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RAG (Retrieval-Augmented Generation) is one of the most promising ways to integrate AI into real-world workflows. Many teams struggle with where to start, so I built a local prototype using Qwen1.5, LangChain, Ollama, and ChromaDB—completely cloud-free, API-free, and frictionless.
Here’s a step-by-step breakdown of the pipeline, along with the GitHub repository:
➡️ Full Process & GitHub Repo
You Should Know:
1. Setting Up the Environment
Before running the RAG pipeline, ensure you have the following installed:
Install Python dependencies pip install langchain ollama chromadb qwen1.5 Pull the Qwen1.5 model via Ollama ollama pull qwen1.5
2. Initializing ChromaDB for Vector Storage
ChromaDB is used for storing and retrieving document embeddings.
from langchain.vectorstores import Chroma from langchain.embeddings import OllamaEmbeddings Initialize embeddings embeddings = OllamaEmbeddings(model="qwen1.5") Create a ChromaDB instance vector_db = Chroma.from_texts( texts=["Your document text here..."], embedding=embeddings, persist_directory="./chroma_db" )
3. Configuring LangChain for RAG
LangChain orchestrates the retrieval and generation process.
from langchain.chains import RetrievalQA
from langchain.llms import Ollama
Load the Qwen1.5 model
llm = Ollama(model="qwen1.5")
Create a retriever from ChromaDB
retriever = vector_db.as_retriever()
Build the RAG pipeline
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever
)
Query the RAG model
response = qa_chain.run("What is RAG?")
print(response)
4. Running the Pipeline Locally
Execute the script and test with custom queries:
python rag_pipeline.py
5. Optimizing Performance
- Use GPU acceleration for faster embeddings (
export CUDA_VISIBLE_DEVICES=0). - Fine-tune retrieval parameters (
k=3for top 3 documents).
What Undercode Say:
This local RAG prototype demonstrates how to leverage Qwen1.5, LangChain, Ollama, and ChromaDB without relying on cloud APIs. Key takeaways:
– Privacy-first AI: No data leaves your machine.
– Cost-effective: Avoids API fees.
– Customizable: Adaptable to domain-specific documents.
For further improvements, consider:
- Adding hybrid search (keyword + semantic).
- Implementing query expansion for better retrieval.
Prediction:
As RAG adoption grows, more enterprises will shift toward self-hosted AI solutions to maintain data control and reduce costs. Expect tighter integrations between local LLMs and enterprise knowledge bases in 2024-2025.
Expected Output:
RAG (Retrieval-Augmented Generation) is a method that enhances AI responses by retrieving relevant documents before generating an answer, improving accuracy and context awareness.
➡️ GitHub Repo: https://lnkd.in/enyys-ze
IT/Security Reporter URL:
Reported By: Ninadurann How – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
