mistralai[patch]: Docs Update APIReference for MistralAIEmbeddings (l…

…angchain-ai#25294)

Update API Reference for MistralAI embeddings

Issue: langchain-ai#24856

libs/partners/mistralai/langchain_mistralai/embeddings.py

@@ -32,20 +32,82 @@ def encode_batch(self, texts: List[str]) -> List[List[str]]:
 
 
 class MistralAIEmbeddings(BaseModel, Embeddings):
-    """MistralAI embedding models.
+    """MistralAI embedding model integration.
 
-    To use, set the environment variable `MISTRAL_API_KEY` is set with your API key or
-    pass it as a named parameter to the constructor.
+    Setup:
+        Install ``langchain_mistralai`` and set environment variable
+        ``MISTRAL_API_KEY``.
 
-    Example:
+        .. code-block:: bash
+
+            pip install -U langchain_mistralai
+            export MISTRAL_API_KEY="your-api-key"
+
+    Key init args — completion params:
+        model: str
+            Name of MistralAI model to use.
+
+    Key init args — client params:
+      api_key: Optional[SecretStr]
+        The API key for the MistralAI API. If not provided, it will be read from the
+        environment variable `MISTRAL_API_KEY`.
+      max_retries: int
+        The number of times to retry a request if it fails.
+      timeout: int
+        The number of seconds to wait for a response before timing out.
+      max_concurrent_requests: int
+        The maximum number of concurrent requests to make to the Mistral API.
+
+    See full list of supported init args and their descriptions in the params section.
+
+    Instantiate:
         .. code-block:: python
 
-            from langchain_mistralai import MistralAIEmbeddings
+            from __module_name__ import MistralAIEmbeddings
 
-            mistral = MistralAIEmbeddings(
+            embed = MistralAIEmbeddings(
                 model="mistral-embed",
-                api_key="my-api-key"
+                # api_key="...",
+                # other params...
             )
+
+    Embed single text:
+        .. code-block:: python
+
+            input_text = "The meaning of life is 42"
+            vector = embed.embed_query(input_text)
+            print(vector[:3])
+
+        .. code-block:: python
+
+            [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
+
+    Embed multiple text:
+        .. code-block:: python
+
+             input_texts = ["Document 1...", "Document 2..."]
+            vectors = embed.embed_documents(input_texts)
+            print(len(vectors))
+            # The first 3 coordinates for the first vector
+            print(vectors[0][:3])
+
+        .. code-block:: python
+
+            2
+            [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
+
+    Async:
+        .. code-block:: python
+
+            vector = await embed.aembed_query(input_text)
+           print(vector[:3])
+
+            # multiple:
+            # await embed.aembed_documents(input_texts)
+
+        .. code-block:: python
+
+            [-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
     """
 
     client: httpx.Client = Field(default=None)  #: :meta private: