How do I build a chatbot that searches our knowledge base before answering?

To build a chatbot that searches your knowledge base before answering, use a retrieval-augmented generation (RAG) setup:

Basic flow

1. User asks a question
2. Search your knowledge base for relevant passages
3. Send those passages + the question to the model
4. Model answers using the retrieved content

Core components

• Knowledge source: docs, PDFs, wiki pages, tickets, etc.
• Chunking: split docs into small overlapping sections
• Embeddings: turn chunks into vectors
• Vector database / search index: store and retrieve chunks
• LLM: generates the final answer from retrieved context

Typical architecture

1. Ingest documents
2. Clean and chunk text
3. Create embeddings for each chunk
4. Store embeddings in a vector DB
5. On each question:
   • embed the question
   • retrieve top-k similar chunks
   • optionally rerank results
   • prompt the LLM with the question + retrieved text

Prompt pattern

Use a system prompt like:

• “Answer only using the provided context.”
• “If the context doesn’t contain the answer, say you don’t know.”
• “Cite sources when possible.”

Example structure:

Context:
[retrieved passages]

Question:
[user question]

Instructions:
Answer based only on the context. If missing, say you don’t know.

Recommended implementation steps

• Start with one data source
• Use a vector DB like:
  • Pinecone
  • Weaviate
  • Qdrant
  • pgvector
  • FAISS for local prototyping
• Add metadata:
  • title
  • URL
  • updated_at
  • access permissions
• Return citations so users can verify answers

Important design tips

• Keep chunks small enough for precise retrieval, but not too small to lose context
• Use overlap between chunks
• Add hybrid search if possible:
  • keyword search + vector search
• Add permission filtering so users only see docs they can access
• Log queries and retrieval quality for tuning
• Have a fallback:
  • “I couldn’t find that in the knowledge base”

Minimal pseudo-code

question = user_input
query_vec = embed(question)
docs = vector_db.search(query_vec, top_k=5)
context = "\n\n".join(doc.text for doc in docs)

prompt = f"""
Answer only using the context below.

Context:
{context}

Question:
{question}
"""

answer = llm.generate(prompt)

If you want faster results

Use a framework such as:

• LlamaIndex
• LangChain
• Haystack

They handle ingestion, retrieval, and prompting scaffolding.

Best practice for production

• Evaluate retrieval quality separately from answer quality
• Cache frequent queries
• Monitor hallucinations
• Refresh embeddings when docs change

If you want, I can give you:

1. a simple Python example,
2. a production architecture diagram, or
3. a step-by-step guide using LangChain or LlamaIndex.