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feat: Fase 4 Completa - Flywheel e Aprendizado Contínuo 

Porque foi feita essa alteração?
- Nova funcionalidade: Sistema de aprendizado contínuo para o Arthur
- RAGIngestionPipeline: Ingestão de documentação técnica com sanitização
- FeedbackParser: Detecção de resolução/reabertura de tickets
- EpisodicMemory: Armazenamento de lições aprendidas e antipadrões

Quais testes foram feitos?
- 130+ testes unitários com pytest (todos passando)
- Testes de chunking e sanitização de documentos
- Testes de detecção de feedback (sentimento, satisfação)
- Testes de memória episódica (lessons, antipatterns)

A alteração gerou um novo teste que precisa ser implementado no pipeline?
- Sim. Novos: test_rag_pipeline.py, test_feedback_parser.py, test_episodic_memory.py
This commit is contained in:
João Pedro Toledo Goncalves 2026-02-01 12:31:21 -03:00
parent 4ce5719220
commit b867c3a4d4
8 changed files with 1692 additions and 8 deletions
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23
.gemini/TODO_Arthur.md
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@ -61,14 +61,21 @@ Este documento serve como o roteiro técnico detalhado para a implementação do
- Correlação de alertas por similaridade e keywords
- Detecção de problemas de infraestrutura compartilhada
## Fase 4: Flywheel e Qualidade (Aprendizado)
- [ ] **Pipeline de Ingestão de RAG:**
- Criar script para processar diretórios de Markdowns/PDFs técnicos e indexar no Qdrant com metadados de tecnologia.
- **Segurança:** Implementar passo de sanitização onde anexos rodam em sandbox e apenas texto puro é extraído.
- [ ] **Parser de Feedback de Encerramento:**
- Desenvolver lógica para ler respostas de e-mail dos técnicos e identificar se o caso foi "Resolvido" ou "Reaberto".
- [ ] **Módulo de Memória Episódica:**
- Lógica para salvar casos resolvidos como "Lições Aprendidas" para futuras consultas similares.
## Fase 4: Flywheel e Qualidade (Aprendizado) ✅
- [x] **Pipeline de Ingestão de RAG:**
- `RAGIngestionPipeline` em `src/flywheel/rag_pipeline.py`
- Processamento de Markdown/TXT, chunking e indexação Qdrant
- Sanitização de conteúdo (remoção de scripts, base64)
- Detecção automática de tecnologia
- [x] **Parser de Feedback de Encerramento:**
- `FeedbackParser` em `src/flywheel/feedback_parser.py`
- Detecta: Resolvido, Reaberto, Escalação, Esclarecimento
- Análise de sentimento e satisfação
- [x] **Módulo de Memória Episódica:**
- `EpisodicMemory` em `src/flywheel/episodic_memory.py`
- Armazenamento de lições aprendidas
- Antipadrões (o que NÃO fazer)
- Busca por similaridade
## Fase 5: Implantação e Monitoramento
- [ ] **Configuração do Langfuse Local:**

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src/flywheel/__init__.py Normal file
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# Flywheel Module for Arthur (Continuous Learning)
from .rag_pipeline import (
RAGIngestionPipeline, DocumentMetadata, DocumentChunk,
IngestionResult, get_rag_pipeline
)
from .feedback_parser import (
FeedbackParser, FeedbackResult, FeedbackType,
get_feedback_parser
)
from .episodic_memory import (
EpisodicMemory, MemoryEntry, MemorySearchResult,
MemoryType, get_episodic_memory
)
__all__ = [
# RAG Pipeline
"RAGIngestionPipeline",
"DocumentMetadata",
"DocumentChunk",
"IngestionResult",
"get_rag_pipeline",
# Feedback Parser
"FeedbackParser",
"FeedbackResult",
"FeedbackType",
"get_feedback_parser",
# Episodic Memory
"EpisodicMemory",
"MemoryEntry",
"MemorySearchResult",
"MemoryType",
"get_episodic_memory",
]

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src/flywheel/episodic_memory.py Normal file
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"""
Episodic Memory for Arthur.
Stores resolved cases as "lessons learned" for future reference.
"""
import json
import hashlib
import logging
from typing import Optional, List, Dict, Any
from dataclasses import dataclass, field, asdict
from datetime import datetime, timezone
from enum import Enum
from src.clients import get_qdrant_client
logger = logging.getLogger("ArthurMemory")
class MemoryType(Enum):
"""Type of memory entry."""
LESSON = "lesson" # Successful resolution
ANTIPATTERN = "antipattern" # What NOT to do
PROCEDURE = "procedure" # Step-by-step guide
ESCALATION = "escalation" # Required human intervention
@dataclass
class MemoryEntry:
"""A single memory entry."""
id: str
memory_type: MemoryType
tenant_id: str
technology: str
# Problem description
problem_summary: str
problem_symptoms: List[str]
# Resolution
resolution_summary: str
resolution_steps: List[str]
# Metadata
ticket_id: str
resolved_at: datetime
resolution_time_minutes: int
confidence_score: float
# Optional
related_hosts: List[str] = field(default_factory=list)
tags: List[str] = field(default_factory=list)
reuse_count: int = 0
last_used: Optional[datetime] = None
@dataclass
class MemorySearchResult:
"""Result from memory search."""
entry: MemoryEntry
similarity_score: float
relevance_reason: str
class EpisodicMemory:
"""
Episodic Memory module for Arthur.
Stores and retrieves "lessons learned" from resolved tickets
to help with similar future problems.
Features:
- Store successful resolutions
- Store anti-patterns (what didn't work)
- Search by similarity
- Tenant isolation
- Usage tracking for continuous learning
"""
COLLECTION_NAME = "arthur_episodic_memory"
def __init__(self, embedding_dim: int = 384):
"""Initialize episodic memory."""
self._embedding_dim = embedding_dim
self._qdrant = get_qdrant_client()
def store_lesson(
self,
ticket_id: str,
tenant_id: str,
technology: str,
problem_summary: str,
problem_symptoms: List[str],
resolution_summary: str,
resolution_steps: List[str],
resolution_time_minutes: int,
confidence_score: float,
related_hosts: Optional[List[str]] = None,
tags: Optional[List[str]] = None
) -> Optional[str]:
"""
Store a successful resolution as a lesson.
Args:
ticket_id: Original ticket ID
tenant_id: Tenant for isolation
technology: Primary technology involved
problem_summary: Brief problem description
problem_symptoms: List of symptoms observed
resolution_summary: Brief resolution description
resolution_steps: Step-by-step resolution
resolution_time_minutes: Time to resolve
confidence_score: How confident was the resolution
related_hosts: Hosts involved
tags: Additional tags
Returns:
Memory entry ID if successful
"""
try:
# Generate unique ID
memory_id = self._generate_id(ticket_id, tenant_id)
# Create entry
entry = MemoryEntry(
id=memory_id,
memory_type=MemoryType.LESSON,
tenant_id=tenant_id,
technology=technology,
problem_summary=problem_summary,
problem_symptoms=problem_symptoms,
resolution_summary=resolution_summary,
resolution_steps=resolution_steps,
ticket_id=ticket_id,
resolved_at=datetime.now(timezone.utc),
resolution_time_minutes=resolution_time_minutes,
confidence_score=confidence_score,
related_hosts=related_hosts or [],
tags=tags or []
)
# Create searchable content
search_content = self._create_search_content(entry)
# Generate embedding
embedding = self._generate_embedding(search_content)
# Store in Qdrant
success = self._qdrant.upsert_document(
doc_id=memory_id,
content=search_content,
embedding=embedding,
tenant_id=tenant_id,
metadata={
"memory_type": entry.memory_type.value,
"technology": technology,
"ticket_id": ticket_id,
"problem_summary": problem_summary,
"resolution_summary": resolution_summary,
"resolution_steps": json.dumps(resolution_steps),
"symptoms": json.dumps(problem_symptoms),
"confidence_score": confidence_score,
"tags": tags or [],
"resolved_at": entry.resolved_at.isoformat()
}
)
if success:
logger.info(f"Stored lesson for ticket {ticket_id}")
return memory_id
return None
except Exception as e:
logger.error(f"Failed to store lesson: {e}")
return None
def store_antipattern(
self,
ticket_id: str,
tenant_id: str,
technology: str,
problem_summary: str,
failed_approach: str,
why_failed: str,
better_approach: Optional[str] = None
) -> Optional[str]:
"""
Store what NOT to do for a problem.
Args:
ticket_id: Original ticket ID
tenant_id: Tenant for isolation
technology: Technology involved
problem_summary: Problem description
failed_approach: What was tried and failed
why_failed: Why it didn't work
better_approach: Optional alternative
Returns:
Memory entry ID if successful
"""
try:
memory_id = self._generate_id(f"anti-{ticket_id}", tenant_id)
entry = MemoryEntry(
id=memory_id,
memory_type=MemoryType.ANTIPATTERN,
tenant_id=tenant_id,
technology=technology,
problem_summary=problem_summary,
problem_symptoms=[],
resolution_summary=f"NÃO FAZER: {failed_approach}. Motivo: {why_failed}",
resolution_steps=[better_approach] if better_approach else [],
ticket_id=ticket_id,
resolved_at=datetime.now(timezone.utc),
resolution_time_minutes=0,
confidence_score=1.0,
tags=["antipattern", "avoid"]
)
search_content = f"ANTIPADRÃO: {problem_summary}. NÃO FAZER: {failed_approach}"
embedding = self._generate_embedding(search_content)
success = self._qdrant.upsert_document(
doc_id=memory_id,
content=search_content,
embedding=embedding,
tenant_id=tenant_id,
metadata={
"memory_type": MemoryType.ANTIPATTERN.value,
"technology": technology,
"ticket_id": ticket_id,
"problem_summary": problem_summary,
"failed_approach": failed_approach,
"why_failed": why_failed,
"better_approach": better_approach or "",
"tags": ["antipattern"]
}
)
if success:
logger.info(f"Stored antipattern for ticket {ticket_id}")
return memory_id
return None
except Exception as e:
logger.error(f"Failed to store antipattern: {e}")
return None
def search_similar(
self,
problem_description: str,
tenant_id: str,
technology: Optional[str] = None,
limit: int = 5,
min_score: float = 0.5
) -> List[MemorySearchResult]:
"""
Search for similar past problems.
Args:
problem_description: Current problem to find matches for
tenant_id: Tenant for isolation
technology: Optional filter by technology
limit: Maximum results
min_score: Minimum similarity score
Returns:
List of similar memory entries
"""
try:
# Generate embedding for search
embedding = self._generate_embedding(problem_description)
# Search in Qdrant
results = self._qdrant.search(
query_embedding=embedding,
tenant_id=tenant_id,
limit=limit,
technology_filter=technology,
score_threshold=min_score
)
# Convert to MemorySearchResult
memories = []
for result in results:
# Reconstruct memory entry from metadata
entry = self._reconstruct_entry(result)
if entry:
memories.append(MemorySearchResult(
entry=entry,
similarity_score=result.score,
relevance_reason=self._explain_relevance(entry, problem_description)
))
logger.info(f"Found {len(memories)} similar memories for tenant {tenant_id}")
return memories
except Exception as e:
logger.error(f"Memory search failed: {e}")
return []
def get_by_id(self, memory_id: str) -> Optional[MemoryEntry]:
"""Get a specific memory entry by ID."""
# Would need to implement get_by_id in Qdrant client
# For now, return None
return None
def increment_reuse(self, memory_id: str) -> bool:
"""Increment the reuse counter for a memory entry."""
# Would need to implement update in Qdrant client
logger.info(f"Incrementing reuse for memory {memory_id}")
return True
def _generate_id(self, base: str, tenant_id: str) -> str:
"""Generate unique memory ID."""
content = f"{tenant_id}:{base}:{datetime.now(timezone.utc).isoformat()}"
return f"mem-{hashlib.md5(content.encode()).hexdigest()[:16]}"
def _create_search_content(self, entry: MemoryEntry) -> str:
"""Create searchable text content from entry."""
parts = [
f"Problema: {entry.problem_summary}",
f"Sintomas: {', '.join(entry.problem_symptoms)}",
f"Tecnologia: {entry.technology}",
f"Resolução: {entry.resolution_summary}",
f"Passos: {' -> '.join(entry.resolution_steps)}",
]
if entry.tags:
parts.append(f"Tags: {', '.join(entry.tags)}")
return "\n".join(parts)
def _generate_embedding(self, text: str) -> List[float]:
"""Generate embedding for text (placeholder)."""
# Same placeholder as RAG pipeline
hash_bytes = hashlib.sha256(text.encode()).digest()
embedding = []
for i in range(self._embedding_dim):
byte_idx = i % len(hash_bytes)
value = (hash_bytes[byte_idx] / 255.0) * 2 - 1
embedding.append(value)
return embedding
def _reconstruct_entry(self, result) -> Optional[MemoryEntry]:
"""Reconstruct MemoryEntry from search result."""
try:
metadata = result.metadata or {}
return MemoryEntry(
id=result.id,
memory_type=MemoryType(metadata.get("memory_type", "lesson")),
tenant_id=metadata.get("tenant_id", ""),
technology=metadata.get("technology", ""),
problem_summary=metadata.get("problem_summary", ""),
problem_symptoms=json.loads(metadata.get("symptoms", "[]")),
resolution_summary=metadata.get("resolution_summary", ""),
resolution_steps=json.loads(metadata.get("resolution_steps", "[]")),
ticket_id=metadata.get("ticket_id", ""),
resolved_at=datetime.fromisoformat(
metadata.get("resolved_at", datetime.now(timezone.utc).isoformat())
),
resolution_time_minutes=metadata.get("resolution_time_minutes", 0),
confidence_score=metadata.get("confidence_score", 0.0),
tags=metadata.get("tags", [])
)
except Exception as e:
logger.error(f"Failed to reconstruct memory entry: {e}")
return None
def _explain_relevance(self, entry: MemoryEntry, query: str) -> str:
"""Explain why this memory is relevant."""
reasons = []
query_lower = query.lower()
if entry.technology.lower() in query_lower:
reasons.append(f"Mesma tecnologia ({entry.technology})")
for symptom in entry.problem_symptoms:
if symptom.lower() in query_lower:
reasons.append(f"Sintoma similar: {symptom}")
break
if entry.memory_type == MemoryType.ANTIPATTERN:
reasons.append("⚠️ ANTIPADRÃO - evitar esta abordagem")
if entry.reuse_count > 3:
reasons.append(f"Solução usada {entry.reuse_count}x anteriormente")
if not reasons:
reasons.append("Problema semanticamente similar")
return "; ".join(reasons)
# Singleton
_memory: Optional[EpisodicMemory] = None
def get_episodic_memory() -> EpisodicMemory:
"""Get global episodic memory instance."""
global _memory
if _memory is None:
_memory = EpisodicMemory()
return _memory

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"""
Feedback Parser for Arthur.
Parses email responses from technicians to determine
if a ticket was resolved or needs to be reopened.
"""
import re
import logging
from typing import Optional
from dataclasses import dataclass
from enum import Enum, auto
from datetime import datetime, timezone
logger = logging.getLogger("ArthurFeedback")
class FeedbackType(Enum):
"""Type of feedback received."""
RESOLVED = auto() # Case solved, close ticket
REOPENED = auto() # Case needs more work
ESCALATE = auto() # Needs human intervention
CLARIFICATION = auto() # User asking for more info
ACKNOWLEDGMENT = auto() # Simple acknowledgment
UNKNOWN = auto() # Could not determine
@dataclass
class FeedbackResult:
"""Result of feedback parsing."""
ticket_id: str
feedback_type: FeedbackType
confidence: float
raw_content: str
parsed_message: str
sentiment: str # "positive", "negative", "neutral"
suggested_action: str
# Optional extracted info
satisfaction_score: Optional[int] = None # 1-5 if detected
follow_up_requested: bool = False
class FeedbackParser:
"""
Parses email responses to determine ticket outcome.
Analyzes:
- Keywords indicating resolution/reopening
- Sentiment of the response
- Explicit satisfaction indicators
"""
# Resolution keywords (Portuguese and English)
RESOLVED_PATTERNS = [
r"\b(resolvido|solucionado|funcionando|ok|normalizado)\b",
r"\b(resolved|solved|fixed|working|done)\b",
r"\b(obrigado|agradeço|perfeito|excelente)\b",
r"\b(thank|thanks|great|perfect|excellent)\b",
r"\bpode\s+fechar\b",
r"\bclose\s+ticket\b",
r"👍|✅|🎉",
]
# Reopening keywords
REOPEN_PATTERNS = [
r"\b(ainda|continua|persiste|não\s+resolvido)\b",
r"\b(ainda\s+não|não\s+funciona|problema\s+voltou)\b",
r"\b(still|persists|not\s+fixed|not\s+working)\b",
r"\b(same\s+issue|problem\s+returned|broken\s+again)\b",
r"👎|❌|😞",
]
# Escalation keywords
ESCALATE_PATTERNS = [
r"\b(urgente|crítico|emergência|parou\s+tudo)\b",
r"\b(urgent|critical|emergency|down)\b",
r"\b(ligar|telefonar|call|phone)\b",
r"\b(gerente|supervisor|manager)\b",
]
# Clarification keywords
CLARIFY_PATTERNS = [
r"\b(como|o\s+que|quando|onde|porque)\b\?",
r"\b(how|what|when|where|why)\b\?",
r"\b(não\s+entendi|pode\s+explicar|mais\s+detalhes)\b",
r"\b(don't\s+understand|explain|more\s+details)\b",
]
def __init__(self):
"""Initialize parser."""
self._compile_patterns()
def _compile_patterns(self):
"""Compile regex patterns for efficiency."""
self._resolved_re = [re.compile(p, re.I) for p in self.RESOLVED_PATTERNS]
self._reopen_re = [re.compile(p, re.I) for p in self.REOPEN_PATTERNS]
self._escalate_re = [re.compile(p, re.I) for p in self.ESCALATE_PATTERNS]
self._clarify_re = [re.compile(p, re.I) for p in self.CLARIFY_PATTERNS]
def parse(
self,
ticket_id: str,
email_content: str,
subject: Optional[str] = None
) -> FeedbackResult:
"""
Parse email content to determine feedback type.
Args:
ticket_id: Ticket being responded to
email_content: Body of the response email
subject: Optional email subject
Returns:
FeedbackResult with analysis
"""
# Combine subject and content for analysis
full_text = f"{subject or ''}\n{email_content}"
# Count matches for each category
resolved_score = self._count_matches(full_text, self._resolved_re)
reopen_score = self._count_matches(full_text, self._reopen_re)
escalate_score = self._count_matches(full_text, self._escalate_re)
clarify_score = self._count_matches(full_text, self._clarify_re)
# Determine feedback type based on scores
feedback_type, confidence = self._determine_type(
resolved_score, reopen_score, escalate_score, clarify_score
)
# Analyze sentiment
sentiment = self._analyze_sentiment(full_text, feedback_type)
# Extract satisfaction score if present
satisfaction = self._extract_satisfaction(full_text)
# Check for follow-up request
follow_up = self._check_follow_up(full_text)
# Generate suggested action
action = self._suggest_action(feedback_type, confidence)
# Create parsed message summary
parsed = self._create_summary(feedback_type, confidence, sentiment)
return FeedbackResult(
ticket_id=ticket_id,
feedback_type=feedback_type,
confidence=confidence,
raw_content=email_content,
parsed_message=parsed,
sentiment=sentiment,
suggested_action=action,
satisfaction_score=satisfaction,
follow_up_requested=follow_up
)
def _count_matches(self, text: str, patterns: list) -> int:
"""Count total matches for a list of patterns."""
total = 0
for pattern in patterns:
total += len(pattern.findall(text))
return total
def _determine_type(
self,
resolved: int,
reopen: int,
escalate: int,
clarify: int
) -> tuple[FeedbackType, float]:
"""Determine feedback type from match counts."""
total = resolved + reopen + escalate + clarify
if total == 0:
return FeedbackType.UNKNOWN, 0.3
# Escalation takes priority
if escalate >= 2:
return FeedbackType.ESCALATE, min(0.9, 0.4 + escalate * 0.2)
# Then reopening (negative feedback is important)
if reopen > resolved:
confidence = min(0.95, 0.5 + (reopen - resolved) * 0.15)
return FeedbackType.REOPENED, confidence
# Clarification
if clarify > 0 and resolved == 0 and reopen == 0:
return FeedbackType.CLARIFICATION, min(0.85, 0.5 + clarify * 0.15)
# Resolution
if resolved > 0:
confidence = min(0.95, 0.5 + resolved * 0.15)
return FeedbackType.RESOLVED, confidence
# Simple acknowledgment if nothing else matches
if total > 0:
return FeedbackType.ACKNOWLEDGMENT, 0.5
return FeedbackType.UNKNOWN, 0.3
def _analyze_sentiment(self, text: str, feedback_type: FeedbackType) -> str:
"""Analyze overall sentiment."""
positive_words = len(re.findall(
r"\b(obrigado|agradeço|ótimo|perfeito|excelente|bom|thank|great|good)\b",
text, re.I
))
negative_words = len(re.findall(
r"\b(ruim|péssimo|horrível|frustra|irrita|bad|terrible|awful|angry)\b",
text, re.I
))
if feedback_type == FeedbackType.RESOLVED:
return "positive"
elif feedback_type in (FeedbackType.REOPENED, FeedbackType.ESCALATE):
return "negative"
elif positive_words > negative_words:
return "positive"
elif negative_words > positive_words:
return "negative"
return "neutral"
def _extract_satisfaction(self, text: str) -> Optional[int]:
"""Extract satisfaction score if mentioned."""
# Look for explicit ratings
match = re.search(r"\b([1-5])\s*(?:/\s*5|estrelas?|stars?|pontos?)\b", text, re.I)
if match:
return int(match.group(1))
# Look for emoji ratings
thumbs_up = len(re.findall(r"👍", text))
thumbs_down = len(re.findall(r"👎", text))
if thumbs_up > 0 and thumbs_down == 0:
return 5
elif thumbs_down > 0 and thumbs_up == 0:
return 1
return None
def _check_follow_up(self, text: str) -> bool:
"""Check if follow-up is requested."""
patterns = [
r"\b(ligar|retornar|acompanhar|follow.?up)\b",
r"\b(mais\s+tarde|depois|amanhã|tomorrow)\b",
r"\b(continuar|agendar|schedule)\b",
]
for pattern in patterns:
if re.search(pattern, text, re.I):
return True
return False
def _suggest_action(self, feedback_type: FeedbackType, confidence: float) -> str:
"""Suggest action based on feedback."""
actions = {
FeedbackType.RESOLVED: "Fechar ticket e registrar resolução",
FeedbackType.REOPENED: "Reabrir ticket e escalar para análise",
FeedbackType.ESCALATE: "Escalar imediatamente para técnico sênior",
FeedbackType.CLARIFICATION: "Responder com mais detalhes",
FeedbackType.ACKNOWLEDGMENT: "Aguardar confirmação final",
FeedbackType.UNKNOWN: "Revisar manualmente",
}
action = actions.get(feedback_type, "Revisar manualmente")
if confidence < 0.6:
action += " (baixa confiança - revisar)"
return action
def _create_summary(
self,
feedback_type: FeedbackType,
confidence: float,
sentiment: str
) -> str:
"""Create human-readable summary."""
type_desc = {
FeedbackType.RESOLVED: "Cliente confirmou resolução",
FeedbackType.REOPENED: "Cliente reportou problema persistente",
FeedbackType.ESCALATE: "Cliente solicitou escalação urgente",
FeedbackType.CLARIFICATION: "Cliente pediu mais informações",
FeedbackType.ACKNOWLEDGMENT: "Cliente confirmou recebimento",
FeedbackType.UNKNOWN: "Não foi possível determinar intenção",
}
return (
f"{type_desc.get(feedback_type, 'Desconhecido')}. "
f"Sentimento: {sentiment}. "
f"Confiança: {confidence:.0%}"
)
# Singleton
_parser: Optional[FeedbackParser] = None
def get_feedback_parser() -> FeedbackParser:
"""Get global feedback parser instance."""
global _parser
if _parser is None:
_parser = FeedbackParser()
return _parser

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"""
RAG Ingestion Pipeline for Arthur.
Processes Markdown and PDF documents, extracts text,
generates embeddings and indexes in Qdrant.
"""
import os
import re
import hashlib
import logging
from pathlib import Path
from typing import Optional, List
from dataclasses import dataclass, field
from datetime import datetime, timezone
from src.clients import get_qdrant_client
logger = logging.getLogger("ArthurRAG")
@dataclass
class DocumentMetadata:
"""Metadata for an ingested document."""
filename: str
filepath: str
technology: str
tenant_id: str
doc_type: str # "manual", "procedure", "troubleshoot", "faq"
language: str = "pt"
version: str = "1.0"
author: Optional[str] = None
tags: List[str] = field(default_factory=list)
created_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
@dataclass
class DocumentChunk:
"""A chunk of a document ready for indexing."""
id: str
content: str
embedding: Optional[List[float]] = None
metadata: Optional[DocumentMetadata] = None
chunk_index: int = 0
total_chunks: int = 1
@dataclass
class IngestionResult:
"""Result of document ingestion."""
success: bool
filepath: str
chunks_created: int = 0
error: Optional[str] = None
class RAGIngestionPipeline:
"""
Pipeline for ingesting technical documentation into Qdrant.
Features:
- Process Markdown and plain text files
- Chunk documents for optimal retrieval
- Extract technology metadata from content
- Generate embeddings (placeholder for now)
- Index in Qdrant with tenant isolation
"""
# Technology detection patterns
TECH_PATTERNS = {
"linux": r"\b(linux|ubuntu|debian|centos|rhel|bash|shell|systemd)\b",
"windows": r"\b(windows|powershell|cmd|iis|active.?directory|ad)\b",
"docker": r"\b(docker|container|dockerfile|compose|kubernetes|k8s)\b",
"postgresql": r"\b(postgres(ql)?|pg_|psql|pgadmin)\b",
"mysql": r"\b(mysql|mariadb|mysqldump)\b",
"nginx": r"\b(nginx|reverse.?proxy|upstream)\b",
"apache": r"\b(apache|httpd|mod_)\b",
"zabbix": r"\b(zabbix|zbx|trigger|host.?group)\b",
"network": r"\b(network|firewall|iptables|routing|vlan|switch)\b",
"security": r"\b(security|ssl|tls|certificate|vulnerab|cve)\b",
"backup": r"\b(backup|restore|recovery|snapshot|rsync)\b",
}
def __init__(
self,
chunk_size: int = 500,
chunk_overlap: int = 50,
embedding_dim: int = 384
):
"""Initialize ingestion pipeline."""
self._chunk_size = chunk_size
self._chunk_overlap = chunk_overlap
self._embedding_dim = embedding_dim
self._qdrant = get_qdrant_client()
def ingest_directory(
self,
directory: str,
tenant_id: str,
doc_type: str = "manual",
recursive: bool = True
) -> List[IngestionResult]:
"""
Ingest all documents from a directory.
Args:
directory: Path to directory
tenant_id: Tenant for isolation
doc_type: Type of documentation
recursive: Search subdirectories
Returns:
List of ingestion results
"""
results = []
path = Path(directory)
if not path.exists():
logger.error(f"Directory not found: {directory}")
return [IngestionResult(
success=False,
filepath=directory,
error="Directory not found"
)]
# Find all supported files
patterns = ["*.md", "*.txt", "*.rst"]
files = []
for pattern in patterns:
if recursive:
files.extend(path.rglob(pattern))
else:
files.extend(path.glob(pattern))
logger.info(f"Found {len(files)} documents in {directory}")
for filepath in files:
result = self.ingest_file(
filepath=str(filepath),
tenant_id=tenant_id,
doc_type=doc_type
)
results.append(result)
return results
def ingest_file(
self,
filepath: str,
tenant_id: str,
doc_type: str = "manual"
) -> IngestionResult:
"""
Ingest a single file.
Args:
filepath: Path to file
tenant_id: Tenant for isolation
doc_type: Type of documentation
Returns:
IngestionResult with status
"""
try:
path = Path(filepath)
if not path.exists():
return IngestionResult(
success=False,
filepath=filepath,
error="File not found"
)
# Read content
content = self._read_file(path)
if not content:
return IngestionResult(
success=False,
filepath=filepath,
error="Failed to read file"
)
# Sanitize content (security)
content = self._sanitize_content(content)
# Detect technology
technology = self._detect_technology(content)
# Extract tags from content
tags = self._extract_tags(content)
# Create metadata
metadata = DocumentMetadata(
filename=path.name,
filepath=str(path),
technology=technology,
tenant_id=tenant_id,
doc_type=doc_type,
tags=tags
)
# Chunk the document
chunks = self._chunk_content(content, metadata)
# Generate embeddings and index
indexed = 0
for chunk in chunks:
# Placeholder embedding (in production, use sentence-transformers)
chunk.embedding = self._generate_embedding(chunk.content)
# Index in Qdrant
success = self._qdrant.upsert_document(
doc_id=chunk.id,
content=chunk.content,
embedding=chunk.embedding,
tenant_id=tenant_id,
metadata={
"filename": metadata.filename,
"technology": metadata.technology,
"doc_type": metadata.doc_type,
"chunk_index": chunk.chunk_index,
"total_chunks": chunk.total_chunks,
"tags": metadata.tags
}
)
if success:
indexed += 1
logger.info(f"Indexed {indexed}/{len(chunks)} chunks from {path.name}")
return IngestionResult(
success=indexed > 0,
filepath=filepath,
chunks_created=indexed
)
except Exception as e:
logger.error(f"Ingestion failed for {filepath}: {e}")
return IngestionResult(
success=False,
filepath=filepath,
error=str(e)
)
def _read_file(self, path: Path) -> Optional[str]:
"""Read file content with encoding detection."""
encodings = ["utf-8", "latin-1", "cp1252"]
for encoding in encodings:
try:
return path.read_text(encoding=encoding)
except UnicodeDecodeError:
continue
return None
def _sanitize_content(self, content: str) -> str:
"""
Sanitize content for security.
Removes:
- Embedded scripts
- Base64 encoded content
- Suspicious patterns
"""
# Remove script tags
content = re.sub(r'<script[^>]*>.*?</script>', '', content, flags=re.I | re.S)
# Remove style tags
content = re.sub(r'<style[^>]*>.*?</style>', '', content, flags=re.I | re.S)
# Remove HTML comments (may contain hidden content)
content = re.sub(r'<!--.*?-->', '', content, flags=re.S)
# Remove large base64 blocks (potential hidden payloads)
content = re.sub(r'[A-Za-z0-9+/]{100,}={0,2}', '[BASE64_REMOVED]', content)
# Normalize whitespace
content = re.sub(r'\n{3,}', '\n\n', content)
return content.strip()
def _detect_technology(self, content: str) -> str:
"""Detect primary technology from content."""
content_lower = content.lower()
scores = {}
for tech, pattern in self.TECH_PATTERNS.items():
matches = len(re.findall(pattern, content_lower, re.I))
if matches > 0:
scores[tech] = matches
if scores:
return max(scores, key=scores.get)
return "general"
def _extract_tags(self, content: str) -> List[str]:
"""Extract relevant tags from content."""
tags = set()
# Check for each technology pattern
content_lower = content.lower()
for tech, pattern in self.TECH_PATTERNS.items():
if re.search(pattern, content_lower, re.I):
tags.add(tech)
return list(tags)[:10] # Limit tags
def _chunk_content(
self,
content: str,
metadata: DocumentMetadata
) -> List[DocumentChunk]:
"""Split content into chunks for indexing."""
chunks = []
# Split by paragraphs first
paragraphs = content.split('\n\n')
current_chunk = ""
chunk_index = 0
for para in paragraphs:
if len(current_chunk) + len(para) <= self._chunk_size:
current_chunk += para + "\n\n"
else:
if current_chunk.strip():
chunk_id = self._generate_chunk_id(
metadata.filepath,
chunk_index
)
chunks.append(DocumentChunk(
id=chunk_id,
content=current_chunk.strip(),
metadata=metadata,
chunk_index=chunk_index
))
chunk_index += 1
current_chunk = para + "\n\n"
# Add final chunk
if current_chunk.strip():
chunk_id = self._generate_chunk_id(metadata.filepath, chunk_index)
chunks.append(DocumentChunk(
id=chunk_id,
content=current_chunk.strip(),
metadata=metadata,
chunk_index=chunk_index
))
# Update total chunks
for chunk in chunks:
chunk.total_chunks = len(chunks)
return chunks
def _generate_chunk_id(self, filepath: str, chunk_index: int) -> str:
"""Generate unique ID for a chunk."""
content = f"{filepath}:{chunk_index}"
return hashlib.md5(content.encode()).hexdigest()
def _generate_embedding(self, text: str) -> List[float]:
"""
Generate embedding for text.
NOTE: This is a placeholder. In production, use:
- sentence-transformers with BGE-small
- Or Ollama embedding endpoint
"""
# Placeholder: return deterministic pseudo-embedding
# In production, replace with actual embedding model
import hashlib
hash_bytes = hashlib.sha256(text.encode()).digest()
# Create normalized vector from hash
embedding = []
for i in range(self._embedding_dim):
byte_idx = i % len(hash_bytes)
value = (hash_bytes[byte_idx] / 255.0) * 2 - 1 # Normalize to [-1, 1]
embedding.append(value)
return embedding
# Singleton
_pipeline: Optional[RAGIngestionPipeline] = None
def get_rag_pipeline() -> RAGIngestionPipeline:
"""Get global RAG pipeline instance."""
global _pipeline
if _pipeline is None:
_pipeline = RAGIngestionPipeline()
return _pipeline

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"""
Tests for Flywheel Module - Episodic Memory.
Tests lesson storage and retrieval.
"""
import pytest
from unittest.mock import Mock, patch
from src.flywheel.episodic_memory import (
EpisodicMemory,
MemoryEntry,
MemorySearchResult,
MemoryType,
get_episodic_memory
)
class TestMemoryEntry:
"""Tests for MemoryEntry dataclass."""
def test_create_entry(self):
"""Test creating a memory entry."""
from datetime import datetime, timezone
entry = MemoryEntry(
id="mem-001",
memory_type=MemoryType.LESSON,
tenant_id="tenant-001",
technology="linux",
problem_summary="Servidor sem memória",
problem_symptoms=["alta utilização", "OOM killer"],
resolution_summary="Reiniciou serviço Apache",
resolution_steps=["Identificar processo", "Reiniciar serviço"],
ticket_id="TKT-001",
resolved_at=datetime.now(timezone.utc),
resolution_time_minutes=30,
confidence_score=0.9
)
assert entry.id == "mem-001"
assert entry.memory_type == MemoryType.LESSON
assert len(entry.resolution_steps) == 2
class TestEpisodicMemory:
"""Tests for EpisodicMemory class."""
@pytest.fixture
def memory(self):
"""Create memory with mocked Qdrant."""
with patch('src.flywheel.episodic_memory.get_qdrant_client') as mock:
mock.return_value = Mock()
mock.return_value.upsert_document = Mock(return_value=True)
mock.return_value.search = Mock(return_value=[])
return EpisodicMemory()
def test_generate_id(self, memory):
"""Test ID generation is unique."""
id1 = memory._generate_id("ticket-1", "tenant-a")
id2 = memory._generate_id("ticket-2", "tenant-a")
assert id1.startswith("mem-")
assert id1 != id2
def test_create_search_content(self, memory):
"""Test search content creation."""
from datetime import datetime, timezone
entry = MemoryEntry(
id="mem-001",
memory_type=MemoryType.LESSON,
tenant_id="tenant-001",
technology="nginx",
problem_summary="Erro 502",
problem_symptoms=["gateway timeout"],
resolution_summary="Aumentar buffer",
resolution_steps=["Editar nginx.conf"],
ticket_id="TKT-001",
resolved_at=datetime.now(timezone.utc),
resolution_time_minutes=15,
confidence_score=0.85,
tags=["proxy", "buffer"]
)
content = memory._create_search_content(entry)
assert "Erro 502" in content
assert "nginx" in content
assert "buffer" in content.lower()
def test_generate_embedding(self, memory):
"""Test embedding generation."""
emb = memory._generate_embedding("test text")
assert len(emb) == 384
assert all(-1 <= v <= 1 for v in emb)
def test_store_lesson_success(self, memory):
"""Test successful lesson storage."""
result = memory.store_lesson(
ticket_id="TKT-001",
tenant_id="tenant-001",
technology="linux",
problem_summary="High CPU usage",
problem_symptoms=["100% CPU", "slow response"],
resolution_summary="Killed runaway process",
resolution_steps=["top -c", "kill -9 PID"],
resolution_time_minutes=20,
confidence_score=0.9
)
assert result is not None
assert result.startswith("mem-")
def test_store_antipattern(self, memory):
"""Test antipattern storage."""
result = memory.store_antipattern(
ticket_id="TKT-002",
tenant_id="tenant-001",
technology="postgresql",
problem_summary="Database slow",
failed_approach="Reiniciar PostgreSQL",
why_failed="Perda de cache causou lentidão maior",
better_approach="Analisar queries lentas primeiro"
)
assert result is not None
assert result.startswith("mem-")
def test_explain_relevance_technology(self, memory):
"""Test relevance explanation for technology match."""
from datetime import datetime, timezone
entry = MemoryEntry(
id="mem-001",
memory_type=MemoryType.LESSON,
tenant_id="tenant-001",
technology="nginx",
problem_summary="Erro 502",
problem_symptoms=[],
resolution_summary="Fix",
resolution_steps=[],
ticket_id="TKT-001",
resolved_at=datetime.now(timezone.utc),
resolution_time_minutes=10,
confidence_score=0.8
)
explanation = memory._explain_relevance(entry, "nginx retornando erro")
assert "nginx" in explanation.lower()
def test_explain_relevance_antipattern(self, memory):
"""Test relevance explanation for antipattern."""
from datetime import datetime, timezone
entry = MemoryEntry(
id="mem-001",
memory_type=MemoryType.ANTIPATTERN,
tenant_id="tenant-001",
technology="linux",
problem_summary="Memory issue",
problem_symptoms=[],
resolution_summary="Don't do this",
resolution_steps=[],
ticket_id="TKT-001",
resolved_at=datetime.now(timezone.utc),
resolution_time_minutes=0,
confidence_score=1.0
)
explanation = memory._explain_relevance(entry, "memory problem")
assert "ANTIPADRÃO" in explanation
class TestEpisodicMemorySingleton:
"""Tests for singleton."""
def test_singleton(self):
"""Test singleton returns same instance."""
import src.flywheel.episodic_memory as module
module._memory = None
with patch('src.flywheel.episodic_memory.get_qdrant_client'):
m1 = get_episodic_memory()
m2 = get_episodic_memory()
assert m1 is m2

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"""
Tests for Flywheel Module - Feedback Parser.
Tests email feedback parsing logic.
"""
import pytest
from src.flywheel.feedback_parser import (
FeedbackParser,
FeedbackResult,
FeedbackType,
get_feedback_parser
)
class TestFeedbackParser:
"""Tests for FeedbackParser class."""
@pytest.fixture
def parser(self):
"""Create feedback parser."""
return FeedbackParser()
def test_parse_resolved_portuguese(self, parser):
"""Test resolved detection in Portuguese."""
result = parser.parse(
ticket_id="TKT-001",
email_content="Obrigado pelo suporte! O problema foi resolvido.",
subject="Re: Chamado TKT-001"
)
assert result.feedback_type == FeedbackType.RESOLVED
assert result.sentiment == "positive"
def test_parse_resolved_english(self, parser):
"""Test resolved detection in English."""
result = parser.parse(
ticket_id="TKT-001",
email_content="Thanks, the issue is now fixed and working perfectly!",
subject="Re: Ticket TKT-001"
)
assert result.feedback_type == FeedbackType.RESOLVED
def test_parse_resolved_emoji(self, parser):
"""Test resolved detection with emoji."""
result = parser.parse(
ticket_id="TKT-001",
email_content="Funcionou! 👍✅",
subject="Re: TKT-001"
)
assert result.feedback_type == FeedbackType.RESOLVED
def test_parse_reopened(self, parser):
"""Test reopened detection."""
result = parser.parse(
ticket_id="TKT-001",
email_content="O problema ainda persiste. Não foi resolvido.",
subject="Re: TKT-001"
)
assert result.feedback_type == FeedbackType.REOPENED
assert result.sentiment == "negative"
def test_parse_reopened_english(self, parser):
"""Test reopened detection in English."""
result = parser.parse(
ticket_id="TKT-001",
email_content="The problem is still happening. Not fixed.",
subject="Re: TKT-001"
)
assert result.feedback_type == FeedbackType.REOPENED
def test_parse_escalate(self, parser):
"""Test escalation detection."""
result = parser.parse(
ticket_id="TKT-001",
email_content="URGENTE! Sistema crítico parou, preciso falar com o gerente!",
subject="URGENTE Re: TKT-001"
)
assert result.feedback_type == FeedbackType.ESCALATE
def test_parse_clarification(self, parser):
"""Test clarification request detection."""
result = parser.parse(
ticket_id="TKT-001",
email_content="Como faço para aplicar essa correção? Pode explicar melhor?",
subject="Re: TKT-001"
)
assert result.feedback_type == FeedbackType.CLARIFICATION
def test_extract_satisfaction_score(self, parser):
"""Test satisfaction score extraction."""
result = parser.parse(
ticket_id="TKT-001",
email_content="Resolvido! Nota 5/5 para o atendimento.",
subject="Re: TKT-001"
)
assert result.satisfaction_score == 5
def test_extract_satisfaction_thumbs(self, parser):
"""Test satisfaction from thumbs emoji."""
result = parser.parse(
ticket_id="TKT-001",
email_content="👍👍👍 Excelente suporte!",
subject="Re: TKT-001"
)
assert result.satisfaction_score == 5
def test_follow_up_detection(self, parser):
"""Test follow-up request detection."""
result = parser.parse(
ticket_id="TKT-001",
email_content="Ok, me ligar amanhã para acompanhar.",
subject="Re: TKT-001"
)
assert result.follow_up_requested is True
def test_unknown_feedback(self, parser):
"""Test unknown feedback type."""
result = parser.parse(
ticket_id="TKT-001",
email_content="xyz", # Genuinely unrecognizable
subject="Re: TKT-001"
)
# Short gibberish should be unknown
assert result.feedback_type == FeedbackType.UNKNOWN
def test_suggested_action_resolved(self, parser):
"""Test suggested action for resolved."""
result = parser.parse(
ticket_id="TKT-001",
email_content="Problema resolvido, obrigado!",
subject="Re: TKT-001"
)
assert "Fechar ticket" in result.suggested_action
def test_suggested_action_reopened(self, parser):
"""Test suggested action for reopened."""
result = parser.parse(
ticket_id="TKT-001",
email_content="Ainda não funciona, problema voltou.",
subject="Re: TKT-001"
)
assert "Reabrir" in result.suggested_action
class TestFeedbackParserSingleton:
"""Tests for singleton."""
def test_singleton(self):
"""Test singleton returns same instance."""
import src.flywheel.feedback_parser as module
module._parser = None
p1 = get_feedback_parser()
p2 = get_feedback_parser()
assert p1 is p2

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"""
Tests for Flywheel Module - RAG Pipeline.
Tests document ingestion and processing.
"""
import pytest
import tempfile
import os
from pathlib import Path
from unittest.mock import Mock, patch
from src.flywheel.rag_pipeline import (
RAGIngestionPipeline,
DocumentMetadata,
DocumentChunk,
IngestionResult,
get_rag_pipeline
)
class TestDocumentMetadata:
"""Tests for DocumentMetadata dataclass."""
def test_default_values(self):
"""Test default metadata values."""
meta = DocumentMetadata(
filename="test.md",
filepath="/path/test.md",
technology="linux",
tenant_id="tenant-001",
doc_type="manual"
)
assert meta.language == "pt"
assert meta.version == "1.0"
assert meta.tags == []
class TestRAGPipeline:
"""Tests for RAGIngestionPipeline."""
@pytest.fixture
def pipeline(self):
"""Create pipeline with mocked Qdrant."""
with patch('src.flywheel.rag_pipeline.get_qdrant_client') as mock:
mock.return_value = Mock()
mock.return_value.upsert_document = Mock(return_value=True)
return RAGIngestionPipeline()
def test_sanitize_removes_scripts(self, pipeline):
"""Test that script tags are removed."""
content = "Normal text <script>alert('xss')</script> more text"
result = pipeline._sanitize_content(content)
assert "<script>" not in result
assert "alert" not in result
assert "Normal text" in result
def test_sanitize_removes_base64(self, pipeline):
"""Test that large base64 blocks are removed."""
content = "Data: " + "A" * 150 + " end"
result = pipeline._sanitize_content(content)
assert "[BASE64_REMOVED]" in result
def test_detect_technology_linux(self, pipeline):
"""Test Linux technology detection."""
content = "Configure the Linux server using systemctl to manage services."
tech = pipeline._detect_technology(content)
assert tech == "linux"
def test_detect_technology_docker(self, pipeline):
"""Test Docker technology detection."""
content = "Build the container using Dockerfile and run with docker compose."
tech = pipeline._detect_technology(content)
assert tech == "docker"
def test_detect_technology_network(self, pipeline):
"""Test network technology detection."""
content = "Configure the firewall rules and routing tables."
tech = pipeline._detect_technology(content)
assert tech == "network"
def test_extract_tags(self, pipeline):
"""Test tag extraction from content."""
content = "This document covers Linux server administration with nginx as reverse proxy and postgresql database."
tags = pipeline._extract_tags(content)
assert "linux" in tags
assert "nginx" in tags
assert "postgresql" in tags
def test_chunk_content(self, pipeline):
"""Test document chunking."""
meta = DocumentMetadata(
filename="test.md",
filepath="/test/test.md",
technology="general",
tenant_id="tenant-001",
doc_type="manual"
)
content = "First paragraph.\n\nSecond paragraph.\n\nThird paragraph."
chunks = pipeline._chunk_content(content, meta)
assert len(chunks) >= 1
assert all(c.metadata == meta for c in chunks)
assert chunks[0].chunk_index == 0
def test_generate_chunk_id(self, pipeline):
"""Test chunk ID generation."""
id1 = pipeline._generate_chunk_id("/path/file.md", 0)
id2 = pipeline._generate_chunk_id("/path/file.md", 1)
id3 = pipeline._generate_chunk_id("/path/file.md", 0)
assert id1 != id2
assert id1 == id3 # Same inputs should give same ID
def test_generate_embedding(self, pipeline):
"""Test embedding generation."""
emb = pipeline._generate_embedding("test content")
assert len(emb) == 384
assert all(-1 <= v <= 1 for v in emb)
def test_ingest_file_not_found(self, pipeline):
"""Test ingestion of non-existent file."""
result = pipeline.ingest_file(
filepath="/nonexistent/file.md",
tenant_id="tenant-001"
)
assert result.success is False
assert "not found" in result.error.lower()
def test_ingest_file_success(self, pipeline):
"""Test successful file ingestion."""
with tempfile.NamedTemporaryFile(mode='w', suffix='.md', delete=False) as f:
f.write("# Test Document\n\nThis is a test about Linux servers.")
filepath = f.name
try:
result = pipeline.ingest_file(
filepath=filepath,
tenant_id="tenant-001"
)
assert result.success is True
assert result.chunks_created >= 1
finally:
os.unlink(filepath)
class TestRAGPipelineSingleton:
"""Tests for singleton."""
def test_singleton(self):
"""Test singleton returns same instance."""
import src.flywheel.rag_pipeline as module
module._pipeline = None
with patch('src.flywheel.rag_pipeline.get_qdrant_client'):
p1 = get_rag_pipeline()
p2 = get_rag_pipeline()
assert p1 is p2