DSPy Neo4j Integration
DSPy is a framework for algorithmically optimizing LM prompts and weights, especially when LMs are used one or more times within a pipeline.
The Neo4j integration allows for vector search.
Here is an overview of the DSPy Integrations.
Functionality Includes
-
Neo4jRM
- is a typical retriever component which can be used to query vector store index and find related Documents.
from dspy.retrieve.neo4j_rm import Neo4jRM
import os
os.environ["NEO4J_URI"] = 'bolt://localhost:7687'
os.environ["NEO4J_USERNAME"] = 'neo4j'
os.environ["NEO4J_PASSWORD"] = 'password'
os.environ["OPENAI_API_KEY"] = 'sk-'
retriever_model = Neo4jRM(
index_name="vector",
text_node_property="text"
)
results = retriever_model("Explore the significance of quantum computing", k=3)
for passage in results:
print("Document:", passage, "\n")