КОНТЕКСТ-ИНЖИНИРИНГ: ПОЛНОЕ РУКОВОДСТВО ДЛЯ ПРОФИ

От теории до production-ready систем

Продвинутая методичка для специалистов с техническим бэкграундом

ВВЕДЕНИЕ: ПАРАДИГМАЛЬНЫЙ СДВИГ В AI-РАЗРАБОТКЕ

Промпт-инжиниринг умер с появлением мощных языковых моделей. Контекст-инжиниринг - это новая дисциплина на стыке информационной архитектуры, ML engineering и системного анализа.

Ключевой инсайт: Современные LLM обладают достаточными reasoning capabilities, но нуждаются в quality context для генерации релевантных и точных ответов.

ГЛАВА 1: АРХИТЕКТУРА ИНФОРМАЦИОННЫХ СЛОЕВ

1.1 Таксономия данных по volatility

Профессиональные контекстные системы используют многослойную архитектуру данных, классифицированную по частоте изменений:

class DataLayer(Enum):

STATIC = "static" # TTL: 30+ days

SEMI_STATIC = "semi_static" # TTL: 7-30 days

DYNAMIC = "dynamic" # TTL: 1-24 hours

REAL_TIME = "real_time" # TTL: < 1 hour

EPHEMERAL = "ephemeral" # Session-based

Техническая реализация:

Static layer: Vector database (Pinecone, Weaviate)
Semi-static layer: Redis с TTL + vector indexing
Dynamic layer: API calls с кэшированием
Real-time layer: WebSocket connections
Ephemeral layer: In-memory session storage

1.2 Информационная энтропия и приоритизация

Используем information-theoretic подходы для ранжирования данных:

def calculate_information_value(document, query_context):

# Shannon entropy для оценки информативности

entropy = -sum(p * log2(p) for p in word_probabilities(document))

# Semantic similarity с query

similarity = cosine_similarity(

embed_text(document),

embed_text(query_context)

)

# Temporal relevance decay

age_factor = exp(-0.1 * days_since_update(document))

return entropy * similarity * age_factor

ГЛАВА 2: ADVANCED RAG ARCHITECTURES

2.1 Multi-step Retrieval Pipeline

Классический RAG (Retrieval-Augmented Generation) эволюционировал в sophisticated multi-stage systems:

Query → Intent Classification → Multi-vector Search →

→ Re-ranking → Context Assembly → LLM Generation

Техническая архитектура:

class AdvancedRAG:

def __init__(self):

self.intent_classifier = IntentClassifier()

self.vector_stores = {

'dense': PineconeVectorStore(),

'sparse': ElasticsearchStore(),

'hybrid': WeaviateStore()

}

self.reranker = CrossEncoderReranker()

async def retrieve_and_generate(self, query: str) -> str:

# Multi-stage retrieval

intent = await self.intent_classifier.predict(query)

# Parallel retrieval from multiple stores

tasks = [

self.dense_search(query, intent),

self.sparse_search(query, intent),

self.hybrid_search(query, intent)

]

raw_results = await asyncio.gather(*tasks)

# Fusion и re-ranking

fused_results = self.reciprocal_rank_fusion(raw_results)

reranked = await self.reranker.rerank(query, fused_results)

# Context assembly с учетом token limits

context = self.assemble_context(reranked, max_tokens=8000)

return await self.llm.generate(query, context)

2.2 Agentic RAG с Tool Use

Следующее поколение RAG-систем использует agentic workflows:

class AgenticRAG:

def __init__(self):

self.tools = {

'vector_search': VectorSearchTool(),

'sql_query': SQLQueryTool(),

'api_call': APICallTool(),

'web_search': WebSearchTool()

}

async def process_query(self, query: str) -> str:

# LLM планирует последовательность actions

plan = await self.planner.create_plan(query, self.tools)

results = []

for action in plan.actions:

if action.tool == 'vector_search':

result = await self.vector_search(action.parameters)

elif action.tool == 'sql_query':

result = await self.execute_sql(action.parameters)

# ... другие tools

results.append(result)

# Adaptive planning - корректировка плана на основе промежуточных результатов

if self.should_replan(results, plan):

plan = await self.planner.replan(query, results, remaining_actions)

return self.synthesize_response(query, results)

ГЛАВА 3: ВЕКТОРИЗАЦИЯ И EMBEDDING STRATEGIES

3.1 Domain-specific Embeddings

Универсальные embedding модели (text-embedding-ada-002) показывают субоптимальную performance на специализированных доменах.

Стратегия fine-tuning:

def create_domain_embeddings(domain_corpus, base_model="sentence-transformers/all-MiniLM-L6-v2"):

# Contrastive learning setup

train_examples = []

for doc in domain_corpus:

# Positive pairs - semantically related chunks

chunks = semantic_chunking(doc)

for i, chunk in enumerate(chunks):

if i > 0:

train_examples.append(InputExample(

texts=[chunks[i-1], chunk],

label=0.8 # High similarity

))

# Hard negative mining

negative_chunk = find_hard_negative(chunk, domain_corpus)

train_examples.append(InputExample(

texts=[chunk, negative_chunk],

label=0.1 # Low similarity

))

# Fine-tune model

model = SentenceTransformer(base_model)

model.fit(train_examples, epochs=3, warmup_steps=1000)

return model

3.2 Hybrid Dense-Sparse Retrieval

Комбинирование dense (semantic) и sparse (keyword-based) search для optimal recall:

class HybridRetriever:

def __init__(self, alpha=0.7):

self.dense_retriever = DensePassageRetriever()

self.sparse_retriever = BM25Retriever()

self.alpha = alpha # Weight for dense vs sparse

def search(self, query: str, k: int = 10) -> List[Document]:

# Parallel retrieval

dense_scores = self.dense_retriever.search(query, k=k*2)

sparse_scores = self.sparse_retriever.search(query, k=k*2)

# Score normalization

dense_scores = self.normalize_scores(dense_scores)

sparse_scores = self.normalize_scores(sparse_scores)

# Weighted combination

combined_scores = {}

for doc_id, score in dense_scores.items():

combined_scores[doc_id] = self.alpha * score

for doc_id, score in sparse_scores.items():

if doc_id in combined_scores:

combined_scores[doc_id] += (1 - self.alpha) * score

else:

combined_scores[doc_id] = (1 - self.alpha) * score

# Return top-k results

return sorted(combined_scores.items(), key=lambda x: x[1], reverse=True)[:k]

ГЛАВА 4: ПРОДВИНУТЫЕ ТЕХНИКИ КОНТЕКСТНОЙ ПАМЯТИ

4.1 Hierarchical Memory Architecture

Inspiration от human cognitive architecture:

class HierarchicalMemory:

def __init__(self):

self.sensory_buffer = SensoryBuffer(capacity=100, ttl=60) # 1 min

self.working_memory = WorkingMemory(capacity=7, ttl=3600) # 1 hour

self.long_term_memory = LongTermMemory() # Persistent

def encode_interaction(self, user_input: str, system_response: str, user_id: str):

# Sensory buffer - raw interaction

interaction = Interaction(user_input, system_response, timestamp=now())

self.sensory_buffer.add(interaction)

# Working memory - processed concepts

concepts = self.extract_concepts(user_input)

for concept in concepts:

self.working_memory.activate(concept, strength=0.8)

# Long-term memory - consolidated patterns

if self.should_consolidate(user_id):

patterns = self.extract_patterns(user_id)

self.long_term_memory.store(user_id, patterns)

def retrieve_context(self, query: str, user_id: str) -> str:

# Multi-level retrieval

sensory_context = self.sensory_buffer.search(query)

working_context = self.working_memory.get_active_concepts()

ltm_context = self.long_term_memory.retrieve(user_id, query)

return self.merge_contexts(sensory_context, working_context, ltm_context)

4.2 Attention-based Context Selection

Механизм attention для dynamic context assembly:

class AttentionBasedContextSelector:

def __init__(self, d_model=512):

self.attention = MultiHeadAttention(d_model, num_heads=8)

self.context_encoder = ContextEncoder(d_model)

def select_context(self, query: str, candidate_docs: List[Document], max_tokens: int) -> List[Document]:

# Encode query and documents

query_emb = self.context_encoder.encode(query)

doc_embs = [self.context_encoder.encode(doc.content) for doc in candidate_docs]

# Attention mechanism для scoring relevance

attention_scores = self.attention(

query=query_emb.unsqueeze(0),

key=torch.stack(doc_embs),

value=torch.stack(doc_embs)

)

# Greedy selection с учетом token budget

selected_docs = []

current_tokens = 0

for idx in attention_scores.argsort(descending=True):

doc = candidate_docs[idx]

doc_tokens = count_tokens(doc.content)

if current_tokens + doc_tokens <= max_tokens:

selected_docs.append(doc)

current_tokens += doc_tokens

else:

break

return selected_docs

ГЛАВА 5: PRODUCTION DEPLOYMENT И OPTIMIZATION

5.1 Latency Optimization Techniques

Production контекстные системы требуют sub-second response times:

class OptimizedContextEngine:

def __init__(self):

self.vector_cache = RedisVectorCache()

self.embedding_service = BatchEmbeddingService() # Batch inference

self.async_preprocessor = AsyncTextPreprocessor()

async def process_query(self, query: str) -> str:

# Preprocessing pipeline с async operations

tasks = [

self.async_preprocessor.clean_text(query),

self.embedding_service.embed_async(query),

self.vector_cache.warm_cache(query) # Predictive caching

]

clean_query, query_embedding, _ = await asyncio.gather(*tasks)

# Parallel retrieval с connection pooling

retrieval_tasks = [

self.vector_search_async(query_embedding),

self.keyword_search_async(clean_query),

self.hybrid_search_async(query_embedding, clean_query)

]

search_results = await asyncio.gather(*retrieval_tasks)

# Fast fusion algorithm

fused_results = self.fast_reciprocal_rank_fusion(search_results)

return await self.generate_response(query, fused_results)

5.2 Monitoring и Observability

Comprehensive monitoring для production систем:

class ContextEngineMonitor:

def __init__(self):

self.metrics_collector = PrometheusMetrics()

self.trace_collector = JaegerTracer()

self.alert_manager = AlertManager()

@trace("context_retrieval")

@monitor_latency("retrieval_latency_seconds")

async def monitored_retrieval(self, query: str):

start_time = time.time()

try:

results = await self.retrieve_context(query)

# Quality metrics

self.metrics_collector.histogram(

"context_relevance_score",

self.calculate_relevance(query, results)

)

return results

except Exception as e:

self.alert_manager.send_alert(f"Retrieval failed: {str(e)}")

raise

finally:

latency = time.time() - start_time

self.metrics_collector.histogram("retrieval_latency_seconds", latency)

ГЛАВА 6: EVALUATION И QUALITY ASSURANCE

6.1 Automated Quality Metrics

Systematic evaluation framework:

class ContextQualityEvaluator:

def __init__(self):

self.retrieval_evaluator = RetrievalEvaluator()

self.generation_evaluator = GenerationEvaluator()

self.faithfulness_checker = FaithfulnessChecker()

def evaluate_system(self, test_queries: List[str], ground_truth: List[str]) -> Dict:

metrics = {}

for query, expected in zip(test_queries, ground_truth):

# Retrieval evaluation

retrieved_docs = self.system.retrieve(query)

metrics['retrieval'] = {

'precision_at_k': self.retrieval_evaluator.precision_at_k(retrieved_docs, expected, k=5),

'recall_at_k': self.retrieval_evaluator.recall_at_k(retrieved_docs, expected, k=5),

'mrr': self.retrieval_evaluator.mean_reciprocal_rank(retrieved_docs, expected)

}

# Generation evaluation

response = self.system.generate(query)

metrics['generation'] = {

'bleu_score': self.generation_evaluator.bleu(response, expected),

'rouge_score': self.generation_evaluator.rouge(response, expected),

'bert_score': self.generation_evaluator.bert_score(response, expected)

}

# Faithfulness check

metrics['faithfulness'] = self.faithfulness_checker.check(response, retrieved_docs)

return self.aggregate_metrics(metrics)

6.2 A/B Testing Framework

Continuous improvement через experimentation:

class ContextSystemABTesting:

def __init__(self):

self.experiment_tracker = ExperimentTracker()

self.statistical_engine = StatisticalEngine()

def create_experiment(self, name: str, treatment_config: Dict, control_config: Dict):

experiment = Experiment(

name=name,

treatment=ContextSystem(treatment_config),

control=ContextSystem(control_config),

traffic_split=0.5,

success_metrics=['response_quality', 'user_satisfaction', 'task_completion']

)

self.experiment_tracker.register(experiment)

async def route_traffic(self, user_request: Request) -> Response:

# Determine experiment assignment

experiment = self.experiment_tracker.get_active_experiment(user_request.path)

if experiment and self.should_include_user(user_request.user_id, experiment):

variant = self.assign_variant(user_request.user_id, experiment)

system = experiment.treatment if variant == 'treatment' else experiment.control

else:

system = self.default_system

response = await system.process(user_request)

# Log experiment data

if experiment:

self.experiment_tracker.log_interaction(

experiment.name, variant, user_request, response

)

return response

ГЛАВА 7: ENTERPRISE INTEGRATION PATTERNS

7.1 Microservices Architecture

Scalable deployment pattern:

# docker-compose.yml

version: '3.8'

services:

context-api:

image: context-engine:latest

ports:

- "8000:8000"

environment:

- VECTOR_DB_URL=http://vector-db:8080

- CACHE_URL=redis://cache:6379

depends_on:

- vector-db

- cache

vector-db:

image: weaviate/weaviate:latest

ports:

- "8080:8080"

volumes:

- vector_data:/var/lib/weaviate

cache:

image: redis:alpine

ports:

- "6379:6379"

volumes:

- cache_data:/data

preprocessing-worker:

image: preprocessing-worker:latest

environment:

- QUEUE_URL=redis://cache:6379

depends_on:

- cache

7.2 Security и Compliance

Data governance для enterprise deployment:

class SecureContextEngine:

def __init__(self):

self.access_control = RoleBasedAccessControl()

self.audit_logger = AuditLogger()

self.data_classifier = DataClassifier()

self.encryption = FieldLevelEncryption()

@require_permission("context.read")

@audit_log("context_access")

async def get_context(self, query: str, user: User) -> str:

# Data classification

sensitivity = self.data_classifier.classify(query)

if sensitivity == "PII" and not user.has_permission("pii.access"):

raise PermissionDenied("Insufficient permissions for PII access")

# Retrieve context с учетом user permissions

raw_context = await self.retrieve_context(query, user.access_level)

# Apply data masking if needed

masked_context = self.apply_data_masking(raw_context, user.clearance_level)

# Encrypt sensitive fields

encrypted_context = self.encryption.encrypt_sensitive_fields(masked_context)

return encrypted_context

ЗАКЛЮЧЕНИЕ: ROADMAP РАЗВИТИЯ КОНТЕКСТ-ИНЖИНИРИНГА

Краткосрочная перспектива (6-12 месяцев):

Multimodal context engines (text + images + audio)
Real-time learning и adaptation
Advanced reasoning capabilities через tool use

Среднесрочная перспектива (1-2 года):

Fully autonomous context management
Cross-domain knowledge transfer
Neuro-symbolic hybrid approaches

Долгосрочная перспектива (2-5 лет):

AGI-ready context architectures
Quantum-enhanced information retrieval
Federated learning для distributed knowledge

Ключевые навыки для развития:

Vector database administration
Distributed systems architecture
ML operations и model lifecycle management
Information theory и cognitive science basics

Автор: Евгений Романов. Практик AI-автоматизации | Опыт внедрения: 100+ проектов. https://t.me/insighter13