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fleet-memory/hindsight-api/hindsight_api/config.py
Nicolò Boschi 9db64ecda3
feat: revisit mental models, directives and reflections (#179) 
* chore: run benchmarks with reflect mode

* chore: run benchmarks with reflect mode

* fixes

* new mm

* bunch of fixes

* initial commit

* fixes

* fixes

* fixes

* fix: sometimes memories gets extracted in the wrong language
2026-01-22 17:13:16 +01:00

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"""
Centralized configuration for Hindsight API.
All environment variables and their defaults are defined here.
"""
import json
import logging
import os
import sys
from dataclasses import dataclass
from datetime import datetime, timezone
from dotenv import find_dotenv, load_dotenv
# Load .env file, searching current and parent directories (overrides existing env vars)
load_dotenv(find_dotenv(usecwd=True), override=True)
logger = logging.getLogger(__name__)
# Environment variable names
ENV_DATABASE_URL = "HINDSIGHT_API_DATABASE_URL"
ENV_LLM_PROVIDER = "HINDSIGHT_API_LLM_PROVIDER"
ENV_LLM_API_KEY = "HINDSIGHT_API_LLM_API_KEY"
ENV_LLM_MODEL = "HINDSIGHT_API_LLM_MODEL"
ENV_LLM_BASE_URL = "HINDSIGHT_API_LLM_BASE_URL"
ENV_LLM_MAX_CONCURRENT = "HINDSIGHT_API_LLM_MAX_CONCURRENT"
ENV_LLM_TIMEOUT = "HINDSIGHT_API_LLM_TIMEOUT"
ENV_LLM_GROQ_SERVICE_TIER = "HINDSIGHT_API_LLM_GROQ_SERVICE_TIER"
# Per-operation LLM configuration (optional, falls back to global LLM config)
ENV_RETAIN_LLM_PROVIDER = "HINDSIGHT_API_RETAIN_LLM_PROVIDER"
ENV_RETAIN_LLM_API_KEY = "HINDSIGHT_API_RETAIN_LLM_API_KEY"
ENV_RETAIN_LLM_MODEL = "HINDSIGHT_API_RETAIN_LLM_MODEL"
ENV_RETAIN_LLM_BASE_URL = "HINDSIGHT_API_RETAIN_LLM_BASE_URL"
ENV_REFLECT_LLM_PROVIDER = "HINDSIGHT_API_REFLECT_LLM_PROVIDER"
ENV_REFLECT_LLM_API_KEY = "HINDSIGHT_API_REFLECT_LLM_API_KEY"
ENV_REFLECT_LLM_MODEL = "HINDSIGHT_API_REFLECT_LLM_MODEL"
ENV_REFLECT_LLM_BASE_URL = "HINDSIGHT_API_REFLECT_LLM_BASE_URL"
ENV_EMBEDDINGS_PROVIDER = "HINDSIGHT_API_EMBEDDINGS_PROVIDER"
ENV_EMBEDDINGS_LOCAL_MODEL = "HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL"
ENV_EMBEDDINGS_TEI_URL = "HINDSIGHT_API_EMBEDDINGS_TEI_URL"
ENV_EMBEDDINGS_OPENAI_API_KEY = "HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY"
ENV_EMBEDDINGS_OPENAI_MODEL = "HINDSIGHT_API_EMBEDDINGS_OPENAI_MODEL"
ENV_EMBEDDINGS_OPENAI_BASE_URL = "HINDSIGHT_API_EMBEDDINGS_OPENAI_BASE_URL"
ENV_COHERE_API_KEY = "HINDSIGHT_API_COHERE_API_KEY"
ENV_EMBEDDINGS_COHERE_MODEL = "HINDSIGHT_API_EMBEDDINGS_COHERE_MODEL"
