RAG and KnowledgeIntermediate
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…
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RAG and Knowledge ·Intermediate
PharmaQuery
About this app
PharmaQuery is an advanced Pharmaceutical Insight Retrieval System designed to help users gain meaningful insights from research papers and documents in the pharmaceutical domain.
- Complete open-source code
- Step-by-step tutorial
Technical sheet
- Category
- RAG and Knowledge
- Difficulty
- Intermediate
- Models
- Gemini
- Frameworks
- LangChain
- Runs locally?
- Needs cloud APIs
- Required keys
- None
License Apache-2.0 · content curated and maintained by Doutor AI.
Full tutorial
Overview
PharmaQuery is an advanced Pharmaceutical Insight Retrieval System designed to help users gain meaningful insights from research papers and documents in the pharmaceutical domain.
Demo
https://github.com/user-attachments/assets/c12ee305-86fe-4f71-9219-57c7f438f291
Features
- Natural Language Querying: Ask complex questions about the pharmaceutical industry and get concise, accurate answers.
- Custom Database: Upload your own research documents to enhance the retrieval system's knowledge base.
- Similarity Search: Retrieves the most relevant documents for your query using AI embeddings.
- Streamlit Interface: User-friendly interface for queries and document uploads.
Technologies Used
- Programming Language: Python 3.10+
- Framework: LangChain
- Database: ChromaDB
- Models:
- Embeddings: Google Gemini API (embedding-001)
- Chat: Google Gemini API (gemini-1.5-pro)
- PDF Processing: PyPDFLoader
- Document Splitter: SentenceTransformersTokenTextSplitter
Requirements
- Install Dependencies:
pip install -r requirements.txt- Run the Application:
streamlit run app.py- Use the Application:
- Paste your Google API Key in the sidebar.
- Enter your query in the main interface.
- Optionally, upload research papers in the sidebar to enhance the database.
:mailbox: Connect With Me
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