Spaces:
Runtime error
Runtime error
File size: 8,383 Bytes
129cd69 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 |
"""Question answering over a graph."""
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import (
AQL_FIX_PROMPT,
AQL_GENERATION_PROMPT,
AQL_QA_PROMPT,
)
from langchain.chains.llm import LLMChain
from langchain.graphs.arangodb_graph import ArangoGraph
class ArangoGraphQAChain(Chain):
"""Chain for question-answering against a graph by generating AQL statements.
*Security note*: Make sure that the database connection uses credentials
that are narrowly-scoped to only include necessary permissions.
Failure to do so may result in data corruption or loss, since the calling
code may attempt commands that would result in deletion, mutation
of data if appropriately prompted or reading sensitive data if such
data is present in the database.
The best way to guard against such negative outcomes is to (as appropriate)
limit the permissions granted to the credentials used with this tool.
See https://python.langchain.com/docs/security for more information.
"""
graph: ArangoGraph = Field(exclude=True)
aql_generation_chain: LLMChain
aql_fix_chain: LLMChain
qa_chain: LLMChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
# Specifies the maximum number of AQL Query Results to return
top_k: int = 10
# Specifies the set of AQL Query Examples that promote few-shot-learning
aql_examples: str = ""
# Specify whether to return the AQL Query in the output dictionary
return_aql_query: bool = False
# Specify whether to return the AQL JSON Result in the output dictionary
return_aql_result: bool = False
# Specify the maximum amount of AQL Generation attempts that should be made
max_aql_generation_attempts: int = 3
@property
def input_keys(self) -> List[str]:
return [self.input_key]
@property
def output_keys(self) -> List[str]:
return [self.output_key]
@property
def _chain_type(self) -> str:
return "graph_aql_chain"
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
*,
qa_prompt: BasePromptTemplate = AQL_QA_PROMPT,
aql_generation_prompt: BasePromptTemplate = AQL_GENERATION_PROMPT,
aql_fix_prompt: BasePromptTemplate = AQL_FIX_PROMPT,
**kwargs: Any,
) -> ArangoGraphQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
aql_generation_chain = LLMChain(llm=llm, prompt=aql_generation_prompt)
aql_fix_chain = LLMChain(llm=llm, prompt=aql_fix_prompt)
return cls(
qa_chain=qa_chain,
aql_generation_chain=aql_generation_chain,
aql_fix_chain=aql_fix_chain,
**kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""
Generate an AQL statement from user input, use it retrieve a response
from an ArangoDB Database instance, and respond to the user input
in natural language.
Users can modify the following ArangoGraphQAChain Class Variables:
:var top_k: The maximum number of AQL Query Results to return
:type top_k: int
:var aql_examples: A set of AQL Query Examples that are passed to
the AQL Generation Prompt Template to promote few-shot-learning.
Defaults to an empty string.
:type aql_examples: str
:var return_aql_query: Whether to return the AQL Query in the
output dictionary. Defaults to False.
:type return_aql_query: bool
:var return_aql_result: Whether to return the AQL Query in the
output dictionary. Defaults to False
:type return_aql_result: bool
:var max_aql_generation_attempts: The maximum amount of AQL
Generation attempts to be made prior to raising the last
AQL Query Execution Error. Defaults to 3.
:type max_aql_generation_attempts: int
"""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
user_input = inputs[self.input_key]
#########################
# Generate AQL Query #
aql_generation_output = self.aql_generation_chain.run(
{
"adb_schema": self.graph.schema,
"aql_examples": self.aql_examples,
"user_input": user_input,
},
callbacks=callbacks,
)
#########################
aql_query = ""
aql_error = ""
aql_result = None
aql_generation_attempt = 1
while (
aql_result is None
and aql_generation_attempt < self.max_aql_generation_attempts + 1
):
#####################
# Extract AQL Query #
pattern = r"```(?i:aql)?(.*?)```"
matches = re.findall(pattern, aql_generation_output, re.DOTALL)
if not matches:
_run_manager.on_text(
"Invalid Response: ", end="\n", verbose=self.verbose
)
_run_manager.on_text(
aql_generation_output, color="red", end="\n", verbose=self.verbose
)
raise ValueError(f"Response is Invalid: {aql_generation_output}")
aql_query = matches[0]
#####################
_run_manager.on_text(
f"AQL Query ({aql_generation_attempt}):", verbose=self.verbose
)
_run_manager.on_text(
aql_query, color="green", end="\n", verbose=self.verbose
)
#####################
# Execute AQL Query #
from arango import AQLQueryExecuteError
try:
aql_result = self.graph.query(aql_query, self.top_k)
except AQLQueryExecuteError as e:
aql_error = e.error_message
_run_manager.on_text(
"AQL Query Execution Error: ", end="\n", verbose=self.verbose
)
_run_manager.on_text(
aql_error, color="yellow", end="\n\n", verbose=self.verbose
)
########################
# Retry AQL Generation #
aql_generation_output = self.aql_fix_chain.run(
{
"adb_schema": self.graph.schema,
"aql_query": aql_query,
"aql_error": aql_error,
},
callbacks=callbacks,
)
########################
#####################
aql_generation_attempt += 1
if aql_result is None:
m = f"""
Maximum amount of AQL Query Generation attempts reached.
Unable to execute the AQL Query due to the following error:
{aql_error}
"""
raise ValueError(m)
_run_manager.on_text("AQL Result:", end="\n", verbose=self.verbose)
_run_manager.on_text(
str(aql_result), color="green", end="\n", verbose=self.verbose
)
########################
# Interpret AQL Result #
result = self.qa_chain(
{
"adb_schema": self.graph.schema,
"user_input": user_input,
"aql_query": aql_query,
"aql_result": aql_result,
},
callbacks=callbacks,
)
########################
# Return results #
result = {self.output_key: result[self.qa_chain.output_key]}
if self.return_aql_query:
result["aql_query"] = aql_query
if self.return_aql_result:
result["aql_result"] = aql_result
return result
|