feat(proxy_server.py): return litellm version in response headers

.pre-commit-config.yaml

@@ -16,11 +16,11 @@ repos:
         name: Check if files match
         entry: python3 ci_cd/check_files_match.py
         language: system
--   repo: local
+# -   repo: local
-    hooks:
+#     hooks:
-    -   id: mypy
+#     -   id: mypy
-        name: mypy
+#         name: mypy
-        entry: python3 -m mypy --ignore-missing-imports
+#         entry: python3 -m mypy --ignore-missing-imports
-        language: system
+#         language: system
-        types: [python]
+#         types: [python]
-        files: ^litellm/
+#         files: ^litellm/

enterprise/utils.py

@@ -291,7 +291,7 @@ def _create_clickhouse_aggregate_tables(client=None, table_names=[]):
 
 
 def _forecast_daily_cost(data: list):
-    import requests
+    import requests  # type: ignore
     from datetime import datetime, timedelta
 
     if len(data) == 0:

litellm/_redis.py

@@ -10,8 +10,8 @@
 # s/o [@Frank Colson](https://www.linkedin.com/in/frank-colson-422b9b183/) for this redis implementation
 import os
 import inspect
-import redis, litellm
+import redis, litellm  # type: ignore
-import redis.asyncio as async_redis
+import redis.asyncio as async_redis  # type: ignore
 from typing import List, Optional
 
 

litellm/budget_manager.py

@@ -10,7 +10,7 @@
 import os, json, time
 import litellm
 from litellm.utils import ModelResponse
-import requests, threading
+import requests, threading  # type: ignore
 from typing import Optional, Union, Literal
 
 

litellm/integrations/aispend.py

@@ -1,7 +1,6 @@
 #### What this does ####
 #    On success + failure, log events to aispend.io
 import dotenv, os
-import requests
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback

litellm/integrations/athina.py

@@ -4,18 +4,30 @@ import datetime
 class AthinaLogger:
     def __init__(self):
         import os
+
         self.athina_api_key = os.getenv("ATHINA_API_KEY")
         self.headers = {
             "athina-api-key": self.athina_api_key,
-            "Content-Type": "application/json"
+            "Content-Type": "application/json",
         }
         self.athina_logging_url = "https://log.athina.ai/api/v1/log/inference"
-        self.additional_keys = ["environment", "prompt_slug", "customer_id", "customer_user_id", "session_id", "external_reference_id", "context", "expected_response", "user_query"]
+        self.additional_keys = [
+            "environment",
+            "prompt_slug",
+            "customer_id",
+            "customer_user_id",
+            "session_id",
+            "external_reference_id",
+            "context",
+            "expected_response",
+            "user_query",
+        ]
 
     def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
-        import requests
+        import requests  # type: ignore
         import json
         import traceback
+
         try:
             response_json = response_obj.model_dump() if response_obj else {}
             data = {
@@ -23,32 +35,51 @@ class AthinaLogger:
                 "request": kwargs,
                 "response": response_json,
                 "prompt_tokens": response_json.get("usage", {}).get("prompt_tokens"),
-                "completion_tokens": response_json.get("usage", {}).get("completion_tokens"),
+                "completion_tokens": response_json.get("usage", {}).get(
+                    "completion_tokens"
+                ),
                 "total_tokens": response_json.get("usage", {}).get("total_tokens"),
             }
-            
+
-            if type(end_time) == datetime.datetime and type(start_time) == datetime.datetime:
+            if (
-                data["response_time"] = int((end_time - start_time).total_seconds() * 1000)
+                type(end_time) == datetime.datetime
+                and type(start_time) == datetime.datetime
+            ):
+                data["response_time"] = int(
+                    (end_time - start_time).total_seconds() * 1000
+                )
 
             if "messages" in kwargs:
                 data["prompt"] = kwargs.get("messages", None)
 
             # Directly add tools or functions if present
             optional_params = kwargs.get("optional_params", {})
-            data.update((k, v) for k, v in optional_params.items() if k in ["tools", "functions"])
+            data.update(
+                (k, v)
+                for k, v in optional_params.items()
+                if k in ["tools", "functions"]
+            )
 
             # Add additional metadata keys
-            metadata = kwargs.get("litellm_params", {}).get("metadata", {})        
+            metadata = kwargs.get("litellm_params", {}).get("metadata", {})
             if metadata:
                 for key in self.additional_keys:
                     if key in metadata:
                         data[key] = metadata[key]
 
