Local RAG Pipeline Development for 1artifactware

Introduction

Why Local RAG Is a Game Changer

Real-World Use Cases

Architecture Overview

[Your Docs] → [Text Splitter] → [Embedder] → [Vector Store (FAISS)]
↓
[Retriever via LangChain]
↓
[Mistral (via Ollama or llama.cpp)]
↓
[Answer + Source Metadata]

Step 1: Chunk and Embed Your Documents

🔪 Text Splitting (Chunking)

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])

🧠 Embedding the Chunks

from sentence_transformers import SentenceTransformer
model = SentenceTransformer(“all-MiniLM-L6-v2”)
texts = [chunk.page_content for chunk in chunks]
vectors = model.encode(texts)

📦 Store Vectors in FAISS

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)

🧪 Bonus: Index More Than One File

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]))

Step 2: Set Up Retrieval + Generation

🧠 Load Your Index and Chunks

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)

⚡ Connect LangChain to Mistral (via Ollama)

from langchain.llms import Ollama
llm = Ollama(model=”mistral”) # You can also try llama3 or mixtral

ollama pull mistral

🔗 Build the RetrievalQA Chain

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
)

🌐 Wrap It in a FastAPI Server

from fastapi import FastAPI
app = FastAPI()
@app.get(“/ask”)
def ask(q: str):
result = qa_chain.run(q)
return {“response”: result}

📊 Example

Step 3: Choose the Right Local Model (and Make It Fast)

🥇 Top Local Models for RAG

⚙️ Hardware Considerations

🔎 How Quantization Works

ollama run mistral:quantized

🚀 Inference Performance Tuning

📈 Evaluation & Benchmarking

from langchain.evaluation import QAEvalChain

Step 4: Deploy Your RAG Pipeline Like a Pro

🐳 Dockerize Your RAG Service

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”]

FROM ollama/ollama
COPY . /app
WORKDIR /app
CMD [“uvicorn”, “api:app”, “ — host”, “0.0.0.0”, “ — port”, “8000”]

docker build -t rag-app .
docker run -p 8000:8000 rag-app

🔁 Managing the Vector Index

with open(“faiss_store.pkl”, “rb”) as f:
index, chunks = pickle.load(f)

📊 Add Monitoring (Basic)

☁️ Where to Host

🔐 Security & Access Control

from fastapi.security import HTTPBearer
security = HTTPBearer()

Step 5: Learn From Real Deployments

🏦 Morgan Stanley — AI for Financial Advisors

🧑‍💼 Assembly — AI-Powered HR Intranet

💼 Causal — Financial Analysis From Raw Data

🧪 1artifactware — Lead Qualification & Personalization

FAQ: Everything You Wanted to Know About Running RAG Locally

❓ Can I do this without a GPU?

❓ What if I don’t want to use Ollama?

❓ Can I support follow-up questions or chat history?

❓ What if I want citations and sources?

❓ How big can my document corpus be?

What to Do Next

🚀 Promote It

💰 Monetize It

🔄 Iterate On It

Final Thoughts