From 4b9425abf1d1cd79d8bf153859aa8c6b45cb91a6 Mon Sep 17 00:00:00 2001 From: rinivasan-redis Date: Fri, 18 Apr 2025 17:19:04 -0400 Subject: [PATCH 1/4] Update index.md to include updates from redis-ai-resources repo Updates listed here: https://docs.google.com/document/d/1DYKD_9h_7yKfRoYTEgeWbwKhE7NgxVj2SOdL_FUJknU/edit?tab=t.0 --- content/develop/ai/index.md | 23 ++++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/content/develop/ai/index.md b/content/develop/ai/index.md index 5afe7a057d..95f144070f 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://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/01_crewai_langgraph_redis.ipynb](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 +* [ArxivChatGuru](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 lib by MSFT 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. From 8b4c9258223fa24a089389c44a0c26ecfae13900 Mon Sep 17 00:00:00 2001 From: rinivasan-redis Date: Fri, 18 Apr 2025 18:13:13 -0400 Subject: [PATCH 2/4] Update content/develop/ai/index.md Co-authored-by: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> --- content/develop/ai/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/develop/ai/index.md b/content/develop/ai/index.md index 95f144070f..d348026ac7 100644 --- a/content/develop/ai/index.md +++ b/content/develop/ai/index.md @@ -73,7 +73,7 @@ Retrieval Augmented Generation (aka RAG) is a technique to enhance the ability o 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://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/01_crewai_langgraph_redis.ipynb](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/02_full_featured_agent.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. From 7018a9466e6d4d4a9cd35f51ba05ae7cf9e2d325 Mon Sep 17 00:00:00 2001 From: rinivasan-redis Date: Fri, 18 Apr 2025 18:16:24 -0400 Subject: [PATCH 3/4] Update content/develop/ai/index.md Co-authored-by: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> --- content/develop/ai/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/develop/ai/index.md b/content/develop/ai/index.md index d348026ac7..d168bcbe10 100644 --- a/content/develop/ai/index.md +++ b/content/develop/ai/index.md @@ -110,7 +110,7 @@ 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 -* [ArxivChatGuru](https://github.com/redis-developer/ArxivChatGuru) - Streamlit demo of RAG over Arxiv documents with Redis & OpenAI +* [ArXiv Chat](https://github.com/redis-developer/ArxivChatGuru) - Streamlit demo of RAG over ArXiv documents with Redis & OpenAI #### 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 From 454af636676ed3db3b0b7f8db5fef76683a1cad1 Mon Sep 17 00:00:00 2001 From: rinivasan-redis Date: Fri, 18 Apr 2025 18:16:35 -0400 Subject: [PATCH 4/4] Update content/develop/ai/index.md Co-authored-by: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> --- content/develop/ai/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/develop/ai/index.md b/content/develop/ai/index.md index d168bcbe10..f502fcf4dc 100644 --- a/content/develop/ai/index.md +++ b/content/develop/ai/index.md @@ -128,7 +128,7 @@ 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 lib by MSFT 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) +* [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