viruPVS/langchain-docs huggingface dataset:
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**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)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, user_id: Optional[str] = None, app_id: Optional[str] = None, number_of_docs: Optional[int] = None, pat: Optional[str] = None, **kwargs: Any) → Clarifai[source]¶
Create a Clarifai vectorstore from a list of documents.
Parameters
user_id (str) – User ID.
app_id (str) – App ID.
documents (List[Document]) – List of documents to add.
number_of_docs (Optional[int]) – Number of documents to return
number_of_docs (Optional[int]) – Number of documents to return
None. (during vector search. Defaults to) – 
Returns
Clarifai vectorstore.
Return type
Clarifai
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, user_id: Optional[str] = None, app_id: Optional[str] = None, number_of_docs: Optional[int] = None, pat: Optional[str] = None, **kwargs: Any) → Clarifai[source]¶
Create a Clarifai vectorstore from a list of texts.
Parameters
user_id (str) – User ID.
app_id (str) – App ID.
texts (List[str]) – List of texts to add.
number_of_docs (Optional[int]) – Number of documents to return
None. (Defaults to) – 
metadatas (Optional[List[dict]]) – Optional list of metadatas.
None. – 
Returns
Clarifai vectorstore.
Return type
Clarifai
max_marginal_relevance_search(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
among selected documents.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – 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.
Returns
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(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.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – 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.
Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Run similarity search using Clarifai.
Parameters
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 and score for each
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query vector.
Returns
List of Documents most similar to the query vector.
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**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)
similarity_search_with_score(query: str, k: int = 4, filters: Optional[dict] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with score using Clarifai.
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) – 
Returns
List of documents most similar to the query text.
Return type
List[Document]
Examples using Clarifai¶
Clarifai
langchain_community.vectorstores.tencentvectordb.IndexParams¶
class langchain_community.vectorstores.tencentvectordb.IndexParams(dimension: int, shard: int = 1, replicas: int = 2, index_type: str = 'HNSW', metric_type: str = 'L2', params: Optional[Dict] = None)[source]¶
Tencent vector DB Index params.
See the following documentation for details:
https://cloud.tencent.com/document/product/1709/95826
Methods
__init__(dimension[, shard, replicas, ...])
__init__(dimension: int, shard: int = 1, replicas: int = 2, index_type: str = 'HNSW', metric_type: str = 'L2', params: Optional[Dict] = None)[source]¶
langchain_community.vectorstores.starrocks.StarRocksSettings¶
class langchain_community.vectorstores.starrocks.StarRocksSettings[source]¶
Bases: BaseSettings
StarRocks client configuration.
Attribute:
StarRocks_host (str)An URL to connect to MyScale backend.Defaults to ‘localhost’.
StarRocks_port (int) : URL port to connect with HTTP. Defaults to 8443.
username (str) : Username to login. Defaults to None.
password (str) : Password to login. Defaults to None.
database (str) : Database name to find the table. Defaults to ‘default’.
table (str) : Table name to operate on.
Defaults to ‘vector_table’.
column_map (Dict)Column type map to project column name onto langchainsemantics. Must have keys: text, id, vector,
must be same size to number of columns. For example:
.. code-block:: python
{‘id’: ‘text_id’,
‘embedding’: ‘text_embedding’,
‘document’: ‘text_plain’,
‘metadata’: ‘metadata_dictionary_in_json’,
}
Defaults to identity map.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param column_map: Dict[str, str] = {'document': 'document', 'embedding': 'embedding', 'id': 'id', 'metadata': 'metadata'}¶
param database: str = 'default'¶
param host: str = 'localhost'¶
param password: str = ''¶
param port: int = 9030¶
param table: str = 'langchain'¶
param username: str = 'root'¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
Examples using StarRocksSettings¶
StarRocks
langchain_community.vectorstores.vald.Vald¶
class langchain_community.vectorstores.vald.Vald(embedding: Embeddings, host: str = 'localhost', port: int = 8080, grpc_options: Tuple = (('grpc.keepalive_time_ms', 10000), ('grpc.keepalive_timeout_ms', 10000)), grpc_use_secure: bool = False, grpc_credentials: Optional[Any] = None)[source]¶
Wrapper around Vald vector database.
To use, you should have the vald-client-python python package installed.
Example
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Vald
texts = ['foo', 'bar', 'baz']
vald = Vald.from_texts(
    texts=texts,
    embedding=HuggingFaceEmbeddings(),
    host="localhost",
    port=8080,
    skip_strict_exist_check=False,
 )
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding[, host, port, ...])
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, grpc_metadata, ...])
param skip_strict_exist_check
Deprecated. This is not used basically.
adelete([ids])
Delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
afrom_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1], asynchronously.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
delete([ids, skip_strict_exist_check, ...])
param skip_strict_exist_check
Deprecated. This is not used basically.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
param skip_strict_exist_check
Deprecated. This is not used basically.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, radius, ...])
Return docs most similar to query.
Return docs most similar to query.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Run similarity search with distance.
similarity_search_with_score_by_vector(embedding)
__init__(embedding: Embeddings, host: str = 'localhost', port: int = 8080, grpc_options: Tuple = (('grpc.keepalive_time_ms', 10000), ('grpc.keepalive_timeout_ms', 10000)), grpc_use_secure: bool = False, grpc_credentials: Optional[Any] = None)[source]¶
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, grpc_metadata: Optional[Any] = None, skip_strict_exist_check: bool = False, **kwargs: Any) → List[str][source]¶
Parameters
skip_strict_exist_check – Deprecated. This is not used basically.
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(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.
async amax_marginal_relevance_search_by_vector(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.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**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)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
delete(ids: Optional[List[str]] = None, skip_strict_exist_check: bool = False, grpc_metadata: Optional[Any] = None, **kwargs: Any) → Optional[bool][source]¶
Parameters
Parameters
skip_strict_exist_check – Deprecated. This is not used basically.
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, host: str = 'localhost', port: int = 8080, grpc_options: Tuple = (('grpc.keepalive_time_ms', 10000), ('grpc.keepalive_timeout_ms', 10000)), grpc_use_secure: bool = False, grpc_credentials: Optional[Any] = None, grpc_metadata: Optional[Any] = None, skip_strict_exist_check: bool = False, **kwargs: Any) → Vald[source]¶
Parameters
skip_strict_exist_check – Deprecated. This is not used basically.
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, radius: float = - 1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[Any] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – 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.
Returns
List of Documents selected by maximal marginal relevance.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, radius: float = - 1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[Any] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – 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.
Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, radius: float = - 1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[Any] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Return docs most similar to query.
similarity_search_by_vector(embedding: List[float], k: int = 4, radius: float = - 1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[Any] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query vector.
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**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)
similarity_search_with_score(query: str, k: int = 4, radius: float = - 1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[Any] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
langchain_community.vectorstores.supabase.SupabaseVectorStore¶
class langchain_community.vectorstores.supabase.SupabaseVectorStore(client: supabase.client.Client, embedding: Embeddings, table_name: str, chunk_size: int = 500, query_name: Union[str, None] = None)[source]¶
Supabase Postgres vector store.
It assumes you have the pgvector
extension installed and a match_documents (or similar) function. For more details:
https://integrations.langchain.com/vectorstores?integration_name=SupabaseVectorStore
You can implement your own match_documents function in order to limit the search
space to a subset of documents based on your own authorization or business logic.
Note that the Supabase Python client does not yet support async operations.
If you’d like to use max_marginal_relevance_search, please review the instructions
below on modifying the match_documents function to return matched embeddings.
Examples:
from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_core.documents import Document
from langchain_community.vectorstores import SupabaseVectorStore
from supabase.client import create_client
docs = [
    Document(page_content="foo", metadata={"id": 1}),
]
embeddings = OpenAIEmbeddings()
supabase_client = create_client("my_supabase_url", "my_supabase_key")
vector_store = SupabaseVectorStore.from_documents(
    docs,
    embeddings,
    client=supabase_client,
    table_name="documents",
    query_name="match_documents",
    chunk_size=500,
)
To load from an existing table:
from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores import SupabaseVectorStore
from supabase.client import create_client