id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
a1668b2fc254-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
a1668b2fc254-1 | return create_model(
"CombineDocumentsOutput",
**{self.output_key: (str, None)}, # type: ignore[call-overload]
)
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def out... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
a1668b2fc254-2 | """
[docs] @abstractmethod
async def acombine_docs(
self, docs: List[Document], **kwargs: Any
) -> Tuple[str, dict]:
"""Combine documents into a single string.
Args:
docs: List[Document], the documents to combine
**kwargs: Other parameters to use in combining d... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
a1668b2fc254-3 | # 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_dict = await self.acombine_docs(
docs, callbacks=_run_manager.get_child(), **other_keys
)
extra_return_dict[self.output_key] = o... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
a1668b2fc254-4 | def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""Split document into chunks and pass to CombineDocumentsChain."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
docu... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/base.html |
ceddc520f316-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/reduce.html |
ceddc520f316-1 | 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 |
ceddc520f316-2 | `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 |
ceddc520f316-3 | 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 |
ceddc520f316-4 | """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 |
ceddc520f316-5 | 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 |
ceddc520f316-6 | 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 |
d0fbccd53f81-0 | 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 langchain.callbacks.manager import Callbacks
from langcha... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
d0fbccd53f81-1 | "you are. Context: {content}"
)
output_parser = RegexParser(
regex=r"(.*?)\nScore: (.*)",
output_keys=["answer", "score"],
)
prompt = PromptTemplate(
template=prompt_template,
input_variables=["context"],
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
d0fbccd53f81-2 | }
if self.return_intermediate_steps:
schema["intermediate_steps"] = (List[str], None)
if self.metadata_keys:
schema.update({key: (Any, None) for key in self.metadata_keys})
return create_model("MapRerankOutput", **schema)
@property
def output_keys(self) -> List[st... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
d0fbccd53f81-3 | )
return values
@root_validator(pre=True)
def get_default_document_variable_name(cls, values: Dict) -> Dict:
"""Get default document variable name, if not provided."""
if "document_variable_name" not in values:
llm_chain_variables = values["llm_chain"].prompt.input_variables
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
d0fbccd53f81-4 | # FYI - this is parallelized and so it is fast.
[{**{self.document_variable_name: d.page_content}, **kwargs} for d in docs],
callbacks=callbacks,
)
return self._process_results(docs, results)
[docs] async def acombine_docs(
self, docs: List[Document], callbacks: Callba... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
d0fbccd53f81-5 | extra_info = {}
if self.metadata_keys is not None:
for key in self.metadata_keys:
extra_info[key] = document.metadata[key]
if self.return_intermediate_steps:
extra_info["intermediate_steps"] = results
return output[self.answer_key], extra_info
@propert... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_rerank.html |
6ee4ed6c314b-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 langchain.callbacks.manager import Callbacks
from langchain.chains.combine_docum... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
6ee4ed6c314b-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 |
6ee4ed6c314b-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 |
6ee4ed6c314b-3 | if "reduce_documents_chain" in values:
raise ValueError(
"Both `reduce_documents_chain` and `combine_document_chain` "
"cannot be provided at the same time. `combine_document_chain` "
"is deprecated, please only provide `reduce_documents_chain`... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
6ee4ed6c314b-4 | 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_variable_name']} was "
f"not found in llm_ch... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
6ee4ed6c314b-5 | 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(
# FYI - this is parallelized and so it is fast.
[{self.document_variable_na... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
6ee4ed6c314b-6 | callbacks=callbacks,
)
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 results from `results`
for i, r in enumerate(map... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/combine_documents/map_reduce.html |
66a0d75e0389-0 | Source code for langchain.chains.llm_math.base
"""Chain that interprets a prompt and executes python code to do math."""
from __future__ import annotations
import math
import re
import warnings
from typing import Any, Dict, List, Optional
import numexpr
from langchain.callbacks.manager import (
AsyncCallbackManager... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
66a0d75e0389-1 | if "llm" in values:
warnings.warn(
"Directly instantiating an LLMMathChain with an llm is deprecated. "
"Please instantiate with llm_chain argument or using the from_llm "
"class method."
