chore: enrich pyproject.toml files with links to new references, others (#33343)

libs/partners/mistralai/README.md

@@ -1,57 +1,3 @@
 # langchain-mistralai
 
-This package contains the LangChain integrations for [MistralAI](https://docs.mistral.ai) through their [mistralai](https://pypi.org/project/mistralai/) SDK.
-
-## Installation
-
-```bash
-pip install -U langchain-mistralai
-```
-
-## Chat Models
-
-This package contains the `ChatMistralAI` class, which is the recommended way to interface with MistralAI models.
-
-To use, install the requirements, and configure your environment.
-
-```bash
-export MISTRAL_API_KEY=your-api-key
-```
-
-Then initialize
-
-```python
-from langchain_core.messages import HumanMessage
-from langchain_mistralai.chat_models import ChatMistralAI
-
-chat = ChatMistralAI(model="mistral-small")
-messages = [HumanMessage(content="say a brief hello")]
-chat.invoke(messages)
-```
-
-`ChatMistralAI` also supports async and streaming functionality:
-
-```python
-# For async...
-await chat.ainvoke(messages)
-
-# For streaming...
-for chunk in chat.stream(messages):
-    print(chunk.content, end="", flush=True)
-```
-
-## Embeddings
-
-With `MistralAIEmbeddings`, you can directly use the default model 'mistral-embed', or set a different one if available.
-
-### Choose model
-
-`embedding.model = 'mistral-embed'`
-
-### Simple query
-
-`res_query = embedding.embed_query("The test information")`
-
-### Documents
-
-`res_document = embedding.embed_documents(["test1", "another test"])`
+View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/mistralai) for more details.