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595 | https://python.langchain.com/docs/integrations/text_embedding/fake | ComponentsText embedding modelsFake EmbeddingsFake EmbeddingsLangChain also provides a fake embedding class. You can use this to test your pipelines.from langchain.embeddings import FakeEmbeddingsembeddings = FakeEmbeddings(size=1352)query_result = embeddings.embed_query("foo")doc_results = embeddings.embed_documents([... |
596 | https://python.langchain.com/docs/integrations/text_embedding/google_vertex_ai_palm | ComponentsText embedding modelsGoogle Vertex AI PaLMGoogle Vertex AI PaLMVertex AI PaLM API is a service on Google Cloud exposing the embedding models. Note: This integration is seperate from the Google PaLM integration.By default, Google Cloud does not use Customer Data to train its foundation models as part of Google... |
597 | https://python.langchain.com/docs/integrations/text_embedding/gpt4all | ComponentsText embedding modelsGPT4AllOn this pageGPT4AllGPT4All is a free-to-use, locally running, privacy-aware chatbot. There is no GPU or internet required. It features popular models and its own models such as GPT4All Falcon, Wizard, etc.This notebook explains how to use GPT4All embeddings with LangChain.Install G... |
598 | https://python.langchain.com/docs/integrations/text_embedding/gradient | ComponentsText embedding modelsGradientOn this pageGradientGradient allows to create Embeddings as well fine tune and get completions on LLMs with a simple web API.This notebook goes over how to use Langchain with Embeddings of Gradient.Importsfrom langchain.embeddings import GradientEmbeddingsSet the Environment API ... |
599 | https://python.langchain.com/docs/integrations/text_embedding/huggingfacehub | ComponentsText embedding modelsHugging FaceOn this pageHugging FaceLet's load the Hugging Face Embedding class.pip install langchain sentence_transformersfrom langchain.embeddings import HuggingFaceEmbeddingsembeddings = HuggingFaceEmbeddings()text = "This is a test document."query_result = embeddings.embed_query(text)... |
600 | https://python.langchain.com/docs/integrations/text_embedding/instruct_embeddings | ComponentsText embedding modelsInstructEmbeddingsInstructEmbeddingsLet's load the HuggingFace instruct Embeddings class.from langchain.embeddings import HuggingFaceInstructEmbeddingsembeddings = HuggingFaceInstructEmbeddings( query_instruction="Represent the query for retrieval: ") load INSTRUCTOR_Transformer ... |
601 | https://python.langchain.com/docs/integrations/text_embedding/jina | ComponentsText embedding modelsJinaJinaLet's load the Jina Embedding class.from langchain.embeddings import JinaEmbeddingsembeddings = JinaEmbeddings( jina_auth_token=jina_auth_token, model_name="ViT-B-32::openai")text = "This is a test document."query_result = embeddings.embed_query(text)doc_result = embeddings.emb... |
602 | https://python.langchain.com/docs/integrations/text_embedding/llamacpp | ComponentsText embedding modelsLlama-cppLlama-cppThis notebook goes over how to use Llama-cpp embeddings within LangChainpip install llama-cpp-pythonfrom langchain.embeddings import LlamaCppEmbeddingsllama = LlamaCppEmbeddings(model_path="/path/to/model/ggml-model-q4_0.bin")text = "This is a test document."query_result... |
603 | https://python.langchain.com/docs/integrations/text_embedding/llm_rails | ComponentsText embedding modelsLLMRailsLLMRailsLet's load the LLMRails Embeddings class.To use LLMRails embedding you need to pass api key by argument or set it in environment with LLM_RAILS_API_KEY key.
To gey API Key you need to sign up in https://console.llmrails.com/signup and then go to https://console.llmrails.co... |
604 | https://python.langchain.com/docs/integrations/text_embedding/localai | ComponentsText embedding modelsLocalAILocalAILet's load the LocalAI Embedding class. In order to use the LocalAI Embedding class, you need to have the LocalAI service hosted somewhere and configure the embedding models. See the documentation at https://localai.io/basics/getting_started/index.html and https://localai.io... |
605 | https://python.langchain.com/docs/integrations/text_embedding/minimax | ComponentsText embedding modelsMiniMaxMiniMaxMiniMax offers an embeddings service.This example goes over how to use LangChain to interact with MiniMax Inference for text embedding.import osos.environ["MINIMAX_GROUP_ID"] = "MINIMAX_GROUP_ID"os.environ["MINIMAX_API_KEY"] = "MINIMAX_API_KEY"from langchain.embeddings impor... |
606 | https://python.langchain.com/docs/integrations/text_embedding/modelscope_hub | ComponentsText embedding modelsModelScopeModelScopeModelScope is big repository of the models and datasets.Let's load the ModelScope Embedding class.from langchain.embeddings import ModelScopeEmbeddingsmodel_id = "damo/nlp_corom_sentence-embedding_english-base"embeddings = ModelScopeEmbeddings(model_id=model_id)text = ... |
607 | https://python.langchain.com/docs/integrations/text_embedding/mosaicml | ComponentsText embedding modelsMosaicMLMosaicMLMosaicML offers a managed inference service. You can either use a variety of open source models, or deploy your own.This example goes over how to use LangChain to interact with MosaicML Inference for text embedding.# sign up for an account: https://forms.mosaicml.com/demo?... |
608 | https://python.langchain.com/docs/integrations/text_embedding/nlp_cloud | ComponentsText embedding modelsNLP CloudNLP CloudNLP Cloud is an artificial intelligence platform that allows you to use the most advanced AI engines, and even train your own engines with your own data. The embeddings endpoint offers the following model:paraphrase-multilingual-mpnet-base-v2: Paraphrase Multilingual MPN... |
609 | https://python.langchain.com/docs/integrations/text_embedding/ollama | ComponentsText embedding modelsOllamaOllamaLet's load the Ollama Embeddings class.from langchain.embeddings import OllamaEmbeddingsembeddings = OllamaEmbeddings()text = "This is a test document."To generate embeddings, you can either query an invidivual text, or you can query a list of texts.query_result = embeddings.e... |
610 | https://python.langchain.com/docs/integrations/text_embedding/openai | ComponentsText embedding modelsOpenAIOpenAILet's load the OpenAI Embedding class.from langchain.embeddings import OpenAIEmbeddingsembeddings = OpenAIEmbeddings()text = "This is a test document."query_result = embeddings.embed_query(text)query_result[:5] [-0.003186025367556387, 0.011071979803637493, -0.004020... |
611 | https://python.langchain.com/docs/integrations/text_embedding/sagemaker-endpoint | ComponentsText embedding modelsSageMakerSageMakerLet's load the SageMaker Endpoints Embeddings class. The class can be used if you host, e.g. your own Hugging Face model on SageMaker.For instructions on how to do this, please see here. Note: In order to handle batched requests, you will need to adjust the return line i... |
612 | https://python.langchain.com/docs/integrations/text_embedding/self-hosted | ComponentsText embedding modelsSelf HostedSelf HostedLet's load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes.from langchain.embeddings import ( SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, SelfHostedHuggingFaceInstructEmbeddings,)impor... |
613 | https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers | ComponentsText embedding modelsSentence TransformersSentence TransformersSentenceTransformers embeddings are called using the HuggingFaceEmbeddings integration. We have also added an alias for SentenceTransformerEmbeddings for users who are more familiar with directly using that package.SentenceTransformers is a python... |
614 | https://python.langchain.com/docs/integrations/text_embedding/spacy_embedding | ComponentsText embedding modelsSpaCyOn this pageSpaCyspaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython.Installation and Setup#!pip install spacyImport the necessary classesfrom langchain.embeddings.spacy_embeddings import SpacyEmbe... |
615 | https://python.langchain.com/docs/integrations/text_embedding/tensorflowhub | ComponentsText embedding modelsTensorflowHubTensorflowHubLet's load the TensorflowHub Embedding class.from langchain.embeddings import TensorflowHubEmbeddingsembeddings = TensorflowHubEmbeddings() 2023-01-30 23:53:01.652176: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized wit... |
616 | https://python.langchain.com/docs/integrations/text_embedding/xinference | ComponentsText embedding modelsXorbits inference (Xinference)On this pageXorbits inference (Xinference)This notebook goes over how to use Xinference embeddings within LangChainInstallationInstall Xinference through PyPI:%pip install "xinference[all]"Deploy Xinference Locally or in a Distributed Cluster.For local depl... |
617 | https://python.langchain.com/docs/integrations/vectorstores | ComponentsVector storesVector stores📄️ Activeloop Deep LakeActiveloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddin... |
618 | https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake | ComponentsVector storesActiveloop Deep LakeOn this pageActiveloop Deep LakeActiveloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search inc... |
619 | https://python.langchain.com/docs/integrations/vectorstores/alibabacloud_opensearch | ComponentsVector storesAlibaba Cloud OpenSearchAlibaba Cloud OpenSearchAlibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thous... |
620 | https://python.langchain.com/docs/integrations/vectorstores/analyticdb | ComponentsVector storesAnalyticDBAnalyticDBAnalyticDB for PostgreSQL is a massively parallel processing (MPP) data warehousing service that is designed to analyze large volumes of data online.AnalyticDB for PostgreSQL is developed based on the open source Greenplum Database project and is enhanced with in-depth extensi... |
621 | https://python.langchain.com/docs/integrations/vectorstores/annoy | ComponentsVector storesAnnoyOn this pageAnnoyAnnoy (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 t... |
622 | https://python.langchain.com/docs/integrations/vectorstores/atlas | ComponentsVector storesAtlasOn this pageAtlasAtlas 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.This notebook shows you how to use functionality related to the AtlasDB vectorstore... |
623 | https://python.langchain.com/docs/integrations/vectorstores/awadb | ComponentsVector storesAwaDBOn this pageAwaDBAwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.This notebook shows how to use functionality related to the AwaDB.pip install awadbfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores imp... |
624 | https://python.langchain.com/docs/integrations/vectorstores/azuresearch | ComponentsVector storesAzure Cognitive SearchOn this pageAzure Cognitive SearchAzure Cognitive Search (formerly known as Azure Search) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise ... |
625 | https://python.langchain.com/docs/integrations/vectorstores/bageldb | ComponentsVector storesBagelDBOn this pageBagelDBBagelDB (Open Vector Database for AI), is like GitHub for AI data.
It is a collaborative platform where users can create,
share, and manage vector datasets. It can support private projects for independent developers,
internal collaborations for enterprises, and public co... |
626 | https://python.langchain.com/docs/integrations/vectorstores/cassandra | ComponentsVector storesCassandraOn this pageCassandraApache Cassandra® is a NoSQL, row-oriented, highly scalable and highly available database.Newest Cassandra releases natively support Vector Similarity Search.To run this notebook you need either a running Cassandra cluster equipped with Vector Search capabilities (in... |
627 | https://python.langchain.com/docs/integrations/vectorstores/chroma | ComponentsVector storesChromaOn this pageChromaChroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.Install Chroma with:pip install chromadbChroma runs in various modes. See below for examples of each integrated with LangChain.in-memory -... |
628 | https://python.langchain.com/docs/integrations/vectorstores/clarifai | ComponentsVector storesClarifaiOn this pageClarifaiClarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. This notebook shows how to use functi... |
629 | https://python.langchain.com/docs/integrations/vectorstores/clickhouse | ComponentsVector storesClickHouseOn this pageClickHouseClickHouse 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... |
630 | https://python.langchain.com/docs/integrations/vectorstores/dashvector | ComponentsVector storesDashVectorOn this pageDashVectorDashVector is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.This notebook shows how to use func... |
631 | https://python.langchain.com/docs/integrations/vectorstores/dingo | ComponentsVector storesDingoOn this pageDingoDingo 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 insig... |
632 | https://python.langchain.com/docs/integrations/vectorstores/docarray_hnsw | ComponentsVector storesDocArray HnswSearchOn this pageDocArray HnswSearchDocArrayHnswSearch is a lightweight Document Index implementation provided by Docarray that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite.This n... |
633 | https://python.langchain.com/docs/integrations/vectorstores/docarray_in_memory | ComponentsVector storesDocArray InMemorySearchOn this pageDocArray InMemorySearchDocArrayInMemorySearch is a document index provided by Docarray that stores documents in memory. It is a great starting point for small datasets, where you may not want to launch a database server.This notebook shows how to use functionali... |
634 | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch | ComponentsVector storesElasticsearchOn this pageElasticsearchElasticsearch is a distributed, RESTful search and analytics engine, capable of performing both vector and lexical search. It is built on top of the Apache Lucene library. This notebook shows how to use functionality related to the Elasticsearch database.pip ... |
635 | https://python.langchain.com/docs/integrations/vectorstores/epsilla | ComponentsVector storesEpsillaEpsillaEpsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0.This notebook shows how to use the functionalities related to the Epsilla vector database.As a prerequisite, you need to h... |
636 | https://python.langchain.com/docs/integrations/vectorstores/faiss | ComponentsVector storesFaissOn this pageFaissFacebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation ... |
637 | https://python.langchain.com/docs/integrations/vectorstores/hologres | ComponentsVector storesHologresHologresHologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time.
