User specific data retrieval from neo4j embedding RAG

[GiteshKambli]

I am trying to build a chatbot based on data specific to each user, I want to retrieve the data for specific user from neo4j. How can I achieve this using llama index.

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To retrieve data specific to each user from a Neo4j database for your chatbot using LlamaIndex, you can utilize the query method of the Neo4jVectorStore class. This method accepts a VectorStoreQuery object, which includes the query embedding and the number of top similar results to return.

Here's an example of how you can use it:

from llama_index.vector_stores.types import VectorStoreQuery
from llama_index.vector_stores.neo4jvector import Neo4jVectorStore

# Initialize the Neo4jVectorStore
store = Neo4jVectorStore(
    username="neo4j_username",
    password="neo4j_password",
    url="bolt://localhost:7687",
    embedding_dimension=300,
    database="neo4j",
    index_name="vector",
    keyword_index_name="keyword",
    node_label="Chunk",
    embedding_node_property="embedding",
    text_node_property="text",
    distance_strategy="cosine",
    hybrid_search=False,
)

# Create a VectorStoreQuery
query = VectorStoreQuery(query_embedding=user_embedding, similarity_top_k=5)

# Query the store
result = store.query(query)

# The result is a VectorStoreQueryResult object
# You can access the nodes, similarities, and ids from the result
nodes = result.nodes
similarities = result.similarities
ids = result.ids

In this example, user_embedding is the embedding vector of the user's input. This vector is used to query the Neo4j database to find the most similar results. The similarity_top_k parameter determines how many of the most similar results to return.

The query method returns a VectorStoreQueryResult object, which contains the nodes, similarities, and ids of the results. You can use these results to retrieve data specific to each user for your chatbot.

Please note that you need to replace "neo4j_username", "neo4j_password", and "bolt://localhost:7687" with your actual Neo4j username, password, and URL. The embedding_dimension should match the dimension of your embeddings. The index_name, keyword_index_name, node_label, embedding_node_property, and text_node_property should match the names used in your Neo4j database.

You can find more details about the Neo4jVectorStore class and its methods in the LlamaIndex repository.

If you have any further questions or need more clarification, feel free to ask.

Sources

• llama_index/vector_stores/neo4jvector.py

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