id stringlengths 14 16 | text stringlengths 45 2.73k | source stringlengths 49 114 |
|---|---|---|
3c0754fd3093-0 | Source code for langchain.chains.pal.base
"""Implements Program-Aided Language Models.
As in https://arxiv.org/pdf/2211.10435.pdf.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMCh... | https://python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
3c0754fd3093-1 | else:
return [self.output_key, "intermediate_steps"]
def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)
code = llm_chain.predict(stop=[self.stop], **inputs)
self.callback_manager.on_text(
code, color="gree... | https://python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
3c0754fd3093-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chains/pal/base.html |
9d24c99c693c-0 | Source code for langchain.chains.llm_bash.base
"""Chain that interprets a prompt and executes bash code to perform bash operations."""
from typing import Dict, List
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_bash.prompt import P... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
9d24c99c693c-1 | bash_executor = BashProcess()
self.callback_manager.on_text(inputs[self.input_key], verbose=self.verbose)
t = llm_executor.predict(question=inputs[self.input_key])
self.callback_manager.on_text(t, color="green", verbose=self.verbose)
t = t.strip()
if t.startswith("```bash"):
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
431d4df81461-0 | Source code for langchain.chains.graph_qa.base
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List
from pydantic import Field
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import ENTITY_EXTRACTION_PROMPT, PROMPT
from langchain.cha... | https://python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
431d4df81461-1 | qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
entity_chain = LLMChain(llm=llm, prompt=entity_prompt)
return cls(qa_chain=qa_chain, entity_extraction_chain=entity_chain, **kwargs)
def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
"""Extract entities, look up info and answer question... | https://python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
dbab2c9f377f-0 | Source code for langchain.chains.llm_checker.base
"""Chain for question-answering with self-verification."""
from typing import Dict, List
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.llm_checker.prompt import (
CHECK_ASSERTIONS_P... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
dbab2c9f377f-1 | def input_keys(self) -> List[str]:
"""Return the singular input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the singular output key.
:meta private:
"""
return [self.output_key]
def _ca... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
dbab2c9f377f-2 | return "llm_checker_chain"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
96a6043c84c0-0 | Source code for langchain.chains.combine_documents.base
"""Base interface for chains combining documents."""
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Tuple
from pydantic import Field
from langchain.chains.base import Chain
from langchain.docstore.document import Document
from la... | https://python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
96a6043c84c0-1 | """Return output key.
:meta private:
"""
return [self.output_key]
def prompt_length(self, docs: List[Document], **kwargs: Any) -> Optional[int]:
"""Return the prompt length given the documents passed in.
Returns None if the method does not depend on the prompt length.
... | https://python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
96a6043c84c0-2 | """Chain that splits documents, then analyzes it in pieces."""
input_key: str = "input_document" #: :meta private:
text_splitter: TextSplitter = Field(default_factory=RecursiveCharacterTextSplitter)
combine_docs_chain: BaseCombineDocumentsChain
@property
def input_keys(self) -> List[str]:
"... | https://python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
2783d80b97f9-0 | Source code for langchain.chains.hyde.base
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Dict, List
import numpy as np
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.hyde.prompts import PROMPT_MAP... | https://python.langchain.com/en/latest/_modules/langchain/chains/hyde/base.html |
2783d80b97f9-1 | """Generate a hypothetical document and embedded it."""
var_name = self.llm_chain.input_keys[0]
result = self.llm_chain.generate([{var_name: text}])
documents = [generation.text for generation in result.generations[0]]
embeddings = self.embed_documents(documents)
return self.comb... | https://python.langchain.com/en/latest/_modules/langchain/chains/hyde/base.html |
3e52db009261-0 | Source code for langchain.chains.qa_generation.base
from __future__ import annotations
import json
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.qa_generation.prompt import PROMPT_SELECTOR
f... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
3e52db009261-1 | docs = self.text_splitter.create_documents([inputs[self.input_key]])
results = self.llm_chain.generate([{"text": d.page_content} for d in docs])
qa = [json.loads(res[0].text) for res in results.generations]
return {self.output_key: qa}
async def _acall(self, inputs: Dict[str, str]) -> Dict[s... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
7e57617c3f06-0 | Source code for langchain.chains.llm_math.base
"""Chain that interprets a prompt and executes python code to do math."""
