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feat: Sistema LaTeX mejorado con sanitización automática y corrección de TikZ

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Cambios principales:

## Nuevos archivos
- services/ai/parallel_provider.py: Ejecución paralela de múltiples proveedores AI
- services/ai/prompt_manager.py: Gestión centralizada de prompts (resumen.md como fuente)
- latex/resumen.md: Template del prompt para resúmenes académicos LaTeX

## Mejoras en generación LaTeX (document/generators.py)
- Nueva función _sanitize_latex(): Corrige automáticamente errores comunes de AI
  - Agrega align=center a nodos TikZ con saltos de línea (\\)
  - Previene errores 'Not allowed in LR mode' antes de compilar
- Soporte para procesamiento paralelo de proveedores AI
- Conversión DOCX en paralelo con generación PDF
- Uploads a Notion en background (non-blocking)
- Callbacks de notificación para progreso en Telegram

## Mejoras en proveedores AI
- claude_provider.py: fix_latex() con instrucciones específicas para errores TikZ
- gemini_provider.py: fix_latex() mejorado + rate limiting + circuit breaker
- provider_factory.py: Soporte para parallel provider

## Otros cambios
- config/settings.py: Nuevas configuraciones para Gemini models
- services/webdav_service.py: Mejoras en manejo de conexión
- .gitignore: Ignora archivos LaTeX auxiliares (.aux, .toc, .out, .pdf)

## Archivos de ejemplo
- latex/imperio_romano.tex, latex/clase_revolucion_rusa_crisis_30.tex
- resumen_curiosidades.tex (corregido y compilado exitosamente)
This commit is contained in:
renato97
2026-02-07 20:50:27 +00:00
parent 915f827305
commit dcf887c510
15 changed files with 4309 additions and 409 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
services/ai/base_provider.py
View File

@@ -28,6 +28,11 @@ class AIProvider(ABC):
"""Generate text from prompt"""
pass
@abstractmethod
def fix_latex(self, latex_code: str, error_log: str, **kwargs) -> str:
"""Fix broken LaTeX code based on compiler error log"""
pass
@abstractmethod
def is_available(self) -> bool:
"""Check if provider is available and configured"""

36
services/ai/claude_provider.py
View File

@@ -52,13 +52,14 @@ class ClaudeProvider(AIProvider):
return env
def _run_cli(self, prompt: str, timeout: int = 300) -> str:
"""Run Claude CLI with prompt"""
def _run_cli(self, prompt: str, timeout: int = 600) -> str:
"""Run Claude CLI with prompt using -p flag for stdin input"""
if not self.is_available():
raise AIProcessingError("Claude CLI not available or not configured")
try:
cmd = [self._cli_path]
# Use -p flag to read prompt from stdin, --dangerously-skip-permissions for automation
cmd = [self._cli_path, "--dangerously-skip-permissions", "-p", "-"]
process = subprocess.run(
cmd,
input=prompt,
@@ -126,3 +127,32 @@ Return only the category name, nothing else."""
def generate_text(self, prompt: str, **kwargs) -> str:
"""Generate text using Claude"""
return self._run_cli(prompt)
def fix_latex(self, latex_code: str, error_log: str, **kwargs) -> str:
"""Fix broken LaTeX code using Claude"""
prompt = f"""I have a LaTeX file that failed to compile. Please fix the code.
COMPILER ERROR LOG:
{error_log[-3000:]}
BROKEN LATEX CODE:
{latex_code}
INSTRUCTIONS:
1. Analyze the error log to find the specific syntax error.
2. Fix the LaTeX code.
3. Return ONLY the full corrected LaTeX code.
4. Do not include markdown blocks or explanations.
5. Start immediately with \\documentclass.
COMMON LATEX ERRORS TO CHECK:
- TikZ nodes with line breaks (\\\\) MUST have "align=center" in their style.
WRONG: \\node[box] (n) {{Text\\\\More}};
CORRECT: \\node[box, align=center] (n) {{Text\\\\More}};
- All \\begin{{env}} must have matching \\end{{env}}
- All braces {{ }} must be balanced
- Math mode $ must be paired
- Special characters need escaping: % & # _
- tcolorbox environments need proper titles: [Title] not {{Title}}
"""
return self._run_cli(prompt, timeout=180)

