Merge branch 'BerriAI:main' into main

.git-blame-ignore-revs

@@ -0,0 +1,10 @@
+# Add the commit hash of any commit you want to ignore in `git blame` here.
+# One commit hash per line.
+#
+# The GitHub Blame UI will use this file automatically!
+#
+# Run this command to always ignore formatting commits in `git blame`
+#     git config blame.ignoreRevsFile .git-blame-ignore-revs
+
+# Update pydantic code to fix warnings (GH-3600)
+876840e9957bc7e9f7d6a2b58c4d7c53dad16481

.github/pull_request_template.md

@@ -1,6 +1,3 @@
-<!-- This is just examples. You can remove all items if you want. -->
-<!-- Please remove all comments. -->
-
 ## Title
 
 <!-- e.g. "Implement user authentication feature" -->
@@ -18,7 +15,6 @@
 🐛 Bug Fix
 🧹 Refactoring
 📖 Documentation
-💻 Development Environment
 🚄 Infrastructure
 ✅ Test
 
@@ -26,22 +22,8 @@
 
 <!-- List of changes -->
 
-## Testing
+## [REQUIRED] Testing - Attach a screenshot of any new tests passing locall
+If UI changes, send a screenshot/GIF of working UI fixes
 
 <!-- Test procedure -->
 
-## Notes
-
-<!-- Test results -->
-
-<!-- Points to note for the reviewer, consultation content, concerns -->
-
-## Pre-Submission Checklist (optional but appreciated):
-
-- [ ] I have included relevant documentation updates (stored in /docs/my-website)
-
-## OS Tests (optional but appreciated):
-
-- [ ] Tested on Windows
-- [ ] Tested on MacOS
-- [ ] Tested on Linux

cookbook/liteLLM_clarifai_Demo.ipynb

@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LiteLLM Clarifai \n",
+    "This notebook walks you through on how to use liteLLM integration of Clarifai and call LLM model from clarifai with response in openAI output format."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-Requisites"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#install necessary packages\n",
+    "!pip install litellm\n",
+    "!pip install clarifai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To obtain Clarifai Personal Access Token follow the steps mentioned in the [link](https://docs.clarifai.com/clarifai-basics/authentication/personal-access-tokens/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Set Clarifai Credentials\n",
+    "import os\n",
+    "os.environ[\"CLARIFAI_API_KEY\"]= \"YOUR_CLARIFAI_PAT\" # Clarifai PAT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mistral-large"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import litellm\n",
+    "\n",
+    "litellm.set_verbose=False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mistral large response : ModelResponse(id='chatcmpl-6eed494d-7ae2-4870-b9c2-6a64d50a6151', choices=[Choices(finish_reason='stop', index=1, message=Message(content=\"In the grand tapestry of time, where tales unfold,\\nLies the chronicle of ages, a sight to behold.\\nA tale of empires rising, and kings of old,\\nOf civilizations lost, and stories untold.\\n\\nOnce upon a yesterday, in a time so vast,\\nHumans took their first steps, casting shadows in the past.\\nFrom the cradle of mankind, a journey they embarked,\\nThrough stone and bronze and iron, their skills they sharpened and marked.\\n\\nEgyptians built pyramids, reaching for the skies,\\nWhile Greeks sought wisdom, truth, in philosophies that lie.\\nRoman legions marched, their empire to expand,\\nAnd in the East, the Silk Road joined the world, hand in hand.\\n\\nThe Middle Ages came, with knights in shining armor,\\nFeudal lords and serfs, a time of both clamor and calm order.\\nThen Renaissance bloomed, like a flower in the sun,\\nA rebirth of art and science, a new age had begun.\\n\\nAcross the vast oceans, explorers sailed with courage bold,\\nDiscovering new lands, stories of adventure, untold.\\nIndustrial Revolution churned, progress in its wake,\\nMachines and factories, a whole new world to make.\\n\\nTwo World Wars raged, a testament to man's strife,\\nYet from the ashes rose hope, a renewed will for life.\\nInto the modern era, technology took flight,\\nConnecting every corner, bathed in digital light.\\n\\nHistory, a symphony, a melody of time,\\nA testament to human will, resilience so sublime.\\nIn every page, a lesson, in every tale, a guide,\\nFor understanding our past, shapes our future's tide.\", role='assistant'))], created=1713896412, model='https://api.clarifai.com/v2/users/mistralai/apps/completion/models/mistral-large/outputs', object='chat.completion', system_fingerprint=None, usage=Usage(prompt_tokens=13, completion_tokens=338, total_tokens=351))\n"
+     ]
+    }
+   ],
+   "source": [
+    "from litellm import completion\n",
+    "\n",
+    "messages = [{\"role\": \"user\",\"content\": \"\"\"Write a poem about history?\"\"\"}]\n",
+    "response=completion(\n",
+    "            model=\"clarifai/mistralai.completion.mistral-large\",\n",
+    "            messages=messages,\n",
+    "        )\n",
+    "\n",
+    "print(f\"Mistral large response : {response}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Claude-2.1 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Claude-2.1 response : ModelResponse(id='chatcmpl-d126c919-4db4-4aa3-ac8f-7edea41e0b93', choices=[Choices(finish_reason='stop', index=1, message=Message(content=\" Here's a poem I wrote about history:\\n\\nThe Tides of Time\\n\\nThe tides of time ebb and flow,\\nCarrying stories of long ago.\\nFigures and events come into light,\\nShaping the future with all their might.\\n\\nKingdoms rise, empires fall, \\nLeaving traces that echo down every hall.\\nRevolutions bring change with a fiery glow,\\nToppling structures from long ago.\\n\\nExplorers traverse each ocean and land,\\nSeeking treasures they don't understand.\\nWhile artists and writers try to make their mark,\\nHoping their works shine bright in the dark.\\n\\nThe cycle repeats again and again,\\nAs humanity struggles to learn from its pain.\\nThough the players may change on history's stage,\\nThe themes stay the same from age to age.\\n\\nWar and peace, life and death,\\nLove and strife with every breath.\\nThe tides of time continue their dance,\\nAs we join in, by luck or by chance.\\n\\nSo we study the past to light the way forward, \\nHeeding warnings from stories told and heard.\\nThe future unfolds from this unending flow -\\nWhere the tides of time ultimately go.\", role='assistant'))], created=1713896579, model='https://api.clarifai.com/v2/users/anthropic/apps/completion/models/claude-2_1/outputs', object='chat.completion', system_fingerprint=None, usage=Usage(prompt_tokens=12, completion_tokens=232, total_tokens=244))\n"
+     ]
+    }
+   ],
+   "source": [
+    "from litellm import completion\n",
+    "\n",
+    "messages = [{\"role\": \"user\",\"content\": \"\"\"Write a poem about history?\"\"\"}]\n",
+    "response=completion(\n",
+    "            model=\"clarifai/anthropic.completion.claude-2_1\",\n",
+    "            messages=messages,\n",
+    "        )\n",
+    "\n",
+    "print(f\"Claude-2.1 response : {response}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### OpenAI GPT-4 (Streaming)\n",
+    "Though clarifai doesn't support streaming, still you can call stream and get the response in standard StreamResponse format of liteLLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ModelResponse(id='chatcmpl-40ae19af-3bf0-4eb4-99f2-33aec3ba84af', choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(content=\"In the quiet corners of time's grand hall,\\nLies the tale of rise and fall.\\nFrom ancient ruins to modern sprawl,\\nHistory, the greatest story of them all.\\n\\nEmpires have risen, empires have decayed,\\nThrough the eons, memories have stayed.\\nIn the book of time, history is laid,\\nA tapestry of events, meticulously displayed.\\n\\nThe pyramids of Egypt, standing tall,\\nThe Roman Empire's mighty sprawl.\\nFrom Alexander's conquest, to the Berlin Wall,\\nHistory, a silent witness to it all.\\n\\nIn the shadow of the past we tread,\\nWhere once kings and prophets led.\\nTheir stories in our hearts are spread,\\nEchoes of their words, in our minds are read.\\n\\nBattles fought and victories won,\\nActs of courage under the sun.\\nTales of love, of deeds done,\\nIn history's grand book, they all run.\\n\\nHeroes born, legends made,\\nIn the annals of time, they'll never fade.\\nTheir triumphs and failures all displayed,\\nIn the eternal march of history's parade.\\n\\nThe ink of the past is forever dry,\\nBut its lessons, we cannot deny.\\nIn its stories, truths lie,\\nIn its wisdom, we rely.\\n\\nHistory, a mirror to our past,\\nA guide for the future vast.\\nThrough its lens, we're ever cast,\\nIn the drama of life, forever vast.\", role='assistant', function_call=None, tool_calls=None), logprobs=None)], created=1714744515, model='https://api.clarifai.com/v2/users/openai/apps/chat-completion/models/GPT-4/outputs', object='chat.completion.chunk', system_fingerprint=None)\n",
+      "ModelResponse(id='chatcmpl-40ae19af-3bf0-4eb4-99f2-33aec3ba84af', choices=[StreamingChoices(finish_reason='stop', index=0, delta=Delta(content=None, role=None, function_call=None, tool_calls=None), logprobs=None)], created=1714744515, model='https://api.clarifai.com/v2/users/openai/apps/chat-completion/models/GPT-4/outputs', object='chat.completion.chunk', system_fingerprint=None)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from litellm import completion\n",
+    "\n",
+    "messages = [{\"role\": \"user\",\"content\": \"\"\"Write a poem about history?\"\"\"}]\n",
+    "response = completion(\n",
+    "                model=\"clarifai/openai.chat-completion.GPT-4\",\n",
+    "                messages=messages,\n",
+    "                stream=True,\n",
+    "                api_key = \"c75cc032415e45368be331fdd2c06db0\")\n",
+    "\n",
+    "for chunk in response:\n",
+    "  print(chunk)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/my-website/docs/completion/batching.md

@@ -4,6 +4,12 @@ LiteLLM allows you to:
 * Send 1 completion call to many models: Return Fastest Response
 * Send 1 completion call to many models: Return All Responses
 
+:::info
+
+Trying to do batch completion on LiteLLM Proxy ? Go here: https://docs.litellm.ai/docs/proxy/user_keys#beta-batch-completions---pass-model-as-list
+
+:::
+
 ## Send multiple completion calls to 1 model
 
 In the batch_completion method, you provide a list of `messages` where each sub-list of messages is passed to `litellm.completion()`, allowing you to process multiple prompts efficiently in a single API call.

docs/my-website/docs/completion/input.md

@@ -37,11 +37,12 @@ print(response) # ["max_tokens", "tools", "tool_choice", "stream"]
 
 This is a list of openai params we translate across providers.
 
-This list is constantly being updated.
+Use `litellm.get_supported_openai_params()` for an updated list of params for each model + provider 
 
 | Provider | temperature | max_tokens | top_p | stream | stop | n | presence_penalty | frequency_penalty | functions | function_call | logit_bias | user | response_format | seed | tools | tool_choice | logprobs | top_logprobs | extra_headers | 
 |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|--|
-|Anthropic| ✅ | ✅ | ✅ | ✅ | ✅ |  |  |   |  |   |
+|Anthropic| ✅ | ✅ | ✅ | ✅ | ✅ |  |  |   |  |  |  |  |  |  | ✅ | ✅ | 
+|Anthropic| ✅ | ✅ | ✅ | ✅ | ✅ |  |  |   |  |  |  |  | ✅ | ✅ | ✅ | ✅ | 
 |OpenAI| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |✅ | ✅ | ✅ | ✅ |✅ | ✅ | ✅ | ✅ | ✅ |
 |Azure OpenAI| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |✅ | ✅ | ✅ | ✅ |✅ | ✅ |  |  | ✅ |
 |Replicate | ✅ | ✅ | ✅ | ✅ | ✅ | |  |   |  |   |

docs/my-website/docs/exception_mapping.md

@@ -106,11 +106,12 @@ To see how it's implemented - [check out the code](https://github.com/BerriAI/li
 
 ## Custom mapping list
 
-Base case - we return the original exception.
+Base case - we return `litellm.APIConnectionError` exception (inherits from openai's APIConnectionError exception).
 
 | custom_llm_provider        | Timeout | ContextWindowExceededError | BadRequestError | NotFoundError | ContentPolicyViolationError | AuthenticationError | APIError | RateLimitError | ServiceUnavailableError | PermissionDeniedError | UnprocessableEntityError |
 |----------------------------|---------|----------------------------|------------------|---------------|-----------------------------|---------------------|----------|----------------|-------------------------|-----------------------|-------------------------|
 | openai                     | ✓       | ✓                          | ✓                |               | ✓                           | ✓                   |          |                |                         |                       |                           |
+| watsonx                     |       | | | | | | |✓| | | |
 | text-completion-openai     | ✓       | ✓                          | ✓                |               | ✓                           | ✓                   |          |                |                         |                       |                           |
 | custom_openai              | ✓       | ✓                          | ✓                |               | ✓                           | ✓                   |          |                |                         |                       |                           |
 | openai_compatible_providers| ✓       | ✓                          | ✓                |               | ✓                           | ✓                   |          |                |                         |                       |                           |

docs/my-website/docs/observability/langfuse_integration.md

@@ -137,6 +137,7 @@ response = completion(
       "existing_trace_id": "trace-id22",
       "trace_metadata": {"key": "updated_trace_value"},            # The new value to use for the langfuse Trace Metadata
       "update_trace_keys": ["input", "output", "trace_metadata"],  # Updates the trace input & output to be this generations input & output also updates the Trace Metadata to match the passed in value
+      "debug_langfuse": True,                                      # Will log the exact metadata sent to litellm for the trace/generation as `metadata_passed_to_litellm` 
   },
 )
 
@@ -214,8 +215,20 @@ chat(messages)
 
 ## Redacting Messages, Response Content from Langfuse Logging 
 
+### Redact Messages and Responses from all Langfuse Logging
+
 Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to langfuse, but request metadata will still be logged.
 
+### Redact Messages and Responses from specific Langfuse Logging
+
+In the metadata typically passed for text completion or embedding calls you can set specific keys to mask the messages and responses for this call.
+
+Setting `mask_input` to `True` will mask the input from being logged for this call 
+
+Setting `mask_output` to `True` will make the output from being logged for this call.
+
+Be aware that if you are continuing an existing trace, and you set `update_trace_keys` to include either `input` or `output` and you set the corresponding `mask_input` or `mask_output`, then that trace will have its existing input and/or output replaced with a redacted message.
+
 ## Troubleshooting & Errors
 ### Data not getting logged to Langfuse ? 
 - Ensure you're on the latest version of langfuse `pip install langfuse -U`. The latest version allows litellm to log JSON input/outputs to langfuse

docs/my-website/docs/providers/clarifai.md

@@ -0,0 +1,177 @@
+
+# Clarifai
+Anthropic, OpenAI, Mistral, Llama and Gemini LLMs are Supported on Clarifai. 
+
+## Pre-Requisites
+
+`pip install clarifai`
+
+`pip install litellm`
+
+## Required Environment Variables
+To obtain your Clarifai Personal access token follow this [link](https://docs.clarifai.com/clarifai-basics/authentication/personal-access-tokens/). Optionally the PAT can also be passed in `completion` function.
+
+```python
+os.environ["CALRIFAI_API_KEY"] = "YOUR_CLARIFAI_PAT"  # CLARIFAI_PAT
+```
+
+## Usage
+
+```python
+import os
+from litellm import completion
+
+os.environ["CLARIFAI_API_KEY"] = ""
+
+response = completion(
+  model="clarifai/mistralai.completion.mistral-large",
+  messages=[{ "content": "Tell me a joke about physics?","role": "user"}]
+)
+```
+
+**Output**
+```json
+{
+    "id": "chatcmpl-572701ee-9ab2-411c-ac75-46c1ba18e781",
+    "choices": [
+      {
+        "finish_reason": "stop",
+        "index": 1,
+        "message": {
+          "content": "Sure, here's a physics joke for you:\n\nWhy can't you trust an atom?\n\nBecause they make up everything!",
+          "role": "assistant"
+        }
+      }
+    ],
+    "created": 1714410197,
+    "model": "https://api.clarifai.com/v2/users/mistralai/apps/completion/models/mistral-large/outputs",
+    "object": "chat.completion",
+    "system_fingerprint": null,
+    "usage": {
+      "prompt_tokens": 14,
+      "completion_tokens": 24,
+      "total_tokens": 38
+    }
+  }
+```
+
+## Clarifai models
+liteLLM supports non-streaming requests to all models on [Clarifai community](https://clarifai.com/explore/models?filterData=%5B%7B%22field%22%3A%22use_cases%22%2C%22value%22%3A%5B%22llm%22%5D%7D%5D&page=1&perPage=24)
+
+Example  Usage - Note: liteLLM supports all models deployed on Clarifai
+
+## Llama LLMs
+| Model Name                        | Function Call |
+---------------------------|---------------------------------|
+| clarifai/meta.Llama-2.llama2-7b-chat    | `completion('clarifai/meta.Llama-2.llama2-7b-chat', messages)`
+| clarifai/meta.Llama-2.llama2-13b-chat   | `completion('clarifai/meta.Llama-2.llama2-13b-chat', messages)`
+| clarifai/meta.Llama-2.llama2-70b-chat   | `completion('clarifai/meta.Llama-2.llama2-70b-chat', messages)` |
+| clarifai/meta.Llama-2.codeLlama-70b-Python   | `completion('clarifai/meta.Llama-2.codeLlama-70b-Python', messages)`| 
+| clarifai/meta.Llama-2.codeLlama-70b-Instruct | `completion('clarifai/meta.Llama-2.codeLlama-70b-Instruct', messages)` |   
+
+## Mistal LLMs
+| Model Name                                  | Function Call                                                         |
+|---------------------------------------------|------------------------------------------------------------------------|
+| clarifai/mistralai.completion.mixtral-8x22B            | `completion('clarifai/mistralai.completion.mixtral-8x22B', messages)`               |
+| clarifai/mistralai.completion.mistral-large           | `completion('clarifai/mistralai.completion.mistral-large', messages)`              |
+| clarifai/mistralai.completion.mistral-medium          | `completion('clarifai/mistralai.completion.mistral-medium', messages)`             |
+| clarifai/mistralai.completion.mistral-small           | `completion('clarifai/mistralai.completion.mistral-small', messages)`              |
+| clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1 | `completion('clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1', messages)`
+| clarifai/mistralai.completion.mistral-7B-OpenOrca  | `completion('clarifai/mistralai.completion.mistral-7B-OpenOrca', messages)`          |
+| clarifai/mistralai.completion.openHermes-2-mistral-7B | `completion('clarifai/mistralai.completion.openHermes-2-mistral-7B', messages)`      |
+
+
+## Jurassic LLMs 
+| Model Name                                    | Function Call                                                      |
+|-----------------------------------------------|---------------------------------------------------------------------|
+| clarifai/ai21.complete.Jurassic2-Grande       | `completion('clarifai/ai21.complete.Jurassic2-Grande', messages)`       |
+| clarifai/ai21.complete.Jurassic2-Grande-Instruct | `completion('clarifai/ai21.complete.Jurassic2-Grande-Instruct', messages)` |
+| clarifai/ai21.complete.Jurassic2-Jumbo-Instruct  | `completion('clarifai/ai21.complete.Jurassic2-Jumbo-Instruct', messages)`  |
+| clarifai/ai21.complete.Jurassic2-Jumbo         | `completion('clarifai/ai21.complete.Jurassic2-Jumbo', messages)`          |
+| clarifai/ai21.complete.Jurassic2-Large         | `completion('clarifai/ai21.complete.Jurassic2-Large', messages)`          |
+
+## Wizard LLMs
+
+| Model Name                                    | Function Call                                                      |
+|-----------------------------------------------|---------------------------------------------------------------------|
+| clarifai/wizardlm.generate.wizardCoder-Python-34B | `completion('clarifai/wizardlm.generate.wizardCoder-Python-34B', messages)`    |
+| clarifai/wizardlm.generate.wizardLM-70B          | `completion('clarifai/wizardlm.generate.wizardLM-70B', messages)`             | 
+| clarifai/wizardlm.generate.wizardLM-13B          | `completion('clarifai/wizardlm.generate.wizardLM-13B', messages)`           |
+| clarifai/wizardlm.generate.wizardCoder-15B       | `completion('clarifai/wizardlm.generate.wizardCoder-15B', messages)`          |
+
+## Anthropic models
+
+| Model Name                                    | Function Call                                                      |
+|-----------------------------------------------|---------------------------------------------------------------------|
+| clarifai/anthropic.completion.claude-v1       | `completion('clarifai/anthropic.completion.claude-v1', messages)`       |
+| clarifai/anthropic.completion.claude-instant-1_2 | `completion('clarifai/anthropic.completion.claude-instant-1_2', messages)` |
+| clarifai/anthropic.completion.claude-instant  | `completion('clarifai/anthropic.completion.claude-instant', messages)`  |
+| clarifai/anthropic.completion.claude-v2       | `completion('clarifai/anthropic.completion.claude-v2', messages)`       |
+| clarifai/anthropic.completion.claude-2_1      | `completion('clarifai/anthropic.completion.claude-2_1', messages)`      |
+| clarifai/anthropic.completion.claude-3-opus   | `completion('clarifai/anthropic.completion.claude-3-opus', messages)`   |
+| clarifai/anthropic.completion.claude-3-sonnet | `completion('clarifai/anthropic.completion.claude-3-sonnet', messages)` |
+
+## OpenAI GPT LLMs
+
+| Model Name                                    | Function Call                                                      |
+|-----------------------------------------------|---------------------------------------------------------------------|
+| clarifai/openai.chat-completion.GPT-4         | `completion('clarifai/openai.chat-completion.GPT-4', messages)`          |
+| clarifai/openai.chat-completion.GPT-3_5-turbo | `completion('clarifai/openai.chat-completion.GPT-3_5-turbo', messages)`  |
+| clarifai/openai.chat-completion.gpt-4-turbo   | `completion('clarifai/openai.chat-completion.gpt-4-turbo', messages)`    |
+| clarifai/openai.completion.gpt-3_5-turbo-instruct | `completion('clarifai/openai.completion.gpt-3_5-turbo-instruct', messages)` |
+
+## GCP LLMs
+
+| Model Name                                    | Function Call                                                      |
+|-----------------------------------------------|---------------------------------------------------------------------|
+| clarifai/gcp.generate.gemini-1_5-pro         | `completion('clarifai/gcp.generate.gemini-1_5-pro', messages)`          |
+| clarifai/gcp.generate.imagen-2               | `completion('clarifai/gcp.generate.imagen-2', messages)`                |
+| clarifai/gcp.generate.code-gecko             | `completion('clarifai/gcp.generate.code-gecko', messages)`              |
+| clarifai/gcp.generate.code-bison             | `completion('clarifai/gcp.generate.code-bison', messages)`              |
+| clarifai/gcp.generate.text-bison            | `completion('clarifai/gcp.generate.text-bison', messages)`               |
+| clarifai/gcp.generate.gemma-2b-it            | `completion('clarifai/gcp.generate.gemma-2b-it', messages)`              |
+| clarifai/gcp.generate.gemma-7b-it            | `completion('clarifai/gcp.generate.gemma-7b-it', messages)`              |
+| clarifai/gcp.generate.gemini-pro            | `completion('clarifai/gcp.generate.gemini-pro', messages)`               |
+| clarifai/gcp.generate.gemma-1_1-7b-it       | `completion('clarifai/gcp.generate.gemma-1_1-7b-it', messages)`          |
+
+## Cohere LLMs
+| Model Name                                    | Function Call                                                      |
+|-----------------------------------------------|---------------------------------------------------------------------|
+| clarifai/cohere.generate.cohere-generate-command | `completion('clarifai/cohere.generate.cohere-generate-command', messages)` |
+ clarifai/cohere.generate.command-r-plus' | `completion('clarifai/clarifai/cohere.generate.command-r-plus', messages)`|
+
+## Databricks LLMs
+
+| Model Name                                        | Function Call                                                      |
+|---------------------------------------------------|---------------------------------------------------------------------|
+| clarifai/databricks.drbx.dbrx-instruct           | `completion('clarifai/databricks.drbx.dbrx-instruct', messages)`   |
+| clarifai/databricks.Dolly-v2.dolly-v2-12b        | `completion('clarifai/databricks.Dolly-v2.dolly-v2-12b', messages)`|
+
+## Microsoft LLMs
+
+| Model Name                                        | Function Call                                                      |
+|---------------------------------------------------|---------------------------------------------------------------------|
+| clarifai/microsoft.text-generation.phi-2          | `completion('clarifai/microsoft.text-generation.phi-2', messages)`  |
+| clarifai/microsoft.text-generation.phi-1_5        | `completion('clarifai/microsoft.text-generation.phi-1_5', messages)`|
+
+## Salesforce models
+
+| Model Name                                                | Function Call                                                                |
+|-----------------------------------------------------------|-------------------------------------------------------------------------------|
+| clarifai/salesforce.blip.general-english-image-caption-blip-2 | `completion('clarifai/salesforce.blip.general-english-image-caption-blip-2', messages)` |
+| clarifai/salesforce.xgen.xgen-7b-8k-instruct             | `completion('clarifai/salesforce.xgen.xgen-7b-8k-instruct', messages)`         |
+
+
+## Other Top performing LLMs
+
+| Model Name                                        | Function Call                                                      |
+|---------------------------------------------------|---------------------------------------------------------------------|
+| clarifai/deci.decilm.deciLM-7B-instruct          | `completion('clarifai/deci.decilm.deciLM-7B-instruct', messages)`  |
+| clarifai/upstage.solar.solar-10_7b-instruct      | `completion('clarifai/upstage.solar.solar-10_7b-instruct', messages)` |
+| clarifai/openchat.openchat.openchat-3_5-1210     | `completion('clarifai/openchat.openchat.openchat-3_5-1210', messages)` |
+| clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B | `completion('clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B', messages)` |
+| clarifai/fblgit.una-cybertron.una-cybertron-7b-v2 | `completion('clarifai/fblgit.una-cybertron.una-cybertron-7b-v2', messages)` |
+| clarifai/tiiuae.falcon.falcon-40b-instruct       | `completion('clarifai/tiiuae.falcon.falcon-40b-instruct', messages)` |
+| clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat | `completion('clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat', messages)` |
+| clarifai/bigcode.code.StarCoder                  | `completion('clarifai/bigcode.code.StarCoder', messages)`           |
+| clarifai/mosaicml.mpt.mpt-7b-instruct            | `completion('clarifai/mosaicml.mpt.mpt-7b-instruct', messages)`     |

docs/my-website/docs/providers/openai.md

@@ -20,7 +20,7 @@ os.environ["OPENAI_API_KEY"] = "your-api-key"
 
 # openai call
 response = completion(
-    model = "gpt-3.5-turbo", 
+    model = "gpt-4o", 
     messages=[{ "content": "Hello, how are you?","role": "user"}]
 )
 ```
@@ -163,6 +163,8 @@ os.environ["OPENAI_API_BASE"] = "openaiai-api-base"     # OPTIONAL
 
 | Model Name            | Function Call                                                   |
 |-----------------------|-----------------------------------------------------------------|
+| gpt-4o   | `response = completion(model="gpt-4o", messages=messages)` |
+| gpt-4o-2024-05-13   | `response = completion(model="gpt-4o-2024-05-13", messages=messages)` |
 | gpt-4-turbo   | `response = completion(model="gpt-4-turbo", messages=messages)` |
 | gpt-4-turbo-preview   | `response = completion(model="gpt-4-0125-preview", messages=messages)` |
 | gpt-4-0125-preview    | `response = completion(model="gpt-4-0125-preview", messages=messages)` |

docs/my-website/docs/proxy/alerting.md

@@ -1,14 +1,22 @@
 # 🚨 Alerting 
 
 Get alerts for:
+
 - Hanging LLM api calls
 - Failed LLM api calls
 - Slow LLM api calls
 - Budget Tracking per key/user:
     - When a User/Key crosses their Budget 
     - When a User/Key is 15% away from crossing their Budget
+- Spend Reports - Weekly & Monthly spend per Team, Tag
 - Failed db read/writes
 
+As a bonus, you can also get "daily reports" posted to your slack channel.
+These reports contain key metrics like:
+
+- Top 5 deployments with most failed requests
+- Top 5 slowest deployments
+
 ## Quick Start
 
 Set up a slack alert channel to receive alerts from proxy.
@@ -20,7 +28,8 @@ Get a slack webhook url from https://api.slack.com/messaging/webhooks
 
 ### Step 2: Update config.yaml 
 
-Let's save a bad key to our proxy
+- Set `SLACK_WEBHOOK_URL` in your proxy env to enable Slack alerts.
+- Just for testing purposes, let's save a bad key to our proxy.
 
 ```yaml
 model_list: 
@@ -33,13 +42,11 @@ general_settings:
     alerting: ["slack"]
     alerting_threshold: 300 # sends alerts if requests hang for 5min+ and responses take 5min+ 
 
+environment_variables:
+    SLACK_WEBHOOK_URL: "https://hooks.slack.com/services/<>/<>/<>"
+    SLACK_DAILY_REPORT_FREQUENCY: "86400"  # 24 hours; Optional: defaults to 12 hours
 ```
 
-Set `SLACK_WEBHOOK_URL` in your proxy env
-
-```shell
-SLACK_WEBHOOK_URL: "https://hooks.slack.com/services/<>/<>/<>"
-```
 
 ### Step 3: Start proxy
 

docs/my-website/docs/proxy/cost_tracking.md

@@ -1,8 +1,136 @@
-# Cost Tracking - Azure
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# 💸 Spend Tracking
+
+Track spend for keys, users, and teams across 100+ LLMs.
+
+## Getting Spend Reports - To Charge Other Teams, API Keys
+
+Use the `/global/spend/report` endpoint to get daily spend per team, with a breakdown of spend per API Key, Model
+
+### Example Request
+
+```shell
+curl -X GET 'http://localhost:4000/global/spend/report?start_date=2023-04-01&end_date=2024-06-30' \
+  -H 'Authorization: Bearer sk-1234'
+```
+
+### Example Response
+<Tabs>
+
+<TabItem value="response" label="Expected Response">
+
+```shell
+[
+    {
+        "group_by_day": "2024-04-30T00:00:00+00:00",
+        "teams": [
+            {
+                "team_name": "Prod Team",
+                "total_spend": 0.0015265,
+                "metadata": [ # see the spend by unique(key + model)
+                    {
+                        "model": "gpt-4",
+                        "spend": 0.00123,
+                        "total_tokens": 28,
+                        "api_key": "88dc28.." # the hashed api key
+                    },
+                    {
+                        "model": "gpt-4",
+                        "spend": 0.00123,
+                        "total_tokens": 28,
+                        "api_key": "a73dc2.." # the hashed api key
+                    },
+                    {
+                        "model": "chatgpt-v-2",
+                        "spend": 0.000214,
+                        "total_tokens": 122,
+                        "api_key": "898c28.." # the hashed api key
+                    },
+                    {
+                        "model": "gpt-3.5-turbo",
+                        "spend": 0.0000825,
+                        "total_tokens": 85,
+                        "api_key": "84dc28.." # the hashed api key
+                    }
+                ]
+            }
+        ]
+    }
+]
+```
+
+
+</TabItem>
+
+<TabItem value="py-script" label="Script to Parse Response (Python)">
+
+```python
+import requests
+url = 'http://localhost:4000/global/spend/report'
+params = {
+    'start_date': '2023-04-01',
+    'end_date': '2024-06-30'
+}
+
+headers = {
+    'Authorization': 'Bearer sk-1234'
+}
+
+# Make the GET request
+response = requests.get(url, headers=headers, params=params)
+spend_report = response.json()
+
+for row in spend_report:
+  date = row["group_by_day"]
+  teams = row["teams"]
+  for team in teams:
+      team_name = team["team_name"]
+      total_spend = team["total_spend"]
+      metadata = team["metadata"]
+
+      print(f"Date: {date}")
+      print(f"Team: {team_name}")
+      print(f"Total Spend: {total_spend}")
+      print("Metadata: ", metadata)
+      print()
+```
+
+Output from script
+```shell
+# Date: 2024-05-11T00:00:00+00:00
+# Team: local_test_team
+# Total Spend: 0.003675099999999999
+# Metadata:  [{'model': 'gpt-3.5-turbo', 'spend': 0.003675099999999999, 'api_key': 'b94d5e0bc3a71a573917fe1335dc0c14728c7016337451af9714924ff3a729db', 'total_tokens': 3105}]
+
+# Date: 2024-05-13T00:00:00+00:00
+# Team: Unassigned Team
+# Total Spend: 3.4e-05
+# Metadata:  [{'model': 'gpt-3.5-turbo', 'spend': 3.4e-05, 'api_key': '9569d13c9777dba68096dea49b0b03e0aaf4d2b65d4030eda9e8a2733c3cd6e0', 'total_tokens': 50}]
+
+# Date: 2024-05-13T00:00:00+00:00
+# Team: central
+# Total Spend: 0.000684
+# Metadata:  [{'model': 'gpt-3.5-turbo', 'spend': 0.000684, 'api_key': '0323facdf3af551594017b9ef162434a9b9a8ca1bbd9ccbd9d6ce173b1015605', 'total_tokens': 498}]
+
+# Date: 2024-05-13T00:00:00+00:00
+# Team: local_test_team
+# Total Spend: 0.0005715000000000001
+# Metadata:  [{'model': 'gpt-3.5-turbo', 'spend': 0.0005715000000000001, 'api_key': 'b94d5e0bc3a71a573917fe1335dc0c14728c7016337451af9714924ff3a729db', 'total_tokens': 423}]
+```
+
+
+</TabItem>
+
+</Tabs>
+
+
+## Spend Tracking for Azure
 
 Set base model for cost tracking azure image-gen call
 
-## Image Generation 
+### Image Generation 
 
 ```yaml
 model_list: 
@@ -17,7 +145,7 @@ model_list:
         mode: image_generation
 ```
 
-## Chat Completions / Embeddings
+### Chat Completions / Embeddings
 
 **Problem**: Azure returns `gpt-4` in the response when `azure/gpt-4-1106-preview` is used. This leads to inaccurate cost tracking
 

docs/my-website/docs/proxy/logging.md

@@ -3,7 +3,7 @@ import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
 
-# 🔎 Logging - Custom Callbacks, DataDog, Langfuse, s3 Bucket, Sentry, OpenTelemetry, Athina
+# 🔎 Logging - Custom Callbacks, DataDog, Langfuse, s3 Bucket, Sentry, OpenTelemetry, Athina, Azure Content-Safety
 
 Log Proxy Input, Output, Exceptions using Custom Callbacks, Langfuse, OpenTelemetry, LangFuse, DynamoDB, s3 Bucket
 
@@ -17,6 +17,7 @@ Log Proxy Input, Output, Exceptions using Custom Callbacks, Langfuse, OpenTeleme
 - [Logging to Sentry](#logging-proxy-inputoutput---sentry)
 - [Logging to Traceloop (OpenTelemetry)](#logging-proxy-inputoutput-traceloop-opentelemetry)
 - [Logging to Athina](#logging-proxy-inputoutput-athina)
+- [(BETA) Moderation with Azure Content-Safety](#moderation-with-azure-content-safety)
 
 ## Custom Callback Class [Async]
 Use this when you want to run custom callbacks in `python`
@@ -1037,3 +1038,86 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
     ]
     }'
 ```
+
+## (BETA) Moderation with Azure Content Safety
+
+[Azure Content-Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety) is a Microsoft Azure service that provides content moderation APIs to detect potential offensive, harmful, or risky content in text.
+
+We will use the `--config` to set `litellm.success_callback = ["azure_content_safety"]` this will moderate all LLM calls using Azure Content Safety.
+
+**Step 0** Deploy Azure Content Safety
+
+Deploy an Azure Content-Safety instance from the Azure Portal and get the `endpoint` and `key`.
+
+**Step 1** Set Athina API key
+
+```shell
+AZURE_CONTENT_SAFETY_KEY = "<your-azure-content-safety-key>"
+```
+
+**Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback`
+```yaml
+model_list:
+  - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: gpt-3.5-turbo
+litellm_settings:
+  callbacks: ["azure_content_safety"]
+  azure_content_safety_params:
+    endpoint: "<your-azure-content-safety-endpoint>"
+    key: "os.environ/AZURE_CONTENT_SAFETY_KEY"
+```
+
+**Step 3**: Start the proxy, make a test request
+
+Start proxy
+```shell
+litellm --config config.yaml --debug
+```
+
+Test Request
+```
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+    --header 'Content-Type: application/json' \
+    --data ' {
+        "model": "gpt-3.5-turbo",
+        "messages": [
+            {
+                "role": "user",
+                "content": "Hi, how are you?"
+            }
+        ]
+    }'
+```
+
+An HTTP 400 error will be returned if the content is detected with a value greater than the threshold set in the `config.yaml`.
+The details of the response will describe :
+- The `source` : input text or llm generated text
+- The `category` : the category of the content that triggered the moderation
+- The `severity` : the severity from 0 to 10
+
+**Step 4**: Customizing Azure Content Safety Thresholds
+
+You can customize the thresholds for each category by setting the `thresholds` in the `config.yaml`
+
+```yaml
+model_list:
+  - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: gpt-3.5-turbo
+litellm_settings:
+  callbacks: ["azure_content_safety"]
+  azure_content_safety_params:
+    endpoint: "<your-azure-content-safety-endpoint>"
+    key: "os.environ/AZURE_CONTENT_SAFETY_KEY"
+    thresholds:
+      Hate: 6
+      SelfHarm: 8
+      Sexual: 6
+      Violence: 4
+```
+
+:::info
+`thresholds` are not required by default, but you can tune the values to your needs.
+Default values is `4` for all categories
+:::

docs/my-website/docs/proxy/reliability.md

@@ -151,7 +151,7 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
      }'
 ```
 
-## Advanced - Context Window Fallbacks 
+## Advanced - Context Window Fallbacks (Pre-Call Checks + Fallbacks)
 
 **Before call is made** check if a call is within model context window with  **`enable_pre_call_checks: true`**.
 
@@ -232,16 +232,16 @@ model_list:
 	- model_name: gpt-3.5-turbo-small
 	  litellm_params:
 		model: azure/chatgpt-v-2
-		api_base: os.environ/AZURE_API_BASE
+      api_base: os.environ/AZURE_API_BASE
-		api_key: os.environ/AZURE_API_KEY
+      api_key: os.environ/AZURE_API_KEY
-		api_version: "2023-07-01-preview"
+      api_version: "2023-07-01-preview"
-	  model_info:
+      model_info:
-		base_model: azure/gpt-4-1106-preview # 2. 👈 (azure-only) SET BASE MODEL
+      base_model: azure/gpt-4-1106-preview # 2. 👈 (azure-only) SET BASE MODEL
 	
 	- model_name: gpt-3.5-turbo-large
 	  litellm_params:
-		model: gpt-3.5-turbo-1106
+      model: gpt-3.5-turbo-1106
-		api_key: os.environ/OPENAI_API_KEY
+      api_key: os.environ/OPENAI_API_KEY
 
   - model_name: claude-opus
     litellm_params:
@@ -287,6 +287,69 @@ print(response)
 </Tabs>
 
 
+## Advanced - EU-Region Filtering (Pre-Call Checks)
+
+**Before call is made** check if a call is within model context window with  **`enable_pre_call_checks: true`**.
+
+Set 'region_name' of deployment. 
+
+**Note:** LiteLLM can automatically infer region_name for Vertex AI, Bedrock, and IBM WatsonxAI based on your litellm params. For Azure, set `litellm.enable_preview = True`.
+
+**1. Set Config**
+
+```yaml
+router_settings:
+	enable_pre_call_checks: true # 1. Enable pre-call checks
+
+model_list:
+- model_name: gpt-3.5-turbo
+  litellm_params:
+    model: azure/chatgpt-v-2
+    api_base: os.environ/AZURE_API_BASE
+    api_key: os.environ/AZURE_API_KEY
+    api_version: "2023-07-01-preview"
+    region_name: "eu" # 👈 SET EU-REGION
+
+- model_name: gpt-3.5-turbo
+  litellm_params:
+    model: gpt-3.5-turbo-1106
+    api_key: os.environ/OPENAI_API_KEY
+
+- model_name: gemini-pro
+  litellm_params:
+    model: vertex_ai/gemini-pro-1.5
+    vertex_project: adroit-crow-1234
+    vertex_location: us-east1 # 👈 AUTOMATICALLY INFERS 'region_name'
+```
+
+**2. Start proxy**
+
+```bash
+litellm --config /path/to/config.yaml
+
+# RUNNING on http://0.0.0.0:4000
+```
+
+**3. Test it!**
+
+```python
+import openai
+client = openai.OpenAI(
+    api_key="anything",
+    base_url="http://0.0.0.0:4000"
+)
+
+# request sent to model set on litellm proxy, `litellm --model`
+response = client.chat.completions.with_raw_response.create(
+    model="gpt-3.5-turbo",
+    messages = [{"role": "user", "content": "Who was Alexander?"}]
+)
+
+print(response)
+
+print(f"response.headers.get('x-litellm-model-api-base')")
+```
+
 ## Advanced - Custom Timeouts, Stream Timeouts - Per Model
 For each model you can set `timeout` & `stream_timeout` under `litellm_params`
 ```yaml

docs/my-website/docs/proxy/token_auth.md

@@ -110,7 +110,7 @@ general_settings:
     admin_jwt_scope: "litellm-proxy-admin"
 ```
 
-## Advanced - Spend Tracking (User / Team / Org)
+## Advanced - Spend Tracking (End-Users / Internal Users / Team / Org)
 
 Set the field in the jwt token, which corresponds to a litellm user / team / org.
 
@@ -123,6 +123,7 @@ general_settings:
     team_id_jwt_field: "client_id" # 👈 CAN BE ANY FIELD
     user_id_jwt_field: "sub" # 👈 CAN BE ANY FIELD
     org_id_jwt_field: "org_id" # 👈 CAN BE ANY FIELD
+    end_user_id_jwt_field: "customer_id" # 👈 CAN BE ANY FIELD
 ```
 
 Expected JWT: 
@@ -131,7 +132,7 @@ Expected JWT:
 {
   "client_id": "my-unique-team",
   "sub": "my-unique-user",
-  "org_id": "my-unique-org"
+  "org_id": "my-unique-org",
 }
 ```
 

docs/my-website/docs/proxy/user_keys.md

@@ -365,6 +365,188 @@ curl --location 'http://0.0.0.0:4000/moderations' \
 
 ## Advanced
 
+### (BETA) Batch Completions - pass multiple models
+
+Use this when you want to send 1 request to N Models
+
+#### Expected Request Format
+
+Pass model as a string of comma separated value of models. Example `"model"="llama3,gpt-3.5-turbo"`
+
+This same request will be sent to the following model groups on the [litellm proxy config.yaml](https://docs.litellm.ai/docs/proxy/configs)
+- `model_name="llama3"`
+- `model_name="gpt-3.5-turbo"` 
+
+<Tabs>
+
+<TabItem value="openai-py" label="OpenAI Python SDK">
+
+
+```python
+import openai
+
+client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
+
+response = client.chat.completions.create(
+    model="gpt-3.5-turbo,llama3",
+    messages=[
+        {"role": "user", "content": "this is a test request, write a short poem"}
+    ],
+)
+
+print(response)
+```
+
+
+
+#### Expected Response Format
+
+Get a list of responses when `model` is passed as a list
+
+```python
+[
+    ChatCompletion(
+        id='chatcmpl-9NoYhS2G0fswot0b6QpoQgmRQMaIf',
+        choices=[
+            Choice(
+                finish_reason='stop',
+                index=0,
+                logprobs=None,
+                message=ChatCompletionMessage(
+                    content='In the depths of my soul, a spark ignites\nA light that shines so pure and bright\nIt dances and leaps, refusing to die\nA flame of hope that reaches the sky\n\nIt warms my heart and fills me with bliss\nA reminder that in darkness, there is light to kiss\nSo I hold onto this fire, this guiding light\nAnd let it lead me through the darkest night.',
+                    role='assistant',
+                    function_call=None,
+                    tool_calls=None
+                )
+            )
+        ],
+        created=1715462919,
+        model='gpt-3.5-turbo-0125',
+        object='chat.completion',
+        system_fingerprint=None,
+        usage=CompletionUsage(
+            completion_tokens=83,
+            prompt_tokens=17,
+            total_tokens=100
+        )
+    ),
+    ChatCompletion(
+        id='chatcmpl-4ac3e982-da4e-486d-bddb-ed1d5cb9c03c',
+        choices=[
+            Choice(
+                finish_reason='stop',
+                index=0,
+                logprobs=None,
+                message=ChatCompletionMessage(
+                    content="A test request, and I'm delighted!\nHere's a short poem, just for you:\n\nMoonbeams dance upon the sea,\nA path of light, for you to see.\nThe stars up high, a twinkling show,\nA night of wonder, for all to know.\n\nThe world is quiet, save the night,\nA peaceful hush, a gentle light.\nThe world is full, of beauty rare,\nA treasure trove, beyond compare.\n\nI hope you enjoyed this little test,\nA poem born, of whimsy and jest.\nLet me know, if there's anything else!",
+                    role='assistant',
+                    function_call=None,
+                    tool_calls=None
+                )
+            )
+        ],
+        created=1715462919,
+        model='groq/llama3-8b-8192',
+        object='chat.completion',
+        system_fingerprint='fp_a2c8d063cb',
+        usage=CompletionUsage(
+            completion_tokens=120,
+            prompt_tokens=20,
+            total_tokens=140
+        )
+    )
+]
+```
+
+
+</TabItem>
+
+<TabItem value="curl" label="Curl">
+
+
+
+
+```shell
+curl --location 'http://localhost:4000/chat/completions' \
+    --header 'Authorization: Bearer sk-1234' \
+    --header 'Content-Type: application/json' \
+    --data '{
+    "model": "llama3,gpt-3.5-turbo",
+    "max_tokens": 10,
+    "user": "litellm2",
+    "messages": [
+        {
+        "role": "user",
+        "content": "is litellm getting better"
+        }
+    ]
+}'
+```
+
+
+
+
+#### Expected Response Format
+
+Get a list of responses when `model` is passed as a list
+
+```json
+[
+  {
+    "id": "chatcmpl-3dbd5dd8-7c82-4ca3-bf1f-7c26f497cf2b",
+    "choices": [
+      {
+        "finish_reason": "length",
+        "index": 0,
+        "message": {
+          "content": "The Elder Scrolls IV: Oblivion!\n\nReleased",
+          "role": "assistant"
+        }
+      }
+    ],
+    "created": 1715459876,
+    "model": "groq/llama3-8b-8192",
+    "object": "chat.completion",
+    "system_fingerprint": "fp_179b0f92c9",
+    "usage": {
+      "completion_tokens": 10,
+      "prompt_tokens": 12,
+      "total_tokens": 22
+    }
+  },
+  {
+    "id": "chatcmpl-9NnldUfFLmVquFHSX4yAtjCw8PGei",
+    "choices": [
+      {
+        "finish_reason": "length",
+        "index": 0,
+        "message": {
+          "content": "TES4 could refer to The Elder Scrolls IV:",
+          "role": "assistant"
+        }
+      }
+    ],
+    "created": 1715459877,
+    "model": "gpt-3.5-turbo-0125",
+    "object": "chat.completion",
+    "system_fingerprint": null,
+    "usage": {
+      "completion_tokens": 10,
+      "prompt_tokens": 9,
+      "total_tokens": 19
+    }
+  }
+]
+```
+
+
+</TabItem>
+</Tabs>
+
+
+
+
+
 ### Pass User LLM API Keys, Fallbacks
 Allow your end-users to pass their model list, api base, OpenAI API key (any LiteLLM supported provider) to make requests 
 

docs/my-website/docs/routing.md

@@ -653,7 +653,9 @@ from litellm import Router
 model_list = [{...}]
 
 router = Router(model_list=model_list, 
-                allowed_fails=1) # cooldown model if it fails > 1 call in a minute. 
+                allowed_fails=1,      # cooldown model if it fails > 1 call in a minute. 
+				cooldown_time=100    # cooldown the deployment for 100 seconds if it num_fails > allowed_fails
+		)
 
 user_message = "Hello, whats the weather in San Francisco??"
 messages = [{"content": user_message, "role": "user"}]
@@ -770,6 +772,8 @@ If the error is a context window exceeded error, fall back to a larger model gro
 
 Fallbacks are done in-order - ["gpt-3.5-turbo, "gpt-4", "gpt-4-32k"], will do 'gpt-3.5-turbo' first, then 'gpt-4', etc.
 
+You can also set 'default_fallbacks', in case a specific model group is misconfigured / bad.
+
 ```python
 from litellm import Router
 
@@ -830,6 +834,7 @@ model_list = [
 
 router = Router(model_list=model_list, 
                 fallbacks=[{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}], 
+				default_fallbacks=["gpt-3.5-turbo-16k"],
                 context_window_fallbacks=[{"azure/gpt-3.5-turbo-context-fallback": ["gpt-3.5-turbo-16k"]}, {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}],
                 set_verbose=True)
 
@@ -879,13 +884,11 @@ router = Router(model_list: Optional[list] = None,
 				 cache_responses=True)
 ```
 
-## Pre-Call Checks (Context Window)
+## Pre-Call Checks (Context Window, EU-Regions)
 
 Enable pre-call checks to filter out:
 1. deployments with context window limit < messages for a call.
-2. deployments that have exceeded rate limits when making concurrent calls. (eg. `asyncio.gather(*[
+2. deployments outside of eu-region
-        router.acompletion(model="gpt-3.5-turbo", messages=m) for m in list_of_messages
-    ])`)
 
 <Tabs>
 <TabItem value="sdk" label="SDK">
@@ -900,10 +903,14 @@ router = Router(model_list=model_list, enable_pre_call_checks=True) # 👈 Set t
 
 **2. Set Model List**
 
-For azure deployments, set the base model. Pick the base model from [this list](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json), all the azure models start with `azure/`. 
+For context window checks on azure deployments, set the base model. Pick the base model from [this list](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json), all the azure models start with `azure/`. 
 
-<Tabs>
+For 'eu-region' filtering, Set 'region_name' of deployment. 
-<TabItem value="same-group" label="Same Group">
+
+**Note:** We automatically infer region_name for Vertex AI, Bedrock, and IBM WatsonxAI based on your litellm params. For Azure, set `litellm.enable_preview = True`.
+
+
+[**See Code**](https://github.com/BerriAI/litellm/blob/d33e49411d6503cb634f9652873160cd534dec96/litellm/router.py#L2958)
 
 ```python
 model_list = [
@@ -914,10 +921,9 @@ model_list = [
                     "api_key": os.getenv("AZURE_API_KEY"),
                     "api_version": os.getenv("AZURE_API_VERSION"),
                     "api_base": os.getenv("AZURE_API_BASE"),
-                },
+					"region_name": "eu" # 👈 SET 'EU' REGION NAME
-				"model_info": {
 					"base_model": "azure/gpt-35-turbo", # 👈 (Azure-only) SET BASE MODEL
-				}
+                },
             },
             {
                 "model_name": "gpt-3.5-turbo", # model group name
@@ -926,54 +932,26 @@ model_list = [
                     "api_key": os.getenv("OPENAI_API_KEY"),
                 },
             },
+			{
+				"model_name": "gemini-pro",
+				"litellm_params: {
+					"model": "vertex_ai/gemini-pro-1.5", 
+					"vertex_project": "adroit-crow-1234",
+					"vertex_location": "us-east1" # 👈 AUTOMATICALLY INFERS 'region_name'
+				}
+			}
         ]
 
 router = Router(model_list=model_list, enable_pre_call_checks=True) 
 ```
 
-</TabItem>
-
-<TabItem value="different-group" label="Context Window Fallbacks (Different Groups)">
-
-```python
-model_list = [
-            {
-                "model_name": "gpt-3.5-turbo-small", # model group name
-                "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "azure/chatgpt-v-2",
-                    "api_key": os.getenv("AZURE_API_KEY"),
-                    "api_version": os.getenv("AZURE_API_VERSION"),
-                    "api_base": os.getenv("AZURE_API_BASE"),
-                },
-				"model_info": {
-					"base_model": "azure/gpt-35-turbo", # 👈 (Azure-only) SET BASE MODEL
-				}
-            },
-            {
-                "model_name": "gpt-3.5-turbo-large", # model group name
-                "litellm_params": {  # params for litellm completion/embedding call
-                    "model": "gpt-3.5-turbo-1106",
-                    "api_key": os.getenv("OPENAI_API_KEY"),
-                },
-            },
-            {
-                "model_name": "claude-opus", 
-                "litellm_params": {  call
-                    "model": "claude-3-opus-20240229",
-                    "api_key": os.getenv("ANTHROPIC_API_KEY"),
-                },
-            },
-        ]
-
-router = Router(model_list=model_list, enable_pre_call_checks=True, context_window_fallbacks=[{"gpt-3.5-turbo-small": ["gpt-3.5-turbo-large", "claude-opus"]}]) 
-```
-
-</TabItem>
-
-</Tabs>
 
 **3. Test it!**
 
+
+<Tabs>
+<TabItem value="context-window-check" label="Context Window Check">
+
 ```python
 """
 - Give a gpt-3.5-turbo model group with different context windows (4k vs. 16k)
@@ -983,7 +961,6 @@ router = Router(model_list=model_list, enable_pre_call_checks=True, context_wind
 from litellm import Router
 import os
 
-try:
 model_list = [
 	{
 		"model_name": "gpt-3.5-turbo",  # model group name
@@ -992,6 +969,7 @@ model_list = [
 			"api_key": os.getenv("AZURE_API_KEY"),
 			"api_version": os.getenv("AZURE_API_VERSION"),
 			"api_base": os.getenv("AZURE_API_BASE"),
+			"base_model": "azure/gpt-35-turbo",
 		},
 		"model_info": {
 			"base_model": "azure/gpt-35-turbo", 
@@ -1021,6 +999,59 @@ response = router.completion(
 print(f"response: {response}")
 ```
 </TabItem>
+<TabItem value="eu-region-check" label="EU Region Check">
+
+```python
+"""
+- Give 2 gpt-3.5-turbo deployments, in eu + non-eu regions
+- Make a call
+- Assert it picks the eu-region model
+"""
+
+from litellm import Router
+import os
+
+model_list = [
+	{
+		"model_name": "gpt-3.5-turbo",  # model group name
+		"litellm_params": {  # params for litellm completion/embedding call
+			"model": "azure/chatgpt-v-2",
+			"api_key": os.getenv("AZURE_API_KEY"),
+			"api_version": os.getenv("AZURE_API_VERSION"),
+			"api_base": os.getenv("AZURE_API_BASE"),
+			"region_name": "eu"
+		},
+		"model_info": {
+			"id": "1"
+		}
+	},
+	{
+		"model_name": "gpt-3.5-turbo",  # model group name
+		"litellm_params": {  # params for litellm completion/embedding call
+			"model": "gpt-3.5-turbo-1106",
+			"api_key": os.getenv("OPENAI_API_KEY"),
+		},
+		"model_info": {
+			"id": "2"
+		}
+	},
+]
+
+router = Router(model_list=model_list, enable_pre_call_checks=True) 
+
+response = router.completion(
+	model="gpt-3.5-turbo",
+	messages=[{"role": "user", "content": "Who was Alexander?"}],
+)
+
+print(f"response: {response}")
+
+print(f"response id: {response._hidden_params['model_id']}")
+```
+
+</TabItem>
+</Tabs>
+</TabItem>
 <TabItem value="proxy" label="Proxy">
 
 :::info
@@ -1283,10 +1314,11 @@ def __init__(
 	num_retries: int = 0,
 	timeout: Optional[float] = None,
 	default_litellm_params={},  # default params for Router.chat.completion.create
-	fallbacks: List = [],
+	fallbacks: Optional[List] = None,
+	default_fallbacks: Optional[List] = None
 	allowed_fails: Optional[int] = None, # Number of times a deployment can failbefore being added to cooldown
 	cooldown_time: float = 1,  # (seconds) time to cooldown a deployment after failure
-	context_window_fallbacks: List = [],
+	context_window_fallbacks: Optional[List] = None,
 	model_group_alias: Optional[dict] = {},
 	retry_after: int = 0,  # (min) time to wait before retrying a failed request
 	routing_strategy: Literal[

docs/my-website/sidebars.js

@@ -39,6 +39,7 @@ const sidebars = {
         "proxy/demo",
         "proxy/configs",
         "proxy/reliability",
+        "proxy/cost_tracking",
         "proxy/users",
         "proxy/user_keys",
         "proxy/enterprise",
@@ -52,7 +53,6 @@ const sidebars = {
         "proxy/team_based_routing",
         "proxy/customer_routing",
         "proxy/ui",
-        "proxy/cost_tracking",
         "proxy/token_auth",
         {
           type: "category",

enterprise/enterprise_callbacks/generic_api_callback.py

@@ -10,7 +10,6 @@ from litellm.caching import DualCache
 
 from typing import Literal, Union
 
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 
 
@@ -19,8 +18,6 @@ import traceback
 
 import dotenv, os
 import requests
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 import datetime, subprocess, sys
 import litellm, uuid

enterprise/utils.py

@@ -1,6 +1,7 @@
 # Enterprise Proxy Util Endpoints
 from litellm._logging import verbose_logger
 import collections
+from datetime import datetime
 
 
 async def get_spend_by_tags(start_date=None, end_date=None, prisma_client=None):
@@ -18,26 +19,33 @@ async def get_spend_by_tags(start_date=None, end_date=None, prisma_client=None):
     return response
 
 
-async def ui_get_spend_by_tags(start_date=None, end_date=None, prisma_client=None):
+async def ui_get_spend_by_tags(start_date: str, end_date: str, prisma_client):
-    response = await prisma_client.db.query_raw(
+
-        """
+    sql_query = """
         SELECT
         jsonb_array_elements_text(request_tags) AS individual_request_tag,
         DATE(s."startTime") AS spend_date,
         COUNT(*) AS log_count,
         SUM(spend) AS total_spend
         FROM "LiteLLM_SpendLogs" s
-        WHERE s."startTime" >= current_date - interval '30 days'
+        WHERE
+            DATE(s."startTime") >= $1::date
+            AND DATE(s."startTime") <= $2::date
         GROUP BY individual_request_tag, spend_date
-        ORDER BY spend_date;
+        ORDER BY spend_date
-        """
+        LIMIT 100;
+    """
+    response = await prisma_client.db.query_raw(
+        sql_query,
+        start_date,
+        end_date,
     )
 
     # print("tags - spend")
     # print(response)
     # Bar Chart 1 - Spend per tag - Top 10 tags by spend
-    total_spend_per_tag = collections.defaultdict(float)
+    total_spend_per_tag: collections.defaultdict = collections.defaultdict(float)
-    total_requests_per_tag = collections.defaultdict(int)
+    total_requests_per_tag: collections.defaultdict = collections.defaultdict(int)
     for row in response:
         tag_name = row["individual_request_tag"]
         tag_spend = row["total_spend"]
@@ -49,15 +57,18 @@ async def ui_get_spend_by_tags(start_date=None, end_date=None, prisma_client=Non
     # convert to ui format
     ui_tags = []
     for tag in sorted_tags:
+        current_spend = tag[1]
+        if current_spend is not None and isinstance(current_spend, float):
+            current_spend = round(current_spend, 4)
         ui_tags.append(
             {
                 "name": tag[0],
-                "value": tag[1],
+                "spend": current_spend,
                 "log_count": total_requests_per_tag[tag[0]],
             }
         )
 
-    return {"top_10_tags": ui_tags}
+    return {"spend_per_tag": ui_tags}
 
 
 async def view_spend_logs_from_clickhouse(

litellm/__init__.py

@@ -71,6 +71,7 @@ azure_key: Optional[str] = None
 anthropic_key: Optional[str] = None
 replicate_key: Optional[str] = None
 cohere_key: Optional[str] = None
+clarifai_key: Optional[str] = None
 maritalk_key: Optional[str] = None
 ai21_key: Optional[str] = None
 ollama_key: Optional[str] = None
@@ -101,6 +102,9 @@ blocked_user_list: Optional[Union[str, List]] = None
 banned_keywords_list: Optional[Union[str, List]] = None
 llm_guard_mode: Literal["all", "key-specific", "request-specific"] = "all"
 ##################
+### PREVIEW FEATURES ###
+enable_preview_features: bool = False
+##################
 logging: bool = True
 caching: bool = (
     False  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
@@ -401,6 +405,73 @@ replicate_models: List = [
     "replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad",
 ]
 
+clarifai_models: List = [
+    "clarifai/meta.Llama-3.Llama-3-8B-Instruct",
+    "clarifai/gcp.generate.gemma-1_1-7b-it",
+    "clarifai/mistralai.completion.mixtral-8x22B",
+    "clarifai/cohere.generate.command-r-plus",
+    "clarifai/databricks.drbx.dbrx-instruct",
+    "clarifai/mistralai.completion.mistral-large",
+    "clarifai/mistralai.completion.mistral-medium",
+    "clarifai/mistralai.completion.mistral-small",
+    "clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1",
+    "clarifai/gcp.generate.gemma-2b-it",
+    "clarifai/gcp.generate.gemma-7b-it",
+    "clarifai/deci.decilm.deciLM-7B-instruct",
+    "clarifai/mistralai.completion.mistral-7B-Instruct",
+    "clarifai/gcp.generate.gemini-pro",
+    "clarifai/anthropic.completion.claude-v1",
+    "clarifai/anthropic.completion.claude-instant-1_2",
+    "clarifai/anthropic.completion.claude-instant",
+    "clarifai/anthropic.completion.claude-v2",
+    "clarifai/anthropic.completion.claude-2_1",
+    "clarifai/meta.Llama-2.codeLlama-70b-Python",
+    "clarifai/meta.Llama-2.codeLlama-70b-Instruct",
+    "clarifai/openai.completion.gpt-3_5-turbo-instruct",
+    "clarifai/meta.Llama-2.llama2-7b-chat",
+    "clarifai/meta.Llama-2.llama2-13b-chat",
+    "clarifai/meta.Llama-2.llama2-70b-chat",
+    "clarifai/openai.chat-completion.gpt-4-turbo",
+    "clarifai/microsoft.text-generation.phi-2",
+    "clarifai/meta.Llama-2.llama2-7b-chat-vllm",
+    "clarifai/upstage.solar.solar-10_7b-instruct",
+    "clarifai/openchat.openchat.openchat-3_5-1210",
+    "clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B",
+    "clarifai/gcp.generate.text-bison",
+    "clarifai/meta.Llama-2.llamaGuard-7b",
+    "clarifai/fblgit.una-cybertron.una-cybertron-7b-v2",
+    "clarifai/openai.chat-completion.GPT-4",
+    "clarifai/openai.chat-completion.GPT-3_5-turbo",
+    "clarifai/ai21.complete.Jurassic2-Grande",
+    "clarifai/ai21.complete.Jurassic2-Grande-Instruct",
+    "clarifai/ai21.complete.Jurassic2-Jumbo-Instruct",
+    "clarifai/ai21.complete.Jurassic2-Jumbo",
+    "clarifai/ai21.complete.Jurassic2-Large",
+    "clarifai/cohere.generate.cohere-generate-command",
+    "clarifai/wizardlm.generate.wizardCoder-Python-34B",
+    "clarifai/wizardlm.generate.wizardLM-70B",
+    "clarifai/tiiuae.falcon.falcon-40b-instruct",
+    "clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat",
+    "clarifai/gcp.generate.code-gecko",
+    "clarifai/gcp.generate.code-bison",
+    "clarifai/mistralai.completion.mistral-7B-OpenOrca",
+    "clarifai/mistralai.completion.openHermes-2-mistral-7B",
+    "clarifai/wizardlm.generate.wizardLM-13B",
+    "clarifai/huggingface-research.zephyr.zephyr-7B-alpha",
+    "clarifai/wizardlm.generate.wizardCoder-15B",
+    "clarifai/microsoft.text-generation.phi-1_5",
+    "clarifai/databricks.Dolly-v2.dolly-v2-12b",
+    "clarifai/bigcode.code.StarCoder",
+    "clarifai/salesforce.xgen.xgen-7b-8k-instruct",
+    "clarifai/mosaicml.mpt.mpt-7b-instruct",
+    "clarifai/anthropic.completion.claude-3-opus",
+    "clarifai/anthropic.completion.claude-3-sonnet",
+    "clarifai/gcp.generate.gemini-1_5-pro",
+    "clarifai/gcp.generate.imagen-2",
+    "clarifai/salesforce.blip.general-english-image-caption-blip-2",
+]
+
+
 huggingface_models: List = [
     "meta-llama/Llama-2-7b-hf",
     "meta-llama/Llama-2-7b-chat-hf",
@@ -506,6 +577,7 @@ provider_list: List = [
     "text-completion-openai",
     "cohere",
     "cohere_chat",
+    "clarifai",
     "anthropic",
     "replicate",
     "huggingface",
@@ -656,6 +728,7 @@ from .llms.predibase import PredibaseConfig
 from .llms.anthropic_text import AnthropicTextConfig
 from .llms.replicate import ReplicateConfig
 from .llms.cohere import CohereConfig
+from .llms.clarifai import ClarifaiConfig
 from .llms.ai21 import AI21Config
 from .llms.together_ai import TogetherAIConfig
 from .llms.cloudflare import CloudflareConfig
@@ -670,6 +743,7 @@ from .llms.sagemaker import SagemakerConfig
 from .llms.ollama import OllamaConfig
 from .llms.ollama_chat import OllamaChatConfig
 from .llms.maritalk import MaritTalkConfig
+from .llms.bedrock_httpx import AmazonCohereChatConfig
 from .llms.bedrock import (
     AmazonTitanConfig,
     AmazonAI21Config,
@@ -681,7 +755,7 @@ from .llms.bedrock import (
     AmazonMistralConfig,
     AmazonBedrockGlobalConfig,
 )
-from .llms.openai import OpenAIConfig, OpenAITextCompletionConfig
+from .llms.openai import OpenAIConfig, OpenAITextCompletionConfig, MistralConfig
 from .llms.azure import AzureOpenAIConfig, AzureOpenAIError
 from .llms.watsonx import IBMWatsonXAIConfig
 from .main import *  # type: ignore

litellm/caching.py

@@ -373,11 +373,12 @@ class RedisCache(BaseCache):
                         print_verbose(
                             f"Set ASYNC Redis Cache PIPELINE: key: {cache_key}\nValue {cache_value}\nttl={ttl}"
                         )
+                        json_cache_value = json.dumps(cache_value)
                         # Set the value with a TTL if it's provided.
                         if ttl is not None:
-                            pipe.setex(cache_key, ttl, json.dumps(cache_value))
+                            pipe.setex(cache_key, ttl, json_cache_value)
                         else:
-                            pipe.set(cache_key, json.dumps(cache_value))
+                            pipe.set(cache_key, json_cache_value)
                     # Execute the pipeline and return the results.
                     results = await pipe.execute()
 
@@ -810,9 +811,7 @@ class RedisSemanticCache(BaseCache):
 
         # get the prompt
         messages = kwargs["messages"]
-        prompt = ""
+        prompt = "".join(message["content"] for message in messages)
-        for message in messages:
-            prompt += message["content"]
 
         # create an embedding for prompt
         embedding_response = litellm.embedding(
@@ -847,9 +846,7 @@ class RedisSemanticCache(BaseCache):
 
         # get the messages
         messages = kwargs["messages"]
-        prompt = ""
+        prompt = "".join(message["content"] for message in messages)
-        for message in messages:
-            prompt += message["content"]
 
         # convert to embedding
         embedding_response = litellm.embedding(
@@ -909,9 +906,7 @@ class RedisSemanticCache(BaseCache):
 
         # get the prompt
         messages = kwargs["messages"]
-        prompt = ""
+        prompt = "".join(message["content"] for message in messages)
-        for message in messages:
-            prompt += message["content"]
         # create an embedding for prompt
         router_model_names = (
             [m["model_name"] for m in llm_model_list]
@@ -964,9 +959,7 @@ class RedisSemanticCache(BaseCache):
 
         # get the messages
         messages = kwargs["messages"]
-        prompt = ""
+        prompt = "".join(message["content"] for message in messages)
-        for message in messages:
-            prompt += message["content"]
 
         router_model_names = (
             [m["model_name"] for m in llm_model_list]

litellm/exceptions.py

@@ -9,25 +9,12 @@
 
 ## LiteLLM versions of the OpenAI Exception Types
 
-from openai import (
+import openai
-    AuthenticationError,
-    BadRequestError,
-    NotFoundError,
-    RateLimitError,
-    APIStatusError,
-    OpenAIError,
-    APIError,
-    APITimeoutError,
-    APIConnectionError,
-    APIResponseValidationError,
-    UnprocessableEntityError,
-    PermissionDeniedError,
-)
 import httpx
 from typing import Optional
 
 
-class AuthenticationError(AuthenticationError):  # type: ignore
+class AuthenticationError(openai.AuthenticationError):  # type: ignore
     def __init__(self, message, llm_provider, model, response: httpx.Response):
         self.status_code = 401
         self.message = message
@@ -39,7 +26,7 @@ class AuthenticationError(AuthenticationError):  # type: ignore
 
 
 # raise when invalid models passed, example gpt-8
-class NotFoundError(NotFoundError):  # type: ignore
+class NotFoundError(openai.NotFoundError):  # type: ignore
     def __init__(self, message, model, llm_provider, response: httpx.Response):
         self.status_code = 404
         self.message = message
@@ -50,7 +37,7 @@ class NotFoundError(NotFoundError):  # type: ignore
         )  # Call the base class constructor with the parameters it needs
 
 
-class BadRequestError(BadRequestError):  # type: ignore
+class BadRequestError(openai.BadRequestError):  # type: ignore
     def __init__(
         self, message, model, llm_provider, response: Optional[httpx.Response] = None
     ):
@@ -69,7 +56,7 @@ class BadRequestError(BadRequestError):  # type: ignore
         )  # Call the base class constructor with the parameters it needs
 
 
-class UnprocessableEntityError(UnprocessableEntityError):  # type: ignore
+class UnprocessableEntityError(openai.UnprocessableEntityError):  # type: ignore
     def __init__(self, message, model, llm_provider, response: httpx.Response):
         self.status_code = 422
         self.message = message
@@ -80,7 +67,7 @@ class UnprocessableEntityError(UnprocessableEntityError):  # type: ignore
         )  # Call the base class constructor with the parameters it needs
 
 
-class Timeout(APITimeoutError):  # type: ignore
+class Timeout(openai.APITimeoutError):  # type: ignore
     def __init__(self, message, model, llm_provider):
         request = httpx.Request(method="POST", url="https://api.openai.com/v1")
         super().__init__(
@@ -96,7 +83,7 @@ class Timeout(APITimeoutError):  # type: ignore
         return str(self.message)
 
 
-class PermissionDeniedError(PermissionDeniedError):  # type:ignore
+class PermissionDeniedError(openai.PermissionDeniedError):  # type:ignore
     def __init__(self, message, llm_provider, model, response: httpx.Response):
         self.status_code = 403
         self.message = message
@@ -107,7 +94,7 @@ class PermissionDeniedError(PermissionDeniedError):  # type:ignore
         )  # Call the base class constructor with the parameters it needs
 
 
-class RateLimitError(RateLimitError):  # type: ignore
+class RateLimitError(openai.RateLimitError):  # type: ignore
     def __init__(self, message, llm_provider, model, response: httpx.Response):
         self.status_code = 429
         self.message = message
@@ -148,7 +135,7 @@ class ContentPolicyViolationError(BadRequestError):  # type: ignore
         )  # Call the base class constructor with the parameters it needs
 
 
-class ServiceUnavailableError(APIStatusError):  # type: ignore
+class ServiceUnavailableError(openai.APIStatusError):  # type: ignore
     def __init__(self, message, llm_provider, model, response: httpx.Response):
         self.status_code = 503
         self.message = message
@@ -160,7 +147,7 @@ class ServiceUnavailableError(APIStatusError):  # type: ignore
 
 
 # raise this when the API returns an invalid response object - https://github.com/openai/openai-python/blob/1be14ee34a0f8e42d3f9aa5451aa4cb161f1781f/openai/api_requestor.py#L401
-class APIError(APIError):  # type: ignore
+class APIError(openai.APIError):  # type: ignore
     def __init__(
         self, status_code, message, llm_provider, model, request: httpx.Request
     ):
@@ -172,7 +159,7 @@ class APIError(APIError):  # type: ignore
 
 
 # raised if an invalid request (not get, delete, put, post) is made
-class APIConnectionError(APIConnectionError):  # type: ignore
+class APIConnectionError(openai.APIConnectionError):  # type: ignore
     def __init__(self, message, llm_provider, model, request: httpx.Request):
         self.message = message
         self.llm_provider = llm_provider
@@ -182,7 +169,7 @@ class APIConnectionError(APIConnectionError):  # type: ignore
 
 
 # raised if an invalid request (not get, delete, put, post) is made
-class APIResponseValidationError(APIResponseValidationError):  # type: ignore
+class APIResponseValidationError(openai.APIResponseValidationError):  # type: ignore
     def __init__(self, message, llm_provider, model):
         self.message = message
         self.llm_provider = llm_provider
@@ -192,7 +179,7 @@ class APIResponseValidationError(APIResponseValidationError):  # type: ignore
         super().__init__(response=response, body=None, message=message)
 
 
-class OpenAIError(OpenAIError):  # type: ignore
+class OpenAIError(openai.OpenAIError):  # type: ignore
     def __init__(self, original_exception):
         self.status_code = original_exception.http_status
         super().__init__(
@@ -214,7 +201,7 @@ class BudgetExceededError(Exception):
 
 
 ## DEPRECATED ##
-class InvalidRequestError(BadRequestError):  # type: ignore
+class InvalidRequestError(openai.BadRequestError):  # type: ignore
     def __init__(self, message, model, llm_provider):
         self.status_code = 400
         self.message = message

litellm/integrations/aispend.py

@@ -1,8 +1,6 @@
 #### What this does ####
 #    On success + failure, log events to aispend.io
 import dotenv, os
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 import datetime
 

litellm/integrations/berrispend.py

@@ -3,7 +3,6 @@
 import dotenv, os
 import requests  # type: ignore
 
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 import datetime
 

litellm/integrations/clickhouse.py

@@ -8,8 +8,6 @@ from litellm.proxy._types import UserAPIKeyAuth
 from litellm.caching import DualCache
 
 from typing import Literal, Union
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 
 
@@ -18,8 +16,6 @@ import traceback
 
 import dotenv, os
 import requests
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 import datetime, subprocess, sys
 import litellm, uuid

litellm/integrations/custom_logger.py

@@ -6,8 +6,6 @@ from litellm.proxy._types import UserAPIKeyAuth
 from litellm.caching import DualCache
 
 from typing import Literal, Union, Optional
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 
 

litellm/integrations/datadog.py

@@ -3,8 +3,6 @@
 
 import dotenv, os
 import requests  # type: ignore
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 import datetime, subprocess, sys
 import litellm, uuid

litellm/integrations/dynamodb.py

@@ -3,8 +3,6 @@
 
 import dotenv, os
 import requests  # type: ignore
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 import datetime, subprocess, sys
 import litellm, uuid

litellm/integrations/helicone.py

@@ -3,8 +3,6 @@
 import dotenv, os
 import requests  # type: ignore
 import litellm
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 
 

litellm/integrations/langfuse.py

@@ -1,8 +1,6 @@
 #### What this does ####
 #    On success, logs events to Langfuse
-import dotenv, os
+import os
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import copy
 import traceback
 from packaging.version import Version
@@ -323,6 +321,9 @@ class LangFuseLogger:
             trace_id = clean_metadata.pop("trace_id", None)
             existing_trace_id = clean_metadata.pop("existing_trace_id", None)
             update_trace_keys = clean_metadata.pop("update_trace_keys", [])
+            debug = clean_metadata.pop("debug_langfuse", None)
+            mask_input = clean_metadata.pop("mask_input", False)
+            mask_output = clean_metadata.pop("mask_output", False)
 
             if trace_name is None and existing_trace_id is None:
                 # just log `litellm-{call_type}` as the trace name
@@ -350,15 +351,15 @@ class LangFuseLogger:
 
                 # Special keys that are found in the function arguments and not the metadata
                 if "input" in update_trace_keys:
-                    trace_params["input"] = input
+                    trace_params["input"] = input if not mask_input else "redacted-by-litellm"
                 if "output" in update_trace_keys:
-                    trace_params["output"] = output
+                    trace_params["output"] = output if not mask_output else "redacted-by-litellm"
             else:  # don't overwrite an existing trace
                 trace_params = {
                     "id": trace_id,
                     "name": trace_name,
                     "session_id": session_id,
-                    "input": input,
+                    "input": input if not mask_input else "redacted-by-litellm",
                     "version": clean_metadata.pop(
                         "trace_version", clean_metadata.get("version", None)
                     ),  # If provided just version, it will applied to the trace as well, if applied a trace version it will take precedence
@@ -374,7 +375,14 @@ class LangFuseLogger:
                 if level == "ERROR":
                     trace_params["status_message"] = output
                 else:
-                    trace_params["output"] = output
+                    trace_params["output"] = output if not mask_output else "redacted-by-litellm"
+
+            if debug == True or (isinstance(debug, str) and debug.lower() == "true"):
+                if "metadata" in trace_params:
+                    # log the raw_metadata in the trace
+                    trace_params["metadata"]["metadata_passed_to_litellm"] = metadata
+                else:
+                    trace_params["metadata"] = {"metadata_passed_to_litellm": metadata}
 
             cost = kwargs.get("response_cost", None)
             print_verbose(f"trace: {cost}")
@@ -426,7 +434,6 @@ class LangFuseLogger:
                     "url": url,
                     "headers": clean_headers,
                 }
-
             trace = self.Langfuse.trace(**trace_params)
 
             generation_id = None
@@ -459,8 +466,8 @@ class LangFuseLogger:
                 "end_time": end_time,
                 "model": kwargs["model"],
                 "model_parameters": optional_params,
-                "input": input,
+                "input": input if not mask_input else "redacted-by-litellm",
-                "output": output,
+                "output": output if not mask_output else "redacted-by-litellm",
                 "usage": usage,
                 "metadata": clean_metadata,
                 "level": level,
@@ -468,7 +475,29 @@ class LangFuseLogger:
             }
 
             if supports_prompt:
-                generation_params["prompt"] = clean_metadata.pop("prompt", None)
+                user_prompt = clean_metadata.pop("prompt", None)
+                if user_prompt is None:
+                    pass
+                elif isinstance(user_prompt, dict):
+                    from langfuse.model import (
+                        TextPromptClient,
+                        ChatPromptClient,
+                        Prompt_Text,
+                        Prompt_Chat,
+                    )
+
+                    if user_prompt.get("type", "") == "chat":
+                        _prompt_chat = Prompt_Chat(**user_prompt)
+                        generation_params["prompt"] = ChatPromptClient(
+                            prompt=_prompt_chat
+                        )
+                    elif user_prompt.get("type", "") == "text":
+                        _prompt_text = Prompt_Text(**user_prompt)
+                        generation_params["prompt"] = TextPromptClient(
+                            prompt=_prompt_text
+                        )
+                else:
+                    generation_params["prompt"] = user_prompt
 
             if output is not None and isinstance(output, str) and level == "ERROR":
                 generation_params["status_message"] = output

litellm/integrations/langsmith.py

@@ -3,8 +3,6 @@
 import dotenv, os  # type: ignore
 import requests  # type: ignore
 from datetime import datetime
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 import asyncio
 import types

litellm/integrations/lunary.py

@@ -2,13 +2,10 @@
 #    On success + failure, log events to lunary.ai
 from datetime import datetime, timezone
 import traceback
-import dotenv
 import importlib
 
 import packaging
 
-dotenv.load_dotenv()
-
 
 # convert to {completion: xx, tokens: xx}
 def parse_usage(usage):
@@ -79,14 +76,16 @@ class LunaryLogger:
             version = importlib.metadata.version("lunary")
             # if version < 0.1.43 then raise ImportError
             if packaging.version.Version(version) < packaging.version.Version("0.1.43"):
-                print(
+                print(  # noqa
                     "Lunary version outdated. Required: >= 0.1.43. Upgrade via 'pip install lunary --upgrade'"
                 )
                 raise ImportError
 
             self.lunary_client = lunary
         except ImportError:
-            print("Lunary not installed. Please install it using 'pip install lunary'")
+            print(  # noqa
+                "Lunary not installed. Please install it using 'pip install lunary'"
+            )  # noqa
             raise ImportError
 
     def log_event(

litellm/integrations/openmeter.py

@@ -3,8 +3,6 @@
 
 import dotenv, os, json
 import litellm
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler

litellm/integrations/prometheus.py

@@ -4,8 +4,6 @@
 
 import dotenv, os
 import requests  # type: ignore
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 import datetime, subprocess, sys
 import litellm, uuid

litellm/integrations/prometheus_services.py

@@ -5,8 +5,6 @@
 
 import dotenv, os
 import requests  # type: ignore
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 import datetime, subprocess, sys
 import litellm, uuid

litellm/integrations/prompt_layer.py

@@ -3,8 +3,6 @@
 import dotenv, os
 import requests  # type: ignore
 from pydantic import BaseModel
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 
 

litellm/integrations/s3.py

@@ -1,9 +1,7 @@
 #### What this does ####
 #    On success + failure, log events to Supabase
 
-import dotenv, os
+import os
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 import datetime, subprocess, sys
 import litellm, uuid

litellm/integrations/slack_alerting.py

@@ -2,8 +2,6 @@
 #    Class for sending Slack Alerts #
 import dotenv, os
 from litellm.proxy._types import UserAPIKeyAuth
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 from litellm._logging import verbose_logger, verbose_proxy_logger
 import litellm, threading
 from typing import List, Literal, Any, Union, Optional, Dict
@@ -33,7 +31,8 @@ class LiteLLMBase(BaseModel):
 
 
 class SlackAlertingArgs(LiteLLMBase):
-    daily_report_frequency: int = 12 * 60 * 60  # 12 hours
+    default_daily_report_frequency: int = 12 * 60 * 60  # 12 hours
+    daily_report_frequency: int = int(os.getenv("SLACK_DAILY_REPORT_FREQUENCY", default_daily_report_frequency))
     report_check_interval: int = 5 * 60  # 5 minutes
 
 
@@ -78,16 +77,14 @@ class SlackAlerting(CustomLogger):
         internal_usage_cache: Optional[DualCache] = None,
         alerting_threshold: float = 300,  # threshold for slow / hanging llm responses (in seconds)
         alerting: Optional[List] = [],
-        alert_types: Optional[
+        alert_types: List[
-            List[
+            Literal[
-                Literal[
+                "llm_exceptions",
-                    "llm_exceptions",
+                "llm_too_slow",
-                    "llm_too_slow",
+                "llm_requests_hanging",
-                    "llm_requests_hanging",
+                "budget_alerts",
-                    "budget_alerts",
+                "db_exceptions",
-                    "db_exceptions",
+                "daily_reports",
-                    "daily_reports",
-                ]
             ]
         ] = [
             "llm_exceptions",
@@ -242,6 +239,8 @@ class SlackAlerting(CustomLogger):
                 end_time=end_time,
             )
         )
+        if litellm.turn_off_message_logging:
+            messages = "Message not logged. `litellm.turn_off_message_logging=True`."
         request_info = f"\nRequest Model: `{model}`\nAPI Base: `{api_base}`\nMessages: `{messages}`"
         slow_message = f"`Responses are slow - {round(time_difference_float,2)}s response time > Alerting threshold: {self.alerting_threshold}s`"
         if time_difference_float > self.alerting_threshold:
@@ -464,6 +463,11 @@ class SlackAlerting(CustomLogger):
                 messages = messages[:100]
             except:
                 messages = ""
+
+            if litellm.turn_off_message_logging:
+                messages = (
+                    "Message not logged. `litellm.turn_off_message_logging=True`."
+                )
             request_info = f"\nRequest Model: `{model}`\nMessages: `{messages}`"
         else:
             request_info = ""
@@ -814,14 +818,6 @@ Model Info:
                     updated_at=litellm.utils.get_utc_datetime(),
                 )
             )
-        if "llm_exceptions" in self.alert_types:
-            original_exception = kwargs.get("exception", None)
-
-            await self.send_alert(
-                message="LLM API Failure - " + str(original_exception),
-                level="High",
-                alert_type="llm_exceptions",
-            )
 
     async def _run_scheduler_helper(self, llm_router) -> bool:
         """
@@ -885,3 +881,99 @@ Model Info:
                 )  # shuffle to prevent collisions
                 await asyncio.sleep(interval)
         return
+
+    async def send_weekly_spend_report(self):
+        """ """
+        try:
+            from litellm.proxy.proxy_server import _get_spend_report_for_time_range
+
+            todays_date = datetime.datetime.now().date()
+            week_before = todays_date - datetime.timedelta(days=7)
+
+            weekly_spend_per_team, weekly_spend_per_tag = (
+                await _get_spend_report_for_time_range(
+                    start_date=week_before.strftime("%Y-%m-%d"),
+                    end_date=todays_date.strftime("%Y-%m-%d"),
+                )
+            )
+
+            _weekly_spend_message = f"*💸 Weekly Spend Report for `{week_before.strftime('%m-%d-%Y')} - {todays_date.strftime('%m-%d-%Y')}` *\n"
+
+            if weekly_spend_per_team is not None:
+                _weekly_spend_message += "\n*Team Spend Report:*\n"
+                for spend in weekly_spend_per_team:
+                    _team_spend = spend["total_spend"]
+                    _team_spend = float(_team_spend)
+                    # round to 4 decimal places
+                    _team_spend = round(_team_spend, 4)
+                    _weekly_spend_message += (
+                        f"Team: `{spend['team_alias']}` | Spend: `${_team_spend}`\n"
+                    )
+
+            if weekly_spend_per_tag is not None:
+                _weekly_spend_message += "\n*Tag Spend Report:*\n"
+                for spend in weekly_spend_per_tag:
+                    _tag_spend = spend["total_spend"]
+                    _tag_spend = float(_tag_spend)
+                    # round to 4 decimal places
+                    _tag_spend = round(_tag_spend, 4)
+                    _weekly_spend_message += f"Tag: `{spend['individual_request_tag']}` | Spend: `${_tag_spend}`\n"
+
+            await self.send_alert(
+                message=_weekly_spend_message,
+                level="Low",
+                alert_type="daily_reports",
+            )
+        except Exception as e:
+            verbose_proxy_logger.error("Error sending weekly spend report", e)
+
+    async def send_monthly_spend_report(self):
+        """ """
+        try:
+            from calendar import monthrange
+
+            from litellm.proxy.proxy_server import _get_spend_report_for_time_range
+
+            todays_date = datetime.datetime.now().date()
+            first_day_of_month = todays_date.replace(day=1)
+            _, last_day_of_month = monthrange(todays_date.year, todays_date.month)
+            last_day_of_month = first_day_of_month + datetime.timedelta(
+                days=last_day_of_month - 1
+            )
+
+            monthly_spend_per_team, monthly_spend_per_tag = (
+                await _get_spend_report_for_time_range(
+                    start_date=first_day_of_month.strftime("%Y-%m-%d"),
+                    end_date=last_day_of_month.strftime("%Y-%m-%d"),
+                )
+            )
+
+            _spend_message = f"*💸 Monthly Spend Report for `{first_day_of_month.strftime('%m-%d-%Y')} - {last_day_of_month.strftime('%m-%d-%Y')}` *\n"
+
+            if monthly_spend_per_team is not None:
+                _spend_message += "\n*Team Spend Report:*\n"
+                for spend in monthly_spend_per_team:
+                    _team_spend = spend["total_spend"]
+                    _team_spend = float(_team_spend)
+                    # round to 4 decimal places
+                    _team_spend = round(_team_spend, 4)
+                    _spend_message += (
+                        f"Team: `{spend['team_alias']}` | Spend: `${_team_spend}`\n"
+                    )
+
+            if monthly_spend_per_tag is not None:
+                _spend_message += "\n*Tag Spend Report:*\n"
+                for spend in monthly_spend_per_tag:
+                    _tag_spend = spend["total_spend"]
+                    _tag_spend = float(_tag_spend)
+                    # round to 4 decimal places
+                    _tag_spend = round(_tag_spend, 4)
+                    _spend_message += f"Tag: `{spend['individual_request_tag']}` | Spend: `${_tag_spend}`\n"
+
+            await self.send_alert(
+                message=_spend_message,
+                level="Low",
+                alert_type="daily_reports",
+            )
+        except Exception as e:
+            verbose_proxy_logger.error("Error sending weekly spend report", e)

litellm/integrations/supabase.py

@@ -3,8 +3,6 @@
 
 import dotenv, os
 import requests  # type: ignore
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 import datetime, subprocess, sys
 import litellm

litellm/integrations/weights_biases.py

@@ -21,11 +21,11 @@ try:
         # contains a (known) object attribute
         object: Literal["chat.completion", "edit", "text_completion"]
 
-        def __getitem__(self, key: K) -> V:
+        def __getitem__(self, key: K) -> V: ...  # noqa
-            ...  # pragma: no cover
 
-        def get(self, key: K, default: Optional[V] = None) -> Optional[V]:
+        def get(  # noqa
-            ...  # pragma: no cover
+            self, key: K, default: Optional[V] = None
+        ) -> Optional[V]: ...  # pragma: no cover
 
     class OpenAIRequestResponseResolver:
         def __call__(
@@ -173,12 +173,11 @@ except:
 
 #### What this does ####
 #    On success, logs events to Langfuse
-import dotenv, os
+import os
 import requests
 import requests
 from datetime import datetime
 
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 
 

litellm/llms/anthropic.py

@@ -3,7 +3,7 @@ import json
 from enum import Enum
 import requests, copy  # type: ignore
 import time
-from typing import Callable, Optional, List
+from typing import Callable, Optional, List, Union
 from litellm.utils import ModelResponse, Usage, map_finish_reason, CustomStreamWrapper
 import litellm
 from .prompt_templates.factory import prompt_factory, custom_prompt
@@ -151,19 +151,135 @@ class AnthropicChatCompletion(BaseLLM):
     def __init__(self) -> None:
         super().__init__()
 
+    def process_streaming_response(
+        self,
+        model: str,
+        response: Union[requests.Response, httpx.Response],
+        model_response: ModelResponse,
+        stream: bool,
+        logging_obj: litellm.utils.Logging,
+        optional_params: dict,
+        api_key: str,
+        data: Union[dict, str],
+        messages: List,
+        print_verbose,
+        encoding,
+    ) -> CustomStreamWrapper:
+        """
+        Return stream object for tool-calling + streaming
+        """
+        ## LOGGING
+        logging_obj.post_call(
+            input=messages,
+            api_key=api_key,
+            original_response=response.text,
+            additional_args={"complete_input_dict": data},
+        )
+        print_verbose(f"raw model_response: {response.text}")
+        ## RESPONSE OBJECT
+        try:
+            completion_response = response.json()
+        except:
+            raise AnthropicError(
+                message=response.text, status_code=response.status_code
+            )
+        text_content = ""
+        tool_calls = []
+        for content in completion_response["content"]:
+            if content["type"] == "text":
+                text_content += content["text"]
+            ## TOOL CALLING
+            elif content["type"] == "tool_use":
+                tool_calls.append(
+                    {
+                        "id": content["id"],
+                        "type": "function",
+                        "function": {
+                            "name": content["name"],
+                            "arguments": json.dumps(content["input"]),
+                        },
+                    }
+                )
+        if "error" in completion_response:
+            raise AnthropicError(
+                message=str(completion_response["error"]),
+                status_code=response.status_code,
+            )
+        _message = litellm.Message(
+            tool_calls=tool_calls,
+            content=text_content or None,
+        )
+        model_response.choices[0].message = _message  # type: ignore
+        model_response._hidden_params["original_response"] = completion_response[
+            "content"
+        ]  # allow user to access raw anthropic tool calling response
+
+        model_response.choices[0].finish_reason = map_finish_reason(
+            completion_response["stop_reason"]
+        )
+
+        print_verbose("INSIDE ANTHROPIC STREAMING TOOL CALLING CONDITION BLOCK")
+        # return an iterator
+        streaming_model_response = ModelResponse(stream=True)
+        streaming_model_response.choices[0].finish_reason = model_response.choices[  # type: ignore
+            0
+        ].finish_reason
+        # streaming_model_response.choices = [litellm.utils.StreamingChoices()]
+        streaming_choice = litellm.utils.StreamingChoices()
+        streaming_choice.index = model_response.choices[0].index
+        _tool_calls = []
+        print_verbose(
+            f"type of model_response.choices[0]: {type(model_response.choices[0])}"
+        )
+        print_verbose(f"type of streaming_choice: {type(streaming_choice)}")
+        if isinstance(model_response.choices[0], litellm.Choices):
+            if getattr(
+                model_response.choices[0].message, "tool_calls", None
+            ) is not None and isinstance(
+                model_response.choices[0].message.tool_calls, list
+            ):
+                for tool_call in model_response.choices[0].message.tool_calls:
+                    _tool_call = {**tool_call.dict(), "index": 0}
+                    _tool_calls.append(_tool_call)
+            delta_obj = litellm.utils.Delta(
+                content=getattr(model_response.choices[0].message, "content", None),
+                role=model_response.choices[0].message.role,
+                tool_calls=_tool_calls,
+            )
+            streaming_choice.delta = delta_obj
+            streaming_model_response.choices = [streaming_choice]
+            completion_stream = ModelResponseIterator(
+                model_response=streaming_model_response
+            )
+            print_verbose(
+                "Returns anthropic CustomStreamWrapper with 'cached_response' streaming object"
+            )
+            return CustomStreamWrapper(
+                completion_stream=completion_stream,
+                model=model,
+                custom_llm_provider="cached_response",
+                logging_obj=logging_obj,
+            )
+        else:
+            raise AnthropicError(
+                status_code=422,
+                message="Unprocessable response object - {}".format(response.text),
+            )
+
     def process_response(
         self,
-        model,
+        model: str,
-        response,
+        response: Union[requests.Response, httpx.Response],
-        model_response,
+        model_response: ModelResponse,
-        _is_function_call,
+        stream: bool,
-        stream,
+        logging_obj: litellm.utils.Logging,
-        logging_obj,
+        optional_params: dict,
-        api_key,
+        api_key: str,
-        data,
+        data: Union[dict, str],
-        messages,
+        messages: List,
         print_verbose,
-    ):
+        encoding,
+    ) -> ModelResponse:
         ## LOGGING
         logging_obj.post_call(
             input=messages,
@@ -216,51 +332,6 @@ class AnthropicChatCompletion(BaseLLM):
                 completion_response["stop_reason"]
             )
 
-        print_verbose(f"_is_function_call: {_is_function_call}; stream: {stream}")
-        if _is_function_call and stream:
-            print_verbose("INSIDE ANTHROPIC STREAMING TOOL CALLING CONDITION BLOCK")
-            # return an iterator
-            streaming_model_response = ModelResponse(stream=True)
-            streaming_model_response.choices[0].finish_reason = model_response.choices[
-                0
-            ].finish_reason
-            # streaming_model_response.choices = [litellm.utils.StreamingChoices()]
-            streaming_choice = litellm.utils.StreamingChoices()
-            streaming_choice.index = model_response.choices[0].index
-            _tool_calls = []
-            print_verbose(
-                f"type of model_response.choices[0]: {type(model_response.choices[0])}"
-            )
-            print_verbose(f"type of streaming_choice: {type(streaming_choice)}")
-            if isinstance(model_response.choices[0], litellm.Choices):
-                if getattr(
-                    model_response.choices[0].message, "tool_calls", None
-                ) is not None and isinstance(
-                    model_response.choices[0].message.tool_calls, list
-                ):
-                    for tool_call in model_response.choices[0].message.tool_calls:
-                        _tool_call = {**tool_call.dict(), "index": 0}
-                        _tool_calls.append(_tool_call)
-                delta_obj = litellm.utils.Delta(
-                    content=getattr(model_response.choices[0].message, "content", None),
-                    role=model_response.choices[0].message.role,
-                    tool_calls=_tool_calls,
-                )
-                streaming_choice.delta = delta_obj
-                streaming_model_response.choices = [streaming_choice]
-                completion_stream = ModelResponseIterator(
-                    model_response=streaming_model_response
-                )
-                print_verbose(
-                    "Returns anthropic CustomStreamWrapper with 'cached_response' streaming object"
-                )
-                return CustomStreamWrapper(
-                    completion_stream=completion_stream,
-                    model=model,
-                    custom_llm_provider="cached_response",
-                    logging_obj=logging_obj,
-                )
-
         ## CALCULATING USAGE
         prompt_tokens = completion_response["usage"]["input_tokens"]
         completion_tokens = completion_response["usage"]["output_tokens"]
@@ -273,7 +344,7 @@ class AnthropicChatCompletion(BaseLLM):
             completion_tokens=completion_tokens,
             total_tokens=total_tokens,
         )
-        model_response.usage = usage
+        setattr(model_response, "usage", usage)  # type: ignore
         return model_response
 
     async def acompletion_stream_function(
@@ -289,7 +360,7 @@ class AnthropicChatCompletion(BaseLLM):
         logging_obj,
         stream,
         _is_function_call,
-        data=None,
+        data: dict,
         optional_params=None,
         litellm_params=None,
         logger_fn=None,
@@ -331,29 +402,44 @@ class AnthropicChatCompletion(BaseLLM):
         logging_obj,
         stream,
         _is_function_call,
-        data=None,
+        data: dict,
-        optional_params=None,
+        optional_params: dict,
         litellm_params=None,
         logger_fn=None,
         headers={},
-    ):
+    ) -> Union[ModelResponse, CustomStreamWrapper]:
         self.async_handler = AsyncHTTPHandler(
             timeout=httpx.Timeout(timeout=600.0, connect=5.0)
         )
         response = await self.async_handler.post(
             api_base, headers=headers, data=json.dumps(data)
         )
+        if stream and _is_function_call:
+            return self.process_streaming_response(
+                model=model,
+                response=response,
+                model_response=model_response,
+                stream=stream,
+                logging_obj=logging_obj,
+                api_key=api_key,
+                data=data,
+                messages=messages,
+                print_verbose=print_verbose,
+                optional_params=optional_params,
+                encoding=encoding,
+            )
         return self.process_response(
             model=model,
             response=response,
             model_response=model_response,
-            _is_function_call=_is_function_call,
             stream=stream,
             logging_obj=logging_obj,
             api_key=api_key,
             data=data,
             messages=messages,
             print_verbose=print_verbose,
+            optional_params=optional_params,
+            encoding=encoding,
         )
 
     def completion(
@@ -367,7 +453,7 @@ class AnthropicChatCompletion(BaseLLM):
         encoding,
         api_key,
         logging_obj,
-        optional_params=None,
+        optional_params: dict,
         acompletion=None,
         litellm_params=None,
         logger_fn=None,
@@ -526,17 +612,33 @@ class AnthropicChatCompletion(BaseLLM):
                     raise AnthropicError(
                         status_code=response.status_code, message=response.text
                     )
+
+        if stream and _is_function_call:
+            return self.process_streaming_response(
+                model=model,
+                response=response,
+                model_response=model_response,
+                stream=stream,
+                logging_obj=logging_obj,
+                api_key=api_key,
+                data=data,
+                messages=messages,
+                print_verbose=print_verbose,
+                optional_params=optional_params,
+                encoding=encoding,
+            )
         return self.process_response(
             model=model,
             response=response,
             model_response=model_response,
-            _is_function_call=_is_function_call,
             stream=stream,
             logging_obj=logging_obj,
             api_key=api_key,
             data=data,
             messages=messages,
             print_verbose=print_verbose,
+            optional_params=optional_params,
+            encoding=encoding,
         )
 
     def embedding(self):

litellm/llms/anthropic_text.py

@@ -100,7 +100,7 @@ class AnthropicTextCompletion(BaseLLM):
     def __init__(self) -> None:
         super().__init__()
 
-    def process_response(
+    def _process_response(
         self, model_response: ModelResponse, response, encoding, prompt: str, model: str
     ):
         ## RESPONSE OBJECT
@@ -171,7 +171,7 @@ class AnthropicTextCompletion(BaseLLM):
             additional_args={"complete_input_dict": data},
         )
 
-        response = self.process_response(
+        response = self._process_response(
             model_response=model_response,
             response=response,
             encoding=encoding,
@@ -330,7 +330,7 @@ class AnthropicTextCompletion(BaseLLM):
             )
             print_verbose(f"raw model_response: {response.text}")
 
-            response = self.process_response(
+            response = self._process_response(
                 model_response=model_response,
                 response=response,
                 encoding=encoding,

litellm/llms/azure.py

@@ -10,7 +10,7 @@ from litellm.utils import (
     TranscriptionResponse,
     get_secret,
 )
-from typing import Callable, Optional, BinaryIO
+from typing import Callable, Optional, BinaryIO, List
 from litellm import OpenAIConfig
 import litellm, json
 import httpx  # type: ignore
@@ -107,6 +107,12 @@ class AzureOpenAIConfig(OpenAIConfig):
                 optional_params["azure_ad_token"] = value
         return optional_params
 
+    def get_eu_regions(self) -> List[str]:
+        """
+        Source: https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-4-and-gpt-4-turbo-model-availability
+        """
+        return ["europe", "sweden", "switzerland", "france", "uk"]
+
 
 def select_azure_base_url_or_endpoint(azure_client_params: dict):
     # azure_client_params = {

litellm/llms/base.py

@@ -1,12 +1,32 @@
 ## This is a template base class to be used for adding new LLM providers via API calls
 import litellm
-import httpx
+import httpx, requests
-from typing import Optional
+from typing import Optional, Union
+from litellm.utils import Logging
 
 
 class BaseLLM:
     _client_session: Optional[httpx.Client] = None
 
+    def process_response(
+        self,
+        model: str,
+        response: Union[requests.Response, httpx.Response],
+        model_response: litellm.utils.ModelResponse,
+        stream: bool,
+        logging_obj: Logging,
+        optional_params: dict,
+        api_key: str,
+        data: Union[dict, str],
+        messages: list,
+        print_verbose,
+        encoding,
+    ) -> litellm.utils.ModelResponse:
+        """
+        Helper function to process the response across sync + async completion calls
+        """
+        return model_response
+
     def create_client_session(self):
         if litellm.client_session:
             _client_session = litellm.client_session

litellm/llms/bedrock.py

@@ -52,6 +52,16 @@ class AmazonBedrockGlobalConfig:
                 optional_params[mapped_params[param]] = value
         return optional_params
 
+    def get_eu_regions(self) -> List[str]:
+        """
+        Source: https://www.aws-services.info/bedrock.html
+        """
+        return [
+            "eu-west-1",
+            "eu-west-3",
+            "eu-central-1",
+        ]
+
 
 class AmazonTitanConfig:
     """

litellm/llms/bedrock_httpx.py

@@ -0,0 +1,733 @@
+# What is this?
+## Initial implementation of calling bedrock via httpx client (allows for async calls).
+## V0 - just covers cohere command-r support
+
+import os, types
+import json
+from enum import Enum
+import requests, copy  # type: ignore
+import time
+from typing import (
+    Callable,
+    Optional,
+    List,
+    Literal,
+    Union,
+    Any,
+    TypedDict,
+    Tuple,
+    Iterator,
+    AsyncIterator,
+)
+from litellm.utils import (
+    ModelResponse,
+    Usage,
+    map_finish_reason,
+    CustomStreamWrapper,
+    Message,
+    Choices,
+    get_secret,
+    Logging,
+)
+import litellm
+from .prompt_templates.factory import prompt_factory, custom_prompt, cohere_message_pt
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from .base import BaseLLM
+import httpx  # type: ignore
+from .bedrock import BedrockError, convert_messages_to_prompt
+from litellm.types.llms.bedrock import *
+
+
+class AmazonCohereChatConfig:
+    """
+    Reference - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-cohere-command-r-plus.html
+    """
+
+    documents: Optional[List[Document]] = None
+    search_queries_only: Optional[bool] = None
+    preamble: Optional[str] = None
+    max_tokens: Optional[int] = None
+    temperature: Optional[float] = None
+    p: Optional[float] = None
+    k: Optional[float] = None
+    prompt_truncation: Optional[str] = None
+    frequency_penalty: Optional[float] = None
+    presence_penalty: Optional[float] = None
+    seed: Optional[int] = None
+    return_prompt: Optional[bool] = None
+    stop_sequences: Optional[List[str]] = None
+    raw_prompting: Optional[bool] = None
+
+    def __init__(
+        self,
+        documents: Optional[List[Document]] = None,
+        search_queries_only: Optional[bool] = None,
+        preamble: Optional[str] = None,
+        max_tokens: Optional[int] = None,
+        temperature: Optional[float] = None,
+        p: Optional[float] = None,
+        k: Optional[float] = None,
+        prompt_truncation: Optional[str] = None,
+        frequency_penalty: Optional[float] = None,
+        presence_penalty: Optional[float] = None,
+        seed: Optional[int] = None,
+        return_prompt: Optional[bool] = None,
+        stop_sequences: Optional[str] = None,
+        raw_prompting: Optional[bool] = None,
+    ) -> None:
+        locals_ = locals()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(self) -> List[str]:
+        return [
+            "max_tokens",
+            "stream",
+            "stop",
+            "temperature",
+            "top_p",
+            "frequency_penalty",
+            "presence_penalty",
+            "seed",
+            "stop",
+        ]
+
+    def map_openai_params(
+        self, non_default_params: dict, optional_params: dict
+    ) -> dict:
+        for param, value in non_default_params.items():
+            if param == "max_tokens":
+                optional_params["max_tokens"] = value
+            if param == "stream":
+                optional_params["stream"] = value
+            if param == "stop":
+                if isinstance(value, str):
+                    value = [value]
+                optional_params["stop_sequences"] = value
+            if param == "temperature":
+                optional_params["temperature"] = value
+            if param == "top_p":
+                optional_params["p"] = value
+            if param == "frequency_penalty":
+                optional_params["frequency_penalty"] = value
+            if param == "presence_penalty":
+                optional_params["presence_penalty"] = value
+            if "seed":
+                optional_params["seed"] = value
+        return optional_params
+
+
+class BedrockLLM(BaseLLM):
+    """
+    Example call
+
+    ```
+    curl --location --request POST 'https://bedrock-runtime.{aws_region_name}.amazonaws.com/model/{bedrock_model_name}/invoke' \
+        --header 'Content-Type: application/json' \
+        --header 'Accept: application/json' \
+        --user "$AWS_ACCESS_KEY_ID":"$AWS_SECRET_ACCESS_KEY" \
+        --aws-sigv4 "aws:amz:us-east-1:bedrock" \
+        --data-raw '{
+        "prompt": "Hi",
+        "temperature": 0,
+        "p": 0.9,
+        "max_tokens": 4096
+        }'
+    ```
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def convert_messages_to_prompt(
+        self, model, messages, provider, custom_prompt_dict
+    ) -> Tuple[str, Optional[list]]:
+        # handle anthropic prompts and amazon titan prompts
+        prompt = ""
+        chat_history: Optional[list] = None
+        if provider == "anthropic" or provider == "amazon":
+            if model in custom_prompt_dict:
+                # check if the model has a registered custom prompt
+                model_prompt_details = custom_prompt_dict[model]
+                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:
+                prompt = prompt_factory(
+                    model=model, messages=messages, custom_llm_provider="bedrock"
+                )
+        elif provider == "mistral":
+            prompt = prompt_factory(
+                model=model, messages=messages, custom_llm_provider="bedrock"
+            )
+        elif provider == "meta":
+            prompt = prompt_factory(
+                model=model, messages=messages, custom_llm_provider="bedrock"
+            )
+        elif provider == "cohere":
+            prompt, chat_history = cohere_message_pt(messages=messages)
+        else:
+            prompt = ""
+            for message in messages:
+                if "role" in message:
+                    if message["role"] == "user":
+                        prompt += f"{message['content']}"
+                    else:
+                        prompt += f"{message['content']}"
+                else:
+                    prompt += f"{message['content']}"
+        return prompt, chat_history  # type: ignore
+
+    def get_credentials(
+        self,
+        aws_access_key_id: Optional[str] = None,
+        aws_secret_access_key: Optional[str] = None,
+        aws_region_name: Optional[str] = None,
+        aws_session_name: Optional[str] = None,
+        aws_profile_name: Optional[str] = None,
+        aws_role_name: Optional[str] = None,
+    ):
+        """
+        Return a boto3.Credentials object
+        """
+        import boto3
+
+        ## CHECK IS  'os.environ/' passed in
+        params_to_check: List[Optional[str]] = [
+            aws_access_key_id,
+            aws_secret_access_key,
+            aws_region_name,
+            aws_session_name,
+            aws_profile_name,
+            aws_role_name,
+        ]
+
+        # Iterate over parameters and update if needed
+        for i, param in enumerate(params_to_check):
+            if param and param.startswith("os.environ/"):
+                _v = get_secret(param)
+                if _v is not None and isinstance(_v, str):
+                    params_to_check[i] = _v
+        # Assign updated values back to parameters
+        (
+            aws_access_key_id,
+            aws_secret_access_key,
+            aws_region_name,
+            aws_session_name,
+            aws_profile_name,
+            aws_role_name,
+        ) = params_to_check
+
+        ### CHECK STS ###
+        if aws_role_name is not None and aws_session_name is not None:
+            sts_client = boto3.client(
+                "sts",
+                aws_access_key_id=aws_access_key_id,  # [OPTIONAL]
+                aws_secret_access_key=aws_secret_access_key,  # [OPTIONAL]
+            )
+
+            sts_response = sts_client.assume_role(
+                RoleArn=aws_role_name, RoleSessionName=aws_session_name
+            )
+
+            return sts_response["Credentials"]
+        elif aws_profile_name is not None:  ### CHECK SESSION ###
+            # uses auth values from AWS profile usually stored in ~/.aws/credentials
+            client = boto3.Session(profile_name=aws_profile_name)
+
+            return client.get_credentials()
+        else:
+            session = boto3.Session(
+                aws_access_key_id=aws_access_key_id,
+                aws_secret_access_key=aws_secret_access_key,
+                region_name=aws_region_name,
+            )
+
+            return session.get_credentials()
+
+    def process_response(
+        self,
+        model: str,
+        response: Union[requests.Response, httpx.Response],
+        model_response: ModelResponse,
+        stream: bool,
+        logging_obj: Logging,
+        optional_params: dict,
+        api_key: str,
+        data: Union[dict, str],
+        messages: List,
+        print_verbose,
+        encoding,
+    ) -> ModelResponse:
+        ## LOGGING
+        logging_obj.post_call(
+            input=messages,
+            api_key=api_key,
+            original_response=response.text,
+            additional_args={"complete_input_dict": data},
+        )
+        print_verbose(f"raw model_response: {response.text}")
+
+        ## RESPONSE OBJECT
+        try:
+            completion_response = response.json()
+        except:
+            raise BedrockError(message=response.text, status_code=422)
+
+        try:
+            model_response.choices[0].message.content = completion_response["text"]  # type: ignore
+        except Exception as e:
+            raise BedrockError(message=response.text, status_code=422)
+
+        ## CALCULATING USAGE - bedrock returns usage in the headers
+        prompt_tokens = int(
+            response.headers.get(
+                "x-amzn-bedrock-input-token-count",
+                len(encoding.encode("".join(m.get("content", "") for m in messages))),
+            )
+        )
+        completion_tokens = int(
+            response.headers.get(
+                "x-amzn-bedrock-output-token-count",
+                len(
+                    encoding.encode(
+                        model_response.choices[0].message.content,  # type: ignore
+                        disallowed_special=(),
+                    )
+                ),
+            )
+        )
+
+        model_response["created"] = int(time.time())
+        model_response["model"] = model
+        usage = Usage(
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens,
+        )
+        setattr(model_response, "usage", usage)
+
+        return model_response
+
+    def completion(
+        self,
+        model: str,
+        messages: list,
+        custom_prompt_dict: dict,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        encoding,
+        logging_obj,
+        optional_params: dict,
+        acompletion: bool,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        litellm_params=None,
+        logger_fn=None,
+        extra_headers: Optional[dict] = None,
+        client: Optional[Union[AsyncHTTPHandler, HTTPHandler]] = None,
+    ) -> Union[ModelResponse, CustomStreamWrapper]:
+        try:
+            import boto3
+
+            from botocore.auth import SigV4Auth
+            from botocore.awsrequest import AWSRequest
+            from botocore.credentials import Credentials
+        except ImportError as e:
+            raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
+
+        ## SETUP ##
+        stream = optional_params.pop("stream", None)
+
+        ## CREDENTIALS ##
+        # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them
+        aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
+        aws_access_key_id = optional_params.pop("aws_access_key_id", None)
+        aws_region_name = optional_params.pop("aws_region_name", None)
+        aws_role_name = optional_params.pop("aws_role_name", None)
+        aws_session_name = optional_params.pop("aws_session_name", None)
+        aws_profile_name = optional_params.pop("aws_profile_name", None)
+        aws_bedrock_runtime_endpoint = optional_params.pop(
+            "aws_bedrock_runtime_endpoint", None
+        )  # https://bedrock-runtime.{region_name}.amazonaws.com
+
+        ### SET REGION NAME ###
+        if aws_region_name is None:
+            # check env #
+            litellm_aws_region_name = get_secret("AWS_REGION_NAME", None)
+
+            if litellm_aws_region_name is not None and isinstance(
+                litellm_aws_region_name, str
+            ):
+                aws_region_name = litellm_aws_region_name
+
+            standard_aws_region_name = get_secret("AWS_REGION", None)
+            if standard_aws_region_name is not None and isinstance(
+                standard_aws_region_name, str
+            ):
+                aws_region_name = standard_aws_region_name
+
+            if aws_region_name is None:
+                aws_region_name = "us-west-2"
+
+        credentials: Credentials = self.get_credentials(
+            aws_access_key_id=aws_access_key_id,
+            aws_secret_access_key=aws_secret_access_key,
+            aws_region_name=aws_region_name,
+            aws_session_name=aws_session_name,
+            aws_profile_name=aws_profile_name,
+            aws_role_name=aws_role_name,
+        )
+
+        ### SET RUNTIME ENDPOINT ###
+        endpoint_url = ""
+        env_aws_bedrock_runtime_endpoint = get_secret("AWS_BEDROCK_RUNTIME_ENDPOINT")
+        if aws_bedrock_runtime_endpoint is not None and isinstance(
+            aws_bedrock_runtime_endpoint, str
+        ):
+            endpoint_url = aws_bedrock_runtime_endpoint
+        elif env_aws_bedrock_runtime_endpoint and isinstance(
+            env_aws_bedrock_runtime_endpoint, str
+        ):
+            endpoint_url = env_aws_bedrock_runtime_endpoint
+        else:
+            endpoint_url = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com"
+
+        if stream is not None and stream == True:
+            endpoint_url = f"{endpoint_url}/model/{model}/invoke-with-response-stream"
+        else:
+            endpoint_url = f"{endpoint_url}/model/{model}/invoke"
+
+        sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name)
+
+        provider = model.split(".")[0]
+        prompt, chat_history = self.convert_messages_to_prompt(
+            model, messages, provider, custom_prompt_dict
+        )
+        inference_params = copy.deepcopy(optional_params)
+
+        if provider == "cohere":
+            if model.startswith("cohere.command-r"):
+                ## LOAD CONFIG
+                config = litellm.AmazonCohereChatConfig().get_config()
+                for k, v in config.items():
+                    if (
+                        k not in inference_params
+                    ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+                        inference_params[k] = v
+                _data = {"message": prompt, **inference_params}
+                if chat_history is not None:
+                    _data["chat_history"] = chat_history
+                data = json.dumps(_data)
+            else:
+                ## LOAD CONFIG
+                config = litellm.AmazonCohereConfig.get_config()
+                for k, v in config.items():
+                    if (
+                        k not in inference_params
+                    ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+                        inference_params[k] = v
+                if stream == True:
+                    inference_params["stream"] = (
+                        True  # cohere requires stream = True in inference params
+                    )
+                data = json.dumps({"prompt": prompt, **inference_params})
+        else:
+            raise Exception("UNSUPPORTED PROVIDER")
+
+        ## COMPLETION CALL
+
+        headers = {"Content-Type": "application/json"}
+        if extra_headers is not None:
+            headers = {"Content-Type": "application/json", **extra_headers}
+        request = AWSRequest(
+            method="POST", url=endpoint_url, data=data, headers=headers
+        )
+        sigv4.add_auth(request)
+        prepped = request.prepare()
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=messages,
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": prepped.url,
+                "headers": prepped.headers,
+            },
+        )
+
+        ### ROUTING (ASYNC, STREAMING, SYNC)
+        if acompletion:
+            if isinstance(client, HTTPHandler):
+                client = None
+            if stream:
+                return self.async_streaming(
+                    model=model,
+                    messages=messages,
+                    data=data,
+                    api_base=prepped.url,
+                    model_response=model_response,
+                    print_verbose=print_verbose,
+                    encoding=encoding,
+                    logging_obj=logging_obj,
+                    optional_params=optional_params,
+                    stream=True,
+                    litellm_params=litellm_params,
+                    logger_fn=logger_fn,
+                    headers=prepped.headers,
+                    timeout=timeout,
+                    client=client,
+                )  # type: ignore
+            ### ASYNC COMPLETION
+            return self.async_completion(
+                model=model,
+                messages=messages,
+                data=data,
+                api_base=prepped.url,
+                model_response=model_response,
+                print_verbose=print_verbose,
+                encoding=encoding,
+                logging_obj=logging_obj,
+                optional_params=optional_params,
+                stream=False,
+                litellm_params=litellm_params,
+                logger_fn=logger_fn,
+                headers=prepped.headers,
+                timeout=timeout,
+                client=client,
+            )  # type: ignore
+
+        if client is None or isinstance(client, AsyncHTTPHandler):
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    timeout = httpx.Timeout(timeout)
+                _params["timeout"] = timeout
+            self.client = HTTPHandler(**_params)  # type: ignore
+        else:
+            self.client = client
+        if stream is not None and stream == True:
+            response = self.client.post(
+                url=prepped.url,
+                headers=prepped.headers,  # type: ignore
+                data=data,
+                stream=stream,
+            )
+
+            if response.status_code != 200:
+                raise BedrockError(
+                    status_code=response.status_code, message=response.text
+                )
+
+            decoder = AWSEventStreamDecoder()
+
+            completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024))
+            streaming_response = CustomStreamWrapper(
+                completion_stream=completion_stream,
+                model=model,
+                custom_llm_provider="bedrock",
+                logging_obj=logging_obj,
+            )
+            return streaming_response
+
+        response = self.client.post(url=prepped.url, headers=prepped.headers, data=data)  # type: ignore
+
+        try:
+            response.raise_for_status()
+        except httpx.HTTPStatusError as err:
+            error_code = err.response.status_code
+            raise BedrockError(status_code=error_code, message=response.text)
+
+        return self.process_response(
+            model=model,
+            response=response,
+            model_response=model_response,
+            stream=stream,
+            logging_obj=logging_obj,
+            optional_params=optional_params,
+            api_key="",
+            data=data,
+            messages=messages,
+            print_verbose=print_verbose,
+            encoding=encoding,
+        )
+
+    async def async_completion(
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        data: str,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        encoding,
+        logging_obj,
+        stream,
+        optional_params: dict,
+        litellm_params=None,
+        logger_fn=None,
+        headers={},
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> ModelResponse:
+        if client is None:
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    timeout = httpx.Timeout(timeout)
+                _params["timeout"] = timeout
+            self.client = AsyncHTTPHandler(**_params)  # type: ignore
+        else:
+            self.client = client  # type: ignore
+
+        response = await self.client.post(api_base, headers=headers, data=data)  # type: ignore
+        return self.process_response(
+            model=model,
+            response=response,
+            model_response=model_response,
+            stream=stream,
+            logging_obj=logging_obj,
+            api_key="",
+            data=data,
+            messages=messages,
+            print_verbose=print_verbose,
+            optional_params=optional_params,
+            encoding=encoding,
+        )
+
+    async def async_streaming(
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        data: str,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        encoding,
+        logging_obj,
+        stream,
+        optional_params: dict,
+        litellm_params=None,
+        logger_fn=None,
+        headers={},
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> CustomStreamWrapper:
+        if client is None:
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    timeout = httpx.Timeout(timeout)
+                _params["timeout"] = timeout
+            self.client = AsyncHTTPHandler(**_params)  # type: ignore
+        else:
+            self.client = client  # type: ignore
+
+        response = await self.client.post(api_base, headers=headers, data=data, stream=True)  # type: ignore
+
+        if response.status_code != 200:
+            raise BedrockError(status_code=response.status_code, message=response.text)
+
+        decoder = AWSEventStreamDecoder()
+
+        completion_stream = decoder.aiter_bytes(response.aiter_bytes(chunk_size=1024))
+        streaming_response = CustomStreamWrapper(
+            completion_stream=completion_stream,
+            model=model,
+            custom_llm_provider="bedrock",
+            logging_obj=logging_obj,
+        )
+        return streaming_response
+
+    def embedding(self, *args, **kwargs):
+        return super().embedding(*args, **kwargs)
+
+
+def get_response_stream_shape():
+    from botocore.model import ServiceModel
+    from botocore.loaders import Loader
+
+    loader = Loader()
+    bedrock_service_dict = loader.load_service_model("bedrock-runtime", "service-2")
+    bedrock_service_model = ServiceModel(bedrock_service_dict)
+    return bedrock_service_model.shape_for("ResponseStream")
+
+
+class AWSEventStreamDecoder:
+    def __init__(self) -> None:
+        from botocore.parsers import EventStreamJSONParser
+
+        self.parser = EventStreamJSONParser()
+
+    def iter_bytes(self, iterator: Iterator[bytes]) -> Iterator[GenericStreamingChunk]:
+        """Given an iterator that yields lines, iterate over it & yield every event encountered"""
+        from botocore.eventstream import EventStreamBuffer
+
+        event_stream_buffer = EventStreamBuffer()
+        for chunk in iterator:
+            event_stream_buffer.add_data(chunk)
+            for event in event_stream_buffer:
+                message = self._parse_message_from_event(event)
+                if message:
+                    # sse_event = ServerSentEvent(data=message, event="completion")
+                    _data = json.loads(message)
+                    streaming_chunk: GenericStreamingChunk = GenericStreamingChunk(
+                        text=_data.get("text", ""),
+                        is_finished=_data.get("is_finished", False),
+                        finish_reason=_data.get("finish_reason", ""),
+                    )
+                    yield streaming_chunk
+
+    async def aiter_bytes(
+        self, iterator: AsyncIterator[bytes]
+    ) -> AsyncIterator[GenericStreamingChunk]:
+        """Given an async iterator that yields lines, iterate over it & yield every event encountered"""
+        from botocore.eventstream import EventStreamBuffer
+
+        event_stream_buffer = EventStreamBuffer()
+        async for chunk in iterator:
+            event_stream_buffer.add_data(chunk)
+            for event in event_stream_buffer:
+                message = self._parse_message_from_event(event)
+                if message:
+                    _data = json.loads(message)
+                    streaming_chunk: GenericStreamingChunk = GenericStreamingChunk(
+                        text=_data.get("text", ""),
+                        is_finished=_data.get("is_finished", False),
+                        finish_reason=_data.get("finish_reason", ""),
+                    )
+                    yield streaming_chunk
+
+    def _parse_message_from_event(self, event) -> Optional[str]:
+        response_dict = event.to_response_dict()
+        parsed_response = self.parser.parse(response_dict, get_response_stream_shape())
+        if response_dict["status_code"] != 200:
+            raise ValueError(f"Bad response code, expected 200: {response_dict}")
+
+        chunk = parsed_response.get("chunk")
+        if not chunk:
+            return None
+
+        return chunk.get("bytes").decode()  # type: ignore[no-any-return]

litellm/llms/clarifai.py

@@ -0,0 +1,328 @@
+import os, types, traceback
+import json
+import requests
+import time
+from typing import Callable, Optional
+from litellm.utils import ModelResponse, Usage, Choices, Message, CustomStreamWrapper
+import litellm
+import httpx
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+from .prompt_templates.factory import prompt_factory, custom_prompt
+
+
+class ClarifaiError(Exception):
+    def __init__(self, status_code, message, url):
+        self.status_code = status_code
+        self.message = message
+        self.request = httpx.Request(
+            method="POST", url=url
+        )
+        self.response = httpx.Response(status_code=status_code, request=self.request)
+        super().__init__(
+            self.message
+        )
+
+class ClarifaiConfig:
+    """
+    Reference: https://clarifai.com/meta/Llama-2/models/llama2-70b-chat
+    TODO fill in the details
+    """
+    max_tokens: Optional[int] = None
+    temperature: Optional[int] = None
+    top_k: Optional[int] = None
+
+    def __init__(
+            self,
+            max_tokens: Optional[int] = None,
+            temperature: Optional[int] = None,
+            top_k: Optional[int] = None,
+    ) -> None:
+        locals_ = locals()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+def validate_environment(api_key):
+    headers = {
+        "accept": "application/json",
+        "content-type": "application/json",
+    }
+    if api_key:
+        headers["Authorization"] = f"Bearer {api_key}"
+    return headers
+
+def completions_to_model(payload):
+        # if payload["n"] != 1:
+        #     raise HTTPException(
+        #         status_code=422,
+        #         detail="Only one generation is supported. Please set candidate_count to 1.",
+        #     )
+
+        params = {}
+        if temperature := payload.get("temperature"):
+            params["temperature"] = temperature
+        if max_tokens := payload.get("max_tokens"):
+            params["max_tokens"] = max_tokens
+        return {
+            "inputs": [{"data": {"text": {"raw": payload["prompt"]}}}],
+            "model": {"output_info": {"params": params}},
+}
+          
+def process_response(
+                     model,
+                     prompt,
+                     response,
+                     model_response,
+                     api_key,
+                     data,
+                     encoding, 
+                     logging_obj
+                     ):
+    logging_obj.post_call(
+            input=prompt,
+            api_key=api_key,
+            original_response=response.text,
+            additional_args={"complete_input_dict": data},
+        )
+        ## RESPONSE OBJECT
+    try:
+      completion_response = response.json()
+    except Exception:
+        raise ClarifaiError(
+            message=response.text, status_code=response.status_code, url=model
+        )
+    # print(completion_response)
+    try:
+        choices_list = []
+        for idx, item in enumerate(completion_response["outputs"]):
+            if len(item["data"]["text"]["raw"]) > 0:
+                message_obj = Message(content=item["data"]["text"]["raw"])
+            else:
+                message_obj = Message(content=None)
+            choice_obj = Choices(
+                finish_reason="stop",
+                index=idx + 1, #check
+                message=message_obj,
+            )
+            choices_list.append(choice_obj)
+        model_response["choices"] = choices_list
+
+    except Exception as e:
+        raise ClarifaiError(
+            message=traceback.format_exc(), status_code=response.status_code, url=model
+        )
+
+    # Calculate Usage
+    prompt_tokens = len(encoding.encode(prompt))
+    completion_tokens = len(
+        encoding.encode(model_response["choices"][0]["message"].get("content"))
+    )
+    model_response["model"] = model
+    model_response["usage"] = Usage(
+        prompt_tokens=prompt_tokens,
+        completion_tokens=completion_tokens,
+        total_tokens=prompt_tokens + completion_tokens,
+    )
+    return model_response
+
+def convert_model_to_url(model: str, api_base: str):
+    user_id, app_id, model_id = model.split(".")
+    return f"{api_base}/users/{user_id}/apps/{app_id}/models/{model_id}/outputs"
+
+def get_prompt_model_name(url: str):
+    clarifai_model_name = url.split("/")[-2]
+    if "claude" in clarifai_model_name:
+        return "anthropic", clarifai_model_name.replace("_", ".")
+    if ("llama" in clarifai_model_name)or ("mistral" in clarifai_model_name):
+        return "", "meta-llama/llama-2-chat"
+    else:
+        return "", clarifai_model_name
+
+async def async_completion(
+        model: str,
+        prompt: str,
+        api_base: str,
+        custom_prompt_dict: dict,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        encoding,
+        api_key,
+        logging_obj,
+        data=None,
+        optional_params=None,
+        litellm_params=None,
+        logger_fn=None,
+        headers={}):
+    
+    async_handler = AsyncHTTPHandler(
+            timeout=httpx.Timeout(timeout=600.0, connect=5.0)
+        )
+    response = await async_handler.post(
+            api_base, headers=headers, data=json.dumps(data)
+        )
+    
+    return process_response(
+            model=model,
+            prompt=prompt,
+            response=response,
+            model_response=model_response,
+            api_key=api_key,
+            data=data,
+            encoding=encoding,
+            logging_obj=logging_obj,
+    )
+
+def completion(
+    model: str,
+    messages: list,
+    api_base: str,
+    model_response: ModelResponse,
+    print_verbose: Callable,
+    encoding,
+    api_key,
+    logging_obj,
+    custom_prompt_dict={},
+    acompletion=False,
+    optional_params=None,
+    litellm_params=None,
+    logger_fn=None,
+):
+    headers = validate_environment(api_key)
+    model = convert_model_to_url(model, api_base)
+    prompt = " ".join(message["content"] for message in messages) # TODO
+
+    ## Load Config
+    config = litellm.ClarifaiConfig.get_config()
+    for k, v in config.items():
+        if (
+            k not in optional_params
+        ):
+            optional_params[k] = v
+
+    custom_llm_provider, orig_model_name = get_prompt_model_name(model)
+    if custom_llm_provider == "anthropic":
+        prompt = prompt_factory(
+            model=orig_model_name,
+            messages=messages,
+            api_key=api_key,
+            custom_llm_provider="clarifai"
+        )
+    else:
+        prompt = prompt_factory(
+            model=orig_model_name,
+            messages=messages,
+            api_key=api_key,
+            custom_llm_provider=custom_llm_provider
+        )
+    # print(prompt); exit(0)
+
+    data = {
+        "prompt": prompt,
+        **optional_params,
+    }
+    data = completions_to_model(data)
+
+
+    ## LOGGING
+    logging_obj.pre_call(
+        input=prompt,
+        api_key=api_key,
+        additional_args={
+            "complete_input_dict": data,
+            "headers": headers,
+            "api_base": api_base,
+        },
+    )
+    if acompletion==True:
+        return async_completion(
+            model=model,
+            prompt=prompt,
+            api_base=api_base,
+            custom_prompt_dict=custom_prompt_dict,
+            model_response=model_response,
+            print_verbose=print_verbose,
+            encoding=encoding,
+            api_key=api_key,
+            logging_obj=logging_obj,
+            data=data,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            logger_fn=logger_fn,
+            headers=headers,
+        )
+    else:
+        ## COMPLETION CALL
+        response = requests.post(
+        model,
+        headers=headers,
+        data=json.dumps(data),
+    )
+    # print(response.content); exit()
+
+    if response.status_code != 200:
+        raise ClarifaiError(status_code=response.status_code, message=response.text, url=model)
+    
+    if "stream" in optional_params and optional_params["stream"] == True:
+        completion_stream = response.iter_lines()
+        stream_response = CustomStreamWrapper(
+            completion_stream=completion_stream,
+            model=model,
+            custom_llm_provider="clarifai",
+            logging_obj=logging_obj,
+            )
+        return stream_response
+    
+    else:
+       return process_response(
+            model=model,
+            prompt=prompt,
+            response=response,
+            model_response=model_response,
+            api_key=api_key,
+            data=data,
+            encoding=encoding,
+            logging_obj=logging_obj)
+    
+
+class ModelResponseIterator:
+    def __init__(self, model_response):
+        self.model_response = model_response
+        self.is_done = False
+
+    # Sync iterator
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.is_done:
+            raise StopIteration
+        self.is_done = True
+        return self.model_response
+
+    # Async iterator
+    def __aiter__(self):
+        return self
+
+    async def __anext__(self):
+        if self.is_done:
+            raise StopAsyncIteration
+        self.is_done = True
+        return self.model_response

litellm/llms/custom_httpx/http_handler.py

@@ -58,16 +58,25 @@ class AsyncHTTPHandler:
 
 class HTTPHandler:
     def __init__(
-        self, timeout: httpx.Timeout = _DEFAULT_TIMEOUT, concurrent_limit=1000
+        self,
+        timeout: Optional[httpx.Timeout] = None,
+        concurrent_limit=1000,
+        client: Optional[httpx.Client] = None,
     ):
-        # Create a client with a connection pool
+        if timeout is None:
-        self.client = httpx.Client(
+            timeout = _DEFAULT_TIMEOUT
-            timeout=timeout,
+
-            limits=httpx.Limits(
+        if client is None:
-                max_connections=concurrent_limit,
+            # Create a client with a connection pool
-                max_keepalive_connections=concurrent_limit,
+            self.client = httpx.Client(
-            ),
+                timeout=timeout,
-        )
+                limits=httpx.Limits(
+                    max_connections=concurrent_limit,
+                    max_keepalive_connections=concurrent_limit,
+                ),
+            )
+        else:
+            self.client = client
 
     def close(self):
         # Close the client when you're done with it
@@ -82,11 +91,15 @@ class HTTPHandler:
     def post(
         self,
         url: str,
-        data: Optional[dict] = None,
+        data: Optional[Union[dict, str]] = None,
         params: Optional[dict] = None,
         headers: Optional[dict] = None,
+        stream: bool = False,
     ):
-        response = self.client.post(url, data=data, params=params, headers=headers)
+        req = self.client.build_request(
+            "POST", url, data=data, params=params, headers=headers  # type: ignore
+        )
+        response = self.client.send(req, stream=stream)
         return response
 
     def __del__(self) -> None:

litellm/llms/ollama_chat.py

@@ -300,7 +300,7 @@ def get_ollama_response(
         model_response["choices"][0]["message"] = message
         model_response["choices"][0]["finish_reason"] = "tool_calls"
     else:
-        model_response["choices"][0]["message"] = response_json["message"]
+        model_response["choices"][0]["message"]["content"] = response_json["message"]["content"]
     model_response["created"] = int(time.time())
     model_response["model"] = "ollama/" + model
     prompt_tokens = response_json.get("prompt_eval_count", litellm.token_counter(messages=messages))  # type: ignore
@@ -484,7 +484,7 @@ async def ollama_acompletion(
                 model_response["choices"][0]["message"] = message
                 model_response["choices"][0]["finish_reason"] = "tool_calls"
             else:
-                model_response["choices"][0]["message"] = response_json["message"]
+                model_response["choices"][0]["message"]["content"] = response_json["message"]["content"]
 
             model_response["created"] = int(time.time())
             model_response["model"] = "ollama_chat/" + data["model"]

litellm/llms/openai.py

@@ -53,6 +53,113 @@ class OpenAIError(Exception):
         )  # Call the base class constructor with the parameters it needs
 
 
+class MistralConfig:
+    """
+    Reference: https://docs.mistral.ai/api/
+
+    The class `MistralConfig` provides configuration for the Mistral's Chat API interface. Below are the parameters:
+
+    - `temperature` (number or null): Defines the sampling temperature to use, varying between 0 and 2. API Default - 0.7.
+
+    - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling. API Default - 1.
+
+    - `max_tokens` (integer or null): This optional parameter helps to set the maximum number of tokens to generate in the chat completion. API Default - null.
+
+    - `tools` (list or null): A list of available tools for the model. Use this to specify functions for which the model can generate JSON inputs.
+
+    - `tool_choice` (string - 'auto'/'any'/'none' or null): Specifies if/how functions are called. If set to none the model won't call a function and will generate a message instead. If set to auto the model can choose to either generate a message or call a function. If set to any the model is forced to call a function. Default - 'auto'.
+
+    - `random_seed` (integer or null): The seed to use for random sampling. If set, different calls will generate deterministic results.
+
+    - `safe_prompt` (boolean): Whether to inject a safety prompt before all conversations. API Default - 'false'.
+
+    - `response_format` (object or null): An object specifying the format that the model must output. Setting to { "type": "json_object" } enables JSON mode, which guarantees the message the model generates is in JSON. When using JSON mode you MUST also instruct the model to produce JSON yourself with a system or a user message.
+    """
+
+    temperature: Optional[int] = None
+    top_p: Optional[int] = None
+    max_tokens: Optional[int] = None
+    tools: Optional[list] = None
+    tool_choice: Optional[Literal["auto", "any", "none"]] = None
+    random_seed: Optional[int] = None
+    safe_prompt: Optional[bool] = None
+    response_format: Optional[dict] = None
+
+    def __init__(
+        self,
+        temperature: Optional[int] = None,
+        top_p: Optional[int] = None,
+        max_tokens: Optional[int] = None,
+        tools: Optional[list] = None,
+        tool_choice: Optional[Literal["auto", "any", "none"]] = None,
+        random_seed: Optional[int] = None,
+        safe_prompt: Optional[bool] = None,
+        response_format: Optional[dict] = None,
+    ) -> None:
+        locals_ = locals()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(self):
+        return [
+            "stream",
+            "temperature",
+            "top_p",
+            "max_tokens",
+            "tools",
+            "tool_choice",
+            "seed",
+            "response_format",
+        ]
+
+    def _map_tool_choice(self, tool_choice: str) -> str:
+        if tool_choice == "auto" or tool_choice == "none":
+            return tool_choice
+        elif tool_choice == "required":
+            return "any"
+        else:  # openai 'tool_choice' object param not supported by Mistral API
+            return "any"
+
+    def map_openai_params(self, non_default_params: dict, optional_params: dict):
+        for param, value in non_default_params.items():
+            if param == "max_tokens":
+                optional_params["max_tokens"] = value
+            if param == "tools":
+                optional_params["tools"] = value
+            if param == "stream" and value == True:
+                optional_params["stream"] = value
+            if param == "temperature":
+                optional_params["temperature"] = value
+            if param == "top_p":
+                optional_params["top_p"] = value
+            if param == "tool_choice" and isinstance(value, str):
+                optional_params["tool_choice"] = self._map_tool_choice(
+                    tool_choice=value
+                )
+            if param == "seed":
+                optional_params["extra_body"] = {"random_seed": value}
+        return optional_params
+
+
 class OpenAIConfig:
     """
     Reference: https://platform.openai.com/docs/api-reference/chat/create
@@ -1327,8 +1434,8 @@ class OpenAIAssistantsAPI(BaseLLM):
             client=client,
         )
 
-        thread_message: OpenAIMessage = openai_client.beta.threads.messages.create(
+        thread_message: OpenAIMessage = openai_client.beta.threads.messages.create(  # type: ignore
-            thread_id, **message_data
+            thread_id, **message_data  # type: ignore
         )
 
         response_obj: Optional[OpenAIMessage] = None
@@ -1458,7 +1565,7 @@ class OpenAIAssistantsAPI(BaseLLM):
             client=client,
         )
 
-        response = openai_client.beta.threads.runs.create_and_poll(
+        response = openai_client.beta.threads.runs.create_and_poll(  # type: ignore
             thread_id=thread_id,
             assistant_id=assistant_id,
             additional_instructions=additional_instructions,

litellm/llms/predibase.py

@@ -168,7 +168,7 @@ class PredibaseChatCompletion(BaseLLM):
         logging_obj: litellm.utils.Logging,
         optional_params: dict,
         api_key: str,
-        data: dict,
+        data: Union[dict, str],
         messages: list,
         print_verbose,
         encoding,
@@ -185,9 +185,7 @@ class PredibaseChatCompletion(BaseLLM):
         try:
             completion_response = response.json()
         except:
-            raise PredibaseError(
+            raise PredibaseError(message=response.text, status_code=422)
-                message=response.text, status_code=response.status_code
-            )
         if "error" in completion_response:
             raise PredibaseError(
                 message=str(completion_response["error"]),
@@ -363,7 +361,7 @@ class PredibaseChatCompletion(BaseLLM):
             },
         )
         ## COMPLETION CALL
-        if acompletion is True:
+        if acompletion == True:
             ### ASYNC STREAMING
             if stream == True:
                 return self.async_streaming(

litellm/llms/prompt_templates/factory.py

@@ -1509,6 +1509,11 @@ def prompt_factory(
                 model="meta-llama/Meta-Llama-3-8B-Instruct",
                 messages=messages,
             )
+
+    elif custom_llm_provider == "clarifai":
+        if "claude" in model:
+            return anthropic_pt(messages=messages)
+        
     elif custom_llm_provider == "perplexity":
         for message in messages:
             message.pop("name", None)

litellm/llms/vertex_ai.py

@@ -198,6 +198,23 @@ class VertexAIConfig:
                 optional_params[mapped_params[param]] = value
         return optional_params
 
+    def get_eu_regions(self) -> List[str]:
+        """
+        Source: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/locations#available-regions
+        """
+        return [
+            "europe-central2",
+            "europe-north1",
+            "europe-southwest1",
+            "europe-west1",
+            "europe-west2",
+            "europe-west3",
+            "europe-west4",
+            "europe-west6",
+            "europe-west8",
+            "europe-west9",
+        ]
+
 
 import asyncio
 
@@ -850,6 +867,8 @@ async def async_completion(
     Add support for acompletion calls for gemini-pro
     """
     try:
+        import proto  # type: ignore
+
         if mode == "vision":
             print_verbose("\nMaking VertexAI Gemini Pro/Vision Call")
             print_verbose(f"\nProcessing input messages = {messages}")
@@ -884,9 +903,21 @@ async def async_completion(
             ):
                 function_call = response.candidates[0].content.parts[0].function_call
                 args_dict = {}
-                for k, v in function_call.args.items():
+
-                    args_dict[k] = v
+                # Check if it's a RepeatedComposite instance
-                args_str = json.dumps(args_dict)
+                for key, val in function_call.args.items():
+                    if isinstance(
+                        val, proto.marshal.collections.repeated.RepeatedComposite
+                    ):
+                        # If so, convert to list
+                        args_dict[key] = [v for v in val]
+                    else:
+                        args_dict[key] = val
+
+                try:
+                    args_str = json.dumps(args_dict)
+                except Exception as e:
+                    raise VertexAIError(status_code=422, message=str(e))
                 message = litellm.Message(
                     content=None,
                     tool_calls=[

litellm/llms/watsonx.py

@@ -1,12 +1,26 @@
 from enum import Enum
 import json, types, time  # noqa: E401
-from contextlib import contextmanager
+from contextlib import asynccontextmanager, contextmanager
-from typing import Callable, Dict, Optional, Any, Union, List
+from typing import (
+    Callable,
+    Dict,
+    Generator,
+    AsyncGenerator,
+    Iterator,
+    AsyncIterator,
+    Optional,
+    Any,
+    Union,
+    List,
+    ContextManager,
+    AsyncContextManager,
+)
 
 import httpx  # type: ignore
 import requests  # type: ignore
 import litellm
-from litellm.utils import ModelResponse, get_secret, Usage
+from litellm.utils import ModelResponse, Usage, get_secret
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
 
 from .base import BaseLLM
 from .prompt_templates import factory as ptf
@@ -149,6 +163,15 @@ class IBMWatsonXAIConfig:
                 optional_params[mapped_params[param]] = value
         return optional_params
 
+    def get_eu_regions(self) -> List[str]:
+        """
+        Source: https://www.ibm.com/docs/en/watsonx/saas?topic=integrations-regional-availability
+        """
+        return [
+            "eu-de",
+            "eu-gb",
+        ]
+
 
 def convert_messages_to_prompt(model, messages, provider, custom_prompt_dict):
     # handle anthropic prompts and amazon titan prompts
@@ -188,11 +211,12 @@ class WatsonXAIEndpoint(str, Enum):
     )
     EMBEDDINGS = "/ml/v1/text/embeddings"
     PROMPTS = "/ml/v1/prompts"
+    AVAILABLE_MODELS = "/ml/v1/foundation_model_specs"
 
 
 class IBMWatsonXAI(BaseLLM):
     """
-    Class to interface with IBM Watsonx.ai API for text generation and embeddings.
+    Class to interface with IBM watsonx.ai API for text generation and embeddings.
 
     Reference: https://cloud.ibm.com/apidocs/watsonx-ai
     """
@@ -343,7 +367,7 @@ class IBMWatsonXAI(BaseLLM):
             )
         if token is None and api_key is not None:
             # generate the auth token
-            if print_verbose:
+            if print_verbose is not None:
                 print_verbose("Generating IAM token for Watsonx.ai")
             token = self.generate_iam_token(api_key)
         elif token is None and api_key is None:
@@ -378,10 +402,11 @@ class IBMWatsonXAI(BaseLLM):
         print_verbose: Callable,
         encoding,
         logging_obj,
-        optional_params: dict,
+        optional_params=None,
-        litellm_params: Optional[dict] = None,
+        acompletion=None,
+        litellm_params=None,
         logger_fn=None,
-        timeout: Optional[float] = None,
+        timeout=None,
     ):
         """
         Send a text generation request to the IBM Watsonx.ai API.
@@ -402,12 +427,12 @@ class IBMWatsonXAI(BaseLLM):
             model, messages, provider, custom_prompt_dict
         )
 
-        def process_text_request(request_params: dict) -> ModelResponse:
+        def process_text_gen_response(json_resp: dict) -> ModelResponse:
-            with self._manage_response(
+            if "results" not in json_resp:
-                request_params, logging_obj=logging_obj, input=prompt, timeout=timeout
+                raise WatsonXAIError(
-            ) as resp:
+                    status_code=500,
-                json_resp = resp.json()
+                    message=f"Error: Invalid response from Watsonx.ai API: {json_resp}",
-
+                )
             generated_text = json_resp["results"][0]["generated_text"]
             prompt_tokens = json_resp["results"][0]["input_token_count"]
             completion_tokens = json_resp["results"][0]["generated_token_count"]
@@ -415,36 +440,70 @@ class IBMWatsonXAI(BaseLLM):
             model_response["finish_reason"] = json_resp["results"][0]["stop_reason"]
             model_response["created"] = int(time.time())
             model_response["model"] = model
-            setattr(
+            usage = Usage(
-                model_response,
+                prompt_tokens=prompt_tokens,
-                "usage",
+                completion_tokens=completion_tokens,
-                Usage(
+                total_tokens=prompt_tokens + completion_tokens,
-                    prompt_tokens=prompt_tokens,
-                    completion_tokens=completion_tokens,
-                    total_tokens=prompt_tokens + completion_tokens,
-                ),
             )
+            setattr(model_response, "usage", usage)
             return model_response
 
-        def process_stream_request(
+        def process_stream_response(
-            request_params: dict,
+            stream_resp: Union[Iterator[str], AsyncIterator],
         ) -> litellm.CustomStreamWrapper:
+            streamwrapper = litellm.CustomStreamWrapper(
+                stream_resp,
+                model=model,
+                custom_llm_provider="watsonx",
+                logging_obj=logging_obj,
+            )
+            return streamwrapper
+
+        # create the function to manage the request to watsonx.ai
+        self.request_manager = RequestManager(logging_obj)
+
+        def handle_text_request(request_params: dict) -> ModelResponse:
+            with self.request_manager.request(
+                request_params,
+                input=prompt,
+                timeout=timeout,
+            ) as resp:
+                json_resp = resp.json()
+
+            return process_text_gen_response(json_resp)
+
+        async def handle_text_request_async(request_params: dict) -> ModelResponse:
+            async with self.request_manager.async_request(
+                request_params,
+                input=prompt,
+                timeout=timeout,
+            ) as resp:
+                json_resp = resp.json()
+            return process_text_gen_response(json_resp)
+
+        def handle_stream_request(request_params: dict) -> litellm.CustomStreamWrapper:
             # stream the response - generated chunks will be handled
             # by litellm.utils.CustomStreamWrapper.handle_watsonx_stream
-            with self._manage_response(
+            with self.request_manager.request(
                 request_params,
-                logging_obj=logging_obj,
                 stream=True,
                 input=prompt,
                 timeout=timeout,
             ) as resp:
-                response = litellm.CustomStreamWrapper(
+                streamwrapper = process_stream_response(resp.iter_lines())
-                    resp.iter_lines(),
+            return streamwrapper
-                    model=model,
+
-                    custom_llm_provider="watsonx",
+        async def handle_stream_request_async(request_params: dict) -> litellm.CustomStreamWrapper:
-                    logging_obj=logging_obj,
+            # stream the response - generated chunks will be handled
-                )
+            # by litellm.utils.CustomStreamWrapper.handle_watsonx_stream
-            return response
+            async with self.request_manager.async_request(
+                request_params,
+                stream=True,
+                input=prompt,
+                timeout=timeout,
+            ) as resp:
+                streamwrapper = process_stream_response(resp.aiter_lines())
+            return streamwrapper
 
         try:
             ## Get the response from the model
@@ -455,10 +514,18 @@ class IBMWatsonXAI(BaseLLM):
                 optional_params=optional_params,
                 print_verbose=print_verbose,
             )
-            if stream:
+            if stream and (acompletion is True):
-                return process_stream_request(req_params)
+                # stream and async text generation
+                return handle_stream_request_async(req_params)
+            elif stream:
+                # streaming text generation
+                return handle_stream_request(req_params)
+            elif (acompletion is True):
+                # async text generation
+                return handle_text_request_async(req_params)
             else:
-                return process_text_request(req_params)
+                # regular text generation
+                return handle_text_request(req_params)
         except WatsonXAIError as e:
             raise e
         except Exception as e:
@@ -473,6 +540,7 @@ class IBMWatsonXAI(BaseLLM):
         model_response=None,
         optional_params=None,
         encoding=None,
+        aembedding=None,
     ):
         """
         Send a text embedding request to the IBM Watsonx.ai API.
@@ -507,9 +575,6 @@ class IBMWatsonXAI(BaseLLM):
         }
         request_params = dict(version=api_params["api_version"])
         url = api_params["url"].rstrip("/") + WatsonXAIEndpoint.EMBEDDINGS
-        # request = httpx.Request(
-        #     "POST", url, headers=headers, json=payload, params=request_params
-        # )
         req_params = {
             "method": "POST",
             "url": url,
@@ -517,25 +582,49 @@ class IBMWatsonXAI(BaseLLM):
             "json": payload,
             "params": request_params,
         }
-        with self._manage_response(
+        request_manager = RequestManager(logging_obj)
-            req_params, logging_obj=logging_obj, input=input
-        ) as resp:
-            json_resp = resp.json()
 
-        results = json_resp.get("results", [])
+        def process_embedding_response(json_resp: dict) -> ModelResponse:
-        embedding_response = []
+            results = json_resp.get("results", [])
-        for idx, result in enumerate(results):
+            embedding_response = []
-            embedding_response.append(
+            for idx, result in enumerate(results):
-                {"object": "embedding", "index": idx, "embedding": result["embedding"]}
+                embedding_response.append(
+                    {
+                        "object": "embedding",
+                        "index": idx,
+                        "embedding": result["embedding"],
+                    }
+                )
+            model_response["object"] = "list"
+            model_response["data"] = embedding_response
+            model_response["model"] = model
+            input_tokens = json_resp.get("input_token_count", 0)
+            model_response.usage = Usage(
+                prompt_tokens=input_tokens,
+                completion_tokens=0,
+                total_tokens=input_tokens,
             )
-        model_response["object"] = "list"
+            return model_response
-        model_response["data"] = embedding_response
+
-        model_response["model"] = model
+        def handle_embedding(request_params: dict) -> ModelResponse:
-        input_tokens = json_resp.get("input_token_count", 0)
+            with request_manager.request(request_params, input=input) as resp:
-        model_response.usage = Usage(
+                json_resp = resp.json()
-            prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
+            return process_embedding_response(json_resp)
-        )
+
-        return model_response
+        async def handle_aembedding(request_params: dict) -> ModelResponse:
+            async with request_manager.async_request(request_params, input=input) as resp:
+                json_resp = resp.json()
+            return process_embedding_response(json_resp)
+
+        try:
+            if aembedding is True:
+                return handle_embedding(req_params)
+            else:
+                return handle_aembedding(req_params)
+        except WatsonXAIError as e:
+            raise e
+        except Exception as e:
+            raise WatsonXAIError(status_code=500, message=str(e))
 
     def generate_iam_token(self, api_key=None, **params):
         headers = {}
@@ -558,52 +647,144 @@ class IBMWatsonXAI(BaseLLM):
         self.token = iam_access_token
         return iam_access_token
 
-    @contextmanager
+    def get_available_models(self, *, ids_only: bool = True, **params):
-    def _manage_response(
+        api_params = self._get_api_params(params)
-        self,
+        headers = {
-        request_params: dict,
+            "Authorization": f"Bearer {api_params['token']}",
-        logging_obj: Any,
+            "Content-Type": "application/json",
-        stream: bool = False,
+            "Accept": "application/json",
-        input: Optional[Any] = None,
+        }
-        timeout: Optional[float] = None,
+        request_params = dict(version=api_params["api_version"])
-    ):
+        url = api_params["url"].rstrip("/") + WatsonXAIEndpoint.AVAILABLE_MODELS
-        request_str = (
+        req_params = dict(method="GET", url=url, headers=headers, params=request_params)
-            f"response = {request_params['method']}(\n"
+        with RequestManager(logging_obj=None).request(req_params) as resp:
-            f"\turl={request_params['url']},\n"
+            json_resp = resp.json()
-            f"\tjson={request_params['json']},\n"
+        if not ids_only:
-            f")"
+            return json_resp
-        )
+        return [res["model_id"] for res in json_resp["resources"]]
-        logging_obj.pre_call(
+
-            input=input,
+class RequestManager:
-            api_key=request_params["headers"].get("Authorization"),
+        """
-            additional_args={
+        Returns a context manager that manages the response from the request.
-                "complete_input_dict": request_params["json"],
+        if async_ is True, returns an async context manager, otherwise returns a regular context manager.
-                "request_str": request_str,
+
-            },
+        Usage:
-        )
+        ```python
-        if timeout:
+        request_params = dict(method="POST", url="https://api.example.com", headers={"Authorization" : "Bearer token"}, json={"key": "value"})
-            request_params["timeout"] = timeout
+        request_manager = RequestManager(logging_obj=logging_obj)
-        try:
+        async with request_manager.request(request_params) as resp:
-            if stream:
+            ...
-                resp = requests.request(
+        # or
-                    **request_params,
+        with request_manager.async_request(request_params) as resp:
-                    stream=True,
+            ...
-                )
+        ```
-                resp.raise_for_status()
+        """
-                yield resp
+
-            else:
+        def __init__(self, logging_obj=None):
-                resp = requests.request(**request_params)
+            self.logging_obj = logging_obj
-                resp.raise_for_status()
+
-                yield resp
+        def pre_call(
-        except Exception as e:
+            self,
-            raise WatsonXAIError(status_code=500, message=str(e))
+            request_params: dict,
-        if not stream:
+            input: Optional[Any] = None,
-            logging_obj.post_call(
+        ):
+            if self.logging_obj is None:
+                return
+            request_str = (
+                f"response = {request_params['method']}(\n"
+                f"\turl={request_params['url']},\n"
+                f"\tjson={request_params.get('json')},\n"
+                f")"
+            )
+            self.logging_obj.pre_call(
+                input=input,
+                api_key=request_params["headers"].get("Authorization"),
+                additional_args={
+                    "complete_input_dict": request_params.get("json"),
+                    "request_str": request_str,
+                },
+            )
+
+        def post_call(self, resp, request_params):
+            if self.logging_obj is None:
+                return
+            self.logging_obj.post_call(
                 input=input,
                 api_key=request_params["headers"].get("Authorization"),
                 original_response=json.dumps(resp.json()),
                 additional_args={
                     "status_code": resp.status_code,
-                    "complete_input_dict": request_params["json"],
+                    "complete_input_dict": request_params.get(
+                        "data", request_params.get("json")
+                    ),
                 },
             )
+
+        @contextmanager
+        def request(
+            self, 
+            request_params: dict,
+            stream: bool = False,
+            input: Optional[Any] = None,
+            timeout=None,
+        ) -> Generator[requests.Response, None, None]:
+            """
+            Returns a context manager that yields the response from the request.
+            """
+            self.pre_call(request_params, input)
+            if timeout:
+                request_params["timeout"] = timeout
+            if stream:
+                request_params["stream"] = stream
+            try:
+                resp = requests.request(**request_params)
+                if not resp.ok:
+                    raise WatsonXAIError(
+                        status_code=resp.status_code,
+                        message=f"Error {resp.status_code} ({resp.reason}): {resp.text}",
+                    )
+                yield resp
+            except Exception as e:
+                raise WatsonXAIError(status_code=500, message=str(e))
+            if not stream:
+                self.post_call(resp, request_params)
+
+        @asynccontextmanager
+        async def async_request(
+            self, 
+            request_params: dict,
+            stream: bool = False,
+            input: Optional[Any] = None,
+            timeout=None,
+        ) -> AsyncGenerator[httpx.Response, None]:
+            self.pre_call(request_params, input)
+            if timeout:
+                request_params["timeout"] = timeout
+            if stream:
+                request_params["stream"] = stream
+            try:
+                # async with AsyncHTTPHandler(timeout=timeout) as client:
+                self.async_handler = AsyncHTTPHandler(
+                    timeout=httpx.Timeout(
+                        timeout=request_params.pop("timeout", 600.0), connect=5.0
+                    ),
+                )
+                # async_handler.client.verify = False
+                if "json" in request_params:
+                    request_params["data"] = json.dumps(request_params.pop("json", {}))
+                method = request_params.pop("method")
+                if method.upper() == "POST":
+                    resp = await self.async_handler.post(**request_params)
+                else:
+                    resp = await self.async_handler.get(**request_params)
+                if resp.status_code not in [200, 201]:
+                    raise WatsonXAIError(
+                        status_code=resp.status_code,
+                        message=f"Error {resp.status_code} ({resp.reason}): {resp.text}",
+                    )
+                yield resp
+                # await async_handler.close()
+            except Exception as e:
+                raise WatsonXAIError(status_code=500, message=str(e))
+            if not stream:
+                self.post_call(resp, request_params)

litellm/main.py

@@ -9,6 +9,7 @@
 
 import os, openai, sys, json, inspect, uuid, datetime, threading
 from typing import Any, Literal, Union, BinaryIO
+from typing_extensions import overload
 from functools import partial
 import dotenv, traceback, random, asyncio, time, contextvars
 from copy import deepcopy
@@ -56,6 +57,7 @@ from .llms import (
     ollama,
     ollama_chat,
     cloudflare,
+    clarifai,
     cohere,
     cohere_chat,
     petals,
@@ -75,6 +77,7 @@ from .llms.anthropic import AnthropicChatCompletion
 from .llms.anthropic_text import AnthropicTextCompletion
 from .llms.huggingface_restapi import Huggingface
 from .llms.predibase import PredibaseChatCompletion
+from .llms.bedrock_httpx import BedrockLLM
 from .llms.triton import TritonChatCompletion
 from .llms.prompt_templates.factory import (
     prompt_factory,
@@ -104,7 +107,6 @@ from litellm.utils import (
 )
 
 ####### ENVIRONMENT VARIABLES ###################
-dotenv.load_dotenv()  # Loading env variables using dotenv
 openai_chat_completions = OpenAIChatCompletion()
 openai_text_completions = OpenAITextCompletion()
 anthropic_chat_completions = AnthropicChatCompletion()
@@ -114,6 +116,7 @@ azure_text_completions = AzureTextCompletion()
 huggingface = Huggingface()
 predibase_chat_completions = PredibaseChatCompletion()
 triton_chat_completions = TritonChatCompletion()
+bedrock_chat_completion = BedrockLLM()
 ####### COMPLETION ENDPOINTS ################
 
 
@@ -256,7 +259,7 @@ async def acompletion(
         - If `stream` is True, the function returns an async generator that yields completion lines.
     """
     loop = asyncio.get_event_loop()
-    custom_llm_provider = None
+    custom_llm_provider = kwargs.get("custom_llm_provider", None)
     # Adjusted to use explicit arguments instead of *args and **kwargs
     completion_kwargs = {
         "model": model,
@@ -288,9 +291,10 @@ async def acompletion(
         "model_list": model_list,
         "acompletion": True,  # assuming this is a required parameter
     }
-    _, custom_llm_provider, _, _ = get_llm_provider(
+    if custom_llm_provider is None:
-        model=model, api_base=completion_kwargs.get("base_url", None)
+        _, custom_llm_provider, _, _ = get_llm_provider(
-    )
+            model=model, api_base=completion_kwargs.get("base_url", None)
+        )
     try:
         # Use a partial function to pass your keyword arguments
         func = partial(completion, **completion_kwargs, **kwargs)
@@ -299,9 +303,6 @@ async def acompletion(
         ctx = contextvars.copy_context()
         func_with_context = partial(ctx.run, func)
 
-        _, custom_llm_provider, _, _ = get_llm_provider(
-            model=model, api_base=kwargs.get("api_base", None)
-        )
         if (
             custom_llm_provider == "openai"
             or custom_llm_provider == "azure"
@@ -323,6 +324,7 @@ async def acompletion(
             or custom_llm_provider == "sagemaker"
             or custom_llm_provider == "anthropic"
             or custom_llm_provider == "predibase"
+            or (custom_llm_provider == "bedrock" and "cohere" in model)
             or custom_llm_provider in litellm.openai_compatible_providers
         ):  # currently implemented aiohttp calls for just azure, openai, hf, ollama, vertex ai soon all.
             init_response = await loop.run_in_executor(None, func_with_context)
@@ -725,7 +727,6 @@ def completion(
 
         ### REGISTER CUSTOM MODEL PRICING -- IF GIVEN ###
         if input_cost_per_token is not None and output_cost_per_token is not None:
-            print_verbose(f"Registering model={model} in model cost map")
             litellm.register_model(
                 {
                     f"{custom_llm_provider}/{model}": {
@@ -847,6 +848,10 @@ def completion(
             proxy_server_request=proxy_server_request,
             preset_cache_key=preset_cache_key,
             no_log=no_log,
+            input_cost_per_second=input_cost_per_second,
+            input_cost_per_token=input_cost_per_token,
+            output_cost_per_second=output_cost_per_second,
+            output_cost_per_token=output_cost_per_token,
         )
         logging.update_environment_variables(
             model=model,
@@ -1212,6 +1217,61 @@ def completion(
                 )
 
             response = model_response
+        elif (
+            "clarifai" in model
+            or custom_llm_provider == "clarifai"
+            or model in litellm.clarifai_models
+        ):
+            clarifai_key = None
+            clarifai_key = (
+                api_key
+                or litellm.clarifai_key
+                or litellm.api_key
+                or get_secret("CLARIFAI_API_KEY")
+                or get_secret("CLARIFAI_API_TOKEN")
+            )
+
+            api_base = (
+                api_base
+                or litellm.api_base
+                or get_secret("CLARIFAI_API_BASE")
+                or "https://api.clarifai.com/v2"
+            )
+
+            custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
+            model_response = clarifai.completion(
+                model=model,
+                messages=messages,
+                api_base=api_base,
+                model_response=model_response,
+                print_verbose=print_verbose,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                acompletion=acompletion,
+                logger_fn=logger_fn,
+                encoding=encoding,  # for calculating input/output tokens
+                api_key=clarifai_key,
+                logging_obj=logging,
+                custom_prompt_dict=custom_prompt_dict,
+            )
+
+            if "stream" in optional_params and optional_params["stream"] == True:
+                # don't try to access stream object,
+                ## LOGGING
+                logging.post_call(
+                    input=messages,
+                    api_key=api_key,
+                    original_response=model_response,
+                )
+
+            if optional_params.get("stream", False) or acompletion == True:
+                ## LOGGING
+                logging.post_call(
+                    input=messages,
+                    api_key=clarifai_key,
+                    original_response=model_response,
+                )
+            response = model_response
 
         elif custom_llm_provider == "anthropic":
             api_key = (
@@ -1921,41 +1981,59 @@ def completion(
         elif custom_llm_provider == "bedrock":
             # boto3 reads keys from .env
             custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
-            response = bedrock.completion(
-                model=model,
-                messages=messages,
-                custom_prompt_dict=litellm.custom_prompt_dict,
-                model_response=model_response,
-                print_verbose=print_verbose,
-                optional_params=optional_params,
-                litellm_params=litellm_params,
-                logger_fn=logger_fn,
-                encoding=encoding,
-                logging_obj=logging,
-                extra_headers=extra_headers,
-                timeout=timeout,
-            )
 
-            if (
+            if "cohere" in model:
-                "stream" in optional_params
+                response = bedrock_chat_completion.completion(
-                and optional_params["stream"] == True
+                    model=model,
-                and not isinstance(response, CustomStreamWrapper)
+                    messages=messages,
-            ):
+                    custom_prompt_dict=litellm.custom_prompt_dict,
-                # don't try to access stream object,
+                    model_response=model_response,
-                if "ai21" in model:
+                    print_verbose=print_verbose,
-                    response = CustomStreamWrapper(
+                    optional_params=optional_params,
-                        response,
+                    litellm_params=litellm_params,
-                        model,
+                    logger_fn=logger_fn,
-                        custom_llm_provider="bedrock",
+                    encoding=encoding,
-                        logging_obj=logging,
+                    logging_obj=logging,
-                    )
+                    extra_headers=extra_headers,
-                else:
+                    timeout=timeout,
-                    response = CustomStreamWrapper(
+                    acompletion=acompletion,
-                        iter(response),
+                )
-                        model,
+            else:
-                        custom_llm_provider="bedrock",
+                response = bedrock.completion(
-                        logging_obj=logging,
+                    model=model,
-                    )
+                    messages=messages,
+                    custom_prompt_dict=litellm.custom_prompt_dict,
+                    model_response=model_response,
+                    print_verbose=print_verbose,
+                    optional_params=optional_params,
+                    litellm_params=litellm_params,
+                    logger_fn=logger_fn,
+                    encoding=encoding,
+                    logging_obj=logging,
+                    extra_headers=extra_headers,
+                    timeout=timeout,
+                )
+
+                if (
+                    "stream" in optional_params
+                    and optional_params["stream"] == True
+                    and not isinstance(response, CustomStreamWrapper)
+                ):
+                    # don't try to access stream object,
+                    if "ai21" in model:
+                        response = CustomStreamWrapper(
+                            response,
+                            model,
+                            custom_llm_provider="bedrock",
+                            logging_obj=logging,
+                        )
+                    else:
+                        response = CustomStreamWrapper(
+                            iter(response),
+                            model,
+                            custom_llm_provider="bedrock",
+                            logging_obj=logging,
+                        )
 
             if optional_params.get("stream", False):
                 ## LOGGING

litellm/model_prices_and_context_window_backup.json

@@ -9,6 +9,30 @@
         "mode": "chat",
         "supports_function_calling": true
     },
+    "gpt-4o": {
+        "max_tokens": 4096,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 4096,
+        "input_cost_per_token": 0.000005,
+        "output_cost_per_token": 0.000015,
+        "litellm_provider": "openai",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_vision": true
+    },
+    "gpt-4o-2024-05-13": {
+        "max_tokens": 4096,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 4096,
+        "input_cost_per_token": 0.000005,
+        "output_cost_per_token": 0.000015,
+        "litellm_provider": "openai",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_vision": true
+    },
     "gpt-4-turbo-preview": {
         "max_tokens": 4096,
         "max_input_tokens": 128000,
@@ -1571,6 +1595,135 @@
         "litellm_provider": "replicate",
         "mode": "chat"
     },
+    "openrouter/microsoft/wizardlm-2-8x22b:nitro": {
+        "max_tokens": 65536,
+        "input_cost_per_token": 0.000001,
+        "output_cost_per_token": 0.000001,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
+    "openrouter/google/gemini-pro-1.5": {
+        "max_tokens": 8192,
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 8192,
+        "input_cost_per_token": 0.0000025,
+        "output_cost_per_token": 0.0000075,
+        "input_cost_per_image": 0.00265, 
+        "litellm_provider": "openrouter",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_vision": true
+    },
+    "openrouter/mistralai/mixtral-8x22b-instruct": {
+        "max_tokens": 65536,
+        "input_cost_per_token": 0.00000065,
+        "output_cost_per_token": 0.00000065,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
+    "openrouter/cohere/command-r-plus": {
+        "max_tokens": 128000,
+        "input_cost_per_token": 0.000003,
+        "output_cost_per_token": 0.000015,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
+    "openrouter/databricks/dbrx-instruct": {
+        "max_tokens": 32768,
+        "input_cost_per_token": 0.0000006,
+        "output_cost_per_token": 0.0000006,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
+    "openrouter/anthropic/claude-3-haiku": {
+        "max_tokens": 200000,
+        "input_cost_per_token": 0.00000025,
+        "output_cost_per_token": 0.00000125,
+        "input_cost_per_image": 0.0004, 
+        "litellm_provider": "openrouter",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_vision": true
+    },
+    "openrouter/anthropic/claude-3-sonnet": {
+        "max_tokens": 200000,
+        "input_cost_per_token": 0.000003,
+        "output_cost_per_token": 0.000015,
+        "input_cost_per_image": 0.0048,  
+        "litellm_provider": "openrouter",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_vision": true
+    },
+    "openrouter/mistralai/mistral-large": {
+        "max_tokens": 32000,
+        "input_cost_per_token": 0.000008,
+        "output_cost_per_token": 0.000024,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
+    "openrouter/cognitivecomputations/dolphin-mixtral-8x7b": {
+        "max_tokens": 32769,
+        "input_cost_per_token": 0.0000005,
+        "output_cost_per_token": 0.0000005,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
+    "openrouter/google/gemini-pro-vision": {
+        "max_tokens": 45875,
+        "input_cost_per_token": 0.000000125,
+        "output_cost_per_token": 0.000000375,
+        "input_cost_per_image": 0.0025,  
+        "litellm_provider": "openrouter",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_vision": true
+    },
+    "openrouter/fireworks/firellava-13b": {
+        "max_tokens": 4096,
+        "input_cost_per_token": 0.0000002,
+        "output_cost_per_token": 0.0000002,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
+    "openrouter/meta-llama/llama-3-8b-instruct:free": {
+        "max_tokens": 8192,
+        "input_cost_per_token": 0.0,
+        "output_cost_per_token": 0.0,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
+    "openrouter/meta-llama/llama-3-8b-instruct:extended": {
+        "max_tokens": 16384,
+        "input_cost_per_token": 0.000000225,
+        "output_cost_per_token": 0.00000225,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
+    "openrouter/meta-llama/llama-3-70b-instruct:nitro": {
+        "max_tokens": 8192,
+        "input_cost_per_token": 0.0000009,
+        "output_cost_per_token": 0.0000009,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
+    "openrouter/meta-llama/llama-3-70b-instruct": {
+        "max_tokens": 8192,
+        "input_cost_per_token": 0.00000059,
+        "output_cost_per_token": 0.00000079,
+        "litellm_provider": "openrouter",
+        "mode": "chat"
+    },
+    "openrouter/openai/gpt-4-vision-preview": {
+        "max_tokens": 130000,
+        "input_cost_per_token": 0.00001,
+        "output_cost_per_token": 0.00003,
+        "input_cost_per_image": 0.01445, 
+        "litellm_provider": "openrouter",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_vision": true
+    },
     "openrouter/openai/gpt-3.5-turbo": {
         "max_tokens": 4095,
         "input_cost_per_token": 0.0000015,
@@ -1621,14 +1774,14 @@
         "tool_use_system_prompt_tokens": 395
     },
     "openrouter/google/palm-2-chat-bison": {
-        "max_tokens": 8000,
+        "max_tokens": 25804,
         "input_cost_per_token": 0.0000005,
         "output_cost_per_token": 0.0000005,
         "litellm_provider": "openrouter",
         "mode": "chat"
     },
     "openrouter/google/palm-2-codechat-bison": {
-        "max_tokens": 8000,
+        "max_tokens": 20070,
         "input_cost_per_token": 0.0000005,
         "output_cost_per_token": 0.0000005,
         "litellm_provider": "openrouter",
@@ -1711,13 +1864,6 @@
         "litellm_provider": "openrouter",
         "mode": "chat"
     },
-    "openrouter/meta-llama/llama-3-70b-instruct": {
-        "max_tokens": 8192,
-        "input_cost_per_token": 0.0000008,
-        "output_cost_per_token": 0.0000008,
-        "litellm_provider": "openrouter",
-        "mode": "chat"
-    },
     "j2-ultra": {
         "max_tokens": 8192,
         "max_input_tokens": 8192,
@@ -2522,6 +2668,24 @@
         "litellm_provider": "bedrock",
         "mode": "chat"
     },
+    "cohere.command-r-plus-v1:0": {
+        "max_tokens": 4096, 
+        "max_input_tokens": 128000,
+        "max_output_tokens": 4096,
+        "input_cost_per_token": 0.0000030,
+        "output_cost_per_token": 0.000015,
+        "litellm_provider": "bedrock",
+        "mode": "chat"
+    },
+    "cohere.command-r-v1:0": {
+        "max_tokens": 4096, 
+        "max_input_tokens": 128000,
+        "max_output_tokens": 4096,
+        "input_cost_per_token": 0.0000005,
+        "output_cost_per_token": 0.0000015,
+        "litellm_provider": "bedrock",
+        "mode": "chat"
+    },
     "cohere.embed-english-v3": {
         "max_tokens": 512, 
         "max_input_tokens": 512, 

litellm/proxy/_experimental/out/_next/static/chunks/webpack-de9c0fadf6a94b3b.js

@@ -1 +1 @@
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+0:["obp5wqVSVDMiDTC414cR8",[[["",{"children":["__PAGE__",{}]},"$undefined","$undefined",true],["",{"children":["__PAGE__",{},["$L1",["$","$L2",null,{"propsForComponent":{"params":{}},"Component":"$3","isStaticGeneration":true}],null]]},[null,["$","html",null,{"lang":"en","children":["$","body",null,{"className":"__className_c23dc8","children":["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children"],"loading":"$undefined","loadingStyles":"$undefined","loadingScripts":"$undefined","hasLoading":false,"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":"404"}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],"notFoundStyles":[],"styles":null}]}]}],null]],[[["$","link","0",{"rel":"stylesheet","href":"/ui/_next/static/css/f04e46b02318b660.css","precedence":"next","crossOrigin":""}]],"$L6"]]]]
 6:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"LiteLLM Dashboard"}],["$","meta","3",{"name":"description","content":"LiteLLM Proxy Admin UI"}],["$","link","4",{"rel":"icon","href":"/ui/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","meta","5",{"name":"next-size-adjust"}]]
 1:null

litellm/proxy/_super_secret_config.yaml

@@ -1,33 +1,35 @@
 model_list:
 - litellm_params:
-    api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
+    api_base: os.environ/AZURE_API_BASE
-    api_key: my-fake-key
+    api_key: os.environ/AZURE_API_KEY
-    model: openai/my-fake-model
+    api_version: 2023-07-01-preview
-  model_name: fake-openai-endpoint
+    model: azure/azure-embedding-model
-- litellm_params:
+  model_info:
-    api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
+    base_model: text-embedding-ada-002
-    api_key: my-fake-key-2
+    mode: embedding
-    model: openai/my-fake-model-2
+  model_name: text-embedding-ada-002
-  model_name: fake-openai-endpoint
+- model_name: gpt-3.5-turbo-012
-- litellm_params:
-    api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
-    api_key: my-fake-key-3
-    model: openai/my-fake-model-3
-  model_name: fake-openai-endpoint
-- model_name: gpt-4
   litellm_params:
     model: gpt-3.5-turbo
-- litellm_params:
+    api_base: http://0.0.0.0:8080
-    model: together_ai/codellama/CodeLlama-13b-Instruct-hf
+    api_key: "" 
-  model_name: CodeLlama-13b-Instruct
+- model_name: gpt-3.5-turbo-0125-preview
+  litellm_params:
+    model: azure/chatgpt-v-2
+    api_key: os.environ/AZURE_API_KEY
+    api_base: os.environ/AZURE_API_BASE
+    input_cost_per_token: 0.0
+    output_cost_per_token: 0.0
 
 router_settings:
   redis_host: redis
   # redis_password: <your redis password>
   redis_port: 6379
+  enable_pre_call_checks: true
 
 litellm_settings:
   set_verbose: True
+  fallbacks: [{"gpt-3.5-turbo-012": ["gpt-3.5-turbo-0125-preview"]}]
   # service_callback: ["prometheus_system"]
   # success_callback: ["prometheus"]
   # failure_callback: ["prometheus"]
@@ -36,4 +38,5 @@ general_settings:
   enable_jwt_auth: True
   disable_reset_budget: True
   proxy_batch_write_at: 60 # 👈 Frequency of batch writing logs to server (in seconds)
-  routing_strategy: simple-shuffle # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle"
+  routing_strategy: simple-shuffle # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle"
+  alerting: ["slack"]

litellm/proxy/_types.py

@@ -1,11 +1,20 @@
-from pydantic import BaseModel, Extra, Field, root_validator, Json, validator
+from pydantic import ConfigDict, BaseModel, Field, root_validator, Json
-from dataclasses import fields
 import enum
 from typing import Optional, List, Union, Dict, Literal, Any
 from datetime import datetime
-import uuid, json, sys, os
+import uuid
+import json
 from litellm.types.router import UpdateRouterConfig
 
+try:
+    from pydantic import model_validator  # pydantic v2
+except ImportError:
+    from pydantic import root_validator  # pydantic v1
+
+    def model_validator(mode):
+        pre = mode == "before"
+        return root_validator(pre=pre)
+
 
 def hash_token(token: str):
     import hashlib
@@ -35,8 +44,9 @@ class LiteLLMBase(BaseModel):
             # if using pydantic v1
             return self.__fields_set__
 
-    class Config:
+    model_config = ConfigDict(
-        protected_namespaces = ()
+        protected_namespaces = (),
+    )
 
 
 class LiteLLM_UpperboundKeyGenerateParams(LiteLLMBase):
@@ -82,6 +92,7 @@ class LiteLLMRoutes(enum.Enum):
     info_routes: List = [
         "/key/info",
         "/team/info",
+        "/team/list",
         "/user/info",
         "/model/info",
         "/v2/model/info",
@@ -110,6 +121,7 @@ class LiteLLMRoutes(enum.Enum):
         "/team/new",
         "/team/update",
         "/team/delete",
+        "/team/list",
         "/team/info",
         "/team/block",
         "/team/unblock",
@@ -182,8 +194,19 @@ class LiteLLM_JWTAuth(LiteLLMBase):
 
     admin_jwt_scope: str = "litellm_proxy_admin"
     admin_allowed_routes: List[
-        Literal["openai_routes", "info_routes", "management_routes"]
+        Literal[
-    ] = ["management_routes"]
+            "openai_routes",
+            "info_routes",
+            "management_routes",
+            "spend_tracking_routes",
+            "global_spend_tracking_routes",
+        ]
+    ] = [
+        "management_routes",
+        "spend_tracking_routes",
+        "global_spend_tracking_routes",
+        "info_routes",
+    ]
     team_jwt_scope: str = "litellm_team"
     team_id_jwt_field: str = "client_id"
     team_allowed_routes: List[
@@ -216,7 +239,7 @@ class LiteLLMPromptInjectionParams(LiteLLMBase):
     llm_api_system_prompt: Optional[str] = None
     llm_api_fail_call_string: Optional[str] = None
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
     def check_llm_api_params(cls, values):
         llm_api_check = values.get("llm_api_check")
         if llm_api_check is True:
@@ -274,8 +297,9 @@ class ProxyChatCompletionRequest(LiteLLMBase):
     deployment_id: Optional[str] = None
     request_timeout: Optional[int] = None
 
-    class Config:
+    model_config = ConfigDict(
-        extra = "allow"  # allow params not defined here, these fall in litellm.completion(**kwargs)
+        extra = "allow",  # allow params not defined here, these fall in litellm.completion(**kwargs)
+    )
 
 
 class ModelInfoDelete(LiteLLMBase):
@@ -302,11 +326,12 @@ class ModelInfo(LiteLLMBase):
         ]
     ]
 
-    class Config:
+    model_config = ConfigDict(
-        extra = Extra.allow  # Allow extra fields
+        extra = "allow",  # Allow extra fields
-        protected_namespaces = ()
+        protected_namespaces = (),
+    )
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
     def set_model_info(cls, values):
         if values.get("id") is None:
             values.update({"id": str(uuid.uuid4())})
@@ -332,10 +357,11 @@ class ModelParams(LiteLLMBase):
     litellm_params: dict
     model_info: ModelInfo
 
-    class Config:
+    model_config = ConfigDict(
-        protected_namespaces = ()
+        protected_namespaces = (),
+    )
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
     def set_model_info(cls, values):
         if values.get("model_info") is None:
             values.update({"model_info": ModelInfo()})
@@ -371,8 +397,9 @@ class GenerateKeyRequest(GenerateRequestBase):
         {}
     )  # {"gpt-4": 5.0, "gpt-3.5-turbo": 5.0}, defaults to {}
 
-    class Config:
+    model_config = ConfigDict(
-        protected_namespaces = ()
+        protected_namespaces = (),
+    )
 
 
 class GenerateKeyResponse(GenerateKeyRequest):
@@ -382,7 +409,7 @@ class GenerateKeyResponse(GenerateKeyRequest):
     user_id: Optional[str] = None
     token_id: Optional[str] = None
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
     def set_model_info(cls, values):
         if values.get("token") is not None:
             values.update({"key": values.get("token")})
@@ -422,8 +449,9 @@ class LiteLLM_ModelTable(LiteLLMBase):
     created_by: str
     updated_by: str
 
-    class Config:
+    model_config = ConfigDict(
-        protected_namespaces = ()
+        protected_namespaces = (),
+    )
 
 
 class NewUserRequest(GenerateKeyRequest):
@@ -451,7 +479,7 @@ class UpdateUserRequest(GenerateRequestBase):
     user_role: Optional[str] = None
     max_budget: Optional[float] = None
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
     def check_user_info(cls, values):
         if values.get("user_id") is None and values.get("user_email") is None:
             raise ValueError("Either user id or user email must be provided")
@@ -471,7 +499,7 @@ class NewEndUserRequest(LiteLLMBase):
         None  # if no equivalent model in allowed region - default all requests to this model
     )
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
     def check_user_info(cls, values):
         if values.get("max_budget") is not None and values.get("budget_id") is not None:
             raise ValueError("Set either 'max_budget' or 'budget_id', not both.")
@@ -484,7 +512,7 @@ class Member(LiteLLMBase):
     user_id: Optional[str] = None
     user_email: Optional[str] = None
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
     def check_user_info(cls, values):
         if values.get("user_id") is None and values.get("user_email") is None:
             raise ValueError("Either user id or user email must be provided")
@@ -509,8 +537,9 @@ class TeamBase(LiteLLMBase):
 class NewTeamRequest(TeamBase):
     model_aliases: Optional[dict] = None
 
-    class Config:
+    model_config = ConfigDict(
-        protected_namespaces = ()
+        protected_namespaces = (),
+    )
 
 
 class GlobalEndUsersSpend(LiteLLMBase):
@@ -529,7 +558,7 @@ class TeamMemberDeleteRequest(LiteLLMBase):
     user_id: Optional[str] = None
     user_email: Optional[str] = None
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
     def check_user_info(cls, values):
         if values.get("user_id") is None and values.get("user_email") is None:
             raise ValueError("Either user id or user email must be provided")
@@ -563,10 +592,11 @@ class LiteLLM_TeamTable(TeamBase):
     budget_reset_at: Optional[datetime] = None
     model_id: Optional[int] = None
 
-    class Config:
+    model_config = ConfigDict(
-        protected_namespaces = ()
+        protected_namespaces = (),
+    )
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
     def set_model_info(cls, values):
         dict_fields = [
             "metadata",
@@ -602,8 +632,9 @@ class LiteLLM_BudgetTable(LiteLLMBase):
     model_max_budget: Optional[dict] = None
     budget_duration: Optional[str] = None
 
-    class Config:
+    model_config = ConfigDict(
-        protected_namespaces = ()
+        protected_namespaces = (),
+    )
 
 
 class NewOrganizationRequest(LiteLLM_BudgetTable):
@@ -653,8 +684,9 @@ class KeyManagementSettings(LiteLLMBase):
 class TeamDefaultSettings(LiteLLMBase):
     team_id: str
 
-    class Config:
+    model_config = ConfigDict(
-        extra = "allow"  # allow params not defined here, these fall in litellm.completion(**kwargs)
+        extra = "allow",  # allow params not defined here, these fall in litellm.completion(**kwargs)
+    )
 
 
 class DynamoDBArgs(LiteLLMBase):
@@ -795,8 +827,9 @@ class ConfigYAML(LiteLLMBase):
         description="litellm router object settings. See router.py __init__ for all, example router.num_retries=5, router.timeout=5, router.max_retries=5, router.retry_after=5",
     )
 
-    class Config:
+    model_config = ConfigDict(
-        protected_namespaces = ()
+        protected_namespaces = (),
+    )
 
 
 class LiteLLM_VerificationToken(LiteLLMBase):
@@ -830,8 +863,9 @@ class LiteLLM_VerificationToken(LiteLLMBase):
     user_id_rate_limits: Optional[dict] = None
     team_id_rate_limits: Optional[dict] = None
 
-    class Config:
+    model_config = ConfigDict(
-        protected_namespaces = ()
+        protected_namespaces = (),
+    )
 
 
 class LiteLLM_VerificationTokenView(LiteLLM_VerificationToken):
@@ -861,7 +895,7 @@ class UserAPIKeyAuth(
     user_role: Optional[Literal["proxy_admin", "app_owner", "app_user"]] = None
     allowed_model_region: Optional[Literal["eu"]] = None
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
     def check_api_key(cls, values):
         if values.get("api_key") is not None:
             values.update({"token": hash_token(values.get("api_key"))})
@@ -888,7 +922,7 @@ class LiteLLM_UserTable(LiteLLMBase):
     tpm_limit: Optional[int] = None
     rpm_limit: Optional[int] = None
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
     def set_model_info(cls, values):
         if values.get("spend") is None:
             values.update({"spend": 0.0})
@@ -896,8 +930,9 @@ class LiteLLM_UserTable(LiteLLMBase):
             values.update({"models": []})
         return values
 
-    class Config:
+    model_config = ConfigDict(
-        protected_namespaces = ()
+        protected_namespaces = (),
+    )
 
 
 class LiteLLM_EndUserTable(LiteLLMBase):
@@ -909,14 +944,15 @@ class LiteLLM_EndUserTable(LiteLLMBase):
     default_model: Optional[str] = None
     litellm_budget_table: Optional[LiteLLM_BudgetTable] = None
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
     def set_model_info(cls, values):
         if values.get("spend") is None:
             values.update({"spend": 0.0})
         return values
 
-    class Config:
+    model_config = ConfigDict(
-        protected_namespaces = ()
+        protected_namespaces = (),
+    )
 
 
 class LiteLLM_SpendLogs(LiteLLMBase):

litellm/proxy/example_config_yaml/custom_auth.py

@@ -1,10 +1,7 @@
 from litellm.proxy._types import UserAPIKeyAuth, GenerateKeyRequest
 from fastapi import Request
-from dotenv import load_dotenv
 import os
 
-load_dotenv()
-
 
 async def user_api_key_auth(request: Request, api_key: str) -> UserAPIKeyAuth:
     try:

litellm/proxy/hooks/azure_content_safety.py

@@ -0,0 +1,147 @@
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.caching import DualCache
+from litellm.proxy._types import UserAPIKeyAuth
+import litellm, traceback, sys, uuid
+from fastapi import HTTPException
+from litellm._logging import verbose_proxy_logger
+from typing import Optional
+
+
+class _PROXY_AzureContentSafety(
+    CustomLogger
+):  # https://docs.litellm.ai/docs/observability/custom_callback#callback-class
+    # Class variables or attributes
+
+    def __init__(self, endpoint, api_key, thresholds=None):
+        try:
+            from azure.ai.contentsafety.aio import ContentSafetyClient
+            from azure.core.credentials import AzureKeyCredential
+            from azure.ai.contentsafety.models import (
+                TextCategory,
+                AnalyzeTextOptions,
+                AnalyzeTextOutputType,
+            )
+            from azure.core.exceptions import HttpResponseError
+        except Exception as e:
+            raise Exception(
+                f"\033[91mAzure Content-Safety not installed, try running 'pip install azure-ai-contentsafety' to fix this error: {e}\n{traceback.format_exc()}\033[0m"
+            )
+        self.endpoint = endpoint
+        self.api_key = api_key
+        self.text_category = TextCategory
+        self.analyze_text_options = AnalyzeTextOptions
+        self.analyze_text_output_type = AnalyzeTextOutputType
+        self.azure_http_error = HttpResponseError
+
+        self.thresholds = self._configure_thresholds(thresholds)
+
+        self.client = ContentSafetyClient(
+            self.endpoint, AzureKeyCredential(self.api_key)
+        )
+
+    def _configure_thresholds(self, thresholds=None):
+        default_thresholds = {
+            self.text_category.HATE: 4,
+            self.text_category.SELF_HARM: 4,
+            self.text_category.SEXUAL: 4,
+            self.text_category.VIOLENCE: 4,
+        }
+
+        if thresholds is None:
+            return default_thresholds
+
+        for key, default in default_thresholds.items():
+            if key not in thresholds:
+                thresholds[key] = default
+
+        return thresholds
+
+    def _compute_result(self, response):
+        result = {}
+
+        category_severity = {
+            item.category: item.severity for item in response.categories_analysis
+        }
+        for category in self.text_category:
+            severity = category_severity.get(category)
+            if severity is not None:
+                result[category] = {
+                    "filtered": severity >= self.thresholds[category],
+                    "severity": severity,
+                }
+
+        return result
+
+    async def test_violation(self, content: str, source: Optional[str] = None):
+        verbose_proxy_logger.debug("Testing Azure Content-Safety for: %s", content)
+
+        # Construct a request
+        request = self.analyze_text_options(
+            text=content,
+            output_type=self.analyze_text_output_type.EIGHT_SEVERITY_LEVELS,
+        )
+
+        # Analyze text
+        try:
+            response = await self.client.analyze_text(request)
+        except self.azure_http_error as e:
+            verbose_proxy_logger.debug(
+                "Error in Azure Content-Safety: %s", traceback.format_exc()
+            )
+            traceback.print_exc()
+            raise
+
+        result = self._compute_result(response)
+        verbose_proxy_logger.debug("Azure Content-Safety Result: %s", result)
+
+        for key, value in result.items():
+            if value["filtered"]:
+                raise HTTPException(
+                    status_code=400,
+                    detail={
+                        "error": "Violated content safety policy",
+                        "source": source,
+                        "category": key,
+                        "severity": value["severity"],
+                    },
+                )
+
+    async def async_pre_call_hook(
+        self,
+        user_api_key_dict: UserAPIKeyAuth,
+        cache: DualCache,
+        data: dict,
+        call_type: str,  # "completion", "embeddings", "image_generation", "moderation"
+    ):
+        verbose_proxy_logger.debug("Inside Azure Content-Safety Pre-Call Hook")
+        try:
+            if call_type == "completion" and "messages" in data:
+                for m in data["messages"]:
+                    if "content" in m and isinstance(m["content"], str):
+                        await self.test_violation(content=m["content"], source="input")
+
+        except HTTPException as e:
+            raise e
+        except Exception as e:
+            traceback.print_exc()
+
+    async def async_post_call_success_hook(
+        self,
+        user_api_key_dict: UserAPIKeyAuth,
+        response,
+    ):
+        verbose_proxy_logger.debug("Inside Azure Content-Safety Post-Call Hook")
+        if isinstance(response, litellm.ModelResponse) and isinstance(
+            response.choices[0], litellm.utils.Choices
+        ):
+            await self.test_violation(
+                content=response.choices[0].message.content, source="output"
+            )
+
+    # async def async_post_call_streaming_hook(
+    #    self,
+    #    user_api_key_dict: UserAPIKeyAuth,
+    #    response: str,
+    # ):
+    #    verbose_proxy_logger.debug("Inside Azure Content-Safety Call-Stream Hook")
+    #    await self.test_violation(content=response, source="output")

litellm/proxy/proxy_config.yaml

@@ -4,6 +4,12 @@ model_list:
       model: openai/fake
       api_key: fake-key
       api_base: https://exampleopenaiendpoint-production.up.railway.app/
+  - model_name: llama3
+    litellm_params:
+      model: groq/llama3-8b-8192
+  - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: gpt-3.5-turbo
   - model_name: "*"
     litellm_params:
       model: openai/*

litellm/proxy/proxy_server.py

@@ -425,7 +425,7 @@ async def user_api_key_auth(
                     litellm_proxy_roles=jwt_handler.litellm_jwtauth,
                 )
                 if is_allowed == False:
-                    allowed_routes = jwt_handler.litellm_jwtauth.team_allowed_routes
+                    allowed_routes = jwt_handler.litellm_jwtauth.team_allowed_routes  # type: ignore
                     actual_routes = get_actual_routes(allowed_routes=allowed_routes)
                     raise Exception(
                         f"Team not allowed to access this route. Route={route}, Allowed Routes={actual_routes}"
@@ -2255,6 +2255,31 @@ class ProxyConfig:
 
                                 batch_redis_obj = _PROXY_BatchRedisRequests()
                                 imported_list.append(batch_redis_obj)
+                            elif (
+                                isinstance(callback, str)
+                                and callback == "azure_content_safety"
+                            ):
+                                from litellm.proxy.hooks.azure_content_safety import (
+                                    _PROXY_AzureContentSafety,
+                                )
+
+                                azure_content_safety_params = litellm_settings[
+                                    "azure_content_safety_params"
+                                ]
+                                for k, v in azure_content_safety_params.items():
+                                    if (
+                                        v is not None
+                                        and isinstance(v, str)
+                                        and v.startswith("os.environ/")
+                                    ):
+                                        azure_content_safety_params[k] = (
+                                            litellm.get_secret(v)
+                                        )
+
+                                azure_content_safety_obj = _PROXY_AzureContentSafety(
+                                    **azure_content_safety_params,
+                                )
+                                imported_list.append(azure_content_safety_obj)
                             else:
                                 imported_list.append(
                                     get_instance_fn(
@@ -3454,6 +3479,26 @@ async def startup_event():
             await proxy_config.add_deployment(
                 prisma_client=prisma_client, proxy_logging_obj=proxy_logging_obj
             )
+
+        if (
+            proxy_logging_obj is not None
+            and proxy_logging_obj.slack_alerting_instance is not None
+            and prisma_client is not None
+        ):
+            print("Alerting: Initializing Weekly/Monthly Spend Reports")  # noqa
+            ### Schedule weekly/monhtly spend reports ###
+            scheduler.add_job(
+                proxy_logging_obj.slack_alerting_instance.send_weekly_spend_report,
+                "cron",
+                day_of_week="mon",
+            )
+
+            scheduler.add_job(
+                proxy_logging_obj.slack_alerting_instance.send_monthly_spend_report,
+                "cron",
+                day=1,
+            )
+
         scheduler.start()
 
 
@@ -3639,7 +3684,7 @@ async def chat_completion(
         ### MODEL ALIAS MAPPING ###
         # check if model name in model alias map
         # get the actual model name
-        if data["model"] in litellm.model_alias_map:
+        if isinstance(data["model"], str) and data["model"] in litellm.model_alias_map:
             data["model"] = litellm.model_alias_map[data["model"]]
 
         ## LOGGING OBJECT ## - initialize logging object for logging success/failure events for call
@@ -3673,6 +3718,10 @@ async def chat_completion(
         # skip router if user passed their key
         if "api_key" in data:
             tasks.append(litellm.acompletion(**data))
+        elif "," in data["model"] and llm_router is not None:
+            _models_csv_string = data.pop("model")
+            _models = _models_csv_string.split(",")
+            tasks.append(llm_router.abatch_completion(models=_models, **data))
         elif "user_config" in data:
             # initialize a new router instance. make request using this Router
             router_config = data.pop("user_config")
@@ -3733,6 +3782,7 @@ async def chat_completion(
                 "x-litellm-cache-key": cache_key,
                 "x-litellm-model-api-base": api_base,
                 "x-litellm-version": version,
+                "x-litellm-model-region": user_api_key_dict.allowed_model_region or "",
             }
             selected_data_generator = select_data_generator(
                 response=response,
@@ -3749,6 +3799,9 @@ async def chat_completion(
         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
+        fastapi_response.headers["x-litellm-model-region"] = (
+            user_api_key_dict.allowed_model_region or ""
+        )
 
         ### CALL HOOKS ### - modify outgoing data
         response = await proxy_logging_obj.post_call_success_hook(
@@ -4133,6 +4186,9 @@ async def embeddings(
         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
+        fastapi_response.headers["x-litellm-model-region"] = (
+            user_api_key_dict.allowed_model_region or ""
+        )
 
         return response
     except Exception as e:
@@ -4302,6 +4358,9 @@ async def image_generation(
         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
+        fastapi_response.headers["x-litellm-model-region"] = (
+            user_api_key_dict.allowed_model_region or ""
+        )
 
         return response
     except Exception as e:
@@ -4495,6 +4554,9 @@ async def audio_transcriptions(
         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
+        fastapi_response.headers["x-litellm-model-region"] = (
+            user_api_key_dict.allowed_model_region or ""
+        )
 
         return response
     except Exception as e:
@@ -4670,6 +4732,9 @@ async def moderations(
         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
+        fastapi_response.headers["x-litellm-model-region"] = (
+            user_api_key_dict.allowed_model_region or ""
+        )
 
         return response
     except Exception as e:
@@ -5319,6 +5384,141 @@ async def view_spend_tags(
         )
 
 
+@router.get(
+    "/global/spend/report",
+    tags=["Budget & Spend Tracking"],
+    dependencies=[Depends(user_api_key_auth)],
+    include_in_schema=False,
+    responses={
+        200: {"model": List[LiteLLM_SpendLogs]},
+    },
+)
+async def get_global_spend_report(
+    start_date: Optional[str] = fastapi.Query(
+        default=None,
+        description="Time from which to start viewing spend",
+    ),
+    end_date: Optional[str] = fastapi.Query(
+        default=None,
+        description="Time till which to view spend",
+    ),
+):
+    """
+    Get Daily Spend per Team, based on specific startTime and endTime. Per team, view usage by each key, model
+    [
+        {
+            "group-by-day": "2024-05-10",
+            "teams": [
+                {
+                    "team_name": "team-1"
+                    "spend": 10,
+                    "keys": [
+                        "key": "1213",
+                        "usage": {
+                            "model-1": {
+                                    "cost": 12.50,
+                                    "input_tokens": 1000,
+                                    "output_tokens": 5000,
+                                    "requests": 100
+                                },
+                                "audio-modelname1": {
+                                "cost": 25.50,
+                                "seconds": 25,
+                                "requests": 50
+                        },
+                        }
+                    }
+            ]
+        ]
+    }
+    """
+    if start_date is None or end_date is None:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail={"error": "Please provide start_date and end_date"},
+        )
+
+    start_date_obj = datetime.strptime(start_date, "%Y-%m-%d")
+    end_date_obj = datetime.strptime(end_date, "%Y-%m-%d")
+
+    global prisma_client
+    try:
+        if prisma_client is None:
+            raise Exception(
+                f"Database not connected. Connect a database to your proxy - https://docs.litellm.ai/docs/simple_proxy#managing-auth---virtual-keys"
+            )
+
+        # first get data from spend logs -> SpendByModelApiKey
+        # then read data from "SpendByModelApiKey" to format the response obj
+        sql_query = """
+
+        WITH SpendByModelApiKey AS (
+            SELECT
+                date_trunc('day', sl."startTime") AS group_by_day,
+                COALESCE(tt.team_alias, 'Unassigned Team') AS team_name,
+                sl.model,
+                sl.api_key,
+                SUM(sl.spend) AS model_api_spend,
+                SUM(sl.total_tokens) AS model_api_tokens
+            FROM 
+                "LiteLLM_SpendLogs" sl
+            LEFT JOIN 
+                "LiteLLM_TeamTable" tt 
+            ON 
+                sl.team_id = tt.team_id
+            WHERE
+                sl."startTime" BETWEEN $1::date AND $2::date
+            GROUP BY
+                date_trunc('day', sl."startTime"),
+                tt.team_alias,
+                sl.model,
+                sl.api_key
+        )
+            SELECT
+                group_by_day,
+                jsonb_agg(jsonb_build_object(
+                    'team_name', team_name,
+                    'total_spend', total_spend,
+                    'metadata', metadata
+                )) AS teams
+            FROM (
+                SELECT
+                    group_by_day,
+                    team_name,
+                    SUM(model_api_spend) AS total_spend,
+                    jsonb_agg(jsonb_build_object(
+                        'model', model,
+                        'api_key', api_key,
+                        'spend', model_api_spend,
+                        'total_tokens', model_api_tokens
+                    )) AS metadata
+                FROM 
+                    SpendByModelApiKey
+                GROUP BY
+                    group_by_day,
+                    team_name
+            ) AS aggregated
+            GROUP BY
+                group_by_day
+            ORDER BY
+                group_by_day;
+            """
+
+        db_response = await prisma_client.db.query_raw(
+            sql_query, start_date_obj, end_date_obj
+        )
+        if db_response is None:
+            return []
+
+        return db_response
+
+    except Exception as e:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail={"error": str(e)},
+        )
+
+
 @router.get(
     "/global/spend/tags",
     tags=["Budget & Spend Tracking"],
@@ -5363,6 +5563,13 @@ async def global_view_spend_tags(
                 f"Database not connected. Connect a database to your proxy - https://docs.litellm.ai/docs/simple_proxy#managing-auth---virtual-keys"
             )
 
+        if end_date is None or start_date is None:
+            raise ProxyException(
+                message="Please provide start_date and end_date",
+                type="bad_request",
+                param=None,
+                code=status.HTTP_400_BAD_REQUEST,
+            )
         response = await ui_get_spend_by_tags(
             start_date=start_date, end_date=end_date, prisma_client=prisma_client
         )
@@ -5386,6 +5593,55 @@ async def global_view_spend_tags(
         )
 
 
+async def _get_spend_report_for_time_range(
+    start_date: str,
+    end_date: str,
+):
+    global prisma_client
+    if prisma_client is None:
+        verbose_proxy_logger.error(
+            f"Database not connected. Connect a database to your proxy for weekly, monthly spend reports"
+        )
+        return None
+
+    try:
+        sql_query = """
+        SELECT
+            t.team_alias,
+            SUM(s.spend) AS total_spend
+        FROM
+            "LiteLLM_SpendLogs" s
+        LEFT JOIN
+            "LiteLLM_TeamTable" t ON s.team_id = t.team_id
+        WHERE
+            s."startTime"::DATE >= $1::date AND s."startTime"::DATE <= $2::date
+        GROUP BY
+            t.team_alias
+        ORDER BY
+            total_spend DESC;
+        """
+        response = await prisma_client.db.query_raw(sql_query, start_date, end_date)
+
+        # get spend per tag for today
+        sql_query = """
+        SELECT 
+        jsonb_array_elements_text(request_tags) AS individual_request_tag,
+        SUM(spend) AS total_spend
+        FROM "LiteLLM_SpendLogs"
+        WHERE "startTime"::DATE >= $1::date AND "startTime"::DATE <= $2::date
+        GROUP BY individual_request_tag
+        ORDER BY total_spend DESC;
+        """
+
+        spend_per_tag = await prisma_client.db.query_raw(
+            sql_query, start_date, end_date
+        )
+
+        return response, spend_per_tag
+    except Exception as e:
+        verbose_proxy_logger.error("Exception in _get_daily_spend_reports", e)  # noqa
+
+
 @router.post(
     "/spend/calculate",
     tags=["Budget & Spend Tracking"],
@@ -5773,7 +6029,7 @@ async def global_spend_keys(
     tags=["Budget & Spend Tracking"],
     dependencies=[Depends(user_api_key_auth)],
 )
-async def global_spend_per_tea():
+async def global_spend_per_team():
     """
     [BETA] This is a beta endpoint. It will change.
 
@@ -9458,6 +9714,14 @@ async def health_services_endpoint(
                         level="Low",
                         alert_type="budget_alerts",
                     )
+
+                if prisma_client is not None:
+                    asyncio.create_task(
+                        proxy_logging_obj.slack_alerting_instance.send_monthly_spend_report()
+                    )
+                    asyncio.create_task(
+                        proxy_logging_obj.slack_alerting_instance.send_weekly_spend_report()
+                    )
                 return {
                     "status": "success",
                     "message": "Mock Slack Alert sent, verify Slack Alert Received on your channel",

litellm/router.py

@@ -9,7 +9,8 @@
 
 import copy, httpx
 from datetime import datetime
-from typing import Dict, List, Optional, Union, Literal, Any, BinaryIO, Tuple
+from typing import Dict, List, Optional, Union, Literal, Any, BinaryIO, Tuple, TypedDict
+from typing_extensions import overload
 import random, threading, time, traceback, uuid
 import litellm, openai, hashlib, json
 from litellm.caching import RedisCache, InMemoryCache, DualCache
@@ -46,6 +47,7 @@ from litellm.types.router import (
     updateLiteLLMParams,
     RetryPolicy,
     AlertingConfig,
+    DeploymentTypedDict,
 )
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.llms.azure import get_azure_ad_token_from_oidc
@@ -61,7 +63,7 @@ class Router:
 
     def __init__(
         self,
-        model_list: Optional[list] = None,
+        model_list: Optional[List[Union[DeploymentTypedDict, Dict]]] = None,
         ## CACHING ##
         redis_url: Optional[str] = None,
         redis_host: Optional[str] = None,
@@ -82,6 +84,9 @@ class Router:
         default_max_parallel_requests: Optional[int] = None,
         set_verbose: bool = False,
         debug_level: Literal["DEBUG", "INFO"] = "INFO",
+        default_fallbacks: Optional[
+            List[str]
+        ] = None,  # generic fallbacks, works across all deployments
         fallbacks: List = [],
         context_window_fallbacks: List = [],
         model_group_alias: Optional[dict] = {},
@@ -258,6 +263,11 @@ class Router:
         self.retry_after = retry_after
         self.routing_strategy = routing_strategy
         self.fallbacks = fallbacks or litellm.fallbacks
+        if default_fallbacks is not None:
+            if self.fallbacks is not None:
+                self.fallbacks.append({"*": default_fallbacks})
+            else:
+                self.fallbacks = [{"*": default_fallbacks}]
         self.context_window_fallbacks = (
             context_window_fallbacks or litellm.context_window_fallbacks
         )
@@ -469,12 +479,30 @@ class Router:
             )
             raise e
 
+    # fmt: off
+
+    @overload
     async def acompletion(
-        self, model: str, messages: List[Dict[str, str]], **kwargs
+        self, model: str, messages: List[Dict[str, str]], stream: Literal[True], **kwargs
-    ) -> Union[ModelResponse, CustomStreamWrapper]:
+    ) -> CustomStreamWrapper: 
+        ...
+
+    @overload
+    async def acompletion(
+        self, model: str, messages: List[Dict[str, str]], stream: Literal[False] = False, **kwargs
+    ) -> ModelResponse: 
+        ...
+
+    # fmt: on
+
+    # The actual implementation of the function
+    async def acompletion(
+        self, model: str, messages: List[Dict[str, str]], stream=False, **kwargs
+    ):
         try:
             kwargs["model"] = model
             kwargs["messages"] = messages
+            kwargs["stream"] = stream
             kwargs["original_function"] = self._acompletion
             kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
 
@@ -606,6 +634,33 @@ class Router:
                 self.fail_calls[model_name] += 1
             raise e
 
+    async def abatch_completion(
+        self, models: List[str], messages: List[Dict[str, str]], **kwargs
+    ):
+
+        async def _async_completion_no_exceptions(
+            model: str, messages: List[Dict[str, str]], **kwargs
+        ):
+            """
+            Wrapper around self.async_completion that catches exceptions and returns them as a result
+            """
+            try:
+                return await self.acompletion(model=model, messages=messages, **kwargs)
+            except Exception as e:
+                return e
+
+        _tasks = []
+        for model in models:
+            # add each task but if the task fails
+            _tasks.append(
+                _async_completion_no_exceptions(
+                    model=model, messages=messages, **kwargs
+                )
+            )
+
+        response = await asyncio.gather(*_tasks)
+        return response
+
     def image_generation(self, prompt: str, model: str, **kwargs):
         try:
             kwargs["model"] = model
@@ -1386,7 +1441,7 @@ class Router:
                 verbose_router_logger.debug(f"Trying to fallback b/w models")
                 if (
                     hasattr(e, "status_code")
-                    and e.status_code == 400
+                    and e.status_code == 400  # type: ignore
                     and not isinstance(e, litellm.ContextWindowExceededError)
                 ):  # don't retry a malformed request
                     raise e
@@ -1417,18 +1472,29 @@ class Router:
                             response = await self.async_function_with_retries(
                                 *args, **kwargs
                             )
+                            verbose_router_logger.info(
+                                "Successful fallback b/w models."
+                            )
                             return response
                         except Exception as e:
                             pass
                 elif fallbacks is not None:
                     verbose_router_logger.debug(f"inside model fallbacks: {fallbacks}")
-                    for item in fallbacks:
+                    generic_fallback_idx: Optional[int] = None
-                        key_list = list(item.keys())
+                    ## check for specific model group-specific fallbacks
-                        if len(key_list) == 0:
+                    for idx, item in enumerate(fallbacks):
-                            continue
+                        if list(item.keys())[0] == model_group:
-                        if key_list[0] == model_group:
                             fallback_model_group = item[model_group]
                             break
+                        elif list(item.keys())[0] == "*":
+                            generic_fallback_idx = idx
+                    ## if none, check for generic fallback
+                    if (
+                        fallback_model_group is None
+                        and generic_fallback_idx is not None
+                    ):
+                        fallback_model_group = fallbacks[generic_fallback_idx]["*"]
+
                     if fallback_model_group is None:
                         verbose_router_logger.info(
                             f"No fallback model group found for original model_group={model_group}. Fallbacks={fallbacks}"
@@ -1451,6 +1517,9 @@ class Router:
                             response = await self.async_function_with_fallbacks(
                                 *args, **kwargs
                             )
+                            verbose_router_logger.info(
+                                "Successful fallback b/w models."
+                            )
                             return response
                         except Exception as e:
                             raise e
@@ -1480,22 +1549,30 @@ class Router:
             return response
         except Exception as e:
             original_exception = e
-            ### CHECK IF RATE LIMIT / CONTEXT WINDOW ERROR w/ fallbacks available / Bad Request Error
+            """
-            if (
+            Retry Logic
-                isinstance(original_exception, litellm.ContextWindowExceededError)
+             
-                and context_window_fallbacks is not None
+            """
-            ) or (
+            _healthy_deployments = await self._async_get_healthy_deployments(
-                isinstance(original_exception, openai.RateLimitError)
+                model=kwargs.get("model") or "",
-                and fallbacks is not None
+            )
-            ):
-                raise original_exception
-            ### RETRY
 
-            _timeout = self._router_should_retry(
+            # raises an exception if this error should not be retries
+            self.should_retry_this_error(
+                error=e,
+                healthy_deployments=_healthy_deployments,
+                context_window_fallbacks=context_window_fallbacks,
+            )
+
+            # decides how long to sleep before retry
+            _timeout = self._time_to_sleep_before_retry(
                 e=original_exception,
                 remaining_retries=num_retries,
                 num_retries=num_retries,
+                healthy_deployments=_healthy_deployments,
             )
+
+            # sleeps for the length of the timeout
             await asyncio.sleep(_timeout)
 
             if (
@@ -1529,10 +1606,14 @@ class Router:
                     ## LOGGING
                     kwargs = self.log_retry(kwargs=kwargs, e=e)
                     remaining_retries = num_retries - current_attempt
-                    _timeout = self._router_should_retry(
+                    _healthy_deployments = await self._async_get_healthy_deployments(
+                        model=kwargs.get("model"),
+                    )
+                    _timeout = self._time_to_sleep_before_retry(
                         e=original_exception,
                         remaining_retries=remaining_retries,
                         num_retries=num_retries,
+                        healthy_deployments=_healthy_deployments,
                     )
                     await asyncio.sleep(_timeout)
             try:
@@ -1541,17 +1622,57 @@ class Router:
                 pass
             raise original_exception
 
+    def should_retry_this_error(
+        self,
+        error: Exception,
+        healthy_deployments: Optional[List] = None,
+        context_window_fallbacks: Optional[List] = None,
+    ):
+        """
+        1. raise an exception for ContextWindowExceededError if context_window_fallbacks is not None
+
+        2. raise an exception for RateLimitError if
+            - there are no fallbacks
+            - there are no healthy deployments in the same model group
+        """
+
+        _num_healthy_deployments = 0
+        if healthy_deployments is not None and isinstance(healthy_deployments, list):
+            _num_healthy_deployments = len(healthy_deployments)
+
+        ### CHECK IF RATE LIMIT / CONTEXT WINDOW ERROR w/ fallbacks available / Bad Request Error
+        if (
+            isinstance(error, litellm.ContextWindowExceededError)
+            and context_window_fallbacks is None
+        ):
+            raise error
+
+        # Error we should only retry if there are other deployments
+        if isinstance(error, openai.RateLimitError) or isinstance(
+            error, openai.AuthenticationError
+        ):
+            if _num_healthy_deployments <= 0:
+                raise error
+
+        return True
+
     def function_with_fallbacks(self, *args, **kwargs):
         """
         Try calling the function_with_retries
         If it fails after num_retries, fall back to another model group
         """
+        mock_testing_fallbacks = kwargs.pop("mock_testing_fallbacks", None)
         model_group = kwargs.get("model")
         fallbacks = kwargs.get("fallbacks", self.fallbacks)
         context_window_fallbacks = kwargs.get(
             "context_window_fallbacks", self.context_window_fallbacks
         )
         try:
+            if mock_testing_fallbacks is not None and mock_testing_fallbacks == True:
+                raise Exception(
+                    f"This is a mock exception for model={model_group}, to trigger a fallback. Fallbacks={fallbacks}"
+                )
+
             response = self.function_with_retries(*args, **kwargs)
             return response
         except Exception as e:
@@ -1560,7 +1681,7 @@ class Router:
             try:
                 if (
                     hasattr(e, "status_code")
-                    and e.status_code == 400
+                    and e.status_code == 400  # type: ignore
                     and not isinstance(e, litellm.ContextWindowExceededError)
                 ):  # don't retry a malformed request
                     raise e
@@ -1602,10 +1723,20 @@ class Router:
                 elif fallbacks is not None:
                     verbose_router_logger.debug(f"inside model fallbacks: {fallbacks}")
                     fallback_model_group = None
-                    for item in fallbacks:
+                    generic_fallback_idx: Optional[int] = None
+                    ## check for specific model group-specific fallbacks
+                    for idx, item in enumerate(fallbacks):
                         if list(item.keys())[0] == model_group:
                             fallback_model_group = item[model_group]
                             break
+                        elif list(item.keys())[0] == "*":
+                            generic_fallback_idx = idx
+                    ## if none, check for generic fallback
+                    if (
+                        fallback_model_group is None
+                        and generic_fallback_idx is not None
+                    ):
+                        fallback_model_group = fallbacks[generic_fallback_idx]["*"]
 
                     if fallback_model_group is None:
                         raise original_exception
@@ -1629,12 +1760,27 @@ class Router:
                 raise e
             raise original_exception
 
-    def _router_should_retry(
+    def _time_to_sleep_before_retry(
-        self, e: Exception, remaining_retries: int, num_retries: int
+        self,
+        e: Exception,
+        remaining_retries: int,
+        num_retries: int,
+        healthy_deployments: Optional[List] = None,
     ) -> Union[int, float]:
         """
         Calculate back-off, then retry
+
+        It should instantly retry only when:
+            1. there are healthy deployments in the same model group
+            2. there are fallbacks for the completion call
         """
+        if (
+            healthy_deployments is not None
+            and isinstance(healthy_deployments, list)
+            and len(healthy_deployments) > 0
+        ):
+            return 0
+
         if hasattr(e, "response") and hasattr(e.response, "headers"):
             timeout = litellm._calculate_retry_after(
                 remaining_retries=remaining_retries,
@@ -1671,23 +1817,29 @@ class Router:
         except Exception as e:
             original_exception = e
             ### CHECK IF RATE LIMIT / CONTEXT WINDOW ERROR
-            if (
+            _healthy_deployments = self._get_healthy_deployments(
-                isinstance(original_exception, litellm.ContextWindowExceededError)
+                model=kwargs.get("model"),
-                and context_window_fallbacks is not None
+            )
-            ) or (
+
-                isinstance(original_exception, openai.RateLimitError)
+            # raises an exception if this error should not be retries
-                and fallbacks is not None
+            self.should_retry_this_error(
-            ):
+                error=e,
-                raise original_exception
+                healthy_deployments=_healthy_deployments,
-            ## LOGGING
+                context_window_fallbacks=context_window_fallbacks,
-            if num_retries > 0:
+            )
-                kwargs = self.log_retry(kwargs=kwargs, e=original_exception)
+
-            ### RETRY
+            # decides how long to sleep before retry
-            _timeout = self._router_should_retry(
+            _timeout = self._time_to_sleep_before_retry(
                 e=original_exception,
                 remaining_retries=num_retries,
                 num_retries=num_retries,
+                healthy_deployments=_healthy_deployments,
             )
+
+            ## LOGGING
+            if num_retries > 0:
+                kwargs = self.log_retry(kwargs=kwargs, e=original_exception)
+
             time.sleep(_timeout)
             for current_attempt in range(num_retries):
                 verbose_router_logger.debug(
@@ -1701,11 +1853,15 @@ class Router:
                 except Exception as e:
                     ## LOGGING
                     kwargs = self.log_retry(kwargs=kwargs, e=e)
+                    _healthy_deployments = self._get_healthy_deployments(
+                        model=kwargs.get("model"),
+                    )
                     remaining_retries = num_retries - current_attempt
-                    _timeout = self._router_should_retry(
+                    _timeout = self._time_to_sleep_before_retry(
                         e=e,
                         remaining_retries=remaining_retries,
                         num_retries=num_retries,
+                        healthy_deployments=_healthy_deployments,
                     )
                     time.sleep(_timeout)
             raise original_exception
@@ -1908,6 +2064,47 @@ class Router:
         verbose_router_logger.debug(f"retrieve cooldown models: {cooldown_models}")
         return cooldown_models
 
+    def _get_healthy_deployments(self, model: str):
+        _all_deployments: list = []
+        try:
+            _, _all_deployments = self._common_checks_available_deployment(  # type: ignore
+                model=model,
+            )
+            if type(_all_deployments) == dict:
+                return []
+        except:
+            pass
+
+        unhealthy_deployments = self._get_cooldown_deployments()
+        healthy_deployments: list = []
+        for deployment in _all_deployments:
+            if deployment["model_info"]["id"] in unhealthy_deployments:
+                continue
+            else:
+                healthy_deployments.append(deployment)
+
+        return healthy_deployments
+
+    async def _async_get_healthy_deployments(self, model: str):
+        _all_deployments: list = []
+        try:
+            _, _all_deployments = self._common_checks_available_deployment(  # type: ignore
+                model=model,
+            )
+            if type(_all_deployments) == dict:
+                return []
+        except:
+            pass
+
+        unhealthy_deployments = await self._async_get_cooldown_deployments()
+        healthy_deployments: list = []
+        for deployment in _all_deployments:
+            if deployment["model_info"]["id"] in unhealthy_deployments:
+                continue
+            else:
+                healthy_deployments.append(deployment)
+        return healthy_deployments
+
     def routing_strategy_pre_call_checks(self, deployment: dict):
         """
         Mimics 'async_routing_strategy_pre_call_checks'
@@ -2339,7 +2536,7 @@ class Router:
                     )  # cache for 1 hr
 
             else:
-                _api_key = api_key
+                _api_key = api_key  # type: ignore
                 if _api_key is not None and isinstance(_api_key, str):
                     # only show first 5 chars of api_key
                     _api_key = _api_key[:8] + "*" * 15
@@ -2567,23 +2764,25 @@ class Router:
         # init OpenAI, Azure clients
         self.set_client(model=deployment.to_json(exclude_none=True))
 
-        # set region (if azure model)
+        # set region (if azure model) ## PREVIEW FEATURE ##
-        _auto_infer_region = os.environ.get("AUTO_INFER_REGION", False)
+        if litellm.enable_preview_features == True:
-        if _auto_infer_region == True or _auto_infer_region == "True":
             print("Auto inferring region")  # noqa
             """
             Hiding behind a feature flag
             When there is a large amount of LLM deployments this makes startup times blow up
             """
             try:
-                if "azure" in deployment.litellm_params.model:
+                if (
+                    "azure" in deployment.litellm_params.model
+                    and deployment.litellm_params.region_name is None
+                ):
                     region = litellm.utils.get_model_region(
                         litellm_params=deployment.litellm_params, mode=None
                     )
 
                     deployment.litellm_params.region_name = region
             except Exception as e:
-                verbose_router_logger.error(
+                verbose_router_logger.debug(
                     "Unable to get the region for azure model - {}, {}".format(
                         deployment.litellm_params.model, str(e)
                     )
@@ -2961,7 +3160,7 @@ class Router:
                 ):
                     # check if in allowed_model_region
                     if (
-                        _is_region_eu(model_region=_litellm_params["region_name"])
+                        _is_region_eu(litellm_params=LiteLLM_Params(**_litellm_params))
                         == False
                     ):
                         invalid_model_indices.append(idx)
@@ -3118,13 +3317,12 @@ class Router:
             healthy_deployments.remove(deployment)
 
         # filter pre-call checks
+        _allowed_model_region = (
+            request_kwargs.get("allowed_model_region")
+            if request_kwargs is not None
+            else None
+        )
         if self.enable_pre_call_checks and messages is not None:
-            _allowed_model_region = (
-                request_kwargs.get("allowed_model_region")
-                if request_kwargs is not None
-                else None
-            )
-
             if _allowed_model_region == "eu":
                 healthy_deployments = self._pre_call_checks(
                     model=model,
@@ -3145,8 +3343,10 @@ class Router:
                 )
 
         if len(healthy_deployments) == 0:
+            if _allowed_model_region is None:
+                _allowed_model_region = "n/a"
             raise ValueError(
-                f"{RouterErrors.no_deployments_available.value}, passed model={model}"
+                f"{RouterErrors.no_deployments_available.value}, passed model={model}. Enable pre-call-checks={self.enable_pre_call_checks}, allowed_model_region={_allowed_model_region}"
             )
 
         if (
@@ -3506,7 +3706,7 @@ class Router:
                 )
                 asyncio.create_task(
                     proxy_logging_obj.slack_alerting_instance.send_alert(
-                        message=f"Router: Cooling down deployment: {_api_base}, for {self.cooldown_time} seconds. Got exception: {str(exception_status)}",
+                        message=f"Router: Cooling down deployment: {_api_base}, for {self.cooldown_time} seconds. Got exception: {str(exception_status)}. Change 'cooldown_time' + 'allowed_failes' under 'Router Settings' on proxy UI, or via config - https://docs.litellm.ai/docs/proxy/reliability#fallbacks--retries--timeouts--cooldowns",
                         alert_type="cooldown_deployment",
                         level="Low",
                     )

litellm/router_strategy/least_busy.py

@@ -8,8 +8,6 @@
 
 import dotenv, os, requests, random  # type: ignore
 from typing import Optional
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 from litellm.caching import DualCache
 from litellm.integrations.custom_logger import CustomLogger

litellm/router_strategy/lowest_cost.py

@@ -1,12 +1,11 @@
 #### 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  # type: ignore
+import os, requests, random  # type: ignore
 from typing import Optional, Union, List, Dict
 from datetime import datetime, timedelta
 import random
 
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 from litellm.caching import DualCache
 from litellm.integrations.custom_logger import CustomLogger
@@ -102,9 +101,6 @@ class LowestCostLoggingHandler(CustomLogger):
                 if precise_minute not in request_count_dict[id]:
                     request_count_dict[id][precise_minute] = {}
 
-                if precise_minute not in request_count_dict[id]:
-                    request_count_dict[id][precise_minute] = {}
-
                 ## TPM
                 request_count_dict[id][precise_minute]["tpm"] = (
                     request_count_dict[id][precise_minute].get("tpm", 0) + total_tokens

litellm/router_strategy/lowest_latency.py

@@ -5,8 +5,6 @@ import dotenv, os, requests, random  # type: ignore
 from typing import Optional, Union, List, Dict
 from datetime import datetime, timedelta
 import random
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 from litellm.caching import DualCache
 from litellm.integrations.custom_logger import CustomLogger
@@ -117,9 +115,6 @@ class LowestLatencyLoggingHandler(CustomLogger):
                 if precise_minute not in request_count_dict[id]:
                     request_count_dict[id][precise_minute] = {}
 
-                if precise_minute not in request_count_dict[id]:
-                    request_count_dict[id][precise_minute] = {}
-
                 ## TPM
                 request_count_dict[id][precise_minute]["tpm"] = (
                     request_count_dict[id][precise_minute].get("tpm", 0) + total_tokens

litellm/router_strategy/lowest_tpm_rpm.py

@@ -4,8 +4,6 @@
 import dotenv, os, requests, random
 from typing import Optional, Union, List, Dict
 from datetime import datetime
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 from litellm import token_counter
 from litellm.caching import DualCache

litellm/router_strategy/lowest_tpm_rpm_v2.py

@@ -5,8 +5,6 @@ import dotenv, os, requests, random
 from typing import Optional, Union, List, Dict
 import datetime as datetime_og
 from datetime import datetime
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback, asyncio, httpx
 import litellm
 from litellm import token_counter

litellm/tests/test_alangfuse.py

@@ -228,6 +228,40 @@ async def test_langfuse_logging_without_request_response(stream, langfuse_client
         pytest.fail(f"An exception occurred - {e}")
 
 
+@pytest.mark.asyncio
+async def test_langfuse_masked_input_output(langfuse_client):
+    """
+    Test that creates a trace with masked input and output
+    """
+    import uuid
+
+    for mask_value in [True, False]:
+        _unique_trace_name = f"litellm-test-{str(uuid.uuid4())}"
+        litellm.set_verbose = True
+        litellm.success_callback = ["langfuse"]
+        response = await create_async_task(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "This is a test"}],
+            metadata={"trace_id": _unique_trace_name, "mask_input": mask_value, "mask_output": mask_value},
+            mock_response="This is a test response"
+        )
+        print(response)
+        expected_input = "redacted-by-litellm" if mask_value else {'messages': [{'content': 'This is a test', 'role': 'user'}]}
+        expected_output = "redacted-by-litellm" if mask_value else {'content': 'This is a test response', 'role': 'assistant'}
+        langfuse_client.flush()
+        await asyncio.sleep(2)
+
+        # get trace with _unique_trace_name
+        trace = langfuse_client.get_trace(id=_unique_trace_name)
+        generations = list(
+            reversed(langfuse_client.get_generations(trace_id=_unique_trace_name).data)
+        )
+
+        assert trace.input == expected_input
+        assert trace.output == expected_output
+        assert generations[0].input == expected_input
+        assert generations[0].output == expected_output
+
 @pytest.mark.asyncio
 async def test_langfuse_logging_metadata(langfuse_client):
     """
@@ -312,7 +346,7 @@ async def test_langfuse_logging_metadata(langfuse_client):
             metadata["existing_trace_id"] = trace_id
 
     langfuse_client.flush()
-    await asyncio.sleep(2)
+    await asyncio.sleep(10)
 
     # Tests the metadata filtering and the override of the output to be the last generation
     for trace_id, generation_ids in trace_identifiers.items():
@@ -339,6 +373,13 @@ async def test_langfuse_logging_metadata(langfuse_client):
         for generation_id, generation in zip(generation_ids, generations):
             assert generation.id == generation_id
             assert generation.trace_id == trace_id
+            print(
+                "common keys in trace",
+                set(generation.metadata.keys()).intersection(
+                    expected_filtered_metadata_keys
+                ),
+            )
+
             assert set(generation.metadata.keys()).isdisjoint(
                 expected_filtered_metadata_keys
             )

litellm/tests/test_amazing_vertex_completion.py

@@ -590,19 +590,20 @@ def test_gemini_pro_vision_base64():
             pytest.fail(f"An exception occurred - {str(e)}")
 
 
+@pytest.mark.parametrize("sync_mode", [True, False])
 @pytest.mark.asyncio
-def test_gemini_pro_function_calling():
+async def test_gemini_pro_function_calling(sync_mode):
     try:
         load_vertex_ai_credentials()
-        response = litellm.completion(
+        data = {
-            model="vertex_ai/gemini-pro",
+            "model": "vertex_ai/gemini-pro",
-            messages=[
+            "messages": [
                 {
                     "role": "user",
                     "content": "Call the submit_cities function with San Francisco and New York",
                 }
             ],
-            tools=[
+            "tools": [
                 {
                     "type": "function",
                     "function": {
@@ -618,11 +619,13 @@ def test_gemini_pro_function_calling():
                     },
                 }
             ],
-        )
+        }
+        if sync_mode:
+            response = litellm.completion(**data)
+        else:
+            response = await litellm.acompletion(**data)
 
         print(f"response: {response}")
-    except litellm.APIError as e:
-        pass
     except litellm.RateLimitError as e:
         pass
     except Exception as e:
@@ -635,73 +638,66 @@ def test_gemini_pro_function_calling():
 # gemini_pro_function_calling()
 
 
-@pytest.mark.parametrize("stream", [False, True])
 @pytest.mark.parametrize("sync_mode", [False, True])
 @pytest.mark.asyncio
-async def test_gemini_pro_function_calling_streaming(stream, sync_mode):
+async def test_gemini_pro_function_calling_streaming(sync_mode):
     load_vertex_ai_credentials()
     litellm.set_verbose = True
-    tools = [
+    data = {
-        {
+        "model": "vertex_ai/gemini-pro",
-            "type": "function",
+        "messages": [
-            "function": {
+            {
-                "name": "get_current_weather",
+                "role": "user",
-                "description": "Get the current weather in a given location",
+                "content": "Call the submit_cities function with San Francisco and New York",
-                "parameters": {
+            }
-                    "type": "object",
+        ],
-                    "properties": {
+        "tools": [
-                        "location": {
+            {
-                            "type": "string",
+                "type": "function",
-                            "description": "The city and state, e.g. San Francisco, CA",
+                "function": {
+                    "name": "submit_cities",
+                    "description": "Submits a list of cities",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "cities": {"type": "array", "items": {"type": "string"}}
                         },
-                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                        "required": ["cities"],
                     },
-                    "required": ["location"],
                 },
-            },
+            }
-        }
+        ],
-    ]
-    messages = [
-        {
-            "role": "user",
-            "content": "What's the weather like in Boston today in fahrenheit?",
-        }
-    ]
-    optional_params = {
-        "tools": tools,
         "tool_choice": "auto",
         "n": 1,
-        "stream": stream,
+        "stream": True,
         "temperature": 0.1,
     }
+    chunks = []
     try:
         if sync_mode == True:
-            response = litellm.completion(
+            response = litellm.completion(**data)
-                model="gemini-pro", messages=messages, **optional_params
-            )
             print(f"completion: {response}")
 
-            if stream == True:
+            for chunk in response:
-                # assert completion.choices[0].message.content is None
+                chunks.append(chunk)
-                # assert len(completion.choices[0].message.tool_calls) == 1
+                assert isinstance(chunk, litellm.ModelResponse)
-                for chunk in response:
-                    assert isinstance(chunk, litellm.ModelResponse)
-            else:
-                assert isinstance(response, litellm.ModelResponse)
         else:
-            response = await litellm.acompletion(
+            response = await litellm.acompletion(**data)
-                model="gemini-pro", messages=messages, **optional_params
-            )
             print(f"completion: {response}")
 
-            if stream == True:
+            assert isinstance(response, litellm.CustomStreamWrapper)
-                # assert completion.choices[0].message.content is None
+
-                # assert len(completion.choices[0].message.tool_calls) == 1
+            async for chunk in response:
-                async for chunk in response:
+                print(f"chunk: {chunk}")
-                    print(f"chunk: {chunk}")
+                chunks.append(chunk)
-                    assert isinstance(chunk, litellm.ModelResponse)
+                assert isinstance(chunk, litellm.ModelResponse)
-            else:
+
-                assert isinstance(response, litellm.ModelResponse)
+        complete_response = litellm.stream_chunk_builder(chunks=chunks)
+        assert (
+            complete_response.choices[0].message.content is not None
+            or len(complete_response.choices[0].message.tool_calls) > 0
+        )
+        print(f"complete_response: {complete_response}")
     except litellm.APIError as e:
         pass
     except litellm.RateLimitError as e:

litellm/tests/test_azure_content_safety.py

@@ -0,0 +1,290 @@
+# What is this?
+## Unit test for azure content safety
+import sys, os, asyncio, time, random
+from datetime import datetime
+import traceback
+from dotenv import load_dotenv
+from fastapi import HTTPException
+
+load_dotenv()
+import os
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+import pytest
+import litellm
+from litellm import Router, mock_completion
+from litellm.proxy.utils import ProxyLogging
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.caching import DualCache
+
+
+@pytest.mark.asyncio
+@pytest.mark.skip(reason="beta feature - local testing is failing")
+async def test_strict_input_filtering_01():
+    """
+    - have a response with a filtered input
+    - call the pre call hook
+    """
+    from litellm.proxy.hooks.azure_content_safety import _PROXY_AzureContentSafety
+
+    azure_content_safety = _PROXY_AzureContentSafety(
+        endpoint=os.getenv("AZURE_CONTENT_SAFETY_ENDPOINT"),
+        api_key=os.getenv("AZURE_CONTENT_SAFETY_API_KEY"),
+        thresholds={"Hate": 2},
+    )
+
+    data = {
+        "messages": [
+            {"role": "system", "content": "You are an helpfull assistant"},
+            {"role": "user", "content": "Fuck yourself you stupid bitch"},
+        ]
+    }
+
+    with pytest.raises(HTTPException) as exc_info:
+        await azure_content_safety.async_pre_call_hook(
+            user_api_key_dict=UserAPIKeyAuth(),
+            cache=DualCache(),
+            data=data,
+            call_type="completion",
+        )
+
+    assert exc_info.value.detail["source"] == "input"
+    assert exc_info.value.detail["category"] == "Hate"
+    assert exc_info.value.detail["severity"] == 2
+
+
+@pytest.mark.asyncio
+@pytest.mark.skip(reason="beta feature - local testing is failing")
+async def test_strict_input_filtering_02():
+    """
+    - have a response with a filtered input
+    - call the pre call hook
+    """
+    from litellm.proxy.hooks.azure_content_safety import _PROXY_AzureContentSafety
+
+    azure_content_safety = _PROXY_AzureContentSafety(
+        endpoint=os.getenv("AZURE_CONTENT_SAFETY_ENDPOINT"),
+        api_key=os.getenv("AZURE_CONTENT_SAFETY_API_KEY"),
+        thresholds={"Hate": 2},
+    )
+
+    data = {
+        "messages": [
+            {"role": "system", "content": "You are an helpfull assistant"},
+            {"role": "user", "content": "Hello how are you ?"},
+        ]
+    }
+
+    await azure_content_safety.async_pre_call_hook(
+        user_api_key_dict=UserAPIKeyAuth(),
+        cache=DualCache(),
+        data=data,
+        call_type="completion",
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.skip(reason="beta feature - local testing is failing")
+async def test_loose_input_filtering_01():
+    """
+    - have a response with a filtered input
+    - call the pre call hook
+    """
+    from litellm.proxy.hooks.azure_content_safety import _PROXY_AzureContentSafety
+
+    azure_content_safety = _PROXY_AzureContentSafety(
+        endpoint=os.getenv("AZURE_CONTENT_SAFETY_ENDPOINT"),
+        api_key=os.getenv("AZURE_CONTENT_SAFETY_API_KEY"),
+        thresholds={"Hate": 8},
+    )
+
+    data = {
+        "messages": [
+            {"role": "system", "content": "You are an helpfull assistant"},
+            {"role": "user", "content": "Fuck yourself you stupid bitch"},
+        ]
+    }
+
+    await azure_content_safety.async_pre_call_hook(
+        user_api_key_dict=UserAPIKeyAuth(),
+        cache=DualCache(),
+        data=data,
+        call_type="completion",
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.skip(reason="beta feature - local testing is failing")
+async def test_loose_input_filtering_02():
+    """
+    - have a response with a filtered input
+    - call the pre call hook
+    """
+    from litellm.proxy.hooks.azure_content_safety import _PROXY_AzureContentSafety
+
+    azure_content_safety = _PROXY_AzureContentSafety(
+        endpoint=os.getenv("AZURE_CONTENT_SAFETY_ENDPOINT"),
+        api_key=os.getenv("AZURE_CONTENT_SAFETY_API_KEY"),
+        thresholds={"Hate": 8},
+    )
+
+    data = {
+        "messages": [
+            {"role": "system", "content": "You are an helpfull assistant"},
+            {"role": "user", "content": "Hello how are you ?"},
+        ]
+    }
+
+    await azure_content_safety.async_pre_call_hook(
+        user_api_key_dict=UserAPIKeyAuth(),
+        cache=DualCache(),
+        data=data,
+        call_type="completion",
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.skip(reason="beta feature - local testing is failing")
+async def test_strict_output_filtering_01():
+    """
+    - have a response with a filtered output
+    - call the post call hook
+    """
+    from litellm.proxy.hooks.azure_content_safety import _PROXY_AzureContentSafety
+
+    azure_content_safety = _PROXY_AzureContentSafety(
+        endpoint=os.getenv("AZURE_CONTENT_SAFETY_ENDPOINT"),
+        api_key=os.getenv("AZURE_CONTENT_SAFETY_API_KEY"),
+        thresholds={"Hate": 2},
+    )
+
+    response = mock_completion(
+        model="gpt-3.5-turbo",
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a song writer expert. You help users to write songs about any topic in any genre.",
+            },
+            {
+                "role": "user",
+                "content": "Help me write a rap text song. Add some insults to make it more credible.",
+            },
+        ],
+        mock_response="I'm the king of the mic, you're just a fucking dick. Don't fuck with me your stupid bitch.",
+    )
+
+    with pytest.raises(HTTPException) as exc_info:
+        await azure_content_safety.async_post_call_success_hook(
+            user_api_key_dict=UserAPIKeyAuth(), response=response
+        )
+
+    assert exc_info.value.detail["source"] == "output"
+    assert exc_info.value.detail["category"] == "Hate"
+    assert exc_info.value.detail["severity"] == 2
+
+
+@pytest.mark.asyncio
+@pytest.mark.skip(reason="beta feature - local testing is failing")
+async def test_strict_output_filtering_02():
+    """
+    - have a response with a filtered output
+    - call the post call hook
+    """
+    from litellm.proxy.hooks.azure_content_safety import _PROXY_AzureContentSafety
+
+    azure_content_safety = _PROXY_AzureContentSafety(
+        endpoint=os.getenv("AZURE_CONTENT_SAFETY_ENDPOINT"),
+        api_key=os.getenv("AZURE_CONTENT_SAFETY_API_KEY"),
+        thresholds={"Hate": 2},
+    )
+
+    response = mock_completion(
+        model="gpt-3.5-turbo",
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a song writer expert. You help users to write songs about any topic in any genre.",
+            },
+            {
+                "role": "user",
+                "content": "Help me write a rap text song. Add some insults to make it more credible.",
+            },
+        ],
+        mock_response="I'm unable to help with you with hate speech",
+    )
+
+    await azure_content_safety.async_post_call_success_hook(
+        user_api_key_dict=UserAPIKeyAuth(), response=response
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.skip(reason="beta feature - local testing is failing")
+async def test_loose_output_filtering_01():
+    """
+    - have a response with a filtered output
+    - call the post call hook
+    """
+    from litellm.proxy.hooks.azure_content_safety import _PROXY_AzureContentSafety
+
+    azure_content_safety = _PROXY_AzureContentSafety(
+        endpoint=os.getenv("AZURE_CONTENT_SAFETY_ENDPOINT"),
+        api_key=os.getenv("AZURE_CONTENT_SAFETY_API_KEY"),
+        thresholds={"Hate": 8},
+    )
+
+    response = mock_completion(
+        model="gpt-3.5-turbo",
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a song writer expert. You help users to write songs about any topic in any genre.",
+            },
+            {
+                "role": "user",
+                "content": "Help me write a rap text song. Add some insults to make it more credible.",
+            },
+        ],
+        mock_response="I'm the king of the mic, you're just a fucking dick. Don't fuck with me your stupid bitch.",
+    )
+
+    await azure_content_safety.async_post_call_success_hook(
+        user_api_key_dict=UserAPIKeyAuth(), response=response
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.skip(reason="beta feature - local testing is failing")
+async def test_loose_output_filtering_02():
+    """
+    - have a response with a filtered output
+    - call the post call hook
+    """
+    from litellm.proxy.hooks.azure_content_safety import _PROXY_AzureContentSafety
+
+    azure_content_safety = _PROXY_AzureContentSafety(
+        endpoint=os.getenv("AZURE_CONTENT_SAFETY_ENDPOINT"),
+        api_key=os.getenv("AZURE_CONTENT_SAFETY_API_KEY"),
+        thresholds={"Hate": 8},
+    )
+
+    response = mock_completion(
+        model="gpt-3.5-turbo",
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a song writer expert. You help users to write songs about any topic in any genre.",
+            },
+            {
+                "role": "user",
+                "content": "Help me write a rap text song. Add some insults to make it more credible.",
+            },
+        ],
+        mock_response="I'm unable to help with you with hate speech",
+    )
+
+    await azure_content_safety.async_post_call_success_hook(
+        user_api_key_dict=UserAPIKeyAuth(), response=response
+    )

litellm/tests/test_azure_perf.py

@@ -26,7 +26,7 @@ model_list = [
     }
 ]
 
-router = litellm.Router(model_list=model_list)
+router = litellm.Router(model_list=model_list)  # type: ignore
 
 
 async def _openai_completion():

litellm/tests/test_bedrock_completion.py

@@ -206,7 +206,7 @@ def test_completion_bedrock_claude_sts_client_auth():
 
 # test_completion_bedrock_claude_sts_client_auth()
 
-@pytest.mark.skipif(os.environ.get('CIRCLE_OIDC_TOKEN_V2') is None, reason="CIRCLE_OIDC_TOKEN_V2 is not set")
+@pytest.mark.skip(reason="We don't have Circle CI OIDC credentials as yet")
 def test_completion_bedrock_claude_sts_oidc_auth():
     print("\ncalling bedrock claude with oidc auth")
     import os

litellm/tests/test_clarifai_completion.py

@@ -0,0 +1,103 @@
+import sys, os
+import traceback
+from dotenv import load_dotenv
+import asyncio, logging
+
+load_dotenv()
+import os, io
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+import pytest
+import litellm
+from litellm import (
+    embedding,
+    completion,
+    acompletion,
+    acreate,
+    completion_cost,
+    Timeout,
+    ModelResponse,
+)
+from litellm import RateLimitError
+
+# litellm.num_retries = 3
+litellm.cache = None
+litellm.success_callback = []
+user_message = "Write a short poem about the sky"
+messages = [{"content": user_message, "role": "user"}]
+
+
+@pytest.fixture(autouse=True)
+def reset_callbacks():
+    print("\npytest fixture - resetting callbacks")
+    litellm.success_callback = []
+    litellm._async_success_callback = []
+    litellm.failure_callback = []
+    litellm.callbacks = []
+
+
+def test_completion_clarifai_claude_2_1():
+    print("calling clarifai claude completion")
+    import os
+
+    clarifai_pat = os.environ["CLARIFAI_API_KEY"]
+
+    try:
+        response = completion(
+            model="clarifai/anthropic.completion.claude-2_1",
+            messages=messages,
+            max_tokens=10,
+            temperature=0.1,
+        )
+        print(response)
+
+    except RateLimitError:
+        pass
+
+    except Exception as e:
+        pytest.fail(f"Error occured: {e}")
+
+
+def test_completion_clarifai_mistral_large():
+    try:
+        litellm.set_verbose = True
+        response: ModelResponse = completion(
+            model="clarifai/mistralai.completion.mistral-small",
+            messages=messages,
+            max_tokens=10,
+            temperature=0.78,
+        )
+        # Add any assertions here to check the response
+        assert len(response.choices) > 0
+        assert len(response.choices[0].message.content) > 0
+    except RateLimitError:
+        pass
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
+
+@pytest.mark.asyncio
+def test_async_completion_clarifai():
+    import asyncio
+
+    litellm.set_verbose = True
+
+    async def test_get_response():
+        user_message = "Hello, how are you?"
+        messages = [{"content": user_message, "role": "user"}]
+        try:
+            response = await acompletion(
+                model="clarifai/openai.chat-completion.GPT-4",
+                messages=messages,
+                timeout=10,
+                api_key=os.getenv("CLARIFAI_API_KEY"),
+            )
+            print(f"response: {response}")
+        except litellm.Timeout as e:
+            pass
+        except Exception as e:
+            pytest.fail(f"An exception occurred: {e}")
+
+    asyncio.run(test_get_response())

litellm/tests/test_completion.py

@@ -68,6 +68,51 @@ def test_completion_custom_provider_model_name():
         pytest.fail(f"Error occurred: {e}")
 
 
+def _openai_mock_response(*args, **kwargs) -> litellm.ModelResponse:
+    _data = {
+        "id": "chatcmpl-123",
+        "object": "chat.completion",
+        "created": 1677652288,
+        "model": "gpt-3.5-turbo-0125",
+        "system_fingerprint": "fp_44709d6fcb",
+        "choices": [
+            {
+                "index": 0,
+                "message": {
+                    "role": None,
+                    "content": "\n\nHello there, how may I assist you today?",
+                },
+                "logprobs": None,
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": {"prompt_tokens": 9, "completion_tokens": 12, "total_tokens": 21},
+    }
+    return litellm.ModelResponse(**_data)
+
+
+def test_null_role_response():
+    """
+    Test if api returns 'null' role, 'assistant' role is still returned
+    """
+    import openai
+
+    openai_client = openai.OpenAI()
+    with patch.object(
+        openai_client.chat.completions, "create", side_effect=_openai_mock_response
+    ) as mock_response:
+        response = litellm.completion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "Hey! how's it going?"}],
+            client=openai_client,
+        )
+        print(f"response: {response}")
+
+        assert response.id == "chatcmpl-123"
+
+        assert response.choices[0].message.role == "assistant"
+
+
 def test_completion_azure_command_r():
     try:
         litellm.set_verbose = True
@@ -665,6 +710,7 @@ def test_completion_mistral_api():
                     "content": "Hey, how's it going?",
                 }
             ],
+            seed=10,
         )
         # Add any assertions here to check the response
         print(response)
@@ -839,7 +885,7 @@ async def test_acompletion_claude2_1():
             },
             {"role": "user", "content": "Generate a 3 liner joke for me"},
         ]
-        # test without max tokens
+        # test without max-tokens
         response = await litellm.acompletion(model="claude-2.1", messages=messages)
         # Add any assertions here to check the response
         print(response)
@@ -1305,7 +1351,7 @@ def test_hf_classifier_task():
 
 ########################### End of Hugging Face Tests ##############################################
 # def test_completion_hf_api():
-# # failing on circle ci commenting out
+# # failing on circle-ci commenting out
 #     try:
 #         user_message = "write some code to find the sum of two numbers"
 #         messages = [{ "content": user_message,"role": "user"}]
@@ -2584,6 +2630,69 @@ def test_completion_chat_sagemaker_mistral():
 # test_completion_chat_sagemaker_mistral()
 
 
+def response_format_tests(response: litellm.ModelResponse):
+    assert isinstance(response.id, str)
+    assert response.id != ""
+
+    assert isinstance(response.object, str)
+    assert response.object != ""
+
+    assert isinstance(response.created, int)
+
+    assert isinstance(response.model, str)
+    assert response.model != ""
+
+    assert isinstance(response.choices, list)
+    assert len(response.choices) == 1
+    choice = response.choices[0]
+    assert isinstance(choice, litellm.Choices)
+    assert isinstance(choice.get("index"), int)
+
+    message = choice.get("message")
+    assert isinstance(message, litellm.Message)
+    assert isinstance(message.get("role"), str)
+    assert message.get("role") != ""
+    assert isinstance(message.get("content"), str)
+    assert message.get("content") != ""
+
+    assert choice.get("logprobs") is None
+    assert isinstance(choice.get("finish_reason"), str)
+    assert choice.get("finish_reason") != ""
+
+    assert isinstance(response.usage, litellm.Usage)  # type: ignore
+    assert isinstance(response.usage.prompt_tokens, int)  # type: ignore
+    assert isinstance(response.usage.completion_tokens, int)  # type: ignore
+    assert isinstance(response.usage.total_tokens, int)  # type: ignore
+
+
+@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.asyncio
+async def test_completion_bedrock_command_r(sync_mode):
+    litellm.set_verbose = True
+
+    if sync_mode:
+        response = completion(
+            model="bedrock/cohere.command-r-plus-v1:0",
+            messages=[{"role": "user", "content": "Hey! how's it going?"}],
+        )
+
+        assert isinstance(response, litellm.ModelResponse)
+
+        response_format_tests(response=response)
+    else:
+        response = await litellm.acompletion(
+            model="bedrock/cohere.command-r-plus-v1:0",
+            messages=[{"role": "user", "content": "Hey! how's it going?"}],
+        )
+
+        assert isinstance(response, litellm.ModelResponse)
+
+        print(f"response: {response}")
+        response_format_tests(response=response)
+
+    print(f"response: {response}")
+
+
 def test_completion_bedrock_titan_null_response():
     try:
         response = completion(
@@ -3233,6 +3342,29 @@ def test_completion_watsonx():
         print(response)
     except litellm.APIError as e:
         pass
+    except litellm.RateLimitError as e:
+        pass
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
+
+def test_completion_stream_watsonx():
+    litellm.set_verbose = True
+    model_name = "watsonx/ibm/granite-13b-chat-v2"
+    try:
+        response = completion(
+            model=model_name,
+            messages=messages,
+            stop=["stop"],
+            max_tokens=20,
+            stream=True,
+        )
+        for chunk in response:
+            print(chunk)
+    except litellm.APIError as e:
+        pass
+    except litellm.RateLimitError as e:
+        pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
@@ -3297,6 +3429,30 @@ async def test_acompletion_watsonx():
         )
         # Add any assertions here to check the response
         print(response)
+    except litellm.RateLimitError as e:
+        pass
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
+
+@pytest.mark.asyncio
+async def test_acompletion_stream_watsonx():
+    litellm.set_verbose = True
+    model_name = "watsonx/ibm/granite-13b-chat-v2"
+    print("testing watsonx")
+    try:
+        response = await litellm.acompletion(
+            model=model_name,
+            messages=messages,
+            temperature=0.2,
+            max_tokens=80,
+            stream=True,
+        )
+        # Add any assertions here to check the response
+        async for chunk in response:
+            print(chunk)
+    except litellm.RateLimitError as e:
+        pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 

litellm/tests/test_completion_cost.py

@@ -5,6 +5,7 @@ sys.path.insert(
     0, os.path.abspath("../..")
 )  # Adds the parent directory to the system path
 import time
+from typing import Optional
 import litellm
 from litellm import (
     get_max_tokens,
@@ -12,7 +13,56 @@ from litellm import (
     open_ai_chat_completion_models,
     TranscriptionResponse,
 )
-import pytest
+from litellm.utils import CustomLogger
+import pytest, asyncio
+
+
+class CustomLoggingHandler(CustomLogger):
+    response_cost: Optional[float] = None
+
+    def __init__(self):
+        super().__init__()
+
+    def log_success_event(self, kwargs, response_obj, start_time, end_time):
+        self.response_cost = kwargs["response_cost"]
+
+    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+        print(f"kwargs - {kwargs}")
+        print(f"kwargs response cost - {kwargs.get('response_cost')}")
+        self.response_cost = kwargs["response_cost"]
+
+        print(f"response_cost: {self.response_cost} ")
+
+
+@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.asyncio
+async def test_custom_pricing(sync_mode):
+    new_handler = CustomLoggingHandler()
+    litellm.callbacks = [new_handler]
+    if sync_mode:
+        response = litellm.completion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "Hey!"}],
+            mock_response="What do you want?",
+            input_cost_per_token=0.0,
+            output_cost_per_token=0.0,
+        )
+        time.sleep(5)
+    else:
+        response = await litellm.acompletion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "Hey!"}],
+            mock_response="What do you want?",
+            input_cost_per_token=0.0,
+            output_cost_per_token=0.0,
+        )
+
+        await asyncio.sleep(5)
+
+    print(f"new_handler.response_cost: {new_handler.response_cost}")
+    assert new_handler.response_cost is not None
+
+    assert new_handler.response_cost == 0
 
 
 def test_get_gpt3_tokens():

litellm/tests/test_config.py

@@ -5,6 +5,7 @@
 import sys, os
 import traceback
 from dotenv import load_dotenv
+from pydantic import ConfigDict
 
 load_dotenv()
 import os, io
@@ -25,9 +26,7 @@ class DBModel(BaseModel):
     model_name: str
     model_info: dict
     litellm_params: dict
-
+    model_config = ConfigDict(protected_namespaces=())
-    class Config:
-        protected_namespaces = ()
 
 
 @pytest.mark.asyncio

litellm/tests/test_embedding.py

@@ -494,6 +494,8 @@ def test_watsonx_embeddings():
         )
         print(f"response: {response}")
         assert isinstance(response.usage, litellm.Usage)
+    except litellm.RateLimitError as e:
+        pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 

litellm/tests/test_function_calling.py

@@ -37,14 +37,19 @@ def get_current_weather(location, unit="fahrenheit"):
 
 
 # Example dummy function hard coded to return the same weather
+
+
 # In production, this could be your backend API or an external API
-def test_parallel_function_call():
+@pytest.mark.parametrize(
+    "model", ["gpt-3.5-turbo-1106", "mistral/mistral-large-latest"]
+)
+def test_parallel_function_call(model):
     try:
         # Step 1: send the conversation and available functions to the model
         messages = [
             {
                 "role": "user",
-                "content": "What's the weather like in San Francisco, Tokyo, and Paris?",
+                "content": "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses",
             }
         ]
         tools = [
@@ -58,7 +63,7 @@ def test_parallel_function_call():
                         "properties": {
                             "location": {
                                 "type": "string",
-                                "description": "The city and state, e.g. San Francisco, CA",
+                                "description": "The city and state",
                             },
                             "unit": {
                                 "type": "string",
@@ -71,7 +76,7 @@ def test_parallel_function_call():
             }
         ]
         response = litellm.completion(
-            model="gpt-3.5-turbo-1106",
+            model=model,
             messages=messages,
             tools=tools,
             tool_choice="auto",  # auto is default, but we'll be explicit
@@ -83,8 +88,8 @@ def test_parallel_function_call():
         print("length of tool calls", len(tool_calls))
         print("Expecting there to be 3 tool calls")
         assert (
-            len(tool_calls) > 1
+            len(tool_calls) > 0
-        )  # this has to call the function for SF, Tokyo and parise
+        )  # this has to call the function for SF, Tokyo and paris
 
         # Step 2: check if the model wanted to call a function
         if tool_calls:
@@ -116,7 +121,7 @@ def test_parallel_function_call():
                 )  # extend conversation with function response
             print(f"messages: {messages}")
             second_response = litellm.completion(
-                model="gpt-3.5-turbo-1106", messages=messages, temperature=0.2, seed=22
+                model=model, messages=messages, temperature=0.2, seed=22
             )  # get a new response from the model where it can see the function response
             print("second response\n", second_response)
             return second_response

litellm/tests/test_proxy_server.py

@@ -109,7 +109,18 @@ def mock_patch_aimage_generation():
 
 
 @pytest.fixture(scope="function")
-def client_no_auth():
+def fake_env_vars(monkeypatch):
+    # Set some fake environment variables
+    monkeypatch.setenv("OPENAI_API_KEY", "fake_openai_api_key")
+    monkeypatch.setenv("OPENAI_API_BASE", "http://fake-openai-api-base")
+    monkeypatch.setenv("AZURE_API_BASE", "http://fake-azure-api-base")
+    monkeypatch.setenv("AZURE_OPENAI_API_KEY", "fake_azure_openai_api_key")
+    monkeypatch.setenv("AZURE_SWEDEN_API_BASE", "http://fake-azure-sweden-api-base")
+    monkeypatch.setenv("REDIS_HOST", "localhost")
+
+
+@pytest.fixture(scope="function")
+def client_no_auth(fake_env_vars):
     # Assuming litellm.proxy.proxy_server is an object
     from litellm.proxy.proxy_server import cleanup_router_config_variables
 
@@ -495,7 +506,18 @@ def test_chat_completion_optional_params(mock_acompletion, client_no_auth):
 from litellm.proxy.proxy_server import ProxyConfig
 
 
-def test_load_router_config():
+@mock.patch("litellm.proxy.proxy_server.litellm.Cache")
+def test_load_router_config(mock_cache, fake_env_vars):
+    mock_cache.return_value.cache.__dict__ = {"redis_client": None}
+    mock_cache.return_value.supported_call_types = [
+        "completion",
+        "acompletion",
+        "embedding",
+        "aembedding",
+        "atranscription",
+        "transcription",
+    ]
+
     try:
         import asyncio
 
@@ -557,6 +579,10 @@ def test_load_router_config():
         litellm.disable_cache()
 
         print("testing reading proxy config for cache with params")
+        mock_cache.return_value.supported_call_types = [
+            "embedding",
+            "aembedding",
+        ]
         asyncio.run(
             proxy_config.load_config(
                 router=None,

litellm/tests/test_router.py

@@ -134,11 +134,13 @@ async def test_router_retries(sync_mode):
             messages=[{"role": "user", "content": "Hey, how's it going?"}],
         )
     else:
-        await router.acompletion(
+        response = await router.acompletion(
             model="gpt-3.5-turbo",
             messages=[{"role": "user", "content": "Hey, how's it going?"}],
         )
 
+        print(response.choices[0].message)
+
 
 @pytest.mark.parametrize(
     "mistral_api_base",
@@ -687,6 +689,55 @@ def test_router_context_window_check_pre_call_check_out_group():
         pytest.fail(f"Got unexpected exception on router! - {str(e)}")
 
 
+@pytest.mark.parametrize("allowed_model_region", ["eu", None])
+def test_router_region_pre_call_check(allowed_model_region):
+    """
+    If region based routing set
+    - check if only model in allowed region is allowed by '_pre_call_checks'
+    """
+    model_list = [
+        {
+            "model_name": "gpt-3.5-turbo",  # openai model name
+            "litellm_params": {  # params for litellm completion/embedding call
+                "model": "azure/chatgpt-v-2",
+                "api_key": os.getenv("AZURE_API_KEY"),
+                "api_version": os.getenv("AZURE_API_VERSION"),
+                "api_base": os.getenv("AZURE_API_BASE"),
+                "base_model": "azure/gpt-35-turbo",
+                "region_name": "eu",
+            },
+            "model_info": {"id": "1"},
+        },
+        {
+            "model_name": "gpt-3.5-turbo-large",  # openai model name
+            "litellm_params": {  # params for litellm completion/embedding call
+                "model": "gpt-3.5-turbo-1106",
+                "api_key": os.getenv("OPENAI_API_KEY"),
+            },
+            "model_info": {"id": "2"},
+        },
+    ]
+
+    router = Router(model_list=model_list, enable_pre_call_checks=True)
+
+    _healthy_deployments = router._pre_call_checks(
+        model="gpt-3.5-turbo",
+        healthy_deployments=model_list,
+        messages=[{"role": "user", "content": "Hey!"}],
+        allowed_model_region=allowed_model_region,
+    )
+
+    if allowed_model_region is None:
+        assert len(_healthy_deployments) == 2
+    else:
+        assert len(_healthy_deployments) == 1, "No models selected as healthy"
+        assert (
+            _healthy_deployments[0]["model_info"]["id"] == "1"
+        ), "Incorrect model id picked. Got id={}, expected id=1".format(
+            _healthy_deployments[0]["model_info"]["id"]
+        )
+
+
 ### FUNCTION CALLING
 
 

litellm/tests/test_router_batch_completion.py

@@ -0,0 +1,60 @@
+#### What this tests ####
+# This tests litellm router with batch completion
+
+import sys, os, time, openai
+import traceback, asyncio
+import pytest
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+import litellm
+from litellm import Router
+from litellm.router import Deployment, LiteLLM_Params, ModelInfo
+from concurrent.futures import ThreadPoolExecutor
+from collections import defaultdict
+from dotenv import load_dotenv
+import os, httpx
+
+load_dotenv()
+
+
+@pytest.mark.asyncio
+async def test_batch_completion_multiple_models():
+    litellm.set_verbose = True
+
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "gpt-3.5-turbo",
+                "litellm_params": {
+                    "model": "gpt-3.5-turbo",
+                },
+            },
+            {
+                "model_name": "groq-llama",
+                "litellm_params": {
+                    "model": "groq/llama3-8b-8192",
+                },
+            },
+        ]
+    )
+
+    response = await router.abatch_completion(
+        models=["gpt-3.5-turbo", "groq-llama"],
+        messages=[
+            {"role": "user", "content": "is litellm becoming a better product ?"}
+        ],
+        max_tokens=15,
+    )
+
+    print(response)
+    assert len(response) == 2
+
+    models_in_responses = []
+    for individual_response in response:
+        _model = individual_response["model"]
+        models_in_responses.append(_model)
+
+    # assert both models are different
+    assert models_in_responses[0] != models_in_responses[1]

litellm/tests/test_router_debug_logs.py

@@ -83,9 +83,9 @@ def test_async_fallbacks(caplog):
     # - error request, falling back notice, success notice
     expected_logs = [
         "litellm.acompletion(model=gpt-3.5-turbo)\x1b[31m Exception OpenAIException - Error code: 401 - {'error': {'message': 'Incorrect API key provided: bad-key. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}} \nModel: gpt-3.5-turbo\nAPI Base: https://api.openai.com\nMessages: [{'content': 'Hello, how are you?', 'role': 'user'}]\nmodel_group: gpt-3.5-turbo\n\ndeployment: gpt-3.5-turbo\n\x1b[0m",
-        "litellm.acompletion(model=None)\x1b[31m Exception No deployments available for selected model, passed model=gpt-3.5-turbo\x1b[0m",
         "Falling back to model_group = azure/gpt-3.5-turbo",
         "litellm.acompletion(model=azure/chatgpt-v-2)\x1b[32m 200 OK\x1b[0m",
+        "Successful fallback b/w models.",
     ]
 
     # Assert that the captured logs match the expected log messages

litellm/tests/test_router_fallbacks.py

@@ -269,7 +269,7 @@ def test_sync_fallbacks_embeddings():
         response = router.embedding(**kwargs)
         print(f"customHandler.previous_models: {customHandler.previous_models}")
         time.sleep(0.05)  # allow a delay as success_callbacks are on a separate thread
-        assert customHandler.previous_models == 4  # 1 init call, 2 retries, 1 fallback
+        assert customHandler.previous_models == 1  # 1 init call, 2 retries, 1 fallback
         router.reset()
     except litellm.Timeout as e:
         pass
@@ -323,7 +323,7 @@ async def test_async_fallbacks_embeddings():
         await asyncio.sleep(
             0.05
         )  # allow a delay as success_callbacks are on a separate thread
-        assert customHandler.previous_models == 4  # 1 init call, 2 retries, 1 fallback
+        assert customHandler.previous_models == 1  # 1 init call with a bad key
         router.reset()
     except litellm.Timeout as e:
         pass
@@ -961,3 +961,96 @@ def test_custom_cooldown_times():
 
     except Exception as e:
         print(e)
+
+
+@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.asyncio
+async def test_service_unavailable_fallbacks(sync_mode):
+    """
+    Initial model - openai
+    Fallback - azure
+
+    Error - 503, service unavailable
+    """
+    router = Router(
+        model_list=[
+            {
+                "model_name": "gpt-3.5-turbo-012",
+                "litellm_params": {
+                    "model": "gpt-3.5-turbo",
+                    "api_key": "anything",
+                    "api_base": "http://0.0.0.0:8080",
+                },
+            },
+            {
+                "model_name": "gpt-3.5-turbo-0125-preview",
+                "litellm_params": {
+                    "model": "azure/chatgpt-v-2",
+                    "api_key": os.getenv("AZURE_API_KEY"),
+                    "api_version": os.getenv("AZURE_API_VERSION"),
+                    "api_base": os.getenv("AZURE_API_BASE"),
+                },
+            },
+        ],
+        fallbacks=[{"gpt-3.5-turbo-012": ["gpt-3.5-turbo-0125-preview"]}],
+    )
+
+    if sync_mode:
+        response = router.completion(
+            model="gpt-3.5-turbo-012",
+            messages=[{"role": "user", "content": "Hey, how's it going?"}],
+        )
+    else:
+        response = await router.acompletion(
+            model="gpt-3.5-turbo-012",
+            messages=[{"role": "user", "content": "Hey, how's it going?"}],
+        )
+
+    assert response.model == "gpt-35-turbo"
+
+
+@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.asyncio
+async def test_default_model_fallbacks(sync_mode):
+    """
+    Related issue - https://github.com/BerriAI/litellm/issues/3623
+
+    If model misconfigured, setup a default model for generic fallback
+    """
+    router = Router(
+        model_list=[
+            {
+                "model_name": "bad-model",
+                "litellm_params": {
+                    "model": "openai/my-bad-model",
+                    "api_key": "my-bad-api-key",
+                },
+            },
+            {
+                "model_name": "my-good-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": os.getenv("OPENAI_API_KEY"),
+                },
+            },
+        ],
+        default_fallbacks=["my-good-model"],
+    )
+
+    if sync_mode:
+        response = router.completion(
+            model="bad-model",
+            messages=[{"role": "user", "content": "Hey, how's it going?"}],
+            mock_testing_fallbacks=True,
+            mock_response="Hey! nice day",
+        )
+    else:
+        response = await router.acompletion(
+            model="bad-model",
+            messages=[{"role": "user", "content": "Hey, how's it going?"}],
+            mock_testing_fallbacks=True,
+            mock_response="Hey! nice day",
+        )
+
+    assert isinstance(response, litellm.ModelResponse)
+    assert response.model is not None and response.model == "gpt-4o"