import math
import re
from typing import Dict, List
import numexpr
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.l... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
7e57617c3f06-1 | try:
local_dict = {"pi": math.pi, "e": math.e}
output = str(
numexpr.evaluate(
expression.strip(),
global_dict={}, # restrict access to globals
local_dict=local_dict, # add common mathematical functions
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
7e57617c3f06-2 | llm_output, color="green", verbose=self.verbose
)
else:
self.callback_manager.on_text(
llm_output, color="green", verbose=self.verbose
)
llm_output = llm_output.strip()
text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
7e57617c3f06-3 | )
return self._process_llm_result(llm_output)
async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]:
llm_executor = LLMChain(
prompt=self.prompt, llm=self.llm, callback_manager=self.callback_manager
)
if self.callback_manager.is_async:
await self.ca... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
7a249646b379-0 | Source code for langchain.chains.conversational_retrieval.base
"""Chain for chatting with a vector database."""
from __future__ import annotations
import warnings
from abc import abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from pydantic import Extra, Fiel... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
7a249646b379-1 | buffer += "\n" + "\n".join([human, ai])
else:
raise ValueError(
f"Unsupported chat history format: {type(dialogue_turn)}."
f" Full chat history: {chat_history} "
)
return buffer
class BaseConversationalRetrievalChain(Chain):
"""Chain for chatting w... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
7a249646b379-2 | if chat_history_str:
new_question = self.question_generator.run(
question=question, chat_history=chat_history_str
)
else:
new_question = question
docs = self._get_docs(new_question, inputs)
new_inputs = inputs.copy()
new_inputs["questio... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
7a249646b379-3 | def save(self, file_path: Union[Path, str]) -> None:
if self.get_chat_history:
raise ValueError("Chain not savable when `get_chat_history` is not None.")
super().save(file_path)
[docs]class ConversationalRetrievalChain(BaseConversationalRetrievalChain):
"""Chain for chatting with an inde... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
7a249646b379-4 | def from_llm(
cls,
llm: BaseLanguageModel,
retriever: BaseRetriever,
condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
qa_prompt: Optional[BasePromptTemplate] = None,
chain_type: str = "stuff",
**kwargs: Any,
) -> BaseConversationalRetri... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
7a249646b379-5 | vectordbkwargs = inputs.get("vectordbkwargs", {})
full_kwargs = {**self.search_kwargs, **vectordbkwargs}
return self.vectorstore.similarity_search(
question, k=self.top_k_docs_for_context, **full_kwargs
)
async def _aget_docs(self, question: str, inputs: Dict[str, Any]) -> List[D... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
a630a1dddcb9-0 | Source code for langchain.chains.sql_database.base
"""Chain for interacting with SQL Database."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sql_... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
a630a1dddcb9-1 | extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Return the singular input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the singular output key.
... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
a630a1dddcb9-2 | self.callback_manager.on_text("\nSQLResult: ", verbose=self.verbose)
self.callback_manager.on_text(result, color="yellow", verbose=self.verbose)
# If return direct, we just set the final result equal to the sql query
if self.return_direct:
final_result = result
else:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
a630a1dddcb9-3 | **kwargs: Any,
) -> SQLDatabaseSequentialChain:
"""Load the necessary chains."""
sql_chain = SQLDatabaseChain(
llm=llm, database=database, prompt=query_prompt, **kwargs
)
decider_chain = LLMChain(
llm=llm, prompt=decider_prompt, output_key="table_names"
... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
a630a1dddcb9-4 | )
self.callback_manager.on_text(
str(table_names_to_use), color="yellow", verbose=self.verbose
)
new_inputs = {
self.sql_chain.input_key: inputs[self.input_key],
"table_names_to_use": table_names_to_use,
}
return self.sql_chain(new_inputs, retu... | https://python.langchain.com/en/latest/_modules/langchain/chains/sql_database/base.html |
6162e2b246bd-0 | Source code for langchain.chains.llm_summarization_checker.base
"""Chain for summarization with self-verification."""
from pathlib import Path
from typing import Dict, List
from pydantic import Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sequential impor... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
6162e2b246bd-1 | revised_summary_prompt: PromptTemplate = REVISED_SUMMARY_PROMPT
are_all_true_prompt: PromptTemplate = ARE_ALL_TRUE_PROMPT
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
max_checks: int = 2
"""Maximum number of times to check the assertions. Default to doubl... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
6162e2b246bd-2 | output_key="revised_summary",
verbose=self.verbose,
),
LLMChain(
llm=self.llm,
output_key="all_true",
prompt=self.are_all_true_prompt,
verbose=self.verbose,
... | https://python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
e0aa645c3f13-0 | Source code for langchain.chains.qa_with_sources.retrieval
"""Question-answering with sources over an index."""
from typing import Any, Dict, List
from pydantic import Field
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
e0aa645c3f13-1 | docs = self.retriever.get_relevant_documents(question)
return self._reduce_tokens_below_limit(docs)
async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
question = inputs[self.question_key]
docs = await self.retriever.aget_relevant_documents(question)
return self._re... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
aeaba721977f-0 | Source code for langchain.chains.qa_with_sources.vector_db
"""Question-answering with sources over a vector database."""
import warnings
from typing import Any, Dict, List
from pydantic import Field, root_validator
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_so... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
aeaba721977f-1 | num_docs -= 1
token_count -= tokens[num_docs]
return docs[:num_docs]
def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
question = inputs[self.question_key]
docs = self.vectorstore.similarity_search(
question, k=self.k, **self.search_kwargs
)
... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
0e72bd4901a4-0 | Source code for langchain.chains.qa_with_sources.base
"""Question answering with sources over documents."""
from __future__ import annotations
import re
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.chains.base import Chain
fro... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
0e72bd4901a4-1 | combine_prompt: BasePromptTemplate = COMBINE_PROMPT,
**kwargs: Any,
) -> BaseQAWithSourcesChain:
"""Construct the chain from an LLM."""
llm_question_chain = LLMChain(llm=llm, prompt=question_prompt)
llm_combine_chain = LLMChain(llm=llm, prompt=combine_prompt)
combine_results_... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
0e72bd4901a4-2 | :meta private:
"""
return [self.question_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
_output_keys = [self.answer_key, self.sources_answer_key]
if self.return_source_documents:
_output_keys = _outp... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
0e72bd4901a4-3 | docs = await self._aget_docs(inputs)
answer = await self.combine_documents_chain.arun(input_documents=docs, **inputs)
if re.search(r"SOURCES:\s", answer):
answer, sources = re.split(r"SOURCES:\s", answer)
else:
sources = ""
result: Dict[str, Any] = {
s... | https://python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
603d8ee01690-0 | Source code for langchain.chains.retrieval_qa.base
"""Chain for question-answering against a vector database."""
from __future__ import annotations
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.chains.base imp... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
603d8ee01690-1 | _output_keys = [self.output_key]
if self.return_source_documents:
_output_keys = _output_keys + ["source_documents"]
return _output_keys
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[PromptTemplate] = None,
**kwargs: Any,
... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
603d8ee01690-2 | def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
"""Run get_relevant_text and llm on input query.
If chain has 'return_source_documents' as 'True', returns
the retrieved documents as well under the key 'source_documents'.
Example:
.. code-block:: python
res = in... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
603d8ee01690-3 | return {self.output_key: answer, "source_documents": docs}
else:
return {self.output_key: answer}
[docs]class RetrievalQA(BaseRetrievalQA):
"""Chain for question-answering against an index.
Example:
.. code-block:: python
from langchain.llms import OpenAI
from... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
603d8ee01690-4 | warnings.warn(
"`VectorDBQA` is deprecated - "
"please use `from langchain.chains import RetrievalQA`"
)
return values
@root_validator()
def validate_search_type(cls, values: Dict) -> Dict:
"""Validate search type."""
if "search_type" in values:
... | https://python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
cf1f987ffa84-0 | Source code for langchain.chains.conversation.base
"""Chain that carries on a conversation and calls an LLM."""
from typing import Dict, List
from pydantic import Extra, Field, root_validator
from langchain.chains.conversation.prompt import PROMPT
from langchain.chains.llm import LLMChain
from langchain.memory.buffer i... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
cf1f987ffa84-1 | f"The input key {input_key} was also found in the memory keys "
f"({memory_keys}) - please provide keys that don't overlap."
)
prompt_variables = values["prompt"].input_variables
expected_keys = memory_keys + [input_key]
if set(expected_keys) != set(prompt_variables):... | https://python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
351fcdc3f0d8-0 | Source code for langchain.agents.agent
"""Chain that takes in an input and produces an action and action input."""
from __future__ import annotations
import asyncio
import json
import logging
import time
from abc import abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Tupl... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-1 | along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
[docs] @abstractmethod
async def aplan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Union[AgentAction, AgentFinish]:
"""Given... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-2 | raise NotImplementedError
@property
def _agent_type(self) -> str:
"""Return Identifier of agent type."""
raise NotImplementedError
[docs] def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary representation of agent."""
