ai vein

soleprint/artery/veins/ia/.env.example

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+AI_API_URL=https://api.openai.com/v1
+AI_API_KEY=your_api_key_here
+AI_MODEL=gpt-4o
+API_PORT=8005

soleprint/artery/veins/ia/__init__.py

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+# IA Vein - AI-powered practice tutor

soleprint/artery/veins/ia/__main__.py

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+"""Run IA vein: python -m ia"""
+
+import uvicorn
+from .core.config import settings
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "ia.main:app",
+        host="0.0.0.0",
+        port=settings.api_port,
+        reload=True,
+    )

soleprint/artery/veins/ia/api/routes.py

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+"""
+Generic API routes for IA vein.
+Provides /health and /chat endpoints.
+Use-case-specific routes are mounted separately.
+"""
+
+import json
+import logging
+from typing import Optional
+
+from fastapi import APIRouter, HTTPException, Header
+
+from ..core.client import chat_completion, health_check as client_health_check, AIClientError
+from ..core.config import settings
+from ..models.chat import ChatRequest, ChatResponse
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+
+def get_api_key(x_ai_token: str | None = None) -> str:
+    """Resolve API key from header or config. Shared by all routes."""
+    if x_ai_token and x_ai_token.strip():
+        return x_ai_token.strip()
+    if settings.ai_api_key:
+        return settings.ai_api_key
+    raise HTTPException(401, "No AI API key configured")
+
+
+_decoder = json.JSONDecoder()
+
+
+def parse_json_response(content: str) -> dict | None:
+    """Extract first valid JSON object from AI response using the JSON parser itself."""
+    for i, ch in enumerate(content):
+        if ch == "{":
+            try:
+                obj, _ = _decoder.raw_decode(content, i)
+                if isinstance(obj, dict):
+                    return obj
+            except json.JSONDecodeError:
+                continue
+    return None
+
+
+@router.get("/health")
+async def health(x_ai_token: Optional[str] = Header(None)):
+    """Test AI API connection."""
+    try:
+        key = get_api_key(x_ai_token)
+        result = await client_health_check(key)
+        return result
+    except AIClientError as e:
+        raise HTTPException(503, str(e))
+
+
+@router.post("/chat")
+async def chat(
+    req: ChatRequest,
+    x_ai_token: Optional[str] = Header(None),
+):
+    """Generic chat completion endpoint."""
+    try:
+        key = get_api_key(x_ai_token)
+        messages = [{"role": m.role, "content": m.content} for m in req.messages]
+        content = await chat_completion(
+            messages,
+            api_key=key,
+            temperature=req.temperature,
+            max_tokens=req.max_tokens,
+        )
+        return ChatResponse(content=content, parsed=parse_json_response(content))
+    except AIClientError as e:
+        raise HTTPException(503, str(e))

soleprint/artery/veins/ia/core/client.py

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+"""
+AI API client - OpenAI-compatible chat completions via httpx.
+"""
+
+import httpx
+from .config import settings
+
+
+class AIClientError(Exception):
+    pass
+
+
+async def chat_completion(
+    messages: list[dict],
+    model: str | None = None,
+    temperature: float = 0.7,
+    max_tokens: int = 1024,
+    api_key: str | None = None,
+) -> str:
+    """Send chat completion request to OpenAI-compatible API."""
+    url = f"{settings.ai_api_url}/chat/completions"
+    key = api_key or settings.ai_api_key
+
+    if not key:
+        raise AIClientError("No API key configured")
+
+    headers = {
+        "Authorization": f"Bearer {key}",
+        "Content-Type": "application/json",
+    }
+
+    payload = {
+        "model": model or settings.ai_model,
+        "messages": messages,
+        "temperature": temperature,
+        "max_tokens": max_tokens,
+    }
+
+    async with httpx.AsyncClient(timeout=30.0) as client:
+        try:
+            response = await client.post(url, json=payload, headers=headers)
+            response.raise_for_status()
+            data = response.json()
+            return data["choices"][0]["message"]["content"]
+        except httpx.HTTPStatusError as e:
+            raise AIClientError(
+                f"API error {e.response.status_code}: {e.response.text}"
+            )
+        except Exception as e:
+            raise AIClientError(f"Request failed: {e}")
+
+
+async def health_check(api_key: str | None = None) -> dict:
+    """Test API connection."""
+    url = f"{settings.ai_api_url}/models"
+    key = api_key or settings.ai_api_key
+
+    if not key:
+        raise AIClientError("No API key configured")
+
+    headers = {"Authorization": f"Bearer {key}"}
+
+    async with httpx.AsyncClient(timeout=10.0) as client:
+        try:
+            response = await client.get(url, headers=headers)
+            response.raise_for_status()
+            return {
+                "status": "ok",
+                "provider": settings.ai_api_url,
+                "model": settings.ai_model,
+            }
+        except Exception as e:
+            raise AIClientError(f"Health check failed: {e}")

soleprint/artery/veins/ia/core/config.py

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+"""
+IA Vein configuration loaded from .env file.
+"""
+
+from pathlib import Path
+from pydantic_settings import BaseSettings
+
+ENV_FILE = Path(__file__).parent.parent / ".env"
+
+
+class IAConfig(BaseSettings):
+    ai_api_url: str = "https://api.openai.com/v1"
+    ai_api_key: str = ""
+    ai_model: str = "gpt-4o"
+    api_port: int = 8005
+
+    model_config = {
+        "env_file": ENV_FILE,
+        "env_file_encoding": "utf-8",
+        "extra": "ignore",
+    }
+
+
+settings = IAConfig()

soleprint/artery/veins/ia/main.py

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+"""
+IA Vein - FastAPI app.
+
+Generic AI vein with use-case-specific routers mounted as sub-routes.
+"""
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+from .api.routes import router as generic_router
+from .usecases.practice.routes import router as practice_router
+from .core.config import settings
+
+app = FastAPI(title="IA Vein", version="0.1.0")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["https://mcrn.ar", "http://localhost:8000", "http://localhost:8765", "http://127.0.0.1:8000"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Generic: /ia/health, /ia/chat
+app.include_router(generic_router, prefix="/ia")
+
+# Use case: /ia/practice/*
+app.include_router(practice_router, prefix="/ia/practice")
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=settings.api_port)

soleprint/artery/veins/ia/models/chat.py

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+"""
+Generic request/response models for IA vein.
+"""
+
+from pydantic import BaseModel
+
+
+class ChatMessage(BaseModel):
+    role: str  # "system" | "user" | "assistant"
+    content: str
+
+
+class ChatRequest(BaseModel):
+    messages: list[ChatMessage]
+    temperature: float = 0.7
+    max_tokens: int = 1024
+
+
+class ChatResponse(BaseModel):
+    content: str
+    parsed: dict | None = None

soleprint/artery/veins/ia/requirements.txt

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+fastapi>=0.104.0
+uvicorn>=0.24.0
+pydantic>=2.0.0
+pydantic-settings>=2.0.0
+httpx>=0.25.0

soleprint/artery/veins/ia/run.py

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+#!/usr/bin/env python
+"""Run the IA vein API."""
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent))
+
+import uvicorn
+from core.config import settings
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "main:app",
+        host="0.0.0.0",
+        port=settings.api_port,
+        reload=True,
+    )

soleprint/artery/veins/ia/usecases/practice/formatter.py

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+"""
+Format practice item context data as text for AI prompt assembly.
+"""
+
+
+def _fmt_str(label, val):
+    return f"{label}: {val}"
+
+
+def _fmt_list(label, val):
+    return f"{label}:\n" + "\n".join(f"  - {v}" for v in val)
+
+
+def _fmt_join(label, val):
+    return f"{label}: {', '.join(val)}"
+
+
+def _fmt_complexity(label, val):
+    return f"{label}: Time {val.get('time', '?')}, Space {val.get('space', '?')}"
+
+
+FIELDS = [
+    ("oneLiner",     "Summary",      _fmt_str),
+    ("howItWorks",   "How it works", _fmt_str),
+    ("structure",    "Structure",    _fmt_str),
+    ("whenToUse",    "When to use",  _fmt_list),
+    ("participants", "Participants", _fmt_join),
+    ("complexity",   "Complexity",   _fmt_complexity),
+]
+
+
+def format_item_context(item: dict) -> str:
+    """Format algorithm/pattern data as context string for prompts."""
+    lines = [
+        f"Name: {item.get('name', 'Unknown')}",
+        f"Category: {item.get('category', 'Unknown')}",
+        f"Topic: {item.get('topic', 'unknown')}",
+    ]
+    for key, label, fmt in FIELDS:
+        val = item.get(key)
+        if val:
+            lines.append(fmt(label, val))
+    return "\n".join(lines)

soleprint/artery/veins/ia/usecases/practice/models.py

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+"""
+Request models for the practice tutor use case.
+"""
+
+from pydantic import BaseModel
+
+
+class ItemContext(BaseModel):
+    """Algorithm or pattern data sent from frontend."""
+    id: str
+    name: str
+    category: str
+    topic: str = "algorithms"
+    whenToUse: list[str] = []
+    howItWorks: str = ""
+    structure: str = ""
+    participants: list[str] = []
+    complexity: dict = {}
+    oneLiner: str = ""
+
+
+class IdentifyRequest(BaseModel):
+    item: ItemContext
+    lang: str = "en"
+
+
+class ReviewExplanationRequest(BaseModel):
+    item: ItemContext
+    userExplanation: str
+    lang: str = "en"
+
+
+class ReviewCodeRequest(BaseModel):
+    item: ItemContext
+    userCode: str
+    language: str = "python"
+    referenceCode: str = ""
+    lang: str = "en"
+
+
+class ReviewStructureRequest(BaseModel):
+    item: ItemContext
+    userExplanation: str
+    lang: str = "en"
+
+
+class HintRequest(BaseModel):
+    item: ItemContext
+    mode: str = "identify"
+    context: str = ""
+    lang: str = "en"
+
+
+class SuggestNextRequest(BaseModel):
+    progress: dict
+    items: list[ItemContext]
+    lang: str = "en"

soleprint/artery/veins/ia/usecases/practice/prompts.py

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+"""
+System prompts for the practice tutor use case.
+Each function returns a system prompt string for a specific drill mode.
+"""
+
+
+def identify(lang: str = "en") -> str:
+    if lang == "es":
+        return (
+            "Eres un tutor de algoritmos y patrones de diseno. "
+            "Genera una descripcion de un problema que se resuelve con la tecnica especificada. "
+            "La descripcion debe ser un escenario realista, sin nombrar la tecnica directamente. "
+            'Responde en JSON: {"problem": "...", "hint": "..."}'
+        )
+    return (
+        "You are an algorithm and design pattern tutor. "
+        "Generate a problem description that is solved by the specified technique. "
+        "The description should be a realistic scenario without naming the technique directly. "
+        'Respond in JSON: {"problem": "...", "hint": "..."}'
+    )
+
+
+def review_explanation(lang: str = "en") -> str:
+    if lang == "es":
+        return (
+            "Eres un tutor evaluando la explicacion de un estudiante sobre un algoritmo o patron. "
+            "Compara con los datos de referencia. Se constructivo pero preciso. "
+            'Responde en JSON: {"score": "good|partial|weak", "feedback": "...", "missing": ["..."]}'
+        )
+    return (
+        "You are a tutor evaluating a student's explanation of an algorithm or pattern. "
+        "Compare against the reference data. Be constructive but precise. "
+        'Respond in JSON: {"score": "good|partial|weak", "feedback": "...", "missing": ["..."]}'
+    )
+
+
+def review_code(lang: str = "en") -> str:
+    if lang == "es":
+        return (
+            "Eres un tutor de codigo evaluando la implementacion de un estudiante. "
+            "Evalua: correctitud, eficiencia, estilo, y manejo de edge cases. "
+            'Responde en JSON: {"verdict": "correct|partial|incorrect", "feedback": "...", "improvements": ["..."]}'
+        )
+    return (
+        "You are a code tutor evaluating a student's implementation. "
+        "Evaluate: correctness, efficiency, style, and edge case handling. "
+        'Respond in JSON: {"verdict": "correct|partial|incorrect", "feedback": "...", "improvements": ["..."]}'
+    )
+
+
+def review_structure(lang: str = "en") -> str:
+    if lang == "es":
+        return (
+            "Eres un tutor evaluando la explicacion de un estudiante sobre la estructura de un patron de diseno. "
+            "Compara participantes, relaciones y responsabilidades con la referencia. "
+            'Responde en JSON: {"score": "good|partial|weak", "feedback": "...", "missing": ["..."]}'
+        )
+    return (
+        "You are a tutor evaluating a student's explanation of a design pattern's structure. "
+        "Compare participants, relationships, and responsibilities against the reference. "
+        'Respond in JSON: {"score": "good|partial|weak", "feedback": "...", "missing": ["..."]}'
+    )
+
+
+def hint(lang: str = "en") -> str:
+    if lang == "es":
+        return (
+            "Eres un tutor. Da una pista sutil sin revelar la respuesta completa. "
+            "Guia al estudiante hacia el enfoque correcto."
+        )
+    return (
+        "You are a tutor. Give a subtle hint without revealing the full answer. "
+        "Guide the student toward the correct approach."
+    )
+
+
+def suggest_next(lang: str = "en") -> str:
+    if lang == "es":
+        return (
+            "Eres un tutor. Basandote en el progreso del estudiante, sugiere que practicar. "
+            "Prioriza tecnicas debiles. "
+            'Responde en JSON: {"suggestions": [{"id": "...", "reason": "..."}], "encouragement": "..."}'
+        )
+    return (
+        "You are a tutor. Based on student progress, suggest what to practice next. "
+        "Prioritize weak techniques. "
+        'Respond in JSON: {"suggestions": [{"id": "...", "reason": "..."}], "encouragement": "..."}'
+    )

soleprint/artery/veins/ia/usecases/practice/routes.py

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+"""
+Practice tutor use case routes.
+Mounted under /ia/practice/ by main.py.
+Uses the generic AI client from core.
+"""
+
+import json
+import logging
+from typing import Optional
+
+from fastapi import APIRouter, HTTPException, Header
+
+from ...core.client import chat_completion, AIClientError
+from ...api.routes import get_api_key, parse_json_response
+from ...models.chat import ChatResponse
+from . import prompts
+from .models import (
+    IdentifyRequest,
+    ReviewExplanationRequest,
+    ReviewCodeRequest,
+    ReviewStructureRequest,
+    HintRequest,
+    SuggestNextRequest,
+)
+from .formatter import format_item_context
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+
+@router.post("/identify")
+async def generate_identify_question(
+    req: IdentifyRequest,
+    x_ai_token: Optional[str] = Header(None),
+):
+    try:
+        key = get_api_key(x_ai_token)
+        ctx = format_item_context(req.item.model_dump())
+        messages = [
+            {"role": "system", "content": prompts.identify(req.lang)},
+            {"role": "user", "content": f"Generate a problem description for:\n\n{ctx}"},
+        ]
+        content = await chat_completion(messages, api_key=key, temperature=0.8)
+        return ChatResponse(content=content, parsed=parse_json_response(content))
+    except AIClientError as e:
+        raise HTTPException(503, str(e))
+
+
+@router.post("/review-explanation")
+async def review_explanation(
+    req: ReviewExplanationRequest,
+    x_ai_token: Optional[str] = Header(None),
+):
+    try:
+        key = get_api_key(x_ai_token)
+        ctx = format_item_context(req.item.model_dump())
+        messages = [
+            {"role": "system", "content": prompts.review_explanation(req.lang)},
+            {"role": "user", "content": f"Reference:\n{ctx}\n\nStudent's explanation:\n{req.userExplanation}"},
+        ]
+        content = await chat_completion(messages, api_key=key, temperature=0.3)
+        return ChatResponse(content=content, parsed=parse_json_response(content))
+    except AIClientError as e:
+        raise HTTPException(503, str(e))
+
+
+@router.post("/review-code")
+async def review_code(
+    req: ReviewCodeRequest,
+    x_ai_token: Optional[str] = Header(None),
+):
+    try:
+        key = get_api_key(x_ai_token)
+        ctx = format_item_context(req.item.model_dump())
+        ref = f"\n\nReference ({req.language}):\n```\n{req.referenceCode}\n```" if req.referenceCode else ""
+        messages = [
+            {"role": "system", "content": prompts.review_code(req.lang)},
+            {"role": "user", "content": f"Algorithm:\n{ctx}{ref}\n\nStudent's code ({req.language}):\n```\n{req.userCode}\n```"},
+        ]
+        content = await chat_completion(messages, api_key=key, temperature=0.3)
+        return ChatResponse(content=content, parsed=parse_json_response(content))
+    except AIClientError as e:
+        raise HTTPException(503, str(e))
+
+
+@router.post("/review-structure")
+async def review_structure(
+    req: ReviewStructureRequest,
+    x_ai_token: Optional[str] = Header(None),
+):
+    try:
+        key = get_api_key(x_ai_token)
+        ctx = format_item_context(req.item.model_dump())
+        messages = [
+            {"role": "system", "content": prompts.review_structure(req.lang)},
+            {"role": "user", "content": f"Reference:\n{ctx}\n\nStudent's explanation:\n{req.userExplanation}"},
+        ]
+        content = await chat_completion(messages, api_key=key, temperature=0.3)
+        return ChatResponse(content=content, parsed=parse_json_response(content))
+    except AIClientError as e:
+        raise HTTPException(503, str(e))
+
+
+@router.post("/hint")
+async def get_hint(
+    req: HintRequest,
+    x_ai_token: Optional[str] = Header(None),
+):
+    try:
+        key = get_api_key(x_ai_token)
+        ctx = format_item_context(req.item.model_dump())
+        messages = [
+            {"role": "system", "content": prompts.hint(req.lang)},
+            {"role": "user", "content": f"Mode: {req.mode}\n{ctx}\n\nStuck on: {req.context or 'general approach'}"},
+        ]
+        content = await chat_completion(messages, api_key=key, temperature=0.7, max_tokens=256)
+        return ChatResponse(content=content)
+    except AIClientError as e:
+        raise HTTPException(503, str(e))
+
+
+@router.post("/suggest-next")
+async def suggest_next(
+    req: SuggestNextRequest,
+    x_ai_token: Optional[str] = Header(None),
+):
+    try:
+        key = get_api_key(x_ai_token)
+        progress_str = json.dumps(req.progress, indent=2)
+        names = [i.name for i in req.items]
+        messages = [
+            {"role": "system", "content": prompts.suggest_next(req.lang)},
+            {"role": "user", "content": f"Available: {', '.join(names)}\n\nProgress:\n{progress_str}"},
+        ]
+        content = await chat_completion(messages, api_key=key, temperature=0.5)
+        return ChatResponse(content=content, parsed=parse_json_response(content))
+    except AIClientError as e:
+        raise HTTPException(503, str(e))