fix(utils.py): ensure last chunk is always empty delta w/ finish reason

makes sure we're openai-compatible with our streaming. Adds stricter tests for this as well

litellm/tests/log.txt

@@ -1,119 +1,6 @@
-============================= test session starts ==============================
+<litellm.utils.CustomStreamWrapper object at 0x118bd82d0>
-platform darwin -- Python 3.11.6, pytest-7.3.1, pluggy-1.3.0
+chunk: ModelResponse(id='chatcmpl-95b7d389-ff5a-4e09-a084-02584ba2cf1e', choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(content='In the United States of America, the Supreme Court has ultimate judicial authority, and it is the one that rules on legal disputes between the states, or on the interpretation of the', role='assistant', function_call=None, tool_calls=None), logprobs=None)], created=1711406570, model='ai21.j2-mid-v1', object='chat.completion.chunk', system_fingerprint=None, usage=Usage())
-rootdir: /Users/krrishdholakia/Documents/litellm/litellm/tests
+extracted chunk: In the United States of America, the Supreme Court has ultimate judicial authority, and it is the one that rules on legal disputes between the states, or on the interpretation of the
-plugins: timeout-2.2.0, asyncio-0.23.2, anyio-3.7.1, xdist-3.3.1
+chunk: ModelResponse(id='chatcmpl-95b7d389-ff5a-4e09-a084-02584ba2cf1e', choices=[StreamingChoices(finish_reason='stop', index=0, delta=Delta(content=None, role=None, function_call=None, tool_calls=None), logprobs=None)], created=1711406570, model='ai21.j2-mid-v1', object='chat.completion.chunk', system_fingerprint=None, usage=Usage())
-asyncio: mode=Mode.STRICT
+extracted chunk: 
-collected 1 item
+completion_response: In the United States of America, the Supreme Court has ultimate judicial authority, and it is the one that rules on legal disputes between the states, or on the interpretation of the
-
-test_completion.py .                                                     [100%]
-
-=============================== warnings summary ===============================
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271
-  /opt/homebrew/lib/python3.11/site-packages/pydantic/_internal/_config.py:271: PydanticDeprecatedSince20: Support for class-based `config` is deprecated, use ConfigDict instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
-    warnings.warn(DEPRECATION_MESSAGE, DeprecationWarning)
-
-../proxy/_types.py:102
-  /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:102: PydanticDeprecatedSince20: `pydantic.config.Extra` is deprecated, use literal values instead (e.g. `extra='allow'`). Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
-    extra = Extra.allow  # Allow extra fields
-
-../proxy/_types.py:105
-  /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:105: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
-    @root_validator(pre=True)
-
-../proxy/_types.py:134
-  /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:134: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
-    @root_validator(pre=True)
-
-../proxy/_types.py:180
-  /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:180: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
-    @root_validator(pre=True)
-
-../proxy/_types.py:241
-  /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:241: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
-    @root_validator(pre=True)
-
-../proxy/_types.py:253
-  /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:253: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
-    @root_validator(pre=True)
-
-../proxy/_types.py:292
-  /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:292: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
-    @root_validator(pre=True)
-
-../proxy/_types.py:319
-  /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:319: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
-    @root_validator(pre=True)
-
-../proxy/_types.py:570
-  /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:570: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
-    @root_validator(pre=True)
-
-../proxy/_types.py:591
-  /Users/krrishdholakia/Documents/litellm/litellm/proxy/_types.py:591: PydanticDeprecatedSince20: Pydantic V1 style `@root_validator` validators are deprecated. You should migrate to Pydantic V2 style `@model_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/
-    @root_validator(pre=True)
-
-../utils.py:35
-  /Users/krrishdholakia/Documents/litellm/litellm/utils.py:35: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
-    import pkg_resources
-
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: 10 warnings
-  /opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.
-  Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
-    declare_namespace(pkg)
-
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
-  /opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.cloud')`.
-  Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
-    declare_namespace(pkg)
-
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2350
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2350
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2350
-  /opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2350: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.
-  Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
-    declare_namespace(parent)
-
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
-  /opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.logging')`.
-  Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
-    declare_namespace(pkg)
-
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
-  /opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.iam')`.
-  Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
-    declare_namespace(pkg)
-
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
-  /opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('mpl_toolkits')`.
-  Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
-    declare_namespace(pkg)
-
-../../../../../../opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871
-  /opt/homebrew/lib/python3.11/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('sphinxcontrib')`.
-  Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
-    declare_namespace(pkg)
-
-../llms/prompt_templates/factory.py:6
-  /Users/krrishdholakia/Documents/litellm/litellm/llms/prompt_templates/factory.py:6: DeprecationWarning: 'imghdr' is deprecated and slated for removal in Python 3.13
-    import imghdr, base64
-
-test_completion.py::test_completion_claude_3_stream
-../utils.py:3249
-../utils.py:3249
-  /Users/krrishdholakia/Documents/litellm/litellm/utils.py:3249: DeprecationWarning: open_text is deprecated. Use files() instead. Refer to https://importlib-resources.readthedocs.io/en/latest/using.html#migrating-from-legacy for migration advice.
-    with resources.open_text(
-
--- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
-======================== 1 passed, 46 warnings in 3.14s ========================

litellm/tests/test_streaming.py

@@ -108,8 +108,19 @@ last_openai_chunk_example = {
     "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
 }
 
+"""
+Final chunk (sdk):
+chunk: ChatCompletionChunk(id='chatcmpl-96mM3oNBlxh2FDWVLKsgaFBBcULmI', 
+choices=[Choice(delta=ChoiceDelta(content=None, function_call=None, role=None, 
+tool_calls=None), finish_reason='stop', index=0, logprobs=None)], 
+created=1711402871, model='gpt-3.5-turbo-0125', object='chat.completion.chunk', system_fingerprint='fp_3bc1b5746c')
+"""
+
 
 def validate_last_format(chunk):
+    """
+    Ensure last chunk has no remaining content or tools
+    """
     assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary."
     assert isinstance(chunk["id"], str), "'id' should be a string."
     assert isinstance(chunk["object"], str), "'object' should be a string."
@@ -119,6 +130,10 @@ def validate_last_format(chunk):
 
     for choice in chunk["choices"]:
         assert isinstance(choice["index"], int), "'index' should be an integer."
+        assert choice["delta"]["content"] is None
+        assert choice["delta"]["function_call"] is None
+        assert choice["delta"]["role"] is None
+        assert choice["delta"]["tool_calls"] is None
         assert isinstance(
             choice["finish_reason"], str
         ), "'finish_reason' should be a string."
@@ -493,13 +508,15 @@ def test_completion_mistral_api_stream():
             stream=True,
         )
         complete_response = ""
+        has_finish_reason = False
         for idx, chunk in enumerate(response):
-            print(chunk)
-            # print(chunk.choices[0].delta)
             chunk, finished = streaming_format_tests(idx, chunk)
             if finished:
+                has_finish_reason = True
                 break
             complete_response += chunk
+        if has_finish_reason == False:
+            raise Exception("finish reason not set")
         if complete_response.strip() == "":
             raise Exception("Empty response received")
         print(f"completion_response: {complete_response}")
@@ -534,11 +551,15 @@ def test_completion_deep_infra_stream():
 
         complete_response = ""
         # Add any assertions here to check the response
+        has_finish_reason = False
         for idx, chunk in enumerate(response):
             chunk, finished = streaming_format_tests(idx, chunk)
             if finished:
+                has_finish_reason = True
                 break
             complete_response += chunk
+        if has_finish_reason == False:
+            raise Exception("finish reason not set")
         if complete_response.strip() == "":
             raise Exception("Empty response received")
         print(f"completion_response: {complete_response}")
@@ -608,11 +629,15 @@ def test_completion_claude_stream_bad_key():
         )
         complete_response = ""
         # Add any assertions here to check the response
+        has_finish_reason = False
         for idx, chunk in enumerate(response):
             chunk, finished = streaming_format_tests(idx, chunk)
             if finished:
+                has_finish_reason = True
                 break
             complete_response += chunk
+        if has_finish_reason == False:
+            raise Exception("finish reason not set")
         if complete_response.strip() == "":
             raise Exception("Empty response received")
         print(f"1234completion_response: {complete_response}")
@@ -626,6 +651,45 @@ def test_completion_claude_stream_bad_key():
 # test_completion_claude_stream_bad_key()
 # test_completion_replicate_stream()
 
