docs: integrations reference updates 5 (#25151)

Added missed references. Added missed provider pages.

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>

docs/docs/integrations/platforms/anthropic.mdx

@@ -26,6 +26,7 @@ from langchain_anthropic import ChatAnthropic
 model = ChatAnthropic(model='claude-3-opus-20240229')
 ```
 
+
 ## LLMs
 
 ### [Legacy] AnthropicLLM

docs/docs/integrations/platforms/aws.mdx

@@ -204,7 +204,7 @@ AWS offers services for computing, databases, storage, analytics, and other func
 See a [usage example](/docs/integrations/vectorstores/documentdb).
 
 ```python
-from langchain.vectorstores import DocumentDBVectorSearch
+from langchain_community.vectorstores import DocumentDBVectorSearch
 ```
 ### Amazon MemoryDB 
 [Amazon MemoryDB](https://aws.amazon.com/memorydb/) is a durable, in-memory database service that delivers ultra-fast performance. MemoryDB is compatible with Redis OSS, a popular open source data store, 
@@ -305,7 +305,7 @@ pip install boto3
 See a [usage example](/docs/integrations/memory/aws_dynamodb).
 
 ```python
-from langchain.memory import DynamoDBChatMessageHistory
+from langchain_community.chat_message_histories import DynamoDBChatMessageHistory
 ```
 
 ## Graphs
@@ -333,6 +333,12 @@ from langchain_community.chains.graph_qa.neptune_sparql import NeptuneSparqlQACh
 
 ## Callbacks
 
+### Bedrock token usage
+
+```python
+from langchain_community.callbacks.bedrock_anthropic_callback import BedrockAnthropicTokenUsageCallbackHandler
+```
+
 ### SageMaker Tracking
 
 >[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that is used to quickly 
@@ -351,7 +357,7 @@ pip install google-search-results sagemaker
 See a [usage example](/docs/integrations/callbacks/sagemaker_tracking).
 
 ```python
-from langchain.callbacks import SageMakerCallbackHandler
+from langchain_community.callbacks import SageMakerCallbackHandler
 ```
 
 ## Chains

docs/docs/integrations/platforms/google.mdx

@@ -72,6 +72,14 @@ See a [usage example](/docs/integrations/chat/google_vertex_ai_palm).
 from langchain_google_vertexai import ChatVertexAI
 ```
 
+### Chat Anthropic on Vertex AI
+
+See a [usage example](/docs/integrations/llms/google_vertex_ai_palm).
+
+```python
+from langchain_google_vertexai.model_garden import ChatAnthropicVertex
+```
+
 ## LLMs
 
 ### Google Generative AI

docs/docs/integrations/providers/arxiv.mdx

@@ -32,5 +32,5 @@ from langchain_community.document_loaders import ArxivLoader
 See a [usage example](/docs/integrations/retrievers/arxiv).
 
 ```python
-from langchain.retrievers import ArxivRetriever
+from langchain_community.retrievers import ArxivRetriever
 ```

docs/docs/integrations/providers/baidu.mdx

@@ -39,6 +39,20 @@ See a [usage example](/docs/integrations/text_embedding/baidu_qianfan_endpoint).
 from langchain_community.embeddings import QianfanEmbeddingsEndpoint
 ```
 
+## Document loaders
+
+### Baidu BOS Directory Loader
+
+```python
+from langchain_community.document_loaders.baiducloud_bos_directory import BaiduBOSDirectoryLoader
+```
+
+### Baidu BOS File Loader
+
+```python
+from langchain_community.document_loaders.baiducloud_bos_file import BaiduBOSFileLoader
+```
+
 ## Vector stores
 
 ### Baidu Cloud ElasticSearch VectorSearch

docs/docs/integrations/providers/bookendai.mdx

@@ -0,0 +1,18 @@
+# bookend.ai
+
+LangChain implements an integration with embeddings provided by [bookend.ai](https://bookend.ai/).
+
+
+## Installation and Setup
+
+
+You need to register and get the `API_KEY` 
+from the [bookend.ai](https://bookend.ai/) website.
+
+## Embedding model
+
+See a [usage example](/docs/integrations/text_embedding/bookend).
+
+```python
+from langchain_community.embeddings import BookendEmbeddings
+```

docs/docs/integrations/providers/coze.mdx

@@ -0,0 +1,19 @@
+# Coze
+
+[Coze](https://www.coze.com/) is an AI chatbot development platform that enables
+the creation and deployment of chatbots for handling diverse conversations across
+various applications.
+
+
+## Installation and Setup
+
+First, you need to get the `API_KEY` from the [Coze](https://www.coze.com/) website.
+
+
+## Chat models
+
+See a [usage example](/docs/integrations/chat/coze/).
+
+```python
+from langchain_community.chat_models import ChatCoze
+```

docs/docs/integrations/providers/rank_bm25.mdx

@@ -0,0 +1,25 @@
+# rank_bm25
+
+[rank_bm25](https://github.com/dorianbrown/rank_bm25) is an open-source collection of algorithms
+designed to query documents and return the most relevant ones, commonly used for creating
+search engines.
+
+See its [project page](https://github.com/dorianbrown/rank_bm25) for available algorithms.
+
+
+## Installation and Setup
+
+First, you need to install `rank_bm25` python package.
+
+```bash
+pip install rank_bm25
+```
+
+
+## Retriever
+
+See a [usage example](/docs/integrations/retrievers/bm25).
+
+```python
+from langchain_community.retrievers import BM25Retriever
+```