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
126 lines
3.2 KiB
Python
126 lines
3.2 KiB
Python
"""
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Ollama API endpoints for local AI status and simple tasks.
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"""
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from fastapi import APIRouter, HTTPException
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from pydantic import BaseModel
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from typing import Optional, List
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from app.services.ollama_service import ollama_service
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from app.config import settings
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router = APIRouter(prefix="/api/ollama", tags=["Local AI"])
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class OllamaStatus(BaseModel):
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available: bool
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model: str
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embed_model: str
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base_url: str
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class GenerateRequest(BaseModel):
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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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class GenerateResponse(BaseModel):
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response: Optional[str]
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model: str
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success: bool
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class SentimentRequest(BaseModel):
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text: str
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class SentimentResponse(BaseModel):
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sentiment: Optional[str]
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confidence: Optional[float]
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success: bool
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class ClassifyRequest(BaseModel):
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text: str
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categories: List[str]
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class ClassifyResponse(BaseModel):
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category: Optional[str]
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success: bool
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class SummarizeRequest(BaseModel):
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text: str
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max_sentences: int = 2
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class SummarizeResponse(BaseModel):
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summary: Optional[str]
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success: bool
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@router.get("/status", response_model=OllamaStatus)
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async def get_ollama_status():
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"""Check if Ollama is available and configured."""
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available = await ollama_service.is_available()
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return OllamaStatus(
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available=available,
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model=settings.OLLAMA_MODEL,
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embed_model=settings.OLLAMA_MODEL_EMBED,
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base_url=settings.OLLAMA_BASE_URL
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)
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@router.post("/generate", response_model=GenerateResponse)
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async def generate_text(request: GenerateRequest):
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"""Generate text using local Ollama model."""
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result = await ollama_service.generate(
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prompt=request.prompt,
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system=request.system,
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temperature=request.temperature,
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max_tokens=request.max_tokens
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)
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return GenerateResponse(
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response=result,
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model=settings.OLLAMA_MODEL,
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success=result is not None
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)
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@router.post("/sentiment", response_model=SentimentResponse)
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async def analyze_sentiment(request: SentimentRequest):
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"""Quick sentiment analysis using local model."""
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result = await ollama_service.quick_sentiment(request.text)
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if result:
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return SentimentResponse(
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sentiment=result.get("sentiment"),
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confidence=result.get("confidence"),
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success=True
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)
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return SentimentResponse(sentiment=None, confidence=None, success=False)
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@router.post("/classify", response_model=ClassifyResponse)
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async def classify_text(request: ClassifyRequest):
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"""Classify text into one of the provided categories."""
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result = await ollama_service.quick_classify(request.text, request.categories)
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return ClassifyResponse(
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category=result,
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success=result is not None
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)
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@router.post("/summarize", response_model=SummarizeResponse)
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async def summarize_text(request: SummarizeRequest):
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"""Quick text summarization using local model."""
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result = await ollama_service.quick_summarize(
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text=request.text,
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max_sentences=request.max_sentences
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)
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return SummarizeResponse(
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summary=result,
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success=result is not None
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)
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