+
+def test_vertex_ai_stream():
+    from litellm.tests.test_amazing_vertex_completion import load_vertex_ai_credentials
+
+    load_vertex_ai_credentials()
+    litellm.set_verbose = True
+    litellm.vertex_project = "reliablekeys"
+    import random
+
+    test_models = ["gemini-1.0-pro"]
+    for model in test_models:
+        try:
+            print("making request", model)
+            response = completion(
+                model=model,
+                messages=[
+                    {"role": "user", "content": "write 10 line code code for saying hi"}
+                ],
+                stream=True,
+            )
+            complete_response = ""
+            is_finished = False
+            for idx, chunk in enumerate(response):
+                print(f"chunk in response: {chunk}")
+                chunk, finished = streaming_format_tests(idx, chunk)
+                if finished:
+                    is_finished = True
+                    break
+                complete_response += chunk
+            if complete_response.strip() == "":
+                raise Exception("Empty response received")
+            print(f"completion_response: {complete_response}")
+            assert is_finished == True
+        except litellm.RateLimitError as e:
+            pass
+        except Exception as e:
+            pytest.fail(f"Error occurred: {e}")
+
+
 # def test_completion_vertexai_stream():
 #     try:
 #         import os
@@ -742,11 +806,15 @@ def test_bedrock_claude_3_streaming():
         )
         complete_response = ""
         # Add any assertions here to check the response
+        has_finish_reason = False
         for idx, chunk in enumerate(response):
             chunk, finished = streaming_format_tests(idx, chunk)
             if finished:
+                has_finish_reason = True
                 break
             complete_response += chunk
+        if has_finish_reason == False:
+            raise Exception("finish reason not set")
         if complete_response.strip() == "":
             raise Exception("Empty response received")
         print(f"completion_response: {complete_response}")
@@ -1705,7 +1773,7 @@ def test_success_callback_streaming():
     messages = [{"role": "user", "content": "hello"}]
     print("TESTING LITELLM COMPLETION CALL")
     response = litellm.completion(
-        model="j2-light",
+        model="gpt-3.5-turbo",
         messages=messages,
         stream=True,
         max_tokens=5,
@@ -2072,7 +2140,7 @@ def test_completion_claude_3_function_call_with_streaming():
         )
         idx = 0
         for chunk in response:
-            # print(f"chunk: {chunk}")
+            print(f"chunk in response: {chunk}")
             if idx == 0:
                 assert (
                     chunk.choices[0].delta.tool_calls[0].function.arguments is not None
@@ -2081,7 +2149,7 @@ def test_completion_claude_3_function_call_with_streaming():
                     chunk.choices[0].delta.tool_calls[0].function.arguments, str
                 )
                 validate_first_streaming_function_calling_chunk(chunk=chunk)
-            elif idx == 1:
+            elif idx == 1 and chunk.choices[0].finish_reason is None:
                 validate_second_streaming_function_calling_chunk(chunk=chunk)
             elif chunk.choices[0].finish_reason is not None:  # last chunk
                 validate_final_streaming_function_calling_chunk(chunk=chunk)
@@ -2136,7 +2204,7 @@ async def test_acompletion_claude_3_function_call_with_streaming():
                     chunk.choices[0].delta.tool_calls[0].function.arguments, str
                 )
                 validate_first_streaming_function_calling_chunk(chunk=chunk)
-            elif idx == 1:
+            elif idx == 1 and chunk.choices[0].finish_reason is None:
                 validate_second_streaming_function_calling_chunk(chunk=chunk)
             elif chunk.choices[0].finish_reason is not None:  # last chunk
                 validate_final_streaming_function_calling_chunk(chunk=chunk)

litellm/utils.py

@@ -8458,6 +8458,7 @@ class CustomStreamWrapper:
         self.completion_stream = completion_stream
         self.sent_first_chunk = False
         self.sent_last_chunk = False
+        self.received_finish_reason: Optional[str] = None
         self.special_tokens = ["<|assistant|>", "<|system|>", "<|user|>", "<s>", "</s>"]
         self.holding_chunk = ""
         self.complete_response = ""
@@ -9131,7 +9132,7 @@ class CustomStreamWrapper:
                 "finish_reason": finish_reason,
             }
 
-    def chunk_creator(self, chunk):
+    def model_response_creator(self):
         model_response = ModelResponse(stream=True, model=self.model)
         if self.response_id is not None:
             model_response.id = self.response_id
@@ -9141,6 +9142,20 @@ class CustomStreamWrapper:
         model_response._hidden_params["created_at"] = time.time()
         model_response.choices = [StreamingChoices()]
         model_response.choices[0].finish_reason = None
+        return model_response
+
+    def is_delta_empty(self, delta: Delta) -> bool:
+        is_empty = True
+        if delta.content is not None:
+            is_empty = False
+        elif delta.tool_calls is not None:
+            is_empty = False
+        elif delta.function_call is not None:
+            is_empty = False
+        return is_empty
+
+    def chunk_creator(self, chunk):
+        model_response = self.model_response_creator()
         response_obj = {}
         try:
             # return this for all models
@@ -9149,30 +9164,22 @@ class CustomStreamWrapper:
                 response_obj = self.handle_anthropic_chunk(chunk)
                 completion_obj["content"] = response_obj["text"]
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif self.model == "replicate" or self.custom_llm_provider == "replicate":
                 response_obj = self.handle_replicate_chunk(chunk)
                 completion_obj["content"] = response_obj["text"]
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif self.custom_llm_provider and self.custom_llm_provider == "together_ai":
                 response_obj = self.handle_together_ai_chunk(chunk)
                 completion_obj["content"] = response_obj["text"]
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif self.custom_llm_provider and self.custom_llm_provider == "huggingface":
                 response_obj = self.handle_huggingface_chunk(chunk)
                 completion_obj["content"] = response_obj["text"]
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif (
                 self.custom_llm_provider and self.custom_llm_provider == "baseten"
             ):  # baseten doesn't provide streaming
@@ -9183,16 +9190,12 @@ class CustomStreamWrapper:
                 response_obj = self.handle_ai21_chunk(chunk)
                 completion_obj["content"] = response_obj["text"]
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif self.custom_llm_provider and self.custom_llm_provider == "maritalk":
                 response_obj = self.handle_maritalk_chunk(chunk)
                 completion_obj["content"] = response_obj["text"]
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif self.custom_llm_provider and self.custom_llm_provider == "vllm":
                 completion_obj["content"] = chunk[0].outputs[0].text
             elif (
@@ -9201,152 +9204,116 @@ class CustomStreamWrapper:
                 response_obj = self.handle_aleph_alpha_chunk(chunk)
                 completion_obj["content"] = response_obj["text"]
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif self.custom_llm_provider == "nlp_cloud":
                 try:
                     response_obj = self.handle_nlp_cloud_chunk(chunk)
                     completion_obj["content"] = response_obj["text"]
                     if response_obj["is_finished"]:
-                        model_response.choices[0].finish_reason = response_obj[
+                        self.received_finish_reason = response_obj["finish_reason"]
-                            "finish_reason"
-                        ]
                 except Exception as e:
-                    if self.sent_last_chunk:
+                    if self.received_finish_reason:
                         raise e
                     else:
                         if self.sent_first_chunk is False:
                             raise Exception("An unknown error occurred with the stream")
-                        model_response.choices[0].finish_reason = "stop"
+                        self.received_finish_reason = "stop"
-                        self.sent_last_chunk = True
             elif self.custom_llm_provider == "gemini":
-                try:
+                if hasattr(chunk, "parts") == True:
-                    if hasattr(chunk, "parts") == True:
+                    try:
-                        try:
+                        if len(chunk.parts) > 0:
-                            if len(chunk.parts) > 0:
+                            completion_obj["content"] = chunk.parts[0].text
-                                completion_obj["content"] = chunk.parts[0].text
+                        if hasattr(chunk.parts[0], "finish_reason"):
-                            if hasattr(chunk.parts[0], "finish_reason"):
+                            self.received_finish_reason = chunk.parts[
-                                model_response.choices[0].finish_reason = (
+                                0
-                                    map_finish_reason(chunk.parts[0].finish_reason.name)
+                            ].finish_reason.name
-                                )
+                    except:
-                        except:
+                        if chunk.parts[0].finish_reason.name == "SAFETY":
-                            if chunk.parts[0].finish_reason.name == "SAFETY":
+                            raise Exception(
-                                raise Exception(
+                                f"The response was blocked by VertexAI. {str(chunk)}"
-                                    f"The response was blocked by VertexAI. {str(chunk)}"
+                            )
-                                )
+                else:
-                    else:
+                    completion_obj["content"] = str(chunk)
-                        completion_obj["content"] = str(chunk)
-                except StopIteration as e:
-                    if self.sent_last_chunk:
-                        raise e
-                    else:
-                        model_response.choices[0].finish_reason = "stop"
-                        self.sent_last_chunk = True
             elif self.custom_llm_provider and (self.custom_llm_provider == "vertex_ai"):
-                try:
+                if hasattr(chunk, "candidates") == True:
-                    if hasattr(chunk, "candidates") == True:
+                    try:
                         try:
-                            try:
+                            completion_obj["content"] = chunk.text
-                                completion_obj["content"] = chunk.text
-                            except Exception as e:
-                                if "Part has no text." in str(e):
-                                    ## check for function calling
-                                    function_call = (
-                                        chunk.candidates[0]
-                                        .content.parts[0]
-                                        .function_call
-                                    )
-                                    args_dict = {}
-                                    for k, v in function_call.args.items():
-                                        args_dict[k] = v
-                                    args_str = json.dumps(args_dict)
-                                    _delta_obj = litellm.utils.Delta(
-                                        content=None,
-                                        tool_calls=[
-                                            {
-                                                "id": f"call_{str(uuid.uuid4())}",
-                                                "function": {
-                                                    "arguments": args_str,
-                                                    "name": function_call.name,
-                                                },
-                                                "type": "function",
-                                            }
-                                        ],
-                                    )
-                                    _streaming_response = StreamingChoices(
-                                        delta=_delta_obj
-                                    )
-                                    _model_response = ModelResponse(stream=True)
-                                    _model_response.choices = [_streaming_response]
-                                    response_obj = {"original_chunk": _model_response}
-                                else:
-                                    raise e
-                            if (
-                                hasattr(chunk.candidates[0], "finish_reason")
-                                and chunk.candidates[0].finish_reason.name
-                                != "FINISH_REASON_UNSPECIFIED"
-                            ):  # every non-final chunk in vertex ai has this
-                                model_response.choices[0].finish_reason = (
-                                    map_finish_reason(
-                                        chunk.candidates[0].finish_reason.name
-                                    )
-                                )
                         except Exception as e:
-                            if chunk.candidates[0].finish_reason.name == "SAFETY":
+                            if "Part has no text." in str(e):
-                                raise Exception(
+                                ## check for function calling
-                                    f"The response was blocked by VertexAI. {str(chunk)}"
+                                function_call = (
+                                    chunk.candidates[0].content.parts[0].function_call
                                 )
-                    else:
+                                args_dict = {}
-                        completion_obj["content"] = str(chunk)
+                                for k, v in function_call.args.items():
-                except StopIteration as e:
+                                    args_dict[k] = v
-                    if self.sent_last_chunk:
+                                args_str = json.dumps(args_dict)
-                        raise e
+                                _delta_obj = litellm.utils.Delta(
-                    else:
+                                    content=None,
-                        model_response.choices[0].finish_reason = "stop"
+                                    tool_calls=[
-                        self.sent_last_chunk = True
+                                        {
+                                            "id": f"call_{str(uuid.uuid4())}",
+                                            "function": {
+                                                "arguments": args_str,
+                                                "name": function_call.name,
+                                            },
+                                            "type": "function",
+                                        }
+                                    ],
+                                )
+                                _streaming_response = StreamingChoices(delta=_delta_obj)
+                                _model_response = ModelResponse(stream=True)
+                                _model_response.choices = [_streaming_response]
+                                response_obj = {"original_chunk": _model_response}
+                            else:
+                                raise e
+                        if (
+                            hasattr(chunk.candidates[0], "finish_reason")
+                            and chunk.candidates[0].finish_reason.name
+                            != "FINISH_REASON_UNSPECIFIED"
+                        ):  # every non-final chunk in vertex ai has this
+                            self.received_finish_reason = chunk.candidates[
+                                0
+                            ].finish_reason.name
+                    except Exception as e:
+                        if chunk.candidates[0].finish_reason.name == "SAFETY":
+                            raise Exception(
+                                f"The response was blocked by VertexAI. {str(chunk)}"
+                            )
+                else:
+                    completion_obj["content"] = str(chunk)
             elif self.custom_llm_provider == "cohere":
                 response_obj = self.handle_cohere_chunk(chunk)
                 completion_obj["content"] = response_obj["text"]
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif self.custom_llm_provider == "cohere_chat":
                 response_obj = self.handle_cohere_chat_chunk(chunk)
                 if response_obj is None:
                     return
                 completion_obj["content"] = response_obj["text"]
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif self.custom_llm_provider == "bedrock":
-                if self.sent_last_chunk:
+                if self.received_finish_reason is not None:
                     raise StopIteration
                 response_obj = self.handle_bedrock_stream(chunk)
                 completion_obj["content"] = response_obj["text"]
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
-                    self.sent_last_chunk = True
             elif self.custom_llm_provider == "sagemaker":
-                verbose_logger.debug(f"ENTERS SAGEMAKER STREAMING for chunk {chunk}")
+                print_verbose(f"ENTERS SAGEMAKER STREAMING for chunk {chunk}")
                 response_obj = self.handle_sagemaker_stream(chunk)
                 completion_obj["content"] = response_obj["text"]
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
-                    self.sent_last_chunk = True
             elif self.custom_llm_provider == "petals":
                 if len(self.completion_stream) == 0:
-                    if self.sent_last_chunk:
+                    if self.received_finish_reason is not None:
                         raise StopIteration
                     else:
-                        model_response.choices[0].finish_reason = "stop"
+                        self.received_finish_reason = "stop"
-                        self.sent_last_chunk = True
                 chunk_size = 30
                 new_chunk = self.completion_stream[:chunk_size]
                 completion_obj["content"] = new_chunk
@@ -9356,11 +9323,10 @@ class CustomStreamWrapper:
                 # fake streaming
                 response_obj = {}
                 if len(self.completion_stream) == 0:
-                    if self.sent_last_chunk:
+                    if self.received_finish_reason is not None:
                         raise StopIteration
                     else:
-                        model_response.choices[0].finish_reason = "stop"
+                        self.received_finish_reason = "stop"
-                        self.sent_last_chunk = True
                 chunk_size = 30
                 new_chunk = self.completion_stream[:chunk_size]
                 completion_obj["content"] = new_chunk
@@ -9371,41 +9337,31 @@ class CustomStreamWrapper:
                 completion_obj["content"] = response_obj["text"]
                 print_verbose(f"completion obj content: {completion_obj['content']}")
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif self.custom_llm_provider == "ollama_chat":
                 response_obj = self.handle_ollama_chat_stream(chunk)
                 completion_obj["content"] = response_obj["text"]
                 print_verbose(f"completion obj content: {completion_obj['content']}")
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif self.custom_llm_provider == "cloudflare":
                 response_obj = self.handle_cloudlfare_stream(chunk)
                 completion_obj["content"] = response_obj["text"]
                 print_verbose(f"completion obj content: {completion_obj['content']}")
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif self.custom_llm_provider == "text-completion-openai":
                 response_obj = self.handle_openai_text_completion_chunk(chunk)
                 completion_obj["content"] = response_obj["text"]
                 print_verbose(f"completion obj content: {completion_obj['content']}")
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif self.custom_llm_provider == "azure_text":
                 response_obj = self.handle_azure_text_completion_chunk(chunk)
                 completion_obj["content"] = response_obj["text"]
                 print_verbose(f"completion obj content: {completion_obj['content']}")
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             elif self.custom_llm_provider == "cached_response":
                 response_obj = {
                     "text": chunk.choices[0].delta.content,
@@ -9419,9 +9375,7 @@ class CustomStreamWrapper:
                 if hasattr(chunk, "id"):
                     model_response.id = chunk.id
                 if response_obj["is_finished"]:
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
             else:  # openai / azure chat model
                 if self.custom_llm_provider == "azure":
                     if hasattr(chunk, "model"):
@@ -9437,9 +9391,7 @@ class CustomStreamWrapper:
                         raise Exception(
                             "Mistral API raised a streaming error - finish_reason: error, no content string given."
                         )
-                    model_response.choices[0].finish_reason = response_obj[
+                    self.received_finish_reason = response_obj["finish_reason"]
-                        "finish_reason"
-                    ]
                 if response_obj.get("original_chunk", None) is not None:
                     model_response.system_fingerprint = getattr(
                         response_obj["original_chunk"], "system_fingerprint", None
@@ -9451,7 +9403,7 @@ class CustomStreamWrapper:
 
