id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
586f36c69ead-3 | docs = self._get_docs(inputs, run_manager=_run_manager)
else:
docs = self._get_docs(inputs) # type: ignore[call-arg]
answer = self.combine_documents_chain.run(
input_documents=docs, callbacks=_run_manager.get_child(), **inputs
)
if re.search(r"SOURCES:\s", answer... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
586f36c69ead-4 | )
if re.search(r"SOURCES:\s", answer):
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:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/base.html |
eddbeddbcc57-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/loading.html |
eddbeddbcc57-1 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/loading.html |
eddbeddbcc57-2 | **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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/loading.html |
eddbeddbcc57-3 | 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_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/loading.html |
eddbeddbcc57-4 | 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,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/loading.html |
3ba86036d774-0 | 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.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChai... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
3ba86036d774-1 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/vector_db.html |
6617ff676242-0 | 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.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains.combine_doc... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
6617ff676242-1 | 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()
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_with_sources/retrieval.html |
fe0bba7f71db-0 | Source code for langchain.chains.llm_bash.base
"""Chain that interprets a prompt and executes bash operations."""
from __future__ import annotations
import logging
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import Callb... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
fe0bba7f71db-1 | if "llm" in values:
warnings.warn(
"Directly instantiating an LLMBashChain with an llm is deprecated. "
"Please instantiate with llm_chain or using the from_llm class method."
)
if "llm_chain" not in values and values["llm"] is not None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
fe0bba7f71db-2 | t = t.strip()
try:
parser = self.llm_chain.prompt.output_parser
command_list = parser.parse(t) # type: ignore[union-attr]
except OutputParserException as e:
_run_manager.on_chain_error(e, verbose=self.verbose)
raise e
if self.verbose:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
91cf248f8f53-0 | Source code for langchain.chains.llm_bash.prompt
# flake8: noqa
from __future__ import annotations
import re
from typing import List
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import BaseOutputParser, OutputParserException
_PROMPT_TEMPLATE = """If someone asks you to perform a task, your ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/prompt.html |
91cf248f8f53-1 | for match in pattern.finditer(t):
matched = match.group(1).strip()
if matched:
code_blocks.extend(
[line for line in matched.split("\n") if line.strip()]
)
return code_blocks
@property
def _type(self) -> str:
return "bas... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/prompt.html |
8006eb438d2b-0 | Source code for langchain.chains.openai_functions.base
"""Methods for creating chains that use OpenAI function-calling APIs."""
import inspect
from typing import (
Any,
Callable,
Dict,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
)
from pydantic import BaseModel
from langchain.base_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
8006eb438d2b-1 | past_descriptors = True
elif not past_descriptors:
descriptors.append(block)
else:
continue
description = " ".join(descriptors)
else:
description = ""
args_block = None
arg_descriptions = {}
if args_block:
arg = None
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
8006eb438d2b-2 | spec = inspect.getfullargspec(function)
required = spec.args[: -len(spec.defaults)] if spec.defaults else spec.args
required += [k for k in spec.kwonlyargs if k not in (spec.kwonlydefaults or {})]
is_class = type(function) is type
if is_class and required[0] == "self":
required = required[1:]
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
8006eb438d2b-3 | OpenAI function-calling API.
"""
if isinstance(function, dict):
return function
elif isinstance(function, type) and issubclass(function, BaseModel):
schema = function.schema()
return {
"name": schema["title"],
"description": schema["description"],
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
8006eb438d2b-4 | llm: BaseLanguageModel,
prompt: BasePromptTemplate,
*,
output_parser: Optional[BaseLLMOutputParser] = None,
**kwargs: Any,
) -> LLMChain:
"""Create an LLM chain that uses OpenAI functions.
