id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 59 127 |
|---|---|---|
d791178e0d57-1 | verbose=verbose,
),
LLMChain(
llm=llm,
prompt=check_assertions_prompt,
output_key="checked_assertions",
verbose=verbose,
),
LLMChain(
llm=llm,
prompt=revised_summary_prompt,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_summarization_checker/base.html |
d791178e0d57-2 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_summarization_checker/base.html |
d791178e0d57-3 | 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 ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_summarization_checker/base.html |
d791178e0d57-4 | create_assertions_prompt,
check_assertions_prompt,
revised_summary_prompt,
are_all_true_prompt,
verbose=verbose,
)
return cls(sequential_chain=chain, verbose=verbose, **kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last up... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_summarization_checker/base.html |
f54b57b10006-0 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/base.html |
f54b57b10006-1 | """
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return [self.output_key]
def prompt_length(self, docs: List[Document], **kwargs: Any) -> Optional[int]:
"""Return the prompt length given the doc... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/base.html |
f54b57b10006-2 | ) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
docs = inputs[self.input_key]
# Other keys are assumed to be needed for LLM prediction
other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
output, extra_return_... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/base.html |
f54b57b10006-3 | other_keys[self.combine_docs_chain.input_key] = docs
return self.combine_docs_chain(
other_keys, return_only_outputs=True, callbacks=_run_manager.get_child()
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/combine_documents/base.html |
cf5e270ef9d0-0 | Source code for langchain.chains.sql_database.base
"""Chain for interacting with SQL Database."""
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.callbac... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/sql_database/base.html |
cf5e270ef9d0-1 | 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 SQL table directly."""
use_query_checker: bool = False
"""Whether or not the query checker too... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/sql_database/base.html |
cf5e270ef9d0-2 | :meta private:
"""
if not self.return_intermediate_steps:
return [self.output_key]
else:
return [self.output_key, INTERMEDIATE_STEPS_KEY]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/sql_database/base.html |
cf5e270ef9d0-3 | result = self.database.run(sql_cmd)
intermediate_steps.append(str(result)) # output: sql exec
else:
query_checker_prompt = self.query_checker_prompt or PromptTemplate(
template=QUERY_CHECKER, input_variables=["query", "dialect"]
)
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/sql_database/base.html |
cf5e270ef9d0-4 | llm_inputs["input"] = input_text
intermediate_steps.append(llm_inputs) # input: final answer
final_result = self.llm_chain.predict(
callbacks=_run_manager.get_child(),
**llm_inputs,
).strip()
intermediate_steps.appe... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/sql_database/base.html |
cf5e270ef9d0-5 | 2. Based on those tables, call the normal SQL database chain.
This is useful in cases where the number of tables in the database is large.
"""
decider_chain: LLMChain
sql_chain: SQLDatabaseChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
return_... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/sql_database/base.html |
cf5e270ef9d0-6 | def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_table_names = self.sql_chain.database.get_usable_table_names()
table_na... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/sql_database/base.html |
f043fac22094-0 | Source code for langchain.chains.hyde.base
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
import numpy as np
from pydantic import Extra
from langchain.base_language import BaseLanguageModel
from langchain.callback... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/hyde/base.html |
f043fac22094-1 | return list(np.array(embeddings).mean(axis=0))
[docs] def embed_query(self, text: str) -> List[float]:
"""Generate a hypothetical document and embedded it."""
var_name = self.llm_chain.input_keys[0]
result = self.llm_chain.generate([{var_name: text}])
documents = [generation.text for ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/hyde/base.html |
c758ad599ce9-0 | Source code for langchain.chains.qa_with_sources.base
"""Question answering with sources over documents."""
from __future__ import annotations
import re
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.base_language import BaseLan... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/base.html |
c758ad599ce9-1 | document_prompt: BasePromptTemplate = EXAMPLE_PROMPT,
question_prompt: BasePromptTemplate = QUESTION_PROMPT,
combine_prompt: BasePromptTemplate = COMBINE_PROMPT,
**kwargs: Any,
) -> BaseQAWithSourcesChain:
"""Construct the chain from an LLM."""
llm_question_chain = LLMChain(l... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/base.html |
c758ad599ce9-2 | def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.question_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
_output_keys = [self.answer_key, self.sources_answer_key]
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/base.html |
c758ad599ce9-3 | }
if self.return_source_documents:
result["source_documents"] = docs
return result
@abstractmethod
async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
"""Get docs to run questioning over."""
