288 lines
8.1 KiB
Python
288 lines
8.1 KiB
Python
"""
|
|
Ollama service for local LLM integration
|
|
"""
|
|
|
|
import httpx
|
|
import json
|
|
import asyncio
|
|
from typing import Dict, List, Optional, AsyncGenerator
|
|
from app.core.config import settings
|
|
from app.models.ai import LanguageType, AICommand
|
|
|
|
class OllamaService:
|
|
"""Service for interacting with Ollama API"""
|
|
|
|
def __init__(self):
|
|
self.base_url = settings.OLLAMA_BASE_URL
|
|
self.default_model = settings.DEFAULT_MODEL
|
|
self.client = httpx.AsyncClient(timeout=60.0)
|
|
|
|
async def __aenter__(self):
|
|
return self
|
|
|
|
async def __aexit__(self, exc_type, exc_val, exc_tb):
|
|
await self.client.aclose()
|
|
|
|
async def is_available(self) -> bool:
|
|
"""Check if Ollama is running and available"""
|
|
try:
|
|
response = await self.client.get(f"{self.base_url}/api/tags")
|
|
return response.status_code == 200
|
|
except Exception:
|
|
return False
|
|
|
|
async def list_models(self) -> List[Dict]:
|
|
"""List available models from Ollama"""
|
|
try:
|
|
response = await self.client.get(f"{self.base_url}/api/tags")
|
|
if response.status_code == 200:
|
|
data = response.json()
|
|
return data.get("models", [])
|
|
except Exception as e:
|
|
print(f"Error listing models: {e}")
|
|
return []
|
|
|
|
async def pull_model(self, model_name: str) -> bool:
|
|
"""Pull/download a model if not available"""
|
|
try:
|
|
payload = {"name": model_name}
|
|
response = await self.client.post(
|
|
f"{self.base_url}/api/pull",
|
|
json=payload,
|
|
timeout=300.0 # 5 minutes for model download
|
|
)
|
|
return response.status_code == 200
|
|
except Exception as e:
|
|
print(f"Error pulling model {model_name}: {e}")
|
|
return False
|
|
|
|
async def generate_completion(
|
|
self,
|
|
prompt: str,
|
|
model: Optional[str] = None,
|
|
temperature: float = settings.TEMPERATURE,
|
|
max_tokens: int = settings.MAX_TOKENS,
|
|
stream: bool = False
|
|
) -> str:
|
|
"""Generate text completion from Ollama"""
|
|
model_name = model or self.default_model
|
|
|
|
payload = {
|
|
"model": model_name,
|
|
"prompt": prompt,
|
|
"stream": stream,
|
|
"options": {
|
|
"temperature": temperature,
|
|
"num_predict": max_tokens,
|
|
"top_p": settings.TOP_P
|
|
}
|
|
}
|
|
|
|
try:
|
|
response = await self.client.post(
|
|
f"{self.base_url}/api/generate",
|
|
json=payload,
|
|
timeout=120.0
|
|
)
|
|
|
|
if response.status_code == 200:
|
|
if stream:
|
|
# Handle streaming response
|
|
full_response = ""
|
|
for line in response.iter_lines():
|
|
if line:
|
|
data = json.loads(line)
|
|
if "response" in data:
|
|
full_response += data["response"]
|
|
if data.get("done", False):
|
|
break
|
|
return full_response
|
|
else:
|
|
# Handle single response
|
|
data = response.json()
|
|
return data.get("response", "")
|
|
else:
|
|
raise Exception(f"Ollama API error: {response.status_code}")
|
|
|
|
except Exception as e:
|
|
print(f"Error generating completion: {e}")
|
|
raise
|
|
|
|
async def generate_streaming(
|
|
self,
|
|
prompt: str,
|
|
model: Optional[str] = None,
|
|
temperature: float = settings.TEMPERATURE,
|
|
max_tokens: int = settings.MAX_TOKENS
|
|
) -> AsyncGenerator[str, None]:
|
|
"""Generate streaming completion from Ollama"""
|
|
model_name = model or self.default_model
|
|
|
|
payload = {
|
|
"model": model_name,
|
|
"prompt": prompt,
|
|
"stream": True,
|
|
"options": {
|
|
"temperature": temperature,
|
|
"num_predict": max_tokens,
|
|
"top_p": settings.TOP_P
|
|
}
|
|
}
|
|
|
|
try:
|
|
async with self.client.stream(
|
|
"POST",
|
|
f"{self.base_url}/api/generate",
|
|
json=payload,
|
|
timeout=120.0
|
|
) as response:
|
|
if response.status_code == 200:
|
|
async for line in response.aiter_lines():
|
|
if line:
|
|
try:
|
|
data = json.loads(line)
|
|
if "response" in data:
|
|
yield data["response"]
|
|
if data.get("done", False):
|
|
break
|
|
except json.JSONDecodeError:
|
|
continue
|
|
else:
|
|
raise Exception(f"Ollama API error: {response.status_code}")
|
|
|
|
except Exception as e:
|
|
print(f"Error in streaming generation: {e}")
|
|
raise
|
|
|
|
def build_prompt(
|
|
self,
|
|
command: AICommand,
|
|
code: str,
|
|
language: Optional[LanguageType] = None,
|
|
context: Optional[str] = None,
|
|
error_message: Optional[str] = None
|
|
) -> str:
|
|
"""Build appropriate prompt based on command and context"""
|
|
|
|
lang_name = language.value if language else "code"
|
|
|
|
prompts = {
|
|
AICommand.EXPLAIN: f"""
|
|
Explain the following {lang_name} code in clear, concise terms:
|
|
|
|
```{lang_name}
|
|
{code}
|
|
```
|
|
|
|
Please provide:
|
|
1. What this code does
|
|
2. Key concepts and algorithms used
|
|
3. Any potential issues or improvements
|
|
|
|
Response:""",
|
|
|
|
AICommand.REFACTOR: f"""
|
|
Refactor the following {lang_name} code to improve readability, performance, and maintainability:
|
|
|
|
```{lang_name}
|
|
{code}
|
|
```
|
|
|
|
Please provide:
|
|
1. Refactored code
|
|
2. Explanation of changes made
|
|
3. Benefits of the refactoring
|
|
|
|
Refactored code:""",
|
|
|
|
AICommand.FIX: f"""
|
|
Fix the bugs or issues in the following {lang_name} code:
|
|
|
|
```{lang_name}
|
|
{code}
|
|
```
|
|
|
|
{f"Error message: {error_message}" if error_message else ""}
|
|
|
|
Please provide:
|
|
1. Fixed code
|
|
2. Explanation of what was wrong
|
|
3. How the fix addresses the issue
|
|
|
|
Fixed code:""",
|
|
|
|
AICommand.COMPLETE: f"""
|
|
Complete the following {lang_name} code based on the context:
|
|
|
|
```{lang_name}
|
|
{code}
|
|
```
|
|
|
|
Please provide the most likely completion that follows naturally from the existing code.
|
|
|
|
Completion:""",
|
|
|
|
AICommand.COMMENT: f"""
|
|
Add clear, helpful comments to the following {lang_name} code:
|
|
|
|
```{lang_name}
|
|
{code}
|
|
```
|
|
|
|
Please provide the same code with appropriate comments explaining the functionality.
|
|
|
|
Commented code:""",
|
|
|
|
AICommand.TEST: f"""
|
|
Generate comprehensive unit tests for the following {lang_name} code:
|
|
|
|
```{lang_name}
|
|
{code}
|
|
```
|
|
|
|
Please provide:
|
|
1. Complete test cases covering different scenarios
|
|
2. Test setup and teardown if needed
|
|
3. Comments explaining what each test validates
|
|
|
|
Test code:""",
|
|
|
|
AICommand.OPTIMIZE: f"""
|
|
Optimize the following {lang_name} code for better performance:
|
|
|
|
```{lang_name}
|
|
{code}
|
|
```
|
|
|
|
Please provide:
|
|
1. Optimized code
|
|
2. Explanation of optimizations made
|
|
3. Expected performance improvements
|
|
|
|
Optimized code:""",
|
|
|
|
AICommand.DOCUMENT: f"""
|
|
Generate comprehensive documentation for the following {lang_name} code:
|
|
|
|
```{lang_name}
|
|
{code}
|
|
```
|
|
|
|
Please provide:
|
|
1. Function/class documentation
|
|
2. Parameter descriptions
|
|
3. Return value descriptions
|
|
4. Usage examples
|
|
|
|
Documentation:"""
|
|
}
|
|
|
|
base_prompt = prompts.get(command, f"Analyze this {lang_name} code:\n\n```{lang_name}\n{code}\n```\n\nResponse:")
|
|
|
|
if context:
|
|
base_prompt = f"Context: {context}\n\n{base_prompt}"
|
|
|
|
return base_prompt
|
|
|
|
# Create singleton instance
|
|
ollama_service = OllamaService() |