mutliple fixes

backend/lib/types.py

@@ -1,5 +1,5 @@
 from pydantic import BaseModel, Field
-from typing import List, Optional, Dict, Union
+from typing import List, Optional, Union
 
 # Add these models after your existing Pydantic models
 class TranscriptionRequest(BaseModel):
@@ -20,7 +20,7 @@ class ImageUrl(BaseModel):
 class MessageContent(BaseModel):
     type: str  # "text" or "image_url"
     text: Optional[str] = None
-    image_url: Optional[ImageUrl] = None
+    image_url: Optional[ImageUrl] = None 
 
 # chat completion request and response models
 class UsageDetails(BaseModel):
@@ -30,14 +30,14 @@ class UsageDetails(BaseModel):
 
 class Usage(BaseModel):
     prompt_tokens: int
-    completion_tokens: int
+    completion_tokens: Optional[int] = None 
     total_tokens: int
     prompt_tokens_details: Optional[UsageDetails] = None
     completion_tokens_details: Optional[UsageDetails] = None
 
 class Message(BaseModel):
     role: str
-    content: Union[str, List[MessageContent]]  # Can be string or list for vision
+    content: Union[str,List[MessageContent]]  # Can be string or list for vision
     name: Optional[str] = None
 
 class ChatCompletionRequest(BaseModel):
@@ -69,7 +69,7 @@ class ChatCompletionResponse(BaseModel):
 # embeddings request and response models
 class EmbeddingInput(BaseModel):
     model: str
-    input: List[str]
+    input: Union[str,List[str]]
     user: Optional[str] = None
 
 class EmbeddingData(BaseModel):
@@ -88,4 +88,4 @@ class SpeechRequest(BaseModel):
     input: str
     voice: str = "alloy"  # alloy, echo, fable, onyx, nova, shimmer
     response_format: Optional[str] = "mp3"  # mp3, opus, aac, flac, wav, pcm
-    speed: Optional[float] = Field(1.0, ge=0.25, le=4.0)  # Speed between 0.25 and 4.0
+    speed: Optional[float] = Field(1.0, ge=0.25, le=4.0)  # Speed between 0.25 and 4.0

backend/main.py

@@ -128,7 +128,7 @@ async def chat_completions(request: ChatCompletionRequest, user_key = Depends(ve
                         if content_item.image_url.url.startswith(("http://", "https://")):
                             content_item.image_url.url = await fetch_image_as_base64(content_item.image_url.url)
 
-    request_data = request.dict(by_alias=True)
+    request_data = request.model_dump(by_alias=True)
 
     # Convert to OpenAI format for vision messages
     if has_images:
@@ -160,7 +160,11 @@ async def chat_completions(request: ChatCompletionRequest, user_key = Depends(ve
         # Keep OpenAI-compatible parameters only
         allowed_params = ["model", "messages", "stream", "max_tokens", "temperature", "top_p", "n", "stop", "presence_penalty", "frequency_penalty", "user"]
         request_data = {k: v for k, v in request_data.items() if k in allowed_params and v is not None}
-
+
+        # Some models don't allow both temperature and top_p
+        if 'temperature' in request_data and 'top_p' in request_data:
+            # Remove top_p, keep temperature (or vice versa based on your preference)
+            request_data.pop('top_p')
     # Don't truncate messages with images
     if model_config['params'].get('max_input_tokens') and not has_images:
         # Only truncate text-only messages
@@ -343,7 +347,7 @@ async def event_generator():
 @app.post("/v1/embeddings")
 async def create_embedding(request: EmbeddingInput, user_key = Depends(verify_token)):
     model_config = get_model_config(request.model, user_key)
-    request_data = request.dict()
+    request_data = request.model_dump()
 
     request_data["model"] = model_config['params']['model']  # Maps "devstral" to "devstral:24b"
 
@@ -599,4 +603,4 @@ async def list_models(user_key = Depends(verify_token)):
 
