| # Python Interview Task |
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| ## Scenario |
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| You are asked to build a Python web API that analyzes plain text transcripts and returns a summary along with a list of next actions. Your implementation should clearly demonstrate good architectural practices. |
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| ## Provided Adapter (do not implement) |
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| - **OpenAI Adapter**: Sends the transcript text to OpenAI's API along with a predefined, hardcoded prompt. This adapter returns a DTO. |
| - The transcript, system prompt, and user prompt are provided. |
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| A file defining the interface (ports) for this adapter will be provided. |
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| ## Requirements (Point 1) |
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| ### Analyze Transcript |
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| - Create an HTTP endpoint (e.g., using FastAPI or Flask) that accepts GET requests containing a plain text transcript. |
| - Perform basic input validation (when the transcript is empty) |
| - Invoke the provided OpenAI adapter to analyze the transcript. |
| - Store the analysis result in memory (an external DB is not required). |
| - Return a response containing: |
| - A unique ID. |
| - A summary of the transcript. |
| - A suggested list of next steps or actions based on the transcript analysis. |
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| ### Get a Transcript by ID |
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| - Create an HTTP endpoint to get transcript analysis by ID. |
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| ### Additional Requirements |
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| - Adhere strictly to the interfaces defined in the provided ports file. |
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| ## Optional Advanced Requirements (Point 2) |
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| - Build an additional endpoint to support concurrent analysis of multiple transcripts within a single request: |
| - Implement asynchronous processing (e.g., using asyncio). |
| - Handle multiple transcript analyses simultaneously without blocking the main API thread. |
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| ## Success Criteria |
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| - Code readability, modularity, and adherence to best practices. |
| - Functional correctness of the API. |
| - Swagger |
| - Clear error handling and appropriate HTTP response statuses. |
| - Testability of the code (clear separation of concerns, ease of unit testing). |
| - (Optional) Effective asynchronous processing implementation. |
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| ## Hints |
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| - You will find a test running openai adapter. This will be the documentation to build the prompt to analyze the transcript |
| - The provided OpenAI adapter utilizes structured output, allowing you to specify a system prompt, a user prompt, and a DTO. The adapter then returns a model instance populated according to the DTO's defined structure. |
| - Create a DTO that contains the requested fields. |
| - Hexagonal (or clean) architecture consists of distinct layers. Consider creating a separate model layer for the LLM responses. Pay attention to avoiding layer coupling. |
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