bump version

litellm/main.py

@@ -659,6 +659,14 @@ def completion(
                 chat_model = CodeChatModel.from_pretrained(model)
 
             chat = chat_model.start_chat()
+
+            if stream:
+                model_response = chat.send_message_streaming(prompt, **optional_params)
+                response = CustomStreamWrapper(
+                    model_response, model, custom_llm_provider="vertexai", logging_obj=logging
+                )
+                return response
+
             completion_response = chat.send_message(prompt, **optional_params)
 
             ## LOGGING
@@ -692,6 +700,13 @@ def completion(
             else:
                 vertex_model = CodeGenerationModel.from_pretrained(model)
 
+            if stream:
+                model_response = vertex_model.predict_streaming(prompt, **optional_params)
+                response = CustomStreamWrapper(
+                    model_response, model, custom_llm_provider="vertexai", logging_obj=logging
+                )
+                return response
+
             completion_response = vertex_model.predict(prompt, **optional_params)
 
             ## LOGGING

litellm/utils.py

@@ -885,8 +885,8 @@ def get_optional_params(  # use the openai defaults
         if stop != None:
             optional_params["stop"] = stop #TG AI expects a list, example ["\n\n\n\n","<|endoftext|>"]
     elif (
-        model == "chat-bison"
-    ):  # chat-bison has diff args from chat-bison@001 ty Google
+        model in litellm.vertex_chat_models or model in litellm.vertex_code_chat_models
+    ):  # chat-bison has diff args from chat-bison@001, ty Google :) 
         if temperature != 1:
             optional_params["temperature"] = temperature
         if top_p != 1:
@@ -900,6 +900,12 @@ def get_optional_params(  # use the openai defaults
         optional_params["temperature"] = temperature
         optional_params["top_p"] = top_p
         optional_params["top_k"] = top_k
+        if max_tokens != float("inf"):
+            optional_params["max_output_tokens"] = max_tokens
+    elif model in model in litellm.vertex_code_text_models:
+        optional_params["temperature"] = temperature
+        if max_tokens != float("inf"):
+            optional_params["max_output_tokens"] = max_tokens
     elif custom_llm_provider == "baseten":
         optional_params["temperature"] = temperature
         optional_params["stream"] = stream
@@ -2482,6 +2488,9 @@ class CustomStreamWrapper:
             elif self.model in litellm.nlp_cloud_models or self.custom_llm_provider == "nlp_cloud":
                 chunk = next(self.completion_stream)
                 completion_obj["content"] = self.handle_nlp_cloud_chunk(chunk)
+            elif self.model in (litellm.vertex_chat_models + litellm.vertex_code_chat_models + litellm.vertex_text_models + litellm.vertex_code_text_models):
+                chunk = next(self.completion_stream)
+                completion_obj["content"] = str(chunk)
             elif self.model in litellm.cohere_models or self.custom_llm_provider == "cohere":
                 chunk = next(self.completion_stream)
                 completion_obj["content"] = self.handle_cohere_chunk(chunk)

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "0.1.639"
+version = "0.1.640"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT License"