hack/tools/scripts/llm-key-hunter/results/_cache/content/abelmaireg_ragnarock-backend.json

{"repo": "abelmaireg/ragnarock-backend", "files": {"agent/.env.example": {"ca06be28686d02526f284d638e72cce061728973": {"t": 1785745164.134076, "n": 2873, "h": "047280f325bcec63", "c": "# ─── LLM Configuration ────────────────────────────────────────────────────────\n# Which provider to use: gemini | anthropic | openai | deepseek\nLLM_PROVIDER=gemini\n\n# ─── Gemini: choose ONE authentication mode ─────────────────────────────────────\n#\n# (A) Vertex AI + Application Default Credentials (recommended on GCP / Enterprise)\n#     Use after: gcloud auth application-default login\n#     Ensure IAM: roles/aiplatform.user (or org-approved equivalent).\nGOOGLE_GENAI_USE_VERTEXAI=false\nGOOGLE_CLOUD_PROJECT=\nGOOGLE_CLOUD_LOCATION=global\n\n# (B) Google AI Studio API key (consumer Gemini API via google-generativeai)\n#     Required when GOOGLE_GENAI_USE_VERTEXAI=false for chat + embeddings.\nGEMINI_API_KEY=\nGEMINI_MODEL=gemini-2.5-flash\n\n# Anthropic (Claude)\nANTHROPIC_API_KEY=your_anthropic_api_key_here\nANTHROPIC_MODEL=claude-sonnet-4-6\n\n# OpenAI\nOPENAI_API_KEY=your_openai_api_key_here\nOPENAI_MODEL=gpt-4o\n\n# DeepSeek\nDEEPSEEK_API_KEY=your_deepseek_api_key_here\nDEEPSEEK_MODEL=deepseek-chat\n\n# ─── Vector Memory / Phase 3 ──────────────────────────────────────────────────\n# Point this at the same PostgreSQL instance NestJS uses.\n# Leave empty to run without memory (Phase 1/2 behaviour).\nDATABASE_URL=postgresql://user:password@localhost:5432/app\n\n# Embedding model (used for both legacy API key and Vertex; name may differ by API surface)\n# Examples: models/text-embedding-004 (legacy) / check Vertex model list for your region\nEMBEDDING_MODEL=models/text-embedding-004\n\n# ─── AI Requirements Queue (Redis Streams) ────────────────────────────────────\n# The agent consumes jobs from NestJS and publishes results back through Redis.\nREDIS_URL=redis://localhost:6379\nAI_REQUIREMENTS_QUEUE_JOBS_STREAM=stream:ai-requirements:jobs\nAI_REQUIREMENTS_QUEUE_JOBS_GROUP=ai-requirements-agent-workers\nAI_REQUIREMENTS_QUEUE_JOBS_CONSUMER=agent-1\nAI_REQUIREMENTS_QUEUE_RESULTS_STREAM=stream:ai-requirements:results\nAI_REQUIREMENTS_QUEUE_BATCH_SIZE=10\nAI_REQUIREMENTS_QUEUE_BLOCK_MS=5000\nAI_REQUIREMENTS_QUEUE_MAX_RETRIES=5\nAI_REQUIREMENTS_QUEUE_CLAIM_IDLE_MS=30000\nAI_REQUIREMENTS_QUEUE_STREAM_MAXLEN=10000\nAI_REQUIREMENTS_QUEUE_JOBS_DLQ_STREAM=stream:ai-requirements:jobs:dlq\n\n# ─── Upload References From NestJS ────────────────────────────────────────────\n# Local mode: mount/share the backend upload directory and set AI_UPLOAD_LOCAL_ROOT.\nAI_UPLOAD_LOCAL_ROOT=\n# S3 mode: agent downloads queued s3://bucket/key or bucket+key references.\nAI_UPLOAD_S3_BUCKET=\nAI_UPLOAD_S3_ENDPOINT_URL=\nAI_UPLOAD_S3_REGION=us-east-1\nAI_UPLOAD_S3_ACCESS_KEY_ID=\nAI_UPLOAD_S3_SECRET_ACCESS_KEY=\n\n# ─── CORS ─────────────────────────────────────────────────────────────────────\n# Comma-separated list of allowed origins (NestJS base URL)\nALLOWED_ORIGINS=http://localhost:3000\n\n# ─── App ─────────────────────────────────────────────────────────────────────\nAPP_ENV=development\nAPP_PORT=8100\n"}, "652df33a6a689405f2db56bb72111016b973e477": {"t": 1785748332.2263744, "n": 2873, "h": "047280f325bcec63", "c": "# ─── LLM Configuration ────────────────────────────────────────────────────────\n# Which provider to use: gemini | anthropic | openai | deepseek\nLLM_PROVIDER=gemini\n\n# ─── Gemini: choose ONE authentication mode ─────────────────────────────────────\n#\n# (A) Vertex AI + Application Default Credentials (recommended on GCP / Enterprise)\n#     Use after: gcloud auth application-default login\n#     Ensure IAM: roles/aiplatform.user (or org-approved equivalent).\nGOOGLE_GENAI_USE_VERTEXAI=false\nGOOGLE_CLOUD_PROJECT=\nGOOGLE_CLOUD_LOCATION=global\n\n# (B) Google AI Studio API key (consumer Gemini API via google-generativeai)\n#     Required when GOOGLE_GENAI_USE_VERTEXAI=false for chat + embeddings.\nGEMINI_API_KEY=\nGEMINI_MODEL=gemini-2.5-flash\n\n# Anthropic (Claude)\nANTHROPIC_API_KEY=your_anthropic_api_key_here\nANTHROPIC_MODEL=claude-sonnet-4-6\n\n# OpenAI\nOPENAI_API_KEY=your_openai_api_key_here\nOPENAI_MODEL=gpt-4o\n\n# DeepSeek\nDEEPSEEK_API_KEY=your_deepseek_api_key_here\nDEEPSEEK_MODEL=deepseek-chat\n\n# ─── Vector Memory / Phase 3 ──────────────────────────────────────────────────\n# Point this at the same PostgreSQL instance NestJS uses.\n# Leave empty to run without memory (Phase 1/2 behaviour).\nDATABASE_URL=postgresql://user:password@localhost:5432/app\n\n# Embedding model (used for both legacy API key and Vertex; name may differ by API surface)\n# Examples: models/text-embedding-004 (legacy) / check Vertex model list for your region\nEMBEDDING_MODEL=models/text-embedding-004\n\n# ─── AI Requirements Queue (Redis Streams) ────────────────────────────────────\n# The agent consumes jobs from NestJS and publishes results back through Redis.\nREDIS_URL=redis://localhost:6379\nAI_REQUIREMENTS_QUEUE_JOBS_STREAM=stream:ai-requirements:jobs\nAI_REQUIREMENTS_QUEUE_JOBS_GROUP=ai-requirements-agent-workers\nAI_REQUIREMENTS_QUEUE_JOBS_CONSUMER=agent-1\nAI_REQUIREMENTS_QUEUE_RESULTS_STREAM=stream:ai-requirements:results\nAI_REQUIREMENTS_QUEUE_BATCH_SIZE=10\nAI_REQUIREMENTS_QUEUE_BLOCK_MS=5000\nAI_REQUIREMENTS_QUEUE_MAX_RETRIES=5\nAI_REQUIREMENTS_QUEUE_CLAIM_IDLE_MS=30000\nAI_REQUIREMENTS_QUEUE_STREAM_MAXLEN=10000\nAI_REQUIREMENTS_QUEUE_JOBS_DLQ_STREAM=stream:ai-requirements:jobs:dlq\n\n# ─── Upload References From NestJS ────────────────────────────────────────────\n# Local mode: mount/share the backend upload directory and set AI_UPLOAD_LOCAL_ROOT.\nAI_UPLOAD_LOCAL_ROOT=\n# S3 mode: agent downloads queued s3://bucket/key or bucket+key references.\nAI_UPLOAD_S3_BUCKET=\nAI_UPLOAD_S3_ENDPOINT_URL=\nAI_UPLOAD_S3_REGION=us-east-1\nAI_UPLOAD_S3_ACCESS_KEY_ID=\nAI_UPLOAD_S3_SECRET_ACCESS_KEY=\n\n# ─── CORS ─────────────────────────────────────────────────────────────────────\n# Comma-separated list of allowed origins (NestJS base URL)\nALLOWED_ORIGINS=http://localhost:3000\n\n# ─── App ─────────────────────────────────────────────────────────────────────\nAPP_ENV=development\nAPP_PORT=8100\n"}, "468cb74ce115c00de94ffff393a66dfc7bd4e926": {"t": 1785748333.9430463, "n": 2777, "h": "53e6f2491fb2d31f", "c": "# ─── LLM Configuration ────────────────────────────────────────────────────────\n# Which provider to use: gemini | anthropic | openai\nLLM_PROVIDER=gemini\n\n# ─── Gemini: choose ONE authentication mode ─────────────────────────────────────\n#\n# (A) Vertex AI + Application Default Credentials (recommended on GCP / Enterprise)\n#     Use after: gcloud auth application-default login\n#     Ensure IAM: roles/aiplatform.user (or org-approved equivalent).\nGOOGLE_GENAI_USE_VERTEXAI=false\nGOOGLE_CLOUD_PROJECT=\nGOOGLE_CLOUD_LOCATION=global\n\n# (B) Google AI Studio API key (consumer Gemini API via google-generativeai)\n#     Required when GOOGLE_GENAI_USE_VERTEXAI=false for chat + embeddings.\nGEMINI_API_KEY=\nGEMINI_MODEL=gemini-2.5-flash\n\n# Anthropic (Claude)\nANTHROPIC_API_KEY=your_anthropic_api_key_here\nANTHROPIC_MODEL=claude-sonnet-4-6\n\n# OpenAI\nOPENAI_API_KEY=your_openai_api_key_here\nOPENAI_MODEL=gpt-4o\n\n# ─── Vector Memory / Phase 3 ──────────────────────────────────────────────────\n# Point this at the same PostgreSQL instance NestJS uses.\n# Leave empty to run without memory (Phase 1/2 behaviour).\nDATABASE_URL=postgresql://user:password@localhost:5432/app\n\n# Embedding model (used for both legacy API key and Vertex; name may differ by API surface)\n# Examples: models/text-embedding-004 (legacy) / check Vertex model list for your region\nEMBEDDING_MODEL=models/text-embedding-004\n\n# ─── AI Requirements Queue (Redis Streams) ────────────────────────────────────\n# The agent consumes jobs from NestJS and publishes results back through Redis.\nREDIS_URL=redis://localhost:6379\nAI_REQUIREMENTS_QUEUE_JOBS_STREAM=stream:ai-requirements:jobs\nAI_REQUIREMENTS_QUEUE_JOBS_GROUP=ai-requirements-agent-workers\nAI_REQUIREMENTS_QUEUE_JOBS_CONSUMER=agent-1\nAI_REQUIREMENTS_QUEUE_RESULTS_STREAM=stream:ai-requirements:results\nAI_REQUIREMENTS_QUEUE_BATCH_SIZE=10\nAI_REQUIREMENTS_QUEUE_BLOCK_MS=5000\nAI_REQUIREMENTS_QUEUE_MAX_RETRIES=5\nAI_REQUIREMENTS_QUEUE_CLAIM_IDLE_MS=30000\nAI_REQUIREMENTS_QUEUE_STREAM_MAXLEN=10000\nAI_REQUIREMENTS_QUEUE_JOBS_DLQ_STREAM=stream:ai-requirements:jobs:dlq\n\n# ─── Upload References From NestJS ────────────────────────────────────────────\n# Local mode: mount/share the backend upload directory and set AI_UPLOAD_LOCAL_ROOT.\nAI_UPLOAD_LOCAL_ROOT=\n# S3 mode: agent downloads queued s3://bucket/key or bucket+key references.\nAI_UPLOAD_S3_BUCKET=\nAI_UPLOAD_S3_ENDPOINT_URL=\nAI_UPLOAD_S3_REGION=us-east-1\nAI_UPLOAD_S3_ACCESS_KEY_ID=\nAI_UPLOAD_S3_SECRET_ACCESS_KEY=\n\n# ─── CORS ─────────────────────────────────────────────────────────────────────\n# Comma-separated list of allowed origins (NestJS base URL)\nALLOWED_ORIGINS=http://localhost:3000\n\n# ─── App ─────────────────────────────────────────────────────────────────────\nAPP_ENV=development\nAPP_PORT=8100\n"}, "e2290b243d0b4a13fcf9172ec8319b853b9494df": {"t": 1785748335.1835425, "n": 2781, "h": "a915edf01faf1c67", "c": "# ─── LLM Configuration ────────────────────────────────────────────────────────\n# Which provider to use: gemini | anthropic | openai\nLLM_PROVIDER=gemini\n\n# ─── Gemini: choose ONE authentication mode ─────────────────────────────────────\n#\n# (A) Vertex AI + Application Default Credentials (recommended on GCP / Enterprise)\n#     Use after: gcloud auth application-default login\n#     Ensure IAM: roles/aiplatform.user (or org-approved equivalent).\nGOOGLE_GENAI_USE_VERTEXAI=false\nGOOGLE_CLOUD_PROJECT=\nGOOGLE_CLOUD_LOCATION=global\n\n# (B) Google AI Studio API key (consumer Gemini API via google-generativeai)\n#     Required when GOOGLE_GENAI_USE_VERTEXAI=false for chat + embeddings.\nGEMINI_API_KEY=\nGEMINI_MODEL=gemini-2.5-flash\n\n# Anthropic (Claude)\nANTHROPIC_API_KEY=your_anthropic_api_key_here\nANTHROPIC_MODEL=claude-sonnet-4-6\n\n# OpenAI\nOPENAI_API_KEY=your_openai_api_key_here\nOPENAI_MODEL=gpt-4o\n\n# ─── Vector Memory / Phase 3 ──────────────────────────────────────────────────\n# Point this at the same PostgreSQL instance NestJS uses.\n# Leave empty to run without memory (Phase 1/2 behaviour).\nDATABASE_URL=postgresql://user:password@localhost:5432/sdlc_db\n\n# Embedding model (used for both legacy API key and Vertex; name may differ by API surface)\n# Examples: models/text-embedding-004 (legacy) / check Vertex model list for your region\nEMBEDDING_MODEL=models/text-embedding-004\n\n# ─── AI Requirements Queue (Redis Streams) ────────────────────────────────────\n# The agent consumes jobs from NestJS and publishes results back through Redis.\nREDIS_URL=redis://localhost:6379\nAI_REQUIREMENTS_QUEUE_JOBS_STREAM=stream:ai-requirements:jobs\nAI_REQUIREMENTS_QUEUE_JOBS_GROUP=ai-requirements-agent-workers\nAI_REQUIREMENTS_QUEUE_JOBS_CONSUMER=agent-1\nAI_REQUIREMENTS_QUEUE_RESULTS_STREAM=stream:ai-requirements:results\nAI_REQUIREMENTS_QUEUE_BATCH_SIZE=10\nAI_REQUIREMENTS_QUEUE_BLOCK_MS=5000\nAI_REQUIREMENTS_QUEUE_MAX_RETRIES=5\nAI_REQUIREMENTS_QUEUE_CLAIM_IDLE_MS=30000\nAI_REQUIREMENTS_QUEUE_STREAM_MAXLEN=10000\nAI_REQUIREMENTS_QUEUE_JOBS_DLQ_STREAM=stream:ai-requirements:jobs:dlq\n\n# ─── Upload References From NestJS ────────────────────────────────────────────\n# Local mode: mount/share the backend upload directory and set AI_UPLOAD_LOCAL_ROOT.\nAI_UPLOAD_LOCAL_ROOT=\n# S3 mode: agent downloads queued s3://bucket/key or bucket+key references.\nAI_UPLOAD_S3_BUCKET=\nAI_UPLOAD_S3_ENDPOINT_URL=\nAI_UPLOAD_S3_REGION=us-east-1\nAI_UPLOAD_S3_ACCESS_KEY_ID=\nAI_UPLOAD_S3_SECRET_ACCESS_KEY=\n\n# ─── CORS ─────────────────────────────────────────────────────────────────────\n# Comma-separated list of allowed origins (NestJS base URL)\nALLOWED_ORIGINS=http://localhost:3000\n\n# ─── App ─────────────────────────────────────────────────────────────────────\nAPP_ENV=development\nAPP_PORT=8100\n"}}}}