diff --git a/content/develop/ai/index.md b/content/develop/ai/index.md index 5afe7a057d..f502fcf4dc 100644 --- a/content/develop/ai/index.md +++ b/content/develop/ai/index.md @@ -53,6 +53,7 @@ Vector search retrieves results based on the similarity of high-dimensional nume * [Implementing hybrid search with Redis](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/02_hybrid_search.ipynb) * [Vector search with Redis Python client](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/00_redispy.ipynb) * [Vector search with Redis Vector Library](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/01_redisvl.ipynb) +* [Shows how to convert a float 32 index to float16 or integer data types](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/03_dtype_support.ipynb) #### RAG Retrieval Augmented Generation (aka RAG) is a technique to enhance the ability of an LLM to respond to user queries. The retrieval part of RAG is supported by a vector database, which can return semantically relevant results to a user’s query, serving as contextual information to augment the generative capabilities of an LLM. @@ -65,12 +66,14 @@ Retrieval Augmented Generation (aka RAG) is a technique to enhance the ability o * [Vector search with Azure](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/vector-similarity-search-with-azure-cache-for-redis-enterprise/3822059) * [RAG with Spring AI](https://redis.io/blog/building-a-rag-application-with-redis-and-spring-ai/) * [RAG with Vertex AI](https://github.com/redis-developer/gcp-redis-llm-stack/tree/main) -* [Notebook for additional tips and techniques to improve RAG quality](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb) +* [Notebook for additional tips and techniques to improve RAG quality](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb) +* [Implement a simple RBAC policy with vector search using Redis](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/07_user_role_based_rag.ipynb) #### Agents AI agents can act autonomously to plan and execute tasks for the user. * [Notebook to get started with LangGraph and agents](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/00_langgraph_redis_agentic_rag.ipynb) * [Build a collaborative movie recommendation system using Redis for data storage, CrewAI for agent-based task execution, and LangGraph for workflow management.](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/01_crewai_langgraph_redis.ipynb) +* [Full-Featured Agent Architecture](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/02_full_featured_agent.ipynb) #### LLM memory LLMs are stateless. To maintain context within a conversation chat sessions must be stored and resent to the LLM. Redis manages the storage and retrieval of chat sessions to maintain context and conversational relevance. @@ -81,6 +84,12 @@ LLMs are stateless. To maintain context within a conversation chat sessions must An estimated 31% of LLM queries are potentially redundant. Redis enables semantic caching to help cut down on LLM costs quickly. * [Build a semantic cache using the Doc2Cache framework and Llama3.1](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/doc2cache_llama3_1.ipynb) * [Build a semantic cache with Redis and Google Gemini](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/semantic_caching_gemini.ipynb) +* [Optimize semantic cache threshold with RedisVL](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-cache/02_semantic_cache_optimization.ipynb) + +#### Semantic routing +Routing is a simple and effective way of preventing misuses with your AI application or for creating branching logic between data sources etc. +* [Simple examples of how to build an allow/block list router in addition to a multi-topic router](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-router/00_semantic_routing.ipynb) +* [Use RouterThresholdOptimizer from redisvl to setup best router config](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/semantic-router/01_routing_optimization.ipynb) #### Computer vision Build a facial recognition system using the Facenet embedding model and RedisVL. @@ -88,7 +97,11 @@ Build a facial recognition system using the Facenet embedding model and RedisVL. #### Recommendation systems * [Intro content filtering example with redisvl](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/00_content_filtering.ipynb) -* [Intro collaborative filtering example with redisvl](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/01_collaborative_filtering.ipynb) +* [Intro collaborative filtering example with redisvl](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/01_collaborative_filtering.ipynb) +* [Intro deep learning two tower example with redisvl](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/02_two_towers.ipynb) + +#### Feature store +* [Credit scoring system using Feast with Redis as the online store](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/feature-store/00_feast_credit_score.ipynb) ## Tutorials Need a deeper-dive through different use cases and topics? @@ -97,10 +110,12 @@ Need a deeper-dive through different use cases and topics? * [Agentic RAG](https://github.com/redis-developer/agentic-rag) - A tutorial focused on agentic RAG with LlamaIndex and Amazon Bedrock * [RAG on Vertex AI](https://github.com/redis-developer/gcp-redis-llm-stack/tree/main) - A RAG tutorial featuring Redis with Vertex AI * [RAG workbench](https://github.com/redis-developer/redis-rag-workbench) - A development playground for exploring RAG techniques with Redis +* [ArXiv Chat](https://github.com/redis-developer/ArxivChatGuru) - Streamlit demo of RAG over ArXiv documents with Redis & OpenAI -#### Recommendation system +#### Recommendations and search * [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 * [Redis product search](https://github.com/redis-developer/redis-product-search) - Build a real-time product search engine using features like full-text search, vector similarity, and real-time data updates +* [ArXiv Search](https://github.com/redis-developer/redis-arxiv-search) - Full stack implementation of Redis with React FE ## Ecosystem integrations @@ -113,6 +128,8 @@ Need a deeper-dive through different use cases and topics? * [Deploy GenAI apps faster with Redis and NVIDIA NIM](https://redis.io/blog/use-redis-with-nvidia-nim-to-deploy-genai-apps-faster/) * [Building LLM Applications with Kernel Memory and Redis](https://redis.io/blog/building-llm-applications-with-kernel-memory-and-redis/) * [DocArray integration of Redis as a vector database by Jina AI](https://docs.docarray.org/user_guide/storing/index_redis/) +* [Semantic Kernel: A popular library by Microsoft to integrate LLMs with plugins](https://learn.microsoft.com/en-us/semantic-kernel/concepts/vector-store-connectors/out-of-the-box-connectors/redis-connector?pivots=programming-language-csharp) +* [LiteLLM integration](https://docs.litellm.ai/docs/caching/all_caches#initialize-cache---in-memory-redis-s3-bucket-redis-semantic-disk-cache-qdrant-semantic) ## Benchmarks See how we stack up against the competition.