Args:
functions: A sequence of either dictionaries, pydantic.BaseModels classes, or
Pyt... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
8006eb438d2b-5 | Example:
.. code-block:: python
from langchain.chains.openai_functions import create_openai_fn_chain
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
from pydantic import Base... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
8006eb438d2b-6 | """ # noqa: E501
if not functions:
raise ValueError("Need to pass in at least one function. Received zero.")
openai_functions = [convert_to_openai_function(f) for f in functions]
fn_names = [oai_fn["name"] for oai_fn in openai_functions]
output_parser = output_parser or _get_openai_output_parse... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
8006eb438d2b-7 | prompt: BasePromptTemplate to pass to the model.
output_parser: BaseLLMOutputParser to use for parsing model outputs. By default
will be inferred from the function types. If pydantic.BaseModels are passed
in, then the OutputParser will try to parse outputs using those. Otherwise
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
8006eb438d2b-8 | chain.run("Harry was a chubby brown beagle who loved chicken")
# -> Dog(name="Harry", color="brown", fav_food="chicken")
""" # noqa: E501
if isinstance(output_schema, dict):
function: Any = {
"name": "output_formatter",
"description": (
"Output fo... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/base.html |
4e2d7368b8ff-0 | Source code for langchain.chains.openai_functions.openapi
import json
import re
from collections import defaultdict
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import requests
from openapi_schema_pydantic import Parameter
from requests import Response
from langchain import LLMChain
from langcha... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
4e2d7368b8ff-1 | sep = f"{clean_param}=" if param[-1] == "*" else ","
new_val = f"{clean_param}=" + sep.join(val)
else:
new_val = ",".join(val)
elif isinstance(val, dict):
kv_sep = "=" if param[-1] == "*" else ","
kv_strs = [kv_sep.join((k, v)) for k, v in val.... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
4e2d7368b8ff-2 | if p.required:
required.append(p.name)
return {"type": "object", "properties": properties, "required": required}
[docs]def openapi_spec_to_openai_fn(
spec: OpenAPISpec,
) -> Tuple[List[Dict[str, Any]], Callable]:
"""Convert a valid OpenAPI spec to the JSON Schema format expected for OpenAI
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
4e2d7368b8ff-3 | params_by_type[param_loc], spec
)
request_body = spec.get_request_body_for_operation(op)
# TODO: Support more MIME types.
if request_body and request_body.content:
media_types = {}
for media_type, media_type_object in request_body.c... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
4e2d7368b8ff-4 | url = _name_to_call_map[name]["url"]
path_params = fn_args.pop("path_params", {})
url = _format_url(url, path_params)
if "data" in fn_args and isinstance(fn_args["data"], dict):
fn_args["data"] = json.dumps(fn_args["data"])
_kwargs = {**fn_args, **kwargs}
if headers i... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
4e2d7368b8ff-5 | _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
name = inputs[self.input_key].pop("name")
args = inputs[self.input_key].pop("arguments")
_pretty_name = get_colored_text(name, "green")
_pretty_args = get_colored_text(json.dumps(args, indent=2), "green")
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
4e2d7368b8ff-6 | spec: OpenAPISpec or url/file/text string corresponding to one.
llm: language model, should be an OpenAI function-calling model, e.g.
`ChatOpenAI(model="gpt-3.5-turbo-0613")`.
prompt: Main prompt template to use.
request_chain: Chain for taking the functions output and executing the ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
4e2d7368b8ff-7 | name, args, headers=headers, params=params
),
verbose=verbose,
)
return SequentialChain(
chains=[llm_chain, request_chain],
input_variables=llm_chain.input_keys,
output_variables=["response"],
verbose=verbose,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/openapi.html |
52256c665eb1-0 | Source code for langchain.chains.openai_functions.extraction
from typing import Any, List
from pydantic import BaseModel
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import (
_convert_schema,
_resolve_schema_references,
get_ll... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/extraction.html |
52256c665eb1-1 | verbose: Whether to run in verbose mode. In verbose mode, some intermediate
logs will be printed to the console. Defaults to `langchain.verbose` value.
Returns:
Chain that can be used to extract information from a passage.
