id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
38c6a19de250-2 | 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:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
38c6a19de250-3 | 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) -> Optional[int]:
"""Return the prompt length given the documents passed in... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
38c6a19de250-4 | 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]:
"""Async stuff all documents into one prompt and pass to LLM.
Args:
docs: List of docu... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
5957d20dc08b-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, Type
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
5957d20dc08b-1 | )
[docs] def get_output_schema(
self, config: Optional[RunnableConfig] = None
) -> Type[BaseModel]:
return create_model(
"CombineDocumentsOutput",
**{self.output_key: (str, None)}, # type: ignore[call-overload]
)
@property
def input_keys(self) -> List[str]... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
5957d20dc08b-2 | other inputs to the prompt.
Returns:
The first element returned is the single string output. The second
element returned is a dictionary of other keys to return.
"""
[docs] @abstractmethod
async def acombine_docs(
self, docs: List[Document], **kwargs: Any
) -> ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
5957d20dc08b-3 | """Prepare inputs, call combine docs, prepare outputs."""
_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.inp... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
5957d20dc08b-4 | return create_model(
"AnalyzeDocumentChain",
**{self.input_key: (str, None)}, # type: ignore[call-overload]
)
[docs] def get_output_schema(
self, config: Optional[RunnableConfig] = None
) -> Type[BaseModel]:
return self.combine_docs_chain.get_output_schema(config)... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
d9c32bc2d44b-0 | Source code for langchain.chains.combine_documents.reduce
"""Combine many documents together by recursively reducing them."""
from __future__ import annotations
from typing import Any, Callable, List, Optional, Protocol, Tuple
from langchain.callbacks.manager import Callbacks
from langchain.chains.combine_documents.bas... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
d9c32bc2d44b-1 | if _num_tokens > token_max:
if len(_sub_result_docs) == 1:
raise ValueError(
"A single document was longer than the context length,"
" we cannot handle this."
)
new_result_doc_list.append(_sub_result_docs[:-1])
_... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
d9c32bc2d44b-2 | [docs]async def acollapse_docs(
docs: List[Document],
combine_document_func: AsyncCombineDocsProtocol,
**kwargs: Any,
) -> Document:
"""Execute a collapse function on a set of documents and merge their metadatas.
Args:
docs: A list of Documents to combine.
combine_document_func: A fu... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
d9c32bc2d44b-3 | `collapse_documents_chain` is used if the documents passed in are too many to all
be passed to `combine_documents_chain` in one go. In this case,
`collapse_documents_chain` is called recursively on as big of groups of documents
as are allowed.
Example:
.. code-block:: python
from lan... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
d9c32bc2d44b-4 | llm_chain = LLMChain(llm=llm, prompt=prompt)
collapse_documents_chain = StuffDocumentsChain(
llm_chain=llm_chain,
document_prompt=document_prompt,
document_variable_name=document_variable_name
)
chain = ReduceDocumentsChain(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
d9c32bc2d44b-5 | """Combine multiple documents recursively.
Args:
docs: List of documents to combine, assumed that each one is less than
`token_max`.
token_max: Recursively creates groups of documents less than this number
of tokens.
callbacks: Callbacks to be ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
d9c32bc2d44b-6 | docs, token_max=token_max, callbacks=callbacks, **kwargs
)
return await self.combine_documents_chain.acombine_docs(
docs=result_docs, callbacks=callbacks, **kwargs
)
def _collapse(
self,
docs: List[Document],
token_max: Optional[int] = None,
callba... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
d9c32bc2d44b-7 | num_tokens = length_func(result_docs, **kwargs)
async def _collapse_docs_func(docs: List[Document], **kwargs: Any) -> str:
return await self._collapse_chain.arun(
input_documents=docs, callbacks=callbacks, **kwargs
)
_token_max = token_max or self.token_max
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
f99e5cbe750d-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, Dict, List, Optional, Tuple, Type
from langchain.callbacks.manager import Callbacks
from langchain.chains.combine... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
f99e5cbe750d-1 | # The prompt here should take as an input variable the
# `document_variable_name`
prompt = PromptTemplate.from_template(
"Summarize this content: {context}"
)
llm_chain = LLMChain(llm=llm, prompt=prompt)
# We now define how to combine these sum... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
f99e5cbe750d-2 | reduce_documents_chain=reduce_documents_chain,
)
"""
llm_chain: LLMChain
"""Chain to apply to each document individually."""
reduce_documents_chain: BaseCombineDocumentsChain
"""Chain to use to reduce the results of applying `llm_chain` to each doc.
This typically either a ReduceDocu... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
f99e5cbe750d-3 | """For backwards compatibility."""
if "combine_document_chain" in values:
if "reduce_documents_chain" in values:
raise ValueError(
"Both `reduce_documents_chain` and `combine_document_chain` "
"cannot be provided at the same time. `combine_docu... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
f99e5cbe750d-4 | "multiple llm_chain input_variables"
)
else:
llm_chain_variables = values["llm_chain"].prompt.input_variables
if values["document_variable_name"] not in llm_chain_variables:
raise ValueError(
f"document_variable_name {values['document_v... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
f99e5cbe750d-5 | ) -> 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 documents).
"""
map_results = self.llm_chain.apply(
# F... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
f99e5cbe750d-6 | # FYI - this is parallelized and so it is fast.
[{**{self.document_variable_name: d.page_content}, **kwargs} for d in docs],
callbacks=callbacks,
)
question_result_key = self.llm_chain.output_key
result_docs = [
Document(page_content=r[question_result_key], me... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
4e0127867a4a-0 | Source code for langchain.chains.combine_documents.refine
"""Combine documents by doing a first pass and then refining on more documents."""
from __future__ import annotations
from typing import Any, Dict, List, Tuple
from langchain.callbacks.manager import Callbacks
from langchain.chains.combine_documents.base import ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
4e0127867a4a-1 | # details.
document_prompt = PromptTemplate(
input_variables=["page_content"],
template="{page_content}"
)
document_variable_name = "context"
llm = OpenAI()
# The prompt here should take as an input variable the
# `... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
4e0127867a4a-2 | """The variable name to format the initial response in when refining."""
document_prompt: BasePromptTemplate = Field(
default_factory=_get_default_document_prompt
)
"""Prompt to use to format each document, gets passed to `format_document`."""
return_intermediate_steps: bool = False
"""Retur... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
4e0127867a4a-3 | "multiple llm_chain input_variables"
)
else:
llm_chain_variables = values["initial_llm_chain"].prompt.input_variables
if values["document_variable_name"] not in llm_chain_variables:
raise ValueError(
f"document_variable_name {values['do... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
4e0127867a4a-4 | ) -> Tuple[str, dict]:
"""Async combine by mapping a first chain over all, then stuffing
into a final chain.
Args:
docs: List of documents to combine
callbacks: Callbacks to be passed through
**kwargs: additional parameters to be passed to LLM calls (like oth... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
4e0127867a4a-5 | ) -> Dict[str, Any]:
base_info = {"page_content": docs[0].page_content}
base_info.update(docs[0].metadata)
document_info = {k: base_info[k] for k in self.document_prompt.input_variables}
base_inputs: dict = {
self.document_variable_name: self.document_prompt.format(**document... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
8a5bba9cfe17-0 | Source code for langchain.chains.router.multi_prompt
"""Use a single chain to route an input to one of multiple llm chains."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.chains import ConversationChain
from langchain.chains.base import Chain
from langchain.chains.llm ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_prompt.html |
8a5bba9cfe17-1 | destination_chains = {}
for p_info in prompt_infos:
name = p_info["name"]
prompt_template = p_info["prompt_template"]
prompt = PromptTemplate(template=prompt_template, input_variables=["input"])
chain = LLMChain(llm=llm, prompt=prompt)
destination_chai... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_prompt.html |
41192c30ff56-0 | Source code for langchain.chains.router.base
"""Base classes for chain routing."""
from __future__ import annotations
from abc import ABC
from typing import Any, Dict, List, Mapping, NamedTuple, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
C... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
41192c30ff56-1 | destination_chains: Mapping[str, Chain]
"""Chains that return final answer to inputs."""
default_chain: Chain
"""Default chain to use when none of the destination chains are suitable."""
silent_errors: bool = False
"""If True, use default_chain when an invalid destination name is provided.
Defa... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
41192c30ff56-2 | else:
raise ValueError(
f"Received invalid destination chain name '{route.destination}'"
)
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = ru... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/router/base.html |
1df5e7648768-0 | Source code for langchain.chains.router.multi_retrieval_qa
"""Use a single chain to route an input to one of multiple retrieval qa chains."""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.chains import ConversationChain
from langchain.chains.base import Chain
fr... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_retrieval_qa.html |
1df5e7648768-1 | default_retriever: Optional[BaseRetriever] = None,
default_prompt: Optional[PromptTemplate] = None,
default_chain: Optional[Chain] = None,
**kwargs: Any,
) -> MultiRetrievalQAChain:
if default_prompt and not default_retriever:
raise ValueError(
"`default_r... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_retrieval_qa.html |
1df5e7648768-2 | prompt = PromptTemplate(
template=prompt_template, input_variables=["history", "query"]
)
_default_chain = ConversationChain(
llm=ChatOpenAI(), prompt=prompt, input_key="query", output_key="result"
)
return cls(
router_chain=router_... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/router/multi_retrieval_qa.html |
297caacedd83-0 | Source code for langchain.chains.router.llm_router
"""Base classes for LLM-powered router chains."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Type, cast
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/router/llm_router.html |
297caacedd83-1 | raise ValueError
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
output = cast(... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/router/llm_router.html |
297caacedd83-2 | [docs] def parse(self, text: str) -> Dict[str, Any]:
try:
expected_keys = ["destination", "next_inputs"]
parsed = parse_and_check_json_markdown(text, expected_keys)
if not isinstance(parsed["destination"], str):
raise ValueError("Expected 'destination' to b... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/router/llm_router.html |
da97f556e112-0 | Source code for langchain.chains.router.embedding_router
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple, Type
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.router.base import RouterChain
from langchain.docstore.document import ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/router/embedding_router.html |
da97f556e112-1 | """Convenience constructor."""
documents = []
for name, descriptions in names_and_descriptions:
for description in descriptions:
documents.append(
Document(page_content=description, metadata={"name": name})
)
vectorstore = vectorsto... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/router/embedding_router.html |
e4a77c41e8a8-0 | Source code for langchain.chains.query_constructor.schema
from langchain.pydantic_v1 import BaseModel
[docs]class AttributeInfo(BaseModel):
"""Information about a data source attribute."""
name: str
description: str
type: str
class Config:
"""Configuration for this pydantic object."""
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/schema.html |
15562f57066a-0 | Source code for langchain.chains.query_constructor.base
"""LLM Chain for turning a user text query into a structured query."""
from __future__ import annotations
import json
from typing import Any, Callable, List, Optional, Sequence, Tuple, Union, cast
from langchain.chains.llm import LLMChain
from langchain.chains.que... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/base.html |
15562f57066a-1 | parsed = parse_and_check_json_markdown(text, expected_keys)
if len(parsed["query"]) == 0:
parsed["query"] = " "
if parsed["filter"] == "NO_FILTER" or not parsed["filter"]:
parsed["filter"] = None
else:
parsed["filter"] = self.ast_parse(... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/base.html |
15562f57066a-2 | allowed_attributes=allowed_attributes,
)
return fixed
else:
ast_parse = get_parser(
allowed_comparators=allowed_comparators,
allowed_operators=allowed_operators,
allowed_attributes=allowed_attributes,
).parse... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/base.html |
15562f57066a-3 | )
for arg in filter.arguments
]
args = [arg for arg in args if arg is not None]
if not args:
return None
elif len(args) == 1 and filter.operator in (Operator.AND, Operator.OR):
return args[0]
else:
return Operation(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/base.html |
15562f57066a-4 | allowed_comparators: Sequence[Comparator] = tuple(Comparator),
allowed_operators: Sequence[Operator] = tuple(Operator),
enable_limit: bool = False,
schema_prompt: Optional[BasePromptTemplate] = None,
**kwargs: Any,
) -> BasePromptTemplate:
"""Create query construction prompt.
Args:
docum... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/base.html |
15562f57066a-5 | schema=schema, content=document_contents, attributes=attribute_str
)
suffix = SUFFIX_WITHOUT_DATA_SOURCE.format(i=len(examples) + 1)
else:
examples = examples or (
EXAMPLES_WITH_LIMIT if enable_limit else DEFAULT_EXAMPLES
)
example_prompt = EXAMPLE_PROMPT
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/base.html |
15562f57066a-6 | allowed_operators: Sequence of allowed operators. Defaults to all Operators.
enable_limit: Whether to enable the limit operator. Defaults to False.
schema_prompt: Prompt for describing query schema. Should have string input
variables allowed_comparators and allowed_operators.
**kwarg... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/base.html |
15562f57066a-7 | schema_prompt: Optional[BasePromptTemplate] = None,
fix_invalid: bool = False,
**kwargs: Any,
) -> Runnable:
"""Load a query constructor runnable chain.
Args:
llm: BaseLanguageModel to use for the chain.
document_contents: The contents of the document to be queried.
attribute_inf... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/base.html |
15562f57066a-8 | allowed_operators=allowed_operators,
allowed_attributes=allowed_attributes,
fix_invalid=fix_invalid,
)
return prompt | llm | output_parser | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/base.html |
067c9d2ec823-0 | Source code for langchain.chains.query_constructor.ir
"""Internal representation of a structured query language."""
from __future__ import annotations
from abc import ABC, abstractmethod
from enum import Enum
from typing import Any, List, Optional, Sequence, Union
from langchain.pydantic_v1 import BaseModel
[docs]class... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/ir.html |
067c9d2ec823-1 | snake_case = ""
for i, char in enumerate(name):
if char.isupper() and i != 0:
snake_case += "_" + char.lower()
else:
snake_case += char.lower()
return snake_case
[docs]class Expr(BaseModel):
"""Base class for all expressions."""
[docs] def accept(self, visitor: Vis... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/ir.html |
067c9d2ec823-2 | """A structured query."""
query: str
"""Query string."""
filter: Optional[FilterDirective]
"""Filtering expression."""
limit: Optional[int]
"""Limit on the number of results.""" | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/ir.html |
5d9c74164a1e-0 | Source code for langchain.chains.query_constructor.parser
import datetime
import warnings
from typing import Any, Literal, Optional, Sequence, Union
from typing_extensions import TypedDict
from langchain.utils import check_package_version
try:
check_package_version("lark", gte_version="1.1.5")
from lark import ... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/parser.html |
5d9c74164a1e-1 | %import common.WS
%ignore WS
"""
[docs]class ISO8601Date(TypedDict):
"""A date in ISO 8601 format (YYYY-MM-DD)."""
date: str
type: Literal["date"]
@v_args(inline=True)
class QueryTransformer(Transformer):
"""Transforms a query string into an intermediate representation."""
def __init__(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/parser.html |
5d9c74164a1e-2 | if func_name in set(Comparator):
if self.allowed_comparators is not None:
if func_name not in self.allowed_comparators:
raise ValueError(
f"Received disallowed comparator {func_name}. Allowed "
f"comparators are {self.allowe... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/parser.html |
5d9c74164a1e-3 | "(YYYY-MM-DD)."
)
return {"date": item, "type": "date"}
def string(self, item: Any) -> str:
# Remove escaped quotes
return str(item).strip("\"'")
[docs]def get_parser(
allowed_comparators: Optional[Sequence[Comparator]] = None,
allowed_operators: Optional[Sequence[Operato... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/query_constructor/parser.html |
f4715a3a37e3-0 | Source code for langchain.chains.qa_with_sources.retrieval
"""Question-answering with sources over an index."""
from typing import Any, Dict, List
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains.combine_documents.stuff import StuffDo... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
f4715a3a37e3-1 | return docs[:num_docs]
def _get_docs(
self, inputs: Dict[str, Any], *, run_manager: CallbackManagerForChainRun
) -> List[Document]:
question = inputs[self.question_key]
docs = self.retriever.get_relevant_documents(
question, callbacks=run_manager.get_child()
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
68eadb54b8b7-0 | Source code for langchain.chains.qa_with_sources.base
"""Question answering with sources over documents."""
from __future__ import annotations
import inspect
import re
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Tuple
from langchain.callbacks.manager import (
AsyncCallbackManag... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
68eadb54b8b7-1 | [docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
document_prompt: BasePromptTemplate = EXAMPLE_PROMPT,
question_prompt: BasePromptTemplate = QUESTION_PROMPT,
combine_prompt: BasePromptTemplate = COMBINE_PROMPT,
**kwargs: Any,
) -> BaseQAWithSource... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
68eadb54b8b7-2 | )
return cls(combine_documents_chain=combine_documents_chain, **kwargs)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta priv... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
68eadb54b8b7-3 | """Get docs to run questioning over."""
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
accepts_run_manager = (
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
68eadb54b8b7-4 | )
if accepts_run_manager:
docs = await self._aget_docs(inputs, run_manager=_run_manager)
else:
docs = await self._aget_docs(inputs) # type: ignore[call-arg]
answer = await self.combine_documents_chain.arun(
input_documents=docs, callbacks=_run_manager.get_chi... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
4270919abe82-0 | Source code for langchain.chains.qa_with_sources.loading
"""Load question answering with sources chains."""
from __future__ import annotations
from typing import Any, Mapping, Optional, Protocol
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.combine_documents.map_red... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/loading.html |
4270919abe82-1 | return MapRerankDocumentsChain(
llm_chain=llm_chain,
rank_key=rank_key,
answer_key=answer_key,
document_variable_name=document_variable_name,
**kwargs,
)
def _load_stuff_chain(
llm: BaseLanguageModel,
prompt: BasePromptTemplate = stuff_prompt.PROMPT,
document_prom... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/loading.html |
4270919abe82-2 | **kwargs: Any,
) -> MapReduceDocumentsChain:
map_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose)
_reduce_llm = reduce_llm or llm
reduce_chain = LLMChain(llm=_reduce_llm, prompt=combine_prompt, verbose=verbose)
combine_documents_chain = StuffDocumentsChain(
llm_chain=reduce_cha... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/loading.html |
4270919abe82-3 | question_prompt: BasePromptTemplate = refine_prompts.DEFAULT_TEXT_QA_PROMPT,
refine_prompt: BasePromptTemplate = refine_prompts.DEFAULT_REFINE_PROMPT,
document_prompt: BasePromptTemplate = refine_prompts.EXAMPLE_PROMPT,
document_variable_name: str = "context_str",
initial_response_name: str = "existing_... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/loading.html |
4270919abe82-4 | verbose: Whether chains should be run in verbose mode or not. Note that this
applies to all chains that make up the final chain.
Returns:
A chain to use for question answering with sources.
"""
loader_mapping: Mapping[str, LoadingCallable] = {
"stuff": _load_stuff_chain,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/loading.html |
568f7b36f232-0 | 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 langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains.combine_docum... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
568f7b36f232-1 | for doc in docs
]
token_count = sum(tokens[:num_docs])
while token_count > self.max_tokens_limit:
num_docs -= 1
token_count -= tokens[num_docs]
return docs[:num_docs]
def _get_docs(
self, inputs: Dict[str, Any], *, run_manager: Call... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
56b7c4702295-0 | Source code for langchain.chains.llm_summarization_checker.base
"""Chain for summarization with self-verification."""
from __future__ import annotations
import warnings
from pathlib import Path
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
56b7c4702295-1 | verbose=verbose,
),
LLMChain(
llm=llm,
prompt=check_assertions_prompt,
output_key="checked_assertions",
verbose=verbose,
),
LLMChain(
llm=llm,
prompt=revised_summary_prompt,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
56b7c4702295-2 | """[Deprecated]"""
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 double-checking."""
class Config:
"""Configuration for this pydantic object."""
extra = Extr... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
56b7c4702295-3 | return [self.output_key]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
all_true = False
count = 0
output =... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
56b7c4702295-4 | llm,
create_assertions_prompt,
check_assertions_prompt,
revised_summary_prompt,
are_all_true_prompt,
verbose=verbose,
)
return cls(sequential_chain=chain, verbose=verbose, **kwargs) | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
1e8d05445472-0 | Source code for langchain.chains.retrieval_qa.base
"""Chain for question-answering against a vector database."""
from __future__ import annotations
import inspect
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManage... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
1e8d05445472-1 | """Input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Output keys.
:meta private:
"""
_output_keys = [self.output_key]
if self.return_source_documents:
_output_keys = _output_keys + [... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
1e8d05445472-2 | _chain_type_kwargs = chain_type_kwargs or {}
combine_documents_chain = load_qa_chain(
llm, chain_type=chain_type, **_chain_type_kwargs
)
return cls(combine_documents_chain=combine_documents_chain, **kwargs)
@abstractmethod
def _get_docs(
self,
question: str,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
1e8d05445472-3 | return {self.output_key: answer, "source_documents": docs}
else:
return {self.output_key: answer}
@abstractmethod
async def _aget_docs(
self,
question: str,
*,
run_manager: AsyncCallbackManagerForChainRun,
) -> List[Document]:
"""Get documents to d... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
1e8d05445472-4 | 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 langchain.chains import RetrievalQA
from langchain.vec... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
1e8d05445472-5 | """Vector Database to connect to."""
k: int = 4
"""Number of documents to query for."""
search_type: str = "similarity"
"""Search type to use over vectorstore. `similarity` or `mmr`."""
search_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Extra search args."""
@root_validator()
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
1e8d05445472-6 | self,
question: str,
*,
run_manager: AsyncCallbackManagerForChainRun,
) -> List[Document]:
"""Get docs."""
raise NotImplementedError("VectorDBQA does not support async")
@property
def _chain_type(self) -> str:
"""Return the chain type."""
return "vecto... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/retrieval_qa/base.html |
6d8a26754629-0 | Source code for langchain.chains.conversational_retrieval.base
"""Chain for chatting with a vector database."""
from __future__ import annotations
import inspect
import warnings
from abc import abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
from langch... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
6d8a26754629-1 | buffer += f"\n{role_prefix}{dialogue_turn.content}"
elif isinstance(dialogue_turn, tuple):
human = "Human: " + dialogue_turn[0]
ai = "Assistant: " + dialogue_turn[1]
buffer += "\n" + "\n".join([human, ai])
else:
raise ValueError(
f"Unsuppor... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
6d8a26754629-2 | return_generated_question: bool = False
"""Return the generated question as part of the final result."""
get_chat_history: Optional[Callable[[List[CHAT_TURN_TYPE]], str]] = None
"""An optional function to get a string of the chat history.
If None is provided, will use a default."""
response_if_no_do... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
6d8a26754629-3 | run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
question = inputs["question"]
get_chat_history = self.get_chat_history or _get_chat_history
chat_history_str = get_chat_history(i... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
6d8a26754629-4 | return output
@abstractmethod
async def _aget_docs(
self,
question: str,
inputs: Dict[str, Any],
*,
run_manager: AsyncCallbackManagerForChainRun,
) -> List[Document]:
"""Get docs."""
async def _acall(
self,
inputs: Dict[str, Any],
r... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
6d8a26754629-5 | if self.return_source_documents:
output["source_documents"] = docs
if self.return_generated_question:
output["generated_question"] = new_question
return output
[docs] def save(self, file_path: Union[Path, str]) -> None:
if self.get_chat_history:
raise Value... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
6d8a26754629-6 | combine_docs_chain = StuffDocumentsChain(...)
vectorstore = ...
retriever = vectorstore.as_retriever()
# This controls how the standalone question is generated.
# Should take `chat_history` and `question` as input variables.
template = (
"Combi... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
6d8a26754629-7 | def _get_docs(
self,
question: str,
inputs: Dict[str, Any],
*,
run_manager: CallbackManagerForChainRun,
) -> List[Document]:
"""Get docs."""
docs = self.retriever.get_relevant_documents(
question, callbacks=run_manager.get_child()
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
6d8a26754629-8 | (eg in both the question generation and the answering)
retriever: The retriever to use to fetch relevant documents from.
condense_question_prompt: The prompt to use to condense the chat history
and new question into a standalone question.
chain_type: The chain type to... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
6d8a26754629-9 | """Chain for chatting with a vector database."""
vectorstore: VectorStore = Field(alias="vectorstore")
top_k_docs_for_context: int = 4
search_kwargs: dict = Field(default_factory=dict)
@property
def _chain_type(self) -> str:
return "chat-vector-db"
@root_validator()
def raise_depreca... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
6d8a26754629-10 | combine_docs_chain_kwargs: Optional[Dict] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> BaseConversationalRetrievalChain:
"""Load chain from LLM."""
combine_docs_chain_kwargs = combine_docs_chain_kwargs or {}
doc_chain = load_qa_chain(
llm,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
0275cedc3677-0 | Source code for langchain.vectorstores.xata
from __future__ import annotations
import time
from itertools import repeat
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.vectorstor... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/xata.html |
0275cedc3677-1 | [docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[Any, Any]]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
ids = ids
docs = self._texts_to_documents(texts, metadatas)
vectors = self._embedding... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/xata.html |
0275cedc3677-2 | if r.status_code != 200:
raise Exception(f"Error adding vectors to Xata: {r.status_code} {r}")
id_list.extend(r["recordIDs"])
return id_list
@staticmethod
def _texts_to_documents(
texts: Iterable[str],
metadatas: Optional[Iterable[Dict[Any, Any]]] = None,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/xata.html |
0275cedc3677-3 | embedding=embedding,
table_name=table_name,
)
vector_db._add_vectors(embeddings, docs, ids)
return vector_db
[docs] def similarity_search(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Document]:
"""Return docs most simila... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/xata.html |
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