dict with the output keys.
result = await chain.acall(
inputs=params,
return_only_outputs=True,
callbacks=logging_callbacks,
)
except NotImplementedError:
# Not ideal, but for now we fall back to sync execution
# if the async is not available
result = fn.run(**params)
else:
# TODO: there should be a common base class here
result = fn.run(**params)
return result, "success"
except Exception as e:
log.exception(f"Error {e} while execution {action_name}")
return None, "failed"
def get_registered_actions(self) -> List[str]:
"""Endpoint called from action server to get the list of available actions"""
return list(self._registered_actions.keys())
@staticmethod
def _load_actions_from_module(filepath: str):
"""Loads the actions from the specified python module."""
action_objects = {}
filename = os.path.basename(filepath)
try:
log.debug(f"Analyzing file {filename}")
# Import the module from the file
spec = importlib.util.spec_from_file_location(filename, filepath)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
# Loop through all members in the module and check for the `@action` decorator
# If class has action decorator is_action class member is true
for name, obj in inspect.getmembers(module):
if inspect.isfunction(obj) and hasattr(obj, "action_meta"):
log.info(f"Adding {obj.__name__} to actions")
action_objects[obj.action_meta["name"]] = obj
if inspect.isclass(obj) and hasattr(obj, "action_meta"):
try:
action_objects[obj.action_meta["name"]] = obj
log.info(f"Added {obj.action_meta['name']} to actions")
except Exception as e:
log.debug(
f"Failed to register {obj.action_meta['name']} in action dispatcher due to exception {e}"
)
except Exception as e:
log.debug(
f"Failed to register {filename} in action dispatcher due to exception {e}"
)
return action_objects
@staticmethod
def _find_actions(directory) -> Dict:
"""Loop through all the subdirectories and check for the class with @action
decorator and add in action_classes dict
"""
action_objects = {}
if not os.path.exists(directory):
return action_objects
# Loop through all files in the directory and its subdirectories
for root, dirs, files in os.walk(directory):
for filename in files:
if filename.endswith(".py"):
filepath = os.path.join(root, filename)
action_objects.update(
ActionDispatcher._load_actions_from_module(filepath)
)
return action_objects
| NeMo-Guardrails-main | nemoguardrails/actions/action_dispatcher.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from langchain.chains import AnalyzeDocumentChain
from langchain.chains.summarize import load_summarize_chain
from langchain.llms import BaseLLM
from nemoguardrails.actions.actions import action
@action(name="summarize_document")
class SummarizeDocument:
"""Sample implementation of a document summarization action.
The implementation uses the summarization chain from LangChain.
"""
def __init__(self, document_path: str, llm: BaseLLM):
self.llm = llm
self.document_path = document_path
def run(self):
summary_chain = load_summarize_chain(self.llm, "map_reduce")
summarize_document_chain = AnalyzeDocumentChain(
combine_docs_chain=summary_chain
)
try:
with open(self.document_path) as f:
document = f.read()
summary = summarize_document_chain.run(document)
return summary
except Exception as e:
print(f"Ran into an error while summarizing the document: {e}")
return None
| NeMo-Guardrails-main | nemoguardrails/actions/summarize_document.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .hallucination import check_hallucination
| NeMo-Guardrails-main | nemoguardrails/actions/hallucination/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from typing import Optional
from langchain import LLMChain, PromptTemplate
from langchain.llms import OpenAI
from langchain.llms.base import BaseLLM
from nemoguardrails.actions.llm.utils import (
get_multiline_response,
llm_call,
strip_quotes,
)
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.taskmanager import LLMTaskManager
from nemoguardrails.llm.types import Task
from nemoguardrails.logging.callbacks import logging_callback_manager_for_chain
from nemoguardrails.rails.llm.config import RailsConfig
log = logging.getLogger(__name__)
HALLUCINATION_NUM_EXTRA_RESPONSES = 2
async def check_hallucination(
llm_task_manager: LLMTaskManager,
context: Optional[dict] = None,
llm: Optional[BaseLLM] = None,
use_llm_checking: bool = True,
):
"""Checks if the last bot response is a hallucination by checking multiple completions for self-consistency.
:return: True if hallucination is detected, False otherwise.
"""
bot_response = context.get("last_bot_message")
last_bot_prompt_string = context.get("_last_bot_prompt")
if bot_response and last_bot_prompt_string:
num_responses = HALLUCINATION_NUM_EXTRA_RESPONSES
# Use beam search for the LLM call, to get several completions with only one call.
# At the current moment, only OpenAI LLM engines are supported for computing the additional completions.
if type(llm) != OpenAI:
log.warning(
f"Hallucination rail can only be used with OpenAI LLM engines."
f"Current LLM engine is {type(llm).__name__}."
)
return False
# Use the "generate" call from langchain to get all completions in the same response.
last_bot_prompt = PromptTemplate(template="{text}", input_variables=["text"])
chain = LLMChain(prompt=last_bot_prompt, llm=llm)
# Generate multiple responses with temperature 1.
with llm_params(llm, temperature=1.0, n=num_responses, best_of=num_responses):
extra_llm_response = await chain.agenerate(
[{"text": last_bot_prompt_string}],
run_manager=logging_callback_manager_for_chain,
)
extra_llm_completions = []
if len(extra_llm_response.generations) > 0:
extra_llm_completions = extra_llm_response.generations[0]
extra_responses = []
i = 0
while i < num_responses and i < len(extra_llm_completions):
result = extra_llm_completions[i].text
# We need the same post-processing of responses as in "generate_bot_message"
result = get_multiline_response(result)
result = strip_quotes(result)
extra_responses.append(result)
i += 1
if len(extra_responses) == 0:
# Log message and return that no hallucination was found
log.warning(
f"No extra LLM responses were generated for '{bot_response}' hallucination check."
)
return False
elif len(extra_responses) < num_responses:
log.warning(
f"Requested {num_responses} extra LLM responses for hallucination check, "
f"received {len(extra_responses)}."
)
if use_llm_checking:
# Only support LLM-based agreement check in current version
prompt = llm_task_manager.render_task_prompt(
task=Task.CHECK_HALLUCINATION,
context={
"statement": bot_response,
"paragraph": ". ".join(extra_responses),
},
)
with llm_params(llm, temperature=0.0):
agreement = await llm_call(llm, prompt)
agreement = agreement.lower().strip()
log.info(f"Agreement result for looking for hallucination is {agreement}.")
# Return True if the hallucination check fails
return "no" in agreement
else:
# TODO Implement BERT-Score based consistency method proposed by SelfCheckGPT paper
# See details: https://arxiv.org/abs/2303.08896
return False
return False
| NeMo-Guardrails-main | nemoguardrails/actions/hallucination/hallucination.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A set of actions for generating various types of completions using an LLMs."""
import asyncio
import logging
import random
import re
import sys
import uuid
from ast import literal_eval
from functools import lru_cache
from time import time
from typing import Callable, List, Optional
from jinja2 import Environment, meta
from langchain.llms import BaseLLM
from nemoguardrails.actions.actions import ActionResult, action
from nemoguardrails.actions.llm.utils import (
flow_to_colang,
get_first_nonempty_line,
get_last_bot_intent_event,
get_last_user_intent_event,
get_last_user_utterance_event,
get_multiline_response,
get_retrieved_relevant_chunks,
llm_call,
strip_quotes,
)
from nemoguardrails.embeddings.index import EmbeddingsIndex, IndexItem
from nemoguardrails.language.parser import parse_colang_file
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.taskmanager import LLMTaskManager
from nemoguardrails.llm.types import Task
from nemoguardrails.rails.llm.config import EmbeddingSearchProvider, RailsConfig
from nemoguardrails.utils import new_event_dict
log = logging.getLogger(__name__)
class LLMGenerationActions:
"""A container objects for multiple related actions."""
def __init__(
self,
config: RailsConfig,
llm: BaseLLM,
llm_task_manager: LLMTaskManager,
get_embedding_search_provider_instance: Callable[
[Optional[EmbeddingSearchProvider]], EmbeddingsIndex
],
verbose: bool = False,
):
self.config = config
self.llm = llm
self.verbose = verbose
# If we have user messages, we build an index with them
self.user_message_index = None
self.bot_message_index = None
self.flows_index = None
self.get_embedding_search_provider_instance = (
get_embedding_search_provider_instance
)
asyncio.run(self.init())
self.llm_task_manager = llm_task_manager
# We also initialize the environment for rendering bot messages
self.env = Environment()
async def init(self):
await asyncio.gather(
self._init_user_message_index(),
self._init_bot_message_index(),
self._init_flows_index(),
)
async def _init_user_message_index(self):
"""Initializes the index of user messages."""
if not self.config.user_messages:
return
items = []
for intent, utterances in self.config.user_messages.items():
for text in utterances:
items.append(IndexItem(text=text, meta={"intent": intent}))
# If we have no patterns, we stop.
if len(items) == 0:
return
self.user_message_index = self.get_embedding_search_provider_instance(
self.config.core.embedding_search_provider
)
await self.user_message_index.add_items(items)
# NOTE: this should be very fast, otherwise needs to be moved to separate thread.
await self.user_message_index.build()
async def _init_bot_message_index(self):
"""Initializes the index of bot messages."""
if not self.config.bot_messages:
return
items = []
for intent, utterances in self.config.bot_messages.items():
for text in utterances:
items.append(IndexItem(text=intent, meta={"text": text}))
# If we have no patterns, we stop.
if len(items) == 0:
return
self.bot_message_index = self.get_embedding_search_provider_instance(
self.config.core.embedding_search_provider
)
await self.bot_message_index.add_items(items)
# NOTE: this should be very fast, otherwise needs to be moved to separate thread.
await self.bot_message_index.build()
async def _init_flows_index(self):
"""Initializes the index of flows."""
if not self.config.flows:
return
items = []
for flow in self.config.flows:
# We don't include the default system flows in the index because we don't want
# the LLM to predict system actions.
if flow.get("id") in [
"generate user intent",
"generate next step",
"generate bot message",
]:
continue
# TODO: check if the flow has system actions and ignore the flow.
colang_flow = flow.get("source_code") or flow_to_colang(flow)
# We index on the full body for now
items.append(IndexItem(text=colang_flow, meta={"flow": colang_flow}))
# If we have no patterns, we stop.
if len(items) == 0:
return
self.flows_index = self.get_embedding_search_provider_instance(
self.config.core.embedding_search_provider
)
await self.flows_index.add_items(items)
# NOTE: this should be very fast, otherwise needs to be moved to separate thread.
await self.flows_index.build()
def _get_general_instruction(self):
"""Helper to extract the general instruction."""
text = ""
for instruction in self.config.instructions:
if instruction.type == "general":
text = instruction.content
# We stop at the first one for now
break
return text
@lru_cache
def _get_sample_conversation_two_turns(self):
"""Helper to extract only the two turns from the sample conversation.
This is needed to be included to "seed" the conversation so that the model
can follow the format more easily.
"""
lines = self.config.sample_conversation.split("\n")
i = 0
user_count = 0
while i < len(lines):
if lines[i].startswith("user "):
user_count += 1
if user_count == 3:
break
i += 1
sample_conversation = "\n".join(lines[0:i])
# Remove any trailing new lines
sample_conversation = sample_conversation.strip()
return sample_conversation
@action(is_system_action=True)
async def generate_user_intent(
self, events: List[dict], llm: Optional[BaseLLM] = None
):
"""Generate the canonical form for what the user said i.e. user intent."""
# The last event should be the "StartInternalSystemAction" and the one before it the "UtteranceUserActionFinished".
event = get_last_user_utterance_event(events)
assert event["type"] == "UtteranceUserActionFinished"
# Use action specific llm if registered else fallback to main llm
llm = llm or self.llm
# TODO: check for an explicit way of enabling the canonical form detection
if self.config.user_messages:
# TODO: based on the config we can use a specific canonical forms model
# or use the LLM to detect the canonical form. The below implementation
# is for the latter.
log.info("Phase 1: Generating user intent")
# We search for the most relevant similar user utterance
examples = ""
potential_user_intents = []
if self.user_message_index:
results = await self.user_message_index.search(
text=event["final_transcript"], max_results=5
)
# We add these in reverse order so the most relevant is towards the end.
for result in reversed(results):
examples += f"user \"{result.text}\"\n {result.meta['intent']}\n\n"
potential_user_intents.append(result.meta["intent"])
prompt = self.llm_task_manager.render_task_prompt(
task=Task.GENERATE_USER_INTENT,
events=events,
context={
"examples": examples,
"potential_user_intents": ", ".join(potential_user_intents),
},
)
# We make this call with temperature 0 to have it as deterministic as possible.
with llm_params(llm, temperature=self.config.lowest_temperature):
result = await llm_call(llm, prompt)
# Parse the output using the associated parser
result = self.llm_task_manager.parse_task_output(
Task.GENERATE_USER_INTENT, output=result
)
user_intent = get_first_nonempty_line(result)
if user_intent is None:
user_intent = "unknown message"
if user_intent and user_intent.startswith("user "):
user_intent = user_intent[5:]
log.info(
"Canonical form for user intent: "
+ (user_intent if user_intent else "None")
)
if user_intent is None:
return ActionResult(
events=[new_event_dict("UserIntent", intent="unknown message")]
)
else:
return ActionResult(
events=[new_event_dict("UserIntent", intent=user_intent)]
)
else:
prompt = self.llm_task_manager.render_task_prompt(
task=Task.GENERAL, events=events
)
# We make this call with temperature 0 to have it as deterministic as possible.
result = await llm_call(llm, prompt)
return ActionResult(
events=[
new_event_dict("StartUtteranceBotAction", script=result.strip())
]
)
@action(is_system_action=True)
async def generate_next_step(
self, events: List[dict], llm: Optional[BaseLLM] = None
):
"""Generate the next step in the current conversation flow.
Currently, only generates a next step after a user intent.
"""
log.info("Phase 2 :: Generating next step ...")
# Use action specific llm if registered else fallback to main llm
llm = llm or self.llm
# The last event should be the "StartInternalSystemAction" and the one before it the "UserIntent".
event = get_last_user_intent_event(events)
# Currently, we only predict next step after a user intent using LLM
if event["type"] == "UserIntent":
user_intent = event["intent"]
# We search for the most relevant similar flows
examples = ""
if self.flows_index:
results = await self.flows_index.search(text=user_intent, max_results=5)
# We add these in reverse order so the most relevant is towards the end.
for result in reversed(results):
examples += f"{result.text}\n\n"
prompt = self.llm_task_manager.render_task_prompt(
task=Task.GENERATE_NEXT_STEPS,
events=events,
context={"examples": examples},
)
# We use temperature 0 for next step prediction as well
with llm_params(llm, temperature=self.config.lowest_temperature):
result = await llm_call(llm, prompt)
# Parse the output using the associated parser
result = self.llm_task_manager.parse_task_output(
Task.GENERATE_NEXT_STEPS, output=result
)
# If we don't have multi-step generation enabled, we only look at the first line.
if not self.config.enable_multi_step_generation:
result = get_first_nonempty_line(result)
if result and result.startswith("bot "):
next_step = {"bot": result[4:]}
else:
next_step = {"bot": "general response"}
# If we have to execute an action, we return the event to start it
if next_step.get("execute"):
return ActionResult(
events=[
new_event_dict(
"StartInternalSystemAction",
action_name=next_step["execute"],
)
]
)
else:
bot_intent = next_step.get("bot")
return ActionResult(
events=[new_event_dict("BotIntent", intent=bot_intent)]
)
else:
# Otherwise, we parse the output as a single flow.
# If we have a parsing error, we try to reduce size of the flow, potentially
# up to a single step.
lines = result.split("\n")
while True:
try:
parse_colang_file("dynamic.co", content="\n".join(lines))
break
except Exception as e:
# If we could not parse the flow on the last line, we return a general response
if len(lines) == 1:
log.info("Exception while parsing single line: %s", e)
return ActionResult(
events=[
new_event_dict(
"BotIntent", intent="general response"
)
]
)
log.info("Could not parse %s lines, reducing size", len(lines))
lines = lines[:-1]
return ActionResult(
events=[
# We generate a random UUID as the flow_id
new_event_dict(
"start_flow",
flow_id=str(uuid.uuid4()),
flow_body="\n".join(lines),
)
]
)
return ActionResult(return_value=None)
def _render_string(
self,
template_str: str,
context: Optional[dict] = None,
) -> str:
"""Render a string using the provided context information.
Args:
template_str: The string template to render.
context: The context for rendering.
Returns:
The rendered string.
"""
# First, if we have any direct usage of variables in the string,
# we replace with correct Jinja syntax.
for param in re.findall(r"\$([^ \"'!?\-,;</]*(?:\w|]))", template_str):
template_str = template_str.replace(f"${param}", "{{" + param + "}}")
template = self.env.from_string(template_str)
# First, we extract all the variables from the template.
variables = meta.find_undeclared_variables(self.env.parse(template_str))
# This is the context that will be passed to the template when rendering.
render_context = {}
# Copy the context variables to the render context.
if context:
for variable in variables:
if variable in context:
render_context[variable] = context[variable]
return template.render(render_context)
@action(is_system_action=True)
async def generate_bot_message(
self, events: List[dict], context: dict, llm: Optional[BaseLLM] = None
):
"""Generate a bot message based on the desired bot intent."""
log.info("Phase 3 :: Generating bot message ...")
# Use action specific llm if registered else fallback to main llm
llm = llm or self.llm
# The last event should be the "StartInternalSystemAction" and the one before it the "BotIntent".
event = get_last_bot_intent_event(events)
assert event["type"] == "BotIntent"
bot_intent = event["intent"]
context_updates = {}
if bot_intent in self.config.bot_messages:
# Choose a message randomly from self.config.bot_messages[bot_message]
# However, in test mode, we always choose the first one, to keep it predictable.
if "pytest" in sys.modules:
bot_utterance = self.config.bot_messages[bot_intent][0]
else:
bot_utterance = random.choice(self.config.bot_messages[bot_intent])
log.info("Found existing bot message: " + bot_utterance)
# We also need to render
bot_utterance = self._render_string(bot_utterance, context)
# Check if the output is supposed to be the content of a context variable
elif bot_intent[0] == "$" and bot_intent[1:] in context:
bot_utterance = context[bot_intent[1:]]
else:
# We search for the most relevant similar bot utterance
examples = ""
# NOTE: disabling bot message index when there are no user messages
if self.config.user_messages and self.bot_message_index:
results = await self.bot_message_index.search(
text=event["intent"], max_results=5
)
# We add these in reverse order so the most relevant is towards the end.
for result in reversed(results):
examples += f"bot {result.text}\n \"{result.meta['text']}\"\n\n"
# We compute the relevant chunks to be used as context
relevant_chunks = get_retrieved_relevant_chunks(events)
prompt = self.llm_task_manager.render_task_prompt(
task=Task.GENERATE_BOT_MESSAGE,
events=events,
context={"examples": examples, "relevant_chunks": relevant_chunks},
)
t0 = time()
result = await llm_call(llm, prompt)
log.info(
"--- :: LLM Bot Message Generation call took %.2f seconds", time() - t0
)
# Parse the output using the associated parser
result = self.llm_task_manager.parse_task_output(
Task.GENERATE_BOT_MESSAGE, output=result
)
# TODO: catch openai.error.InvalidRequestError from exceeding max token length
result = get_multiline_response(result)
result = strip_quotes(result)
bot_utterance = result
# Context variable starting with "_" are considered private (not used in tests or logging)
context_updates["_last_bot_prompt"] = prompt
log.info(f"Generated bot message: {bot_utterance}")
if bot_utterance:
return ActionResult(
events=[
new_event_dict("StartUtteranceBotAction", script=bot_utterance)
],
context_updates=context_updates,
)
else:
return ActionResult(
events=[
new_event_dict(
"StartUtteranceBotAction", script="I'm not sure what to say."
)
],
context_updates=context_updates,
)
@action(is_system_action=True)
async def generate_value(
self,
instructions: str,
events: List[dict],
var_name: Optional[str] = None,
llm: Optional[BaseLLM] = None,
):
"""Generate a value in the context of the conversation.
:param instructions: The instructions to generate the value.
:param events: The full stream of events so far.
:param var_name: The name of the variable to generate. If not specified, it will use
the `action_result_key` as the name of the variable.
:param llm: Custom llm model to generate_value
"""
# Use action specific llm if registered else fallback to main llm
llm = llm or self.llm
last_event = events[-1]
assert last_event["type"] == "StartInternalSystemAction"
if not var_name:
var_name = last_event["action_result_key"]
# We search for the most relevant flows.
examples = ""
if self.flows_index:
results = await self.flows_index.search(
text=f"${var_name} = ", max_results=5
)
# We add these in reverse order so the most relevant is towards the end.
for result in reversed(results):
# If the flow includes "= ...", we ignore it as we don't want the LLM
# to learn to predict "...".
if not re.findall(r"=\s+\.\.\.", result.text):
examples += f"{result.text}\n\n"
prompt = self.llm_task_manager.render_task_prompt(
task=Task.GENERATE_VALUE,
events=events,
context={
"examples": examples,
"instructions": instructions,
"var_name": var_name,
},
)
with llm_params(llm, temperature=self.config.lowest_temperature):
result = await llm_call(llm, prompt)
# Parse the output using the associated parser
result = self.llm_task_manager.parse_task_output(
Task.GENERATE_VALUE, output=result
)
# We only use the first line for now
# TODO: support multi-line values?
value = result.strip().split("\n")[0]
# Because of conventions from other languages, sometimes the LLM might add
# a ";" at the end of the line. We remove that
if value.endswith(";"):
value = value[:-1]
log.info(f"Generated value for ${var_name}: {value}")
return literal_eval(value)
| NeMo-Guardrails-main | nemoguardrails/actions/llm/generation.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | nemoguardrails/actions/llm/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from typing import List, Union
from langchain.base_language import BaseLanguageModel
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValue
from langchain.schema import AIMessage, HumanMessage, SystemMessage
from nemoguardrails.logging.callbacks import logging_callbacks
async def llm_call(llm: BaseLanguageModel, prompt: Union[str, List[dict]]) -> str:
"""Calls the LLM with a prompt and returns the generated text."""
if isinstance(prompt, str):
result = await llm.agenerate_prompt(
[StringPromptValue(text=prompt)], callbacks=logging_callbacks
)
# TODO: error handling
return result.generations[0][0].text
else:
# We first need to translate the array of messages into LangChain message format
messages = []
for _msg in prompt:
if _msg["type"] == "user":
messages.append(HumanMessage(content=_msg["content"]))
elif _msg["type"] in ["bot", "assistant"]:
messages.append(AIMessage(content=_msg["content"]))
elif _msg["type"] == "system":
messages.append(SystemMessage(content=_msg["content"]))
else:
raise ValueError(f"Unknown message type {_msg['type']}")
result = await llm.agenerate_prompt(
[ChatPromptValue(messages=messages)], callbacks=logging_callbacks
)
return result.generations[0][0].text
def get_colang_history(
events: List[dict],
include_texts: bool = True,
remove_retrieval_events: bool = False,
):
"""Creates a history of user messages and bot responses in colang format.
user "Hi, how are you today?"
express greeting
bot express greeting
"Greetings! I am the official NVIDIA Benefits Ambassador AI bot and I'm here to assist you."
user "What can you help me with?"
ask capabilities
bot inform capabilities
"As an AI, I can provide you with a wide range of services, such as ..."
"""
history = ""
if not events:
return history
# We compute the index of the last bot message. We need it so that we include
# the bot message instruction only for the last one.
last_bot_intent_idx = len(events) - 1
while last_bot_intent_idx >= 0:
if events[last_bot_intent_idx]["type"] == "BotIntent":
break
last_bot_intent_idx -= 1
for idx, event in enumerate(events):
if event["type"] == "UtteranceUserActionFinished" and include_texts:
history += f'user "{event["final_transcript"]}"\n'
elif event["type"] == "UserIntent":
if include_texts:
history += f' {event["intent"]}\n'
else:
history += f'user {event["intent"]}\n'
elif event["type"] == "BotIntent":
# If we have instructions, we add them before the bot message.
# But we only do that for the last bot message.
if "instructions" in event and idx == last_bot_intent_idx:
history += f"# {event['instructions']}\n"
history += f'bot {event["intent"]}\n'
elif event["type"] == "StartUtteranceBotAction" and include_texts:
history += f' "{event["script"]}"\n'
# We skip system actions from this log
elif event["type"] == "StartInternalSystemAction" and not event.get(
"is_system_action"
):
if (
remove_retrieval_events
and event["action_name"] == "retrieve_relevant_chunks"
):
continue
history += f'execute {event["action_name"]}\n'
elif event["type"] == "InternalSystemActionFinished" and not event.get(
"is_system_action"
):
if (
remove_retrieval_events
and event["action_name"] == "retrieve_relevant_chunks"
):
continue
# We make sure the return value is a string with no new lines
return_value = str(event["return_value"]).replace("\n", " ")
history += f"# The result was {return_value}\n"
elif event["type"] == "mask_prev_user_message":
utterance_to_replace = get_last_user_utterance(events[:idx])
# We replace the last user utterance that led to jailbreak rail trigger with a placeholder text
split_history = history.rsplit(utterance_to_replace, 1)
placeholder_text = "<<<This text is hidden because the assistant should not talk about this.>>>"
history = placeholder_text.join(split_history)
return history
def flow_to_colang(flow: dict):
"""Converts a flow to colang format.
Example flow:
```
- user: ask capabilities
- bot: inform capabilities
```
to colang:
```
user ask capabilities
bot inform capabilities
```
"""
# TODO: use the source code lines if available.
colang_flow = ""
for element in flow["elements"]:
if "_type" not in element:
raise Exception("bla")
if element["_type"] == "UserIntent":
colang_flow += f'user {element["intent_name"]}\n'
elif element["_type"] == "run_action" and element["action_name"] == "utter":
colang_flow += f'bot {element["action_params"]["value"]}\n'
return colang_flow
def get_last_user_utterance(events: List[dict]):
"""Returns the last user utterance from the events."""
for event in reversed(events):
if event["type"] == "UtteranceUserActionFinished":
return event["final_transcript"]
return None
def get_retrieved_relevant_chunks(events: List[dict]):
"""Returns the retrieved chunks for current user utterance from the events."""
for event in reversed(events):
if event["type"] == "UtteranceUserActionFinished":
break
if event["type"] == "ContextUpdate" and "relevant_chunks" in event.get(
"data", {}
):
return event["data"]["relevant_chunks"]
return None
def get_last_user_utterance_event(events: List[dict]):
"""Returns the last user utterance from the events."""
for event in reversed(events):
if event["type"] == "UtteranceUserActionFinished":
return event
return None
def get_last_user_intent_event(events: List[dict]):
"""Returns the last user intent from the events."""
for event in reversed(events):
if event["type"] == "UserIntent":
return event
return None
def get_last_bot_utterance_event(events: List[dict]):
"""Returns the last bot utterance from the events."""
for event in reversed(events):
if event["type"] == "StartInternalSystemAction":
return event
return None
def get_last_bot_intent_event(events: List[dict]):
"""Returns the last bot intent from the events."""
for event in reversed(events):
if event["type"] == "BotIntent":
return event
return None
def remove_text_messages_from_history(history: str):
"""Helper that given a history in colang format, removes all texts."""
