# Application local development environment.
#
# Usage:
#   cp .env.example .env
#   docker compose -f docker-compose.yml up -d
#
# All values below are non-secret local-development defaults matching
# docker-compose.yml. Do not commit .env.

# Application
APP_ENV=local
APP_READINESS_CHECK_TIMEOUT_SECONDS=2.0

# Logging
LOG_LEVEL=INFO
LOG_JSON_FORMAT=false

# Postgres (application database, separate from Langfuse's Postgres)
# Use 127.0.0.1 rather than localhost: some environments resolve localhost to
# the IPv6 loopback first, and Docker only publishes these ports on IPv4.
POSTGRES_HOST=127.0.0.1
POSTGRES_PORT=5433
POSTGRES_USER=chatbot
POSTGRES_PASSWORD=chatbot
POSTGRES_DB=chatbot

# MinIO (application bucket, separate from Langfuse's MinIO)
MINIO_ENDPOINT=127.0.0.1:9100
MINIO_ACCESS_KEY=chatbot
MINIO_SECRET_KEY=chatbot-secret
MINIO_SECURE=false
MINIO_BUCKET=chatbot-source-files

# Inline ingestion bounds (ADR-0017; no broker, no queue).
# INGESTION_TIMEOUT_SECONDS must stay below any proxy/client read timeout.
INGESTION_MAX_CONCURRENCY=4
INGESTION_THREAD_POOL_SIZE=8
INGESTION_TIMEOUT_SECONDS=120.0
INGESTION_MAX_UPLOAD_SIZE_MB=25
INGESTION_MAX_CHUNKS_PER_FILE=5000
INGESTION_EMBED_BATCH_SIZE=128
INGESTION_EMBED_CONCURRENCY=4

# Qdrant
QDRANT_URL=http://127.0.0.1:6343
QDRANT_API_KEY=
QDRANT_COLLECTION=chunks
QDRANT_UPSERT_BATCH_SIZE=128
QDRANT_UPSERT_CONCURRENCY=4

# Dense embedders (ADR-0001). Both speak an OpenAI-compatible /embeddings
# endpoint, so one adapter serves both. Models and endpoints are the ones the
# `emet` evaluation lab benchmarked as winners on the Farsi corpus.
#
# dense_nomic runs behind Ollama's OpenAI-compat shim, which accepts any
# non-empty API key. KEEP_ALIVE holds the model resident: a cold load of
# nomic-embed-text-v2-moe takes >150s, well past INGESTION_TIMEOUT_SECONDS,
# so an idle-then-upload would otherwise 504.
EMBEDDING_NOMIC_BASE_URL=http://192.168.10.10:11435/v1
EMBEDDING_NOMIC_MODEL=nomic-embed-text-v2-moe
EMBEDDING_NOMIC_API_KEY=sk-not-set
EMBEDDING_NOMIC_KEEP_ALIVE=30m
EMBEDDING_NOMIC_TIMEOUT_SECONDS=30.0
# Empty = emet parity. The model card specifies `search_document: ` (ADR-0004),
# but the benchmark ran without it and the prefix shifts the vector a lot
# (cosine 0.57 on identical text) — so if you set this, the query side must
# send `search_query: ` to match, or retrieval gets worse rather than better.
EMBEDDING_NOMIC_DOCUMENT_PREFIX=

# Leave DIMENSIONS empty for text-embedding-3-large's native 3072, which is
# what was benchmarked. Setting it truncates via Matryoshka and is a
# re-embedding migration, not a config tweak.
EMBEDDING_OPENAI_BASE_URL=https://api.openai.com/v1
EMBEDDING_OPENAI_MODEL=text-embedding-3-large
EMBEDDING_OPENAI_API_KEY=
EMBEDDING_OPENAI_DIMENSIONS=
EMBEDDING_OPENAI_DOCUMENT_PREFIX=
EMBEDDING_OPENAI_TIMEOUT_SECONDS=30.0

# Sparse BM25 (ADR-0001, ADR-0005): the benchmarked `bm25-fa-norm-stop`.
# k/b saturation is applied client-side; IDF comes from Qdrant's
# modifier="idf" on the sparse vector field. AVG_LEN is the average document
# length in analyzer tokens — emet's placeholder, worth recalibrating from
# real corpus statistics.
EMBEDDING_SPARSE_ANALYZER=fa_norm_stop
EMBEDDING_SPARSE_K=1.2
EMBEDDING_SPARSE_B=0.75
EMBEDDING_SPARSE_AVG_LEN=256.0

# Parsing and chunking (ADR-0018).
# max_chunk_tokens is nomic-embed-text-v2-moe's sequence length; text past it
# is silently truncated by the model, so the cap is enforced before embedding.
# chunk_size sits under it to leave room for the `search_document: ` prefix.
CHUNKING_STRATEGY=fixed_size
CHUNKING_CHUNK_SIZE=400
CHUNKING_CHUNK_OVERLAP=60
CHUNKING_MAX_CHUNK_TOKENS=512
CHUNKING_ENCODING_NAME=cl100k_base

# tiktoken downloads its vocabulary on first use; point this at a
# pre-populated directory for offline/air-gapped deployments.
# TIKTOKEN_CACHE_DIR=