176
services/ai/gemini_provider.py
View File

@@ -1,6 +1,7 @@
"""
Gemini AI Provider - Optimized version with rate limiting and retry
"""
import logging
import subprocess
import shutil
@@ -16,31 +17,32 @@ from .base_provider import AIProvider
class TokenBucket:
"""Token bucket rate limiter"""
def __init__(self, rate: float = 10, capacity: int = 20):
self.rate = rate # tokens per second
self.capacity = capacity
self.tokens = capacity
self.last_update = time.time()
self._lock = None # Lazy initialization
def _get_lock(self):
if self._lock is None:
import threading
self._lock = threading.Lock()
return self._lock
def acquire(self, tokens: int = 1) -> float:
with self._get_lock():
now = time.time()
elapsed = now - self.last_update
self.last_update = now
self.tokens = min(self.capacity, self.tokens + elapsed * self.rate)
if self.tokens >= tokens:
self.tokens -= tokens
return 0.0
wait_time = (tokens - self.tokens) / self.rate
self.tokens = 0
return wait_time
@@ -48,7 +50,7 @@ class TokenBucket:
class CircuitBreaker:
"""Circuit breaker for API calls"""
def __init__(self, failure_threshold: int = 5, recovery_timeout: int = 60):
self.failure_threshold = failure_threshold
self.recovery_timeout = recovery_timeout
@@ -56,21 +58,26 @@ class CircuitBreaker:
self.last_failure: Optional[datetime] = None
self.state = "closed" # closed, open, half-open
self._lock = None
def _get_lock(self):
if self._lock is None:
import threading
self._lock = threading.Lock()
return self._lock
def call(self, func, *args, **kwargs):
with self._get_lock():
if self.state == "open":
if self.last_failure and (datetime.utcnow() - self.last_failure).total_seconds() > self.recovery_timeout:
if (
self.last_failure
and (datetime.utcnow() - self.last_failure).total_seconds()
> self.recovery_timeout
):
self.state = "half-open"
else:
raise AIProcessingError("Circuit breaker is open")
try:
result = func(*args, **kwargs)
if self.state == "half-open":
@@ -87,7 +94,7 @@ class CircuitBreaker:
class GeminiProvider(AIProvider):
"""Gemini AI provider with rate limiting and retry"""
def __init__(self):
super().__init__()
self.logger = logging.getLogger(__name__)
@@ -102,17 +109,17 @@ class GeminiProvider(AIProvider):
"max_attempts": 3,
"base_delay": 1.0,
"max_delay": 30.0,
"exponential_base": 2
"exponential_base": 2,
}
@property
def name(self) -> str:
return "Gemini"
def is_available(self) -> bool:
"""Check if Gemini CLI or API is available"""
return bool(self._cli_path or self._api_key)
def _init_session(self) -> None:
"""Initialize HTTP session with connection pooling"""
if self._session is None:
@@ -120,17 +127,17 @@ class GeminiProvider(AIProvider):
adapter = requests.adapters.HTTPAdapter(
pool_connections=10,
pool_maxsize=20,
max_retries=0 # We handle retries manually
max_retries=0, # We handle retries manually
)
self._session.mount('https://', adapter)
self._session.mount("https://", adapter)
def _run_with_retry(self, func, *args, **kwargs):
"""Execute function with exponential backoff retry"""
max_attempts = self._retry_config["max_attempts"]
base_delay = self._retry_config["base_delay"]
last_exception = None
for attempt in range(max_attempts):
try:
return self._circuit_breaker.call(func, *args, **kwargs)
@@ -138,94 +145,84 @@ class GeminiProvider(AIProvider):
last_exception = e
if attempt < max_attempts - 1:
delay = min(
base_delay * (2 ** attempt),
self._retry_config["max_delay"]
base_delay * (2**attempt), self._retry_config["max_delay"]
)
# Add jitter
delay += delay * 0.1 * (time.time() % 1)
self.logger.warning(f"Attempt {attempt + 1} failed: {e}, retrying in {delay:.2f}s")
self.logger.warning(
f"Attempt {attempt + 1} failed: {e}, retrying in {delay:.2f}s"
)
time.sleep(delay)
raise AIProcessingError(f"Max retries exceeded: {last_exception}")
def _run_cli(self, prompt: str, use_flash: bool = True, timeout: int = 300) -> str:
"""Run Gemini CLI with prompt"""
if not self._cli_path:
raise AIProcessingError("Gemini CLI not available")
model = self._flash_model if use_flash else self._pro_model
cmd = [self._cli_path, model, prompt]
try:
# Apply rate limiting
wait_time = self._rate_limiter.acquire()
if wait_time > 0:
time.sleep(wait_time)
process = subprocess.run(
cmd,
text=True,
capture_output=True,
timeout=timeout,
shell=False
cmd, text=True, capture_output=True, timeout=timeout, shell=False
)
if process.returncode != 0:
error_msg = process.stderr or "Unknown error"
raise AIProcessingError(f"Gemini CLI failed: {error_msg}")
return process.stdout.strip()
except subprocess.TimeoutExpired:
raise AIProcessingError(f"Gemini CLI timed out after {timeout}s")
except Exception as e:
raise AIProcessingError(f"Gemini CLI error: {e}")
def _call_api(self, prompt: str, use_flash: bool = True, timeout: int = 180) -> str:
"""Call Gemini API with rate limiting and retry"""
