We are developing AI chatbots using open-source LLM models, ensuring that our knowledge base operates within a secure sandbox environment, with no data being transmitted over the internet. By leveraging Retrieval-Augmented Generation (RAG), we can utilize our existing knowledge base to enhance the foundation LLM, generating accurate and privacy-preserving results. This approach benefits both our customers/end users and the company by maintaining strict data privacy.
- Used open source entirely
- Can read unstructured documents like PDF
- Ensures guardrails to stick to a context
- Ensures no hallucination in the responses
- Ensures Data Governance
- Optimal search from existing knowledge base using foundation LLM models so that customer support can be self-serving (level 0)
- Lesser reliance on level 1 support
- Only the most important and complex calls are forwarded for manual intervention
- PlantUML: Write code and generate sequence diagrams on the fly
- LLM (Large Language Models): Llama3, Mistral, etc.
- Ollama: Handle the NLP tasks effectively, e.g., embeddings
- Unstructured: Read unstructured data, i.e., PDF
- NLP (Natural Language Processing)
- RAG (Retrieval Augmented Generation)
- Python
- ChromaDB
- Langchain