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One of the common types of databases that we can build Q&A systems for are graph databases. LangChain comes with a number of built-in chains and agents that are compatible with graph query language dialects like Cypher, SparQL, and others (e.g., Neo4j, MemGraph, Amazon Neptune, Kùzu, OntoText, Tigergraph). They enable use cases such as:

  • Generating queries that will be run based on natural language questions,
  • Creating chatbots that can answer questions based on database data,
  • Building custom dashboards based on insights a user wants to analyze,

and much more.

⚠️ Security note ⚠️​

Building Q&A systems of graph databases might require executing model-generated database queries. There are inherent risks in doing this. Make sure that your database connection permissions are always scoped as narrowly as possible for your chain/agent's needs. This will mitigate though not eliminate the risks of building a model-driven system. For more on general security best practices, see here.


Employing database query templates within a semantic layer provides the advantage of bypassing the need for database query generation. This approach effectively eradicates security vulnerabilities linked to the generation of database queries.


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