(KNN))Vector Range Search (e.g. find all vectors within a radius of a query vector)Incremental indexing without performance lossDocument ranking (using tf-idf, with optional user-provided weights)Field weightingComplex boolean queries with AND, OR, and NOT operatorsPrefix matching, fuzzy matching, and exact-phrase queriesSupport for double-metaphone phonetic matchingAuto-complete suggestions (with fuzzy prefix suggestions)Stemming-based query expansion in many languages (using Snowball)Support for Chinese-language tokenization and querying (using Friso)Numeric filters and rangesGeospatial searches using Redis geospatial indexingA powerful aggregations engineSupports for all utf-8 encoded textRetrieve full documents, selected fields, or only the document IDsSorting results (for example, by creation date)Clientsâ€‹Since redis is much more than just a vector database, there are often use cases that demand usage of a Redis client besides just the langchain integration. You can use any standard Redis client library to run Search and Query commands, but it's easiest to use a library that wraps the Search and Query API. Below are a few examples, but you can find more client libraries here.ProjectLanguageLicenseAuthorStarsjedisJavaMITRedisredisvlPythonMITRedisredis-pyPythonMITRedisnode-redisNode.jsMITRedisnredisstack.NETMITRedisDeployment Optionsâ€‹There are many ways to deploy Redis with RediSearch. The easiest way to get started is to use Docker, but there are are many potential options for deployment such asRedis CloudDocker (Redis Stack)Cloud marketplaces: AWS Marketplace, Google Marketplace, or Azure MarketplaceOn-premise: Redis Enterprise SoftwareKubernetes: Redis Enterprise Software on KubernetesExamplesâ€‹Many examples can be found in the Redis AI team's GitHubAwesome Redis AI Resources - List of examples of using Redis in AI workloadsAzure OpenAI Embeddings Q&A - OpenAI and Redis as a Q&A service on Azure.ArXiv Paper Search - Semantic search over arXiv scholarly
Redis vector database introduction and langchain integration guide.
Redis vector database introduction and langchain integration guide. ->: (KNN))Vector Range Search (e.g. find all vectors within a radius of a query vector)Incremental indexing without performance lossDocument ranking (using tf-idf, with optional user-provided weights)Field weightingComplex boolean queries with AND, OR, and NOT operatorsPrefix matching, fuzzy matching, and exact-phrase queriesSupport for double-metaphone phonetic matchingAuto-complete suggestions (with fuzzy prefix suggestions)Stemming-based query expansion in many languages (using Snowball)Support for Chinese-language tokenization and querying (using Friso)Numeric filters and rangesGeospatial searches using Redis geospatial indexingA powerful aggregations engineSupports for all utf-8 encoded textRetrieve full documents, selected fields, or only the document IDsSorting results (for example, by creation date)Clientsâ€‹Since redis is much more than just a vector database, there are often use cases that demand usage of a Redis client besides just the langchain integration. You can use any standard Redis client library to run Search and Query commands, but it's easiest to use a library that wraps the Search and Query API. Below are a few examples, but you can find more client libraries here.ProjectLanguageLicenseAuthorStarsjedisJavaMITRedisredisvlPythonMITRedisredis-pyPythonMITRedisnode-redisNode.jsMITRedisnredisstack.NETMITRedisDeployment Optionsâ€‹There are many ways to deploy Redis with RediSearch. The easiest way to get started is to use Docker, but there are are many potential options for deployment such asRedis CloudDocker (Redis Stack)Cloud marketplaces: AWS Marketplace, Google Marketplace, or Azure MarketplaceOn-premise: Redis Enterprise SoftwareKubernetes: Redis Enterprise Software on KubernetesExamplesâ€‹Many examples can be found in the Redis AI team's GitHubAwesome Redis AI Resources - List of examples of using Redis in AI workloadsAzure OpenAI Embeddings Q&A - OpenAI and Redis as a Q&A service on Azure.ArXiv Paper Search - Semantic search over arXiv scholarly