id
stringlengths 14
16
| text
stringlengths 31
2.41k
| source
stringlengths 53
121
|
|---|---|---|
aa6f172b81d3-9 | log_system_params=log_system_params
if log_system_params
else self.log_system_params,
) | https://api.python.langchain.com/en/stable/_modules/langchain/callbacks/aim_callback.html |
ac014ce0369e-0 | Source code for langchain.callbacks.streamlit.streamlit_callback_handler
"""Callback Handler that prints to streamlit."""
from __future__ import annotations
from enum import Enum
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.streamlit.mutable_expander import MutableExpander
from langchain.schema import AgentAction, AgentFinish, LLMResult
if TYPE_CHECKING:
from streamlit.delta_generator import DeltaGenerator
def _convert_newlines(text: str) -> str:
"""Convert newline characters to markdown newline sequences
(space, space, newline).
"""
return text.replace("\n", " \n")
CHECKMARK_EMOJI = "✅"
THINKING_EMOJI = ":thinking_face:"
HISTORY_EMOJI = ":books:"
EXCEPTION_EMOJI = "⚠️"
class LLMThoughtState(Enum):
# The LLM is thinking about what to do next. We don't know which tool we'll run.
THINKING = "THINKING"
# The LLM has decided to run a tool. We don't have results from the tool yet.
RUNNING_TOOL = "RUNNING_TOOL"
# We have results from the tool.
COMPLETE = "COMPLETE"
class ToolRecord(NamedTuple):
name: str
input_str: str
[docs]class LLMThoughtLabeler:
"""
Generates markdown labels for LLMThought containers. Pass a custom
subclass of this to StreamlitCallbackHandler to override its default
labeling logic.
"""
[docs] def get_initial_label(self) -> str:
"""Return the markdown label for a new LLMThought that doesn't have | https://api.python.langchain.com/en/stable/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ac014ce0369e-1 | """Return the markdown label for a new LLMThought that doesn't have
an associated tool yet.
"""
return f"{THINKING_EMOJI} **Thinking...**"
[docs] def get_tool_label(self, tool: ToolRecord, is_complete: bool) -> str:
"""Return the label for an LLMThought that has an associated
tool.
Parameters
----------
tool
The tool's ToolRecord
is_complete
True if the thought is complete; False if the thought
is still receiving input.
Returns
-------
The markdown label for the thought's container.
"""
input = tool.input_str
name = tool.name
emoji = CHECKMARK_EMOJI if is_complete else THINKING_EMOJI
if name == "_Exception":
emoji = EXCEPTION_EMOJI
name = "Parsing error"
idx = min([60, len(input)])
input = input[0:idx]
if len(tool.input_str) > idx:
input = input + "..."
input = input.replace("\n", " ")
label = f"{emoji} **{name}:** {input}"
return label
[docs] def get_history_label(self) -> str:
"""Return a markdown label for the special 'history' container
that contains overflow thoughts.
"""
return f"{HISTORY_EMOJI} **History**"
[docs] def get_final_agent_thought_label(self) -> str:
"""Return the markdown label for the agent's final thought -
the "Now I have the answer" thought, that doesn't involve
a tool.
"""
return f"{CHECKMARK_EMOJI} **Complete!**" | https://api.python.langchain.com/en/stable/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ac014ce0369e-2 | """
return f"{CHECKMARK_EMOJI} **Complete!**"
class LLMThought:
def __init__(
self,
parent_container: DeltaGenerator,
labeler: LLMThoughtLabeler,
expanded: bool,
collapse_on_complete: bool,
):
self._container = MutableExpander(
parent_container=parent_container,
label=labeler.get_initial_label(),
expanded=expanded,
)
self._state = LLMThoughtState.THINKING
self._llm_token_stream = ""
self._llm_token_writer_idx: Optional[int] = None
self._last_tool: Optional[ToolRecord] = None
self._collapse_on_complete = collapse_on_complete
self._labeler = labeler
@property
def container(self) -> MutableExpander:
"""The container we're writing into."""
return self._container
@property
def last_tool(self) -> Optional[ToolRecord]:
"""The last tool executed by this thought"""
return self._last_tool
def _reset_llm_token_stream(self) -> None:
self._llm_token_stream = ""
self._llm_token_writer_idx = None
def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str]) -> None:
self._reset_llm_token_stream()
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
# This is only called when the LLM is initialized with `streaming=True`
self._llm_token_stream += _convert_newlines(token)
self._llm_token_writer_idx = self._container.markdown( | https://api.python.langchain.com/en/stable/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ac014ce0369e-3 | self._llm_token_writer_idx = self._container.markdown(
self._llm_token_stream, index=self._llm_token_writer_idx
)
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
# `response` is the concatenation of all the tokens received by the LLM.
# If we're receiving streaming tokens from `on_llm_new_token`, this response
# data is redundant
self._reset_llm_token_stream()
def on_llm_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
self._container.markdown("**LLM encountered an error...**")
self._container.exception(error)
def on_tool_start(
self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
) -> None:
# Called with the name of the tool we're about to run (in `serialized[name]`),
# and its input. We change our container's label to be the tool name.
self._state = LLMThoughtState.RUNNING_TOOL
tool_name = serialized["name"]
self._last_tool = ToolRecord(name=tool_name, input_str=input_str)
self._container.update(
new_label=self._labeler.get_tool_label(self._last_tool, is_complete=False)
)
def on_tool_end(
self,
output: str,
color: Optional[str] = None,
observation_prefix: Optional[str] = None,
llm_prefix: Optional[str] = None,
**kwargs: Any,
) -> None:
self._container.markdown(f"**{output}**")
def on_tool_error( | https://api.python.langchain.com/en/stable/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ac014ce0369e-4 | def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
self._container.markdown("**Tool encountered an error...**")
self._container.exception(error)
def on_agent_action(
self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
) -> Any:
# Called when we're about to kick off a new tool. The `action` data
# tells us the tool we're about to use, and the input we'll give it.
# We don't output anything here, because we'll receive this same data
# when `on_tool_start` is called immediately after.
pass
def complete(self, final_label: Optional[str] = None) -> None:
"""Finish the thought."""
if final_label is None and self._state == LLMThoughtState.RUNNING_TOOL:
assert (
self._last_tool is not None
), "_last_tool should never be null when _state == RUNNING_TOOL"
final_label = self._labeler.get_tool_label(
self._last_tool, is_complete=True
)
self._state = LLMThoughtState.COMPLETE
if self._collapse_on_complete:
self._container.update(new_label=final_label, new_expanded=False)
else:
self._container.update(new_label=final_label)
def clear(self) -> None:
"""Remove the thought from the screen. A cleared thought can't be reused."""
self._container.clear()
class StreamlitCallbackHandler(BaseCallbackHandler):
def __init__(
self,
parent_container: DeltaGenerator,
*,
max_thought_containers: int = 4, | https://api.python.langchain.com/en/stable/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ac014ce0369e-5 | *,
max_thought_containers: int = 4,
expand_new_thoughts: bool = True,
collapse_completed_thoughts: bool = True,
thought_labeler: Optional[LLMThoughtLabeler] = None,
):
"""Create a StreamlitCallbackHandler instance.
Parameters
----------
parent_container
The `st.container` that will contain all the Streamlit elements that the
Handler creates.
max_thought_containers
The max number of completed LLM thought containers to show at once. When
this threshold is reached, a new thought will cause the oldest thoughts to
be collapsed into a "History" expander. Defaults to 4.
expand_new_thoughts
Each LLM "thought" gets its own `st.expander`. This param controls whether
that expander is expanded by default. Defaults to True.
collapse_completed_thoughts
If True, LLM thought expanders will be collapsed when completed.
Defaults to True.
thought_labeler
An optional custom LLMThoughtLabeler instance. If unspecified, the handler
will use the default thought labeling logic. Defaults to None.
"""
self._parent_container = parent_container
self._history_parent = parent_container.container()
self._history_container: Optional[MutableExpander] = None
self._current_thought: Optional[LLMThought] = None
self._completed_thoughts: List[LLMThought] = []
self._max_thought_containers = max(max_thought_containers, 1)
self._expand_new_thoughts = expand_new_thoughts
self._collapse_completed_thoughts = collapse_completed_thoughts | https://api.python.langchain.com/en/stable/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ac014ce0369e-6 | self._collapse_completed_thoughts = collapse_completed_thoughts
self._thought_labeler = thought_labeler or LLMThoughtLabeler()
def _require_current_thought(self) -> LLMThought:
"""Return our current LLMThought. Raise an error if we have no current
thought.
"""
if self._current_thought is None:
raise RuntimeError("Current LLMThought is unexpectedly None!")
return self._current_thought
def _get_last_completed_thought(self) -> Optional[LLMThought]:
"""Return our most recent completed LLMThought, or None if we don't have one."""
if len(self._completed_thoughts) > 0:
return self._completed_thoughts[len(self._completed_thoughts) - 1]
return None
@property
def _num_thought_containers(self) -> int:
"""The number of 'thought containers' we're currently showing: the
number of completed thought containers, the history container (if it exists),
and the current thought container (if it exists).
"""
count = len(self._completed_thoughts)
if self._history_container is not None:
count += 1
if self._current_thought is not None:
count += 1
return count
def _complete_current_thought(self, final_label: Optional[str] = None) -> None:
"""Complete the current thought, optionally assigning it a new label.
Add it to our _completed_thoughts list.
"""
thought = self._require_current_thought()
thought.complete(final_label)
self._completed_thoughts.append(thought)
self._current_thought = None | https://api.python.langchain.com/en/stable/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ac014ce0369e-7 | self._current_thought = None
def _prune_old_thought_containers(self) -> None:
"""If we have too many thoughts onscreen, move older thoughts to the
'history container.'
"""
while (
self._num_thought_containers > self._max_thought_containers
and len(self._completed_thoughts) > 0
):
# Create our history container if it doesn't exist, and if
# max_thought_containers is > 1. (if max_thought_containers is 1, we don't
# have room to show history.)
if self._history_container is None and self._max_thought_containers > 1:
self._history_container = MutableExpander(
self._history_parent,
label=self._thought_labeler.get_history_label(),
expanded=False,
)
oldest_thought = self._completed_thoughts.pop(0)
if self._history_container is not None:
self._history_container.markdown(oldest_thought.container.label)
self._history_container.append_copy(oldest_thought.container)
oldest_thought.clear()
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
if self._current_thought is None:
self._current_thought = LLMThought(
parent_container=self._parent_container,
expanded=self._expand_new_thoughts,
collapse_on_complete=self._collapse_completed_thoughts,
labeler=self._thought_labeler,
)
self._current_thought.on_llm_start(serialized, prompts) | https://api.python.langchain.com/en/stable/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ac014ce0369e-8 | )
self._current_thought.on_llm_start(serialized, prompts)
# We don't prune_old_thought_containers here, because our container won't
# be visible until it has a child.
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
self._require_current_thought().on_llm_new_token(token, **kwargs)
self._prune_old_thought_containers()
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
self._require_current_thought().on_llm_end(response, **kwargs)
self._prune_old_thought_containers()
def on_llm_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
self._require_current_thought().on_llm_error(error, **kwargs)
self._prune_old_thought_containers()
def on_tool_start(
self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
) -> None:
self._require_current_thought().on_tool_start(serialized, input_str, **kwargs)
self._prune_old_thought_containers()
def on_tool_end(
self,
output: str,
color: Optional[str] = None,
observation_prefix: Optional[str] = None,
llm_prefix: Optional[str] = None,
**kwargs: Any,
) -> None:
self._require_current_thought().on_tool_end(
output, color, observation_prefix, llm_prefix, **kwargs
)
self._complete_current_thought()
def on_tool_error( | https://api.python.langchain.com/en/stable/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
ac014ce0369e-9 | )
self._complete_current_thought()
def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
self._require_current_thought().on_tool_error(error, **kwargs)
self._prune_old_thought_containers()
def on_text(
self,
text: str,
color: Optional[str] = None,
end: str = "",
**kwargs: Any,
) -> None:
pass
def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> None:
pass
def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
pass
def on_chain_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
pass
def on_agent_action(
self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
) -> Any:
self._require_current_thought().on_agent_action(action, color, **kwargs)
self._prune_old_thought_containers()
def on_agent_finish(
self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any
) -> None:
if self._current_thought is not None:
self._current_thought.complete(
self._thought_labeler.get_final_agent_thought_label()
)
self._current_thought = None | https://api.python.langchain.com/en/stable/_modules/langchain/callbacks/streamlit/streamlit_callback_handler.html |
c49aa138d29f-0 | Source code for langchain.output_parsers.list
from __future__ import annotations
from abc import abstractmethod
from typing import List
from langchain.schema import BaseOutputParser
[docs]class ListOutputParser(BaseOutputParser):
"""Class to parse the output of an LLM call to a list."""
@property
def _type(self) -> str:
return "list"
[docs] @abstractmethod
def parse(self, text: str) -> List[str]:
"""Parse the output of an LLM call."""
[docs]class CommaSeparatedListOutputParser(ListOutputParser):
"""Parse out comma separated lists."""