_dict = super().dict()
_dict["_type"] = str... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-3 | def return_values(self) -> List[str]:
"""Return values of the agent."""
return ["output"]
[docs] def get_allowed_tools(self) -> Optional[List[str]]:
return None
[docs] @abstractmethod
def plan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Unio... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-4 | return AgentFinish({"output": "Agent stopped due to max iterations."}, "")
else:
raise ValueError(
f"Got unsupported early_stopping_method `{early_stopping_method}`"
)
@property
def _agent_type(self) -> str:
"""Return Identifier of agent type."""
r... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-5 | [docs] def tool_run_logging_kwargs(self) -> Dict:
return {}
[docs]class AgentOutputParser(BaseOutputParser):
[docs] @abstractmethod
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
"""Parse text into agent action/finish."""
[docs]class LLMSingleActionAgent(BaseSingleActionAgent):... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-6 | """
output = await self.llm_chain.arun(
intermediate_steps=intermediate_steps, stop=self.stop, **kwargs
)
return self.output_parser.parse(output)
[docs] def tool_run_logging_kwargs(self) -> Dict:
return {
"llm_prefix": "",
"observation_prefix": "" i... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-7 | thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
return thoughts
[docs] def plan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) ... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-8 | thoughts = self._construct_scratchpad(intermediate_steps)
new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
full_inputs = {**kwargs, **new_inputs}
return full_inputs
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-9 | def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
"""Validate that appropriate tools are passed in."""
pass
@classmethod
@abstractmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
"""Get default output parser for this class."""
[docs] @clas... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-10 | {"output": "Agent stopped due to iteration limit or time limit."}, ""
)
elif early_stopping_method == "generate":
# Generate does one final forward pass
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-11 | [docs]class AgentExecutor(Chain):
"""Consists of an agent using tools."""
agent: Union[BaseSingleActionAgent, BaseMultiActionAgent]
tools: Sequence[BaseTool]
return_intermediate_steps: bool = False
max_iterations: Optional[int] = 15
max_execution_time: Optional[float] = None
early_stopping_m... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-12 | for tool in tools:
if tool.return_direct:
raise ValueError(
"Tools that have `return_direct=True` are not allowed "
"in multi-action agents"
)
return values
[docs] def save(self, file_path: Union[Path, str... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-13 | and time_elapsed >= self.max_execution_time
):
return False
return True
def _return(self, output: AgentFinish, intermediate_steps: list) -> Dict[str, Any]:
self.callback_manager.on_agent_finish(
output, color="green", verbose=self.verbose
)
final_outpu... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-14 | if isinstance(output, AgentFinish):
return output
actions: List[AgentAction]
if isinstance(output, AgentAction):
actions = [output]
else:
actions = output
result = []
for agent_action in actions:
self.callback_manager.on_agent_actio... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-15 | Override this to take control of how the agent makes and acts on choices.
"""
# Call the LLM to see what to do.
output = await self.agent.aplan(intermediate_steps, **inputs)
# If the tool chosen is the finishing tool, then we end and return.
if isinstance(output, AgentFinish):
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-16 | verbose=self.verbose,
color=None,
**tool_run_kwargs,
)
return agent_action, observation
# Use asyncio.gather to run multiple tool.arun() calls concurrently
result = await asyncio.gather(
*[_aperform_agent_action(agent_action... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-17 | if tool_return is not None:
return self._return(tool_return, intermediate_steps)
iterations += 1
time_elapsed = time.time() - start_time
output = self.agent.return_stopped_response(
self.early_stopping_method, intermediate_steps, **inputs
)
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
351fcdc3f0d8-18 | tool_return = self._get_tool_return(next_step_action)
if tool_return is not None:
return await self._areturn(tool_return, intermediate_steps)
iterations += 1
time_elapsed = time.time() - start_time
output = self.... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
385e8533319f-0 | Source code for langchain.agents.initialize
"""Load agent."""
from typing import Any, Optional, Sequence
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.agents.loading import AGENT_TO_CLASS, load_agent
from langchain.callbacks.base import BaseCallbackMa... | https://python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
385e8533319f-1 | "but at most only one should be."
