langchain.vectorstores.neo4j_vector.Neo4jVector

class langchain.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)

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.vectorstores.neo4j_vector import Neo4jVector
from langchain.embeddings.openai import OpenAIEmbeddings

url="bolt://localhost:7687"
username="neo4j"
password="pleaseletmein"
embeddings = OpenAIEmbeddings()
vectorestore = Neo4jVector.from_documents(
    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 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.
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

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]

add_embeddings(texts: Iterable[str], embeddings: List[List[float]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str]

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]

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.

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

Return VectorStoreRetriever initialized from this VectorStore.

Parameters

• search_type (Optional[str]) – Defines the type of search that the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.

• search_kwargs (Optional[Dict]) –

  Keyword arguments to pass to the search function. Can include things like:

  k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold

  for similarity_score_threshold

  fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR;

  1 for minimum diversity and 0 for maximum. (Default: 0.5)

  filter: Filter by document metadata

Returns

Retriever class for VectorStore.

Return type

VectorStoreRetriever

Examples:

# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)

# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)

# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.8}
)

# Only get the single most similar document from the dataset
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.

create_new_index() → None

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

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]

classmethod from_documents(documents: List[Document], embedding: Embeddings, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, **kwargs: Any) → Neo4jVector

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

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.vectorstores.neo4j_vector import Neo4jVector
from langchain.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

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

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

Return Neo4jVector initialized from texts and embeddings. Neo4j credentials are required in the form of url, username, 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]]

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]

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]

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]

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]

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]]

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]]

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

• 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 containing

a Document object and its similarity score.

Return type

List[Tuple[Document, float]]

verify_version() → None

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