docs: integrations updates 21 (#27380)

Added missed provider pages. Added descriptions and links. Fixed
inconsistency in text formatting.

---------

Co-authored-by: Erick Friis <erickfriis@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
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You can find the access instructions [here](https://open.larksuite.com/document)
## Document Loader
### Lark Suite
## Document Loaders
>[Lark Suite](https://www.larksuite.com/) is an enterprise collaboration platform
> developed by `ByteDance`.
See a [usage example](/docs/integrations/document_loaders/larksuite).
### Lark Suite for Document
See a [usage example](/docs/integrations/document_loaders/larksuite/#load-from-document).
```python
from langchain_community.document_loaders.larksuite import LarkSuiteDocLoader
```
### Lark Suite for Wiki
See a [usage example](/docs/integrations/document_loaders/larksuite/#load-from-wiki).
```python
from langchain_community.document_loaders.larksuite import LarkSuiteWikiLoader
```

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# LocalAI
>[LocalAI](https://localai.io/) is the free, Open Source OpenAI alternative.
> `LocalAI` act as a drop-in replacement REST API thats compatible with OpenAI API
> specifications for local inferencing. It allows you to run LLMs, generate images,
> audio (and not only) locally or on-prem with consumer grade hardware,
> supporting multiple model families and architectures.
## Installation and Setup
We have to install several python packages:
```bash
pip install tenacity openai
```
## Embedding models
See a [usage example](/docs/integrations/text_embedding/localai).
```python
from langchain_community.embeddings import LocalAIEmbeddings
```

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Install the Python SDK:
```bash
pip install pymilvus
pip install langchain-milvus
```
## Vector Store
There exists a wrapper around `Milvus` indexes, allowing you to use it as a vectorstore,
whether for semantic search or example selection.
See a [usage example](/docs/integrations/vectorstores/milvus).
To import this vectorstore:
```python
from langchain_community.vectorstores import Milvus
from langchain_milvus import Milvus
```
## Retrievers
See a [usage example](/docs/integrations/retrievers/milvus_hybrid_search).
To import this vectorstore:
```python
from langchain_milvus.retrievers import MilvusCollectionHybridSearchRetriever
from langchain_milvus.utils.sparse import BM25SparseEmbedding
```
For a more detailed walkthrough of the `Miluvs` wrapper, see [this notebook](/docs/integrations/vectorstores/milvus)

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# MongoDB
>[MongoDB](https://www.mongodb.com/) is a NoSQL, document-oriented
> database that supports JSON-like documents with a dynamic schema.
**NOTE:**
- See other `MongoDB` integrations on the [MongoDB Atlas page](/docs/integrations/providers/mongodb_atlas).
## Installation and Setup
Install the Python package:
```bash
pip install langchain-mongodb
```
## Message Histories
See a [usage example](/docs/integrations/memory/mongodb_chat_message_history).
To import this vectorstore:
```python
from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
```

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from langchain_mongodb import MongoDBAtlasVectorSearch
```
## Retrievers
## LLM Caches
### Full Text Search Retriever
>`Hybrid Search Retriever` performs full-text searches using
> Lucenes standard (`BM25`) analyzer.
```python
from langchain_mongodb.retrievers import MongoDBAtlasFullTextSearchRetriever
```
### Hybrid Search Retriever
>`Hybrid Search Retriever` combines vector and full-text searches weighting
> them the via `Reciprocal Rank Fusion` (`RRF`) algorithm.
```python
from langchain_mongodb.retrievers import MongoDBAtlasHybridSearchRetriever
```
## Model Caches
### MongoDBCache
An abstraction to store a simple cache in MongoDB. This does not use Semantic Caching, nor does it require an index to be made on the collection before generation.
To import this cache: