Integrate retrieval steps before inference to boost relevancy

You now have: ✔️ A knowledge base made of embedded text chunks ✔️ A vector index (e.g., FAISS) to search for the most relevant passages ✔️ An LLM that generates answers

Next, you’ll tie these pieces together so your assistant: 1️⃣ Takes a user query 2️⃣ Searches your knowledge base for matching info 3️⃣ Feeds that info into the LLM as extra context 4️⃣ Generates a final, grounded answer

This is the core of Retrieval-Augmented Generation (RAG).

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✅ Why Integrate Retrieval?

By default, your LLM:

• Uses only what it learned during training.

• Might hallucinate or guess if it doesn’t know.

Adding a retrieval step:

• Grounds the model with real, up-to-date info.

• Makes answers more factual and traceable.

• Reduces hallucinations.

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✅ How It Works – Basic Flow

1️⃣ Embed the user’s query with the same embedding model you used for your chunks. 2️⃣ Search your FAISS index to find the top N relevant text chunks. 3️⃣ Combine these retrieved chunks with the original user query to build a prompt. 4️⃣ Run the LLM with this augmented prompt. 5️⃣ Return the final answer + optionally the source passages for transparency.

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✅ Practical Example

1️⃣ User Query

user_query = "How do I reset my device?"
query_embedding = embedder.encode([user_query])

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2️⃣ Retrieve Matches

D, I = index.search(np.array(query_embedding), k=3)  # Top 3 matches

retrieved_chunks = [docs[idx] for idx in I[0]]
print("Top chunks:", retrieved_chunks)

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3️⃣ Build the Final Prompt

Combine the context + question:

context = "\n\n".join(retrieved_chunks)

prompt = f"""You are a helpful assistant. Use the information below to answer the question.

Context:
{context}

Question:
{user_query}

Answer:"""

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4️⃣ Run the LLM

from transformers import pipeline

generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

response = generator(
    prompt,
    max_length=512,
    do_sample=True,
    temperature=0.3,
)

print("Assistant:", response[0]["generated_text"])

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✅ Now your LLM sees the relevant passages and generates a response based on real data, not just memory.

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✅ Tips for Good Prompts

✔️ Clearly mark sections: “Context: ...” and “Question: ...” ✔️ Limit the number of chunks to avoid hitting token limits. ✔️ Use clean, short chunks (100–300 words) for best results. ✔️ Adjust temperature low (0.2–0.4) to reduce randomness.

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✅ What You Just Did

🔗 Connect retrieval to generation: This makes your chatbot or assistant:

• Context-aware

• Fact-based

• Dynamic, using fresh or private knowledge you control

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✅ Where This Goes Next

Many modern production AI assistants (like Bing Chat, Perplexity AI, ChatGPT with plugins) use RAG under the hood. You just built a mini version — all open-source!

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🗝️ Key Takeaway

RAG is the best way to turn a static LLM into a live knowledge assistant — without retraining the base model every time your information changes.

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➡️ Next: Connect this to your chat UI (like Gradio) so users get real-time, grounded answers!