Cypher search is now part of the official LangChain library, learn more in: https://towardsdatascience.com/langchain-has-added-cypher-search-cb9d821120d5
The Langchain2Neo4j is a proof of concept application of how to integrate Neo4j into the Langchain ecosystem. This project took heavy inspiration from IMDB-LLM. The IMDB-LLM integrated graph search using networkx library into langchain ecosystem. I borrowed the idea and changed the project to use Neo4j as the source of information for the LLM.
The project now supports three modes of context search:
- Generating Cypher statements to query the database
- Full-text search of relevant entities
- Vector similarity search
Learn more: https://towardsdatascience.com/integrating-neo4j-into-the-langchain-ecosystem-df0e988344d2
The project uses the Recommendation project that is available as part of the Neo4j Sandbox. If you want a local instance of Neo4j, you can restore a database dump that is available here.
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Create a
.envfile and fill-in the environment variables as shown in theenv.example -
You need to create a full text index and import the movie embeddings:
sh seed_db.shIf you are using Windows, you can also execute the code in data/database_seed.ipynb notebook to seed the database.
- Run the project using docker-compose
docker-compose up- Open the application in your browser at http://localhost:3000
# Who played in Top Gun?
MATCH (m:Movie)<-[r:ACTED_IN]-(a)
RETURN {{actor: a.name, role: r.role}} AS result
# What is the plot of the Copycat movie?
MATCH (m:Movie {{title: "Copycat"}})
RETURN {{plot: m.plot}} AS result
# Did Luis Guzmán appear in any other movies?
MATCH (p:Person {{name:"Luis Guzmán"}})-[r:ACTED_IN]->(movie)
RETURN {{movie: movie.title, role: r.role}} AS result
# Do you know of any matrix movies?
MATCH (m:Movie)
WHERE toLower(m.title) CONTAINS toLower("matrix")
RETURN {{movie:m.title}} AS result