id stringlengths 14 15 | text stringlengths 35 2.51k | source stringlengths 61 154 |
|---|---|---|
173f0a856c90-6 | lambda_mult=lambda_mult,
)
candidates = _results_to_docs(results)
selected_results = [r for i, r in enumerate(candidates) if i in mmr_selected]
return selected_results
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = DEFAULT_K,
fetch... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
173f0a856c90-7 | )
return docs
[docs] def delete_collection(self) -> None:
"""Delete the collection."""
self._client.delete_collection(self._collection.name)
[docs] def get(
self,
ids: Optional[OneOrMany[ID]] = None,
where: Optional[Where] = None,
limit: Optional[int] = None... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
173f0a856c90-8 | kwargs["include"] = include
return self._collection.get(**kwargs)
[docs] def persist(self) -> None:
"""Persist the collection.
This can be used to explicitly persist the data to disk.
It will also be called automatically when the object is destroyed.
"""
if self._persi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
173f0a856c90-9 | client: Optional[chromadb.Client] = None,
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a raw documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Args:
texts (Li... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
173f0a856c90-10 | client: Optional[chromadb.Client] = None, # Add this line
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a list of documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Args:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/chroma.html |
4a21a2dd68dc-0 | Source code for langchain.vectorstores.matching_engine
"""Vertex Matching Engine implementation of the vector store."""
from __future__ import annotations
import json
import logging
import time
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Type
from langchain.docstore.document import Docu... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/matching_engine.html |
4a21a2dd68dc-1 | using this module.
See usage in
docs/modules/indexes/vectorstores/examples/matchingengine.ipynb.
Note that this implementation is mostly meant for reading if you are
planning to do a real time implementation. While reading is a real time
operation, updating the index takes close ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/matching_engine.html |
4a21a2dd68dc-2 | "to use the MatchingEngine Vectorstore."
)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
te... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/matching_engine.html |
4a21a2dd68dc-3 | )
logger.debug("Updated index with new configuration.")
return ids
def _upload_to_gcs(self, data: str, gcs_location: str) -> None:
"""Uploads data to gcs_location.
Args:
data: The data that will be stored.
gcs_location: The location where the data will be stor... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/matching_engine.html |
4a21a2dd68dc-4 | page_content = self._download_from_gcs(f"documents/{doc.id}")
results.append(Document(page_content=page_content))
logger.debug("Downloaded documents for query.")
return results
def _get_index_id(self) -> str:
"""Gets the correct index id for the endpoint.
Returns:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/matching_engine.html |
4a21a2dd68dc-5 | )
[docs] @classmethod
def from_components(
cls: Type["MatchingEngine"],
project_id: str,
region: str,
gcs_bucket_name: str,
index_id: str,
endpoint_id: str,
credentials_path: Optional[str] = None,
embedding: Optional[Embeddings] = None,
) -> "Ma... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/matching_engine.html |
4a21a2dd68dc-6 | return cls(
project_id=project_id,
index=index,
endpoint=endpoint,
embedding=embedding or cls._get_default_embeddings(),
gcs_client=gcs_client,
credentials=credentials,
gcs_bucket_name=gcs_bucket_name,
)
@classmethod
def... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/matching_engine.html |
4a21a2dd68dc-7 | ) -> MatchingEngineIndex:
"""Creates a MatchingEngineIndex object by id.
Args:
index_id: The created index id.
project_id: The project to retrieve index from.
region: Location to retrieve index from.
credentials: GCS credentials.
Returns:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/matching_engine.html |
4a21a2dd68dc-8 | A configured GCS client.
"""
from google.cloud import storage
return storage.Client(credentials=credentials, project=project_id)
@classmethod
def _init_aiplatform(
cls,
project_id: str,
region: str,
gcs_bucket_name: str,
credentials: "Credentials",... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/matching_engine.html |
6b03f8d18f8b-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
6b03f8d18f8b-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
6b03f8d18f8b-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
6b03f8d18f8b-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
6b03f8d18f8b-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
6b03f8d18f8b-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
6b03f8d18f8b-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
6b03f8d18f8b-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/atlas.html |
b8c55f08f1e5-0 | Source code for langchain.vectorstores.supabase
from __future__ import annotations
import uuid
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 la... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
b8c55f08f1e5-1 | 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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
b8c55f08f1e5-2 | """Return VectorStore initialized from texts and embeddings."""
if not client:
raise ValueError("Supabase client is required.")
if not table_name:
raise ValueError("Supabase document table_name is required.")
embeddings = embedding.embed_documents(texts)
ids = [st... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
b8c55f08f1e5-3 | ) -> List[Tuple[Document, float]]:
vectors = self._embedding.embed_documents([query])
return self.similarity_search_by_vector_with_relevance_scores(vectors[0], k)
[docs] def similarity_search_by_vector_with_relevance_scores(
self, query: List[float], k: int
) -> List[Tuple[Document, float... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
b8c55f08f1e5-4 | ),
)
for search in res.data
if search.get("content")
]
return match_result
@staticmethod
def _texts_to_documents(
texts: Iterable[str],
metadatas: Optional[Iterable[dict[Any, Any]]] = None,
) -> List[Document]:
"""Return list of Doc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
b8c55f08f1e5-5 | if len(result.data) == 0:
raise Exception("Error inserting: No rows added")
# VectorStore.add_vectors returns ids as strings
ids = [str(i.get("id")) for i in result.data if i.get("id")]
id_list.extend(ids)
return id_list
[docs] def max_marginal_relevance_se... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
b8c55f08f1e5-6 | matched_embeddings,
k=k,
lambda_mult=lambda_mult,
)
filtered_documents = [matched_documents[i] for i in mmr_selected]
return filtered_documents
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
b8c55f08f1e5-7 | SELECT
id,
content,
metadata,
embedding,
1 -(docstore.embedding <=> query_embedding) AS similarity
FROM
docstore
ORDER BY
docstore.embedding <=> query_embedding
LIMIT match... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/supabase.html |
6937ad042eb1-0 | Source code for langchain.vectorstores.awadb
"""Wrapper around AwaDB for embedding vectors"""
from __future__ import annotations
import logging
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
6937ad042eb1-1 | self.table2embeddings: dict[str, Embeddings] = {}
if embedding is not None:
self.table2embeddings[table_name] = embedding
self.using_table_name = table_name
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
is_duplica... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
6937ad042eb1-2 | [docs] def similarity_search(
self,
query: str,
k: int = DEFAULT_TOPN,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query."""
if self.awadb_client is None:
raise ValueError("AwaDB client is None!!!")
embedding = None
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
6937ad042eb1-3 | retrieval_docs = self.similarity_search_by_vector(embedding, k, scores)
L2_Norm = 0.0
for score in scores:
L2_Norm = L2_Norm + score * score
L2_Norm = pow(L2_Norm, 0.5)
doc_no = 0
for doc in retrieval_docs:
doc_tuple = (doc, 1 - (scores[doc_no] / L2_Norm))... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
6937ad042eb1-4 | L2_Norm = L2_Norm + score * score
L2_Norm = pow(L2_Norm, 0.5)
doc_no = 0
for doc in retrieval_docs:
doc_tuple = (doc, 1 - scores[doc_no] / L2_Norm)
results.append(doc_tuple)
doc_no = doc_no + 1
return results
[docs] def similarity_search_by_vector(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
6937ad042eb1-5 | content = item_detail[item_key]
elif (
item_key == "Field@1" or item_key == "text_embedding"
): # embedding field for the document
continue
elif item_key == "score": # L2 distance
if scores is not None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
6937ad042eb1-6 | ) -> str:
"""Get the current table."""
return self.using_table_name
[docs] @classmethod
def from_texts(
cls: Type[AwaDB],
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
table_name: str = _DEFAULT_TABLE_NAME... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
6937ad042eb1-7 | table_name: str = _DEFAULT_TABLE_NAME,
log_and_data_dir: Optional[str] = None,
client: Optional[awadb.Client] = None,
**kwargs: Any,
) -> AwaDB:
"""Create an AwaDB vectorstore from a list of documents.
If a log_and_data_dir specified, the table will be persisted there.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/awadb.html |
945561975e29-0 | 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 (
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Mapping,
Optional,
Tuple,
Union,
)
from l... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
945561975e29-1 | # defined as an abstract base class itself, allowing the creation of subclasses with
# their own specific implementations. If you plan to subclass ElasticVectorSearch,
# you can inherit from it and define your own implementation of the necessary methods
# and attributes.
[docs]class ElasticVectorSearch(VectorStore, ABC... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
945561975e29-2 | 4. Click "Reset password"
5. Follow the prompts to reset the password
The format for Elastic Cloud URLs is
https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
Example:
.. code-block:: python
from langchain import ElasticVectorSearch
from langchain.embeddi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
945561975e29-3 | self.index_name = index_name
_ssl_verify = ssl_verify or {}
try:
self.client = elasticsearch.Elasticsearch(elasticsearch_url, **_ssl_verify)
except ValueError as e:
raise ValueError(
f"Your elasticsearch client string is mis-formatted. Got error: {e} "
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
945561975e29-4 | # just to save expensive steps for last
self.create_index(self.client, self.index_name, mapping)
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
request = {
"_op_type": "index",
"_index": self.index_name,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
945561975e29-5 | Returns:
List of Documents most similar to the query.
