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This page organized into a few sections depending on what you’re trying to do:
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***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.
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***Concepts** - Explanations of foundational ideas and core principles to help you understand the reason behind the product’s features and design.
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***Quickstarts** - Short, focused guides to get you started with key features or workflows in minutes.
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***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.
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***Integrations** - Guides and resources to help you connect and use the product with popular tools, frameworks, or platforms.
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***Benchmarks** - Performance comparisons and metrics to demonstrate how the product performs under various scenarios. This helps you understand its efficiency and capabilities.
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***Best practices** - Recommendations and guidelines for maximizing effectiveness and avoiding common pitfalls. This section equips you to use the product effectively and efficiently.
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## How to's
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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.
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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.
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1.[**Search with vectors**]({{< baseurl >}}/develop/interact/search-and-query/advanced-concepts/vectors#search-with-vectors): Redis supports several advanced querying strategies with vector fields including k-nearest neighbor ([KNN]({{< baseurl >}}/develop/interact/search-and-query/advanced-concepts/vectors#knn-vector-search)), [vector range queries]({{< baseurl >}}/develop/interact/search-and-query/advanced-concepts/vectors#vector-range-queries), and [metadata filters]({{< baseurl >}}/develop/interact/search-and-query/advanced-concepts/vectors#filters).
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Learn to perform vector search and use gateways and semantic caching in your AI/ML projects.
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| Search | AI Gateways | Semantic Caching |
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| :-- | :-- | :-- |
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| {{<imagefilename="images/ai-search.png"alt="AI Redis icon.">}}[Vector search guide]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) | {{<imagefilename="images/ai-model.png"alt="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/)| {{<imagefilename="images/ai-brain-2.png"alt="AI Redis icon.">}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching)|
| {{<image filename="images/ai-search.png" alt="AI Redis icon.">}}[Vector search guide]({{< relref "/develop/interact/search-and-query/query/vector-search" >}}) | {{<image filename="images/ai-LLM-memory.png" alt="LLM memory icon.">}}[Store memory for LLMs](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{<image filename="images/ai-brain-2.png" alt="AI Redis icon.">}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) | {{<image filename="images/ai-semantic-routing.png" alt="Semantic routing icon.">}}[Semantic routing chooses the best tool](https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/) | {{<image filename="images/ai-model.png" alt="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/) | {{<image filename="images/ai-brain-2.png" alt="AI Redis icon.">}}[Semantic caching for faster, smarter LLM apps](https://redis.io/blog/what-is-semantic-caching) |
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## Examples
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## Quickstarts
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Get started with the following Redis Python notebooks.
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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.
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*[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)
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#### Hybrid and vector search
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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.
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*[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)
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*[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)
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*[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)
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#### RAG
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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.
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*[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)
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*[RAG using Redis and LangChain](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/02_langchain.ipynb)
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*[RAG using Redis and LlamaIndex](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/03_llamaindex.ipynb)
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*[Advanced RAG with redisvl](https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/main/python-recipes/RAG/04_advanced_redisvl.ipynb)
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*[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)
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*[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)
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*[Vector search with Azure](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/vector-similarity-search-with-azure-cache-for-redis-enterprise/3822059)
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*[RAG with Spring AI](https://redis.io/blog/building-a-rag-application-with-redis-and-spring-ai/)
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*[RAG with Vertex AI](https://github.com/redis-developer/gcp-redis-llm-stack/tree/main)
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*[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)
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#### LLM session management
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#### Agents
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AI agents can act autonomously to plan and execute tasks for the user.
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*[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)
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*[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)
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#### LLM memory
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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.
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*[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)
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*[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)
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#### Semantic caching
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An estimated 31% of LLM queries are potentially redundant. Redis enables semantic caching to help cut down on LLM costs quickly.
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*[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)
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*[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)
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#### Agents
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*[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)
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*[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)
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#### Computer vision
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Build a facial recognition system using the Facenet embedding model and RedisVL.
*[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)
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*[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)
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*[Intro content filtering example with redisvl](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/00_content_filtering.ipynb)
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*[Intro collaborative filtering example with redisvl](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/recommendation-systems/01_collaborative_filtering.ipynb)
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## Tutorials
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Need a deeper-dive through different use cases and topics?
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#### RAG
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*[Agentic RAG](https://github.com/redis-developer/agentic-rag) - A tutorial focused on agentic RAG with LlamaIndex and Amazon Bedrock
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*[RAG on Vertex AI](https://github.com/redis-developer/gcp-redis-llm-stack/tree/main) - A RAG tutorial featuring Redis with Vertex AI
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*[RAG workbench](https://github.com/redis-developer/redis-rag-workbench) - A development playground for exploring RAG techniques with Redis
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#### Recommendation system
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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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*[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
features in [Redis Query Engine]({{< relref "/develop/interact/search-and-query" >}}).
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Although there are some similarities between these two features, the data type is intended
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for simpler use cases and doesn't have the range of format options and queries
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available in Redis Query Engine.
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{{< /note >}}
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## Basic commands
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*[`GEOADD`]({{< relref "/commands/geoadd" >}}) adds a location to a given geospatial index (note that longitude comes before latitude with this command).
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