id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 115 |
|---|---|---|
df52c41c6f0c-1 | return values
[docs] def compress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Filter documents based on similarity of their embeddings to the query."""
stateful_documents = get_stateful_documents(documents)
embedded_documents = _get_embed... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
6508c12ebb30-0 | Source code for langchain.retrievers.document_compressors.cohere_rerank
from __future__ import annotations
from typing import TYPE_CHECKING, Dict, Sequence
from pydantic import root_validator
from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
from langchain.schema import Document
from lan... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/cohere_rerank.html |
6508c12ebb30-1 | doc.metadata["relevance_score"] = r.relevance_score
final_results.append(doc)
return final_results
[docs] async def acompress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
raise NotImplementedError
By Harrison Chase
© Copyright ... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/cohere_rerank.html |
8dbe23d4a445-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/base.html |
8dbe23d4a445-1 | 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... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/base.html |
7d4ec64c94f0-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
7d4ec64c94f0-1 | [docs] def compress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Compress page content of raw documents."""
compressed_docs = []
for doc in documents:
_input = self.get_input(query, doc)
output = self.llm_chain.pred... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
7d4ec64c94f0-2 | _get_input = get_input if get_input is not None else default_get_input
llm_chain = LLMChain(llm=llm, prompt=_prompt, **(llm_chain_kwargs or {}))
return cls(llm_chain=llm_chain, get_input=_get_input)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
23709cdfa505-0 | Source code for langchain.vectorstores.pinecone
"""Wrapper around Pinecone vector database."""
from __future__ import annotations
import uuid
from typing import Any, Callable, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
23709cdfa505-1 | self._embedding_function = embedding_function
self._text_key = text_key
self._namespace = namespace
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
namespace: Optional[str] = None,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
23709cdfa505-2 | filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
23709cdfa505-3 | namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents most similar to the query and score for each
"""
if namespace is None:
namespace = self._namespace
query_obj = self._embedding_function(query)
docs = []
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
23709cdfa505-4 | pinecone.init(api_key="***", environment="...")
embeddings = OpenAIEmbeddings()
pinecone = Pinecone.from_texts(
texts,
embeddings,
index_name="langchain-demo"
)
"""
try:
import pinecon... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
23709cdfa505-5 | metadata = metadatas[i:i_end]
else:
metadata = [{} for _ in range(i, i_end)]
for j, line in enumerate(lines_batch):
metadata[j][text_key] = line
to_upsert = zip(ids_batch, embeds, metadata)
# upsert to Pinecone
index.upsert(vect... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
0ce1eb24cd30-0 | Source code for langchain.vectorstores.elastic_vector_search
"""Wrapper around Elasticsearch vector database."""
from __future__ import annotations
import uuid
from abc import ABC
from typing import Any, Dict, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.bas... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
0ce1eb24cd30-1 | # and attributes.
[docs]class ElasticVectorSearch(VectorStore, ABC):
"""Wrapper around Elasticsearch as a vector database.
To connect to an Elasticsearch instance that does not require
login credentials, pass the Elasticsearch URL and index name along with the
embedding object to the constructor.
Ex... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
0ce1eb24cd30-2 | Example:
.. code-block:: python
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_host = "cluster_id.region_id.gcp.cloud.es.io"
elasticsearch_url = f"https://username:pass... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
0ce1eb24cd30-3 | [docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
refresh_indices: bool = True,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
0ce1eb24cd30-4 | "metadata": metadata,
"_id": _id,
}
ids.append(_id)
requests.append(request)
bulk(self.client, requests)
if refresh_indices:
self.client.indices.refresh(index=self.index_name)
return ids
[docs] def similarity_search(
self... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
0ce1eb24cd30-5 | (
Document(
page_content=hit["_source"]["text"],
metadata=hit["_source"]["metadata"],
),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
[docs] @classmethod
def from_texts(
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
0ce1eb24cd30-6 | except ValueError as e:
raise ValueError(
"Your elasticsearch client string is misformatted. " f"Got error: {e} "
)
index_name = kwargs.get("index_name", uuid.uuid4().hex)
embeddings = embedding.embed_documents(texts)
dim = len(embeddings[0])
mappi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
73e0ed260a80-0 | Source code for langchain.vectorstores.atlas
"""Wrapper around Atlas by Nomic."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Iterable, List, Optional, Type
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
73e0ed260a80-1 | is_public (bool): Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool): Whether to reset this project if it
already exists. Default False.
Generally userful during development and testing.
"""
try:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
73e0ed260a80-2 | metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]]): An optional list of ids.
refresh(bool): Whether or not to refresh indices with the updated data.
