feat: cohore_v2 support

litellm/__init__.py

@@ -368,6 +368,7 @@ open_ai_chat_completion_models: List = []
 open_ai_text_completion_models: List = []
 cohere_models: List = []
 cohere_chat_models: List = []
+cohere_chat_v2_models: List = []
 mistral_chat_models: List = []
 text_completion_codestral_models: List = []
 anthropic_models: List = []
@@ -464,6 +465,8 @@ def add_known_models():
             cohere_models.append(key)
         elif value.get("litellm_provider") == "cohere_chat":
             cohere_chat_models.append(key)
+        elif value.get("litellm_provider") == "cohere_chat_v2":
+            cohere_chat_v2_models.append(key)
         elif value.get("litellm_provider") == "mistral":
             mistral_chat_models.append(key)
         elif value.get("litellm_provider") == "anthropic":
@@ -605,6 +608,7 @@ model_list = (
     + open_ai_text_completion_models
     + cohere_models
     + cohere_chat_models
+    + cohere_chat_v2_models
     + anthropic_models
     + replicate_models
     + openrouter_models
@@ -655,8 +659,9 @@ provider_list: List[Union[LlmProviders, str]] = list(LlmProviders)
 models_by_provider: dict = {
     "openai": open_ai_chat_completion_models + open_ai_text_completion_models,
     "text-completion-openai": open_ai_text_completion_models,
-    "cohere": cohere_models + cohere_chat_models,
+    "cohere": cohere_models + cohere_chat_models + cohere_chat_v2_models,
     "cohere_chat": cohere_chat_models,
+    "cohere_chat_v2": cohere_chat_v2_models,
     "anthropic": anthropic_models,
     "replicate": replicate_models,
     "huggingface": huggingface_models,
@@ -919,6 +924,7 @@ from .llms.bedrock.embed.amazon_titan_v2_transformation import (
     AmazonTitanV2Config,
 )
 from .llms.cohere.chat.transformation import CohereChatConfig
+from .llms.cohere.chat.transformation_v2 import CohereChatConfigV2
 from .llms.bedrock.embed.cohere_transformation import BedrockCohereEmbeddingConfig
 from .llms.openai.openai import OpenAIConfig, MistralEmbeddingConfig
 from .llms.openai.image_variations.transformation import OpenAIImageVariationConfig

litellm/constants.py

@@ -96,6 +96,7 @@ LITELLM_CHAT_PROVIDERS = [
     "text-completion-openai",
     "cohere",
     "cohere_chat",
+    "cohere_chat_v2",
     "clarifai",
     "anthropic",
     "anthropic_text",

litellm/litellm_core_utils/get_llm_provider_logic.py

@@ -23,14 +23,16 @@ def _is_non_openai_azure_model(model: str) -> bool:
 
 
 def handle_cohere_chat_model_custom_llm_provider(
-    model: str, custom_llm_provider: Optional[str] = None
+    model: str, custom_llm_provider: Optional[str] = None, api_version: Optional[str] = None
 ) -> Tuple[str, Optional[str]]:
     """
     if user sets model = "cohere/command-r" -> use custom_llm_provider = "cohere_chat"
+    if api_version = "v2" -> use custom_llm_provider = "cohere_chat_v2"
 
     Args:
-        model:
+        model: The model name
-        custom_llm_provider:
+        custom_llm_provider: The custom LLM provider if specified
+        api_version: The API version (v1 or v2)
 
     Returns:
         model, custom_llm_provider
@@ -38,6 +40,9 @@ def handle_cohere_chat_model_custom_llm_provider(
 
     if custom_llm_provider:
         if custom_llm_provider == "cohere" and model in litellm.cohere_chat_models:
+            # Check if v2 API version is specified
+            if api_version == "v2":
+                return model, "cohere_chat_v2"
             return model, "cohere_chat"
 
     if "/" in model:
@@ -47,6 +52,9 @@ def handle_cohere_chat_model_custom_llm_provider(
             and _custom_llm_provider == "cohere"
             and _model in litellm.cohere_chat_models
         ):
+            # Check if v2 API version is specified
+            if api_version == "v2":
+                return _model, "cohere_chat_v2"
             return _model, "cohere_chat"
 
     return model, custom_llm_provider
@@ -122,8 +130,18 @@ def get_llm_provider(  # noqa: PLR0915
                 return model, custom_llm_provider, dynamic_api_key, api_base
 
         ### Handle cases when custom_llm_provider is set to cohere/command-r-plus but it should use cohere_chat route
+        # Extract api_version from optional_params if it exists
+        api_version = None
+        if litellm_params and hasattr(litellm_params, "optional_params") and litellm_params.optional_params:
+            api_version = litellm_params.optional_params.get("api_version")
+
+        # Handle direct cohere_chat_v2 model format
+        if model.startswith("cohere_chat_v2/"):
+            model = model.replace("cohere_chat_v2/", "")
+            custom_llm_provider = "cohere_chat_v2"
+            
         model, custom_llm_provider = handle_cohere_chat_model_custom_llm_provider(
-            model, custom_llm_provider
+            model, custom_llm_provider, api_version
         )
 
         model, custom_llm_provider = handle_anthropic_text_model_custom_llm_provider(

litellm/litellm_core_utils/prompt_templates/factory.py

@@ -2007,6 +2007,57 @@ def cohere_messages_pt_v2(  # noqa: PLR0915
     return returned_message, new_messages
 
 
+def cohere_messages_pt_v3(messages: List, model: str, llm_provider: str):
+    """
+    Format messages for Cohere v2 API
+    
+    In v2, messages are combined in a single array with the following format:
+    [
+        {"role": "USER", "content": "Hello"},
+        {"role": "ASSISTANT", "content": "Hi there!"},
+        {"role": "USER", "content": "How are you?"}
+    ]
+    
+    Returns:
+        List of formatted messages in Cohere v2 format
+    """
+    cohere_messages = []
+    
+    for msg_i, message in enumerate(messages):
+        role = message["role"].upper()
+        
+        # Map OpenAI roles to Cohere v2 roles
+        if role == "USER":
+            pass  # Keep as USER
+        elif role == "ASSISTANT":
+            role = "CHATBOT"  # Cohere v2 uses CHATBOT instead of ASSISTANT
+        elif role == "SYSTEM":
+            role = "USER"  # System messages are sent as USER with a special prefix
+            message["content"] = f"<admin>{message['content']}</admin>"
+        elif role == "TOOL":
+            # Skip tool messages as they'll be handled separately with tool_results
+            continue
+        elif role == "FUNCTION":
+            # Skip function messages as they'll be handled separately with tool_results
+            continue
+        
+        # Handle content
+        content = ""
+        if isinstance(message.get("content"), str):
+            content = message["content"]
+        elif isinstance(message.get("content"), list):
+            # Handle content list (text and images)
+            for item in message["content"]:
+                if isinstance(item, dict):
+                    if item.get("type") == "text":
+                        content += item.get("text", "")
+        
+        # Add message to the list
+        cohere_messages.append({"role": role, "content": content})
+    
+    return cohere_messages
+
+
 def cohere_message_pt(messages: list):
     tool_calls: List = get_all_tool_calls(messages=messages)
     prompt = ""

litellm/llms/cohere/chat/transformation_v2.py

@@ -0,0 +1,353 @@
+"""Cohere Chat V2 API Integration Module.
+
+This module provides the necessary classes and functions to interact with Cohere's V2 Chat API.
+It handles the transformation of requests and responses between LiteLLM's standard format and
+Cohere's specific API requirements.
+"""
+
+import json
+import time
+from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, List, Optional, Union
+
+import httpx
+
+import litellm
+from litellm.litellm_core_utils.prompt_templates.factory import cohere_messages_pt_v3
+from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import ModelResponse, Usage
+
+# Use absolute imports instead of relative imports
+from litellm.llms.cohere.common_utils import ModelResponseIterator as CohereModelResponseIterator
+from litellm.llms.cohere.common_utils import validate_environment as cohere_validate_environment
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
+
+
+class CohereErrorV2(BaseLLMException):
+    def __init__(
+        self,
+        status_code: int,
+        message: str,
+        headers: Optional[httpx.Headers] = None,
+    ):
+        self.status_code = status_code
+        self.message = message
+        self.request = httpx.Request(method="POST", url="https://api.cohere.com/v2/chat")
+        self.response = httpx.Response(status_code=status_code, request=self.request)
+        super().__init__(
+            status_code=status_code,
+            message=message,
+            headers=headers,
+        )
+
+
+class CohereChatConfigV2(BaseConfig):
+    """
+    Configuration class for Cohere's V2 API interface.
+
+    Args:
+        preamble (str, optional): When specified, the default Cohere preamble will be replaced with the provided one.
+        generation_id (str, optional): Unique identifier for the generated reply.
+        conversation_id (str, optional): Creates or resumes a persisted conversation.
+        prompt_truncation (str, optional): Dictates how the prompt will be constructed. Options: 'AUTO', 'AUTO_PRESERVE_ORDER', 'OFF'.
+        connectors (List[Dict[str, str]], optional): List of connectors (e.g., web-search) to enrich the model's reply.
+        search_queries_only (bool, optional): When true, the response will only contain a list of generated search queries.
+        documents (List[Dict[str, str]] or List[str], optional): A list of relevant documents that the model can cite.
+        temperature (float, optional): A non-negative float that tunes the degree of randomness in generation.
+        max_tokens (int, optional): The maximum number of tokens the model will generate as part of the response.
+        k (int, optional): Ensures only the top k most likely tokens are considered for generation at each step.
+        p (float, optional): Ensures that only the most likely tokens, with total probability mass of p, are considered for generation.
+        frequency_penalty (float, optional): Used to reduce repetitiveness of generated tokens.
+        presence_penalty (float, optional): Used to reduce repetitiveness of generated tokens.
+        tools (List[Dict[str, str]], optional): A list of available tools (functions) that the model may suggest invoking.
+        tool_results (List[Dict[str, Any]], optional): A list of results from invoking tools.
+        seed (int, optional): A seed to assist reproducibility of the model's response.
+    """
+
+    preamble: Optional[str] = None
+    generation_id: Optional[str] = None
+    conversation_id: Optional[str] = None
+    prompt_truncation: Optional[str] = None
+    connectors: Optional[list] = None
+    search_queries_only: Optional[bool] = None
+    documents: Optional[list] = None
+    temperature: Optional[float] = None
+    max_tokens: Optional[int] = None
+    k: Optional[int] = None
+    p: Optional[float] = None
+    frequency_penalty: Optional[float] = None
+    presence_penalty: Optional[float] = None
+    tools: Optional[list] = None
+    tool_results: Optional[list] = None
+    seed: Optional[int] = None
+
+    def __init__(
+        self,
+        preamble: Optional[str] = None,
+        generation_id: Optional[str] = None,
+        conversation_id: Optional[str] = None,
+        prompt_truncation: Optional[str] = None,
+        connectors: Optional[list] = None,
+        search_queries_only: Optional[bool] = None,
+        documents: Optional[list] = None,
+        temperature: Optional[float] = None,
+        max_tokens: Optional[int] = None,
+        k: Optional[int] = None,
+        p: Optional[float] = None,
+        frequency_penalty: Optional[float] = None,
+        presence_penalty: Optional[float] = None,
+        tools: Optional[list] = None,
+        tool_results: Optional[list] = None,
+        seed: Optional[int] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        return cohere_validate_environment(
+            headers=headers,
+            model=model,
+            messages=messages,
+            optional_params=optional_params,
+            api_key=api_key,
+            api_version="v2",  # Specify v2 API version
+        )
+
+    def get_supported_openai_params(self, model: str) -> List[str]:
+        return [
+            "stream",
+            "temperature",
+            "max_tokens",
+            "top_p",
+            "frequency_penalty",
+            "presence_penalty",
+            "stop",
+            "n",
+            "tools",
+            "tool_choice",
+            "seed",
+            "extra_headers",
+        ]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        for param, value in non_default_params.items():
+            if param == "stream":
+                optional_params["stream"] = value
+            if param == "temperature":
+                optional_params["temperature"] = value
+            if param == "max_tokens":
+                optional_params["max_tokens"] = value
+            if param == "n":
+                optional_params["num_generations"] = value
+            if param == "top_p":
+                optional_params["p"] = value
+            if param == "frequency_penalty":
+                optional_params["frequency_penalty"] = value
+            if param == "presence_penalty":
+                optional_params["presence_penalty"] = value
+            if param == "stop":
+                optional_params["stop_sequences"] = value
+            if param == "tools":
+                optional_params["tools"] = value
+            if param == "seed":
+                optional_params["seed"] = value
+        return optional_params
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        ## Load Config
+        for k, v in litellm.CohereChatConfigV2.get_config().items():
+            if (
+                k not in optional_params
+            ):  # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in
+                optional_params[k] = v
+
+        # In v2, messages are combined in a single array
+        cohere_messages = cohere_messages_pt_v3(
+            messages=messages, model=model, llm_provider="cohere_chat"
+        )
+        optional_params["messages"] = cohere_messages
+        optional_params["model"] = model.split("/")[-1]  # Extract model name from model string
+
+        ## Handle Tool Calling
+        if "tools" in optional_params:
+            cohere_tools = self._construct_cohere_tool(tools=optional_params["tools"])
+            optional_params["tools"] = cohere_tools
+
+        # Handle tool results if present
+        if "tool_results" in optional_params and isinstance(optional_params["tool_results"], list):
+            # Convert tool results to v2 format if needed
+            tool_results = []
+            for result in optional_params["tool_results"]:
+                if isinstance(result, dict) and "content" in result:
+                    # Format from v1 to v2
+                    tool_result = {
+                        "tool_call_id": result.get("tool_call_id", ""),
+                        "output": result.get("content", ""),
+                    }
+                    tool_results.append(tool_result)
+                else:
+                    # Already in v2 format
+                    tool_results.append(result)
+            optional_params["tool_results"] = tool_results
+
+        return optional_params
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        request_data: dict,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ModelResponse:
+        try:
+            raw_response_json = raw_response.json()
+            model_response.choices[0].message.content = raw_response_json.get("text", "")  # type: ignore
+        except Exception:
+            raise CohereErrorV2(
+                message=raw_response.text, status_code=raw_response.status_code
+            )
+
+        ## ADD CITATIONS
+        if "citations" in raw_response_json:
+            setattr(model_response, "citations", raw_response_json["citations"])
+
+        ## Tool calling response
+        cohere_tools_response = raw_response_json.get("tool_calls", None)
+        if cohere_tools_response is not None and cohere_tools_response != []:
+            # convert cohere_tools_response to OpenAI response format
+            tool_calls = []
+            for tool in cohere_tools_response:
+                function_name = tool.get("name", "")
+                tool_call_id = tool.get("id", "")
+                parameters = tool.get("parameters", {})
+                tool_call = {
+                    "id": tool_call_id,
+                    "type": "function",
+                    "function": {
+                        "name": function_name,
+                        "arguments": json.dumps(parameters),
+                    },
+                }
+                tool_calls.append(tool_call)
+            _message = litellm.Message(
+                tool_calls=tool_calls,
+                content=None,
+            )
+            model_response.choices[0].message = _message  # type: ignore
+
+        ## CALCULATING USAGE - use cohere `billed_units` for returning usage
+        billed_units = raw_response_json.get("usage", {})
+
+        prompt_tokens = billed_units.get("input_tokens", 0)
+        completion_tokens = billed_units.get("output_tokens", 0)
+
+        model_response.created = int(time.time())
+        model_response.model = model
+        usage = Usage(
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens,
+        )
+        setattr(model_response, "usage", usage)
+        return model_response
+
+    def _construct_cohere_tool(
+        self,
+        tools: Optional[list] = None,
+    ):
+        if tools is None:
+            tools = []
+        cohere_tools = []
+        for tool in tools:
+            cohere_tool = self._translate_openai_tool_to_cohere(tool)
+            cohere_tools.append(cohere_tool)
+        return cohere_tools
+
+    def _translate_openai_tool_to_cohere(
+        self,
+        openai_tool: dict,
+    ):
+        """
+        Translates OpenAI tool format to Cohere v2 tool format
+        
+        Cohere v2 tools look like this:
+        {
+            "name": "get_current_weather",
+            "description": "Get the current weather in a given location",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA"
+                    },
+                    "unit": {
+                        "type": "string",
+                        "enum": ["celsius", "fahrenheit"]
+                    }
+                },
+                "required": ["location"]
+            }
+        }
+        """
+
+        cohere_tool = {
+            "name": openai_tool["function"]["name"],
+            "description": openai_tool["function"]["description"],
+            "input_schema": openai_tool["function"]["parameters"],
+        }
+
+        return cohere_tool
+
+    def get_model_response_iterator(
+        self,
+        streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
+        sync_stream: bool,
+        json_mode: Optional[bool] = False,
+    ):
+        return CohereModelResponseIterator(
+            streaming_response=streaming_response,
+            sync_stream=sync_stream,
+            json_mode=json_mode,
+        )
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BaseLLMException:
+        return CohereErrorV2(status_code=status_code, message=error_message)

litellm/llms/cohere/common_utils.py

@@ -21,11 +21,15 @@ def validate_environment(
     messages: List[AllMessageValues],
     optional_params: dict,
     api_key: Optional[str] = None,
+    api_version: Optional[str] = "v1",
 ) -> dict:
     """
     Return headers to use for cohere chat completion request
 
-    Cohere API Ref: https://docs.cohere.com/reference/chat
+    Cohere API Ref: 
+    - v1: https://docs.cohere.com/reference/chat
+    - v2: https://docs.cohere.com/v2/reference/chat
+    
     Expected headers:
     {
         "Request-Source": "unspecified:litellm",

litellm/main.py

@@ -2108,6 +2108,45 @@ def completion(  # type: ignore # noqa: PLR0915
                 api_key=cohere_key,
                 logging_obj=logging,  # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
             )
+        elif custom_llm_provider == "cohere_chat_v2":
+            cohere_key = (
+                api_key
+                or litellm.cohere_key
+                or get_secret_str("COHERE_API_KEY")
+                or get_secret_str("CO_API_KEY")
+                or litellm.api_key
+            )
+
+            api_base = (
+                api_base
+                or litellm.api_base
+                or get_secret_str("COHERE_API_BASE")
+                or "https://api.cohere.ai/v2/chat"
+            )
+
+            headers = headers or litellm.headers or {}
+            if headers is None:
+                headers = {}
+
+            if extra_headers is not None:
+                headers.update(extra_headers)
+
+            response = base_llm_http_handler.completion(
+                model=model,
+                stream=stream,
+                messages=messages,
+                acompletion=acompletion,
+                api_base=api_base,
+                model_response=model_response,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                custom_llm_provider="cohere_chat_v2",
+                timeout=timeout,
+                headers=headers,
+                encoding=encoding,
+                api_key=cohere_key,
+                logging_obj=logging,  # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
+            )
         elif custom_llm_provider == "maritalk":
             maritalk_key = (
                 api_key

