Connecting Haystack to Neo4j for advanced vector search
With the movie embeddings now stored in Neo4j, we need to configure a vector index on the embedding property, which will allow us to efficiently search for movies based on their vector similarity. By creating a vector index in Neo4j, we enable rapid retrieval of nodes that are close in the high-dimensional embedding space, making it possible to perform sophisticated queries, such as finding movies with similar plot summaries.
Once the vector index has been created, it will be integrated with Haystack to perform vector-based retrieval from Neo4j. This search will be based on vector similarity mechanisms such as cosine similarity.
Creating a vector search index in Neo4j
You will first want to drop any existing vector index on the embedding property (if one exists) and then create a new one for performing vector searches. This is how you can do that using Cypher queries in your Python script:
def create_or_reset_vector_index...