Default True.
Returns:
List[str]: List of IDs of the added texts... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
73e0ed260a80-3 | else:
if metadatas is None:
data = [
{"text": text, AtlasDB._ATLAS_DEFAULT_ID_FIELD: ids[i]}
for i, text in enumerate(texts)
]
else:
for i, text in enumerate(texts):
metadatas[i]["text"] =... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
73e0ed260a80-4 | """
if self._embedding_function is None:
raise NotImplementedError(
"AtlasDB requires an embedding_function for text similarity search!"
)
_embedding = self._embedding_function.embed_documents([query])[0]
embedding = np.array(_embedding).reshape(1, -1)
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
73e0ed260a80-5 | ids (Optional[List[str]]): Optional list of document IDs. If None,
ids will be auto created
description (str): A description for your project.
is_public (bool): Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
73e0ed260a80-6 | ids: Optional[List[str]] = None,
name: Optional[str] = None,
api_key: Optional[str] = None,
persist_directory: Optional[str] = None,
description: str = "A description for your project",
is_public: bool = True,
reset_project_if_exists: bool = False,
index_kwargs: O... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
73e0ed260a80-7 | return cls.from_texts(
name=name,
api_key=api_key,
texts=texts,
embedding=embedding,
metadatas=metadatas,
ids=ids,
description=description,
is_public=is_public,
reset_project_if_exists=reset_project_if_exists,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
329a57d26012-0 | Source code for langchain.vectorstores.supabase
from __future__ import annotations
from itertools import repeat
from typing import (
TYPE_CHECKING,
Any,
Iterable,
List,
Optional,
Tuple,
Type,
Union,
)
import numpy as np
from langchain.docstore.document import Document
from langchain.embe... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
329a57d26012-1 | embedding: Embeddings,
table_name: str,
query_name: Union[str, None] = None,
) -> None:
"""Initialize with supabase client."""
try:
import supabase # noqa: F401
except ImportError:
raise ValueError(
"Could not import supabase python pa... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
329a57d26012-2 | if not table_name:
raise ValueError("Supabase document table_name is required.")
embeddings = embedding.embed_documents(texts)
docs = cls._texts_to_documents(texts, metadatas)
_ids = cls._add_vectors(client, table_name, embeddings, docs)
return cls(
client=client,... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
329a57d26012-3 | self, query: List[float], k: int
) -> List[Tuple[Document, float]]:
match_documents_params = dict(query_embedding=query, match_count=k)
res = self._client.rpc(self.query_name, match_documents_params).execute()
match_result = [
(
Document(
metad... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
329a57d26012-4 | metadatas: Optional[Iterable[dict[Any, Any]]] = None,
) -> List[Document]:
"""Return list of Documents from list of texts and metadatas."""
if metadatas is None:
metadatas = repeat({})
docs = [
Document(page_content=text, metadata=metadata)
for text, metad... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
329a57d26012-5 | return id_list
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
329a57d26012-6 | k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
329a57d26012-7 | $$;```
"""
embedding = self._embedding.embed_documents([query])
docs = self.max_marginal_relevance_search_by_vector(
embedding[0], k, fetch_k, lambda_mult=lambda_mult
)
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
22671331ea2a-0 | Source code for langchain.vectorstores.faiss
"""Wrapper around FAISS vector database."""
from __future__ import annotations
import math
import pickle
import uuid
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base import Addabl... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
22671331ea2a-1 | [docs]class FAISS(VectorStore):
"""Wrapper around FAISS vector database.
To use, you should have the ``faiss`` python package installed.
Example:
.. code-block:: python
from langchain import FAISS
faiss = FAISS(embedding_function, index, docstore, index_to_docstore_id)
""... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
22671331ea2a-2 | starting_len = len(self.index_to_docstore_id)
self.index.add(np.array(embeddings, dtype=np.float32))
# Get list of index, id, and docs.
full_info = [
(starting_len + i, str(uuid.uuid4()), doc)
for i, doc in enumerate(documents)
]
# Add information to docst... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
22671331ea2a-3 | self,
text_embeddings: Iterable[Tuple[str, List[float]]],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
text_embeddings: Iterable pairs of string and embedding to
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
22671331ea2a-4 | # This happens when not enough docs are returned.
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}")
docs.append... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
22671331ea2a-5 | """Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(quer... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
22671331ea2a-6 | np.array([embedding], dtype=np.float32),
embeddings,
k=k,
lambda_mult=lambda_mult,
)
selected_indices = [indices[0][i] for i in mmr_selected]
docs = []
for i in selected_indices:
if i == -1:
# This happens when not enough do... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
22671331ea2a-7 | embedding, k, fetch_k, lambda_mult=lambda_mult
)
return docs
[docs] def merge_from(self, target: FAISS) -> None:
"""Merge another FAISS object with the current one.
Add the target FAISS to the current one.
Args:
target: FAISS object you wish to merge into the curre... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
22671331ea2a-8 | ) -> FAISS:
faiss = dependable_faiss_import()
index = faiss.IndexFlatL2(len(embeddings[0]))
index.add(np.array(embeddings, dtype=np.float32))
documents = []
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
documents.append(Docu... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
22671331ea2a-9 | metadatas,
**kwargs,
)
[docs] @classmethod
def from_embeddings(
cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> FAISS:
"""Construct FAISS wrapper from ra... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
22671331ea2a-10 | path = Path(folder_path)
path.mkdir(exist_ok=True, parents=True)
# save index separately since it is not picklable
faiss = dependable_faiss_import()
faiss.write_index(
self.index, str(path / "{index_name}.faiss".format(index_name=index_name))
)
# save docstore... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
22671331ea2a-11 | self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and their similarity scores on a scale from 0 to 1."""
if self.relevance_score_fn is None:
raise ValueError(
"normalize_score_fn must be provided to"
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/faiss.html |
65111daf92ee-0 | Source code for langchain.vectorstores.zilliz
from __future__ import annotations
import logging
from typing import Any, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.milvus import Milvus
logger = logging.getLogger(__name__)
[docs]class Zilliz(Milvus):
def _create_index(... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/zilliz.html |
65111daf92ee-1 | "Failed to create an index on collection: %s", self.collection_name
)
raise e
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection_name: str = "LangChainCollecti... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/zilliz.html |
65111daf92ee-2 | Zilliz: Zilliz Vector Store
"""
vector_db = cls(
embedding_function=embedding,
collection_name=collection_name,
connection_args=connection_args,
consistency_level=consistency_level,
index_params=index_params,
search_params=search_pa... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/zilliz.html |
14acadc75e2f-0 | Source code for langchain.vectorstores.myscale
"""Wrapper around MyScale vector database."""
from __future__ import annotations
import json
import logging
from hashlib import sha1
from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple
from pydantic import BaseSettings
from langchain.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
14acadc75e2f-1 | .. code-block:: python
{
'id': 'text_id',
'vector': 'text_embedding',
'text': 'text_plain',
'metadata': 'metadata_dictionary_in_json',
}... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
14acadc75e2f-2 | config: Optional[MyScaleSettings] = None,
**kwargs: Any,
) -> None:
"""MyScale Wrapper to LangChain
embedding_function (Embeddings):
config (MyScaleSettings): Configuration to MyScale Client
Other keyword arguments will pass into
[clickhouse-connect](https://docs.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
14acadc75e2f-3 | CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}(
{self.config.column_map['id']} String,
{self.config.column_map['text']} String,
{self.config.column_map['vector']} Array(Float32),
{self.config.column_map['metadata']} JSON,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
14acadc75e2f-4 | _data.append(f"({n})")
i_str = f"""
INSERT INTO TABLE
{self.config.database}.{self.config.table}({ks})
VALUES
{','.join(_data)}
"""
return i_str
def _insert(self, transac: Iterable, column_names: Iterable[str]) -> N... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
14acadc75e2f-5 | column_names[colmap_["metadata"]] = map(json.dumps, metadatas)
assert len(set(colmap_) - set(column_names)) >= 0
keys, values = zip(*column_names.items())
try:
t = None
for v in self.pgbar(
zip(*values), desc="Inserting data...", total=len(metadatas)
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
14acadc75e2f-6 | texts (Iterable[str]): List or tuple of strings to be added
config (MyScaleSettings, Optional): Myscale configuration
text_ids (Optional[Iterable], optional): IDs for the texts.
Defaults to None.
batch_size (int, optional): Batchsi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
14acadc75e2f-7 | ).named_results():
_repr += (
f"|\033[94m{r['name']:24s}\033[0m|\033[96m{r['type']:24s}\033[0m|\n"
)
_repr += "-" * 51 + "\n"
return _repr
def _build_qstr(
self, q_emb: List[float], topk: int, where_str: Optional[str] = None
) -> str:
q_emb... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
14acadc75e2f-8 | of SQL injection. When dealing with metadatas, remember to
use `{self.metadata_column}.attribute` instead of `attribute`
alone. The default name for it is `metadata`.
Returns:
List[Document]: List of Documents
"""
return self.similarity_search_by_v... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
14acadc75e2f-9 | ]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
[docs] def similarity_search_with_relevance_scores(
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
) -> List[Tuple[Document, float]]:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
14acadc75e2f-10 | return []
[docs] def drop(self) -> None:
"""
Helper function: Drop data
"""
self.client.command(
f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}"
)
@property
def metadata_column(self) -> str:
return self.config.column_map["metadata... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
c711e22f0f73-0 | Source code for langchain.vectorstores.lancedb
"""Wrapper around LanceDB vector database"""
from __future__ import annotations
import uuid
from typing import Any, Iterable, List, Optional
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base i... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/lancedb.html |
c711e22f0f73-1 | self._id_key = id_key
self._text_key = text_key
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Turn texts into embedding and add it to the database... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/lancedb.html |
c711e22f0f73-2 | """
embedding = self._embedding.embed_query(query)
docs = self._connection.search(embedding).limit(k).to_df()
return [
Document(
page_content=row[self._text_key],
metadata=row[docs.columns != self._text_key],
)
for _, row in doc... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/lancedb.html |
c9e9346875b9-0 | Source code for langchain.vectorstores.tair
"""Wrapper around Tair Vector."""
from __future__ import annotations
import json
import logging
import uuid
from typing import Any, Iterable, List, Optional, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
c9e9346875b9-1 | data_type: str,
**kwargs: Any,
) -> bool:
index = self.client.tvs_get_index(self.index_name)
if index is not None:
logger.info("Index already exists")
return False
self.client.tvs_create_index(
self.index_name,
dim,
distance... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
c9e9346875b9-2 | Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
# Creates embedding vector from user quer... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
c9e9346875b9-3 | if "tair_url" in kwargs:
kwargs.pop("tair_url")
distance_type = tairvector.DistanceMetric.InnerProduct
if "distance_type" in kwargs:
distance_type = kwargs.pop("distance_typ")
index_type = tairvector.IndexType.HNSW
if "index_type" in kwargs:
index_type... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
c9e9346875b9-4 | cls,
documents: List[Document],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: str = "langchain",
content_key: str = "content",
metadata_key: str = "metadata",
**kwargs: Any,
) -> Tair:
texts = [d.page_content for d in docum... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
c9e9346875b9-5 | # index not exist
logger.info("Index does not exist")
return False
return True
[docs] @classmethod
def from_existing_index(
cls,
embedding: Embeddings,
index_name: str = "langchain",
content_key: str = "content",
metadata_key: str = "metadat... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
7ae0fff4890a-0 | Source code for langchain.vectorstores.qdrant
"""Wrapper around Qdrant vector database."""
from __future__ import annotations
import uuid
from hashlib import md5
from operator import itemgetter
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union
from langchain.docstore.document import D... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
7ae0fff4890a-1 | if not isinstance(client, qdrant_client.QdrantClient):
raise ValueError(
f"client should be an instance of qdrant_client.QdrantClient, "
f"got {type(client)}"
)
self.client: qdrant_client.QdrantClient = client
self.collection_name = collection_name... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
7ae0fff4890a-2 | k: int = 4,
filter: Optional[MetadataFilter] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by met... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
7ae0fff4890a-3 | self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
amon... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
7ae0fff4890a-4 | embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = N... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
7ae0fff4890a-5 | grpc_port: Port of the gRPC interface. Default: 6334
prefer_grpc:
If true - use gPRC interface whenever possible in custom methods.
Default: False
https: If true - use HTTPS(SSL) protocol. Default: None
api_key: API key for authentication in Qdrant Clo... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
7ae0fff4890a-6 | 2. Initializes the Qdrant database as an in-memory docstore by default
(and overridable to a remote docstore)
3. Adds the text embeddings to the Qdrant database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
7ae0fff4890a-7 | ),
)
# Now generate the embeddings for all the texts
embeddings = embedding.embed_documents(texts)
client.upsert(
collection_name=collection_name,
points=rest.Batch.construct(
ids=[md5(text.encode("utf-8")).hexdigest() for text in texts],
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
7ae0fff4890a-8 | metadata_payload_key: str,
) -> Document:
return Document(
page_content=scored_point.payload.get(content_payload_key),
metadata=scored_point.payload.get(metadata_payload_key) or {},
)
def _qdrant_filter_from_dict(self, filter: Optional[MetadataFilter]) -> Any:
if ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
39a24f0b5c5f-0 | Source code for langchain.vectorstores.opensearch_vector_search
"""Wrapper around OpenSearch vector database."""
from __future__ import annotations
import uuid
from typing import Any, Dict, Iterable, List, Optional
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from la... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
39a24f0b5c5f-1 | try:
opensearch = _import_opensearch()
client = opensearch(opensearch_url, **kwargs)
except ValueError as e:
raise ValueError(
f"OpenSearch client string provided is not in proper format. "
f"Got error: {e} "
)
return client
def _validate_embeddings_and_bu... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
39a24f0b5c5f-2 | request = {
"_op_type": "index",
"_index": index_name,
vector_field: embeddings[i],
text_field: text,
"metadata": metadata,
"_id": _id,
}
requests.append(request)
ids.append(_id)
bulk(client, requests)
client.indices... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
39a24f0b5c5f-3 | "parameters": {"ef_construction": ef_construction, "m": m},
},
}
}
},
}
def _default_approximate_search_query(
query_vector: List[float],
size: int = 4,
k: int = 4,
vector_field: str = "vector_field",
) -> Dict:
"""For Approximate k-NN Sear... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
39a24f0b5c5f-4 | query_vector, size, k, vector_field
)
search_query["query"]["knn"][vector_field]["filter"] = lucene_filter
return search_query
def _default_script_query(
query_vector: List[float],
space_type: str = "l2",
pre_filter: Dict = MATCH_ALL_QUERY,
vector_field: str = "vector_field",
) -> Dict:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
39a24f0b5c5f-5 | vector_field: str = "vector_field",
) -> Dict:
"""For Painless Scripting Search, this is the default query."""
source = __get_painless_scripting_source(space_type, query_vector)
return {
"query": {
"script_score": {
"query": pre_filter,
"script": {
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
39a24f0b5c5f-6 | bulk_size: int = 500,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
bulk_size... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
39a24f0b5c5f-7 | texts,
metadatas,
vector_field,
text_field,
mapping,
)
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
By default supports Approximate Search.
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
39a24f0b5c5f-8 | search_type: "script_scoring"; default: "approximate_search"
space_type: "l2", "l1", "linf", "cosinesimil", "innerproduct",
"hammingbit"; default: "l2"
pre_filter: script_score query to pre-filter documents before identifying
nearest neighbors; default: {"match_all": {}}
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
39a24f0b5c5f-9 | "is invalid"
)
if boolean_filter != {}:
search_query = _approximate_search_query_with_boolean_filter(
embedding, boolean_filter, size, k, vector_field, subquery_clause
)
elif lucene_filter != {}:
search_query = _... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
39a24f0b5c5f-10 | for hit in hits
]
return documents
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
bulk_size: int = 500,
**kwargs: Any,
) -> OpenSearchVectorSearch:
"""Construct O... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
39a24f0b5c5f-11 | ef_construction: Size of the dynamic list used during k-NN graph creation.
Higher values lead to more accurate graph but slower indexing speed;
default: 512
m: Number of bidirectional links created for each new element. Large impact
on memory consumption. Between 2 and 10... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
39a24f0b5c5f-12 | if is_appx_search:
engine = _get_kwargs_value(kwargs, "engine", "nmslib")
space_type = _get_kwargs_value(kwargs, "space_type", "l2")
ef_search = _get_kwargs_value(kwargs, "ef_search", 512)
ef_construction = _get_kwargs_value(kwargs, "ef_construction", 512)
m =... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/opensearch_vector_search.html |
5350863b55b6-0 | Source code for langchain.vectorstores.analyticdb
"""VectorStore wrapper around a Postgres/PGVector database."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple
import sqlalchemy
from sqlalchemy import REAL, Index
from sqlalchemy.dialects.postg... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
5350863b55b6-1 | """
created = False
collection = cls.get_by_name(session, name)
if collection:
return collection, created
collection = cls(name=name, cmetadata=cmetadata)
session.add(collection)
session.commit()
created = True
return collection, created
class ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
5350863b55b6-2 | - `connection_string` is a postgres connection string.
- `embedding_function` any embedding function implementing
`langchain.embeddings.base.Embeddings` interface.
- `collection_name` is the name of the collection to use. (default: langchain)
- NOTE: This is not the name of the table, but the na... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
5350863b55b6-3 | return conn
[docs] def create_tables_if_not_exists(self) -> None:
Base.metadata.create_all(self._conn)
[docs] def drop_tables(self) -> None:
Base.metadata.drop_all(self._conn)
[docs] def create_collection(self) -> None:
if self.pre_delete_collection:
self.delete_collection()... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
5350863b55b6-4 | embeddings = self.embedding_function.embed_documents(list(texts))
if not metadatas:
metadatas = [{} for _ in texts]
with Session(self._conn) as session:
collection = self.get_collection(session)
if not collection:
raise ValueError("Collection not found... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
5350863b55b6-5 | """Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to th... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
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