Merge pull request #4158 from BerriAI/litellm_fix_clarifai

[Fix] Add ClarifAI support for LiteLLM Proxy

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

@@ -1,10 +1,13 @@
 # Clarifai
 Anthropic, OpenAI, Mistral, Llama and Gemini LLMs are Supported on Clarifai. 
 
+:::warning 
+
+Streaming is not yet supported on using clarifai and litellm. Tracking support here: https://github.com/BerriAI/litellm/issues/4162
+
+:::
+
 ## Pre-Requisites
-
-`pip install clarifai`
-
 `pip install litellm`
 
 ## Required Environment Variables
@@ -12,6 +15,7 @@ To obtain your Clarifai Personal access token follow this [link](https://docs.cl
 
 ```python
 os.environ["CLARIFAI_API_KEY"] = "YOUR_CLARIFAI_PAT"  # CLARIFAI_PAT
+
 ```
 
 ## Usage
@@ -68,7 +72,7 @@ Example  Usage - Note: liteLLM supports all models deployed on Clarifai
 | 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
+## Mistral LLMs
 | Model Name                                  | Function Call                                                         |
 |---------------------------------------------|------------------------------------------------------------------------|
 | clarifai/mistralai.completion.mixtral-8x22B            | `completion('clarifai/mistralai.completion.mixtral-8x22B', messages)`               |

litellm/llms/clarifai.py

@@ -139,6 +139,7 @@ def process_response(
 
 def convert_model_to_url(model: str, api_base: str):
     user_id, app_id, model_id = model.split(".")
+    model_id = model_id.lower()
     return f"{api_base}/users/{user_id}/apps/{app_id}/models/{model_id}/outputs"
 
 
@@ -171,19 +172,55 @@ async def async_completion(
 
     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)
+        url=model, headers=headers, data=json.dumps(data)
     )
 
-    return process_response(
-        model=model,
-        prompt=prompt,
-        response=response,
-        model_response=model_response,
+    logging_obj.post_call(
+        input=prompt,
         api_key=api_key,
-        data=data,
-        encoding=encoding,
-        logging_obj=logging_obj,
+        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 completion(
@@ -241,7 +278,7 @@ def completion(
         additional_args={
             "complete_input_dict": data,
             "headers": headers,
-            "api_base": api_base,
+            "api_base": model,
         },
     )
     if acompletion == True:

litellm/main.py

@@ -335,6 +335,7 @@ async def acompletion(
             or custom_llm_provider == "predibase"
             or custom_llm_provider == "bedrock"
             or custom_llm_provider == "databricks"
+            or custom_llm_provider == "clarifai"
             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)