[docs] def get_format_instructions(self) -> str:
return (
"Your response should be a list of comma separated values, "
"eg: `foo, bar, baz`"
)
[docs] def parse(self, text: str) -> List[str]:
"""Parse the output of an LLM call."""
return text.strip().split(", ") | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/list.html |
c005aa3b591c-0 | Source code for langchain.output_parsers.fix
from __future__ import annotations
from typing import TypeVar
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import BaseOutputParser, OutputParserException
T = TypeVar("T")
[docs]class OutputFixingParser(BaseOutputParser[T]):
"""Wraps a parser and tries to fix parsing errors."""
parser: BaseOutputParser[T]
retry_chain: LLMChain
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
parser: BaseOutputParser[T],
prompt: BasePromptTemplate = NAIVE_FIX_PROMPT,
) -> OutputFixingParser[T]:
chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain)
[docs] def parse(self, completion: str) -> T:
try:
parsed_completion = self.parser.parse(completion)
except OutputParserException as e:
new_completion = self.retry_chain.run(
instructions=self.parser.get_format_instructions(),
completion=completion,
error=repr(e),
)
parsed_completion = self.parser.parse(new_completion)
return parsed_completion
[docs] def get_format_instructions(self) -> str:
return self.parser.get_format_instructions()
@property
def _type(self) -> str:
return "output_fixing" | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/fix.html |
27e19fe977f3-0 | Source code for langchain.output_parsers.combining
from __future__ import annotations
from typing import Any, Dict, List
from pydantic import root_validator
from langchain.schema import BaseOutputParser
[docs]class CombiningOutputParser(BaseOutputParser):
"""Class to combine multiple output parsers into one."""
parsers: List[BaseOutputParser]
@root_validator()
def validate_parsers(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Validate the parsers."""
parsers = values["parsers"]
if len(parsers) < 2:
raise ValueError("Must have at least two parsers")
for parser in parsers:
if parser._type == "combining":
raise ValueError("Cannot nest combining parsers")
if parser._type == "list":
raise ValueError("Cannot comine list parsers")
return values
@property
def _type(self) -> str:
"""Return the type key."""
return "combining"
[docs] def get_format_instructions(self) -> str:
"""Instructions on how the LLM output should be formatted."""
initial = f"For your first output: {self.parsers[0].get_format_instructions()}"
subsequent = "\n".join(
f"Complete that output fully. Then produce another output, separated by two newline characters: {p.get_format_instructions()}" # noqa: E501
for p in self.parsers[1:]
)
return f"{initial}\n{subsequent}"
[docs] def parse(self, text: str) -> Dict[str, Any]:
"""Parse the output of an LLM call."""
texts = text.split("\n\n")
output = dict() | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/combining.html |
27e19fe977f3-1 | texts = text.split("\n\n")
output = dict()
for txt, parser in zip(texts, self.parsers):
output.update(parser.parse(txt.strip()))
return output | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/combining.html |
e031d8758eb5-0 | Source code for langchain.output_parsers.boolean
from langchain.schema import BaseOutputParser
[docs]class BooleanOutputParser(BaseOutputParser[bool]):
true_val: str = "YES"
false_val: str = "NO"
[docs] def parse(self, text: str) -> bool:
"""Parse the output of an LLM call to a boolean.
Args:
text: output of language model
Returns:
boolean
"""
cleaned_text = text.strip()
if cleaned_text.upper() not in (self.true_val.upper(), self.false_val.upper()):
raise ValueError(
f"BooleanOutputParser expected output value to either be "
f"{self.true_val} or {self.false_val}. Received {cleaned_text}."
)
return cleaned_text.upper() == self.true_val.upper()
@property
def _type(self) -> str:
"""Snake-case string identifier for output parser type."""
return "boolean_output_parser" | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/boolean.html |
95cfc04ee623-0 | Source code for langchain.output_parsers.rail_parser
from __future__ import annotations
from typing import Any, Callable, Dict, Optional
from langchain.schema import BaseOutputParser
[docs]class GuardrailsOutputParser(BaseOutputParser):
guard: Any
api: Optional[Callable]
args: Any
kwargs: Any
@property
def _type(self) -> str:
return "guardrails"
[docs] @classmethod
def from_rail(
cls,
rail_file: str,
num_reasks: int = 1,
api: Optional[Callable] = None,
*args: Any,
**kwargs: Any,
) -> GuardrailsOutputParser:
try:
from guardrails import Guard
except ImportError:
raise ValueError(
"guardrails-ai package not installed. "
"Install it by running `pip install guardrails-ai`."
)
return cls(
guard=Guard.from_rail(rail_file, num_reasks=num_reasks),
api=api,
args=args,
kwargs=kwargs,
)
[docs] @classmethod
def from_rail_string(
cls,
rail_str: str,
num_reasks: int = 1,
api: Optional[Callable] = None,
*args: Any,
**kwargs: Any,
) -> GuardrailsOutputParser:
try:
from guardrails import Guard
except ImportError:
raise ValueError(
"guardrails-ai package not installed. "
"Install it by running `pip install guardrails-ai`."
)
return cls( | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/rail_parser.html |
95cfc04ee623-1 | )
return cls(
guard=Guard.from_rail_string(rail_str, num_reasks=num_reasks),
api=api,
args=args,
kwargs=kwargs,
)
[docs] @classmethod
def from_pydantic(
cls,
output_class: Any,
num_reasks: int = 1,
api: Optional[Callable] = None,
*args: Any,
**kwargs: Any,
) -> GuardrailsOutputParser:
try:
from guardrails import Guard
except ImportError:
raise ValueError(
"guardrails-ai package not installed. "
"Install it by running `pip install guardrails-ai`."
)
return cls(
guard=Guard.from_pydantic(output_class, "", num_reasks=num_reasks),
api=api,
args=args,
kwargs=kwargs,
)
[docs] def get_format_instructions(self) -> str:
return self.guard.raw_prompt.format_instructions
[docs] def parse(self, text: str) -> Dict:
return self.guard.parse(text, llm_api=self.api, *self.args, **self.kwargs) | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/rail_parser.html |
1aa72675691c-0 | Source code for langchain.output_parsers.enum
from enum import Enum
from typing import Any, Dict, List, Type
from pydantic import root_validator
from langchain.schema import BaseOutputParser, OutputParserException
[docs]class EnumOutputParser(BaseOutputParser):
enum: Type[Enum]
@root_validator()
def raise_deprecation(cls, values: Dict) -> Dict:
enum = values["enum"]
if not all(isinstance(e.value, str) for e in enum):
raise ValueError("Enum values must be strings")
return values
@property
def _valid_values(self) -> List[str]:
return [e.value for e in self.enum]
[docs] def parse(self, response: str) -> Any:
try:
return self.enum(response.strip())
except ValueError:
raise OutputParserException(
f"Response '{response}' is not one of the "
f"expected values: {self._valid_values}"
)
[docs] def get_format_instructions(self) -> str:
return f"Select one of the following options: {', '.join(self._valid_values)}" | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/enum.html |
81dbbfeab141-0 | Source code for langchain.output_parsers.regex
from __future__ import annotations
import re
from typing import Dict, List, Optional
from langchain.schema import BaseOutputParser
[docs]class RegexParser(BaseOutputParser):
"""Class to parse the output into a dictionary."""
regex: str
output_keys: List[str]
default_output_key: Optional[str] = None
@property
def _type(self) -> str:
"""Return the type key."""
return "regex_parser"
[docs] def parse(self, text: str) -> Dict[str, str]:
"""Parse the output of an LLM call."""
match = re.search(self.regex, text)
if match:
return {key: match.group(i + 1) for i, key in enumerate(self.output_keys)}
else:
if self.default_output_key is None:
raise ValueError(f"Could not parse output: {text}")
else:
return {
key: text if key == self.default_output_key else ""
for key in self.output_keys
} | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/regex.html |
f3fd6fb718c9-0 | Source code for langchain.output_parsers.retry
from __future__ import annotations
from typing import TypeVar
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import (
BaseOutputParser,
OutputParserException,
PromptValue,
)
NAIVE_COMPLETION_RETRY = """Prompt:
{prompt}
Completion:
{completion}
Above, the Completion did not satisfy the constraints given in the Prompt.
Please try again:"""
NAIVE_COMPLETION_RETRY_WITH_ERROR = """Prompt:
{prompt}
Completion:
{completion}
Above, the Completion did not satisfy the constraints given in the Prompt.
Details: {error}
Please try again:"""
NAIVE_RETRY_PROMPT = PromptTemplate.from_template(NAIVE_COMPLETION_RETRY)
NAIVE_RETRY_WITH_ERROR_PROMPT = PromptTemplate.from_template(
NAIVE_COMPLETION_RETRY_WITH_ERROR
)
T = TypeVar("T")
[docs]class RetryOutputParser(BaseOutputParser[T]):
"""Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt and the completion to another
LLM, and telling it the completion did not satisfy criteria in the prompt.
"""
parser: BaseOutputParser[T]
retry_chain: LLMChain
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
parser: BaseOutputParser[T],
prompt: BasePromptTemplate = NAIVE_RETRY_PROMPT,
) -> RetryOutputParser[T]:
chain = LLMChain(llm=llm, prompt=prompt) | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/retry.html |
f3fd6fb718c9-1 | chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain)
[docs] def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
try:
parsed_completion = self.parser.parse(completion)
except OutputParserException:
new_completion = self.retry_chain.run(
prompt=prompt_value.to_string(), completion=completion
)
parsed_completion = self.parser.parse(new_completion)
return parsed_completion
[docs] def parse(self, completion: str) -> T:
raise NotImplementedError(
"This OutputParser can only be called by the `parse_with_prompt` method."
)
[docs] def get_format_instructions(self) -> str:
return self.parser.get_format_instructions()
@property
def _type(self) -> str:
return "retry"
[docs]class RetryWithErrorOutputParser(BaseOutputParser[T]):
"""Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt, the completion, AND the error
that was raised to another language model and telling it that the completion
did not work, and raised the given error. Differs from RetryOutputParser
in that this implementation provides the error that was raised back to the
LLM, which in theory should give it more information on how to fix it.
"""
parser: BaseOutputParser[T]
retry_chain: LLMChain
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
parser: BaseOutputParser[T],
prompt: BasePromptTemplate = NAIVE_RETRY_WITH_ERROR_PROMPT,
) -> RetryWithErrorOutputParser[T]: | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/retry.html |
f3fd6fb718c9-2 | ) -> RetryWithErrorOutputParser[T]:
chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain)
[docs] def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
try:
parsed_completion = self.parser.parse(completion)
except OutputParserException as e:
new_completion = self.retry_chain.run(
prompt=prompt_value.to_string(), completion=completion, error=repr(e)
)
parsed_completion = self.parser.parse(new_completion)
return parsed_completion
[docs] def parse(self, completion: str) -> T:
raise NotImplementedError(
"This OutputParser can only be called by the `parse_with_prompt` method."
)
[docs] def get_format_instructions(self) -> str:
return self.parser.get_format_instructions()
@property
def _type(self) -> str:
return "retry_with_error" | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/retry.html |
55b85fe66110-0 | Source code for langchain.output_parsers.pydantic
import json
import re
from typing import Type, TypeVar
from pydantic import BaseModel, ValidationError
from langchain.output_parsers.format_instructions import PYDANTIC_FORMAT_INSTRUCTIONS
from langchain.schema import BaseOutputParser, OutputParserException
T = TypeVar("T", bound=BaseModel)
[docs]class PydanticOutputParser(BaseOutputParser[T]):
pydantic_object: Type[T]
[docs] def parse(self, text: str) -> T:
try:
# Greedy search for 1st json candidate.
match = re.search(
r"\{.*\}", text.strip(), re.MULTILINE | re.IGNORECASE | re.DOTALL
)
json_str = ""
if match:
json_str = match.group()
json_object = json.loads(json_str, strict=False)
return self.pydantic_object.parse_obj(json_object)
except (json.JSONDecodeError, ValidationError) as e:
name = self.pydantic_object.__name__
msg = f"Failed to parse {name} from completion {text}. Got: {e}"
raise OutputParserException(msg)
[docs] def get_format_instructions(self) -> str:
schema = self.pydantic_object.schema()
# Remove extraneous fields.
reduced_schema = schema
if "title" in reduced_schema:
del reduced_schema["title"]
if "type" in reduced_schema:
del reduced_schema["type"]
# Ensure json in context is well-formed with double quotes.
schema_str = json.dumps(reduced_schema)
return PYDANTIC_FORMAT_INSTRUCTIONS.format(schema=schema_str)
@property
def _type(self) -> str: | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/pydantic.html |
55b85fe66110-1 | @property
def _type(self) -> str:
return "pydantic" | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/pydantic.html |
2d90c02b0f0a-0 | Source code for langchain.output_parsers.datetime
import random
from datetime import datetime, timedelta
from typing import List
from langchain.schema import BaseOutputParser, OutputParserException
from langchain.utils import comma_list
def _generate_random_datetime_strings(
pattern: str,
n: int = 3,
start_date: datetime = datetime(1, 1, 1),
end_date: datetime = datetime.now() + timedelta(days=3650),
) -> List[str]:
"""
Generates n random datetime strings conforming to the
given pattern within the specified date range.
Pattern should be a string containing the desired format codes.
start_date and end_date should be datetime objects representing
the start and end of the date range.
"""
examples = []
delta = end_date - start_date
for i in range(n):
random_delta = random.uniform(0, delta.total_seconds())
dt = start_date + timedelta(seconds=random_delta)
date_string = dt.strftime(pattern)
examples.append(date_string)
return examples
[docs]class DatetimeOutputParser(BaseOutputParser[datetime]):
format: str = "%Y-%m-%dT%H:%M:%S.%fZ"
[docs] def get_format_instructions(self) -> str:
examples = comma_list(_generate_random_datetime_strings(self.format))
return f"""Write a datetime string that matches the
following pattern: "{self.format}". Examples: {examples}"""
[docs] def parse(self, response: str) -> datetime:
try:
return datetime.strptime(response.strip(), self.format)
except ValueError as e:
raise OutputParserException(
f"Could not parse datetime string: {response}"
) from e
@property | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/datetime.html |
2d90c02b0f0a-1 | ) from e
@property
def _type(self) -> str:
return "datetime" | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/datetime.html |
707b1f3b9ca9-0 | Source code for langchain.output_parsers.regex_dict
from __future__ import annotations
import re
from typing import Dict, Optional
from langchain.schema import BaseOutputParser
[docs]class RegexDictParser(BaseOutputParser):
"""Class to parse the output into a dictionary."""
regex_pattern: str = r"{}:\s?([^.'\n']*)\.?" # : :meta private:
output_key_to_format: Dict[str, str]
no_update_value: Optional[str] = None
@property
def _type(self) -> str:
"""Return the type key."""
return "regex_dict_parser"
[docs] def parse(self, text: str) -> Dict[str, str]:
"""Parse the output of an LLM call."""
result = {}
for output_key, expected_format in self.output_key_to_format.items():
specific_regex = self.regex_pattern.format(re.escape(expected_format))
matches = re.findall(specific_regex, text)
if not matches:
raise ValueError(
f"No match found for output key: {output_key} with expected format \
{expected_format} on text {text}"
)
elif len(matches) > 1:
raise ValueError(
f"Multiple matches found for output key: {output_key} with \
expected format {expected_format} on text {text}"
)
elif (
self.no_update_value is not None and matches[0] == self.no_update_value
):
continue
else:
result[output_key] = matches[0]
return result | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/regex_dict.html |
086209193aff-0 | Source code for langchain.output_parsers.structured
from __future__ import annotations
from typing import Any, List
from pydantic import BaseModel
from langchain.output_parsers.format_instructions import STRUCTURED_FORMAT_INSTRUCTIONS
from langchain.output_parsers.json import parse_and_check_json_markdown
from langchain.schema import BaseOutputParser
line_template = '\t"{name}": {type} // {description}'
[docs]class ResponseSchema(BaseModel):
name: str
description: str
type: str = "string"
def _get_sub_string(schema: ResponseSchema) -> str:
return line_template.format(
name=schema.name, description=schema.description, type=schema.type
)
[docs]class StructuredOutputParser(BaseOutputParser):
response_schemas: List[ResponseSchema]
[docs] @classmethod
def from_response_schemas(
cls, response_schemas: List[ResponseSchema]
) -> StructuredOutputParser:
return cls(response_schemas=response_schemas)
[docs] def get_format_instructions(self) -> str:
schema_str = "\n".join(
[_get_sub_string(schema) for schema in self.response_schemas]
)
return STRUCTURED_FORMAT_INSTRUCTIONS.format(format=schema_str)
[docs] def parse(self, text: str) -> Any:
expected_keys = [rs.name for rs in self.response_schemas]
return parse_and_check_json_markdown(text, expected_keys)
@property
def _type(self) -> str:
return "structured" | https://api.python.langchain.com/en/stable/_modules/langchain/output_parsers/structured.html |
3324155329d5-0 | Source code for langchain.utilities.searx_search
"""Utility for using SearxNG meta search API.
SearxNG is a privacy-friendly free metasearch engine that aggregates results from
`multiple search engines
<https://docs.searxng.org/admin/engines/configured_engines.html>`_ and databases and
supports the `OpenSearch
<https://github.com/dewitt/opensearch/blob/master/opensearch-1-1-draft-6.md>`_
specification.
More details on the installation instructions `here. <../../integrations/searx.html>`_
For the search API refer to https://docs.searxng.org/dev/search_api.html
Quick Start
-----------
In order to use this utility you need to provide the searx host. This can be done
by passing the named parameter :attr:`searx_host <SearxSearchWrapper.searx_host>`
or exporting the environment variable SEARX_HOST.
Note: this is the only required parameter.
Then create a searx search instance like this:
.. code-block:: python
from langchain.utilities import SearxSearchWrapper
# when the host starts with `http` SSL is disabled and the connection
# is assumed to be on a private network
searx_host='http://self.hosted'
search = SearxSearchWrapper(searx_host=searx_host)
You can now use the ``search`` instance to query the searx API.
Searching
---------
Use the :meth:`run() <SearxSearchWrapper.run>` and
:meth:`results() <SearxSearchWrapper.results>` methods to query the searx API.
Other methods are available for convenience.
:class:`SearxResults` is a convenience wrapper around the raw json result. | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/searx_search.html |
3324155329d5-1 | :class:`SearxResults` is a convenience wrapper around the raw json result.
Example usage of the ``run`` method to make a search:
.. code-block:: python
s.run(query="what is the best search engine?")
Engine Parameters
-----------------
You can pass any `accepted searx search API
<https://docs.searxng.org/dev/search_api.html>`_ parameters to the
:py:class:`SearxSearchWrapper` instance.
In the following example we are using the
:attr:`engines <SearxSearchWrapper.engines>` and the ``language`` parameters:
.. code-block:: python
# assuming the searx host is set as above or exported as an env variable
s = SearxSearchWrapper(engines=['google', 'bing'],
language='es')
Search Tips
-----------
Searx offers a special
`search syntax <https://docs.searxng.org/user/index.html#search-syntax>`_
that can also be used instead of passing engine parameters.
For example the following query:
.. code-block:: python
s = SearxSearchWrapper("langchain library", engines=['github'])
# can also be written as:
s = SearxSearchWrapper("langchain library !github")
# or even:
s = SearxSearchWrapper("langchain library !gh")
In some situations you might want to pass an extra string to the search query.
For example when the `run()` method is called by an agent. The search suffix can
also be used as a way to pass extra parameters to searx or the underlying search
engines.
.. code-block:: python
# select the github engine and pass the search suffix | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/searx_search.html |
3324155329d5-2 | .. code-block:: python
# select the github engine and pass the search suffix
s = SearchWrapper("langchain library", query_suffix="!gh")
s = SearchWrapper("langchain library")
# select github the conventional google search syntax
s.run("large language models", query_suffix="site:github.com")
*NOTE*: A search suffix can be defined on both the instance and the method level.
The resulting query will be the concatenation of the two with the former taking
precedence.
See `SearxNG Configured Engines
<https://docs.searxng.org/admin/engines/configured_engines.html>`_ and
`SearxNG Search Syntax <https://docs.searxng.org/user/index.html#id1>`_
for more details.
Notes
-----
This wrapper is based on the SearxNG fork https://github.com/searxng/searxng which is
better maintained than the original Searx project and offers more features.
Public searxNG instances often use a rate limiter for API usage, so you might want to
use a self hosted instance and disable the rate limiter.
If you are self-hosting an instance you can customize the rate limiter for your
own network as described
`here <https://docs.searxng.org/src/searx.botdetection.html#limiter-src>`_.
For a list of public SearxNG instances see https://searx.space/
"""
import json
from typing import Any, Dict, List, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra, Field, PrivateAttr, root_validator, validator
from langchain.utils import get_from_dict_or_env
def _get_default_params() -> dict: | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/searx_search.html |
3324155329d5-3 | def _get_default_params() -> dict:
return {"language": "en", "format": "json"}
class SearxResults(dict):
"""Dict like wrapper around search api results."""
_data = ""
def __init__(self, data: str):
"""Take a raw result from Searx and make it into a dict like object."""
json_data = json.loads(data)
super().__init__(json_data)
self.__dict__ = self
def __str__(self) -> str:
"""Text representation of searx result."""
return self._data
@property
def results(self) -> Any:
"""Silence mypy for accessing this field.
:meta private:
"""
return self.get("results")
@property
def answers(self) -> Any:
"""Helper accessor on the json result."""
return self.get("answers")
[docs]class SearxSearchWrapper(BaseModel):
"""Wrapper for Searx API.
To use you need to provide the searx host by passing the named parameter
``searx_host`` or exporting the environment variable ``SEARX_HOST``.
In some situations you might want to disable SSL verification, for example
if you are running searx locally. You can do this by passing the named parameter
``unsecure``. You can also pass the host url scheme as ``http`` to disable SSL.
Example:
.. code-block:: python
from langchain.utilities import SearxSearchWrapper
searx = SearxSearchWrapper(searx_host="http://localhost:8888")
Example with SSL disabled:
.. code-block:: python
from langchain.utilities import SearxSearchWrapper | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/searx_search.html |
3324155329d5-4 | .. code-block:: python
from langchain.utilities import SearxSearchWrapper
# note the unsecure parameter is not needed if you pass the url scheme as
# http
searx = SearxSearchWrapper(searx_host="http://localhost:8888",
unsecure=True)
"""
_result: SearxResults = PrivateAttr()
searx_host: str = ""
unsecure: bool = False
params: dict = Field(default_factory=_get_default_params)
headers: Optional[dict] = None
engines: Optional[List[str]] = []
categories: Optional[List[str]] = []
query_suffix: Optional[str] = ""
k: int = 10
aiosession: Optional[Any] = None
@validator("unsecure")
def disable_ssl_warnings(cls, v: bool) -> bool:
"""Disable SSL warnings."""
if v:
# requests.urllib3.disable_warnings()
try:
import urllib3
urllib3.disable_warnings()
except ImportError as e:
print(e)
return v
@root_validator()
def validate_params(cls, values: Dict) -> Dict:
"""Validate that custom searx params are merged with default ones."""
user_params = values["params"]
default = _get_default_params()
values["params"] = {**default, **user_params}
engines = values.get("engines")
if engines:
values["params"]["engines"] = ",".join(engines)
categories = values.get("categories")
if categories:
values["params"]["categories"] = ",".join(categories) | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/searx_search.html |
3324155329d5-5 | if categories:
values["params"]["categories"] = ",".join(categories)
searx_host = get_from_dict_or_env(values, "searx_host", "SEARX_HOST")
if not searx_host.startswith("http"):
print(
f"Warning: missing the url scheme on host \
! assuming secure https://{searx_host} "
)
searx_host = "https://" + searx_host
elif searx_host.startswith("http://"):
values["unsecure"] = True
cls.disable_ssl_warnings(True)
values["searx_host"] = searx_host
return values
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def _searx_api_query(self, params: dict) -> SearxResults:
"""Actual request to searx API."""
raw_result = requests.get(
self.searx_host,
headers=self.headers,
params=params,
verify=not self.unsecure,
)
# test if http result is ok
if not raw_result.ok:
raise ValueError("Searx API returned an error: ", raw_result.text)
res = SearxResults(raw_result.text)
self._result = res
return res
async def _asearx_api_query(self, params: dict) -> SearxResults:
if not self.aiosession:
async with aiohttp.ClientSession() as session:
async with session.get(
self.searx_host,
headers=self.headers,
params=params,
ssl=(lambda: False if self.unsecure else None)(),
) as response: | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/searx_search.html |
3324155329d5-6 | ) as response:
if not response.ok:
raise ValueError("Searx API returned an error: ", response.text)
result = SearxResults(await response.text())
self._result = result
else:
async with self.aiosession.get(
self.searx_host,
headers=self.headers,
params=params,
verify=not self.unsecure,
) as response:
if not response.ok:
raise ValueError("Searx API returned an error: ", response.text)
result = SearxResults(await response.text())
self._result = result
return result
[docs] def run(
self,
query: str,
engines: Optional[List[str]] = None,
categories: Optional[List[str]] = None,
query_suffix: Optional[str] = "",
**kwargs: Any,
) -> str:
"""Run query through Searx API and parse results.
You can pass any other params to the searx query API.
Args:
query: The query to search for.
query_suffix: Extra suffix appended to the query.
engines: List of engines to use for the query.
categories: List of categories to use for the query.
**kwargs: extra parameters to pass to the searx API.
Returns:
str: The result of the query.
Raises:
ValueError: If an error occurred with the query.
Example:
This will make a query to the qwant engine:
.. code-block:: python
from langchain.utilities import SearxSearchWrapper
searx = SearxSearchWrapper(searx_host="http://my.searx.host") | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/searx_search.html |
3324155329d5-7 | searx.run("what is the weather in France ?", engine="qwant")
# the same result can be achieved using the `!` syntax of searx
# to select the engine using `query_suffix`
searx.run("what is the weather in France ?", query_suffix="!qwant")
"""
_params = {
"q": query,
}
params = {**self.params, **_params, **kwargs}
if self.query_suffix and len(self.query_suffix) > 0:
params["q"] += " " + self.query_suffix
if isinstance(query_suffix, str) and len(query_suffix) > 0:
params["q"] += " " + query_suffix
if isinstance(engines, list) and len(engines) > 0:
params["engines"] = ",".join(engines)
if isinstance(categories, list) and len(categories) > 0:
params["categories"] = ",".join(categories)
res = self._searx_api_query(params)
if len(res.answers) > 0:
toret = res.answers[0]
# only return the content of the results list
elif len(res.results) > 0:
toret = "\n\n".join([r.get("content", "") for r in res.results[: self.k]])
else:
toret = "No good search result found"
return toret
[docs] async def arun(
self,
query: str,
engines: Optional[List[str]] = None,
query_suffix: Optional[str] = "",
**kwargs: Any,
) -> str:
"""Asynchronously version of `run`.""" | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/searx_search.html |
3324155329d5-8 | ) -> str:
"""Asynchronously version of `run`."""
_params = {
"q": query,
}
params = {**self.params, **_params, **kwargs}
if self.query_suffix and len(self.query_suffix) > 0:
params["q"] += " " + self.query_suffix
if isinstance(query_suffix, str) and len(query_suffix) > 0:
params["q"] += " " + query_suffix
if isinstance(engines, list) and len(engines) > 0:
params["engines"] = ",".join(engines)
res = await self._asearx_api_query(params)
if len(res.answers) > 0:
toret = res.answers[0]
# only return the content of the results list
elif len(res.results) > 0:
toret = "\n\n".join([r.get("content", "") for r in res.results[: self.k]])
else:
toret = "No good search result found"
return toret
[docs] def results(
self,
query: str,
num_results: int,
engines: Optional[List[str]] = None,
categories: Optional[List[str]] = None,
query_suffix: Optional[str] = "",
**kwargs: Any,
) -> List[Dict]:
"""Run query through Searx API and returns the results with metadata.