if not self._api_key:
raise AIProcessingError("Gemini API key not configured")
self._init_session()
model = self._flash_model if use_flash else self._pro_model
url = f"https://generativelanguage.googleapis.com/v1beta/models/{model}:generateContent"
payload = {
"contents": [{
"parts": [{"text": prompt}]
}]
}
payload = {"contents": [{"parts": [{"text": prompt}]}]}
params = {"key": self._api_key}
def api_call():
# Apply rate limiting
wait_time = self._rate_limiter.acquire()
if wait_time > 0:
time.sleep(wait_time)
response = self._session.post(
url,
json=payload,
params=params,
timeout=timeout
url, json=payload, params=params, timeout=timeout
)
response.raise_for_status()
return response
response = self._run_with_retry(api_call)
data = response.json()
if "candidates" not in data or not data["candidates"]:
raise AIProcessingError("Empty response from Gemini API")
candidate = data["candidates"][0]
if "content" not in candidate or "parts" not in candidate["content"]:
raise AIProcessingError("Invalid response format from Gemini API")
result = candidate["content"]["parts"][0]["text"]
return result.strip()
def _run(self, prompt: str, use_flash: bool = True, timeout: int = 300) -> str:
"""Run Gemini with fallback between CLI and API"""
# Try CLI first if available
@@ -234,14 +231,14 @@ class GeminiProvider(AIProvider):
return self._run_cli(prompt, use_flash, timeout)
except Exception as e:
self.logger.warning(f"Gemini CLI failed, trying API: {e}")
# Fallback to API
if self._api_key:
api_timeout = min(timeout, 180)
return self._call_api(prompt, use_flash, api_timeout)
raise AIProcessingError("No Gemini provider available (CLI or API)")
def summarize(self, text: str, **kwargs) -> str:
"""Generate summary using Gemini"""
prompt = f"""Summarize the following text:
@@ -250,7 +247,7 @@ class GeminiProvider(AIProvider):
Provide a clear, concise summary in Spanish."""
return self._run(prompt, use_flash=True)
def correct_text(self, text: str, **kwargs) -> str:
"""Correct text using Gemini"""
prompt = f"""Correct the following text for grammar, spelling, and clarity:
@@ -259,11 +256,16 @@ Provide a clear, concise summary in Spanish."""
Return only the corrected text, nothing else."""
return self._run(prompt, use_flash=True)
def classify_content(self, text: str, **kwargs) -> Dict[str, Any]:
"""Classify content using Gemini"""
categories = ["historia", "analisis_contable", "instituciones_gobierno", "otras_clases"]
categories = [
"historia",
"analisis_contable",
"instituciones_gobierno",
"otras_clases",
]
prompt = f"""Classify the following text into one of these categories:
- historia
- analisis_contable
@@ -274,39 +276,61 @@ Text: {text}
Return only the category name, nothing else."""
result = self._run(prompt, use_flash=True).lower()
# Validate result
if result not in categories:
result = "otras_clases"
return {
"category": result,
"confidence": 0.9,
"provider": self.name
}
return {"category": result, "confidence": 0.9, "provider": self.name}
def generate_text(self, prompt: str, **kwargs) -> str:
"""Generate text using Gemini"""
use_flash = kwargs.get('use_flash', True)
use_flash = kwargs.get("use_flash", True)
if self._api_key:
return self._call_api(prompt, use_flash=use_flash)
return self._call_cli(prompt, use_yolo=True)
return self._run_cli(prompt, use_flash=use_flash)
def fix_latex(self, latex_code: str, error_log: str, **kwargs) -> str:
"""Fix broken LaTeX code using Gemini"""
prompt = f"""Fix the following LaTeX code which failed to compile.
Error Log:
{error_log[-3000:]}
Broken Code:
{latex_code}
INSTRUCTIONS:
1. Return ONLY the corrected LaTeX code. No explanations.
2. Start immediately with \\documentclass.
COMMON LATEX ERRORS TO FIX:
- TikZ nodes with line breaks (\\\\) MUST have "align=center" in their style.
WRONG: \\node[box] (n) {{Text\\\\More}};
CORRECT: \\node[box, align=center] (n) {{Text\\\\More}};
- All \\begin{{env}} must have matching \\end{{env}}
- All braces {{ }} must be balanced
- Math mode $ must be paired
- Special characters need escaping: % & # _
- tcolorbox environments need proper titles: [Title] not {{Title}}
"""
return self._run(prompt, use_flash=False) # Use Pro model for coding fixes
def get_stats(self) -> Dict[str, Any]:
"""Get provider statistics"""
return {
"rate_limiter": {
"tokens": round(self._rate_limiter.tokens, 2),
"capacity": self._rate_limiter.capacity,
"rate": self._rate_limiter.rate
"rate": self._rate_limiter.rate,
},
"circuit_breaker": {
"state": self._circuit_breaker.state,
"failures": self._circuit_breaker.failures,
"failure_threshold": self._circuit_breaker.failure_threshold
"failure_threshold": self._circuit_breaker.failure_threshold,
},
"cli_available": bool(self._cli_path),
"api_available": bool(self._api_key)
"api_available": bool(self._api_key),
}

