id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
053d28d89110-2 | Returns:
List of embeddings, one for each text.
"""
return self._generate_embeddings(texts)
[docs] def embed_query(self, text: str) -> List[float]:
"""Embed a query using EdenAI.
Args:
text: The text to embed.
Returns:
Embeddings for the tex... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/edenai.html |
a5b51ff0a9bf-0 | Source code for langchain.embeddings.gpt4all
from typing import Any, Dict, List
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
[docs]class GPT4AllEmbeddings(BaseModel, Embeddings):
"""GPT4All embedding models.
To use, you should have the gpt4all py... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/gpt4all.html |
a5b51ff0a9bf-1 | Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self.embed_documents([text])[0] | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/gpt4all.html |
71c07b4045a3-0 | Source code for langchain.embeddings.openai
from __future__ import annotations
import logging
import os
import warnings
from importlib.metadata import version
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Mapping,
Optional,
Sequence,
Set,
Tuple,
Union,
cast,
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-1 | | retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def _async_retry_decorator(embeddings: OpenAIEmbeddings) -> Any:
import openai
min_seconds = 4
max_seconds = 10
# Wait 2^x * 1 second between each retry starti... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-2 | import openai
raise openai.error.APIError("OpenAI API returned an empty embedding")
return response
[docs]def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
"""Use tenacity to retry the embedding call."""
if _is_openai_v1():
return embeddings.client.create(**kwargs)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-3 | Example:
.. code-block:: python
from langchain.embeddings import OpenAIEmbeddings
openai = OpenAIEmbeddings(openai_api_key="my-api-key")
In order to use the library with Microsoft Azure endpoints, you need to set
the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and OPENAI_API... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-4 | # to support Azure OpenAI Service custom deployment names
deployment: Optional[str] = model
# TODO: Move to AzureOpenAIEmbeddings.
openai_api_version: Optional[str] = Field(default=None, alias="api_version")
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
# to support... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-5 | default=None, alias="timeout"
)
"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
None."""
headers: Any = None
tiktoken_model_name: Optional[str] = None
"""The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of t... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-6 | """Optional httpx.Client."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
allow_population_by_field_name = True
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional ... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-7 | "OPENAI_API_BASE"
)
values["openai_api_type"] = get_from_dict_or_env(
values,
"openai_api_type",
"OPENAI_API_TYPE",
default="",
)
values["openai_proxy"] = get_from_dict_or_env(
values,
"openai_proxy",
"OP... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-8 | warnings.warn(
"If you have openai>=1.0.0 installed and are using Azure, "
"please use the `AzureOpenAIEmbeddings` class."
)
client_params = {
"api_key": values["openai_api_key"],
"organization": ... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-9 | **self.model_kwargs,
}
if self.openai_api_type in ("azure", "azure_ad", "azuread"):
openai_args["engine"] = self.deployment
# TODO: Look into proxy with openai v1.
if self.openai_proxy:
try:
import openai
... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-10 | model = "cl100k_base"
encoding = tiktoken.get_encoding(model)
for i, text in enumerate(texts):
if self.model.endswith("001"):
# See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
# replace newlines, which can negatively affect ... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-11 | for i in range(len(indices)):
if self.skip_empty and len(batched_embeddings[i]) == 1:
continue
results[indices[i]].append(batched_embeddings[i])
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
for i in range(len(texts)):
_result = results[i]... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-12 | encoding = tiktoken.encoding_for_model(model_name)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
model = "cl100k_base"
encoding = tiktoken.get_encoding(model)
for i, text in enumerate(texts):
if self.model.endswit... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-13 | for i in range(len(texts)):
_result = results[i]
if len(_result) == 0:
average_embedded = embed_with_retry(
self,
input="",
**self._invocation_params,
)
if not isinstance(average_embedded,... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
71c07b4045a3-14 | Args:
texts: The list of texts to embed.
chunk_size: The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns:
List of embeddings, one for each text.
"""
# NOTE: to keep things simple, we assume the list ma... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
81ddc58c16a5-0 | Source code for langchain.embeddings.fastembed
from typing import Any, Dict, List, Literal, Optional
import numpy as np
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
[docs]class FastEmbedEmbeddings(BaseModel, Embeddings):
"""Qdrant FastEmbeddin... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/fastembed.html |
81ddc58c16a5-1 | """Type of embedding to use for documents
"default": Uses FastEmbed's default embedding method
"passage": Prefixes the text with "passage" before embedding.
