Krikorios
48e60d015f
- Add advanced metrics dashboard with trade analytics - Add new trading components (EntryTypeAnalysis, MultiDayPositionTracker, NewsEventTracker, etc.) - Add strategy mode selector and trend confirmation - Add risk automation panel and slippage correlation analysis - Add daily trading plan enhancements with modal components - Add custom hooks (useApi, useLocalStorage, useAdvancedTradeMetrics) - Add broker service integration and trading API - Add test setup and vitest configuration - Include parquet data files for live market data - Add comprehensive documentation in docs/ folder
327 lines
10 KiB
Python
327 lines
10 KiB
Python
"""
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Ollama Local AI Service
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Provides local AI capabilities for lightweight tasks like:
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- Quick sentiment analysis
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- Simple text summarization
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- Fast pattern classification
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- Embeddings generation
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Falls back to OpenRouter for complex tasks.
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"""
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import httpx
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import logging
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from typing import Optional, List, Dict, Any
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from app.config import settings
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logger = logging.getLogger(__name__)
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class OllamaService:
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"""Service for local AI using Ollama."""
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def __init__(self):
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self.base_url = settings.OLLAMA_BASE_URL
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self.model = settings.OLLAMA_MODEL
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self.embed_model = settings.OLLAMA_MODEL_EMBED
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self.timeout = settings.OLLAMA_TIMEOUT
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self._available = None # Cached availability status
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async def is_available(self) -> bool:
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"""Check if Ollama is running and has the required model."""
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try:
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async with httpx.AsyncClient(timeout=5.0) as client:
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response = await client.get(f"{self.base_url}/api/tags")
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if response.status_code == 200:
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data = response.json()
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models = [m["name"] for m in data.get("models", [])]
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self._available = self.model in models or any(self.model.split(":")[0] in m for m in models)
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return self._available
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except Exception as e:
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logger.debug(f"Ollama not available: {e}")
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self._available = False
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return False
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async def generate(
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self,
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prompt: str,
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system: Optional[str] = None,
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temperature: float = 0.7,
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max_tokens: int = 500,
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model: Optional[str] = None
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) -> Optional[str]:
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"""
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Generate text using local Ollama model.
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Args:
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prompt: The user prompt
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system: Optional system prompt
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temperature: Sampling temperature (0-1)
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max_tokens: Maximum tokens to generate
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model: Override default model
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Returns:
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Generated text or None if failed
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"""
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if not await self.is_available():
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logger.warning("Ollama not available, skipping local generation")
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return None
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use_model = model or self.model
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payload = {
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"model": use_model,
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"prompt": prompt,
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"stream": False,
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"options": {
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"temperature": temperature,
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"num_predict": max_tokens,
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}
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}
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if system:
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payload["system"] = system
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try:
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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response = await client.post(
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f"{self.base_url}/api/generate",
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json=payload
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)
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if response.status_code == 200:
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data = response.json()
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return data.get("response", "").strip()
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else:
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logger.error(f"Ollama generate failed: {response.status_code}")
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return None
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except Exception as e:
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logger.error(f"Ollama generate error: {e}")
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return None
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async def chat(
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self,
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messages: List[Dict[str, str]],
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temperature: float = 0.7,
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max_tokens: int = 500,
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model: Optional[str] = None
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) -> Optional[str]:
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"""
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Chat completion using local Ollama model.
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Args:
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messages: List of {"role": "user/assistant/system", "content": "..."}
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temperature: Sampling temperature
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max_tokens: Maximum tokens to generate
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model: Override default model
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Returns:
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Assistant response or None if failed
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"""
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if not await self.is_available():
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return None
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use_model = model or self.model
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payload = {
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"model": use_model,
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"messages": messages,
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"stream": False,
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"options": {
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"temperature": temperature,
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"num_predict": max_tokens,
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}
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}
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try:
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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response = await client.post(
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f"{self.base_url}/api/chat",
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json=payload
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)
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if response.status_code == 200:
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data = response.json()
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return data.get("message", {}).get("content", "").strip()
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else:
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logger.error(f"Ollama chat failed: {response.status_code}")
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return None
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except Exception as e:
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logger.error(f"Ollama chat error: {e}")
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return None
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async def embed(
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self,
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text: str,
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model: Optional[str] = None
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) -> Optional[List[float]]:
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"""
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Generate embeddings using local model.
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Args:
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text: Text to embed
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model: Override default embedding model
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Returns:
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Embedding vector or None if failed
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"""
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if not await self.is_available():
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return None
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use_model = model or self.embed_model
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try:
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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response = await client.post(
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f"{self.base_url}/api/embeddings",
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json={"model": use_model, "prompt": text}
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)
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if response.status_code == 200:
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data = response.json()
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return data.get("embedding")
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else:
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logger.error(f"Ollama embed failed: {response.status_code}")
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return None
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except Exception as e:
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logger.error(f"Ollama embed error: {e}")
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return None
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async def quick_sentiment(self, text: str) -> Optional[Dict[str, Any]]:
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"""
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Quick sentiment analysis using local model.
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Optimized for speed over accuracy.
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Args:
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text: Text to analyze
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Returns:
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{"sentiment": "positive/negative/neutral", "confidence": 0.0-1.0}
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"""
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system = """You are a sentiment analyzer. Respond ONLY with JSON in this exact format:
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{"sentiment": "positive" or "negative" or "neutral", "confidence": 0.0 to 1.0}
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No other text."""
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prompt = f"Analyze the sentiment of this text:\n\n{text[:500]}" # Limit input
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result = await self.generate(
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prompt=prompt,
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system=system,
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temperature=0.1,
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max_tokens=50
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)
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if result:
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try:
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import json
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# Try to extract JSON from response
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if "{" in result:
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json_str = result[result.find("{"):result.rfind("}")+1]
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return json.loads(json_str)
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except:
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pass
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return None
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async def quick_classify(
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self,
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text: str,
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categories: List[str]
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) -> Optional[str]:
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"""
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Quick text classification into predefined categories.
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Args:
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text: Text to classify
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categories: List of possible categories
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Returns:
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Selected category or None
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"""
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categories_str = ", ".join(categories)
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system = f"You are a classifier. Respond with ONLY one of these categories: {categories_str}. No other text."
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prompt = f"Classify this text into one category:\n\n{text[:500]}"
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result = await self.generate(
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prompt=prompt,
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system=system,
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temperature=0.1,
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max_tokens=20
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)
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if result:
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# Find matching category
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result_lower = result.lower().strip()
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for cat in categories:
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if cat.lower() in result_lower:
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return cat
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return None
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async def quick_summarize(self, text: str, max_sentences: int = 2) -> Optional[str]:
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"""
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Quick text summarization.
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Args:
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text: Text to summarize
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max_sentences: Maximum sentences in summary
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Returns:
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Summary or None
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"""
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system = f"Summarize in {max_sentences} sentence(s) or less. Be concise and direct."
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result = await self.generate(
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prompt=text[:2000], # Limit input
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system=system,
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temperature=0.3,
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max_tokens=150
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)
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return result
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# Global instance
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ollama_service = OllamaService()
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async def get_ai_response(
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prompt: str,
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system: Optional[str] = None,
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use_local: bool = True,
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fallback_to_cloud: bool = True
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) -> Optional[str]:
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"""
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Unified AI response function that tries local first, then cloud.
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Args:
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prompt: User prompt
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system: System prompt
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use_local: Whether to try Ollama first
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fallback_to_cloud: Whether to fallback to OpenRouter if local fails
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Returns:
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AI response or None
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"""
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# Try local first if enabled
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if use_local and settings.USE_LOCAL_AI:
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result = await ollama_service.generate(prompt, system)
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if result:
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logger.info("Used local Ollama for AI response")
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return result
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# Fallback to cloud
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if fallback_to_cloud and settings.OPENROUTER_API_KEY:
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from app.services.openrouter import openrouter_service
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# This would need a simple generate method in openrouter
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logger.info("Falling back to OpenRouter for AI response")
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# For now, return None - full integration would go here
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pass
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return None
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