Build Local Document Intelligence Agent with LlamaIndex and VectorAI DB

This tutorial demonstrates how to build a local document intelligence agent using LlamaIndex and Actian VectorAI DB. The integration allows all components, including LlamaIndex for orchestration, nomic-embed-text for embeddings, VectorAI DB for retrieval, and a local LLM for generation, to run entirely on local hardware. This approach is beneficial for air-gapped environments or when data classification prohibits external API calls. The tutorial details the implementation of LlamaIndex's VectorStore interface methods: add, delete, query, and get_nodes, using the VectorAIClient from the Python SDK. AI

IMPACT Enables local, air-gapped LLM applications by integrating VectorAI DB with LlamaIndex.

RANK_REASON Tutorial on integrating existing tools for a specific use case.

Read on dev.to — LLM tag →

• Actian VectorAI DB
• ActianVectorAIVectorStore
• LlamaIndex
• nomic-embed-text
• Ollama
• Pinecone
• Qdrant Cloud
• VectorAI DB
• Weaviate

• product
• infra

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