mirror of https://github.com/Shubhamsaboo/awesome-llm-apps.git synced 2026-05-25 13:30:16 +00:00

Files

T

Ryan Alexander Alberts 78ee6b005f fix: unpin duckduckgo-search==6.4.1 (yanked from PyPI)

Version 6.4.1 is no longer distributed on PyPI (6.4.2 is the
closest available in the 6.4.x line). Four requirements.txt
files still pin the unavailable version, causing a hard
`pip install` failure on a clean checkout.

Widen to `>=6.4.2,<9` so the resolver picks the latest
compatible release (currently 8.1.1); the lower bound preserves
the original intent of pinning within the 6.4.x family, and the
upper bound matches the loosest existing pin elsewhere in the
repo (ai_competitor_intelligence_agent_team uses 7.2.1, the
ADK crash course uses `>=6.0.0`).

Affected projects:
- starter_ai_agents/ai_medical_imaging_agent
- rag_tutorials/rag_database_routing
- rag_tutorials/rag_agent_cohere
- advanced_ai_agents/multi_agent_apps/agent_teams/ai_teaching_agent_team

Fixes #63

Made-with: Cursor

2026-04-17 17:00:45 -04:00

rag_agent_cohere.py

changess

2024-12-15 10:16:56 +05:30

README.md

chore: updated README

2024-12-14 22:26:28 -06:00

requirements.txt

fix: unpin duckduckgo-search==6.4.1 (yanked from PyPI)

2026-04-17 17:00:45 -04:00

README.md

RAG Agent with Cohere ⌘R

A RAG Agentic system built with Cohere's new model Command-r7b-12-2024, Qdrant for vector storage, Langchain for RAG and LangGraph for orchestration. This application allows users to upload documents, ask questions about them, and get AI-powered responses with fallback to web search when needed.

Features

Document Processing
- PDF document upload and processing
- Automatic text chunking and embedding
- Vector storage in Qdrant cloud
Intelligent Querying
- RAG-based document retrieval
- Similarity search with threshold filtering
- Automatic fallback to web search when no relevant documents found
- Source attribution for answers
Advanced Capabilities
- DuckDuckGo web search integration
- LangGraph agent for web research
- Context-aware response generation
- Long answer summarization
Model Specific Features
- Command-r7b-12-2024 model for Chat and RAG
- cohere embed-english-v3.0 model for embeddings
- create_react_agent function from langgraph
- DuckDuckGoSearchRun tool for web search

Prerequisites

1. Cohere API Key

Go to Cohere Platform
Sign up or log in to your account
Navigate to API Keys section
Create a new API key

2. Qdrant Cloud Setup

Visit Qdrant Cloud
Create an account or sign in
Create a new cluster
Get your credentials:
- Qdrant API Key: Found in API Keys section
- Qdrant URL: Your cluster URL (format: https://xxx-xxx.aws.cloud.qdrant.io)

How to Run

Clone the repository:

git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd rag_tutorials/rag_agent_cohere

Install dependencies:

pip install -r requirements.txt

streamlit run rag_agent_cohere.py