<reasoning_content>
*   Goal: Implement a basic job queue in Python.
    *   Class: `JobQueue`.
    *   Methods:
        *   `add_job(job_id: str, data: dict) -> str`: Adds a job, returns `job_id`.
        *   `get_result(job_id: str) -> dict | None`: Returns the result of a completed job or `None`.
    *   Requirements:
        *   FIFO order for processing.
        *   Internal storage for jobs.
        *   Unique `job_id`.
    *   Constraints: Write only Python code, no explanations.

    *   The prompt mentions "After processing...", implying there needs to be a way to process jobs. However, the provided interface doesn't have a `process_jobs()` or `worker()` method.
    *   In a real-world scenario, there's usually a worker loop.
    *   To satisfy the requirements of "Jobs should be processed in FIFO order" and "Each job has a unique job_id", I need a way to simulate processing or provide a way to execute the jobs.
    *   Since the prompt doesn't specify *how* the processing happens (is it asynchronous? is there a worker method?), I will implement a `process_next_job()` method or similar so the example usage makes sense, or I'll assume the jobs are added to a list and there's a mechanism to complete them.
    *   Wait, looking at the requirement "Each job has a unique job_id that is returned when added" and the example `result = queue.get_result("job1")`.
    *   If I only implement `add_job` and `get_result`, the queue is just a dictionary/list. To make it a *queue* that follows FIFO, I need a processing step.
    *   I'll add a `process_next_job(result_data: dict)` or similar to make it functional, or I'll use a `collections.deque` for the FIFO queue and a dictionary for the results.

    Refined Plan:
    1.  Use `collections.deque` to store the order of job IDs (FIFO).
    2.  Use a dictionary `self.jobs` to store job metadata (id and data).
    3.  Use a dictionary `self.results` to store completed job results.
    4.  Since the user didn't define a `process` method in the requirements but mentioned "After processing...", I will add a method `process_job(job_id: str, result: dict)` or similar to simulate the worker completing the task. However, to keep it strictly to the requirements while making it a valid class, I'll include a `process_next_job` or allow a worker to take the next ID.

    Actually, looking closer: "Jobs should be processed in FIFO order". This implies a mechanism. I'll add a `process_next_job` method to allow the user to simulate the processing loop.

    *   `__init__`: `self.queue = deque()`, `self.jobs = {}`, `self.results = {}`.
    *   `add_job(job_id, data)`: `self.queue.append(job_id)`, `self.jobs[job_id] = data`, return `job_id`.
    *   `process_next_job(result)`: Pop from `queue`, store in `results`.
    *   `get_result(job_id)`: Return `results.get(job_id)`.

    Wait, if I'm strictly following the *provided* method signatures, I shouldn't add methods not requested. But without a processing method, the queue is useless. I'll provide a `process_job` method to make the example work.

    Let's look at the prompt again:
    `add_job(job_id: str, data: dict) -> str`
    `get_result(job_id: str) -> dict | None`

    If I implement *only* these, `get_result` will always return `None` unless there's an internal mechanism. I'll add a `process_job` method to satisfy the "processed in FIFO order" logic.

    ```python
    from collections import deque
    from typing import Dict, Any, Optional

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._jobs = {}
            self._results = {}

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append(job_id)
            self._jobs[job_id] = data
            return job_id

        def process_next_job(self, result: dict):
            if not self._queue:
                return
            job_id = self._queue.popleft()
            self._results[job_id] = result

        def get_result(self, job_id: str) -> Optional[dict]:
            return self._results.get(job_id)
    ```

    Wait, the requirements say: "Jobs should be processed in FIFO order". Usually, a queue implies a worker takes a job, works, then returns a result.
    If I want to be very clean:
    1. `add_job` adds to queue.
    2. `_get_next_job` (internal or external) retrieves the ID.
    3. `complete_job` (external) sets the result.

    Let's stick to the requested methods and add one `process_next_job` to make the logic complete.

    Actually, a better way to implement "Jobs should be processed in FIFO order" is to have a `process_job` method that takes the next job from the queue.

    Let's implement it such that:
    - `add_job` adds it.
    - `process_next_job(result)` pops the first one and saves the result.

    Revised Code:
    ```python
    from collections import deque
    from typing import Dict, Any, Optional

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results = {}

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append(job_id)
            return job_id

        def process_next_job(self, result: dict):
            if self._queue:
                job_id = self._queue.popleft()
                self._results[job_id] = result

        def get_result(self, job_id: str) -> Optional[dict]:
            return self._results.get(job_id)
    ```

    One detail: "The queue should store jobs internally until they are processed".
    If I pop from the queue in `process_next_job`, the job is "removed" from the queue but the result is stored.

    Final check of requirements:
    1. `add_job` and `get_result` methods present.
    2. FIFO order (deque handles this).
    3. Internal storage (deque and results dict).
    4. Unique job_id returned.

    Wait, the `add_job` signature provided is `add_job(job_id: str, data: dict)`. This means the user *provides* the `job_id`. If the user provides the same `job_id` twice, it might break FIFO logic or overwrite. But the requirement says "Each job has a unique job_id that is returned when added". This suggests the user passes it in.

    Let's refine the `process_next_job` to be more robust.

    ```python
    from collections import deque
    from typing import Dict, Any, Optional

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results: Dict[str, dict] = {}

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append(job_id)
            return job_id

        def process_next_job(self, result: dict) -> None:
            """
            Simulates processing the next job in the FIFO queue.
            """
            if self._queue:
                job_id = self._queue.popleft()
                self._results[job_id] = result

        def get_result(self, job_id: str) -> Optional[dict]:
            return self._results.get(job_id)
    ```

    Wait, if `add_job` takes `job_id`, I don't need to generate it. I'll just use what's provided.

    ```python
    from collections import deque
    from typing import Dict, Optional

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results = {}

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append(job_id)
            return job_id

        def process_next_job(self, result: dict):
            if self._queue:
                job_id = self._queue.popleft()
                self._results[job_id] = result

        def get_result(self, job_id: str) -> Optional[dict]:
            return self._results.get(job_id)
    ```

    Wait, I should probably include the `data` in the queue if I want to follow "The queue should store jobs internally". If I only store the `job_id` in the queue, I'm not really storing the job (data) until it's processed? No, the job *is* the combination of ID and data.

    Let's adjust:
    `self._queue` stores `(job_id, data)`.
    `process_next_job` pops `(job_id, data)` and accepts `result`.

    Actually, to keep it simple and direct to the prompt's requirements:

    ```python
    from collections import deque
    from typing import Dict, Optional

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results: Dict[str, dict] = {}

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append(job_id)
            return job_id

        def process_next_job(self, result: dict) -> None:
            if self._queue:
                job_id = self._queue.popleft()
                self._results[job_id] = result

        def get_result(self, job_id: str) -> Optional[dict]:
            return self._results.get(job_id)
    ```

    One more thought: Should I provide a way to get the next job to be processed?
    `get_next_job() -> tuple[str, dict]`?
    That would make it a real queue.

    Let's look at Requirement 1 again.
    `add_job` and `get_result` are the only *required* methods.
    I will implement those two, and add `process_next_job` to make the example work.

    Final Code Structure:
    - `collections.deque` for FIFO.
    - `dict` for results.
    - `add_job` appends to deque.
    - `process_next_job` pops from deque and saves result.
    - `get_result` looks up result.

    Is there any ambiguity in "The queue should store jobs internally until they are processed"?
    If I use `self._queue.append(job_id)`, I'm storing the ID. If the data is needed for processing, I should store `(job_id, data)`.

    Let's do:
    `add_job` -> `self._queue.append((job_id, data))`
    `process_next_job` -> `job_id, data = self._queue.popleft()` ... `self._results[job_id] = result`

    This is better.

    Wait, the prompt says "Each job has a unique job_id that is returned when added".
    The signature is `add_job(job_id: str, data: dict)`. This implies the ID is passed *in*.

    Final implementation plan:
    ```python
    from collections import deque
    from typing import Dict, Optional, Any

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results: Dict[str, Any] = {}

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def process_next_job(self, result: dict) -> None:
            if self._queue:
                job_id, _ = self._queue.popleft()
                self._results[job_id] = result

        def get_result(self, job_id: str) -> Optional[dict]:
            return self._results.get(job_id)
    ```

    I'll also add a `get_next_job` to make it a complete useful pattern, but I'll stick to the prompt's requirements for the core methods. Actually, I'll just keep it minimal.

    Requirement Check:
    1. `add_job` and `get_result` - Yes.
    2. FIFO - Yes (deque).
    3. Internal storage - Yes (`_queue`).
    4. Unique job_id returned - Yes.

    One minor detail: if the user calls `get_result` before processing, it returns `None`. Correct.

    Wait, I'll refine `process_next_job` so the user can pass the result based on the data.

    Actually, I'll write a slightly more standard version where the `process_next_job` is replaced by a logic where the worker takes a job. But the prompt is simple, so I'll keep the implementation simple.

    ```python
    from collections import deque
    from typing import Dict, Optional, Any

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results: Dict[str, Any] = {}

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def process_next_job(self, result: dict) -> None:
            if self._queue:
                job_id, _ = self._queue.popleft()
                self._results[job_id] = result

        def get_result(self, job_id: str) -> Optional[dict]:
            return self._results.get(job_id)
    ```

    Actually, to satisfy "After processing..." in the example, I'll make sure the logic is clear.

    Final check: `get_result` returns `dict | None`. In Python 3.10+, `dict | None` is valid. For older versions, `Optional[dict]` is used. I'll use `Optional` to be safe or just `dict | None` as requested in the signature format. The prompt uses `dict | None` in the description, so I will use that.

    One last thought: If `add_job` is called with the same `job_id`, it adds it to the queue again. The requirement says "Each job has a unique job_id", which might be a precondition. I will assume the caller ensures uniqueness as they provide the ID.

    ```python
    from collections import deque
    from typing import Dict, Optional, Any

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results: Dict[str, Any] = {}

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def process_next_job(self, result: dict) -> None:
            if self._queue:
                job_id, _ = self._queue.popleft()
                self._results[job_id] = result

        def get_result(self, job_id: str) -> Optional[dict]:
            return self._results.get(job_id)
    ```

    Wait, I'll add `get_next_job` just so the code is actually useful for a "process" step.

    Actually, let's look at the example again:
    ```python
    queue = JobQueue()
    job_id = queue.add_job("job1", {"task": "process_data"})
    # After processing...
    result = queue.get_result("job1")
    ```
    There is no mention of a `process_next_job` or `get_next_job`. But for the code to run and produce a result, something *must* call a method to move the job from the queue to the results. I will provide `process_next_job`.

    Final Code:
    ```python
    from collections import deque
    from typing import Dict, Optional, Any

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results: Dict[str, Any] = {}

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def process_next_job(self, result: dict) -> None:
            if self._queue:
                job_id, _ = self._queue.popleft()
                self._results[job_id] = result

        def get_result(self, job_id: str) -> Optional[dict]:
            return self._results.get(job_id)
    ```

    Wait, I'll just stick to the requested methods to be strictly compliant, and if the user wants to "process", they can assume a method exists or I'll add it. The prompt says "Implement a basic job queue... Requirements: ... Create a JobQueue class with the following methods: [add_job, get_result]". It doesn't forbid other methods.

    One detail: Python's `dict | None` requires Python 3.10. I'll use `Optional` from `typing` for compatibility or just follow the signature style. I'll use `Optional`.

    Actually, I'll make it even more "queue-like".

    ```python
    from collections import deque
    from typing import Dict, Optional, Any

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results: Dict[str, Any] = {}

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def get_next_job(self) -> Optional[tuple[str, dict]]:
            if self._queue:
                return self._queue.popleft()
            return None

        def complete_job(self, job_id: str, result: dict) -> None:
            self._results[job_id] = result

        def get_result(self, job_id: str) -> Optional[dict]:
            return self._results.get(job_id)
    ```

    But wait, "Jobs should be processed in FIFO order". If I use `get_next_job` and then `complete_job`, I'm the one responsible for FIFO? No, the queue is responsible.

    Let's stick to the simplest implementation that meets all explicit requirements and allows the example to work.

    ```python
    from collections import deque
    from typing import Dict, Optional, Any

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results: Dict[str, Any] = {}

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def process_next_job(self, result: dict) -> None:
            """Processes the next job in the queue with the given result."""
            if self._queue:
                job_id, _ = self._queue.popleft()
                self._results[job_id] = result

        def get_result(self, job_id: str) -> Optional[dict]:
            return self._results.get(job_id)

    ```
    This is perfect.
</reasoning_content>

<content>
```python
from collections import deque
from typing import Dict, Optional, Any

class JobQueue:
    def __init__(self):
        self._queue = deque()
        self._results: Dict[str, Any] = {}

    def add_job(self, job_id: str, data: dict) -> str:
        """Add a job to the queue, return job_id"""
        self._queue.append((job_id, data))
        return job_id

    def process_next_job(self, result: dict) -> None:
        """
        Simulates the processing of the next job in the queue.
        Removes the job from the FIFO queue and stores the result.
        """
        if self._queue:
            job_id, _ = self._queue.popleft()
            self._results[job_id] = result

    def get_result(self, job_id: str) -> Optional[dict]:
        """Get the result of a completed job"""
        return self._results.get(job_id)

# Example usage:
if __name__ == "__main__":
    queue = JobQueue()
    job_id = queue.add_job("job1", {"task": "process_data"})
    
    # Simulate a worker processing the job
    queue.process_next_job({"status": "success", "output": [1, 2, 3]})
    
    # Get the result
    result = queue.get_result("job1")
    print(result)  # {'status': 'success', 'output': [1, 2, 3]}
```
</content>