ENV_EMBEDDINGS_COHERE_BASE_URL = "HINDSIGHT_API_EMBEDDINGS_COHERE_BASE_URL"
ENV_RERANKER_COHERE_MODEL = "HINDSIGHT_API_RERANKER_COHERE_MODEL"
ENV_RERANKER_COHERE_BASE_URL = "HINDSIGHT_API_RERANKER_COHERE_BASE_URL"
# LiteLLM gateway configuration (for embeddings and reranker via LiteLLM proxy)
ENV_LITELLM_API_BASE = "HINDSIGHT_API_LITELLM_API_BASE"
ENV_LITELLM_API_KEY = "HINDSIGHT_API_LITELLM_API_KEY"
ENV_EMBEDDINGS_LITELLM_MODEL = "HINDSIGHT_API_EMBEDDINGS_LITELLM_MODEL"
ENV_RERANKER_LITELLM_MODEL = "HINDSIGHT_API_RERANKER_LITELLM_MODEL"
ENV_RERANKER_PROVIDER = "HINDSIGHT_API_RERANKER_PROVIDER"
ENV_RERANKER_LOCAL_MODEL = "HINDSIGHT_API_RERANKER_LOCAL_MODEL"
ENV_RERANKER_LOCAL_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_LOCAL_MAX_CONCURRENT"
ENV_RERANKER_TEI_URL = "HINDSIGHT_API_RERANKER_TEI_URL"
ENV_RERANKER_TEI_BATCH_SIZE = "HINDSIGHT_API_RERANKER_TEI_BATCH_SIZE"
ENV_RERANKER_TEI_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_TEI_MAX_CONCURRENT"
ENV_RERANKER_MAX_CANDIDATES = "HINDSIGHT_API_RERANKER_MAX_CANDIDATES"
ENV_RERANKER_FLASHRANK_MODEL = "HINDSIGHT_API_RERANKER_FLASHRANK_MODEL"
ENV_RERANKER_FLASHRANK_CACHE_DIR = "HINDSIGHT_API_RERANKER_FLASHRANK_CACHE_DIR"
ENV_HOST = "HINDSIGHT_API_HOST"
ENV_PORT = "HINDSIGHT_API_PORT"
ENV_LOG_LEVEL = "HINDSIGHT_API_LOG_LEVEL"
ENV_LOG_FORMAT = "HINDSIGHT_API_LOG_FORMAT"
ENV_WORKERS = "HINDSIGHT_API_WORKERS"
ENV_MCP_ENABLED = "HINDSIGHT_API_MCP_ENABLED"
ENV_GRAPH_RETRIEVER = "HINDSIGHT_API_GRAPH_RETRIEVER"
ENV_MPFP_TOP_K_NEIGHBORS = "HINDSIGHT_API_MPFP_TOP_K_NEIGHBORS"
ENV_RECALL_MAX_CONCURRENT = "HINDSIGHT_API_RECALL_MAX_CONCURRENT"
ENV_RECALL_CONNECTION_BUDGET = "HINDSIGHT_API_RECALL_CONNECTION_BUDGET"
ENV_MCP_LOCAL_BANK_ID = "HINDSIGHT_API_MCP_LOCAL_BANK_ID"
ENV_MCP_INSTRUCTIONS = "HINDSIGHT_API_MCP_INSTRUCTIONS"
ENV_MENTAL_MODEL_REFRESH_CONCURRENCY = "HINDSIGHT_API_MENTAL_MODEL_REFRESH_CONCURRENCY"
# Observation thresholds
ENV_OBSERVATION_MIN_FACTS = "HINDSIGHT_API_OBSERVATION_MIN_FACTS"
ENV_OBSERVATION_TOP_ENTITIES = "HINDSIGHT_API_OBSERVATION_TOP_ENTITIES"
# Retain settings
ENV_RETAIN_MAX_COMPLETION_TOKENS = "HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS"
ENV_RETAIN_CHUNK_SIZE = "HINDSIGHT_API_RETAIN_CHUNK_SIZE"
ENV_RETAIN_EXTRACT_CAUSAL_LINKS = "HINDSIGHT_API_RETAIN_EXTRACT_CAUSAL_LINKS"
ENV_RETAIN_EXTRACTION_MODE = "HINDSIGHT_API_RETAIN_EXTRACTION_MODE"
ENV_RETAIN_OBSERVATIONS_ASYNC = "HINDSIGHT_API_RETAIN_OBSERVATIONS_ASYNC"
# Mental models settings
ENV_ENABLE_MENTAL_MODELS = "HINDSIGHT_API_ENABLE_MENTAL_MODELS"
ENV_CONSOLIDATION_SIMILARITY_THRESHOLD = "HINDSIGHT_API_CONSOLIDATION_SIMILARITY_THRESHOLD"
ENV_CONSOLIDATION_BATCH_SIZE = "HINDSIGHT_API_CONSOLIDATION_BATCH_SIZE"
# Optimization flags
ENV_SKIP_LLM_VERIFICATION = "HINDSIGHT_API_SKIP_LLM_VERIFICATION"
ENV_LAZY_RERANKER = "HINDSIGHT_API_LAZY_RERANKER"
# Database migrations
ENV_RUN_MIGRATIONS_ON_STARTUP = "HINDSIGHT_API_RUN_MIGRATIONS_ON_STARTUP"
# Database connection pool
ENV_DB_POOL_MIN_SIZE = "HINDSIGHT_API_DB_POOL_MIN_SIZE"
ENV_DB_POOL_MAX_SIZE = "HINDSIGHT_API_DB_POOL_MAX_SIZE"
ENV_DB_COMMAND_TIMEOUT = "HINDSIGHT_API_DB_COMMAND_TIMEOUT"
ENV_DB_ACQUIRE_TIMEOUT = "HINDSIGHT_API_DB_ACQUIRE_TIMEOUT"
# Worker configuration (distributed task processing)
ENV_WORKER_ENABLED = "HINDSIGHT_API_WORKER_ENABLED"
ENV_WORKER_ID = "HINDSIGHT_API_WORKER_ID"
ENV_WORKER_POLL_INTERVAL_MS = "HINDSIGHT_API_WORKER_POLL_INTERVAL_MS"
ENV_WORKER_MAX_RETRIES = "HINDSIGHT_API_WORKER_MAX_RETRIES"
ENV_WORKER_BATCH_SIZE = "HINDSIGHT_API_WORKER_BATCH_SIZE"
ENV_WORKER_HTTP_PORT = "HINDSIGHT_API_WORKER_HTTP_PORT"
# Reflect agent settings
ENV_REFLECT_MAX_ITERATIONS = "HINDSIGHT_API_REFLECT_MAX_ITERATIONS"
# Default values
DEFAULT_DATABASE_URL = "pg0"
DEFAULT_LLM_PROVIDER = "openai"
DEFAULT_LLM_MODEL = "gpt-5-mini"
DEFAULT_LLM_MAX_CONCURRENT = 32
DEFAULT_LLM_TIMEOUT = 120.0 # seconds
DEFAULT_EMBEDDINGS_PROVIDER = "local"
DEFAULT_EMBEDDINGS_LOCAL_MODEL = "BAAI/bge-small-en-v1.5"
DEFAULT_EMBEDDINGS_OPENAI_MODEL = "text-embedding-3-small"
DEFAULT_EMBEDDING_DIMENSION = 384
DEFAULT_RERANKER_PROVIDER = "local"
DEFAULT_RERANKER_LOCAL_MODEL = "cross-encoder/ms-marco-MiniLM-L-6-v2"
DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT = 4 # Limit concurrent CPU-bound reranking to prevent thrashing
DEFAULT_RERANKER_TEI_BATCH_SIZE = 128
DEFAULT_RERANKER_TEI_MAX_CONCURRENT = 8
DEFAULT_RERANKER_MAX_CANDIDATES = 300
DEFAULT_RERANKER_FLASHRANK_MODEL = "ms-marco-MiniLM-L-12-v2" # Best balance of speed and quality
DEFAULT_RERANKER_FLASHRANK_CACHE_DIR = None # Use default cache directory
DEFAULT_EMBEDDINGS_COHERE_MODEL = "embed-english-v3.0"
DEFAULT_RERANKER_COHERE_MODEL = "rerank-english-v3.0"
# LiteLLM defaults
DEFAULT_LITELLM_API_BASE = "http://localhost:4000"
DEFAULT_EMBEDDINGS_LITELLM_MODEL = "text-embedding-3-small"
DEFAULT_RERANKER_LITELLM_MODEL = "cohere/rerank-english-v3.0"
DEFAULT_HOST = "0.0.0.0"
DEFAULT_PORT = 8888
DEFAULT_LOG_LEVEL = "info"
DEFAULT_LOG_FORMAT = "text" # Options: "text", "json"
