id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
1dda809fbc47-2 | )
chains = [
create_draft_answer_chain,
list_assertions_chain,
check_assertions_chain,
revised_answer_chain,
]
question_to_checked_assertions_chain = SequentialChain(
chains=chains,
input_variables=["question"],
output_variables=["revised_statement"],
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
1dda809fbc47-3 | """[Deprecated] LLM wrapper to use."""
create_draft_answer_prompt: PromptTemplate = CREATE_DRAFT_ANSWER_PROMPT
"""[Deprecated]"""
list_assertions_prompt: PromptTemplate = LIST_ASSERTIONS_PROMPT
"""[Deprecated]"""
check_assertions_prompt: PromptTemplate = CHECK_ASSERTIONS_PROMPT
"""[Deprecated]""... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
1dda809fbc47-4 | if "llm" in values:
warnings.warn(
"Directly instantiating an LLMCheckerChain with an llm is deprecated. "
"Please instantiate with question_to_checked_assertions_chain "
"or using the from_llm class method."
)
if (
"que... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
1dda809fbc47-5 | )
)
values[
"question_to_checked_assertions_chain"
] = question_to_checked_assertions_chain
return values
@property
def input_keys(self) -> List[str]:
"""Return the singular input key.
:meta private:
"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
1dda809fbc47-6 | {"question": question}, callbacks=_run_manager.get_child()
)
return {self.output_key: output["revised_statement"]}
@property
def _chain_type(self) -> str:
return "llm_checker_chain"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
create_draft... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
1dda809fbc47-7 | create_draft_answer_prompt,
list_assertions_prompt,
check_assertions_prompt,
revised_answer_prompt,
)
)
return cls(
question_to_checked_assertions_chain=question_to_checked_assertions_chain,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_checker/base.html |
9943286c9494-0 | Source code for langchain.chains.constitutional_ai.base
"""Chain for applying constitutional principles to the outputs of another chain."""
from typing import Any, Dict, List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
9943286c9494-1 | from langchain.llms import OpenAI
from langchain.chains import LLMChain, ConstitutionalChain
from langchain.chains.constitutional_ai.models \
import ConstitutionalPrinciple
llm = OpenAI()
qa_prompt = PromptTemplate(
template="Q: {question} ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
9943286c9494-2 | """
chain: LLMChain
constitutional_principles: List[ConstitutionalPrinciple]
critique_chain: LLMChain
revision_chain: LLMChain
return_intermediate_steps: bool = False
[docs] @classmethod
def get_principles(
cls, names: Optional[List[str]] = None
) -> List[ConstitutionalPrinciple]:... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
9943286c9494-3 | **kwargs: Any,
) -> "ConstitutionalChain":
"""Create a chain from an LLM."""
critique_chain = LLMChain(llm=llm, prompt=critique_prompt)
revision_chain = LLMChain(llm=llm, prompt=revision_prompt)
return cls(
chain=chain,
critique_chain=critique_chain,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
9943286c9494-4 | def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
response = self.chain.run(
**inputs,
callbacks=_run_mana... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
9943286c9494-5 | input_prompt=input_prompt,
output_from_model=response,
critique_request=constitutional_principle.critique_request,
callbacks=_run_manager.get_child("critique"),
)
critique = self._parse_critique(
output_string=raw_critique,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
9943286c9494-6 | callbacks=_run_manager.get_child("revision"),
).strip()
response = revision
critiques_and_revisions.append((critique, revision))
_run_manager.on_text(
text=f"Applying {constitutional_principle.name}..." + "\n\n",
verbose=self.verbose,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
9943286c9494-7 | final_output["critiques_and_revisions"] = critiques_and_revisions
return final_output
@staticmethod
def _parse_critique(output_string: str) -> str:
if "Revision request:" not in output_string:
return output_string
output_string = output_string.split("Revision request:")[0]
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
85317467bd17-0 | 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://api.python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
85317467bd17-1 | """Default conversation prompt to use."""
input_key: str = "input" #: :meta private:
output_key: str = "response" #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self)... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
85317467bd17-2 | 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):
raise ValueError(
"Got unexpected prompt input vari... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
6b66fd16a9a3-0 | 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://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
6b66fd16a9a3-1 | """Restrict the docs to return from store based on tokens,
enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true"""
def _reduce_tokens_below_limit(self, docs: List[Document]) -> List[Document]:
num_docs = len(docs)
if self.reduce_k_below_max_tokens and isinstance(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
6b66fd16a9a3-2 | question = inputs[self.question_key]
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_relevan... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
71dcabde18dd-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.base_language import BaseLan... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
71dcabde18dd-1 | COMBINE_PROMPT,
EXAMPLE_PROMPT,
QUESTION_PROMPT,
)
from langchain.docstore.document import Document
from langchain.prompts.base import BasePromptTemplate
class BaseQAWithSourcesChain(Chain, ABC):
"""Question answering with sources over documents."""
combine_documents_chain: BaseCombineDocumentsChain
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
71dcabde18dd-2 | llm: BaseLanguageModel,
document_prompt: BasePromptTemplate = EXAMPLE_PROMPT,
question_prompt: BasePromptTemplate = QUESTION_PROMPT,
combine_prompt: BasePromptTemplate = COMBINE_PROMPT,
**kwargs: Any,
) -> BaseQAWithSourcesChain:
"""Construct the chain from an LLM."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
71dcabde18dd-3 | document_variable_name="context",
)
return cls(
combine_documents_chain=combine_document_chain,
**kwargs,
)
@classmethod
def from_chain_type(
cls,
llm: BaseLanguageModel,
chain_type: str = "stuff",
chain_type_kwargs: Optional[dict] ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
71dcabde18dd-4 | @property
def input_keys(self) -> List[str]:
"""Expect input key.
: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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
71dcabde18dd-5 | return values
@abstractmethod
def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
"""Get docs to run questioning over."""
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manage... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
71dcabde18dd-6 | self.answer_key: answer,
self.sources_answer_key: sources,
}
if self.return_source_documents:
result["source_documents"] = docs
return result
@abstractmethod
async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
"""Get docs to run questioni... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
71dcabde18dd-7 | answer, sources = re.split(r"SOURCES:\s", answer)
else:
sources = ""
result: Dict[str, Any] = {
self.answer_key: answer,
self.sources_answer_key: sources,
}
if self.return_source_documents:
result["source_documents"] = docs
return r... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
71dcabde18dd-8 | return inputs.pop(self.input_docs_key)
async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
return inputs.pop(self.input_docs_key)
@property
def _chain_type(self) -> str:
return "qa_with_sources_chain" | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
e2dd8beaff78-0 | 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://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
e2dd8beaff78-1 | max_tokens_limit: int = 3375
"""Restrict the docs to return from store based on tokens,
enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true"""
search_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Extra search args."""
def _reduce_tokens_below_limit(self, docs: ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
e2dd8beaff78-2 | 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
)
return self._reduce_tok... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
e2dd8beaff78-3 | return values
@property
def _chain_type(self) -> str:
return "vector_db_qa_with_sources_chain" | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
43fb1bf9abe0-0 | Source code for langchain.chains.openai_functions.extraction
from typing import Any, List
from pydantic import BaseModel
from langchain.base_language import BaseLanguageModel
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import (
_conv... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/extraction.html |
43fb1bf9abe0-1 | "properties": {
"info": {"type": "array", "items": _convert_schema(entity_schema)}
},
"required": ["info"],
},
}
_EXTRACTION_TEMPLATE = """Extract and save the relevant entities mentioned\
in the following passage together with their properties.
Passage:
{input}
"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/extraction.html |
43fb1bf9abe0-2 | llm_kwargs = get_llm_kwargs(function)
chain = LLMChain(
llm=llm,
prompt=prompt,
llm_kwargs=llm_kwargs,
output_parser=output_parser,
)
return chain
[docs]def create_extraction_chain_pydantic(
pydantic_schema: Any, llm: BaseLanguageModel
) -> Chain:
"""Creates a chain t... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/extraction.html |
43fb1bf9abe0-3 | openai_schema = _resolve_schema_references(
openai_schema, openai_schema["definitions"]
)
function = _get_extraction_function(openai_schema)
prompt = ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE)
output_parser = PydanticAttrOutputFunctionsParser(
pydantic_schema=PydanticSchema, ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/extraction.html |
2946d065c346-0 | Source code for langchain.chains.openai_functions.qa_with_structure
from typing import Any, List, Optional, Type, Union
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import get_llm_kwargs... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/qa_with_structure.html |
2946d065c346-1 | sources: List[str] = Field(
..., description="List of sources used to answer the question"
)
[docs]def create_qa_with_structure_chain(
llm: BaseLanguageModel,
schema: Union[dict, Type[BaseModel]],
output_parser: str = "base",
prompt: Optional[Union[PromptTemplate, ChatPromptTemplate]] = None... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/qa_with_structure.html |
2946d065c346-2 | Returns:
"""
if output_parser == "pydantic":
if not (isinstance(schema, type) and issubclass(schema, BaseModel)):
raise ValueError(
"Must provide a pydantic class for schema when output_parser is "
"'pydantic'."
)
_output_parser: BaseLLMOut... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/qa_with_structure.html |
2946d065c346-3 | function = {
"name": schema_dict["title"],
"description": schema_dict["description"],
"parameters": schema_dict,
}
llm_kwargs = get_llm_kwargs(function)
messages = [
SystemMessage(
content=(
"You are a world class algorithm to answer "
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/qa_with_structure.html |
2946d065c346-4 | llm_kwargs=llm_kwargs,
output_parser=_output_parser,
)
return chain
[docs]def create_qa_with_sources_chain(llm: BaseLanguageModel, **kwargs: Any) -> LLMChain:
"""Create a question answering chain that returns an answer with sources.
Args:
llm: Language model to use for the chain.
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/qa_with_structure.html |
555b11586e57-0 | Source code for langchain.chains.openai_functions.citation_fuzzy_match
from typing import Iterator, List
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import get_llm_kwargs
from langchain... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
555b11586e57-1 | """
fact: str = Field(..., description="Body of the sentence, as part of a response")
substring_quote: List[str] = Field(
...,
description=(
"Each source should be a direct quote from the context, "
"as a substring of the original content"
),
)
def _get_sp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
555b11586e57-2 | yield from s.spans()
def get_spans(self, context: str) -> Iterator[str]:
for quote in self.substring_quote:
yield from self._get_span(quote, context)
class QuestionAnswer(BaseModel):
"""A question and its answer as a list of facts each one should have a source.
each sentence contains a b... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
555b11586e57-3 | Args:
llm: Language model to use for the chain.
Returns:
Chain (LLMChain) that can be used to answer questions with citations.
"""
output_parser = PydanticOutputFunctionsParser(pydantic_schema=QuestionAnswer)
schema = QuestionAnswer.schema()
function = {
"name": schema["title... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
555b11586e57-4 | HumanMessage(
content=(
"Tips: Make sure to cite your sources, "
"and use the exact words from the context."
)
),
]
prompt = ChatPromptTemplate(messages=messages)
chain = LLMChain(
llm=llm,
prompt=prompt,
llm_kwargs=llm_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
e5913540bc07-0 | Source code for langchain.chains.openai_functions.tagging
from typing import Any
from langchain.base_language import BaseLanguageModel
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import _convert_schema, get_llm_kwargs
from langchain.outp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/tagging.html |
e5913540bc07-1 | Passage:
{input}
"""
[docs]def create_tagging_chain(schema: dict, llm: BaseLanguageModel) -> Chain:
"""Creates a chain that extracts information from a passage.
Args:
schema: The schema of the entities to extract.
llm: The language model to use.
Returns:
Chain (LLMChain) that can be ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/tagging.html |
e5913540bc07-2 | )
return chain
[docs]def create_tagging_chain_pydantic(
pydantic_schema: Any, llm: BaseLanguageModel
) -> Chain:
"""Creates a chain that extracts information from a passage.
Args:
pydantic_schema: The pydantic schema of the entities to extract.
llm: The language model to use.
Returns... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/tagging.html |
e5913540bc07-3 | llm=llm,
prompt=prompt,
llm_kwargs=llm_kwargs,
output_parser=output_parser,
)
return chain | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/tagging.html |
080598dc8e42-0 | Source code for langchain.chains.api.base
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.ca... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
080598dc8e42-1 | api_answer_chain: LLMChain
requests_wrapper: TextRequestsWrapper = Field(exclude=True)
api_docs: str
question_key: str = "question" #: :meta private:
output_key: str = "output" #: :meta private:
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
080598dc8e42-2 | expected_vars = {"question", "api_docs"}
if set(input_vars) != expected_vars:
raise ValueError(
f"Input variables should be {expected_vars}, got {input_vars}"
)
return values
@root_validator(pre=True)
def validate_api_answer_prompt(cls, values: Dict) -> Di... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
080598dc8e42-3 | ) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
question = inputs[self.question_key]
api_url = self.api_request_chain.predict(
question=question,
api_docs=self.api_docs,
callbacks=_run_manager.get_child(),
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
080598dc8e42-4 | )
return {self.output_key: answer}
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
question = input... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
080598dc8e42-5 | await _run_manager.on_text(
api_response, color="yellow", end="\n", verbose=self.verbose
)
answer = await self.api_answer_chain.apredict(
question=question,
api_docs=self.api_docs,
api_url=api_url,
api_response=api_response,
callbac... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
080598dc8e42-6 | get_request_chain = LLMChain(llm=llm, prompt=api_url_prompt)
requests_wrapper = TextRequestsWrapper(headers=headers)
get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt)
return cls(
api_request_chain=get_request_chain,
api_answer_chain=get_answer_chain,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
eeec4df69ed7-0 | Source code for langchain.chains.api.openapi.chain
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
import json
from typing import Any, Dict, List, NamedTuple, Optional, cast
from pydantic import BaseModel, Field
from requests import Response
from la... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
eeec4df69ed7-1 | body_params: List[str]
path_params: List[str]
[docs]class OpenAPIEndpointChain(Chain, BaseModel):
"""Chain interacts with an OpenAPI endpoint using natural language."""
api_request_chain: LLMChain
api_response_chain: Optional[LLMChain]
api_operation: APIOperation
requests: Requests = Field(exclu... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
eeec4df69ed7-2 | :meta private:
"""
return [self.instructions_key]
@property
def output_keys(self) -> List[str]:
"""Expect output key.
:meta private:
"""
if not self.return_intermediate_steps:
return [self.output_key]
else:
return [self.output_key, ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
eeec4df69ed7-3 | query_params = {}
for param in self.param_mapping.query_params:
if param in args:
query_params[param] = args.pop(param)
return query_params
def _extract_body_params(self, args: Dict[str, str]) -> Optional[Dict[str, str]]:
"""Extract the request body params from th... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
eeec4df69ed7-4 | path = self._construct_path(args)
body_params = self._extract_body_params(args)
query_params = self._extract_query_params(args)
return {
"url": path,
"data": body_params,
"params": query_params,
}
def _get_output(self, output: str, intermediate_ste... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
eeec4df69ed7-5 | ) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
intermediate_steps = {}
instructions = inputs[self.instructions_key]
instructions = instructions[: self.max_text_length]
_api_arguments = self.api_request_chain.predict_and_parse(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
eeec4df69ed7-6 | )
try:
request_args = self.deserialize_json_input(api_arguments)
method = getattr(self.requests, self.api_operation.method.value)
api_response: Response = method(**request_args)
if api_response.status_code != 200:
method_str = str(self.api_operatio... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
eeec4df69ed7-7 | _run_manager.on_text(
response_text, color="blue", end="\n", verbose=self.verbose
)
if self.api_response_chain is not None:
_answer = self.api_response_chain.predict_and_parse(
response=response_text,
instructions=instructions,
call... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
eeec4df69ed7-8 | return_intermediate_steps: bool = False,
**kwargs: Any
# TODO: Handle async
) -> "OpenAPIEndpointChain":
"""Create an OpenAPIEndpoint from a spec at the specified url."""
operation = APIOperation.from_openapi_url(spec_url, path, method)
return cls.from_api_operation(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
eeec4df69ed7-9 | callbacks: Callbacks = None,
**kwargs: Any
# TODO: Handle async
) -> "OpenAPIEndpointChain":
"""Create an OpenAPIEndpointChain from an operation and a spec."""
param_mapping = _ParamMapping(
query_params=operation.query_params,
body_params=operation.body_param... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
eeec4df69ed7-10 | api_request_chain=requests_chain,
api_response_chain=response_chain,
api_operation=operation,
requests=_requests,
param_mapping=param_mapping,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
callbacks=callbacks,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
a3670ac1815b-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.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManag... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
a3670ac1815b-1 | required_metadata = [
iv for iv in prompt.input_variables if iv != "page_content"
]
raise ValueError(
f"Document prompt requires documents to have metadata variables: "
f"{required_metadata}. Received document with missing metadata: "
f"{list(missing_metad... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
a3670ac1815b-2 | @property
def output_keys(self) -> List[str]:
"""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 i... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
a3670ac1815b-3 | def _call(
self,
inputs: Dict[str, List[Document]],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
docs = inputs[self.input_key]
# Other keys are assumed to be ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
a3670ac1815b-4 | ) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
docs = inputs[self.input_key]
# Other keys are assumed to be needed for LLM prediction
other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
output, extra_return_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
a3670ac1815b-5 | @property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return self.combine_docs_chain.output_keys
d... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
a3670ac1815b-6 | other_keys: Dict = {k: v for k, v in inputs.items() if k != self.input_key}
other_keys[self.combine_docs_chain.input_key] = docs
return self.combine_docs_chain(
other_keys, return_only_outputs=True, callbacks=_run_manager.get_child()
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
116c57848a85-0 | Source code for langchain.chains.combine_documents.stuff
"""Chain that combines documents by stuffing into context."""
from typing import Any, Dict, List, Optional, Tuple
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import Callbacks
from langchain.chains.combine_documents.base impo... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
116c57848a85-1 | document_prompt: BasePromptTemplate = Field(
default_factory=_get_default_document_prompt
)
"""Prompt to use to format each document."""
document_variable_name: str
"""The variable name in the llm_chain to put the documents in.
If only one variable in the llm_chain, this need not be provided... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
116c57848a85-2 | if len(llm_chain_variables) == 1:
values["document_variable_name"] = llm_chain_variables[0]
else:
raise ValueError(
"document_variable_name must be provided if there are "
"multiple llm_chain_variables"
)
else:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
116c57848a85-3 | inputs = {
k: v
for k, v in kwargs.items()
if k in self.llm_chain.prompt.input_variables
}
inputs[self.document_variable_name] = self.document_separator.join(doc_strings)
return inputs
[docs] def prompt_length(self, docs: List[Document], **kwargs: Any) -> O... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
116c57848a85-4 | # Call predict on the LLM.
return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
[docs] async def acombine_docs(
self, docs: List[Document], callbacks: Callbacks = None, **kwargs: Any
) -> Tuple[str, dict]:
"""Stuff all documents into one prompt and pass to LLM."""
inpu... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
2c865be41cea-0 | Source code for langchain.chains.combine_documents.map_reduce
"""Combining documents by mapping a chain over them first, then combining results."""
from __future__ import annotations
from typing import Any, Callable, Dict, List, Optional, Protocol, Tuple
from pydantic import Extra, root_validator
from langchain.callbac... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
2c865be41cea-1 | ) -> List[List[Document]]:
new_result_doc_list = []
_sub_result_docs = []
for doc in docs:
_sub_result_docs.append(doc)
_num_tokens = length_func(_sub_result_docs, **kwargs)
if _num_tokens > token_max:
if len(_sub_result_docs) == 1:
raise ValueError(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
2c865be41cea-2 | return new_result_doc_list
def _collapse_docs(
docs: List[Document],
combine_document_func: CombineDocsProtocol,
**kwargs: Any,
) -> Document:
result = combine_document_func(docs, **kwargs)
combined_metadata = {k: str(v) for k, v in docs[0].metadata.items()}
for doc in docs[1:]:
for k, v... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
2c865be41cea-3 | combine_document_chain: BaseCombineDocumentsChain
"""Chain to use to combine results of applying llm_chain to documents."""
collapse_document_chain: Optional[BaseCombineDocumentsChain] = None
"""Chain to use to collapse intermediary results if needed.
If None, will use the combine_document_chain."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
2c865be41cea-4 | return _output_keys
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator(pre=True)
def get_return_intermediate_steps(cls, values: Dict) -> Dict:
"""For backwards compatibility."""
if "return_ma... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
2c865be41cea-5 | values["document_variable_name"] = llm_chain_variables[0]
else:
raise ValueError(
"document_variable_name must be provided if there are "
"multiple llm_chain input_variables"
)
else:
llm_chain_variables = values["llm... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
2c865be41cea-6 | self,
docs: List[Document],
token_max: int = 3000,
callbacks: Callbacks = None,
**kwargs: Any,
) -> Tuple[str, dict]:
"""Combine documents in a map reduce manner.
Combine by mapping first chain over all documents, then reducing the results.
This reducing can b... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
2c865be41cea-7 | self, docs: List[Document], callbacks: Callbacks = None, **kwargs: Any
) -> Tuple[str, dict]:
"""Combine documents in a map reduce manner.
Combine by mapping first chain over all documents, then reducing the results.
This reducing can be done recursively if needed (if there are many document... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
2c865be41cea-8 | callbacks: Callbacks = None,
**kwargs: Any,
) -> Tuple[List[Document], dict]:
question_result_key = self.llm_chain.output_key
result_docs = [
Document(page_content=r[question_result_key], metadata=docs[i].metadata)
# This uses metadata from the docs, and the textual r... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
2c865be41cea-9 | result_docs, length_func, token_max, **kwargs
)
result_docs = []
for docs in new_result_doc_list:
new_doc = _collapse_docs(docs, _collapse_docs_func, **kwargs)
result_docs.append(new_doc)
num_tokens = length_func(result_docs, **kwargs)
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
2c865be41cea-10 | ) -> Tuple[str, dict]:
result_docs, extra_return_dict = self._process_results_common(
results, docs, token_max, callbacks=callbacks, **kwargs
)
output = self.combine_document_chain.run(
input_documents=result_docs, callbacks=callbacks, **kwargs
)
return ou... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
2c865be41cea-11 | @property
def _chain_type(self) -> str:
return "map_reduce_documents_chain" | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
021e87a50f55-0 | Source code for langchain.chains.combine_documents.map_rerank
"""Combining documents by mapping a chain over them first, then reranking results."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast
from pydantic import Extra, root_validator
from langchain.call... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
021e87a50f55-1 | If only one variable in the llm_chain, this need not be provided."""
rank_key: str
"""Key in output of llm_chain to rank on."""
answer_key: str
"""Key in output of llm_chain to return as answer."""
metadata_keys: Optional[List[str]] = None
return_intermediate_steps: bool = False
class Config... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
021e87a50f55-2 | return _output_keys
@root_validator()
def validate_llm_output(cls, values: Dict) -> Dict:
"""Validate that the combine chain outputs a dictionary."""
output_parser = values["llm_chain"].prompt.output_parser
if not isinstance(output_parser, RegexParser):
raise ValueError(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
021e87a50f55-3 | raise ValueError(
f"Got {values['answer_key']} as key to return, but did not find "
f"it in the llm_chain output keys ({output_keys})"
)
return values
@root_validator(pre=True)
def get_default_document_variable_name(cls, values: Dict) -> Dict:
"""Get d... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
021e87a50f55-4 | if values["document_variable_name"] not in llm_chain_variables:
raise ValueError(
f"document_variable_name {values['document_variable_name']} was "
f"not found in llm_chain input_variables: {llm_chain_variables}"
)
return values
[docs] d... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
021e87a50f55-5 | callbacks=callbacks,
)
return self._process_results(docs, results)
[docs] async def acombine_docs(
self, docs: List[Document], callbacks: Callbacks = None, **kwargs: Any
) -> Tuple[str, dict]:
"""Combine documents in a map rerank manner.
Combine by mapping first chain over... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
021e87a50f55-6 | docs: List[Document],
results: Sequence[Union[str, List[str], Dict[str, str]]],
) -> Tuple[str, dict]:
typed_results = cast(List[dict], results)
sorted_res = sorted(
zip(typed_results, docs), key=lambda x: -int(x[0][self.rank_key])
)
output, document = sorted_res[... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
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