LITELLM: Remove requests library usage (#7235)

* fix(generic_api_callback.py): remove requests lib usage

* fix(budget_manager.py): remove requests lib usgae

* fix(main.py): cleanup requests lib usage

* fix(utils.py): remove requests lib usage

* fix(argilla.py): fix argilla test

* fix(athina.py): replace 'requests' lib usage with litellm module

* fix(greenscale.py): replace 'requests' lib usage with httpx

* fix: remove unused 'requests' lib import + replace usage in some places

* fix(prompt_layer.py): remove 'requests' lib usage from prompt layer

* fix(ollama_chat.py): remove 'requests' lib usage

* fix(baseten.py): replace 'requests' lib usage

* fix(codestral/): replace 'requests' lib usage

* fix(predibase/): replace 'requests' lib usage

* refactor: cleanup unused 'requests' lib imports

* fix(oobabooga.py): cleanup 'requests' lib usage

* fix(invoke_handler.py): remove unused 'requests' lib usage

* refactor: cleanup unused 'requests' lib import

* fix: fix linting errors

* refactor(ollama/): move ollama to using base llm http handler

removes 'requests' lib dep for ollama integration

* fix(ollama_chat.py): fix linting errors

* fix(ollama/completion/transformation.py): convert non-jpeg/png image to jpeg/png before passing to ollama

enterprise/enterprise_callbacks/generic_api_callback.py

@@ -3,7 +3,6 @@
 #### What this does ####
 #    On success, logs events to Promptlayer
 import dotenv, os
-import requests
 
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.caching.caching import DualCache
@@ -17,7 +16,6 @@ import traceback
 #    On success + failure, log events to Supabase
 
 import dotenv, os
-import requests
 import traceback
 import datetime, subprocess, sys
 import litellm, uuid
@@ -116,7 +114,9 @@ class GenericAPILogger:
             print_verbose(f"\nGeneric Logger - Logging payload = {data}")
 
             # make request to endpoint with payload
-            response = requests.post(self.endpoint, json=data, headers=self.headers)
+            response = litellm.module_level_client.post(
+                self.endpoint, json=data, headers=self.headers
+            )
 
             response_status = response.status_code
             response_text = response.text

litellm/budget_manager.py

@@ -13,8 +13,6 @@ import threading
 import time
 from typing import Literal, Optional, Union
 
-import requests  # type: ignore
-
 import litellm
 from litellm.utils import ModelResponse
 
@@ -58,7 +56,9 @@ class BudgetManager:
             # Load the user_dict from hosted db
             url = self.api_base + "/get_budget"
             data = {"project_name": self.project_name}
-            response = requests.post(url, headers=self.headers, json=data)
+            response = litellm.module_level_client.post(
+                url, headers=self.headers, json=data
+            )
             response = response.json()
             if response["status"] == "error":
                 self.user_dict = (
@@ -215,6 +215,8 @@ class BudgetManager:
         elif self.client_type == "hosted":
             url = self.api_base + "/set_budget"
             data = {"project_name": self.project_name, "user_dict": self.user_dict}
-            response = requests.post(url, headers=self.headers, json=data)
+            response = litellm.module_level_client.post(
+                url, headers=self.headers, json=data
+            )
             response = response.json()
             return response

litellm/integrations/argilla.py

@@ -15,19 +15,20 @@ from typing import Any, Dict, List, Optional, TypedDict, Union
 
 import dotenv  # type: ignore
 import httpx
-import requests  # type: ignore
 from pydantic import BaseModel  # type: ignore
 
 import litellm
 from litellm._logging import verbose_logger
 from litellm.integrations.custom_batch_logger import CustomBatchLogger
 from litellm.integrations.custom_logger import CustomLogger
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    get_content_from_model_response,
+)
 from litellm.llms.custom_httpx.http_handler import (
     AsyncHTTPHandler,
     get_async_httpx_client,
     httpxSpecialProvider,
 )
-from litellm.litellm_core_utils.prompt_templates.common_utils import get_content_from_model_response
 from litellm.types.integrations.argilla import (
     SUPPORTED_PAYLOAD_FIELDS,
     ArgillaCredentialsObject,
@@ -223,7 +224,7 @@ class ArgillaLogger(CustomBatchLogger):
         headers = {"X-Argilla-Api-Key": argilla_api_key}
 
         try:
-            response = requests.post(
+            response = litellm.module_level_client.post(
                 url=url,
                 json=self.log_queue,
                 headers=headers,

litellm/integrations/athina.py

@@ -1,5 +1,7 @@
 import datetime
 
+import litellm
+
 
 class AthinaLogger:
     def __init__(self):
@@ -27,8 +29,6 @@ class AthinaLogger:
         import json
         import traceback
 
-        import requests  # type: ignore
-
         try:
             is_stream = kwargs.get("stream", False)
             if is_stream:
@@ -81,7 +81,7 @@ class AthinaLogger:
                     if key in metadata:
                         data[key] = metadata[key]
 
-            response = requests.post(
+            response = litellm.module_level_client.post(
                 self.athina_logging_url,
                 headers=self.headers,
                 data=json.dumps(data, default=str),

litellm/integrations/dynamodb.py

@@ -8,7 +8,6 @@ import uuid
 from typing import Any
 
 import dotenv
-import requests  # type: ignore
 
 import litellm
 

litellm/integrations/greenscale.py

@@ -2,7 +2,7 @@ import json
 import traceback
 from datetime import datetime, timezone
 
-import requests  # type: ignore
+import litellm
 
 
 class GreenscaleLogger:
@@ -54,7 +54,7 @@ class GreenscaleLogger:
             if self.greenscale_logging_url is None:
                 raise Exception("Greenscale Logger Error - No logging URL found")
 
-            response = requests.post(
+            response = litellm.module_level_client.post(
                 self.greenscale_logging_url,
                 headers=self.headers,
                 data=json.dumps(data, default=str),

litellm/integrations/helicone.py

@@ -4,7 +4,6 @@ import os
 import traceback
 
 import dotenv
-import requests  # type: ignore
 
 import litellm
 from litellm._logging import verbose_logger
@@ -179,7 +178,7 @@ class HeliconeLogger:
                     },
                 },  # {"seconds": .., "milliseconds": ..}
             }
-            response = requests.post(url, headers=headers, json=data)
+            response = litellm.module_level_client.post(url, headers=headers, json=data)
             if response.status_code == 200:
                 print_verbose("Helicone Logging - Success!")
             else:

litellm/integrations/langsmith.py

@@ -12,7 +12,6 @@ from typing import Any, Dict, List, Optional, TypedDict, Union
 
 import dotenv  # type: ignore
 import httpx
-import requests  # type: ignore
 from pydantic import BaseModel  # type: ignore
 
 import litellm
@@ -481,7 +480,7 @@ class LangsmithLogger(CustomBatchLogger):
         langsmith_api_base = self.default_credentials["LANGSMITH_BASE_URL"]
 
         url = f"{langsmith_api_base}/runs/{run_id}"
-        response = requests.get(
+        response = litellm.module_level_client.get(
             url=url,
             headers={"x-api-key": langsmith_api_key},
         )

litellm/integrations/prometheus.py

@@ -9,9 +9,6 @@ import uuid
 from datetime import date, datetime, timedelta
 from typing import Optional, TypedDict, Union
 
-import dotenv
-import requests  # type: ignore
-
 import litellm
 from litellm._logging import print_verbose, verbose_logger
 from litellm.integrations.custom_logger import CustomLogger

litellm/integrations/prometheus_services.py

@@ -11,9 +11,6 @@ import traceback
 import uuid
 from typing import List, Optional, Union
 
-import dotenv
-import requests  # type: ignore
-
 import litellm
 from litellm._logging import print_verbose, verbose_logger
 from litellm.types.integrations.prometheus import LATENCY_BUCKETS

litellm/integrations/prompt_layer.py

@@ -3,10 +3,10 @@
 import os
 import traceback
 
-import dotenv
-import requests  # type: ignore
 from pydantic import BaseModel
 
+import litellm
+
 
 class PromptLayerLogger:
     # Class variables or attributes
@@ -47,7 +47,7 @@ class PromptLayerLogger:
             if isinstance(response_obj, BaseModel):
                 response_obj = response_obj.model_dump()
 
-            request_response = requests.post(
+            request_response = litellm.module_level_client.post(
                 "https://api.promptlayer.com/rest/track-request",
                 json={
                     "function_name": "openai.ChatCompletion.create",
@@ -74,7 +74,7 @@ class PromptLayerLogger:
 
             if "request_id" in response_json:
                 if metadata:
-                    response = requests.post(
+                    response = litellm.module_level_client.post(
                         "https://api.promptlayer.com/rest/track-metadata",
                         json={
                             "request_id": response_json["request_id"],

litellm/integrations/supabase.py

@@ -8,7 +8,6 @@ import sys
 import traceback
 
 import dotenv
-import requests  # type: ignore
 
 import litellm
 

litellm/integrations/weights_biases.py

@@ -177,8 +177,6 @@ import os
 import traceback
 from datetime import datetime
 
-import requests
-
 
 class WeightsBiasesLogger:
     # Class variables or attributes

litellm/litellm_core_utils/streaming_handler.py

@@ -570,40 +570,6 @@ class CustomStreamWrapper:
             )
             return ""
 
-    def handle_ollama_stream(self, chunk):
-        try:
-            if isinstance(chunk, dict):
-                json_chunk = chunk
-            else:
-                json_chunk = json.loads(chunk)
-            if "error" in json_chunk:
-                raise Exception(f"Ollama Error - {json_chunk}")
-
-            text = ""
-            is_finished = False
-            finish_reason = None
-            if json_chunk["done"] is True:
-                text = ""
-                is_finished = True
-                finish_reason = "stop"
-                return {
-                    "text": text,
-                    "is_finished": is_finished,
-                    "finish_reason": finish_reason,
-                }
-            elif json_chunk["response"]:
-                print_verbose(f"delta content: {json_chunk}")
-                text = json_chunk["response"]
-                return {
-                    "text": text,
-                    "is_finished": is_finished,
-                    "finish_reason": finish_reason,
-                }
-            else:
-                raise Exception(f"Ollama Error - {json_chunk}")
-        except Exception as e:
-            raise e
-
     def handle_ollama_chat_stream(self, chunk):
         # for ollama_chat/ provider
         try:
@@ -1111,12 +1077,6 @@ class CustomStreamWrapper:
                 new_chunk = self.completion_stream[:chunk_size]
                 completion_obj["content"] = new_chunk
                 self.completion_stream = self.completion_stream[chunk_size:]
-            elif self.custom_llm_provider == "ollama":
-                response_obj = self.handle_ollama_stream(chunk)
-                completion_obj["content"] = response_obj["text"]
-                print_verbose(f"completion obj content: {completion_obj['content']}")
-                if response_obj["is_finished"]:
-                    self.received_finish_reason = response_obj["finish_reason"]
             elif self.custom_llm_provider == "ollama_chat":
                 response_obj = self.handle_ollama_chat_stream(chunk)
                 completion_obj["content"] = response_obj["text"]

litellm/llms/anthropic/chat/handler.py

@@ -13,7 +13,6 @@ from functools import partial
 from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
 
 import httpx  # type: ignore
-import requests  # type: ignore
 from openai.types.chat.chat_completion_chunk import Choice as OpenAIStreamingChoice
 
 import litellm

litellm/llms/anthropic/chat/transformation.py

@@ -16,7 +16,6 @@ from typing import (
 )
 
 import httpx
-import requests
 
 import litellm
 from litellm.constants import RESPONSE_FORMAT_TOOL_NAME

litellm/llms/azure/completion/handler.py

@@ -4,11 +4,14 @@ import uuid
 from typing import Any, Callable, Optional, Union
 
 import httpx
-import requests
 from openai import AsyncAzureOpenAI, AzureOpenAI
 
 import litellm
 from litellm import OpenAIConfig
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    custom_prompt,
+    prompt_factory,
+)
 from litellm.utils import (
     Choices,
     CustomStreamWrapper,
@@ -22,7 +25,6 @@ from litellm.utils import (
 from ...base import BaseLLM
 from ...openai.completion.handler import OpenAITextCompletion
 from ...openai.completion.transformation import OpenAITextCompletionConfig
-from litellm.litellm_core_utils.prompt_templates.factory import custom_prompt, prompt_factory
 from ..common_utils import AzureOpenAIError
 
 openai_text_completion_config = OpenAITextCompletionConfig()

litellm/llms/baseten.py

@@ -4,9 +4,8 @@ import time
 from enum import Enum
 from typing import Callable
 
-import requests  # type: ignore
+import litellm
-
+from litellm.types.utils import ModelResponse, Usage
-from litellm.utils import ModelResponse, Usage
 
 
 class BasetenError(Exception):
@@ -71,7 +70,7 @@ def completion(
         additional_args={"complete_input_dict": data},
     )
     ## COMPLETION CALL
-    response = requests.post(
+    response = litellm.module_level_client.post(
         completion_url_fragment_1 + model + completion_url_fragment_2,
         headers=headers,
         data=json.dumps(data),

litellm/llms/bedrock/base_aws_llm.py

@@ -10,8 +10,6 @@ from litellm._logging import verbose_logger
 from litellm.caching.caching import DualCache, InMemoryCache
 from litellm.secret_managers.main import get_secret, get_secret_str
 
-from litellm.llms.base import BaseLLM
-
 if TYPE_CHECKING:
     from botocore.credentials import Credentials
 else:
@@ -37,7 +35,7 @@ class AwsAuthError(Exception):
         )  # Call the base class constructor with the parameters it needs
 
 
-class BaseAWSLLM(BaseLLM):
+class BaseAWSLLM:
     def __init__(self) -> None:
         self.iam_cache = DualCache()
         super().__init__()

litellm/llms/bedrock/chat/invoke_handler.py

@@ -25,7 +25,6 @@ from typing import (
 )
 
 import httpx  # type: ignore
-import requests  # type: ignore
 
 import litellm
 from litellm import verbose_logger
@@ -316,7 +315,7 @@ class BedrockLLM(BaseAWSLLM):
     def process_response(  # noqa: PLR0915
         self,
         model: str,
-        response: Union[requests.Response, httpx.Response],
+        response: httpx.Response,
         model_response: ModelResponse,
         stream: bool,
         logging_obj: Logging,
@@ -1041,9 +1040,6 @@ class BedrockLLM(BaseAWSLLM):
         )
         return streaming_response
 
-    def embedding(self, *args, **kwargs):
-        return super().embedding(*args, **kwargs)
-
 
 def get_response_stream_shape():
     global _response_stream_shape_cache

litellm/llms/codestral/completion/handler.py

@@ -12,7 +12,6 @@ from functools import partial
 from typing import Callable, List, Literal, Optional, Union
 
 import httpx  # type: ignore
-import requests  # type: ignore
 
 import litellm
 from litellm import verbose_logger
@@ -22,7 +21,6 @@ from litellm.litellm_core_utils.prompt_templates.factory import (
     custom_prompt,
     prompt_factory,
 )
-from litellm.llms.base import BaseLLM
 from litellm.llms.custom_httpx.http_handler import (
     AsyncHTTPHandler,
     get_async_httpx_client,
@@ -95,7 +93,7 @@ async def make_call(
     return completion_stream
 
 
-class CodestralTextCompletion(BaseLLM):
+class CodestralTextCompletion:
     def __init__(self) -> None:
         super().__init__()
 
@@ -139,7 +137,7 @@ class CodestralTextCompletion(BaseLLM):
     def process_text_completion_response(
         self,
         model: str,
-        response: Union[requests.Response, httpx.Response],
+        response: httpx.Response,
         model_response: TextCompletionResponse,
         stream: bool,
         logging_obj: LiteLLMLogging,
@@ -317,7 +315,7 @@ class CodestralTextCompletion(BaseLLM):
 
         ### SYNC STREAMING
         if stream is True:
-            response = requests.post(
+            response = litellm.module_level_client.post(
                 completion_url,
                 headers=headers,
                 data=json.dumps(data),
@@ -333,7 +331,7 @@ class CodestralTextCompletion(BaseLLM):
         ### SYNC COMPLETION
         else:
 
-            response = requests.post(
+            response = litellm.module_level_client.post(
                 url=completion_url,
                 headers=headers,
                 data=json.dumps(data),

litellm/llms/cohere/embed/handler.py

@@ -6,8 +6,7 @@ import types
 from enum import Enum
 from typing import Any, Callable, Optional, Union
 
-import httpx  # type: ignore
+import httpx
-import requests  # type: ignore
 
 import litellm
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj

litellm/llms/custom_httpx/http_handler.py

@@ -491,7 +491,7 @@ class HTTPHandler:
         self,
         url: str,
         data: Optional[Union[dict, str]] = None,
-        json: Optional[Union[dict, str]] = None,
+        json: Optional[Union[dict, str, List]] = None,
         params: Optional[dict] = None,
         headers: Optional[dict] = None,
         stream: bool = False,

litellm/llms/custom_llm.py

@@ -29,8 +29,7 @@ from typing import (
     Union,
 )
 
-import httpx  # type: ignore
+import httpx
-import requests  # type: ignore
 
 import litellm
 from litellm.litellm_core_utils.core_helpers import map_finish_reason
@@ -46,6 +45,7 @@ from litellm.utils import (
 
 from .base import BaseLLM
 
+
 class CustomLLMError(Exception):  # use this for all your exceptions
     def __init__(
         self,

litellm/llms/deprecated_providers/aleph_alpha.py

@@ -6,7 +6,6 @@ from enum import Enum
 from typing import Callable, Optional
 
 import httpx  # type: ignore
-import requests  # type: ignore
 
 import litellm
 from litellm.utils import Choices, Message, ModelResponse, Usage
@@ -240,7 +239,7 @@ def completion(
         additional_args={"complete_input_dict": data},
     )
     ## COMPLETION CALL
-    response = requests.post(
+    response = litellm.module_level_client.post(
         completion_url,
         headers=headers,
         data=json.dumps(data),

litellm/llms/huggingface/chat/handler.py

@@ -20,7 +20,6 @@ from typing import (
 )
 
 import httpx
-import requests
 
 import litellm
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj

litellm/llms/ollama/common_utils.py

@@ -10,3 +10,36 @@ class OllamaError(BaseLLMException):
         self, status_code: int, message: str, headers: Union[dict, httpx.Headers]
     ):
         super().__init__(status_code=status_code, message=message, headers=headers)
+
+
+def _convert_image(image):
+    """
+    Convert image to base64 encoded image if not already in base64 format
+
+    If image is already in base64 format AND is a jpeg/png, return it
+
+    If image is not JPEG/PNG, convert it to JPEG base64 format
+    """
+    import base64
+    import io
+
+    try:
+        from PIL import Image
+    except Exception:
+        raise Exception(
+            "ollama image conversion failed please run `pip install Pillow`"
+        )
+
+    orig = image
+    if image.startswith("data:"):
+        image = image.split(",")[-1]
+    try:
+        image_data = Image.open(io.BytesIO(base64.b64decode(image)))
+        if image_data.format in ["JPEG", "PNG"]:
+            return image
+    except Exception:
+        return orig
+    jpeg_image = io.BytesIO()
+    image_data.convert("RGB").save(jpeg_image, "JPEG")
+    jpeg_image.seek(0)
+    return base64.b64encode(jpeg_image.getvalue()).decode("utf-8")

litellm/llms/ollama/completion/handler.py

@@ -1,3 +1,9 @@
+"""
+Ollama /chat/completion calls handled in llm_http_handler.py
+
+[TODO]: migrate embeddings to a base handler as well.
+"""
+
 import asyncio
 import json
 import time
@@ -8,10 +14,6 @@ from copy import deepcopy
 from itertools import chain
 from typing import Any, Dict, List, Optional
 
-import aiohttp
-import httpx  # type: ignore
-import requests  # type: ignore
-
 import litellm
 from litellm import verbose_logger
 from litellm.litellm_core_utils.prompt_templates.factory import (
@@ -31,370 +33,8 @@ from litellm.types.utils import (
 from ..common_utils import OllamaError
 from .transformation import OllamaConfig
 