)
if "llm_chain" not in values and values["llm"]... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
66a0d75e0389-2 | ) -> Dict[str, str]:
run_manager.on_text(llm_output, color="green", verbose=self.verbose)
llm_output = llm_output.strip()
text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
if text_match:
expression = text_match.group(1)
output = self._evaluate_exp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
66a0d75e0389-3 | elif llm_output.startswith("Answer:"):
answer = llm_output
elif "Answer:" in llm_output:
answer = "Answer: " + llm_output.split("Answer:")[-1]
else:
raise ValueError(f"unknown format from LLM: {llm_output}")
return {self.output_key: answer}
def _call(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
66a0d75e0389-4 | [docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: BasePromptTemplate = PROMPT,
**kwargs: Any,
) -> LLMMathChain:
llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(llm_chain=llm_chain, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_math/base.html |
d63617f169f1-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
d63617f169f1-1 | 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 |
d63617f169f1-2 | """[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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
d63617f169f1-3 | 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 =... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
d63617f169f1-4 | llm,
create_assertions_prompt,
check_assertions_prompt,
revised_summary_prompt,
are_all_true_prompt,
verbose=verbose,
)
return cls(sequential_chain=chain, verbose=verbose, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_summarization_checker/base.html |
9b097ba73753-0 | 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 |
9b097ba73753-1 | 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 |
121708d2bad9-0 | 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 langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base i... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
121708d2bad9-1 | def raise_deprecation(cls, values: Dict) -> Dict:
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" n... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
121708d2bad9-2 | )
_run_manager.on_text(t, color="green", verbose=self.verbose)
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, ... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/llm_bash/base.html |
20935118e100-0 | Source code for langchain.chains.conversation.base
"""Chain that carries on a conversation and calls an LLM."""
from typing import Dict, List
from langchain.chains.conversation.prompt import PROMPT
from langchain.chains.llm import LLMChain
from langchain.memory.buffer import ConversationBufferMemory
from langchain.pyda... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
20935118e100-1 | f"The input key {input_key} was also found in the memory keys "
f"({memory_keys}) - please provide keys that don't overlap."
)
prompt_variables = values["prompt"].input_variables
expected_keys = memory_keys + [input_key]
if set(expected_keys) != set(prompt_variables):... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversation/base.html |
b812e3b8d681-0 | Source code for langchain.chains.elasticsearch_database.base
"""Chain for interacting with Elasticsearch Database."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
b812e3b8d681-1 | output_key: str = "result" #: :meta private:
sample_documents_in_index_info: int = 3
return_intermediate_steps: bool = False
"""Whether or not to return the intermediate steps along with the final answer."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbi... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
b812e3b8d681-2 | if self.sample_documents_in_index_info > 0:
for k, v in mappings.items():
hits = self.database.search(
index=k,
query={"match_all": {}},
size=self.sample_documents_in_index_info,
)["hits"]["hits"]
hit... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
b812e3b8d681-3 | es_cmd = self.query_chain.run(
callbacks=_run_manager.get_child(),
**query_inputs,
)
_run_manager.on_text(es_cmd, color="green", verbose=self.verbose)
intermediate_steps.append(
es_cmd
) # output: elasticsearch dsl generati... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
b812e3b8d681-4 | [docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
database: Elasticsearch,
*,
query_prompt: Optional[BasePromptTemplate] = None,
answer_prompt: Optional[BasePromptTemplate] = None,
query_output_parser: Optional[BaseLLMOutputParser] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/elasticsearch_database/base.html |
25f83aeb6232-0 | 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 langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
f... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
25f83aeb6232-1 | 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 |
25f83aeb6232-2 | api_response=api_response,
callbacks=_run_manager.get_child(),
)
return {self.output_key: answer}
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_man... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
25f83aeb6232-3 | """Load chain from just an LLM and the api docs."""
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_... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/base.html |
55304d3ec343-0 | Source code for langchain.chains.api.openapi.requests_chain
"""request parser."""
import json
import re
from typing import Any
from langchain.chains.api.openapi.prompts import REQUEST_TEMPLATE
from langchain.chains.llm import LLMChain
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import Base... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/requests_chain.html |
55304d3ec343-1 | ) -> LLMChain:
"""Get the request parser."""
output_parser = APIRequesterOutputParser()
prompt = PromptTemplate(
template=REQUEST_TEMPLATE,
output_parser=output_parser,
partial_variables={"schema": typescript_definition},
input_variables=["instruct... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/requests_chain.html |
88c729bdd8cd-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 requests import Response
from langchain.callbacks.manager import Callb... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
88c729bdd8cd-1 | :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 |
88c729bdd8cd-2 | 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 |
88c729bdd8cd-3 | 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_operation.method.value)
response_text = (
f"{api_response.status_code... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
88c729bdd8cd-4 | # 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(
operation,
requests=requests,
llm=llm,
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
88c729bdd8cd-5 | requests=_requests,
param_mapping=param_mapping,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
callbacks=callbacks,
**kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/chain.html |
0a81ddcd3544-0 | Source code for langchain.chains.api.openapi.response_chain
"""Response parser."""
import json
import re
from typing import Any
from langchain.chains.api.openapi.prompts import RESPONSE_TEMPLATE
from langchain.chains.llm import LLMChain
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import Ba... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/response_chain.html |
0a81ddcd3544-1 | template=RESPONSE_TEMPLATE,
output_parser=output_parser,
input_variables=["response", "instructions"],
)
return cls(prompt=prompt, llm=llm, verbose=verbose, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/chains/api/openapi/response_chain.html |
515b85ab6c97-0 | Source code for langchain.chains.graph_qa.cypher
"""Question answering over a graph."""
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prom... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
515b85ab6c97-1 | for k, v in structured_schema.get("node_props", {}).items()
if filter_func(k)
},
"rel_props": {
k: v
for k, v in structured_schema.get("rel_props", {}).items()
if filter_func(k)
},
"relationships": [
r
for r in struc... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
515b85ab6c97-2 | """Whether or not to return the result of querying the graph directly."""
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the output keys.
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
515b85ab6c97-3 | ", and 'llm', but not all three simultaneously."
)
qa_chain = LLMChain(llm=qa_llm or llm, prompt=qa_prompt)
cypher_generation_chain = LLMChain(llm=cypher_llm or llm, prompt=cypher_prompt)
if exclude_types and include_types:
raise ValueError(
"Either `exclu... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
515b85ab6c97-4 | )
intermediate_steps.append({"query": generated_cypher})
# Retrieve and limit the number of results
context = self.graph.query(generated_cypher)[: self.top_k]
if self.return_direct:
final_result = context
else:
_run_manager.on_text("Full Context:", end="\n... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/cypher.html |
e5ba83ad1ccf-0 | Source code for langchain.chains.graph_qa.hugegraph
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts imp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/hugegraph.html |
e5ba83ad1ccf-1 | **kwargs: Any,
) -> HugeGraphQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
gremlin_generation_chain = LLMChain(llm=llm, prompt=gremlin_prompt)
return cls(
qa_chain=qa_chain,
gremlin_generation_chain=gremlin_generation_chain... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/hugegraph.html |
8c1cc13421ad-0 | Source code for langchain.chains.graph_qa.base
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import E... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
8c1cc13421ad-1 | ) -> GraphQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
entity_chain = LLMChain(llm=llm, prompt=entity_prompt)
return cls(
qa_chain=qa_chain,
entity_extraction_chain=entity_chain,
**kwargs,
)
def _call(
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/base.html |
3c5bf027e1a0-0 | Source code for langchain.chains.graph_qa.kuzu
"""Question answering over a graph."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import C... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/kuzu.html |
3c5bf027e1a0-1 | *,
qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
cypher_prompt: BasePromptTemplate = KUZU_GENERATION_PROMPT,
**kwargs: Any,
) -> KuzuQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
cypher_generation_chain = LLMChain(llm=llm, prom... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/kuzu.html |
3c5bf027e1a0-2 | )
result = self.qa_chain(
{"question": question, "context": context},
callbacks=callbacks,
)
return {self.output_key: result[self.qa_chain.output_key]} | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/kuzu.html |
9b06638bd331-0 | 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 langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chai... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
9b06638bd331-1 | max_aql_generation_attempts: int = 3
@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... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
9b06638bd331-2 | Users can modify the following ArangoGraphQAChain Class Variables:
: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.
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/arangodb.html |
9b06638bd331-3 | ):
#####################
# 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 |
9b06638bd331-4 | },
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 |
f6a9abe2e0e3-0 | Source code for langchain.chains.graph_qa.sparql
"""
Question answering over an RDF or OWL graph using SPARQL.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.cha... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/sparql.html |
f6a9abe2e0e3-1 | cls,
llm: BaseLanguageModel,
*,
qa_prompt: BasePromptTemplate = SPARQL_QA_PROMPT,
sparql_select_prompt: BasePromptTemplate = SPARQL_GENERATION_SELECT_PROMPT,
sparql_update_prompt: BasePromptTemplate = SPARQL_GENERATION_UPDATE_PROMPT,
sparql_intent_prompt: BasePromptTempla... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/sparql.html |
f6a9abe2e0e3-2 | callbacks = _run_manager.get_child()
prompt = inputs[self.input_key]
_intent = self.sparql_intent_chain.run({"prompt": prompt}, callbacks=callbacks)
intent = _intent.strip()
if "SELECT" in intent and "UPDATE" not in intent:
sparql_generation_chain = self.sparql_generation_sel... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/sparql.html |
f6a9abe2e0e3-3 | callbacks=callbacks,
)
res = result[self.qa_chain.output_key]
elif intent == "UPDATE":
self.graph.update(generated_sparql)
res = "Successfully inserted triples into the graph."
