viruPVS/langchain-docs huggingface dataset:
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, filter: Optional[dict] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return documents most similar to query, along with scores.
Parameters
query – Text to look up documents similar to.
size – Number of Documents to return. Defaults to 4.
filter – Array of Elasticsearch filter clauses to apply to the query.
Returns
List of Documents most similar to the query and score for each
Examples using BESVectorStore¶
Baidu Cloud VectorSearch
langchain_community.vectorstores.neo4j_vector.Neo4jVector¶
class langchain_community.vectorstores.neo4j_vector.Neo4jVector(embedding: Embeddings, *, search_type: SearchType = SearchType.VECTOR, username: Optional[str] = None, password: Optional[str] = None, url: Optional[str] = None, keyword_index_name: Optional[str] = 'keyword', database: str = 'neo4j', index_name: str = 'vector', node_label: str = 'Chunk', embedding_node_property: str = 'embedding', text_node_property: str = 'text', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, logger: Optional[Logger] = None, pre_delete_collection: bool = False, retrieval_query: str = '', relevance_score_fn: Optional[Callable[[float], float]] = None)[source]¶
Neo4j vector index.
To use, you should have the neo4j python package installed.
Parameters
url – Neo4j connection url
username – Neo4j username.
password – Neo4j password
database – Optionally provide Neo4j database
Defaults to “neo4j”
embedding – Any embedding function implementing
langchain.embeddings.base.Embeddings interface.
distance_strategy – The distance strategy to use. (default: COSINE)
pre_delete_collection – If True, will delete existing data if it exists.
(default: False). Useful for testing.
Example
from langchain_community.vectorstores.neo4j_vector import Neo4jVector
from langchain_community.embeddings.openai import OpenAIEmbeddings
url="bolt://localhost:7687"
username="neo4j"
password="pleaseletmein"
embeddings = OpenAIEmbeddings()
vectorestore = Neo4jVector.from_documents(
    embedding=embeddings,
    documents=docs,
    url=url
embedding=embeddings,
    documents=docs,
    url=url
    username=username,
    password=password,
)
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding, *[, search_type, ...])
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_embeddings(texts, embeddings[, ...])
Add embeddings to the vectorstore.
add_texts(texts[, metadatas, ids])
Run more texts through the embeddings and add to the vectorstore.
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])
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 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.
create_new_index()
This method constructs a Cypher query and executes it to create a new vector index in Neo4j.
create_new_keyword_index([text_node_properties])
This method constructs a Cypher query and executes it to create a new full text index in Neo4j.
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding[, ...])
Return Neo4jVector initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)
Construct Neo4jVector wrapper from raw documents and pre- generated embeddings.
from_existing_graph(embedding, node_label, ...)
Initialize and return a Neo4jVector instance from an existing graph.
from_existing_index(embedding, index_name[, ...])
Get instance of an existing Neo4j vector index.
from_texts(texts, embedding[, metadatas, ...])
Return Neo4jVector initialized from texts and embeddings.
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.
query(query, *[, params])
This method sends a Cypher query to the connected Neo4j database and returns the results as a list of dictionaries.
retrieve_existing_fts_index([...])
Check if the fulltext index exists in the Neo4j database
retrieve_existing_index()
Check if the vector index exists in the Neo4j database and returns its embedding dimension.
Check if the vector index exists in the Neo4j database and returns its embedding dimension.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k])
Run similarity search with Neo4jVector.
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])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
Perform a similarity search in the Neo4j database using a given vector and return the top k similar documents with their scores.
verify_version()
Check if the connected Neo4j database version supports vector indexing.
__init__(embedding: Embeddings, *, search_type: SearchType = SearchType.VECTOR, username: Optional[str] = None, password: Optional[str] = None, url: Optional[str] = None, keyword_index_name: Optional[str] = 'keyword', database: str = 'neo4j', index_name: str = 'vector', node_label: str = 'Chunk', embedding_node_property: str = 'embedding', text_node_property: str = 'text', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, logger: Optional[Logger] = None, pre_delete_collection: bool = False, retrieval_query: str = '', relevance_score_fn: Optional[Callable[[float], float]] = None) → 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[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]
add_embeddings(texts: Iterable[str], embeddings: List[List[float]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Add embeddings to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
embeddings – List of list of embedding vectors.
metadatas – List of metadatas associated with the texts.
kwargs – vectorstore specific parameters
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
kwargs – vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
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.
create_new_index() → None[source]¶
This method constructs a Cypher query and executes it
to create a new vector index in Neo4j.
create_new_keyword_index(text_node_properties: List[str] = []) → None[source]¶
This method constructs a Cypher query and executes it
to create a new full text index in Neo4j.
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]
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, **kwargs: Any) → Neo4jVector[source]¶
Return Neo4jVector initialized from documents and embeddings.
Neo4j credentials are required in the form of url, username,
and password and optional database parameters.
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → Neo4jVector[source]¶
Construct Neo4jVector wrapper from raw documents and pre-
generated embeddings.
Return Neo4jVector initialized from documents and embeddings.
Neo4j credentials are required in the form of url, username,
and password and optional database parameters.
Example
from langchain_community.vectorstores.neo4j_vector import Neo4jVector
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
vectorstore = Neo4jVector.from_embeddings(
    text_embedding_pairs, embeddings)
classmethod from_existing_graph(embedding: Embeddings, node_label: str, embedding_node_property: str, text_node_properties: List[str], *, keyword_index_name: Optional[str] = 'keyword', index_name: str = 'vector', search_type: SearchType = SearchType.VECTOR, retrieval_query: str = '', **kwargs: Any) → Neo4jVector[source]¶
Initialize and return a Neo4jVector instance from an existing graph.
This method initializes a Neo4jVector instance using the provided
parameters and the existing graph. It validates the existence of
the indices and creates new ones if they don’t exist.
Returns:
Neo4jVector: An instance of Neo4jVector initialized with the provided parameters
and existing graph.
Example:
>>> neo4j_vector = Neo4jVector.from_existing_graph(
…     embedding=my_embedding,
…     node_label=”Document”,
…     embedding_node_property=”embedding”,
…     text_node_properties=[“title”, “content”]
… )
Note:
Neo4j credentials are required in the form of url, username, and password,
and optional database parameters passed as additional keyword arguments.
classmethod from_existing_index(embedding: Embeddings, index_name: str, search_type: SearchType = SearchType.VECTOR, keyword_index_name: Optional[str] = None, **kwargs: Any) → Neo4jVector[source]¶
Get instance of an existing Neo4j vector index. This method will
return the instance of the store without inserting any new
embeddings.
Neo4j credentials are required in the form of url, username,
and password and optional database parameters along with
the index_name definition.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, **kwargs: Any) → Neo4jVector[source]¶
Return Neo4jVector initialized from texts and embeddings.
Neo4j credentials are required in the form of url, username,
and password and optional database parameters.
and password and optional database parameters.
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
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.
query(query: str, *, params: Optional[dict] = None) → List[Dict[str, Any]][source]¶
This method sends a Cypher query to the connected Neo4j database
and returns the results as a list of dictionaries.
Parameters
query (str) – The Cypher query to execute.
params (dict, optional) – Dictionary of query parameters. Defaults to {}.
Returns
List of dictionaries containing the query results.
Return type
List[Dict[str, Any]]
retrieve_existing_fts_index(text_node_properties: List[str] = []) → Optional[str][source]¶
Check if the fulltext index exists in the Neo4j database
This method queries the Neo4j database for existing fts indexes
with the specified name.
Returns
keyword index information
Return type
(Tuple)
retrieve_existing_index() → Optional[int][source]¶
Check if the vector index exists in the Neo4j database
and returns its embedding dimension.
This method queries the Neo4j database for existing indexes
and attempts to retrieve the dimension of the vector index
with the specified name. If the index exists, its dimension is returned.
If the index doesn’t exist, None is returned.
Returns
The embedding dimension of the existing index if found.
Return type
int or None
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 with Neo4jVector.
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: List[float], k: int = 4, **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) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
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_with_score_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Perform a similarity search in the Neo4j database using a
given vector and return the top k similar documents with their scores.
This method uses a Cypher query to find the top k documents that
are most similar to a given embedding. The similarity is measured
using a vector index in the Neo4j database. The results are returned
as a list of tuples, each containing a Document object and
its similarity score.
Parameters
as a list of tuples, each containing a Document object and
its similarity score.
Parameters
embedding (List[float]) – The embedding vector to compare against.
k (int, optional) – The number of top similar documents to retrieve.
Returns
A list of tuples, each containinga Document object and its similarity score.
Return type
List[Tuple[Document, float]]
verify_version() → None[source]¶
Check if the connected Neo4j database version supports vector indexing.
Queries the Neo4j database to retrieve its version and compares it
against a target version (5.11.0) that is known to support vector
indexing. Raises a ValueError if the connected Neo4j version is
not supported.
Examples using Neo4jVector¶
Neo4j
Neo4j Vector Index
langchain_community.vectorstores.redis.schema.RedisDistanceMetric¶
class langchain_community.vectorstores.redis.schema.RedisDistanceMetric(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Distance metrics for Redis vector fields.
l2 = 'L2'¶
cosine = 'COSINE'¶
ip = 'IP'¶
langchain_community.vectorstores.redis.schema.RedisModel¶
class langchain_community.vectorstores.redis.schema.RedisModel[source]¶
Bases: BaseModel
Schema for Redis index.
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 content_key: str = 'content'¶
param content_vector_key: str = 'content_vector'¶
param extra: Optional[List[langchain_community.vectorstores.redis.schema.RedisField]] = None¶
param numeric: Optional[List[langchain_community.vectorstores.redis.schema.NumericFieldSchema]] = None¶
param tag: Optional[List[langchain_community.vectorstores.redis.schema.TagFieldSchema]] = None¶
param text: List[langchain_community.vectorstores.redis.schema.TextFieldSchema] = [TextFieldSchema(name='content', weight=1, no_stem=False, phonetic_matcher=None, withsuffixtrie=False, no_index=False, sortable=False)]¶
param vector: Optional[List[Union[langchain_community.vectorstores.redis.schema.FlatVectorField, langchain_community.vectorstores.redis.schema.HNSWVectorField]]] = None¶
add_content_field() → None[source]¶
add_vector_field(vector_field: Dict[str, Any]) → None[source]¶
as_dict() → Dict[str, List[Any]][source]¶
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
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¶
get_fields() → List[RedisField][source]¶
get_fields() → List[RedisField][source]¶
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¶
property content_vector: Union[langchain_community.vectorstores.redis.schema.FlatVectorField, langchain_community.vectorstores.redis.schema.HNSWVectorField]¶