"""
function = _get_extraction_function(schema)
prompt = Chat... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/extraction.html |
52256c665eb1-2 | pydantic_schema=PydanticSchema, attr_name="info"
)
llm_kwargs = get_llm_kwargs(function)
chain = LLMChain(
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/extraction.html |
07ddbbc00ffa-0 | Source code for langchain.chains.openai_functions.tagging
from typing import Any, Optional
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.output_parsers.openai_functions import (
Jso... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/tagging.html |
07ddbbc00ffa-1 | llm=llm,
prompt=prompt,
llm_kwargs=llm_kwargs,
output_parser=output_parser,
**kwargs,
)
return chain
[docs]def create_tagging_chain_pydantic(
pydantic_schema: Any,
llm: BaseLanguageModel,
prompt: Optional[ChatPromptTemplate] = None,
**kwargs: Any
) -> Chain:
"... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/tagging.html |
25fc914019ee-0 | Source code for langchain.chains.openai_functions.citation_fuzzy_match
from typing import Iterator, List
from pydantic import BaseModel, Field
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import get_llm_kwargs
from langchain.output_parsers.openai_functions import (
Pydantic... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
25fc914019ee-1 | if s is not None:
yield from s.spans()
[docs] def get_spans(self, context: str) -> Iterator[str]:
for quote in self.substring_quote:
yield from self._get_span(quote, context)
[docs]class QuestionAnswer(BaseModel):
"""A question and its answer as a list of facts each one should hav... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
25fc914019ee-2 | HumanMessagePromptTemplate.from_template("Question: {question}"),
HumanMessage(
content=(
"Tips: Make sure to cite your sources, "
"and use the exact words from the context."
)
),
]
prompt = ChatPromptTemplate(messages=messages)
chain =... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/citation_fuzzy_match.html |
9ff602e09242-0 | 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.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import get_llm_kwargs
from langchain.output_parsers.openai_functions import... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/qa_with_structure.html |
9ff602e09242-1 | 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 |
9ff602e09242-2 | 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.
**kwargs: Keyword arguments to... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/qa_with_structure.html |
bbd8d1a9a241-0 | Source code for langchain.chains.openai_functions.utils
from typing import Any, Dict
def _resolve_schema_references(schema: Any, definitions: Dict[str, Any]) -> Any:
"""
Resolves the $ref keys in a JSON schema object using the provided definitions.
"""
if isinstance(schema, list):
for i, item in... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/openai_functions/utils.html |
92322bd09305-0 | 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 pydantic import Extra, root_validator
from langchain.callbacks.manager impor... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
92322bd09305-1 | verbose=verbose,
),
LLMChain(
llm=llm,
prompt=check_assertions_prompt,
output_key="checked_assertions",
verbose=verbose,
),
LLMChain(
llm=llm,
prompt=revised_summary_prompt,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
92322bd09305-2 | 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 = Extra.forbid
arbitr... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
92322bd09305-3 | 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 = None
original_input ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
92322bd09305-4 | create_assertions_prompt,
check_assertions_prompt,
revised_summary_prompt,
are_all_true_prompt,
verbose=verbose,
)
return cls(sequential_chain=chain, verbose=verbose, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
9416ab94220d-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.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.consti... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
9416ab94220d-1 | critique_chain: LLMChain
revision_chain: LLMChain
return_intermediate_steps: bool = False
[docs] @classmethod
def get_principles(
cls, names: Optional[List[str]] = None
) -> List[ConstitutionalPrinciple]:
if names is None:
return list(PRINCIPLES.values())
else:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
9416ab94220d-2 | ) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
response = self.chain.run(
**inputs,
callbacks=_run_manager.get_child("original"),
)
initial_response = response
input_prompt = self.chain.prompt.format(**inpu... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
9416ab94220d-3 | _run_manager.on_text(
text=f"Applying {constitutional_principle.name}..." + "\n\n",
verbose=self.verbose,
color="green",
)
_run_manager.on_text(
text="Critique: " + critique + "\n\n",
verbose=self.verbose,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/base.html |
56625d218a56-0 | Source code for langchain.chains.constitutional_ai.models
"""Models for the Constitutional AI chain."""
from pydantic import BaseModel
[docs]class ConstitutionalPrinciple(BaseModel):
"""Class for a constitutional principle."""
critique_request: str
revision_request: str
name: str = "Constitutional Princ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/constitutional_ai/models.html |
0d9a73c05157-0 | Source code for langchain.chains.sql_database.query
from typing import List, Optional, TypedDict, Union
from langchain.chains.sql_database.prompt import PROMPT, SQL_PROMPTS
from langchain.schema.language_model import BaseLanguageModel
from langchain.schema.output_parser import NoOpOutputParser
from langchain.schema.pro... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/query.html |
0d9a73c05157-1 | prompt_to_use = SQL_PROMPTS[db.dialect]
else:
prompt_to_use = PROMPT
inputs = {
"input": lambda x: x["question"] + "\nSQLQuery: ",
"top_k": lambda _: k,
"table_info": lambda x: db.get_table_info(
table_names=x.get("table_names_to_use")
),
}
if "dialect... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/sql_database/query.html |
e17a3935c093-0 | 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.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
e17a3935c093-1 | """
_prompt = prompt or PROMPT_SELECTOR.get_prompt(llm)
chain = LLMChain(llm=llm, prompt=_prompt)
return cls(llm_chain=chain, **kwargs)
@property
def _chain_type(self) -> str:
raise NotImplementedError
@property
def input_keys(self) -> List[str]:
return [self.inpu... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/qa_generation/base.html |
77db7f780f3a-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 |
77db7f780f3a-1 | """Return the prompt length given the documents passed in.
This can be used by a caller to determine whether passing in a list
of documents would exceed a certain prompt length. This useful when
trying to ensure that the size of a prompt remains below a certain
context limit.
Arg... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
77db7f780f3a-2 | run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""Prepare inputs, call combine docs, prepare outputs."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
docs = inputs[self.input_key]
# Other keys are assumed to be needed for... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
77db7f780f3a-3 | This chain takes a single document as input, and then splits it up into chunks
and then passes those chucks to the CombineDocumentsChain.
"""
input_key: str = "input_document" #: :meta private:
text_splitter: TextSplitter = Field(default_factory=RecursiveCharacterTextSplitter)
combine_docs_chain: B... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
fc545f71c6ae-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 pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import Callbacks
fr... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
fc545f71c6ae-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
# `... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
fc545f71c6ae-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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
fc545f71c6ae-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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
fc545f71c6ae-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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
fc545f71c6ae-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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/refine.html |
0c63d08ed8b7-0 | 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
from pydantic import Extra, root_validator
from langchain.callbacks.manager import Ca... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
0c63d08ed8b7-1 | # `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 summaries
reduce_prompt = PromptTemplate.from_template(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
0c63d08ed8b7-2 | )
"""
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 ReduceDocumentChain or StuffDocumentChain."""
document_variable_n... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
0c63d08ed8b7-3 | collapse_documents_chain=collapse_chain,
)
values["reduce_documents_chain"] = reduce_chain
del values["combine_document_chain"]
if "collapse_document_chain" in values:
del values["collapse_document_chain"]
return values
@root_validator(pre=True... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
0c63d08ed8b7-4 | if self.reduce_documents_chain.collapse_documents_chain:
return self.reduce_documents_chain.collapse_documents_chain
else:
return self.reduce_documents_chain.combine_documents_chain
else:
raise ValueError(
f"`reduce_documents_chain` is of t... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
0c63d08ed8b7-5 | # This uses metadata from the docs, and the textual results from `results`
for i, r in enumerate(map_results)
]
result, extra_return_dict = self.reduce_documents_chain.combine_docs(
result_docs, token_max=token_max, callbacks=callbacks, **kwargs
)
if self.return_i... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
0c63d08ed8b7-6 | )
if self.return_intermediate_steps:
intermediate_steps = [r[question_result_key] for r in map_results]
extra_return_dict["intermediate_steps"] = intermediate_steps
return result, extra_return_dict
@property
def _chain_type(self) -> str:
return "map_reduce_documen... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
71b572e3985b-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 |
71b572e3985b-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)
chain = StuffDocumentsChain(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
71b572e3985b-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 |
71b572e3985b-3 | """Return the prompt length given the documents passed in.
This can be used by a caller to determine whether passing in a list
of documents would exceed a certain prompt length. This useful when
trying to ensure that the size of a prompt remains below a certain
context limit.
Arg... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
71b572e3985b-4 | """Async stuff all documents into one prompt and pass to LLM.
Args:
docs: List of documents to join together into one variable
callbacks: Optional callbacks to pass along
**kwargs: additional parameters to use to get inputs to LLMChain.
Returns:
The first ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/stuff.html |
982a136a68f8-0 | 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 pydantic import Extra
from langchain.callbacks.manager import Callbacks
from langchain.c... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
982a136a68f8-1 | 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 in doc.metadata.items():
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
982a136a68f8-2 | `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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
982a136a68f8-3 | 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(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
982a136a68f8-4 | """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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
982a136a68f8-5 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
982a136a68f8-6 | 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
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
c3378698c10a-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 |
c3378698c10a-1 | )
output_parser = RegexParser(
regex=r"(.*?)\nScore: (.*)",
output_keys=["answer", "score"],
)
prompt = PromptTemplate(
template=prompt_template,
input_variables=["context"],
output_parser=output_parser,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
c3378698c10a-2 | _output_keys = _output_keys + ["intermediate_steps"]
if self.metadata_keys is not None:
_output_keys += self.metadata_keys
return _output_keys
@root_validator()
def validate_llm_output(cls, values: Dict) -> Dict:
"""Validate that the combine chain outputs a dictionary."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
c3378698c10a-3 | "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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
c3378698c10a-4 | Args:
docs: List of documents to combine
callbacks: Callbacks to be passed through
**kwargs: additional parameters to be passed to LLM calls (like other
input variables besides the documents)
Returns:
The first element returned is the single string... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
cffbb0f769b2-0 | Source code for langchain.chains.graph_qa.arangodb
"""Question answering over a graph."""
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForC... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
cffbb0f769b2-1 | @property
def input_keys(self) -> List[str]:
return [self.input_key]
@property
def output_keys(self) -> List[str]:
return [self.output_key]
@property
def _chain_type(self) -> str:
return "graph_aql_chain"
[docs] @classmethod
def from_llm(
cls,
llm: Base... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
cffbb0f769b2-2 | :var top_k: The maximum number of AQL Query Results to return
:type top_k: int
:var aql_examples: A set of AQL Query Examples that are passed to
the AQL Generation Prompt Template to promote few-shot-learning.
Defaults to an empty string.
:type aql_examples: str
:... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
cffbb0f769b2-3 | ):
#####################
# Extract AQL Query #
pattern = r"```(?i:aql)?(.*?)```"
matches = re.findall(pattern, aql_generation_output, re.DOTALL)
if not matches:
_run_manager.on_text(
"Invalid Response: ", end="\n", verbose=s... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
cffbb0f769b2-4 | },
callbacks=callbacks,
)
########################
#####################
aql_generation_attempt += 1
if aql_result is None:
m = f"""
Maximum amount of AQL Query Generation attempts reached.
Un... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
25ed9e5ea744-0 | Source code for langchain.chains.graph_qa.nebulagraph
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchai... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/nebulagraph.html |
25ed9e5ea744-1 | **kwargs: Any,
) -> NebulaGraphQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
ngql_generation_chain = LLMChain(llm=llm, prompt=ngql_prompt)
return cls(
qa_chain=qa_chain,
ngql_generation_chain=ngql_generation_chain,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/nebulagraph.html |
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