else:
raise ValueError("Unsupported SPARQL query type.")
re... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/sparql.html |
acb21419e1b3-0 | Source code for langchain.chains.graph_qa.neptune_cypher
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langcha... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/neptune_cypher.html |
acb21419e1b3-1 | # The pattern to find Cypher code enclosed in triple backticks
pattern = r"```(.*?)```"
# Find all matches in the input text
matches = re.findall(pattern, text, re.DOTALL)
return matches[0] if matches else text
[docs]def use_simple_prompt(llm: BaseLanguageModel) -> bool:
"""Decides whether to use th... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/neptune_cypher.html |
acb21419e1b3-2 | top_k: int = 10
return_intermediate_steps: bool = False
"""Whether or not to return the intermediate steps along with the final answer."""
return_direct: bool = False
"""Whether or not to return the result of querying the graph directly."""
@property
def input_keys(self) -> List[str]:
""... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/neptune_cypher.html |
acb21419e1b3-3 | """Generate Cypher statement, use it to look up in db and answer question."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
question = inputs[self.input_key]
intermediate_steps: List = []
generated_cypher = self.c... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/neptune_cypher.html |
971f5e2c9bc8-0 | Source code for langchain.chains.graph_qa.falkordb
"""Question answering over a graph."""
from __future__ import annotations
import re
from typing import Any, Dict, List, Optional
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chai... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/falkordb.html |
971f5e2c9bc8-1 | """Number of results to return from the query"""
return_intermediate_steps: bool = False
"""Whether or not to return the intermediate steps along with the final answer."""
return_direct: bool = False
"""Whether or not to return the result of querying the graph directly."""
@property
def input_ke... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/falkordb.html |
971f5e2c9bc8-2 | ) -> Dict[str, Any]:
"""Generate Cypher statement, use it to look up in db and answer question."""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
question = inputs[self.input_key]
intermediate_steps: List = []
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/falkordb.html |
3fe63f7bfed4-0 | 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 langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts i... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/graph_qa/nebulagraph.html |
3fe63f7bfed4-1 | **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 |
1003b619db12-0 | 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, Union
from langchain.ca... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
1003b619db12-1 | 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"Unsupported chat history format: {type(dialogue_turn)}."
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
1003b619db12-2 | """An optional function to get a string of the chat history.
If None is provided, will use a default."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
allow_population_by_field_name = True
@property
def inp... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
1003b619db12-3 | )
else:
new_question = question
accepts_run_manager = (
"run_manager" in inspect.signature(self._get_docs).parameters
)
if accepts_run_manager:
docs = self._get_docs(new_question, inputs, run_manager=_run_manager)
else:
docs = self.... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
1003b619db12-4 | if chat_history_str:
callbacks = _run_manager.get_child()
new_question = await self.question_generator.arun(
question=question, chat_history=chat_history_str, callbacks=callbacks
)
else:
new_question = question
accepts_run_manager = (
... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
1003b619db12-5 | The algorithm for this chain consists of three parts:
1. Use the chat history and the new question to create a "standalone question".
This is done so that this question can be passed into the retrieval step to fetch
relevant documents. If only the new question was passed in, then relevant context
may be... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
1003b619db12-6 | retriever=retriever,
question_generator=question_generator_chain,
)
"""
retriever: BaseRetriever
"""Retriever to use to fetch documents."""
max_tokens_limit: Optional[int] = None
"""If set, enforces that the documents returned are less than this limit.
This is only en... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
1003b619db12-7 | question, callbacks=run_manager.get_child()
)
return self._reduce_tokens_below_limit(docs)
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
retriever: BaseRetriever,
condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT,
chai... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
1003b619db12-8 | callbacks: Callbacks to pass to all subchains.
**kwargs: Additional parameters to pass when initializing
ConversationalRetrievalChain
"""
combine_docs_chain_kwargs = combine_docs_chain_kwargs or {}
doc_chain = load_qa_chain(
llm,
chain_type=cha... | https://api.python.langchain.com/en/latest/_modules/langchain/chains/conversational_retrieval/base.html |
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