-
 # ollama wants plain base64 jpeg/png files as images.  strip any leading dataURI
 # and convert to jpeg if necessary.
-def _convert_image(image):
-    import base64
-    import io
-
-    try:
-        from PIL import Image
-    except Exception:
-        raise Exception(
-            "ollama image conversion failed please run `pip install Pillow`"
-        )
-
-    orig = image
-    if image.startswith("data:"):
-        image = image.split(",")[-1]
-    try:
-        image_data = Image.open(io.BytesIO(base64.b64decode(image)))
-        if image_data.format in ["JPEG", "PNG"]:
-            return image
-    except Exception:
-        return orig
-    jpeg_image = io.BytesIO()
-    image_data.convert("RGB").save(jpeg_image, "JPEG")
-    jpeg_image.seek(0)
-    return base64.b64encode(jpeg_image.getvalue()).decode("utf-8")
-
-
-# ollama implementation
-def get_ollama_response(
-    model_response: ModelResponse,
-    model: str,
-    prompt: str,
-    optional_params: dict,
-    logging_obj: Any,
-    encoding: Any,
-    acompletion: bool = False,
-    api_base="http://localhost:11434",
-):
-    if api_base.endswith("/api/generate"):
-        url = api_base
-    else:
-        url = f"{api_base}/api/generate"
-
-    ## Load Config
-    config = litellm.OllamaConfig.get_config()
-    for k, v in 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
-
-    stream = optional_params.pop("stream", False)
-    format = optional_params.pop("format", None)
-    images = optional_params.pop("images", None)
-    data = {
-        "model": model,
-        "prompt": prompt,
-        "options": optional_params,
-        "stream": stream,
-    }
-    if format is not None:
-        data["format"] = format
-    if images is not None:
-        data["images"] = [_convert_image(image) for image in images]
-
-    ## LOGGING
-    logging_obj.pre_call(
-        input=None,
-        api_key=None,
-        additional_args={
-            "api_base": url,
-            "complete_input_dict": data,
-            "headers": {},
-            "acompletion": acompletion,
-        },
-    )
-    if acompletion is True:
-        if stream is True:
-            response = ollama_async_streaming(
-                url=url,
-                data=data,
-                model_response=model_response,
-                encoding=encoding,
-                logging_obj=logging_obj,
-            )
-        else:
-            response = ollama_acompletion(
-                url=url,
-                data=data,
-                model_response=model_response,
-                encoding=encoding,
-                logging_obj=logging_obj,
-            )
-        return response
-    elif stream is True:
-        return ollama_completion_stream(url=url, data=data, logging_obj=logging_obj)
-
-    response = requests.post(
-        url=f"{url}", json={**data, "stream": stream}, timeout=litellm.request_timeout
-    )
-    if response.status_code != 200:
-        raise OllamaError(
-            status_code=response.status_code,
-            message=response.text,
-            headers=dict(response.headers),
-        )
-
-    ## LOGGING
-    logging_obj.post_call(
-        input=prompt,
-        api_key="",
-        original_response=response.text,
-        additional_args={
-            "headers": None,
-            "api_base": api_base,
-        },
-    )
-
-    response_json = response.json()
-
-    ## RESPONSE OBJECT
-    model_response.choices[0].finish_reason = "stop"
-    if data.get("format", "") == "json":
-        function_call = json.loads(response_json["response"])
-        message = litellm.Message(
-            content=None,
-            tool_calls=[
-                {
-                    "id": f"call_{str(uuid.uuid4())}",
-                    "function": {
-                        "name": function_call["name"],
-                        "arguments": json.dumps(function_call["arguments"]),
-                    },
-                    "type": "function",
-                }
-            ],
-        )
-        model_response.choices[0].message = message  # type: ignore
-        model_response.choices[0].finish_reason = "tool_calls"
-    else:
-        model_response.choices[0].message.content = response_json["response"]  # type: ignore
-    model_response.created = int(time.time())
-    model_response.model = "ollama/" + model
-    prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(prompt, disallowed_special=())))  # type: ignore
-    completion_tokens = response_json.get(
-        "eval_count", len(response_json.get("message", dict()).get("content", ""))
-    )
-    setattr(
-        model_response,
-        "usage",
-        litellm.Usage(
-            prompt_tokens=prompt_tokens,
-            completion_tokens=completion_tokens,
-            total_tokens=prompt_tokens + completion_tokens,
-        ),
-    )
-    return model_response
-
-
-def ollama_completion_stream(url, data, logging_obj):
-    with httpx.stream(
-        url=url, json=data, method="POST", timeout=litellm.request_timeout
-    ) as response:
-        try:
-            if response.status_code != 200:
-                raise OllamaError(
-                    status_code=response.status_code,
-                    message=str(response.read()),
-                    headers=response.headers,
-                )
-
-            streamwrapper = litellm.CustomStreamWrapper(
-                completion_stream=response.iter_lines(),
-                model=data["model"],
-                custom_llm_provider="ollama",
-                logging_obj=logging_obj,
-            )
-            # If format is JSON, this was a function call
-            # Gather all chunks and return the function call as one delta to simplify parsing
-            if data.get("format", "") == "json":
-                first_chunk = next(streamwrapper)
-                content_chunks = []
-                for chunk in chain([first_chunk], streamwrapper):
-                    content_chunk = chunk.choices[0]
-                    if (
-                        isinstance(content_chunk, StreamingChoices)
-                        and hasattr(content_chunk, "delta")
-                        and hasattr(content_chunk.delta, "content")
-                        and content_chunk.delta.content is not None
-                    ):
-                        content_chunks.append(content_chunk.delta.content)
-                response_content = "".join(content_chunks)
-
-                function_call = json.loads(response_content)
-                delta = litellm.utils.Delta(
-                    content=None,
-                    tool_calls=[
-                        {
-                            "id": f"call_{str(uuid.uuid4())}",
-                            "function": {
-                                "name": function_call["name"],
-                                "arguments": json.dumps(function_call["arguments"]),
-                            },
-                            "type": "function",
-                        }
-                    ],
-                )
-                model_response = first_chunk
-                model_response.choices[0].delta = delta  # type: ignore
-                model_response.choices[0].finish_reason = "tool_calls"
-                yield model_response
-            else:
-                for transformed_chunk in streamwrapper:
-                    yield transformed_chunk
-        except Exception as e:
-            raise e
-
-
-async def ollama_async_streaming(url, data, model_response, encoding, logging_obj):
-    try:
-        _async_http_client = get_async_httpx_client(
-            llm_provider=litellm.LlmProviders.OLLAMA
-        )
-        client = _async_http_client.client
-        async with client.stream(
-            url=f"{url}", json=data, method="POST", timeout=litellm.request_timeout
-        ) as response:
-            if response.status_code != 200:
-                raise OllamaError(
-                    status_code=response.status_code,
-                    message=str(await response.aread()),
-                    headers=dict(response.headers),
-                )
-
-            streamwrapper = litellm.CustomStreamWrapper(
-                completion_stream=response.aiter_lines(),
-                model=data["model"],
-                custom_llm_provider="ollama",
-                logging_obj=logging_obj,
-            )
-
-            # If format is JSON, this was a function call
-            # Gather all chunks and return the function call as one delta to simplify parsing
-            if data.get("format", "") == "json":
-                first_chunk = await anext(streamwrapper)  # noqa F821
-                chunk_choice = first_chunk.choices[0]
-                if (
-                    isinstance(chunk_choice, StreamingChoices)
-                    and hasattr(chunk_choice, "delta")
-                    and hasattr(chunk_choice.delta, "content")
-                ):
-                    first_chunk_content = chunk_choice.delta.content or ""
-                else:
-                    first_chunk_content = ""
-
-                content_chunks = []
-                async for chunk in streamwrapper:
-                    chunk_choice = chunk.choices[0]
-                    if (
-                        isinstance(chunk_choice, StreamingChoices)
-                        and hasattr(chunk_choice, "delta")
-                        and hasattr(chunk_choice.delta, "content")
-                    ):
-                        content_chunks.append(chunk_choice.delta.content)
-                response_content = first_chunk_content + "".join(content_chunks)
-                function_call = json.loads(response_content)
-                delta = litellm.utils.Delta(
-                    content=None,
-                    tool_calls=[
-                        {
-                            "id": f"call_{str(uuid.uuid4())}",
-                            "function": {
-                                "name": function_call["name"],
-                                "arguments": json.dumps(function_call["arguments"]),
-                            },
-                            "type": "function",
-                        }
-                    ],
-                )
-                model_response = first_chunk
-                model_response.choices[0].delta = delta  # type: ignore
-                model_response.choices[0].finish_reason = "tool_calls"
-                yield model_response
-            else:
-                async for transformed_chunk in streamwrapper:
-                    yield transformed_chunk
-    except Exception as e:
-        raise e  # don't use verbose_logger.exception, if exception is raised
-
-
-async def ollama_acompletion(
-    url, data, model_response: litellm.ModelResponse, encoding, logging_obj
-):
-    data["stream"] = False
-    try:
-        timeout = aiohttp.ClientTimeout(total=litellm.request_timeout)  # 10 minutes
-        async with aiohttp.ClientSession(timeout=timeout) as session:
-            resp = await session.post(url, json=data)
-
-            if resp.status != 200:
-                text = await resp.text()
-                raise OllamaError(
-                    status_code=resp.status,
-                    message=text,
-                    headers=dict(resp.headers),
-                )
-
-            ## LOGGING
-            logging_obj.post_call(
-                input=data["prompt"],
-                api_key="",
-                original_response=resp.text,
-                additional_args={
-                    "headers": None,
-                    "api_base": url,
-                },
-            )
-
-            response_json = await resp.json()
-            ## RESPONSE OBJECT
-            model_response.choices[0].finish_reason = "stop"
-            if data.get("format", "") == "json":
-                function_call = json.loads(response_json["response"])
-                message = litellm.Message(
-                    content=None,
-                    tool_calls=[
-                        {
-                            "id": f"call_{str(uuid.uuid4())}",
-                            "function": {
-                                "name": function_call.get(
-                                    "name", function_call.get("function", None)
-                                ),
-                                "arguments": json.dumps(function_call["arguments"]),
-                            },
-                            "type": "function",
-                        }
-                    ],
-                )
-                model_response.choices[0].message = message  # type: ignore
-                model_response.choices[0].finish_reason = "tool_calls"
-            else:
-                model_response.choices[0].message.content = response_json["response"]  # type: ignore
-            model_response.created = int(time.time())
-            model_response.model = "ollama/" + data["model"]
-            prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(data["prompt"], disallowed_special=())))  # type: ignore
-            completion_tokens = response_json.get(
-                "eval_count",
-                len(response_json.get("message", dict()).get("content", "")),
-            )
-            setattr(
-                model_response,
-                "usage",
-                litellm.Usage(
-                    prompt_tokens=prompt_tokens,
-                    completion_tokens=completion_tokens,
-                    total_tokens=prompt_tokens + completion_tokens,
-                ),
-            )
-            return model_response
-    except Exception as e:
-        raise e  # don't use verbose_logger.exception, if exception is raised
-
 
 async def ollama_aembeddings(
     api_base: str,
@@ -432,39 +72,18 @@ async def ollama_aembeddings(
     total_input_tokens = 0
     output_data = []
 
