id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 53 121 |
|---|---|---|
2b28b54605b7-22 | Returns
List of IDs of the added texts.
Return type
List[str]
similarity_search_with_score_id_by_vector(embedding, k=4)[source]
Return docs most similar to embedding vector.
No support for filter query (on metadata) along with vector search.
Parameters
embedding (str) – Embedding to look up documents similar to.
k (in... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-23 | Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) –
Returns
List of Documents most similar to the query vector.
Return type
List[langchain.schema.Document]
similarity_search_... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-24 | Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
:param query: Text to look up documents similar to.
:param k: Number of Documents to return.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
:param lambda_mult: Number between 0 and 1 that determi... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-25 | Bases: langchain.vectorstores.base.VectorStore
Wrapper around ChromaDB embeddings platform.
To use, you should have the chromadb python package installed.
Example
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Chroma("lang... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-26 | Return docs most similar to embedding vector.
:param embedding: Embedding to look up documents similar to.
:type embedding: str
:param k: Number of Documents to return. Defaults to 4.
:type k: int
:param filter: Filter by metadata. Defaults to None.
:type filter: Optional[Dict[str, str]]
Returns
List of Documents most ... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-27 | lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –
Returns
List of Documents selected by ma... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-28 | where (Optional[Where]) – A Where type dict used to filter results by.
E.g. {“color” : “red”, “price”: 4.20}. Optional.
limit (Optional[int]) – The number of documents to return. Optional.
offset (Optional[int]) – The offset to start returning results from.
Useful for paging results with limit. Optional.
where_document... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-29 | collection_name (str) – Name of the collection to create.
persist_directory (Optional[str]) – Directory to persist the collection.
embedding (Optional[Embeddings]) – Embedding function. Defaults to None.
metadatas (Optional[List[dict]]) – List of metadatas. Defaults to None.
ids (Optional[List[str]]) – List of document... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-30 | Bases: langchain.vectorstores.base.VectorStore
Wrapper around ClickHouse vector database
You need a clickhouse-connect python package, and a valid account
to connect to ClickHouse.
ClickHouse can not only search with simple vector indexes,
it also supports complex query with multiple conditions,
constraints and even su... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-31 | Defaults to None.
batch_size (int, optional) – Batchsize when transmitting data to ClickHouse.
Defaults to 32.
metadata (List[dict], optional) – metadata to texts. Defaults to None.
into (Other keyword arguments will pass) – [clickhouse-connect](https://clickhouse.com/docs/en/integrations/python#clickhouse-connect-driv... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-32 | 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.
embedding (List[float]) –
kwargs (Any) –
Returns
List of (Document, similarity)
Return type
List[Document]
similarity_search_with_relevance_scores(query, k=4... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-33 | index_param (list): index build parameter.
index_query_params(dict): index query parameters.
database (str) : Database name to find the table. Defaults to ‘default’.
table (str) : Table name to operate on.
Defaults to ‘vector_table’.
metric (str)Metric to compute distance,supported are (‘angular’, ‘euclidean’, ‘manhatt... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-34 | Show JSON schema{
"title": "ClickhouseSettings",
"description": "ClickHouse Client Configuration\n\nAttribute:\n clickhouse_host (str) : An URL to connect to MyScale backend.\n Defaults to 'localhost'.\n clickhouse_port (int) : URL port to connect with HTTP. Defaults to 8443.\n us... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-35 | "type": "object",
"properties": {
"host": {
"title": "Host",
"default": "localhost",
"env_names": "{'clickhouse_host'}",
"type": "string"
},
"port": {
"title": "Port",
"default": 8123,
"env_names": "{'clickhouse_port'}",
"type"... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-36 | "type": "string"
}
},
"column_map": {
"title": "Column Map",
"default": {
"id": "id",
"uuid": "uuid",
"document": "document",
"embedding": "embedding",
"metadata": "metadata"
},
"env_names": "{'clickhous... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-37 | port (int)
table (str)
username (Optional[str])
attribute column_map: Dict[str, str] = {'document': 'document', 'embedding': 'embedding', 'id': 'id', 'metadata': 'metadata', 'uuid': 'uuid'}
attribute database: str = 'default'
attribute host: str = 'localhost'
attribute index_param: Optional[Union[List, Dict]] = ["'L... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-38 | To use, you should have the deeplake python package installed.
Example
from langchain.vectorstores import DeepLake
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = DeepLake("langchain_store", embeddings.embed_query)
Parameters
dataset_path (str) –
token (Optional[s... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-39 | ... )
>>> # Run tql search:
>>> data = vector_store.tql_search(
... tql_query="SELECT * WHERE id == <id>",
... exec_option="compute_engine",
... )
Parameters
k (int) – Number of Documents to return. Defaults to 4.
query (str) – Text to look up similar documents.
**kwargs – Additional keyword arguments include:
... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-40 | similarity_search_by_vector(embedding, k=4, **kwargs)[source]
Return docs most similar to embedding vector.
Examples
>>> # Search using an embedding
>>> data = vector_store.similarity_search_by_vector(
... embedding=<your_embedding>,
... k=<num_items_to_return>,
... exec_option=<preferred_exec_option>,
... )
... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-41 | runtime = {“db_engine”: True} during dataset creation.
distance_metric (str): L2 for Euclidean, L1 for Nuclear,max for L-infinity distance, cos for cosine similarity,
‘dot’ for dot product. Defaults to L2.
kwargs (Any) –
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
similarity_... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-42 | any data stored in or connected to Deep Lake. It cannot be used
with in-memory or local datasets.
”tensor_db” - Performant, fully-hosted Managed Tensor Database.Responsible for storage and query execution. Only available for
data stored in the Deep Lake Managed Database. To store datasets
in this database, specify runt... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-43 | “python”.
- “python” - Pure-python implementation running on the client.
Can be used for data stored anywhere. WARNING: using this
option with big datasets is discouraged due to potential
memory issues.
”compute_engine” - Performant C++ implementation of the DeepLake Compute Engine. Runs on the client and can be used f... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-44 | lambda_mult (float) – Value between 0 and 1. 0 corresponds
to maximum diversity and 1 to minimum.
Defaults to 0.5.
exec_option (str) – Supports 3 ways to perform searching.
- “python” - Pure-python implementation running on the client.
Can be used for data stored anywhere. WARNING: using this
option with big datasets i... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-45 | … exec_option = <preferred_exec_option>,
… )
Parameters
dataset_path (str) –
The full path to the dataset. Can be:
Deep Lake cloud path of the form hub://username/dataset_name.To write to Deep Lake cloud datasets,
ensure that you are logged in to Deep Lake
(use ‘activeloop login’ from command line)
AWS S3 path ... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-46 | filter (Optional[Dict[str, str]], optional) – The filter to delete by.
Defaults to None.
delete_all (Optional[bool], optional) – Whether to drop the dataset.
Defaults to None.
Returns
Whether the delete operation was successful.
Return type
bool
classmethod force_delete_by_path(path)[source]
Force delete dataset by pa... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-47 | “cosine”, “ip”, and “l2”. Defaults to “cosine”.
max_elements (int) – Maximum number of vectors that can be stored.
Defaults to 1024.
index (bool) – Whether an index should be built for this field.
Defaults to True.
ef_construction (int) – defines a construction time/accuracy trade-off.
Defaults to 200.
ef (int) – param... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-48 | Return type
langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch
class langchain.vectorstores.DocArrayInMemorySearch(doc_index, embedding)[source]
Bases: langchain.vectorstores.docarray.base.DocArrayIndex
Wrapper around in-memory storage for exact search.
To use it, you should have the docarray package with version... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-49 | Defaults to “cosine_sim”.
kwargs (Any) –
Returns
DocArrayInMemorySearch Vector Store
Return type
langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch
class langchain.vectorstores.ElasticVectorSearch(elasticsearch_url, index_name, embedding, *, ssl_verify=None)[source]
Bases: langchain.vectorstores.base.Ve... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-50 | 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
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_host = "cluster_id.region_id.gcp.c... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-51 | Return docs most similar to query.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[dict]) –
kwargs (Any) –
Returns
List of Documents most similar to the query.
Return type
List[langchain.schema.Document]
similarity_search_with_sco... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-52 | elasticsearch_url (Optional[str]) –
index_name (Optional[str]) –
refresh_indices (bool) –
kwargs (Any) –
Return type
langchain.vectorstores.elastic_vector_search.ElasticVectorSearch
create_index(client, index_name, mapping)[source]
Parameters
client (Any) –
index_name (str) –
mapping (Dict) –
Return type
None
c... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-53 | Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) – Optional list of unique IDs.
kwargs (Any) –
Returns
Li... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-54 | filter the resulting set of retrieved docs
kwargs (Any) –
Returns
List of documents most similar to the query text and L2 distance
in float for each. Lower score represents more similarity.
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search_with_score(query, k=4, filter=None, fetch_k=20, **kwa... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-55 | Return docs most similar to query.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
fetch_k (int) – (Optional[int]) Number of Documents to fetch bef... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-56 | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number o... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-57 | Return type
List[langchain.schema.Document]
merge_from(target)[source]
Merge another FAISS object with the current one.
Add the target FAISS to the current one.
Parameters
target (langchain.vectorstores.faiss.FAISS) – FAISS object you wish to merge into the current one
Returns
None.
Return type
None
classmethod from_t... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-58 | faiss = FAISS.from_embeddings(text_embedding_pairs, embeddings)
Parameters
text_embeddings (List[Tuple[str, List[float]]]) –
embedding (langchain.embeddings.base.Embeddings) –
metadatas (Optional[List[dict]]) –
ids (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.vectorstores.faiss.FAISS
save_local(fol... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-59 | - NOTE: The table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
pre_delete_table if True, will delete the table if it exists.(default: False)
- Useful for testing.
Parameters
connection_string (str) –
embedding_function (Embeddings) –
nd... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-60 | Run similarity search with Hologres with distance.
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) –
Returns
List of Documents most similar to the query.
Return type
List[lang... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-61 | Return type
List[Tuple[langchain.schema.Document, float]]
classmethod from_texts(texts, embedding, metadatas=None, ndims=1536, table_name='langchain_pg_embedding', ids=None, pre_delete_table=False, **kwargs)[source]
Return VectorStore initialized from texts and embeddings.
Postgres connection string is required
“Eithe... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-62 | metadatas (Optional[List[dict]]) –
ndims (int) –
table_name (str) –
ids (Optional[List[str]]) –
pre_delete_table (bool) –
kwargs (Any) –
Return type
langchain.vectorstores.hologres.Hologres
classmethod from_existing_index(embedding, ndims=1536, table_name='langchain_pg_embedding', pre_delete_table=False, **kwargs... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-63 | Return connection string from database parameters.
Parameters
host (str) –
port (int) –
database (str) –
user (str) –
password (str) –
Return type
str
class langchain.vectorstores.LanceDB(connection, embedding, vector_key='vector', id_key='id', text_key='text')[source]
Bases: langchain.vectorstores.base.VectorSto... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-64 | kwargs (Any) –
Returns
List of documents most similar to the query.
Return type
List[langchain.schema.Document]
classmethod from_texts(texts, embedding, metadatas=None, connection=None, vector_key='vector', id_key='id', text_key='text', **kwargs)[source]
Return VectorStore initialized from texts and embeddings.
Param... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-65 | gcs_bucket_name (str) –
credentials (Optional[Credentials]) –
add_texts(texts, metadatas=None, **kwargs)[source]
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of meta... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-66 | regional. (the same location as the GCS bucket and must be) –
gcs_bucket_name (str) – The location where the vectors will be stored in
created. (order for the index to be) –
index_id (str) – The id of the created index.
endpoint_id (str) – The id of the created endpoint.
credentials_path (Optional[str]) – (Optional) ... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-67 | embedding and the columns are decided by the first metadata dict.
Metada keys will need to be present for all inserted values. At
the moment there is no None equivalent in Milvus.
Parameters
texts (Iterable[str]) – The texts to embed, it is assumed
that they all fit in memory.
metadatas (Optional[List[dict]]) – Metadat... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-68 | k (int, optional) – How many results to return. Defaults to 4.
param (dict, optional) – The search params for the index type.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – How long to wait before timeout error.
Defaults to None.
kwargs (Any) – Collection.sear... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-69 | documentation found here:
https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md
Parameters
embedding (List[float]) – The embedding vector being searched.
k (int, optional) – The amount of results ot return. Defaults to 4.
param (dict) – The search params for the specified index.
Defaults to None.
expr ... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-70 | Returns
Document results for search.
Return type
List[Document]
max_marginal_relevance_search_by_vector(embedding, k=4, fetch_k=20, lambda_mult=0.5, param=None, expr=None, timeout=None, **kwargs)[source]
Perform a search and return results that are reordered by MMR.
Parameters
embedding (str) – The embedding vector be... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-71 | metadatas (Optional[List[dict]]) – Metadata for each text if it exists.