async def _acall(
self,
inputs: Dict[str, Any],... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/base.html |
c758ad599ce9-4 | return inputs.pop(self.input_docs_key)
@property
def _chain_type(self) -> str:
return "qa_with_sources_chain"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/base.html |
92de44e5f839-0 | Source code for langchain.chains.qa_with_sources.vector_db
"""Question-answering with sources over a vector database."""
import warnings
from typing import Any, Dict, List
from pydantic import Field, root_validator
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_so... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/vector_db.html |
92de44e5f839-1 | num_docs -= 1
token_count -= tokens[num_docs]
return docs[:num_docs]
def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
question = inputs[self.question_key]
docs = self.vectorstore.similarity_search(
question, k=self.k, **self.search_kwargs
)
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/vector_db.html |
022fb0123bab-0 | Source code for langchain.chains.qa_with_sources.retrieval
"""Question-answering with sources over an index."""
from typing import Any, Dict, List
from pydantic import Field
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/retrieval.html |
022fb0123bab-1 | docs = self.retriever.get_relevant_documents(question)
return self._reduce_tokens_below_limit(docs)
async def _aget_docs(self, inputs: Dict[str, Any]) -> List[Document]:
question = inputs[self.question_key]
docs = await self.retriever.aget_relevant_documents(question)
return self._re... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/qa_with_sources/retrieval.html |
947118fa9f6e-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 pydantic import Extra, root_validator
from langchain.base_lan... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_math/base.html |
947118fa9f6e-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"]... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_math/base.html |
947118fa9f6e-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_math/base.html |
947118fa9f6e-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(
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_math/base.html |
947118fa9f6e-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)
By Harrison Chase
© Copyright... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm_math/base.html |
a3093a112b78-0 | Source code for langchain.chains.retrieval_qa.base
"""Chain for question-answering against a vector database."""
from __future__ import annotations
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.base_language i... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/retrieval_qa/base.html |
a3093a112b78-1 | def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
if self.return_source_documents:
_output_keys = _output_keys + ["source_documents"]
return _output_keys
@classmethod
def from_llm(
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/retrieval_qa/base.html |
a3093a112b78-2 | def _get_docs(self, question: str) -> List[Document]:
"""Get documents to do question answering over."""
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run get_relevant_text and llm on input query... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/retrieval_qa/base.html |
a3093a112b78-3 | the retrieved documents as well under the key 'source_documents'.
Example:
.. code-block:: python
res = indexqa({'query': 'This is my query'})
answer, docs = res['result'], res['source_documents']
"""
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/retrieval_qa/base.html |
a3093a112b78-4 | def _chain_type(self) -> str:
"""Return the chain type."""
return "retrieval_qa"
[docs]class VectorDBQA(BaseRetrievalQA):
"""Chain for question-answering against a vector database."""
vectorstore: VectorStore = Field(exclude=True, alias="vectorstore")
"""Vector Database to connect to."""
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/retrieval_qa/base.html |
a3093a112b78-5 | question, k=self.k, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def _aget_docs(self, question: str) -> List[Document]:
raise NotImplementedError("VectorDBQA does not support async")
@property
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/retrieval_qa/base.html |
24db6450623b-0 | Source code for langchain.chains.conversation.base
"""Chain that carries on a conversation and calls an LLM."""
from typing import Dict, List
from pydantic import Extra, Field, root_validator
from langchain.chains.conversation.prompt import PROMPT
from langchain.chains.llm import LLMChain
from langchain.memory.buffer i... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/conversation/base.html |
24db6450623b-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):... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/conversation/base.html |
df77e24cd9d0-0 | Source code for langchain.chains.api.base
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Field, root_validator
from langchain.base_language import BaseLanguageModel
from langchain.ca... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/api/base.html |
df77e24cd9d0-1 | 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) -> Dict:
"""Check that api answer prompt expec... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/api/base.html |
df77e24cd9d0-2 | return {self.output_key: answer}
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
question = inputs[self.que... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/api/base.html |
df77e24cd9d0-3 | requests_wrapper = TextRequestsWrapper(headers=headers)
get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt)
return cls(
api_request_chain=get_request_chain,
api_answer_chain=get_answer_chain,
requests_wrapper=requests_wrapper,
api_docs=api_doc... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/api/base.html |
8f0fcfc20bfb-0 | Source code for langchain.chains.api.openapi.chain
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
import json
from typing import Any, Dict, List, NamedTuple, Optional, cast
from pydantic import BaseModel, Field
from requests import Response
from la... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/api/openapi/chain.html |
8f0fcfc20bfb-1 | """
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, "intermediate_steps"]
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/api/openapi/chain.html |
8f0fcfc20bfb-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/api/openapi/chain.html |
8f0fcfc20bfb-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... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/api/openapi/chain.html |
8f0fcfc20bfb-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,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/api/openapi/chain.html |
8f0fcfc20bfb-5 | requests=_requests,
param_mapping=param_mapping,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
callbacks=callbacks,
**kwargs,
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/api/openapi/chain.html |
a4536c037b83-0 | Source code for langchain.retrievers.chatgpt_plugin_retriever
from __future__ import annotations
from typing import List, Optional
import aiohttp
import requests
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs]class ChatGPTPluginRetriever(BaseRetriever, BaseModel):
url: str... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
a4536c037b83-1 | ) as response:
res = await response.json()
results = res["results"][0]["results"]
docs = []
for d in results:
content = d.pop("text")
metadata = d.pop("metadata", d)
if metadata.get("source_id"):
metadata["source"] = metadata.po... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
e739168c01d9-0 | Source code for langchain.retrievers.elastic_search_bm25
"""Wrapper around Elasticsearch vector database."""
from __future__ import annotations
import uuid
from typing import Any, Iterable, List
from langchain.docstore.document import Document
from langchain.schema import BaseRetriever
[docs]class ElasticSearchBM25Retr... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/elastic_search_bm25.html |
e739168c01d9-1 | self.index_name = index_name
[docs] @classmethod
def create(
cls, elasticsearch_url: str, index_name: str, k1: float = 2.0, b: float = 0.75
) -> ElasticSearchBM25Retriever:
from elasticsearch import Elasticsearch
# Create an Elasticsearch client instance
es = Elasticsearch(ela... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/elastic_search_bm25.html |
e739168c01d9-2 | raise ValueError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
requests = []
ids = []
for i, text in enumerate(texts):
_id = str(uuid.uuid4())
request = {
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/elastic_search_bm25.html |
19f6955e3979-0 | Source code for langchain.retrievers.tfidf
"""TF-IDF Retriever.
Largely based on
https://github.com/asvskartheek/Text-Retrieval/blob/master/TF-IDF%20Search%20Engine%20(SKLEARN).ipynb"""
from __future__ import annotations
from typing import Any, Dict, Iterable, List, Optional
from pydantic import BaseModel
from langchai... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/tfidf.html |
19f6955e3979-1 | return cls(vectorizer=vectorizer, docs=docs, tfidf_array=tfidf_array, **kwargs)
[docs] @classmethod
def from_documents(
cls,
documents: Iterable[Document],
*,
tfidf_params: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> TFIDFRetriever:
texts, metadatas = ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/tfidf.html |
f7819b69d7e5-0 | Source code for langchain.retrievers.zep
from __future__ import annotations
from typing import TYPE_CHECKING, Dict, List, Optional
from langchain.schema import BaseRetriever, Document
if TYPE_CHECKING:
from zep_python import MemorySearchResult
[docs]class ZepRetriever(BaseRetriever):
"""A Retriever implementati... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/zep.html |
f7819b69d7e5-1 | )
for r in results
if r.message
]
[docs] def get_relevant_documents(
self, query: str, metadata: Optional[Dict] = None
) -> List[Document]:
from zep_python import MemorySearchPayload
payload: MemorySearchPayload = MemorySearchPayload(
text=query... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/zep.html |
ba158238f972-0 | Source code for langchain.retrievers.remote_retriever
from typing import List, Optional
import aiohttp
import requests
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs]class RemoteLangChainRetriever(BaseRetriever, BaseModel):
url: str
headers: Optional[dict] = None
i... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/remote_retriever.html |
5ab5b5df176e-0 | Source code for langchain.retrievers.aws_kendra_index_retriever
"""Retriever wrapper for AWS Kendra."""
import re
from typing import Any, Dict, List
from langchain.schema import BaseRetriever, Document
[docs]class AwsKendraIndexRetriever(BaseRetriever):
"""Wrapper around AWS Kendra."""
kendraindex: str
"""K... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/aws_kendra_index_retriever.html |
5ab5b5df176e-1 | doc_excerpt = self._clean_result(res_text)
combined_text = f"""Document Title: {doc_title}
Document Excerpt: {doc_excerpt}
"""
return Document(
page_content=combined_text,
metadata={
"source": doc_uri,
"title": doc_title,
"excerpt":... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/aws_kendra_index_retriever.html |
67cb7bb02754-0 | Source code for langchain.retrievers.svm
"""SMV Retriever.
Largely based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
from __future__ import annotations
import concurrent.futures
from typing import Any, List, Optional
import numpy as np
from pydantic import BaseModel
from langchain.embedding... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/svm.html |
67cb7bb02754-1 | y[0] = 1
clf = svm.LinearSVC(
class_weight="balanced", verbose=False, max_iter=10000, tol=1e-6, C=0.1
)
clf.fit(x, y)
similarities = clf.decision_function(x)
sorted_ix = np.argsort(-similarities)
# svm.LinearSVC in scikit-learn is non-deterministic.
# ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/svm.html |
dfb467d7ec76-0 | Source code for langchain.retrievers.contextual_compression
"""Retriever that wraps a base retriever and filters the results."""
from typing import List
from pydantic import BaseModel, Extra
from langchain.retrievers.document_compressors.base import (
BaseDocumentCompressor,
)
from langchain.schema import BaseRetri... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/contextual_compression.html |
dfb467d7ec76-1 | return list(compressed_docs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/contextual_compression.html |
bb8e89143840-0 | Source code for langchain.retrievers.time_weighted_retriever
"""Retriever that combines embedding similarity with recency in retrieving values."""
import datetime
from copy import deepcopy
from typing import Any, Dict, List, Optional, Tuple
from pydantic import BaseModel, Field
from langchain.schema import BaseRetrieve... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/time_weighted_retriever.html |
bb8e89143840-1 | """
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
def _get_combined_score(
self,
document: Document,
vector_relevance: Optional[float],
current_time: datetime.datetime,
) -> float:
"""Return the combined sco... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/time_weighted_retriever.html |
bb8e89143840-2 | for doc in self.memory_stream[-self.k :]
}
# If a doc is considered salient, update the salience score
docs_and_scores.update(self.get_salient_docs(query))
rescored_docs = [
(doc, self._get_combined_score(doc, relevance, current_time))
for doc, relevance in docs_a... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/time_weighted_retriever.html |
bb8e89143840-3 | doc.metadata["buffer_idx"] = len(self.memory_stream) + i
self.memory_stream.extend(dup_docs)
return self.vectorstore.add_documents(dup_docs, **kwargs)
[docs] async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Add documents to vectorstore.""... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/time_weighted_retriever.html |
f33751026e13-0 | Source code for langchain.retrievers.knn
"""KNN Retriever.
Largely based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
from __future__ import annotations
import concurrent.futures
from typing import Any, List, Optional
import numpy as np
from pydantic import BaseModel
from langchain.embedding... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/knn.html |
f33751026e13-1 | similarities = index_embeds.dot(query_embeds)
sorted_ix = np.argsort(-similarities)
denominator = np.max(similarities) - np.min(similarities) + 1e-6
normalized_similarities = (similarities - np.min(similarities)) / denominator
top_k_results = [
Document(page_content=self.text... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/knn.html |
a9c0445e574d-0 | Source code for langchain.retrievers.azure_cognitive_search
"""Retriever wrapper for Azure Cognitive Search."""