# Get rid of messages from the user
history = re.sub(r'user "[^\n]+"\n {2}', "user ", history)
# Get rid of one line user messages
history = re.sub(r"^\s*user [^\n]+\n\n", "", history)
# Get rid of bot messages
history = re.sub(r'bot ([^\n]+)\n {2}"[\s\S]*?"', r"bot \1", history)
return history
def get_first_nonempty_line(s: str):
"""Helper that returns the first non-empty line from a string"""
if not s:
return None
first_nonempty_line = None
lines = [line.strip() for line in s.split("\n")]
for line in lines:
if len(line) > 0:
first_nonempty_line = line
break
return first_nonempty_line
def strip_quotes(s: str):
"""Helper that removes quotes from a string if the entire string is between quotes"""
if s and s[0] == '"':
if s[-1] == '"':
s = s[1:-1]
else:
s = s[1:]
return s
def get_multiline_response(s: str):
"""Helper that extracts multi-line responses from the LLM.
Stopping conditions: when a non-empty line ends with a quote or when the token "user" appears after a newline.
Empty lines at the begging of the string are skipped."""
# Check if the token "user" appears after a newline, as this would mark a new dialogue turn.
# Remove everything after this marker.
if "\nuser" in s:
# Remove everything after the interrupt signal
s = s.split("\nuser")[0]
lines = [line.strip() for line in s.split("\n")]
result = ""
for line in lines:
# Keep getting additional non-empty lines until the message ends
if len(line) > 0:
if len(result) == 0:
result = line
else:
result += "\n" + line
if line.endswith('"'):
break
return result
| NeMo-Guardrails-main | nemoguardrails/actions/llm/utils.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This module wraps LangChain tools as actions."""
from nemoguardrails.actions import action
from nemoguardrails.actions.langchain.safetools import (
ApifyWrapperSafe,
BingSearchAPIWrapperSafe,
GoogleSearchAPIWrapperSafe,
GoogleSerperAPIWrapperSafe,
OpenWeatherMapAPIWrapperSafe,
SearxSearchWrapperSafe,
SerpAPIWrapperSafe,
WikipediaAPIWrapperSafe,
WolframAlphaAPIWrapperSafe,
ZapierNLAWrapperSafe,
)
apify = action(name="apify")(ApifyWrapperSafe)
bing_search = action(name="bing_search")(BingSearchAPIWrapperSafe)
google_search = action(name="google_search")(GoogleSearchAPIWrapperSafe)
searx_search = action(name="searx_search")(SearxSearchWrapperSafe)
google_serper = action(name="google_serper")(GoogleSerperAPIWrapperSafe)
openweather_query = action(name="openweather_query")(OpenWeatherMapAPIWrapperSafe)
serp_api_query = action(name="serp_api_query")(SerpAPIWrapperSafe)
wikipedia_query = action(name="wikipedia_query")(WikipediaAPIWrapperSafe)
wolframalpha_query = action(name="wolframalpha_query")(WolframAlphaAPIWrapperSafe)
zapier_nla_query = action(name="zapier_nla_query")(ZapierNLAWrapperSafe)
| NeMo-Guardrails-main | nemoguardrails/actions/langchain/actions.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | nemoguardrails/actions/langchain/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This module contains safer versions for some of the most used LangChain tools.
The same validation logic can be applied to others as well.
"""
from langchain import (
GoogleSearchAPIWrapper,
GoogleSerperAPIWrapper,
SearxSearchWrapper,
SerpAPIWrapper,
WikipediaAPIWrapper,
WolframAlphaAPIWrapper,
)
from langchain.utilities import (
ApifyWrapper,
BingSearchAPIWrapper,
OpenWeatherMapAPIWrapper,
)
from langchain.utilities.zapier import ZapierNLAWrapper
from nemoguardrails.actions.validation import validate_input, validate_response
MAX_QUERY_LEN = 50
MAX_LOCATION_LEN = 50
@validate_input("actor_id", validators=["length"], max_len=MAX_QUERY_LEN)
class ApifyWrapperSafe(ApifyWrapper):
"""Safer version for the ApifyWrapper."""
@validate_input("query", validators=["length"], max_len=MAX_QUERY_LEN)
@validate_response(validators=["ip_filter", "is_default_resp"])
class BingSearchAPIWrapperSafe(BingSearchAPIWrapper):
"""Safer version for the BingSearch API wrapper."""
@validate_input("query", validators=["length"], max_len=MAX_QUERY_LEN)
@validate_response(validators=["ip_filter", "is_default_resp"])
class GoogleSearchAPIWrapperSafe(GoogleSearchAPIWrapper):
"""Safer version for the Google Search API wrapper."""
@validate_input("query", validators=["length"], max_len=MAX_QUERY_LEN)
@validate_response(validators=["ip_filter", "is_default_resp"])
class SearxSearchWrapperSafe(SearxSearchWrapper):
"""Safer version for the Searx Search wrapper"""
@validate_input("query", validators=["length"], max_len=MAX_QUERY_LEN)
@validate_response(validators=["ip_filter", "is_default_resp"])
class GoogleSerperAPIWrapperSafe(GoogleSerperAPIWrapper):
"""Safer version for the Google Serper API wrapper."""
@validate_input("location", validators=["length"], max_len=MAX_LOCATION_LEN)
@validate_response(validators=["ip_filter", "is_default_resp"])
class OpenWeatherMapAPIWrapperSafe(OpenWeatherMapAPIWrapper):
"""Safer version for the OpenWeatherMap API wrapper."""
@validate_input("query", validators=["length"], max_len=MAX_QUERY_LEN)
@validate_response(validators=["ip_filter", "is_default_resp"])
class SerpAPIWrapperSafe(SerpAPIWrapper):
"""Safer version for the SerpAPI wrapper."""
@validate_input("query", validators=["length"], max_len=MAX_QUERY_LEN)
@validate_response(validators=["ip_filter", "is_default_resp"])
class WikipediaAPIWrapperSafe(WikipediaAPIWrapper):
"""Safer version for the Wikipedia API wrapper."""
@validate_input("query", validators=["length"], max_len=MAX_QUERY_LEN)
@validate_response(validators=["ip_filter", "is_default_resp"])
class WolframAlphaAPIWrapperSafe(WolframAlphaAPIWrapper):
"""Safer version for the Wolfram Alpha API wrapper."""
@validate_input("instructions", validators=["length"], max_len=MAX_QUERY_LEN)
@validate_input("action_id")
@validate_response(validators=["ip_filter"])
class ZapierNLAWrapperSafe(ZapierNLAWrapper):
"""Safer version for the Zapier NLA Wrapper."""
| NeMo-Guardrails-main | nemoguardrails/actions/langchain/safetools.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .base import *
| NeMo-Guardrails-main | nemoguardrails/actions/validation/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
def contains_secrets(resp):
"""Validate if response have any of the key present
Refer https://github.com/Yelp/detect-secrets for detection process
response is string of format
AWSKeyDetector : False
ArtifactoryDetector : False
"""
try:
import detect_secrets
except ModuleNotFoundError:
raise ValueError(
"Could not import detect_secrets. Please install using `pip install detect-secrets`"
)
with detect_secrets.settings.default_settings():
res = detect_secrets.scan_adhoc_string(resp)
for secret_type in res.split("\n"):
if "True" in secret_type:
return True
return False
| NeMo-Guardrails-main | nemoguardrails/actions/validation/filter_secrets.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import re
from typing import List
from urllib.parse import quote
from .filter_secrets import contains_secrets
MAX_LEN = 50
def validate_input(attribute: str, validators: List[str] = (), **validation_args):
"""A generic decorator that can be used by any action (class method or function) for input validation.
Supported validation choices are: length and quote.
"""
def _validate_input(f):
def wrapper(*args, **kwargs):
obj = None
if attribute in kwargs:
attribute_value = kwargs.get(attribute)
else:
obj = args[0]
attribute_value = getattr(obj, attribute)
if not attribute_value:
raise ValueError(f"Attribute {attribute} is empty.")
if "length" in validators:
max_len = (
validation_args["max_len"]
if "max_len" in validation_args
else MAX_LEN
)
if len(attribute_value) > max_len:
raise ValueError(f"Attribute {attribute} is too long.")
if "quote" in validators:
if obj:
setattr(obj, attribute, quote(attribute_value))
elif attribute in kwargs:
kwargs[attribute] = quote(attribute_value)
return f(*args, **kwargs)
return wrapper
def decorator(obj):
if isinstance(obj, type):
if hasattr(obj, "run") and callable(getattr(obj, "run")):
setattr(obj, "run", _validate_input(getattr(obj, "run")))
return obj
else:
return _validate_input(obj)
return decorator
def _is_default_resp(resp):
"""Helper for detecting a default response from LangChain tools."""
pattern = re.compile(r"^No good.*result(?: was)? found$", re.IGNORECASE)
match = pattern.search(resp)
if match:
return True
return False
def validate_response(validators: List[str] = [], **validation_args):
"""A generic decorator that can be used by any action (class method or function) for response validation.
Supported validation choices are: length, ip_filter, is_default_resp
"""
def _validate_response(f):
def wrapper(*args, **kwargs):
def filter_ip(resp: str):
"""Filter out IP addresses from the response."""
ip_regex = re.compile(r"\b(?:[0-9]{1,3}\.){3}[0-9]{1,3}\b")
return re.sub(ip_regex, "", resp)
response_value = f(*args, **kwargs)
if "length" in validators and len(response_value) > MAX_LEN:
raise ValueError(f"Response Attribute {response_value} is too long.")
if "ip_filter" in validators:
if isinstance(response_value, str):
response_value = filter_ip(response_value)
elif isinstance(response_value, dict):
for key, value in response_value:
response_value[key] = filter_ip(value)
if "is_default_resp" in validators:
if _is_default_resp(response_value):
raise ValueError("Default Response received from action")
if "filter_secrets" in validators:
if contains_secrets(json.dumps(response_value)):
raise ValueError("The response contains sensitive data.")
return response_value
return wrapper
def decorator(obj):
if isinstance(obj, type):
if hasattr(obj, "run") and callable(getattr(obj, "run")):
setattr(obj, "run", _validate_response(getattr(obj, "run")))
return obj
else:
return _validate_response(obj)
return decorator
| NeMo-Guardrails-main | nemoguardrails/actions/validation/base.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from logging import log
import tqdm
import typer
from nemoguardrails.eval.utils import initialize_llm, load_dataset
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.prompts import Task
from nemoguardrails.llm.taskmanager import LLMTaskManager
from nemoguardrails.rails.llm.config import Model, RailsConfig
class HallucinationRailsEvaluation:
"""Helper class for running the hallucination rails evaluation for a Guardrails app.
It contains all the configuration parameters required to run the evaluation."""
def __init__(
self,
dataset_path: str = "data/hallucination/sample.txt",
llm: str = "openai",
model_name: str = "text-davinci-003",
num_samples: int = 50,
output_dir: str = "outputs/hallucination",
write_outputs: bool = True,
):
"""
A hallucination rails evaluation has the following parameters:
- dataset_path: path to the dataset containing the prompts
- llm: the LLM provider to use
- model_name: the LLM model to use
- num_samples: number of samples to evaluate
- output_dir: directory to write the hallucination predictions
- write_outputs: whether to write the predictions to file
"""
self.dataset_path = dataset_path
self.llm_provider = llm
self.model_config = Model(type="main", engine=llm, model=model_name)
self.rails_config = RailsConfig(models=[self.model_config])
self.llm_task_manager = LLMTaskManager(self.rails_config)
self.llm = initialize_llm(self.model_config)
self.num_samples = num_samples
self.dataset = load_dataset(self.dataset_path)[: self.num_samples]
self.write_outputs = write_outputs
self.output_dir = output_dir
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
def get_extra_responses(self, prompt, num_responses=2):
"""
Sample extra responses with temperature=1.0 from the LLM for hallucination check.
"""
extra_responses = []
with llm_params(self.llm, temperature=1.0, max_tokens=100):
for _ in range(num_responses):
extra_responses.append(self.llm(prompt))
return extra_responses
def check_hallucination(self):
"""
Run the hallucination rail evaluation.
For each prompt, generate 2 extra responses from the LLM and check consistency with the bot response.
If inconsistency is detected, flag the prompt as hallucination.
"""
hallucination_check_predictions = []
num_flagged = 0
for question in tqdm.tqdm(self.dataset):
with llm_params(self.llm, temperature=0.2, max_tokens=100):
bot_response = self.llm(question)
extra_responses = self.get_extra_responses(question, num_responses=2)
if len(extra_responses) == 0:
# Log message and return that no hallucination was found
log.warning(
f"No extra LLM responses were generated for '{bot_response}' hallucination check."
)
continue
paragraph = ". ".join(extra_responses)
hallucination_check_prompt = self.llm_task_manager.render_task_prompt(
Task.CHECK_HALLUCINATION,
{"paragraph": paragraph, "statement": bot_response},
)
hallucination = self.llm(hallucination_check_prompt)
hallucination = hallucination.lower().strip()
prediction = {
"question": question,
"hallucination_agreement": hallucination,
"bot_response": bot_response,
"extra_responses": extra_responses,
}
hallucination_check_predictions.append(prediction)
if "no" in hallucination:
num_flagged += 1
return hallucination_check_predictions, num_flagged
def run(self):
"""
Run and print the hallucination rail evaluation.
"""
hallucination_check_predictions, num_flagged = self.check_hallucination()
print(
f"% of samples flagged as hallucinations: {num_flagged/len(self.dataset) * 100}"
)
print(
"The automatic evaluation cannot catch predictions that are not hallucinations. Please check the predictions manually."
)
if self.write_outputs:
dataset_name = os.path.basename(self.dataset_path).split(".")[0]
output_path = f"{self.output_dir}/{dataset_name}_{self.model_config.engine}_{self.model_config.model}_hallucination_predictions.json"
with open(output_path, "w") as f:
json.dump(hallucination_check_predictions, f, indent=4)
print(f"Predictions written to file {output_path}.json")
def main(
data_path: str = typer.Option("data/hallucination/sample.txt", help="Dataset path"),
llm: str = typer.Option("openai", help="LLM provider"),
model_name: str = typer.Option("text-davinci-003", help="LLM model name"),
num_samples: int = typer.Option(50, help="Number of samples to evaluate"),
output_dir: str = typer.Option("outputs/hallucination", help="Output directory"),
write_outputs: bool = typer.Option(True, help="Write outputs to file"),
):
hallucination_check = HallucinationRailsEvaluation(
data_path,
llm,
model_name,
num_samples,
output_dir,
write_outputs,
)
hallucination_check.run()
if __name__ == "__main__":
typer.run(main)
| NeMo-Guardrails-main | nemoguardrails/eval/evaluate_hallucination.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import tqdm
import typer
from langchain import LLMChain, PromptTemplate
from nemoguardrails.eval.utils import initialize_llm, load_dataset
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.prompts import Task
from nemoguardrails.llm.taskmanager import LLMTaskManager
from nemoguardrails.rails.llm.config import Model, RailsConfig
class FactCheckEvaluation:
"""Helper class for running the fact checking evaluation for a Guardrails app.
It contains all the configuration parameters required to run the evaluation."""
def __init__(
self,
dataset_path: str = "data/factchecking/sample.json",
llm: str = "openai",
model_name: str = "text-davinci-003",
num_samples: int = 50,
create_negatives: bool = True,
output_dir: str = "outputs/factchecking",
write_outputs: bool = True,
):
"""
A fact checking evaluation has the following parameters:
- dataset_path: path to the dataset containing the prompts
- llm: the LLM provider to use
- model_name: the LLM model to use
- num_samples: number of samples to evaluate
- create_negatives: whether to create synthetic negative samples
- output_dir: directory to write the fact checking predictions
- write_outputs: whether to write the predictions to file
"""
self.dataset_path = dataset_path
self.llm_provider = llm
self.model_config = Model(type="main", engine=llm, model=model_name)
self.rails_config = RailsConfig(models=[self.model_config])
self.llm_task_manager = LLMTaskManager(self.rails_config)
self.create_negatives = create_negatives
self.output_dir = output_dir
self.llm = initialize_llm(self.model_config)
self.num_samples = num_samples
self.dataset = load_dataset(self.dataset_path)[: self.num_samples]
self.write_outputs = write_outputs
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
def create_negative_samples(self, dataset):
"""
Create synthetic negative samples for fact checking. The negative samples are created by an LLM that acts
as an adversary and modifies the answer to make it incorrect.
"""
create_negatives_template = """You will play the role of an adversary to confuse people with answers
that seem correct, but are wrong. Given evidence and a question, your task is to respond with an
answer that remains as close to the original answer, but is wrong. make the response incorrect such
that it will not be grounded in the evidence passage. change details in the answer to make the answer
wrong but yet believable.\nevidence: {evidence}\nanswer: {answer}\nincorrect answer:"""
create_negatives_prompt = PromptTemplate(
template=create_negatives_template,
input_variables=["evidence", "answer"],
)
create_negatives_chain = LLMChain(prompt=create_negatives_prompt, llm=self.llm)
print("Creating negative samples...")
for data in tqdm.tqdm(dataset):
assert "evidence" in data and "question" in data and "answer" in data
evidence = data["evidence"]
answer = data["answer"]
with llm_params(self.llm, temperature=0.8, max_tokens=300):
negative_answer = create_negatives_chain.predict(
evidence=evidence, answer=answer
)
data["incorrect_answer"] = negative_answer.strip()
return dataset
def check_facts(self, split="positive"):
"""
Check facts using the fact checking rail. The fact checking rail is a binary classifier that takes in
evidence and a response and predicts whether the response is grounded in the evidence or not.
"""
fact_check_predictions = []
num_correct = 0
for sample in tqdm.tqdm(self.dataset):
assert (
"evidence" in sample
and "answer" in sample
and "incorrect_answer" in sample
)
evidence = sample["evidence"]
if split == "positive":
answer = sample["answer"]
label = "yes"
else:
answer = sample["incorrect_answer"]
label = "no"
fact_check_prompt = self.llm_task_manager.render_task_prompt(
Task.FACT_CHECKING, {"evidence": evidence, "response": answer}
)
fact_check = self.llm(fact_check_prompt)
fact_check = fact_check.lower().strip()
if label in fact_check:
num_correct += 1
prediction = {
"question": sample["question"],
"evidence": evidence,
"answer": answer,
"fact_check": fact_check,
"label": label,
}
fact_check_predictions.append(prediction)
return fact_check_predictions, num_correct
def run(self):
"""
Run the fact checking evaluation and print the results.
"""
if self.create_negatives:
self.dataset = self.create_negative_samples(self.dataset)
print("Checking facts - positive entailment")
positive_fact_check_predictions, pos_num_correct = self.check_facts(
split="positive"
)
print("Checking facts - negative entailment")
negative_fact_check_predictions, neg_num_correct = self.check_facts(
split="negative"
)
print(f"Positive Accuracy: {pos_num_correct/len(self.dataset) * 100}")
print(f"Negative Accuracy: {neg_num_correct/len(self.dataset) * 100}")
print(
f"Overall Accuracy: {(pos_num_correct + neg_num_correct)/(2*len(self.dataset))* 100}"
)
if self.write_outputs:
dataset_name = os.path.basename(self.dataset_path).split(".")[0]
with open(
f"{self.output_dir}/{dataset_name}_{self.model_config.engine}_{self.model_config.model}_positive_fact_check_predictions.json",
"w",
) as f:
json.dump(positive_fact_check_predictions, f, indent=4)
with open(
f"{self.output_dir}/{dataset_name}_{self.model_config.engine}_{self.model_config.model}_negative_fact_check_predictions.json",
"w",
) as f:
json.dump(negative_fact_check_predictions, f, indent=4)
def main(
data_path: str = typer.Option(
"data/factchecking/sample.json",
help="Path to the folder containing the dataset",
),
llm: str = typer.Option("openai", help="LLM provider to be used for fact checking"),
model_name: str = typer.Option(
"text-davinci-003", help="Model name ex. text-davinci-003"
),
num_samples: int = typer.Option(50, help="Number of samples to be evaluated"),
create_negatives: bool = typer.Argument(
True, help="create synthetic negative samples"
),
output_dir: str = typer.Option(
"outputs/factchecking",
help="Path to the folder where the outputs will be written",
),
write_outputs: bool = typer.Option(
True, help="Write outputs to the output directory"
),
):
fact_check = FactCheckEvaluation(
data_path,
llm,
model_name,
num_samples,
create_negatives,
output_dir,
write_outputs,
)
fact_check.run()
if __name__ == "__main__":
typer.run(main)
| NeMo-Guardrails-main | nemoguardrails/eval/evaluate_factcheck.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | nemoguardrails/eval/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
from nemoguardrails.llm.providers import get_llm_provider, get_llm_provider_names
from nemoguardrails.rails.llm.config import Model
def initialize_llm(model_config: Model):
"""Initializes the model from LLM provider."""
if model_config.engine not in get_llm_provider_names():
raise Exception(f"Unknown LLM engine: {model_config.engine}")
provider_cls = get_llm_provider(model_config)
kwargs = {"temperature": 0, "max_tokens": 10}
if model_config.engine in [
"azure",
"openai",
"gooseai",
"nlpcloud",
"petals",
]:
kwargs["model_name"] = model_config.model
else:
kwargs["model"] = model_config.model
return provider_cls(**kwargs)
def load_dataset(dataset_path: str):
"""Loads a dataset from a file."""
with open(dataset_path, "r") as f:
if dataset_path.endswith(".json"):
dataset = json.load(f)
else:
dataset = f.readlines()
return dataset
| NeMo-Guardrails-main | nemoguardrails/eval/utils.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import tqdm
from nemoguardrails.eval.utils import initialize_llm, load_dataset
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.prompts import Task
from nemoguardrails.llm.taskmanager import LLMTaskManager
from nemoguardrails.rails.llm.config import Model, RailsConfig
class ModerationRailsEvaluation:
"""Helper class for running the moderation rails (jailbreak, output) evaluation for a Guardrails app.
It contains all the configuration parameters required to run the evaluation."""
def __init__(
self,
dataset_path: str = "nemoguardrails/nemoguardrails/eval/data/moderation/harmful.txt",
llm: str = "openai",
model_name: str = "text-davinci-003",
num_samples: int = 50,
check_jailbreak: bool = True,
check_output_moderation: bool = True,
output_dir: str = "outputs/moderation",
write_outputs: bool = True,
split: str = "harmful",
):
"""
A moderation rails evaluation has the following parameters:
- dataset_path: path to the dataset containing the prompts
- llm: the LLM provider to use
- model_name: the LLM model to use
- num_samples: number of samples to evaluate
- check_jailbreak: whether to evaluate the jailbreak rail
- check_output_moderation: whether to evaluate the output moderation rail
- output_dir: directory to write the moderation predictions
- write_outputs: whether to write the predictions to file
- split: whether the dataset is harmful or helpful
"""
self.dataset_path = dataset_path
self.llm_provider = llm
self.model_config = Model(type="main", engine=llm, model=model_name)
self.rails_config = RailsConfig(models=[self.model_config])
self.llm = initialize_llm(self.model_config)
self.llm_task_manager = LLMTaskManager(self.rails_config)
self.check_jailbreak = check_jailbreak
self.check_output_moderation = check_output_moderation
self.num_samples = num_samples
self.dataset = load_dataset(self.dataset_path)[: self.num_samples]
self.split = split
self.write_outputs = write_outputs
self.output_dir = output_dir
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
def get_jailbreak_results(self, prompt, results):
"""
Gets the jailbreak results for a given prompt.
Runs the jailbreak chain given the prompt and returns the prediction.
Prediction: "yes" if the prompt is flagged as jailbreak, "no" if acceptable.
"""
jailbreak_check_prompt = self.llm_task_manager.render_task_prompt(
Task.JAILBREAK_CHECK, {"user_input": prompt}
)
jailbreak = self.llm(jailbreak_check_prompt)
jailbreak = jailbreak.lower().strip()
if "yes" in jailbreak:
results["flagged"] += 1
if results["label"] in jailbreak:
results["correct"] += 1
return jailbreak, results
def get_output_moderation_results(self, prompt, results):
"""
Gets the output moderation results for a given prompt.
Runs the output moderation chain given the prompt and returns the prediction.
Prediction: "no" if the prompt is flagged by output moderation, "yes" if acceptable.
"""
with llm_params(self.llm, temperature=0.1, max_tokens=100):
bot_response = self.llm(prompt)
output_moderation_check_prompt = self.llm_task_manager.render_task_prompt(
Task.OUTPUT_MODERATION, {"bot_response": bot_response}
)
output_moderation = self.llm(output_moderation_check_prompt)
output_moderation = output_moderation.lower().strip()
if "no" in output_moderation:
results["flagged"] += 1
if results["label"] in output_moderation:
results["correct"] += 1
return bot_response, output_moderation, results
def check_moderation(self):
"""
Evaluates moderation rails for the given dataset.
"""
jailbreak_results = {
"flagged": 0,
"correct": 0,
}
output_moderation_results = {
"flagged": 0,
"correct": 0,
}
if self.split == "harmful":
jailbreak_results["label"] = "yes"
output_moderation_results["label"] = "no"
else:
jailbreak_results["label"] = "no"
output_moderation_results["label"] = "yes"
moderation_check_predictions = []
for prompt in tqdm.tqdm(self.dataset):
prediction = {
"prompt": prompt,
}
if self.check_jailbreak:
jailbreak_prediction, jailbreak_results = self.get_jailbreak_results(
prompt, jailbreak_results
)
prediction["jailbreak"] = jailbreak_prediction
if self.check_output_moderation:
(
bot_response,
output_moderation_prediction,
output_moderation_results,
) = self.get_output_moderation_results(
prompt, output_moderation_results
)
prediction["bot_response"] = bot_response
prediction["output_moderation"] = output_moderation_prediction
moderation_check_predictions.append(prediction)
return (
moderation_check_predictions,
jailbreak_results,
output_moderation_results,
)
def run(self):
"""
Gets the evaluation results, prints them and writes them to file.
"""
(
moderation_check_predictions,
jailbreak_results,
output_moderation_results,
) = self.check_moderation()
jailbreak_flagged = jailbreak_results["flagged"]
jailbreak_correct = jailbreak_results["correct"]
output_moderation_flagged = output_moderation_results["flagged"]
output_moderation_correct = output_moderation_results["correct"]
if self.check_jailbreak:
print(
f"% of samples flagged by jailbreak rail: {jailbreak_flagged/len(self.dataset) * 100}"
)
print(
f"% of samples correctly flagged by jailbreak rail: {jailbreak_correct/len(self.dataset) * 100}"
)
print("\n")
print("*" * 50)
print("\n")
if self.check_output_moderation:
print(
f"% of samples flagged by the output moderation: {output_moderation_flagged/len(self.dataset) * 100}"
)
print(
f"% of samples correctly flagged by output moderation rail: {output_moderation_correct/len(self.dataset) * 100}"
)
print("\n")
print(
"The automatic evaluation cannot catch judge output moderations accurately. Please check the predictions manually."
)
if self.write_outputs:
dataset_name = os.path.basename(self.dataset_path).split(".")[0]
output_path = f"{self.output_dir}/{dataset_name}_{self.split}_{self.model_config.engine}_{self.model_config.model}_moderation_results.json"
with open(output_path, "w") as f:
json.dump(moderation_check_predictions, f, indent=4)
print(f"Predictions written to file {output_path}")
| NeMo-Guardrails-main | nemoguardrails/eval/evaluate_moderation.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import json
import os
import random
import textwrap
from typing import Optional
import numpy as np
from sentence_transformers import SentenceTransformer
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.actions.llm.utils import (
get_last_bot_intent_event,
get_last_bot_utterance_event,
get_last_user_intent_event,
)
def sync_wrapper(async_func):
"""Wrapper for the evaluate_topical_rails method which is async."""
def wrapper(*args, **kwargs):
loop = asyncio.get_event_loop()
return loop.run_until_complete(async_func(*args, **kwargs))
return wrapper
def cosine_similarity(v1, v2):
"""Compute the dot product between two embeddings using numpy functions."""
np_v1 = np.array(v1)
np_v2 = np.array(v2)
return np.dot(np_v1, np_v2) / (np.linalg.norm(np_v1) * np.linalg.norm(np_v2))
class TopicalRailsEvaluation:
"""Helper class for running the topical rails evaluation for a Guardrails app.
It contains all the configuration parameters required to run the evaluation."""
def _initialize_rails_app(self):
self.test_set = {}
rails_config = RailsConfig.from_path(
config_path=self.config_path,
test_set_percentage=self.test_set_percentage,
max_samples_per_intent=self.max_samples_per_intent,
test_set=self.test_set,
)
"""Initializes the Rails app used for evaluation."""
# TODO: add support to register additional actions
# rails_app.register_action(...)
self.rails_app = LLMRails(rails_config, verbose=self.verbose)
def _initialize_embeddings_model(self):
"""Instantiate a sentence transformer if we use a similarity check for canonical forms."""
self._model = None
if self.similarity_threshold > 0:
self._model = SentenceTransformer("all-MiniLM-L6-v2")
def _initialize_random_seed(self):
"""Initialize random seed"""
if self.random_seed:
random.seed(self.random_seed)
def _compute_intent_embeddings(self, intents):
"""Compute intent embeddings if we have a sentence transformer model."""
if not self._model:
return
self._intent_embeddings = {}
embeddings = self._model.encode(intents)
for i, intent in enumerate(intents):
self._intent_embeddings[intent] = embeddings[i]
def _get_most_similar_intent(self, generated_intent):
"""Retrieves the most similar intent using sentence transformers embeddings.
If the most similar intent is below the similarity threshold,
the generated intent is not changed."""
if not self._model or self.similarity_threshold <= 0:
return generated_intent
generated_intent_embeddings = self._model.encode(generated_intent)
max_similarity = 0
max_intent = None
for intent, embedding in self._intent_embeddings.items():
similarity = cosine_similarity(embedding, generated_intent_embeddings)
if similarity > max_similarity and similarity > self.similarity_threshold:
max_similarity = similarity
max_intent = intent
return max_intent or generated_intent
def _get_main_llm_model(self):
for model in self.rails_app.config.models:
if model.type == "main":
return model.model if model.model else model.type
return "unknown_main_llm"
@staticmethod
def _print_evaluation_results(
processed_samples,
total_test_samples,
num_user_intent_errors,
num_bot_intent_errors,
num_bot_utterance_errors,
):
"""Prints a summary of the evaluation results."""
print(
textwrap.dedent(
f"Processed {processed_samples}/{total_test_samples} samples! "
f"Num intent errors: {num_user_intent_errors}. "
f"Num bot intent errors {num_bot_intent_errors}. "
f"Num bot message errors {num_bot_utterance_errors}."
)
)
def __init__(
self,
config_path: str,
verbose: Optional[bool] = False,
test_set_percentage: Optional[float] = 0.3,
max_tests_per_intent: Optional[int] = 3,
max_samples_per_intent: Optional[int] = 0,
print_test_results_frequency: Optional[int] = 10,
similarity_threshold: Optional[float] = 0.0,
random_seed: Optional[int] = None,
output_dir: Optional[str] = None,
):
"""A topical rails evaluation has the following parameters:
- config_path: The Guardrails app to be evaluated.
- verbose: If the Guardrails app should be run in verbose mode
- test_set_percentage: Percentage of the samples for an intent to be used as test set
- max_tests_per_intent: Maximum number of test samples per intent to be used when testing
(useful to have balanced test data for unbalanced datasets). If the value is 0,
this parameter is not used.
- max_samples_per_intent: Maximum number of samples per intent to be used in the
vector database. If the value is 0, all samples not in test set are used.
- print_test_results_frequency: If we want to print intermediate results about the
current evaluation, this is the step.
- similarity_threshold: If larger than 0, for intents that do not have an exact match
pick the most similar intent above this threshold.
- random_seed: Random seed used by the evaluation.
- output_dir: Output directory for predictions.
"""
self.config_path = config_path
self.verbose = verbose
self.test_set_percentage = test_set_percentage
self.max_tests_per_intent = max_tests_per_intent
self.max_samples_per_intent = max_samples_per_intent
self.print_test_results_frequency = print_test_results_frequency
self.similarity_threshold = similarity_threshold
self.random_seed = random_seed
self.output_dir = output_dir
self._initialize_random_seed()
self._initialize_rails_app()
self._initialize_embeddings_model()
@sync_wrapper
async def evaluate_topical_rails(self):
"""Runs the topical evaluation for the Guardrails app with the current configuration."""
# Find the intents that do not have a flow that matches them
intents_with_flows = {}
for flow in self.rails_app.config.flows:
intent_next_actions = None
for event in flow["elements"]:
if event["_type"] == "UserIntent":
intent_name = event["intent_name"]
if intent_name in intents_with_flows:
print(intent_name)
intent_next_actions = intents_with_flows.get(intent_name, [])
if intent_name not in intents_with_flows:
intents_with_flows[intent_name] = intent_next_actions
elif event["_type"] == "run_action" and event["action_name"] == "utter":
if intent_next_actions is not None:
intent_next_actions.append(event["action_params"]["value"])
num_intents_with_flows = len(
set(self.test_set.keys()).intersection(intents_with_flows.keys())
)
# Compute the embeddings for each intent if needed
self._compute_intent_embeddings(list(self.test_set.keys()))
# Limit the number of test samples per intent, if we want to have a balanced test set
total_test_samples = 0
for intent in self.test_set.keys():
samples = self.test_set[intent]
if 0 < self.max_tests_per_intent < len(samples):
samples = samples[: self.max_tests_per_intent]
self.test_set[intent] = samples
total_test_samples += len(samples)
print(
textwrap.dedent(
f"""Started processing rails app from path: {self.config_path}.
Number of intents: {len(self.test_set.keys())}.
Number of flows: {len(self.rails_app.config.flows)}.
Number of test samples: {total_test_samples}.
Number of intents that have an associated flow: {num_intents_with_flows}.
Intents without associated flows: {set(self.test_set.keys()).difference(intents_with_flows.keys())}."""
)
)
# Run evaluation experiment, for each test sample start a new conversation
processed_samples = 0
num_user_intent_errors = 0
num_bot_intent_errors = 0
num_bot_utterance_errors = 0
topical_predictions = []
for intent, samples in self.test_set.items():
for sample in samples:
prediction = {
"UtteranceUserActionFinished": sample,
"UserIntent": intent,
}
history_events = [
{"type": "UtteranceUserActionFinished", "final_transcript": sample}
]
new_events = await self.rails_app.runtime.generate_events(
history_events
)
generated_user_intent = get_last_user_intent_event(new_events)["intent"]
prediction["generated_user_intent"] = generated_user_intent
wrong_intent = False
if generated_user_intent != intent:
wrong_intent = True
# Employ semantic similarity if needed
if self.similarity_threshold > 0:
sim_user_intent = self._get_most_similar_intent(
generated_user_intent
)
prediction["sim_user_intent"] = sim_user_intent
if sim_user_intent == intent:
wrong_intent = False
if wrong_intent:
num_user_intent_errors += 1
if self.similarity_threshold > 0:
print(
f"Error!: Generated intent: {generated_user_intent} ; "
f"Most similar intent: {sim_user_intent} <> "
f"Expected intent: {intent}"
)
else:
print(
f"Error!: Generated intent: {generated_user_intent} <> "
f"Expected intent: {intent}"
)
# If the intent is correct, the generated bot intent and bot message
# are also correct. For user intent similarity check,
# the bot intent (next step) and bot message may appear different in
# the verbose logs as they are generated using the generated user intent,
# before applying similarity checking.
if wrong_intent:
generated_bot_intent = get_last_bot_intent_event(new_events)[
"intent"
]
prediction["generated_bot_intent"] = generated_bot_intent
prediction["bot_intents"] = intents_with_flows[intent]
if generated_bot_intent not in intents_with_flows[intent]:
num_bot_intent_errors += 1
print(
f"Error!: Generated bot intent: {generated_bot_intent} <> "
f"Expected bot intent: {intents_with_flows[intent]}"
)
generated_bot_utterance = get_last_bot_utterance_event(new_events)[
"content"
]
prediction["generated_bot_said"] = generated_bot_utterance
found_utterance = False
found_bot_message = False
for bot_intent in intents_with_flows[intent]:
bot_messages = self.rails_app.config.bot_messages
if bot_intent in bot_messages:
found_bot_message = True
if generated_bot_utterance in bot_messages[bot_intent]:
found_utterance = True
if found_bot_message and not found_utterance:
prediction["bot_said"] = bot_messages[bot_intent]
num_bot_utterance_errors += 1
print(
f"Error!: Generated bot message: {generated_bot_utterance} <> "
f"Expected bot message: {bot_messages[bot_intent]}"
)
topical_predictions.append(prediction)
processed_samples += 1
if (
self.print_test_results_frequency
and processed_samples % self.print_test_results_frequency == 0
):
TopicalRailsEvaluation._print_evaluation_results(
processed_samples,
total_test_samples,
num_user_intent_errors,
num_bot_intent_errors,
num_bot_utterance_errors,
)
TopicalRailsEvaluation._print_evaluation_results(
processed_samples,
total_test_samples,
num_user_intent_errors,
num_bot_intent_errors,
num_bot_utterance_errors,
)
if self.output_dir:
# Extract filename from config path (use last 2 directory names if possible)
filename = "default"
words = self.config_path.split(os.path.sep)
if len(words) > 2:
filename = "_".join(words[-2:])
elif len(words) == 1:
filename = words[0]
model_name = self._get_main_llm_model()
filename += (
f"_{model_name}_shots{self.max_samples_per_intent}"
f"_sim{self.similarity_threshold}"
f"_topical_results.json"
)
output_path = f"{self.output_dir}/{filename}"
with open(output_path, "w") as f:
json.dump(topical_predictions, f, indent=4)
print(f"Predictions written to file {output_path}")
| NeMo-Guardrails-main | nemoguardrails/eval/evaluate_topical.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | nemoguardrails/eval/cli/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from typing import List
import typer
from nemoguardrails.eval.evaluate_factcheck import FactCheckEvaluation
from nemoguardrails.eval.evaluate_hallucination import HallucinationRailsEvaluation
from nemoguardrails.eval.evaluate_moderation import ModerationRailsEvaluation
from nemoguardrails.eval.evaluate_topical import TopicalRailsEvaluation
from nemoguardrails.logging.verbose import set_verbose
app = typer.Typer()
logging.getLogger().setLevel(logging.WARNING)
@app.command()
def topical(
config: List[str] = typer.Option(
default=[""],
exists=True,
help="Path to a directory containing configuration files of the Guardrails application for evaluation. "
"Can also point to a single configuration file.",
),
verbose: bool = typer.Option(
default=False,
help="If the chat should be verbose and output the prompts.",
),
test_percentage: float = typer.Option(
default=0.3,
help="Percentage of the samples for an intent to be used as test set.",
),
max_tests_intent: int = typer.Option(
default=3,
help="Maximum number of test samples per intent to be used when testing. "
"If value is 0, no limit is used.",
),
max_samples_intent: int = typer.Option(
default=0,
help="Maximum number of samples per intent indexed in vector database. "
"If value is 0, all samples are used.",
),
results_frequency: int = typer.Option(
default=10,
help="Print evaluation intermediate results using this step.",
),
sim_threshold: float = typer.Option(
default=0.0,
help="Minimum similarity score to select the intent when exact match fails.",
),
random_seed: int = typer.Option(
default=None, help="Random seed used by the evaluation."
),
output_dir: str = typer.Option(
default=None, help="Output directory for predictions."
),
):
"""Evaluates the performance of the topical rails defined in a Guardrails application.
Computes accuracy for canonical form detection, next step generation, and next bot message generation.
Only a single Guardrails application can be specified in the config option.
"""
if verbose:
set_verbose(True)
if len(config) > 1:
typer.secho(f"Multiple configurations are not supported.", fg=typer.colors.RED)
typer.echo("Please provide a single config path (folder or config file).")
raise typer.Exit(1)
if config[0] == "":
typer.echo("Please provide a value for the config path.")
raise typer.Exit(1)
typer.echo(f"Starting the evaluation for app: {config[0]}...")
topical_eval = TopicalRailsEvaluation(
config_path=config[0],
verbose=verbose,
test_set_percentage=test_percentage,
max_samples_per_intent=max_samples_intent,
max_tests_per_intent=max_tests_intent,
print_test_results_frequency=results_frequency,
similarity_threshold=sim_threshold,
random_seed=random_seed,
output_dir=output_dir,
)
topical_eval.evaluate_topical_rails()
@app.command()
def moderation(
dataset_path: str = typer.Option(
"nemoguardrails/eval/data/moderation/harmful.txt",
help="Path to dataset containing prompts",
),
llm: str = typer.Option("openai", help="LLM provider ex. OpenAI"),
model_name: str = typer.Option(
"text-davinci-003", help="LLM model ex. text-davinci-003"
),
num_samples: int = typer.Option(50, help="Number of samples to evaluate"),
check_jailbreak: bool = typer.Option(True, help="Evaluate jailbreak rail"),
check_output_moderation: bool = typer.Option(
True, help="Evaluate output moderation rail"
),
output_dir: str = typer.Option(
"eval_outputs/moderation", help="Output directory for predictions"
),
write_outputs: bool = typer.Option(True, help="Write outputs to file"),
split: str = typer.Option("harmful", help="Whether prompts are harmful or helpful"),
):
"""
Evaluates the performance of the moderation rails defined in a Guardrails application.
Computes accuracy for jailbreak detection and output moderation.
"""
moderation_check = ModerationRailsEvaluation(
dataset_path,
llm,
model_name,
num_samples,
check_jailbreak,
check_output_moderation,
output_dir,
write_outputs,
split,
)
typer.echo(
f"Starting the moderation evaluation for data: {dataset_path} using LLM {llm}-{model_name}..."
)
moderation_check.run()
@app.command()
def hallucination(
dataset_path: str = typer.Option(
"nemoguardrails/eval/data/hallucination/sample.txt", help="Dataset path"
),
llm: str = typer.Option("openai", help="LLM provider"),
model_name: str = typer.Option("text-davinci-003", help="LLM model name"),
num_samples: int = typer.Option(50, help="Number of samples to evaluate"),
output_dir: str = typer.Option(
"eval_outputs/hallucination", help="Output directory"
),
write_outputs: bool = typer.Option(True, help="Write outputs to file"),
):
"""
Evaluates the performance of the hallucination rails defined in a Guardrails application.
Computes accuracy for hallucination detection.
"""
hallucination_check = HallucinationRailsEvaluation(
dataset_path,
llm,
model_name,
num_samples,
output_dir,
write_outputs,
)
typer.echo(
f"Starting the hallucination evaluation for data: {dataset_path} using LLM {llm}-{model_name}..."
)
hallucination_check.run()
@app.command()
def fact_checking(
dataset_path: str = typer.Option(
"nemoguardrails/eval/data/factchecking/sample.json",
help="Path to the folder containing the dataset",
),
llm: str = typer.Option("openai", help="LLM provider to be used for fact checking"),
model_name: str = typer.Option(
"text-davinci-003", help="Model name ex. text-davinci-003"
),
num_samples: int = typer.Option(50, help="Number of samples to be evaluated"),
create_negatives: bool = typer.Argument(
True, help="create synthetic negative samples"
),
output_dir: str = typer.Option(
"eval_outputs/factchecking",
help="Path to the folder where the outputs will be written",
),
write_outputs: bool = typer.Option(
True, help="Write outputs to the output directory"
),
):
"""
Evaluates the performance of the fact checking rails defined in a Guardrails application.
Computes accuracy for fact checking.
Negatives can be created synthetically by an LLM that acts as an adversary and modifies the answer to make it incorrect.
"""
fact_check = FactCheckEvaluation(
dataset_path,
llm,
model_name,
num_samples,
create_negatives,
output_dir,
write_outputs,
)
typer.echo(
f"Starting the fact checking evaluation for data: {dataset_path} using LLM {llm}-{model_name}..."
)
fact_check.run()
| NeMo-Guardrails-main | nemoguardrails/eval/cli/evaluate.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | nemoguardrails/eval/data/hallucination/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | nemoguardrails/eval/data/factchecking/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import pandas as pd
import tqdm
from datasets import load_dataset
# Install the datasets library using pip install datasets
# Load the dataset
dataset = load_dataset("ms_marco", "v2.1")
# Use the validation split and convert to pandas dataframe
df = pd.DataFrame(dataset["validation"])
# Convert the dataframe to a json file with "question", "answers" and "evidence" as keys
fact_check_data = []
for idx, row in tqdm.tqdm(df.iterrows()):
sample = {}
sample["question"] = row["query"]
sample["answer"] = row["answers"][0]
if row["passages"]["is_selected"].count(1) == 1:
sample["evidence"] = row["passages"]["passage_text"][
row["passages"]["is_selected"].index(1)
]
fact_check_data.append(sample)
# Save the json file
with open("msmarco.json", "w") as f:
json.dump(fact_check_data, f)
| NeMo-Guardrails-main | nemoguardrails/eval/data/factchecking/process_msmarco_data.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | nemoguardrails/eval/data/topical/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import csv
import json
import logging
from dataclasses import dataclass, field
from random import shuffle
from typing import Dict, List, Optional, Set
log = logging.getLogger(__name__)
@dataclass(eq=True, frozen=True)
class Intent:
"""An intent tag having optional domain and canonical_form attributes"""
intent_name: str
domain: Optional[str] = None
canonical_form: Optional[str] = None
@dataclass
class IntentExample:
"""A turn labeled with a specific intent tag/name"""
SPLIT_TRAIN = "train"
SPLIT_TEST = "test"
SPLIT_VAL = "val"
SPLIT_FULL = "full"
intent: Intent
text: str
# Dataset split values are usually "train", "test" or "val"; however some datasets have other splits as well
dataset_split: Optional[str] = None
@dataclass
class DatasetConnector:
"""A wrapper class to extract NLU specific data from a conversation dataset.
In its current form it can be used to extract intent samples and build
the corresponding `user.co` Colang file.
"""
name: str
intents: Set[Intent] = field(default_factory=set)
slot_names: Set[str] = field(default_factory=set)
domain_names: Set[str] = field(default_factory=set)
intent_examples: List[IntentExample] = field(default_factory=list)
def read_dataset(self, dataset_path: str) -> None:
"""Reads the dataset from the specified path, instantiating some or all of the fields of the object.
E.g. can instantiate intent names, slot names, intent examples etc.
"""
raise NotImplemented
def get_intent_sample(self, intent_name: str, num_samples: int = 10) -> List[str]:
"""Generates a random sample of `num_samples` texts for the `intent_name`.
Inefficient implementation for now, as it passes through all intent samples to get the random subset.
"""
all_samples_intent_name = []
for intent in self.intent_examples:
if intent.intent.intent_name == intent_name:
all_samples_intent_name.append(intent.text)
shuffle(all_samples_intent_name)
if num_samples > 0:
all_samples_intent_name = all_samples_intent_name[:num_samples]
return all_samples_intent_name
def write_colang_output(
self, output_file_name: str = None, num_samples_per_intent: int = 20
):
"""Creates an output file with pairs of turns and canonical forms"""
if output_file_name is None:
return
sample_turns: Dict[str, List[str]] = dict()
for intent in self.intents:
if intent.canonical_form is None:
print(f"Intent with no canonical form: {intent.intent_name} !")
continue
sample_intent_turns = self.get_intent_sample(
intent_name=intent.intent_name, num_samples=num_samples_per_intent
)
sample_turns[intent.canonical_form] = sample_intent_turns
for intent in self.intents:
for intent2 in self.intents:
if intent.canonical_form is None or intent2.canonical_form is None:
continue
if (
intent.intent_name != intent2.intent_name
and intent.canonical_form == intent2.canonical_form
):
print(intent.intent_name + " -- " + intent2.intent_name)
with open(output_file_name, "w", newline="\n") as output_file:
for intent_canonical_form, intent_samples in sample_turns.items():
output_file.write("define user " + intent_canonical_form + "\n")
for intent_sample in intent_samples:
intent_sample = intent_sample.replace('"', "")
intent_sample = intent_sample.replace("\n", "")
output_file.write(' "' + intent_sample + '"\n')
output_file.write("\n")
class Banking77Connector(DatasetConnector):
BANKING77_FOLDER = "./banking/original_dataset/"
BANKING77_CANONICAL_FORMS_FILE = "./banking/categories_canonical_forms.json"
def __init__(self, name: str = "banking77"):
super().__init__(name=name)
@staticmethod
def _read_canonical_forms(
canonical_path: str = BANKING77_CANONICAL_FORMS_FILE,
) -> Dict[str, str]:
"""Reads the intent-canonical form mapping and returns it."""
intent_canonical_forms = dict()
with open(canonical_path) as canonical_file:
data = json.load(canonical_file)
for intent_canonical_entry in data:
if len(intent_canonical_entry) != 2:
print(
f"Problem: no canonical form found or too many canonical forms "
f"for entry {intent_canonical_entry}!"
)
continue
intent = intent_canonical_entry[0]
canonical_form = intent_canonical_entry[1]
intent_canonical_forms[intent] = canonical_form
return intent_canonical_forms
def read_dataset(self, dataset_path: str = BANKING77_FOLDER) -> None:
"""Reads the dataset from the specified path, instantiating some or all of the fields of the object.
E.g. can instantiate intent names, slot names, intent examples etc.
"""
train_path = dataset_path + "train.csv"
test_path = dataset_path + "test.csv"
path_dict = {
IntentExample.SPLIT_TRAIN: train_path,
IntentExample.SPLIT_TEST: test_path,
}
intent_canonical_forms = Banking77Connector._read_canonical_forms()
for dataset_type, dataset_path in path_dict.items():
with open(dataset_path, "r") as banking_file:
intent_examples = csv.reader(banking_file)
for intent_example in intent_examples:
text = intent_example[0]
intent_name = intent_example[1]
# skip header if needed
if text == "text" and intent_name == "category":
continue
intent_canonical = None
if intent_name in intent_canonical_forms:
intent_canonical = intent_canonical_forms[intent_name]
intent = Intent(
intent_name=intent_name, canonical_form=intent_canonical
)
self.intents.add(intent)
self.intent_examples.append(
IntentExample(
intent=intent, text=text, dataset_split=dataset_type
)
)
class ChitChatConnector(DatasetConnector):
CHITCHAT_FOLDER = "./chitchat/original_dataset/"
CHITCHAT_CANONICAL_FORMS_FILE = "./chitchat/intent_canonical_forms.json"
def __init__(self, name: str = "chitchat"):
super().__init__(name=name)
@staticmethod
def _read_canonical_forms(
canonical_path: str = CHITCHAT_CANONICAL_FORMS_FILE,
) -> Dict[str, str]:
"""Reads the intent-canonical form mapping and returns it."""
intent_canonical_forms = dict()
with open(canonical_path) as canonical_file:
data = json.load(canonical_file)
for intent_canonical_entry in data:
if len(intent_canonical_entry) != 2:
print(
f"Problem: no canonical form found or too many canonical forms "
f"for entry {intent_canonical_entry}!"
)
continue
intent = intent_canonical_entry[0]
canonical_form = intent_canonical_entry[1]
intent_canonical_forms[intent] = canonical_form
return intent_canonical_forms
def read_dataset(self, dataset_path: str = CHITCHAT_FOLDER) -> None:
"""Reads the dataset from the specified path, instantiating some or all of the fields of the object.
E.g. can instantiate intent names, slot names, intent examples etc.
"""
full_dataset_path = dataset_path + "nlu.md"
path_dict = {
IntentExample.SPLIT_FULL: full_dataset_path,
}
intent_canonical_forms = ChitChatConnector._read_canonical_forms()
intent_name = None
intent_canonical = None
intent = None
for dataset_type, dataset_path in path_dict.items():
with open(dataset_path, "r") as banking_file:
# Read the markdown file in the Rasa markdown format
lines = banking_file.readlines()
for line in lines:
if line.startswith("##"):
intent_name = line[2:]
intent_start = "intent:"
pos = intent_name.find(intent_start)
if pos > 0:
intent_name = line[pos + len(intent_start) + 2 :]
intent_name = intent_name.strip()
intent_canonical = intent_canonical_forms.get(
intent_name, None
)
intent = Intent(
intent_name=intent_name, canonical_form=intent_canonical
)
self.intents.add(intent)
if line.startswith("- "):
text = line[2:]
text = text.strip()
if intent:
self.intent_examples.append(
IntentExample(
intent=intent, text=text, dataset_split=dataset_type
)
)
| NeMo-Guardrails-main | nemoguardrails/eval/data/topical/dataset_tools.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import typer
from dataset_tools import Banking77Connector, ChitChatConnector
app = typer.Typer()
@app.command()
def main(
dataset_name: str = typer.Option(
default="banking",
exists=True,
help="Name of the dataset. Possible values: banking, chitchat.",
),
dataset_path: str = typer.Option(
default="./banking/original-data",
help="Path to the dataset data, can be downloaded from original repos.",
),
max_samples_intent: int = typer.Option(
default=0,
help="Maximum samples per intent. If value is 0, use all samples.",
),
):
"""
Create the user.co files needed to run the topical evaluation and Guardrails app
for the banking and chit-chat datasets used in the evaluation experiments.
This code can be easily adapted for any other public chatbot dataset.
"""
print(
f"Creating user.co file for {dataset_name} dataset. "
f"Path: {dataset_path} , max samples per intent: {max_samples_intent}"
)
if dataset_name == "banking":
dataset = Banking77Connector()
dataset.read_dataset()
dataset.write_colang_output(
output_file_name="./banking/user.co",
num_samples_per_intent=max_samples_intent,
)
print("Created user.co file for banking dataset.")
elif dataset_name == "chitchat":
dataset = ChitChatConnector()
dataset.read_dataset()
dataset.write_colang_output(
output_file_name="./chitchat/user.co",
num_samples_per_intent=max_samples_intent,
)
print("Created user.co file for banking dataset.")
else:
print(f"Unknown dataset {dataset_name}, cannot create user.co file!")
if __name__ == "__main__":
app()
| NeMo-Guardrails-main | nemoguardrails/eval/data/topical/create_colang_intent_file.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import typer
def load_dataset(input_path, split="harmful"):
"""
Loads the dataset from the given path.