Args:
query: The query to search for.
query_suffix: Extra suffix appended to the query.
num_results: Limit the number of results to return.
engines: List of engines to use for the query. | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/searx_search.html |
3324155329d5-9 | engines: List of engines to use for the query.
categories: List of categories to use for the query.
**kwargs: extra parameters to pass to the searx API.
Returns:
Dict with the following keys:
{
snippet: The description of the result.
title: The title of the result.
link: The link to the result.
engines: The engines used for the result.
category: Searx category of the result.
}
"""
_params = {
"q": query,
}
params = {**self.params, **_params, **kwargs}
if self.query_suffix and len(self.query_suffix) > 0:
params["q"] += " " + self.query_suffix
if isinstance(query_suffix, str) and len(query_suffix) > 0:
params["q"] += " " + query_suffix
if isinstance(engines, list) and len(engines) > 0:
params["engines"] = ",".join(engines)
if isinstance(categories, list) and len(categories) > 0:
params["categories"] = ",".join(categories)
results = self._searx_api_query(params).results[:num_results]
if len(results) == 0:
return [{"Result": "No good Search Result was found"}]
return [
{
"snippet": result.get("content", ""),
"title": result["title"],
"link": result["url"],
"engines": result["engines"],
"category": result["category"],
}
for result in results
]
[docs] async def aresults(
self, | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/searx_search.html |
3324155329d5-10 | ]
[docs] async def aresults(
self,
query: str,
num_results: int,
engines: Optional[List[str]] = None,
query_suffix: Optional[str] = "",
**kwargs: Any,
) -> List[Dict]:
"""Asynchronously query with json results.
Uses aiohttp. See `results` for more info.
"""
_params = {
"q": query,
}
params = {**self.params, **_params, **kwargs}
if self.query_suffix and len(self.query_suffix) > 0:
params["q"] += " " + self.query_suffix
if isinstance(query_suffix, str) and len(query_suffix) > 0:
params["q"] += " " + query_suffix
if isinstance(engines, list) and len(engines) > 0:
params["engines"] = ",".join(engines)
results = (await self._asearx_api_query(params)).results[:num_results]
if len(results) == 0:
return [{"Result": "No good Search Result was found"}]
return [
{
"snippet": result.get("content", ""),
"title": result["title"],
"link": result["url"],
"engines": result["engines"],
"category": result["category"],
}
for result in results
] | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/searx_search.html |
bf6fc09db221-0 | Source code for langchain.utilities.duckduckgo_search
"""Util that calls DuckDuckGo Search.
No setup required. Free.
https://pypi.org/project/duckduckgo-search/
"""
from typing import Dict, List, Optional
from pydantic import BaseModel, Extra
from pydantic.class_validators import root_validator
[docs]class DuckDuckGoSearchAPIWrapper(BaseModel):
"""Wrapper for DuckDuckGo Search API.
Free and does not require any setup
"""
k: int = 10
region: Optional[str] = "wt-wt"
safesearch: str = "moderate"
time: Optional[str] = "y"
max_results: int = 5
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that python package exists in environment."""
try:
from duckduckgo_search import DDGS # noqa: F401
except ImportError:
raise ValueError(
"Could not import duckduckgo-search python package. "
"Please install it with `pip install duckduckgo-search`."
)
return values
[docs] def get_snippets(self, query: str) -> List[str]:
"""Run query through DuckDuckGo and return concatenated results."""
from duckduckgo_search import DDGS
with DDGS() as ddgs:
results = ddgs.text(
query,
region=self.region,
safesearch=self.safesearch,
timelimit=self.time,
)
if results is None: | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/duckduckgo_search.html |
bf6fc09db221-1 | timelimit=self.time,
)
if results is None:
return ["No good DuckDuckGo Search Result was found"]
snippets = []
for i, res in enumerate(results, 1):
if res is not None:
snippets.append(res["body"])
if len(snippets) == self.max_results:
break
return snippets
[docs] def run(self, query: str) -> str:
snippets = self.get_snippets(query)
return " ".join(snippets)
[docs] def results(self, query: str, num_results: int) -> List[Dict[str, str]]:
"""Run query through DuckDuckGo and return metadata.
Args:
query: The query to search for.
num_results: The number of results to return.
Returns:
A list of dictionaries with the following keys:
snippet - The description of the result.
title - The title of the result.
link - The link to the result.
"""
from duckduckgo_search import DDGS
with DDGS() as ddgs:
results = ddgs.text(
query,
region=self.region,
safesearch=self.safesearch,
timelimit=self.time,
)
if results is None:
return [{"Result": "No good DuckDuckGo Search Result was found"}]
def to_metadata(result: Dict) -> Dict[str, str]:
return {
"snippet": result["body"],
"title": result["title"],
"link": result["href"],
}
formatted_results = []
for i, res in enumerate(results, 1):
if res is not None: | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/duckduckgo_search.html |
bf6fc09db221-2 | if res is not None:
formatted_results.append(to_metadata(res))
if len(formatted_results) == num_results:
break
return formatted_results | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/duckduckgo_search.html |
c6f24c2566fb-0 | Source code for langchain.utilities.serpapi
"""Chain that calls SerpAPI.
Heavily borrowed from https://github.com/ofirpress/self-ask
"""
import os
import sys
from typing import Any, Dict, Optional, Tuple
import aiohttp
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.utils import get_from_dict_or_env
class HiddenPrints:
"""Context manager to hide prints."""
def __enter__(self) -> None:
"""Open file to pipe stdout to."""
self._original_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
def __exit__(self, *_: Any) -> None:
"""Close file that stdout was piped to."""
sys.stdout.close()
sys.stdout = self._original_stdout
[docs]class SerpAPIWrapper(BaseModel):
"""Wrapper around SerpAPI.
To use, you should have the ``google-search-results`` python package installed,
and the environment variable ``SERPAPI_API_KEY`` set with your API key, or pass
`serpapi_api_key` as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.utilities import SerpAPIWrapper
serpapi = SerpAPIWrapper()
"""
search_engine: Any #: :meta private:
params: dict = Field(
default={
"engine": "google",
"google_domain": "google.com",
"gl": "us",
"hl": "en",
}
)
serpapi_api_key: Optional[str] = None
aiosession: Optional[aiohttp.ClientSession] = None
class Config: | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/serpapi.html |
c6f24c2566fb-1 | aiosession: Optional[aiohttp.ClientSession] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
serpapi_api_key = get_from_dict_or_env(
values, "serpapi_api_key", "SERPAPI_API_KEY"
)
values["serpapi_api_key"] = serpapi_api_key
try:
from serpapi import GoogleSearch
values["search_engine"] = GoogleSearch
except ImportError:
raise ValueError(
"Could not import serpapi python package. "
"Please install it with `pip install google-search-results`."
)
return values
[docs] async def arun(self, query: str, **kwargs: Any) -> str:
"""Run query through SerpAPI and parse result async."""
return self._process_response(await self.aresults(query))
[docs] def run(self, query: str, **kwargs: Any) -> str:
"""Run query through SerpAPI and parse result."""
return self._process_response(self.results(query))
[docs] def results(self, query: str) -> dict:
"""Run query through SerpAPI and return the raw result."""
params = self.get_params(query)
with HiddenPrints():
search = self.search_engine(params)
res = search.get_dict()
return res
[docs] async def aresults(self, query: str) -> dict:
"""Use aiohttp to run query through SerpAPI and return the results async.""" | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/serpapi.html |
c6f24c2566fb-2 | """Use aiohttp to run query through SerpAPI and return the results async."""
def construct_url_and_params() -> Tuple[str, Dict[str, str]]:
params = self.get_params(query)
params["source"] = "python"
if self.serpapi_api_key:
params["serp_api_key"] = self.serpapi_api_key
params["output"] = "json"
url = "https://serpapi.com/search"
return url, params
url, params = construct_url_and_params()
if not self.aiosession:
async with aiohttp.ClientSession() as session:
async with session.get(url, params=params) as response:
res = await response.json()
else:
async with self.aiosession.get(url, params=params) as response:
res = await response.json()
return res
[docs] def get_params(self, query: str) -> Dict[str, str]:
"""Get parameters for SerpAPI."""
_params = {
"api_key": self.serpapi_api_key,
"q": query,
}
params = {**self.params, **_params}
return params
@staticmethod
def _process_response(res: dict) -> str:
"""Process response from SerpAPI."""
if "error" in res.keys():
raise ValueError(f"Got error from SerpAPI: {res['error']}")
if "answer_box" in res.keys() and type(res["answer_box"]) == list:
res["answer_box"] = res["answer_box"][0]
if "answer_box" in res.keys() and "answer" in res["answer_box"].keys(): | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/serpapi.html |
c6f24c2566fb-3 | toret = res["answer_box"]["answer"]
elif "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
toret = res["answer_box"]["snippet"]
elif (
"answer_box" in res.keys()
and "snippet_highlighted_words" in res["answer_box"].keys()
):
toret = res["answer_box"]["snippet_highlighted_words"][0]
elif (
"sports_results" in res.keys()
and "game_spotlight" in res["sports_results"].keys()
):
toret = res["sports_results"]["game_spotlight"]
elif (
"shopping_results" in res.keys()
and "title" in res["shopping_results"][0].keys()
):
toret = res["shopping_results"][:3]
elif (
"knowledge_graph" in res.keys()
and "description" in res["knowledge_graph"].keys()
):
toret = res["knowledge_graph"]["description"]
elif "snippet" in res["organic_results"][0].keys():
toret = res["organic_results"][0]["snippet"]
elif "link" in res["organic_results"][0].keys():
toret = res["organic_results"][0]["link"]
else:
toret = "No good search result found"
return toret | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/serpapi.html |
9144df94e48d-0 | Source code for langchain.utilities.twilio
"""Util that calls Twilio."""
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class TwilioAPIWrapper(BaseModel):
"""Messaging Client using Twilio.
To use, you should have the ``twilio`` python package installed,
and the environment variables ``TWILIO_ACCOUNT_SID``, ``TWILIO_AUTH_TOKEN``, and
``TWILIO_FROM_NUMBER``, or pass `account_sid`, `auth_token`, and `from_number` as
named parameters to the constructor.
Example:
.. code-block:: python
from langchain.utilities.twilio import TwilioAPIWrapper
twilio = TwilioAPIWrapper(
account_sid="ACxxx",
auth_token="xxx",
from_number="+10123456789"
)
twilio.run('test', '+12484345508')
"""
client: Any #: :meta private:
account_sid: Optional[str] = None
"""Twilio account string identifier."""
auth_token: Optional[str] = None
"""Twilio auth token."""
from_number: Optional[str] = None
"""A Twilio phone number in [E.164](https://www.twilio.com/docs/glossary/what-e164)
format, an
[alphanumeric sender ID](https://www.twilio.com/docs/sms/send-messages#use-an-alphanumeric-sender-id),
or a [Channel Endpoint address](https://www.twilio.com/docs/sms/channels#channel-addresses)
that is enabled for the type of message you want to send. Phone numbers or | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/twilio.html |
9144df94e48d-1 | that is enabled for the type of message you want to send. Phone numbers or
[short codes](https://www.twilio.com/docs/sms/api/short-code) purchased from
Twilio also work here. You cannot, for example, spoof messages from a private
cell phone number. If you are using `messaging_service_sid`, this parameter
must be empty.
""" # noqa: E501
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = False
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
try:
from twilio.rest import Client
except ImportError:
raise ImportError(
"Could not import twilio python package. "
"Please install it with `pip install twilio`."
)
account_sid = get_from_dict_or_env(values, "account_sid", "TWILIO_ACCOUNT_SID")
auth_token = get_from_dict_or_env(values, "auth_token", "TWILIO_AUTH_TOKEN")
values["from_number"] = get_from_dict_or_env(
values, "from_number", "TWILIO_FROM_NUMBER"
)
values["client"] = Client(account_sid, auth_token)
return values
[docs] def run(self, body: str, to: str) -> str:
"""Run body through Twilio and respond with message sid.
Args:
body: The text of the message you want to send. Can be up to 1,600
characters in length.
to: The destination phone number in | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/twilio.html |
9144df94e48d-2 | characters in length.
to: The destination phone number in
[E.164](https://www.twilio.com/docs/glossary/what-e164) format for
SMS/MMS or
[Channel user address](https://www.twilio.com/docs/sms/channels#channel-addresses)
for other 3rd-party channels.
""" # noqa: E501
message = self.client.messages.create(to, from_=self.from_number, body=body)
return message.sid | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/twilio.html |
668214326740-0 | Source code for langchain.utilities.metaphor_search
"""Util that calls Metaphor Search API.
In order to set this up, follow instructions at:
"""
import json
from typing import Dict, List, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
METAPHOR_API_URL = "https://api.metaphor.systems"
[docs]class MetaphorSearchAPIWrapper(BaseModel):
"""Wrapper for Metaphor Search API."""
metaphor_api_key: str
k: int = 10
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def _metaphor_search_results(
self,
query: str,
num_results: int,
include_domains: Optional[List[str]] = None,
exclude_domains: Optional[List[str]] = None,
start_crawl_date: Optional[str] = None,
end_crawl_date: Optional[str] = None,
start_published_date: Optional[str] = None,
end_published_date: Optional[str] = None,
) -> List[dict]:
headers = {"X-Api-Key": self.metaphor_api_key}
params = {
"numResults": num_results,
"query": query,
"includeDomains": include_domains,
"excludeDomains": exclude_domains,
"startCrawlDate": start_crawl_date,
"endCrawlDate": end_crawl_date,
"startPublishedDate": start_published_date,
"endPublishedDate": end_published_date,
}
response = requests.post(
# type: ignore
f"{METAPHOR_API_URL}/search", | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/metaphor_search.html |
668214326740-1 | # type: ignore
f"{METAPHOR_API_URL}/search",
headers=headers,
json=params,
)
response.raise_for_status()
search_results = response.json()
print(search_results)
return search_results["results"]
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and endpoint exists in environment."""
metaphor_api_key = get_from_dict_or_env(
values, "metaphor_api_key", "METAPHOR_API_KEY"
)
values["metaphor_api_key"] = metaphor_api_key
return values
[docs] def results(
self,
query: str,
num_results: int,
include_domains: Optional[List[str]] = None,
exclude_domains: Optional[List[str]] = None,
start_crawl_date: Optional[str] = None,
end_crawl_date: Optional[str] = None,
start_published_date: Optional[str] = None,
end_published_date: Optional[str] = None,
) -> List[Dict]:
"""Run query through Metaphor Search and return metadata.