Hologres supports standard SQL syntax, is compatible with PostgreSQL, and supports most PostgreSQL f... |
638 | https://python.langchain.com/docs/integrations/vectorstores/lancedb | ComponentsVector storesLanceDBLanceDBLanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings. Fully open source.This notebook shows how to use functionality related to the LanceDB vector database based on the Lance da... |
639 | https://python.langchain.com/docs/integrations/vectorstores/llm_rails | ComponentsVector storesLLMRailsOn this pageLLMRailsLLMRails 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.
See the LLMRails API documentation for more information on how to u... |
640 | https://python.langchain.com/docs/integrations/vectorstores/marqo | ComponentsVector storesMarqoOn this pageMarqoThis notebook shows how to use functionality related to the Marqo vectorstore.Marqo is an open-source vector search engine. Marqo allows you to store and query multimodal data such as text and images. Marqo creates the vectors for you using a huge selection of opensource mod... |
641 | https://python.langchain.com/docs/integrations/vectorstores/matchingengine | ComponentsVector storesGoogle Vertex AI MatchingEngineOn this pageGoogle Vertex AI MatchingEngineThis notebook shows how to use functionality related to the GCP Vertex AI MatchingEngine vector database.Vertex AI Matching Engine provides the industry's leading high-scale low latency vector database. These vector databas... |
642 | https://python.langchain.com/docs/integrations/vectorstores/meilisearch | ComponentsVector storesMeilisearchOn this pageMeilisearchMeilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. You can self-host Meilisearch or run on Meilisearch Cloud.Meilisearch v1.3 supports vector search. Th... |
643 | https://python.langchain.com/docs/integrations/vectorstores/milvus | ComponentsVector storesMilvusOn this pageMilvusMilvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.This notebook shows how to use functionality related to the Milvus vector database.To run, you should have a Milvus ins... |
644 | https://python.langchain.com/docs/integrations/vectorstores/momento_vector_index | ComponentsVector storesMomento Vector Index (MVI)On this pageMomento Vector Index (MVI)MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is ... |
645 | https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas | ComponentsVector storesMongoDB AtlasMongoDB AtlasMongoDB 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.This notebook shows how to use MongoDB Atlas Vector Search to store your embeddings in MongoDB documents, create a... |
646 | https://python.langchain.com/docs/integrations/vectorstores/myscale | ComponentsVector storesMyScaleOn this pageMyScaleMyScale is a cloud-based database optimized for AI applications and solutions, built on the open-source ClickHouse. This notebook shows how to use functionality related to the MyScale vector database.Setting up envrionmentspip install clickhouse-connectWe want to use Op... |
647 | https://python.langchain.com/docs/integrations/vectorstores/neo4jvector | ComponentsVector storesNeo4j Vector IndexOn this pageNeo4j Vector IndexNeo4j is an open-source graph database with integrated support for vector similarity searchIt supports:approximate nearest neighbor searchEuclidean similarity and cosine similarityHybrid search combining vector and keyword searchesThis notebook show... |
648 | https://python.langchain.com/docs/integrations/vectorstores/nucliadb | ComponentsVector storesNucliaDBOn this pageNucliaDBYou can use a local NucliaDB instance or use Nuclia Cloud.When using a local instance, you need a Nuclia Understanding API key, so your texts are properly vectorized and indexed. You can get a key by creating a free account at https://nuclia.cloud, and then create a NU... |
649 | https://python.langchain.com/docs/integrations/vectorstores/opensearch | ComponentsVector storesOpenSearchOn this pageOpenSearchOpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. OpenSearch is a distributed search and analytics engine based on Apache Lucene.This notebook shows how to ... |
650 | https://python.langchain.com/docs/integrations/vectorstores/pgembedding | ComponentsVector storesPostgres EmbeddingOn this pagePostgres EmbeddingPostgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search.It supports:exact and approximate nearest neighbor search using HNSWL2 distanceT... |
651 | https://python.langchain.com/docs/integrations/vectorstores/pgvector | ComponentsVector storesPGVectorOn this pagePGVectorPGVector is an open-source vector similarity search for PostgresIt supports:exact and approximate nearest neighbor searchL2 distance, inner product, and cosine distanceThis notebook shows how to use the Postgres vector database (PGVector).See the installation instructi... |
652 | https://python.langchain.com/docs/integrations/vectorstores/pinecone | ComponentsVector storesPineconeOn this pagePineconePinecone is a vector database with broad functionality.This notebook shows how to use functionality related to the Pinecone vector database.To use Pinecone, you must have an API key.
Here are the installation instructions.pip install pinecone-client openai tiktoken lan... |
653 | https://python.langchain.com/docs/integrations/vectorstores/qdrant | ComponentsVector storesQdrantOn this pageQdrantQdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for al... |
654 | https://python.langchain.com/docs/integrations/vectorstores/redis | ComponentsVector storesRedisOn this pageRedisRedis vector database introduction and langchain integration guide.What is Redis?Most developers from a web services background are probably familiar with Redis. At it's core, Redis is an open-source key-value store that can be used as a cache, message broker, and database.... |
655 | https://python.langchain.com/docs/integrations/vectorstores/rockset | ComponentsVector storesRocksetOn this pageRocksetRockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ 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 ha... |
656 | https://python.langchain.com/docs/integrations/vectorstores/scann | ComponentsVector storesScaNNOn this pageScaNNScaNN (Scalable Nearest Neighbors) is a method for efficient vector similarity search at scale.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 o... |
657 | https://python.langchain.com/docs/integrations/vectorstores/singlestoredb | ComponentsVector storesSingleStoreDBSingleStoreDBSingleStoreDB is a high-performance distributed SQL database that supports deployment both in the cloud and on-premises. It provides vector storage, and vector functions including dot_product and euclidean_distance, thereby supporting AI applications that require text si... |
658 | https://python.langchain.com/docs/integrations/vectorstores/sklearn | ComponentsVector storesscikit-learnOn this pagescikit-learnscikit-learn is an open source collection of machine learning algorithms, including some implementations of the k nearest neighbors. SKLearnVectorStore wraps this implementation and adds the possibility to persist the vector store in json, bson (binary json) or... |
659 | https://python.langchain.com/docs/integrations/vectorstores/sqlitevss | ComponentsVector storessqlite-vssOn this pagesqlite-vsssqlite-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.This no... |
660 | https://python.langchain.com/docs/integrations/vectorstores/starrocks | ComponentsVector storesStarRocksOn this pageStarRocksStarRocks is a High-Performance Analytical Database.
StarRocks is a next-gen sub-second MPP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics and ad-hoc query.Usually StarRocks is categorized into OLAP, and it has showe... |
661 | https://python.langchain.com/docs/integrations/vectorstores/supabase | ComponentsVector storesSupabase (Postgres)On this pageSupabase (Postgres)Supabase is an open source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks.PostgreSQL also known as Postgres, is ... |
662 | https://python.langchain.com/docs/integrations/vectorstores/tair | ComponentsVector storesTairTairTair is a cloud native in-memory database service developed by Alibaba Cloud.
It provides rich data models and enterprise-grade capabilities to support your real-time online scenarios while maintaining full compatibility with open source Redis. Tair also introduces persistent memory-optim... |
663 | https://python.langchain.com/docs/integrations/vectorstores/tencentvectordb | ComponentsVector storesTencent Cloud VectorDBTencent Cloud VectorDBTencent Cloud VectorDB is a fully managed, self-developed, enterprise-level distributed database service designed for storing, retrieving, and analyzing multi-dimensional vector data. The database supports multiple index types and similarity calculation... |
664 | https://python.langchain.com/docs/integrations/vectorstores/tigris | ComponentsVector storesTigrisOn this pageTigrisTigris is an open source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications.
Tigris eliminates the infrastructure complexity of managing, operating, and synchronizing multiple tools, allowing you to focus... |
665 | https://python.langchain.com/docs/integrations/vectorstores/timescalevector | ComponentsVector storesTimescale Vector (Postgres)On this pageTimescale Vector (Postgres)This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed ... |
666 | https://python.langchain.com/docs/integrations/vectorstores/typesense | ComponentsVector storesTypesenseOn this pageTypesenseTypesense is an open source, in-memory search engine, that you can either self-host or run on Typesense Cloud.Typesense focuses on performance by storing the entire index in RAM (with a backup on disk) and also focuses on providing an out-of-the-box developer experie... |
667 | https://python.langchain.com/docs/integrations/vectorstores/usearch | ComponentsVector storesUSearchOn this pageUSearchUSearch is a Smaller & Faster Single-File Vector Search EngineUSearch's base functionality is identical to FAISS, and the interface should look familiar if you have ever investigated Approximate Nearest Neigbors search. FAISS is a widely recognized standard for high-perf... |
668 | https://python.langchain.com/docs/integrations/vectorstores/vald | ComponentsVector storesValdOn this pageValdVald is a highly scalable distributed fast approximate nearest neighbor (ANN) dense vector search engine.This notebook shows how to use functionality related to the Vald database.To run this notebook you need a running Vald cluster.
Check Get Started for more information.See t... |
669 | https://python.langchain.com/docs/integrations/vectorstores/vearch | ComponentsVector storesvearchvearchfrom langchain.document_loaders import TextLoaderfrom langchain.embeddings.huggingface import HuggingFaceEmbeddingsfrom langchain.text_splitter import RecursiveCharacterTextSplitterfrom transformers import AutoModel, AutoTokenizerfrom langchain.vectorstores.vearch import Vearch# repal... |
670 | https://python.langchain.com/docs/integrations/vectorstores/vectara | ComponentsVector storesVectaraOn this pageVectaraVectara is a API platform for building GenAI applications. It provides an easy-to-use API for document indexing and querying that is managed by Vectara and is optimized for performance and accuracy.
See the Vectara API documentation for more information on how to use th... |
671 | https://python.langchain.com/docs/integrations/vectorstores/vespa | ComponentsVector storesVespaOn this pageVespaVespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query.This notebook shows how to use Vespa.ai as a LangChain vector store.In order to create the vector store, we use
... |
672 | https://python.langchain.com/docs/integrations/vectorstores/weaviate | ComponentsVector storesWeaviateOn this pageWeaviateWeaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects.This notebook shows how to use functionality related to the Weaviatevector database.Se... |
673 | https://python.langchain.com/docs/integrations/vectorstores/xata | ComponentsVector storesXataOn this pageXataXata is a serverless data platform, based on PostgreSQL. It provides a Python SDK for interacting with your database, and a UI for managing your data.
Xata has a native vector type, which can be added to any table, and supports similarity search. LangChain inserts vectors dire... |
674 | https://python.langchain.com/docs/integrations/vectorstores/zep | ComponentsVector storesZepOn this pageZepZep is an open source long-term memory store for LLM applications. Zep makes it easy to add relevant documents,
chat history memory & rich user data to your LLM app's prompts.Note: The ZepVectorStore works with Documents and is intended to be used as a Retriever.
It offers separ... |
675 | https://python.langchain.com/docs/integrations/vectorstores/zilliz | ComponentsVector storesZillizZillizZilliz Cloud is a fully managed service on cloud for LF AI Milvus®,This notebook shows how to use functionality related to the Zilliz Cloud managed vector database.To run, you should have a Zilliz Cloud instance up and running. Here are the installation instructionspip install pymilvu... |
676 | https://python.langchain.com/docs/integrations/retrievers | ComponentsRetrieversRetrievers📄️ Amazon KendraAmazon Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. Kendra is... |
677 | https://python.langchain.com/docs/integrations/retrievers/amazon_kendra_retriever | ComponentsRetrieversAmazon KendraOn this pageAmazon KendraAmazon Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization... |
678 | https://python.langchain.com/docs/integrations/retrievers/arcee | ComponentsRetrieversArcee RetrieverOn this pageArcee RetrieverThis notebook demonstrates how to use the ArceeRetriever class to retrieve relevant document(s) for Arcee's Domain Adapted Language Models (DALMs).SetupBefore using ArceeRetriever, make sure the Arcee API key is set as ARCEE_API_KEY environment variable. Yo... |
679 | https://python.langchain.com/docs/integrations/retrievers/arxiv | ComponentsRetrieversArxivOn this pageArxivarXiv is an open-access archive for 2 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.This notebook shows how to retrieve scient... |
680 | https://python.langchain.com/docs/integrations/retrievers/azure_cognitive_search | ComponentsRetrieversAzure Cognitive SearchOn this pageAzure Cognitive SearchAzure Cognitive Search (formerly known as Azure Search) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise app... |
681 | https://python.langchain.com/docs/integrations/retrievers/bm25 | ComponentsRetrieversBM25On this pageBM25BM25 also known as the Okapi BM25, is a ranking function used in information retrieval systems to estimate the relevance of documents to a given search query.This notebook goes over how to use a retriever that under the hood uses BM25 using rank_bm25 package.# !pip install rank_b... |
682 | https://python.langchain.com/docs/integrations/retrievers/chaindesk | ComponentsRetrieversChaindeskOn this pageChaindeskChaindesk platform brings data from anywhere (Datsources: Text, PDF, Word, PowerPpoint, Excel, Notion, Airtable, Google Sheets, etc..) into Datastores (container of multiple Datasources).
Then your Datastores can be connected to ChatGPT via Plugins or any other Large La... |
683 | https://python.langchain.com/docs/integrations/retrievers/chatgpt-plugin | ComponentsRetrieversChatGPT PluginOn this pageChatGPT PluginOpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions.Plugins can allow ChatGPT to do things lik... |
684 | https://python.langchain.com/docs/integrations/retrievers/cohere-reranker | ComponentsRetrieversCohere RerankerOn this pageCohere RerankerCohere is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.This notebook shows how to use Cohere's rerank endpoint in a retriever. This builds on top of ideas in the ContextualCompress... |
685 | https://python.langchain.com/docs/integrations/retrievers/docarray_retriever | ComponentsRetrieversDocArrayOn this pageDocArrayDocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray d... |
686 | https://python.langchain.com/docs/integrations/retrievers/elastic_search_bm25 | ComponentsRetrieversElasticSearch BM25On this pageElasticSearch BM25Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.In information retrieval, Okapi BM25 (BM is an abbrevi... |
687 | https://python.langchain.com/docs/integrations/retrievers/google_cloud_enterprise_search | ComponentsRetrieversGoogle Cloud Enterprise SearchOn this pageGoogle Cloud Enterprise SearchEnterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud.Gen AI App Builder lets developers, even those with limited machine learning skills, quickly and easily tap into the power of Go... |
688 | https://python.langchain.com/docs/integrations/retrievers/google_drive | ComponentsRetrieversGoogle DriveOn this pageGoogle DriveThis notebook covers how to retrieve documents from Google Drive.PrerequisitesCreate a Google Cloud project or use an existing projectEnable the Google Drive APIAuthorize credentials for desktop apppip install --upgrade google-api-python-client google-auth-httpli... |
689 | https://python.langchain.com/docs/integrations/retrievers/google_vertex_ai_search | ComponentsRetrieversGoogle Vertex AI SearchOn this pageGoogle Vertex AI SearchVertex AI Search (formerly known as Enterprise Search on Generative AI App Builder) is a part of the Vertex AI machine learning platform offered by Google Cloud.Vertex AI Search lets organizations quickly build generative AI powered search en... |
690 | https://python.langchain.com/docs/integrations/retrievers/kay | ComponentsRetrieversKay.aiOn this pageKay.aiData API built for RAG 🕵️ We are curating the world's largest datasets as high-quality embeddings so your AI agents can retrieve context on the fly. Latest models, fast retrieval, and zero infra.This notebook shows you how to retrieve datasets supported by Kay. You can curre... |
691 | https://python.langchain.com/docs/integrations/retrievers/knn | ComponentsRetrieverskNNOn this pagekNNIn statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression.This notebook goes over how to use a retr... |
692 | https://python.langchain.com/docs/integrations/retrievers/merger_retriever | ComponentsRetrieversLOTR (Merger Retriever)On this pageLOTR (Merger Retriever)Lord of the Retrievers, also known as MergerRetriever, takes a list of retrievers as input and merges the results of their get_relevant_documents() methods into a single list. The merged results will be a list of documents that are relevant t... |
693 | https://python.langchain.com/docs/integrations/retrievers/metal | ComponentsRetrieversMetalOn this pageMetalMetal is a managed service for ML Embeddings.This notebook shows how to use Metal's retriever.First, you will need to sign up for Metal and get an API key. You can do so here# !pip install metal_sdkfrom metal_sdk.metal import MetalAPI_KEY = ""CLIENT_ID = ""INDEX_ID = ""metal = ... |
694 | https://python.langchain.com/docs/integrations/retrievers/pinecone_hybrid_search | ComponentsRetrieversPinecone Hybrid SearchOn this pagePinecone Hybrid SearchPinecone is a vector database with broad functionality.This notebook goes over how to use a retriever that under the hood uses Pinecone and Hybrid Search.The logic of this retriever is taken from this documentaionTo use Pinecone, you must have ... |
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