"""
_model: Any # : :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/fastembed.html |
81ddc58c16a5-2 | [docs] def embed_query(self, text: str) -> List[float]:
"""Generate query embeddings using FastEmbed.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
query_embeddings: np.ndarray = next(self._model.query_embed(text))
return ... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/fastembed.html |
f4a6324df779-0 | Source code for langchain.embeddings.modelscope_hub
from typing import Any, List, Optional
from langchain.pydantic_v1 import BaseModel, Extra
from langchain.schema.embeddings import Embeddings
[docs]class ModelScopeEmbeddings(BaseModel, Embeddings):
"""ModelScopeHub embedding models.
To use, you should have the... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/modelscope_hub.html |
f4a6324df779-1 | Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
texts = list(map(lambda x: x.replace("\n", " "), texts))
inputs = {"source_sentence": texts}
embeddings = self.embed(input=inputs)["text_embedding"]
return... | lang/api.python.langchain.com/en/latest/_modules/langchain/embeddings/modelscope_hub.html |
a267d1d5ff44-0 | Source code for langchain.retrievers.web_research
import logging
import re
from typing import List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.chains import LLMChain
from langchain.chains.prompt_selector import Conditi... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/web_research.html |
a267d1d5ff44-1 | )
DEFAULT_SEARCH_PROMPT = PromptTemplate(
input_variables=["question"],
template="""You are an assistant tasked with improving Google search \
results. Generate THREE Google search queries that are similar to \
this question. The output should be a numbered list of questions and each \
should have a question ma... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/web_research.html |
a267d1d5ff44-2 | )
[docs] @classmethod
def from_llm(
cls,
vectorstore: VectorStore,
llm: BaseLLM,
search: GoogleSearchAPIWrapper,
prompt: Optional[BasePromptTemplate] = None,
num_search_results: int = 1,
text_splitter: RecursiveCharacterTextSplitter = RecursiveCharacterText... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/web_research.html |
a267d1d5ff44-3 | [docs] def clean_search_query(self, query: str) -> str:
# Some search tools (e.g., Google) will
# fail to return results if query has a
# leading digit: 1. "LangCh..."
# Check if the first character is a digit
if query[0].isdigit():
# Find the position of the first... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/web_research.html |
a267d1d5ff44-4 | # Get urls
logger.info("Searching for relevant urls...")
urls_to_look = []
for query in questions:
# Google search
search_results = self.search_tool(query, self.num_search_results)
logger.info("Searching for relevant urls...")
logger.info(f"Search ... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/web_research.html |
a267d1d5ff44-5 | self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
) -> List[Document]:
raise NotImplementedError | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/web_research.html |
d0c14b2482d8-0 | Source code for langchain.retrievers.metal
from typing import Any, List, Optional
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.pydantic_v1 import root_validator
from langchain.schema import BaseRetriever, Document
[docs]class MetalRetriever(BaseRetriever):
"""`Metal API` ret... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/metal.html |
7822f1c2bd99-0 | Source code for langchain.retrievers.azure_cognitive_search
from __future__ import annotations
import json
from typing import Dict, List, Optional
import aiohttp
import requests
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.pydant... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/azure_cognitive_search.html |
7822f1c2bd99-1 | values["service_name"] = get_from_dict_or_env(
values, "service_name", "AZURE_COGNITIVE_SEARCH_SERVICE_NAME"
)
values["index_name"] = get_from_dict_or_env(
values, "index_name", "AZURE_COGNITIVE_SEARCH_INDEX_NAME"
)
values["api_key"] = get_from_dict_or_env(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/azure_cognitive_search.html |
7822f1c2bd99-2 | async with session.get(search_url, headers=self._headers) as response:
response_json = await response.json()
else:
async with self.aiosession.get(
search_url, headers=self._headers
) as response:
response_json = await response.json()
... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/azure_cognitive_search.html |
396e28bfca69-0 | Source code for langchain.retrievers.chaindesk
from typing import Any, List, Optional
import aiohttp
import requests
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.schema import BaseRetriever, Document
[docs]class ChaindeskRetrieve... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/chaindesk.html |
396e28bfca69-1 | )
for r in data["results"]
]
async def _aget_relevant_documents(
self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
**kwargs: Any,
) -> List[Document]:
async with aiohttp.ClientSession() as session:
async with se... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/chaindesk.html |
c29c869a2afd-0 | Source code for langchain.retrievers.tavily_search_api
import os
from enum import Enum
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.schema import Document
from langchain.schema.retriever import BaseRetriever
[docs]class SearchDepth(En... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/tavily_search_api.html |
c29c869a2afd-1 | include_domains=self.include_domains,
exclude_domains=self.exclude_domains,
include_raw_content=self.include_raw_content,
include_images=self.include_images,
**self.kwargs,
)
docs = [
Document(
page_content=result.get("content",... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/tavily_search_api.html |
89893d4a26fb-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.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.docstore.document import Document
from ... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
89893d4a26fb-1 | [docs] @classmethod
def create(
cls, elasticsearch_url: str, index_name: str, k1: float = 2.0, b: float = 0.75
) -> ElasticSearchBM25Retriever:
"""
Create a ElasticSearchBM25Retriever from a list of texts.