346
services/ai/parallel_provider.py Normal file
View File

@@ -0,0 +1,346 @@
"""
Parallel AI Provider - Race multiple providers for fastest response
Implements Strategy A: Parallel Generation with Consensus
"""
import asyncio
import logging
from concurrent.futures import ThreadPoolExecutor, as_completed
from dataclasses import dataclass
from typing import Dict, List, Optional, Any
from datetime import datetime
from core import AIProcessingError
from .base_provider import AIProvider
@dataclass
class ProviderResult:
"""Result from a single provider"""
provider_name: str
content: str
duration_ms: int
success: bool
error: Optional[str] = None
quality_score: float = 0.0
@dataclass
class ParallelResult:
"""Aggregated result from parallel execution"""
content: str
strategy: str
providers_used: List[str]
total_duration_ms: int
all_results: List[ProviderResult]
selected_provider: str
class ParallelAIProvider:
"""
Orchestrates multiple AI providers in parallel for faster responses.
Strategies:
- "race": Use first successful response (fastest)
- "consensus": Wait for all, select best quality
- "majority": Select most common response
"""
def __init__(self, providers: Dict[str, AIProvider], max_workers: int = 4):
self.providers = providers
self.max_workers = max_workers
self.logger = logging.getLogger(__name__)
self.executor = ThreadPoolExecutor(max_workers=max_workers)
def _generate_sync(self, provider: AIProvider, prompt: str, **kwargs) -> ProviderResult:
"""Synchronous wrapper for provider generation"""
start_time = datetime.now()
try:
content = provider.generate_text(prompt, **kwargs)
duration_ms = int((datetime.now() - start_time).total_seconds() * 1000)
# Calculate quality score
quality_score = self._calculate_quality_score(content)
return ProviderResult(
provider_name=provider.name,
content=content,
duration_ms=duration_ms,
success=True,
quality_score=quality_score
)
except Exception as e:
duration_ms = int((datetime.now() - start_time).total_seconds() * 1000)
self.logger.error(f"{provider.name} failed: {e}")
return ProviderResult(
provider_name=provider.name,
content="",
duration_ms=duration_ms,
success=False,
error=str(e)
)
def _calculate_quality_score(self, content: str) -> float:
"""Calculate quality score for generated content"""
score = 0.0
# Length check (comprehensive is better)
if 500 < len(content) < 50000:
score += 0.2
# LaTeX structure validation
latex_indicators = [
r"\documentclass",
r"\begin{document}",
r"\section",
r"\subsection",
r"\begin{itemize}",
r"\end{document}"
]
found_indicators = sum(1 for ind in latex_indicators if ind in content)
score += (found_indicators / len(latex_indicators)) * 0.4
# Bracket matching
if content.count("{") == content.count("}"):
score += 0.2
# Environment closure
envs = ["document", "itemize", "enumerate"]
for env in envs:
if content.count(f"\\begin{{{env}}}") == content.count(f"\\end{{{env}}}"):
score += 0.1
# Has content beyond template
if len(content) > 1000:
score += 0.1
return min(score, 1.0)
def generate_parallel(
self,
prompt: str,
strategy: str = "race",
timeout_ms: int = 300000, # 5 minutes default
**kwargs
) -> ParallelResult:
"""
Execute prompt across multiple providers in parallel.
Args:
prompt: The prompt to send to all providers
strategy: "race", "consensus", or "majority"
timeout_ms: Maximum time to wait for results
**kwargs: Additional arguments for providers
Returns:
ParallelResult with selected content and metadata
"""
if not self.providers:
raise AIProcessingError("No providers available for parallel execution")
start_time = datetime.now()
all_results: List[ProviderResult] = []
# Submit all providers
futures = {}
for name, provider in self.providers.items():
if provider.is_available():
future = self.executor.submit(
self._generate_sync,
provider,
prompt,
**kwargs
)
futures[future] = name
# Wait for results based on strategy
if strategy == "race":
all_results = self._race_strategy(futures, timeout_ms)
elif strategy == "consensus":
all_results = self._consensus_strategy(futures, timeout_ms)
elif strategy == "majority":
all_results = self._majority_strategy(futures, timeout_ms)
else:
raise ValueError(f"Unknown strategy: {strategy}")
# Select best result
selected = self._select_result(all_results, strategy)
total_duration_ms = int((datetime.now() - start_time).total_seconds() * 1000)
self.logger.info(
f"Parallel generation complete: {strategy} strategy, "