Args:
query: The query to search for.
num_results: The number of results to return.
Returns:
A list of dictionaries with the following keys:
title - The title of the
url - The url
author - Author of the content, if applicable. Otherwise, None.
published_date - Estimated date published
in YYYY-MM-DD format. Otherwise, None.
"""
raw_search_results = self._metaphor_search_results(
query,
num_results=num_results,
include_domains=include_domains, | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/metaphor_search.html |
668214326740-2 | query,
num_results=num_results,
include_domains=include_domains,
exclude_domains=exclude_domains,
start_crawl_date=start_crawl_date,
end_crawl_date=end_crawl_date,
start_published_date=start_published_date,
end_published_date=end_published_date,
)
return self._clean_results(raw_search_results)
[docs] async def results_async(
self,
query: str,
num_results: int,
include_domains: Optional[List[str]] = None,
exclude_domains: Optional[List[str]] = None,
start_crawl_date: Optional[str] = None,
end_crawl_date: Optional[str] = None,
start_published_date: Optional[str] = None,
end_published_date: Optional[str] = None,
) -> List[Dict]:
"""Get results from the Metaphor Search API asynchronously."""
# Function to perform the API call
async def fetch() -> str:
headers = {"X-Api-Key": self.metaphor_api_key}
params = {
"numResults": num_results,
"query": query,
"includeDomains": include_domains,
"excludeDomains": exclude_domains,
"startCrawlDate": start_crawl_date,
"endCrawlDate": end_crawl_date,
"startPublishedDate": start_published_date,
"endPublishedDate": end_published_date,
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"{METAPHOR_API_URL}/search", json=params, headers=headers
) as res:
if res.status == 200:
data = await res.text()
return data
else: | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/metaphor_search.html |
668214326740-3 | data = await res.text()
return data
else:
raise Exception(f"Error {res.status}: {res.reason}")
results_json_str = await fetch()
results_json = json.loads(results_json_str)
return self._clean_results(results_json["results"])
def _clean_results(self, raw_search_results: List[Dict]) -> List[Dict]:
cleaned_results = []
for result in raw_search_results:
cleaned_results.append(
{
"title": result["title"],
"url": result["url"],
"author": result["author"],
"published_date": result["publishedDate"],
}
)
return cleaned_results | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/metaphor_search.html |
12b68bc086df-0 | Source code for langchain.utilities.pupmed
import json
import logging
import time
import urllib.error
import urllib.request
from typing import List
from pydantic import BaseModel, Extra
from langchain.schema import Document
logger = logging.getLogger(__name__)
[docs]class PubMedAPIWrapper(BaseModel):
"""
Wrapper around PubMed API.
This wrapper will use the PubMed API to conduct searches and fetch
document summaries. By default, it will return the document summaries
of the top-k results of an input search.
Parameters:
top_k_results: number of the top-scored document used for the PubMed tool
load_max_docs: a limit to the number of loaded documents
load_all_available_meta:
if True: the `metadata` of the loaded Documents gets all available meta info
(see https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch)
if False: the `metadata` gets only the most informative fields.
"""
base_url_esearch = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?"
base_url_efetch = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?"
max_retry = 5
sleep_time = 0.2
# Default values for the parameters
top_k_results: int = 3
load_max_docs: int = 25
ARXIV_MAX_QUERY_LENGTH = 300
doc_content_chars_max: int = 2000
load_all_available_meta: bool = False
email: str = "your_email@example.com"
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] def run(self, query: str) -> str: | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/pupmed.html |
12b68bc086df-1 | [docs] def run(self, query: str) -> str:
"""
Run PubMed search and get the article meta information.
See https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch
It uses only the most informative fields of article meta information.
"""
try:
# Retrieve the top-k results for the query
docs = [
f"Published: {result['pub_date']}\nTitle: {result['title']}\n"
f"Summary: {result['summary']}"
for result in self.load(query[: self.ARXIV_MAX_QUERY_LENGTH])
]
# Join the results and limit the character count
return (
"\n\n".join(docs)[: self.doc_content_chars_max]
if docs
else "No good PubMed Result was found"
)
except Exception as ex:
return f"PubMed exception: {ex}"
[docs] def load(self, query: str) -> List[dict]:
"""
Search PubMed for documents matching the query.
Return a list of dictionaries containing the document metadata.
"""
url = (
self.base_url_esearch
+ "db=pubmed&term="
+ str({urllib.parse.quote(query)})
+ f"&retmode=json&retmax={self.top_k_results}&usehistory=y"
)
result = urllib.request.urlopen(url)
text = result.read().decode("utf-8")
json_text = json.loads(text)
articles = []
webenv = json_text["esearchresult"]["webenv"]
for uid in json_text["esearchresult"]["idlist"]:
article = self.retrieve_article(uid, webenv)
articles.append(article) | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/pupmed.html |
12b68bc086df-2 | article = self.retrieve_article(uid, webenv)
articles.append(article)
# Convert the list of articles to a JSON string
return articles
def _transform_doc(self, doc: dict) -> Document:
summary = doc.pop("summary")
return Document(page_content=summary, metadata=doc)
[docs] def load_docs(self, query: str) -> List[Document]:
document_dicts = self.load(query=query)
return [self._transform_doc(d) for d in document_dicts]
[docs] def retrieve_article(self, uid: str, webenv: str) -> dict:
url = (
self.base_url_efetch
+ "db=pubmed&retmode=xml&id="
+ uid
+ "&webenv="
+ webenv
)
retry = 0
while True:
try:
result = urllib.request.urlopen(url)
break
except urllib.error.HTTPError as e:
if e.code == 429 and retry < self.max_retry:
# Too Many Requests error
# wait for an exponentially increasing amount of time
print(
f"Too Many Requests, "
f"waiting for {self.sleep_time:.2f} seconds..."
)
time.sleep(self.sleep_time)
self.sleep_time *= 2
retry += 1
else:
raise e
xml_text = result.read().decode("utf-8")
# Get title
title = ""
if "<ArticleTitle>" in xml_text and "</ArticleTitle>" in xml_text:
start_tag = "<ArticleTitle>"
end_tag = "</ArticleTitle>"
title = xml_text[ | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/pupmed.html |
12b68bc086df-3 | end_tag = "</ArticleTitle>"
title = xml_text[
xml_text.index(start_tag) + len(start_tag) : xml_text.index(end_tag)
]
# Get abstract
abstract = ""
if "<AbstractText>" in xml_text and "</AbstractText>" in xml_text:
start_tag = "<AbstractText>"
end_tag = "</AbstractText>"
abstract = xml_text[
xml_text.index(start_tag) + len(start_tag) : xml_text.index(end_tag)
]
# Get publication date
pub_date = ""
if "<PubDate>" in xml_text and "</PubDate>" in xml_text:
start_tag = "<PubDate>"
end_tag = "</PubDate>"
pub_date = xml_text[
xml_text.index(start_tag) + len(start_tag) : xml_text.index(end_tag)
]
# Return article as dictionary
article = {
"uid": uid,
"title": title,
"summary": abstract,
"pub_date": pub_date,
}
return article | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/pupmed.html |
099e4809bc4e-0 | Source code for langchain.utilities.spark_sql
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Iterable, List, Optional
if TYPE_CHECKING:
from pyspark.sql import DataFrame, Row, SparkSession
[docs]class SparkSQL:
def __init__(
self,
spark_session: Optional[SparkSession] = None,
catalog: Optional[str] = None,
schema: Optional[str] = None,
ignore_tables: Optional[List[str]] = None,
include_tables: Optional[List[str]] = None,
sample_rows_in_table_info: int = 3,
):
try:
from pyspark.sql import SparkSession
except ImportError:
raise ValueError(
"pyspark is not installed. Please install it with `pip install pyspark`"
)
self._spark = (
spark_session if spark_session else SparkSession.builder.getOrCreate()
)
if catalog is not None:
self._spark.catalog.setCurrentCatalog(catalog)
if schema is not None:
self._spark.catalog.setCurrentDatabase(schema)
self._all_tables = set(self._get_all_table_names())
self._include_tables = set(include_tables) if include_tables else set()
if self._include_tables:
missing_tables = self._include_tables - self._all_tables
if missing_tables:
raise ValueError(
f"include_tables {missing_tables} not found in database"
)
self._ignore_tables = set(ignore_tables) if ignore_tables else set()
if self._ignore_tables:
missing_tables = self._ignore_tables - self._all_tables
if missing_tables:
raise ValueError(
f"ignore_tables {missing_tables} not found in database"
) | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/spark_sql.html |
099e4809bc4e-1 | f"ignore_tables {missing_tables} not found in database"
)
usable_tables = self.get_usable_table_names()
self._usable_tables = set(usable_tables) if usable_tables else self._all_tables
if not isinstance(sample_rows_in_table_info, int):
raise TypeError("sample_rows_in_table_info must be an integer")
self._sample_rows_in_table_info = sample_rows_in_table_info
[docs] @classmethod
def from_uri(
cls, database_uri: str, engine_args: Optional[dict] = None, **kwargs: Any
) -> SparkSQL:
"""Creating a remote Spark Session via Spark connect.
For example: SparkSQL.from_uri("sc://localhost:15002")
"""
try:
from pyspark.sql import SparkSession
except ImportError:
raise ValueError(
"pyspark is not installed. Please install it with `pip install pyspark`"
)
spark = SparkSession.builder.remote(database_uri).getOrCreate()
return cls(spark, **kwargs)
[docs] def get_usable_table_names(self) -> Iterable[str]:
"""Get names of tables available."""
if self._include_tables:
return self._include_tables
# sorting the result can help LLM understanding it.
return sorted(self._all_tables - self._ignore_tables)
def _get_all_table_names(self) -> Iterable[str]:
rows = self._spark.sql("SHOW TABLES").select("tableName").collect()
return list(map(lambda row: row.tableName, rows))
def _get_create_table_stmt(self, table: str) -> str:
statement = (
self._spark.sql(f"SHOW CREATE TABLE {table}").collect()[0].createtab_stmt | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/spark_sql.html |
099e4809bc4e-2 | )
# Ignore the data source provider and options to reduce the number of tokens.
using_clause_index = statement.find("USING")
return statement[:using_clause_index] + ";"
[docs] def get_table_info(self, table_names: Optional[List[str]] = None) -> str:
all_table_names = self.get_usable_table_names()
if table_names is not None:
missing_tables = set(table_names).difference(all_table_names)
if missing_tables:
raise ValueError(f"table_names {missing_tables} not found in database")
all_table_names = table_names
tables = []
for table_name in all_table_names:
table_info = self._get_create_table_stmt(table_name)
if self._sample_rows_in_table_info:
table_info += "\n\n/*"
table_info += f"\n{self._get_sample_spark_rows(table_name)}\n"
table_info += "*/"
tables.append(table_info)
final_str = "\n\n".join(tables)
return final_str
def _get_sample_spark_rows(self, table: str) -> str:
query = f"SELECT * FROM {table} LIMIT {self._sample_rows_in_table_info}"
df = self._spark.sql(query)
columns_str = "\t".join(list(map(lambda f: f.name, df.schema.fields)))
try:
sample_rows = self._get_dataframe_results(df)
# save the sample rows in string format
sample_rows_str = "\n".join(["\t".join(row) for row in sample_rows])
except Exception:
sample_rows_str = ""
return (
f"{self._sample_rows_in_table_info} rows from {table} table:\n" | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/spark_sql.html |
099e4809bc4e-3 | f"{columns_str}\n"
f"{sample_rows_str}"
)
def _convert_row_as_tuple(self, row: Row) -> tuple:
return tuple(map(str, row.asDict().values()))
def _get_dataframe_results(self, df: DataFrame) -> list:
return list(map(self._convert_row_as_tuple, df.collect()))
[docs] def run(self, command: str, fetch: str = "all") -> str:
df = self._spark.sql(command)
if fetch == "one":
df = df.limit(1)
return str(self._get_dataframe_results(df))
[docs] def get_table_info_no_throw(self, table_names: Optional[List[str]] = None) -> str:
"""Get information about specified tables.
Follows best practices as specified in: Rajkumar et al, 2022
(https://arxiv.org/abs/2204.00498)
If `sample_rows_in_table_info`, the specified number of sample rows will be
appended to each table description. This can increase performance as
demonstrated in the paper.
"""
try:
return self.get_table_info(table_names)
except ValueError as e:
"""Format the error message"""
return f"Error: {e}"
[docs] def run_no_throw(self, command: str, fetch: str = "all") -> str:
"""Execute a SQL command and return a string representing the results.
If the statement returns rows, a string of the results is returned.
If the statement returns no rows, an empty string is returned.
If the statement throws an error, the error message is returned.
"""
try:
from pyspark.errors import PySparkException | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/spark_sql.html |
099e4809bc4e-4 | """
try:
from pyspark.errors import PySparkException
except ImportError:
raise ValueError(
"pyspark is not installed. Please install it with `pip install pyspark`"
)
try:
return self.run(command, fetch)
except PySparkException as e:
"""Format the error message"""
return f"Error: {e}" | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/spark_sql.html |
7b96770b6e67-0 | Source code for langchain.utilities.google_places_api
"""Chain that calls Google Places API.
