metadata

title: Document AI Agent
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 4.36.1
app_file: app.py
pinned: false
license: apache-2.0

NVIDIA AI Document Chatbot

This project is a document-based chatbot application. It helps users ask questions about specific documents and receive accurate responses based on those documents.

Models and Components Used:

NVIDIAEmbeddings (NV-Embed-QA):
- This model extracts vector representations of texts to better understand documents.
- The NV-Embed-QA model is used to find relevant information in documents to answer questions.
ChatDocument (mistralai/mixtral-8x7b-instruct-v0.1):
- The Mistral-8x7B Instruct model is responsible for answering user questions about documents. It specializes in extracting information from documents and responding conversationally.

Application Workflow:

Loading Documents:
- Specific academic papers are loaded using ArxivLoader. These documents are split into text chunks and filtered based on predefined rules.
- The documents are then added to a FAISS vector store, which allows for efficient and fast document chunk retrieval.
Chat and Document Querying:
- The user's questions are processed according to a predefined chat template. The response is generated based on both the conversation history and information retrieved from the documents.
- The chat_gen function takes the user's input and generates responses using NVIDIA models, pulling relevant information from the documents.
Remembering Document Content and Conversation History:
- Previous user messages and responses are stored in a conversation memory and used for answering future questions more effectively.

Conclusion:

This application leverages NVIDIA's powerful language models and embedding tools to generate intelligent, document-driven conversational responses.

An example chatbot using Gradio, huggingface_hub, and the Hugging Face Inference API.