mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-24 07:35:18 +00:00
docs: platform pages update (#17836)
`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"
This commit is contained in:
Leonid Ganeline
GitHub
parent
07c518ad3e
commit
3dabd3f214
@ -67,7 +67,7 @@ See a [usage example](/docs/integrations/chat/bedrock).
|
||||
from langchain_community.chat_models import BedrockChat
|
||||
```
|
||||
|
||||
## Text Embedding Models
|
||||
## Embedding Models
|
||||
|
||||
### Bedrock
|
||||
|
||||
@ -84,26 +84,6 @@ from langchain_community.embeddings import SagemakerEndpointEmbeddings
|
||||
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
|
||||
|
||||
### AWS S3 Directory and File
|
||||
@ -132,71 +112,6 @@ See a [usage example](/docs/integrations/document_loaders/amazon_textract).
|
||||
from langchain_community.document_loaders import AmazonTextractPDFLoader
|
||||
```
|
||||
|
||||
## 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
|
||||
```
|
||||
|
||||
## Retrievers
|
||||
|
||||
### Amazon Kendra
|
||||
|
||||
> [Amazon Kendra](https://docs.aws.amazon.com/kendra/latest/dg/what-is-kendra.html) is an intelligent search service
|
||||
> provided by `Amazon Web Services` (`AWS`). It utilizes advanced natural language processing (NLP) and machine
|
||||
> learning algorithms to enable powerful search capabilities across various data sources within an organization.
|
||||
> `Kendra` is designed to help users find the information they need quickly and accurately,
|
||||
> improving productivity and decision-making.
|
||||
|
||||
> With `Kendra`, we can search across a wide range of content types, including documents, FAQs, knowledge bases,
|
||||
> manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and
|
||||
> contextual meanings to provide highly relevant search results.
|
||||
|
||||
We need to install the `boto3` library.
|
||||
|
||||
```bash
|
||||
pip install boto3
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/retrievers/amazon_kendra_retriever).
|
||||
|
||||
```python
|
||||
from langchain.retrievers import AmazonKendraRetriever
|
||||
```
|
||||
|
||||
### Amazon Bedrock (Knowledge Bases)
|
||||
|
||||
> [Knowledge bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an
|
||||
> `Amazon Web Services` (`AWS`) offering which lets you quickly build RAG applications by using your
|
||||
> private data to customize foundation model response.
|
||||
|
||||
We need to install the `boto3` library.
|
||||
|
||||
```bash
|
||||
pip install boto3
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/retrievers/bedrock).
|
||||
|
||||
```python
|
||||
from langchain.retrievers import AmazonKnowledgeBasesRetriever
|
||||
```
|
||||
|
||||
## Vector stores
|
||||
|
||||
### Amazon OpenSearch Service
|
||||
@ -248,6 +163,49 @@ See a [usage example](/docs/integrations/vectorstores/documentdb).
|
||||
from langchain.vectorstores import DocumentDBVectorSearch
|
||||
```
|
||||
|
||||
## Retrievers
|
||||
|
||||
### Amazon Kendra
|
||||
|
||||
> [Amazon Kendra](https://docs.aws.amazon.com/kendra/latest/dg/what-is-kendra.html) is an intelligent search service
|
||||
> provided by `Amazon Web Services` (`AWS`). It utilizes advanced natural language processing (NLP) and machine
|
||||
> learning algorithms to enable powerful search capabilities across various data sources within an organization.
|
||||
> `Kendra` is designed to help users find the information they need quickly and accurately,
|
||||
> improving productivity and decision-making.
|
||||
|
||||
> With `Kendra`, we can search across a wide range of content types, including documents, FAQs, knowledge bases,
|
||||
> manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and
|
||||
> contextual meanings to provide highly relevant search results.
|
||||
|
||||
We need to install the `boto3` library.
|
||||
|
||||
```bash
|
||||
pip install boto3
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/retrievers/amazon_kendra_retriever).
|
||||
|
||||
```python
|
||||
from langchain.retrievers import AmazonKendraRetriever
|
||||
```
|
||||
|
||||
### Amazon Bedrock (Knowledge Bases)
|
||||
|
||||
> [Knowledge bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an
|
||||
> `Amazon Web Services` (`AWS`) offering which lets you quickly build RAG applications by using your
|
||||
> private data to customize foundation model response.
|
||||
|
||||
We need to install the `boto3` library.
|
||||
|
||||
```bash
|
||||
pip install boto3
|
||||
```
|
||||
|
||||
See a [usage example](/docs/integrations/retrievers/bedrock).
|
||||
|
||||
```python
|
||||
from langchain.retrievers import AmazonKnowledgeBasesRetriever
|
||||
```
|
||||
|
||||
## Tools
|
||||
|
||||
@ -267,6 +225,26 @@ pip install boto3
|
||||
|
||||
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
|
||||
|
||||
@ -290,3 +268,23 @@ See a [usage example](/docs/integrations/callbacks/sagemaker_tracking).
|
||||
```python
|
||||
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
|
||||
```
|
||||
|
||||
@ -20,25 +20,9 @@ See a [usage example](/docs/integrations/llms/google_ai).
|
||||
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.
|
||||
|
||||
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.
|
||||
Access `PaLM` and hundreds of OSS models via `Vertex AI Model Garden` service.
|
||||
|
||||
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
|
||||
```
|
||||
|
||||
|
||||
## Chat models
|
||||
|
||||
### Google Generative AI
|
||||
@ -119,6 +104,40 @@ See a [usage example](/docs/integrations/chat/google_vertex_ai_palm).
|
||||
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
|
||||
|
||||
### 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
|
||||
```
|
||||
|
||||
## 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
|
||||
|
||||
> [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
|
||||
```
|
||||
|
||||
## El Carro Oracle Operator
|
||||
### El Carro: The Oracle Operator for Kubernetes
|
||||
|
||||
> 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,
|
||||
> 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,
|
||||
community driven, no vendor lock-in container orchestration system.
|
||||
|
||||
```bash
|
||||
|
||||
@ -2,6 +2,15 @@
|
||||
|
||||
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
|
||||
### Azure OpenAI
|
||||
|
||||
@ -29,7 +38,7 @@ See a [usage example](/docs/integrations/chat/azure_chat_openai)
|
||||
from langchain_openai import AzureChatOpenAI
|
||||
```
|
||||
|
||||
## Text Embedding Models
|
||||
## Embedding Models
|
||||
### Azure OpenAI
|
||||
|
||||
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
|
||||
```
|
||||
|
||||
## LLMs
|
||||
### Azure OpenAI
|
||||
|
||||
See a [usage example](/docs/integrations/llms/azure_openai).
|
||||
|
||||
```python
|
||||
from langchain_openai import AzureOpenAI
|
||||
```
|
||||
|
||||
## Document loaders
|
||||
|
||||
### 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
|
||||
```
|
||||
|
||||
|
||||
## Vector stores
|
||||
|
||||
### Azure Cosmos DB
|
||||
@ -262,19 +261,6 @@ See a [usage example](/docs/integrations/retrievers/azure_cognitive_search).
|
||||
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
|
||||
|
||||
### Azure Cognitive Services
|
||||
@ -320,6 +306,19 @@ from langchain_community.agent_toolkits import PowerBIToolkit
|
||||
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
|
||||
|
||||
### Microsoft Presidio
|
||||
|
||||
@ -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)
|
||||
|
||||
|
||||
## Chat model
|
||||
|
||||
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)
|
||||
|
||||
|
||||
## Text Embedding Model
|
||||
## Embedding Model
|
||||
|
||||
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
|
||||
```
|
||||
|
||||
|
||||
## 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
|
||||
|
||||
See a [usage example](/docs/integrations/document_loaders/chatgpt_loader).
|
||||
@ -89,22 +74,6 @@ See a [usage example](/docs/integrations/retrievers/chatgpt-plugin).
|
||||
from langchain.retrievers import ChatGPTPluginRetriever
|
||||
```
|
||||
|
||||
## Chain
|
||||
|
||||
See a [usage example](/docs/guides/safety/moderation).
|
||||
|
||||
```python
|
||||
from langchain.chains import OpenAIModerationChain
|
||||
```
|
||||
|
||||
## Adapter
|
||||
|
||||
See a [usage example](/docs/integrations/adapters/openai).
|
||||
|
||||
```python
|
||||
from langchain.adapters import openai as lc_openai
|
||||
```
|
||||
|
||||
## Tools
|
||||
|
||||
### Dall-E Image Generator
|
||||
@ -119,3 +88,33 @@ See a [usage example](/docs/integrations/tools/dalle_image_generator).
|
||||
```python
|
||||
from langchain_community.utilities.dalle_image_generator import DallEAPIWrapper
|
||||
```
|
||||
|
||||
## Adapter
|
||||
|
||||
See a [usage example](/docs/integrations/adapters/openai).
|
||||
|
||||
```python
|
||||
from langchain.adapters import openai as lc_openai
|
||||
```
|
||||
|
||||
## 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_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/guides/safety/moderation).
|
||||
|
||||
```python
|
||||
from langchain.chains import OpenAIModerationChain
|
||||
```
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user