Ireneusz Bachanowicz
0c468c0a69
- Added FastAPI application to handle Jira webhooks. - Created Pydantic models for Jira payload and LLM output. - Integrated LangChain with OpenAI and Ollama for LLM processing. - Set up Langfuse for tracing and monitoring. - Implemented analysis logic for Jira tickets, including sentiment analysis and label suggestions. - Added test endpoint for LLM integration. - Updated requirements.txt to include necessary dependencies and versions.
65 lines
2.8 KiB
YAML
65 lines
2.8 KiB
YAML
name: jira-llm-stack
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services:
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# Service for the Ollama server
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ollama:
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image: ollama/ollama:latest
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# Map port 11434 from the container to the host machine
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# This allows you to access Ollama directly from your host if needed (e.g., via curl http://localhost:11434)
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ports:
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- "11434:11434"
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# Mount a volume to persist Ollama models and data
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# This prevents redownloading models every time the container restarts
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volumes:
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- ollama_data:/root/.ollama
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# CORRECTED COMMAND:
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# We explicitly tell Docker to use 'bash -c' to execute the string.
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# This ensures that 'ollama pull' and 'ollama serve' are run sequentially.
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entrypoint: ["sh"]
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command: ["-c", "ollama serve && ollama pull phi4-mini:latest"]
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# Restart the container if it exits unexpectedly
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restart: unless-stopped
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# Service for your FastAPI application
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app:
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# Build the Docker image for your app from the current directory (where Dockerfile is located)
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build: .
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# Map port 8000 from the container to the host machine
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# This allows you to access your FastAPI app at http://localhost:8000
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ports:
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- "8000:8000"
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# Define environment variables for your FastAPI application
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# These will be read by pydantic-settings in your app
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environment:
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# Set the LLM mode to 'ollama'
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LLM_MODE: ollama
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# Point to the Ollama service within the Docker Compose network
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# 'ollama' is the service name, which acts as a hostname within the network
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OLLAMA_BASE_URL: http://192.168.0.122:11434
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# Specify the model to use
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OLLAMA_MODEL: gemma3:1b
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# If you have an OpenAI API key in your settings, but want to ensure it's not used
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# when LLM_MODE is ollama, you can explicitly set it to empty or omit it.
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# OPENAI_API_KEY: ""
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# OPENAI_MODEL: ""
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# Ensure the Ollama service starts and is healthy before starting the app
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depends_on:
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- ollama
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# Restart the container if it exits unexpectedly
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restart: unless-stopped
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# Mount your current project directory into the container
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# This is useful for development, as changes to your code will be reflected
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# without rebuilding the image (if you're using a hot-reloading server like uvicorn --reload)
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# For production, you might remove this and rely solely on the Dockerfile copy.
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volumes:
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- .:/app
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# Command to run your FastAPI application using Uvicorn
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# --host 0.0.0.0 is crucial for the app to be accessible from outside the container
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# --reload is good for development; remove for production
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command: uvicorn jira-webhook-llm:app --host 0.0.0.0 --port 8000 --reload
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# Define named volumes for persistent data
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volumes:
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ollama_data:
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driver: local |