RAG Knowledge Base Chatbot
Ingests PDF knowledge bases via Jotform and answers user chat questions using semantic search and Google Gemini.
What it does
You get instant, accurate answers to your questions by chatting with a bot trained specifically on your uploaded PDF documents. The system automatically processes your files from Jotform and uses semantic search to find the right information in Supabase before Google Gemini generates precise responses based only on that knowledge base.
Step by step
- Configure the Jotform webhook to trigger an HTTP request upon form submission.
- Implement a text chunking function that splits PDF content into fixed-size segments with overlap.
- Use Together AI API to generate vector embeddings for each text chunk.
- Store the resulting vectors and metadata in Supabase using pgvector extension.
Where the LLM does the work
- Prompt engineering is required to instruct Google Gemini to answer only using the retrieved context.
- LLM judgment is needed to handle ambiguous queries that lack sufficient context matches.
- Generate natural language summaries if the retrieved chunks are too fragmented for direct citation.
Watch out for
PDF parsing can fail on scanned images so add an OCR step like Tesseract before chunking.
Vector search might return irrelevant results if embeddings are noisy, so implement a relevance threshold filter.
Context window limits can truncate important information, so prioritize chunks by semantic similarity score.
Supabase query latency may increase with large datasets, so add indexing on the vector column.
Tools that fit
Google Gemini Chat Model LLM LLM for answer generation
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal RAG Knowledge Base Chatbot: Ingests PDF knowledge bases via Jotform and answers user chat questions using semantic search and Google Gemini. ## Specification - What it does: You get instant, accurate answers to your questions by chatting with a bot trained specifically on your uploaded PDF documents. The system automatically processes your files from Jotform and uses semantic search to find the right information in Supabase before Google Gemini generates precise responses based only on that knowledge base. - Trigger: Triggered by an event (Event · on new Jotform submission or chat message) - Autonomy: Fully hands-off - Expected setup effort: a few focused days - Tools/services involved: - Jotform: Trigger and file upload intake - Supabase: Vector database storage and retrieval - Together AI: Generating text embeddings - Google Gemini Chat Model: LLM for answer generation ## Known pitfalls, handle each one explicitly in your implementation 1. PDF parsing can fail on scanned images so add an OCR step like Tesseract before chunking. 2. Vector search might return irrelevant results if embeddings are noisy, so implement a relevance threshold filter. 3. Context window limits can truncate important information, so prioritize chunks by semantic similarity score. 4. Supabase query latency may increase with large datasets, so add indexing on the vector column. ## Reference implementation https://n8n.io/workflows/9626 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/rag-knowledge-base-chatbot/ via usecasesforagents.com
Frequently asked questions
Can I use a different Forms & Surveys than Jotform?
Yes. Jotform is only the example forms & surveys in this recipe. The same flow works with Tally and Typeform. Swap the forms & surveys connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for support teams drowning in inbound and looking to triage faster, not just harder.
Developers can adapt this to their own repo or ticket queue with the same trigger and tools.