from __future__ import annotations
import json
from typing import Dict, List, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.schema import BaseRet... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/azure_cognitive_search.html |
a9c0445e574d-1 | )
values["api_key"] = get_from_dict_or_env(
values, "api_key", "AZURE_COGNITIVE_SEARCH_API_KEY"
)
return values
def _build_search_url(self, query: str) -> str:
base_url = f"https://{self.service_name}.search.windows.net/"
endpoint_path = f"indexes/{self.index_name... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/azure_cognitive_search.html |
a9c0445e574d-2 | search_results = self._search(query)
return [
Document(page_content=result.pop(self.content_key), metadata=result)
for result in search_results
]
[docs] async def aget_relevant_documents(self, query: str) -> List[Document]:
search_results = await self._asearch(query)
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/azure_cognitive_search.html |
c7671d075d44-0 | Source code for langchain.retrievers.weaviate_hybrid_search
"""Wrapper around weaviate vector database."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from uuid import uuid4
from pydantic import Extra
from langchain.docstore.document import Document
from langchain.schema import BaseR... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/weaviate_hybrid_search.html |
c7671d075d44-1 | "properties": [{"name": self._text_key, "dataType": ["text"]}],
"vectorizer": "text2vec-openai",
}
if not self._client.schema.exists(self._index_name):
self._client.schema.create_class(class_obj)
[docs] class Config:
"""Configuration for this pydantic object."""
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/weaviate_hybrid_search.html |
c7671d075d44-2 | if where_filter:
query_obj = query_obj.with_where(where_filter)
result = query_obj.with_hybrid(query, alpha=self.alpha).with_limit(self.k).do()
if "errors" in result:
raise ValueError(f"Error during query: {result['errors']}")
docs = []
for res in result["data"]["... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/weaviate_hybrid_search.html |
daed6da266ed-0 | Source code for langchain.retrievers.pinecone_hybrid_search
"""Taken from: https://docs.pinecone.io/docs/hybrid-search"""
import hashlib
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRe... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/pinecone_hybrid_search.html |
daed6da266ed-1 | # create dense vectors
dense_embeds = embeddings.embed_documents(context_batch)
# create sparse vectors
sparse_embeds = sparse_encoder.encode_documents(context_batch)
for s in sparse_embeds:
s["values"] = [float(s1) for s1 in s["values"]]
vectors = []
# loop t... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/pinecone_hybrid_search.html |
daed6da266ed-2 | """Validate that api key and python package exists in environment."""
try:
from pinecone_text.hybrid import hybrid_convex_scale # noqa:F401
from pinecone_text.sparse.base_sparse_encoder import (
BaseSparseEncoder, # noqa:F401
)
except ImportError:
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/pinecone_hybrid_search.html |
cdf5c77bf365-0 | Source code for langchain.retrievers.vespa_retriever
"""Wrapper for retrieving documents from Vespa."""
from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Sequence, Union
from langchain.schema import BaseRetriever, Document
if TYPE_CHECKING:
from ves... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/vespa_retriever.html |
cdf5c77bf365-1 | docs.append(Document(page_content=page_content, metadata=metadata))
return docs
[docs] def get_relevant_documents(self, query: str) -> List[Document]:
body = self._query_body.copy()
body["query"] = query
return self._query(body)
[docs] async def aget_relevant_documents(self, query:... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/vespa_retriever.html |
cdf5c77bf365-2 | document metadata. Defaults to empty tuple ().
sources (Sequence[str] or "*" or None): Sources to retrieve
from. Defaults to None.
_filter (Optional[str]): Document filter condition expressed in YQL.
Defaults to None.
yql (Optional[str]): Full YQL quer... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/vespa_retriever.html |
d05d3131bd44-0 | Source code for langchain.retrievers.pupmed
from typing import List
from langchain.schema import BaseRetriever, Document
from langchain.utilities.pupmed import PubMedAPIWrapper
[docs]class PubMedRetriever(BaseRetriever, PubMedAPIWrapper):
"""
It is effectively a wrapper for PubMedAPIWrapper.
It wraps load()... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/pupmed.html |
d336c3d5ecde-0 | Source code for langchain.retrievers.arxiv
from typing import List
from langchain.schema import BaseRetriever, Document
from langchain.utilities.arxiv import ArxivAPIWrapper
[docs]class ArxivRetriever(BaseRetriever, ArxivAPIWrapper):
"""
It is effectively a wrapper for ArxivAPIWrapper.
It wraps load() to ge... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/arxiv.html |
2448275fad5e-0 | Source code for langchain.retrievers.databerry
from typing import List, Optional
import aiohttp
import requests
from langchain.schema import BaseRetriever, Document
[docs]class DataberryRetriever(BaseRetriever):
datastore_url: str
top_k: Optional[int]
api_key: Optional[str]
def __init__(
self,
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/databerry.html |
2448275fad5e-1 | self.datastore_url,
json={
"query": query,
**({"topK": self.top_k} if self.top_k is not None else {}),
},
headers={
"Content-Type": "application/json",
**(
{"Authorizat... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/databerry.html |
655fad512421-0 | Source code for langchain.retrievers.wikipedia
from typing import List
from langchain.schema import BaseRetriever, Document
from langchain.utilities.wikipedia import WikipediaAPIWrapper
[docs]class WikipediaRetriever(BaseRetriever, WikipediaAPIWrapper):
"""
It is effectively a wrapper for WikipediaAPIWrapper.
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/wikipedia.html |
0910faed59a3-0 | Source code for langchain.retrievers.merger_retriever
from typing import List
from langchain.schema import BaseRetriever, Document
[docs]class MergerRetriever(BaseRetriever):
"""
This class merges the results of multiple retrievers.
Args:
retrievers: A list of retrievers to merge.
"""
def __... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/merger_retriever.html |
0910faed59a3-1 | Returns:
A list of merged documents.
"""
# Get the results of all retrievers.
retriever_docs = [
retriever.get_relevant_documents(query) for retriever in self.retrievers
]
# Merge the results of the retrievers.
merged_documents = []
max_doc... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/merger_retriever.html |
60d44a6814ff-0 | Source code for langchain.retrievers.metal
from typing import Any, List, Optional
from langchain.schema import BaseRetriever, Document
[docs]class MetalRetriever(BaseRetriever):
def __init__(self, client: Any, params: Optional[dict] = None):
from metal_sdk.metal import Metal
if not isinstance(client... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/metal.html |
10f467bd2847-0 | Source code for langchain.retrievers.self_query.base
"""Retriever that generates and executes structured queries over its own data source."""
from typing import Any, Dict, List, Optional, Type, cast
from pydantic import BaseModel, Field, root_validator
from langchain import LLMChain
from langchain.base_language import ... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/self_query/base.html |
10f467bd2847-1 | return QdrantTranslator(metadata_key=vectorstore.metadata_payload_key)
return BUILTIN_TRANSLATORS[vectorstore_cls]()
[docs]class SelfQueryRetriever(BaseRetriever, BaseModel):
"""Retriever that wraps around a vector store and uses an LLM to generate
the vector store queries."""
vectorstore: VectorStore
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/self_query/base.html |
10f467bd2847-2 | )
if self.verbose:
print(structured_query)
new_query, new_kwargs = self.structured_query_translator.visit_structured_query(
structured_query
)
if structured_query.limit is not None:
new_kwargs["k"] = structured_query.limit
search_kwargs = {**se... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/self_query/base.html |
10f467bd2847-3 | **chain_kwargs,
)
return cls(
llm_chain=llm_chain,
vectorstore=vectorstore,
structured_query_translator=structured_query_translator,
**kwargs,
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/self_query/base.html |
d29c02dfb229-0 | Source code for langchain.retrievers.document_compressors.base
"""Interface for retrieved document compressors."""
from abc import ABC, abstractmethod
from typing import List, Sequence, Union
from pydantic import BaseModel
from langchain.schema import BaseDocumentTransformer, Document
class BaseDocumentCompressor(BaseM... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/document_compressors/base.html |
d29c02dfb229-1 | self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Compress retrieved documents given the query context."""
for _transformer in self.transformers:
if isinstance(_transformer, BaseDocumentCompressor):
documents = await _transformer.acompress_docume... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/document_compressors/base.html |
f11df7c5b63e-0 | Source code for langchain.retrievers.document_compressors.cohere_rerank
from __future__ import annotations
from typing import TYPE_CHECKING, Dict, Sequence
from pydantic import Extra, root_validator
from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
from langchain.schema import Document
f... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/document_compressors/cohere_rerank.html |
f11df7c5b63e-1 | return []
doc_list = list(documents)
_docs = [d.page_content for d in doc_list]
results = self.client.rerank(
model=self.model, query=query, documents=_docs, top_n=self.top_n
)
final_results = []
for r in results:
doc = doc_list[r.index]
... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/document_compressors/cohere_rerank.html |
e81835ae1e31-0 | Source code for langchain.retrievers.document_compressors.chain_extract
"""DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
from __future__ import annotations
import asyncio
from typing import Any, Callable, Dict, Optional, Sequence
from langchain import LLMChain, PromptTemplate
from... | rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/document_compressors/chain_extract.html |
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