"""
import logging
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class GooglePlacesAPIWrapper(BaseModel):
"""Wrapper around Google Places API.
To use, you should have the ``googlemaps`` python package installed,
**an API key for the google maps platform**,
and the enviroment variable ''GPLACES_API_KEY''
set with your API key , or pass 'gplaces_api_key'
as a named parameter to the constructor.
By default, this will return the all the results on the input query.
You can use the top_k_results argument to limit the number of results.
Example:
.. code-block:: python
from langchain import GooglePlacesAPIWrapper
gplaceapi = GooglePlacesAPIWrapper()
"""
gplaces_api_key: Optional[str] = None
google_map_client: Any #: :meta private:
top_k_results: Optional[int] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key is in your environment variable."""
gplaces_api_key = get_from_dict_or_env(
values, "gplaces_api_key", "GPLACES_API_KEY"
)
values["gplaces_api_key"] = gplaces_api_key
try:
import googlemaps
values["google_map_client"] = googlemaps.Client(gplaces_api_key)
except ImportError:
raise ImportError( | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/google_places_api.html |
7b96770b6e67-1 | except ImportError:
raise ImportError(
"Could not import googlemaps python package. "
"Please install it with `pip install googlemaps`."
)
return values
[docs] def run(self, query: str) -> str:
"""Run Places search and get k number of places that exists that match."""
search_results = self.google_map_client.places(query)["results"]
num_to_return = len(search_results)
places = []
if num_to_return == 0:
return "Google Places did not find any places that match the description"
num_to_return = (
num_to_return
if self.top_k_results is None
else min(num_to_return, self.top_k_results)
)
for i in range(num_to_return):
result = search_results[i]
details = self.fetch_place_details(result["place_id"])
if details is not None:
places.append(details)
return "\n".join([f"{i+1}. {item}" for i, item in enumerate(places)])
[docs] def fetch_place_details(self, place_id: str) -> Optional[str]:
try:
place_details = self.google_map_client.place(place_id)
formatted_details = self.format_place_details(place_details)
return formatted_details
except Exception as e:
logging.error(f"An Error occurred while fetching place details: {e}")
return None
[docs] def format_place_details(self, place_details: Dict[str, Any]) -> Optional[str]:
try:
name = place_details.get("result", {}).get("name", "Unkown")
address = place_details.get("result", {}).get(
"formatted_address", "Unknown"
) | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/google_places_api.html |
7b96770b6e67-2 | "formatted_address", "Unknown"
)
phone_number = place_details.get("result", {}).get(
"formatted_phone_number", "Unknown"
)
website = place_details.get("result", {}).get("website", "Unknown")
formatted_details = (
f"{name}\nAddress: {address}\n"
f"Phone: {phone_number}\nWebsite: {website}\n\n"
)
return formatted_details
except Exception as e:
logging.error(f"An error occurred while formatting place details: {e}")
return None | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/google_places_api.html |
c3733490c1c8-0 | Source code for langchain.utilities.scenexplain
"""Util that calls SceneXplain.
In order to set this up, you need API key for the SceneXplain API.
You can obtain a key by following the steps below.
- Sign up for a free account at https://scenex.jina.ai/.
- Navigate to the API Access page (https://scenex.jina.ai/api) and create a new API key.
"""
from typing import Dict
import requests
from pydantic import BaseModel, BaseSettings, Field, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class SceneXplainAPIWrapper(BaseSettings, BaseModel):
"""Wrapper for SceneXplain API.
In order to set this up, you need API key for the SceneXplain API.
You can obtain a key by following the steps below.
- Sign up for a free account at https://scenex.jina.ai/.
- Navigate to the API Access page (https://scenex.jina.ai/api)
and create a new API key.
"""
scenex_api_key: str = Field(..., env="SCENEX_API_KEY")
scenex_api_url: str = (
"https://us-central1-causal-diffusion.cloudfunctions.net/describe"
)
def _describe_image(self, image: str) -> str:
headers = {
"x-api-key": f"token {self.scenex_api_key}",
"content-type": "application/json",
}
payload = {
"data": [
{
"image": image,
"algorithm": "Ember",
"languages": ["en"],
}
]
} | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/scenexplain.html |
c3733490c1c8-1 | "languages": ["en"],
}
]
}
response = requests.post(self.scenex_api_url, headers=headers, json=payload)
response.raise_for_status()
result = response.json().get("result", [])
img = result[0] if result else {}
return img.get("text", "")
[docs] @root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
scenex_api_key = get_from_dict_or_env(
values, "scenex_api_key", "SCENEX_API_KEY"
)
values["scenex_api_key"] = scenex_api_key
return values
[docs] def run(self, image: str) -> str:
"""Run SceneXplain image explainer."""
description = self._describe_image(image)
if not description:
return "No description found."
return description | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/scenexplain.html |
84a51f7b299d-0 | Source code for langchain.utilities.bash
"""Wrapper around subprocess to run commands."""
from __future__ import annotations
import platform
import re
import subprocess
from typing import TYPE_CHECKING, List, Union
from uuid import uuid4
if TYPE_CHECKING:
import pexpect
def _lazy_import_pexpect() -> pexpect:
"""Import pexpect only when needed."""
if platform.system() == "Windows":
raise ValueError("Persistent bash processes are not yet supported on Windows.")
try:
import pexpect
except ImportError:
raise ImportError(
"pexpect required for persistent bash processes."
" To install, run `pip install pexpect`."
)
return pexpect
[docs]class BashProcess:
"""Executes bash commands and returns the output."""
def __init__(
self,
strip_newlines: bool = False,
return_err_output: bool = False,
persistent: bool = False,
):
"""Initialize with stripping newlines."""
self.strip_newlines = strip_newlines
self.return_err_output = return_err_output
self.prompt = ""
self.process = None
if persistent:
self.prompt = str(uuid4())
self.process = self._initialize_persistent_process(self.prompt)
@staticmethod
def _initialize_persistent_process(prompt: str) -> pexpect.spawn:
# Start bash in a clean environment
# Doesn't work on windows
pexpect = _lazy_import_pexpect()
process = pexpect.spawn(
"env", ["-i", "bash", "--norc", "--noprofile"], encoding="utf-8"
)
# Set the custom prompt
process.sendline("PS1=" + prompt) | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/bash.html |
84a51f7b299d-1 | # Set the custom prompt
process.sendline("PS1=" + prompt)
process.expect_exact(prompt, timeout=10)
return process
[docs] def run(self, commands: Union[str, List[str]]) -> str:
"""Run commands and return final output."""
if isinstance(commands, str):
commands = [commands]
commands = ";".join(commands)
if self.process is not None:
return self._run_persistent(
commands,
)
else:
return self._run(commands)
def _run(self, command: str) -> str:
"""Run commands and return final output."""
try:
output = subprocess.run(
command,
shell=True,
check=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
).stdout.decode()
except subprocess.CalledProcessError as error:
if self.return_err_output:
return error.stdout.decode()
return str(error)
if self.strip_newlines:
output = output.strip()
return output
[docs] def process_output(self, output: str, command: str) -> str:
# Remove the command from the output using a regular expression
pattern = re.escape(command) + r"\s*\n"
output = re.sub(pattern, "", output, count=1)
return output.strip()
def _run_persistent(self, command: str) -> str:
"""Run commands and return final output."""
pexpect = _lazy_import_pexpect()
if self.process is None:
raise ValueError("Process not initialized")
self.process.sendline(command)
# Clear the output with an empty string
self.process.expect(self.prompt, timeout=10) | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/bash.html |
84a51f7b299d-2 | self.process.expect(self.prompt, timeout=10)
self.process.sendline("")
try:
self.process.expect([self.prompt, pexpect.EOF], timeout=10)
except pexpect.TIMEOUT:
return f"Timeout error while executing command {command}"
if self.process.after == pexpect.EOF:
return f"Exited with error status: {self.process.exitstatus}"
output = self.process.before
output = self.process_output(output, command)
if self.strip_newlines:
return output.strip()
return output | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/bash.html |
fbd3f5dde681-0 | Source code for langchain.utilities.brave_search
import json
import requests
from pydantic import BaseModel, Field
[docs]class BraveSearchWrapper(BaseModel):
api_key: str
search_kwargs: dict = Field(default_factory=dict)
[docs] def run(self, query: str) -> str:
headers = {
"X-Subscription-Token": self.api_key,
"Accept": "application/json",
}
base_url = "https://api.search.brave.com/res/v1/web/search"
req = requests.PreparedRequest()
params = {**self.search_kwargs, **{"q": query}}
req.prepare_url(base_url, params)
if req.url is None:
raise ValueError("prepared url is None, this should not happen")
response = requests.get(req.url, headers=headers)
if not response.ok:
raise Exception(f"HTTP error {response.status_code}")
parsed_response = response.json()
web_search_results = parsed_response.get("web", {}).get("results", [])
final_results = []
if isinstance(web_search_results, list):
for item in web_search_results:
final_results.append(
{
"title": item.get("title"),
"link": item.get("url"),
"snippet": item.get("description"),
}
)
return json.dumps(final_results) | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/brave_search.html |
7d208f4c9053-0 | Source code for langchain.utilities.wikipedia
"""Util that calls Wikipedia."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.schema import Document
logger = logging.getLogger(__name__)
WIKIPEDIA_MAX_QUERY_LENGTH = 300
[docs]class WikipediaAPIWrapper(BaseModel):
"""Wrapper around WikipediaAPI.
To use, you should have the ``wikipedia`` python package installed.
This wrapper will use the Wikipedia API to conduct searches and
fetch page summaries. By default, it will return the page summaries
of the top-k results.
It limits the Document content by doc_content_chars_max.
"""
wiki_client: Any #: :meta private:
top_k_results: int = 3
lang: str = "en"
load_all_available_meta: bool = False
doc_content_chars_max: int = 4000
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that the python package exists in environment."""
try:
import wikipedia
wikipedia.set_lang(values["lang"])
values["wiki_client"] = wikipedia
except ImportError:
raise ImportError(
"Could not import wikipedia python package. "
"Please install it with `pip install wikipedia`."
)
return values
[docs] def run(self, query: str) -> str:
"""Run Wikipedia search and get page summaries."""
page_titles = self.wiki_client.search(query[:WIKIPEDIA_MAX_QUERY_LENGTH])
summaries = []
for page_title in page_titles[: self.top_k_results]: | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/wikipedia.html |
7d208f4c9053-1 | summaries = []
for page_title in page_titles[: self.top_k_results]:
if wiki_page := self._fetch_page(page_title):
if summary := self._formatted_page_summary(page_title, wiki_page):
summaries.append(summary)
if not summaries:
return "No good Wikipedia Search Result was found"
return "\n\n".join(summaries)[: self.doc_content_chars_max]
@staticmethod
def _formatted_page_summary(page_title: str, wiki_page: Any) -> Optional[str]:
return f"Page: {page_title}\nSummary: {wiki_page.summary}"
def _page_to_document(self, page_title: str, wiki_page: Any) -> Document:
main_meta = {
"title": page_title,
"summary": wiki_page.summary,
"source": wiki_page.url,
}
add_meta = (
{
"categories": wiki_page.categories,
"page_url": wiki_page.url,
"image_urls": wiki_page.images,
"related_titles": wiki_page.links,
"parent_id": wiki_page.parent_id,
"references": wiki_page.references,
"revision_id": wiki_page.revision_id,
"sections": wiki_page.sections,
}
if self.load_all_available_meta
else {}
)
doc = Document(
page_content=wiki_page.content[: self.doc_content_chars_max],
metadata={
**main_meta,
**add_meta,
},
)
return doc
def _fetch_page(self, page: str) -> Optional[str]:
try:
return self.wiki_client.page(title=page, auto_suggest=False)
except (
self.wiki_client.exceptions.PageError, | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/wikipedia.html |
7d208f4c9053-2 | except (
self.wiki_client.exceptions.PageError,
self.wiki_client.exceptions.DisambiguationError,
):
return None
[docs] def load(self, query: str) -> List[Document]:
"""
Run Wikipedia search and get the article text plus the meta information.
See
Returns: a list of documents.
"""
page_titles = self.wiki_client.search(query[:WIKIPEDIA_MAX_QUERY_LENGTH])
docs = []
for page_title in page_titles[: self.top_k_results]:
if wiki_page := self._fetch_page(page_title):
if doc := self._page_to_document(page_title, wiki_page):
docs.append(doc)
return docs | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/wikipedia.html |
6e1255e503c9-0 | Source code for langchain.utilities.zapier
"""Util that can interact with Zapier NLA.
Full docs here: https://nla.zapier.com/start/
Note: this wrapper currently only implemented the `api_key` auth method for testing
and server-side production use cases (using the developer's connected accounts on
Zapier.com)
For use-cases where LangChain + Zapier NLA is powering a user-facing application, and
LangChain needs access to the end-user's connected accounts on Zapier.com, you'll need
to use oauth. Review the full docs above and reach out to nla@zapier.com for
developer support.
"""
import json
from typing import Any, Dict, List, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra, root_validator
from requests import Request, Session
from langchain.utils import get_from_dict_or_env
[docs]class ZapierNLAWrapper(BaseModel):
"""Wrapper for Zapier NLA.
Full docs here: https://nla.zapier.com/start/
This wrapper supports both API Key and OAuth Credential auth methods. API Key
is the fastest way to get started using this wrapper.
Call this wrapper with either `zapier_nla_api_key` or
`zapier_nla_oauth_access_token` arguments, or set the `ZAPIER_NLA_API_KEY`
environment variable. If both arguments are set, the Access Token will take
precedence.