-            response = requests.post(self.athina_logging_url, headers=self.headers, data=json.dumps(data, default=str))
+            response = requests.post(
+                self.athina_logging_url,
+                headers=self.headers,
+                data=json.dumps(data, default=str),
+            )
             if response.status_code != 200:
-                print_verbose(f"Athina Logger Error - {response.text}, {response.status_code}")
+                print_verbose(
+                    f"Athina Logger Error - {response.text}, {response.status_code}"
+                )
             else:
                 print_verbose(f"Athina Logger Succeeded - {response.text}")
         except Exception as e:
-            print_verbose(f"Athina Logger Error - {e}, Stack trace: {traceback.format_exc()}")
+            print_verbose(
-            pass
+                f"Athina Logger Error - {e}, Stack trace: {traceback.format_exc()}"
+            )
+            pass

litellm/integrations/berrispend.py

@@ -1,7 +1,7 @@
 #### What this does ####
 #    On success + failure, log events to aispend.io
 import dotenv, os
-import requests
+import requests  # type: ignore
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback

litellm/integrations/clickhouse.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 import DualCache

litellm/integrations/custom_logger.py

@@ -1,7 +1,6 @@
 #### What this does ####
 #    On success, logs events to Promptlayer
 import dotenv, os
-import requests
 
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.caching import DualCache

litellm/integrations/datadog.py

@@ -2,7 +2,7 @@
 #    On success + failure, log events to Supabase
 
 import dotenv, os
-import requests
+import requests  # type: ignore
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback

litellm/integrations/dynamodb.py

@@ -2,7 +2,7 @@
 #    On success + failure, log events to Supabase
 
 import dotenv, os
-import requests
+import requests  # type: ignore
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback

litellm/integrations/greenscale.py

@@ -1,15 +1,17 @@
-import requests
+import requests  # type: ignore
 import json
 import traceback
 from datetime import datetime, timezone
 
+
 class GreenscaleLogger:
     def __init__(self):
         import os
+
         self.greenscale_api_key = os.getenv("GREENSCALE_API_KEY")
         self.headers = {
             "api-key": self.greenscale_api_key,
-            "Content-Type": "application/json"
+            "Content-Type": "application/json",
         }
         self.greenscale_logging_url = os.getenv("GREENSCALE_ENDPOINT")
 
@@ -19,33 +21,48 @@ class GreenscaleLogger:
             data = {
                 "modelId": kwargs.get("model"),
                 "inputTokenCount": response_json.get("usage", {}).get("prompt_tokens"),
-                "outputTokenCount": response_json.get("usage", {}).get("completion_tokens"),
+                "outputTokenCount": response_json.get("usage", {}).get(
+                    "completion_tokens"
+                ),
             }
-            data["timestamp"] = datetime.now(timezone.utc).strftime('%Y-%m-%dT%H:%M:%SZ')
+            data["timestamp"] = datetime.now(timezone.utc).strftime(
-            
+                "%Y-%m-%dT%H:%M:%SZ"
-            if type(end_time) == datetime and type(start_time) == datetime:
+            )
-                data["invocationLatency"] = int((end_time - start_time).total_seconds() * 1000)
 
+            if type(end_time) == datetime and type(start_time) == datetime:
+                data["invocationLatency"] = int(
+                    (end_time - start_time).total_seconds() * 1000
+                )
 
             # Add additional metadata keys to tags
             tags = []
             metadata = kwargs.get("litellm_params", {}).get("metadata", {})
             for key, value in metadata.items():
-                if key.startswith("greenscale"): 
+                if key.startswith("greenscale"):
                     if key == "greenscale_project":
                         data["project"] = value
                     elif key == "greenscale_application":
                         data["application"] = value
                     else:
-                        tags.append({"key": key.replace("greenscale_", ""), "value": str(value)})
+                        tags.append(
-            
+                            {"key": key.replace("greenscale_", ""), "value": str(value)}
+                        )
+
             data["tags"] = tags
 
-            response = requests.post(self.greenscale_logging_url, headers=self.headers, data=json.dumps(data, default=str))
+            response = requests.post(
+                self.greenscale_logging_url,
+                headers=self.headers,
+                data=json.dumps(data, default=str),
+            )
             if response.status_code != 200:
-                print_verbose(f"Greenscale Logger Error - {response.text}, {response.status_code}")
+                print_verbose(
+                    f"Greenscale Logger Error - {response.text}, {response.status_code}"
+                )
             else:
                 print_verbose(f"Greenscale Logger Succeeded - {response.text}")
         except Exception as e:
-            print_verbose(f"Greenscale Logger Error - {e}, Stack trace: {traceback.format_exc()}")
+            print_verbose(
-            pass
+                f"Greenscale Logger Error - {e}, Stack trace: {traceback.format_exc()}"
+            )
+            pass

litellm/integrations/helicone.py

@@ -1,7 +1,7 @@
 #### What this does ####
 #    On success, logs events to Helicone
 import dotenv, os
-import requests
+import requests  # type: ignore
 import litellm
 
 dotenv.load_dotenv()  # Loading env variables using dotenv

litellm/integrations/langsmith.py

@@ -1,15 +1,14 @@
 #### What this does ####
 #    On success, logs events to Langsmith
-import dotenv, os
+import dotenv, os  # type: ignore
-import requests
+import requests  # type: ignore
-import requests
 from datetime import datetime
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 import asyncio
 import types
-from pydantic import BaseModel
+from pydantic import BaseModel  # type: ignore
 
 
 def is_serializable(value):
@@ -79,8 +78,6 @@ class LangsmithLogger:
                 except:
                     response_obj = response_obj.dict()  # type: ignore
 
-            print(f"response_obj: {response_obj}")
-
             data = {
                 "name": run_name,
                 "run_type": "llm",  # this should always be llm, since litellm always logs llm calls. Langsmith allow us to log "chain"
@@ -90,7 +87,6 @@ class LangsmithLogger:
                 "start_time": start_time,
                 "end_time": end_time,
             }
-            print(f"data: {data}")
 
             response = requests.post(
                 "https://api.smith.langchain.com/runs",

litellm/integrations/openmeter.py

@@ -2,7 +2,6 @@
 ## On Success events log cost to OpenMeter - https://github.com/BerriAI/litellm/issues/1268
 
 import dotenv, os, json
-import requests
 import litellm
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
@@ -60,7 +59,7 @@ class OpenMeterLogger(CustomLogger):
                 "total_tokens": response_obj["usage"].get("total_tokens"),
             }
 
-        subject = kwargs.get("user", None),  # end-user passed in via 'user' param
+        subject = (kwargs.get("user", None),)  # end-user passed in via 'user' param
         if not subject:
             raise Exception("OpenMeter: user is required")
 

litellm/integrations/prometheus.py

@@ -3,7 +3,7 @@
 #    On success, log events to Prometheus
 
 import dotenv, os
-import requests
+import requests  # type: ignore
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
@@ -19,7 +19,6 @@ class PrometheusLogger:
         **kwargs,
     ):
         try:
-            print(f"in init prometheus metrics")
             from prometheus_client import Counter
 
             self.litellm_llm_api_failed_requests_metric = Counter(

litellm/integrations/prometheus_services.py

@@ -4,7 +4,7 @@
 
 
 import dotenv, os
-import requests
+import requests  # type: ignore
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
@@ -183,7 +183,6 @@ class PrometheusServicesLogger:
                     )
 
     async def async_service_failure_hook(self, payload: ServiceLoggerPayload):
-        print(f"received error payload: {payload.error}")
         if self.mock_testing:
             self.mock_testing_failure_calls += 1
 

litellm/integrations/prompt_layer.py

@@ -1,12 +1,13 @@
 #### What this does ####
 #    On success, logs events to Promptlayer
 import dotenv, os
-import requests
+import requests  # type: ignore
 from pydantic import BaseModel
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 
+
 class PromptLayerLogger:
     # Class variables or attributes
     def __init__(self):
@@ -32,7 +33,11 @@ class PromptLayerLogger:
                     tags = kwargs["litellm_params"]["metadata"]["pl_tags"]
 
                 # Remove "pl_tags" from metadata
-                metadata = {k:v for k, v in kwargs["litellm_params"]["metadata"].items() if k != "pl_tags"}
+                metadata = {
+                    k: v
+                    for k, v in kwargs["litellm_params"]["metadata"].items()
+                    if k != "pl_tags"
+                }
 
             print_verbose(
                 f"Prompt Layer Logging - Enters logging function for model kwargs: {new_kwargs}\n, response: {response_obj}"

litellm/integrations/s3.py

@@ -2,7 +2,6 @@
 #    On success + failure, log events to Supabase
 
 import dotenv, os
-import requests
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback

litellm/integrations/supabase.py

@@ -2,7 +2,7 @@
 #    On success + failure, log events to Supabase
 
 import dotenv, os
-import requests
+import requests  # type: ignore
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback

litellm/llms/ai21.py

@@ -1,8 +1,8 @@
 import os, types, traceback
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
-import time, httpx
+import time, httpx  # type: ignore
 from typing import Callable, Optional
 from litellm.utils import ModelResponse, Choices, Message
 import litellm

litellm/llms/aleph_alpha.py

@@ -1,12 +1,12 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 import litellm
 from litellm.utils import ModelResponse, Choices, Message, Usage
-import httpx
+import httpx  # type: ignore
 
 
 class AlephAlphaError(Exception):

litellm/llms/anthropic.py

@@ -1,7 +1,7 @@
 import os, types
 import json
 from enum import Enum
-import requests, copy
+import requests, copy  # type: ignore
 import time
 from typing import Callable, Optional, List
 from litellm.utils import ModelResponse, Usage, map_finish_reason, CustomStreamWrapper
@@ -9,7 +9,7 @@ import litellm
 from .prompt_templates.factory import prompt_factory, custom_prompt
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
 from .base import BaseLLM
-import httpx
+import httpx  # type: ignore
 
 
 class AnthropicConstants(Enum):

litellm/llms/azure.py

@@ -1,5 +1,5 @@
 from typing import Optional, Union, Any
-import types, requests
+import types, requests  # type: ignore
 from .base import BaseLLM
 from litellm.utils import (
     ModelResponse,
@@ -12,7 +12,7 @@ from litellm.utils import (
 from typing import Callable, Optional, BinaryIO
 from litellm import OpenAIConfig
 import litellm, json
-import httpx
+import httpx  # type: ignore
 from .custom_httpx.azure_dall_e_2 import CustomHTTPTransport, AsyncCustomHTTPTransport
 from openai import AzureOpenAI, AsyncAzureOpenAI
 import uuid

litellm/llms/azure_text.py

@@ -1,5 +1,5 @@
 from typing import Optional, Union, Any
-import types, requests
+import types, requests  # type: ignore
 from .base import BaseLLM
 from litellm.utils import (
     ModelResponse,

litellm/llms/baseten.py

@@ -1,7 +1,7 @@
 import os
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable
 from litellm.utils import ModelResponse, Usage

litellm/llms/bedrock.py

@@ -163,10 +163,9 @@ class AmazonAnthropicClaude3Config:
             "stop",
             "temperature",
             "top_p",
-            "extra_headers"
+            "extra_headers",
         ]
 
-
     def map_openai_params(self, non_default_params: dict, optional_params: dict):
         for param, value in non_default_params.items():
             if param == "max_tokens":
@@ -534,10 +533,12 @@ class AmazonStabilityConfig:
 
 def add_custom_header(headers):
     """Closure to capture the headers and add them."""
+
     def callback(request, **kwargs):
         """Actual callback function that Boto3 will call."""
         for header_name, header_value in headers.items():
             request.headers.add_header(header_name, header_value)
+
     return callback
 
 
@@ -672,7 +673,9 @@ def init_bedrock_client(
             config=config,
         )
     if extra_headers:
-        client.meta.events.register('before-sign.bedrock-runtime.*', add_custom_header(extra_headers))
+        client.meta.events.register(
+            "before-sign.bedrock-runtime.*", add_custom_header(extra_headers)
+        )
 
     return client
 
@@ -1224,7 +1227,7 @@ def _embedding_func_single(
             "input_type", "search_document"
         )  # aws bedrock example default - https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/providers?model=cohere.embed-english-v3
         data = {"texts": [input], **inference_params}  # type: ignore
-    body = json.dumps(data).encode("utf-8")
+    body = json.dumps(data).encode("utf-8")  # type: ignore
     ## LOGGING
     request_str = f"""
     response = client.invoke_model(
@@ -1416,7 +1419,7 @@ def image_generation(
     ## LOGGING
     request_str = f"""
     response = client.invoke_model(
-        body={body},
+        body={body}, # type: ignore
         modelId={modelId},
         accept="application/json",
         contentType="application/json",

litellm/llms/cloudflare.py

@@ -1,11 +1,11 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 import litellm
-import httpx
+import httpx  # type: ignore
 from litellm.utils import ModelResponse, Usage
 from .prompt_templates.factory import prompt_factory, custom_prompt
 

litellm/llms/cohere.py

@@ -1,12 +1,12 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time, traceback
 from typing import Callable, Optional
 from litellm.utils import ModelResponse, Choices, Message, Usage
 import litellm
-import httpx
+import httpx  # type: ignore
 
 
 class CohereError(Exception):

litellm/llms/cohere_chat.py

@@ -1,12 +1,12 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time, traceback
 from typing import Callable, Optional
 from litellm.utils import ModelResponse, Choices, Message, Usage
 import litellm
-import httpx
+import httpx  # type: ignore
 from .prompt_templates.factory import cohere_message_pt
 
 

litellm/llms/maritalk.py

@@ -1,7 +1,7 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time, traceback
 from typing import Callable, Optional, List
 from litellm.utils import ModelResponse, Choices, Message, Usage

litellm/llms/nlp_cloud.py

@@ -1,7 +1,7 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 import litellm

litellm/llms/ollama.py

@@ -1,10 +1,10 @@
 from itertools import chain
-import requests, types, time
+import requests, types, time  # type: ignore
 import json, uuid
 import traceback
 from typing import Optional
 import litellm
-import httpx, aiohttp, asyncio
+import httpx, aiohttp, asyncio  # type: ignore
 from .prompt_templates.factory import prompt_factory, custom_prompt
 
 
@@ -220,7 +220,10 @@ def get_ollama_response(
             tool_calls=[
                 {
                     "id": f"call_{str(uuid.uuid4())}",
-                    "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
+                    "function": {
+                        "name": function_call["name"],
+                        "arguments": json.dumps(function_call["arguments"]),
+                    },
                     "type": "function",
                 }
             ],
@@ -232,7 +235,9 @@ def get_ollama_response(
     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", "")))
+    completion_tokens = response_json.get(
+        "eval_count", len(response_json.get("message", dict()).get("content", ""))
+    )
     model_response["usage"] = litellm.Usage(
         prompt_tokens=prompt_tokens,
         completion_tokens=completion_tokens,
@@ -273,7 +278,10 @@ def ollama_completion_stream(url, data, logging_obj):
                     tool_calls=[
                         {
                             "id": f"call_{str(uuid.uuid4())}",
-                            "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
+                            "function": {
+                                "name": function_call["name"],
+                                "arguments": json.dumps(function_call["arguments"]),
+                            },
                             "type": "function",
                         }
                     ],
@@ -314,9 +322,10 @@ async def ollama_async_streaming(url, data, model_response, encoding, logging_ob
                 first_chunk_content = first_chunk.choices[0].delta.content or ""
                 response_content = first_chunk_content + "".join(
                     [
-                    chunk.choices[0].delta.content
+                        chunk.choices[0].delta.content
-                    async for chunk in streamwrapper
+                        async for chunk in streamwrapper
-                    if chunk.choices[0].delta.content]
+                        if chunk.choices[0].delta.content
+                    ]
                 )
                 function_call = json.loads(response_content)
                 delta = litellm.utils.Delta(
@@ -324,7 +333,10 @@ async def ollama_async_streaming(url, data, model_response, encoding, logging_ob
                     tool_calls=[
                         {
                             "id": f"call_{str(uuid.uuid4())}",
-                            "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
+                            "function": {
+                                "name": function_call["name"],
+                                "arguments": json.dumps(function_call["arguments"]),
+                            },
                             "type": "function",
                         }
                     ],
@@ -373,7 +385,10 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj):
                     tool_calls=[
                         {
                             "id": f"call_{str(uuid.uuid4())}",
-                            "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
+                            "function": {
+                                "name": function_call["name"],
+                                "arguments": json.dumps(function_call["arguments"]),
+                            },
                             "type": "function",
                         }
                     ],
@@ -387,7 +402,10 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj):
             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", "")))
+            completion_tokens = response_json.get(
+                "eval_count",
+                len(response_json.get("message", dict()).get("content", "")),
+            )
             model_response["usage"] = litellm.Usage(
                 prompt_tokens=prompt_tokens,
                 completion_tokens=completion_tokens,
@@ -475,6 +493,7 @@ async def ollama_aembeddings(
     }
     return model_response
 
+
 def ollama_embeddings(
     api_base: str,
     model: str,
@@ -492,5 +511,6 @@ def ollama_embeddings(
             optional_params,
             logging_obj,
             model_response,
-            encoding)
+            encoding,
         )
+    )

litellm/llms/oobabooga.py

@@ -1,7 +1,7 @@
 import os
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 from litellm.utils import ModelResponse, Usage

litellm/llms/openai.py

@@ -22,7 +22,6 @@ from litellm.utils import (
     TextCompletionResponse,
 )
 from typing import Callable, Optional
-import aiohttp, requests
 import litellm
 from .prompt_templates.factory import prompt_factory, custom_prompt
 from openai import OpenAI, AsyncOpenAI

litellm/llms/petals.py

@@ -1,7 +1,7 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 import litellm

litellm/llms/replicate.py

@@ -1,11 +1,11 @@
 import os, types
 import json
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 from litellm.utils import ModelResponse, Usage
 import litellm
-import httpx
+import httpx  # type: ignore
 from .prompt_templates.factory import prompt_factory, custom_prompt
 
 

litellm/llms/sagemaker.py

@@ -1,14 +1,14 @@
 import os, types, traceback
 from enum import Enum
 import json
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional, Any
 import litellm
 from litellm.utils import ModelResponse, EmbeddingResponse, get_secret, Usage
 import sys
 from copy import deepcopy
-import httpx
+import httpx  # type: ignore
 from .prompt_templates.factory import prompt_factory, custom_prompt
 
 
@@ -295,7 +295,7 @@ def completion(
                 EndpointName={model},
                 InferenceComponentName={model_id},
                 ContentType="application/json",
-                Body={data},
+                Body={data}, # type: ignore
                 CustomAttributes="accept_eula=true",
             )
             """  # type: ignore
@@ -321,7 +321,7 @@ def completion(
             response = client.invoke_endpoint(
                 EndpointName={model},
                 ContentType="application/json",
-                Body={data},
+                Body={data}, # type: ignore
                 CustomAttributes="accept_eula=true",
             )
             """  # type: ignore
@@ -688,7 +688,7 @@ def embedding(
     response = client.invoke_endpoint(
         EndpointName={model},
         ContentType="application/json",
-        Body={data},
+        Body={data}, # type: ignore
         CustomAttributes="accept_eula=true",
     )"""  # type: ignore
     logging_obj.pre_call(

litellm/llms/together_ai.py

@@ -6,11 +6,11 @@ Reference: https://docs.together.ai/docs/openai-api-compatibility
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 import litellm
-import httpx
+import httpx  # type: ignore
 from litellm.utils import ModelResponse, Usage
 from .prompt_templates.factory import prompt_factory, custom_prompt
 

litellm/llms/vertex_ai.py

@@ -1,12 +1,12 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional, Union, List
 from litellm.utils import ModelResponse, Usage, CustomStreamWrapper, map_finish_reason
 import litellm, uuid
-import httpx, inspect
+import httpx, inspect  # type: ignore
 
 
 class VertexAIError(Exception):

litellm/llms/vertex_ai_anthropic.py

@@ -3,7 +3,7 @@
 import os, types
 import json
 from enum import Enum
-import requests, copy
+import requests, copy  # type: ignore
 import time, uuid
 from typing import Callable, Optional, List
 from litellm.utils import ModelResponse, Usage, map_finish_reason, CustomStreamWrapper
@@ -17,7 +17,7 @@ from .prompt_templates.factory import (
     extract_between_tags,
     parse_xml_params,
 )
-import httpx
+import httpx  # type: ignore
 
 
 class VertexAIError(Exception):

litellm/llms/vllm.py

@@ -1,8 +1,8 @@
 import os
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
-import time, httpx
+import time, httpx  # type: ignore
 from typing import Callable, Any
 from litellm.utils import ModelResponse, Usage
 from .prompt_templates.factory import prompt_factory, custom_prompt

litellm/llms/watsonx.py

@@ -3,8 +3,8 @@ import json, types, time  # noqa: E401
 from contextlib import contextmanager
 from typing import Callable, Dict, Optional, Any, Union, List
 
-import httpx
+import httpx  # type: ignore
-import requests
+import requests  # type: ignore
 import litellm
 from litellm.utils import ModelResponse, get_secret, Usage
 

litellm/proxy/proxy_cli.py

@@ -252,7 +252,7 @@ def run_server(
     if model and "ollama" in model and api_base is None:
         run_ollama_serve()
     if test_async is True:
-        import requests, concurrent, time
+        import requests, concurrent, time  # type: ignore
 
         api_base = f"http://{host}:{port}"
 
@@ -418,7 +418,7 @@ def run_server(
             read from there and save it to os.env['DATABASE_URL']
             """
             try:
-                import yaml, asyncio
+                import yaml, asyncio  # type: ignore
             except:
                 raise ImportError(
                     "yaml needs to be imported. Run - `pip install 'litellm[proxy]'`"

litellm/proxy/proxy_server.py

@@ -30,7 +30,7 @@ sys.path.insert(
 try:
     import fastapi
     import backoff
-    import yaml
+    import yaml  # type: ignore
     import orjson
     import logging
     from apscheduler.schedulers.asyncio import AsyncIOScheduler
@@ -3719,6 +3719,7 @@ async def chat_completion(
                 "x-litellm-model-id": model_id,
                 "x-litellm-cache-key": cache_key,
                 "x-litellm-model-api-base": api_base,
+                "x-litellm-version": version,
             }
             selected_data_generator = select_data_generator(
                 response=response,
@@ -3734,6 +3735,7 @@ async def chat_completion(
         fastapi_response.headers["x-litellm-model-id"] = model_id
         fastapi_response.headers["x-litellm-cache-key"] = cache_key
         fastapi_response.headers["x-litellm-model-api-base"] = api_base
+        fastapi_response.headers["x-litellm-version"] = version
 
         ### CALL HOOKS ### - modify outgoing data
         response = await proxy_logging_obj.post_call_success_hook(
@@ -3890,14 +3892,10 @@ async def completion(
                 },
             )
 
-        if hasattr(response, "_hidden_params"):
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
-            model_id = response._hidden_params.get("model_id", None) or ""
+        model_id = hidden_params.get("model_id", None) or ""
-            original_response = (
+        cache_key = hidden_params.get("cache_key", None) or ""
-                response._hidden_params.get("original_response", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
-            )
-        else:
-            model_id = ""
-            original_response = ""
 
         verbose_proxy_logger.debug("final response: %s", response)
         if (
@@ -3905,6 +3903,9 @@ async def completion(
         ):  # use generate_responses to stream responses
             custom_headers = {
                 "x-litellm-model-id": model_id,
+                "x-litellm-cache-key": cache_key,
+                "x-litellm-model-api-base": api_base,
+                "x-litellm-version": version,
             }
             selected_data_generator = select_data_generator(
                 response=response,
@@ -3919,6 +3920,10 @@ async def completion(
             )
 
         fastapi_response.headers["x-litellm-model-id"] = model_id
+        fastapi_response.headers["x-litellm-cache-key"] = cache_key
+        fastapi_response.headers["x-litellm-model-api-base"] = api_base
+        fastapi_response.headers["x-litellm-version"] = version
+
         return response
     except Exception as e:
         data["litellm_status"] = "fail"  # used for alerting
@@ -3958,6 +3963,7 @@ async def completion(
 )  # azure compatible endpoint
 async def embeddings(
     request: Request,
+    fastapi_response: Response,
     model: Optional[str] = None,
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
@@ -4104,6 +4110,17 @@ async def embeddings(
         ### ALERTING ###
         data["litellm_status"] = "success"  # used for alerting
 
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers["x-litellm-model-id"] = model_id
+        fastapi_response.headers["x-litellm-cache-key"] = cache_key
+        fastapi_response.headers["x-litellm-model-api-base"] = api_base
+        fastapi_response.headers["x-litellm-version"] = version
+
         return response
     except Exception as e:
         data["litellm_status"] = "fail"  # used for alerting
@@ -4142,6 +4159,7 @@ async def embeddings(
 )
 async def image_generation(
     request: Request,
+    fastapi_response: Response,
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
     global proxy_logging_obj
@@ -4261,6 +4279,17 @@ async def image_generation(
         ### ALERTING ###
         data["litellm_status"] = "success"  # used for alerting
 
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers["x-litellm-model-id"] = model_id
+        fastapi_response.headers["x-litellm-cache-key"] = cache_key
+        fastapi_response.headers["x-litellm-model-api-base"] = api_base
+        fastapi_response.headers["x-litellm-version"] = version
+
         return response
     except Exception as e:
         data["litellm_status"] = "fail"  # used for alerting
@@ -4297,6 +4326,7 @@ async def image_generation(
 )
 async def audio_transcriptions(
     request: Request,
+    fastapi_response: Response,
     file: UploadFile = File(...),
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
@@ -4441,6 +4471,18 @@ async def audio_transcriptions(
 
         ### ALERTING ###
         data["litellm_status"] = "success"  # used for alerting
+
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers["x-litellm-model-id"] = model_id
+        fastapi_response.headers["x-litellm-cache-key"] = cache_key
+        fastapi_response.headers["x-litellm-model-api-base"] = api_base
+        fastapi_response.headers["x-litellm-version"] = version
+
         return response
     except Exception as e:
         data["litellm_status"] = "fail"  # used for alerting
@@ -4480,6 +4522,7 @@ async def audio_transcriptions(
 )
 async def moderations(
     request: Request,
+    fastapi_response: Response,
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
     """
@@ -4604,6 +4647,17 @@ async def moderations(
         ### ALERTING ###
         data["litellm_status"] = "success"  # used for alerting
 
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers["x-litellm-model-id"] = model_id
+        fastapi_response.headers["x-litellm-cache-key"] = cache_key
+        fastapi_response.headers["x-litellm-model-api-base"] = api_base
+        fastapi_response.headers["x-litellm-version"] = version
+
         return response
     except Exception as e:
         data["litellm_status"] = "fail"  # used for alerting

litellm/proxy/utils.py

@@ -1689,12 +1689,12 @@ def get_instance_fn(value: str, config_file_path: Optional[str] = None) -> Any:
             module_file_path = os.path.join(directory, *module_name.split("."))
             module_file_path += ".py"
 
-            spec = importlib.util.spec_from_file_location(module_name, module_file_path)
+            spec = importlib.util.spec_from_file_location(module_name, module_file_path)  # type: ignore
             if spec is None:
                 raise ImportError(
                     f"Could not find a module specification for {module_file_path}"
                 )
-            module = importlib.util.module_from_spec(spec)
+            module = importlib.util.module_from_spec(spec)  # type: ignore
             spec.loader.exec_module(module)  # type: ignore
         else:
             # Dynamically import the module

litellm/router_strategy/least_busy.py

@@ -6,7 +6,7 @@
 #   - use litellm.success + failure callbacks to log when a request completed
 #   - in get_available_deployment, for a given model group name -> pick based on traffic
 
-import dotenv, os, requests, random
+import dotenv, os, requests, random  # type: ignore
 from typing import Optional
 
 dotenv.load_dotenv()  # Loading env variables using dotenv

litellm/router_strategy/lowest_cost.py

@@ -1,7 +1,7 @@
 #### What this does ####
 #   picks based on response time (for streaming, this is time to first token)
 from pydantic import BaseModel, Extra, Field, root_validator
-import dotenv, os, requests, random
+import dotenv, os, requests, random  # type: ignore
 from typing import Optional, Union, List, Dict
 from datetime import datetime, timedelta
 import random

litellm/router_strategy/lowest_latency.py

@@ -1,7 +1,7 @@
 #### What this does ####
 #   picks based on response time (for streaming, this is time to first token)
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import BaseModel, Extra, Field, root_validator  # type: ignore
-import dotenv, os, requests, random
+import dotenv, os, requests, random  # type: ignore
 from typing import Optional, Union, List, Dict
 from datetime import datetime, timedelta
 import random

litellm/utils.py

@@ -14,7 +14,7 @@ import subprocess, os
 from os.path import abspath, join, dirname
 import litellm, openai
 import itertools
-import random, uuid, requests
+import random, uuid, requests  # type: ignore
 from functools import wraps
 import datetime, time
 import tiktoken
@@ -36,7 +36,7 @@ import litellm._service_logger  # for storing API inputs, outputs, and metadata
 
 try:
     # this works in python 3.8
-    import pkg_resources
+    import pkg_resources  # type: ignore
 
     filename = pkg_resources.resource_filename(__name__, "llms/tokenizers")
 # try:
@@ -7732,11 +7732,11 @@ def _calculate_retry_after(
             try:
                 retry_after = int(retry_header)
             except Exception:
-                retry_date_tuple = email.utils.parsedate_tz(retry_header)
+                retry_date_tuple = email.utils.parsedate_tz(retry_header)  # type: ignore
                 if retry_date_tuple is None:
                     retry_after = -1
                 else:
-                    retry_date = email.utils.mktime_tz(retry_date_tuple)
+                    retry_date = email.utils.mktime_tz(retry_date_tuple)  # type: ignore
                     retry_after = int(retry_date - time.time())
         else:
             retry_after = -1
@@ -9423,7 +9423,9 @@ def get_secret(
         else:
             secret = os.environ.get(secret_name)
             try:
-                secret_value_as_bool = ast.literal_eval(secret) if secret is not None else None
+                secret_value_as_bool = (
+                    ast.literal_eval(secret) if secret is not None else None
+                )
                 if isinstance(secret_value_as_bool, bool):
                     return secret_value_as_bool
                 else: