Agentic RAG Embedding Gemma
This Streamlit app demonstrates an agentic Retrieval-Augmented Generation (RAG) Agent using Google's EmbeddingGemma for embeddings and Llama 3.2 as th…
- Llama
- local
RAG and Knowledge ·Intermediate
A powerful document Q&A application that leverages Hybrid Search (RAG) and Claude's advanced language capabilities to provide comprehensive answers. Built with RAGLite for robust document processing and retrieval, and Streamlit for an intuitive chat interface, this system seamlessly combines document-specific knowledge with Claude's general intelligence to deliver accurate and contextual responses.
ANTHROPIC_API_KEYCOHERE_API_KEYOPENAI_API_KEYLicense Apache-2.0 · content curated and maintained by Doutor AI.
A powerful document Q&A application that leverages Hybrid Search (RAG) and Claude's advanced language capabilities to provide comprehensive answers. Built with RAGLite for robust document processing and retrieval, and Streamlit for an intuitive chat interface, this system seamlessly combines document-specific knowledge with Claude's general intelligence to deliver accurate and contextual responses.
You'll need the following API keys and database setup:
postgresql://user:pass@ep-xyz.region.aws.neon.tech/dbname)git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps/rag_tutorials/hybrid_search_rag
pip install -r requirements.txt
pip install https://github.com/explosion/spacy-models/releases/download/xx_sent_ud_sm-3.7.0/xx_sent_ud_sm-3.7.0-py3-none-any.whl
streamlit run main.py
The application supports multiple database backends:
postgresql://user:pass@ep-xyz.region.aws.neon.tech/dbnamemysql://user:pass@host:port/db
sqlite:///path/to/db.sqlite
Contributions are welcome! Please feel free to submit a Pull Request.
This Streamlit app demonstrates an agentic Retrieval-Augmented Generation (RAG) Agent using Google's EmbeddingGemma for embeddings and Llama 3.2 as th…
An agentic RAG application built with the Agno framework, featuring GPT-5 and LanceDB for efficient knowledge retrieval and question answering.
Architecture Flow <img width="465" alt="Screenshot 2025-05-04 at 3 45 58 PM" src="" /
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