Build a powerful Retrieval-Augmented Generation (RAG) pipeline using LangChain, FAISS, and local LLMs like Mistral or LLaMA 3. This is your full-stack, production-ready guide.
Large Language Models (LLMs) are amazing, but they have a memory problem. Out of the box, they don’t know anything about your internal documentation, client projects, CRM entries, or product catalogs. That’s where Retrieval-Augmented Generation (RAG) comes in — by connecting your own documents to the model, you unlock grounded, factual, context-aware outputs.
But here’s the twist: you don’t need to use expensive, privacy-compromising cloud APIs to do this. Thanks to advances in local LLMs like Mistral, LLaMA 3, and Phi-2, and the incredible flexibility of LangChain and FAISS, you can now build a complete RAG system that runs entirely on your machine.
This guide is your blueprint for doing just that. Whether you’re a solo dev, a founder with technical chops, or running a small agency — this article shows how to build a fully local, fast, private RAG pipeline from scratch, using production-ready tools.
We’ll cover:
And yes — with code you can copy, use, and ship.
Retrieval-Augmented Generation systems improve LLM accuracy by injecting relevant snippets from your knowledge base into the model’s prompt at runtime. But most implementations rely on OpenAI or Anthropic APIs, which:
With local RAG, you take control:
In short: local RAG turns an LLM into a private, intelligent assistant for your business or product.
Before we dive into architecture and code, here are just a few companies using RAG in production:
At 1artifactware, we use RAG pipelines to:
You can do the same.
Here’s a simplified overview of the system you’re about to build:
[Your Docs] → [Text Splitter] → [Embedder] → [Vector Store (FAISS)]
↓
[Retriever via LangChain]
↓
[Mistral (via Ollama or llama.cpp)]
↓
[Answer + Source Metadata]
Each piece can be swapped, optimized, and deployed as needed. We’ll walk through it all — including:
Before you can search anything, you need to break your documents down into manageable chunks and convert them into vectors (numerical representations of meaning). This step is critical for semantic search — the heart of your RAG pipeline.
LLMs can't process unlimited text, and embeddings become fuzzy when a chunk is too long. You want chunks that:
📌 Pro tip: 250–300 tokens (~1–2 paragraphs) with 20–50 token overlap is a sweet spot.
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(
chunk_size=512,
chunk_overlap=64,
separators=["\\n\\n", "\\n", ".", "!", "?", " ", ""]
)
with open("docs/agency-intro.md") as f:
raw_text = f.read()
chunks = splitter.create_documents([raw_text])
Once the text is chunked, each chunk must be embedded into a vector. You’ll use a model like all-MiniLM-L6-v2 from Sentence Transformers — fast and performant for most tasks.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")
texts = [chunk.page_content for chunk in chunks]
vectors = model.encode(texts)
Alternatives for embeddings:
E5-large (more accurate, slower)MPNet (very robust across domains)text-embedding-ada-002 (if you want to go cloud)FAISS is the go-to vector store for blazing-fast local similarity search.
import faiss
import pickle
dimension = len(vectors[0]) # Typically 384
index = faiss.IndexFlatL2(dimension)
index.add(vectors)
with open("faiss_store.pkl", "wb") as f:
pickle.dump((index, chunks), f)
💡 Note: FAISS is in-memory. You’ll save the index and reload it during app startup.
For a real application, you’ll want to ingest multiple Markdown, PDF, or HTML files.
import os
all_chunks = []
for filename in os.listdir("docs"):
if filename.endswith(".md"):
text = open(os.path.join("docs", filename)).read()
all_chunks.extend(splitter.create_documents([text]))
Store filename and page in metadata if you want traceability for citations later.
Now that your documents are chunked, embedded, and indexed, it’s time to build the actual Retrieval-Augmented Generation (RAG) pipeline. This means:
We’ll use LangChain to orchestrate the entire flow — and Ollama to run Mistral locally.
import pickle
from langchain.vectorstores import FAISS
with open("faiss_store.pkl", "rb") as f:
index, chunks = pickle.load(f)
texts = [chunk.page_content for chunk in chunks]
# FAISS expects metadata alongside texts (even if it's just placeholders)
vectorstore = FAISS(embedding_function=None, index=index, texts=texts)
from langchain.llms import Ollama
llm = Ollama(model="mistral") # You can also try llama3 or mixtral
💡 Ollama is a local runtime that supports multiple open-source LLMs. It’s fast, reliable, and doesn’t require special setup — just install the model you want:
ollama pull mistral
You can run Ollama on CPU or GPU depending on your hardware.
from langchain.chains import RetrievalQA
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=retriever,
return_source_documents=True
)
🔧 k=4 controls how many chunks are retrieved per query. Start small (3–5), or tune dynamically based on prompt size.
from fastapi import FastAPI
app = FastAPI()
@app.get("/ask")
def ask(q: str):
result = qa_chain.run(q)
return {"response": result}
This lets you send natural language questions and get grounded answers — served from your local system.
If you indexed a proposal titled agency-intro.md, a query like:
“What services does 1artifactware provide?”
Will trigger FAISS to pull the right chunks, and Mistral will generate an answer like:
“1artifactware offers cloud migration, API integration, and scalable backend development tailored to marketing agencies.”
All without ever touching OpenAI or sending data outside your system.
Not all LLMs are created equal — and not all are optimized for local use. Choosing the right one for your RAG pipeline means balancing:
Let’s break it down.
| Model | Params | Strengths | RAM (fp16 / 4-bit) |
|---|---|---|---|
| Mistral | 7B | Fast, accurate, great out-of-the-box | ~13GB / ~6GB |
| Mixtral | 12.9B (MoE) | Very strong factual recall (MoE active) | ~24GB / ~12GB |
| LLaMA 3 | 8B | Well-rounded, strong long-form generation | ~12GB / ~6GB |
| Phi-2 | 2.7B | Tiny, CPU-friendly, surprising reasoning | ~6GB / ~2GB |
Most users will start with Mistral or LLaMA 3 for the balance of quality and footprint.
Quantization shrinks model size and speeds up inference by reducing precision.
Use GPTQ, AWQ, or Ollama’s built-in quantizers to load 4-bit weights with minimal performance hit.
ollama run mistral:quantized
Or use llama.cpp with AVX2 acceleration for bare-metal CPU serving.
Rule of thumb: A 7B 4-bit model with 4K context will run well on most dev-class machines. A 13B or 70B model will need dedicated hardware.
Want to see how your setup compares?
Check:
QAEvalChain or LlamaIndex’s evaluate() functionsExample:
from langchain.evaluation import QAEvalChain
You can even compare:
You’ve got a working RAG system locally. Now let’s deploy it so it can run 24/7, scale with traffic, and be managed like a real application.
We'll cover:
Docker allows you to package your whole app — embeddings, FastAPI, Ollama or llama.cpp — into a portable unit.
Here’s a sample Dockerfile for your FastAPI server:
FROM python:3.10-slim
WORKDIR /app
COPY . /app
RUN pip install -r requirements.txt
CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]
If you want Ollama inside Docker too:
FROM ollama/ollama
COPY . /app
WORKDIR /app
CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]
Then build + run:
docker build -t rag-app .
docker run -p 8000:8000 rag-app
You don’t need to re-index every time. Save the vector store with FAISS or Chroma and load on startup:
with open("faiss_store.pkl", "rb") as f:
index, chunks = pickle.load(f)
Or store it in:
pgvector if you prefer PostgresQdrant for metadata filteringChroma if you want quick reloadsYou can track:
Simple tools:
loguru or logging module for logstracing or CallbackHandler for tracing prompt + retrieval flowSome users run RAG inside:
If your RAG app is client-facing:
slowapi or NGINX)Example middleware:
from fastapi.security import HTTPBearer
security = HTTPBearer()
Let’s look at how other teams — including us — are using RAG systems in production:
They built a private assistant that answers financial queries using internal research. All answers are grounded in proprietary content that gets retrieved and injected on-the-fly. 🔐 Result: Increased advisor productivity while maintaining strict data governance.
Employees use a chatbot to ask things like:
"What’s the PTO carryover policy?" RAG retrieves the right chunk from internal docs and the LLM paraphrases it. 🧠 No hallucination. Just clarity.
RAG pipeline reads Xero and QuickBooks exports to generate on-demand financial metrics like burn rate, gross profit, and runway. 📈 A literal “AI financial analyst.”
We scraped and embedded over 8,000 agency websites. Then we used a private RAG pipeline to:
🚀 Result: High-response outreach with zero manual effort.
Yes. Use a 4-bit quantized model like Phi-2, Mistral, or LLaMA 3 on CPU. It's slower, but works — especially for internal tools.
Try:
llama.cpp — lightweight and great on CPUvLLM — high-throughput transformer inference for serving APIsLM Studio — GUI + playground for local modelsYes. LangChain and LlamaIndex support conversational retrieval chains. Just feed in past messages as part of the prompt.
LangChain’s RetrievalQA can return the original documents. Display titles, filenames, or snippets with the answer.
FAISS handles millions of entries. For 10K–100K chunks:
IndexFlatL2 for speedIVF or HNSW for larger corpora (tune for balance)You’ve just built a private, flexible RAG pipeline that:
Use your blog, social posts, or a demo video to show it in action. People love seeing:
This isn’t just a “tutorial.” It’s a launchpad.
RAG lets you combine the power of LLMs with the depth of your data — privately, securely, and scalably.
You’ve now got the blueprint.
If you want help turning it into a real product or internal tool:
👉 Schedule a Free Consultation with 1artifactware
💬 Need help deploying your own RAG pipeline? We offer private AI stack setup, optimization, and integrations.
👉 Check our blog for more AI engineering insights
Let’s build something powerful.
Contact us now
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