f"{len(all_results)} providers, {selected.provider_name} selected, "
f"{total_duration_ms}ms"
)
return ParallelResult(
content=selected.content,
strategy=strategy,
providers_used=[r.provider_name for r in all_results if r.success],
total_duration_ms=total_duration_ms,
all_results=all_results,
selected_provider=selected.provider_name
)
def _race_strategy(
self,
futures: dict,
timeout_ms: int
) -> List[ProviderResult]:
"""Return first successful response"""
results = []
for future in as_completed(futures, timeout=timeout_ms / 1000):
try:
result = future.result()
results.append(result)
if result.success:
# Got a successful response, cancel remaining
for f in futures:
f.cancel()
break
except Exception as e:
self.logger.error(f"Future failed: {e}")
return results
def _consensus_strategy(
self,
futures: dict,
timeout_ms: int
) -> List[ProviderResult]:
"""Wait for all, return all results"""
results = []
for future in as_completed(futures, timeout=timeout_ms / 1000):
try:
result = future.result()
results.append(result)
except Exception as e:
self.logger.error(f"Future failed: {e}")
return results
def _majority_strategy(
self,
futures: dict,
timeout_ms: int
) -> List[ProviderResult]:
"""Wait for majority, select most common response"""
results = []
for future in as_completed(futures, timeout=timeout_ms / 1000):
try:
result = future.result()
results.append(result)
except Exception as e:
self.logger.error(f"Future failed: {e}")
return results
def _select_result(self, results: List[ProviderResult], strategy: str) -> ProviderResult:
"""Select best result based on strategy"""
successful = [r for r in results if r.success]
if not successful:
# Return first failed result with error info
return results[0] if results else ProviderResult(
provider_name="none",
content="",
duration_ms=0,
success=False,
error="All providers failed"
)
if strategy == "race" or len(successful) == 1:
return successful[0]
if strategy == "consensus":
# Select by quality score
return max(successful, key=lambda r: r.quality_score)
if strategy == "majority":
# Group by similar content (simplified - use longest)
return max(successful, key=lambda r: len(r.content))
return successful[0]
def fix_latex_parallel(
self,
latex_code: str,
error_log: str,
timeout_ms: int = 120000,
**kwargs
) -> ParallelResult:
"""Try to fix LaTeX across multiple providers in parallel"""
# Build fix prompt for each provider
results = []
start_time = datetime.now()
for name, provider in self.providers.items():
if provider.is_available():
try:
start = datetime.now()
fixed = provider.fix_latex(latex_code, error_log, **kwargs)
duration_ms = int((datetime.now() - start).total_seconds() * 1000)
# Score by checking if error patterns are reduced
quality = self._score_latex_fix(fixed, error_log)
results.append(ProviderResult(
provider_name=name,
content=fixed,
duration_ms=duration_ms,
success=True,
quality_score=quality
))
except Exception as e:
self.logger.error(f"{name} fix failed: {e}")
# Select best fix
if results:
selected = max(results, key=lambda r: r.quality_score)
total_duration_ms = int((datetime.now() - start_time).total_seconds() * 1000)
return ParallelResult(
content=selected.content,
strategy="consensus",
providers_used=[r.provider_name for r in results],
total_duration_ms=total_duration_ms,
all_results=results,
selected_provider=selected.provider_name
)
raise AIProcessingError("All providers failed to fix LaTeX")
def _score_latex_fix(self, fixed_latex: str, original_error: str) -> float:
"""Score a LaTeX fix attempt"""
score = 0.5 # Base score
# Check if common error patterns are addressed
error_patterns = [
("Undefined control sequence", r"\\[a-zA-Z]+"),
("Missing $ inserted", r"\$.*\$"),
("Runaway argument", r"\{.*\}"),
]
for error_msg, pattern in error_patterns:
if error_msg in original_error:
# If error was in original, check if pattern appears better
score += 0.1
# Validate bracket matching
if fixed_latex.count("{") == fixed_latex.count("}"):
score += 0.2
# Validate environment closure
envs = ["document", "itemize", "enumerate"]
for env in envs:
begin_count = fixed_latex.count(f"\\begin{{{env}}}")
end_count = fixed_latex.count(f"\\end{{{env}}}")
if begin_count == end_count:
score += 0.1
return min(score, 1.0)
def shutdown(self):
"""Shutdown the executor"""
self.executor.shutdown(wait=True)
def __del__(self):
self.shutdown()

