add oobabooga text web api support

litellm/__init__.py

@@ -277,6 +277,7 @@ provider_list: List = [
     "nlp_cloud",
     "bedrock",
     "petals",
+    "oobabooga",
     "custom", # custom apis
 ]
 

litellm/llms/oobabooga.py

@@ -0,0 +1,123 @@
+import os
+import json
+from enum import Enum
+import requests
+import time
+from typing import Callable, Optional
+from litellm.utils import ModelResponse
+from .prompt_templates.factory import prompt_factory, custom_prompt
+
+class OobaboogaError(Exception):
+    def __init__(self, status_code, message):
+        self.status_code = status_code
+        self.message = message
+        super().__init__(
+            self.message
+        )  # Call the base class constructor with the parameters it needs
+
+def validate_environment(api_key):
+    headers = {
+        "accept": "application/json",
+        "content-type": "application/json",
+    }
+    if api_key:
+        headers["Authorization"] = f"Token {api_key}"
+    return headers
+
+def completion(
+    model: str,
+    messages: list,
+    api_base: Optional[str],
+    model_response: ModelResponse,
+    print_verbose: Callable,
+    encoding,
+    api_key,
+    logging_obj,
+    custom_prompt_dict={},
+    optional_params=None,
+    litellm_params=None,
+    logger_fn=None,
+    default_max_tokens_to_sample=None,
+):
+    headers = validate_environment(api_key)
+    if "https" in model:
+        completion_url = model
+    elif api_base:
+        completion_url = api_base
+    else: 
+        raise OobaboogaError(status_code=404, message="API Base not set. Set one via completion(..,api_base='your-api-url')")
+    model = model
+    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)
+    
+    completion_url = completion_url + "/api/v1/generate"
+    data = {
+        "prompt": prompt,
+        **optional_params,
+    }
+    ## LOGGING
+    logging_obj.pre_call(
+            input=prompt,
+            api_key=api_key,
+            additional_args={"complete_input_dict": data},
+        )
+    ## COMPLETION CALL
+    response = requests.post(
+        completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False
+    )
+    if "stream" in optional_params and optional_params["stream"] == True:
+        return response.iter_lines()
+    else:
+        ## LOGGING
+        logging_obj.post_call(
+                input=prompt,
+                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 OobaboogaError(message=response.text, status_code=response.status_code)
+        if "error" in completion_response:
+            raise OobaboogaError(
+                message=completion_response["error"],
+                status_code=response.status_code,
+            )
+        else:
+            try:
+                model_response["choices"][0]["message"]["content"] = completion_response['results'][0]['text']
+            except:
+                raise OobaboogaError(message=json.dumps(completion_response), status_code=response.status_code)
+
+        ## CALCULATING USAGE
+        prompt_tokens = len(
+            encoding.encode(prompt)
+        ) 
+        completion_tokens = len(
+            encoding.encode(model_response["choices"][0]["message"]["content"])
+        )
+
+        model_response["created"] = time.time()
+        model_response["model"] = model
+        model_response["usage"] = {
+            "prompt_tokens": prompt_tokens,
+            "completion_tokens": completion_tokens,
+            "total_tokens": prompt_tokens + completion_tokens,
+        }
+        return model_response
+
+def embedding():
+    # logic for parsing in - calling - parsing out model embedding calls
+    pass

litellm/main.py

@@ -35,6 +35,7 @@ from .llms import vllm
 from .llms import ollama
 from .llms import cohere
 from .llms import petals
+from .llms import oobabooga
 import tiktoken
 from concurrent.futures import ThreadPoolExecutor
 from typing import Callable, List, Optional, Dict
@@ -686,6 +687,28 @@ def completion(
                 )
                 return response
             response = model_response
+        elif custom_llm_provider == "oobabooga":
+            custom_llm_provider = "oobabooga"
+            model_response = oobabooga.completion(
+                model=model,
+                messages=messages,
+                model_response=model_response,
+                api_base=api_base, # type: ignore
+                print_verbose=print_verbose,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                api_key=None,
+                logger_fn=logger_fn,
+                encoding=encoding,
+                logging_obj=logging
+            )
+            if "stream" in optional_params and optional_params["stream"] == True:
+                # don't try to access stream object,
+                response = CustomStreamWrapper(
+                    model_response, model, custom_llm_provider="oobabooga", logging_obj=logging
+                )
+                return response
+            response = model_response
         elif custom_llm_provider == "together_ai" or ("togethercomputer" in model) or (model  in litellm.together_ai_models):
             custom_llm_provider = "together_ai"
             together_ai_key = (

litellm/tests/test_completion.py

@@ -47,6 +47,18 @@ def test_completion_claude():
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 # test_completion_claude()
+
+# def test_completion_oobabooga():
+#     try:
+#         response = completion(
+#             model="oobabooga/vicuna-1.3b", messages=messages, api_base="http://127.0.0.1:5000"
+#         )
+#         # Add any assertions here to check the response
+#         print(response)
+#     except Exception as e:
+#         pytest.fail(f"Error occurred: {e}")
+
+# test_completion_oobabooga()
 # aleph alpha
 # def test_completion_aleph_alpha():
 #     try:

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "0.1.704"
+version = "0.1.705"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT License"