Demos | Recipes | Tutorials | Integrations | Content | Benchmarks | Docs
No faster way to get started than by diving in and playing around with a demo.
| Demo | Description |
|---|---|
| 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. |
| ArxivChatGuru | Streamlit demo of RAG over Arxiv documents with Redis & OpenAI |
| Redis VSS - Simple Streamlit Demo | Streamlit demo of Redis Vector Search |
| ArXiv Search | Full stack implementation of Redis with React FE |
| Product Search | Vector search with Redis Stack and Redis Enterprise |
Need quickstarts to begin your Redis AI journey? Start here.
| Recipe | Description |
|---|---|
| /redis-intro/00_redis_intro.ipynb | The place to start if brand new to Redis |
| /vector-search/00_redispy.ipynb | Vector search with Redis python client |
| /vector-search/01_redisvl.ipynb | Vector search with Redis Vector Library |
| /vector-search/02_hybrid_search.ipynb | Hybrid search techniques with Redis (BM25 + Vector) |
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.
To get started with RAG, either from scratch or using a popular framework like Llamaindex or LangChain, go with these recipes:
| Recipe | Description |
|---|---|
| /RAG/01_redisvl.ipynb | RAG from scratch with the Redis Vector Library |
| /RAG/02_langchain.ipynb | RAG using Redis and LangChain |
| /RAG/03_llamaindex.ipynb | RAG using Redis and LlamaIndex |
| /RAG/04_advanced_redisvl.ipynb | Advanced RAG techniques |
| /RAG/05_nvidia_ai_rag_redis.ipynb | RAG using Redis and Nvidia NIMs |
| /RAG/06_ragas_evaluation.ipynb | Utilize the RAGAS framework to evaluate RAG performance |
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.
| Recipe | Description |
|---|---|
| /llm-session-manager/00_session_manager.ipynb | LLM session manager with semantic similarity |
| /llm-session-manager/01_multiple_sessions.ipynb | Handle multiple simultaneous chats with one instance |
An estimated 31% of LLM queries are potentially redundant (source). Redis enables semantic caching to help cut down on LLM costs quickly.
| Recipe | Description |
|---|---|
| /semantic-cache/doc2cache_llama3_1.ipynb | Build a semantic cache using the Doc2Cache framework and Llama3.1 |
| /semantic-cache/semantic_caching_gemini.ipynb | Build a semantic cache with Redis and Google Gemini |
| Recipe | Description |
|---|---|
| /agents/00_langgraph_redis_agentic_rag.ipynb | Notebook to get started with lang-graph and agents |
| /agents/01_crewai_langgraph_redis.ipynb | Notebook to get started with lang-graph and agents |
| Recipe | Description |
|---|---|
| /recommendation-systems/00_content_filtering.ipynb | Intro content filtering example with redisvl |
| /recommendation-systems/01_collaborative_filtering.ipynb | Intro collaborative filtering example with redisvl |
Need a deeper-dive through different use cases and topics?
| Tutorial | Description |
|---|---|
| RAG on VertexAI | A RAG tutorial featuring Redis with Vertex AI |
| Agentic RAG | A tutorial focused on agentic RAG with LlamaIndex and Cohere |
| Recommendation Systems w/ NVIDIA Merlin & Redis | Three examples, each escalating in complexity, showcasing the process of building a realtime recsys with NVIDIA and Redis |
Redis integrates with many different players in the AI ecosystem. Here's a curated list below:
| Integration | Description |
|---|---|
| RedisVL | A dedicated Python client lib for Redis as a Vector DB |
| AWS Bedrock | Streamlines GenAI deployment by offering foundational models as a unified API |
| LangChain Python | Popular Python client lib for building LLM applications powered by Redis |
| LangChain JS | Popular JS client lib for building LLM applications powered by Redis |
| LlamaIndex | LlamaIndex Integration for Redis as a vector Database (formerly GPT-index) |
| LiteLLM | Popular LLM proxy layer to help manage and streamline usage of multiple foundation models |
| Semantic Kernel | Popular lib by MSFT to integrate LLMs with plugins |
| RelevanceAI | Platform to tag, search and analyze unstructured data faster, built on Redis |
| DocArray | DocArray Integration of Redis as a VectorDB by Jina AI |
- Vector Similarity Search: From Basics to Production - Introductory blog post to VSS and Redis as a VectorDB.
- Improving RAG quality with RAGAs
- Level-up RAG with RedisVL
- Vector Databases and Large Language Models - Talk given at LLMs in Production Part 1 by Sam Partee.
- Vector Databases and AI-powered Search Talk - Video "Vector Databases and AI-powered Search" given by Sam Partee at SDSC 2023.
- Real-Time Product Recommendations - Content-based recsys design with Redis and DocArray.
- NVIDIA Recsys with Redis
- LabLab AI Redis Tech Page
- Storing and querying for embeddings with Redis
- Building Intelligent Apps with Redis Vector Similarity Search
- RedisDays Trading Signals - Video "Using AI to Reveal Trading Signals Buried in Corporate Filings".
- Benchmarking results for vector databases - Benchmarking results for vector databases, including Redis and 7 other Vector Database players.
- ANN Benchmarks - Standard ANN Benchmarks site. Only using single Redis OSS instance/client.
- Redis Vector Library Docs
- Redis Vector Database QuickStart
- Redis Vector Similarity Docs - Official Redis literature for Vector Similarity Search.
- Redis-py Search Docs - Redis-py client library docs for RediSearch.
- Redis-py General Docs - Redis-py client library documentation.
- Redis Stack - Redis Stack documentation.
- Redis Clients - Redis client list.