diff --git a/docs/docs/integrations/platforms/google.mdx b/docs/docs/integrations/platforms/google.mdx index 19da4679c5..bc8d17d257 100644 --- a/docs/docs/integrations/platforms/google.mdx +++ b/docs/docs/integrations/platforms/google.mdx @@ -625,6 +625,7 @@ from langchain.retrievers import GoogleVertexAISearchRetriever > from Google Cloud allows enterprises to search, store, govern, and manage documents and their AI-extracted > data and metadata in a single platform. +Note: `GoogleDocumentAIWarehouseRetriever` is deprecated, use `DocumentAIWarehouseRetriever` (see below). ```python from langchain.retrievers import GoogleDocumentAIWarehouseRetriever docai_wh_retriever = GoogleDocumentAIWarehouseRetriever( @@ -636,6 +637,10 @@ documents = docai_wh_retriever.invoke( ) ``` +```python +from langchain_google_community.documentai_warehouse import DocumentAIWarehouseRetriever +``` + ## Tools ### Text-to-Speech diff --git a/docs/docs/integrations/platforms/microsoft.mdx b/docs/docs/integrations/platforms/microsoft.mdx index a0d6807ed7..ed282710cc 100644 --- a/docs/docs/integrations/platforms/microsoft.mdx +++ b/docs/docs/integrations/platforms/microsoft.mdx @@ -466,6 +466,22 @@ See a [usage example](/docs/integrations/tools/playwright). from langchain_community.agent_toolkits import PlayWrightBrowserToolkit ``` +#### PlayWright Browser individual tools + +You can use individual tools from the PlayWright Browser Toolkit. + +```python +from langchain_community.tools.playwright import ClickTool +from langchain_community.tools.playwright import CurrentWebPageTool +from langchain_community.tools.playwright import ExtractHyperlinksTool +from langchain_community.tools.playwright import ExtractTextTool +from langchain_community.tools.playwright import GetElementsTool +from langchain_community.tools.playwright import NavigateTool +from langchain_community.tools.playwright import NavigateBackTool +``` + + +```python ## Graphs ### Azure Cosmos DB for Apache Gremlin diff --git a/docs/docs/integrations/providers/connery.mdx b/docs/docs/integrations/providers/connery.mdx new file mode 100644 index 0000000000..36684a97fa --- /dev/null +++ b/docs/docs/integrations/providers/connery.mdx @@ -0,0 +1,28 @@ +# Connery + +>[Connery SDK](https://github.com/connery-io/connery-sdk) is an NPM package that +> includes both an SDK and a CLI, designed for the development of plugins and actions. +> +>The CLI automates many things in the development process. The SDK +> offers a JavaScript API for defining plugins and actions and packaging them +> into a plugin server with a standardized REST API generated from the metadata. +> The plugin server handles authorization, input validation, and logging. +> So you can focus on the logic of your actions. +> +> See the use cases and examples in the [Connery SDK documentation](https://sdk.connery.io/docs/use-cases/) + +## Toolkit + +See [usage example](/docs/integrations/tools/connery). + +```python +from langchain_community.agent_toolkits.connery import ConneryToolkit +``` + +## Tools + +### ConneryAction + +```python +from langchain_community.tools.connery import ConneryService +``` diff --git a/docs/docs/integrations/providers/dashvector.mdx b/docs/docs/integrations/providers/dashvector.mdx index b18fca590b..b7ded751dd 100644 --- a/docs/docs/integrations/providers/dashvector.mdx +++ b/docs/docs/integrations/providers/dashvector.mdx @@ -6,12 +6,27 @@ This document demonstrates to leverage DashVector within the LangChain ecosystem It is broken into two parts: installation and setup, and then references to specific DashVector wrappers. ## Installation and Setup + + Install the Python SDK: + ```bash pip install dashvector ``` -## VectorStore +You must have an API key. Here are the [installation instructions](https://help.aliyun.com/document_detail/2510223.html). + + +## Embedding models + +```python +from langchain_community.embeddings import DashScopeEmbeddings +``` + +See the [use example](/docs/integrations/vectorstores/dashvector). + + +## Vector Store A DashVector Collection is wrapped as a familiar VectorStore for native usage within LangChain, which allows it to be readily used for various scenarios, such as semantic search or example selection. diff --git a/docs/docs/integrations/providers/dataforseo.mdx b/docs/docs/integrations/providers/dataforseo.mdx index 3be8ed0f2b..37d8884fa4 100644 --- a/docs/docs/integrations/providers/dataforseo.mdx +++ b/docs/docs/integrations/providers/dataforseo.mdx @@ -19,7 +19,7 @@ os.environ["DATAFORSEO_PASSWORD"] = "your_password" ## Utility -The DataForSEO utility wraps the API. To import this utility, use: +The `DataForSEO` utility wraps the API. To import this utility, use: ```python from langchain_community.utilities.dataforseo_api_search import DataForSeoAPIWrapper @@ -36,6 +36,13 @@ from langchain.agents import load_tools tools = load_tools(["dataforseo-api-search"]) ``` +This will load the following tools: + +```python +from langchain_community.tools import DataForSeoAPISearchRun +from langchain_community.tools import DataForSeoAPISearchResults +``` + ## Example usage ```python diff --git a/docs/docs/integrations/providers/dingo.mdx b/docs/docs/integrations/providers/dingo.mdx index be0c9f83fa..b12a6a72cb 100644 --- a/docs/docs/integrations/providers/dingo.mdx +++ b/docs/docs/integrations/providers/dingo.mdx @@ -1,10 +1,21 @@ # DingoDB -This page covers how to use the DingoDB ecosystem within LangChain. -It is broken into two parts: installation and setup, and then references to specific DingoDB wrappers. +>[DingoDB](https://github.com/dingodb) is a distributed multi-modal vector +> database. It combines the features of a data lake and a vector database, +> allowing for the storage of any type of data (key-value, PDF, audio, +> video, etc.) regardless of its size. Utilizing DingoDB, you can construct +> your own Vector Ocean (the next-generation data architecture following data +> warehouse and data lake). This enables +> the analysis of both structured and unstructured data through +> a singular SQL with exceptionally low latency in real time. ## Installation and Setup -- Install the Python SDK with `pip install dingodb` + +Install the Python SDK + +```bash +pip install dingodb +``` ## VectorStore @@ -12,6 +23,7 @@ There exists a wrapper around DingoDB indexes, allowing you to use it as a vecto whether for semantic search or example selection. To import this vectorstore: + ```python from langchain_community.vectorstores import Dingo ``` diff --git a/docs/docs/integrations/providers/docarray.mdx b/docs/docs/integrations/providers/docarray.mdx index 5895fa30f7..d1d41a1983 100644 --- a/docs/docs/integrations/providers/docarray.mdx +++ b/docs/docs/integrations/providers/docarray.mdx @@ -20,7 +20,7 @@ LangChain provides an access to the `In-memory` and `HNSW` vector stores from th See a [usage example](/docs/integrations/vectorstores/docarray_hnsw). ```python -from langchain_community.vectorstores DocArrayHnswSearch +from langchain_community.vectorstores import DocArrayHnswSearch ``` See a [usage example](/docs/integrations/vectorstores/docarray_in_memory). @@ -28,3 +28,10 @@ See a [usage example](/docs/integrations/vectorstores/docarray_in_memory). from langchain_community.vectorstores DocArrayInMemorySearch ``` +## Retriever + +See a [usage example](/docs/integrations/retrievers/docarray_retriever). + +```python +from langchain_community.retrievers import DocArrayRetriever +``` diff --git a/docs/docs/integrations/providers/pandas.mdx b/docs/docs/integrations/providers/pandas.mdx new file mode 100644 index 0000000000..15519b0b0f --- /dev/null +++ b/docs/docs/integrations/providers/pandas.mdx @@ -0,0 +1,29 @@ +# Pandas + +>[pandas](https://pandas.pydata.org) is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, +built on top of the `Python` programming language. + +## Installation and Setup + +Install the `pandas` package using `pip`: + +```bash +pip install pandas +``` + + +## Document loader + +See a [usage example](/docs/integrations/document_loaders/pandas_dataframe). + +```python +from langchain_community.document_loaders import DataFrameLoader +``` + +## Toolkit + +See a [usage example](/docs/integrations/tools/pandas). + +```python +from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent +```