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docs: platform pages update (#17836)

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`Integrations` platform page ToC-s: sections there are placed without
order. For example, the
[google](https://python.langchain.com/docs/integrations/platforms/google)
page. The `LLM` section is not the first section, as it is in the
[Components](https://python.langchain.com/docs/integrations/components)
menu.
Updates:
* reorganized the page sections so they follow the Component menu order.
* fixed names for the section names: "Text Embedding Models" ->
"Embedding Models"
pull/13988/merge
Leonid Ganeline 7 months ago committed by GitHub
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164
docs/docs/integrations/platforms/aws.mdx
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@ -67,7 +67,7 @@ See a [usage example](/docs/integrations/chat/bedrock).
from langchain_community.chat_models import BedrockChat from langchain_community.chat_models import BedrockChat
``` ```
## Text Embedding Models ## Embedding Models
### Bedrock ### Bedrock
@ -84,26 +84,6 @@ from langchain_community.embeddings import SagemakerEndpointEmbeddings
from langchain_community.llms.sagemaker_endpoint import ContentHandlerBase from langchain_community.llms.sagemaker_endpoint import ContentHandlerBase
``` ```
## Chains
### Amazon Comprehend Moderation Chain
>[Amazon Comprehend](https://aws.amazon.com/comprehend/) is a natural-language processing (NLP) service that
> uses machine learning to uncover valuable insights and connections in text.
We need to install the `boto3` and `nltk` libraries.
```bash
pip install boto3 nltk
```
See a [usage example](/docs/guides/safety/amazon_comprehend_chain).
```python
from langchain_experimental.comprehend_moderation import AmazonComprehendModerationChain
```
## Document loaders ## Document loaders
### AWS S3 Directory and File ### AWS S3 Directory and File
@ -132,25 +112,55 @@ See a [usage example](/docs/integrations/document_loaders/amazon_textract).
from langchain_community.document_loaders import AmazonTextractPDFLoader from langchain_community.document_loaders import AmazonTextractPDFLoader
``` ```
## Memory ## Vector stores
### AWS DynamoDB ### Amazon OpenSearch Service
>[AWS DynamoDB](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/dynamodb/index.html) > [Amazon OpenSearch Service](https://aws.amazon.com/opensearch-service/) performs
> is a fully managed `NoSQL` database service that provides fast and predictable performance with seamless scalability. > interactive log analytics, real-time application monitoring, website search, and more. `OpenSearch` is
> an open source,
We have to configure the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html). > distributed search and analytics suite derived from `Elasticsearch`. `Amazon OpenSearch Service` offers the
> latest versions of `OpenSearch`, support for many versions of `Elasticsearch`, as well as
> visualization capabilities powered by `OpenSearch Dashboards` and `Kibana`.
We need to install the `boto3` library. We need to install several python libraries.
```bash ```bash
pip install boto3 pip install boto3 requests requests-aws4auth
``` ```
See a [usage example](/docs/integrations/memory/aws_dynamodb). See a [usage example](/docs/integrations/vectorstores/opensearch#using-aos-amazon-opensearch-service).
```python ```python
from langchain.memory import DynamoDBChatMessageHistory from langchain_community.vectorstores import OpenSearchVectorSearch
```
### Amazon DocumentDB Vector Search
>[Amazon DocumentDB (with MongoDB Compatibility)](https://docs.aws.amazon.com/documentdb/) makes it easy to set up, operate, and scale MongoDB-compatible databases in the cloud.
> With Amazon DocumentDB, you can run the same application code and use the same drivers and tools that you use with MongoDB.
> Vector search for Amazon DocumentDB combines the flexibility and rich querying capability of a JSON-based document database with the power of vector search.
#### Installation and Setup
See [detail configuration instructions](/docs/integrations/vectorstores/documentdb).
We need to install the `pymongo` python package.
```bash
pip install pymongo
```
#### Deploy DocumentDB on AWS
[Amazon DocumentDB (with MongoDB Compatibility)](https://docs.aws.amazon.com/documentdb/) is a fast, reliable, and fully managed database service. Amazon DocumentDB makes it easy to set up, operate, and scale MongoDB-compatible databases in the cloud.
AWS offers services for computing, databases, storage, analytics, and other functionality. For an overview of all AWS services, see [Cloud Computing with Amazon Web Services](https://aws.amazon.com/what-is-aws/).
See a [usage example](/docs/integrations/vectorstores/documentdb).
```python
from langchain.vectorstores import DocumentDBVectorSearch
``` ```
## Retrievers ## Retrievers
@ -197,58 +207,6 @@ See a [usage example](/docs/integrations/retrievers/bedrock).
from langchain.retrievers import AmazonKnowledgeBasesRetriever from langchain.retrievers import AmazonKnowledgeBasesRetriever
``` ```
## Vector stores
### Amazon OpenSearch Service
> [Amazon OpenSearch Service](https://aws.amazon.com/opensearch-service/) performs
> interactive log analytics, real-time application monitoring, website search, and more. `OpenSearch` is
> an open source,
> distributed search and analytics suite derived from `Elasticsearch`. `Amazon OpenSearch Service` offers the
> latest versions of `OpenSearch`, support for many versions of `Elasticsearch`, as well as
> visualization capabilities powered by `OpenSearch Dashboards` and `Kibana`.
We need to install several python libraries.
```bash
pip install boto3 requests requests-aws4auth
```
See a [usage example](/docs/integrations/vectorstores/opensearch#using-aos-amazon-opensearch-service).
```python
from langchain_community.vectorstores import OpenSearchVectorSearch
```
### Amazon DocumentDB Vector Search
>[Amazon DocumentDB (with MongoDB Compatibility)](https://docs.aws.amazon.com/documentdb/) makes it easy to set up, operate, and scale MongoDB-compatible databases in the cloud.
> With Amazon DocumentDB, you can run the same application code and use the same drivers and tools that you use with MongoDB.
> Vector search for Amazon DocumentDB combines the flexibility and rich querying capability of a JSON-based document database with the power of vector search.
#### Installation and Setup
See [detail configuration instructions](/docs/integrations/vectorstores/documentdb).
We need to install the `pymongo` python package.
```bash
pip install pymongo
```
#### Deploy DocumentDB on AWS
[Amazon DocumentDB (with MongoDB Compatibility)](https://docs.aws.amazon.com/documentdb/) is a fast, reliable, and fully managed database service. Amazon DocumentDB makes it easy to set up, operate, and scale MongoDB-compatible databases in the cloud.
AWS offers services for computing, databases, storage, analytics, and other functionality. For an overview of all AWS services, see [Cloud Computing with Amazon Web Services](https://aws.amazon.com/what-is-aws/).
See a [usage example](/docs/integrations/vectorstores/documentdb).
```python
from langchain.vectorstores import DocumentDBVectorSearch
```
## Tools ## Tools
### AWS Lambda ### AWS Lambda
@ -267,6 +225,26 @@ pip install boto3
See a [usage example](/docs/integrations/tools/awslambda). See a [usage example](/docs/integrations/tools/awslambda).
## Memory
### AWS DynamoDB
>[AWS DynamoDB](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/dynamodb/index.html)
> is a fully managed `NoSQL` database service that provides fast and predictable performance with seamless scalability.
We have to configure the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html).
We need to install the `boto3` library.
```bash
pip install boto3
```
See a [usage example](/docs/integrations/memory/aws_dynamodb).
```python
from langchain.memory import DynamoDBChatMessageHistory
```
## Callbacks ## Callbacks
@ -290,3 +268,23 @@ See a [usage example](/docs/integrations/callbacks/sagemaker_tracking).
```python ```python
from langchain.callbacks import SageMakerCallbackHandler from langchain.callbacks import SageMakerCallbackHandler
``` ```
## Chains
### Amazon Comprehend Moderation Chain
>[Amazon Comprehend](https://aws.amazon.com/comprehend/) is a natural-language processing (NLP) service that
> uses machine learning to uncover valuable insights and connections in text.
We need to install the `boto3` and `nltk` libraries.
```bash
pip install boto3 nltk
```
See a [usage example](/docs/guides/safety/amazon_comprehend_chain).
```python
from langchain_experimental.comprehend_moderation import AmazonComprehendModerationChain
```

77
docs/docs/integrations/platforms/google.mdx
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@ -20,25 +20,9 @@ See a [usage example](/docs/integrations/llms/google_ai).
from langchain_google_genai import GoogleGenerativeAI from langchain_google_genai import GoogleGenerativeAI
``` ```
### Vertex AI ### Vertex AI Model Garden
Access to `Gemini` and `PaLM` LLMs (like `text-bison` and `code-bison`) via `Vertex AI` on Google Cloud. Access `PaLM` and hundreds of OSS models via `Vertex AI Model Garden` service.
We need to install `langchain-google-vertexai` python package.
```bash
pip install langchain-google-vertexai
```
See a [usage example](/docs/integrations/llms/google_vertex_ai_palm).
```python
from langchain_google_vertexai import VertexAI
```
### Model Garden
Access PaLM and hundreds of OSS models via `Vertex AI Model Garden` on Google Cloud.
We need to install `langchain-google-vertexai` python package. We need to install `langchain-google-vertexai` python package.
@ -52,6 +36,7 @@ See a [usage example](/docs/integrations/llms/google_vertex_ai_palm#vertex-model
from langchain_google_vertexai import VertexAIModelGarden from langchain_google_vertexai import VertexAIModelGarden
``` ```
## Chat models ## Chat models
### Google Generative AI ### Google Generative AI
@ -119,6 +104,40 @@ See a [usage example](/docs/integrations/chat/google_vertex_ai_palm).
from langchain_google_vertexai import ChatVertexAI from langchain_google_vertexai import ChatVertexAI
``` ```
## Embedding models
### Google Generative AI Embeddings
See a [usage example](/docs/integrations/text_embedding/google_generative_ai).
```bash
pip install -U langchain-google-genai
```
Configure your API key.
```bash
export GOOGLE_API_KEY=your-api-key
```
```python
from langchain_google_genai import GoogleGenerativeAIEmbeddings
```
### Vertex AI
We need to install `langchain-google-vertexai` python package.
```bash
pip install langchain-google-vertexai
```
See a [usage example](/docs/integrations/text_embedding/google_vertex_ai_palm).
```python
from langchain_google_vertexai import VertexAIEmbeddings
```
## Document Loaders ## Document Loaders
### AlloyDB for PostgreSQL ### AlloyDB for PostgreSQL
@ -797,22 +816,6 @@ See [usage example](/docs/integrations/memory/google_cloud_sql_mssql).
from langchain_google_cloud_sql_mssql import MSSQLEngine, MSSQLChatMessageHistory from langchain_google_cloud_sql_mssql import MSSQLEngine, MSSQLChatMessageHistory
``` ```
## El Carro for Oracle Workloads
> Google [El Carro Oracle Operator](https://github.com/GoogleCloudPlatform/elcarro-oracle-operator)
offers a way to run Oracle databases in Kubernetes as a portable, open source,
community driven, no vendor lock-in container orchestration system.
```bash
pip install langchain-google-el-carro
```
See [usage example](/docs/integrations/memory/google_el_carro).
```python
from langchain_google_el_carro import ElCarroChatMessageHistory
```
### Spanner ### Spanner
> [Google Cloud Spanner](https://cloud.google.com/spanner/docs) is a fully managed, mission-critical, relational database service on Google Cloud that offers transactional consistency at global scale, automatic, synchronous replication for high availability, and support for two SQL dialects: GoogleSQL (ANSI 2011 with extensions) and PostgreSQL. > [Google Cloud Spanner](https://cloud.google.com/spanner/docs) is a fully managed, mission-critical, relational database service on Google Cloud that offers transactional consistency at global scale, automatic, synchronous replication for high availability, and support for two SQL dialects: GoogleSQL (ANSI 2011 with extensions) and PostgreSQL.
@ -889,10 +892,10 @@ See [usage example](/docs/integrations/memory/google_datastore).
from langchain_google_datastore import DatastoreChatMessageHistory from langchain_google_datastore import DatastoreChatMessageHistory
``` ```
## El Carro Oracle Operator ### El Carro: The Oracle Operator for Kubernetes
> Google [El Carro Oracle Operator](https://github.com/GoogleCloudPlatform/elcarro-oracle-operator) > Google [El Carro Oracle Operator for Kubernetes](https://github.com/GoogleCloudPlatform/elcarro-oracle-operator)
offers a way to run Oracle databases in Kubernetes as a portable, open source, offers a way to run `Oracle` databases in `Kubernetes` as a portable, open source,
community driven, no vendor lock-in container orchestration system. community driven, no vendor lock-in container orchestration system.
```bash ```bash

47
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@ -2,6 +2,15 @@
All functionality related to `Microsoft Azure` and other `Microsoft` products. All functionality related to `Microsoft Azure` and other `Microsoft` products.
## LLMs
### Azure OpenAI
See a [usage example](/docs/integrations/llms/azure_openai).
```python
from langchain_openai import AzureOpenAI
```
## Chat Models ## Chat Models
### Azure OpenAI ### Azure OpenAI
@ -29,7 +38,7 @@ See a [usage example](/docs/integrations/chat/azure_chat_openai)
from langchain_openai import AzureChatOpenAI from langchain_openai import AzureChatOpenAI
``` ```
## Text Embedding Models ## Embedding Models
### Azure OpenAI ### Azure OpenAI
See a [usage example](/docs/integrations/text_embedding/azureopenai) See a [usage example](/docs/integrations/text_embedding/azureopenai)
@ -38,15 +47,6 @@ See a [usage example](/docs/integrations/text_embedding/azureopenai)
from langchain_openai import AzureOpenAIEmbeddings from langchain_openai import AzureOpenAIEmbeddings
``` ```
## LLMs
### Azure OpenAI
See a [usage example](/docs/integrations/llms/azure_openai).
```python
from langchain_openai import AzureOpenAI
```
## Document loaders ## Document loaders
### Azure AI Data ### Azure AI Data
@ -209,7 +209,6 @@ See a [usage example](/docs/integrations/document_loaders/microsoft_onenote).
from langchain_community.document_loaders.onenote import OneNoteLoader from langchain_community.document_loaders.onenote import OneNoteLoader
``` ```
## Vector stores ## Vector stores
### Azure Cosmos DB ### Azure Cosmos DB
@ -262,19 +261,6 @@ See a [usage example](/docs/integrations/retrievers/azure_cognitive_search).
from langchain.retrievers import AzureCognitiveSearchRetriever from langchain.retrievers import AzureCognitiveSearchRetriever
``` ```
## Utilities
### Bing Search API
>[Microsoft Bing](https://www.bing.com/), commonly referred to as `Bing` or `Bing Search`,
> is a web search engine owned and operated by `Microsoft`.
See a [usage example](/docs/integrations/tools/bing_search).
```python
from langchain_community.utilities import BingSearchAPIWrapper
```
## Toolkits ## Toolkits
### Azure Cognitive Services ### Azure Cognitive Services
@ -320,6 +306,19 @@ from langchain_community.agent_toolkits import PowerBIToolkit
from langchain_community.utilities.powerbi import PowerBIDataset from langchain_community.utilities.powerbi import PowerBIDataset
``` ```
## Utilities
### Bing Search API
>[Microsoft Bing](https://www.bing.com/), commonly referred to as `Bing` or `Bing Search`,
> is a web search engine owned and operated by `Microsoft`.
See a [usage example](/docs/integrations/tools/bing_search).
```python
from langchain_community.utilities import BingSearchAPIWrapper
```
## More ## More
### Microsoft Presidio ### Microsoft Presidio

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docs/docs/integrations/platforms/openai.mdx
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@ -36,7 +36,6 @@ from langchain_openai import AzureOpenAI
``` ```
For a more detailed walkthrough of the `Azure` wrapper, see [here](/docs/integrations/llms/azure_openai) For a more detailed walkthrough of the `Azure` wrapper, see [here](/docs/integrations/llms/azure_openai)
## Chat model ## Chat model
See a [usage example](/docs/integrations/chat/openai). See a [usage example](/docs/integrations/chat/openai).
@ -51,8 +50,7 @@ from langchain_openai import AzureChatOpenAI
``` ```
For a more detailed walkthrough of the `Azure` wrapper, see [here](/docs/integrations/chat/azure_chat_openai) For a more detailed walkthrough of the `Azure` wrapper, see [here](/docs/integrations/chat/azure_chat_openai)
## Embedding Model
## Text Embedding Model
See a [usage example](/docs/integrations/text_embedding/openai) See a [usage example](/docs/integrations/text_embedding/openai)
@ -60,19 +58,6 @@ See a [usage example](/docs/integrations/text_embedding/openai)
from langchain_openai import OpenAIEmbeddings from langchain_openai import OpenAIEmbeddings
``` ```
## Tokenizer
There are several places you can use the `tiktoken` tokenizer. By default, it is used to count tokens
for OpenAI LLMs.
You can also use it to count tokens when splitting documents with
```python
from langchain_text_splitters import CharacterTextSplitter
CharacterTextSplitter.from_tiktoken_encoder(...)
```
For a more detailed walkthrough of this, see [this notebook](/docs/modules/data_connection/document_transformers/split_by_token#tiktoken)
## Document Loader ## Document Loader
See a [usage example](/docs/integrations/document_loaders/chatgpt_loader). See a [usage example](/docs/integrations/document_loaders/chatgpt_loader).
@ -89,12 +74,19 @@ See a [usage example](/docs/integrations/retrievers/chatgpt-plugin).
from langchain.retrievers import ChatGPTPluginRetriever from langchain.retrievers import ChatGPTPluginRetriever
``` ```
## Chain ## Tools
See a [usage example](/docs/guides/safety/moderation). ### Dall-E Image Generator
>[OpenAI Dall-E](https://openai.com/dall-e-3) are text-to-image models developed by `OpenAI`
> using deep learning methodologies to generate digital images from natural language descriptions,
> called "prompts".
See a [usage example](/docs/integrations/tools/dalle_image_generator).
```python ```python
from langchain.chains import OpenAIModerationChain from langchain_community.utilities.dalle_image_generator import DallEAPIWrapper
``` ```
## Adapter ## Adapter
@ -105,17 +97,24 @@ See a [usage example](/docs/integrations/adapters/openai).
from langchain.adapters import openai as lc_openai from langchain.adapters import openai as lc_openai
``` ```
## Tools ## Tokenizer
### Dall-E Image Generator There are several places you can use the `tiktoken` tokenizer. By default, it is used to count tokens
for OpenAI LLMs.
>[OpenAI Dall-E](https://openai.com/dall-e-3) are text-to-image models developed by `OpenAI` You can also use it to count tokens when splitting documents with
> using deep learning methodologies to generate digital images from natural language descriptions, ```python
> called "prompts". from langchain.text_splitter import CharacterTextSplitter
CharacterTextSplitter.from_tiktoken_encoder(...)
```
For a more detailed walkthrough of this, see [this notebook](/docs/modules/data_connection/document_transformers/split_by_token#tiktoken)
## Chain
See a [usage example](/docs/integrations/tools/dalle_image_generator). See a [usage example](/docs/guides/safety/moderation).
```python ```python
from langchain_community.utilities.dalle_image_generator import DallEAPIWrapper from langchain.chains import OpenAIModerationChain
``` ```

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