DEFAULT_WORKERS = 1
DEFAULT_MCP_ENABLED = True
DEFAULT_GRAPH_RETRIEVER = "link_expansion" # Options: "link_expansion", "mpfp", "bfs"
DEFAULT_MPFP_TOP_K_NEIGHBORS = 20 # Fan-out limit per node in MPFP graph traversal
DEFAULT_RECALL_MAX_CONCURRENT = 32 # Max concurrent recall operations per worker
DEFAULT_RECALL_CONNECTION_BUDGET = 4 # Max concurrent DB connections per recall operation
DEFAULT_MCP_LOCAL_BANK_ID = "mcp"
DEFAULT_MENTAL_MODEL_REFRESH_CONCURRENCY = 8 # Max concurrent mental model refreshes
# Observation thresholds
DEFAULT_OBSERVATION_MIN_FACTS = 5 # Min facts required to generate entity observations
DEFAULT_OBSERVATION_TOP_ENTITIES = 5 # Max entities to process per retain batch
# Retain settings
DEFAULT_RETAIN_MAX_COMPLETION_TOKENS = 64000 # Max tokens for fact extraction LLM call
DEFAULT_RETAIN_CHUNK_SIZE = 3000 # Max chars per chunk for fact extraction
DEFAULT_RETAIN_EXTRACT_CAUSAL_LINKS = True # Extract causal links between facts
DEFAULT_RETAIN_EXTRACTION_MODE = "concise" # Extraction mode: "concise" or "verbose"
RETAIN_EXTRACTION_MODES = ("concise", "verbose") # Allowed extraction modes
DEFAULT_RETAIN_OBSERVATIONS_ASYNC = False # Run observation generation async (after retain completes)
# Mental models defaults
DEFAULT_ENABLE_MENTAL_MODELS = False # Mental models disabled by default (experimental)
DEFAULT_CONSOLIDATION_SIMILARITY_THRESHOLD = 0.75 # Minimum similarity to consider a learning related
DEFAULT_CONSOLIDATION_BATCH_SIZE = 50 # Memories to load per batch (internal memory optimization)
# Database migrations
DEFAULT_RUN_MIGRATIONS_ON_STARTUP = True
# Database connection pool
DEFAULT_DB_POOL_MIN_SIZE = 5
DEFAULT_DB_POOL_MAX_SIZE = 100
DEFAULT_DB_COMMAND_TIMEOUT = 60 # seconds
DEFAULT_DB_ACQUIRE_TIMEOUT = 30 # seconds
# Worker configuration (distributed task processing)
DEFAULT_WORKER_ENABLED = True # API runs worker by default (standalone mode)
DEFAULT_WORKER_ID = None # Will use hostname if not specified
DEFAULT_WORKER_POLL_INTERVAL_MS = 500 # Poll database every 500ms
DEFAULT_WORKER_MAX_RETRIES = 3 # Max retries before marking task failed
DEFAULT_WORKER_BATCH_SIZE = 10 # Tasks to claim per poll cycle
DEFAULT_WORKER_HTTP_PORT = 8889 # HTTP port for worker metrics/health
# Reflect agent settings
DEFAULT_REFLECT_MAX_ITERATIONS = 10 # Max tool call iterations before forcing response
# Default MCP tool descriptions (can be customized via env vars)
DEFAULT_MCP_RETAIN_DESCRIPTION = """Store important information to long-term memory.
Use this tool PROACTIVELY whenever the user shares:
- Personal facts, preferences, or interests
- Important events or milestones
- User history, experiences, or background
- Decisions, opinions, or stated preferences
- Goals, plans, or future intentions
- Relationships or people mentioned
- Work context, projects, or responsibilities"""
DEFAULT_MCP_RECALL_DESCRIPTION = """Search memories to provide personalized, context-aware responses.
Use this tool PROACTIVELY to:
- Check user's preferences before making suggestions
- Recall user's history to provide continuity
- Remember user's goals and context
- Personalize responses based on past interactions"""
# Default embedding dimension (used by initial migration, adjusted at runtime)
EMBEDDING_DIMENSION = DEFAULT_EMBEDDING_DIMENSION
class JsonFormatter(logging.Formatter):
"""JSON formatter for structured logging.
Outputs logs in JSON format with a 'severity' field that cloud logging
systems (GCP, AWS CloudWatch, etc.) can parse to correctly categorize log levels.
"""
SEVERITY_MAP = {
logging.DEBUG: "DEBUG",
logging.INFO: "INFO",
logging.WARNING: "WARNING",
logging.ERROR: "ERROR",
logging.CRITICAL: "CRITICAL",
}
def format(self, record: logging.LogRecord) -> str:
log_entry = {
"severity": self.SEVERITY_MAP.get(record.levelno, "DEFAULT"),
"message": record.getMessage(),
"timestamp": datetime.now(timezone.utc).isoformat(),
"logger": record.name,
}
# Add exception info if present
if record.exc_info:
log_entry["exception"] = self.formatException(record.exc_info)
return json.dumps(log_entry)
def _validate_extraction_mode(mode: str) -> str:
"""Validate and normalize extraction mode."""
mode_lower = mode.lower()
if mode_lower not in RETAIN_EXTRACTION_MODES:
logger.warning(
f"Invalid extraction mode '{mode}', must be one of {RETAIN_EXTRACTION_MODES}. "
f"Defaulting to '{DEFAULT_RETAIN_EXTRACTION_MODE}'."
)
return DEFAULT_RETAIN_EXTRACTION_MODE
return mode_lower
@dataclass
class HindsightConfig:
"""Configuration container for Hindsight API."""
# Database
database_url: str
# LLM (default, used as fallback for per-operation config)
llm_provider: str
llm_api_key: str | None
llm_model: str
llm_base_url: str | None
llm_max_concurrent: int
llm_timeout: float
# Per-operation LLM configuration (None = use default LLM config)
retain_llm_provider: str | None
retain_llm_api_key: str | None
retain_llm_model: str | None
retain_llm_base_url: str | None
reflect_llm_provider: str | None
reflect_llm_api_key: str | None
reflect_llm_model: str | None
reflect_llm_base_url: str | None
# Embeddings
embeddings_provider: str
embeddings_local_model: str
embeddings_tei_url: str | None
embeddings_openai_base_url: str | None
embeddings_cohere_base_url: str | None
# Reranker
reranker_provider: str
reranker_local_model: str
reranker_tei_url: str | None
reranker_tei_batch_size: int
reranker_tei_max_concurrent: int
reranker_max_candidates: int
reranker_cohere_base_url: str | None
# Server
host: str
port: int
log_level: str
log_format: str
mcp_enabled: bool
# Recall
graph_retriever: str
mpfp_top_k_neighbors: int
recall_max_concurrent: int
recall_connection_budget: int
mental_model_refresh_concurrency: int
# Observation thresholds
observation_min_facts: int
observation_top_entities: int
# Retain settings
retain_max_completion_tokens: int
retain_chunk_size: int
retain_extract_causal_links: bool
retain_extraction_mode: str
retain_observations_async: bool
# Mental models settings
enable_mental_models: bool
consolidation_similarity_threshold: float
consolidation_batch_size: int
# Optimization flags
skip_llm_verification: bool
lazy_reranker: bool
# Database migrations
run_migrations_on_startup: bool
# Database connection pool
db_pool_min_size: int
db_pool_max_size: int
db_command_timeout: int
db_acquire_timeout: int
# Worker configuration (distributed task processing)
worker_enabled: bool
worker_id: str | None
worker_poll_interval_ms: int
worker_max_retries: int
worker_batch_size: int
worker_http_port: int
# Reflect agent settings
reflect_max_iterations: int
@classmethod
def from_env(cls) -> "HindsightConfig":
"""Create configuration from environment variables."""
