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docs: integrations reference updates 16 (#26059)
Added missed provider pages and links. Fixed inconsistent formatting. Co-authored-by: Erick Friis <erick@langchain.dev>
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@ -436,6 +436,8 @@ See a [usage example](/docs/integrations/tools/azure_ai_services).
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from langchain_community.agent_toolkits import azure_ai_services
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```
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#### Azure AI Services individual tools
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The `azure_ai_services` toolkit includes the following tools:
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- Image Analysis: [AzureAiServicesImageAnalysisTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.azure_ai_services.image_analysis.AzureAiServicesImageAnalysisTool.html)
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@ -460,6 +462,23 @@ See a [usage example](/docs/integrations/tools/office365).
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from langchain_community.agent_toolkits import O365Toolkit
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```
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#### Office 365 individual tools
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You can use individual tools from the Office 365 Toolkit:
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- `O365CreateDraftMessage`: tool for creating a draft email in Office 365
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- `O365SearchEmails`: tool for searching email messages in Office 365
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- `O365SearchEvents`: tool for searching calendar events in Office 365
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- `O365SendEvent`: tool for sending calendar events in Office 365
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- `O365SendMessage`: tool for sending an email in Office 365
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```python
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from langchain_community.tools.office365 import O365CreateDraftMessage
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from langchain_community.tools.office365 import O365SearchEmails
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from langchain_community.tools.office365 import O365SearchEvents
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from langchain_community.tools.office365 import O365SendEvent
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from langchain_community.tools.office365 import O365SendMessage
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```
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### Microsoft Azure PowerBI
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We need to install `azure-identity` python package.
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@ -475,6 +494,20 @@ from langchain_community.agent_toolkits import PowerBIToolkit
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from langchain_community.utilities.powerbi import PowerBIDataset
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```
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#### PowerBI individual tools
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You can use individual tools from the Azure PowerBI Toolkit:
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- `InfoPowerBITool`: tool for getting metadata about a PowerBI Dataset
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- `ListPowerBITool`: tool for getting tables names
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- `QueryPowerBITool`: tool for querying a PowerBI Dataset
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```python
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from langchain_community.tools.powerbi.tool import InfoPowerBITool
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from langchain_community.tools.powerbi.tool import ListPowerBITool
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from langchain_community.tools.powerbi.tool import QueryPowerBITool
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```
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### PlayWright Browser Toolkit
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>[Playwright](https://github.com/microsoft/playwright) is an open-source automation tool
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63
docs/docs/integrations/providers/apache.mdx
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63
docs/docs/integrations/providers/apache.mdx
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# Apache Software Foundation
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>[The Apache Software Foundation (Wikipedia)](https://en.wikipedia.org/wiki/The_Apache_Software_Foundation)
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> is a decentralized open source community of developers. The software they
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> produce is distributed under the terms of the Apache License, a permissive
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> open-source license for free and open-source software (FOSS). The Apache projects
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> are characterized by a collaborative, consensus-based development process
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> and an open and pragmatic software license, which is to say that it
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> allows developers, who receive the software freely, to redistribute
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> it under non-free terms. Each project is managed by a self-selected
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> team of technical experts who are active contributors to the project.
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## Apache AGE
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>[Apache AGE](https://age.apache.org/) is a `PostgreSQL` extension that provides
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> graph database functionality. `AGE` is an acronym for `A Graph Extension`, and
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> is inspired by Bitnine’s fork of `PostgreSQL 10`, `AgensGraph`, which is
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> a multimodal database. The goal of the project is to create single
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> storage that can handle both relational and graph model data so that users
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> can use standard ANSI SQL along with `openCypher`, the Graph query language.
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> The data elements `Apache AGE` stores are nodes, edges connecting them, and
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> attributes of nodes and edges.
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See more about [integrating with Apache AGE](/docs/integrations/graphs/apache_age).
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## Apache Cassandra
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>[Apache Cassandra](https://cassandra.apache.org/) is a NoSQL, row-oriented,
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> highly scalable and highly available database. Starting with version 5.0,
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> the database ships with vector search capabilities.
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See more about [integrating with Apache Cassandra](/docs/integrations/providers/cassandra/).
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## Apache Doris
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>[Apache Doris](https://doris.apache.org/) is a modern data warehouse for
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> real-time analytics. It delivers lightning-fast analytics on real-time data at scale.
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>
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>Usually `Apache Doris` is categorized into OLAP, and it has showed excellent
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> performance in ClickBench — a Benchmark For Analytical DBMS. Since it has
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> a super-fast vectorized execution engine, it could also be used as a fast vectordb.
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See more about [integrating with Apache Doris](/docs/integrations/providers/apache_doris/).
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## Apache Kafka
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>[Apache Kafka](https://github.com/apache/kafka) is a distributed messaging system
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> that is used to publish and subscribe to streams of records.
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See more about [integrating with Apache Kafka](/docs/integrations/memory/kafka_chat_message_history).
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## Apache Spark
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>[Apache Spark](https://spark.apache.org/) is a unified analytics engine for
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> large-scale data processing. It provides high-level APIs in Scala, Java,
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> Python, and R, and an optimized engine that supports general computation
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> graphs for data analysis. It also supports a rich set of higher-level
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> tools including `Spark SQL` for SQL and DataFrames, `pandas API on Spark`
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> for pandas workloads, `MLlib` for machine learning,
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> `GraphX` for graph processing, and `Structured Streaming` for stream processing.
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See more about [integrating with Apache Spark](/docs/integrations/providers/spark).
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49
docs/docs/integrations/providers/spark.mdx
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49
docs/docs/integrations/providers/spark.mdx
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# Spark
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>[Apache Spark](https://spark.apache.org/) is a unified analytics engine for
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> large-scale data processing. It provides high-level APIs in Scala, Java,
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> Python, and R, and an optimized engine that supports general computation
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> graphs for data analysis. It also supports a rich set of higher-level
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> tools including `Spark SQL` for SQL and DataFrames, `pandas API on Spark`
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> for pandas workloads, `MLlib` for machine learning,
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> `GraphX` for graph processing, and `Structured Streaming` for stream processing.
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## Document loaders
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### PySpark
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It loads data from a `PySpark` DataFrame.
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See a [usage example](/docs/integrations/document_loaders/pyspark_dataframe).
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```python
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from langchain_community.document_loaders import PySparkDataFrameLoader
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```
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## Tools/Toolkits
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### Spark SQL toolkit
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Toolkit for interacting with `Spark SQL`.
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See a [usage example](/docs/integrations/tools/spark_sql).
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```python
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from langchain_community.agent_toolkits import SparkSQLToolkit, create_spark_sql_agent
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from langchain_community.utilities.spark_sql import SparkSQL
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```
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#### Spark SQL individual tools
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You can use individual tools from the Spark SQL Toolkit:
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- `InfoSparkSQLTool`: tool for getting metadata about a Spark SQL
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- `ListSparkSQLTool`: tool for getting tables names
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- `QueryCheckerTool`: tool uses an LLM to check if a query is correct
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- `QuerySparkSQLTool`: tool for querying a Spark SQL
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```python
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from langchain_community.tools.spark_sql.tool import InfoSparkSQLTool
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from langchain_community.tools.spark_sql.tool import ListSparkSQLTool
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from langchain_community.tools.spark_sql.tool import QueryCheckerTool
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from langchain_community.tools.spark_sql.tool import QuerySparkSQLTool
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```
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@ -4,11 +4,26 @@
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It has cross-domain knowledge and language understanding ability by learning a large amount of texts, codes and images.
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It can understand and perform tasks based on natural dialogue.
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## SparkLLM LLM Model
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An example is available at [example](/docs/integrations/llms/sparkllm).
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## Chat models
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## SparkLLM Chat Model
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An example is available at [example](/docs/integrations/chat/sparkllm).
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See a [usage example](/docs/integrations/chat/sparkllm).
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## SparkLLM Text Embedding Model
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An example is available at [example](/docs/integrations/text_embedding/sparkllm)
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```python
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from langchain_community.chat_models import ChatSparkLLM
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```
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## LLMs
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See a [usage example](/docs/integrations/llms/sparkllm).
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```python
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from langchain_community.llms import SparkLLM
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```
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## Embedding models
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See a [usage example](/docs/integrations/text_embedding/sparkllm)
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```python
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from langchain_community.embeddings import SparkLLMTextEmbeddings
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```
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