             model_response.model = self.model
             print_verbose(
-                f"model_response finish reason 3: {model_response.choices[0].finish_reason}; response_obj={response_obj}"
+                f"model_response finish reason 3: {self.received_finish_reason}; response_obj={response_obj}"
             )
             ## FUNCTION CALL PARSING
             if (
@@ -9580,7 +9532,7 @@ class CustomStreamWrapper:
                     return model_response
                 else:
                     return
-            elif model_response.choices[0].finish_reason is not None:
+            elif self.received_finish_reason is not None:
                 # flush any remaining holding chunk
                 if len(self.holding_chunk) > 0:
                     if model_response.choices[0].delta.content is None:
@@ -9590,10 +9542,17 @@ class CustomStreamWrapper:
                             self.holding_chunk + model_response.choices[0].delta.content
                         )
                     self.holding_chunk = ""
-                # get any function call arguments
+                # if delta is None
-                model_response.choices[0].finish_reason = map_finish_reason(
+                is_delta_empty = self.is_delta_empty(
-                    model_response.choices[0].finish_reason
+                    delta=model_response.choices[0].delta
-                )  # ensure consistent output to openai
+                )
+                if is_delta_empty:
+                    # get any function call arguments
+                    model_response.choices[0].finish_reason = map_finish_reason(
+                        finish_reason=self.received_finish_reason
+                    )  # ensure consistent output to openai
+                    self.sent_last_chunk = True
+
                 return model_response
             elif (
                 model_response.choices[0].delta.tool_calls is not None
@@ -9653,6 +9612,16 @@ class CustomStreamWrapper:
         ## SYNC LOGGING
         self.logging_obj.success_handler(processed_chunk)
 
+    def finish_reason_handler(self):
+        model_response = self.model_response_creator()
+        if self.received_finish_reason is not None:
+            model_response.choices[0].finish_reason = map_finish_reason(
+                finish_reason=self.received_finish_reason
+            )
+        else:
+            model_response.choices[0].finish_reason = "stop"
+        return model_response
+
     ## needs to handle the empty string case (even starting chunk can be an empty string)
     def __next__(self):
         try:
@@ -9687,7 +9656,11 @@ class CustomStreamWrapper:
                     # RETURN RESULT
                     return response
         except StopIteration:
-            raise  # Re-raise StopIteration
+            if self.sent_last_chunk == True:
+                raise  # Re-raise StopIteration
+            else:
+                self.sent_last_chunk = True
+                return self.finish_reason_handler()
         except Exception as e:
             traceback_exception = traceback.format_exc()
             # LOG FAILURE - handle streaming failure logging in the _next_ object, remove `handle_failure` once it's deprecated
@@ -9792,9 +9765,17 @@ class CustomStreamWrapper:
                         # RETURN RESULT
                         return processed_chunk
         except StopAsyncIteration:
-            raise
+            if self.sent_last_chunk == True:
+                raise  # Re-raise StopIteration
+            else:
+                self.sent_last_chunk = True
+                return self.finish_reason_handler()
         except StopIteration:
-            raise StopAsyncIteration  # Re-raise StopIteration
+            if self.sent_last_chunk == True:
+                raise StopAsyncIteration
+            else:
+                self.sent_last_chunk = True
+                return self.finish_reason_handler()
         except Exception as e:
             traceback_exception = traceback.format_exc()
             # Handle any exceptions that might occur during streaming