diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
index bcec062de1..598de09be3 100644
--- a/litellm/cost_calculator.py
+++ b/litellm/cost_calculator.py
@@ -829,18 +829,11 @@ def response_cost_calculator(
         )
         return None
     except Exception as e:
-        if litellm.suppress_debug_info:  # allow cli tools to suppress this information.
-            verbose_logger.debug(
-                "litellm.cost_calculator.py::response_cost_calculator - Returning None. Exception occurred - {}/n{}".format(
-                    str(e), traceback.format_exc()
-                )
-            )
-        else:
-            verbose_logger.warning(
-                "litellm.cost_calculator.py::response_cost_calculator - Returning None. Exception occurred - {}/n{}".format(
-                    str(e), traceback.format_exc()
-                )
+        verbose_logger.debug(
+            "litellm.cost_calculator.py::response_cost_calculator - Returning None. Exception occurred - {}/n{}".format(
+                str(e), traceback.format_exc()
             )
+        )
         return None
 
 
diff --git a/litellm/integrations/slack_alerting.py b/litellm/integrations/slack_alerting.py
index 42e289b871..3d757fa3dc 100644
--- a/litellm/integrations/slack_alerting.py
+++ b/litellm/integrations/slack_alerting.py
@@ -269,10 +269,11 @@ class SlackAlerting(CustomLogger):
                     break
                 await asyncio.sleep(3)  # wait 3s before retrying for trace id
 
-            if litellm.litellm_core_utils.litellm_logging.langFuseLogger is not None:
-                base_url = (
-                    litellm.litellm_core_utils.litellm_logging.langFuseLogger.Langfuse.base_url
-                )
+            _langfuse_object = litellm_logging_obj._get_callback_object(
+                service_name="langfuse"
+            )
+            if _langfuse_object is not None:
+                base_url = _langfuse_object.Langfuse.base_url
                 return f"{base_url}/trace/{trace_id}"
         return None
 
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index 43273224cb..0d3e59db5d 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -924,6 +924,7 @@ class Logging:
                             else:
                                 print_verbose("reaches langfuse for streaming logging!")
                                 result = kwargs["complete_streaming_response"]
+                        temp_langfuse_logger = langFuseLogger
                         if langFuseLogger is None or (
                             (
                                 self.langfuse_public_key is not None
@@ -940,12 +941,12 @@ class Logging:
                                 and self.langfuse_host != langFuseLogger.langfuse_host
                             )
                         ):
-                            langFuseLogger = LangFuseLogger(
+                            temp_langfuse_logger = LangFuseLogger(
                                 langfuse_public_key=self.langfuse_public_key,
                                 langfuse_secret=self.langfuse_secret,
                                 langfuse_host=self.langfuse_host,
                             )
-                        _response = langFuseLogger.log_event(
+                        _response = temp_langfuse_logger.log_event(
                             kwargs=kwargs,
                             response_obj=result,
                             start_time=start_time,
@@ -1925,6 +1926,38 @@ class Logging:
 
         return trace_id
 
+    def _get_callback_object(self, service_name: Literal["langfuse"]) -> Optional[Any]:
+        """
+        Return dynamic callback object.
+
+        Meant to solve issue when doing key-based/team-based logging
+        """
+        global langFuseLogger
+
+        if service_name == "langfuse":
+            if langFuseLogger is None or (
+                (
+                    self.langfuse_public_key is not None
+                    and self.langfuse_public_key != langFuseLogger.public_key
+                )
+                or (
+                    self.langfuse_public_key is not None
+                    and self.langfuse_public_key != langFuseLogger.public_key
+                )
+                or (
+                    self.langfuse_host is not None
+                    and self.langfuse_host != langFuseLogger.langfuse_host
+                )
+            ):
+                return LangFuseLogger(
+                    langfuse_public_key=self.langfuse_public_key,
+                    langfuse_secret=self.langfuse_secret,
+                    langfuse_host=self.langfuse_host,
+                )
+            return langFuseLogger
+
+        return None
+
 
 def set_callbacks(callback_list, function_id=None):
     """
diff --git a/litellm/llms/databricks/cost_calculator.py b/litellm/llms/databricks/cost_calculator.py
index 3d40f2aa62..695a6055f8 100644
--- a/litellm/llms/databricks/cost_calculator.py
+++ b/litellm/llms/databricks/cost_calculator.py
@@ -25,7 +25,30 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]:
         "dbrx-instruct"
     ):
         base_model = "databricks-dbrx-instruct"
-
+    elif model.startswith("databricks/meta-llama-3.1-70b-instruct") or model.startswith(
+        "meta-llama-3.1-70b-instruct"
+    ):
+        base_model = "databricks-meta-llama-3-1-70b-instruct"
+    elif model.startswith(
+        "databricks/meta-llama-3.1-405b-instruct"
+    ) or model.startswith("meta-llama-3.1-405b-instruct"):
+        base_model = "databricks-meta-llama-3-1-405b-instruct"
+    elif model.startswith("databricks/mixtral-8x7b-instruct-v0.1") or model.startswith(
+        "mixtral-8x7b-instruct-v0.1"
+    ):
+        base_model = "databricks-mixtral-8x7b-instruct"
+    elif model.startswith("databricks/mixtral-8x7b-instruct-v0.1") or model.startswith(
+        "mixtral-8x7b-instruct-v0.1"
+    ):
+        base_model = "databricks-mixtral-8x7b-instruct"
+    elif model.startswith("databricks/bge-large-en") or model.startswith(
+        "bge-large-en"
+    ):
+        base_model = "databricks-bge-large-en"
+    elif model.startswith("databricks/gte-large-en") or model.startswith(
+        "gte-large-en"
+    ):
+        base_model = "databricks-gte-large-en"
     ## GET MODEL INFO
     model_info = get_model_info(model=base_model, custom_llm_provider="databricks")
 
diff --git a/litellm/main.py b/litellm/main.py
index 1d20cf4240..8df5d604d8 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -5310,7 +5310,7 @@ def stream_chunk_builder(
         ]
 
         if len(tool_call_chunks) > 0:
-            argument_list = []
+            argument_list: List = []
             delta = tool_call_chunks[0]["choices"][0]["delta"]
             message = response["choices"][0]["message"]
             message["tool_calls"] = []
@@ -5319,6 +5319,7 @@ def stream_chunk_builder(
             type = None
             tool_calls_list = []
             prev_index = None
+            prev_name = None
             prev_id = None
             curr_id = None
             curr_index = 0
@@ -5346,27 +5347,32 @@ def stream_chunk_builder(
                             type = tool_calls[0].type
                 if prev_index is None:
                     prev_index = curr_index
+                if prev_name is None:
+                    prev_name = name
                 if curr_index != prev_index:  # new tool call
                     combined_arguments = "".join(argument_list)
                     tool_calls_list.append(
                         {
                             "id": prev_id,
-                            "index": prev_index,
-                            "function": {"arguments": combined_arguments, "name": name},
+                            "function": {
+                                "arguments": combined_arguments,
+                                "name": prev_name,
+                            },
                             "type": type,
                         }
                     )
                     argument_list = []  # reset
                     prev_index = curr_index
                     prev_id = curr_id
+                    prev_name = name
 
             combined_arguments = (
                 "".join(argument_list) or "{}"
             )  # base case, return empty dict
+
             tool_calls_list.append(
                 {
                     "id": id,
-                    "index": curr_index,
                     "function": {"arguments": combined_arguments, "name": name},
                     "type": type,
                 }
@@ -5422,7 +5428,7 @@ def stream_chunk_builder(
                 for choice in choices:
                     delta = choice.get("delta", {})
                     content = delta.get("content", "")
-                    if content == None:
+                    if content is None:
                         continue  # openai v1.0.0 sets content = None for chunks
                     content_list.append(content)
 
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 3bb65a666a..61aa5183e6 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -5461,90 +5461,129 @@
         "max_input_tokens": 128000,
         "max_output_tokens": 128000, 
         "input_cost_per_token": 0.000005,
-        "output_cost_per_token": 0.000015,
+        "input_dbu_cost_per_token": 0.000071429,
+        "output_cost_per_token": 0.00001500002,
+        "output_db_cost_per_token": 0.000214286,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-meta-llama-3-1-70b-instruct": {
         "max_tokens": 128000,
         "max_input_tokens": 128000,
         "max_output_tokens": 128000, 
-        "input_cost_per_token": 0.000001,
-        "output_cost_per_token": 0.000003,
+        "input_cost_per_token": 0.00000100002,
+        "input_dbu_cost_per_token": 0.000014286,
+        "output_cost_per_token": 0.00000299999,
+        "output_dbu_cost_per_token": 0.000042857,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-dbrx-instruct": {
         "max_tokens": 32768,
         "max_input_tokens": 32768,
         "max_output_tokens": 32768, 
-        "input_cost_per_token": 0.00000075,
-        "output_cost_per_token": 0.00000225,
+        "input_cost_per_token": 0.00000074998,
+        "input_dbu_cost_per_token": 0.000010714,
+        "output_cost_per_token": 0.00000224901,
+        "output_dbu_cost_per_token": 0.000032143,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-meta-llama-3-70b-instruct": {
-        "max_tokens": 8192,
-        "max_input_tokens": 8192,
-        "max_output_tokens": 8192, 
-        "input_cost_per_token": 0.000001,
-        "output_cost_per_token": 0.000003,
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000, 
+        "input_cost_per_token": 0.00000100002,
+        "input_dbu_cost_per_token": 0.000014286,
+        "output_cost_per_token": 0.00000299999,
+        "output_dbu_cost_per_token": 0.000042857,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-llama-2-70b-chat": {
         "max_tokens": 4096,
         "max_input_tokens": 4096,
         "max_output_tokens": 4096, 
-        "input_cost_per_token": 0.0000005,
+        "input_cost_per_token": 0.00000050001,
+        "input_dbu_cost_per_token": 0.000007143,
         "output_cost_per_token": 0.0000015,
+        "output_dbu_cost_per_token": 0.000021429,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
-
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-mixtral-8x7b-instruct": {
         "max_tokens": 4096,
         "max_input_tokens": 4096,
         "max_output_tokens": 4096, 
-        "input_cost_per_token": 0.0000005,
-        "output_cost_per_token": 0.000001,
+        "input_cost_per_token": 0.00000050001,
+        "input_dbu_cost_per_token": 0.000007143,
+        "output_cost_per_token": 0.00000099902,
+        "output_dbu_cost_per_token": 0.000014286,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-mpt-30b-instruct": {
         "max_tokens": 8192,
         "max_input_tokens": 8192,
         "max_output_tokens": 8192, 
-        "input_cost_per_token": 0.000001,
-        "output_cost_per_token": 0.000001,
+        "input_cost_per_token": 0.00000099902,
+        "input_dbu_cost_per_token": 0.000014286,
+        "output_cost_per_token": 0.00000099902,
+        "output_dbu_cost_per_token": 0.000014286,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-mpt-7b-instruct": {
         "max_tokens": 8192,
         "max_input_tokens": 8192,
         "max_output_tokens": 8192, 
-        "input_cost_per_token": 0.0000005,
-        "output_cost_per_token": 0.0000005,
+        "input_cost_per_token": 0.00000050001,
+        "input_dbu_cost_per_token": 0.000007143,
+        "output_cost_per_token": 0.0,
+        "output_dbu_cost_per_token": 0.0,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-bge-large-en": {
         "max_tokens": 512,
         "max_input_tokens": 512,
         "output_vector_size": 1024, 
-        "input_cost_per_token": 0.0000001,
+        "input_cost_per_token": 0.00000010003,
+        "input_dbu_cost_per_token": 0.000001429,
         "output_cost_per_token": 0.0,
+        "output_dbu_cost_per_token": 0.0,
         "litellm_provider": "databricks",
         "mode": "embedding",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
+    },
+    "databricks/databricks-gte-large-en": {
+        "max_tokens": 8192,
+        "max_input_tokens": 8192,
+        "output_vector_size": 1024, 
+        "input_cost_per_token": 0.00000012999,
+        "input_dbu_cost_per_token": 0.000001857,
+        "output_cost_per_token": 0.0,
+        "output_dbu_cost_per_token": 0.0,
+        "litellm_provider": "databricks",
+        "mode": "embedding",
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     }
 }
\ No newline at end of file
diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml
index bf86da1e12..9a3ce96921 100644
--- a/litellm/proxy/_new_secret_config.yaml
+++ b/litellm/proxy/_new_secret_config.yaml
@@ -1,9 +1,9 @@
 model_list:
-  - model_name: "gpt-turbo"
+  - model_name: "gpt-4o"
     litellm_params:
-      model: azure/chatgpt-v-2
-      api_key: os.environ/AZURE_API_KEY
-      api_base: os.environ/AZURE_API_BASE
+      model: gpt-4o
 
-router_settings:
-  model_group_alias: {"gpt-4": "gpt-turbo"} 
\ No newline at end of file
+litellm_settings:
+  cache: true
+  cache_params:
+    type: local
\ No newline at end of file
diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index 3559a4792f..da98b6a101 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -600,7 +600,7 @@ class GenerateRequestBase(LiteLLMBase):
     soft_budget: Optional[float] = None
 
 
-class GenerateKeyRequest(GenerateRequestBase):
+class _GenerateKeyRequest(GenerateRequestBase):
     key_alias: Optional[str] = None
     key: Optional[str] = None
     duration: Optional[str] = None
@@ -618,7 +618,11 @@ class GenerateKeyRequest(GenerateRequestBase):
     guardrails: Optional[List[str]] = None
 
 
-class GenerateKeyResponse(GenerateKeyRequest):
+class GenerateKeyRequest(_GenerateKeyRequest):
+    tags: Optional[List[str]] = None
+
+
+class GenerateKeyResponse(_GenerateKeyRequest):
     key: str
     key_name: Optional[str] = None
     expires: Optional[datetime]
@@ -677,9 +681,10 @@ class LiteLLM_ModelTable(LiteLLMBase):
     model_config = ConfigDict(protected_namespaces=())
 
 
-class NewUserRequest(GenerateKeyRequest):
+class NewUserRequest(_GenerateKeyRequest):
     max_budget: Optional[float] = None
     user_email: Optional[str] = None
+    user_alias: Optional[str] = None
     user_role: Optional[
         Literal[
             LitellmUserRoles.PROXY_ADMIN,
@@ -713,6 +718,7 @@ class NewUserResponse(GenerateKeyResponse):
     ] = None
     teams: Optional[list] = None
     organization_id: Optional[str] = None
+    user_alias: Optional[str] = None
 
 
 class UpdateUserRequest(GenerateRequestBase):
diff --git a/litellm/proxy/management_endpoints/internal_user_endpoints.py b/litellm/proxy/management_endpoints/internal_user_endpoints.py
index 8774136272..859c8aeb84 100644
--- a/litellm/proxy/management_endpoints/internal_user_endpoints.py
+++ b/litellm/proxy/management_endpoints/internal_user_endpoints.py
@@ -156,6 +156,7 @@ async def new_user(
         user_id=response["user_id"],
         user_role=response.get("user_role", None),
         user_email=response.get("user_email", None),
+        user_alias=response.get("user_alias", None),
         teams=response.get("teams", None),
         team_id=response.get("team_id", None),
         metadata=response.get("metadata", None),
diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py
index 3173e38864..90e7728d0a 100644
--- a/litellm/proxy/management_endpoints/key_management_endpoints.py
+++ b/litellm/proxy/management_endpoints/key_management_endpoints.py
@@ -202,6 +202,15 @@ async def generate_key_fn(
         if "budget_duration" in data_json:
             data_json["key_budget_duration"] = data_json.pop("budget_duration", None)
 
+        # Set tags on the new key
+        if "tags" in data_json:
+            if data_json["metadata"] is None:
+                data_json["metadata"] = {"tags": data_json["tags"]}
+            else:
+                data_json["metadata"]["tags"] = data_json["tags"]
+
+            data_json.pop("tags")
+
         response = await generate_key_helper_fn(
             request_type="key", **data_json, table_name="key"
         )
@@ -257,12 +266,11 @@ async def generate_key_fn(
 
         return GenerateKeyResponse(**response)
     except Exception as e:
-        verbose_proxy_logger.error(
+        verbose_proxy_logger.exception(
             "litellm.proxy.proxy_server.generate_key_fn(): Exception occured - {}".format(
                 str(e)
             )
         )
-        verbose_proxy_logger.debug(traceback.format_exc())
         if isinstance(e, HTTPException):
             raise ProxyException(
                 message=getattr(e, "detail", f"Authentication Error({str(e)})"),
@@ -731,6 +739,7 @@ async def generate_key_helper_fn(
         str
     ] = None,  # dev-friendly alt param for 'token'. Exposed on `/key/generate` for setting key value yourself.
     user_id: Optional[str] = None,
+    user_alias: Optional[str] = None,
     team_id: Optional[str] = None,
     user_email: Optional[str] = None,
     user_role: Optional[str] = None,
@@ -816,6 +825,7 @@ async def generate_key_helper_fn(
             "max_budget": max_budget,
             "user_email": user_email,
             "user_id": user_id,
+            "user_alias": user_alias,
             "team_id": team_id,
             "organization_id": organization_id,
             "user_role": user_role,
diff --git a/litellm/tests/test_completion_cost.py b/litellm/tests/test_completion_cost.py
index ed9eebedb1..835cced457 100644
--- a/litellm/tests/test_completion_cost.py
+++ b/litellm/tests/test_completion_cost.py
@@ -1221,11 +1221,37 @@ def test_completion_cost_anthropic_prompt_caching():
     assert cost_1 > cost_2
 
 
-def test_completion_cost_databricks():
-    model, messages = "databricks/databricks-dbrx-instruct", [
-        {"role": "user", "content": "What is 2+2?"}
-    ]
+@pytest.mark.parametrize(
+    "model",
+    [
+        "databricks/databricks-meta-llama-3-1-70b-instruct",
+        "databricks/databricks-meta-llama-3-70b-instruct",
+        "databricks/databricks-dbrx-instruct",
+        "databricks/databricks-mixtral-8x7b-instruct",
+    ],
+)
+def test_completion_cost_databricks(model):
+    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+    model, messages = model, [{"role": "user", "content": "What is 2+2?"}]
 
     resp = litellm.completion(model=model, messages=messages)  # works fine
 
+    print(resp)
+    cost = completion_cost(completion_response=resp)
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        "databricks/databricks-bge-large-en",
+        "databricks/databricks-gte-large-en",
+    ],
+)
+def test_completion_cost_databricks_embedding(model):
+    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+    resp = litellm.embedding(model=model, input=["hey, how's it going?"])  # works fine
+
+    print(resp)
     cost = completion_cost(completion_response=resp)
diff --git a/litellm/tests/test_key_generate_prisma.py b/litellm/tests/test_key_generate_prisma.py
index 995d5c0f75..09f12442a0 100644
--- a/litellm/tests/test_key_generate_prisma.py
+++ b/litellm/tests/test_key_generate_prisma.py
@@ -2762,6 +2762,7 @@ async def test_generate_key_with_model_tpm_limit(prisma_client):
         "team": "litellm-team3",
         "model_tpm_limit": {"gpt-4": 100},
         "model_rpm_limit": {"gpt-4": 2},
+        "tags": None,
     }
 
     # Update model tpm_limit and rpm_limit
@@ -2782,6 +2783,7 @@ async def test_generate_key_with_model_tpm_limit(prisma_client):
         "team": "litellm-team3",
         "model_tpm_limit": {"gpt-4": 200},
         "model_rpm_limit": {"gpt-4": 3},
+        "tags": None,
     }
 
 
@@ -2818,6 +2820,7 @@ async def test_generate_key_with_guardrails(prisma_client):
     assert result["info"]["metadata"] == {
         "team": "litellm-team3",
         "guardrails": ["aporia-pre-call"],
+        "tags": None,
     }
 
     # Update model tpm_limit and rpm_limit
@@ -2836,6 +2839,7 @@ async def test_generate_key_with_guardrails(prisma_client):
     assert result["info"]["metadata"] == {
         "team": "litellm-team3",
         "guardrails": ["aporia-pre-call", "aporia-post-call"],
+        "tags": None,
     }
 
 
diff --git a/litellm/tests/test_stream_chunk_builder.py b/litellm/tests/test_stream_chunk_builder.py
index 100cf4ece9..477f28f2ed 100644
--- a/litellm/tests/test_stream_chunk_builder.py
+++ b/litellm/tests/test_stream_chunk_builder.py
@@ -210,7 +210,6 @@ def test_stream_chunk_builder_litellm_mixed_calls():
 
     assert len(response.choices[0].message.tool_calls) == 1
     assert response.choices[0].message.tool_calls[0].to_dict() == {
-        "index": 1,
         "function": {
             "arguments": '{"query": "SELECT COUNT(*) FROM users;"}',
             "name": "sql_query",
@@ -226,3 +225,400 @@ def test_stream_chunk_builder_litellm_empty_chunks():
 
     response = stream_chunk_builder(chunks=[])
     assert response is None
+
+
+def test_stream_chunk_builder_multiple_tool_calls():
+    init_chunks = [
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "id": "call_X9P9B6STj7ze8OsJCGkfoN94",
+                                "function": {"arguments": "", "name": "exponentiate"},
+                                "type": "function",
+                                "index": 0,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "function": {"arguments": '{"ba'},
+                                "type": "function",
+                                "index": 0,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "function": {"arguments": 'se": '},
+                                "type": "function",
+                                "index": 0,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "function": {"arguments": '3, "ex'},
+                                "type": "function",
+                                "index": 0,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "function": {"arguments": "pone"},
+                                "type": "function",
+                                "index": 0,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "function": {"arguments": 'nt": '},
+                                "type": "function",
+                                "index": 0,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "function": {"arguments": "5}"},
+                                "type": "function",
+                                "index": 0,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "id": "call_Qq8yDeRx7v276abRcLrYORdW",
+                                "function": {"arguments": "", "name": "add"},
+                                "type": "function",
+                                "index": 1,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "function": {"arguments": '{"fi'},
+                                "type": "function",
+                                "index": 1,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "function": {"arguments": "rst_i"},
+                                "type": "function",
+                                "index": 1,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "function": {"arguments": 'nt": 1'},
+                                "type": "function",
+                                "index": 1,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "function": {"arguments": '2, "'},
+                                "type": "function",
+                                "index": 1,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "function": {"arguments": "secon"},
+                                "type": "function",
+                                "index": 1,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "function": {"arguments": 'd_int"'},
+                                "type": "function",
+                                "index": 1,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "function": {"arguments": ": 3}"},
+                                "type": "function",
+                                "index": 1,
+                            }
+                        ],
+                    },
+                }
+            ],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+        {
+            "id": "chatcmpl-A5kCnzaxRsknd6008552ZhDi71yPt",
+            "choices": [{"finish_reason": "tool_calls", "index": 0, "delta": {}}],
+            "created": 1725932618,
+            "model": "gpt-4o-2024-08-06",
+            "object": "chat.completion.chunk",
+            "system_fingerprint": "fp_b2ffeb16ee",
+        },
+    ]
+
+    chunks = []
+    for chunk in init_chunks:
+        chunks.append(litellm.ModelResponse(**chunk, stream=True))
+    response = stream_chunk_builder(chunks=chunks)
+
+    print(f"Returned response: {response}")
+    completed_response = {
+        "id": "chatcmpl-A61mXjvcRX0Xr2IiojN9TPiy1P3Fm",
+        "choices": [
+            {
+                "finish_reason": "tool_calls",
+                "index": 0,
+                "message": {
+                    "content": None,
+                    "role": "assistant",
+                    "tool_calls": [
+                        {
+                            "function": {
+                                "arguments": '{"base": 3, "exponent": 5}',
+                                "name": "exponentiate",
+                            },
+                            "id": "call_X9P9B6STj7ze8OsJCGkfoN94",
+                            "type": "function",
+                        },
+                        {
+                            "function": {
+                                "arguments": '{"first_int": 12, "second_int": 3}',
+                                "name": "add",
+                            },
+                            "id": "call_Qq8yDeRx7v276abRcLrYORdW",
+                            "type": "function",
+                        },
+                    ],
+                    "function_call": None,
+                },
+            }
+        ],
+        "created": 1726000181,
+        "model": "gpt-4o-2024-05-13",
+        "object": "chat.completion",
+        "system_fingerprint": "fp_25624ae3a5",
+        "usage": {"completion_tokens": 55, "prompt_tokens": 127, "total_tokens": 182},
+        "service_tier": None,
+    }
+
+    expected_response = litellm.ModelResponse(**completed_response)
+
+    print(f"\n\nexpected_response:\n{expected_response}\n\n")
+    assert (
+        expected_response.choices == response.choices
+    ), "\nGot={}\n, Expected={}\n".format(response.choices, expected_response.choices)
diff --git a/litellm/types/utils.py b/litellm/types/utils.py
index 96c25e5b51..c2a708663b 100644
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@@ -325,7 +325,7 @@ class Message(OpenAIObject):
     ):
         init_values = {
             "content": content,
-            "role": "assistant",
+            "role": role or "assistant",  # handle null input
             "function_call": (
                 FunctionCall(**function_call) if function_call is not None else None
             ),
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 7883ddc81a..37ac239664 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -5492,90 +5492,129 @@
         "max_input_tokens": 128000,
         "max_output_tokens": 128000, 
         "input_cost_per_token": 0.000005,
-        "output_cost_per_token": 0.000015,
+        "input_dbu_cost_per_token": 0.000071429,
+        "output_cost_per_token": 0.00001500002,
+        "output_db_cost_per_token": 0.000214286,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-meta-llama-3-1-70b-instruct": {
         "max_tokens": 128000,
         "max_input_tokens": 128000,
         "max_output_tokens": 128000, 
-        "input_cost_per_token": 0.000001,
-        "output_cost_per_token": 0.000003,
+        "input_cost_per_token": 0.00000100002,
+        "input_dbu_cost_per_token": 0.000014286,
+        "output_cost_per_token": 0.00000299999,
+        "output_dbu_cost_per_token": 0.000042857,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-dbrx-instruct": {
         "max_tokens": 32768,
         "max_input_tokens": 32768,
         "max_output_tokens": 32768, 
-        "input_cost_per_token": 0.00000075,
-        "output_cost_per_token": 0.00000225,
+        "input_cost_per_token": 0.00000074998,
+        "input_dbu_cost_per_token": 0.000010714,
+        "output_cost_per_token": 0.00000224901,
+        "output_dbu_cost_per_token": 0.000032143,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-meta-llama-3-70b-instruct": {
-        "max_tokens": 8192,
-        "max_input_tokens": 8192,
-        "max_output_tokens": 8192, 
-        "input_cost_per_token": 0.000001,
-        "output_cost_per_token": 0.000003,
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000, 
+        "input_cost_per_token": 0.00000100002,
+        "input_dbu_cost_per_token": 0.000014286,
+        "output_cost_per_token": 0.00000299999,
+        "output_dbu_cost_per_token": 0.000042857,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-llama-2-70b-chat": {
         "max_tokens": 4096,
         "max_input_tokens": 4096,
         "max_output_tokens": 4096, 
-        "input_cost_per_token": 0.0000005,
+        "input_cost_per_token": 0.00000050001,
+        "input_dbu_cost_per_token": 0.000007143,
         "output_cost_per_token": 0.0000015,
+        "output_dbu_cost_per_token": 0.000021429,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
-
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-mixtral-8x7b-instruct": {
         "max_tokens": 4096,
         "max_input_tokens": 4096,
         "max_output_tokens": 4096, 
-        "input_cost_per_token": 0.0000005,
-        "output_cost_per_token": 0.000001,
+        "input_cost_per_token": 0.00000050001,
+        "input_dbu_cost_per_token": 0.000007143,
+        "output_cost_per_token": 0.00000099902,
+        "output_dbu_cost_per_token": 0.000014286,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-mpt-30b-instruct": {
         "max_tokens": 8192,
         "max_input_tokens": 8192,
         "max_output_tokens": 8192, 
-        "input_cost_per_token": 0.000001,
-        "output_cost_per_token": 0.000001,
+        "input_cost_per_token": 0.00000099902,
+        "input_dbu_cost_per_token": 0.000014286,
+        "output_cost_per_token": 0.00000099902,
+        "output_dbu_cost_per_token": 0.000014286,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-mpt-7b-instruct": {
         "max_tokens": 8192,
         "max_input_tokens": 8192,
         "max_output_tokens": 8192, 
-        "input_cost_per_token": 0.0000005,
-        "output_cost_per_token": 0.0000005,
+        "input_cost_per_token": 0.00000050001,
+        "input_dbu_cost_per_token": 0.000007143,
+        "output_cost_per_token": 0.0,
+        "output_dbu_cost_per_token": 0.0,
         "litellm_provider": "databricks",
         "mode": "chat",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     },
     "databricks/databricks-bge-large-en": {
         "max_tokens": 512,
         "max_input_tokens": 512,
         "output_vector_size": 1024, 
-        "input_cost_per_token": 0.0000001,
+        "input_cost_per_token": 0.00000010003,
+        "input_dbu_cost_per_token": 0.000001429,
         "output_cost_per_token": 0.0,
+        "output_dbu_cost_per_token": 0.0,
         "litellm_provider": "databricks",
         "mode": "embedding",
-        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
+    },
+    "databricks/databricks-gte-large-en": {
+        "max_tokens": 8192,
+        "max_input_tokens": 8192,
+        "output_vector_size": 1024, 
+        "input_cost_per_token": 0.00000012999,
+        "input_dbu_cost_per_token": 0.000001857,
+        "output_cost_per_token": 0.0,
+        "output_dbu_cost_per_token": 0.0,
+        "litellm_provider": "databricks",
+        "mode": "embedding",
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "metadata": {"notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."}
     }
 }