)
if agent is not None:
if agent not in AGENT_TO_CLASS:
raise ValueError(
f"Got unknown agent type: {agent}. "
f"Valid types are: {AGENT_TO_CLASS.keys()}."
)
agent_cls = AGENT_TO_CLASS[agent]
ag... | https://python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
7a24e355993c-0 | Source code for langchain.agents.load_tools
# flake8: noqa
"""Load tools."""
import warnings
from typing import Any, List, Optional
from langchain.agents.tools import Tool
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.api import news_docs, open_meteo_docs, podcast_docs, tmdb_docs
from l... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
7a24e355993c-1 | from langchain.utilities.serpapi import SerpAPIWrapper
from langchain.utilities.wikipedia import WikipediaAPIWrapper
from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
def _get_python_repl() -> BaseTool:
return PythonREPLTool()
def _get_tools_requests_get() -> BaseTool:
return RequestsGetTool(... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
7a24e355993c-2 | return Tool(
name="PAL-MATH",
description="A language model that is really good at solving complex word math problems. Input should be a fully worded hard word math problem.",
func=PALChain.from_math_prompt(llm).run,
)
def _get_pal_colored_objects(llm: BaseLLM) -> BaseTool:
return Tool(
... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
7a24e355993c-3 | _LLM_TOOLS = {
"pal-math": _get_pal_math,
"pal-colored-objects": _get_pal_colored_objects,
"llm-math": _get_llm_math,
"open-meteo-api": _get_open_meteo_api,
}
def _get_news_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
news_api_key = kwargs["news_api_key"]
chain = APIChain.from_llm_and_api_docs(... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
7a24e355993c-4 | chain = APIChain.from_llm_and_api_docs(
llm,
podcast_docs.PODCAST_DOCS,
headers={"X-ListenAPI-Key": listen_api_key},
)
return Tool(
name="Podcast API",
description="Use the Listen Notes Podcast API to search all podcasts or episodes. The input should be a question in natu... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
7a24e355993c-5 | func=SerpAPIWrapper(**kwargs).run,
coroutine=SerpAPIWrapper(**kwargs).arun,
)
def _get_searx_search(**kwargs: Any) -> BaseTool:
return SearxSearchRun(wrapper=SearxSearchWrapper(**kwargs))
def _get_searx_search_results_json(**kwargs: Any) -> BaseTool:
wrapper_kwargs = {k: v for k, v in kwargs.items()... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
7a24e355993c-6 | ),
"bing-search": (_get_bing_search, ["bing_subscription_key", "bing_search_url"]),
"google-serper": (_get_google_serper, ["serper_api_key"]),
"serpapi": (_get_serpapi, ["serpapi_api_key", "aiosession"]),
"searx-search": (_get_searx_search, ["searx_host", "engines", "aiosession"]),
"wikipedia": (_ge... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
7a24e355993c-7 | tool_names.extend(requests_method_tools)
elif name in _BASE_TOOLS:
tools.append(_BASE_TOOLS[name]())
elif name in _LLM_TOOLS:
if llm is None:
raise ValueError(f"Tool {name} requires an LLM to be provided")
tool = _LLM_TOOLS[name](llm)
if ca... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
7a24e355993c-8 | return tools
[docs]def get_all_tool_names() -> List[str]:
"""Get a list of all possible tool names."""
return (
list(_BASE_TOOLS)
+ list(_EXTRA_OPTIONAL_TOOLS)
+ list(_EXTRA_LLM_TOOLS)
+ list(_LLM_TOOLS)
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
12e25db920f7-0 | Source code for langchain.agents.loading
"""Functionality for loading agents."""
import json
from pathlib import Path
from typing import Any, Dict, List, Optional, Type, Union
import yaml
from langchain.agents.agent import BaseSingleActionAgent
from langchain.agents.agent_types import AgentType
from langchain.agents.ch... | https://python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
12e25db920f7-1 | if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
agent_cls = AGENT_TO_CLASS[config_type]
combined_config = {**config, **kwargs}
return agent_cls.from_llm_and_tools(llm, tools, **combined_config)
def load_agent_from_config(
config: dict,
llm... | https://python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
12e25db920f7-2 | config["llm_chain"] = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` and `llm_chain_path` should be specified.")
combined_config = {**config, **kwargs}
return agent_cls(**combined_config) # type: ignore
[docs]def load_agent(path: Union[str, Path], **kwargs: Any)... | https://python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
6c618c34f139-0 | Source code for langchain.agents.agent_types
from enum import Enum
[docs]class AgentType(str, Enum):
ZERO_SHOT_REACT_DESCRIPTION = "zero-shot-react-description"
REACT_DOCSTORE = "react-docstore"
SELF_ASK_WITH_SEARCH = "self-ask-with-search"
CONVERSATIONAL_REACT_DESCRIPTION = "conversational-react-descri... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_types.html |
082d3e9a08b7-0 | Source code for langchain.agents.tools
"""Interface for tools."""
from inspect import signature
from typing import Any, Awaitable, Callable, Optional, Type, Union
from pydantic import BaseModel, validate_arguments
from langchain.tools.base import BaseTool
[docs]class Tool(BaseTool):
"""Tool that takes in function o... | https://python.langchain.com/en/latest/_modules/langchain/agents/tools.html |
082d3e9a08b7-1 | name=name, func=func, description=description, **kwargs
)
class InvalidTool(BaseTool):
"""Tool that is run when invalid tool name is encountered by agent."""
name = "invalid_tool"
description = "Called when tool name is invalid."
def _run(self, tool_name: str) -> str:
"""Use the tool."""... | https://python.langchain.com/en/latest/_modules/langchain/agents/tools.html |
082d3e9a08b7-2 | def search_api(query: str) -> str:
# Searches the API for the query.
return
"""
def _make_with_name(tool_name: str) -> Callable:
def _make_tool(func: Callable) -> Tool:
assert func.__doc__, "Function must have a docstring"
# Description example:
... | https://python.langchain.com/en/latest/_modules/langchain/agents/tools.html |
082d3e9a08b7-3 | def _partial(func: Callable[[str], str]) -> BaseTool:
return _make_with_name(func.__name__)(func)
return _partial
else:
raise ValueError("Too many arguments for tool decorator")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/_modules/langchain/agents/tools.html |
fd6c020ac383-0 | Source code for langchain.agents.conversational_chat.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence, Tuple
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from lang... | https://python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
fd6c020ac383-1 | return "Thought:"
[docs] @classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
system_message: str = PREFIX,
human_message: str = SUFFIX,
input_variables: Optional[List[str]] = None,
output_parser: Optional[BaseOutputParser] = None,
) -> BasePromptTem... | https://python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
fd6c020ac383-2 | content=TEMPLATE_TOOL_RESPONSE.format(observation=observation)
)
thoughts.append(human_message)
return thoughts
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallba... | https://python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
66e042b94918-0 | Source code for langchain.agents.agent_toolkits.csv.base
"""Agent for working with csvs."""
from typing import Any, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain.llms.base import BaseLLM
[docs]def create_csv... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/csv/base.html |
b45e541370ee-0 | Source code for langchain.agents.agent_toolkits.pandas.base
"""Agent for working with pandas objects."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.prompt import PREFIX, SUFFIX
from langchain.agents.mrkl.base import ZeroShotAgent
f... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
b45e541370ee-1 | llm_chain = LLMChain(
llm=llm,
prompt=partial_prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent,
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
7814f49b1b9e-0 | Source code for langchain.agents.agent_toolkits.vectorstore.base
"""VectorStore agent."""
from typing import Any, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX, ROUTER_PREFIX
from langchain.agents.agent_toolkits.vectorstore.toolkit import... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
7814f49b1b9e-1 | prefix: str = ROUTER_PREFIX,
verbose: bool = False,
**kwargs: Any,
) -> AgentExecutor:
"""Construct a vectorstore router agent from an LLM and tools."""
tools = toolkit.get_tools()
prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
llm_chain = LLMChain(
llm=llm,
prompt=pr... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
6110879e5f52-0 | Source code for langchain.agents.agent_toolkits.json.base
"""Json agent."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
from l... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
6110879e5f52-1 | )
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
3ad5576d7969-0 | Source code for langchain.agents.agent_toolkits.openapi.base
"""OpenAPI spec agent."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.openapi.prompt import (
OPENAPI_PREFIX,
OPENAPI_SUFFIX,
)
from langchain.agents.agent_toolkits.opena... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
3ad5576d7969-1 | prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=toolkit.get_tools(),
verbose=verbose... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
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