Building a Private AI: A Comprehensive Look at a Retrieval-Augmented Generation (RAG) Streamlit Project
Naresh Matta
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Building a Private AI: A Comprehensive Look at a Retrieval-Augmented Generation (RAG) Streamlit Project
Our Retrieval-Augmented Generation (RAG) system is powered by a local language model that incorporates internal chain-of-thought reasoning. This means that, internally, the model works through intermediate steps and logical reasoning to generate a robust, context-aware response. However, to ensure clarity and simplicity for end users, our system is engineered to only display the final, concise answer.
Key Points:
This approach allows our RAG system to function as a reasoning model—leveraging sophisticated internal processing while keeping the user experience straightforward and focused solely on the answer.
Table of Contents
1. Introduction
In the rapidly evolving landscape of artificial intelligence (AI), Retrieval-Augmented Generation (RAG) has emerged as a powerful technique that combines document retrieval with language model generation to produce more accurate, context-aware answers. This article provides a deep dive into a Private AI RAG Streamlit project, explaining how each piece fits together and how you can build or extend such a system for your own needs.
We’ll explore document ingestion, embedding with SentenceTransformers, similarity search with FAISS, language model inference (on GPU if available), and the Streamlit user interface. Additionally, we’ll detail the post-processing steps that ensure the final AI response is free of chain-of-thought markers like <think>...</think>—making the system more user-friendly and production-ready.
This article is meant for engineers, data scientists, and AI enthusiasts looking to understand how to build a RAG system that respects user privacy and can run on local or on-premise hardware.
2. Understanding Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation is a technique that addresses one of the key limitations of large language models: context windows. Traditional LLMs rely on the text input you provide (the “prompt”) and their internal parameters, but they can’t directly “see” or “search” external documents. This often leads to hallucinations or incomplete answers, especially when the model’s training data is outdated or limited.
RAG mitigates this by introducing a retrieval step:
By doing so, RAG ensures the AI system can provide up-to-date, context-specific responses without relying solely on the LLM’s parametric memory. This approach is especially beneficial for private or proprietary documents, as it keeps everything on your local machine or private server.
3. Project Goals and High-Level Architecture
3.1 Goals
3.2 High-Level Architecture
4. Key Components and Libraries
4.1 Streamlit
4.2 pdfplumber, python-docx, python-pptx
4.3 SentenceTransformers
4.4 FAISS
4.5 Hugging Face Transformers
4.6 Python Standard Libraries
5. Document Ingestion and Text Extraction
Document ingestion is critical to any RAG system. The steps are:
This ensures you get the highest quality text for embedding.
6. Chunking and Embedding
6.1 Why Chunk?
Large language models and retrieval systems often have a context window limit. If you feed them massive text, they may ignore or truncate it. Chunking ensures each chunk is small enough to handle but large enough to be meaningful.
6.2 Overlap
By having a slight overlap (e.g., chunk_size=1000 with overlap=100), you minimize the risk of cutting important information in half. Each chunk can still carry enough context.
6.3 Embedding
After chunking, we use SentenceTransformers (all-mpnet-base-v2 or another model) to generate embeddings:
7. FAISS for Similarity Search
FAISS (Facebook AI Similarity Search) is the library we use to index and search embeddings:
This step is crucial because it retrieves the relevant chunks that the language model will use to answer questions. Without retrieval, the model might rely solely on its internal parameters and produce incomplete or incorrect answers.
8. Language Model and Post-Processing
8.1 Local Language Model
We rely on a local model (e.g., DeepSeek-R1 or Gemma-3). This approach ensures privacy—your data never leaves your environment. We use:
8.2 Prompt Construction
When a user asks a question:
8.3 Removing lt;thinkgt;...lt;/thinkgt;
Some local models might output chain-of-thought text in <think> blocks. We do a regex removal step:
re.sub(r"<think>.*?</think>", "", raw_answer, flags=re.DOTALL)
This ensures the final user sees only the direct answer, not the internal reasoning tokens.
8.4 Stripping “Final Answer:”
If the model echoes “Final Answer:”, we remove it so the user sees a clean answer.
9. Streamlit UI and Workflow
9.1 Sidebar
9.2 Main Chat Interface
9.3 Under the Hood
领英推荐
10. Detailed Walkthrough of the Code
Below, we’ll break down the code from top to bottom, referencing the Python script (or Jupyter cells).
10.1 Project Structure
A typical layout might look like:
MyRAGProject/
├─ rag_app.py
├─ requirements.txt
├─ .gitignore
└─ README.md
10.2 Text Cleaning Helpers
def collapse_repeated_chars(text: str, threshold=3) -> str:
# ...
def clean_text(text: str) -> str:
# ...
10.3 Post-Processing Output
def post_process_response(raw_answer: str) -> str:
# Remove <think> blocks
processed = re.sub(r"<think>.*?</think>", "", raw_answer, flags=re.DOTALL).strip()
# Remove "Final Answer:"
processed = re.sub(r"(?i)^final answer:\s*", "", processed)
return processed
10.4 The DocumentRAG Class
Initialization:
Document Extraction:
Chunking:
Ingestion:
def ingest_document(self, file_path: str, original_filename: str):
# 1. Extract text
# 2. Chunk text
# 3. Embed each chunk
# 4. Add embeddings to FAISS
# 5. Extend doc_store
This method updates the FAISS index and local doc store so that future queries can retrieve these chunks.
Query:
def query(self, question: str, top_k: int = 3, max_length: int = 512) -> str:
# 1. If no docs, return a message
# 2. Embed the question
# 3. FAISS search for top_k chunks
# 4. Build prompt with context + question
# 5. Generate answer with the LLM
# 6. Post-process answer
# 7. Return final cleaned answer
Ensures each user query is answered with the best possible context from the user’s own documents.
10.5 Streamlit UI Logic
Initialization:
if "rag" not in st.session_state:
st.session_state.rag = DocumentRAG()
We store the DocumentRAG instance in session state so it persists across interactions.
File Uploader:
uploaded_files = st.file_uploader(..., accept_multiple_files=True)
if uploaded_files:
for file in uploaded_files:
# Save temp file with correct extension
# rag.ingest_document(...)
We preserve the extension so _extract_text can detect file type.
Chat Input:
prompt = st.chat_input("Ask a question...")
if prompt:
response = st.session_state.rag.query(prompt)
# Display the answer
A user can type any question, and the system calls rag.query(...) to generate a final response.
11. Deployment Considerations
11.1 Python Environment
Ensure you have the correct versions of:
11.2 Virtual Environments
Use conda or venv to isolate dependencies. This helps avoid conflicts, especially with GPU builds of PyTorch.
11.3 Docker
If you want a portable solution, containerize your app. A Dockerfile might install conda, faiss-cpu, torch, and run streamlit.
11.4 GPU Memory
Large models can require significant VRAM (8GB+). If you have a smaller GPU, consider using a smaller model or quantized approach (like 8-bit or 4-bit quantization).
12. Challenges and Future Enhancements
13. Conclusion
Building a Private AI system that retrieves from local documents and generates answers with a local model is entirely feasible with open-source libraries:
By combining these components, you have a Retrieval-Augmented Generation pipeline that ensures data privacy (since everything runs locally) and contextual answers (since the system references your specific documents).
We walked through:
Armed with this knowledge, you can adapt the project to various domains—legal documents, research papers, internal wikis, or any private text corpora. The code base is modular, letting you swap out embedding models or language models to suit your hardware and data constraints.
14.1 Additional Prompting Strategies
Prompt engineering is an ongoing field of experimentation. Some strategies to refine your model’s behavior include:
14.2 Advanced PDF Layout Handling
Some PDFs contain multi-column text, tables, or embedded images. pdfplumber does a good job in many cases, but you can:
14.3 Indexing Large Corpora
When you have thousands or millions of chunks, the IndexFlatL2 approach might become slow or memory-intensive. Consider:
14.4 Monitoring and Logging
To keep track of usage:
14.5 Security and Access Control
When deploying a private AI, consider:
14.6 Potential Future Upgrades
Downloading Local Models from Hugging Face (Online & Offline Methods)
To kickstart your private AI solution, the first step is to download pre-trained models from the Hugging Face Model Hub and store them locally. This ensures that all model data remains on-premise—essential for maintaining data privacy and optimizing inference speed. Online, you can leverage the Transformers library by using the from_pretrained method with a specified cache directory. For example, calling from_pretrained("model-identifier", cache_dir="<LOCAL_MODEL_DIRECTORY>") (with "model-identifier" replaced by the desired model such as DeepSeek-R1-Distill-Qwen-1.5B or Gemma-3-1b-it, and <LOCAL_MODEL_DIRECTORY> replaced by your chosen folder) will download the model automatically into that folder. Alternatively, you can use the Hugging Face CLI to download the model by running commands like huggingface-cli login followed by huggingface-cli download model-identifier, which gives you additional control over the download process.
For situations where you need to work completely offline or prefer to manually manage the files, you can also download the model directly from the Hugging Face Model Hub website. Simply navigate to your desired model’s page, and use the “Download” option to get the model files as a ZIP archive. Once downloaded, unzip the contents into a local directory of your choice. You can then load the model in your code using from_pretrained("<LOCAL_DIRECTORY>"), where <LOCAL_DIRECTORY> points to the folder containing the unzipped model files. This offline approach not only enables you to work in environments without internet access but also gives you complete control over the model version and storage, ensuring that all components of your AI system remain secure and on-premise.
Final Thoughts
By following this comprehensive guide, you can stand up a private AI system that harnesses the power of large language models while keeping data firmly under your control. The combination of FAISS for retrieval, SentenceTransformers for embeddings, Hugging Face for generation, and Streamlit for UI is flexible, powerful, and relatively straightforward to customize.
Whether you’re building an internal knowledge base, assisting with legal document Q&A, or simply exploring the latest in AI—this RAG architecture is a solid foundation for advanced use cases. As the AI ecosystem continues to evolve, you can swap out or update each layer (embedding model, LLM, or UI) to keep your system at the cutting edge.
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