fireworks: Add APIReference for the FireworksEmbeddings model (langchain-ai#25292)

Add API Reference documentation for the FireworksEmbedding model.

Issue: langchain-ai#24856

Author: eyurtsev

libs/partners/fireworks/langchain_fireworks/embeddings.py

@@ -8,16 +8,62 @@
 
 
 class FireworksEmbeddings(BaseModel, Embeddings):
-    """FireworksEmbeddings embedding model.
+    """Fireworks embedding model integration.
 
-    Example:
-        .. code-block:: python
+     Setup:
+         Install ``langchain_fireworks`` and set environment variable
+         ``FIREWORKS_API_KEY``.
 
-            from langchain_fireworks import FireworksEmbeddings
+         .. code-block:: bash
 
-            model = FireworksEmbeddings(
-                model='nomic-ai/nomic-embed-text-v1.5'
-            )
+             pip install -U langchain_fireworks
+             export FIREWORKS_API_KEY="your-api-key"
+
+     Key init args — completion params:
+         model: str
+             Name of Fireworks model to use.
+
+    Key init args — client params:
+        fireworks_api_key: SecretStr
+            Fireworks API key.
+
+     See full list of supported init args and their descriptions in the params section.
+
+     Instantiate:
+         .. code-block:: python
+
+             from __module_name__ import FireworksEmbeddings
+
+             model = FireworksEmbeddings(
+                 model='nomic-ai/nomic-embed-text-v1.5'
+                 # Use FIREWORKS_API_KEY env var or pass it in directly
+                 # fireworks_api_key="..."
+             )
+
+     Embed multiple texts:
+         .. code-block:: python
+
+             vectors = embeddings.embed_documents(['hello', 'goodbye'])
+             # Showing only the first 3 coordinates
+             print(len(vectors))
+             print(vectors[0][:3])
+
+         .. code-block:: python
+
+             2
+             [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
+
+
+     Embed single text:
+         .. code-block:: python
+
+             input_text = "The meaning of life is 42"
+             vector = embeddings.embed_query('hello')
+             print(vector[:3])
+
+         .. code-block:: python
+
+             [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
     """
 
     _client: OpenAI = Field(default=None)