">`AnalyticDB for PostgreSQL` is developed based on the open-source `Greenplum Database` project and is enhanced with in-depth extensions by `Alibaba Cloud`. AnalyticDB for PostgreSQL is compatible with the ANSI SQL 2003 syntax and the PostgreSQL and Oracle database ecosystems. AnalyticDB for PostgreSQL also supports row store and column store. AnalyticDB for PostgreSQL processes petabytes of data offline at a high performance level and supports highly concurrent online queries.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"This notebook shows how to use functionality related to the `AnalyticDB` vector database.\n",
"To run, you should have an [AnalyticDB](https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview) instance up and running:\n",
"\n",
"- Using [AnalyticDB Cloud Vector Database](https://www.alibabacloud.com/product/hybriddb-postgresql). Click here to fast deploy it."
"> [Annoy](https://github.com/spotify/annoy) (`Approximate Nearest Neighbors Oh Yeah`) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"This notebook shows how to use functionality related to the `Annoy` vector database."
">Usually `Apache Doris` is categorized into OLAP, and it has showed excellent performance in [ClickBench — a Benchmark For Analytical DBMS](https://benchmark.clickhouse.com/). Since it has a super-fast vectorized execution engine, it could also be used as a fast vectordb.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"Here we'll show how to use the Apache Doris Vector Store."
">[Atlas](https://docs.nomic.ai/index.html) is a platform by Nomic made for interacting with both small and internet scale unstructured datasets. It enables anyone to visualize, search, and share massive datasets in their browser.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"This notebook shows you how to use functionality related to the `AtlasDB` vectorstore."
"[Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) (formerly known as `Azure Search` and `Azure Cognitive Search`) is a cloud search service that gives developers infrastructure, APIs, and tools for information retrieval of vector, keyword, and hybrid queries at scale.\n"
"[Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) (formerly known as `Azure Search` and `Azure Cognitive Search`) is a cloud search service that gives developers infrastructure, APIs, and tools for information retrieval of vector, keyword, and hybrid queries at scale.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration"
">This database service supports a diverse range of index types and similarity calculation methods, catering to various use cases. A standout feature of VectorDB is its capacity to manage an immense vector scale of up to 10 billion, while maintaining impressive query performance, supporting millions of queries per second (QPS) with millisecond-level query latency.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"This notebook shows how to use functionality related to the Baidu VectorDB. \n",
"\n",
"To run, you should have a [Database instance.](https://cloud.baidu.com/doc/VDB/s/hlrsoazuf)."
"> [ClickHouse](https://clickhouse.com/) is the fastest and most resource efficient open-source database for real-time apps and analytics with full SQL support and a wide range of functions to assist users in writing analytical queries. Lately added data structures and distance search functions (like `L2Distance`) as well as [approximate nearest neighbor search indexes](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) enable ClickHouse to be used as a high performance and scalable vector database to store and search vectors with SQL.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"This notebook shows how to use functionality related to the `ClickHouse` vector search."
">[DingoDB](https://dingodb.readthedocs.io/en/latest/) is a distributed multi-mode vector database, which combines the characteristics of data lakes and vector databases, and can store data of any type and size (Key-Value, PDF, audio, video, etc.). It has real-time low-latency processing capabilities to achieve rapid insight and response, and can efficiently conduct instant analysis and process multi-modal data.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"This notebook shows how to use functionality related to the DingoDB vector database.\n",
"\n",
"To run, you should have a [DingoDB instance up and running](https://github.com/dingodb/dingo-deploy/blob/main/README.md)."
">[DocArrayHnswSearch](https://docs.docarray.org/user_guide/storing/index_hnswlib/) is a lightweight Document Index implementation provided by [Docarray](https://github.com/docarray/docarray) that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in [hnswlib](https://github.com/nmslib/hnswlib), and stores all other data in [SQLite](https://www.sqlite.org/index.html).\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"This notebook shows how to use functionality related to the `DocArrayHnswSearch`."
">[China Mobile ECloud VectorSearch](https://ecloud.10086.cn/portal/product/elasticsearch) is a fully managed, enterprise-level distributed search and analysis service. China Mobile ECloud VectorSearch provides low-cost, high-performance, and reliable retrieval and analysis platform level product services for structured/unstructured data. As a vector database , it supports multiple index types and similarity distance methods. \n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"This notebook shows how to use functionality related to the `ECloud ElasticSearch VectorStore`.\n",
"To run, you should have an [China Mobile ECloud VectorSearch](https://ecloud.10086.cn/portal/product/elasticsearch) instance up and running:\n",
">[Epsilla](https://www.epsilla.com) is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"This notebook shows how to use the functionalities related to the `Epsilla` vector database.\n",
"\n",
"As a prerequisite, you need to have a running Epsilla vector database (for example, through our docker image), and install the ``pyepsilla`` package. View full docs at [docs](https://epsilla-inc.gitbook.io/epsilladb/quick-start)."
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"This notebook shows how to use functionality related to the `FAISS` vector database. It will show functionality specific to this integration. After going through, it may be useful to explore [relevant use-case pages](/docs/how_to#qa-with-rag) to learn how to use this vectorstore as part of a larger chain."
">[LLMRails](https://www.llmrails.com/) is a API platform for building GenAI applications. It provides an easy-to-use API for document indexing and querying that is managed by LLMRails and is optimized for performance and accuracy. \n",
"See the [LLMRails API documentation ](https://docs.llmrails.com/) for more information on how to use the API.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"This notebook shows how to use functionality related to the `LLMRails`'s integration with langchain.\n",
"Note that unlike many other integrations in this category, LLMRails provides an end-to-end managed service for retrieval augmented generation, which includes:\n",
"1. A way to extract text from document files and chunk them into sentences.\n",
">[Marqo](https://www.marqo.ai/) is an open-source vector search engine. Marqo allows you to store and query multi-modal data such as text and images. Marqo creates the vectors for you using a huge selection of open-source models, you can also provide your own fine-tuned models and Marqo will handle the loading and inference for you.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"To run this notebook with our docker image please run the following commands first to get Marqo:\n",
"> You can [self-host Meilisearch](https://www.meilisearch.com/docs/learn/getting_started/installation#local-installation) or run on [Meilisearch Cloud](https://www.meilisearch.com/pricing).\n",
"\n",
"Meilisearch v1.3 supports vector search. This page guides you through integrating Meilisearch as a vector store and using it to perform vector search."
"Meilisearch v1.3 supports vector search. This page guides you through integrating Meilisearch as a vector store and using it to perform vector search.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration"
">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database available in AWS, Azure, and GCP. It now has support for native Vector Search on your MongoDB document data.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"This notebook shows how to use [MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search) to store your embeddings in MongoDB documents, create a vector search index, and perform KNN search with an approximate nearest neighbor algorithm (`Hierarchical Navigable Small Worlds`). It uses the [$vectorSearch MQL Stage](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/). \n",
"We will connect to the index using a `VectorStore` client, which implements the `similarity_search` function to retrieve matching documents.\n",
"\n",
"The basic pipeline used in this document allows to effortlessly build a simple vector index of files stored in a cloud location. However, Pathway provides everything needed to build realtime data pipelines and apps, including SQL-like able operations such as groupby-reductions and joins between disparate data sources, time-based grouping and windowing of data, and a wide array of connectors.\n"
"The basic pipeline used in this document allows to effortlessly build a simple vector index of files stored in a cloud location. However, Pathway provides everything needed to build realtime data pipelines and apps, including SQL-like able operations such as groupby-reductions and joins between disparate data sources, time-based grouping and windowing of data, and a wide array of connectors.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration"
">[Rockset](https://rockset.com/) is a real-time search and analytics database built for the cloud. Rockset uses a [Converged Index™](https://rockset.com/blog/converged-indexing-the-secret-sauce-behind-rocksets-fast-queries/) with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data.\n",
"\n",
"This notebook demonstrates how to use`Rockset` as a vector store in LangChain. Before getting started, make sure you have access to a `Rockset` account and an API key available. [Start your free trial today.](https://rockset.com/create/)\n"
"This notebook demonstrates how to use`Rockset` as a vector store in LangChain. Before getting started, make sure you have access to a `Rockset` account and an API key available. [Start your free trial today.](https://rockset.com/create/)\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration"
">[SAP HANA Cloud Vector Engine](https://www.sap.com/events/teched/news-guide/ai.html#article8) is a vector store fully integrated into the `SAP HANA Cloud` database."
">[SAP HANA Cloud Vector Engine](https://www.sap.com/events/teched/news-guide/ai.html#article8) is a vector store fully integrated into the `SAP HANA Cloud` database.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration"
"ScaNN (Scalable Nearest Neighbors) is a method for efficient vector similarity search at scale.\n",
"\n",
"ScaNN includes search space pruning and quantization for Maximum Inner Product Search and also supports other distance functions such as Euclidean distance. The implementation is optimized for x86 processors with AVX2 support. See its [Google Research github](https://github.com/google-research/google-research/tree/master/scann) for more details."
"ScaNN includes search space pruning and quantization for Maximum Inner Product Search and also supports other distance functions such as Euclidean distance. The implementation is optimized for x86 processors with AVX2 support. See its [Google Research github](https://github.com/google-research/google-research/tree/master/scann) for more details.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration"
"The full documentation of the API along with examples and an interactive playground is available on [RapidAPI](https://rapidapi.com/semafind-semadb/api/semadb).\n",
"\n",
"This notebook demonstrates usage of the `SemaDB Cloud` vector store."
"This notebook demonstrates usage of the `SemaDB Cloud` vector store.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration"
"What sets SingleStoreDB apart is its ability to combine vector and full-text searches in various ways, offering flexibility and versatility. Whether prefiltering by text or vector similarity and selecting the most relevant data, or employing a weighted sum approach to compute a final similarity score, developers have multiple options at their disposal.\n",
"\n",
"In essence, SingleStoreDB provides a comprehensive solution for managing and querying vector data, offering unparalleled performance and flexibility for AI-driven applications."
"In essence, SingleStoreDB provides a comprehensive solution for managing and querying vector data, offering unparalleled performance and flexibility for AI-driven applications.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration"
">[scikit-learn](https://scikit-learn.org/stable/) is an open-source collection of machine learning algorithms, including some implementations of the [k nearest neighbors](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html). `SKLearnVectorStore` wraps this implementation and adds the possibility to persist the vector store in json, bson (binary json) or Apache Parquet format.\n",
"\n",
"This notebook shows how to use the `SKLearnVectorStore` vector database."
"This notebook shows how to use the `SKLearnVectorStore` vector database.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration"
">[SQLite-VSS](https://alexgarcia.xyz/sqlite-vss/) is an `SQLite` extension designed for vector search, emphasizing local-first operations and easy integration into applications without external servers. Leveraging the `Faiss` library, it offers efficient similarity search and clustering capabilities.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"This notebook shows how to use the `SQLiteVSS` vector database."
"See the [Vectara API documentation](https://docs.vectara.com/docs/) for more information on how to use the API.\n",
"\n",
"This notebook shows how to use the basic retrieval functionality, when utilizing Vectara just as a Vector Store (without summarization), incuding: `similarity_search` and `similarity_search_with_score` as well as using the LangChain `as_retriever` functionality."
"This notebook shows how to use the basic retrieval functionality, when utilizing Vectara just as a Vector Store (without summarization), incuding: `similarity_search` and `similarity_search_with_score` as well as using the LangChain `as_retriever` functionality.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration"
"VLite is a simple and blazing fast vector database that allows you to store and retrieve data semantically using embeddings. Made with numpy, vlite is a lightweight batteries-included database to implement RAG, similarity search, and embeddings into your projects.\n",
"\n",
"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
"\n",
"## Installation\n",
"\n",
"To use the VLite in LangChain, you need to install the `vlite` package:\n",