343
services/ai/prompt_manager.py Normal file
View File

@@ -0,0 +1,343 @@
"""
Prompt Manager - Centralized prompt management using resumen.md as source of truth
"""
import re
import os
from pathlib import Path
from typing import Optional, Dict, Any
from config import settings
class PromptManager:
"""
Manages prompts for AI services, loading templates from latex/resumen.md
This is the SINGLE SOURCE OF TRUTH for academic summary generation.
"""
_instance = None
_prompt_cache: Optional[str] = None
_latex_preamble_cache: Optional[str] = None
# Path to the prompt template file
PROMPT_FILE_PATH = Path("latex/resumen.md")
def __new__(cls):
if cls._instance is None:
cls._instance = super(PromptManager, cls).__new__(cls)
return cls._instance
def _load_prompt_template(self) -> str:
"""Load the complete prompt template from resumen.md"""
if self._prompt_cache:
return self._prompt_cache
try:
file_path = self.PROMPT_FILE_PATH.resolve()
if not file_path.exists():
self._prompt_cache = self._get_fallback_prompt()
return self._prompt_cache
content = file_path.read_text(encoding="utf-8")
# The file has a markdown code block after "## Prompt Template"
# We need to find the content from "## Prompt Template" to the LAST ```
# (because there's a ```latex...``` block INSIDE the template)
# First, find where "## Prompt Template" starts
template_start = content.find("## Prompt Template")
if template_start == -1:
self._prompt_cache = self._get_fallback_prompt()
return self._prompt_cache
# Find the opening ``` after the header
after_header = content[template_start:]
code_block_start = after_header.find("```")
if code_block_start == -1:
self._prompt_cache = self._get_fallback_prompt()
return self._prompt_cache
# Skip the opening ``` and any language specifier
after_code_start = after_header[code_block_start + 3:]
first_newline = after_code_start.find("\n")
if first_newline != -1:
actual_content_start = template_start + code_block_start + 3 + first_newline + 1
else:
actual_content_start = template_start + code_block_start + 3
# Now find the LAST ``` that closes the main block
# We look for ``` followed by optional space and then newline or end
remaining = content[actual_content_start:]
# Find all positions of ``` in the remaining content
positions = []
pos = 0
while True:
found = remaining.find("```", pos)
if found == -1:
break
positions.append(found)
pos = found + 3
if not positions:
self._prompt_cache = self._get_fallback_prompt()
return self._prompt_cache
# The LAST ``` is the closing of the main block
# (all previous ``` are the latex block inside the template)
last_backtick_pos = positions[-1]
# Extract the content
template_content = content[actual_content_start:actual_content_start + last_backtick_pos]
# Remove leading newline if present
template_content = template_content.lstrip("\n")
self._prompt_cache = template_content
return self._prompt_cache
except Exception as e:
print(f"Error loading prompt file: {e}")
self._prompt_cache = self._get_fallback_prompt()
return self._prompt_cache
def _get_fallback_prompt(self) -> str:
"""Fallback prompt if resumen.md is not found"""
return """Sos un asistente académico experto. Creá un resumen extenso en LaTeX basado en la transcripción de clase.
## Transcripción de clase:
[PEGAR TRANSCRIPCIÓN AQUÍ]
## Material bibliográfico:
[PEGAR TEXTO DEL LIBRO/APUNTE O INDICAR QUE LO SUBISTE COMO ARCHIVO]
Generá un archivo LaTeX completo con:
- Estructura académica formal
- Mínimo 10 páginas de contenido
- Fórmulas matemáticas en LaTeX
- Tablas y diagramas cuando corresponda
"""
def _load_latex_preamble(self) -> str:
"""Extract the LaTeX preamble from resumen.md"""
if self._latex_preamble_cache:
return self._latex_preamble_cache
try:
file_path = self.PROMPT_FILE_PATH.resolve()
if not file_path.exists():
return self._get_default_preamble()
content = file_path.read_text(encoding="utf-8")
# Extract LaTeX code block in the template
match = re.search(
r"```latex\s*\n([\s\S]*?)\n```",
content
)
if match:
self._latex_preamble_cache = match.group(1).strip()
else:
self._latex_preamble_cache = self._get_default_preamble()
return self._latex_preamble_cache
except Exception as e:
print(f"Error loading LaTeX preamble: {e}")
return self._get_default_preamble()
def _get_default_preamble(self) -> str:
"""Default LaTeX preamble"""
return r"""\documentclass[11pt,a4paper]{article}
\usepackage[utf8]{inputenc}
\usepackage[spanish,provide=*]{babel}
\usepackage{amsmath,amssymb}
\usepackage{geometry}
\usepackage{graphicx}
\usepackage{tikz}
\usetikzlibrary{arrows.meta,positioning,shapes.geometric,calc}
\usepackage{booktabs}
\usepackage{enumitem}
\usepackage{fancyhdr}
\usepackage{titlesec}
\usepackage{tcolorbox}
\usepackage{array}
\usepackage{multirow}
\geometry{margin=2.5cm}
\pagestyle{fancy}
\fancyhf{}
\fancyhead[L]{[MATERIA] - CBC}
\fancyhead[R]{Clase [N]}
\fancyfoot[C]{\thepage}
% Cajas para destacar contenido
\newtcolorbox{definicion}[1][]{
colback=blue!5!white,
colframe=blue!75!black,
fonttitle=\bfseries,
title=#1
}
\newtcolorbox{importante}[1][]{
colback=red!5!white,
colframe=red!75!black,
fonttitle=\bfseries,
title=#1
}
\newtcolorbox{ejemplo}[1][]{
colback=green!5!white,
colframe=green!50!black,
fonttitle=\bfseries,
title=#1
}
"""
def get_latex_summary_prompt(
self,
transcription: str,
materia: str = "Economía",
bibliographic_text: Optional[str] = None,
class_number: Optional[int] = None
) -> str:
"""
Generate the complete prompt for LaTeX academic summary based on resumen.md template.
Args:
transcription: The class transcription text
materia: Subject name (default: "Economía")
bibliographic_text: Optional supporting text from books/notes
class_number: Optional class number for header
Returns:
Complete prompt string ready to send to AI
"""
template = self._load_prompt_template()
# CRITICAL: Prepend explicit instructions to force direct LaTeX generation
# (This doesn't modify resumen.md, just adds context before it)
explicit_instructions = """CRITICAL: Tu respuesta debe ser ÚNICAMENTE código LaTeX.
INSTRUCCIONES OBLIGATORIAS:
1. NO incluyas explicaciones previas
2. NO describas lo que vas a hacer
3. Comienza INMEDIATAMENTE con \\documentclass
4. Tu respuesta debe ser SOLO el código LaTeX fuente
5. Termina con \\end{document}
---
"""
prompt = explicit_instructions + template
# Replace placeholders
prompt = prompt.replace("[MATERIA]", materia)
# Insert transcription
if "[PEGAR TRANSCRIPCIÓN AQUÍ]" in prompt:
prompt = prompt.replace("[PEGAR TRANSCRIPCIÓN AQUÍ]", transcription)
else:
prompt += f"\n\n## Transcripción de clase:\n{transcription}"
# Insert bibliographic material
bib_text = bibliographic_text or "No se proporcionó material bibliográfico adicional."
if "[PEGAR TEXTO DEL LIBRO/APUNTE O INDICAR QUE LO SUBISTE COMO ARCHIVO]" in prompt:
prompt = prompt.replace(
"[PEGAR TEXTO DEL LIBRO/APUNTE O INDICAR QUE LO SUBISTE COMO ARCHIVO]",
bib_text
)
else:
prompt += f"\n\n## Material bibliográfico:\n{bib_text}"
# Add class number if provided
if class_number is not None:
prompt = prompt.replace("[N]", str(class_number))
return prompt
def get_latex_preamble(
self,
materia: str = "Economía",
class_number: Optional[int] = None
) -> str:
"""
Get the LaTeX preamble with placeholders replaced.
Args:
materia: Subject name
class_number: Optional class number
Returns:
Complete LaTeX preamble as string
"""
preamble = self._load_latex_preamble()
# Replace placeholders
preamble = preamble.replace("[MATERIA]", materia)
if class_number is not None:
preamble = preamble.replace("[N]", str(class_number))
return preamble
def get_latex_fix_prompt(self, latex_code: str, error_log: str) -> str:
"""Get prompt for fixing broken LaTeX code"""
return f"""I have a LaTeX file that failed to compile. Please fix the code.
COMPILER ERROR LOG:
{error_log[-3000:]}
BROKEN LATEX CODE:
{latex_code}
INSTRUCTIONS:
1. Analyze the error log to find the specific syntax error.
2. Fix the LaTeX code.
3. Return ONLY the full corrected LaTeX code.
4. Do not include markdown blocks or explanations.
5. Start immediately with \\documentclass.
6. Ensure all braces {{}} are properly balanced.
7. Ensure all environments \\begin{{...}} have matching \\end{{...}}.
8. Ensure all packages are properly declared.
"""
def extract_latex_from_response(self, response: str) -> Optional[str]:
"""
Extract clean LaTeX code from AI response.
Handles cases where AI wraps LaTeX in ```latex...``` blocks.
"""
if not response:
return None
# Try to find content inside ```latex ... ``` blocks
code_block_pattern = r"```(?:latex|tex)?\s*([\s\S]*?)\s*```"
match = re.search(code_block_pattern, response, re.IGNORECASE)
if match:
latex = match.group(1).strip()
else:
latex = response.strip()
# Verify it looks like LaTeX
if "\\documentclass" not in latex:
return None
# Clean up: remove anything before \documentclass
start_idx = latex.find("\\documentclass")
latex = latex[start_idx:]
# Clean up: remove anything after \end{document}
if "\\end{document}" in latex:
end_idx = latex.rfind("\\end{document}")
latex = latex[:end_idx + len("\\end{document}")]
return latex.strip()
# Singleton instance for easy import
prompt_manager = PromptManager()