"""
embedding = self.embedding.embed_query(query)
script_query = _default_script_query(embedding, filter)
response = self.client_search(
self.client, self.index_name, script_query, size=k
)
hits... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
945561975e29-6 | elasticsearch_url="http://localhost:9200"
)
"""
elasticsearch_url = elasticsearch_url or get_from_env(
"elasticsearch_url", "ELASTICSEARCH_URL"
)
index_name = index_name or uuid.uuid4().hex
vectorsearch = cls(elasticsearch_url, index_name, embedding, *... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
945561975e29-7 | # TODO: Check if this can be done in bulk
for id in ids:
self.client.delete(index=self.index_name, id=id)
[docs]class ElasticKnnSearch(ElasticVectorSearch):
"""
A class for performing k-Nearest Neighbors (k-NN) search on an Elasticsearch index.
The class is designed for a text search sce... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
945561975e29-8 | )
self.embedding = embedding
self.index_name = index_name
self.query_field = query_field
self.vector_query_field = vector_query_field
# If a pre-existing Elasticsearch connection is provided, use it.
if es_connection is not None:
self.client = es_connection
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
945561975e29-9 | "k": k,
"num_candidates": num_candidates,
}
# Case 1: `query_vector` is provided, but not `model_id` -> use query_vector
if query_vector and not model_id:
knn["query_vector"] = query_vector
# Case 2: `query` and `model_id` are provided, -> use query_vector_builder... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
945561975e29-10 | search on the Elasticsearch index and returns the results.
Args:
query: The query or queries to be used for the search. Required if
`query_vector` is not provided.
k: The number of nearest neighbors to return. Defaults to 10.
query_vector: The query vector to ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
945561975e29-11 | model_id: Optional[str] = None,
size: Optional[int] = 10,
source: Optional[bool] = True,
knn_boost: Optional[float] = 0.9,
query_boost: Optional[float] = 0.1,
fields: Optional[
Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...], None]
] = None,
)... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
945561975e29-12 | included. Defaults to None.
vector_query_field: Field name to use in knn search if not default 'vector'
query_field: Field name to use in search if not default 'text'
Returns:
The search results.
Raises:
ValueError: If neither `query_vector` nor `model_id`... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
fa0bf0c43f71-0 | Source code for langchain.vectorstores.mongodb_atlas
from __future__ import annotations
import logging
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Iterable,
List,
Optional,
Tuple,
TypeVar,
Union,
)
from langchain.docstore.document import Document
from langchain.embe... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
fa0bf0c43f71-1 | """
Args:
collection: MongoDB collection to add the texts to.
embedding: Text embedding model to use.
text_key: MongoDB field that will contain the text for each
document.
embedding_key: MongoDB field that will contain the embedding for
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
fa0bf0c43f71-2 | """
batch_size = kwargs.get("batch_size", DEFAULT_INSERT_BATCH_SIZE)
_metadatas: Union[List, Generator] = metadatas or ({} for _ in texts)
texts_batch = []
metadatas_batch = []
result_ids = []
for i, (text, metadata) in enumerate(zip(texts, _metadatas)):
texts... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
fa0bf0c43f71-3 | """Return MongoDB documents most similar to query, along with scores.
Use the knnBeta Operator available in MongoDB Atlas Search
This feature is in early access and available only for evaluation purposes, to
validate functionality, and to gather feedback from a small closed group of
earl... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
fa0bf0c43f71-4 | docs.append((Document(page_content=text, metadata=res), score))
return docs
[docs] def similarity_search(
self,
query: str,
k: int = 4,
pre_filter: Optional[dict] = None,
post_filter_pipeline: Optional[List[Dict]] = None,
**kwargs: Any,
) -> List[Document]:... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
fa0bf0c43f71-5 | collection: Optional[Collection[MongoDBDocumentType]] = None,
**kwargs: Any,
) -> MongoDBAtlasVectorSearch:
"""Construct MongoDBAtlasVectorSearch wrapper from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Adds the documents to a provid... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/mongodb_atlas.html |
f76a16c2f8cf-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
f76a16c2f8cf-1 | index_type: str,
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,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
f76a16c2f8cf-2 | """
Returns the most similar indexed documents to the query text.
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 simila... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
f76a16c2f8cf-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
f76a16c2f8cf-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
f76a16c2f8cf-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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
88c876174b1f-0 | Source code for langchain.vectorstores.deeplake
"""Wrapper around Activeloop Deep Lake."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
try:
import deeplake
from deeplake.core.fast_forwarding import version_co... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-1 | vectorstore = DeepLake("langchain_store", embeddings.embed_query)
"""
_LANGCHAIN_DEFAULT_DEEPLAKE_PATH = "./deeplake/"
def __init__(
self,
dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
token: Optional[str] = None,
embedding_function: Optional[Embeddings] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-2 | read_only (bool): Open dataset in read-only mode. Default is False.
ingestion_batch_size (int): During data ingestion, data is divided
into batches. Batch size is the size of each batch.
Default is 1000.
num_workers (int): Number of workers to use during data inge... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-3 | "Please install it with `pip install deeplake`."
)
if version_compare(deeplake.__version__, "3.6.2") == -1:
raise ValueError(
"deeplake version should be >= 3.6.3, but you've installed"
f" {deeplake.__version__}. Consider upgrading deeplake version \
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-4 | ids (Optional[List[str]], optional): Optional list of IDs.
**kwargs: other optional keyword arguments.
Returns:
List[str]: List of IDs of the added texts.
"""
kwargs = {}
if ids:
if self._id_tensor_name == "ids": # for backwards compatibility
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-5 | Engine for the client. Not for in-memory or local datasets.
- ``tensor_db`` - Hosted Managed Tensor Database for storage
and query execution. Only for data in Deep Lake Managed Database.
Use runtime = {"db_engine": True} during dataset creation.
re... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-6 | """
Return docs similar to query.
Args:
query (str, optional): Text to look up similar docs.
embedding (Union[List[float], np.ndarray], optional): Query's embedding.
embedding_function (Callable, optional): Function to convert `query`
into embedding.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-7 | and query execution. Only for data in Deep Lake Managed Database.
Use runtime = {"db_engine": True} during dataset creation.
**kwargs: Additional keyword arguments.
Returns:
List of Documents by the specified distance metric,
if return_score True, return a... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-8 | )
scores = result["score"]
embeddings = result["embedding"]
metadatas = result["metadata"]
texts = result["text"]
if use_maximal_marginal_relevance:
lambda_mult = kwargs.get("lambda_mult", 0.5)
indices = maximal_marginal_relevance( # type: ignore
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-9 | ... exec_option="compute_engine",
... )
Args:
k (int): Number of Documents to return. Defaults to 4.
query (str): Text to look up similar documents.
**kwargs: Additional keyword arguments include:
embedding (Callable): Embedding function to use... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-10 | k=k,
use_maximal_marginal_relevance=False,
return_score=False,
**kwargs,
)
[docs] def similarity_search_by_vector(
self,
embedding: Union[List[float], np.ndarray],
k: int = 4,
**kwargs: Any,
) -> List[Document]:
"""
Retur... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-11 | - "compute_engine" - Performant C++ implementation of the Deep
Lake Compute Engine. Runs on the client and can be used for
any data stored in or connected to Deep Lake. It cannot be
used with in-memory or local datasets.
- "tens... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-12 | ... )
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
**kwargs: Additional keyword arguments. Some of these arguments are:
distance_metric: `L2` for Euclidean, `L1` for Nuclear, `max` L-infinity
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-13 | text with distance in float."""
return self._search(
query=query,
k=k,
return_score=True,
**kwargs,
)
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-14 | option with big datasets is discouraged due to potential
memory issues.
- "compute_engine" - Performant C++ implementation of the Deep
Lake Compute Engine. Runs on the client and can be used for
any data stored in or connected to Deep Lake. It ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-15 | ... embedding_function = <embedding_function_for_query>,
... k = <number_of_items_to_return>,
... exec_option = <preferred_exec_option>,
... )
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-16 | "For MMR search, you must specify an embedding function on"
" `creation` or during add call."
)
return self._search(
query=query,
k=k,
fetch_k=fetch_k,
use_maximal_marginal_relevance=True,
lambda_mult=lambda_mult,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-17 | (use 'activeloop login' from command line)
- AWS S3 path of the form ``s3://bucketname/path/to/dataset``.
Credentials are required in either the environment
- Google Cloud Storage path of the form
``gcs://bucketname/path/to/dataset`` Credentials ar... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
88c876174b1f-18 | metadatas=metadatas,
ids=ids,
embedding_function=embedding.embed_documents, # type: ignore
)
return deeplake_dataset
[docs] def delete(
self,
ids: Any[List[str], None] = None,
filter: Any[Dict[str, str], None] = None,
delete_all: Any[bool, None... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
1958d10fb430-0 | 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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1958d10fb430-1 | column_map (Dict) : Column type map to project column name onto langchain
semantics. Must have keys: `text`, `id`, `vector`,
must be same size to number of columns. For example:
.. code-block:: python
{
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1958d10fb430-2 | constraints and even sub-queries.
For more information, please visit
[myscale official site](https://docs.myscale.com/en/overview/)
"""
def __init__(
self,
embedding: Embeddings,
config: Optional[MyScaleSettings] = None,
**kwargs: Any,
) -> None:
"""MyScal... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1958d10fb430-3 | dim = len(embedding.embed_query("try this out"))
index_params = (
", " + ",".join([f"'{k}={v}'" for k, v in self.config.index_param.items()])
if self.config.index_param
else ""
)
schema_ = f"""
CREATE TABLE IF NOT EXISTS {self.config.database}.{sel... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1958d10fb430-4 | def _build_istr(self, transac: Iterable, column_names: Iterable[str]) -> str:
ks = ",".join(column_names)
_data = []
for n in transac:
n = ",".join([f"'{self.escape_str(str(_n))}'" for _n in n])
_data.append(f"({n})")
i_str = f"""
INSERT INTO TABLE... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1958d10fb430-5 | column_names = {
colmap_["id"]: ids,
colmap_["text"]: texts,
colmap_["vector"]: map(self.embedding_function, texts),
}
metadatas = metadatas or [{} for _ in texts]
column_names[colmap_["metadata"]] = map(json.dumps, metadatas)
assert len(set(colmap_) -... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1958d10fb430-6 | batch_size: int = 32,
**kwargs: Any,
) -> MyScale:
"""Create Myscale wrapper with existing texts
Args:
embedding_function (Embeddings): Function to extract text embedding
texts (Iterable[str]): List or tuple of strings to be added
config (MyScaleSettings, ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1958d10fb430-7 | for r in self.client.query(
f"DESC {self.config.database}.{self.config.table}"
).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(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1958d10fb430-8 | NOTE: Please do not let end-user to fill this and always be aware
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... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1958d10fb430-9 | ]
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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
1958d10fb430-10 | ]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
[docs] def drop(self) -> None:
"""
Helper function: Drop data
"""
self.client.command(
f"DROP TABLE IF EXISTS {self.config.database}.... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
27c778c1385a-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, Sequence, Tuple, Type
from sqlalchemy import REAL, Column, String, Table, create_engine, ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
27c778c1385a-1 | """
def __init__(
self,
connection_string: str,
embedding_function: Embeddings,
embedding_dimension: int = _LANGCHAIN_DEFAULT_EMBEDDING_DIM,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
pre_delete_collection: bool = False,
logger: Optional[loggin... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
27c778c1385a-2 | """
)
result = conn.execute(index_query).scalar()
# Create the index if it doesn't exist
if not result:
index_statement = text(
f"""
CREATE INDEX {index_name}
ON {s... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
27c778c1385a-3 | if not metadatas:
metadatas = [{} for _ in texts]
# Define the table schema
chunks_table = Table(
self.collection_name,
Base.metadata,
Column("id", TEXT, primary_key=True),
Column("embedding", ARRAY(REAL)),
Column("document", String... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
27c778c1385a-4 | k (int): Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query.
"""
embedding = self.embedding_function.embed_query(text=query)
return self.similari... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
27c778c1385a-5 | **kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of Tuples of (doc, similarity_score)
"""
return self.si... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
27c778c1385a-6 | (
Document(
page_content=result.document,
metadata=result.metadata,
),
result.distance if self.embedding_function is not None else None,
)
for result in results
]
return documents_with_scores
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
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