Search - Semantic search over arXiv scholarly papersVector Search on Azure - Vector search on Azure using Azure Cache for Redis and Azure OpenAIMore Resourcesâ€‹For more information on how to use Redis as a vector database, check out the following resources:RedisVL Documentation - Documentation for the Redis Vector Library ClientRedis Vector Similarity Docs - Redis official docs for Vector Search.Redis-py Search Docs - Documentation for redis-py client libraryVector Similarity Search: From Basics to Production - Introductory blog post to VSS and Redis as a VectorDB.Install Redis Python Clientâ€‹Redis-py is the officially supported client by Redis. Recently released is the RedisVL client which is purpose-built for the Vector Database use cases. Both can be installed with pip.pip install redis redisvl openai tiktokenWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")from langchain.embeddings import OpenAIEmbeddingsembeddings = OpenAIEmbeddings()Sample Dataâ€‹First we will describe some sample data so that the various attributes of the Redis vector store can be demonstrated.metadata = [    {        "user": "john",        "age": 18,        "job": "engineer",        "credit_score": "high",    },    {        "user": "derrick",        "age": 45,        "job": "doctor",        "credit_score": "low",    },    {        "user": "nancy",        "age": 94,        "job": "doctor",        "credit_score": "high",    },    {        "user": "tyler",        "age": 100,        "job": "engineer",        "credit_score": "high",    },    {        "user": "joe",        "age": 35,        "job": "dentist",        "credit_score": "medium",    },]texts = ["foo", "foo", "foo", "bar", "bar"]Initializing Redisâ€‹To locally deploy Redis, run:docker run -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latestIf things are running correctly you should see a nice Redis UI at http://localhost:8001.
Redis vector database introduction and langchain integration guide.
Redis vector database introduction and langchain integration guide. ->: Search - Semantic search over arXiv scholarly papersVector Search on Azure - Vector search on Azure using Azure Cache for Redis and Azure OpenAIMore Resourcesâ€‹For more information on how to use Redis as a vector database, check out the following resources:RedisVL Documentation - Documentation for the Redis Vector Library ClientRedis Vector Similarity Docs - Redis official docs for Vector Search.Redis-py Search Docs - Documentation for redis-py client libraryVector Similarity Search: From Basics to Production - Introductory blog post to VSS and Redis as a VectorDB.Install Redis Python Clientâ€‹Redis-py is the officially supported client by Redis. Recently released is the RedisVL client which is purpose-built for the Vector Database use cases. Both can be installed with pip.pip install redis redisvl openai tiktokenWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")from langchain.embeddings import OpenAIEmbeddingsembeddings = OpenAIEmbeddings()Sample Dataâ€‹First we will describe some sample data so that the various attributes of the Redis vector store can be demonstrated.metadata = [    {        "user": "john",        "age": 18,        "job": "engineer",        "credit_score": "high",    },    {        "user": "derrick",        "age": 45,        "job": "doctor",        "credit_score": "low",    },    {        "user": "nancy",        "age": 94,        "job": "doctor",        "credit_score": "high",    },    {        "user": "tyler",        "age": 100,        "job": "engineer",        "credit_score": "high",    },    {        "user": "joe",        "age": 35,        "job": "dentist",        "credit_score": "medium",    },]texts = ["foo", "foo", "foo", "bar", "bar"]Initializing Redisâ€‹To locally deploy Redis, run:docker run -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latestIf things are running correctly you should see a nice Redis UI at http://localhost:8001.