docs: add installation note for extensions (#291)

examples/extensions/__init__.py

@@ -3,4 +3,10 @@
 
 Use large language models with your favourite framework, like LangChain / LLamaIndex or Transformers (HuggingFace).
 If you want to use your model but still use the SDK, try the LocalServer extension provided by the SDK.
+
+.. admonition:: Before you start
+    :class: important
+
+    To use one of the following extensions, first install it by running
+    :bash:`pip install 'ibm-generative-ai[extension_name]'` (the extension name is always lower-cased).
 """

examples/extensions/huggingface/__init__.py

@@ -1,3 +1,9 @@
 """
 Transformers (HuggingFace)
+
+.. admonition:: Before you start
+    :class: important
+
+    To use the following extension, first install it by running
+    :bash:`pip install 'ibm-generative-ai[huggingface]'`.
 """

examples/extensions/langchain/__init__.py

@@ -1,3 +1,9 @@
 """
 LangChain
+
+.. admonition:: Before you start
+    :class: important
+
+    To use the following extension, first install it by running
+    :bash:`pip install 'ibm-generative-ai[langchain]'`.
 """

examples/extensions/llama_index/__init__.py

@@ -1,3 +1,9 @@
 """
 LLamaIndex
+
+.. admonition:: Before you start
+    :class: important
+
+    To use the following extension, first install it by running
+    :bash:`pip install 'ibm-generative-ai[llama-index]'`.
 """

examples/extensions/localserver/__init__.py

@@ -2,4 +2,10 @@
 LocalServer
 
 Create a minimal Rest API compatible with the SDK interface and run inference on your model.
+
+.. admonition:: Before you start
+    :class: important
+
+    To use the following extension, first install it by running
+    :bash:`pip install 'ibm-generative-ai[localserver]'`.
 """

src/genai/extensions/langchain/chat_llm.py

@@ -85,6 +85,7 @@ class LangChainChatInterface(BaseChatModel):
         from genai import Client, Credentials
         from genai.extensions.langchain import LangChainChatInterface
         from langchain_core.messages import HumanMessage, SystemMessage
+        from genai.text.generation import TextGenerationParameters
 
         client = Client(credentials=Credentials.from_env())
         llm = LangChainChatInterface(

src/genai/extensions/langchain/llm.py

@@ -55,6 +55,7 @@ class LangChainInterface(LLM):
 
         from genai import Client, Credentials
         from genai.extensions.langchain import LangChainInterface
+        from genai.text.generation import TextGenerationParameters
 
         client = Client(credentials=Credentials.from_env())
         llm = LangChainInterface(

src/genai/text/embedding/embedding_service.py

@@ -94,12 +94,13 @@ def create(
 
             client = Client(credentials=Credentials.from_env())
 
-            # Create a new conversation
-            response = client.text.embedding.create(
-                model_id="sentence-transformers/all-minilm-l6-v2",
-                input="Write a tagline for an alumni association: Together we"
+            responses = list(
+                client.text.embedding.create(
+                    model_id="sentence-transformers/all-minilm-l6-v2",
+                    input="Write a tagline for an alumni association: Together we"
+                )
             )
-            print("Output vectors", response.results)
+            print("Output vector", responses[0].results[0])
 
         Yields:
             TextEmbeddingCreateResponse object.