docs : Integrations vector stores with langchain-community install (#22028)

- **Description:** Adding installation instruction for integrations
requiring `langchain-community` package since 0.2
  - **Issue:** #22005

---------

Co-authored-by: Maxime Perrin <mperrin@doing.fr>

docs/docs/integrations/vectorstores/activeloop_deeplake.ipynb

@@ -26,7 +26,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain-openai 'deeplake[enterprise]' tiktoken"
+    "%pip install --upgrade --quiet  langchain-openai langchain-community 'deeplake[enterprise]' tiktoken"
    ]
   },
   {

docs/docs/integrations/vectorstores/aerospike.ipynb

@@ -51,7 +51,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install --upgrade --quiet aerospike-vector-search==0.6.1 sentence-transformers langchain"
+    "!pip install --upgrade --quiet aerospike-vector-search==0.6.1 langchain-community sentence-transformers langchain"
    ]
   },
   {

docs/docs/integrations/vectorstores/alibabacloud_opensearch.ipynb

@@ -76,7 +76,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  alibabacloud_ha3engine_vector"
+    "%pip install --upgrade --quiet  langchain-community alibabacloud_ha3engine_vector"
    ]
   },
   {

docs/docs/integrations/vectorstores/analyticdb.ipynb

@@ -10,8 +10,11 @@
     "\n",
     ">`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."
    ]
   },

docs/docs/integrations/vectorstores/annoy.ipynb

@@ -9,6 +9,8 @@
     "\n",
     "> [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."
    ]
   },

docs/docs/integrations/vectorstores/apache_doris.ipynb

@@ -14,6 +14,8 @@
     "\n",
     ">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."
    ]
   },

docs/docs/integrations/vectorstores/astradb.ipynb

@@ -23,6 +23,8 @@
    "id": "d2d6ca14-fb7e-4172-9aa0-a3119a064b96",
    "metadata": {},
    "source": [
+    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
+    "\n",
     "_Note: in addition to access to the database, an OpenAI API Key is required to run the full example._"
    ]
   },

docs/docs/integrations/vectorstores/atlas.ipynb

@@ -10,6 +10,8 @@
     "\n",
     ">[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."
    ]
   },

docs/docs/integrations/vectorstores/awadb.ipynb

@@ -8,6 +8,8 @@
     "# AwaDB\n",
     ">[AwaDB](https://github.com/awa-ai/awadb) is an AI Native database for the search and storage of embedding vectors used by LLM Applications.\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 `AwaDB`."
    ]
   },

docs/docs/integrations/vectorstores/azure_cosmos_db.ipynb

@@ -51,7 +51,7 @@
     }
    ],
    "source": [
-    "%pip install --upgrade --quiet  pymongo"
+    "%pip install --upgrade --quiet  pymongo langchain-openai langchain-community"
    ]
   },
   {

docs/docs/integrations/vectorstores/azuresearch.ipynb

@@ -8,7 +8,9 @@
    "source": [
     "# Azure AI Search\n",
     "\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"
+    "[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"
    ]
   },
   {

docs/docs/integrations/vectorstores/bagel.ipynb

@@ -14,7 +14,7 @@
     "### Installation and Setup\n",
     "\n",
     "```bash\n",
-    "pip install bagelML\n",
+    "pip install bagelML langchain-community\n",
     "```\n",
     "\n"
    ]

docs/docs/integrations/vectorstores/bageldb.ipynb

@@ -14,7 +14,7 @@
     "### Installation and Setup\n",
     "\n",
     "```bash\n",
-    "pip install betabageldb\n",
+    "pip install betabageldb langchain-community\n",
     "```\n",
     "\n"
    ]

docs/docs/integrations/vectorstores/baiducloud_vector_search.ipynb

@@ -39,7 +39,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  elasticsearch == 7.11.0"
+    "%pip install --upgrade --quiet langchain-community elasticsearch == 7.11.0"
    ]
   },
   {

docs/docs/integrations/vectorstores/baiduvectordb.ipynb

@@ -15,6 +15,8 @@
     "\n",
     ">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)."

docs/docs/integrations/vectorstores/cassandra.ipynb

@@ -49,7 +49,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet \"cassio>=0.1.4\""
+    "%pip install --upgrade --quiet langchain-community \"cassio>=0.1.4\""
    ]
   },
   {

docs/docs/integrations/vectorstores/clarifai.ipynb

@@ -35,7 +35,7 @@
    "outputs": [],
    "source": [
     "# Install required dependencies\n",
-    "%pip install --upgrade --quiet  clarifai"
+    "%pip install --upgrade --quiet  clarifai langchain-community"
    ]
   },
   {

docs/docs/integrations/vectorstores/clickhouse.ipynb

@@ -9,6 +9,8 @@
     "\n",
     "> [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."
    ]
   },

docs/docs/integrations/vectorstores/couchbase.ipynb

@@ -28,7 +28,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet langchain langchain-openai couchbase"
+    "%pip install --upgrade --quiet langchain langchain-openai langchain-community couchbase"
    ]
   },
   {

docs/docs/integrations/vectorstores/dashvector.ipynb

@@ -39,7 +39,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  dashvector dashscope"
+    "%pip install --upgrade --quiet  langchain-community dashvector dashscope"
    ]
   },
   {

docs/docs/integrations/vectorstores/dingo.ipynb

@@ -9,6 +9,8 @@
     "\n",
     ">[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)."

docs/docs/integrations/vectorstores/docarray_hnsw.ipynb

@@ -9,6 +9,8 @@
     "\n",
     ">[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`."
    ]
   },

docs/docs/integrations/vectorstores/docarray_in_memory.ipynb

@@ -31,7 +31,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  \"docarray\""
+    "%pip install --upgrade --quiet  langchain-community \"docarray\""
    ]
   },
   {

docs/docs/integrations/vectorstores/duckdb.ipynb

@@ -14,7 +14,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install duckdb"
+    "! pip install duckdb langchain-community"
    ]
   },
   {
@@ -42,7 +42,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.vectorstores import DuckDB\n",
+    "from langchain_community.vectorstores import DuckDB\n",
     "from langchain_openai import OpenAIEmbeddings"
    ]
   },

docs/docs/integrations/vectorstores/ecloud_vector_search.ipynb

@@ -9,6 +9,8 @@
     "\n",
     ">[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",
     "\n",
@@ -66,8 +68,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.vectorstores import EcloudESVectorStore\n",
     "from langchain_community.document_loaders import TextLoader\n",
+    "from langchain_community.vectorstores import EcloudESVectorStore\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "from langchain_text_splitters import CharacterTextSplitter"
    ]

docs/docs/integrations/vectorstores/epsilla.ipynb

@@ -9,6 +9,8 @@
     "\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)."

docs/docs/integrations/vectorstores/faiss.ipynb

@@ -11,6 +11,8 @@
     "\n",
     "[Faiss documentation](https://faiss.ai/).\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 `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."
    ]
   },

docs/docs/integrations/vectorstores/faiss_async.ipynb

@@ -11,6 +11,8 @@
     "\n",
     "[Faiss documentation](https://faiss.ai/).\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 `FAISS` vector database using `asyncio`.\n",
     "LangChain implemented the synchronous and asynchronous vector store functions.\n",
     "\n",

docs/docs/integrations/vectorstores/hippo.ipynb

@@ -59,7 +59,7 @@
     }
    ],
    "source": [
-    "%pip install --upgrade --quiet  langchain tiktoken langchain-openai\n",
+    "%pip install --upgrade --quiet  langchain langchain_community tiktoken langchain-openai\n",
     "%pip install --upgrade --quiet  hippo-api==1.1.0.rc3"
    ]
   },

docs/docs/integrations/vectorstores/hologres.ipynb

@@ -22,7 +22,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  hologres-vector"
+    "%pip install --upgrade --quiet  langchain_community hologres-vector"
    ]
   },
   {

docs/docs/integrations/vectorstores/jaguar.ipynb

@@ -35,7 +35,9 @@
     "2. You must install the http client package for JaguarDB:\n",
     "   ```\n",
     "       pip install -U jaguardb-http-client\n",
-    "   ```\n"
+    "   ```\n",
+    "   \n",
+    "3. You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n"
    ]
   },
   {

docs/docs/integrations/vectorstores/kdbai.ipynb

@@ -17,6 +17,8 @@
     "\n",
     "The following examples demonstrate some of the ways you can interact with KDB.AI through LangChain.\n",
     "\n",
+    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
+    "\n",
     "## Import required packages"
    ]
   },
@@ -47,7 +49,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "KDB.AI endpoint:  https://ui.qa.cld.kx.com/instance/pcnvlmi860\n",

docs/docs/integrations/vectorstores/kinetica.ipynb

@@ -60,7 +60,7 @@
    ],
    "source": [
     "# Pip install necessary package\n",
-    "%pip install --upgrade --quiet  langchain-openai\n",
+    "%pip install --upgrade --quiet  langchain-openai langchain-community\n",
     "%pip install gpudb==7.2.0.1\n",
     "%pip install --upgrade --quiet  tiktoken"
    ]

docs/docs/integrations/vectorstores/lancedb.ipynb

@@ -19,7 +19,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install -U langchain-openai"
+    "! pip install -U langchain-openai langchain-community"
    ]
   },
   {

docs/docs/integrations/vectorstores/lantern.ipynb

@@ -12,6 +12,8 @@
     "- Exact and approximate nearest neighbor search\n",
     "- L2 squared distance, hamming distance, and cosine distance\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 Postgres vector database (`Lantern`)."
    ]
   },
@@ -50,7 +52,7 @@
    },
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "OpenAI API Key: ········\n"
@@ -144,7 +146,7 @@
    },
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "DB Connection String: ········\n"

docs/docs/integrations/vectorstores/llm_rails.ipynb

@@ -10,6 +10,8 @@
     ">[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",

docs/docs/integrations/vectorstores/marqo.ipynb

@@ -12,6 +12,8 @@
     "\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",
     "\n",
     "```\n",

docs/docs/integrations/vectorstores/meilisearch.ipynb

@@ -10,7 +10,9 @@
     ">\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"
    ]
   },
   {

docs/docs/integrations/vectorstores/milvus.ipynb

@@ -11,6 +11,8 @@
     "\n",
     "This notebook shows how to use functionality related to the Milvus vector database.\n",
     "\n",
+    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
+    "\n",
     "To run, you should have a [Milvus instance up and running](https://milvus.io/docs/install_standalone-docker.md)."
    ]
   },

docs/docs/integrations/vectorstores/momento_vector_index.ipynb

@@ -48,7 +48,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  momento langchain-openai tiktoken"
+    "%pip install --upgrade --quiet  momento langchain-openai langchain-community tiktoken"
    ]
   },
   {

docs/docs/integrations/vectorstores/mongodb_atlas.ipynb

@@ -9,6 +9,8 @@
     "\n",
     ">[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",
     "\n",
     "\n",

docs/docs/integrations/vectorstores/myscale.ipynb

@@ -31,7 +31,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  clickhouse-connect"
+    "%pip install --upgrade --quiet  clickhouse-connect langchain-community"
    ]
   },
   {

docs/docs/integrations/vectorstores/neo4jvector.ipynb

@@ -34,7 +34,7 @@
    "source": [
     "# Pip install necessary package\n",
     "%pip install --upgrade --quiet  neo4j\n",
-    "%pip install --upgrade --quiet  langchain-openai\n",
+    "%pip install --upgrade --quiet  langchain-openai langchain-community\n",
     "%pip install --upgrade --quiet  tiktoken"
    ]
   },

docs/docs/integrations/vectorstores/nucliadb.ipynb

@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain nuclia"
+    "%pip install --upgrade --quiet  langchain langchain-community nuclia"
    ]
   },
   {

docs/docs/integrations/vectorstores/opensearch.ipynb

@@ -37,7 +37,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  opensearch-py"
+    "%pip install --upgrade --quiet  opensearch-py langchain-community"
    ]
   },
   {

docs/docs/integrations/vectorstores/oracle.ipynb

@@ -43,6 +43,8 @@
    "source": [
     "### Prerequisites for using Langchain with Oracle AI Vector Search\n",
     "\n",
+    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
+    "\n",
     "Please install Oracle Python Client driver to use Langchain with Oracle AI Vector Search. "
    ]
   },

docs/docs/integrations/vectorstores/pathway.ipynb

@@ -18,7 +18,9 @@
     "\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"
    ]
   },
   {

docs/docs/integrations/vectorstores/pgembedding.ipynb

@@ -29,7 +29,7 @@
    "outputs": [],
    "source": [
     "# Pip install necessary package\n",
-    "%pip install --upgrade --quiet  langchain-openai\n",
+    "%pip install --upgrade --quiet  langchain-openai langchain-community\n",
     "%pip install --upgrade --quiet  psycopg2-binary\n",
     "%pip install --upgrade --quiet  tiktoken"
    ]

docs/docs/integrations/vectorstores/pgvecto_rs.ipynb

@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install \"pgvecto_rs[sdk]\""
+    "%pip install \"pgvecto_rs[sdk]\" langchain-community"
    ]
   },
   {

docs/docs/integrations/vectorstores/pinecone.ipynb

@@ -29,6 +29,7 @@
     "    langchain-pinecone \\\n",
     "    langchain-openai \\\n",
     "    langchain \\\n",
+    "    langchain-community \\\n",
     "    pinecone-notebooks"
    ]
   },

docs/docs/integrations/vectorstores/qdrant.ipynb

@@ -30,7 +30,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain-qdrant langchain-openai langchain"
+    "%pip install --upgrade --quiet  langchain-qdrant langchain-openai langchain langchain-community"
    ]
   },
   {

docs/docs/integrations/vectorstores/relyt.ipynb

@@ -22,7 +22,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install \"pgvecto_rs[sdk]\""
+    "%pip install \"pgvecto_rs[sdk]\" langchain-community"
    ]
   },
   {

docs/docs/integrations/vectorstores/rockset.ipynb

@@ -9,7 +9,9 @@
     "\n",
     ">[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"
    ]
   },
   {

docs/docs/integrations/vectorstores/sap_hanavector.ipynb

@@ -6,7 +6,9 @@
    "source": [
     "# SAP HANA Cloud Vector Engine\n",
     "\n",
-    ">[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"
    ]
   },
   {

docs/docs/integrations/vectorstores/scann.ipynb

@@ -9,7 +9,9 @@
     "\n",
     "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"
    ]
   },
   {

docs/docs/integrations/vectorstores/semadb.ipynb

@@ -11,7 +11,9 @@
     "\n",
     "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"
    ]
   },
   {
@@ -88,7 +90,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "SemaDB API Key: ········\n"

docs/docs/integrations/vectorstores/singlestoredb.ipynb

@@ -16,7 +16,9 @@
     "\n",
     "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"
    ]
   },
   {

docs/docs/integrations/vectorstores/sklearn.ipynb

@@ -8,7 +8,9 @@
     "\n",
     ">[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"
    ]
   },
   {

docs/docs/integrations/vectorstores/sqlitevss.ipynb

@@ -13,6 +13,8 @@
     "\n",
     ">[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."
    ]
   },

docs/docs/integrations/vectorstores/starrocks.ipynb

@@ -30,7 +30,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  pymysql"
+    "%pip install --upgrade --quiet  pymysql langchain-community"
    ]
   },
   {

docs/docs/integrations/vectorstores/supabase.ipynb

@@ -19,6 +19,8 @@
     "\n",
     "This notebook shows how to use `Supabase` and `pgvector` as your VectorStore.\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, please ensure:\n",
     "- the `pgvector` extension is enabled\n",
     "- you have installed the `supabase-py` package\n",

docs/docs/integrations/vectorstores/tair.ipynb

@@ -11,6 +11,8 @@
     "\n",
     "This notebook shows how to use functionality related to the `Tair` vector database.\n",
     "\n",
+    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
+    "\n",
     "To run, you should have a `Tair` instance up and running."
    ]
   },

docs/docs/integrations/vectorstores/tencentvectordb.ipynb

@@ -23,7 +23,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip3 install tcvectordb"
+    "!pip3 install tcvectordb langchain-community"
    ]
   },
   {

docs/docs/integrations/vectorstores/thirdai_neuraldb.ipynb

@@ -16,7 +16,9 @@
     "\n",
     "For all of the following initialization methods, the `thirdai_key` parameter can be omitted if the `THIRDAI_KEY` environment variable is set.\n",
     "\n",
-    "ThirdAI API keys can be obtained at https://www.thirdai.com/try-bolt/"
+    "ThirdAI API keys can be obtained at https://www.thirdai.com/try-bolt/\n",
+    "\n",
+    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration"
    ]
   },
   {
@@ -25,7 +27,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.vectorstores import NeuralDBVectorStore\n",
+    "from langchain_community.vectorstores import NeuralDBVectorStore\n",
     "\n",
     "# From scratch\n",
     "vectorstore = NeuralDBVectorStore.from_scratch(thirdai_key=\"your-thirdai-key\")\n",

docs/docs/integrations/vectorstores/tidb_vector.ipynb

@@ -31,7 +31,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install langchain\n",
+    "%pip install langchain langchain-community\n",
     "%pip install langchain-openai\n",
     "%pip install pymysql\n",
     "%pip install tidb-vector"

docs/docs/integrations/vectorstores/tigris.ipynb

@@ -44,7 +44,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  tigrisdb openapi-schema-pydantic langchain-openai tiktoken"
+    "%pip install --upgrade --quiet  tigrisdb openapi-schema-pydantic langchain-openai langchain-community tiktoken"
    ]
   },
   {

docs/docs/integrations/vectorstores/tiledb.ipynb

@@ -25,7 +25,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  tiledb-vector-search"
+    "%pip install --upgrade --quiet  tiledb-vector-search langchain-community"
    ]
   },
   {

docs/docs/integrations/vectorstores/timescalevector.ipynb

@@ -53,7 +53,7 @@
    "source": [
     "# Pip install necessary packages\n",
     "%pip install --upgrade --quiet  timescale-vector\n",
-    "%pip install --upgrade --quiet  langchain-openai\n",
+    "%pip install --upgrade --quiet  langchain-openai langchain-community\n",
     "%pip install --upgrade --quiet  tiktoken"
    ]
   },

docs/docs/integrations/vectorstores/typesense.ipynb

@@ -38,7 +38,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  typesense openapi-schema-pydantic langchain-openai tiktoken"
+    "%pip install --upgrade --quiet  typesense openapi-schema-pydantic langchain-openai langchain-community tiktoken"
    ]
   },
   {

docs/docs/integrations/vectorstores/upstash.ipynb

@@ -38,7 +38,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install langchain-openai langchain upstash-vector"
+    "%pip install langchain-openai langchain langchain-community upstash-vector"
    ]
   },
   {

docs/docs/integrations/vectorstores/usearch.ipynb

@@ -20,7 +20,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  usearch"
+    "%pip install --upgrade --quiet  usearch langchain-community"
    ]
   },
   {

docs/docs/integrations/vectorstores/vald.ipynb

@@ -24,7 +24,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  vald-client-python"
+    "%pip install --upgrade --quiet  vald-client-python langchain-community"
    ]
   },
   {

docs/docs/integrations/vectorstores/vdms.ipynb

@@ -19,6 +19,8 @@
     "\n",
     "This notebook shows how to use VDMS as a vector store using the docker image.\n",
     "\n",
+    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
+    "\n",
     "To begin, install the Python packages for the VDMS client and Sentence Transformers:"
    ]
   },

docs/docs/integrations/vectorstores/vearch.ipynb

@@ -15,7 +15,9 @@
    "source": [
     "## Setting up\n",
     "\n",
-    "Follow [instructions](https://vearch.readthedocs.io/en/latest/quick-start-guide.html#)."
+    "Follow [instructions](https://vearch.readthedocs.io/en/latest/quick-start-guide.html#).\n",
+    "\n",
+    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration"
    ]
   },
   {

docs/docs/integrations/vectorstores/vectara.ipynb

@@ -21,7 +21,9 @@
     "\n",
     "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"
    ]
   },
   {

docs/docs/integrations/vectorstores/vespa.ipynb

@@ -11,6 +11,8 @@
     "\n",
     "This notebook shows how to use `Vespa.ai` as a LangChain vector store.\n",
     "\n",
+    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
+    "\n",
     "In order to create the vector store, we use\n",
     "[pyvespa](https://pyvespa.readthedocs.io/en/latest/index.html) to create a\n",
     "connection a `Vespa` service."

docs/docs/integrations/vectorstores/vikingdb.ipynb

@@ -13,6 +13,8 @@
     "\n",
     "This notebook shows how to use functionality related to the VikingDB vector database.\n",
     "\n",
+    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
+    "\n",
     "To run, you should have a [viking DB instance up and running](https://www.volcengine.com/docs/6459/1165058).\n",
     "\n",
     "\n"

docs/docs/integrations/vectorstores/vlite.ipynb

@@ -9,6 +9,8 @@
     "\n",
     "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",
@@ -20,7 +22,7 @@
     "## Importing VLite\n",
     "\n",
     "```python\n",
-    "from langchain.vectorstores import VLite\n",
+    "from langchain_community.vectorstores import VLite\n",
     "```\n",
     "\n",
     "## Basic Example\n",

docs/docs/integrations/vectorstores/xata.ipynb

@@ -52,7 +52,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  xata langchain-openai tiktoken langchain"
+    "%pip install --upgrade --quiet  xata langchain-openai langchain-community tiktoken langchain"
    ]
   },
   {

docs/docs/integrations/vectorstores/yellowbrick.ipynb

@@ -34,7 +34,7 @@
    "source": [
     "# Install all needed libraries\n",
     "%pip install --upgrade --quiet  langchain\n",
-    "%pip install --upgrade --quiet  langchain-openai\n",
+    "%pip install --upgrade --quiet  langchain-openai langchain-community\n",
     "%pip install --upgrade --quiet  psycopg2-binary\n",
     "%pip install --upgrade --quiet  tiktoken"
    ]

docs/docs/integrations/vectorstores/zep.ipynb

@@ -22,6 +22,8 @@
     ">\n",
     "> Zep Open Source Docs: [https://docs.getzep.com/](https://docs.getzep.com/)\n",
     "\n",
+    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
+    "\n",
     "## Usage\n",
     "\n",
     "In the examples below, we're using Zep's auto-embedding feature which automatically embeds documents on the Zep server \n",

docs/docs/integrations/vectorstores/zilliz.ipynb

@@ -11,6 +11,8 @@
     "\n",
     "This notebook shows how to use functionality related to the Zilliz Cloud managed vector database.\n",
     "\n",
+    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
+    "\n",
     "To run, you should have a `Zilliz Cloud` instance up and running. Here are the [installation instructions](https://zilliz.com/cloud)"
    ]
   },