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from fastapi import FastAPI, HTTPException, BackgroundTasks
from pydantic import BaseModel, Field
from typing import List, Optional, Dict, Any
import httpx
import asyncio
import logging
import time
import json
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# FastAPI app
app = FastAPI(
title="Ollama API Server",
description="REST API for running Ollama models",
version="1.0.0",
docs_url="/docs",
redoc_url="/redoc"
)
# Ollama server configuration
OLLAMA_BASE_URL = "http://localhost:11434"
# Pydantic models
class ChatMessage(BaseModel):
role: str = Field(..., description="Role of the message sender (user, assistant, system)")
content: str = Field(..., description="Content of the message")
class ChatRequest(BaseModel):
model: str = Field(..., description="Model name to use for chat")
messages: List[ChatMessage] = Field(..., description="List of chat messages")
temperature: Optional[float] = Field(0.7, ge=0.0, le=2.0, description="Sampling temperature")
top_p: Optional[float] = Field(0.9, ge=0.0, le=1.0, description="Top-p sampling parameter")
max_tokens: Optional[int] = Field(512, ge=1, le=4096, description="Maximum tokens to generate")
stream: Optional[bool] = Field(False, description="Whether to stream the response")
class GenerateRequest(BaseModel):
model: str = Field(..., description="Model name to use for generation")
prompt: str = Field(..., description="Input prompt for text generation")
temperature: Optional[float] = Field(0.7, ge=0.0, le=2.0, description="Sampling temperature")
top_p: Optional[float] = Field(0.9, ge=0.0, le=1.0, description="Top-p sampling parameter")
max_tokens: Optional[int] = Field(512, ge=1, le=4096, description="Maximum tokens to generate")
stream: Optional[bool] = Field(False, description="Whether to stream the response")
class ModelPullRequest(BaseModel):
model: str = Field(..., description="Model name to pull (e.g., 'llama2:7b')")
class ChatResponse(BaseModel):
model: str
response: str
done: bool
total_duration: Optional[int] = None
load_duration: Optional[int] = None
prompt_eval_count: Optional[int] = None
eval_count: Optional[int] = None
class GenerateResponse(BaseModel):
model: str
response: str
done: bool
total_duration: Optional[int] = None
load_duration: Optional[int] = None
prompt_eval_count: Optional[int] = None
eval_count: Optional[int] = None
# HTTP client for Ollama API
async def get_ollama_client():
return httpx.AsyncClient(timeout=300.0) # 5 minute timeout
@app.get("/health")
async def health_check():
"""Health check endpoint"""
try:
async with await get_ollama_client() as client:
response = await client.get(f"{OLLAMA_BASE_URL}/api/version")
if response.status_code == 200:
return {
"status": "healthy",
"ollama_status": "running",
"ollama_version": response.json(),
"timestamp": time.time()
}
else:
return {
"status": "degraded",
"ollama_status": "error",
"error": f"Ollama returned status {response.status_code}",
"timestamp": time.time()
}
except Exception as e:
logger.error(f"Health check failed: {e}")
return {
"status": "unhealthy",
"ollama_status": "unreachable",
"error": str(e),
"timestamp": time.time()
}
@app.get("/models")
async def list_models():
"""List available models"""
try:
async with await get_ollama_client() as client:
response = await client.get(f"{OLLAMA_BASE_URL}/api/tags")
response.raise_for_status()
return response.json()
except httpx.HTTPError as e:
logger.error(f"Failed to list models: {e}")
raise HTTPException(status_code=500, detail=f"Failed to list models: {str(e)}")
@app.post("/models/pull")
async def pull_model(request: ModelPullRequest, background_tasks: BackgroundTasks):
"""Pull a model from Ollama registry"""
try:
async with await get_ollama_client() as client:
# Start the pull request
pull_data = {"name": request.model}
response = await client.post(
f"{OLLAMA_BASE_URL}/api/pull",
json=pull_data,
timeout=1800.0 # 30 minute timeout for model pulling
)
if response.status_code == 200:
return {
"status": "success",
"message": f"Successfully initiated pull for model '{request.model}'",
"model": request.model
}
else:
error_detail = response.text
logger.error(f"Failed to pull model: {error_detail}")
raise HTTPException(
status_code=response.status_code,
detail=f"Failed to pull model: {error_detail}"
)
except httpx.TimeoutException:
raise HTTPException(
status_code=408,
detail="Model pull request timed out. Large models may take longer to download."
)
except Exception as e:
logger.error(f"Error pulling model: {e}")
raise HTTPException(status_code=500, detail=f"Error pulling model: {str(e)}")
@app.delete("/models/{model_name}")
async def delete_model(model_name: str):
"""Delete a model"""
try:
async with await get_ollama_client() as client:
response = await client.delete(f"{OLLAMA_BASE_URL}/api/delete", json={"name": model_name})
response.raise_for_status()
return {"status": "success", "message": f"Model '{model_name}' deleted successfully"}
except httpx.HTTPError as e:
logger.error(f"Failed to delete model: {e}")
raise HTTPException(status_code=500, detail=f"Failed to delete model: {str(e)}")
@app.post("/chat", response_model=ChatResponse)
async def chat_with_model(request: ChatRequest):
"""Chat with a model"""
try:
# Convert messages to Ollama format
chat_data = {
"model": request.model,
"messages": [{"role": msg.role, "content": msg.content} for msg in request.messages],
"stream": request.stream,
"options": {
"temperature": request.temperature,
"top_p": request.top_p,
"num_predict": request.max_tokens
}
}
async with await get_ollama_client() as client:
response = await client.post(
f"{OLLAMA_BASE_URL}/api/chat",
json=chat_data,
timeout=300.0
)
response.raise_for_status()
result = response.json()
return ChatResponse(
model=result.get("model", request.model),
response=result.get("message", {}).get("content", ""),
done=result.get("done", True),
total_duration=result.get("total_duration"),
load_duration=result.get("load_duration"),
prompt_eval_count=result.get("prompt_eval_count"),
eval_count=result.get("eval_count")
)
except httpx.HTTPError as e:
logger.error(f"Chat request failed: {e}")
if e.response.status_code == 404:
raise HTTPException(
status_code=404,
detail=f"Model '{request.model}' not found. Try pulling it first with POST /models/pull"
)
raise HTTPException(status_code=500, detail=f"Chat request failed: {str(e)}")
except Exception as e:
logger.error(f"Unexpected error in chat: {e}")
raise HTTPException(status_code=500, detail=f"Unexpected error: {str(e)}")
@app.post("/generate", response_model=GenerateResponse)
async def generate_text(request: GenerateRequest):
"""Generate text completion"""
try:
generate_data = {
"model": request.model,
"prompt": request.prompt,
"stream": request.stream,
"options": {
"temperature": request.temperature,
"top_p": request.top_p,
"num_predict": request.max_tokens
}
}
async with await get_ollama_client() as client:
response = await client.post(
f"{OLLAMA_BASE_URL}/api/generate",
json=generate_data,
timeout=300.0
)
response.raise_for_status()
result = response.json()
return GenerateResponse(
model=result.get("model", request.model),
response=result.get("response", ""),
done=result.get("done", True),
total_duration=result.get("total_duration"),
load_duration=result.get("load_duration"),
prompt_eval_count=result.get("prompt_eval_count"),
eval_count=result.get("eval_count")
)
except httpx.HTTPError as e:
logger.error(f"Generate request failed: {e}")
if e.response.status_code == 404:
raise HTTPException(
status_code=404,
detail=f"Model '{request.model}' not found. Try pulling it first with POST /models/pull"
)
raise HTTPException(status_code=500, detail=f"Generate request failed: {str(e)}")
except Exception as e:
logger.error(f"Unexpected error in generate: {e}")
raise HTTPException(status_code=500, detail=f"Unexpected error: {str(e)}")
@app.get("/")
async def root():
"""Root endpoint with API information"""
return {
"message": "Ollama API Server",
"version": "1.0.0",
"endpoints": {
"health": "/health",
"models": "/models",
"pull_model": "/models/pull",
"chat": "/chat",
"generate": "/generate",
"docs": "/docs"
},
"status": "running"
}
if __name__ == "__main__":
import uvicorn
logger.info("Starting Ollama API server...")
uvicorn.run(app, host="0.0.0.0", port=7860, log_level="info") |