-    timeout = aiohttp.ClientTimeout(total=litellm.request_timeout)  # 10 minutes
+    response = await litellm.module_level_aclient.post(url=url, json=data)
-    async with aiohttp.ClientSession(timeout=timeout) as session:
-        ## LOGGING
-        logging_obj.pre_call(
-            input=None,
-            api_key=None,
-            additional_args={
-                "api_base": url,
-                "complete_input_dict": data,
-                "headers": {},
-            },
-        )
 
-        response = await session.post(url, json=data)
+    response_json = await response.json()
 
-        if response.status != 200:
+    embeddings: List[List[float]] = response_json["embeddings"]
-            text = await response.text()
+    for idx, emb in enumerate(embeddings):
-            raise OllamaError(
+        output_data.append({"object": "embedding", "index": idx, "embedding": emb})
-                status_code=response.status,
-                message=text,
-                headers=dict(response.headers),
-            )
 
-        response_json = await response.json()
+    input_tokens = response_json.get("prompt_eval_count") or len(
-
+        encoding.encode("".join(prompt for prompt in prompts))
-        embeddings: List[List[float]] = response_json["embeddings"]
+    )
-        for idx, emb in enumerate(embeddings):
+    total_input_tokens += input_tokens
-            output_data.append({"object": "embedding", "index": idx, "embedding": emb})
-
-        input_tokens = response_json.get("prompt_eval_count") or len(
-            encoding.encode("".join(prompt for prompt in prompts))
-        )
-        total_input_tokens += input_tokens
 
     model_response.object = "list"
     model_response.data = output_data

litellm/llms/ollama/completion/transformation.py

@@ -1,20 +1,34 @@
+import json
+import time
 import types
-from typing import TYPE_CHECKING, Any, List, Optional, Union
+import uuid
+from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, List, Optional, Union
 
 from httpx._models import Headers, Response
 
 import litellm
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    convert_to_ollama_image,
+    custom_prompt,
+    ollama_pt,
+)
+from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
 from litellm.llms.base_llm.transformation import BaseConfig, BaseLLMException
 from litellm.secret_managers.main import get_secret_str
-from litellm.types.llms.openai import AllMessageValues
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    ChatCompletionToolCallChunk,
+    ChatCompletionUsageBlock,
+)
 from litellm.types.utils import (
+    GenericStreamingChunk,
     ModelInfo,
     ModelResponse,
     ProviderField,
     StreamingChoices,
 )
 
-from ..common_utils import OllamaError
+from ..common_utils import OllamaError, _convert_image
 
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
@@ -247,7 +261,47 @@ class OllamaConfig(BaseConfig):
         api_key: Optional[str] = None,
         json_mode: Optional[bool] = None,
     ) -> ModelResponse:
-        raise NotImplementedError("transformation currently done in handler.py")
+        response_json = raw_response.json()
+        ## RESPONSE OBJECT
+        model_response.choices[0].finish_reason = "stop"
+        if request_data.get("format", "") == "json":
+            function_call = json.loads(response_json["response"])
+            message = litellm.Message(
+                content=None,
+                tool_calls=[
+                    {
+                        "id": f"call_{str(uuid.uuid4())}",
+                        "function": {
+                            "name": function_call["name"],
+                            "arguments": json.dumps(function_call["arguments"]),
+                        },
+                        "type": "function",
+                    }
+                ],
+            )
+            model_response.choices[0].message = message  # type: ignore
+            model_response.choices[0].finish_reason = "tool_calls"
+        else:
+            model_response.choices[0].message.content = response_json["response"]  # type: ignore
+        model_response.created = int(time.time())
+        model_response.model = "ollama/" + model
+        _prompt = request_data.get("prompt", "")
+        prompt_tokens = response_json.get(
+            "prompt_eval_count", len(encoding.encode(_prompt, disallowed_special=()))  # type: ignore
+        )
+        completion_tokens = response_json.get(
+            "eval_count", len(response_json.get("message", dict()).get("content", ""))
+        )
+        setattr(
+            model_response,
+            "usage",
+            litellm.Usage(
+                prompt_tokens=prompt_tokens,
+                completion_tokens=completion_tokens,
+                total_tokens=prompt_tokens + completion_tokens,
+            ),
+        )
+        return model_response
 
     def transform_request(
         self,
@@ -257,7 +311,46 @@ class OllamaConfig(BaseConfig):
         litellm_params: dict,
         headers: dict,
     ) -> dict:
-        raise NotImplementedError("transformation currently done in handler.py")
+        custom_prompt_dict = (
+            litellm_params.get("custom_prompt_dict") or litellm.custom_prompt_dict
+        )
+        if model in custom_prompt_dict:
+            # check if the model has a registered custom prompt
+            model_prompt_details = custom_prompt_dict[model]
+            ollama_prompt = custom_prompt(
+                role_dict=model_prompt_details["roles"],
+                initial_prompt_value=model_prompt_details["initial_prompt_value"],
+                final_prompt_value=model_prompt_details["final_prompt_value"],
+                messages=messages,
+            )
+        else:
+            modified_prompt = ollama_pt(model=model, messages=messages)
+            if isinstance(modified_prompt, dict):
+                ollama_prompt, images = (
+                    modified_prompt["prompt"],
+                    modified_prompt["images"],
+                )
+                optional_params["images"] = images
+            else:
+                ollama_prompt = modified_prompt
+        stream = optional_params.pop("stream", False)
+        format = optional_params.pop("format", None)
+        images = optional_params.pop("images", None)
+        data = {
+            "model": model,
+            "prompt": ollama_prompt,
+            "options": optional_params,
+            "stream": stream,
+        }
+
+        if format is not None:
+            data["format"] = format
+        if images is not None:
+            data["images"] = [
+                _convert_image(convert_to_ollama_image(image)) for image in images
+            ]
+
+        return data
 
     def validate_environment(
         self,
@@ -267,4 +360,77 @@ class OllamaConfig(BaseConfig):
         optional_params: dict,
         api_key: Optional[str] = None,
     ) -> dict:
-        raise NotImplementedError("validation currently done in handler.py")
+        return headers
+
+    def get_complete_url(self, api_base: str, model: str) -> str:
+        """
+        OPTIONAL
+
+        Get the complete url for the request
+
+        Some providers need `model` in `api_base`
+        """
+        if api_base.endswith("/api/generate"):
+            url = api_base
+        else:
+            url = f"{api_base}/api/generate"
+
+        return url
+
+    def get_model_response_iterator(
+        self,
+        streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
+        sync_stream: bool,
+        json_mode: Optional[bool] = False,
+    ):
+        return OllamaTextCompletionResponseIterator(
+            streaming_response=streaming_response,
+            sync_stream=sync_stream,
+            json_mode=json_mode,
+        )
+
+
+class OllamaTextCompletionResponseIterator(BaseModelResponseIterator):
+    def _handle_string_chunk(self, str_line: str) -> GenericStreamingChunk:
+        return self.chunk_parser(json.loads(str_line))
+
+    def chunk_parser(self, chunk: dict) -> GenericStreamingChunk:
+        try:
+            if "error" in chunk:
+                raise Exception(f"Ollama Error - {chunk}")
+
+            text = ""
+            is_finished = False
+            finish_reason = None
+            if chunk["done"] is True:
+                text = ""
+                is_finished = True
+                finish_reason = "stop"
+                prompt_eval_count: Optional[int] = chunk.get("prompt_eval_count", None)
+                eval_count: Optional[int] = chunk.get("eval_count", None)
+
+                usage: Optional[ChatCompletionUsageBlock] = None
+                if prompt_eval_count is not None and eval_count is not None:
+                    usage = ChatCompletionUsageBlock(
+                        prompt_tokens=prompt_eval_count,
+                        completion_tokens=eval_count,
+                        total_tokens=prompt_eval_count + eval_count,
+                    )
+                return GenericStreamingChunk(
+                    text=text,
+                    is_finished=is_finished,
+                    finish_reason=finish_reason,
+                    usage=usage,
+                )
+            elif chunk["response"]:
+                text = chunk["response"]
+                return GenericStreamingChunk(
+                    text=text,
+                    is_finished=is_finished,
+                    finish_reason="stop",
+                    usage=None,
+                )
+            else:
+                raise Exception(f"Unable to parse ollama chunk - {chunk}")
+        except Exception as e:
+            raise e

litellm/llms/ollama_chat.py

@@ -8,7 +8,6 @@ from typing import Any, List, Optional
 
 import aiohttp
 import httpx
-import requests
 from pydantic import BaseModel
 
 import litellm
@@ -297,13 +296,14 @@ def get_ollama_response(  # noqa: PLR0915
             url=url, api_key=api_key, data=data, logging_obj=logging_obj
         )
 
-    _request = {
+    headers: Optional[dict] = None
-        "url": f"{url}",
-        "json": data,
-    }
     if api_key is not None:
-        _request["headers"] = {"Authorization": "Bearer {}".format(api_key)}
+        headers = {"Authorization": "Bearer {}".format(api_key)}
-    response = requests.post(**_request)  # type: ignore
+    response = litellm.module_level_client.post(
+        url=url,
+        json=data,
+        headers=headers,
+    )
     if response.status_code != 200:
         raise OllamaError(status_code=response.status_code, message=response.text)
 

litellm/llms/oobabooga/chat/oobabooga.py

@@ -4,12 +4,14 @@ import time
 from enum import Enum
 from typing import Any, Callable, Optional
 
-import requests  # type: ignore
+import litellm
-
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    custom_prompt,
+    prompt_factory,
+)
 from litellm.llms.custom_httpx.http_handler import HTTPHandler, _get_httpx_client
 from litellm.utils import EmbeddingResponse, ModelResponse, Usage
 
-from litellm.litellm_core_utils.prompt_templates.factory import custom_prompt, prompt_factory
 from ..common_utils import OobaboogaError
 from .transformation import OobaboogaConfig
 
@@ -129,9 +131,9 @@ def embedding(
         messages=[],
         optional_params=optional_params,
     )
-    response = requests.post(embeddings_url, headers=headers, json=data)
+    response = litellm.module_level_client.post(
-    if not response.ok:
+        embeddings_url, headers=headers, json=data
-        raise OobaboogaError(message=response.text, status_code=response.status_code)
+    )
     completion_response = response.json()
 
     # Check for errors in response

litellm/llms/openai_like/chat/handler.py

@@ -13,8 +13,7 @@ from enum import Enum
 from functools import partial
 from typing import Any, Callable, List, Literal, Optional, Tuple, Union
 
-import httpx  # type: ignore
+import httpx
-import requests  # type: ignore
 
 import litellm
 from litellm import LlmProviders

litellm/llms/predibase/chat/handler.py

@@ -12,7 +12,6 @@ from functools import partial
 from typing import Callable, List, Literal, Optional, Union
 
 import httpx  # type: ignore
-import requests  # type: ignore
 
 import litellm
 import litellm.litellm_core_utils
@@ -63,7 +62,7 @@ async def make_call(
     return completion_stream
 
 
-class PredibaseChatCompletion(BaseLLM):
+class PredibaseChatCompletion:
     def __init__(self) -> None:
         super().__init__()
 
@@ -90,7 +89,7 @@ class PredibaseChatCompletion(BaseLLM):
     def process_response(  # noqa: PLR0915
         self,
         model: str,
-        response: Union[requests.Response, httpx.Response],
+        response: httpx.Response,
         model_response: ModelResponse,
         stream: bool,
         logging_obj: LiteLLMLoggingBaseClass,
@@ -347,7 +346,7 @@ class PredibaseChatCompletion(BaseLLM):
 
         ### SYNC STREAMING
         if stream is True:
-            response = requests.post(
+            response = litellm.module_level_client.post(
                 completion_url,
                 headers=headers,
                 data=json.dumps(data),
@@ -363,7 +362,7 @@ class PredibaseChatCompletion(BaseLLM):
             return _response
         ### SYNC COMPLETION
         else:
-            response = requests.post(
+            response = litellm.module_level_client.post(
                 url=completion_url,
                 headers=headers,
                 data=json.dumps(data),

litellm/llms/sagemaker/completion/handler.py

@@ -10,12 +10,16 @@ from enum import Enum
 from functools import partial
 from typing import Any, AsyncIterator, Callable, Dict, Iterator, List, Optional, Union
 
-import httpx  # type: ignore
+import httpx
-import requests  # type: ignore
 
 import litellm
 from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.asyncify import asyncify
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    custom_prompt,
+    prompt_factory,
+)
+from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
 from litellm.llms.custom_httpx.http_handler import (
     AsyncHTTPHandler,
     HTTPHandler,
@@ -31,8 +35,6 @@ from litellm.utils import (
     get_secret,
 )
 
-from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
-from litellm.litellm_core_utils.prompt_templates.factory import custom_prompt, prompt_factory
 from ..common_utils import AWSEventStreamDecoder, SagemakerError
 from .transformation import SagemakerConfig
 

litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py

@@ -24,23 +24,22 @@ from typing import (
 )
 
 import httpx  # type: ignore
-import requests  # type: ignore
 
 import litellm
 import litellm.litellm_core_utils
 import litellm.litellm_core_utils.litellm_logging
 from litellm import verbose_logger
 from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    convert_generic_image_chunk_to_openai_image_obj,
+    convert_to_anthropic_image_obj,
+)
 from litellm.llms.base_llm.transformation import BaseConfig, BaseLLMException
 from litellm.llms.custom_httpx.http_handler import (
     AsyncHTTPHandler,
     HTTPHandler,
     get_async_httpx_client,
 )
-from litellm.litellm_core_utils.prompt_templates.factory import (
-    convert_generic_image_chunk_to_openai_image_obj,
-    convert_to_anthropic_image_obj,
-)
 from litellm.types.llms.openai import (
     AllMessageValues,
     ChatCompletionResponseMessage,

litellm/llms/vertex_ai/vertex_ai_non_gemini.py

@@ -7,19 +7,18 @@ import uuid
 from enum import Enum
 from typing import Any, Callable, List, Literal, Optional, Union, cast
 