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    uvicorn.run(app, host="0.0.0.0", port=8000)

backend/requirements.txt

@@ -7,4 +7,6 @@ codecarbon
 prometheus-client
 python-multipart
 tiktoken
-requests
+requests
+pytest
+httpx

backend/test_main.py

@@ -0,0 +1,122 @@
+from fastapi.testclient import TestClient
+from main import app
+from lib.types import ChatCompletionResponse, EmbeddingResponse
+import os
+
+
+token = os.environ["TOKEN"]
+print("TOKEN", token )
+client = TestClient(app)
+
+b64_image = "data:image/gif;base64,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"
+url_image = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
+chatRequest = {
+    "model": "gemma3",  # Replace with actual model name (e.g., "gpt-4")
+    "messages": [
+        {
+            "role": "user",  # or "system", "assistant"
+            "content": "Hello, how are you?",  # Replace with your actual message
+            # "name": "optional_name"  # Only needed for named users in some APIs
+        }
+    ],
+    "max_tokens": 100,  # Replace with your desired max tokens
+    "temperature": 0.7,  # Replace with your desired temperature (0-2)
+    "top_p": 1,
+    "n": 1,
+    "stream": False,
+    "stop": None,  # or ["string"] if you want to stop at certain phrases
+    "presence_penalty": 0,
+    "frequency_penalty": 0
+    # Note: 'usage' is typically part of the response, not the request
+}
+
+embeddingRequest = {
+  "model": "text-embedding-3-small",
+  "input": [
+    "Message a embeder"
+  ],
+  "user": "string"
+}
+
+
+def test_read_main():
+    response = client.get("/")
+    assert response.status_code == 404
+    assert response.json() == {"detail":"Not Found"}
+def test_list_models():
+    response = client.get("/v1/models", headers={"Authorization": "Bearer " + token})
+    assert response.status_code == 200
+    data = response.json()
+    assert isinstance(data["data"], list)
+def test_completion_chat():
+    response = client.post(
+        "/v1/chat/completions",
+        headers={"Authorization": "Bearer " + token},
+        json=chatRequest 
+    )
+    data = response.json()
+    # check type using pydantic class
+    assert ChatCompletionResponse(**data)
+
+def test_embedding_array():
+    response = client.post(
+        "/v1/embeddings",
+        headers={"Authorization": "Bearer " + token},
+        json=embeddingRequest        
+    ,)
+    data = response.json()
+    assert EmbeddingResponse(**data)
+
+def test_embedding_string():
+    embeddingRequest["input"] = "Message to embed" 
+    response = client.post(
+        "/v1/embeddings",
+        headers={"Authorization": "Bearer " + token},
+        json=embeddingRequest        
+    ,)
+    data = response.json()
+    assert EmbeddingResponse(**data) 
+
+# def test_completion_chat_b64Image():
+#     chatRequest["messages"][0]["content"] = [
+#         {
+#             "type": "text",
+#             "text": "What is image ?"
+#         },
+#         {
+#             "type": "image_url",
+#             "image_url": {"url": b64_image}
+#         }
+#     ]
+#     print(chatRequest) 
+#     response = client.post(
+#         "/v1/chat/completions",
+#         headers={"Authorization": "Bearer " + token},
+#         json=chatRequest 
+#     )
+#     data = response.json()
+#     print(data)
+#     # check type using pydantic class
+#     assert ChatCompletionResponse(**data) 
+
+# def test_completion_chat_urlImage():
+#     chatRequest["messages"][0]["content"] = [
+#         {
+#             "type": "text",
+#             "text": "What is image ?"
+#         },
+#         {
+#             "type": "image_url",
+#             "image_url": {"url": url_image}
+#         }
+#     ]
+#     print(chatRequest) 
+#     response = client.post(
+#         "/v1/chat/completions",
+#         headers={"Authorization": "Bearer " + token},
+#         json=chatRequest 
+#     )
+#     data = response.json()
+#     print(data)
+#     # check type using pydantic class
+#     assert ChatCompletionResponse(**data) 

docker-compose.yaml

@@ -8,20 +8,17 @@ services:
       context: ./backend
       dockerfile: Dockerfile
     container_name: ai-proxy-test
-    depends_on:
-      - api
     volumes:
       - ./backend:/app
+      - ./config.yaml:/config.yaml
+    env_file: .env
     environment:
       - PYTHONUNBUFFERED=1
-      - API_URL=http://api:8000
-      - API_TOKEN=autobot3000
-    command: ["python", "tests.py"]
+    command: ["pytest"]
 
   api:
     profiles:
       - api
-      - test
     image: erasme/ai-proxy:latest
     build:
       context: ./backend
@@ -76,4 +73,4 @@ services:
 
 
 volumes:
-  grafana-storage:
+  grafana-storage: