[Question]: neo4j vector retrieval

[navaneethstabilix]

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Question

How to retrieve embeddings from neo4j . I have neo4j db created for storing my information and have a text data to it and also embeddings are created and stored in neo4j .I need to retrieve that embeddings from neo4j graph for further processing ,How to do the retrieval process.

[dosubot[bot]]

Hey @navaneethstabilix! 👋 I'm here to help you with any questions or issues you have. I'm a bot designed to assist you with bug fixes, problem-solving, and contributing.

To retrieve embeddings from a Neo4j database for further processing, you can use the Neo4jVectorStore class provided in the llama_index.vector_stores.neo4jvector module. Here is an example of how to do this:

1. Initialize the Neo4jVectorStore:

   from llama_index.vector_stores.neo4jvector import Neo4jVectorStore
   
   username = "neo4j"
   password = "pleaseletmein"
   url = "bolt://localhost:7687"
   embed_dim = 1536
   
   neo4j_vector = Neo4jVectorStore(username, password, url, embed_dim)

2. Define a query to retrieve the embeddings:

   from llama_index.core.vector_stores.types import VectorStoreQuery
   
   query = VectorStoreQuery(
      query_embedding=[0.1, 0.2, 0.3, ...],  # Replace with your query embedding
      similarity_top_k=10,  # Number of top similar embeddings to retrieve
      query_str="example query"  # Optional: keyword query for hybrid search
   )

3. Execute the query to retrieve the embeddings:

   result = neo4j_vector.query(query)

4. Process the retrieved embeddings:

   for node, similarity, node_id in zip(result.nodes, result.similarities, result.ids):
      print(f"Node ID: {node_id}, Similarity: {similarity}")
      print(f"Embedding: {node.get_embedding()}")
      print(f"Text: {node.get_content()}")

Additionally, you can check if the vector index exists in the Neo4j database and retrieve its embedding dimension using the retrieve_existing_index method:

if neo4j_vector.retrieve_existing_index():
    print(f"Index exists with embedding dimension: {neo4j_vector.embedding_dimension}")
else:
    print("Index does not exist.")

This code initializes the Neo4jVectorStore, defines a query to retrieve embeddings, executes the query, processes the retrieved embeddings, and checks if the vector index exists in the Neo4j database ^[1][2].

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[navaneethstabilix]

@dosu thanks for the reply one more clarification need my embedding is actually stored in neo4j graph database as embedding : [-0.042840488255023956,0.03162284940481186,-0.020775185897946358,0.012170901522040367,-0.051600754261016846,-0.027891861274838448,0.061851561069488525,0.07208336144685745,-0.05090389400720596,-0.038099255412817,0.011754359118640423......................] how do i retrieve it should i use cypher query for it or is there any other method

[dosubot[bot]]

To retrieve embeddings stored in a Neo4j graph database, you should use a Cypher query. The Neo4jVectorStore class includes a query method that constructs and executes a Cypher query to retrieve the embeddings.

Here is an example of how the Cypher query is constructed and executed:

def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
    default_retrieval = (
        f"RETURN node.`{self.text_node_property}` AS text, score, "
        "node.id AS id, "
        f"node {{.*, `{self.text_node_property}`: Null, "
        f"`{self.embedding_node_property}`: Null, id: Null }} AS metadata"
    )

    retrieval_query = self.retrieval_query or default_retrieval
    read_query = _get_search_index_query(self.hybrid_search) + retrieval_query

    parameters = {
        "index": self.index_name,
        "k": query.similarity_top_k,
        "embedding": query.query_embedding,
        "keyword_index": self.keyword_index_name,
        "query": remove_lucene_chars(query.query_str),
    }

    results = self.database_query(read_query, params=parameters)

    nodes = []
    similarities = []
    ids = []
    for record in results:
        node = metadata_dict_to_node(record["metadata"])
        node.set_content(str(record["text"]))
        nodes.append(node)
        similarities.append(record["score"])
        ids.append(record["id"])

    return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)

This method constructs a Cypher query using the _get_search_index_query function and additional retrieval parameters, then executes it using the database_query method. The results are processed and returned as a VectorStoreQueryResult object ^[1].

Additionally, the Neo4jQueryToolSpec class provides a more advanced way to query a Neo4j graph database. It can generate Cypher queries based on a provided schema definition and a given question, using a language model (LLM) to assist in query construction. Here is an example of how it works:

class Neo4jQueryToolSpec(BaseToolSpec):
    def __init__(self, url, user, password, database, llm: LLM, validate_cypher: bool = False):
        self.graph_store = Neo4jGraphStore(url=url, username=user, password=password, database=database)
        self.llm = llm
        self.cypher_query_corrector = None
        if validate_cypher:
            corrector_schema = [
                Schema(el["start"], el["type"], el["end"])
                for el in self.graph_store.structured_schema.get("relationships")
            ]
            self.cypher_query_corrector = CypherQueryCorrector(corrector_schema)

    def query_graph_db(self, neo4j_query, params=None):
        if params are None:
            params = {}
        with self.graph_store.client.session() as session:
            result = session.run(neo4j_query, params)
            output = [r.values() for r in result]
            output.insert(0, list(result.keys()))
            return output

    def construct_cypher_query(self, question, history=None):
        messages = [
            ChatMessage(role=MessageRole.SYSTEM, content=self.get_system_message()),
            ChatMessage(role=MessageRole.USER, content=question),
        ]
        if history:
            messages.extend(history)

        completions = self.llm.chat(messages)
        return completions.message.content

    def run_request(self, question, history=None, retry=True):
        from neo4j.exceptions import CypherSyntaxError

        cypher = self.construct_cypher_query(question, history)
        if self.cypher_query_corrector:
            cypher = self.cypher_query_corrector(cypher)
        try:
            return self.query_graph_db(cypher)
        except CypherSyntaxError as e:
            if not retry:
                return "Invalid Cypher syntax"
            return self.run_request(
                question,
                [
                    ChatMessage(role=MessageRole.ASSISTANT, content=cypher),
                    ChatMessage(
                        role=MessageRole.SYSTEM,
                        content=f"This query returns an error: {e!s}\n"
                        "Give me an improved query that works without any explanations or apologies",
                    ),
                ],
                retry=False,
            )

This class can be particularly useful if you need to generate complex Cypher queries dynamically based on user input or other criteria ^[2].

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[navaneethstabilix]

@dosu i have my embedding available as a node property in neo4j db how do i take those embeeding and do a similiarity search i need to query from existiing index available from my neo4j database

[dosubot[bot]]

To perform a similarity search using embeddings stored as a node property in an existing index in your Neo4j database, you can use the following Cypher query within a Python function:

def similarity_search(self, query_embedding, top_k=10):
    """
    Perform a similarity search using the embeddings stored as a node property
    in an existing index in the Neo4j database.

    Args:
        query_embedding (list): The embedding vector to search for.
        top_k (int): The number of top similar results to return.

    Returns:
        list: The top_k most similar nodes.
    """
    search_query = (
        "CALL db.index.vector.queryNodes($index_name, $k, $embedding) YIELD node, score "
        "RETURN node, score "
        "ORDER BY score DESC "
        "LIMIT $top_k"
    )

    parameters = {
        "index_name": self.index_name,
        "embedding": query_embedding,
        "k": top_k,
    }

    results = self.database_query(search_query, params=parameters)
    return results

This function constructs and executes a Cypher query to perform a similarity search using the specified embedding vector and returns the top k most similar nodes. The query uses the db.index.vector.queryNodes procedure to find nodes with embeddings similar to the provided query_embedding ^[1][2].

Make sure to replace self.index_name and self.database_query with the appropriate values or methods from your Neo4jVectorStore class.

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