"""
if split == "harmful":
with open(input_path, "r", encoding="utf-8") as f:
data = json.load(f)
else:
with open(input_path, "r", encoding="utf-8") as f:
data = []
for line in f:
data.append(json.loads(line))
return data
def split_messages(message):
"""
Splits a message into two lists of human and assistant messages.
Args:
message (str): The message to split.
Returns:
two lists - one for human messages and one for assistant messages.
"""
messages = message.split("\n\n")[1:]
human = [m.replace("Human: ", "") for i, m in enumerate(messages) if i % 2 == 0]
assistant = [
m.replace("Assistant: ", "") for i, m in enumerate(messages) if i % 2 != 0
]
return human, assistant
def process_anthropic_harmful_data(input_path: str, rating: float):
"""
Extracts the first turn harmful prompts from the red team attempts dataset.
The dataset can be downloaded from Huggingface and has to be unzipped.
"""
dataset = load_dataset(input_path, split="harmful")
first_turn_data = []
for d in dataset:
human_utterance, assistant_response = split_messages(d["transcript"])
if d["rating"] == rating:
first_turn_data.append(human_utterance[0])
with open(f"anthropic_harmful.txt", "w", encoding="utf-8") as f:
for line in first_turn_data:
f.write(line + "\n")
def process_anthropic_helpful_data(input_path: str):
"""
Extracts the first turn helpful prompts from the helpful-base dataset.
The dataset can be downloaded from Huggingface and it has to be unzipped.
"""
dataset = load_dataset(input_path, split="helpful")
first_turn_data = []
for d in dataset:
human_utterance, assistant_response = split_messages(d["chosen"])
first_turn_data.append(human_utterance[0])
with open(f"anthropic_helpful.txt", "w", encoding="utf-8") as f:
for line in first_turn_data:
f.write(line + "\n")
def main(
dataset_path: str = typer.Option(
"red_team_attempts.jsonl",
help="Path to the red team attempts dataset or the Anthropic Helpful-Base dataset - Can be downloaded from https://huggingface.co/datasets/Anthropic/hh-rlhf/",
),
rating: float = typer.Option(
4.0,
help="Rating by which to filter the Red Team Attempts dataset. Values range from 0.0 to 4.0 with higher numbers indicating prompts that got more inappropriate responses from the model. Default is 4.0",
),
split: str = typer.Option("harmful", help="Whether prompts are harmful or helpful"),
):
"""
Extracts the first turn harmful or helpful prompts from the red team attempts dataset.
"""
if split == "harmful":
process_anthropic_harmful_data(dataset_path, rating)
else:
process_anthropic_helpful_data(dataset_path)
if __name__ == "__main__":
typer.run(main)
| NeMo-Guardrails-main | nemoguardrails/eval/data/moderation/process_anthropic_dataset.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | nemoguardrails/eval/data/moderation/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from typing import Optional
from nemoguardrails.flows.eval import eval_expression
log = logging.getLogger(__name__)
def slide(state: "State", flow_config: "FlowConfig", head: int) -> Optional[int]:
"""Tries to slide a flow with the provided head.
Sliding is the operation of moving through "non-matching" elements e.g. check,
if, jump, expect etc. It also includes calling sub-flows.
:param state: The current state of the dialog.
:param flow_config: The config of the flow that should be advanced.
:param head: The current head.
:return:
"""
context = state.context
if head is None:
return None
# The active label is the label that can be reached going backwards
# only through sliding elements.
active_label = None
active_label_data = None
# This might get called directly at the end of a flow, in which case
# we put the prev_head on the last element.
prev_head = head if head < len(flow_config.elements) else head - 1
while True:
# if we reached the end, we stop
if head == len(flow_config.elements) or head < 0:
# We make a convention to return the last head, multiplied by -1 when the flow finished
return -1 * (prev_head + 1)
prev_head = head
pattern_item = flow_config.elements[head]
# Updated the active label if needed
if "_label" in pattern_item:
active_label = pattern_item["_label"]
active_label_data = pattern_item.get("_label_value", None)
# We make sure the active label is propagated to all the other elements
if active_label:
pattern_item["_active_label"] = active_label
pattern_item["_active_label_data"] = active_label_data
p_type = pattern_item["_type"]
# CHECK, IF, JUMP
if p_type in ["check", "if", "jump"]:
# for check and if, we need to evaluate the expression
if p_type in ["check", "if"]:
expr = pattern_item["expression"]
check = eval_expression(expr, context)
if p_type == "check":
if not check:
return None
else:
head += int(pattern_item.get("_next", 1))
elif p_type == "if":
if check:
head += 1
else:
head += int(pattern_item["_next_else"])
elif p_type == "jump":
if not pattern_item.get("_absolute"):
head += int(pattern_item["_next"])
else:
head = int(pattern_item["_next"])
elif p_type in ["while"]:
expr = pattern_item["expression"]
check = eval_expression(expr, context)
if check:
head += int(pattern_item.get("_next", 1))
else:
head += int(pattern_item["_next_on_break"])
# CONTINUE
elif p_type == "continue":
head += int(pattern_item.get("_next_on_continue", 1))
# STOP
elif p_type == "stop":
return None
# BREAK
elif p_type == "break":
head += int(pattern_item.get("_next_on_break", 1))
# SET
elif p_type == "set":
value = eval_expression(pattern_item["expression"], context)
# We transform tuples into arrays
if isinstance(value, tuple):
value = list(value)
key_name = pattern_item["key"]
# Update the context with the result of the expression and also record
# the explicit update.
context.update({key_name: value})
state.context_updates.update({key_name: value})
head += int(pattern_item.get("_next", 1))
else:
break
# If we got this far, it means we had a match and the flow advanced
return head
| NeMo-Guardrails-main | nemoguardrails/flows/sliding.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | nemoguardrails/flows/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import logging
import uuid
from textwrap import indent
from typing import Any, Dict, List, Optional, Tuple
from urllib.parse import urljoin
import aiohttp
from langchain.chains.base import Chain
from nemoguardrails.actions.action_dispatcher import ActionDispatcher
from nemoguardrails.actions.actions import ActionResult
from nemoguardrails.actions.fact_checking import check_facts
from nemoguardrails.actions.hallucination import check_hallucination
from nemoguardrails.actions.jailbreak_check import check_jailbreak
from nemoguardrails.actions.math import wolfram_alpha_request
from nemoguardrails.actions.output_moderation import output_moderation
from nemoguardrails.actions.retrieve_relevant_chunks import retrieve_relevant_chunks
from nemoguardrails.flows.flows import FlowConfig, compute_context, compute_next_steps
from nemoguardrails.language.parser import parse_colang_file
from nemoguardrails.llm.taskmanager import LLMTaskManager
from nemoguardrails.rails.llm.config import RailsConfig
from nemoguardrails.utils import new_event_dict
log = logging.getLogger(__name__)
class Runtime:
"""Runtime for executing the guardrails."""
def __init__(self, config: RailsConfig, verbose: bool = False):
self.config = config
self.verbose = verbose
# Register the actions with the dispatcher.
self.action_dispatcher = ActionDispatcher(config_path=config.config_path)
# The list of additional parameters that can be passed to the actions.
self.registered_action_params = {}
self._init_flow_configs()
# Initialize the prompt renderer as well.
self.llm_task_manager = LLMTaskManager(config)
def _load_flow_config(self, flow: dict):
"""Loads a flow into the list of flow configurations."""
elements = flow["elements"]
# If we have an element with meta information, we move the relevant properties
# to top level.
if elements and elements[0].get("_type") == "meta":
meta_data = elements[0]["meta"]
if "priority" in meta_data:
flow["priority"] = meta_data["priority"]
if "is_extension" in meta_data:
flow["is_extension"] = meta_data["is_extension"]
if "interruptable" in meta_data:
flow["is_interruptible"] = meta_data["interruptable"]
# Finally, remove the meta element
elements = elements[1:]
# If we don't have an id, we generate a random UID.
flow_id = flow.get("id") or str(uuid.uuid4())
self.flow_configs[flow_id] = FlowConfig(
id=flow_id,
elements=elements,
priority=flow.get("priority", 1.0),
is_extension=flow.get("is_extension", False),
is_interruptible=flow.get("is_interruptible", True),
source_code=flow.get("source_code"),
)
# We also compute what types of events can trigger this flow, in addition
# to the default ones.
for element in elements:
if element.get("UtteranceUserActionFinished"):
self.flow_configs[flow_id].trigger_event_types.append(
"UtteranceUserActionFinished"
)
def _init_flow_configs(self):
"""Initializes the flow configs based on the config."""
self.flow_configs = {}
for flow in self.config.flows:
self._load_flow_config(flow)
def register_action(
self, action: callable, name: Optional[str] = None, override: bool = True
):
"""Registers an action with the given name.
:param name: The name of the action.
:param action: The action function.
:param override: If an action already exists, whether it should be overriden or not.
"""
self.action_dispatcher.register_action(action, name, override=override)
def register_actions(self, actions_obj: any, override: bool = True):
"""Registers all the actions from the given object."""
self.action_dispatcher.register_actions(actions_obj, override=override)
@property
def registered_actions(self):
return self.action_dispatcher.registered_actions
def register_action_param(self, name: str, value: any):
"""Registers an additional parameter that can be passed to the actions.
:param name: The name of the parameter.
:param value: The value of the parameter.
"""
self.registered_action_params[name] = value
async def generate_events(self, events: List[dict]) -> List[dict]:
"""Generates the next events based on the provided history.
This is a wrapper around the `process_events` method, that will keep
processing the events until the `listen` event is produced.
:return: The list of events.
"""
events = events.copy()
new_events = []
while True:
last_event = events[-1]
log.info("Processing event: %s", last_event)
event_type = last_event["type"]
log.info(
"Event :: %s %s",
event_type,
str({k: v for k, v in last_event.items() if k != "type"}),
)
# If we need to execute an action, we start doing that.
if last_event["type"] == "StartInternalSystemAction":
next_events = await self._process_start_action(events)
# If we need to start a flow, we parse the content and register it.
elif last_event["type"] == "start_flow":
next_events = await self._process_start_flow(events)
else:
# We need to slide all the flows based on the current event,
# to compute the next steps.
next_events = await self.compute_next_steps(events)
if len(next_events) == 0:
next_events = [new_event_dict("Listen")]
# Otherwise, we append the event and continue the processing.
events.extend(next_events)
new_events.extend(next_events)
# If the next event is a listen, we stop the processing.
if next_events[-1]["type"] == "Listen":
break
# As a safety measure, we stop the processing if we have too many events.
if len(new_events) > 100:
raise Exception("Too many events.")
return new_events
async def compute_next_steps(self, events: List[dict]) -> List[dict]:
"""Computes the next step based on the current flow."""
next_steps = compute_next_steps(events, self.flow_configs)
# If there are any StartInternalSystemAction events, we mark if they are system actions or not
for event in next_steps:
if event["type"] == "StartInternalSystemAction":
is_system_action = False
fn = self.action_dispatcher.get_action(event["action_name"])
if fn:
action_meta = getattr(fn, "action_meta", {})
is_system_action = action_meta.get("is_system_action", False)
event["is_system_action"] = is_system_action
return next_steps
@staticmethod
def _internal_error_action_result(message: str):
"""Helper to construct an action result for an internal error."""
return ActionResult(
events=[
{
"type": "BotIntent",
"intent": "inform internal error occurred",
},
{
"type": "StartUtteranceBotAction",
"script": message,
},
# We also want to hide this from now from the history moving forward
{"type": "hide_prev_turn"},
]
)
async def _process_start_action(self, events: List[dict]) -> List[dict]:
"""Starts the specified action, waits for it to finish and posts back the result."""
event = events[-1]
action_name = event["action_name"]
action_params = event["action_params"]
action_result_key = event["action_result_key"]
action_uid = event["action_uid"]
context = {}
action_meta = {}
fn = self.action_dispatcher.get_action(action_name)
# TODO: check action is available in action server
if fn is None:
status = "failed"
result = self._internal_error_action_result(
f"Action '{action_name}' not found."
)
else:
context = compute_context(events)
# We pass all the parameters that are passed explicitly to the action.
kwargs = {**action_params}
action_meta = getattr(fn, "action_meta", {})
parameters = []
action_type = "class"
if inspect.isfunction(fn) or inspect.ismethod(fn):
# We also add the "special" parameters.
parameters = inspect.signature(fn).parameters
action_type = "function"
elif isinstance(fn, Chain):
# If we're dealing with a chain, we list the annotations
# TODO: make some additional type checking here
parameters = fn.input_keys
action_type = "chain"
# For every parameter that start with "__context__", we pass the value
for parameter_name in parameters:
if parameter_name.startswith("__context__"):
var_name = parameter_name[11:]
kwargs[parameter_name] = context.get(var_name)
# If there are parameters which are variables, we replace with actual values.
for k, v in kwargs.items():
if isinstance(v, str) and v.startswith("$"):
var_name = v[1:]
if var_name in context:
kwargs[k] = context[var_name]
# If we have an action server, we use it for non-system/non-chain actions
if (
self.config.actions_server_url
and not action_meta.get("is_system_action")
and action_type != "chain"
):
result, status = await self._get_action_resp(
action_meta, action_name, kwargs
)
else:
# We don't send these to the actions server;
# TODO: determine if we should
if "events" in parameters:
kwargs["events"] = events
if "context" in parameters:
kwargs["context"] = context
if "config" in parameters:
kwargs["config"] = self.config
if "llm_task_manager" in parameters:
kwargs["llm_task_manager"] = self.llm_task_manager
# Add any additional registered parameters
for k, v in self.registered_action_params.items():
if k in parameters:
kwargs[k] = v
if (
"llm" in kwargs
and f"{action_name}_llm" in self.registered_action_params
):
kwargs["llm"] = self.registered_action_params[f"{action_name}_llm"]
log.info("Executing action :: %s", action_name)
result, status = await self.action_dispatcher.execute_action(
action_name, kwargs
)
# If the action execution failed, we return a hardcoded message
if status == "failed":
# TODO: make this message configurable.
result = self._internal_error_action_result(
"I'm sorry, an internal error has occurred."
)
return_value = result
return_events = []
context_updates = {}
if isinstance(result, ActionResult):
return_value = result.return_value
return_events = result.events
context_updates.update(result.context_updates)
# If we have an action result key, we also record the update.
if action_result_key:
context_updates[action_result_key] = return_value
next_steps = []
if context_updates:
# We check if at least one key changed
changes = False
for k, v in context_updates.items():
if context.get(k) != v:
changes = True
break
if changes:
next_steps.append(new_event_dict("ContextUpdate", data=context_updates))
next_steps.append(
new_event_dict(
"InternalSystemActionFinished",
action_uid=action_uid,
action_name=action_name,
action_params=action_params,
action_result_key=action_result_key,
status=status,
is_success=status != "failed",
failure_reason=status,
return_value=return_value,
events=return_events,
is_system_action=action_meta.get("is_system_action", False),
)
)
# If the action returned additional events, we also add them to the next steps.
if return_events:
next_steps.extend(return_events)
return next_steps
async def _get_action_resp(
self, action_meta: Dict[str, Any], action_name: str, kwargs: Dict[str, Any]
) -> Tuple[Dict[str, Any], str]:
"""Interact with actions and get response from action-server and system actions."""
result, status = {}, "failed" # default response
try:
# Call the Actions Server if it is available.
# But not for system actions, those should still run locally.
if (
action_meta.get("is_system_action", False)
or self.config.actions_server_url is None
):
result, status = await self.action_dispatcher.execute_action(
action_name, kwargs
)
else:
url = urljoin(
self.config.actions_server_url, "/v1/actions/run"
) # action server execute action path
data = {"action_name": action_name, "action_parameters": kwargs}
async with aiohttp.ClientSession() as session:
try:
async with session.post(url, json=data) as resp:
if resp.status != 200:
raise ValueError(
f"Got status code {resp.status} while getting response from {action_name}"
)
resp = await resp.json()
result, status = resp.get("result", result), resp.get(
"status", status
)
except Exception as e:
log.info(f"Exception {e} while making request to {action_name}")
return result, status
except Exception as e:
log.info(f"Failed to get response from {action_name} due to exception {e}")
return result, status
async def _process_start_flow(self, events: List[dict]) -> List[dict]:
"""Starts a flow."""
event = events[-1]
flow_id = event["flow_id"]
# Up to this point, the body will be the sequence of instructions.
# We need to alter it to be an actual flow definition, i.e., add `define flow xxx`
# and intent the body.
body = event["flow_body"]
body = "define flow " + flow_id + ":\n" + indent(body, " ")
# We parse the flow
parsed_data = parse_colang_file("dynamic.co", content=body)
assert len(parsed_data["flows"]) == 1
flow = parsed_data["flows"][0]
# To make sure that the flow will start now, we add a start_flow element at
# the beginning as well.
flow["elements"].insert(0, {"_type": "start_flow", "flow_id": flow_id})
# We add the flow to the list of flows.
self._load_flow_config(flow)
# And we compute the next steps. The new flow should match the current event,
# and start.
next_steps = await self.compute_next_steps(events)
return next_steps
| NeMo-Guardrails-main | nemoguardrails/flows/runtime.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
class AttributeDict(dict):
"""Simple utility to allow accessing dict members as attributes."""
def __getattr__(self, attr):
val = self.get(attr, None)
if isinstance(val, dict):
return AttributeDict(val)
elif isinstance(val, list) and len(val) > 0 and isinstance(val[0], dict):
return [AttributeDict(x) for x in val]
else:
return val
def __setattr__(self, attr, value):
self[attr] = value
| NeMo-Guardrails-main | nemoguardrails/flows/utils.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A simplified modeling of the CoFlows engine."""
import uuid
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional
from nemoguardrails.flows.sliding import slide
from nemoguardrails.utils import new_event_dict
@dataclass
class FlowConfig:
"""The configuration of a flow."""
# A unique id of the flow.
id: str
# The sequence of elements that compose the flow.
elements: List[dict]
# The priority of the flow. Higher priority flows are executed first.
priority: float = 1.0
# Whether it is an extension flow or not.
# Extension flows can interrupt other flows on actionable steps.
is_extension: bool = False
# Weather this flow can be interrupted or not
is_interruptible: bool = True
# Weather this flow is a subflow
is_subflow: bool = False
# The events that can trigger this flow to advance.
trigger_event_types = [
"UserIntent",
"BotIntent",
"run_action",
"InternalSystemActionFinished",
]
# The actual source code, if available
source_code: Optional[str] = None
class FlowStatus(Enum):
"""The status of a flow."""
ACTIVE = "active"
INTERRUPTED = "interrupted"
ABORTED = "aborted"
COMPLETED = "completed"
@dataclass
class FlowState:
"""The state of a flow."""
# The unique id of an instance of a flow.
uid: str
# The id of the flow.
flow_id: str
# The position in the sequence of elements that compose the flow.
head: int
# The current state of the flow
status: FlowStatus = FlowStatus.ACTIVE
# The UID of the flows that interrupted this one
interrupted_by = None
@dataclass
class State:
"""A state of a flow-driven system."""
# The current set of variables in the state.
context: dict
# The current set of flows in the state.
flow_states: List[FlowState]
# The configuration of all the flows that are available.
flow_configs: Dict[str, FlowConfig]
# The next step of the flow-driven system
next_step: Optional[dict] = None
next_step_by_flow_uid: Optional[str] = None
next_step_priority: float = 0.0
# The comment is extract from the source code
next_step_comment: Optional[str] = None
# The updates to the context that should be applied before the next step
context_updates: dict = field(default_factory=dict)
def _is_actionable(element: dict) -> bool:
"""Checks if the given element is actionable."""
if element["_type"] == "run_action":
if (
element["action_name"] == "utter"
and element["action_params"]["value"] == "..."
):
return False
return True
return False
def _is_match(element: dict, event: dict) -> bool:
"""Checks if the given element matches the given event."""
# The element type is the first key in the element dictionary
element_type = element["_type"]
if event["type"] == "UserIntent":
return element_type == "UserIntent" and (
element["intent_name"] == "..." or element["intent_name"] == event["intent"]
)
elif event["type"] == "BotIntent":
return (
element_type == "run_action"
and element["action_name"] == "utter"
and (
element["action_params"]["value"] == "..."
or element["action_params"]["value"] == event["intent"]
)
)
elif event["type"] == "InternalSystemActionFinished":
# Currently, we only match successful execution of actions
if event["status"] != "success":
return False
return (
element_type == "run_action"
and element["action_name"] == event["action_name"]
)
elif event["type"] == "UtteranceUserActionFinished":
return element_type == "UtteranceUserActionFinished" and (
element["final_transcript"] == "..."
or element["final_transcript"] == event["final_transcript"]
)
elif event["type"] == "StartUtteranceBotAction":
return element_type == "StartUtteranceBotAction" and (
element["script"] == "..." or element["script"] == event["script"]
)
else:
# In this case, we try to match the event by type explicitly, and all the properties.
if event["type"] != element_type:
return False
# We need to match all properties used in the element. We also use the "..." wildcard
# to mach anything.
for key, value in element.items():
# Skip potentially private keys.
if key.startswith("_"):
continue
if value == "...":
continue
if event.get(key) != value:
return False
return True
def _record_next_step(
new_state: State,
flow_state: FlowState,
flow_config: FlowConfig,
priority_modifier: float = 1.0,
):
"""Helper to record the next step."""
if (
new_state.next_step is None
or new_state.next_step_priority < flow_config.priority
) and _is_actionable(flow_config.elements[flow_state.head]):
new_state.next_step = flow_config.elements[flow_state.head]
new_state.next_step_by_flow_uid = flow_state.uid
new_state.next_step_priority = flow_config.priority * priority_modifier
# Extract the comment, if any.
new_state.next_step_comment = (
flow_config.elements[flow_state.head]
.get("_source_mapping", {})
.get("comment")
)
def _call_subflow(new_state: State, flow_state: FlowState) -> Optional[FlowState]:
"""Helper to call a subflow.
The head for `flow_state` is expected to be on a "flow" element.
"""
flow_config = new_state.flow_configs[flow_state.flow_id]
subflow_state = FlowState(
flow_id=flow_config.elements[flow_state.head]["flow_name"],
status=FlowStatus.ACTIVE,
head=0,
uid=str(uuid.uuid4()),
)
# Move the head by 1, so that when it will resume, it will be on the next element.
flow_state.head += 1
# We slide the subflow.
_slide_with_subflows(new_state, subflow_state)
# If the subflow finished immediately, we just return with the head advanced
if subflow_state.head < 0:
return None
# We mark the current flow as interrupted.
flow_state.status = FlowStatus.INTERRUPTED
# Record the id of the flow that interrupted the current flow.
flow_state.interrupted_by = subflow_state.uid
# Add any new subflow to the new state
new_state.flow_states.append(subflow_state)
# Check if we have a next step from the subflow
subflow_config = new_state.flow_configs[subflow_state.flow_id]
_record_next_step(new_state, subflow_state, subflow_config)
return subflow_state
def _slide_with_subflows(state: State, flow_state: FlowState) -> Optional[int]:
"""Slides the provided flow and also calls subflows, if applicable."""
flow_config = state.flow_configs[flow_state.flow_id]
should_continue = True
while should_continue:
should_continue = False
flow_state.head = slide(state, flow_config, flow_state.head)
# We check if we reached a point where we need to call a subflow
if flow_state.head >= 0:
if flow_config.elements[flow_state.head]["_type"] == "flow":
# We create a new flow state for the subflow
subflow_state = _call_subflow(state, flow_state)
if subflow_state is None:
should_continue = True
else:
# And if we don't have a next step yet, we set it to the next element
_record_next_step(state, flow_state, flow_config)
def compute_next_state(state: State, event: dict) -> State:
"""Computes the next state of the flow-driven system.
Currently, this is a very simplified implementation, with the following assumptions:
- All flows are singleton i.e. you can't have multiple instances of the same flow.
- Flows can be interrupted by one flow at a time.
- Flows are resumed when the interruption flow completes.
- No prioritization between flows, the first one that can decide something will be used.