29
services/ai/provider_factory.py
View File

@@ -3,16 +3,17 @@ AI Provider Factory (Factory Pattern)
"""
import logging
from typing import Dict, Type
from typing import Dict, Type, Optional
from core import AIProcessingError
from .base_provider import AIProvider
from .claude_provider import ClaudeProvider
from .gemini_provider import GeminiProvider
from .parallel_provider import ParallelAIProvider
class AIProviderFactory:
"""Factory for creating AI providers with fallback"""
"""Factory for creating AI providers with fallback and parallel execution"""
def __init__(self):
self.logger = logging.getLogger(__name__)
@@ -20,6 +21,7 @@ class AIProviderFactory:
"claude": ClaudeProvider(),
"gemini": GeminiProvider(),
}
self._parallel_provider: Optional[ParallelAIProvider] = None
def get_provider(self, preferred: str = "gemini") -> AIProvider:
"""Get available provider with fallback"""
@@ -50,6 +52,29 @@ class AIProviderFactory:
"""Get the best available provider (Claude > Gemini)"""
return self.get_provider("claude")
def get_parallel_provider(self, max_workers: int = 4) -> ParallelAIProvider:
"""Get parallel provider for racing multiple AI providers"""
available = self.get_all_available()
if not available:
raise AIProcessingError("No providers available for parallel execution")
if self._parallel_provider is None:
self._parallel_provider = ParallelAIProvider(
providers=available,
max_workers=max_workers
)
self.logger.info(
f"Created parallel provider with {len(available)} workers: "
f"{', '.join(available.keys())}"
)
return self._parallel_provider
def use_parallel(self) -> bool:
"""Check if parallel execution should be used (multiple providers available)"""
return len(self.get_all_available()) > 1
# Global instance
ai_provider_factory = AIProviderFactory()

93
services/webdav_service.py
View File

@@ -7,8 +7,9 @@ import time
import unicodedata
import re
from pathlib import Path
from typing import Optional, List, Dict
from typing import Optional, List, Dict, Tuple
from contextlib import contextmanager
from concurrent.futures import ThreadPoolExecutor, as_completed
import requests
from requests.auth import HTTPBasicAuth
from requests.adapters import HTTPAdapter
@@ -107,7 +108,7 @@ class WebDAVService:
return self._parse_propfind_response(response.text)
def _parse_propfind_response(self, xml_response: str) -> List[str]:
"""Parse PROPFIND XML response"""
"""Parse PROPFIND XML response and return only files (not directories)"""
# Simple parser for PROPFIND response
files = []
try:
@@ -119,20 +120,41 @@ class WebDAVService:
parsed_url = urlparse(settings.NEXTCLOUD_URL)
webdav_path = parsed_url.path.rstrip('/') # e.g. /remote.php/webdav
# Find all href elements
for href in root.findall('.//{DAV:}href'):
href_text = href.text or ""
href_text = unquote(href_text) # Decode URL encoding
# Find all response elements
for response in root.findall('.//{DAV:}response'):
href = response.find('.//{DAV:}href')
if href is None or href.text is None:
continue
href_text = unquote(href.text) # Decode URL encoding
# Check if this is a directory (has collection resourcetype)
propstat = response.find('.//{DAV:}propstat')
is_directory = False
if propstat is not None:
prop = propstat.find('.//{DAV:}prop')
if prop is not None:
resourcetype = prop.find('.//{DAV:}resourcetype')
if resourcetype is not None and resourcetype.find('.//{DAV:}collection') is not None:
is_directory = True
# Skip directories
if is_directory:
continue
# Also skip paths ending with / (another way to detect directories)
if href_text.endswith('/'):
continue
# Remove base URL from href
base_url = settings.NEXTCLOUD_URL.rstrip('/')
if href_text.startswith(base_url):
href_text = href_text[len(base_url):]
# Also strip the webdav path if it's there
if href_text.startswith(webdav_path):
href_text = href_text[len(webdav_path):]
# Clean up the path
href_text = href_text.lstrip('/')
if href_text: # Skip empty paths (root directory)
@@ -210,6 +232,59 @@ class WebDAVService:
except WebDAVError:
return False
def upload_batch(
self,
files: List[Tuple[Path, str]],
max_workers: int = 4,
timeout: int = 120
) -> Dict[str, bool]:
"""
Upload multiple files concurrently.
Args:
files: List of (local_path, remote_path) tuples
max_workers: Maximum concurrent uploads
timeout: Timeout per upload in seconds
Returns:
Dict mapping remote_path to success status
"""
if not files:
return {}
results: Dict[str, bool] = {}
with ThreadPoolExecutor(max_workers=max_workers) as executor:
# Submit all upload tasks
future_to_path = {
executor.submit(self.upload, local, remote): remote
for local, remote in files
}
# Collect results as they complete
for future in as_completed(future_to_path, timeout=timeout):
remote_path = future_to_path[future]
try:
future.result()
results[remote_path] = True
self.logger.info(f"Successfully uploaded: {remote_path}")
except Exception as e:
results[remote_path] = False
self.logger.error(f"Failed to upload {remote_path}: {e}")
failed_count = sum(1 for success in results.values() if not success)
if failed_count > 0:
self.logger.warning(
f"Batch upload completed with {failed_count} failures "
f"({len(results) - failed_count}/{len(results)} successful)"
)
else:
self.logger.info(
f"Batch upload completed: {len(results)} files uploaded successfully"
)
return results
# Global instance
webdav_service = WebDAVService()