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This project builds a knowledge graph about movies, allowing users to ask complex questions about movies and receive refined, human-like responses.

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Farhaj499/KnowledgeGraph_with_Neo4j_DB

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Movies Knowledge Graph with Neo4j, GROQ, and Llama3.1

This project builds a knowledge graph about movies using Neo4j, a powerful graph database. The knowledge graph is populated with data from a movie dataset, allowing users to ask complex questions about movies and receive refined, human-like responses through the integration of Llama-3.1-70b-Versatile, a large language model (LLM).

Technologies Used

  • Neo4j: Graph database for storing and querying relationships between entities.
  • Cypher Query Language (CQL): Language for creating, manipulating, and querying data in Neo4j.
  • GraphCypherQAChain: Python library facilitating conversion of natural language questions into CQL queries.
  • GROQ: Python library for interacting with the Llama-3.1-70b-Versatile LLM model.
  • Llama-3.1-70b-Versatile: Large language model for refining extracted information and providing human-like responses.

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This project builds a knowledge graph about movies, allowing users to ask complex questions about movies and receive refined, human-like responses.

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