From 6548052f9e7da9932f1d20695831540c52ac1b35 Mon Sep 17 00:00:00 2001 From: Maxime Perrin <63123596+maximeperrindev@users.noreply.github.com> Date: Wed, 22 May 2024 17:32:01 +0200 Subject: [PATCH] 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 --- .../integrations/vectorstores/activeloop_deeplake.ipynb | 2 +- docs/docs/integrations/vectorstores/aerospike.ipynb | 2 +- .../integrations/vectorstores/alibabacloud_opensearch.ipynb | 2 +- docs/docs/integrations/vectorstores/analyticdb.ipynb | 3 +++ docs/docs/integrations/vectorstores/annoy.ipynb | 2 ++ docs/docs/integrations/vectorstores/apache_doris.ipynb | 2 ++ docs/docs/integrations/vectorstores/astradb.ipynb | 2 ++ docs/docs/integrations/vectorstores/atlas.ipynb | 2 ++ docs/docs/integrations/vectorstores/awadb.ipynb | 2 ++ docs/docs/integrations/vectorstores/azure_cosmos_db.ipynb | 2 +- docs/docs/integrations/vectorstores/azuresearch.ipynb | 4 +++- docs/docs/integrations/vectorstores/bagel.ipynb | 2 +- docs/docs/integrations/vectorstores/bageldb.ipynb | 2 +- .../vectorstores/baiducloud_vector_search.ipynb | 2 +- docs/docs/integrations/vectorstores/baiduvectordb.ipynb | 2 ++ docs/docs/integrations/vectorstores/cassandra.ipynb | 2 +- docs/docs/integrations/vectorstores/clarifai.ipynb | 2 +- docs/docs/integrations/vectorstores/clickhouse.ipynb | 2 ++ docs/docs/integrations/vectorstores/couchbase.ipynb | 2 +- docs/docs/integrations/vectorstores/dashvector.ipynb | 2 +- docs/docs/integrations/vectorstores/dingo.ipynb | 2 ++ docs/docs/integrations/vectorstores/docarray_hnsw.ipynb | 2 ++ .../docs/integrations/vectorstores/docarray_in_memory.ipynb | 2 +- docs/docs/integrations/vectorstores/duckdb.ipynb | 4 ++-- .../integrations/vectorstores/ecloud_vector_search.ipynb | 4 +++- docs/docs/integrations/vectorstores/epsilla.ipynb | 2 ++ docs/docs/integrations/vectorstores/faiss.ipynb | 2 ++ docs/docs/integrations/vectorstores/faiss_async.ipynb | 2 ++ docs/docs/integrations/vectorstores/hippo.ipynb | 2 +- docs/docs/integrations/vectorstores/hologres.ipynb | 2 +- docs/docs/integrations/vectorstores/jaguar.ipynb | 4 +++- docs/docs/integrations/vectorstores/kdbai.ipynb | 4 +++- docs/docs/integrations/vectorstores/kinetica.ipynb | 2 +- docs/docs/integrations/vectorstores/lancedb.ipynb | 2 +- docs/docs/integrations/vectorstores/lantern.ipynb | 6 ++++-- docs/docs/integrations/vectorstores/llm_rails.ipynb | 2 ++ docs/docs/integrations/vectorstores/marqo.ipynb | 2 ++ docs/docs/integrations/vectorstores/meilisearch.ipynb | 4 +++- docs/docs/integrations/vectorstores/milvus.ipynb | 2 ++ .../integrations/vectorstores/momento_vector_index.ipynb | 2 +- docs/docs/integrations/vectorstores/mongodb_atlas.ipynb | 2 ++ docs/docs/integrations/vectorstores/myscale.ipynb | 2 +- docs/docs/integrations/vectorstores/neo4jvector.ipynb | 2 +- docs/docs/integrations/vectorstores/nucliadb.ipynb | 2 +- docs/docs/integrations/vectorstores/opensearch.ipynb | 2 +- docs/docs/integrations/vectorstores/oracle.ipynb | 2 ++ docs/docs/integrations/vectorstores/pathway.ipynb | 4 +++- docs/docs/integrations/vectorstores/pgembedding.ipynb | 2 +- docs/docs/integrations/vectorstores/pgvecto_rs.ipynb | 2 +- docs/docs/integrations/vectorstores/pinecone.ipynb | 1 + docs/docs/integrations/vectorstores/qdrant.ipynb | 2 +- docs/docs/integrations/vectorstores/relyt.ipynb | 2 +- docs/docs/integrations/vectorstores/rockset.ipynb | 4 +++- docs/docs/integrations/vectorstores/sap_hanavector.ipynb | 4 +++- docs/docs/integrations/vectorstores/scann.ipynb | 4 +++- docs/docs/integrations/vectorstores/semadb.ipynb | 6 ++++-- docs/docs/integrations/vectorstores/singlestoredb.ipynb | 4 +++- docs/docs/integrations/vectorstores/sklearn.ipynb | 4 +++- docs/docs/integrations/vectorstores/sqlitevss.ipynb | 2 ++ docs/docs/integrations/vectorstores/starrocks.ipynb | 2 +- docs/docs/integrations/vectorstores/supabase.ipynb | 2 ++ docs/docs/integrations/vectorstores/tair.ipynb | 2 ++ docs/docs/integrations/vectorstores/tencentvectordb.ipynb | 2 +- docs/docs/integrations/vectorstores/thirdai_neuraldb.ipynb | 6 ++++-- docs/docs/integrations/vectorstores/tidb_vector.ipynb | 2 +- docs/docs/integrations/vectorstores/tigris.ipynb | 2 +- docs/docs/integrations/vectorstores/tiledb.ipynb | 2 +- docs/docs/integrations/vectorstores/timescalevector.ipynb | 2 +- docs/docs/integrations/vectorstores/typesense.ipynb | 2 +- docs/docs/integrations/vectorstores/upstash.ipynb | 2 +- docs/docs/integrations/vectorstores/usearch.ipynb | 2 +- docs/docs/integrations/vectorstores/vald.ipynb | 2 +- docs/docs/integrations/vectorstores/vdms.ipynb | 2 ++ docs/docs/integrations/vectorstores/vearch.ipynb | 4 +++- docs/docs/integrations/vectorstores/vectara.ipynb | 4 +++- docs/docs/integrations/vectorstores/vespa.ipynb | 2 ++ docs/docs/integrations/vectorstores/vikingdb.ipynb | 2 ++ docs/docs/integrations/vectorstores/vlite.ipynb | 4 +++- docs/docs/integrations/vectorstores/xata.ipynb | 2 +- docs/docs/integrations/vectorstores/yellowbrick.ipynb | 2 +- docs/docs/integrations/vectorstores/zep.ipynb | 2 ++ docs/docs/integrations/vectorstores/zilliz.ipynb | 2 ++ 82 files changed, 147 insertions(+), 59 deletions(-) diff --git a/docs/docs/integrations/vectorstores/activeloop_deeplake.ipynb b/docs/docs/integrations/vectorstores/activeloop_deeplake.ipynb index 770251137ff..805166dd23f 100644 --- a/docs/docs/integrations/vectorstores/activeloop_deeplake.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/aerospike.ipynb b/docs/docs/integrations/vectorstores/aerospike.ipynb index 9a72250db10..b2ad324b055 100644 --- a/docs/docs/integrations/vectorstores/aerospike.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/alibabacloud_opensearch.ipynb b/docs/docs/integrations/vectorstores/alibabacloud_opensearch.ipynb index 6c2f03c223e..50ea0b13977 100644 --- a/docs/docs/integrations/vectorstores/alibabacloud_opensearch.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/analyticdb.ipynb b/docs/docs/integrations/vectorstores/analyticdb.ipynb index dc3404651a4..594adc5560b 100644 --- a/docs/docs/integrations/vectorstores/analyticdb.ipynb +++ b/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." ] }, diff --git a/docs/docs/integrations/vectorstores/annoy.ipynb b/docs/docs/integrations/vectorstores/annoy.ipynb index 72792259e6e..4e3e0486d39 100644 --- a/docs/docs/integrations/vectorstores/annoy.ipynb +++ b/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." ] }, diff --git a/docs/docs/integrations/vectorstores/apache_doris.ipynb b/docs/docs/integrations/vectorstores/apache_doris.ipynb index a4970ed912f..92239a7a60b 100644 --- a/docs/docs/integrations/vectorstores/apache_doris.ipynb +++ b/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." ] }, diff --git a/docs/docs/integrations/vectorstores/astradb.ipynb b/docs/docs/integrations/vectorstores/astradb.ipynb index d7fa83ef08f..b185df23f1c 100644 --- a/docs/docs/integrations/vectorstores/astradb.ipynb +++ b/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._" ] }, diff --git a/docs/docs/integrations/vectorstores/atlas.ipynb b/docs/docs/integrations/vectorstores/atlas.ipynb index 0887b4c03f5..39cc4f70202 100644 --- a/docs/docs/integrations/vectorstores/atlas.ipynb +++ b/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." ] }, diff --git a/docs/docs/integrations/vectorstores/awadb.ipynb b/docs/docs/integrations/vectorstores/awadb.ipynb index a7592e03c17..96c3f55de34 100644 --- a/docs/docs/integrations/vectorstores/awadb.ipynb +++ b/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`." ] }, diff --git a/docs/docs/integrations/vectorstores/azure_cosmos_db.ipynb b/docs/docs/integrations/vectorstores/azure_cosmos_db.ipynb index 2fa1ce9794c..06403c7475c 100644 --- a/docs/docs/integrations/vectorstores/azure_cosmos_db.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/azuresearch.ipynb b/docs/docs/integrations/vectorstores/azuresearch.ipynb index f6345272f5e..06c8c812632 100644 --- a/docs/docs/integrations/vectorstores/azuresearch.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/bagel.ipynb b/docs/docs/integrations/vectorstores/bagel.ipynb index 29ac3cf9eec..6f8665f6c56 100644 --- a/docs/docs/integrations/vectorstores/bagel.ipynb +++ b/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" ] diff --git a/docs/docs/integrations/vectorstores/bageldb.ipynb b/docs/docs/integrations/vectorstores/bageldb.ipynb index e3605d7ee3b..71dbcd6bf84 100644 --- a/docs/docs/integrations/vectorstores/bageldb.ipynb +++ b/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" ] diff --git a/docs/docs/integrations/vectorstores/baiducloud_vector_search.ipynb b/docs/docs/integrations/vectorstores/baiducloud_vector_search.ipynb index 6ccbcbde0fa..4ddc2b34efa 100644 --- a/docs/docs/integrations/vectorstores/baiducloud_vector_search.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/baiduvectordb.ipynb b/docs/docs/integrations/vectorstores/baiduvectordb.ipynb index 89cdc1a22ae..b8515f1e6ae 100644 --- a/docs/docs/integrations/vectorstores/baiduvectordb.ipynb +++ b/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)." diff --git a/docs/docs/integrations/vectorstores/cassandra.ipynb b/docs/docs/integrations/vectorstores/cassandra.ipynb index ab43444f623..e7f9a19a62d 100644 --- a/docs/docs/integrations/vectorstores/cassandra.ipynb +++ b/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\"" ] }, { diff --git a/docs/docs/integrations/vectorstores/clarifai.ipynb b/docs/docs/integrations/vectorstores/clarifai.ipynb index c91ff706408..03baea6e201 100644 --- a/docs/docs/integrations/vectorstores/clarifai.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/clickhouse.ipynb b/docs/docs/integrations/vectorstores/clickhouse.ipynb index 90527963b97..2b0136dff6b 100644 --- a/docs/docs/integrations/vectorstores/clickhouse.ipynb +++ b/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." ] }, diff --git a/docs/docs/integrations/vectorstores/couchbase.ipynb b/docs/docs/integrations/vectorstores/couchbase.ipynb index 9e18bf91970..8035376aebd 100644 --- a/docs/docs/integrations/vectorstores/couchbase.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/dashvector.ipynb b/docs/docs/integrations/vectorstores/dashvector.ipynb index adb4c8a420c..f42b6d4f072 100644 --- a/docs/docs/integrations/vectorstores/dashvector.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/dingo.ipynb b/docs/docs/integrations/vectorstores/dingo.ipynb index 72f5ac3f75d..3d0934d4b07 100644 --- a/docs/docs/integrations/vectorstores/dingo.ipynb +++ b/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)." diff --git a/docs/docs/integrations/vectorstores/docarray_hnsw.ipynb b/docs/docs/integrations/vectorstores/docarray_hnsw.ipynb index 63f8baecb52..87931f415b1 100644 --- a/docs/docs/integrations/vectorstores/docarray_hnsw.ipynb +++ b/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`." ] }, diff --git a/docs/docs/integrations/vectorstores/docarray_in_memory.ipynb b/docs/docs/integrations/vectorstores/docarray_in_memory.ipynb index 81a4f49ec91..7185bf26391 100644 --- a/docs/docs/integrations/vectorstores/docarray_in_memory.ipynb +++ b/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\"" ] }, { diff --git a/docs/docs/integrations/vectorstores/duckdb.ipynb b/docs/docs/integrations/vectorstores/duckdb.ipynb index bff41b1278e..e87f1c4142f 100644 --- a/docs/docs/integrations/vectorstores/duckdb.ipynb +++ b/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" ] }, diff --git a/docs/docs/integrations/vectorstores/ecloud_vector_search.ipynb b/docs/docs/integrations/vectorstores/ecloud_vector_search.ipynb index 0082c5c0b6e..ffe976f5b14 100644 --- a/docs/docs/integrations/vectorstores/ecloud_vector_search.ipynb +++ b/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" ] diff --git a/docs/docs/integrations/vectorstores/epsilla.ipynb b/docs/docs/integrations/vectorstores/epsilla.ipynb index e0bfbd84c4d..5d0e01678a7 100644 --- a/docs/docs/integrations/vectorstores/epsilla.ipynb +++ b/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)." diff --git a/docs/docs/integrations/vectorstores/faiss.ipynb b/docs/docs/integrations/vectorstores/faiss.ipynb index 13425e7cc90..e95d5f76c4b 100644 --- a/docs/docs/integrations/vectorstores/faiss.ipynb +++ b/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." ] }, diff --git a/docs/docs/integrations/vectorstores/faiss_async.ipynb b/docs/docs/integrations/vectorstores/faiss_async.ipynb index 09a638b9a45..770f8b78e0f 100644 --- a/docs/docs/integrations/vectorstores/faiss_async.ipynb +++ b/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", diff --git a/docs/docs/integrations/vectorstores/hippo.ipynb b/docs/docs/integrations/vectorstores/hippo.ipynb index dd2013edef5..eecb5c874fc 100644 --- a/docs/docs/integrations/vectorstores/hippo.ipynb +++ b/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" ] }, diff --git a/docs/docs/integrations/vectorstores/hologres.ipynb b/docs/docs/integrations/vectorstores/hologres.ipynb index 31541f85e06..5bf70d6e25e 100644 --- a/docs/docs/integrations/vectorstores/hologres.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/jaguar.ipynb b/docs/docs/integrations/vectorstores/jaguar.ipynb index 0e520a0d299..c538a33532d 100644 --- a/docs/docs/integrations/vectorstores/jaguar.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/kdbai.ipynb b/docs/docs/integrations/vectorstores/kdbai.ipynb index 302bf5cb7d8..74d177548de 100644 --- a/docs/docs/integrations/vectorstores/kdbai.ipynb +++ b/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", diff --git a/docs/docs/integrations/vectorstores/kinetica.ipynb b/docs/docs/integrations/vectorstores/kinetica.ipynb index ef29f4b97be..5df1dfd96e8 100644 --- a/docs/docs/integrations/vectorstores/kinetica.ipynb +++ b/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" ] diff --git a/docs/docs/integrations/vectorstores/lancedb.ipynb b/docs/docs/integrations/vectorstores/lancedb.ipynb index bcdd38756fe..fd59f044264 100644 --- a/docs/docs/integrations/vectorstores/lancedb.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/lantern.ipynb b/docs/docs/integrations/vectorstores/lantern.ipynb index 01fb3447283..bad29bea1d7 100644 --- a/docs/docs/integrations/vectorstores/lantern.ipynb +++ b/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" diff --git a/docs/docs/integrations/vectorstores/llm_rails.ipynb b/docs/docs/integrations/vectorstores/llm_rails.ipynb index 1ac0a57b6f4..0cbb34036c3 100644 --- a/docs/docs/integrations/vectorstores/llm_rails.ipynb +++ b/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", diff --git a/docs/docs/integrations/vectorstores/marqo.ipynb b/docs/docs/integrations/vectorstores/marqo.ipynb index 879661fc697..6583563203f 100644 --- a/docs/docs/integrations/vectorstores/marqo.ipynb +++ b/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", diff --git a/docs/docs/integrations/vectorstores/meilisearch.ipynb b/docs/docs/integrations/vectorstores/meilisearch.ipynb index 97c8d5cade4..176671bfa6b 100644 --- a/docs/docs/integrations/vectorstores/meilisearch.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/milvus.ipynb b/docs/docs/integrations/vectorstores/milvus.ipynb index 6eaabfef79e..97a7b1d45f6 100644 --- a/docs/docs/integrations/vectorstores/milvus.ipynb +++ b/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)." ] }, diff --git a/docs/docs/integrations/vectorstores/momento_vector_index.ipynb b/docs/docs/integrations/vectorstores/momento_vector_index.ipynb index 50990c5f400..7df6c4fddf5 100644 --- a/docs/docs/integrations/vectorstores/momento_vector_index.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb index 24081245dbf..9ceceee0fd1 100644 --- a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb +++ b/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", diff --git a/docs/docs/integrations/vectorstores/myscale.ipynb b/docs/docs/integrations/vectorstores/myscale.ipynb index 760a55b4620..c75ee44ac79 100644 --- a/docs/docs/integrations/vectorstores/myscale.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/neo4jvector.ipynb b/docs/docs/integrations/vectorstores/neo4jvector.ipynb index 20788d0d28a..72ca016a351 100644 --- a/docs/docs/integrations/vectorstores/neo4jvector.ipynb +++ b/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" ] }, diff --git a/docs/docs/integrations/vectorstores/nucliadb.ipynb b/docs/docs/integrations/vectorstores/nucliadb.ipynb index 8112a25c3c2..74bc916d3ef 100644 --- a/docs/docs/integrations/vectorstores/nucliadb.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/opensearch.ipynb b/docs/docs/integrations/vectorstores/opensearch.ipynb index e377273ebe8..febe4b117d6 100644 --- a/docs/docs/integrations/vectorstores/opensearch.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/oracle.ipynb b/docs/docs/integrations/vectorstores/oracle.ipynb index 862db4673cb..fbdf8085b7e 100644 --- a/docs/docs/integrations/vectorstores/oracle.ipynb +++ b/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. " ] }, diff --git a/docs/docs/integrations/vectorstores/pathway.ipynb b/docs/docs/integrations/vectorstores/pathway.ipynb index 9664f0386d2..985344d3420 100644 --- a/docs/docs/integrations/vectorstores/pathway.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/pgembedding.ipynb b/docs/docs/integrations/vectorstores/pgembedding.ipynb index 838a1be2d8b..e1a75c5545e 100644 --- a/docs/docs/integrations/vectorstores/pgembedding.ipynb +++ b/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" ] diff --git a/docs/docs/integrations/vectorstores/pgvecto_rs.ipynb b/docs/docs/integrations/vectorstores/pgvecto_rs.ipynb index 2eb6aa9f755..e366ee478a8 100644 --- a/docs/docs/integrations/vectorstores/pgvecto_rs.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/pinecone.ipynb b/docs/docs/integrations/vectorstores/pinecone.ipynb index c305b5053df..8efd1a4d0d5 100644 --- a/docs/docs/integrations/vectorstores/pinecone.ipynb +++ b/docs/docs/integrations/vectorstores/pinecone.ipynb @@ -29,6 +29,7 @@ " langchain-pinecone \\\n", " langchain-openai \\\n", " langchain \\\n", + " langchain-community \\\n", " pinecone-notebooks" ] }, diff --git a/docs/docs/integrations/vectorstores/qdrant.ipynb b/docs/docs/integrations/vectorstores/qdrant.ipynb index ed825beb002..038ca0d6b66 100644 --- a/docs/docs/integrations/vectorstores/qdrant.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/relyt.ipynb b/docs/docs/integrations/vectorstores/relyt.ipynb index 4692e06b027..6fd9aed64be 100644 --- a/docs/docs/integrations/vectorstores/relyt.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/rockset.ipynb b/docs/docs/integrations/vectorstores/rockset.ipynb index 8d1f5bf147b..3c664daa5d6 100644 --- a/docs/docs/integrations/vectorstores/rockset.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/sap_hanavector.ipynb b/docs/docs/integrations/vectorstores/sap_hanavector.ipynb index ff84478a188..4eb477da0e8 100644 --- a/docs/docs/integrations/vectorstores/sap_hanavector.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/scann.ipynb b/docs/docs/integrations/vectorstores/scann.ipynb index 9f38d86253f..9ff42e8b2cf 100644 --- a/docs/docs/integrations/vectorstores/scann.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/semadb.ipynb b/docs/docs/integrations/vectorstores/semadb.ipynb index b9f94c97b1b..65eca6cebae 100644 --- a/docs/docs/integrations/vectorstores/semadb.ipynb +++ b/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" diff --git a/docs/docs/integrations/vectorstores/singlestoredb.ipynb b/docs/docs/integrations/vectorstores/singlestoredb.ipynb index 47cf6cb3075..ba5d9b11746 100644 --- a/docs/docs/integrations/vectorstores/singlestoredb.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/sklearn.ipynb b/docs/docs/integrations/vectorstores/sklearn.ipynb index 66a16d31d60..af1c93c02d1 100644 --- a/docs/docs/integrations/vectorstores/sklearn.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/sqlitevss.ipynb b/docs/docs/integrations/vectorstores/sqlitevss.ipynb index 03b30e347fd..1d036ca1947 100644 --- a/docs/docs/integrations/vectorstores/sqlitevss.ipynb +++ b/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." ] }, diff --git a/docs/docs/integrations/vectorstores/starrocks.ipynb b/docs/docs/integrations/vectorstores/starrocks.ipynb index f00cec80492..e6b55b600eb 100644 --- a/docs/docs/integrations/vectorstores/starrocks.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/supabase.ipynb b/docs/docs/integrations/vectorstores/supabase.ipynb index e13d11e5ab6..ca9b2968862 100644 --- a/docs/docs/integrations/vectorstores/supabase.ipynb +++ b/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", diff --git a/docs/docs/integrations/vectorstores/tair.ipynb b/docs/docs/integrations/vectorstores/tair.ipynb index ad0f066256d..81aa08652c0 100644 --- a/docs/docs/integrations/vectorstores/tair.ipynb +++ b/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." ] }, diff --git a/docs/docs/integrations/vectorstores/tencentvectordb.ipynb b/docs/docs/integrations/vectorstores/tencentvectordb.ipynb index f07d46449ed..532b22a2f01 100644 --- a/docs/docs/integrations/vectorstores/tencentvectordb.ipynb +++ b/docs/docs/integrations/vectorstores/tencentvectordb.ipynb @@ -23,7 +23,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip3 install tcvectordb" + "!pip3 install tcvectordb langchain-community" ] }, { diff --git a/docs/docs/integrations/vectorstores/thirdai_neuraldb.ipynb b/docs/docs/integrations/vectorstores/thirdai_neuraldb.ipynb index 4eb85227605..eb830485a97 100644 --- a/docs/docs/integrations/vectorstores/thirdai_neuraldb.ipynb +++ b/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", diff --git a/docs/docs/integrations/vectorstores/tidb_vector.ipynb b/docs/docs/integrations/vectorstores/tidb_vector.ipynb index 0f453cb9c9a..ee40990ac59 100644 --- a/docs/docs/integrations/vectorstores/tidb_vector.ipynb +++ b/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" diff --git a/docs/docs/integrations/vectorstores/tigris.ipynb b/docs/docs/integrations/vectorstores/tigris.ipynb index 3d5ea550f25..5ca1e57935a 100644 --- a/docs/docs/integrations/vectorstores/tigris.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/tiledb.ipynb b/docs/docs/integrations/vectorstores/tiledb.ipynb index c5fc0aec26a..4da8ebd17a1 100644 --- a/docs/docs/integrations/vectorstores/tiledb.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/timescalevector.ipynb b/docs/docs/integrations/vectorstores/timescalevector.ipynb index 8bd067979f7..8e11ff203cf 100644 --- a/docs/docs/integrations/vectorstores/timescalevector.ipynb +++ b/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" ] }, diff --git a/docs/docs/integrations/vectorstores/typesense.ipynb b/docs/docs/integrations/vectorstores/typesense.ipynb index 77726f1cbb9..8b3c11b7e29 100644 --- a/docs/docs/integrations/vectorstores/typesense.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/upstash.ipynb b/docs/docs/integrations/vectorstores/upstash.ipynb index fe49cd59666..03b1909e74f 100644 --- a/docs/docs/integrations/vectorstores/upstash.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/usearch.ipynb b/docs/docs/integrations/vectorstores/usearch.ipynb index c691799ce8d..f944c3360da 100644 --- a/docs/docs/integrations/vectorstores/usearch.ipynb +++ b/docs/docs/integrations/vectorstores/usearch.ipynb @@ -20,7 +20,7 @@ }, "outputs": [], "source": [ - "%pip install --upgrade --quiet usearch" + "%pip install --upgrade --quiet usearch langchain-community" ] }, { diff --git a/docs/docs/integrations/vectorstores/vald.ipynb b/docs/docs/integrations/vectorstores/vald.ipynb index 96dc0df063d..4a994886327 100644 --- a/docs/docs/integrations/vectorstores/vald.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/vdms.ipynb b/docs/docs/integrations/vectorstores/vdms.ipynb index 752c8876232..8aca74c9a52 100644 --- a/docs/docs/integrations/vectorstores/vdms.ipynb +++ b/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:" ] }, diff --git a/docs/docs/integrations/vectorstores/vearch.ipynb b/docs/docs/integrations/vectorstores/vearch.ipynb index 657ae0f7012..da2efb893a7 100644 --- a/docs/docs/integrations/vectorstores/vearch.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/vectara.ipynb b/docs/docs/integrations/vectorstores/vectara.ipynb index 70756b58d91..496ba565659 100644 --- a/docs/docs/integrations/vectorstores/vectara.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/vespa.ipynb b/docs/docs/integrations/vectorstores/vespa.ipynb index abacdc296ca..a6991f7f965 100644 --- a/docs/docs/integrations/vectorstores/vespa.ipynb +++ b/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." diff --git a/docs/docs/integrations/vectorstores/vikingdb.ipynb b/docs/docs/integrations/vectorstores/vikingdb.ipynb index 3e8d32f4ae2..14cd3573185 100644 --- a/docs/docs/integrations/vectorstores/vikingdb.ipynb +++ b/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" diff --git a/docs/docs/integrations/vectorstores/vlite.ipynb b/docs/docs/integrations/vectorstores/vlite.ipynb index 46a2f46a447..8a5dd0d2a72 100644 --- a/docs/docs/integrations/vectorstores/vlite.ipynb +++ b/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", diff --git a/docs/docs/integrations/vectorstores/xata.ipynb b/docs/docs/integrations/vectorstores/xata.ipynb index 10074c047a4..83afa4d89eb 100644 --- a/docs/docs/integrations/vectorstores/xata.ipynb +++ b/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" ] }, { diff --git a/docs/docs/integrations/vectorstores/yellowbrick.ipynb b/docs/docs/integrations/vectorstores/yellowbrick.ipynb index a8ccb56eb7d..3b3b234479a 100644 --- a/docs/docs/integrations/vectorstores/yellowbrick.ipynb +++ b/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" ] diff --git a/docs/docs/integrations/vectorstores/zep.ipynb b/docs/docs/integrations/vectorstores/zep.ipynb index 5e5bd852483..afff8415f8e 100644 --- a/docs/docs/integrations/vectorstores/zep.ipynb +++ b/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", diff --git a/docs/docs/integrations/vectorstores/zilliz.ipynb b/docs/docs/integrations/vectorstores/zilliz.ipynb index 3a9b4bf2005..928d12fde36 100644 --- a/docs/docs/integrations/vectorstores/zilliz.ipynb +++ b/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)" ] },