From 273df923cbad32e324415a778c5d077386d3a2fc Mon Sep 17 00:00:00 2001 From: Jim Allen Wallace Date: Thu, 19 Dec 2024 15:42:40 -0700 Subject: [PATCH 1/2] I updated the AI Dev Hub index page with the following changes: - Added additional resources and links for AI learning materials - Added brief descriptions for each section and guide to the page - Add 2 image files used in the AI Dev Hub page - Fixed broken links that went to Colab notebooks MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit I worked to follow Michelle’s previous layout while adding new content. Please let me know any feedback on my submitted changes. Thanks. JAWS --- content/develop/ai/index.md | 87 +++++++++++++++++++------- static/images/ai-LLM-memory.png | Bin 0 -> 1153 bytes static/images/ai-semantic-routing.png | Bin 0 -> 1722 bytes 3 files changed, 65 insertions(+), 22 deletions(-) create mode 100644 static/images/ai-LLM-memory.png create mode 100644 static/images/ai-semantic-routing.png diff --git a/content/develop/ai/index.md b/content/develop/ai/index.md index f251cde01..bc49566f2 100644 --- a/content/develop/ai/index.md +++ b/content/develop/ai/index.md @@ -16,7 +16,18 @@ Redis stores and indexes vector embeddings that semantically represent unstructu | :-- | :-- | :-- | | {{AI Redis icon.}}[Redis vector database quick start guide]({{< relref "/develop/get-started/vector-database" >}}) |{{AI Redis icon.}} [Retrieval-Augmented Generation quick start guide]({{< relref "/develop/get-started/rag" >}}) | {{AI Redis icon.}}[Redis vector Python client library documentation]({{< relref "/integrate/redisvl/overview/" >}}) | -#### Overview +## Overview + +This page organized into a few sections depending on what you’re trying to do: +* **Docs** - The comprehensive reference section for every feature, API, and setting. It’s your source for detailed, technical information to support any level of development. +* **Concepts** - Explanations of foundational ideas and core principles to help you understand the "why" behind the product’s features and design. +* **Quickstarts** - Short, focused guides to get you up and running with key features or workflows in minutes. +* **Tutorials** - In-depth walkthroughs that dive deeper into specific use cases or processes. These step-by-step guides help you master essential tasks and workflows. +* **Integrations** - Guides and resources to help you connect and use the product with popular tools, frameworks, or platforms. +* **Benchmarks** - Performance comparisons and metrics to demonstrate how the product performs under various scenarios. This helps you understand its efficiency and capabilities. +* **Best practices** - Recommendations and guidelines for maximizing effectiveness and avoiding common pitfalls. This section equips you to use the product effectively and efficiently. + +## Docs 1. [**Create a vector index**]({{< baseurl >}}/develop/interact/search-and-query/advanced-concepts/vectors#create-a-vector-index): Redis maintains a secondary index over your data with a defined schema (including vector fields and metadata). Redis supports [`FLAT`]({{< baseurl >}}/develop/interact/search-and-query/advanced-concepts/vectors#flat-index) and [`HNSW`]({{< baseurl >}}/develop/interact/search-and-query/advanced-concepts/vectors#hnsw-index) vector index types. 1. [**Store and update vectors**]({{< baseurl >}}/develop/interact/search-and-query/advanced-concepts/vectors#store-and-update-vectors): Redis stores vectors and metadata in hashes or JSON objects. @@ -27,56 +38,88 @@ Redis stores and indexes vector embeddings that semantically represent unstructu Learn to perform vector search and use gateways and semantic caching in your AI/ML projects. -| Search | AI Gateways | Semantic Caching | +| Search | LLM memory | Semantic caching | Semantic routing | AI Gateways | | :-- | :-- | :-- | -| {{AI Redis icon.}}[Vector search guide]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) | {{AI Redis icon.}}[Deploy an enhanced gateway with Redis](https://redis.io/blog/ai-gateways-what-are-they-how-can-you-deploy-an-enhanced-gateway-with-redis/) | {{AI Redis icon.}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) | - -## Ecosystem integrations - -* [Amazon Bedrock setup guide]({{< relref "/integrate/amazon-bedrock/set-up-redis" >}}) -* [LangChain Redis Package: Smarter AI apps with advanced vector storage and faster caching](https://redis.io/blog/langchain-redis-partner-package/)) -* [Redis Cloud available on Vercel](https://redis.io/blog/redis-cloud-now-available-on-vercel-marketplace/) -* [Create a Redis Cloud database with the Vercel integration]({{< relref "/operate/rc/cloud-integrations/vercel" >}}) -* [Building a RAG application with Redis and Spring AI](https://redis.io/blog/building-a-rag-application-with-redis-and-spring-ai/) -* [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/) +| {{AI Redis icon.}}[Vector search guide]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) | {{LLM memory icon.}}[Store memory for LLMs](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{AI Redis icon.}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) | {{Semantic routing icon.}}[Semantic routing chooses the best tool](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{AI Redis icon.}}[Deploy an enhanced gateway with Redis](https://redis.io/blog/ai-gateways-what-are-they-how-can-you-deploy-an-enhanced-gateway-with-redis/) | {{AI Redis icon.}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) | +## Quickstarts -## Examples - -Get started with the following Redis Python notebooks. +Quickstarts or recipes are for when you are trying to build specific functionality. For example, you might want to do RAG with LangChain or set up LLM memory for you AI agent. Get started with the following Redis Python notebooks. * [The place to start if you are brand new to Redis](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/redis-intro/00_redis_intro.ipynb) #### Hybrid and vector search +Vector search retrieves results based on the similarity of high-dimensional numerical embeddings, while hybrid search combines this with traditional keyword or metadata-based filtering for more comprehensive results. * [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) #### 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. * [RAG from scratch with the Redis Vector Library](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/01_redisvl.ipynb) * [RAG using Redis and LangChain](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/02_langchain.ipynb) * [RAG using Redis and LlamaIndex](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/03_llamaindex.ipynb) * [Advanced RAG with redisvl](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb) * [RAG using Redis and Nvidia](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb) * [Utilize RAGAS framework to evaluate RAG performance](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/06_ragas_evaluation.ipynb) +* [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) -#### LLM session management +#### Agents +AI agents can act autonomously to plan and execute tasks for the user. +* [Notebook to get started with lang-graph 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) + +#### 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. * [LLM session manager with semantic similarity](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/llm-session-manager/00_llm_session_manager.ipynb) * [Handle multiple simultaneous chats with one instance](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/llm-session-manager/01_multiple_sessions.ipynb) #### Semantic caching +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) -#### Agents -* [Notebook to get started with lang-graph and agents](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/00_langgraph_redis_agentic_rag.ipynb) -* [Notebook to get started with lang-graph and agents](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/01_crewai_langgraph_redis.ipynb) +#### Computer vision +Build a facial recognition system using the Facenet embedding model and RedisVL. +* [Facial recognition](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/computer-vision/00_facial_recognition_facenet.ipynb) #### Recommendation systems -* [Intro content filtering example with redisvl](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/content_filtering.ipynb) -* [Intro collaborative filtering example with redisvl](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/collaborative_filtering.ipynb) +* [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) + +## Tutorials +Need a deeper-dive through different use cases and topics? + +#### RAG +* [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. + +#### Recommendation system +* [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. + +## Ecosystem integrations +* [Amazon Bedrock setup guide]({{< relref "/integrate/amazon-bedrock/set-up-redis" >}}) +* [LangChain Redis Package: Smarter AI apps with advanced vector storage and faster caching](https://redis.io/blog/langchain-redis-partner-package/) +* [LlamaIndex integration for Redis as a vector store](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/RedisIndexDemo.html) +* [Redis Cloud available on Vercel](https://redis.io/blog/redis-cloud-now-available-on-vercel-marketplace/) +* [Create a Redis Cloud database with the Vercel integration]({{< relref "/operate/rc/cloud-integrations/vercel" >}}) +* [Building a RAG application with Redis and Spring AI](https://redis.io/blog/building-a-rag-application-with-redis-and-spring-ai/) +* [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/) +## Benchmarks +See how we stack up against the competition. +* [Benchmarking results for vector databases](https://redis.io/blog/benchmarking-results-for-vector-databases/) +* [1 billion vectors](https://redis.io/blog/redis-8-0-m02-the-fastest-redis-ever/) +## Best practices +See how leaders in the industry are building their RAG apps. +* [Advanced RAG example](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb) +* [get better RAG responses with Ragas](https://redis.io/blog/get-better-rag-responses-with-ragas/) diff --git a/static/images/ai-LLM-memory.png b/static/images/ai-LLM-memory.png new file mode 100644 index 0000000000000000000000000000000000000000..5dddad586cdcf9fa6c22c30860d38e66ffd347cb GIT binary patch literal 1153 zcmeAS@N?(olHy`uVBq!ia0y~yU{nCIe{wJb$qmvH4}lbOv6E*A2M5RPhyD+MT)6S100Y^X)Ap6n}wrN0>93?@1!3+l+8h}W1*CG*9pjp2?T^vIy=DfXo zaoQ{g0fs=)z(4=x{rr5)EYCSie0MB#P1XCrcT#8jbY%GKrHy5O%?+|G4t!JNUG`Sy zF4x-&rGW1Etv@cZ;smH2q`$vp|ya?72z>moN?miYDiv9H-Z?i#5DUGq~? 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I also fixed the format typo for LangGraph --- content/develop/ai/index.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/develop/ai/index.md b/content/develop/ai/index.md index bc49566f2..19934a734 100644 --- a/content/develop/ai/index.md +++ b/content/develop/ai/index.md @@ -16,18 +16,18 @@ Redis stores and indexes vector embeddings that semantically represent unstructu | :-- | :-- | :-- | | {{AI Redis icon.}}[Redis vector database quick start guide]({{< relref "/develop/get-started/vector-database" >}}) |{{AI Redis icon.}} [Retrieval-Augmented Generation quick start guide]({{< relref "/develop/get-started/rag" >}}) | {{AI Redis icon.}}[Redis vector Python client library documentation]({{< relref "/integrate/redisvl/overview/" >}}) | -## Overview +#### Overview This page organized into a few sections depending on what you’re trying to do: -* **Docs** - The comprehensive reference section for every feature, API, and setting. It’s your source for detailed, technical information to support any level of development. -* **Concepts** - Explanations of foundational ideas and core principles to help you understand the "why" behind the product’s features and design. -* **Quickstarts** - Short, focused guides to get you up and running with key features or workflows in minutes. +* **How to's** - The comprehensive reference section for every feature, API, and setting. It’s your source for detailed, technical information to support any level of development. +* **Concepts** - Explanations of foundational ideas and core principles to help you understand the reason behind the product’s features and design. +* **Quickstarts** - Short, focused guides to get you started with key features or workflows in minutes. * **Tutorials** - In-depth walkthroughs that dive deeper into specific use cases or processes. These step-by-step guides help you master essential tasks and workflows. * **Integrations** - Guides and resources to help you connect and use the product with popular tools, frameworks, or platforms. * **Benchmarks** - Performance comparisons and metrics to demonstrate how the product performs under various scenarios. This helps you understand its efficiency and capabilities. * **Best practices** - Recommendations and guidelines for maximizing effectiveness and avoiding common pitfalls. This section equips you to use the product effectively and efficiently. -## Docs +## How to's 1. [**Create a vector index**]({{< baseurl >}}/develop/interact/search-and-query/advanced-concepts/vectors#create-a-vector-index): Redis maintains a secondary index over your data with a defined schema (including vector fields and metadata). Redis supports [`FLAT`]({{< baseurl >}}/develop/interact/search-and-query/advanced-concepts/vectors#flat-index) and [`HNSW`]({{< baseurl >}}/develop/interact/search-and-query/advanced-concepts/vectors#hnsw-index) vector index types. 1. [**Store and update vectors**]({{< baseurl >}}/develop/interact/search-and-query/advanced-concepts/vectors#store-and-update-vectors): Redis stores vectors and metadata in hashes or JSON objects. @@ -39,12 +39,12 @@ This page organized into a few sections depending on what you’re trying to do: Learn to perform vector search and use gateways and semantic caching in your AI/ML projects. | Search | LLM memory | Semantic caching | Semantic routing | AI Gateways | -| :-- | :-- | :-- | +| :-- | :-- | :-- | :-- | :-- | | {{AI Redis icon.}}[Vector search guide]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) | {{LLM memory icon.}}[Store memory for LLMs](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{AI Redis icon.}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) | {{Semantic routing icon.}}[Semantic routing chooses the best tool](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{AI Redis icon.}}[Deploy an enhanced gateway with Redis](https://redis.io/blog/ai-gateways-what-are-they-how-can-you-deploy-an-enhanced-gateway-with-redis/) | {{AI Redis icon.}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) | ## Quickstarts -Quickstarts or recipes are for when you are trying to build specific functionality. For example, you might want to do RAG with LangChain or set up LLM memory for you AI agent. Get started with the following Redis Python notebooks. +Quickstarts or recipes are useful when you are trying to build specific functionality. For example, you might want to do RAG with LangChain or set up LLM memory for you AI agent. Get started with the following Redis Python notebooks. * [The place to start if you are brand new to Redis](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/redis-intro/00_redis_intro.ipynb) @@ -69,7 +69,7 @@ Retrieval Augmented Generation (aka RAG) is a technique to enhance the ability o #### Agents AI agents can act autonomously to plan and execute tasks for the user. -* [Notebook to get started with lang-graph and agents](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/agents/00_langgraph_redis_agentic_rag.ipynb) +* [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) #### LLM memory @@ -96,11 +96,11 @@ Need a deeper-dive through different use cases and topics? #### RAG * [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. +* [RAG workbench](https://github.com/redis-developer/redis-rag-workbench) - A development playground for exploring RAG techniques with Redis #### Recommendation system * [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. +* [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 ## Ecosystem integrations @@ -116,10 +116,10 @@ Need a deeper-dive through different use cases and topics? ## Benchmarks See how we stack up against the competition. -* [Benchmarking results for vector databases](https://redis.io/blog/benchmarking-results-for-vector-databases/) +* [Redis vector benchmark results](https://redis.io/blog/benchmarking-results-for-vector-databases/) * [1 billion vectors](https://redis.io/blog/redis-8-0-m02-the-fastest-redis-ever/) ## Best practices See how leaders in the industry are building their RAG apps. * [Advanced RAG example](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb) -* [get better RAG responses with Ragas](https://redis.io/blog/get-better-rag-responses-with-ragas/) +* [Get better RAG responses with Ragas](https://redis.io/blog/get-better-rag-responses-with-ragas/)