For use-cases where LangChain + Zapier NLA is powering a user-facing application,
and LangChain needs access to the end-user's connected accounts on Zapier.com,
you'll need to use OAuth. Review the full docs above to learn how to create
your own provider and generate credentials.
""" | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/zapier.html |
6e1255e503c9-1 | your own provider and generate credentials.
"""
zapier_nla_api_key: str
zapier_nla_oauth_access_token: str
zapier_nla_api_base: str = "https://nla.zapier.com/api/v1/"
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def _format_headers(self) -> Dict[str, str]:
"""Format headers for requests."""
headers = {
"Accept": "application/json",
"Content-Type": "application/json",
}
if self.zapier_nla_oauth_access_token:
headers.update(
{"Authorization": f"Bearer {self.zapier_nla_oauth_access_token}"}
)
else:
headers.update({"X-API-Key": self.zapier_nla_api_key})
return headers
def _get_session(self) -> Session:
session = requests.Session()
session.headers.update(self._format_headers())
return session
async def _arequest(self, method: str, url: str, **kwargs: Any) -> Dict[str, Any]:
"""Make an async request."""
async with aiohttp.ClientSession(headers=self._format_headers()) as session:
async with session.request(method, url, **kwargs) as response:
response.raise_for_status()
return await response.json()
def _create_action_payload( # type: ignore[no-untyped-def]
self, instructions: str, params: Optional[Dict] = None, preview_only=False
) -> Dict:
"""Create a payload for an action."""
data = params if params else {}
data.update(
{
"instructions": instructions,
}
) | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/zapier.html |
6e1255e503c9-2 | {
"instructions": instructions,
}
)
if preview_only:
data.update({"preview_only": True})
return data
def _create_action_url(self, action_id: str) -> str:
"""Create a url for an action."""
return self.zapier_nla_api_base + f"exposed/{action_id}/execute/"
def _create_action_request( # type: ignore[no-untyped-def]
self,
action_id: str,
instructions: str,
params: Optional[Dict] = None,
preview_only=False,
) -> Request:
data = self._create_action_payload(instructions, params, preview_only)
return Request(
"POST",
self._create_action_url(action_id),
json=data,
)
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
zapier_nla_api_key_default = None
# If there is a oauth_access_key passed in the values
# we don't need a nla_api_key it can be blank
if "zapier_nla_oauth_access_token" in values:
zapier_nla_api_key_default = ""
else:
values["zapier_nla_oauth_access_token"] = ""
# we require at least one API Key
zapier_nla_api_key = get_from_dict_or_env(
values,
"zapier_nla_api_key",
"ZAPIER_NLA_API_KEY",
zapier_nla_api_key_default,
)
values["zapier_nla_api_key"] = zapier_nla_api_key
return values | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/zapier.html |
6e1255e503c9-3 | return values
[docs] async def alist(self) -> List[Dict]:
"""Returns a list of all exposed (enabled) actions associated with
current user (associated with the set api_key). Change your exposed
actions here: https://nla.zapier.com/demo/start/
The return list can be empty if no actions exposed. Else will contain
a list of action objects:
[{
"id": str,
"description": str,
"params": Dict[str, str]
}]
`params` will always contain an `instructions` key, the only required
param. All others optional and if provided will override any AI guesses
(see "understanding the AI guessing flow" here:
https://nla.zapier.com/api/v1/docs)
"""
response = await self._arequest("GET", self.zapier_nla_api_base + "exposed/")
return response["results"]
[docs] def list(self) -> List[Dict]:
"""Returns a list of all exposed (enabled) actions associated with
current user (associated with the set api_key). Change your exposed
actions here: https://nla.zapier.com/demo/start/
The return list can be empty if no actions exposed. Else will contain
a list of action objects:
[{
"id": str,
"description": str,
"params": Dict[str, str]
}]
`params` will always contain an `instructions` key, the only required
param. All others optional and if provided will override any AI guesses
(see "understanding the AI guessing flow" here:
https://nla.zapier.com/docs/using-the-api#ai-guessing)
""" | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/zapier.html |
6e1255e503c9-4 | """
session = self._get_session()
try:
response = session.get(self.zapier_nla_api_base + "exposed/")
response.raise_for_status()
except requests.HTTPError as http_err:
if response.status_code == 401:
if self.zapier_nla_oauth_access_token:
raise requests.HTTPError(
f"An unauthorized response occurred. Check that your "
f"access token is correct and doesn't need to be "
f"refreshed. Err: {http_err}"
)
raise requests.HTTPError(
f"An unauthorized response occurred. Check that your api "
f"key is correct. Err: {http_err}"
)
raise http_err
return response.json()["results"]
[docs] def run(
self, action_id: str, instructions: str, params: Optional[Dict] = None
) -> Dict:
"""Executes an action that is identified by action_id, must be exposed
(enabled) by the current user (associated with the set api_key). Change
your exposed actions here: https://nla.zapier.com/demo/start/
The return JSON is guaranteed to be less than ~500 words (350
tokens) making it safe to inject into the prompt of another LLM
call.
"""
session = self._get_session()
request = self._create_action_request(action_id, instructions, params)
response = session.send(session.prepare_request(request))
response.raise_for_status()
return response.json()["result"]
[docs] async def arun(
self, action_id: str, instructions: str, params: Optional[Dict] = None
) -> Dict: | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/zapier.html |
6e1255e503c9-5 | ) -> Dict:
"""Executes an action that is identified by action_id, must be exposed
(enabled) by the current user (associated with the set api_key). Change
your exposed actions here: https://nla.zapier.com/demo/start/
The return JSON is guaranteed to be less than ~500 words (350
tokens) making it safe to inject into the prompt of another LLM
call.
"""
response = await self._arequest(
"POST",
self._create_action_url(action_id),
json=self._create_action_payload(instructions, params),
)
return response["result"]
[docs] def preview(
self, action_id: str, instructions: str, params: Optional[Dict] = None
) -> Dict:
"""Same as run, but instead of actually executing the action, will
instead return a preview of params that have been guessed by the AI in
case you need to explicitly review before executing."""
session = self._get_session()
params = params if params else {}
params.update({"preview_only": True})
request = self._create_action_request(action_id, instructions, params, True)
response = session.send(session.prepare_request(request))
response.raise_for_status()
return response.json()["input_params"]
[docs] async def apreview(
self, action_id: str, instructions: str, params: Optional[Dict] = None
) -> Dict:
"""Same as run, but instead of actually executing the action, will
instead return a preview of params that have been guessed by the AI in
case you need to explicitly review before executing."""
response = await self._arequest(
"POST", | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/zapier.html |
6e1255e503c9-6 | response = await self._arequest(
"POST",
self._create_action_url(action_id),
json=self._create_action_payload(instructions, params, preview_only=True),
)
return response["result"]
[docs] def run_as_str(self, *args, **kwargs) -> str: # type: ignore[no-untyped-def]
"""Same as run, but returns a stringified version of the JSON for
insertting back into an LLM."""
data = self.run(*args, **kwargs)
return json.dumps(data)
[docs] async def arun_as_str(self, *args, **kwargs) -> str: # type: ignore[no-untyped-def]
"""Same as run, but returns a stringified version of the JSON for
insertting back into an LLM."""
data = await self.arun(*args, **kwargs)
return json.dumps(data)
[docs] def preview_as_str(self, *args, **kwargs) -> str: # type: ignore[no-untyped-def]
"""Same as preview, but returns a stringified version of the JSON for
insertting back into an LLM."""
data = self.preview(*args, **kwargs)
return json.dumps(data)
[docs] async def apreview_as_str( # type: ignore[no-untyped-def]
self, *args, **kwargs
) -> str:
"""Same as preview, but returns a stringified version of the JSON for
insertting back into an LLM."""
data = await self.apreview(*args, **kwargs)
return json.dumps(data)
[docs] def list_as_str(self) -> str: # type: ignore[no-untyped-def] | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/zapier.html |
6e1255e503c9-7 | """Same as list, but returns a stringified version of the JSON for
insertting back into an LLM."""
actions = self.list()
return json.dumps(actions)
[docs] async def alist_as_str(self) -> str: # type: ignore[no-untyped-def]
"""Same as list, but returns a stringified version of the JSON for
insertting back into an LLM."""
actions = await self.alist()
return json.dumps(actions) | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/zapier.html |
c9ec52d8ac85-0 | Source code for langchain.utilities.powerbi
"""Wrapper around a Power BI endpoint."""
from __future__ import annotations
import asyncio
import logging
import os
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Union
import aiohttp
import requests
from aiohttp import ServerTimeoutError
from pydantic import BaseModel, Field, root_validator, validator
from requests.exceptions import Timeout
_LOGGER = logging.getLogger(__name__)
BASE_URL = os.getenv("POWERBI_BASE_URL", "https://api.powerbi.com/v1.0/myorg")
if TYPE_CHECKING:
from azure.core.credentials import TokenCredential
[docs]class PowerBIDataset(BaseModel):
"""Create PowerBI engine from dataset ID and credential or token.
Use either the credential or a supplied token to authenticate.
If both are supplied the credential is used to generate a token.
The impersonated_user_name is the UPN of a user to be impersonated.
If the model is not RLS enabled, this will be ignored.
"""
dataset_id: str
table_names: List[str]
group_id: Optional[str] = None
credential: Optional[TokenCredential] = None
token: Optional[str] = None
impersonated_user_name: Optional[str] = None
sample_rows_in_table_info: int = Field(default=1, gt=0, le=10)
schemas: Dict[str, str] = Field(default_factory=dict)
aiosession: Optional[aiohttp.ClientSession] = None
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@validator("table_names", allow_reuse=True)
def fix_table_names(cls, table_names: List[str]) -> List[str]:
"""Fix the table names.""" | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/powerbi.html |
c9ec52d8ac85-1 | """Fix the table names."""
return [fix_table_name(table) for table in table_names]
@root_validator(pre=True, allow_reuse=True)
def token_or_credential_present(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Validate that at least one of token and credentials is present."""
if "token" in values or "credential" in values:
return values
raise ValueError("Please provide either a credential or a token.")
@property
def request_url(self) -> str:
"""Get the request url."""
if self.group_id:
return f"{BASE_URL}/groups/{self.group_id}/datasets/{self.dataset_id}/executeQueries" # noqa: E501 # pylint: disable=C0301
return f"{BASE_URL}/datasets/{self.dataset_id}/executeQueries" # noqa: E501 # pylint: disable=C0301
@property
def headers(self) -> Dict[str, str]:
"""Get the token."""
if self.token:
return {
"Content-Type": "application/json",
"Authorization": "Bearer " + self.token,
}
from azure.core.exceptions import (
ClientAuthenticationError, # pylint: disable=import-outside-toplevel
)
if self.credential:
try:
token = self.credential.get_token(
"https://analysis.windows.net/powerbi/api/.default"
).token
return {
"Content-Type": "application/json",
"Authorization": "Bearer " + token,
}
except Exception as exc: # pylint: disable=broad-exception-caught
raise ClientAuthenticationError(
"Could not get a token from the supplied credentials."
) from exc | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/powerbi.html |
c9ec52d8ac85-2 | "Could not get a token from the supplied credentials."
) from exc
raise ClientAuthenticationError("No credential or token supplied.")
[docs] def get_table_names(self) -> Iterable[str]:
"""Get names of tables available."""
return self.table_names
[docs] def get_schemas(self) -> str:
"""Get the available schema's."""
if self.schemas:
return ", ".join([f"{key}: {value}" for key, value in self.schemas.items()])
return "No known schema's yet. Use the schema_powerbi tool first."
@property
def table_info(self) -> str:
"""Information about all tables in the database."""
return self.get_table_info()
def _get_tables_to_query(
self, table_names: Optional[Union[List[str], str]] = None
) -> Optional[List[str]]:
"""Get the tables names that need to be queried, after checking they exist."""
if table_names is not None:
if (
isinstance(table_names, list)
and len(table_names) > 0
and table_names[0] != ""
):
fixed_tables = [fix_table_name(table) for table in table_names]
non_existing_tables = [
table for table in fixed_tables if table not in self.table_names
]
if non_existing_tables:
_LOGGER.warning(
"Table(s) %s not found in dataset.",
", ".join(non_existing_tables),
)
tables = [
table for table in fixed_tables if table not in non_existing_tables
]
return tables if tables else None
if isinstance(table_names, str) and table_names != "": | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/powerbi.html |
c9ec52d8ac85-3 | if isinstance(table_names, str) and table_names != "":
if table_names not in self.table_names:
_LOGGER.warning("Table %s not found in dataset.", table_names)
return None
return [fix_table_name(table_names)]
return self.table_names
def _get_tables_todo(self, tables_todo: List[str]) -> List[str]:
"""Get the tables that still need to be queried."""
return [table for table in tables_todo if table not in self.schemas]
def _get_schema_for_tables(self, table_names: List[str]) -> str:
"""Create a string of the table schemas for the supplied tables."""
schemas = [
schema for table, schema in self.schemas.items() if table in table_names
]
return ", ".join(schemas)
[docs] def get_table_info(
self, table_names: Optional[Union[List[str], str]] = None
) -> str:
"""Get information about specified tables."""
tables_requested = self._get_tables_to_query(table_names)
if tables_requested is None:
return "No (valid) tables requested."
tables_todo = self._get_tables_todo(tables_requested)
for table in tables_todo:
self._get_schema(table)
return self._get_schema_for_tables(tables_requested)
[docs] async def aget_table_info(
self, table_names: Optional[Union[List[str], str]] = None
) -> str:
"""Get information about specified tables."""
tables_requested = self._get_tables_to_query(table_names)
if tables_requested is None:
return "No (valid) tables requested."
tables_todo = self._get_tables_todo(tables_requested) | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/powerbi.html |
c9ec52d8ac85-4 | tables_todo = self._get_tables_todo(tables_requested)
await asyncio.gather(*[self._aget_schema(table) for table in tables_todo])
return self._get_schema_for_tables(tables_requested)
def _get_schema(self, table: str) -> None:
"""Get the schema for a table."""
try:
result = self.run(
f"EVALUATE TOPN({self.sample_rows_in_table_info}, {table})"
)
self.schemas[table] = json_to_md(result["results"][0]["tables"][0]["rows"])
except Timeout:
_LOGGER.warning("Timeout while getting table info for %s", table)
self.schemas[table] = "unknown"
except Exception as exc: # pylint: disable=broad-exception-caught
_LOGGER.warning("Error while getting table info for %s: %s", table, exc)
self.schemas[table] = "unknown"
async def _aget_schema(self, table: str) -> None:
"""Get the schema for a table."""
try:
result = await self.arun(
f"EVALUATE TOPN({self.sample_rows_in_table_info}, {table})"
)
self.schemas[table] = json_to_md(result["results"][0]["tables"][0]["rows"])
except ServerTimeoutError:
_LOGGER.warning("Timeout while getting table info for %s", table)
self.schemas[table] = "unknown"
except Exception as exc: # pylint: disable=broad-exception-caught
_LOGGER.warning("Error while getting table info for %s: %s", table, exc)
self.schemas[table] = "unknown" | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/powerbi.html |
c9ec52d8ac85-5 | self.schemas[table] = "unknown"
def _create_json_content(self, command: str) -> dict[str, Any]:
"""Create the json content for the request."""
return {
"queries": [{"query": rf"{command}"}],
"impersonatedUserName": self.impersonated_user_name,
"serializerSettings": {"includeNulls": True},
}
[docs] def run(self, command: str) -> Any:
"""Execute a DAX command and return a json representing the results."""