"""
# We don't advance flow on `StartInternalSystemAction`, but on `InternalSystemActionFinished`.
if event["type"] == "StartInternalSystemAction":
return state
# We don't need to decide any next step on context updates.
if event["type"] == "ContextUpdate":
# TODO: add support to also remove keys from the context.
# maybe with a special context key e.g. "__remove__": ["key1", "key2"]
state.context.update(event["data"])
state.context_updates = {}
state.next_step = None
return state
# Update the default context variables
# TODO: refactor this logic in a single lace
if event["type"] == "UtteranceUserActionFinished":
state.context["last_user_message"] = event["final_transcript"]
elif event["type"] == "StartUtteranceBotAction":
state.context["last_bot_message"] = event["script"]
# Initialize the new state
new_state = State(
context=state.context, flow_states=[], flow_configs=state.flow_configs
)
# The UID of the flow that will determine the next step
new_state.next_step_by_flow_uid = None
# This is to handle an edge case in the simplified implementation
extension_flow_completed = False
# First, we try to advance the existing flows
for flow_state in state.flow_states:
flow_config = state.flow_configs[flow_state.flow_id]
# We skip processing any completed/aborted flows
if (
flow_state.status == FlowStatus.COMPLETED
or flow_state.status == FlowStatus.ABORTED
):
continue
# If the flow was interrupted, we just copy it to the new state
if flow_state.status == FlowStatus.INTERRUPTED:
new_state.flow_states.append(flow_state)
continue
# If it's not a completed flow, we have a valid head element
flow_head_element = flow_config.elements[flow_state.head]
# If the flow is not triggered by the current even type, we copy it as is
if event["type"] not in flow_config.trigger_event_types:
new_state.flow_states.append(flow_state)
# If we don't have a next step, up to this point, and the current flow is on
# an actionable item, we set it as the next step. We adjust the priority
# with 0.9 so that flows that decide on the current event have a higher priority.
_record_next_step(new_state, flow_state, flow_config, priority_modifier=0.9)
continue
# If we're at a branching point, we look at all individual heads.
matching_head = None
if flow_head_element["_type"] == "branch":
for branch_head in flow_head_element["branch_heads"]:
if _is_match(
flow_config.elements[flow_state.head + branch_head], event
):
matching_head = flow_state.head + branch_head + 1
else:
if _is_match(flow_head_element, event):
matching_head = flow_state.head + 1
if matching_head:
# The flow can advance
flow_state.head = matching_head
_slide_with_subflows(new_state, flow_state)
if flow_state.head < 0:
# If a flow finished, we mark it as completed
flow_state.status = FlowStatus.COMPLETED
if flow_config.is_extension:
extension_flow_completed = True
# we don't interrupt on executable elements or if the flow is not interruptible
elif (
_is_actionable(flow_config.elements[flow_state.head])
or not flow_config.is_interruptible
):
flow_state.status = FlowStatus.ABORTED
else:
flow_state.status = FlowStatus.INTERRUPTED
# We copy the flow to the new state
new_state.flow_states.append(flow_state)
# Next, we try to start new flows
for flow_config in state.flow_configs.values():
# We don't allow subflow to start on their own
if flow_config.is_subflow:
continue
# If a flow with the same id is started, we skip
if flow_config.id in [fs.flow_id for fs in new_state.flow_states]:
continue
# We try to slide first, just in case a flow starts with sliding logic
start_head = slide(new_state, flow_config, 0)
# If the first element matches the current event, we start a new flow
if _is_match(flow_config.elements[start_head], event):
flow_uid = str(uuid.uuid4())
flow_state = FlowState(
uid=flow_uid, flow_id=flow_config.id, head=start_head + 1
)
new_state.flow_states.append(flow_state)
_slide_with_subflows(new_state, flow_state)
# If there's any extension flow that has completed, we re-activate all aborted flows
if extension_flow_completed:
for flow_state in new_state.flow_states:
if flow_state.status == FlowStatus.ABORTED:
flow_state.status = FlowStatus.ACTIVE
# And potentially use them for the next decision
flow_config = state.flow_configs[flow_state.flow_id]
_record_next_step(new_state, flow_state, flow_config)
# If there are any flows that have been interrupted in this iteration, we consider
# them to be interrupted by the flow that determined the next step.
for flow_state in new_state.flow_states:
if (
flow_state.status == FlowStatus.INTERRUPTED
and flow_state.interrupted_by is None
):
flow_state.interrupted_by = new_state.next_step_by_flow_uid
# We compute the decision flow config and state
decision_flow_config = None
decision_flow_state = None
for flow_state in new_state.flow_states:
if flow_state.uid == new_state.next_step_by_flow_uid:
decision_flow_config = state.flow_configs[flow_state.flow_id]
decision_flow_state = flow_state
# If we have aborted flows, and the current flow is an extension, when we interrupt them.
# We are only interested when the extension flow actually decided, not just started.
if (
decision_flow_config
and decision_flow_config.is_extension
and decision_flow_state.head > 1
):
for flow_state in new_state.flow_states:
if (
flow_state.status == FlowStatus.ABORTED
and state.flow_configs[flow_state.flow_id].is_interruptible
):
flow_state.status = FlowStatus.INTERRUPTED
flow_state.interrupted_by = new_state.next_step_by_flow_uid
# If there are flows that were waiting on completed flows, we reactivate them
for flow_state in new_state.flow_states:
if flow_state.status == FlowStatus.INTERRUPTED:
# TODO: optimize this with a dict of statuses
# If already there are no more flows to interrupt, we should resume
should_resume = flow_state.interrupted_by is None
# Check if it was waiting on a completed flow
if not should_resume:
for _flow_state in new_state.flow_states:
if _flow_state.uid == flow_state.interrupted_by:
if _flow_state.status == FlowStatus.COMPLETED:
should_resume = True
break
if should_resume:
flow_state.status = FlowStatus.ACTIVE
flow_state.interrupted_by = None
_slide_with_subflows(new_state, flow_state)
if flow_state.head < 0:
flow_state.status = FlowStatus.COMPLETED
return new_state
def _step_to_event(step: dict) -> dict:
"""Helper to convert a next step coming from a flow element into the actual event."""
step_type = step["_type"]
if step_type == "run_action":
if step["action_name"] == "utter":
return {
"type": "BotIntent",
"intent": step["action_params"]["value"],
}
else:
action_name = step["action_name"]
action_params = step.get("action_params", {})
action_result_key = step.get("action_result_key")
return new_event_dict(
"StartInternalSystemAction",
action_name=action_name,
action_params=action_params,
action_result_key=action_result_key,
)
else:
raise ValueError(f"Unknown next step type: {step_type}")
def compute_next_steps(
history: List[dict], flow_configs: Dict[str, FlowConfig]
) -> List[dict]:
"""Computes the next step in a flow-driven system given a history of events."""
state = State(context={}, flow_states=[], flow_configs=flow_configs)
# First, we process the history and apply any alterations e.g. 'hide_prev_turn'
actual_history = []
for event in history:
if event["type"] == "hide_prev_turn":
# we look up the last `UtteranceUserActionFinished` event and remove everything after
end = len(actual_history) - 1
while (
end > 0 and actual_history[end]["type"] != "UtteranceUserActionFinished"
):
end -= 1
assert actual_history[end]["type"] == "UtteranceUserActionFinished"
actual_history = actual_history[0:end]
else:
actual_history.append(event)
for event in actual_history:
state = compute_next_state(state, event)
# NOTE (Jul 24, Razvan): this is a quick fix. Will debug further.
if event["type"] == "BotIntent" and event["intent"] == "stop":
# Reset all flows
state.flow_states = []
next_steps = []
# If we have context updates after this event, we first add that.
if state.context_updates:
next_steps.append(new_event_dict("ContextUpdate", data=state.context_updates))
# If we have a next step, we make sure to convert it to proper event structure.
if state.next_step:
next_step_event = _step_to_event(state.next_step)
if next_step_event["type"] == "BotIntent" and state.next_step_comment:
# For bot intents, we use the comment as instructions
next_step_event["instructions"] = state.next_step_comment
next_steps.append(next_step_event)
# Finally, we check if there was an explicit "stop" request
if actual_history:
last_event = actual_history[-1]
if last_event["type"] == "BotIntent" and last_event["intent"] == "stop":
# In this case, we remove any next steps
next_steps = []
return next_steps
def compute_context(history: List[dict]):
"""Computes the context given a history of events.
# We also include a few special context variables:
- $last_user_message: the last message sent by the user.
- $last_bot_message: the last message sent by the bot.
"""
context = {
"last_user_message": None,
"last_bot_message": None,
}
for event in history:
if event["type"] == "ContextUpdate":
context.update(event["data"])
if event["type"] == "UtteranceUserActionFinished":
context["last_user_message"] = event["final_transcript"]
elif event["type"] == "StartUtteranceBotAction":
context["last_bot_message"] = event["script"]
return context
| NeMo-Guardrails-main | nemoguardrails/flows/flows.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from simpleeval import simple_eval
from nemoguardrails.flows.utils import AttributeDict
def eval_expression(expr, context):
"""Evaluates the provided expression in the given context."""
# If it's not a string, we should return it as such
if expr is None:
return None
if not isinstance(expr, str):
assert isinstance(expr, bool) or isinstance(expr, int)
return expr
# We search for all variable names starting with $, remove the $ and add
# the value in the globals dict for eval
var_names = re.findall(r"\$([a-zA-Z_][a-zA-Z0-9_]*)", expr)
updated_expr = re.sub(r"\$([a-zA-Z_][a-zA-Z0-9_]*)", r"var_\1", expr)
expr_locals = {}
for var_name in var_names:
# if we've already computed the value, we skip
if f"var_{var_name}" in expr_locals:
continue
val = context.get(var_name)
# We transform dicts to AttributeDict so we can access their keys as attributes
# e.g. write things like $speaker.name
if isinstance(val, dict):
val = AttributeDict(val)
expr_locals[f"var_{var_name}"] = val
# Finally, just evaluate the expression
try:
# TODO: replace this with something even more restrictive.
return simple_eval(updated_expr, names=expr_locals, functions={"len": len})
except Exception as ex:
raise Exception(f"Error evaluating '{expr}': {str(ex)}")
| NeMo-Guardrails-main | nemoguardrails/flows/eval.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import threading
import time
class Styles:
"""The set of standard colors."""
BLACK = "\033[30m"
RED = "\033[31m"
GREEN = "\033[32m"
YELLOW = "\033[33m"
BLUE = "\033[34m"
MAGENTA = "\033[35m"
CYAN = "\033[36m"
WHITE = "\033[37m"
GREY = "\033[38;5;246m"
WHITE_ON_GREEN = "\033[42m\033[97m"
RESET = "\033[38m"
RESET_ALL = "\033[0m"
PROMPT = "\033[38;5;232m\033[48;5;254m"
COMPLETION = "\033[38;5;236m\033[48;5;84m"
COMPLETION_GREEN = "\033[48;5;84m"
COMPLETION_RED = "\033[48;5;196m"
EVENT_NAME = "\033[38;5;32m"
# Mapping of colors associated with various sections
SECTION_COLOR = {
"Phase 1": {"title": Styles.GREEN},
"Phase 2": {"title": Styles.GREEN},
"Phase 3": {"title": Styles.GREEN},
"Event": {"title": Styles.CYAN},
"Executing action": {"title": Styles.CYAN},
"Prompt": {
"title": Styles.BLUE,
"body": Styles.PROMPT,
},
"Prompt Messages": {
"title": Styles.BLUE,
"body": Styles.PROMPT,
},
"Completion": {"title": Styles.BLUE, "body": Styles.COMPLETION},
"---": {"title": Styles.GREY, "body": Styles.GREY},
}
class BlinkingCursor:
"""Helper class for a blinking cursor."""
def __init__(self):
self._stop_event = threading.Event()
self._thread = threading.Thread(target=self._blink, daemon=True)
def _blink(self):
first = True
cursors = [f"{Styles.COMPLETION_RED} ", f"{Styles.COMPLETION_GREEN} "]
i = 0
while not self._stop_event.is_set():
i += 1
if first:
first = False
else:
print("\b", end="", flush=True)
print(f"{cursors[i%2]}", end="", flush=True)
for _ in range(25):
time.sleep(0.01)
if self._stop_event.is_set():
break
print("\b \b", end="", flush=True)
def start(self):
if self._thread.is_alive():
return
self._stop_event.clear()
self._thread = threading.Thread(target=self._blink)
self._thread.start()
def stop(self):
if not self._thread.is_alive():
return
self._stop_event.set()
self._thread.join()
class VerboseHandler(logging.StreamHandler):
"""A log handler for verbose mode."""
def __init__(self, *args, **kwargs):
super(VerboseHandler, self).__init__(*args, **kwargs)
self.blinking_cursor = BlinkingCursor()
def emit(self, record) -> None:
msg = self.format(record)
# We check if we're using the spacial syntax with "::" which denotes a title.
if "::" in msg:
title, body = msg.split(" :: ", 1)
title = title.strip()
title_style = SECTION_COLOR.get(title, {}).get("title", "")
body_style = SECTION_COLOR.get(title, {}).get("body", "")
# We remove the title for completion messages and stop the blinking cursor.
if title == "Completion":
self.blinking_cursor.stop()
print(body_style + body + Styles.RESET_ALL)
# For prompts, we also start the blinking cursor.
elif title == "Prompt":
msg = (
title_style
+ title
+ Styles.RESET_ALL
+ "\n"
+ body_style
+ body
+ Styles.RESET_ALL
)
print(msg, end="")
self.blinking_cursor.start()
elif title == "Event":
# For events, we also color differently the type of event.
event_name, body = body.split(" ", 1)
title = title_style + title + Styles.RESET_ALL
event_name = Styles.EVENT_NAME + event_name + Styles.RESET_ALL
body = body_style + body + Styles.RESET_ALL
msg = title + " " + event_name + " " + body
print(msg)
else:
title = title_style + title + Styles.RESET_ALL
body = body_style + body + Styles.RESET_ALL
msg = title + " " + body
print(msg)
def set_verbose(verbose: bool):
"""Configure the verbose mode."""
if verbose:
root_logger = logging.getLogger()
# We set the root logger to INFO so that we can see the messages from the VerboseHandler.
root_logger.setLevel(logging.INFO)
# We make sure the log level for the default root console handler is set to WARNING.
for handler in root_logger.handlers:
if isinstance(handler, logging.StreamHandler):
handler.setLevel(logging.WARNING)
# Next, we also add an instance of the VerboseHandler.
verbose_handler = VerboseHandler()
verbose_handler.setLevel(logging.INFO)
root_logger.addHandler(verbose_handler)
# Also, we make sure the sentence_transformers log level is set to WARNING.
logging.getLogger("sentence_transformers").setLevel(logging.WARNING)
print("Entered verbose mode.")
| NeMo-Guardrails-main | nemoguardrails/logging/verbose.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | nemoguardrails/logging/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Union
class LLMStats:
"""Simple class to store stats for the LLM usage."""
def __init__(self):
self._stats = self._get_empty_stats()
@staticmethod
def _get_empty_stats():
return {
"total_calls": 0,
"total_time": 0,
"total_tokens": 0,
"total_prompt_tokens": 0,
"total_completion_tokens": 0,
}
def inc(self, name: str, value: Union[float, int] = 1):
"""Increment a stat."""
if name not in self._stats:
self._stats[name] = 0
self._stats[name] += value
def get_stat(self, name):
return self._stats[name]
def get_stats(self):
return self._stats
def reset(self):
self._stats = self._get_empty_stats()
def __str__(self):
return (
f"{self._stats['total_calls']} total calls, "
f"{self._stats['total_time']} total time, "
f"{self._stats['total_tokens']} total tokens, "
f"{self._stats['total_prompt_tokens']} total prompt tokens, "
f"{self._stats['total_completion_tokens']} total completion tokens"
)
# Global stats object
# TODO: make this per async context, otherwise, in the server setup it will be shared.
llm_stats = LLMStats()
| NeMo-Guardrails-main | nemoguardrails/logging/stats.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import uuid
from time import time
from typing import Any, Dict, List, Optional, Union
from uuid import UUID
from langchain.callbacks import StdOutCallbackHandler
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackManager
from langchain.callbacks.manager import AsyncCallbackManagerForChainRun
from langchain.schema import AgentAction, AgentFinish, BaseMessage, LLMResult
from nemoguardrails.logging.stats import llm_stats
from nemoguardrails.logging.verbose import Styles
log = logging.getLogger(__name__)
class LoggingCallbackHandler(AsyncCallbackHandler, StdOutCallbackHandler):
"""Async callback handler that can be used to handle callbacks from langchain."""
# The timestamp when the last prompt was sent to the LLM.
last_prompt_timestamp: Optional[float] = 0
async def on_llm_start(
self,
serialized: Dict[str, Any],
prompts: List[str],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
"""Run when LLM starts running."""
log.info("Invocation Params :: %s", kwargs.get("invocation_params", {}))
log.info("Prompt :: %s", prompts[0])
self.last_prompt_timestamp = time()
llm_stats.inc("total_calls")
async def on_chat_model_start(
self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
"""Run when a chat model starts running."""
prompt = "\n" + "\n".join(
[
Styles.CYAN
+ (
"User"
if msg.type == "human"
else "Bot"
if msg.type == "ai"
else "System"
)
+ Styles.PROMPT
+ "\n"
+ msg.content
for msg in messages[0]
]
)
log.info("Invocation Params :: %s", kwargs.get("invocation_params", {}))
log.info("Prompt Messages :: %s", prompt)
self.last_prompt_timestamp = time()
llm_stats.inc("total_calls")
async def on_llm(self, *args, **kwargs) -> Any:
"""NOTE: this needs to be implemented to avoid a warning by LangChain."""
pass
async def on_llm_new_token(
self,
token: str,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
"""Run on new LLM token. Only available when streaming is enabled."""
async def on_llm_end(
self,
response: LLMResult,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
"""Run when LLM ends running."""
log.info("Completion :: %s", response.generations[0][0].text)
# If there are additional completions, we show them as well
if len(response.generations[0]) > 1:
for i, generation in enumerate(response.generations[0][1:]):
log.info("--- :: Completion %d", i + 2)
log.info("Completion :: %s", generation.text)
log.info("Output Stats :: %s", response.llm_output)
took = time() - self.last_prompt_timestamp
log.info("--- :: LLM call took %.2f seconds", took)
llm_stats.inc("total_time", took)
# Update the token usage stats as well
if response.llm_output:
token_usage = response.llm_output.get("token_usage", {})
llm_stats.inc("total_tokens", token_usage.get("total_tokens", 0))
llm_stats.inc("total_prompt_tokens", token_usage.get("prompt_tokens", 0))
llm_stats.inc(
"total_completion_tokens", token_usage.get("completion_tokens", 0)
)
async def on_llm_error(
self,
error: Union[Exception, KeyboardInterrupt],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
"""Run when LLM errors."""
async def on_chain_start(
self,
serialized: Dict[str, Any],
inputs: Dict[str, Any],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
"""Run when chain starts running."""
async def on_chain_end(
self,
outputs: Dict[str, Any],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
"""Run when chain ends running."""
async def on_chain_error(
self,
error: Union[Exception, KeyboardInterrupt],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
"""Run when chain errors."""
async def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
"""Run when tool starts running."""
async def on_tool_end(
self,
output: str,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
"""Run when tool ends running."""
async def on_tool_error(
self,
error: Union[Exception, KeyboardInterrupt],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
"""Run when tool errors."""
async def on_text(
self,
text: str,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
"""Run on arbitrary text."""
async def on_agent_action(
self,
action: AgentAction,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
"""Run on agent action."""
async def on_agent_finish(
self,
finish: AgentFinish,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> None:
"""Run on agent end."""
handlers = [LoggingCallbackHandler()]
logging_callbacks = BaseCallbackManager(
handlers=handlers, inheritable_handlers=handlers
)
logging_callback_manager_for_chain = AsyncCallbackManagerForChainRun(
run_id=uuid.uuid4(),
parent_run_id=None,
handlers=handlers,
inheritable_handlers=handlers,
tags=[],
inheritable_tags=[],
)
| NeMo-Guardrails-main | nemoguardrails/logging/callbacks.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | nemoguardrails/actions_server/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from typing import Dict, Optional
from fastapi import FastAPI
from pydantic import BaseModel, Field
from nemoguardrails.actions.action_dispatcher import ActionDispatcher
log = logging.getLogger(__name__)
api_description = """Guardrails Action Sever API."""
app = FastAPI(
title="Guardrails Action Server API",
description=api_description,
version="0.1.0",
license_info={"name": "Apache License, Version 2.0"},
)
# Create action dispatcher object to communicate with actions
app.action_dispatcher = ActionDispatcher(load_all_actions=True)
class RequestBody(BaseModel):
action_name: str = ""
action_parameters: Dict = Field(
default={}, description="The list of action parameters."
)
class ResponseBody(BaseModel):
status: str = "success" # success / failed
result: Optional[str]
@app.post(
"/v1/actions/run",
summary="Execute action",
response_model=ResponseBody,
)
async def run_action(body: RequestBody):
"""Execute action_name with action_parameters and return result."""
log.info(f"Request body: {body}")
result, status = await app.action_dispatcher.execute_action(
body.action_name, body.action_parameters
)
resp = {"status": status, "result": result}
log.info(f"Response: {resp}")
return resp
@app.get(
"/v1/actions/list",
summary="List available actions",
)
async def get_actions_list():
"""Returns the list of available actions."""
return app.action_dispatcher.get_registered_actions()
| NeMo-Guardrails-main | nemoguardrails/actions_server/actions_server.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .llm.config import RailsConfig
from .llm.llmrails import LLMRails
| NeMo-Guardrails-main | nemoguardrails/rails/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module for the configuration of rails."""
import os
import random
from typing import Any, Dict, List, Optional, Union
import yaml
from pydantic import BaseModel, ValidationError, root_validator
from pydantic.fields import Field
from nemoguardrails.language.coyml_parser import parse_flow_elements
from nemoguardrails.language.parser import parse_colang_file
class Model(BaseModel):
"""Configuration of a model used by the rails engine.
Typically, the main model is configured e.g.:
{
"type": "main",
"engine": "openai",
"model": "text-davinci-003"
}
"""
type: str
engine: str
model: Optional[str] = Field(
default=None,
description="The name of the model. If not specified, it should be specified through the parameters attribute.",
)
parameters: Dict[str, Any] = Field(default_factory=dict)
class Instruction(BaseModel):
"""Configuration for instructions in natural language that should be passed to the LLM."""
type: str
content: str
class Document(BaseModel):
"""Configuration for documents that should be used for question answering."""
format: str
content: str
class MessageTemplate(BaseModel):
"""Template for a message structure."""
type: str = Field(
description="The type of message, e.g., 'assistant', 'user', 'system'."
)
content: str = Field(description="The content of the message.")
class TaskPrompt(BaseModel):
"""Configuration for prompts that will be used for a specific task."""
task: str = Field(description="The id of the task associated with this prompt.")
content: Optional[str] = Field(
default=None, description="The content of the prompt, if it's a string."
)
messages: Optional[List[Union[MessageTemplate, str]]] = Field(
default=None,
description="The list of messages included in the prompt. Used for chat models.",
)
models: Optional[List[str]] = Field(
default=None,
description="If specified, the prompt will be used only for the given LLM engines/models. "
"The format is a list of strings with the format: <engine> or <engine>/<model>.",
)
output_parser: Optional[str] = Field(
default=None,
description="The name of the output parser to use for this prompt.",
)
@root_validator(pre=True, allow_reuse=True)
def check_fields(cls, values):
if not values.get("content") and not values.get("messages"):
raise ValidationError("One of `content` or `messages` must be provided.")
if values.get("content") and values.get("messages"):
raise ValidationError(
"Only one of `content` or `messages` must be provided."
)
return values
class EmbeddingSearchProvider(BaseModel):
"""Configuration of a embedding search provider."""
name: str = Field(
default="default",
description="The name of the embedding search provider. If not specified, default is used.",
)
parameters: Dict[str, Any] = Field(default_factory=dict)
class KnowledgeBaseConfig(BaseModel):
folder: str = Field(
default="kb",
description="The folder from which the documents should be loaded.",
)
embedding_search_provider: EmbeddingSearchProvider = Field(
default_factory=EmbeddingSearchProvider,
description="The search provider used to search the knowledge base.",
)
class CoreConfig(BaseModel):
"""Settings for core internal mechanics."""
embedding_search_provider: EmbeddingSearchProvider = Field(
default_factory=EmbeddingSearchProvider,
description="The search provider used to search the most similar canonical forms/flows.",
)
# Load the default config values from the file
with open(os.path.join(os.path.dirname(__file__), "default_config.yml")) as _fc:
_default_config = yaml.safe_load(_fc)
def _join_config(dest_config: dict, additional_config: dict):
"""Helper to join two configuration."""
dest_config["user_messages"] = {
**dest_config.get("user_messages", {}),
**additional_config.get("user_messages", {}),
}
dest_config["bot_messages"] = {
**dest_config.get("bot_messages", {}),
**additional_config.get("bot_messages", {}),
}
dest_config["instructions"] = dest_config.get(
"instructions", []
) + additional_config.get("instructions", [])
dest_config["flows"] = dest_config.get("flows", []) + additional_config.get(
"flows", []
)
dest_config["models"] = dest_config.get("models", []) + additional_config.get(
"models", []
)
dest_config["prompts"] = dest_config.get("prompts", []) + additional_config.get(
"prompts", []
)
dest_config["docs"] = dest_config.get("docs", []) + additional_config.get(
"docs", []
)
dest_config["actions_server_url"] = dest_config.get(
"actions_server_url", None
) or additional_config.get("actions_server_url", None)
dest_config["embedding_search_provider"] = dest_config.get(
"embedding_search_provider", {}
) or additional_config.get("embedding_search_provider", {})
additional_fields = [
"sample_conversation",
"lowest_temperature",
"enable_multi_step_generation",
"custom_data",
"knowledge_base",
"core",
]
for field in additional_fields:
if additional_config.get(field):
dest_config[field] = additional_config[field]
class RailsConfig(BaseModel):
"""Configuration object for the models and the rails.
TODO: add typed config for user_messages, bot_messages, and flows.
"""
models: List[Model] = Field(
description="The list of models used by the rails configuration."
)
user_messages: Dict[str, List[str]] = Field(
default_factory=dict,
description="The list of user messages that should be used for the rails.",
)
bot_messages: Dict[str, List[str]] = Field(
default_factory=dict,
description="The list of bot messages that should be used for the rails.",
)
flows: List[Dict] = Field(
default_factory=list,
description="The list of flows that should be used for the rails.",
)
instructions: Optional[List[Instruction]] = Field(
default=[Instruction.parse_obj(obj) for obj in _default_config["instructions"]],
description="List of instructions in natural language that the LLM should use.",
)
docs: Optional[List[Document]] = Field(
default=None,
description="List of documents that should be used for question answering.",
)
actions_server_url: Optional[str] = Field(
default=None,
description="The URL of the actions server that should be used for the rails.",
)
sample_conversation: Optional[str] = Field(
default=_default_config["sample_conversation"],
description="The sample conversation that should be used inside the prompts.",
)
prompts: Optional[List[TaskPrompt]] = Field(
default=None,
description="The prompts that should be used for the various LLM tasks.",
)
config_path: Optional[str] = Field(
default=None, description="The path from which the configuration was loaded."
)
# Some tasks need to be as deterministic as possible. The lowest possible temperature
# will be used for those tasks. Models like dolly don't allow for a temperature of 0.0,
# for example, in which case a custom one can be set.
lowest_temperature: Optional[float] = Field(
default=0.0,
description="The lowest temperature that should be used for the LLM.",
)
# This should only be enabled for highly capable LLMs i.e. ~text-davinci-003.
enable_multi_step_generation: Optional[bool] = Field(
default=False,
description="Whether to enable multi-step generation for the LLM.",
)
custom_data: Dict = Field(
default_factory=dict,
description="Any custom configuration data that might be needed.",
)
knowledge_base: KnowledgeBaseConfig = Field(
default_factory=KnowledgeBaseConfig,
description="Configuration for the built-in knowledge base support.",
)
core: CoreConfig = Field(
default_factory=CoreConfig,
description="Configuration for core internal mechanics.",
)
@staticmethod
def from_path(
config_path: str,
test_set_percentage: Optional[float] = 0.0,
test_set: Optional[Dict[str, List]] = {},
max_samples_per_intent: Optional[int] = 0,
):
"""Loads a configuration from a given path.
Supports loading a from a single file, or from a directory.
Also used for testing Guardrails apps, in which case the test_set is
randomly created from the intent samples in the config files.
In this situation test_set_percentage should be larger than 0.
If we want to limit the number of samples for an intent, set the
max_samples_per_intent to a positive number. It is useful for testing apps, but
also for limiting the number of samples for an intent in some scenarios.
The chosen samples are selected randomly for each intent.
"""
# If the config path is a file, we load the YAML content.
# Otherwise, if it's a folder, we iterate through all files.
if config_path.endswith(".yaml") or config_path.endswith(".yml"):
with open(config_path) as f:
raw_config = yaml.safe_load(f.read())
elif os.path.isdir(config_path):
# Iterate all .yml files and join them
raw_config = {}
for root, dirs, files in os.walk(config_path):
for file in files:
# This is the raw configuration that will be loaded from the file.
_raw_config = {}
# Extract the full path for the file and compute relative path
full_path = os.path.join(root, file)
rel_path = os.path.relpath(full_path, config_path)
# If it's a file in the `kb` folder we need to append it to the docs
if rel_path.startswith("kb"):
_raw_config = {"docs": []}
if rel_path.endswith(".md"):
with open(full_path, encoding="utf-8") as f:
_raw_config["docs"].append(
{"format": "md", "content": f.read()}
)
elif file.endswith(".yml") or file.endswith(".yaml"):
with open(full_path, "r", encoding="utf-8") as f:
_raw_config = yaml.safe_load(f.read())
elif file.endswith(".co"):
with open(full_path, "r", encoding="utf-8") as f:
_raw_config = parse_colang_file(file, content=f.read())
# Extract test set if needed before adding the _raw_config to the app config in raw_config
if "user_messages" in _raw_config and test_set_percentage > 0:
for intent, samples in _raw_config["user_messages"].items():
# We need at least 2 samples to create a test split
if len(samples) > 1:
random.shuffle(samples)
num_test_elements = int(
len(samples) * test_set_percentage
)
test_set[intent] = samples[:num_test_elements]
_raw_config["user_messages"][intent] = samples[
num_test_elements:
]
# Limit the number of samples per intent if specified
if (
0
< max_samples_per_intent
< len(_raw_config["user_messages"][intent])
):
_raw_config["user_messages"][intent] = _raw_config[
"user_messages"
][intent][:max_samples_per_intent]
_join_config(raw_config, _raw_config)
else:
raise ValueError(f"Invalid config path {config_path}.")
