Set Up a RAG Pipeline in 5 Steps

Retrieval-Augmented Generation (RAG) is the secret sauce behind smarter, context-aware AI. Imagine ChatGPT with a library card -- it pulls relevant info before answering. As an AI Engineer, I've built RAG pipelines for everything from Q&A bots to expense trackers. Here's how you can set one up in a couple of hours.

Step 1: Pick Your LLM

Start with a lightweight, pre-trained model like LLaMA or Mistral 7B from Hugging Face. They're fast, open-source, and perfect for experimentation. Install the transformers library (pip install transformers) and load it up:

                    from transformers import AutoModelForCausalLM
                    model = AutoModelForCausalLM.from_pretrained("mistral-7b")
                

Step 2: Vectorize Your Documents

Turn your docs into numbers an LLM can understand. Use Sentence Transformers (pip install sentence-transformers) to encode text into embeddings:

                    from sentence_transformers import SentenceTransformer
                    encoder = SentenceTransformer('all-MiniLM-L6-v2')
                    embeddings = encoder.encode(["doc1 text", "doc2 text"])
                

Step 3: Index with FAISS

Speed up retrieval with FAISS (pip install faiss-cpu). Index your embeddings so the pipeline can grab relevant docs fast:

                    import faiss
                    index = faiss.IndexFlatL2(embeddings.shape[1])
                    index.add(embeddings)
                

Step 4: Retrieve and Prompt

When a query comes in, encode it, search the index, and fetch the top-k results. Feed them to your LLM with a clear prompt:

                    query = encoder.encode(["What's the budget?"])
                    D, I = index.search(query, k=3) # Top 3 docs
                    context = " ".join([docs[i] for i in I[0]])
                    prompt = f"Answer based on this: {context}"
                

Step 5: Test and Tweak

Run a test query. If the answers are off, adjust retrieval (e.g., increase k) or refine your docs. My last RAG bot nailed client FAQs after tweaking the context window - precision matters.

RAG combines the best of retrieval and generation: accuracy from real data, fluency from LLMs. I built one last week for a support bot, and it cut response time by 40%. What's your next RAG project? Let's talk pipelines - I'm always up for a challenge.

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