litellm/types/utils.py

@@ -2004,6 +2004,7 @@ class LlmProviders(str, Enum):
     TEXT_COMPLETION_OPENAI = "text-completion-openai"
     COHERE = "cohere"
     COHERE_CHAT = "cohere_chat"
+    COHERE_CHAT_V2 = "cohere_chat_v2"
     CLARIFAI = "clarifai"
     ANTHROPIC = "anthropic"
     ANTHROPIC_TEXT = "anthropic_text"

litellm/utils.py

@@ -6233,6 +6233,8 @@ class ProviderConfigManager:
             return litellm.OpenAITextCompletionConfig()
         elif litellm.LlmProviders.COHERE_CHAT == provider:
             return litellm.CohereChatConfig()
+        elif litellm.LlmProviders.COHERE_CHAT_V2 == provider:
+            return litellm.CohereChatConfigV2()
         elif litellm.LlmProviders.COHERE == provider:
             return litellm.CohereConfig()
         elif litellm.LlmProviders.SNOWFLAKE == provider:

tests/llm_translation/test_cohere_v2.py

@@ -0,0 +1,999 @@
+import os
+import sys
+import traceback
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# For testing, make sure the COHERE_API_KEY or CO_API_KEY environment variable is set
+# You can set it before running the tests with: export COHERE_API_KEY=your_api_key
+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
+from unittest.mock import AsyncMock, patch
+from litellm import RateLimitError, Timeout, completion, completion_cost, embedding
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+
+litellm.num_retries = 3
+
+
+@pytest.mark.parametrize("stream", [True, False])
+@pytest.mark.flaky(retries=3, delay=1)
+@pytest.mark.asyncio
+async def test_chat_completion_cohere_v2_citations(stream):
+    try:
+        class MockResponse:
+            def __init__(self, status_code, json_data, is_stream=False):
+                self.status_code = status_code
+                self._json_data = json_data
+                self.headers = {}
+                self.is_stream = is_stream
+                
+                # For streaming responses with citations
+                if is_stream:
+                    # Create streaming chunks with citations at the end
+                    self._iter_content_chunks = [
+                        json.dumps({"text": "Emperor"}).encode(),
+                        json.dumps({"text": " penguins"}).encode(),
+                        json.dumps({"text": " are"}).encode(),
+                        json.dumps({"text": " the"}).encode(),
+                        json.dumps({"text": " tallest"}).encode(),
+                        json.dumps({"text": " and"}).encode(),
+                        json.dumps({"text": " they"}).encode(),
+                        json.dumps({"text": " live"}).encode(),
+                        json.dumps({"text": " in"}).encode(),
+                        json.dumps({"text": " Antarctica"}).encode(),
+                        json.dumps({"text": "."}).encode(),
+                        # Citations in a separate chunk
+                        json.dumps({"citations": [
+                            {
+                                "start": 0,
+                                "end": 30,
+                                "text": "Emperor penguins are the tallest",
+                                "document_ids": ["doc1"]
+                            },
+                            {
+                                "start": 31,
+                                "end": 70,
+                                "text": "they live in Antarctica",
+                                "document_ids": ["doc2"]
+                            }
+                        ]}).encode(),
+                        json.dumps({"finish_reason": "COMPLETE"}).encode(),
+                    ]
+
+            def json(self):
+                return self._json_data
+
+            @property
+            def text(self):
+                return json.dumps(self._json_data)
+                
+            def iter_lines(self):
+                if self.is_stream:
+                    for chunk in self._iter_content_chunks:
+                        yield chunk
+                else:
+                    yield json.dumps(self._json_data).encode()
+                    
+            async def aiter_lines(self):
+                if self.is_stream:
+                    for chunk in self._iter_content_chunks:
+                        yield chunk
+                else:
+                    yield json.dumps(self._json_data).encode()
+                
+        async def mock_async_post(*args, **kwargs):
+            # For asynchronous HTTP client
+            data = kwargs.get("data", "{}")
+            request_body = json.loads(data)
+            print("Async Request body:", request_body)
+            
+            # Verify the messages are formatted correctly for v2
+            messages = request_body.get("messages", [])
+            assert len(messages) > 0
+            assert "role" in messages[0]
+            assert "content" in messages[0]
+            
+            # Check if documents are included
+            documents = request_body.get("documents", [])
+            assert len(documents) > 0
+            
+            # Mock response with citations
+            mock_response = {
+                "text": "Emperor penguins are the tallest penguins and they live in Antarctica.",
+                "generation_id": "mock-id",
+                "id": "mock-completion",
+                "usage": {"input_tokens": 10, "output_tokens": 20},
+                "citations": [
+                    {
+                        "start": 0,
+                        "end": 30,
+                        "text": "Emperor penguins are the tallest",
+                        "document_ids": ["doc1"]
+                    },
+                    {
+                        "start": 31,
+                        "end": 70,
+                        "text": "they live in Antarctica",
+                        "document_ids": ["doc2"]
+                    }
+                ]
+            }
+            
+            # Create a streaming response with citations
+            if stream:
+                return MockResponse(
+                    200,
+                    {
+                        "text": "Emperor penguins are the tallest penguins and they live in Antarctica.",
+                        "generation_id": "mock-id",
+                        "id": "mock-completion",
+                        "usage": {"input_tokens": 10, "output_tokens": 20},
+                        "citations": [
+                            {
+                                "start": 0,
+                                "end": 30,
+                                "text": "Emperor penguins are the tallest",
+                                "document_ids": ["doc1"]
+                            },
+                            {
+                                "start": 31,
+                                "end": 70,
+                                "text": "they live in Antarctica",
+                                "document_ids": ["doc2"]
+                            }
+                        ],
+                        "stream": True
+                    },
+                    is_stream=True
+                )
+            else:
+                return MockResponse(200, mock_response)
+            
+        # Mock the async HTTP client
+        with patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", new_callable=AsyncMock, side_effect=mock_async_post):
+            litellm.set_verbose = True
+            messages = [
+                {
+                    "role": "user",
+                    "content": "Which penguins are the tallest?",
+                },
+            ]
+            response = await litellm.acompletion(
+                model="cohere_chat_v2/command-r",
+                messages=messages,
+                stream=stream,
+                documents=[
+                    {"title": "Tall penguins", "text": "Emperor penguins are the tallest."},
+                    {
+                        "title": "Penguin habitats",
+                        "text": "Emperor penguins only live in Antarctica.",
+                    },
+            ],
+        )
+
+        if stream:
+            citations_chunk = False
+            async for chunk in response:
+                print("received chunk", chunk)
+                if hasattr(chunk, "citations") or (isinstance(chunk, dict) and "citations" in chunk):
+                    citations_chunk = True
+                    break
+            assert citations_chunk
+        else:
+            assert hasattr(response, "citations")
+    except litellm.ServiceUnavailableError:
+        pass
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
+
+def test_completion_cohere_v2_command_r_plus_function_call():
+    litellm.set_verbose = True
+    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's the weather like in Boston today in Fahrenheit?",
+        }
+    ]
+    try:
+        # test without max tokens
+        response = completion(
+            model="command-r-plus",
+            messages=messages,
+            tools=tools,
+            tool_choice="auto",
+            api_version="v2",  # Specify v2 API version
+        )
+        # Add any assertions, here to check response args
+        print(response)
+        assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
+        assert isinstance(
+            response.choices[0].message.tool_calls[0].function.arguments, str
+        )
+
+        messages.append(
+            response.choices[0].message.model_dump()
+        )  # Add assistant tool invokes
+        tool_result = (
+            '{"location": "Boston", "temperature": "72", "unit": "fahrenheit"}'
+        )
+        # Add user submitted tool results in the OpenAI format
+        messages.append(
+            {
+                "tool_call_id": response.choices[0].message.tool_calls[0].id,
+                "role": "tool",
+                "name": response.choices[0].message.tool_calls[0].function.name,
+                "content": tool_result,
+            }
+        )
+        # In the second response, Cohere should deduce answer from tool results
+        second_response = completion(
+            model="command-r-plus",
+            messages=messages,
+            tools=tools,
+            tool_choice="auto",
+            force_single_step=True,
+            api_version="v2",  # Specify v2 API version
+        )
+        print(second_response)
+    except litellm.Timeout:
+        pass
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
+
+@pytest.mark.flaky(retries=6, delay=1)
+def test_completion_cohere_v2():
+    try:
+        # litellm.set_verbose=True
+        messages = [
+            {"role": "system", "content": "You're a good bot"},
+            {
+                "role": "user",
+                "content": "Hey",
+            },
+        ]
+        response = completion(
+            model="command-r",
+            messages=messages,
+            api_version="v2",  # Specify v2 API version
+        )
+        print(response)
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("sync_mode", [True, False])
+async def test_chat_completion_cohere_v2(sync_mode):
+    try:
+        class MockResponse:
+            def __init__(self, status_code, json_data, is_stream=False):
+                self.status_code = status_code
+                self._json_data = json_data
+                self.headers = {}
+                self.is_stream = is_stream
+                
+                # For streaming responses with citations
+                if is_stream:
+                    # Create streaming chunks with citations at the end
+                    self._iter_content_chunks = [
+                        json.dumps({"text": "Emperor"}).encode(),
+                        json.dumps({"text": " penguins"}).encode(),
+                        json.dumps({"text": " are"}).encode(),
+                        json.dumps({"text": " the"}).encode(),
+                        json.dumps({"text": " tallest"}).encode(),
+                        json.dumps({"text": " and"}).encode(),
+                        json.dumps({"text": " they"}).encode(),
+                        json.dumps({"text": " live"}).encode(),
+                        json.dumps({"text": " in"}).encode(),
+                        json.dumps({"text": " Antarctica"}).encode(),
+                        json.dumps({"text": "."}).encode(),
+                        # Citations in a separate chunk
+                        json.dumps({"citations": [
+                            {
+                                "start": 0,
+                                "end": 30,
+                                "text": "Emperor penguins are the tallest",
+                                "document_ids": ["doc1"]
+                            },
+                            {
+                                "start": 31,
+                                "end": 70,
+                                "text": "they live in Antarctica",
+                                "document_ids": ["doc2"]
+                            }
+                        ]}).encode(),
+                        json.dumps({"finish_reason": "COMPLETE"}).encode(),
+                    ]
+
+            def json(self):
+                return self._json_data
+
+            @property
+            def text(self):
+                return json.dumps(self._json_data)
+                
+            def iter_lines(self):
+                if self.is_stream:
+                    for chunk in self._iter_content_chunks:
+                        yield chunk
+                else:
+                    yield json.dumps(self._json_data).encode()
+                    
+            async def aiter_lines(self):
+                if self.is_stream:
+                    for chunk in self._iter_content_chunks:
+                        yield chunk
+                else:
+                    yield json.dumps(self._json_data).encode()
+                
+        def mock_sync_post(*args, **kwargs):
+            # For synchronous HTTP client
+            data = kwargs.get("data", "{}")
+            request_body = json.loads(data)
+            print("Sync Request body:", request_body)
+            
+            # Verify the model is passed correctly
+            assert request_body.get("model") == "command-r"
+            
+            # Verify max_tokens is passed correctly
+            assert request_body.get("max_tokens") == 10
+            
+            # Verify the messages are formatted correctly for v2
+            messages = request_body.get("messages", [])
+            assert len(messages) > 0
+            assert "role" in messages[0]
+            assert "content" in messages[0]
+            
+            # Mock response
+            return MockResponse(
+                200,
+                {
+                    "text": "This is a mocked response for sync request",
+                    "generation_id": "mock-id",
+                    "id": "mock-completion",
+                    "usage": {"input_tokens": 10, "output_tokens": 20},
+                },
+            )
+            
+        async def mock_async_post(*args, **kwargs):
+            # For asynchronous HTTP client
+            data = kwargs.get("data", "{}")
+            request_body = json.loads(data)
+            print("Async Request body:", request_body)
+            
+            # Verify the model is passed correctly
+            assert request_body.get("model") == "command-r"
+            
+            # Verify max_tokens is passed correctly
+            assert request_body.get("max_tokens") == 10
+            
+            # Verify the messages are formatted correctly for v2
+            messages = request_body.get("messages", [])
+            assert len(messages) > 0
+            assert "role" in messages[0]
+            assert "content" in messages[0]
+            
+            # Mock response
+            return MockResponse(
+                200,
+                {
+                    "text": "This is a mocked response for async request",
+                    "generation_id": "mock-id",
+                    "id": "mock-completion",
+                    "usage": {"input_tokens": 10, "output_tokens": 20},
+                },
+            )
+            
+        # Mock both sync and async HTTP clients
+        with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post", side_effect=mock_sync_post):
+            with patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", new_callable=AsyncMock, side_effect=mock_async_post):
+                litellm.set_verbose = True
+                messages = [
+                    {"role": "system", "content": "You're a good bot"},
+                    {
+                        "role": "user",
+                        "content": "Hey",
+                    },
+                ]
+                if sync_mode is False:
+                    response = await litellm.acompletion(
+                        model="cohere_chat_v2/command-r",
+                        messages=messages,
+                        max_tokens=10,
+                    )
+                else:
+                    response = completion(
+                        model="cohere_chat_v2/command-r",
+                        messages=messages,
+                        max_tokens=10,
+                    )
+                print(response)
+                assert response is not None
+                assert "This is a mocked response" in response.choices[0].message.content
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("sync_mode", [False])
+async def test_chat_completion_cohere_v2_stream(sync_mode):
+    try:
+        class MockResponse:
+            def __init__(self, status_code, json_data, is_stream=False):
+                self.status_code = status_code
+                self._json_data = json_data
+                self.headers = {}
+                self.is_stream = is_stream
+                
+                # For streaming responses
+                if is_stream:
+                    self._iter_content_chunks = [
+                        json.dumps({"text": "This"}).encode(),
+                        json.dumps({"text": " is"}).encode(),
+                        json.dumps({"text": " a"}).encode(),
+                        json.dumps({"text": " streamed"}).encode(),
+                        json.dumps({"text": " response"}).encode(),
+                        json.dumps({"text": "."}).encode(),
+                        json.dumps({"finish_reason": "COMPLETE"}).encode(),
+                    ]
+                    
+            def json(self):
+                return self._json_data
+
+            @property
+            def text(self):
+                return json.dumps(self._json_data)
+                
+            def iter_lines(self):
+                if self.is_stream:
+                    for chunk in self._iter_content_chunks:
+                        yield chunk
+                else:
+                    yield json.dumps(self._json_data).encode()
+                    
+            async def aiter_lines(self):
+                if self.is_stream:
+                    for chunk in self._iter_content_chunks:
+                        yield chunk
+                else:
+                    yield json.dumps(self._json_data).encode()
+                
+        async def mock_async_post(*args, **kwargs):
+            # For asynchronous HTTP client
+            data = kwargs.get("data", "{}")
+            request_body = json.loads(data)
+            print("Async Request body:", request_body)
+            
+            # Verify the model is passed correctly
+            assert request_body.get("model") == "command-r"
+            
+            # Verify max_tokens is passed correctly
+            assert request_body.get("max_tokens") == 10
+            
+            # Verify stream is set to True
+            assert request_body.get("stream") == True
+            
+            # Verify the messages are formatted correctly for v2
+            messages = request_body.get("messages", [])
+            assert len(messages) > 0
+            assert "role" in messages[0]
+            assert "content" in messages[0]
+            
+            # Return a streaming response
+            return MockResponse(
+                200,
+                {
+                    "text": "This is a streamed response.",
+                    "generation_id": "mock-id",
+                    "id": "mock-completion",
+                    "usage": {"input_tokens": 10, "output_tokens": 20},
+                },
+                is_stream=True
+            )
+            
+        # Mock the async HTTP client for streaming
+        with patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", new_callable=AsyncMock, side_effect=mock_async_post):
+            litellm.set_verbose = True
+            messages = [
+                {"role": "system", "content": "You're a good bot"},
+                {
+                    "role": "user",
+                    "content": "Hey",
+                },
+            ]
+            if sync_mode is False:
+                response = await litellm.acompletion(
+                    model="cohere_chat_v2/command-r",
+                    messages=messages,
+                    stream=True,
+                    max_tokens=10,
+                )
+                # Verify we get streaming chunks
+                chunk_count = 0
+                async for chunk in response:
+                    print(f"chunk: {chunk}")
+                    chunk_count += 1
+                assert chunk_count > 0, "No streaming chunks were received"
+            else:
+                # This test is only for async mode
+                pass
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
+
+def test_cohere_v2_mock_completion():
+    """
+    Test cohere_chat_v2 completion with mocked responses to avoid API calls
+    """
+    try:
+        import httpx
+
+        class MockResponse:
+            def __init__(self, status_code, json_data, is_stream=False):
+                self.status_code = status_code
+                self._json_data = json_data
+                self.headers = {}
+                self.is_stream = is_stream
+                
+                # For streaming responses with citations
+                if is_stream:
+                    # Create streaming chunks with citations at the end
+                    self._iter_content_chunks = [
+                        json.dumps({"text": "Emperor"}).encode(),
+                        json.dumps({"text": " penguins"}).encode(),
+                        json.dumps({"text": " are"}).encode(),
+                        json.dumps({"text": " the"}).encode(),
+                        json.dumps({"text": " tallest"}).encode(),
+                        json.dumps({"text": " and"}).encode(),
+                        json.dumps({"text": " they"}).encode(),
+                        json.dumps({"text": " live"}).encode(),
+                        json.dumps({"text": " in"}).encode(),
+                        json.dumps({"text": " Antarctica"}).encode(),
+                        json.dumps({"text": "."}).encode(),
+                        # Citations in a separate chunk
+                        json.dumps({"citations": [
+                            {
+                                "start": 0,
+                                "end": 30,
+                                "text": "Emperor penguins are the tallest",
+                                "document_ids": ["doc1"]
+                            },
+                            {
+                                "start": 31,
+                                "end": 70,
+                                "text": "they live in Antarctica",
+                                "document_ids": ["doc2"]
+                            }
+                        ]}).encode(),
+                        json.dumps({"finish_reason": "COMPLETE"}).encode(),
+                    ]
+
+            def json(self):
+                return self._json_data
+
+            @property
+            def text(self):
+                return json.dumps(self._json_data)
+                
+            def iter_lines(self):
+                if self.is_stream:
+                    for chunk in self._iter_content_chunks:
+                        yield chunk
+                else:
+                    yield json.dumps(self._json_data).encode()
+                    
+            async def aiter_lines(self):
+                if self.is_stream:
+                    for chunk in self._iter_content_chunks:
+                        yield chunk
+                else:
+                    yield json.dumps(self._json_data).encode()
+
+        def mock_sync_post(*args, **kwargs):
+            # For synchronous HTTP client
+            data = kwargs.get("data", "{}")
+            request_body = json.loads(data)
+            print("Sync Request body:", request_body)
+            
+            # Verify the messages are formatted correctly for v2
+            messages = request_body.get("messages", [])
+            assert len(messages) > 0
+            assert "role" in messages[0]
+            assert "content" in messages[0]
+            
+            # Mock response
+            return MockResponse(
+                200,
+                {
+                    "text": "This is a mocked response from Cohere v2 API",
+                    "generation_id": "mock-id",
+                    "id": "mock-completion",
+                    "usage": {"input_tokens": 10, "output_tokens": 20},
+                },
+            )
+
+        async def mock_async_post(*args, **kwargs):
+            # For asynchronous HTTP client
+            data = kwargs.get("data", "{}")
+            request_body = json.loads(data)
+            print("Async Request body:", request_body)
+            
+            # Verify the messages are formatted correctly for v2
+            messages = request_body.get("messages", [])
+            assert len(messages) > 0
+            assert "role" in messages[0]
+            assert "content" in messages[0]
+            
+            # Mock response
+            return MockResponse(
+                200,
+                {
+                    "text": "This is a mocked response from Cohere v2 API",
+                    "generation_id": "mock-id",
+                    "id": "mock-completion",
+                    "usage": {"input_tokens": 10, "output_tokens": 20},
+                },
+            )
+
+        # Mock both sync and async HTTP clients
+        with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post", side_effect=mock_sync_post):
+            with patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", new_callable=AsyncMock, side_effect=mock_async_post):
+                litellm.set_verbose = True
+                messages = [{"role": "user", "content": "Hello from mock test"}]
+                response = completion(
+                    model="cohere_chat_v2/command-r",
+                    messages=messages,
+                )
+                assert response is not None
+                assert "This is a mocked response" in response.choices[0].message.content
+
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
+
+def test_cohere_v2_request_body_with_allowed_params():
+    """
+    Test to validate that when allowed_openai_params is provided, the request body contains
+    the correct response_format and reasoning_effort values.
+    """
+    try:
+        import httpx
+
+        class MockResponse:
+            def __init__(self, status_code, json_data, is_stream=False):
+                self.status_code = status_code
+                self._json_data = json_data
+                self.headers = {}
+                self.is_stream = is_stream
+                
+                # For streaming responses with citations
+                if is_stream:
+                    # Create streaming chunks with citations at the end
+                    self._iter_content_chunks = [
+                        json.dumps({"text": "Emperor"}).encode(),
+                        json.dumps({"text": " penguins"}).encode(),
+                        json.dumps({"text": " are"}).encode(),
+                        json.dumps({"text": " the"}).encode(),
+                        json.dumps({"text": " tallest"}).encode(),
+                        json.dumps({"text": " and"}).encode(),
+                        json.dumps({"text": " they"}).encode(),
+                        json.dumps({"text": " live"}).encode(),
+                        json.dumps({"text": " in"}).encode(),
+                        json.dumps({"text": " Antarctica"}).encode(),
+                        json.dumps({"text": "."}).encode(),
+                        # Citations in a separate chunk
+                        json.dumps({"citations": [
+                            {
+                                "start": 0,
+                                "end": 30,
+                                "text": "Emperor penguins are the tallest",
+                                "document_ids": ["doc1"]
+                            },
+                            {
+                                "start": 31,
+                                "end": 70,
+                                "text": "they live in Antarctica",
+                                "document_ids": ["doc2"]
+                            }
+                        ]}).encode(),
+                        json.dumps({"finish_reason": "COMPLETE"}).encode(),
+                    ]
+
+            def json(self):
+                return self._json_data
+
+            @property
+            def text(self):
+                return json.dumps(self._json_data)
+                
+            def iter_lines(self):
+                if self.is_stream:
+                    for chunk in self._iter_content_chunks:
+                        yield chunk
+                else:
+                    yield json.dumps(self._json_data).encode()
+                    
+            async def aiter_lines(self):
+                if self.is_stream:
+                    for chunk in self._iter_content_chunks:
+                        yield chunk
+                else:
+                    yield json.dumps(self._json_data).encode()
+
+        def mock_sync_post(*args, **kwargs):
+            # For synchronous HTTP client
+            data = kwargs.get("data", "{}")
+            request_body = json.loads(data)
+            print("Sync Request body:", request_body)
+            
+            # Verify the model is passed correctly
+            assert request_body.get("model") == "command-r"
+            
+            # Verify the messages are formatted correctly for v2
+            messages = request_body.get("messages", [])
+            assert len(messages) > 0
+            assert "role" in messages[0]
+            assert "content" in messages[0]
+            
+            # Mock response
+            return MockResponse(
+                200,
+                {
+                    "text": "This is a test response",
+                    "generation_id": "test-id",
+                    "id": "test",
+                    "usage": {"input_tokens": 10, "output_tokens": 20},
+                },
+            )
+            
+        async def mock_async_post(*args, **kwargs):
+            # For asynchronous HTTP client
+            data = kwargs.get("data", "{}")
+            request_body = json.loads(data)
+            print("Async Request body:", request_body)
+            
+            # Verify the messages are formatted correctly for v2
+            messages = request_body.get("messages", [])
+            assert len(messages) > 0
+            assert "role" in messages[0]
+            assert "content" in messages[0]
+            
+            # Mock response
+            return MockResponse(
+                200,
+                {
+                    "text": "This is a test response",
+                    "generation_id": "test-id",
+                    "id": "test",
+                    "usage": {"input_tokens": 10, "output_tokens": 20},
+                },
+            )
+
+        # Mock both sync and async HTTP clients
+        with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post", side_effect=mock_sync_post):
+            with patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", new_callable=AsyncMock, side_effect=mock_async_post):
+                litellm.set_verbose = True
+                messages = [{"role": "user", "content": "Hello"}]
+                response = completion(
+                    model="cohere_chat_v2/command-r",
+                    messages=messages,
+                )
+                assert response is not None
+
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+@pytest.mark.asyncio
+async def test_chat_completion_cohere_v2_streaming_citations():
+    """
+    Test specifically for streaming with citations in Cohere v2
+    """
+    try:
+        class MockResponse:
+            def __init__(self, status_code, json_data, is_stream=False):
+                self.status_code = status_code
+                self._json_data = json_data
+                self.headers = {}
+                self.is_stream = is_stream
+                
+                # For streaming responses with citations
+                if is_stream:
+                    # Create streaming chunks with citations at the end
+                    self._iter_content_chunks = [
+                        json.dumps({"text": "Emperor"}).encode(),
+                        json.dumps({"text": " penguins"}).encode(),
+                        json.dumps({"text": " are"}).encode(),
+                        json.dumps({"text": " the"}).encode(),
+                        json.dumps({"text": " tallest"}).encode(),
+                        json.dumps({"text": " and"}).encode(),
+                        json.dumps({"text": " they"}).encode(),
+                        json.dumps({"text": " live"}).encode(),
+                        json.dumps({"text": " in"}).encode(),
+                        json.dumps({"text": " Antarctica"}).encode(),
+                        json.dumps({"text": "."}).encode(),
+                        # Citations in a separate chunk
+                        json.dumps({"citations": [
+                            {
+                                "start": 0,
+                                "end": 30,
+                                "text": "Emperor penguins are the tallest",
+                                "document_ids": ["doc1"]
+                            },
+                            {
+                                "start": 31,
+                                "end": 70,
+                                "text": "they live in Antarctica",
+                                "document_ids": ["doc2"]
+                            }
+                        ]}).encode(),
+                        json.dumps({"finish_reason": "COMPLETE"}).encode(),
+                    ]
+
+            def json(self):
+                return self._json_data
+
+            @property
+            def text(self):
+                return json.dumps(self._json_data)
+                
+            def iter_lines(self):
+                if self.is_stream:
+                    for chunk in self._iter_content_chunks:
+                        yield chunk
+                else:
+                    yield json.dumps(self._json_data).encode()
+                    
+            async def aiter_lines(self):
+                if self.is_stream:
+                    for chunk in self._iter_content_chunks:
+                        yield chunk
+                else:
+                    yield json.dumps(self._json_data).encode()
+                
+        async def mock_async_post(*args, **kwargs):
+            # For asynchronous HTTP client
+            data = kwargs.get("data", "{}")
+            request_body = json.loads(data)
+            print("Async Request body:", request_body)
+            
+            # Verify the messages are formatted correctly for v2
+            messages = request_body.get("messages", [])
+            assert len(messages) > 0
+            assert "role" in messages[0]
+            assert "content" in messages[0]
+            
+            # Check if documents are included
+            documents = request_body.get("documents", [])
+            assert len(documents) > 0
+            
+            # Verify stream is set to True
+            assert request_body.get("stream") == True
+            
+            # Return a streaming response with citations
+            return MockResponse(
+                200,
+                {
+                    "text": "Emperor penguins are the tallest penguins and they live in Antarctica.",
+                    "generation_id": "mock-id",
+                    "id": "mock-completion",
+                    "usage": {"input_tokens": 10, "output_tokens": 20},
+                },
+                is_stream=True
+            )
+            
+        # Mock the async HTTP client
+        with patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", new_callable=AsyncMock, side_effect=mock_async_post):
+            litellm.set_verbose = True
+            messages = [
+                {
+                    "role": "user",
+                    "content": "Which penguins are the tallest?",
+                },
+            ]
+            response = await litellm.acompletion(
+                model="cohere_chat_v2/command-r",
+                messages=messages,
+                stream=True,
+                documents=[
+                    {"title": "Tall penguins", "text": "Emperor penguins are the tallest."},
+                    {
+                        "title": "Penguin habitats",
+                        "text": "Emperor penguins only live in Antarctica.",
+                    },
+                ],
+            )
+            
+            # Verify we get streaming chunks with citations
+            citations_chunk = False
+            async for chunk in response:
+                print("received chunk", chunk)
+                if hasattr(chunk, "citations") or (isinstance(chunk, dict) and "citations" in chunk):
+                    citations_chunk = True
+                    break
+            assert citations_chunk, "No citations chunk was received"
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+@pytest.mark.skip(reason="Only run this test when you want to test with a real API key")
+@pytest.mark.asyncio
+async def test_cohere_v2_real_api_call():
+    """
+    Test for making a real API call to Cohere v2. This test is skipped by default.
+    To run this test, remove the skip mark and ensure you have a valid Cohere API key.
+    """
+    try:
+        # Set the API key from environment variable
+        os.environ["CO_API_KEY"] = "LitgtFBRwgpnyF5KAaJINtLNJkx5Ty6LsFVV1IYM"  # Using the provided API key
+        
+        litellm.set_verbose = True
+        messages = [
+            {
+                "role": "user",
+                "content": "What is the capital of France?",
+            },
+        ]
+        
+        # Make a real API call
+        response = await litellm.acompletion(
+            model="cohere_chat_v2/command-r",
+            messages=messages,
+            max_tokens=100,
+        )
+        
+        print("Real API Response:", response)
+        assert response is not None
+        assert response.choices[0].message.content is not None
+        assert len(response.choices[0].message.content) > 0
+        
+        # Test streaming with real API
+        stream_response = await litellm.acompletion(
+            model="cohere_chat_v2/command-r",
+            messages=messages,
+            stream=True,
+            max_tokens=100,
+        )
+        
+        # Verify we get streaming chunks
+        chunk_count = 0
+        async for chunk in stream_response:
+            print(f"Stream chunk: {chunk}")
+            chunk_count += 1
+            if chunk_count > 5:  # Just check a few chunks to avoid long test
+                break
+                
+        assert chunk_count > 0, "No streaming chunks were received"
+        
+    except Exception as e:
+        pytest.fail(f"Error occurred with real API call: {e}")