| # ProBas RAG Assistant configuration | |
| # Copy this file to .env and fill in the values for your deployment. | |
| OPENAI_API_KEY=your_openai_compatible_api_key_here | |
| OPENAI_BASE_URL=https://chat-ai.academiccloud.de/v1 | |
| PROBAS_EMBEDDING_MODEL=qwen3-embedding-4b | |
| PROBAS_MAX_RECORDS=0 | |
| PORT=7860 | |
| # Index build tuning | |
| PROBAS_EMBED_BATCH_SIZE=12 # texts per embedding request (smaller = fewer timeouts) | |
| PROBAS_EMBED_CONCURRENCY=4 # parallel embedding requests (main speed lever) | |
| PROBAS_EMBED_TIMEOUT_SECONDS=180 # per-request timeout for the embedding model | |
| PROBAS_EMBED_MAX_RETRIES=1 # retries before a batch is split in half | |
| PROBAS_CHECKPOINT_EVERY=5 # save a resume checkpoint every N waves | |
| # Retrieval and answer-quality tuning | |
| PROBAS_BM25_WEIGHT=0.30 # lexical weight in the hybrid score | |
| PROBAS_VECTOR_WEIGHT=0.70 # dense embedding weight (carries cross-lingual queries) | |
| PROBAS_MIN_RELEVANCE=0.42 # below this top cosine, a query is answered conversationally | |
| PROBAS_MAX_CONTEXT_CHARS=5000 # per-record excerpt size fed to the model | |
| PROBAS_EVIDENCE_SNIPPET_CHARS=320 # per-record snippet shown in the UI evidence panel (compact) | |
| # PROBAS_DISABLE_AUTOSTART=1 # skip background index build on import (useful for tests) | |