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add tests and update readme
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.github/workflows/test.yml

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GCP_REGION: ${{ secrets.GCP_REGION }}
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GCP_PROJECT_ID: ${{ secrets.GCP_PROJECT_ID }}
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run: |
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pytest --verbose --nbval-lax python-recipes/RAG/ python-recipes/vector-search python-recipes/redis-intro python-recipes/recommendation-systems python-recipes/agents --ignore python-recipes/agents/01_crewai_langgraph_redis.ipynb --ignore python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb --ignore python-recipes/semantic-cache/doc2cache_llama3_1.ipynb
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pytest --verbose --nbval-lax python-recipes/RAG/ python-recipes/vector-search python-recipes/redis-intro python-recipes/recommendation-systems python-recipes/agents python-recipes/computer-vision --ignore python-recipes/agents/01_crewai_langgraph_redis.ipynb --ignore python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb --ignore python-recipes/semantic-cache/doc2cache_llama3_1.ipynb

README.md

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| Demo | Description |
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| --- | --- |
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| [Redis RAG Workbench](https://github.com/redis-developer/redis-rag-workbench) | Interactive demo to build a RAG-based chatbot over a user-uploaded PDF. Toggle different settings and configurations to improve chatbot performance and quality. Utilizes RedisVL, LangChain, RAGAs, and more. |
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| [ArxivChatGuru](https://github.com/redis-developer/ArxivChatGuru) | Streamlit demo of RAG over Arxiv documents with Redis & OpenAI |
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| [Redis VSS - Simple Streamlit Demo](https://github.com/antonum/Redis-VSS-Streamlit) | Streamlit demo of Redis Vector Search |
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| [ArXiv Search](https://github.com/redis-developer/redis-arxiv-search) | Full stack implementation of Redis with React FE |
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| [Product Search](https://github.com/redis-developer/redis-product-search) | Vector search with Redis Stack and Redis Enterprise |
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| [ArxivChatGuru](https://github.com/redis-developer/ArxivChatGuru) | Streamlit demo of RAG over Arxiv documents with Redis & OpenAI |
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## Recipes
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| [/semantic-cache/doc2cache_llama3_1.ipynb](python-recipes/semantic-cache/doc2cache_llama3_1.ipynb) | Build a semantic cache using the Doc2Cache framework and Llama3.1 |
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| [/semantic-cache/semantic_caching_gemini.ipynb](python-recipes/semantic-cache/semantic_caching_gemini.ipynb) | Build a semantic cache with Redis and Google Gemini |
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### Agents
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| Recipe | Description |
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| --- | --- |
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[/agents/00_langgraph_redis_agentic_rag.ipynb](python-recipes/agents/00_langgraph_redis_agentic_rag.ipynb) | Notebook to get started with lang-graph and agents |
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[/agents/01_crewai_langgraph_redis.ipynb](python-recipes/agents/01_crewai_langgraph_redis.ipynb) | Notebook to get started with lang-graph and agents |
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### Recommendation systems
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### Computer Vision
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| Recipe | Description |
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| ------ | ----------- |
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| [/computer-vision/00_facial_recognition_facenet.ipynb](python-recipes/computer-vision/00_facial_recognition_facenet.ipynb) | Build a facial recognition system using the Facenet embedding model and RedisVL.
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### Recommendation Systems
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| Recipe | Description |
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| --- | --- |
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| Tutorial | Description |
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| -------- | ------------ |
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| [RAG on VertexAI](https://github.com/redis-developer/gcp-redis-llm-stack/tree/main) | A RAG tutorial featuring Redis with Vertex AI |
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| [Agentic RAG](https://github.com/redis-developer/agentic-rag) | A tutorial focused on agentic RAG with LlamaIndex and Cohere |
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| [RAG on VertexAI](https://github.com/redis-developer/gcp-redis-llm-stack/tree/main) | A RAG tutorial featuring Redis with Vertex AI |
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| [Recommendation Systems w/ NVIDIA Merlin & Redis]((https://github.com/redis-developer/redis-nvidia-recsys)) | Three examples, each escalating in complexity, showcasing the process of building a realtime recsys with NVIDIA and Redis |
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