RAG (Retrieval Augmented Generation) lets your AI agent search through uploaded documents using genuine semantic understanding — not keyword matching. Upload once, query forever.
Traditional search matches words. RAG understands meaning. Ask a question in plain English and your agent finds the most relevant passages from every file you have ever uploaded.
Documents are converted into numerical vectors that capture meaning, not just words. When your agent searches, it finds content that is conceptually similar to the query — even if the exact words never appear.
Embeddings are stored in a ChromaDB vector database that persists between sessions and backs up to cloud storage automatically. Your knowledge base grows over time without re-processing unchanged files.
Drop PDFs, Word docs, spreadsheets, presentations, images, or code files into your project storage.
Documents are chunked and converted to high-dimensional vectors using OpenAI embeddings. OCR handles scanned images automatically.
Your agent calls the rag_search tool with a plain English query. The vector store returns the most semantically similar passages.
Results are written into your Streamlit app automatically, ready for display, further processing, or database storage.
No conversion needed. Upload in any format — the platform handles extraction, OCR, and chunking automatically.
Smart incremental updates — only changed or new files are re-processed. Existing embeddings are preserved, keeping costs low and re-indexing fast.
Every business sits on years of accumulated documents, policies, and data. RAG turns that static archive into a live, queryable intelligence layer.
Upload tender documents, compliance frameworks, and your company capabilities. An agent searches all of them simultaneously to draft responses that precisely match requirements.
Index product manuals, FAQs, and support history. Agents instantly retrieve accurate, source-cited answers rather than hallucinating generic responses.
Upload research papers, industry reports, and internal studies. Ask cross-document questions and have the agent synthesise findings without reading each file manually.
Make policies, procedures, and training materials searchable. New employees get instant answers from authoritative documents without waiting for HR responses.
Index your entire code repository. Agents search across files for relevant implementations, patterns, and documentation before writing new code — reducing duplication and errors.
Store financial statements, regulatory documents, and audit trails. Agents surface exact clauses or figures on demand, with source citations for full traceability.
RAG is not a bolt-on feature. It is a first-class tool available to every agent you build.
The agent calls rag_search with a natural language query and a similarity threshold. Results are returned ranked by relevance and automatically written into your Streamlit application.
Agents inspect the health of your knowledge base, check how many documents are indexed, view collection statistics, and trigger a refresh when new files are uploaded.
rag_search_results.pyUpload your files, deploy an agent, and start querying your knowledge base in minutes. No ML expertise required.