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feat: apply all 15 upgrades — BGE embeddings, cosine FAISS, streaming LLM, tenacity retry, dotenv, Dockerfile, tests

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metadata

title: Kerdos AI — Custom LLM Chat (Demo)
emoji: 🤖
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.6.0
app_file: app.py
pinned: false
license: mit
tags:
  - rag
  - document-qa
  - enterprise
  - llama
  - faiss
  - gradio
  - nlp
  - question-answering
  - kerdos
  - private-llm
  - demo

🤖 Kerdos AI — Custom LLM Chat & Document Q&A

A Demo Product by Kerdos Infrasoft Private Limited Upload your company documents. Ask questions. Get answers — strictly from your data.

⚠️ This is a Demo Version. Features, model selection, and customisation are limited. The full product will support private, on-premise LLM deployments tailored to your organisation.

🚀 About Kerdos Infrasoft

Kerdos Infrasoft Private Limited is an Indian technology startup incorporated in December 2023, headquartered in Bengaluru, Karnataka, India (CIN: U62099KA2023PTC182869). We specialise in:

🧠 Artificial Intelligence & Machine Learning
🔗 Blockchain Development
💻 Custom Software Development
☁️ Infrastructure & Digital Consulting

🌐 Website: https://kerdos.in 📞 Contact: https://kerdos.in/contact ✉️ Email: partnership@kerdos.in

💰 Seeking Investment & Partnerships

We are actively seeking investment and strategic partnerships to build the fully customisable enterprise edition, which will include:

🔒 Private LLM Hosting — on-premise or private-cloud deployments
🎛️ Custom Model Fine-tuning — trained on your domain data
🛡️ Data Privacy Guarantees — your data never leaves your environment
🏷️ White-label Deployments — fully branded for your organisation

Interested? Reach out at partnership@kerdos.in or visit kerdos.in/contact

✨ Features (Demo)

Feature	Details
📄 Multi-format ingestion	PDF, DOCX, TXT, MD, CSV
🧠 Open-source LLM	`meta-llama/Llama-3.1-8B-Instruct` via HF Inference API
🔒 Strictly grounded	Answers only from your uploaded documents
📦 Multi-document	Upload and query across multiple files simultaneously
💬 Multi-turn chat	Maintains conversation context across questions
⚡ Fast	CPU-friendly embeddings (`BAAI/bge-small-en-v1.5` + FAISS cosine)
🔑 Secure	Files processed in-session only — never stored permanently

🚀 How to Use

On Hugging Face Spaces

Upload your documents (PDF, DOCX, TXT) using the left panel
Click Index Documents
Enter your Hugging Face API token (Write access required for Llama 3)
Ask questions in the chat!

Self-Hosted / Local

git clone https://huggingface.co/spaces/kerdosdotio/Custom-LLM-Chat
cd Custom-LLM-Chat
pip install -r requirements.txt
HF_TOKEN=hf_your_token python app.py

🏗️ Architecture

User Uploads Files
      ↓
Document Parser (PDF / DOCX / TXT)
      ↓
Text Chunking (512 chars, 64 overlap)
      ↓
Embeddings (BAAI/bge-small-en-v1.5)
      ↓
FAISS Vector Index (cosine similarity, in-memory)
      ↓
User Question → Similarity Search → Top-K Chunks
      ↓
Llama 3.1 8B — answers ONLY from retrieved chunks
      ↓
Response + Source Citations

🔧 Tech Stack

UI: Gradio
LLM: meta-llama/Llama-3.1-8B-Instruct
Embeddings: BAAI/bge-small-en-v1.5 (cosine similarity via FAISS)
Vector Store: FAISS
Document Parsing: PyMuPDF, python-docx

💼 Use Cases

Customer Support: Index your product manuals, FAQs, and policies
HR & Legal: Query employee handbooks, contracts, and compliance docs
Sales Enablement: Search product specs, case studies, and pricing docs
IT Helpdesk: Query runbooks, troubleshooting guides, and SOPs

🔐 Privacy

Uploaded documents are processed in-memory and not stored after your session ends
For persistent storage or on-premise deployment with full privacy, contact us

📄 License

MIT License — free for commercial and private use.