New approach

app.py

@@ -1,119 +1,133 @@
-import os
-import logging
-import requests
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import StreamingResponse
-from pydantic import BaseModel
-from openai import OpenAI
+# import os
+# import logging
+# import requests
+# from fastapi import FastAPI, HTTPException
+# from fastapi.responses import StreamingResponse
+# from pydantic import BaseModel
+# from openai import OpenAI
+
+# logging.basicConfig(level=logging.INFO)
+# logger = logging.getLogger(__name__)
+
+# app = FastAPI()
+# MODEL_NAME = 'llama3.1:8b'
+
+# OLLAMA_URL = "http://localhost:11434"
+
+
+# # def create_model(query):
+# #     message = [
+# #         {"role": "system", "content": "You are a general chat bot."},
+# #         {"role": "user", "content": f"{query}"}
+# #     ]
+
+# #     completion = ollama_client.chat.completions.create(
+# #         model="llama3.1:8b",
+# #         messages=message
+# #         # response_format=base_model,
+# #         #temperature = 0.1
+# #     )
+# #     return completion
+
+# class Question(BaseModel):
+#     text: str
+
+# @app.get("/")
+# def read_root():
+#     return {"Hello": f"Welcome to {MODEL_NAME} FastAPI"}
+
+
+# # # POST endpoint to query the LLM
+# # @app.post("/ask")
+# # async def ask_question(question: Question):
+# #     try:
+# #         response = create_model(question.text)
+# #         return {"response": response}
+# #     except Exception as e:
+# #         raise HTTPException(status_code=500, detail=f"Error querying the model: {str(e)}")
+
+
+# @app.get("/list_models")
+# async def list_models():
+#     """List all available models in Ollama."""
+#     try:
+#         response = requests.get(f"{OLLAMA_URL}/api/tags")
+#     except Exception as e:
+#         return {"error": str(e)}
 
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+#     return response.json()
 
-app = FastAPI()
-MODEL_NAME = 'llama3.1:8b'
+# @app.post("/pull_model")
+# async def pull_model(model_name: str):
+#     """Pull a model from Ollama's repository."""
+#     response = requests.post(f"{OLLAMA_URL}/api/pull", json={"name": model_name})
+#     # print(response)
 
-OLLAMA_URL = "http://localhost:11434"
+#     return response.json()
 
 
-# def create_model(query):
-#     message = [
-#         {"role": "system", "content": "You are a general chat bot."},
-#         {"role": "user", "content": f"{query}"}
-#     ]
-
-#     completion = ollama_client.chat.completions.create(
-#         model="llama3.1:8b",
-#         messages=message
-#         # response_format=base_model,
-#         #temperature = 0.1
-#     )
-#     return completion
+# @app.post("/generate")
+# async def generate_text(model: str, prompt: str, system: str = "You are a helpful AI assistant.", stream: bool = False):
+#     """Generate text from a given prompt using a specific model."""
+#     try:
+#         response =  requests.post(
+#             f"{OLLAMA_URL}/api/generate",
+#             json={"model": model, "prompt": prompt, "system": system, "stream": stream}
+#         )
+#     except Exception as e:
+#         return {"error": str(e)}
+#     # print(response)
 
-class Question(BaseModel):
-    text: str
+#     return response.json()
 
-@app.get("/")
-def read_root():
-    return {"Hello": f"Welcome to {MODEL_NAME} FastAPI"}
+# @app.post("/embed")
+# async def get_embedding(model: str, text: str):
+#     """Generate embeddings for the given text using a model."""
+#     try:
+#         response = requests.post(
+#             f"{OLLAMA_URL}/api/embeddings",
+#             json={"model": model, "prompt": text}
+#         )
+#     except Exception as e:
+#         return {"error": str(e)}
+#     # print(response)
 
+#     return response.json()
 
-# # POST endpoint to query the LLM
-# @app.post("/ask")
-# async def ask_question(question: Question):
+# @app.post("/chat")
+# async def chat(model: str, message: str, system: str = "You are a helpful chatbot."):
+#     """Chat with the model while maintaining context."""
 #     try:
-#         response = create_model(question.text)
-#         return {"response": response}
+#         response = requests.post(
+#             f"{OLLAMA_URL}/api/chat",
+#             json={"model": model, "messages": [{"role": "system", "content": system}, {"role": "user", "content": message}]}
+#         )
 #     except Exception as e:
-#         raise HTTPException(status_code=500, detail=f"Error querying the model: {str(e)}")
-
-
-@app.get("/list_models")
-async def list_models():
-    """List all available models in Ollama."""
-    try:
-        response = requests.get(f"{OLLAMA_URL}/api/tags")
-    except Exception as e:
-        return {"error": str(e)}
-
-    return response.json()
-
-@app.post("/pull_model")
-async def pull_model(model_name: str):
-    """Pull a model from Ollama's repository."""
-    response = requests.post(f"{OLLAMA_URL}/api/pull", json={"name": model_name})
-    # print(response)
-
-    return response.json()
-
-
-@app.post("/generate")
-async def generate_text(model: str, prompt: str, system: str = "You are a helpful AI assistant.", stream: bool = False):
-    """Generate text from a given prompt using a specific model."""
-    try:
-        response =  requests.post(
-            f"{OLLAMA_URL}/api/generate",
-            json={"model": model, "prompt": prompt, "system": system, "stream": stream}
-        )
-    except Exception as e:
-        return {"error": str(e)}
-    # print(response)
-
-    return response.json()
-
-@app.post("/embed")
-async def get_embedding(model: str, text: str):
-    """Generate embeddings for the given text using a model."""
-    try:
-        response = requests.post(
-            f"{OLLAMA_URL}/api/embeddings",
-            json={"model": model, "prompt": text}
-        )
-    except Exception as e:
-        return {"error": str(e)}
-    # print(response)
-
-    return response.json()
-
-@app.post("/chat")
-async def chat(model: str, message: str, system: str = "You are a helpful chatbot."):
-    """Chat with the model while maintaining context."""
-    try:
-        response = requests.post(
-            f"{OLLAMA_URL}/api/chat",
-            json={"model": model, "messages": [{"role": "system", "content": system}, {"role": "user", "content": message}]}
-        )
-    except Exception as e:
-        return {"error": str(e)}
-    # print(response)
-
-    return response.json()
-
-
-
-@app.on_event("startup")
-async def startup_event():
-    logger.info(f"Starting up with model: {MODEL_NAME}")
-
-@app.on_event("shutdown")
-async def shutdown_event():
-    logger.info("Shutting down")
+#         return {"error": str(e)}
+#     # print(response)
+
+#     return response.json()
+
+
+
+# @app.on_event("startup")
+# async def startup_event():
+#     logger.info(f"Starting up with model: {MODEL_NAME}")
+
+# @app.on_event("shutdown")
+# async def shutdown_event():
+#     logger.info("Shutting down")
+
+
+
+from fastapi import FastAPI
+import httpx
+
+app = FastAPI()
+TARGET_SERVER = "http://localhost:11434"
+
+@app.get("/proxy/{path:path}")
+async def proxy_request(path: str):
+    async with httpx.AsyncClient() as client:
+        response = await client.get(f"{TARGET_SERVER}/{path}")
+    return response.json()

requirements.txt

@@ -3,4 +3,5 @@ uvicorn
 ollama
 openai
 pydantic
-requests
+requests
+httpx