# If there are no instructions, we use the default ones.
if len(raw_config.get("instructions", [])) == 0:
raw_config["instructions"] = _default_config["instructions"]
raw_config["config_path"] = config_path
return RailsConfig.parse_object(raw_config)
@staticmethod
def from_content(
colang_content: Optional[str] = None, yaml_content: Optional[str] = None
):
"""Loads a configuration from the provided colang/YAML content."""
raw_config = {}
if colang_content:
_join_config(
raw_config, parse_colang_file("main.co", content=colang_content)
)
if yaml_content:
_join_config(raw_config, yaml.safe_load(yaml_content))
# If there are no instructions, we use the default ones.
if len(raw_config.get("instructions", [])) == 0:
raw_config["instructions"] = _default_config["instructions"]
return RailsConfig.parse_object(raw_config)
@classmethod
def parse_object(cls, obj):
"""Parses a configuration object from a given dictionary."""
# If we have flows, we need to process them further from CoYML to CIL.
for flow_data in obj.get("flows", []):
# If the first element in the flow does not have a "_type", we need to convert
if flow_data.get("elements") and not flow_data["elements"][0].get("_type"):
flow_data["elements"] = parse_flow_elements(flow_data["elements"])
return RailsConfig.parse_obj(obj)
| NeMo-Guardrails-main | nemoguardrails/rails/llm/config.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | nemoguardrails/rails/llm/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
from typing import List
def get_history_cache_key(messages: List[dict]) -> str:
"""Compute the cache key for a sequence of messages.
Args:
messages: The list of messages.
Returns:
A unique string that can be used as a key for the provides sequence of messages.
"""
if len(messages) == 0:
return ""
key_items = []
for msg in messages:
if msg["role"] == "user":
key_items.append(msg["content"])
elif msg["role"] == "assistant":
key_items.append(msg["content"])
elif msg["role"] == "context":
key_items.append(json.dumps(msg["content"]))
elif msg["role"] == "event":
key_items.append(json.dumps(msg["event"]))
history_cache_key = ":".join(key_items)
return history_cache_key
| NeMo-Guardrails-main | nemoguardrails/rails/llm/utils.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""LLM Rails entry point."""
import asyncio
import importlib.util
import logging
import os
import time
from typing import Any, List, Optional, Type, Union
from langchain.llms.base import BaseLLM
from nemoguardrails.actions.fact_checking import check_facts
from nemoguardrails.actions.hallucination import check_hallucination
from nemoguardrails.actions.jailbreak_check import check_jailbreak
from nemoguardrails.actions.llm.generation import LLMGenerationActions
from nemoguardrails.actions.llm.utils import get_colang_history
from nemoguardrails.actions.math import wolfram_alpha_request
from nemoguardrails.actions.output_moderation import output_moderation
from nemoguardrails.actions.retrieve_relevant_chunks import retrieve_relevant_chunks
from nemoguardrails.embeddings.index import EmbeddingsIndex
from nemoguardrails.flows.runtime import Runtime
from nemoguardrails.kb.kb import KnowledgeBase
from nemoguardrails.language.parser import parse_colang_file
from nemoguardrails.llm.providers import get_llm_provider, get_llm_provider_names
from nemoguardrails.logging.stats import llm_stats
from nemoguardrails.patch_asyncio import check_sync_call_from_async_loop
from nemoguardrails.rails.llm.config import EmbeddingSearchProvider, RailsConfig
from nemoguardrails.rails.llm.utils import get_history_cache_key
log = logging.getLogger(__name__)
class LLMRails:
"""Rails based on a given configuration."""
def __init__(
self, config: RailsConfig, llm: Optional[BaseLLM] = None, verbose: bool = False
):
"""Initializes the LLMRails instance.
Args:
config: A rails configuration.
llm: An optional LLM engine to use.
verbose: Whether the logging should be verbose or not.
"""
self.config = config
self.llm = llm
self.verbose = verbose
# We allow the user to register additional embedding search providers, so we keep
# an index of them.
self.embedding_search_providers = {}
# The default embeddings model is using SentenceTransformers
self.default_embedding_model = "all-MiniLM-L6-v2"
self.default_embedding_engine = "SentenceTransformers"
# We keep a cache of the events history associated with a sequence of user messages.
# TODO: when we update the interface to allow to return a "state object", this
# should be removed
self.events_history_cache = {}
# We also load the default flows from the `default_flows.yml` file in the current folder.
current_folder = os.path.dirname(__file__)
default_flows_path = os.path.join(current_folder, "llm_flows.co")
with open(default_flows_path, "r") as f:
default_flows_content = f.read()
default_flows = parse_colang_file("llm_flows.co", default_flows_content)[
"flows"
]
# We add the default flows to the config.
self.config.flows.extend(default_flows)
# We check if the configuration has a config.py module associated with it.
config_module = None
if self.config.config_path:
filepath = os.path.join(self.config.config_path, "config.py")
if os.path.exists(filepath):
filename = os.path.basename(filepath)
spec = importlib.util.spec_from_file_location(filename, filepath)
config_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(config_module)
# First, we initialize the runtime.
self.runtime = Runtime(config=config, verbose=verbose)
# If we have a config_module with an `init` function, we call it.
# We need to call this here because the `init` might register additional
# LLM providers.
if config_module is not None and hasattr(config_module, "init"):
config_module.init(self)
# Register any default actions that have not yet been registered in the custom
# init function from config.py.
default_actions = {
"wolfram alpha request": wolfram_alpha_request,
"check_facts": check_facts,
"check_jailbreak": check_jailbreak,
"output_moderation": output_moderation,
"check_hallucination": check_hallucination,
"retrieve_relevant_chunks": retrieve_relevant_chunks,
}
for action_name, action_fn in default_actions.items():
self.runtime.register_action(action_fn, action_name, override=False)
# If we have a customized embedding model, we'll use it.
for model in self.config.models:
if model.type == "embeddings":
self.default_embedding_model = model.model
self.default_embedding_engine = model.engine
break
# Next, we initialize the LLM engines (main engine and action engines if specified).
self._init_llms()
# Next, we initialize the LLM Generate actions and register them.
self.llm_generation_actions = LLMGenerationActions(
config=config,
llm=self.llm,
llm_task_manager=self.runtime.llm_task_manager,
get_embedding_search_provider_instance=self._get_embeddings_search_provider_instance,
verbose=verbose,
)
# If there's already an action registered, we don't override.
self.runtime.register_actions(self.llm_generation_actions, override=False)
# Next, we initialize the Knowledge Base
asyncio.run(self._init_kb())
# We also register the kb as a parameter that can be passed to actions.
self.runtime.register_action_param("kb", self.kb)
async def _init_kb(self):
"""Initializes the knowledge base."""
self.kb = None
if not self.config.docs:
return
documents = [doc.content for doc in self.config.docs]
self.kb = KnowledgeBase(
documents=documents,
config=self.config.knowledge_base,
get_embedding_search_provider_instance=self._get_embeddings_search_provider_instance,
)
self.kb.init()
await self.kb.build()
def _init_llms(self):
"""
Initializes the right LLM engines based on the configuration.
There can be multiple LLM engines and types that can be specified in the config.
The main LLM engine is the one that will be used for all the core guardrails generations.
Other LLM engines can be specified for use in specific actions.
The reason we provide an option for decoupling the main LLM engine from the action LLM
is to allow for flexibility in using specialized LLM engines for specific actions.
"""
# If we already have a pre-configured one, we do nothing.
if self.llm is not None:
return
# TODO: Currently we assume the first model is the main one. Add proper support
# to search for the main model config.
for llm_config in self.config.models:
if llm_config.type == "embeddings":
pass
else:
if llm_config.engine not in get_llm_provider_names():
raise Exception(f"Unknown LLM engine: {llm_config.engine}")
provider_cls = get_llm_provider(llm_config)
# We need to compute the kwargs for initializing the LLM
kwargs = llm_config.parameters
# We also need to pass the model, if specified
if llm_config.model:
# Some LLM providers use `model_name` instead of model. For backward compatibility
# we keep this hard-coded mapping.
if llm_config.engine in [
"azure",
"openai",
"gooseai",
"nlpcloud",
"petals",
]:
kwargs["model_name"] = llm_config.model
else:
# The `__fields__` attribute is computed dynamically by pydantic.
if "model" in provider_cls.__fields__:
kwargs["model"] = llm_config.model
if llm_config.type == "main" or len(self.config.models) == 1:
self.llm = provider_cls(**kwargs)
self.runtime.register_action_param("llm", self.llm)
else:
model_name = f"{llm_config.type}_llm"
setattr(self, model_name, provider_cls(**kwargs))
self.runtime.register_action_param(
model_name, getattr(self, model_name)
)
def _get_embeddings_search_provider_instance(
self, esp_config: Optional[EmbeddingSearchProvider] = None
) -> EmbeddingsIndex:
if esp_config is None:
esp_config = EmbeddingSearchProvider()
if esp_config.name == "default":
from nemoguardrails.embeddings.basic import BasicEmbeddingsIndex
return BasicEmbeddingsIndex(
embedding_model=esp_config.parameters.get(
"embedding_model", self.default_embedding_model
),
embedding_engine=esp_config.parameters.get(
"embedding_engine", self.default_embedding_engine
),
)
else:
if esp_config.name not in self.embedding_search_providers:
raise Exception(f"Unknown embedding search provider: {esp_config.name}")
else:
kwargs = esp_config.parameters
return self.embedding_search_providers[esp_config.name](**kwargs)
def _get_events_for_messages(self, messages: List[dict]):
"""Return the list of events corresponding to the provided messages.
Tries to find a prefix of messages for which we have already a list of events
in the cache. For the rest, they are converted as is.
The reason this cache exists is that we want to benefit from events generated in
previous turns, which can't be computed again because it would be expensive (e.g.,
involving multiple LLM calls).
When an explicit state object will be added, this mechanism can be removed.
Args:
messages: The list of messages.
Returns:
A list of events.
"""
events = []
# We try to find the longest prefix of messages for which we have a cache
# of events.
p = len(messages) - 1
while p > 0:
cache_key = get_history_cache_key(messages[0:p])
if cache_key in self.events_history_cache:
events = self.events_history_cache[cache_key].copy()
break
p -= 1
# For the rest of the messages, we transform them directly into events.
# TODO: Move this to separate function once more types of messages are supported.
for msg in messages[p:]:
if msg["role"] == "user":
events.append(
{
"type": "UtteranceUserActionFinished",
"final_transcript": msg["content"],
}
)
elif msg["role"] == "assistant":
events.append(
{"type": "StartUtteranceBotAction", "script": msg["content"]}
)
elif msg["role"] == "context":
events.append({"type": "ContextUpdate", "data": msg["content"]})
elif msg["role"] == "event":
events.append(msg["event"])
return events
async def generate_async(
self, prompt: Optional[str] = None, messages: Optional[List[dict]] = None
) -> Union[str, dict]:
"""Generate a completion or a next message.
The format for messages is the following:
```python
[
{"role": "context", "content": {"user_name": "John"}},
{"role": "user", "content": "Hello! How are you?"},
{"role": "assistant", "content": "I am fine, thank you!"},
{"role": "event", "event": {"type": "UserSilent"}},
...
]
```
Args:
prompt: The prompt to be used for completion.
messages: The history of messages to be used to generate the next message.
Returns:
The completion (when a prompt is provided) or the next message.
System messages are not yet supported."""
if prompt is not None:
# Currently, we transform the prompt request into a single turn conversation
new_message = await self.generate_async(
messages=[{"role": "user", "content": prompt}]
)
assert new_message["role"] == "assistant"
return new_message["content"]
# TODO: Add support to load back history of events, next to history of messages
# This is important as without it, the LLM prediction is not as good.
t0 = time.time()
llm_stats.reset()
# The array of events corresponding to the provided sequence of messages.
events = self._get_events_for_messages(messages)
# Compute the new events.
new_events = await self.runtime.generate_events(events)
# Extract and join all the messages from StartUtteranceBotAction events as the response.
responses = []
for event in new_events:
if event["type"] == "StartUtteranceBotAction":
# Check if we need to remove a message
if event["script"] == "(remove last message)":
responses = responses[0:-1]
else:
responses.append(event["script"])
new_message = {"role": "assistant", "content": "\n".join(responses)}
# Save the new events in the history and update the cache
events.extend(new_events)
cache_key = get_history_cache_key(messages + [new_message])
self.events_history_cache[cache_key] = events
# If logging is enabled, we log the conversation
# TODO: add support for logging flag
if self.verbose:
history = get_colang_history(events)
log.info(f"Conversation history so far: \n{history}")
log.info("--- :: Total processing took %.2f seconds." % (time.time() - t0))
log.info("--- :: Stats: %s" % llm_stats)
return new_message
def generate(
self, prompt: Optional[str] = None, messages: Optional[List[dict]] = None
):
"""Synchronous version of generate_async."""
if check_sync_call_from_async_loop():
raise RuntimeError(
"You are using the sync `generate` inside async code. "
"You should replace with `await generate_async(...)."
)
return asyncio.run(self.generate_async(prompt=prompt, messages=messages))
async def generate_events_async(self, events: List[dict]) -> List[dict]:
"""Generate the next events based on the provided history.
The format for events is the following:
```python
[
{"type": "...", ...},
...
]
```
Args:
events: The history of events to be used to generate the next events.
Returns:
The newly generate event(s).
"""
t0 = time.time()
llm_stats.reset()
# Compute the new events.
new_events = await self.runtime.generate_events(events)
# If logging is enabled, we log the conversation
# TODO: add support for logging flag
if self.verbose:
history = get_colang_history(events)
log.info(f"Conversation history so far: \n{history}")
log.info("--- :: Total processing took %.2f seconds." % (time.time() - t0))
log.info("--- :: Stats: %s" % llm_stats)
return new_events
def generate_events(self, events: List[dict]) -> List[dict]:
"""Synchronous version of `LLMRails.generate_events_async`."""
if check_sync_call_from_async_loop():
raise RuntimeError(
"You are using the sync `generate_events` inside async code. "
"You should replace with `await generate_events_async(...)."
)
return asyncio.run(self.generate_events_async(events=events))
def register_action(self, action: callable, name: Optional[str] = None):
"""Register a custom action for the rails configuration."""
self.runtime.register_action(action, name)
def register_action_param(self, name: str, value: Any):
"""Registers a custom action parameter."""
self.runtime.register_action_param(name, value)
def register_filter(self, filter_fn: callable, name: Optional[str] = None):
"""Register a custom filter for the rails configuration."""
self.runtime.llm_task_manager.register_filter(filter_fn, name)
def register_output_parser(self, output_parser: callable, name: str):
"""Register a custom output parser for the rails configuration."""
self.runtime.llm_task_manager.register_output_parser(output_parser, name)
def register_prompt_context(self, name: str, value_or_fn: Any):
"""Register a value to be included in the prompt context.
:name: The name of the variable or function that will be used.
:value_or_fn: The value or function that will be used to generate the value.
"""
self.runtime.llm_task_manager.register_prompt_context(name, value_or_fn)
def register_embedding_search_provider(
self, name: str, cls: Type[EmbeddingsIndex]
) -> None:
"""Register a new embedding search provider.
Args:
name: The name of the embedding search provider that will be used.
cls: The class that will be used to generate and search embedding
"""
self.embedding_search_providers[name] = cls
| NeMo-Guardrails-main | nemoguardrails/rails/llm/llmrails.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, List
from langchain import LLMChain, PromptTemplate
from pydantic import Extra, root_validator
class ContextVarChain(LLMChain):
"""Chain that always returns the value of a context variable.
The context variable must be provided as input in a key that starts with "__context__".
"""
var_name: str
output_key: str = "value"
prompt: Any = None
llm: Any = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator()
def validate_all(cls, values: Dict) -> Dict:
"""Validate that prompt input variables are consistent."""
_input = f"__context__{values['var_name']}"
values["prompt"] = PromptTemplate(
template="{" + _input + "}", input_variables=[_input]
)
return values
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return ["__context__" + self.var_name]
@property
def output_keys(self) -> List[str]:
"""Expect output key.
:meta private:
"""
return [self.output_key]
def run(self, *args: Any, **kwargs: Any) -> str:
value = kwargs.get(f"__context__{self.var_name}")
return value
async def arun(self, *args: Any, **kwargs: Any) -> str:
return self.run(*args, **kwargs)
@property
def _chain_type(self) -> str:
return "context__var_chain"
| NeMo-Guardrails-main | nemoguardrails/rails/llm/context_var_chain.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Demo script."""
import logging
from langchain.chains import ConstitutionalChain
from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.rails.llm.context_var_chain import ContextVarChain
logging.basicConfig(level=logging.INFO)
COLANG_CONFIG = """
define user express greeting
"hi"
define bot remove last message
"(remove last message)"
define flow
user ...
bot respond
$updated_msg = execute check_if_constitutional
if $updated_msg != $last_bot_message
bot remove last message
bot $updated_msg
"""
YAML_CONFIG = """
models:
- type: main
engine: openai
model: text-davinci-003
"""
def demo():
"""Demo of using a chain as a custom action."""
config = RailsConfig.from_content(COLANG_CONFIG, YAML_CONFIG)
app = LLMRails(config)
constitutional_chain = ConstitutionalChain.from_llm(
llm=app.llm,
chain=ContextVarChain(var_name="last_bot_message"),
constitutional_principles=[
ConstitutionalPrinciple(
critique_request="Tell if this answer is good.",
revision_request="Give a better answer.",
)
],
)
app.register_action(constitutional_chain, name="check_if_constitutional")
history = [{"role": "user", "content": "Tell me if the service is up"}]
result = app.generate(messages=history)
print(result)
if __name__ == "__main__":
demo()
| NeMo-Guardrails-main | examples/demo_chain_as_action.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Example of using a QnA chain with guardrails."""
import logging
import os
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from nemoguardrails import LLMRails, RailsConfig
logging.basicConfig(level=logging.INFO)
COLANG_CONFIG = """
define user express greeting
"hi"
define user express insult
"You are stupid"
# Basic guardrail against insults.
define flow
user express insult
bot express calmly willingness to help
# Here we use the QA chain for anything else.
define flow
user ...
$answer = execute qa_chain(query=$last_user_message)
bot $answer
"""
YAML_CONFIG = """
models:
- type: main
engine: openai
model: text-davinci-003
"""
def _get_qa_chain(llm):
"""Initializes a QA chain using the jobs report.
It uses OpenAI embeddings.
"""
loader = TextLoader(
os.path.join(
os.path.dirname(__file__),
"..",
"examples/grounding_rail/kb/report.md",
)
)
docs = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(docs)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(texts, embeddings)
qa_chain = RetrievalQA.from_chain_type(
llm=llm, chain_type="stuff", retriever=docsearch.as_retriever()
)
return qa_chain
def demo():
"""Demo of using a chain as a custom action."""
config = RailsConfig.from_content(COLANG_CONFIG, YAML_CONFIG)
app = LLMRails(config)
# Create and register the chain directly as an action.
qa_chain = _get_qa_chain(app.llm)
app.register_action(qa_chain, name="qa_chain")
# Change to mode here to experiment with the multiple ways of using the chain
# mode = "chain"
mode = "chain_with_guardrails"
# mode = "chat_with_guardrails"
if mode == "chain":
query = "What is the current unemployment rate?"
result = qa_chain.run(query)
print(result)
elif mode == "chain_with_guardrails":
history = [
{"role": "user", "content": "What is the current unemployment rate?"}
]
result = app.generate(messages=history)
print(result)
elif mode == "chat_with_guardrails":
history = []
while True:
user_message = input("> ")
history.append({"role": "user", "content": user_message})
bot_message = app.generate(messages=history)
history.append(bot_message)
# We print bot messages in green.
print(f"\033[92m{bot_message['content']}\033[0m")
if __name__ == "__main__":
demo()
| NeMo-Guardrails-main | examples/demo_chain_with_guardrails.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Callable, Coroutine
from langchain.llms.base import BaseLLM
from nemoguardrails import LLMRails, RailsConfig
COLANG_CONFIG = """
define user express greeting
"hi"
define user express ill intent
"I hate you"
"I want to destroy the world"
define bot express cannot respond
"I'm sorry I cannot help you with that."
define user express question
"What is the current unemployment rate?"
# Basic guardrail example
define flow
user express ill intent
bot express cannot respond
# Question answering flow
define flow
user ...
$answer = execute llama_index_query(query=$last_user_message)
bot $answer
"""
YAML_CONFIG = """
models:
- type: main
engine: openai
model: text-davinci-003
"""
def demo():
try:
import llama_index
from llama_index.indices.query.base import BaseQueryEngine
from llama_index.response.schema import StreamingResponse
except ImportError:
raise ImportError(
"Could not import llama_index, please install it with "
"`pip install llama_index`."
)
config = RailsConfig.from_content(COLANG_CONFIG, YAML_CONFIG)
app = LLMRails(config)
def _get_llama_index_query_engine(llm: BaseLLM):
docs = llama_index.SimpleDirectoryReader(
input_files=["../examples/grounding_rail/kb/report.md"]
).load_data()
llm_predictor = llama_index.LLMPredictor(llm=llm)
index = llama_index.GPTVectorStoreIndex.from_documents(
docs, llm_predictor=llm_predictor
)
default_query_engine = index.as_query_engine()
return default_query_engine
def _get_callable_query_engine(
query_engine: BaseQueryEngine,
) -> Callable[[str], Coroutine[Any, Any, str]]:
async def get_query_response(query: str) -> str:
response = query_engine.query(query)
if isinstance(response, StreamingResponse):
typed_response = response.get_response()
else:
typed_response = response
response_str = typed_response.response
if response_str is None:
return ""
return response_str
return get_query_response
query_engine = _get_llama_index_query_engine(app.llm)
app.register_action(
_get_callable_query_engine(query_engine), name="llama_index_query"
)
history = [{"role": "user", "content": "What is the current unemployment rate?"}]
result = app.generate(messages=history)
print(result)
if __name__ == "__main__":
demo()
| NeMo-Guardrails-main | examples/demo_llama_index_guardrails.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from nemoguardrails import LLMRails, RailsConfig
config = RailsConfig.from_path(".")
rails = LLMRails(config)
new_message = rails.generate(
messages=[{"role": "user", "content": "How can you help me?"}]
)
print(new_message)
| NeMo-Guardrails-main | examples/topical_rail/api_client.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import lru_cache
from langchain import HuggingFacePipeline
from torch import bfloat16
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, pipeline
from nemoguardrails.llm.helpers import get_llm_instance_wrapper
from nemoguardrails.llm.providers import register_llm_provider
@lru_cache
def get_mpt_7b_instruct_llm():
# For Mosaic MBT LLM, need to use from_pretrained instead of HuggingFacePipeline.from_model_id
# in order to use the GPU. Default config uses CPU and cannot be modified.
# Bug submitted here: https://github.com/huggingface/transformers/issues/24471#issuecomment-1606549042
name = "mosaicml/mpt-7b-instruct"
config = AutoConfig.from_pretrained(name, trust_remote_code=True)
# Use GPU (with id 0 in this case) for fast initialization
device = "cuda:0"
config.init_device = device
config.max_seq_len = 450
params = {"temperature": 0.01, "max_new_tokens": 100, "max_length": 450}
model = AutoModelForCausalLM.from_pretrained(
name,
config=config,
torch_dtype=bfloat16, # Load model weights in bfloat16
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
pipe = pipeline(
model=model,
task="text-generation",
tokenizer=tokenizer,
device=device,
do_sample=True,
use_cache=True,
**params,
)
llm = HuggingFacePipeline(pipeline=pipe, model_kwargs=params)
return llm
HFPipelineMosaic = get_llm_instance_wrapper(
llm_instance=get_mpt_7b_instruct_llm(), llm_type="hf_pipeline_mosaic"
)
register_llm_provider("hf_pipeline_mosaic", HFPipelineMosaic)
| NeMo-Guardrails-main | examples/llm/hf_pipeline_mosaic/config.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import lru_cache
from langchain import HuggingFacePipeline
from torch import float16
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, pipeline
from nemoguardrails.llm.helpers import get_llm_instance_wrapper
from nemoguardrails.llm.providers import register_llm_provider
@lru_cache
def get_vicuna_7b_llm():
"""Loads the Vicuna 7B LLM."""
repo_id = "lmsys/vicuna-7b-v1.3"
params = {"temperature": 0, "max_length": 530}
# Using the first GPU
device = 0
llm = HuggingFacePipeline.from_model_id(
model_id=repo_id,
device=device,
task="text-generation",
model_kwargs=params,
)
return llm
def get_vicuna_13b_llm():
"""Loads the Vicuna 13B LLM."""
repo_id = "lmsys/vicuna-13b-v1.3"
# If you want Bloke Wizard Vicuna, comment one of the next lines
# repo_id = "TheBloke/wizard-vicuna-13B-HF"
# repo_id = "TheBloke/Wizard-Vicuna-13B-Uncensored-HF"
params = {"temperature": 0, "max_length": 500}
# Using the first GPU
device = 0
llm = HuggingFacePipeline.from_model_id(
model_id=repo_id,
device=device,
task="text-generation",
model_kwargs=params,
)
return llm
def _load_model(model_name, device, num_gpus, debug=False):
"""Helper function to load the model."""
if device == "cpu":
kwargs = {}
elif device == "cuda":
kwargs = {"torch_dtype": float16}
if num_gpus == "auto":
kwargs["device_map"] = "auto"
else:
num_gpus = int(num_gpus)
if num_gpus != 1:
kwargs.update(
{
"device_map": "auto",
"max_memory": {i: "13GiB" for i in range(num_gpus)},
}
)
elif device == "mps":
kwargs = {"torch_dtype": float16}
# Avoid bugs in mps backend by not using in-place operations.
print("mps not supported")
else:
raise ValueError(f"Invalid device: {device}")
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
model_name, low_cpu_mem_usage=True, **kwargs
)
if device == "cuda" and num_gpus == 1:
model.to(device)
if debug:
print(model)
return model, tokenizer
def get_vicuna_13b_llm_from_path(model_path: str = "/workspace/ckpt/"):
"""Loads the Vicuna 13B LLM from a local path."""
device = "cuda"
num_gpus = 2 # making sure GPU-GPU are NVlinked, GPUs-GPUS with NVSwitch
model, tokenizer = _load_model(model_path, device, num_gpus, debug=False)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=100,
temperature=0,
do_sample=True,
)
llm = HuggingFacePipeline(pipeline=pipe)
return llm
# On the next line, change the Vicuna LLM instance depending on your needs
HFPipelineVicuna = get_llm_instance_wrapper(
llm_instance=get_vicuna_7b_llm(), llm_type="hf_pipeline_vicuna"
)
register_llm_provider("hf_pipeline_vicuna", HFPipelineVicuna)
| NeMo-Guardrails-main | examples/llm/hf_pipeline_vicuna/config.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import lru_cache
from langchain import HuggingFacePipeline
from torch.cuda import device_count
from nemoguardrails.llm.helpers import get_llm_instance_wrapper
from nemoguardrails.llm.providers import register_llm_provider
@lru_cache
def get_dolly_v2_3b_llm():
repo_id = "databricks/dolly-v2-3b"
params = {"temperature": 0, "max_length": 1024}
# Use the first CUDA-enabled GPU, if any
device = 0 if device_count() else -1
llm = HuggingFacePipeline.from_model_id(
model_id=repo_id, device=device, task="text-generation", model_kwargs=params
)
return llm
HFPipelineDolly = get_llm_instance_wrapper(
llm_instance=get_dolly_v2_3b_llm(), llm_type="hf_pipeline_dolly"
)
register_llm_provider("hf_pipeline_dolly", HFPipelineDolly)
| NeMo-Guardrails-main | examples/llm/hf_pipeline_dolly/config.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from datetime import datetime
from nemoguardrails import LLMRails
def get_current_date_str():
"""Helper function returning a string of the form: "{month} {day}, {year}. It's a {weekday}." """
return datetime.now().strftime("%B %d, %Y. It's a %A.")
def init(llm_rails: LLMRails):
# We register the additional prompt context for the current date.
llm_rails.register_prompt_context("current_date", get_current_date_str)
| NeMo-Guardrails-main | examples/custom_prompt_context/config.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import csv
import json
from nemoguardrails.server.api import register_logger
async def custom_logger(item):
"""Custom logger that writes the ratings to a CSV file in the current directory."""
data = json.loads(item["body"])
config_id = data["config_id"]
messages = data["messages"]
# We only track on rating events
if messages[-1]["role"] != "event" or messages[-1]["event"].get("type") != "rating":
print("Skipping")
return
# Extract the data from the event
str_messages = ""
for message in messages:
if message["role"] == "user":
str_messages += f"User: {message['content']}\n"
if message["role"] == "assistant":
str_messages += f"Assistant: {message['content']}\n"
event_data = messages[-1]["event"]["data"]
row = [
config_id,
event_data["challenge"]["id"],
event_data["challenge"]["name"],
event_data["challenge"]["description"],
event_data["success"],
event_data["effort"],
event_data["comment"],
str_messages.strip(),
]
with open("ratings.csv", "a", newline="") as f:
writer = csv.writer(f)
writer.writerow(row)
register_logger(custom_logger)
| NeMo-Guardrails-main | examples/red-teaming/config.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os.path
import pickle
from pathlib import Path
from typing import Optional
import faiss
import pandas as pd
import torch
from gpt4pandas import GPT4Pandas
from langchain import HuggingFacePipeline
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import BaseLLM
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from examples.multi_kb.tabular_llm import TabularLLM
from nemoguardrails import LLMRails, RailsConfig
from nemoguardrails.actions import action
from nemoguardrails.actions.actions import ActionResult
from nemoguardrails.llm.helpers import get_llm_instance_wrapper
from nemoguardrails.llm.providers import register_llm_provider
def _get_model_config(config: RailsConfig, type: str):
"""Quick helper to return the config for a specific model type."""
for model_config in config.models:
if model_config.type == type:
return model_config
def _load_model(model_name_or_path, device, num_gpus, debug=False):
"""Load an HF locally saved checkpoint."""
if device == "cpu":
kwargs = {}
elif device == "cuda":
kwargs = {"torch_dtype": torch.float16}
if num_gpus == "auto":
kwargs["device_map"] = "auto"
else:
num_gpus = int(num_gpus)
if num_gpus != 1:
kwargs.update(
{
"device_map": "auto",
"max_memory": {i: "13GiB" for i in range(num_gpus)},
}
)
elif device == "mps":
kwargs = {"torch_dtype": torch.float16}
# Avoid bugs in mps backend by not using in-place operations.
print("mps not supported")
else:
raise ValueError(f"Invalid device: {device}")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path, low_cpu_mem_usage=True, **kwargs
)
if device == "cuda" and num_gpus == 1:
model.to(device)
if debug:
print(model)
return model, tokenizer
def _make_faiss_gpu(data_path, out_path, embeddings):
# Here we process the txt files under the data_path folder.
ps = list(Path(data_path).glob("**/*.txt"))
print(ps)
data = []
sources = []
for p in ps:
with open(p) as f:
data.append(f.read())
sources.append(p)
# We do this due to the context limits of the LLMs.
# Here we split the documents, as needed, into smaller chunks.
# We do this due to the context limits of the LLMs.
text_splitter = CharacterTextSplitter(chunk_size=200, separator="\n")
docs = []
metadatas = []
for i, d in enumerate(data):
splits = text_splitter.split_text(d)
docs.extend(splits)
metadatas.extend([{"source": sources[i]}] * len(splits))
# Here we create a vector store from the documents and save it to disk.
store = FAISS.from_texts(docs, embeddings, metadatas=metadatas)
os.makedirs(out_path, exist_ok=True)
faiss.write_index(store.index, out_path + "docs.index")
store.index = None
with open(out_path + "faiss_store.pkl", "wb") as f:
pickle.dump(store, f)
return store
def _get_vector_db(model_name: str, data_path: str, persist_path: str):
"""Creates a vector DB for a given data path.
If it's the first time, the index will be persisted at the given path.
Otherwise, it will be loaded directly (if the `persist_path` exists).
"""
# use other embeddings from huggingface
model_kwargs = {"device": "cuda"}
hf_embedding = HuggingFaceEmbeddings(
model_name=model_name, model_kwargs=model_kwargs
)
using_vectorstore = "faiss"
if using_vectorstore == "faiss":
if os.path.exists(persist_path):
index = faiss.read_index(os.path.join(persist_path, "docs.index"))
with open(os.path.join(persist_path, "faiss_store.pkl"), "rb") as f:
vectordb = pickle.load(f)
vectordb.index = index
else:
data_path = data_path
vectordb = _make_faiss_gpu(data_path, persist_path, hf_embedding)
return vectordb
def init_main_llm(config: RailsConfig):
"""Initialize the main model from a locally saved path.
The path is taken from the main model config.
models:
- type: main
engine: hf_pipeline_bloke
parameters:
path: "<PATH TO THE LOCALLY SAVED CHECKPOINT>"
"""
# loading custom llm from disk with multiGPUs support
# model_name = "< path_to_the_saved_custom_llm_checkpoints >" # loading model ckpt from disk
model_config = _get_model_config(config, "main")
model_path = model_config.parameters.get("path")
device = model_config.parameters.get("device", "cuda")
num_gpus = model_config.parameters.get("num_gpus", 1)
model, tokenizer = _load_model(model_path, device, num_gpus, debug=False)
# repo_id="TheBloke/Wizard-Vicuna-13B-Uncensored-HF"
# pipe = pipeline("text-generation", model=repo_id, device_map={"":"cuda:0"}, max_new_tokens=256, temperature=0.1, do_sample=True,use_cache=True)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=256,
temperature=0.1,
do_sample=True,
)
hf_llm = HuggingFacePipeline(pipeline=pipe)
provider = get_llm_instance_wrapper(
llm_instance=hf_llm, llm_type="hf_pipeline_bloke"
)
register_llm_provider("hf_pipeline_bloke", provider)
def _get_titanic_raw_data_frame(csv_path: str):
"""Reads the Titanic CSV file and returns a tweaked data frame."""
df = pd.read_csv(csv_path, sep=",")
# working on the data
Embarked_d = {"C": "Cherbourg", "Q": "Queenstown", "S": "Southampton"}
class_d = {1: "first class", 2: "second class", 3: "third class"}
df["Class"] = df["Pclass"].apply(lambda x: class_d[x])
# changing the embark port to full name
n = len(df)
col_ls = list(df.columns)
idx = col_ls.index("Embarked")
ls = []
for i in range(n):
temp = df.iloc[i, idx]
if type(temp) == str:
out = Embarked_d[temp]
ls.append(out)
else:
ls.append("N/A")
df["port"] = ls
df["Lived"] = df["Survived"].apply(lambda x: "survived" if x == 1 else "died")
# dropping duplicated and re-worked column
df.drop("Survived", inplace=True, axis=1)
df.drop("Pclass", inplace=True, axis=1)
df.drop("Embarked", inplace=True, axis=1)
return df
def init_tabular_llm(config: RailsConfig):
"""Initialize the model for searching tabular data."""
# We just compute the titanic raw data frame
titanic_csv_path = config.custom_data.get("tabular_data_path")
raw_data_frame = _get_titanic_raw_data_frame(titanic_csv_path)
model_config = _get_model_config(config, "tabular")
model_path = model_config.parameters.get("path")
# We just need to provide an empty data frame when initializing the model.
empty_data_frame = pd.DataFrame()
gpt = GPT4Pandas(model_path, empty_data_frame, verbose=False)
tabular_llm = TabularLLM(
gpt=gpt, raw_data_path=titanic_csv_path, raw_data_frame=raw_data_frame
)
register_llm_provider("tabular", get_llm_instance_wrapper(tabular_llm, "tabular"))
vectordb = None
def init_vectordb_model(config: RailsConfig):
global vectordb
model_config = _get_model_config(config, "vectordb")
vectordb = _get_vector_db(
model_name=model_config.model,
data_path=config.custom_data["kb_data_path"],
persist_path=model_config.parameters.get("persist_path"),
)
register_llm_provider("faiss", vectordb)
@action(is_system_action=True)
async def retrieve_relevant_chunks(
context: Optional[dict] = None,
llm: Optional[BaseLLM] = None,
tabular_llm: Optional[BaseLLM] = None,
):
"""Retrieve relevant chunks from the knowledge base and add them to the context."""
user_message = context.get("last_user_message")
# TODO: do this better using a separate canonical form
if "csv" in user_message:
llm_output = await tabular_llm.agenerate(prompts=[user_message])
result, source_ref, citing_text = llm_output.generations[0][0].text.split("###")
else:
# using faiss vector database , pip install faiss-gpu if you have gpu, otherwise please use faiss-cpu
retriever = vectordb.as_retriever(
search_type="similarity", search_kwargs={"k": 3}
)
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True,
)
out = qa_chain(user_message)
result = out["result"]
citing_text = out["source_documents"][0].page_content
source_ref = str(out["source_documents"][0].metadata["source"])
context_updates = {
"relevant_chunks": f"""
Question: {user_message}
Answer: {result},
Citing : {citing_text},
Source : {source_ref}
"""
}
return ActionResult(
return_value=context_updates["relevant_chunks"],
context_updates=context_updates,
)
def init(llm_rails: LLMRails):
config = llm_rails.config
# Initialize the various models
init_main_llm(config)
init_vectordb_model(config)
init_tabular_llm(config)
# Register the custom `retrieve_relevant_chunks` for custom retrieval
llm_rails.register_action(retrieve_relevant_chunks, "retrieve_relevant_chunks")
| NeMo-Guardrails-main | examples/multi_kb/config.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
def query_tabular_data(usr_query: str, gpt: any, raw_data_frame: any):
"""Answer a question based on some tabular data."""
cut_idx = usr_query.find("based on")
usr_query = usr_query[:cut_idx] + "?"
# TODO: check if there's a way to do this grouping dynamically
grouped_by_cols = []
if any(
word in usr_query for word in ["first class", "second class", "third class"]
):
grouped_by_cols.append("Class")
elif any(
word in usr_query for word in ["port", "Queenstown", "Southampton", "Cherbourg"]
):
grouped_by_cols.append("port")
elif any(
word in usr_query for word in ["female", "male", "man", "woman", "men", "women"]
):
grouped_by_cols.append("Sex")
else:
pass
d = raw_data_frame.groupby(grouped_by_cols, as_index=False)["Lived"].value_counts()
# flatten the grouped by pandas series to flatten dictionary
d2 = d.reset_index(inplace=False)
gpt.set_dataframe(d2)
out = gpt.ask(usr_query)
return out, d2.to_string()
class TabularLLM(LLM):
"""LLM wrapping for GPT4Pandas."""
model: str = ""
temperature: float = 0.7
tokens_to_generate: int = 256
stop: Optional[List[str]] = None
# This is the GPT4Pandas instance
gpt: Any
# The path to the raw data
raw_data_path: str
# This is the raw data frame associated with the tabular LLM
raw_data_frame: Any
@property
def _default_params(self) -> Dict[str, Any]:
return {}
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{"model": self.model}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "tabular_llm"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs,
) -> str:
raise Exception("Sync mode not supported.")
async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs,
) -> str:
result, processed_data = query_tabular_data(
usr_query=prompt, gpt=self.gpt, raw_data_frame=self.raw_data_frame
)
return "###".join([result, self.raw_data_path, processed_data])
| NeMo-Guardrails-main | examples/multi_kb/tabular_llm.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| NeMo-Guardrails-main | examples/multi_kb/__init__.py |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Any, List, Optional
from nemoguardrails.actions import action
@action()
async def block_list(file_name: Optional[str] = None, context: Optional[dict] = None):
bot_response = context.get("last_bot_message")
root_path = os.path.dirname(__file__)
with open(os.path.join(root_path, file_name)) as f:
lines = [line.rstrip() for line in f]
for line in lines:
if line in bot_response:
return True
return False
| NeMo-Guardrails-main | examples/moderation_rail/actions.py |
"""
AmgX base image: x86_64-ubuntu20.04-nvhpc20.9
"""
import posixpath
Stage0 += comment(__doc__, reformat=False)
Stage0 += baseimage(image='ubuntu:20.04')
compiler = nvhpc(eula=True, version='20.9', cuda_multi=False, cuda='11.0')
# WAR: nvhpc should be doing this
compiler.toolchain.CUDA_HOME = '/opt/nvidia/hpc_sdk/Linux_x86_64/20.9/cuda/11.0'
Stage0 += compiler
# Current minimum version required by AMGX
Stage0 += cmake(eula=True, version='3.7.0')
# MPI
Stage0 += mlnx_ofed(version='5.0-2.1.8.0')
Stage0 += gdrcopy(ldconfig=True, version='2.0')
Stage0 += knem(ldconfig=True, version='1.1.3')
# BUG: this should just work
# Stage0 += ucx(gdrcopy=True,knem=True,ofed=True,cuda=True)
Stage0 += ucx(
# WAR: should not be necessary:
build_environment={
'LD_LIBRARY_PATH': '/usr/lib/x86_64-linux-gnu:${LD_LIBRARY_PATH}',
},
gdrcopy=True,
knem=True,
ofed=True,
cuda=compiler.toolchain.CUDA_HOME,
# WAR: should not be necessary, required because OpenMPI cannot find UCX at
# default install location
prefix='/usr/local/ucx'
)
Stage0 += openmpi(
cuda=True,
infiniband=True,
version='4.0.3',
pmix=True,
# WAR: should not be necessary: ucx=True should do the right thing
ucx='/usr/local/ucx',
toolchain=compiler.toolchain
)
Stage0 += environment(multinode_vars = {
'OMPI_MCA_pml': 'ucx',
'OMPI_MCA_btl': '^smcuda,vader,tcp,uct,openib',
'UCX_MEMTYPE_CACHE': 'n',
'UCX_TLS': 'rc,cuda_copy,cuda_ipc,gdr_copy,sm'
},
# WAR: we should have a `compiler.toolchain.environment()` API to do this properly
variables={
'CUDA_HOME': compiler.toolchain.CUDA_HOME,
'CC': compiler.toolchain.CC,
'CXX': compiler.toolchain.CXX,
'FC': compiler.toolchain.FC,
'FC': compiler.toolchain.FC,
'F90': compiler.toolchain.F90,
'F77': compiler.toolchain.F77
}
)
| AMGX-main | ci/containers/x86_64-ubuntu20.04-nvhpc20.9.py |
"""
AmgX base image: x86_64-ubuntu18.04-gnu-cuda11.0
"""
Stage0 += comment(__doc__, reformat=False)
Stage0 += baseimage(image='nvidia/cuda:11.0-devel-ubuntu18.04')
# Last compiler supported for Ubuntu 18.04 by CUDA 11.0
# https://docs.nvidia.com/cuda/archive/11.0/cuda-installation-guide-linux/index.html#system-requirements
compiler = gnu(version='8')
Stage0 += compiler
# Current minimum version required by AMGX
Stage0 += cmake(eula=True, version='3.7.0')
# MPI
Stage0 += mlnx_ofed(version='5.0-2.1.8.0')
Stage0 += gdrcopy(ldconfig=True, version='2.0')
Stage0 += knem(ldconfig=True, version='1.1.3')
Stage0 += ucx(gdrcopy=True, knem=True, ofed=True, cuda=True)
Stage0 += openmpi(
cuda=True,
infiniband=True,
version='4.0.3',
pmix=True,
ucx=True,
toolchain=compiler.toolchain
)
Stage0 += environment(multinode_vars = {
'OMPI_MCA_pml': 'ucx',
'OMPI_MCA_btl': '^smcuda,vader,tcp,uct,openib',
'UCX_MEMTYPE_CACHE': 'n',
'UCX_TLS': 'rc,cuda_copy,cuda_ipc,gdr_copy,sm'
})
| AMGX-main | ci/containers/x86_64-ubuntu18.04-gnu8-cuda11.0.py |
"""
AmgX base image: x86_64-ubuntu18.04-gnu-cuda10.2
"""
Stage0 += comment(__doc__, reformat=False)
Stage0 += baseimage(image='nvidia/cuda:10.2-devel-ubuntu18.04')
# Last compiler supported for Ubuntu 18.04 by CUDA 10.2
# https://docs.nvidia.com/cuda/archive/10.2/cuda-installation-guide-linux/index.html#system-requirements
compiler = gnu()
Stage0 += compiler
# Current minimum version required by AMGX
Stage0 += cmake(eula=True, version='3.7.0')
# MPI
Stage0 += mlnx_ofed(version='5.0-2.1.8.0')
Stage0 += gdrcopy(ldconfig=True, version='2.0')
Stage0 += knem(ldconfig=True, version='1.1.3')
Stage0 += ucx(gdrcopy=True, knem=True, ofed=True, cuda=True)
Stage0 += openmpi(
cuda=True,
infiniband=True,
version='4.0.3',
pmix=True,
ucx=True,
toolchain=compiler.toolchain
)
Stage0 += environment(multinode_vars = {
'OMPI_MCA_pml': 'ucx',
'OMPI_MCA_btl': '^smcuda,vader,tcp,uct,openib',
'UCX_MEMTYPE_CACHE': 'n',
'UCX_TLS': 'rc,cuda_copy,cuda_ipc,gdr_copy,sm'
})
| AMGX-main | ci/containers/x86_64-ubuntu18.04-gnu7-cuda10.2.py |
"""
AmgX base image: x86_64-ubuntu18.04-llvm-cuda11.0
"""
Stage0 += comment(__doc__, reformat=False)
Stage0 += baseimage(image='nvidia/cuda:11.0-devel-ubuntu18.04')
# Last compiler supported for Ubuntu 18.04 by CUDA 11.0
# https://docs.nvidia.com/cuda/archive/11.0/cuda-installation-guide-linux/index.html#system-requirements
compiler = llvm(version='9')
Stage0 += compiler
Stage0 += shell(commands=[
'update-alternatives --install /usr/bin/cc cc /usr/bin/clang-9 40',
'update-alternatives --install /usr/bin/c++ c++ /usr/bin/clang++-9 60'
])
# Current minimum version required by AMGX
Stage0 += cmake(eula=True, version='3.7.0')
# MPI
Stage0 += mlnx_ofed(version='5.0-2.1.8.0')
Stage0 += gdrcopy(ldconfig=True, version='2.0')
Stage0 += knem(ldconfig=True, version='1.1.3')
Stage0 += ucx(gdrcopy=True, knem=True, ofed=True, cuda=True)
Stage0 += openmpi(
cuda=True,
infiniband=True,
version='4.0.3',
pmix=True,
ucx=True,
toolchain=compiler.toolchain
)
Stage0 += environment(multinode_vars = {
'OMPI_MCA_pml': 'ucx',
'OMPI_MCA_btl': '^smcuda,vader,tcp,uct,openib',
'UCX_MEMTYPE_CACHE': 'n',
'UCX_TLS': 'rc,cuda_copy,cuda_ipc,gdr_copy,sm'
})
| AMGX-main | ci/containers/x86_64-ubuntu18.04-llvm9-cuda11.0.py |
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# *****************************************************************************
import copy
import torch
from torch.autograd import Variable
import torch.nn.functional as F
@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a+input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
acts = t_act * s_act
return acts
class WaveGlowLoss(torch.nn.Module):
def __init__(self, sigma=1.0):
super(WaveGlowLoss, self).__init__()
self.sigma = sigma
def forward(self, model_output):
z, log_s_list, log_det_W_list = model_output
for i, log_s in enumerate(log_s_list):
if i == 0:
log_s_total = torch.sum(log_s)
log_det_W_total = log_det_W_list[i]
else:
log_s_total = log_s_total + torch.sum(log_s)
log_det_W_total += log_det_W_list[i]
loss = torch.sum(z*z)/(2*self.sigma*self.sigma) - log_s_total - log_det_W_total
return loss/(z.size(0)*z.size(1)*z.size(2))
class Invertible1x1Conv(torch.nn.Module):
"""
The layer outputs both the convolution, and the log determinant
of its weight matrix. If reverse=True it does convolution with
inverse
"""
def __init__(self, c):
super(Invertible1x1Conv, self).__init__()
self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=0,
bias=False)
# Sample a random orthonormal matrix to initialize weights
W = torch.qr(torch.FloatTensor(c, c).normal_())[0]
# Ensure determinant is 1.0 not -1.0
if torch.det(W) < 0:
W[:,0] = -1*W[:,0]
W = W.view(c, c, 1)
self.conv.weight.data = W
def forward(self, z, reverse=False):
# shape
batch_size, group_size, n_of_groups = z.size()
W = self.conv.weight.squeeze()
if reverse:
if not hasattr(self, 'W_inverse'):
# Reverse computation
W_inverse = W.float().inverse()
W_inverse = Variable(W_inverse[..., None])
if z.type() == 'torch.cuda.HalfTensor':
W_inverse = W_inverse.half()
self.W_inverse = W_inverse
z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
return z
else:
# Forward computation
log_det_W = batch_size * n_of_groups * torch.logdet(W)
z = self.conv(z)
return z, log_det_W
class WN(torch.nn.Module):
"""
This is the WaveNet like layer for the affine coupling. The primary difference
from WaveNet is the convolutions need not be causal. There is also no dilation
size reset. The dilation only doubles on each layer
"""
def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels,
kernel_size):
super(WN, self).__init__()
assert(kernel_size % 2 == 1)
assert(n_channels % 2 == 0)
self.n_layers = n_layers
self.n_channels = n_channels
self.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
start = torch.nn.utils.weight_norm(start, name='weight')
self.start = start
# Initializing last layer to 0 makes the affine coupling layers
# do nothing at first. This helps with training stability
end = torch.nn.Conv1d(n_channels, 2*n_in_channels, 1)
end.weight.data.zero_()
end.bias.data.zero_()
self.end = end
cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels*n_layers, 1)
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
for i in range(n_layers):
dilation = 2 ** i
padding = int((kernel_size*dilation - dilation)/2)
in_layer = torch.nn.Conv1d(n_channels, 2*n_channels, kernel_size,
dilation=dilation, padding=padding)
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
self.in_layers.append(in_layer)
# last one is not necessary
if i < n_layers - 1:
res_skip_channels = 2*n_channels
else:
res_skip_channels = n_channels
res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1)
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
self.res_skip_layers.append(res_skip_layer)
def forward(self, forward_input):
audio, spect = forward_input
audio = self.start(audio)
output = torch.zeros_like(audio)
n_channels_tensor = torch.IntTensor([self.n_channels])
spect = self.cond_layer(spect)
for i in range(self.n_layers):
spect_offset = i*2*self.n_channels
acts = fused_add_tanh_sigmoid_multiply(
self.in_layers[i](audio),
spect[:,spect_offset:spect_offset+2*self.n_channels,:],
n_channels_tensor)
res_skip_acts = self.res_skip_layers[i](acts)
if i < self.n_layers - 1:
audio = audio + res_skip_acts[:,:self.n_channels,:]
output = output + res_skip_acts[:,self.n_channels:,:]
else:
output = output + res_skip_acts
return self.end(output)
class WaveGlow(torch.nn.Module):
def __init__(self, n_mel_channels, n_flows, n_group, n_early_every,
n_early_size, WN_config):
super(WaveGlow, self).__init__()
self.upsample = torch.nn.ConvTranspose1d(n_mel_channels,
n_mel_channels,
1024, stride=256)
assert(n_group % 2 == 0)
self.n_flows = n_flows
self.n_group = n_group
self.n_early_every = n_early_every
self.n_early_size = n_early_size
self.WN = torch.nn.ModuleList()
self.convinv = torch.nn.ModuleList()
n_half = int(n_group/2)
# Set up layers with the right sizes based on how many dimensions
# have been output already
n_remaining_channels = n_group
for k in range(n_flows):
if k % self.n_early_every == 0 and k > 0:
n_half = n_half - int(self.n_early_size/2)
n_remaining_channels = n_remaining_channels - self.n_early_size
self.convinv.append(Invertible1x1Conv(n_remaining_channels))
self.WN.append(WN(n_half, n_mel_channels*n_group, **WN_config))
self.n_remaining_channels = n_remaining_channels # Useful during inference
def forward(self, forward_input):
"""
forward_input[0] = mel_spectrogram: batch x n_mel_channels x frames
forward_input[1] = audio: batch x time
"""
spect, audio = forward_input
# Upsample spectrogram to size of audio
spect = self.upsample(spect)
assert(spect.size(2) >= audio.size(1))
if spect.size(2) > audio.size(1):
spect = spect[:, :, :audio.size(1)]
spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
output_audio = []
log_s_list = []
log_det_W_list = []
for k in range(self.n_flows):
if k % self.n_early_every == 0 and k > 0:
output_audio.append(audio[:,:self.n_early_size,:])
audio = audio[:,self.n_early_size:,:]
audio, log_det_W = self.convinv[k](audio)
log_det_W_list.append(log_det_W)
n_half = int(audio.size(1)/2)
audio_0 = audio[:,:n_half,:]
audio_1 = audio[:,n_half:,:]
output = self.WN[k]((audio_0, spect))
log_s = output[:, n_half:, :]
b = output[:, :n_half, :]
audio_1 = torch.exp(log_s)*audio_1 + b
log_s_list.append(log_s)
audio = torch.cat([audio_0, audio_1],1)
output_audio.append(audio)
return torch.cat(output_audio,1), log_s_list, log_det_W_list
def infer(self, spect, sigma=1.0):
spect = self.upsample(spect)
# trim conv artifacts. maybe pad spec to kernel multiple
time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0]
spect = spect[:, :, :-time_cutoff]
spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
if spect.type() == 'torch.cuda.HalfTensor':
audio = torch.cuda.HalfTensor(spect.size(0),
self.n_remaining_channels,
spect.size(2)).normal_()
else:
audio = torch.cuda.FloatTensor(spect.size(0),
self.n_remaining_channels,
spect.size(2)).normal_()
audio = torch.autograd.Variable(sigma*audio)
for k in reversed(range(self.n_flows)):
n_half = int(audio.size(1)/2)
audio_0 = audio[:,:n_half,:]
audio_1 = audio[:,n_half:,:]
output = self.WN[k]((audio_0, spect))
s = output[:, n_half:, :]
b = output[:, :n_half, :]
audio_1 = (audio_1 - b)/torch.exp(s)
audio = torch.cat([audio_0, audio_1],1)
audio = self.convinv[k](audio, reverse=True)
if k % self.n_early_every == 0 and k > 0:
if spect.type() == 'torch.cuda.HalfTensor':
z = torch.cuda.HalfTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
else:
z = torch.cuda.FloatTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
audio = torch.cat((sigma*z, audio),1)
audio = audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data
return audio
@staticmethod
def remove_weightnorm(model):
waveglow = model
for WN in waveglow.WN:
WN.start = torch.nn.utils.remove_weight_norm(WN.start)
WN.in_layers = remove(WN.in_layers)
WN.cond_layer = torch.nn.utils.remove_weight_norm(WN.cond_layer)
WN.res_skip_layers = remove(WN.res_skip_layers)
return waveglow
def remove(conv_list):
new_conv_list = torch.nn.ModuleList()
for old_conv in conv_list:
old_conv = torch.nn.utils.remove_weight_norm(old_conv)
new_conv_list.append(old_conv)
return new_conv_list
| waveglow-master | glow.py |
import copy
import torch
from glow import Invertible1x1Conv, remove
@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a+input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
acts = t_act * s_act
return acts
class WN(torch.nn.Module):
"""
This is the WaveNet like layer for the affine coupling. The primary difference
from WaveNet is the convolutions need not be causal. There is also no dilation
size reset. The dilation only doubles on each layer
"""
def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels,
kernel_size):
super(WN, self).__init__()
assert(kernel_size % 2 == 1)
assert(n_channels % 2 == 0)
self.n_layers = n_layers
self.n_channels = n_channels
self.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
self.cond_layers = torch.nn.ModuleList()
start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
start = torch.nn.utils.weight_norm(start, name='weight')
self.start = start
# Initializing last layer to 0 makes the affine coupling layers
# do nothing at first. This helps with training stability
end = torch.nn.Conv1d(n_channels, 2*n_in_channels, 1)
end.weight.data.zero_()
end.bias.data.zero_()
self.end = end
for i in range(n_layers):
dilation = 2 ** i
padding = int((kernel_size*dilation - dilation)/2)
in_layer = torch.nn.Conv1d(n_channels, 2*n_channels, kernel_size,
dilation=dilation, padding=padding)
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
self.in_layers.append(in_layer)
cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels, 1)
cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
self.cond_layers.append(cond_layer)
# last one is not necessary
if i < n_layers - 1:
res_skip_channels = 2*n_channels
else:
res_skip_channels = n_channels
res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1)
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
self.res_skip_layers.append(res_skip_layer)
def forward(self, forward_input):
audio, spect = forward_input
audio = self.start(audio)
for i in range(self.n_layers):
acts = fused_add_tanh_sigmoid_multiply(
self.in_layers[i](audio),
self.cond_layers[i](spect),
torch.IntTensor([self.n_channels]))
res_skip_acts = self.res_skip_layers[i](acts)
if i < self.n_layers - 1:
audio = res_skip_acts[:,:self.n_channels,:] + audio
skip_acts = res_skip_acts[:,self.n_channels:,:]
else:
skip_acts = res_skip_acts
if i == 0:
output = skip_acts
else:
output = skip_acts + output
return self.end(output)
class WaveGlow(torch.nn.Module):
def __init__(self, n_mel_channels, n_flows, n_group, n_early_every,
n_early_size, WN_config):
super(WaveGlow, self).__init__()
self.upsample = torch.nn.ConvTranspose1d(n_mel_channels,
n_mel_channels,
1024, stride=256)
assert(n_group % 2 == 0)
self.n_flows = n_flows
self.n_group = n_group
self.n_early_every = n_early_every
self.n_early_size = n_early_size
self.WN = torch.nn.ModuleList()
self.convinv = torch.nn.ModuleList()
n_half = int(n_group/2)
# Set up layers with the right sizes based on how many dimensions
# have been output already
n_remaining_channels = n_group
for k in range(n_flows):
if k % self.n_early_every == 0 and k > 0:
n_half = n_half - int(self.n_early_size/2)
n_remaining_channels = n_remaining_channels - self.n_early_size
self.convinv.append(Invertible1x1Conv(n_remaining_channels))
self.WN.append(WN(n_half, n_mel_channels*n_group, **WN_config))
self.n_remaining_channels = n_remaining_channels # Useful during inference
def forward(self, forward_input):
return None
"""
forward_input[0] = audio: batch x time
forward_input[1] = upsamp_spectrogram: batch x n_cond_channels x time
"""
"""
spect, audio = forward_input
# Upsample spectrogram to size of audio
spect = self.upsample(spect)
assert(spect.size(2) >= audio.size(1))
if spect.size(2) > audio.size(1):
spect = spect[:, :, :audio.size(1)]
spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
output_audio = []
s_list = []
s_conv_list = []
for k in range(self.n_flows):
if k%4 == 0 and k > 0:
output_audio.append(audio[:,:self.n_multi,:])
audio = audio[:,self.n_multi:,:]
# project to new basis
audio, s = self.convinv[k](audio)
s_conv_list.append(s)
n_half = int(audio.size(1)/2)
if k%2 == 0:
audio_0 = audio[:,:n_half,:]
audio_1 = audio[:,n_half:,:]
else:
audio_1 = audio[:,:n_half,:]
audio_0 = audio[:,n_half:,:]
output = self.nn[k]((audio_0, spect))
s = output[:, n_half:, :]
b = output[:, :n_half, :]
audio_1 = torch.exp(s)*audio_1 + b
s_list.append(s)
if k%2 == 0:
audio = torch.cat([audio[:,:n_half,:], audio_1],1)
else:
audio = torch.cat([audio_1, audio[:,n_half:,:]], 1)
output_audio.append(audio)
return torch.cat(output_audio,1), s_list, s_conv_list
"""
def infer(self, spect, sigma=1.0):
spect = self.upsample(spect)
# trim conv artifacts. maybe pad spec to kernel multiple
time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0]
spect = spect[:, :, :-time_cutoff]
spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
if spect.type() == 'torch.cuda.HalfTensor':
audio = torch.cuda.HalfTensor(spect.size(0),
self.n_remaining_channels,
spect.size(2)).normal_()
else:
audio = torch.cuda.FloatTensor(spect.size(0),
self.n_remaining_channels,
spect.size(2)).normal_()
audio = torch.autograd.Variable(sigma*audio)
for k in reversed(range(self.n_flows)):
n_half = int(audio.size(1)/2)
if k%2 == 0:
audio_0 = audio[:,:n_half,:]
audio_1 = audio[:,n_half:,:]
else:
audio_1 = audio[:,:n_half,:]
audio_0 = audio[:,n_half:,:]
output = self.WN[k]((audio_0, spect))
s = output[:, n_half:, :]
b = output[:, :n_half, :]
audio_1 = (audio_1 - b)/torch.exp(s)
if k%2 == 0:
audio = torch.cat([audio[:,:n_half,:], audio_1],1)
else:
audio = torch.cat([audio_1, audio[:,n_half:,:]], 1)
audio = self.convinv[k](audio, reverse=True)
if k%4 == 0 and k > 0:
if spect.type() == 'torch.cuda.HalfTensor':
z = torch.cuda.HalfTensor(spect.size(0),
self.n_early_size,
spect.size(2)).normal_()
else:
z = torch.cuda.FloatTensor(spect.size(0),
self.n_early_size,
spect.size(2)).normal_()
audio = torch.cat((sigma*z, audio),1)
return audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data
@staticmethod
def remove_weightnorm(model):
waveglow = model
for WN in waveglow.WN:
WN.start = torch.nn.utils.remove_weight_norm(WN.start)
WN.in_layers = remove(WN.in_layers)
WN.cond_layers = remove(WN.cond_layers)
WN.res_skip_layers = remove(WN.res_skip_layers)
return waveglow
| waveglow-master | glow_old.py |
import sys
sys.path.append('tacotron2')
import torch
from layers import STFT
class Denoiser(torch.nn.Module):
""" Removes model bias from audio produced with waveglow """
def __init__(self, waveglow, filter_length=1024, n_overlap=4,
win_length=1024, mode='zeros'):
super(Denoiser, self).__init__()
self.stft = STFT(filter_length=filter_length,
hop_length=int(filter_length/n_overlap),
win_length=win_length).cuda()
if mode == 'zeros':
mel_input = torch.zeros(
(1, 80, 88),
dtype=waveglow.upsample.weight.dtype,
device=waveglow.upsample.weight.device)
elif mode == 'normal':
mel_input = torch.randn(
(1, 80, 88),
dtype=waveglow.upsample.weight.dtype,
device=waveglow.upsample.weight.device)
else:
raise Exception("Mode {} if not supported".format(mode))
with torch.no_grad():
bias_audio = waveglow.infer(mel_input, sigma=0.0).float()
bias_spec, _ = self.stft.transform(bias_audio)
self.register_buffer('bias_spec', bias_spec[:, :, 0][:, :, None])
def forward(self, audio, strength=0.1):
audio_spec, audio_angles = self.stft.transform(audio.cuda().float())
audio_spec_denoised = audio_spec - self.bias_spec * strength
audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0)
audio_denoised = self.stft.inverse(audio_spec_denoised, audio_angles)
return audio_denoised
| waveglow-master | denoiser.py |
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# *****************************************************************************
import os
import sys
import time
import subprocess
import argparse
import torch
import torch.distributed as dist
from torch.autograd import Variable
def reduce_tensor(tensor, num_gpus):
rt = tensor.clone()
dist.all_reduce(rt, op=dist.reduce_op.SUM)
rt /= num_gpus
return rt
def init_distributed(rank, num_gpus, group_name, dist_backend, dist_url):
assert torch.cuda.is_available(), "Distributed mode requires CUDA."
print("Initializing Distributed")
# Set cuda device so everything is done on the right GPU.
torch.cuda.set_device(rank % torch.cuda.device_count())
# Initialize distributed communication
dist.init_process_group(dist_backend, init_method=dist_url,
world_size=num_gpus, rank=rank,
group_name=group_name)
def _flatten_dense_tensors(tensors):
"""Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
same dense type.
Since inputs are dense, the resulting tensor will be a concatenated 1D
buffer. Element-wise operation on this buffer will be equivalent to
operating individually.
Arguments:
tensors (Iterable[Tensor]): dense tensors to flatten.
Returns:
A contiguous 1D buffer containing input tensors.
"""
if len(tensors) == 1:
return tensors[0].contiguous().view(-1)
flat = torch.cat([t.contiguous().view(-1) for t in tensors], dim=0)
return flat
def _unflatten_dense_tensors(flat, tensors):
"""View a flat buffer using the sizes of tensors. Assume that tensors are of
same dense type, and that flat is given by _flatten_dense_tensors.
Arguments:
flat (Tensor): flattened dense tensors to unflatten.
tensors (Iterable[Tensor]): dense tensors whose sizes will be used to
unflatten flat.
Returns:
Unflattened dense tensors with sizes same as tensors and values from
flat.
"""
outputs = []
offset = 0
for tensor in tensors:
numel = tensor.numel()
outputs.append(flat.narrow(0, offset, numel).view_as(tensor))
offset += numel
return tuple(outputs)
def apply_gradient_allreduce(module):
"""
Modifies existing model to do gradient allreduce, but doesn't change class
so you don't need "module"
"""
if not hasattr(dist, '_backend'):
module.warn_on_half = True
else:
module.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
for p in module.state_dict().values():
if not torch.is_tensor(p):
continue
dist.broadcast(p, 0)
def allreduce_params():
if(module.needs_reduction):
module.needs_reduction = False
buckets = {}
for param in module.parameters():
if param.requires_grad and param.grad is not None:
tp = type(param.data)
if tp not in buckets:
buckets[tp] = []
buckets[tp].append(param)
if module.warn_on_half:
if torch.cuda.HalfTensor in buckets:
print("WARNING: gloo dist backend for half parameters may be extremely slow." +
" It is recommended to use the NCCL backend in this case. This currently requires" +
"PyTorch built from top of tree master.")
module.warn_on_half = False
for tp in buckets:
bucket = buckets[tp]
grads = [param.grad.data for param in bucket]
coalesced = _flatten_dense_tensors(grads)
dist.all_reduce(coalesced)
coalesced /= dist.get_world_size()
for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
buf.copy_(synced)
for param in list(module.parameters()):
def allreduce_hook(*unused):
Variable._execution_engine.queue_callback(allreduce_params)
if param.requires_grad:
param.register_hook(allreduce_hook)
dir(param)
def set_needs_reduction(self, input, output):
self.needs_reduction = True
module.register_forward_hook(set_needs_reduction)
return module
def main(config, stdout_dir, args_str):
args_list = ['train.py']
args_list += args_str.split(' ') if len(args_str) > 0 else []
args_list.append('--config={}'.format(config))
num_gpus = torch.cuda.device_count()
args_list.append('--num_gpus={}'.format(num_gpus))
args_list.append("--group_name=group_{}".format(time.strftime("%Y_%m_%d-%H%M%S")))
if not os.path.isdir(stdout_dir):
os.makedirs(stdout_dir)
os.chmod(stdout_dir, 0o775)
workers = []
for i in range(num_gpus):
args_list[-2] = '--rank={}'.format(i)
stdout = None if i == 0 else open(
os.path.join(stdout_dir, "GPU_{}.log".format(i)), "w")
print(args_list)
p = subprocess.Popen([str(sys.executable)]+args_list, stdout=stdout)
workers.append(p)
for p in workers:
p.wait()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, required=True,
help='JSON file for configuration')
parser.add_argument('-s', '--stdout_dir', type=str, default=".",
help='directory to save stoud logs')
parser.add_argument(
'-a', '--args_str', type=str, default='',
help='double quoted string with space separated key value pairs')
args = parser.parse_args()
main(args.config, args.stdout_dir, args.args_str)
| waveglow-master | distributed.py |
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# *****************************************************************************
import argparse
import json
import os
import torch
#=====START: ADDED FOR DISTRIBUTED======
from distributed import init_distributed, apply_gradient_allreduce, reduce_tensor
from torch.utils.data.distributed import DistributedSampler
#=====END: ADDED FOR DISTRIBUTED======
from torch.utils.data import DataLoader
from glow import WaveGlow, WaveGlowLoss
from mel2samp import Mel2Samp
def load_checkpoint(checkpoint_path, model, optimizer):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
iteration = checkpoint_dict['iteration']
optimizer.load_state_dict(checkpoint_dict['optimizer'])
model_for_loading = checkpoint_dict['model']
model.load_state_dict(model_for_loading.state_dict())
print("Loaded checkpoint '{}' (iteration {})" .format(
checkpoint_path, iteration))
return model, optimizer, iteration
def save_checkpoint(model, optimizer, learning_rate, iteration, filepath):
print("Saving model and optimizer state at iteration {} to {}".format(
iteration, filepath))
model_for_saving = WaveGlow(**waveglow_config).cuda()
model_for_saving.load_state_dict(model.state_dict())
torch.save({'model': model_for_saving,
'iteration': iteration,
'optimizer': optimizer.state_dict(),
'learning_rate': learning_rate}, filepath)
def train(num_gpus, rank, group_name, output_directory, epochs, learning_rate,
sigma, iters_per_checkpoint, batch_size, seed, fp16_run,
checkpoint_path, with_tensorboard):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
#=====START: ADDED FOR DISTRIBUTED======
if num_gpus > 1:
init_distributed(rank, num_gpus, group_name, **dist_config)
#=====END: ADDED FOR DISTRIBUTED======
criterion = WaveGlowLoss(sigma)
model = WaveGlow(**waveglow_config).cuda()
#=====START: ADDED FOR DISTRIBUTED======
if num_gpus > 1:
model = apply_gradient_allreduce(model)
#=====END: ADDED FOR DISTRIBUTED======
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
if fp16_run:
from apex import amp
model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
# Load checkpoint if one exists
iteration = 0
if checkpoint_path != "":
model, optimizer, iteration = load_checkpoint(checkpoint_path, model,
optimizer)
iteration += 1 # next iteration is iteration + 1
trainset = Mel2Samp(**data_config)
# =====START: ADDED FOR DISTRIBUTED======
train_sampler = DistributedSampler(trainset) if num_gpus > 1 else None
# =====END: ADDED FOR DISTRIBUTED======
train_loader = DataLoader(trainset, num_workers=1, shuffle=False,
sampler=train_sampler,
batch_size=batch_size,
pin_memory=False,
drop_last=True)
# Get shared output_directory ready
if rank == 0:
if not os.path.isdir(output_directory):
os.makedirs(output_directory)
os.chmod(output_directory, 0o775)
print("output directory", output_directory)
if with_tensorboard and rank == 0:
from tensorboardX import SummaryWriter
logger = SummaryWriter(os.path.join(output_directory, 'logs'))
model.train()
epoch_offset = max(0, int(iteration / len(train_loader)))
# ================ MAIN TRAINNIG LOOP! ===================
for epoch in range(epoch_offset, epochs):
print("Epoch: {}".format(epoch))
for i, batch in enumerate(train_loader):
model.zero_grad()
mel, audio = batch
mel = torch.autograd.Variable(mel.cuda())
audio = torch.autograd.Variable(audio.cuda())
outputs = model((mel, audio))
loss = criterion(outputs)
if num_gpus > 1:
reduced_loss = reduce_tensor(loss.data, num_gpus).item()
else:
reduced_loss = loss.item()
if fp16_run:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
print("{}:\t{:.9f}".format(iteration, reduced_loss))
if with_tensorboard and rank == 0:
logger.add_scalar('training_loss', reduced_loss, i + len(train_loader) * epoch)
if (iteration % iters_per_checkpoint == 0):
if rank == 0:
checkpoint_path = "{}/waveglow_{}".format(
output_directory, iteration)
save_checkpoint(model, optimizer, learning_rate, iteration,
checkpoint_path)
iteration += 1
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str,
help='JSON file for configuration')
parser.add_argument('-r', '--rank', type=int, default=0,
help='rank of process for distributed')
parser.add_argument('-g', '--group_name', type=str, default='',
help='name of group for distributed')
args = parser.parse_args()
# Parse configs. Globals nicer in this case
with open(args.config) as f:
data = f.read()
config = json.loads(data)
train_config = config["train_config"]
global data_config
data_config = config["data_config"]
global dist_config
dist_config = config["dist_config"]
global waveglow_config
waveglow_config = config["waveglow_config"]
num_gpus = torch.cuda.device_count()
if num_gpus > 1:
if args.group_name == '':
print("WARNING: Multiple GPUs detected but no distributed group set")
print("Only running 1 GPU. Use distributed.py for multiple GPUs")
num_gpus = 1
if num_gpus == 1 and args.rank != 0:
raise Exception("Doing single GPU training on rank > 0")
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
train(num_gpus, args.rank, args.group_name, **train_config)
| waveglow-master | train.py |
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# *****************************************************************************
import os
from scipy.io.wavfile import write
import torch
from mel2samp import files_to_list, MAX_WAV_VALUE
from denoiser import Denoiser
def main(mel_files, waveglow_path, sigma, output_dir, sampling_rate, is_fp16,
denoiser_strength):
mel_files = files_to_list(mel_files)
waveglow = torch.load(waveglow_path)['model']
waveglow = waveglow.remove_weightnorm(waveglow)
waveglow.cuda().eval()
if is_fp16:
from apex import amp
waveglow, _ = amp.initialize(waveglow, [], opt_level="O3")
if denoiser_strength > 0:
denoiser = Denoiser(waveglow).cuda()
for i, file_path in enumerate(mel_files):
file_name = os.path.splitext(os.path.basename(file_path))[0]
mel = torch.load(file_path)
mel = torch.autograd.Variable(mel.cuda())
mel = torch.unsqueeze(mel, 0)
mel = mel.half() if is_fp16 else mel
with torch.no_grad():
audio = waveglow.infer(mel, sigma=sigma)
if denoiser_strength > 0:
audio = denoiser(audio, denoiser_strength)
audio = audio * MAX_WAV_VALUE
audio = audio.squeeze()
audio = audio.cpu().numpy()
audio = audio.astype('int16')
audio_path = os.path.join(
output_dir, "{}_synthesis.wav".format(file_name))
write(audio_path, sampling_rate, audio)
print(audio_path)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-f', "--filelist_path", required=True)
parser.add_argument('-w', '--waveglow_path',
help='Path to waveglow decoder checkpoint with model')
parser.add_argument('-o', "--output_dir", required=True)
parser.add_argument("-s", "--sigma", default=1.0, type=float)
parser.add_argument("--sampling_rate", default=22050, type=int)
parser.add_argument("--is_fp16", action="store_true")
parser.add_argument("-d", "--denoiser_strength", default=0.0, type=float,
help='Removes model bias. Start with 0.1 and adjust')
args = parser.parse_args()
main(args.filelist_path, args.waveglow_path, args.sigma, args.output_dir,
args.sampling_rate, args.is_fp16, args.denoiser_strength)
| waveglow-master | inference.py |
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# *****************************************************************************\
import os
import random
import argparse
import json
import torch
import torch.utils.data
import sys
from scipy.io.wavfile import read
# We're using the audio processing from TacoTron2 to make sure it matches
sys.path.insert(0, 'tacotron2')
from tacotron2.layers import TacotronSTFT
MAX_WAV_VALUE = 32768.0
def files_to_list(filename):
"""
Takes a text file of filenames and makes a list of filenames
"""
with open(filename, encoding='utf-8') as f:
files = f.readlines()
files = [f.rstrip() for f in files]
return files
def load_wav_to_torch(full_path):
"""
Loads wavdata into torch array
"""
sampling_rate, data = read(full_path)
return torch.from_numpy(data).float(), sampling_rate
class Mel2Samp(torch.utils.data.Dataset):
"""
This is the main class that calculates the spectrogram and returns the
spectrogram, audio pair.
"""
def __init__(self, training_files, segment_length, filter_length,
hop_length, win_length, sampling_rate, mel_fmin, mel_fmax):
self.audio_files = files_to_list(training_files)
random.seed(1234)
random.shuffle(self.audio_files)
self.stft = TacotronSTFT(filter_length=filter_length,
hop_length=hop_length,
win_length=win_length,
sampling_rate=sampling_rate,
mel_fmin=mel_fmin, mel_fmax=mel_fmax)
self.segment_length = segment_length
self.sampling_rate = sampling_rate
def get_mel(self, audio):
audio_norm = audio / MAX_WAV_VALUE
audio_norm = audio_norm.unsqueeze(0)
audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
melspec = self.stft.mel_spectrogram(audio_norm)
melspec = torch.squeeze(melspec, 0)
return melspec
def __getitem__(self, index):
# Read audio
filename = self.audio_files[index]
audio, sampling_rate = load_wav_to_torch(filename)
if sampling_rate != self.sampling_rate:
raise ValueError("{} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate))
# Take segment
if audio.size(0) >= self.segment_length:
max_audio_start = audio.size(0) - self.segment_length
audio_start = random.randint(0, max_audio_start)
audio = audio[audio_start:audio_start+self.segment_length]
else:
audio = torch.nn.functional.pad(audio, (0, self.segment_length - audio.size(0)), 'constant').data
mel = self.get_mel(audio)
audio = audio / MAX_WAV_VALUE
return (mel, audio)
def __len__(self):
return len(self.audio_files)
# ===================================================================
# Takes directory of clean audio and makes directory of spectrograms
# Useful for making test sets
# ===================================================================
if __name__ == "__main__":
# Get defaults so it can work with no Sacred
parser = argparse.ArgumentParser()
parser.add_argument('-f', "--filelist_path", required=True)
parser.add_argument('-c', '--config', type=str,
help='JSON file for configuration')
parser.add_argument('-o', '--output_dir', type=str,
help='Output directory')
args = parser.parse_args()
with open(args.config) as f:
data = f.read()
data_config = json.loads(data)["data_config"]
mel2samp = Mel2Samp(**data_config)
filepaths = files_to_list(args.filelist_path)
# Make directory if it doesn't exist
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir)
os.chmod(args.output_dir, 0o775)
for filepath in filepaths:
audio, sr = load_wav_to_torch(filepath)
melspectrogram = mel2samp.get_mel(audio)
filename = os.path.basename(filepath)
new_filepath = args.output_dir + '/' + filename + '.pt'
print(new_filepath)
torch.save(melspectrogram, new_filepath)
| waveglow-master | mel2samp.py |
import sys
import copy
import torch
def _check_model_old_version(model):
if hasattr(model.WN[0], 'res_layers') or hasattr(model.WN[0], 'cond_layers'):
return True
else:
return False
def _update_model_res_skip(old_model, new_model):
for idx in range(0, len(new_model.WN)):
wavenet = new_model.WN[idx]
n_channels = wavenet.n_channels
n_layers = wavenet.n_layers
wavenet.res_skip_layers = torch.nn.ModuleList()
for i in range(0, n_layers):
if i < n_layers - 1:
res_skip_channels = 2*n_channels
else:
res_skip_channels = n_channels
res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1)
skip_layer = torch.nn.utils.remove_weight_norm(wavenet.skip_layers[i])
if i < n_layers - 1:
res_layer = torch.nn.utils.remove_weight_norm(wavenet.res_layers[i])
res_skip_layer.weight = torch.nn.Parameter(torch.cat([res_layer.weight, skip_layer.weight]))
res_skip_layer.bias = torch.nn.Parameter(torch.cat([res_layer.bias, skip_layer.bias]))
else:
res_skip_layer.weight = torch.nn.Parameter(skip_layer.weight)
res_skip_layer.bias = torch.nn.Parameter(skip_layer.bias)
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
wavenet.res_skip_layers.append(res_skip_layer)
del wavenet.res_layers
del wavenet.skip_layers
def _update_model_cond(old_model, new_model):
for idx in range(0, len(new_model.WN)):
wavenet = new_model.WN[idx]
n_channels = wavenet.n_channels
n_layers = wavenet.n_layers
n_mel_channels = wavenet.cond_layers[0].weight.shape[1]
cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels*n_layers, 1)
cond_layer_weight = []
cond_layer_bias = []
for i in range(0, n_layers):
_cond_layer = torch.nn.utils.remove_weight_norm(wavenet.cond_layers[i])
cond_layer_weight.append(_cond_layer.weight)
cond_layer_bias.append(_cond_layer.bias)
cond_layer.weight = torch.nn.Parameter(torch.cat(cond_layer_weight))
cond_layer.bias = torch.nn.Parameter(torch.cat(cond_layer_bias))
cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
wavenet.cond_layer = cond_layer
del wavenet.cond_layers
def update_model(old_model):
if not _check_model_old_version(old_model):
return old_model
new_model = copy.deepcopy(old_model)
if hasattr(old_model.WN[0], 'res_layers'):
_update_model_res_skip(old_model, new_model)
if hasattr(old_model.WN[0], 'cond_layers'):
_update_model_cond(old_model, new_model)
for m in new_model.modules():
if 'Conv' in str(type(m)) and not hasattr(m, 'padding_mode'):
setattr(m, 'padding_mode', 'zeros')
return new_model
if __name__ == '__main__':
old_model_path = sys.argv[1]
new_model_path = sys.argv[2]
model = torch.load(old_model_path, map_location='cpu')
model['model'] = update_model(model['model'])
torch.save(model, new_model_path)
| waveglow-master | convert_model.py |