Args:
elasticsearch_url: URL of the Elasticsearch instance ... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
89893d4a26fb-2 | """Run more texts through the embeddings and add to the retriever.
Args:
texts: Iterable of strings to add to the retriever.
refresh_indices: bool to refresh ElasticSearch indices
Returns:
List of ids from adding the texts into the retriever.
"""
try:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
7fb52dd5a4e8-0 | Source code for langchain.retrievers.remote_retriever
from typing import List, Optional
import aiohttp
import requests
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.schema import BaseRetriever, Document
[docs]class RemoteLangChain... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/remote_retriever.html |
7fb52dd5a4e8-1 | async with aiohttp.ClientSession() as session:
async with session.request(
"POST", self.url, headers=self.headers, json={self.input_key: query}
) as response:
result = await response.json()
return [
Document(
page_content=r[self... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/remote_retriever.html |
d9852de95864-0 | Source code for langchain.retrievers.svm
from __future__ import annotations
import concurrent.futures
from typing import Any, Iterable, List, Optional
import numpy as np
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.schema import BaseRetriever, Document
from langchain.schema.embe... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/svm.html |
d9852de95864-1 | cls,
texts: List[str],
embeddings: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> SVMRetriever:
index = create_index(texts, embeddings)
return cls(
embeddings=embeddings,
index=index,
texts=texts,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/svm.html |
d9852de95864-2 | clf.fit(x, y)
similarities = clf.decision_function(x)
sorted_ix = np.argsort(-similarities)
# svm.LinearSVC in scikit-learn is non-deterministic.
# if a text is the same as a query, there is no guarantee
# the query will be in the first index.
# this performs a simple swa... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/svm.html |
af07c67c9ead-0 | Source code for langchain.retrievers.zep
from __future__ import annotations
from enum import Enum
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.pydantic_v1 import root_va... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/zep.html |
af07c67c9ead-1 | top_k: Number of documents to return (default: 3, optional)
search_type: Type of search to perform (similarity / mmr) (default: similarity,
optional)
mmr_lambda: Lambda value for MMR search. Defaults to 0.5 (optional)
Zep - Fast, sc... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/zep.html |
af07c67c9ead-2 | "Please install it with `pip install zep-python`."
)
values["zep_client"] = values.get(
"zep_client",
ZepClient(base_url=values["url"], api_key=values.get("api_key")),
)
return values
def _messages_search_result_to_doc(
self, results: List[MemorySe... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/zep.html |
af07c67c9ead-3 | mmr_lambda=self.mmr_lambda,
)
results: List[MemorySearchResult] = self.zep_client.memory.search_memory(
self.session_id, payload, limit=self.top_k
)
if self.search_scope == SearchScope.summary:
return self._summary_search_result_to_doc(results)
return self... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/zep.html |
d4cbc66f961c-0 | Source code for langchain.retrievers.kay
from __future__ import annotations
from typing import Any, List
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.schema import BaseRetriever, Document
[docs]class KayAiRetriever(BaseRetriever):
"""
Retriever for Kay.ai datasets.
T... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/kay.html |
d4cbc66f961c-1 | def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
ctxs = self.client.query(query=query, num_context=self.num_contexts)
docs = []
for ctx in ctxs:
page_content = ctx.pop("chunk_embed_text", None)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/kay.html |
dc63a3d96191-0 | Source code for langchain.retrievers.zilliz
import warnings
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.pydantic_v1 import root_validator
from langchain.schema import BaseRetriever, Document
from langchain.schema.embeddings import Em... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/zilliz.html |
dc63a3d96191-1 | )
return values
[docs] def add_texts(
self, texts: List[str], metadatas: Optional[List[dict]] = None
) -> None:
"""Add text to the Zilliz store
Args:
texts (List[str]): The text
metadatas (List[dict]): Metadata dicts, must line up with existing store
... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/zilliz.html |
ab57e9ad404c-0 | Source code for langchain.retrievers.contextual_compression
from typing import Any, List
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.retrievers.document_compressors.base import (
BaseDocumentCompressor,
)
from langchain.sche... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/contextual_compression.html |
ab57e9ad404c-1 | run_manager: AsyncCallbackManagerForRetrieverRun,
**kwargs: Any,
) -> List[Document]:
"""Get documents relevant for a query.
Args:
query: string to find relevant documents for
Returns:
List of relevant documents
"""
docs = await self.base_retri... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/contextual_compression.html |
26c624d131a2-0 | 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 langchain.callbacks.manager import CallbackManager... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/knn.html |
26c624d131a2-1 | index = create_index(texts, embeddings)
return cls(embeddings=embeddings, index=index, texts=texts, **kwargs)
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
query_embeds = np.array(self.embeddings.embed_query(query))
... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/knn.html |
6287afc97d46-0 | Source code for langchain.retrievers.cohere_rag_retriever
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.chat_models.base import BaseChatModel
fro... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/cohere_rag_retriever.html |
6287afc97d46-1 | """Cohere ChatModel to use."""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
"""Allow arbitrary types."""
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
) -> List[Docume... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/cohere_rag_retriever.html |
e2732978521a-0 | Source code for langchain.retrievers.kendra
import re
from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Union
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.docstore.document import Document
from langchain.pydantic_v1 im... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
e2732978521a-1 | """Information that highlights the keywords in the excerpt."""
BeginOffset: int
"""The zero-based location in the excerpt where the highlight starts."""
EndOffset: int
"""The zero-based location in the excerpt where the highlight ends."""
TopAnswer: Optional[bool]
"""Indicates whether the result... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
e2732978521a-2 | return self.Value.TextWithHighlightsValue.Text
# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
[docs]class DocumentAttributeValue(BaseModel, extra=Extra.allow): # type: ignore[call-arg]
"""Value of a document attribute."""
DateValue: Optional[str]
"""The date expressed as an ISO 8... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
e2732978521a-3 | Id: Optional[str]
"""The ID of the relevant result item."""
DocumentId: Optional[str]
"""The document ID."""
DocumentURI: Optional[str]
"""The document URI."""
DocumentAttributes: Optional[List[DocumentAttribute]] = []
"""The document attributes."""
[docs] @abstractmethod
def get_titl... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
e2732978521a-4 | [docs]class QueryResultItem(ResultItem):
"""Query API result item."""
DocumentTitle: TextWithHighLights
"""The document title."""
FeedbackToken: Optional[str]
"""Identifies a particular result from a particular query."""
Format: Optional[str]
"""
If the Type is ANSWER, then format is eit... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
e2732978521a-5 | [docs]class RetrieveResultItem(ResultItem):
"""Retrieve API result item."""
DocumentTitle: Optional[str]
"""The document title."""
Content: Optional[str]
"""The content of the item."""
[docs] def get_title(self) -> str:
return self.DocumentTitle or ""
[docs] def get_excerpt(self) -> st... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
e2732978521a-6 | Fallsback to AWS_DEFAULT_REGION env variable
or region specified in ~/.aws/config.
credentials_profile_name: The name of the profile in the ~/.aws/credentials
or ~/.aws/config files, which has either access keys or role information
specified. If not specified, the default cre... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
e2732978521a-7 | return value
@root_validator(pre=True)
def create_client(cls, values: Dict[str, Any]) -> Dict[str, Any]:
if values.get("client") is not None:
return values
try:
import boto3
if values.get("credentials_profile_name"):
session = boto3.Session(pro... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
e2732978521a-8 | # Retrieve API returned 0 results, fall back to Query API
response = self.client.query(**kendra_kwargs)
q_result = QueryResult.parse_obj(response)
return q_result.ResultItems
def _get_top_k_docs(self, result_items: Sequence[ResultItem]) -> List[Document]:
top_docs = [
ite... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/kendra.html |
b2a31e689324-0 | Source code for langchain.retrievers.bm25
from __future__ import annotations
from typing import Any, Callable, Dict, Iterable, List, Optional
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.schema import BaseRetriever, Document
[docs]def default_preprocessing_func(text: str) -> Lis... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/bm25.html |
b2a31e689324-1 | **kwargs: Any other arguments to pass to the retriever.
Returns:
A BM25Retriever instance.
"""
try:
from rank_bm25 import BM25Okapi
except ImportError:
raise ImportError(
"Could not import rank_bm25, please install with `pip install "
... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/bm25.html |
b2a31e689324-2 | Returns:
A BM25Retriever instance.
"""
texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
return cls.from_texts(
texts=texts,
bm25_params=bm25_params,
metadatas=metadatas,
preprocess_func=preprocess_func,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/bm25.html |
a8cdc189842b-0 | Source code for langchain.retrievers.tfidf
from __future__ import annotations
import pickle
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.schema import BaseRetriever, Document
[docs]class TFIDFRetriev... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/tfidf.html |
a8cdc189842b-1 | tfidf_array = vectorizer.fit_transform(texts)
metadatas = metadatas or ({} for _ in texts)
docs = [Document(page_content=t, metadata=m) for t, m in zip(texts, metadatas)]
return cls(vectorizer=vectorizer, docs=docs, tfidf_array=tfidf_array, **kwargs)
[docs] @classmethod
def from_documents... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/tfidf.html |
a8cdc189842b-2 | ) -> None:
try:
import joblib
except ImportError:
raise ImportError(
"Could not import joblib, please install with `pip install joblib`."
)
path = Path(folder_path)
path.mkdir(exist_ok=True, parents=True)
# Save vectorizer with ... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/tfidf.html |
7be6cae5f816-0 | Source code for langchain.retrievers.multi_query
import asyncio
import logging
from typing import List, Sequence
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
from... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/multi_query.html |
7be6cae5f816-1 | )
def _unique_documents(documents: Sequence[Document]) -> List[Document]:
return [doc for i, doc in enumerate(documents) if doc not in documents[:i]]
[docs]class MultiQueryRetriever(BaseRetriever):
"""Given a query, use an LLM to write a set of queries.
Retrieve docs for each query. Return the unique union ... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/multi_query.html |
7be6cae5f816-2 | )
async def _aget_relevant_documents(
self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
) -> List[Document]:
"""Get relevant documents given a user query.
Args:
question: user query
Returns:
Unique union of rele... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/multi_query.html |
7be6cae5f816-3 | )
for query in queries
)
)
return [doc for docs in document_lists for doc in docs]
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> List[Document]:
"""Get relevant documents giv... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/multi_query.html |
7be6cae5f816-4 | for query in queries:
docs = self.retriever.get_relevant_documents(
query, callbacks=run_manager.get_child()
)
documents.extend(docs)
return documents
[docs] def unique_union(self, documents: List[Document]) -> List[Document]:
"""Get unique Document... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/multi_query.html |
a649a063a12a-0 | Source code for langchain.retrievers.google_cloud_documentai_warehouse
"""Retriever wrapper for Google Cloud Document AI Warehouse."""
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.docstore.document import Document
from ... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/google_cloud_documentai_warehouse.html |
a649a063a12a-1 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validates the environment."""
try: # noqa: F401
from google.cloud.contentwarehouse_v1 import DocumentServiceClient
except ImportError as exc:
raise ImportError(
"google.cloud.co... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/google_cloud_documentai_warehouse.html |
a649a063a12a-2 | schemas = []
if self.schema_id:
schemas.append(
self.client.document_schema_path(
project=self.project_number,
location=self.location,
document_schema=self.schema_id,
)
)
return SearchDocu... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/google_cloud_documentai_warehouse.html |
6388295a2869-0 | Source code for langchain.retrievers.arcee
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.docstore.document import Document
from langchain.pydantic_v1 import Extra, root_validator
from langchain.schema import BaseRetriever
from langchai... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/arcee.html |
6388295a2869-1 | """Keyword arguments to pass to the model."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
underscore_attrs_are_private = True
def __init__(self, **data: Any) -> None:
"""Initializes private fields."""
super().__init__(**data)
s... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/arcee.html |
6388295a2869-2 | if not kw.get("size") >= 0:
raise ValueError("`size` must not be negative.")
# validate filters
if kw.get("filters") is not None:
if not isinstance(kw.get("filters"), List):
raise ValueError("`filters` must be a list.")
for ... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/arcee.html |
c8a9f523757a-0 | Source code for langchain.retrievers.databerry
from typing import List, Optional
import aiohttp
import requests
from langchain.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain.schema import BaseRetriever, Document
[docs]class DataberryRetriever(Bas... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/databerry.html |
c8a9f523757a-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... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/databerry.html |
a03384beff91-0 | Source code for langchain.retrievers.multi_vector
from typing import List
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.pydantic_v1 import Field
from langchain.schema import BaseRetriever, BaseStore, Document
from langchain.schema.vectorstore import VectorStore
[docs]class MultiV... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/multi_vector.html |
3eb11acf49eb-0 | Source code for langchain.retrievers.llama_index
from typing import Any, Dict, List, cast
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.pydantic_v1 import Field
from langchain.schema import BaseRetriever, Document
[docs]class LlamaIndexRetriever(BaseRetriever):
"""`LlamaIndex... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/llama_index.html |
3eb11acf49eb-1 | It is used for question-answering with sources over an LlamaIndex
graph data structure."""
graph: Any
"""LlamaIndex graph to query."""
query_configs: List[Dict] = Field(default_factory=list)
"""List of query configs to pass to the query method."""
def _get_relevant_documents(
self, query... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/llama_index.html |
734db843c82e-0 | Source code for langchain.retrievers.arxiv
from typing import List
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.schema import BaseRetriever, Document
from langchain.utilities.arxiv import ArxivAPIWrapper
[docs]class ArxivRetriever(BaseRetriever, ArxivAPIWrapper):
"""`Arxiv` ... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/arxiv.html |
4434ac392004-0 | Source code for langchain.retrievers.docarray
from enum import Enum
from typing import Any, Dict, List, Optional, Union
import numpy as np
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.schema import BaseRetriever, Document
from langchain.schema.embeddings import Embeddings
from l... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/docarray.html |
4434ac392004-1 | """Configuration for this pydantic object."""
arbitrary_types_allowed = True
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> List[Document]:
"""Get documents relevant for a query.
Args:
query:... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/docarray.html |
4434ac392004-2 | else:
filter_args["filter_query"] = self.filters
if self.filters:
query = (
self.index.build_query() # get empty query object
.find(
query=query_emb, search_field=search_field
) # add vector similarity search
... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/docarray.html |
4434ac392004-3 | [
doc[self.search_field]
if isinstance(doc, dict)
else getattr(doc, self.search_field)
for doc in docs
],
k=self.top_k,
)
results = [self._docarray_to_langchain_doc(docs[idx]) for idx in mmr_selected]
return ... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/docarray.html |
81c91d92a589-0 | Source code for langchain.retrievers.milvus
"""Milvus Retriever"""
import warnings
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.pydantic_v1 import root_validator
from langchain.schema import BaseRetriever, Document
from langchain.sche... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/milvus.html |
81c91d92a589-1 | Args:
texts (List[str]): The text
metadatas (List[dict]): Metadata dicts, must line up with existing store
"""
self.store.add_texts(texts, metadatas)
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/milvus.html |
faef2f9faf23-0 | Source code for langchain.retrievers.wikipedia
from typing import List
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.schema import BaseRetriever, Document
from langchain.utilities.wikipedia import WikipediaAPIWrapper
[docs]class WikipediaRetriever(BaseRetriever, WikipediaAPIWrapp... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/wikipedia.html |
012377404a81-0 | Source code for langchain.retrievers.parent_document_retriever
import uuid
from typing import List, Optional
from langchain.retrievers import MultiVectorRetriever
from langchain.schema.document import Document
from langchain.text_splitter import TextSplitter
[docs]class ParentDocumentRetriever(MultiVectorRetriever):
... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/parent_document_retriever.html |
012377404a81-1 | # The vectorstore to use to index the child chunks
vectorstore = Chroma(embedding_function=OpenAIEmbeddings())
# The storage layer for the parent documents
store = InMemoryStore()
# Initialize the retriever
retriever = ParentDocumentRetriever(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/retrievers/parent_document_retriever.html |
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