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| # ========================================================================= | |
| # Legal Case Law RAG - Environment Variables Template | |
| # ========================================================================= | |
| # Copy this file to a new file named '.env' and fill in your values. | |
| # Do NOT commit your actual '.env' file with secrets to Git. | |
| # LLM Providers Configuration | |
| # Supported Options: "gemini", "groq" | |
| ACTIVE_LLM="groq" | |
| # --- 1. Google Gemini --- | |
| # Get your API key from Google AI Studio: https://aistudio.google.com/ | |
| GEMINI_API_KEY="your-gemini-api-key-here" | |
| LLM_MODEL_NAME="gemini-2.5-flash" | |
| LLM_TEMPERATURE=0.5 | |
| LLM_MAX_RETRIES=5 | |
| # --- 2. Groq --- | |
| # Get your API key from Groq Console: https://console.groq.com/ | |
| GROQ_API_KEY="your-groq-api-key-here" | |
| GROQ_MODEL_NAME="meta-llama/llama-4-scout-17b-16e-instruct" | |
| # RAG & Legal Embeddings Configuration | |
| # law-ai/InLegalBERT is optimized for Indian legal texts | |
| EMBEDDING_MODEL_NAME="law-ai/InLegalBERT" | |
| RERANKER_MODEL_NAME="BAAI/bge-reranker-v2-m3" | |
| # Chunking & Retrieval Parameters | |
| PARENT_CHUNK_SIZE=1500 | |
| PARENT_CHUNK_OVERLAP=200 | |
| CHILD_CHUNK_SIZE=400 | |
| CHILD_CHUNK_OVERLAP=100 | |
| SEARCH_K=10 | |
| FETCH_K=40 | |
| RERANK_TOP_K=5 | |
| # Database Configuration (PostgreSQL or SQLite) | |
| USE_POSTGRES=false | |
| POSTGRES_USER=postgres | |
| POSTGRES_PASSWORD=your_db_password | |
| POSTGRES_SERVER=localhost | |
| POSTGRES_PORT=5432 | |
| POSTGRES_DB=legal_rag | |
| # Security Options (FastAPI session tokens) | |
| SECRET_KEY="generate-a-secure-random-string-here" | |
| # Webhooks & External Services (Optional) | |
| N8N_WEBHOOK_URL="" | |
| PINECONE_API_KEY="" | |
| PINECONE_INDEX_NAME="legal-rag" | |
| # LangSmith Tracing & Observability (Optional) | |
| LANGSMITH_TRACING=false | |
| LANGSMITH_ENDPOINT="https://api.smith.langchain.com" | |
| LANGSMITH_API_KEY="" | |
| LANGSMITH_PROJECT="legal-case-law-rag" | |