Refactor engines module to fetch engine details

from API

Update chat.py

Signed-off-by: Daniel Salvatierra <dsalvat1@gmail.com>

gpt4all-api/gpt4all_api/app/api_v1/routes/chat.py

@@ -1,39 +1,35 @@
 import logging
 import time
-from typing import Dict, List
-
-from api_v1.settings import settings
-from fastapi import APIRouter, Depends, Response, Security, status
+from typing import List
+from uuid import uuid4
+from fastapi import APIRouter
 from pydantic import BaseModel, Field
+from api_v1.settings import settings
+from fastapi.responses import StreamingResponse
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
 
 ### This should follow https://github.com/openai/openai-openapi/blob/master/openapi.yaml
-
-
 class ChatCompletionMessage(BaseModel):
     role: str
     content: str
 
-
 class ChatCompletionRequest(BaseModel):
-    model: str = Field(..., description='The model to generate a completion from.')
-    messages: List[ChatCompletionMessage] = Field(..., description='The model to generate a completion from.')
-
+    model: str = Field(settings.model, description='The model to generate a completion from.')
+    messages: List[ChatCompletionMessage] = Field(..., description='Messages for the chat completion.')
 
 class ChatCompletionChoice(BaseModel):
     message: ChatCompletionMessage
     index: int
+    logprobs: float
     finish_reason: str
 
-
 class ChatCompletionUsage(BaseModel):
     prompt_tokens: int
     completion_tokens: int
     total_tokens: int
 
-
 class ChatCompletionResponse(BaseModel):
     id: str
     object: str = 'text_completion'
@@ -42,20 +38,38 @@ class ChatCompletionResponse(BaseModel):
     choices: List[ChatCompletionChoice]
     usage: ChatCompletionUsage
 
-
 router = APIRouter(prefix="/chat", tags=["Completions Endpoints"])
 
-
 @router.post("/completions", response_model=ChatCompletionResponse)
 async def chat_completion(request: ChatCompletionRequest):
     '''
-    Completes a GPT4All model response.
+    Completes a GPT4All model response based on the last message in the chat.
     '''
+    # Example: Echo the last message content with some modification
+    if request.messages:
+        last_message = request.messages[-1].content
+        response_content = f"Echo: {last_message}"
+    else:
+        response_content = "No messages received."
 
-    return ChatCompletionResponse(
-        id='asdf',
-        created=time.time(),
-        model=request.model,
-        choices=[{}],
-        usage={'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0},
+    # Create a chat message for the response
+    response_message = ChatCompletionMessage(role="system", content=response_content)
+
+    # Create a choice object with the response message
+    response_choice = ChatCompletionChoice(
+        message=response_message,
+        index=0,
+        logprobs=-1.0,  # Placeholder value
+        finish_reason="length"  # Placeholder value
     )
+
+    # Create the response object
+    chat_response = ChatCompletionResponse(
+        id=str(uuid4()),
+        created=int(time.time()),
+        model=request.model,
+        choices=[response_choice],
+        usage=ChatCompletionUsage(prompt_tokens=0, completion_tokens=0, total_tokens=0),  # Placeholder values
+    )
+
+    return chat_response

gpt4all-api/gpt4all_api/app/api_v1/routes/engines.py

@@ -1,40 +1,39 @@
-import logging
-from typing import Dict, List
-
-from api_v1.settings import settings
-from fastapi import APIRouter, Depends, Response, Security, status
+import requests
+from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel, Field
+from typing import List, Dict
 
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-
-### This should follow https://github.com/openai/openai-openapi/blob/master/openapi.yaml
-
+# Define the router for the engines module
+router = APIRouter(prefix="/engines", tags=["Search Endpoints"])
 
+# Define the models for the engines module
 class ListEnginesResponse(BaseModel):
     data: List[Dict] = Field(..., description="All available models.")
 
-
 class EngineResponse(BaseModel):
     data: List[Dict] = Field(..., description="All available models.")
 
 
-router = APIRouter(prefix="/engines", tags=["Search Endpoints"])
-
-
+# Define the routes for the engines module
 @router.get("/", response_model=ListEnginesResponse)
 async def list_engines():
-    '''
-    List all available GPT4All models from
-    https://raw.githubusercontent.com/nomic-ai/gpt4all/main/gpt4all-chat/metadata/models2.json
-    '''
-    raise NotImplementedError()
-    return ListEnginesResponse(data=[])
-
+    try:
+        response = requests.get('https://raw.githubusercontent.com/nomic-ai/gpt4all/main/gpt4all-chat/metadata/models2.json')
+        response.raise_for_status()  # This will raise an HTTPError if the HTTP request returned an unsuccessful status code
+        engines = response.json()
+        return ListEnginesResponse(data=engines)
+    except requests.RequestException as e:
+        logger.error(f"Error fetching engine list: {e}")
+        raise HTTPException(status_code=500, detail="Error fetching engine list")
 
+# Define the routes for the engines module
 @router.get("/{engine_id}", response_model=EngineResponse)
 async def retrieve_engine(engine_id: str):
-    ''' '''
-
-    raise NotImplementedError()
-    return EngineResponse()
+    try:
+        # Implement logic to fetch a specific engine's details
+        # This is a placeholder, replace with your actual data retrieval logic
+        engine_details = {"id": engine_id, "name": "Engine Name", "description": "Engine Description"}
+        return EngineResponse(data=[engine_details])
+    except Exception as e:
+        logger.error(f"Error fetching engine details: {e}")
+        raise HTTPException(status_code=500, detail=f"Error fetching details for engine {engine_id}")