Defaults to None.
collection_name (str, optional) – Collection name to use. Defaults to
“LangChainCollection”.
connection_args (dict[str, Any], optional) – Connection args to use. Defaults
to DEFAULT_MILVUS_CONNECTION.
consistency_level (str, optio... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-72 | Parameters
texts (List[str]) – Text data.
embedding (Embeddings) – Embedding function.
metadatas (Optional[List[dict]]) – Metadata for each text if it exists.
Defaults to None.
collection_name (str, optional) – Collection name to use. Defaults to
“LangChainCollection”.
connection_args (dict[str, Any], optional) – Conne... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-73 | distance_strategy (DistanceStrategy) –
table_name (str) –
content_field (str) –
metadata_field (str) –
vector_field (str) –
pool_size (int) –
max_overflow (int) –
timeout (float) –
kwargs (Any) –
vector_field
Pass the rest of the kwargs to the connection.
connection_kwargs
Add program name and version to con... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-74 | Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[dict]) – A dictionary of metadata fields and values to filter by.
Defaults to None.
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[langch... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-75 | Return type
langchain.vectorstores.singlestoredb.SingleStoreDBRetriever
class langchain.vectorstores.Clarifai(user_id=None, app_id=None, pat=None, number_of_docs=None, api_base=None)[source]
Bases: langchain.vectorstores.base.VectorStore
Wrapper around Clarifai AI platform’s vector store.
To use, you should have the c... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-76 | Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata.
None. (Defaults to) –
namespace (Optional[str]) –
kwargs (Any) –
Returns
List of documents most simmilar to the query text.
Return type
List[Document]
simil... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-77 | Returns
Clarifai vectorstore.
Return type
Clarifai
classmethod from_documents(documents, embedding=None, user_id=None, app_id=None, pat=None, number_of_docs=None, api_base=None, **kwargs)[source]
Create a Clarifai vectorstore from a list of documents.
Parameters
user_id (str) – User ID.
app_id (str) – App ID.
document... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-78 | Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) – Optional list of ids to associate with the texts.
bulk_size (int) – Bulk API request count; Default: 500
kwargs (Any) –
... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-79 | subquery_clause: Query clause on the knn vector field; default: “must”
lucene_filter: the Lucene algorithm decides whether to perform an exact
k-NN search with pre-filtering or an approximate search with modified
post-filtering.
Optional Args for Script Scoring Search:search_type: “script_scoring”; default: “approximat... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-80 | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fe... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-81 | space_type: “l2”, “l1”, “cosinesimil”, “linf”, “innerproduct”; default: “l2”
ef_search: Size of the dynamic list used during k-NN searches. Higher values
lead to more accurate but slower searches; default: 512
ef_construction: Size of the dynamic list used during k-NN graph creation.
Higher values lead to more accurate... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-82 | Parameters
collection (Collection[MongoDBDocumentType]) –
embedding (Embeddings) –
index_name (str) –
text_key (str) –
embedding_key (str) –
classmethod from_connection_string(connection_string, namespace, embedding, **kwargs)[source]
Parameters
connection_string (str) –
namespace (str) –
embedding (langchain.e... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-83 | fields.
post_filter_pipeline (Optional[List[Dict]]) – Optional Pipeline of MongoDB aggregation stages
following the knnBeta search.
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search(query, k=4, pre_filter=None, post_filter_... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-84 | embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
collection (Optional[Collection[MongoDBDocumentType]]) –
kwargs (Any) –
Return type
MongoDBAtlasVectorSearch
class langchain.vectorstores.MyScale(embedding, config=None, **kwargs)[source]
Bases: langchain.vectorstores.base.VectorStore
Wrapper around MySca... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-85 | Create Myscale wrapper with existing texts
Parameters
embedding_function (Embeddings) – Function to extract text embedding
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... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-86 | Perform a similarity search with MyScale by vectors
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional) – where condition string.
Defaults to None.
NOTE – Please do not let end-user to fill this and always be aware
of SQL injection. W... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-87 | Bases: pydantic.env_settings.BaseSettings
MyScale Client Configuration
Attribute:
myscale_host (str)An URL to connect to MyScale backend.Defaults to ‘localhost’.
myscale_port (int) : URL port to connect with HTTP. Defaults to 8443.
username (str) : Username to login. Defaults to None.
password (str) : Password to login... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-88 | Show JSON schema{
"title": "MyScaleSettings",
"description": "MyScale Client Configuration\n\nAttribute:\n myscale_host (str) : An URL to connect to MyScale backend.\n Defaults to 'localhost'.\n myscale_port (int) : URL port to connect with HTTP. Defaults to 8443.\n username (str)... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-89 | },
"port": {
"title": "Port",
"default": 8443,
"env_names": "{'myscale_port'}",
"type": "integer"
},
"username": {
"title": "Username",
"env_names": "{'myscale_username'}",
"type": "string"
},
"password": {
"title": "P... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-90 | },
"table": {
"title": "Table",
"default": "langchain",
"env_names": "{'myscale_table'}",
"type": "string"
},
"metric": {
"title": "Metric",
"default": "cosine",
"env_names": "{'myscale_metric'}",
"type": "string"
}
},
... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-91 | Bases: langchain.vectorstores.base.VectorStore
Wrapper around Pinecone vector database.
To use, you should have the pinecone-client python package installed.
Example
from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
import pinecone
# The environment should be the one s... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-92 | k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[dict]) – Dictionary of argument(s) to filter on metadata
namespace (Optional[str]) – Namespace to search in. Default will search in ‘’ namespace.
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[langcha... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-93 | Defaults to 0.5.
filter (Optional[dict]) –
namespace (Optional[str]) –
kwargs (Any) –
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[langchain.schema.Document]
max_marginal_relevance_search(query, k=4, fetch_k=20, lambda_mult=0.5, filter=None, namespace=None, **kwargs)[source]
Re... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-94 | # in your Pinecone console
pinecone.init(api_key="***", environment="...")
embeddings = OpenAIEmbeddings()
pinecone = Pinecone.from_texts(
texts,
embeddings,
index_name="langchain-demo"
)
Parameters
texts (List[str]) –
embedding (langchain.embeddings.base.Embeddings) –
metadatas (Optional[List[dict]]) –
... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-95 | client = QdrantClient()
collection_name = "MyCollection"
qdrant = Qdrant(client, collection_name, embedding_function)
Parameters
client (Any) –
collection_name (str) –
embeddings (Optional[Embeddings]) –
content_payload_key (str) –
metadata_payload_key (str) –
embedding_function (Optional[Callable]) –
CONTENT_KEY... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-96 | May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold (Optional[float]) – Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-97 | If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency (Optional[common_types.ReadConsistency]) – Read consistency of the search... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-98 | Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency (Optional[common_types.ReadConsistency]) – Read consistency of the search. Defines how many replicas should be
queried before re... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-99 | E.g. for cosine similarity only higher scores will be returned.
consistency (Optional[common_types.ReadConsistency]) – Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
’majo... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-100 | Return type
List[langchain.schema.Document]
classmethod from_texts(texts, embedding, metadatas=None, ids=None, location=None, url=None, port=6333, grpc_port=6334, prefer_grpc=False, https=None, api_key=None, prefix=None, timeout=None, host=None, path=None, collection_name=None, distance_func='Cosine', content_payload_k... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-101 | Default: False
https (Optional[bool]) – If true - use HTTPS(SSL) protocol. Default: None
api_key (Optional[str]) – API key for authentication in Qdrant Cloud. Default: None
prefix (Optional[str]) – If not None - add prefix to the REST URL path.
Example: service/v1 will result in
http://localhost:6333/service/v1/{qdrant... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-102 | Defines how many copies of each shard will be created.
Have effect only in distributed mode.
write_consistency_factor (Optional[int]) – Write consistency factor for collection. Default is 1, minimum is 1.
Defines how many replicas should apply the operation for us to consider
it successful. Increasing this number will ... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-103 | This is intended to be a quick way to get started.
Example
from langchain import Qdrant
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
class langchain.vectorstores.Redis(redis_url, index_name, embedding_function, content_key='... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-104 | embeddings. Defaults to None.
keys (List[str]) or ids (List[str]) – Identifiers of entries.
Defaults to None.
batch_size (int, optional) – Batch size to use for writes. Defaults to 1000.
kwargs (Any) –
Returns
List of ids added to the vectorstore
Return type
List[str]
similarity_search(query, k=4, **kwargs)[source]
R... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-105 | k (int) –
score_threshold (float) –
kwargs (Any) –
Note
If there are no documents that satisfy the score_threshold value,
an empty list is returned.
similarity_search_with_score(query, k=4)[source]
Return docs most similar to query.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of ... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-106 | Return type
Tuple[langchain.vectorstores.redis.Redis, List[str]]
classmethod from_texts(texts, embedding, metadatas=None, index_name=None, content_key='content', metadata_key='metadata', vector_key='content_vector', **kwargs)[source]
Create a Redis vectorstore from raw documents.
This is a user-friendly interface that... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-107 | Connect to an existing Redis index.
Parameters
embedding (langchain.embeddings.base.Embeddings) –
index_name (str) –
content_key (str) –
metadata_key (str) –
vector_key (str) –
kwargs (Any) –
Return type
langchain.vectorstores.redis.Redis
as_retriever(**kwargs)[source]
Parameters
kwargs (Any) –
Return type
lang... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-108 | Parameters
client (Any) –
embeddings (Embeddings) –
collection_name (str) –
text_key (str) –
embedding_key (str) –
add_texts(texts, metadatas=None, ids=None, batch_size=32, **kwargs)[source]
Run more texts through the embeddings and add to the vectorstore
Args:
texts: Iterable of strings to add to the vectorstore... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-109 | Return type
langchain.vectorstores.rocksetdb.Rockset
class DistanceFunction(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]
Bases: enum.Enum
COSINE_SIM = 'COSINE_SIM'
EUCLIDEAN_DIST = 'EUCLIDEAN_DIST'
DOT_PRODUCT = 'DOT_PRODUCT'
order_by()[source]
Return type
str
simila... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-110 | kwargs (Any) –
Return type
List[Document]
similarity_search_by_vector(embedding, k=4, distance_func=DistanceFunction.COSINE_SIM, where_str=None, **kwargs)[source]
Accepts a query_embedding (vector), and returns documents with
similar embeddings.
Parameters
embedding (List[float]) –
k (int) –
distance_func (Distance... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-111 | Return type
None
persist()[source]
Return type
None
add_texts(texts, metadatas=None, ids=None, **kwargs)[source]
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metad... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-112 | to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
Parameters
embedding (List[float]) –
k (int) –
fetch_k (int) –
lambda_mult (float) –
kwargs (Any) –
Return type
List[langchain.schema.Document]
max_marginal_relevance_search(query, k=... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-113 | persist_path (Optional[str]) –
kwargs (Any) –
Return type
langchain.vectorstores.sklearn.SKLearnVectorStore
class langchain.vectorstores.StarRocks(embedding, config=None, **kwargs)[source]
Bases: langchain.vectorstores.base.VectorStore
Wrapper around StarRocks vector database
You need a pymysql python package, and a... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-114 | List of ids from adding the texts into the VectorStore.
Return type
List[str]
classmethod from_texts(texts, embedding, metadatas=None, config=None, text_ids=None, batch_size=32, **kwargs)[source]
Create StarRocks wrapper with existing texts
Parameters
embedding_function (Embeddings) – Function to extract text embeddin... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-115 | Returns
List of Documents
Return type
List[Document]
similarity_search_by_vector(embedding, k=4, where_str=None, **kwargs)[source]
Perform a similarity search with StarRocks by vectors
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], option... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-116 | Bases: langchain.vectorstores.base.VectorStore
VectorStore for a Supabase postgres database. Assumes you have the pgvector
extension installed and a match_documents (or similar) function. For more details:
https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabase
You can implement your own match... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-117 | Parameters
texts (List[str]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
client (Optional[supabase.client.Client]) –
table_name (Optional[str]) –
query_name (Union[str, None]) –
ids (Optional[List[str]]) –
kwargs (Any) –
Return type
SupabaseVectorStore
add_vectors(vectors, documents, ids)[sour... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-118 | **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
kwargs (Any) –
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_searc... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-119 | Return type
List[langchain.schema.Document]
max_marginal_relevance_search(query, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) – T... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-120 | Parameters
ids (List[str]) – List of ids to delete.
Return type
None
class langchain.vectorstores.Tair(embedding_function, url, index_name, content_key='content', metadata_key='metadata', search_params=None, **kwargs)[source]
Bases: langchain.vectorstores.base.VectorStore
Wrapper around Tair Vector store.
Parameters
e... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
2b28b54605b7-121 | Return type
List[Document]
classmethod from_texts(texts, embedding, metadatas=None, index_name='langchain', content_key='content', metadata_key='metadata', **kwargs)[source]
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) –
embedding (langchain.embeddings.base.Embeddings) –
met... | https://api.python.langchain.com/en/stable/modules/vectorstores.html |
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