return cls(
# Database
database_url=os.getenv(ENV_DATABASE_URL, DEFAULT_DATABASE_URL),
# LLM
llm_provider=os.getenv(ENV_LLM_PROVIDER, DEFAULT_LLM_PROVIDER),
llm_api_key=os.getenv(ENV_LLM_API_KEY),
llm_model=os.getenv(ENV_LLM_MODEL, DEFAULT_LLM_MODEL),
llm_base_url=os.getenv(ENV_LLM_BASE_URL) or None,
llm_max_concurrent=int(os.getenv(ENV_LLM_MAX_CONCURRENT, str(DEFAULT_LLM_MAX_CONCURRENT))),
llm_timeout=float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT))),
# Per-operation LLM config (None = use default)
retain_llm_provider=os.getenv(ENV_RETAIN_LLM_PROVIDER) or None,
retain_llm_api_key=os.getenv(ENV_RETAIN_LLM_API_KEY) or None,
retain_llm_model=os.getenv(ENV_RETAIN_LLM_MODEL) or None,
retain_llm_base_url=os.getenv(ENV_RETAIN_LLM_BASE_URL) or None,
reflect_llm_provider=os.getenv(ENV_REFLECT_LLM_PROVIDER) or None,
reflect_llm_api_key=os.getenv(ENV_REFLECT_LLM_API_KEY) or None,
reflect_llm_model=os.getenv(ENV_REFLECT_LLM_MODEL) or None,
reflect_llm_base_url=os.getenv(ENV_REFLECT_LLM_BASE_URL) or None,
# Embeddings
embeddings_provider=os.getenv(ENV_EMBEDDINGS_PROVIDER, DEFAULT_EMBEDDINGS_PROVIDER),
embeddings_local_model=os.getenv(ENV_EMBEDDINGS_LOCAL_MODEL, DEFAULT_EMBEDDINGS_LOCAL_MODEL),
embeddings_tei_url=os.getenv(ENV_EMBEDDINGS_TEI_URL),
embeddings_openai_base_url=os.getenv(ENV_EMBEDDINGS_OPENAI_BASE_URL) or None,
embeddings_cohere_base_url=os.getenv(ENV_EMBEDDINGS_COHERE_BASE_URL) or None,
# Reranker
reranker_provider=os.getenv(ENV_RERANKER_PROVIDER, DEFAULT_RERANKER_PROVIDER),
reranker_local_model=os.getenv(ENV_RERANKER_LOCAL_MODEL, DEFAULT_RERANKER_LOCAL_MODEL),
reranker_tei_url=os.getenv(ENV_RERANKER_TEI_URL),
reranker_tei_batch_size=int(os.getenv(ENV_RERANKER_TEI_BATCH_SIZE, str(DEFAULT_RERANKER_TEI_BATCH_SIZE))),
reranker_tei_max_concurrent=int(
os.getenv(ENV_RERANKER_TEI_MAX_CONCURRENT, str(DEFAULT_RERANKER_TEI_MAX_CONCURRENT))
),
reranker_max_candidates=int(os.getenv(ENV_RERANKER_MAX_CANDIDATES, str(DEFAULT_RERANKER_MAX_CANDIDATES))),
reranker_cohere_base_url=os.getenv(ENV_RERANKER_COHERE_BASE_URL) or None,
# Server
host=os.getenv(ENV_HOST, DEFAULT_HOST),
port=int(os.getenv(ENV_PORT, DEFAULT_PORT)),
log_level=os.getenv(ENV_LOG_LEVEL, DEFAULT_LOG_LEVEL),
log_format=os.getenv(ENV_LOG_FORMAT, DEFAULT_LOG_FORMAT).lower(),
mcp_enabled=os.getenv(ENV_MCP_ENABLED, str(DEFAULT_MCP_ENABLED)).lower() == "true",
# Recall
graph_retriever=os.getenv(ENV_GRAPH_RETRIEVER, DEFAULT_GRAPH_RETRIEVER),
mpfp_top_k_neighbors=int(os.getenv(ENV_MPFP_TOP_K_NEIGHBORS, str(DEFAULT_MPFP_TOP_K_NEIGHBORS))),
recall_max_concurrent=int(os.getenv(ENV_RECALL_MAX_CONCURRENT, str(DEFAULT_RECALL_MAX_CONCURRENT))),
recall_connection_budget=int(
os.getenv(ENV_RECALL_CONNECTION_BUDGET, str(DEFAULT_RECALL_CONNECTION_BUDGET))
),
mental_model_refresh_concurrency=int(
os.getenv(ENV_MENTAL_MODEL_REFRESH_CONCURRENCY, str(DEFAULT_MENTAL_MODEL_REFRESH_CONCURRENCY))
),
# Optimization flags
skip_llm_verification=os.getenv(ENV_SKIP_LLM_VERIFICATION, "false").lower() == "true",
lazy_reranker=os.getenv(ENV_LAZY_RERANKER, "false").lower() == "true",
# Observation thresholds
observation_min_facts=int(os.getenv(ENV_OBSERVATION_MIN_FACTS, str(DEFAULT_OBSERVATION_MIN_FACTS))),
observation_top_entities=int(
os.getenv(ENV_OBSERVATION_TOP_ENTITIES, str(DEFAULT_OBSERVATION_TOP_ENTITIES))
),
# Retain settings
retain_max_completion_tokens=int(
os.getenv(ENV_RETAIN_MAX_COMPLETION_TOKENS, str(DEFAULT_RETAIN_MAX_COMPLETION_TOKENS))
),
retain_chunk_size=int(os.getenv(ENV_RETAIN_CHUNK_SIZE, str(DEFAULT_RETAIN_CHUNK_SIZE))),
retain_extract_causal_links=os.getenv(
ENV_RETAIN_EXTRACT_CAUSAL_LINKS, str(DEFAULT_RETAIN_EXTRACT_CAUSAL_LINKS)
).lower()
== "true",
retain_extraction_mode=_validate_extraction_mode(
os.getenv(ENV_RETAIN_EXTRACTION_MODE, DEFAULT_RETAIN_EXTRACTION_MODE)
),
retain_observations_async=os.getenv(
ENV_RETAIN_OBSERVATIONS_ASYNC, str(DEFAULT_RETAIN_OBSERVATIONS_ASYNC)
).lower()
== "true",
# Mental models settings
enable_mental_models=os.getenv(ENV_ENABLE_MENTAL_MODELS, str(DEFAULT_ENABLE_MENTAL_MODELS)).lower()
== "true",
consolidation_similarity_threshold=float(
os.getenv(ENV_CONSOLIDATION_SIMILARITY_THRESHOLD, str(DEFAULT_CONSOLIDATION_SIMILARITY_THRESHOLD))
),
consolidation_batch_size=int(
os.getenv(ENV_CONSOLIDATION_BATCH_SIZE, str(DEFAULT_CONSOLIDATION_BATCH_SIZE))
),
# Database migrations
run_migrations_on_startup=os.getenv(ENV_RUN_MIGRATIONS_ON_STARTUP, "true").lower() == "true",
# Database connection pool
db_pool_min_size=int(os.getenv(ENV_DB_POOL_MIN_SIZE, str(DEFAULT_DB_POOL_MIN_SIZE))),
db_pool_max_size=int(os.getenv(ENV_DB_POOL_MAX_SIZE, str(DEFAULT_DB_POOL_MAX_SIZE))),
db_command_timeout=int(os.getenv(ENV_DB_COMMAND_TIMEOUT, str(DEFAULT_DB_COMMAND_TIMEOUT))),
db_acquire_timeout=int(os.getenv(ENV_DB_ACQUIRE_TIMEOUT, str(DEFAULT_DB_ACQUIRE_TIMEOUT))),
# Worker configuration
worker_enabled=os.getenv(ENV_WORKER_ENABLED, str(DEFAULT_WORKER_ENABLED)).lower() == "true",
worker_id=os.getenv(ENV_WORKER_ID) or DEFAULT_WORKER_ID,
worker_poll_interval_ms=int(os.getenv(ENV_WORKER_POLL_INTERVAL_MS, str(DEFAULT_WORKER_POLL_INTERVAL_MS))),
worker_max_retries=int(os.getenv(ENV_WORKER_MAX_RETRIES, str(DEFAULT_WORKER_MAX_RETRIES))),
worker_batch_size=int(os.getenv(ENV_WORKER_BATCH_SIZE, str(DEFAULT_WORKER_BATCH_SIZE))),
worker_http_port=int(os.getenv(ENV_WORKER_HTTP_PORT, str(DEFAULT_WORKER_HTTP_PORT))),
# Reflect agent settings
reflect_max_iterations=int(os.getenv(ENV_REFLECT_MAX_ITERATIONS, str(DEFAULT_REFLECT_MAX_ITERATIONS))),
)
def get_llm_base_url(self) -> str:
"""Get the LLM base URL, with provider-specific defaults."""
if self.llm_base_url:
return self.llm_base_url
provider = self.llm_provider.lower()
if provider == "groq":
return "https://api.groq.com/openai/v1"
elif provider == "ollama":
return "http://localhost:11434/v1"
elif provider == "lmstudio":
return "http://localhost:1234/v1"
else:
return ""
def get_python_log_level(self) -> int:
"""Get the Python logging level from the configured log level string."""
log_level_map = {
"critical": logging.CRITICAL,
"error": logging.ERROR,
"warning": logging.WARNING,
"info": logging.INFO,
"debug": logging.DEBUG,
"trace": logging.DEBUG, # Python doesn't have TRACE, use DEBUG
}
return log_level_map.get(self.log_level.lower(), logging.INFO)
def configure_logging(self) -> None:
"""Configure Python logging based on the log level and format.
When log_format is "json", outputs structured JSON logs with a severity
field that GCP Cloud Logging can parse for proper log level categorization.
"""
root_logger = logging.getLogger()
root_logger.setLevel(self.get_python_log_level())
# Remove existing handlers
for handler in root_logger.handlers[:]:
root_logger.removeHandler(handler)
# Create handler writing to stdout (GCP treats stderr as ERROR)
handler = logging.StreamHandler(sys.stdout)
handler.setLevel(self.get_python_log_level())
if self.log_format == "json":
handler.setFormatter(JsonFormatter())
else:
handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(name)s - %(message)s"))
root_logger.addHandler(handler)
def log_config(self) -> None:
"""Log the current configuration (without sensitive values)."""
logger.info(f"Database: {self.database_url}")
logger.info(f"LLM: provider={self.llm_provider}, model={self.llm_model}")
if self.retain_llm_provider or self.retain_llm_model:
retain_provider = self.retain_llm_provider or self.llm_provider
retain_model = self.retain_llm_model or self.llm_model
logger.info(f"LLM (retain): provider={retain_provider}, model={retain_model}")
if self.reflect_llm_provider or self.reflect_llm_model:
reflect_provider = self.reflect_llm_provider or self.llm_provider
reflect_model = self.reflect_llm_model or self.llm_model
logger.info(f"LLM (reflect): provider={reflect_provider}, model={reflect_model}")
logger.info(f"Embeddings: provider={self.embeddings_provider}")
logger.info(f"Reranker: provider={self.reranker_provider}")
logger.info(f"Graph retriever: {self.graph_retriever}")
# Cached config instance
_config_cache: HindsightConfig | None = None
def get_config() -> HindsightConfig:
"""Get the cached configuration, loading from environment on first call."""
global _config_cache
if _config_cache is None:
_config_cache = HindsightConfig.from_env()
return _config_cache
def clear_config_cache() -> None:
"""Clear the config cache. Useful for testing or reloading config."""
global _config_cache
_config_cache = None
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