-import httpx  # type: ignore
+import httpx
-import requests  # type: ignore
 from pydantic import BaseModel
 
 import litellm
 from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.core_helpers import map_finish_reason
-from litellm.llms.custom_httpx.http_handler import _DEFAULT_TTL_FOR_HTTPX_CLIENTS
 from litellm.litellm_core_utils.prompt_templates.factory import (
     convert_to_anthropic_image_obj,
     convert_to_gemini_tool_call_invoke,
     convert_to_gemini_tool_call_result,
 )
+from litellm.llms.custom_httpx.http_handler import _DEFAULT_TTL_FOR_HTTPX_CLIENTS
 from litellm.types.files import (
     get_file_mime_type_for_file_type,
     get_file_type_from_extension,

litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py

@@ -9,11 +9,19 @@ import uuid
 from enum import Enum
 from typing import Any, Callable, List, Optional, Tuple, Union
 
-import httpx  # type: ignore
+import httpx
-import requests  # type: ignore
 
 import litellm
 from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    construct_tool_use_system_prompt,
+    contains_tag,
+    custom_prompt,
+    extract_between_tags,
+    parse_xml_params,
+    prompt_factory,
+    response_schema_prompt,
+)
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
 from litellm.types.llms.openai import (
     AllMessageValues,
@@ -24,15 +32,6 @@ from litellm.types.utils import ResponseFormatChunk
 from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
 
 from ....anthropic.chat.transformation import AnthropicConfig
-from litellm.litellm_core_utils.prompt_templates.factory import (
-    construct_tool_use_system_prompt,
-    contains_tag,
-    custom_prompt,
-    extract_between_tags,
-    parse_xml_params,
-    prompt_factory,
-    response_schema_prompt,
-)
 
 
 class VertexAIError(Exception):

litellm/llms/vllm/completion/handler.py

@@ -5,12 +5,13 @@ from enum import Enum
 from typing import Any, Callable
 
 import httpx
-import requests  # type: ignore
 
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    custom_prompt,
+    prompt_factory,
+)
 from litellm.utils import ModelResponse, Usage
 
-from litellm.litellm_core_utils.prompt_templates.factory import custom_prompt, prompt_factory
-
 llm = None
 
 

litellm/main.py

@@ -2645,45 +2645,24 @@ def completion(  # type: ignore # noqa: PLR0915
                 or get_secret("OLLAMA_API_BASE")
                 or "http://localhost:11434"
             )
-            custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
+            response = base_llm_http_handler.completion(
-            if model in custom_prompt_dict:
-                # check if the model has a registered custom prompt
-                model_prompt_details = custom_prompt_dict[model]
-                ollama_prompt = custom_prompt(
-                    role_dict=model_prompt_details["roles"],
-                    initial_prompt_value=model_prompt_details["initial_prompt_value"],
-                    final_prompt_value=model_prompt_details["final_prompt_value"],
-                    messages=messages,
-                )
-            else:
-                modified_prompt = ollama_pt(model=model, messages=messages)
-                if isinstance(modified_prompt, dict):
-                    # for multimode models - ollama/llava prompt_factory returns a dict {
-                    #     "prompt": prompt,
-                    #     "images": images
-                    # }
-                    ollama_prompt, images = (
-                        modified_prompt["prompt"],
-                        modified_prompt["images"],
-                    )
-                    optional_params["images"] = images
-                else:
-                    ollama_prompt = modified_prompt
-            ## LOGGING
-            generator = ollama.get_ollama_response(
-                api_base=api_base,
                 model=model,
-                prompt=ollama_prompt,
+                stream=stream,
-                optional_params=optional_params,
+                messages=messages,
-                logging_obj=logging,
                 acompletion=acompletion,
+                api_base=api_base,
                 model_response=model_response,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                custom_llm_provider="ollama",
+                timeout=timeout,
+                headers=headers,
                 encoding=encoding,
+                api_key=api_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
+                client=client,
             )
-            if acompletion is True or optional_params.get("stream", False) is True:
-                return generator
 
-            response = generator
         elif custom_llm_provider == "ollama_chat":
             api_base = (
                 litellm.api_base
@@ -2833,8 +2812,6 @@ def completion(  # type: ignore # noqa: PLR0915
                 return response
             response = model_response
         elif custom_llm_provider == "custom":
-            import requests
-
             url = litellm.api_base or api_base or ""
             if url is None or url == "":
                 raise ValueError(
@@ -2843,7 +2820,7 @@ def completion(  # type: ignore # noqa: PLR0915
 
             """
             assume input to custom LLM api bases follow this format:
-            resp = requests.post(
+            resp = litellm.module_level_client.post(
                 api_base,
                 json={
                     'model': 'meta-llama/Llama-2-13b-hf', # model name
@@ -2859,7 +2836,7 @@ def completion(  # type: ignore # noqa: PLR0915
 
             """
             prompt = " ".join([message["content"] for message in messages])  # type: ignore
-            resp = requests.post(
+            resp = litellm.module_level_client.post(
                 url,
                 json={
                     "model": model,
@@ -2871,7 +2848,6 @@ def completion(  # type: ignore # noqa: PLR0915
                         "top_k": kwargs.get("top_k", 40),
                     },
                 },
-                verify=litellm.ssl_verify,
             )
             response_json = resp.json()
             """

litellm/proxy/proxy_cli.py

@@ -303,7 +303,7 @@ def run_server(  # noqa: PLR0915
         return
     if model and "ollama" in model and api_base is None:
         run_ollama_serve()
-    import requests
+    import httpx
 
     if test_async is True:
         import concurrent
@@ -319,7 +319,7 @@ def run_server(  # noqa: PLR0915
                 ],
             }
 
-            response = requests.post("http://0.0.0.0:4000/queue/request", json=data)
+            response = httpx.post("http://0.0.0.0:4000/queue/request", json=data)
 
             response = response.json()
 
@@ -327,7 +327,7 @@ def run_server(  # noqa: PLR0915
                 try:
                     url = response["url"]
                     polling_url = f"{api_base}{url}"
-                    polling_response = requests.get(polling_url)
+                    polling_response = httpx.get(polling_url)
                     polling_response = polling_response.json()
                     print("\n RESPONSE FROM POLLING JOB", polling_response)  # noqa
                     status = polling_response["status"]
@@ -378,7 +378,7 @@ def run_server(  # noqa: PLR0915
     if health is not False:
 
         print("\nLiteLLM: Health Testing models in config")  # noqa
-        response = requests.get(url=f"http://{host}:{port}/health")
+        response = httpx.get(url=f"http://{host}:{port}/health")
         print(json.dumps(response.json(), indent=4))  # noqa
         return
     if test is not False:

litellm/router_strategy/least_busy.py

@@ -11,9 +11,6 @@ import random
 import traceback
 from typing import Optional
 
-import dotenv  # type: ignore
-import requests
-
 from litellm.caching.caching import DualCache
 from litellm.integrations.custom_logger import CustomLogger
 

litellm/router_strategy/lowest_tpm_rpm.py

@@ -6,10 +6,6 @@ import traceback
 from datetime import datetime
 from typing import Dict, List, Optional, Union
 
-import dotenv
-import requests
-from pydantic import BaseModel
-
 from litellm import token_counter
 from litellm._logging import verbose_router_logger
 from litellm.caching.caching import DualCache

litellm/utils.py

@@ -43,7 +43,6 @@ import aiohttp
 import dotenv
 import httpx
 import openai
-import requests
 import tiktoken
 from httpx import Proxy
 from httpx._utils import get_environment_proxies
@@ -4175,7 +4174,7 @@ def get_max_tokens(model: str) -> Optional[int]:
         config_url = f"https://huggingface.co/{model_name}/raw/main/config.json"
         try:
             # Make the HTTP request to get the raw JSON file
-            response = requests.get(config_url)
+            response = litellm.module_level_client.get(config_url)
             response.raise_for_status()  # Raise an exception for bad responses (4xx or 5xx)
 
             # Parse the JSON response
@@ -4186,7 +4185,7 @@ def get_max_tokens(model: str) -> Optional[int]:
                 return max_position_embeddings
             else:
                 return None
-        except requests.exceptions.RequestException:
+        except Exception:
             return None
 
     try:
@@ -4361,7 +4360,7 @@ def get_model_info(  # noqa: PLR0915
 
         try:
             # Make the HTTP request to get the raw JSON file
-            response = requests.get(config_url)
+            response = litellm.module_level_client.get(config_url)
             response.raise_for_status()  # Raise an exception for bad responses (4xx or 5xx)
 
             # Parse the JSON response
@@ -4374,7 +4373,7 @@ def get_model_info(  # noqa: PLR0915
                 return max_position_embeddings
             else:
                 return None
-        except requests.exceptions.RequestException:
+        except Exception:
             return None
 
     try:

tests/documentation_tests/test_requests_lib_usage.py

@@ -0,0 +1,183 @@
+"""
+Prevent usage of 'requests' library in the codebase.
+"""
+
+import os
+import ast
+import sys
+from typing import List, Tuple
+
+
+def find_requests_usage(directory: str) -> List[Tuple[str, int, str]]:
+    """
+    Recursively search for Python files in the given directory
+    and find usages of the 'requests' library.
+
+    Args:
+        directory (str): The root directory to search for Python files
+
+    Returns:
+        List of tuples containing (file_path, line_number, usage_type)
+    """
+    requests_usages = []
+
+    def is_likely_requests_usage(node):
+        """
+        More precise check to avoid false positives
+        """
+        try:
+            # Convert node to string representation
+            node_str = ast.unparse(node)
+
+            # Specific checks to ensure it's the requests library
+            requests_identifiers = [
+                # HTTP methods
+                "requests.get",
+                "requests.post",
+                "requests.put",
+                "requests.delete",
+                "requests.head",
+                "requests.patch",
+                "requests.options",
+                "requests.request",
+                "requests.session",
+                # Types and exceptions
+                "requests.Response",
+                "requests.Request",
+                "requests.Session",
+                "requests.ConnectionError",
+                "requests.HTTPError",
+                "requests.Timeout",
+                "requests.TooManyRedirects",
+                "requests.RequestException",
+                # Additional modules and attributes
+                "requests.api",
+                "requests.exceptions",
+                "requests.models",
+                "requests.auth",
+                "requests.cookies",
+                "requests.structures",
+            ]
+
+            # Check for specific requests library identifiers
+            return any(identifier in node_str for identifier in requests_identifiers)
+        except:
+            return False
+
+    def scan_file(file_path: str):
+        """
+        Scan a single Python file for requests library usage
+        """
+        try:
+            # Use utf-8-sig to handle files with BOM, ignore errors
+            with open(file_path, "r", encoding="utf-8-sig", errors="ignore") as file:
+                tree = ast.parse(file.read())
+
+            for node in ast.walk(tree):
+                # Check import statements
+                if isinstance(node, ast.Import):
+                    for alias in node.names:
+                        if alias.name == "requests":
+                            requests_usages.append(
+                                (file_path, node.lineno, f"Import: {alias.name}")
+                            )
+
+                # Check import from statements
+                elif isinstance(node, ast.ImportFrom):
+                    if node.module == "requests":
+                        requests_usages.append(
+                            (file_path, node.lineno, f"Import from: {node.module}")
+                        )
+
+                # Check method calls
+                elif isinstance(node, ast.Call):
+                    # More precise check for requests usage
+                    try:
+                        if is_likely_requests_usage(node.func):
+                            requests_usages.append(
+                                (
+                                    file_path,
+                                    node.lineno,
+                                    f"Method Call: {ast.unparse(node.func)}",
+                                )
+                            )
+                    except:
+                        pass
+
+                # Check attribute access
+                elif isinstance(node, ast.Attribute):
+                    try:
+                        # More precise check
+                        if is_likely_requests_usage(node):
+                            requests_usages.append(
+                                (
+                                    file_path,
+                                    node.lineno,
+                                    f"Attribute Access: {ast.unparse(node)}",
+                                )
+                            )
+                    except:
+                        pass
+
+        except SyntaxError as e:
+            print(f"Syntax error in {file_path}: {e}", file=sys.stderr)
+        except Exception as e:
+            print(f"Error processing {file_path}: {e}", file=sys.stderr)
+
+    # Recursively walk through directory
+    for root, dirs, files in os.walk(directory):
+        # Remove virtual environment and cache directories from search
+        dirs[:] = [
+            d
+            for d in dirs
+            if not any(
+                venv in d
+                for venv in [
+                    "venv",
+                    "env",
+                    "myenv",
+                    ".venv",
+                    "__pycache__",
+                    ".pytest_cache",
+                ]
+            )
+        ]
+
+        for file in files:
+            if file.endswith(".py"):
+                full_path = os.path.join(root, file)
+                # Skip files in virtual environment or cache directories
+                if not any(
+                    venv in full_path
+                    for venv in [
+                        "venv",
+                        "env",
+                        "myenv",
+                        ".venv",
+                        "__pycache__",
+                        ".pytest_cache",
+                    ]
+                ):
+                    scan_file(full_path)
+
+    return requests_usages
+
+
+def main():
+    # Get directory from command line argument or use current directory
+    directory = "../../litellm"
+
+    # Find requests library usages
+    results = find_requests_usage(directory)
+
+    # Print results
+    if results:
+        print("Requests Library Usages Found:")
+        for file_path, line_num, usage_type in results:
+            print(f"{file_path}:{line_num} - {usage_type}")
+    else:
+        print("No requests library usages found.")
+
+
+if __name__ == "__main__":
+    main()

tests/local_testing/test_completion.py

@@ -1940,10 +1940,11 @@ def test_ollama_image():
         mock_response = MagicMock()
         mock_response.status_code = 200
         mock_response.headers = {"Content-Type": "application/json"}
+        data_json = json.loads(kwargs["data"])
         mock_response.json.return_value = {
             # return the image in the response so that it can be tested
             # against the original
-            "response": kwargs["json"]["images"]
+            "response": data_json["images"]
         }
         return mock_response
 
@@ -1971,9 +1972,10 @@ def test_ollama_image():
         [datauri_base64_data, datauri_base64_data],
     ]
 
+    client = HTTPHandler()
     for test in tests:
         try:
-            with patch("requests.post", side_effect=mock_post):
+            with patch.object(client, "post", side_effect=mock_post):
                 response = completion(
                     model="ollama/llava",
                     messages=[
@@ -1988,6 +1990,7 @@ def test_ollama_image():
                             ],
                         }
                     ],
+                    client=client,
                 )
                 if not test[1]:
                     # the conversion process may not always generate the same image,
@@ -2387,8 +2390,8 @@ def test_completion_ollama_hosted():
         response = completion(
             model="ollama/phi",
             messages=messages,
-            max_tokens=2,
+            max_tokens=20,
-            api_base="https://test-ollama-endpoint.onrender.com",
+            # api_base="https://test-ollama-endpoint.onrender.com",
         )
         # Add any assertions here to check the response
         print(response)

tests/local_testing/test_streaming.py

@@ -606,14 +606,14 @@ def test_completion_azure_function_calling_stream():
 @pytest.mark.skip("Flaky ollama test - needs to be fixed")
 def test_completion_ollama_hosted_stream():
     try:
-        litellm.set_verbose = True
+        # litellm.set_verbose = True
         response = completion(
             model="ollama/phi",
             messages=messages,
-            max_tokens=10,
+            max_tokens=100,
             num_retries=3,
             timeout=20,
-            api_base="https://test-ollama-endpoint.onrender.com",
+            # api_base="https://test-ollama-endpoint.onrender.com",
             stream=True,
         )
         # Add any assertions here to check the response