Chat With Your Documents Using Garudust Agent — No Vector Database Required
📰 Dev.to AI
Learn how to use Garudust Agent to chat with your documents without needing a vector database, simplifying the RAG setup process
Action Steps
- Install Garudust Agent using the provided instructions
- Drop a PDF or other supported file into the agent
- Configure the trigram tokenizer in SQLite FTS5 for optimal results
- Test the RAG functionality by asking questions about the document
- Compare the performance of Garudust Agent with traditional vector database-based RAG approaches
Who Needs to Know This
Developers and data scientists can benefit from using Garudust Agent to streamline their RAG workflow and improve document analysis
Key Insight
💡 Garudust Agent eliminates the need for a vector database and embedding API calls, making it easier to set up and use RAG for document analysis
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🚀 Simplify RAG with Garudust Agent! No vector database required 📄💡
Key Takeaways
Learn how to use Garudust Agent to chat with your documents without needing a vector database, simplifying the RAG setup process
Full Article
Most RAG tutorials start the same way: "First, install a vector database…" Then come the embedding models, the chunking strategies, the similarity thresholds. By the time you can ask a question about a PDF, you've deployed three services and written 200 lines of boilerplate. Garudust Agent takes a different path. RAG is built in — backed by SQLite FTS5 with a trigram tokenizer. No vector database. No embedding API calls. Drop a PDF (or TXT, CSV, Markdown, JSON
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