_LOGGER.debug("Running command: %s", command)
result = requests.post(
self.request_url,
json=self._create_json_content(command),
headers=self.headers,
timeout=10,
)
return result.json()
[docs] async def arun(self, command: str) -> Any:
"""Execute a DAX command and return the result asynchronously."""
_LOGGER.debug("Running command: %s", command)
if self.aiosession:
async with self.aiosession.post(
self.request_url,
headers=self.headers,
json=self._create_json_content(command),
timeout=10,
) as response:
response_json = await response.json(content_type=response.content_type)
return response_json
async with aiohttp.ClientSession() as session:
async with session.post(
self.request_url,
headers=self.headers,
json=self._create_json_content(command),
timeout=10,
) as response:
response_json = await response.json(content_type=response.content_type)
return response_json
def json_to_md(
json_contents: List[Dict[str, Union[str, int, float]]], | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/powerbi.html |
c9ec52d8ac85-6 | json_contents: List[Dict[str, Union[str, int, float]]],
table_name: Optional[str] = None,
) -> str:
"""Converts a JSON object to a markdown table."""
output_md = ""
headers = json_contents[0].keys()
for header in headers:
header.replace("[", ".").replace("]", "")
if table_name:
header.replace(f"{table_name}.", "")
output_md += f"| {header} "
output_md += "|\n"
for row in json_contents:
for value in row.values():
output_md += f"| {value} "
output_md += "|\n"
return output_md
def fix_table_name(table: str) -> str:
"""Add single quotes around table names that contain spaces."""
if " " in table and not table.startswith("'") and not table.endswith("'"):
return f"'{table}'"
return table | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/powerbi.html |
3ed1995a44a3-0 | Source code for langchain.utilities.bibtex
"""Util that calls bibtexparser."""
import logging
from typing import Any, Dict, List, Mapping
from pydantic import BaseModel, Extra, root_validator
logger = logging.getLogger(__name__)
OPTIONAL_FIELDS = [
"annotate",
"booktitle",
"editor",
"howpublished",
"journal",
"keywords",
"note",
"organization",
"publisher",
"school",
"series",
"type",
"doi",
"issn",
"isbn",
]
[docs]class BibtexparserWrapper(BaseModel):
"""Wrapper around bibtexparser.
To use, you should have the ``bibtexparser`` python package installed.
https://bibtexparser.readthedocs.io/en/master/
This wrapper will use bibtexparser to load a collection of references from
a bibtex file and fetch document summaries.
"""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that the python package exists in environment."""
try:
import bibtexparser # noqa
except ImportError:
raise ImportError(
"Could not import bibtexparser python package. "
"Please install it with `pip install bibtexparser`."
)
return values
[docs] def load_bibtex_entries(self, path: str) -> List[Dict[str, Any]]:
"""Load bibtex entries from the bibtex file at the given path."""
import bibtexparser | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/bibtex.html |
3ed1995a44a3-1 | import bibtexparser
with open(path) as file:
entries = bibtexparser.load(file).entries
return entries
[docs] def get_metadata(
self, entry: Mapping[str, Any], load_extra: bool = False
) -> Dict[str, Any]:
"""Get metadata for the given entry."""
publication = entry.get("journal") or entry.get("booktitle")
if "url" in entry:
url = entry["url"]
elif "doi" in entry:
url = f'https://doi.org/{entry["doi"]}'
else:
url = None
meta = {
"id": entry.get("ID"),
"published_year": entry.get("year"),
"title": entry.get("title"),
"publication": publication,
"authors": entry.get("author"),
"abstract": entry.get("abstract"),
"url": url,
}
if load_extra:
for field in OPTIONAL_FIELDS:
meta[field] = entry.get(field)
return {k: v for k, v in meta.items() if v is not None} | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/bibtex.html |
5ef65071eba8-0 | Source code for langchain.utilities.python
import sys
from io import StringIO
from typing import Dict, Optional
from pydantic import BaseModel, Field
[docs]class PythonREPL(BaseModel):
"""Simulates a standalone Python REPL."""
globals: Optional[Dict] = Field(default_factory=dict, alias="_globals")
locals: Optional[Dict] = Field(default_factory=dict, alias="_locals")
[docs] def run(self, command: str) -> str:
"""Run command with own globals/locals and returns anything printed."""
old_stdout = sys.stdout
sys.stdout = mystdout = StringIO()
try:
exec(command, self.globals, self.locals)
sys.stdout = old_stdout
output = mystdout.getvalue()
except Exception as e:
sys.stdout = old_stdout
output = repr(e)
return output | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/python.html |
5d5c64551f14-0 | Source code for langchain.utilities.openweathermap
"""Util that calls OpenWeatherMap using PyOWM."""
from typing import Any, Dict, Optional
from pydantic import Extra, root_validator
from langchain.tools.base import BaseModel
from langchain.utils import get_from_dict_or_env
[docs]class OpenWeatherMapAPIWrapper(BaseModel):
"""Wrapper for OpenWeatherMap API using PyOWM.
Docs for using:
1. Go to OpenWeatherMap and sign up for an API key
2. Save your API KEY into OPENWEATHERMAP_API_KEY env variable
3. pip install pyowm
"""
owm: Any
openweathermap_api_key: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
openweathermap_api_key = get_from_dict_or_env(
values, "openweathermap_api_key", "OPENWEATHERMAP_API_KEY"
)
try:
import pyowm
except ImportError:
raise ImportError(
"pyowm is not installed. Please install it with `pip install pyowm`"
)
owm = pyowm.OWM(openweathermap_api_key)
values["owm"] = owm
return values
def _format_weather_info(self, location: str, w: Any) -> str:
detailed_status = w.detailed_status
wind = w.wind()
humidity = w.humidity
temperature = w.temperature("celsius")
rain = w.rain
heat_index = w.heat_index
clouds = w.clouds | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/openweathermap.html |
5d5c64551f14-1 | heat_index = w.heat_index
clouds = w.clouds
return (
f"In {location}, the current weather is as follows:\n"
f"Detailed status: {detailed_status}\n"
f"Wind speed: {wind['speed']} m/s, direction: {wind['deg']}°\n"
f"Humidity: {humidity}%\n"
f"Temperature: \n"
f" - Current: {temperature['temp']}°C\n"
f" - High: {temperature['temp_max']}°C\n"
f" - Low: {temperature['temp_min']}°C\n"
f" - Feels like: {temperature['feels_like']}°C\n"
f"Rain: {rain}\n"
f"Heat index: {heat_index}\n"
f"Cloud cover: {clouds}%"
)
[docs] def run(self, location: str) -> str:
"""Get the current weather information for a specified location."""
mgr = self.owm.weather_manager()
observation = mgr.weather_at_place(location)
w = observation.weather
return self._format_weather_info(location, w) | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/openweathermap.html |
eed5c1e3d997-0 | Source code for langchain.utilities.google_serper
"""Util that calls Google Search using the Serper.dev API."""
from typing import Any, Dict, List, Optional
import aiohttp
import requests
from pydantic.class_validators import root_validator
from pydantic.main import BaseModel
from typing_extensions import Literal
from langchain.utils import get_from_dict_or_env
[docs]class GoogleSerperAPIWrapper(BaseModel):
"""Wrapper around the Serper.dev Google Search API.
You can create a free API key at https://serper.dev.
To use, you should have the environment variable ``SERPER_API_KEY``
set with your API key, or pass `serper_api_key` as a named parameter
to the constructor.
Example:
.. code-block:: python
from langchain import GoogleSerperAPIWrapper
google_serper = GoogleSerperAPIWrapper()
"""
k: int = 10
gl: str = "us"
hl: str = "en"
# "places" and "images" is available from Serper but not implemented in the
# parser of run(). They can be used in results()
type: Literal["news", "search", "places", "images"] = "search"
result_key_for_type = {
"news": "news",
"places": "places",
"images": "images",
"search": "organic",
}
tbs: Optional[str] = None
serper_api_key: Optional[str] = None
aiosession: Optional[aiohttp.ClientSession] = None
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@root_validator() | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/google_serper.html |
eed5c1e3d997-1 | arbitrary_types_allowed = True
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
serper_api_key = get_from_dict_or_env(
values, "serper_api_key", "SERPER_API_KEY"
)
values["serper_api_key"] = serper_api_key
return values
[docs] def results(self, query: str, **kwargs: Any) -> Dict:
"""Run query through GoogleSearch."""
return self._google_serper_api_results(
query,
gl=self.gl,
hl=self.hl,
num=self.k,
tbs=self.tbs,
search_type=self.type,
**kwargs,
)
[docs] def run(self, query: str, **kwargs: Any) -> str:
"""Run query through GoogleSearch and parse result."""
results = self._google_serper_api_results(
query,
gl=self.gl,
hl=self.hl,
num=self.k,
tbs=self.tbs,
search_type=self.type,
**kwargs,
)
return self._parse_results(results)
[docs] async def aresults(self, query: str, **kwargs: Any) -> Dict:
"""Run query through GoogleSearch."""
results = await self._async_google_serper_search_results(
query,
gl=self.gl,
hl=self.hl,
num=self.k,
search_type=self.type,
tbs=self.tbs,
**kwargs,
)
return results
[docs] async def arun(self, query: str, **kwargs: Any) -> str: | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/google_serper.html |
eed5c1e3d997-2 | """Run query through GoogleSearch and parse result async."""
results = await self._async_google_serper_search_results(
query,
gl=self.gl,
hl=self.hl,
num=self.k,
search_type=self.type,
tbs=self.tbs,
**kwargs,
)
return self._parse_results(results)
def _parse_snippets(self, results: dict) -> List[str]:
snippets = []
if results.get("answerBox"):
answer_box = results.get("answerBox", {})
if answer_box.get("answer"):
return [answer_box.get("answer")]
elif answer_box.get("snippet"):
return [answer_box.get("snippet").replace("\n", " ")]
elif answer_box.get("snippetHighlighted"):
return answer_box.get("snippetHighlighted")
if results.get("knowledgeGraph"):
kg = results.get("knowledgeGraph", {})
title = kg.get("title")
entity_type = kg.get("type")
if entity_type:
snippets.append(f"{title}: {entity_type}.")
description = kg.get("description")
if description:
snippets.append(description)
for attribute, value in kg.get("attributes", {}).items():
snippets.append(f"{title} {attribute}: {value}.")
for result in results[self.result_key_for_type[self.type]][: self.k]:
if "snippet" in result:
snippets.append(result["snippet"])
for attribute, value in result.get("attributes", {}).items():
snippets.append(f"{attribute}: {value}.")
if len(snippets) == 0:
return ["No good Google Search Result was found"]
return snippets | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/google_serper.html |
eed5c1e3d997-3 | return ["No good Google Search Result was found"]
return snippets
def _parse_results(self, results: dict) -> str:
return " ".join(self._parse_snippets(results))
def _google_serper_api_results(
self, search_term: str, search_type: str = "search", **kwargs: Any
) -> dict:
headers = {
"X-API-KEY": self.serper_api_key or "",
"Content-Type": "application/json",
}
params = {
"q": search_term,
**{key: value for key, value in kwargs.items() if value is not None},
}
response = requests.post(
f"https://google.serper.dev/{search_type}", headers=headers, params=params
)
response.raise_for_status()
search_results = response.json()
return search_results
async def _async_google_serper_search_results(
self, search_term: str, search_type: str = "search", **kwargs: Any
) -> dict:
headers = {
"X-API-KEY": self.serper_api_key or "",
"Content-Type": "application/json",
}
url = f"https://google.serper.dev/{search_type}"
params = {
"q": search_term,
**{key: value for key, value in kwargs.items() if value is not None},
}
if not self.aiosession:
async with aiohttp.ClientSession() as session:
async with session.post(
url, params=params, headers=headers, raise_for_status=False
) as response:
search_results = await response.json()
else:
async with self.aiosession.post( | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/google_serper.html |
eed5c1e3d997-4 | else:
async with self.aiosession.post(
url, params=params, headers=headers, raise_for_status=True
) as response:
search_results = await response.json()
return search_results | https://api.python.langchain.com/en/stable/_modules/langchain/utilities/google_serper.html |