LiteLLM Minor Fixes & Improvements (11/29/2024) (#6965)

* fix(factory.py): ensure tool call converts image url

Fixes https://github.com/BerriAI/litellm/issues/6953

* fix(transformation.py): support mp4 + pdf url's for vertex ai

Fixes https://github.com/BerriAI/litellm/issues/6936

* fix(http_handler.py): mask gemini api key in error logs

Fixes https://github.com/BerriAI/litellm/issues/6963

* docs(prometheus.md): update prometheus FAQs

* feat(auth_checks.py): ensure specific model access > wildcard model access

if wildcard model is in access group, but specific model is not - deny access

* fix(auth_checks.py): handle auth checks for team based model access groups

handles scenario where model access group used for wildcard models

* fix(internal_user_endpoints.py): support adding guardrails on `/user/update`

Fixes https://github.com/BerriAI/litellm/issues/6942

* fix(key_management_endpoints.py): fix prepare_metadata_fields helper

* fix: fix tests

* build(requirements.txt): bump openai dep version

fixes proxies argument

* test: fix tests

* fix(http_handler.py): fix error message masking

* fix(bedrock_guardrails.py): pass in prepped data

* test: fix test

* test: fix nvidia nim test

* fix(http_handler.py): return original response headers

* fix: revert maskedhttpstatuserror

* test: update tests

* test: cleanup test

* fix(key_management_endpoints.py): fix metadata field update logic

* fix(key_management_endpoints.py): maintain initial order of guardrails in key update

* fix(key_management_endpoints.py): handle prepare metadata

* fix: fix linting errors

* fix: fix linting errors

* fix: fix linting errors

* fix: fix key management errors

* fix(key_management_endpoints.py): update metadata

* test: update test

* refactor: add more debug statements

* test: skip flaky test

* test: fix test

* fix: fix test

* fix: fix update metadata logic

* fix: fix test

* ci(config.yml): change db url for e2e ui testing

litellm/__init__.py

@@ -17,7 +17,11 @@ from litellm._logging import (
     _turn_on_json,
     log_level,
 )
-from litellm.constants import ROUTER_MAX_FALLBACKS
+from litellm.constants import (
+    DEFAULT_BATCH_SIZE,
+    DEFAULT_FLUSH_INTERVAL_SECONDS,
+    ROUTER_MAX_FALLBACKS,
+)
 from litellm.types.guardrails import GuardrailItem
 from litellm.proxy._types import (
     KeyManagementSystem,

litellm/constants.py

@@ -1 +1,3 @@
 ROUTER_MAX_FALLBACKS = 5
+DEFAULT_BATCH_SIZE = 512
+DEFAULT_FLUSH_INTERVAL_SECONDS = 5

litellm/integrations/custom_batch_logger.py

@@ -8,20 +8,18 @@ import asyncio
 import time
 from typing import List, Literal, Optional
 
+import litellm
 from litellm._logging import verbose_logger
 from litellm.integrations.custom_logger import CustomLogger
 
-DEFAULT_BATCH_SIZE = 512
-DEFAULT_FLUSH_INTERVAL_SECONDS = 5
-
 
 class CustomBatchLogger(CustomLogger):
 
     def __init__(
         self,
         flush_lock: Optional[asyncio.Lock] = None,
-        batch_size: Optional[int] = DEFAULT_BATCH_SIZE,
-        flush_interval: Optional[int] = DEFAULT_FLUSH_INTERVAL_SECONDS,
+        batch_size: Optional[int] = None,
+        flush_interval: Optional[int] = None,
         **kwargs,
     ) -> None:
         """
@@ -29,13 +27,12 @@ class CustomBatchLogger(CustomLogger):
             flush_lock (Optional[asyncio.Lock], optional): Lock to use when flushing the queue. Defaults to None. Only used for custom loggers that do batching
         """
         self.log_queue: List = []
-        self.flush_interval = flush_interval or DEFAULT_FLUSH_INTERVAL_SECONDS
-        self.batch_size: int = batch_size or DEFAULT_BATCH_SIZE
+        self.flush_interval = flush_interval or litellm.DEFAULT_FLUSH_INTERVAL_SECONDS
+        self.batch_size: int = batch_size or litellm.DEFAULT_BATCH_SIZE
         self.last_flush_time = time.time()
         self.flush_lock = flush_lock
 
         super().__init__(**kwargs)
-        pass
 
     async def periodic_flush(self):
         while True:

litellm/integrations/langsmith.py

@@ -68,8 +68,13 @@ class LangsmithLogger(CustomBatchLogger):
         if _batch_size:
             self.batch_size = int(_batch_size)
         self.log_queue: List[LangsmithQueueObject] = []
-        asyncio.create_task(self.periodic_flush())
+        loop = asyncio.get_event_loop_policy().get_event_loop()
+        if not loop.is_running():
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+        loop.create_task(self.periodic_flush())
         self.flush_lock = asyncio.Lock()
+
         super().__init__(**kwargs, flush_lock=self.flush_lock)
 
     def get_credentials_from_env(
@@ -122,7 +127,7 @@ class LangsmithLogger(CustomBatchLogger):
                 "project_name", credentials["LANGSMITH_PROJECT"]
             )
             run_name = metadata.get("run_name", self.langsmith_default_run_name)
-            run_id = metadata.get("id", None)
+            run_id = metadata.get("id", metadata.get("run_id", None))
             parent_run_id = metadata.get("parent_run_id", None)
             trace_id = metadata.get("trace_id", None)
             session_id = metadata.get("session_id", None)
@@ -173,14 +178,28 @@ class LangsmithLogger(CustomBatchLogger):
             if dotted_order:
                 data["dotted_order"] = dotted_order
 
+            run_id: Optional[str] = data.get("id")  # type: ignore
             if "id" not in data or data["id"] is None:
                 """
                 for /batch langsmith requires id, trace_id and dotted_order passed as params
                 """
                 run_id = str(uuid.uuid4())
-                data["id"] = str(run_id)
-                data["trace_id"] = str(run_id)
-                data["dotted_order"] = self.make_dot_order(run_id=run_id)
+
+                data["id"] = run_id
+
+            if (
+                "trace_id" not in data
+                or data["trace_id"] is None
+                and (run_id is not None and isinstance(run_id, str))
+            ):
+                data["trace_id"] = run_id
+
+            if (
+                "dotted_order" not in data
+                or data["dotted_order"] is None
+                and (run_id is not None and isinstance(run_id, str))
+            ):
+                data["dotted_order"] = self.make_dot_order(run_id=run_id)  # type: ignore
 
             verbose_logger.debug("Langsmith Logging data on langsmith: %s", data)
 

litellm/litellm_core_utils/streaming_handler.py

@@ -437,29 +437,6 @@ class CustomStreamWrapper:
         except Exception:
             raise ValueError(f"Unable to parse response. Original response: {chunk}")
 
-    def handle_cohere_chat_chunk(self, chunk):
-        chunk = chunk.decode("utf-8")
-        data_json = json.loads(chunk)
-        print_verbose(f"chunk: {chunk}")
-        try:
-            text = ""
-            is_finished = False
-            finish_reason = ""
-            if "text" in data_json:
-                text = data_json["text"]
-            elif "is_finished" in data_json and data_json["is_finished"] is True:
-                is_finished = data_json["is_finished"]
-                finish_reason = data_json["finish_reason"]
-            else:
-                return
-            return {
-                "text": text,
-                "is_finished": is_finished,
-                "finish_reason": finish_reason,
-            }
-        except Exception:
-            raise ValueError(f"Unable to parse response. Original response: {chunk}")
-
     def handle_azure_chunk(self, chunk):
         is_finished = False
         finish_reason = ""
@@ -949,7 +926,12 @@ class CustomStreamWrapper:
                 "function_call" in completion_obj
                 and completion_obj["function_call"] is not None
             )
+            or (
+                "provider_specific_fields" in response_obj
+                and response_obj["provider_specific_fields"] is not None
+            )
         ):  # cannot set content of an OpenAI Object to be an empty string
+
             self.safety_checker()
             hold, model_response_str = self.check_special_tokens(
                 chunk=completion_obj["content"],
@@ -1058,6 +1040,7 @@ class CustomStreamWrapper:
             and model_response.choices[0].delta.audio is not None
         ):
             return model_response
+
         else:
             if hasattr(model_response, "usage"):
                 self.chunks.append(model_response)
@@ -1066,6 +1049,7 @@ class CustomStreamWrapper:
     def chunk_creator(self, chunk):  # type: ignore  # noqa: PLR0915
         model_response = self.model_response_creator()
         response_obj: dict = {}
+
         try:
             # return this for all models
             completion_obj = {"content": ""}
@@ -1256,14 +1240,6 @@ class CustomStreamWrapper:
                 completion_obj["content"] = response_obj["text"]
                 if response_obj["is_finished"]:
                     self.received_finish_reason = response_obj["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"]:
-                    self.received_finish_reason = response_obj["finish_reason"]
-
             elif self.custom_llm_provider == "petals":
                 if len(self.completion_stream) == 0:
                     if self.received_finish_reason is not None:

litellm/llms/cohere/chat.py

@@ -4,13 +4,20 @@ import time
 import traceback
 import types
 from enum import Enum
-from typing import Callable, Optional
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 import httpx  # type: ignore
 import requests  # type: ignore
 
 import litellm
+from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
 from litellm.types.llms.cohere import ToolResultObject
+from litellm.types.utils import (
+    ChatCompletionToolCallChunk,
+    ChatCompletionUsageBlock,
+    GenericStreamingChunk,
+)
 from litellm.utils import Choices, Message, ModelResponse, Usage
 
 from ..prompt_templates.factory import cohere_message_pt, cohere_messages_pt_v2
@@ -198,7 +205,107 @@ def construct_cohere_tool(tools=None):
     return cohere_tools
 
 
-def completion(
+async def make_call(
+    client: Optional[AsyncHTTPHandler],
+    api_base: str,
+    headers: dict,
+    data: str,
+    model: str,
+    messages: list,
+    logging_obj,
+    timeout: Optional[Union[float, httpx.Timeout]],
+    json_mode: bool,
+) -> Tuple[Any, httpx.Headers]:
+    if client is None:
+        client = litellm.module_level_aclient
+
+    try:
+        response = await client.post(
+            api_base, headers=headers, data=data, stream=True, timeout=timeout
+        )
+    except httpx.HTTPStatusError as e:
+        error_headers = getattr(e, "headers", None)
+        error_response = getattr(e, "response", None)
+        if error_headers is None and error_response:
+            error_headers = getattr(error_response, "headers", None)
+        raise CohereError(
+            status_code=e.response.status_code,
+            message=await e.response.aread(),
+        )
+    except Exception as e:
+        for exception in litellm.LITELLM_EXCEPTION_TYPES:
+            if isinstance(e, exception):
+                raise e
+        raise CohereError(status_code=500, message=str(e))
+
+    completion_stream = ModelResponseIterator(
+        streaming_response=response.aiter_lines(),
+        sync_stream=False,
+        json_mode=json_mode,
+    )
+
+    # LOGGING
+    logging_obj.post_call(
+        input=messages,
+        api_key="",
+        original_response=completion_stream,  # Pass the completion stream for logging
+        additional_args={"complete_input_dict": data},
+    )
+
+    return completion_stream, response.headers
+
+
+def make_sync_call(
+    client: Optional[HTTPHandler],
+    api_base: str,
+    headers: dict,
+    data: str,
+    model: str,
+    messages: list,
+    logging_obj,
+    timeout: Optional[Union[float, httpx.Timeout]],
+) -> Tuple[Any, httpx.Headers]:
+    if client is None:
+        client = litellm.module_level_client  # re-use a module level client
+
+    try:
+        response = client.post(
+            api_base, headers=headers, data=data, stream=True, timeout=timeout
+        )
+    except httpx.HTTPStatusError as e:
+        raise CohereError(
+            status_code=e.response.status_code,
+            message=e.response.read(),
+        )
+    except Exception as e:
+        for exception in litellm.LITELLM_EXCEPTION_TYPES:
+            if isinstance(e, exception):
+                raise e
+        raise CohereError(status_code=500, message=str(e))
+
+    if response.status_code != 200:
+
+        raise CohereError(
+            status_code=response.status_code,
+            message=response.read(),
+        )
+
+    completion_stream = ModelResponseIterator(
+        streaming_response=response.iter_lines(), sync_stream=True
+    )
+
+    # LOGGING
+    logging_obj.post_call(
+        input=messages,
+        api_key="",
+        original_response="first stream response received",
+        additional_args={"complete_input_dict": data},
+    )
+
+    return completion_stream, response.headers
+
+
+def completion(  # noqa: PLR0915
     model: str,
     messages: list,
     api_base: str,
@@ -211,6 +318,8 @@ def completion(
     logging_obj,
     litellm_params=None,
     logger_fn=None,
+    client=None,
+    timeout=None,
 ):
     headers = validate_environment(api_key, headers=headers)
     completion_url = api_base
@@ -269,7 +378,23 @@ def completion(
         raise CohereError(message=response.text, status_code=response.status_code)
 
     if "stream" in optional_params and optional_params["stream"] is True:
-        return response.iter_lines()
+        completion_stream, cohere_headers = make_sync_call(
+            client=client,
+            api_base=api_base,
+            headers=headers,  # type: ignore
+            data=json.dumps(data),
+            model=model,
+            messages=messages,
+            logging_obj=logging_obj,
+            timeout=timeout,
+        )
+        return CustomStreamWrapper(
+            completion_stream=completion_stream,
+            model=model,
+            custom_llm_provider="cohere_chat",
+            logging_obj=logging_obj,
+            _response_headers=dict(cohere_headers),
+        )
     else:
         ## LOGGING
         logging_obj.post_call(
@@ -286,6 +411,10 @@ def completion(
         except Exception:
             raise CohereError(message=response.text, status_code=response.status_code)
 
+        ## ADD CITATIONS
+        if "citations" in completion_response:
+            setattr(model_response, "citations", completion_response["citations"])
+
         ## Tool calling response
         cohere_tools_response = completion_response.get("tool_calls", None)
         if cohere_tools_response is not None and cohere_tools_response != []:
@@ -325,3 +454,103 @@ def completion(
         )
         setattr(model_response, "usage", usage)
         return model_response
+
+
+class ModelResponseIterator:
+    def __init__(
+        self, streaming_response, sync_stream: bool, json_mode: Optional[bool] = False
+    ):
+        self.streaming_response = streaming_response
+        self.response_iterator = self.streaming_response
+        self.content_blocks: List = []
+        self.tool_index = -1
+        self.json_mode = json_mode
+
+    def chunk_parser(self, chunk: dict) -> GenericStreamingChunk:
+        try:
+            text = ""
+            tool_use: Optional[ChatCompletionToolCallChunk] = None
+            is_finished = False
+            finish_reason = ""
+            usage: Optional[ChatCompletionUsageBlock] = None
+            provider_specific_fields = None
+
+            index = int(chunk.get("index", 0))
+
+            if "text" in chunk:
+                text = chunk["text"]
+            elif "is_finished" in chunk and chunk["is_finished"] is True:
+                is_finished = chunk["is_finished"]
+                finish_reason = chunk["finish_reason"]
+
+            if "citations" in chunk:
+                provider_specific_fields = {"citations": chunk["citations"]}
+
+            returned_chunk = GenericStreamingChunk(
+                text=text,
+                tool_use=tool_use,
+                is_finished=is_finished,
+                finish_reason=finish_reason,
+                usage=usage,
+                index=index,
+                provider_specific_fields=provider_specific_fields,
+            )
+
+            return returned_chunk
+
+        except json.JSONDecodeError:
+            raise ValueError(f"Failed to decode JSON from chunk: {chunk}")
+
+    # Sync iterator
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        try:
+            chunk = self.response_iterator.__next__()
+        except StopIteration:
+            raise StopIteration
+        except ValueError as e:
+            raise RuntimeError(f"Error receiving chunk from stream: {e}")
+
+        try:
+            str_line = chunk
+            if isinstance(chunk, bytes):  # Handle binary data
+                str_line = chunk.decode("utf-8")  # Convert bytes to string
+                index = str_line.find("data:")
+                if index != -1:
+                    str_line = str_line[index:]
+            data_json = json.loads(str_line)
+            return self.chunk_parser(chunk=data_json)
+        except StopIteration:
+            raise StopIteration
+        except ValueError as e:
+            raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}")
+
+    # Async iterator
+    def __aiter__(self):
+        self.async_response_iterator = self.streaming_response.__aiter__()
+        return self
+
+    async def __anext__(self):
+        try:
+            chunk = await self.async_response_iterator.__anext__()
+        except StopAsyncIteration:
+            raise StopAsyncIteration
+        except ValueError as e:
+            raise RuntimeError(f"Error receiving chunk from stream: {e}")
+
+        try:
+            str_line = chunk
+            if isinstance(chunk, bytes):  # Handle binary data
+                str_line = chunk.decode("utf-8")  # Convert bytes to string
+                index = str_line.find("data:")
+                if index != -1:
+                    str_line = str_line[index:]
+
+            data_json = json.loads(str_line)
+            return self.chunk_parser(chunk=data_json)
+        except StopAsyncIteration:
+            raise StopAsyncIteration
+        except ValueError as e:
+            raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}")

litellm/main.py

@@ -1970,15 +1970,16 @@ def completion(  # type: ignore # noqa: PLR0915
                 logging_obj=logging,  # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
             )
 
-            if "stream" in optional_params and optional_params["stream"] is True:
-                # don't try to access stream object,
-                response = CustomStreamWrapper(
-                    model_response,
-                    model,
-                    custom_llm_provider="cohere_chat",
-                    logging_obj=logging,
-                )
-                return response
+            # if "stream" in optional_params and optional_params["stream"] is True:
+            #     # don't try to access stream object,
+            #     response = CustomStreamWrapper(
+            #         model_response,
+            #         model,
+            #         custom_llm_provider="cohere_chat",
+            #         logging_obj=logging,
+            #         _response_headers=headers,
+            #     )
+            #     return response
             response = model_response
         elif custom_llm_provider == "maritalk":
             maritalk_key = (

litellm/proxy/_new_secret_config.yaml

@@ -39,16 +39,4 @@ router_settings:
   redis_port: "os.environ/REDIS_PORT"
 
 litellm_settings:
-  cache: true
-  cache_params:
-    type: redis
-    host: "os.environ/REDIS_HOST"
-    port: "os.environ/REDIS_PORT"
-    namespace: "litellm.caching"
-    ttl: 600
-#   key_generation_settings:
-#     team_key_generation:
-#       allowed_team_member_roles: ["admin"]
-#       required_params: ["tags"] # require team admins to set tags for cost-tracking when generating a team key
-#     personal_key_generation: # maps to 'Default Team' on UI 
-#       allowed_user_roles: ["proxy_admin"]
+  success_callback: ["langsmith"]

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.53.2"
+version = "1.53.1"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -91,7 +91,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.53.2"
+version = "1.53.1"
 version_files = [
     "pyproject.toml:^version"
 ]

tests/llm_translation/test_cohere.py

@@ -0,0 +1,59 @@
+import os
+import sys
+import traceback
+
+from dotenv import load_dotenv
+
+load_dotenv()
+import io
+import os
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+import json
+
+import pytest
+
+import litellm
+from litellm import RateLimitError, Timeout, completion, completion_cost, embedding
+
+litellm.num_retries = 3
+
+
+@pytest.mark.parametrize("stream", [True, False])
+@pytest.mark.asyncio
+async def test_chat_completion_cohere_citations(stream):
+    try:
+        litellm.set_verbose = True
+        messages = [
+            {
+                "role": "user",
+                "content": "Which penguins are the tallest?",
+            },
+        ]
+        response = await litellm.acompletion(
+            model="cohere_chat/command-r",
+            messages=messages,
+            documents=[
+                {"title": "Tall penguins", "text": "Emperor penguins are the tallest."},
+                {
+                    "title": "Penguin habitats",
+                    "text": "Emperor penguins only live in Antarctica.",
+                },
+            ],
+            stream=stream,
+        )
+
+        if stream:
+            citations_chunk = False
+            async for chunk in response:
+                print("received chunk", chunk)
+                if "citations" in chunk:
+                    citations_chunk = True
+                    break
+            assert citations_chunk
+        else:
+            assert response.citations is not None
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")

tests/local_testing/test_cohere_completion.py

@@ -1,210 +0,0 @@
-import os
-import sys
-import traceback
-
-from dotenv import load_dotenv
-
-load_dotenv()
-import io
-import os
-
-sys.path.insert(
-    0, os.path.abspath("../..")
-)  # Adds the parent directory to the system path
-import json
-
-import pytest
-
-import litellm
-from litellm import RateLimitError, Timeout, completion, completion_cost, embedding
-
-litellm.num_retries = 3
-
-
-# FYI - cohere_chat looks quite unstable, even when testing locally
-def test_chat_completion_cohere():
-    try:
-        litellm.set_verbose = True
-        messages = [
-            {
-                "role": "user",
-                "content": "Hey",
-            },
-        ]
-        response = completion(
-            model="cohere_chat/command-r",
-            messages=messages,
-            max_tokens=10,
-        )
-        print(response)
-    except Exception as e:
-        pytest.fail(f"Error occurred: {e}")
-
-
-def test_chat_completion_cohere_tool_calling():
-    try:
-        litellm.set_verbose = True
-        messages = [
-            {
-                "role": "user",
-                "content": "What is the weather like in Boston?",
-            },
-        ]
-        response = completion(
-            model="cohere_chat/command-r",
-            messages=messages,
-            tools=[
-                {
-                    "type": "function",
-                    "function": {
-                        "name": "get_current_weather",
-                        "description": "Get the current weather in a given location",
-                        "parameters": {
-                            "type": "object",
-                            "properties": {
-                                "location": {
-                                    "type": "string",
-                                    "description": "The city and state, e.g. San Francisco, CA",
-                                },
-                                "unit": {
-                                    "type": "string",
-                                    "enum": ["celsius", "fahrenheit"],
-                                },
-                            },
-                            "required": ["location"],
-                        },
-                    },
-                }
-            ],
-        )
-        print(response)
-    except Exception as e:
-        pytest.fail(f"Error occurred: {e}")
-
-    # def get_current_weather(location, unit="fahrenheit"):
-    #     """Get the current weather in a given location"""
-    #     if "tokyo" in location.lower():
-    #         return json.dumps({"location": "Tokyo", "temperature": "10", "unit": unit})
-    #     elif "san francisco" in location.lower():
-    #         return json.dumps({"location": "San Francisco", "temperature": "72", "unit": unit})
-    #     elif "paris" in location.lower():
-    #         return json.dumps({"location": "Paris", "temperature": "22", "unit": unit})
-    #     else:
-    #         return json.dumps({"location": location, "temperature": "unknown"})
-
-    # def test_chat_completion_cohere_tool_with_result_calling():
-    #     # end to end cohere command-r with tool calling
-    #     # Step 1 - Send available tools
-    #     # Step 2 - Execute results
-    #     # Step 3 - Send results to command-r
-    #     try:
-    #         litellm.set_verbose = True
-    #         import json
-
-    #         # Step 1 - Send available tools
-    #         tools = [
-    #                 {
-    #                     "type": "function",
-    #                     "function": {
-    #                         "name": "get_current_weather",
-    #                         "description": "Get the current weather in a given location",
-    #                         "parameters": {
-    #                             "type": "object",
-    #                             "properties": {
-    #                                 "location": {
-    #                                     "type": "string",
-    #                                     "description": "The city and state, e.g. San Francisco, CA",
-    #                                 },
-    #                                 "unit": {
-    #                                     "type": "string",
-    #                                     "enum": ["celsius", "fahrenheit"],
-    #                                 },
-    #                             },
-    #                             "required": ["location"],
-    #                         },
-    #                     },
-    #                 }
-    #         ]
-
-    #         messages = [
-    #             {
-    #                 "role": "user",
-    #                 "content": "What is the weather like in Boston?",
-    #             },
-    #         ]
-    #         response = completion(
-    #             model="cohere_chat/command-r",
-    #             messages=messages,
-    #             tools=tools,
-    #         )
-    #         print("Response with tools to call", response)
-    #         print(response)
-
-    #         # step 2 - Execute results
-    #         tool_calls = response.tool_calls
-
-    #         available_functions = {
-    #             "get_current_weather": get_current_weather,
-    #         }  # only one function in this example, but you can have multiple
-
-    #         for tool_call in tool_calls:
-    #             function_name = tool_call.function.name
-    #             function_to_call = available_functions[function_name]
-    #             function_args = json.loads(tool_call.function.arguments)
-    #             function_response = function_to_call(
-    #                 location=function_args.get("location"),
-    #                 unit=function_args.get("unit"),
-    #             )
-    #             messages.append(
-    #                 {
-    #                     "tool_call_id": tool_call.id,
-    #                     "role": "tool",
-    #                     "name": function_name,
-    #                     "content": function_response,
-    #                 }
-    #             )  # extend conversation with function response
-
-    #         print("messages with tool call results", messages)
-
-    # messages = [
-    #     {
-    #         "role": "user",
-    #         "content": "What is the weather like in Boston?",
-    #     },
-    #     {
-    #             "tool_call_id": "tool_1",
-    #             "role": "tool",
-    #             "name": "get_current_weather",
-    #             "content": {"location": "San Francisco, CA", "unit": "fahrenheit", "temperature": "72"},
-    #     },
-    # ]
-    # respone = completion(
-    #     model="cohere_chat/command-r",
-    #     messages=messages,
-    #     tools=[
-    #         {
-    #             "type": "function",
-    #             "function": {
-    #                 "name": "get_current_weather",
-    #                 "description": "Get the current weather in a given location",
-    #                 "parameters": {
-    #                     "type": "object",
-    #                     "properties": {
-    #                         "location": {
-    #                             "type": "string",
-    #                             "description": "The city and state, e.g. San Francisco, CA",
-    #                         },
-    #                         "unit": {
-    #                             "type": "string",
-    #                             "enum": ["celsius", "fahrenheit"],
-    #                         },
-    #                     },
-    #                     "required": ["location"],
-    #                 },
-    #             },
-    #         }
-    #     ],
-    # )
-    # print(respone)
-    except Exception as e:
-        pytest.fail(f"Error occurred: {e}")

tests/local_testing/test_function_calling.py

@@ -46,11 +46,12 @@ def get_current_weather(location, unit="fahrenheit"):
     "model",
     [
         "gpt-3.5-turbo-1106",
-        # "mistral/mistral-large-latest",
+        "mistral/mistral-large-latest",
         "claude-3-haiku-20240307",
         "gemini/gemini-1.5-pro",
         "anthropic.claude-3-sonnet-20240229-v1:0",
-        # "groq/llama3-8b-8192",
+        "groq/llama3-8b-8192",
+        "cohere_chat/command-r",
     ],
 )
 @pytest.mark.flaky(retries=3, delay=1)

tests/local_testing/test_langsmith.py

@@ -53,10 +53,17 @@ def test_async_langsmith_logging_with_metadata():
 @pytest.mark.asyncio
 async def test_async_langsmith_logging_with_streaming_and_metadata(sync_mode):
     try:
+        litellm.DEFAULT_BATCH_SIZE = 1
+        litellm.DEFAULT_FLUSH_INTERVAL_SECONDS = 1
         test_langsmith_logger = LangsmithLogger()
         litellm.success_callback = ["langsmith"]
         litellm.set_verbose = True
-        run_id = str(uuid.uuid4())
+        run_id = "497f6eca-6276-4993-bfeb-53cbbbba6f08"
+        run_name = "litellmRUN"
+        test_metadata = {
+            "run_name": run_name,  # langsmith run name
+            "run_id": run_id,  # langsmith run id
+        }
 
         messages = [{"role": "user", "content": "what llm are u"}]
         if sync_mode is True:
@@ -66,7 +73,7 @@ async def test_async_langsmith_logging_with_streaming_and_metadata(sync_mode):
                 max_tokens=10,
                 temperature=0.2,
                 stream=True,
-                metadata={"id": run_id},
+                metadata=test_metadata,
             )
             for cb in litellm.callbacks:
                 if isinstance(cb, LangsmithLogger):
@@ -82,7 +89,7 @@ async def test_async_langsmith_logging_with_streaming_and_metadata(sync_mode):
                 temperature=0.2,
                 mock_response="This is a mock request",
                 stream=True,
-                metadata={"id": run_id},
+                metadata=test_metadata,
             )
             for cb in litellm.callbacks:
                 if isinstance(cb, LangsmithLogger):
@@ -100,11 +107,16 @@ async def test_async_langsmith_logging_with_streaming_and_metadata(sync_mode):
 
         input_fields_on_langsmith = logged_run_on_langsmith.get("inputs")
 
-        extra_fields_on_langsmith = logged_run_on_langsmith.get("extra").get(
+        extra_fields_on_langsmith = logged_run_on_langsmith.get("extra", {}).get(
             "invocation_params"
         )
 
-        assert logged_run_on_langsmith.get("run_type") == "llm"
+        assert (
+            logged_run_on_langsmith.get("run_type") == "llm"
+        ), f"run_type should be llm. Got: {logged_run_on_langsmith.get('run_type')}"
+        assert (
+            logged_run_on_langsmith.get("name") == run_name
+        ), f"run_type should be llm. Got: {logged_run_on_langsmith.get('run_type')}"
         print("\nLogged INPUT ON LANGSMITH", input_fields_on_langsmith)
 
         print("\nextra fields on langsmith", extra_fields_on_langsmith)