LLM Background Task Solution

2025-04-10 11:36 UTC o3-mini Open in ChatGPT ↗

When you run into HTTP timeouts on platforms like Heroku or Railway, the fundamental challenge is that the HTTP request–response cycle is not designed for long-running processes. Your application’s long processing time—such as generating a response from a large language model (LLM)—needs to be decoupled from the immediate HTTP response expected by these hosts. Here are several strategies to work around this problem:

1. Offload Processing to Background Tasks

Use a Task Queue or Worker System
Rather than processing the LLM query synchronously within an HTTP request (which might exceed the platform’s timeout limit), you can offload the heavy processing to a background task. Here’s how to do it:

Enqueue the Task:
When an HTTP request comes in, immediately enqueue a job (using tools like Celery, RQ, or a similar task-queue system) that will handle the LLM processing in the background.
Immediate Response:
Return an HTTP 202 (Accepted) response to the client along with a unique job ID. This lets the client know that the request has been received and is being processed.
Polling or Push Notifications:
Implement a separate endpoint or use real-time techniques (such as WebSockets or server-sent events) to allow the client to check on the status of the task or receive the result once it’s ready.

This architecture means that the HTTP endpoint stays responsive while offloading the long-running LLM work to a component that isn’t bound by strict HTTP timeouts.

2. Asynchronous HTTP Patterns

Long Polling:
If real-time updates are not crucial, the client can periodically poll the server using the job ID to check the status or fetch results. This polling endpoint should return quickly, avoiding the extended processing within a single HTTP call.

WebSocket or Server-Sent Events (SSE):
For scenarios where you need real-time communication, consider a persistent connection like WebSocket or SSE. These technologies allow you to push data to the client as soon as the background task completes, bypassing the typical HTTP timeout constraints. However, note that while WebSockets are less susceptible to idle timeout issues, you may still have to deal with connection management and keepalive signals.

3. Leveraging Platform-Specific Techniques

While platforms like Heroku have well-known timeout limits (often around 30 seconds for web dynos), some providers or configurations may offer extensions or alternative solutions:

Custom Timeout Settings:
Check if your provider allows modifications to the timeout settings. This is rare for HTTP endpoints but may be possible through reverse proxies (like configuring Nginx) if you control that layer.
Platform-Specific Queuing Options:
Some cloud services come with their own task queue or worker options (e.g., Heroku’s worker dynos) which can be used to handle longer processing times outside the web request cycle.

4. Architectural Considerations

API Design:
Design your API endpoints so that immediate acknowledgment and separate retrieval of results is part of your workflow. For example:
1. /start: Initiates the process and returns a job ID.
2. /status/{job_id}: Allows clients to query the status and eventually retrieve results.
User Experience:
Ensure you give proper feedback on the status of the request. Inform users that the process may take a while and provide a progress or status indication on the client side.
Error Handling and Retries:
Build robust error handling into both the background processing and the polling mechanism. This ensures that if a background task fails or takes too long, the client can be notified accordingly.

Conclusion

The key to working around strict HTTP timeouts on platforms like Heroku or Railway is to avoid keeping the HTTP connection open for the duration of the long-running LLM processing. By delegating the heavy computation to background tasks or asynchronous processing frameworks and then handling communication via polling or real-time channels, you maintain responsiveness and adhere to platform constraints without compromising on functionality.

This architecture not only provides a practical solution to timeout issues but also improves scalability and enhances the overall user experience by decoupling processing from immediate HTTP responses.

You can design your long polling strategy to handle the entire processing pipeline rather than having the client poll multiple endpoints for each LLM invocation. Here’s how to approach these two specific challenges:

1. Handling a Queued Request (Overall Process Latency)

Because requests can be delayed while waiting in the queue—and the queue itself may vary based on load—it’s often best to separate job submission from job monitoring:

Job Submission:
When a client submits a request, immediately enqueue it and return a job ID. The client then uses this ID to check the status of the request. This means that the initial HTTP endpoint simply acknowledges the task instead of waiting for processing to complete.
Status/Progress Endpoint:
Create a single long polling endpoint (or a sequence of short polling calls) that the client can call with the job ID. Each poll will return the current state of the job—whether it’s queued, processing, or complete—and, if possible, an estimated progress indicator or a timestamp when it will next update. This abstraction allows the client to not worry about the underlying queuing mechanisms.
Keepalive and Timeouts:
Design the polling response to either provide a “heartbeat” message or to keep the connection open only for a brief period before having the client re-issue the poll. You can set a reasonable timeout on the polling connection (for example, 20–30 seconds) and then rely on the client to reconnect if the job is not yet complete.

2. Handling Multiple LLM Invocations (Multiple Process Steps)

Rather than long polling for each individual LLM call, consider aggregating the progress of multiple steps into one overall status update. Here are two approaches:

Single Aggregated Status Endpoint

Unified Progress Reporting:
Update the job’s progress as it moves from one step to the next. For example, you might return a status payload that includes:
- Queue Status: “Waiting in Queue” or “Dequeued”
- Current Step: “Step 1: Understanding Query”, “Step 2: Generating Response”, “Step 3: Refining Output”, etc.
- Overall Progress: A percentage or description that shows how far along the entire process is. This way, the client’s long polling endpoint reports the cumulative progress, and the client only needs to poll one endpoint.

Separate Polling per Step (If Granularity is Necessary)

Step-Specific Feedback:
In scenarios where steps have vastly different processing times or if you need to expose detailed state changes for user feedback, you might design the system so that the job itself contains sub-states.
- The main job ID remains the same.
- Each poll returns the current active step and its status (e.g., “processing”, “completed”, “error”). With this setup, even though the client is still polling a single endpoint, the API responds with detailed information for each LLM invocation phase. This lets the client know exactly which LLM call is taking time and provides contextual feedback for the waiting user.

Recommendations

Prefer Aggregated Progress Feedback:
In most cases, a unified status endpoint that reports overall progress (including current step details) is simpler to implement and easier to manage on the client side. It avoids the overhead of managing multiple long polling connections concurrently.
Progress Updates and Partial Results:
Consider returning intermediate results (if available) so that users see that progress is being made. For instance, as each LLM invocation completes, update the job status with the result or a progress message.
Error Handling:
Ensure that if any individual step fails, the status endpoint reflects that, so the client can report errors or decide to retry processing.
Timeout Management on the Client:
If the client’s connection drops during long polling, make it resilient by automatically re-issuing the poll with the same job ID.

Summary

For long polling in a multi-step LLM workflow:

Queue and Process the Job Asynchronously:
Accept the job immediately and return a job ID to the client.
Use a Single, Unified Polling Endpoint:
Let the client poll a single endpoint that returns the current processing state and progress, with details for each LLM invocation as part of the response.
Maintain a Smooth User Experience:
With aggregated progress and proper error handling, the user remains informed about what’s happening—even if there’s a queue or multiple processing steps.

This approach keeps the client’s polling logic simple while allowing you to manage a complex, multi-step process efficiently on the server side.

it’s weird, but my LLM invocation (using Huggingface inference endpoints) actually did return heartbeats. hoevwer my request wasnt queued at the time, it got processed right away. see the log:

data: {“msg”:“estimation”,“event_id”:“a4c5180aa6f5445cbd9816c8c2eb3418”,“rank”:0,“queue_size”:1,“rank_eta”:null}

data: {“msg”:“process_starts”,“event_id”:“a4c5180aa6f5445cbd9816c8c2eb3418”,“eta”:null}

data: {“msg”:“heartbeat”,“event_id”:null}

data: {“msg”:“process_completed”,“event_id”:“a4c5180aa6f5445cbd9816c8c2eb3418”,“output”:{“data”:[[[“tell me about a day in 1945 in Travny”,“in 1945, the beskydy mountains were cloaked in a silence that spoke of both resilience and upheaval. travny, nestled within this rugged landscape, was a place where the whispers of the past mingled with the tentative steps of a new beginning. the forests, dense with beech, spruce, and my own kind, the silver fir, stood as silent witnesses to the turmoil that had swept through the region.\n\nthe year marked the end of world war ii, a time when the echoes of conflict still lingered in the air. the community of travny, like many others, was grappling with the aftermath of displacement and loss. the once-bustling streets now bore the scars of war, yet there was an undercurrent of determination. people returned to their homes, rebuilding not just their lives but also the fabric of their community.\n\nthe forests, too, bore the marks of human activity. industrialization had begun to encroach on the natural landscape, and the post-war years brought a renewed focus on resource extraction. yet, amidst this change, the resilience of both the people and the environment shone through. the trees, standing tall and steadfast, offered a sense of continuity in a world that felt anything but certain.\n\nas i reflect on that day in 1945, i am reminded of the interconnectedness of nature and human endeavor. the forests of travny, with their rich biodiversity, were not just a backdrop to the story of post-war reconstruction but an integral part of it. they provided shelter, sustenance, and a sense of hope to those who had endured so much.\n\nin the years that followed, the region would undergo further transformations, shaped by the ever-evolving interplay between human activity and the natural world. yet, the lessons of 1945 remain etched in the rings of the trees, a testament to the enduring spirit of resilience and the delicate balance that must be maintained between progress and preservation.”]],"",null],“is_generating”:false,“duration”:73.13649654388428,“average_duration”:73.13649654388428,“render_config”:null,“changed_state_ids”:[]},“success”:true,“title”:null}

data: {“msg”:“close_stream”,“event_id”:null}

The log you shared shows that the Hugging Face inference endpoint sends periodic updates—even when it’s not actually waiting in a traditional queue—to confirm that the process is active and the connection is still alive. Here’s how to interpret these messages and what they imply for your long polling design:

Breakdown of the Log Messages

Estimation Message:
```
{"msg":"estimation","event_id":"a4c5180aa6f5445cbd9816c8c2eb3418","rank":0,"queue_size":1,"rank_eta":null}
```
Although it reports a nonzero queue size, the rank of 0 means your request is at the front (or immediately eligible for processing). Some systems report a queue even if processing starts immediately, possibly as a means of providing status feedback.
Process Start:
```
{"msg":"process_starts","event_id":"a4c5180aa6f5445cbd9816c8c2eb3418","eta":null}
```
This signals that processing has begun. The eta is null because it may not be calculating a time estimate if the process is expected to start immediately.
Heartbeat Messages:
```
{"msg":"heartbeat","event_id":null}
```
These periodic heartbeats serve as a keep-alive signal. They confirm that the request is still being worked on. Even in cases where there is no queuing delay, sending heartbeats prevents client-side timeouts and informs the client that the process is ongoing.
Process Completion:
```
{"msg":"process_completed","event_id":"a4c5180aa6f5445cbd9816c8c2eb3418", ... }
```
Finally, when processing is finished, the complete output is returned.

Implications for Long Polling Design

Given the above behavior, you can leverage these periodic heartbeats in your long polling strategy in the following ways:

Single Unified Long Polling Endpoint:
- Aggregation Over Steps: Instead of creating separate endpoints for each LLM invocation step, aggregate the status updates into one progress report.
- Heartbeat Handling: When your client connects to the long polling endpoint, it should expect intermittent heartbeat messages. These messages let the client know that despite long processing times (or even immediate processing), the underlying connection is alive.
Client-Side Behavior:
- Connection Persistence: Ensure that your client’s long polling implementation is designed to handle intermittent responses like heartbeats—essentially, treating them as “still working” notifications.
- Update UI Accordingly: Optionally, you could use the heartbeats to trigger UI updates that indicate the task is not stalled, even though no new substantive data (like a final result) has been received yet.
Backend Aggregation:
- Unified State Reporting: Implement a status tracker that compiles various states (queue status, processing, heartbeats, completion) into a consolidated response. This way, even if a request goes through multiple underlying LLM invocations, your long polling endpoint only needs to report a cumulative progress state rather than maintaining separate connections for each step.
- Flexibility for Multiple Phases: In your response payload, you can include a field such as current_step or progress_detail that informs the client what stage the process is in (e.g., “starting”, “generating”, “finalizing”).

In Summary

Heartbeats Are Normal:
The heartbeats you see in the log are intended to keep the connection active and reassure the client that processing is still occurring, even if the request is processed immediately without significant queuing.
Single Endpoint Is Preferable:
You generally don’t need to implement separate long polling connections for each LLM step. Instead, adopt an aggregated status endpoint that communicates overall progress—including handling of heartbeats and final results.
Design for Resilience:
Your long polling client should be resilient to intermittent heartbeat messages, re-establishing the connection as needed if timeouts occur, and update the UI to reflect ongoing progress until the final result arrives.

This unified approach simplifies both server and client logic while ensuring that users remain informed throughout the process.

When using services like Railway that enforce a strict timeout on web requests (often around 30 seconds), you need to decouple your long-running LLM process from the HTTP connection that serves your client. This is true even if the LLM endpoint provides periodic heartbeats. Here are some practical suggestions and code examples on how to handle the stream of messages and avoid keeping a single open connection too long:

1. Capturing Streaming Messages in Your Python App

Using a Streaming HTTP Client

Python HTTP libraries like httpx or requests (in streaming mode) can help you capture streamed responses (such as SSE events or chunked transfer encoded messages). For example, with httpx:

import httpx

def get_llm_stream(url):
    with httpx.stream("GET", url) as response:
        for chunk in response.iter_lines():
            if chunk:
                print("Received:", chunk.decode("utf-8"))
                # Process each message (e.g., update internal status or push to a client)

# Example URL for your Hugging Face endpoint or a proxy endpoint that wraps it
stream_url = "https://api.example.com/llm-stream"
get_llm_stream(stream_url)

In this snippet, the iter_lines() function will yield each line as it arrives. This lets your Python app consume the heartbeat and state messages in real time.

Using FastAPI for a Streaming Response

If you need your server to relay these messages to a client, you can use FastAPI (or Flask, using streaming responses) to create an endpoint that yields data:

from fastapi import FastAPI
from fastapi.responses import StreamingResponse
import time

app = FastAPI()

def event_generator():
    # Simulate the LLM's streaming responses
    yield "data: {\"msg\":\"estimation\"}\n\n"
    time.sleep(2)
    yield "data: {\"msg\":\"process_starts\"}\n\n"
    for _ in range(4):
        time.sleep(5)
        yield "data: {\"msg\":\"heartbeat\"}\n\n"
    yield "data: {\"msg\":\"process_completed\", \"output\": \"final result here\"}\n\n"

@app.get("/stream")
def stream():
    return StreamingResponse(event_generator(), media_type="text/event-stream")

This creates an SSE (Server-Sent Events) endpoint that sends chunked data to the client.

2. Preventing Railway from Keeping a Single Long HTTP Connection Open

Even though your LLM endpoint is streaming heartbeats, Railway still enforces its maximum connection lifetime. To work within these constraints, consider the following strategies:

a. Use Short-Lived Connections (Reconnection Pattern)

Instead of maintaining one continuous HTTP connection, design your client to reconnect periodically:

Short Polling Endpoint:
Your server can offer an endpoint (e.g., /status) that returns the current progress in a very lightweight response. The client polls this endpoint every few seconds.
Reconnect on Timeout:
If you want to simulate long polling, design the endpoint to hold for a limited period (say 20-25 seconds) and then close. On the client side, have logic that automatically reconnects using the same job ID. This prevents any single connection from exceeding Railway’s timeout.

For example, with a client using JavaScript’s fetch, you can do:

function pollStatus(jobId) {
  fetch(`/status?job_id=${jobId}`)
    .then(response => response.json())
    .then(data => {
      console.log("Job Status:", data);
      if (!data.complete) {
        setTimeout(() => pollStatus(jobId), 1000); // Poll again after 1 second
      }
    })
    .catch(err => {
      console.error("Polling error", err);
      setTimeout(() => pollStatus(jobId), 3000);
    });
}

b. Offload LLM Processing to Background Tasks

Since the LLM processing may take longer than allowed by Railway’s timeout:

Queue the Job:
When a user request comes in, immediately enqueue the job (e.g., using Celery, RQ, or another task queue). Return a job ID immediately.
Separate Status Endpoint:
The client polls a separate /status endpoint that checks the background job’s state. This decoupling ensures that no single HTTP request is kept open for the duration of the processing.

This pattern decouples the long-running work from the direct HTTP request–response cycle and avoids Railway’s hard limits.

c. Periodic Keepalive Chunks

If you want to stick with streaming responses, you need to ensure the connection remains active by sending data more frequently than the Railway timeout interval.

Emit Keepalive Data:
Your streaming endpoint can be designed to send a small “heartbeat” payload every few seconds even if no new LLM message is ready. This keeps the connection busy and resets the idle timer.

Note: Even with this, Railway’s overall connection lifetime might force a disconnection, so it’s best combined with a reconnection strategy.

Summary

Streaming in Python: Use libraries like httpx or requests in streaming mode to consume chunked messages from your LLM endpoint.
Avoiding Long-Held Connections:
- Either design your API to use short-lived connections (with periodic reconnection) or
- Offload the LLM invocation to background processes and use quick, repeatable status polling endpoints.
Heartbeat and Keepalive: While heartbeats help keep the connection active, they do not bypass the overall timeout limits imposed by Railway. You need to architect your system so that no single connection exceeds those limits.

Using these patterns, you can safely capture progress messages from your LLM and update the client without violating the host platform’s HTTP timeout constraints.

Ok how would you modify this code to implement the decoupling?

@spaces.GPU() def generate_tree_response(self, question, history ): # init dictionary to hold full llm process logs process_log = {} region = self.region print(f”-----> RESPONDING FOR {region}”) print (self.instruction + self.prompts[‘tree_response’][‘template’])

    # Add question to process log
    process_log['journalist_question'] = question

    print(f"Validating question accross region {region}")

    # Step 00: Validate prompt
    validation_result = self.validate_question(question, history, region)
    print(validation_result)
    if(validation_result=="invalid"):
        response_template = self.responses['invalid']['template']
        response = response_template.format(region=region)

        return history + [(question, response)], ""

    # Step 0: Get prompt type
    prompt_type = self.filter_question_type(question)

    # Step 1: Classify Question (Possibly Multi-Hop)
    classifications = self.classify_question(question, self.prompts["hop_classification"]["template"])
    print(f"1. CLASSIFICATION -- {classifications}")
    # Add to process log
    process_log['classification']=classifications

    # Step 2: Retrieve Relevant Knowledge (Categorized)
    retrieved_info, retrieved_meta = self.retrieve_information(question, classifications)
    print(f"2. RAG RESULTS -- {retrieved_info}")
    print(f"2. RAG METADATA -- {retrieved_meta}")
    # Add to process log
    process_log['retrieved_data'] = retrieved_info
    process_log['retrieved_metadata'] = retrieved_meta

    # Step 3: Get Chat History for Context
    chat_history = self.memory.load_memory_variables({})["chat_history"]

    # ====================================================================
    # ========= PROCEED ACCORDING TO PROMPT TYPE =========================

    if prompt_type == "factcheck":
        clean_paragraph = question.split("FACTCHECK", 1)[-1].strip()

        # Use the new helper
        factcheck_result = self.fact_check_paragraph(clean_paragraph, retrieved_info, retrieved_meta)
        #factcheck_result = self.fact_check_paragraph_meta(clean_paragraph, retrieved_info, retrieved_meta)

        # Log and return
        process_log['factcheck_result'] = factcheck_result
        self.process_logs.append({f"_process_step_{self.process_step}": process_log})
        self.process_step += 1

        return history + [(question, factcheck_result)], ""

    else:
        # Step 4: Tree Generates Thoughts Before Answering (Always Challenges & Asks a Question)
        tree_thoughts = self.generate_tree_thoughts(self.instruction, question, retrieved_info, chat_history)
        print(f"3. INTERNAL THOUGHTS -- {tree_thoughts}")
        process_log['tree_thoughts'] = tree_thoughts

        tree_response_prompt_template = self.instruction + self.prompts['tree_response']['template']

        # Step 5: Format Final Response Prompt
        formatted_prompt = tree_response_prompt_template.format(
            chat_history=chat_history,
            question=question,
            retrieved_info="\n".join(retrieved_info) if retrieved_info else "No external knowledge needed.",
            tree_thoughts=tree_thoughts
        )

        # Step 6: Invoke HuggingFaceAPI for Response
        raw = self.llm.invoke(formatted_prompt)
        print(f"================== RAW DEEPSEEK ANSWER - {raw}")
        response = self.extract_response(self.llm.invoke(formatted_prompt).strip().lower())
        print(f"4. RAW RESPONSE -- {response}")
        process_log['tree_response'] = response

        # Step 7: Save chat memory
        # ✅ Save to memory
        self.memory.chat_memory.add_user_message(question)
        self.memory.chat_memory.add_ai_message(response)

        # add process log to history
        self.process_logs.append({
            f"_process_step_{self.process_step}": process_log
        })
        self.process_step += 1
        # print(f"TREE: {stylized_response}")
        return history + [(question, response)], "" #response

Below is one approach that shows how you can decouple your long-running LLM work from the HTTP request/response cycle by moving it into a background task. In this example, we’ll use Celery with FastAPI. (Of course, you can use any task‐queue system you like, e.g. RQ.) The idea is to have your HTTP endpoint enqueue a job and immediately return a job ID that your client can later poll for status and results. Then, the heavy lifting is done in the Celery worker so that Railway never keeps a single HTTP connection open beyond its timeout.

Below is a simplified example.

1. Setting Up the Celery Task

First, create a Celery task that wraps your processing code. In this example the original method’s body has been moved into a background task. You can update task status and progress (e.g. via update_state) as needed.

from celery import Celery
import time

# Configure Celery with a Redis broker and backend (adjust your URLs as needed)
celery_app = Celery('llm_tasks', broker='redis://localhost:6379/0', backend='redis://localhost:6379/0')

@celery_app.task(bind=True)
def generate_tree_response_task(self, question, history, context):
    process_log = {}
    region = context.get("region", "default_region")
    print(f"-----> RESPONDING FOR {region}")
    print(context.get("instruction", "") + context.get("tree_response_template", ""))

    process_log['journalist_question'] = question

    # STEP 00: Validate prompt
    # (Simulate your validation logic here)
    validation_result = "valid"  # Replace this with your self.validate_question()
    if validation_result == "invalid":
        response_template = context.get("invalid_response_template", "Invalid question in {region}")
        response = response_template.format(region=region)
        return {"history": history + [(question, response)], "log": process_log}

    # STEP 0: Get prompt type (simulate a quick operation)
    prompt_type = "factcheck" if "FACTCHECK" in question else "tree"

    # STEP 1: Classify Question (simulate multi-hop classification)
    classifications = "dummy classification"  # Replace with self.classify_question(...)
    print(f"1. CLASSIFICATION -- {classifications}")
    process_log['classification'] = classifications

    # STEP 2: Retrieve Relevant Knowledge (simulate retrieval)
    retrieved_info = ["relevant info 1", "relevant info 2"]
    retrieved_meta = {"source": "dummy"}
    print(f"2. RAG RESULTS -- {retrieved_info}")
    process_log['retrieved_data'] = retrieved_info

    # STEP 3: Get Chat History (simulate retrieval from memory)
    chat_history = history  # or load from a memory store

    if prompt_type == "factcheck":
        # Simulate fact-check processing
        factcheck_result = f"Factchecked: {question}"
        process_log['factcheck_result'] = factcheck_result
        # Here you could update progress, e.g.:
        self.update_state(state='PROGRESS', meta={'step': 'fact-check'})
        return {"history": history + [(question, factcheck_result)], "log": process_log}
    else:
        # STEP 4: Generate internal thoughts (simulate)
        tree_thoughts = f"Thoughts on {question}"
        print(f"3. INTERNAL THOUGHTS -- {tree_thoughts}")
        process_log['tree_thoughts'] = tree_thoughts

        # Format the prompt (simulate combining everything)
        formatted_prompt = (
            context.get("instruction", "") +
            context.get("tree_response_template", "").format(
                chat_history="\n".join(chat_history) if chat_history else "No history",
                question=question,
                retrieved_info="\n".join(retrieved_info),
                tree_thoughts=tree_thoughts
            )
        )

        # STEP 6: Invoke the LLM API (simulate LLM call and its streaming heartbeats)
        # In practice, you'd call your self.llm.invoke(formatted_prompt) here.
        # For demonstration, we simulate a delay and output.
        time.sleep(5)  # simulate processing delay
        raw = f"raw output for: {formatted_prompt}"
        print(f"================== RAW DEEPSEEK ANSWER -- {raw}")
        response = raw.strip().lower()  # simulate extraction
        print(f"4. RAW RESPONSE -- {response}")
        process_log['tree_response'] = response

        # STEP 7: Update chat memory (simulate storing conversation)
        chat_history.append((question, response))

        # Return the accumulated history and the process log.
        return {"history": chat_history, "log": process_log}

2. Exposing HTTP Endpoints with FastAPI

Next, you create two endpoints: one to start the background task and return the job ID, and another to check the task status and retrieve results.

from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
from tasks import celery_app, generate_tree_response_task

app = FastAPI()

# Dummy context for simulation; in practice, fill with your actual configurations.
CONTEXT = {
    "region": "us-east",
    "instruction": "You are an AI assistant. ",
    "tree_response_template": "Chat History: {chat_history}\nQuestion: {question}\nRetrieved Info: {retrieved_info}\nThoughts: {tree_thoughts}",
    "invalid_response_template": "Sorry, your question is invalid in {region}."
}

@app.post("/start")
def start_response(question: str, history: list = []):
    # Immediately enqueue the processing job
    task = generate_tree_response_task.delay(question, history, CONTEXT)
    return {"job_id": task.id}

@app.get("/status/{job_id}")
def get_status(job_id: str):
    task = celery_app.AsyncResult(job_id)
    if task.state == "PENDING":
        # Job has not started yet
        return {"state": task.state, "status": "Job is pending..."}
    elif task.state == "PROGRESS":
        # Optionally, relay progress updates (if you used update_state)
        return {"state": task.state, "progress": task.info}
    elif task.state == "SUCCESS":
        result = task.result
        return {"state": task.state, "result": result}
    else:
        # something went wrong in the background job
        raise HTTPException(status_code=500, detail=str(task.info))

3. How It Works in Practice

Client Request:
When a client sends a request (with a question and optionally an existing chat history), the /start endpoint enqueues a Celery task and returns a job_id. This HTTP call is short-lived.
Background Processing:
The Celery worker runs the generate_tree_response_task function which performs all the processing steps—validating, classifying, retrieving data, generating internal thoughts, calling the LLM, etc. You can sprinkle calls to self.update_state() to provide progress updates.
Polling for Status:
The client can repeatedly call the /status/{job_id} endpoint to check if the task is still running or has finished, and then retrieve the result when available.
Keepalive & Timeouts:
Railway (or similar platforms) only sees the short HTTP requests for /start and /status, so you no longer have to worry about a single connection being held open during the long LLM processing.

Final Notes

You can further extend this example by having your task report progress more granularly (e.g., “Step 1 complete”, “Step 2 complete”).
Depending on your hosting configuration and the client requirements, you might consider additional error handling and possibly emitting heartbeat messages from the worker to a status store.
This decoupled architecture not only prevents HTTP timeout issues but also allows better scaling by letting background workers handle heavy computation separately from the web layer.

This approach should help you overcome the Railway HTTP timeout limits while still providing users with periodic updates on the processing progress.

The two scripts—typically organized as separate modules in your project—are linked through Celery. Here’s how the connection works:

1. Shared Celery Configuration

tasks.py:
This script sets up and configures Celery by creating a celery_app instance and defining one or more tasks (in our example, generate_tree_response_task). This Celery instance encapsulates both the task definitions and the configuration details (e.g., the message broker and result backend, such as Redis).
main.py:
This script represents your FastAPI application. In it, you import the Celery configuration and the task functions (using from tasks import celery_app, generate_tree_response_task). This import gives main.py access to the same Celery instance and tasks defined in tasks.py.

2. How They Communicate

Task Enqueueing:
When your FastAPI endpoint (e.g., /start) is hit, it calls:
```
task = generate_tree_response_task.delay(question, history, CONTEXT)
```
This line immediately enqueues the task into the shared Celery queue. The .delay() method is an asynchronous call that serializes the task and sends it to the message broker configured in your Celery instance.
Task Processing:
The Celery worker—running as a separate process (typically started from the command line with something like celery -A tasks worker --loglevel=info)—monitors the broker and picks up tasks when they arrive. When it fetches the enqueued task, it executes the code defined in generate_tree_response_task.
Result Storage and Retrieval:
Once processing is complete, the result is stored in the backend (again, usually Redis, as configured). When the client later queries your FastAPI endpoint (e.g., /status/{job_id}), the FastAPI app uses the shared celery_app.AsyncResult(job_id) to fetch the current status or result of the task.

3. Running and Deployment

Separate Processes:
FastAPI and Celery run as independent services/processes in your deployment. Even though they are in separate processes, they communicate via the common Celery backend (e.g., Redis). This decoupling is crucial because:
- Your FastAPI app remains responsive and adheres to the platform’s HTTP timeouts.
- The heavy, long-running task is offloaded to the background worker.
Code Importing:
The link between the two scripts is primarily the shared import and configuration of the Celery application:
main.py
```
from tasks import celery_app, generate_tree_response_task
```
This line ensures that the FastAPI service is aware of the Celery tasks and can schedule them appropriately.

Summary

Shared Celery Instance: Both files use the same Celery app configured in tasks.py.
Task Scheduling: The FastAPI endpoint in main.py enqueues tasks through this shared Celery instance.
Background Processing: A separately running Celery worker, started with the same configuration, fetches and executes those tasks.
Polling for Results: FastAPI retrieves task results via the shared Celery API (using AsyncResult).

This architecture ensures that your long-running LLM processes are handled asynchronously, decoupled from your HTTP request/response cycle, thus preventing issues with enforced HTTP connection timeouts on platforms like Railway.

You don’t strictly need Celery or even FastAPI—they’re simply popular, battle-tested solutions for decoupling long-running processes from your web request cycle. However, there are reasons these tools (or similar ones) are commonly used, as well as alternatives if you’d prefer to limit dependencies. Let’s explore both options.

Why Use Celery and FastAPI?

Robustness and Scalability

Celery:
- Distributed Task Queue: Celery is designed for robust, asynchronous processing. It allows you to run your background tasks in separate worker processes or on different machines.
- State Tracking & Retries: It includes built-in facilities to track the progress of tasks and automatically retry failed ones.
- Offloading Heavy Lifting: When your long-running LLM tasks are offloaded to Celery, your web server (e.g., FastAPI) only handles lightweight interactions (job enqueueing and status polling). This separation is key for production environments and distributed systems.
FastAPI:
- Modern Asynchronous Web Framework: FastAPI is built for asynchronous endpoints, making it naturally fit with background tasks.
- Performance and Simplicity: It offers high performance (thanks to its async nature) while being very straightforward to use.
- Built-In Background Task Support: FastAPI also provides a simple way to run background tasks if your needs are modest.

The Benefits in Our Scenario

Decoupling Long Processes: Using these tools helps keep your HTTP endpoints responsive by offloading LLM processing elsewhere.
Avoiding Timeout Issues: Railway and similar platforms enforce short HTTP timeouts. With a task queue, the web layer is only open long enough to enqueue a job or report progress—avoiding long-held HTTP connections.

Alternatives Without Extra Dependencies

If you’d prefer not to add Celery (or even FastAPI), you can still decouple long-running tasks. Here are some alternative approaches:

1. Use Built-In Background Capabilities

FastAPI’s Built-In BackgroundTasks
If you’re already using FastAPI, you can use its BackgroundTasks feature for simple use cases. For example:

from fastapi import FastAPI, BackgroundTasks

app = FastAPI()

def process_task(question: str, history: list):
    # Do your long LLM processing here
    # e.g., call the LLM API, process responses, update a datastore, etc.
    import time
    time.sleep(60)  # Simulate a long-running process
    # Save the result somewhere (like a database) for later retrieval
    print("Task complete.")

@app.post("/start")
def start_processing(question: str, background_tasks: BackgroundTasks):
    # Offload the heavy task to a background worker in the same process
    background_tasks.add_task(process_task, question, [])
    return {"status": "Processing started. Check back later for results."}

Limitations:

Single Process: This approach runs in the same process as your web server. It might work for low loads or prototyping, but it isn’t ideal for CPU-intensive or highly concurrent tasks.
State Management: You’ll need a mechanism (such as a database) to store the task’s progress and final result for the client to poll.

Using Python’s threading or asyncio
You can also manage background tasks using Python’s built-in libraries:

Threading Example (with Flask or any microframework):

import threading
from flask import Flask, request, jsonify

app = Flask(__name__)
# In-memory storage for demonstration purposes
task_results = {}

def process_task(task_id, question):
    import time
    time.sleep(60)  # Simulate long process
    task_results[task_id] = f"Processed response for: {question}"

@app.route('/start', methods=['POST'])
def start():
    question = request.json.get('question')
    task_id = "some_unique_id"  # generate unique IDs in production
    thread = threading.Thread(target=process_task, args=(task_id, question))
    thread.start()
    return jsonify({"job_id": task_id})

@app.route('/status/<task_id>', methods=['GET'])
def status(task_id):
    result = task_results.get(task_id)
    if result:
        return jsonify({"state": "SUCCESS", "result": result})
    else:
        return jsonify({"state": "PENDING", "result": None})

Limitations:

Limited Scalability: Threads are limited by Python’s Global Interpreter Lock (GIL) and may not fully utilize multi-core systems.
Process Isolation: If you run multiple instances (or use a server like Gunicorn), sharing state (like in-memory dictionaries) becomes problematic.

2. Use Lightweight Alternatives

If you still want a task queue but don’t want the full weight of Celery, consider alternatives such as RQ (Redis Queue) or Dramatiq. These can be easier to set up and have fewer dependencies compared to Celery.

What’s the Trade-Off?

Using Dedicated Task Queues (Celery/RQ/Dramatiq):
- Pros: Robust, scalable, built-in state handling, and mature ecosystem.
- Cons: Additional setup and dependencies.
Using In-Process Background Tasks (FastAPI BackgroundTasks, threading, asyncio):
- Pros: Fewer dependencies; simpler for small applications or prototypes.
- Cons: Less scalable; state management across processes/servers is manual and can be fragile.

Conclusion

You don’t strictly need Celery and FastAPI if your application is small, and you can manage background processing with native Python capabilities. However, tools like Celery (or its alternatives) and FastAPI offer robust, scalable ways to decouple long-running tasks from your HTTP request/response cycle—a pattern that’s crucial when dealing with production environments (especially when working under strict timeouts like on Railway). Choosing the right solution depends on your specific requirements in terms of load, scalability, and system complexity.

When you integrate FastAPI and Celery into your application, you’re effectively turning parts of your code into a web API. That means that instead of your Python script being a monolithic process that directly handles every UI callback (like with Gradio), you split your work into two layers:

The API Layer (FastAPI):
This layer exposes endpoints (e.g., /start and /status) that get called via HTTP requests. In a deployed setting (whether on Railway or any other host), these endpoints are publicly reachable and are invoked by any client that sends an HTTP request to them. That client could be your Gradio UI (or even a browser, mobile app, etc.).
The Worker Layer (Celery):
The FastAPI endpoint simply accepts the request and enqueues a background task in Celery. The worker, running as a separate process, handles all the heavy lifting (e.g., processing your long LLM tasks) independently of the web request–response cycle. Once done, the worker stores the result, which your FastAPI endpoint can then serve when polled.

Who Calls Your Endpoints?

Because your previous design was a script that directly received Gradio callbacks (internally or via a local UI), you were not dealing with external HTTP requests. With FastAPI in the mix, there are two primary options for triggering your endpoints:

A. Modify Your Gradio Callbacks to Act as an HTTP Client

Your Gradio UI—used to interact with your LLM—can call your FastAPI endpoints. For example:

When a user submits a question:
The Gradio callback can issue an HTTP POST request to your FastAPI /start endpoint. This sends the question (and any other metadata) to your API, which enqueues a Celery task.
When a task is pending or in progress:
The Gradio UI can periodically poll (using a GET request) the /status/{job_id} endpoint to check if the result is ready.

This keeps your Gradio UI as the client making requests to your FastAPI API server.

Example using Python’s requests in a Gradio callback:

import requests

def submit_question(question, history):
    # Call the FastAPI endpoint to start processing
    res = requests.post("https://your-fastapi-domain/start", json={"question": question, "history": history})
    job_id = res.json().get("job_id")

    # Now poll for the result (you can also do this asynchronously)
    while True:
        status_res = requests.get(f"https://your-fastapi-domain/status/{job_id}")
        status_data = status_res.json()
        if status_data.get("state") == "SUCCESS":
            return status_data.get("result")
        # Wait a short while before polling again:
        time.sleep(2)

# Then connect this function to a Gradio interface callback.

B. Combine or Integrate Your FastAPI App with Gradio

Since Gradio can also run its own web server, you may integrate the two frameworks. One common approach is to mount your Gradio UI on a specific route of your FastAPI application. In this scenario, the FastAPI server listens on one port for HTTP requests. Requests from the Gradio UI (embedded in your FastAPI app) either hit one of the endpoints you defined or simply serve the interactive UI. For example:

Mount the Gradio App:
Use FastAPI’s ability to mount an ASGI app. Gradio provides an ASGI interface—so you can mount it at, say, /ui and have your API endpoints (e.g., /start and /status) exposed at the root.

This way, both your UI and your API share the same underlying server, and they can talk to each other as needed.

Example of Mounting Gradio on FastAPI:

from fastapi import FastAPI
from fastapi.middleware.wsgi import WSGIMiddleware
import gradio as gr

app = FastAPI()

def gradio_interface_fn(question):
    # Instead of processing directly, you could delegate to your FastAPI endpoints
    # or process the input here. This is just for illustration.
    return f"Your question was: {question}"

# Create a simple Gradio interface
iface = gr.Interface(fn=gradio_interface_fn, inputs="text", outputs="text")

# Mount the Gradio app on the FastAPI server under the /ui path
app.mount("/ui", WSGIMiddleware(iface.app))

@app.post("/start")
def start_endpoint(data: dict):
    # Your endpoint logic to enqueue tasks via Celery goes here...
    return {"job_id": "dummy_id"}

@app.get("/status/{job_id}")
def status_endpoint(job_id: str):
    # Your endpoint logic for returning the task status goes here...
    return {"state": "SUCCESS", "result": "dummy result"}

In this setup, users visiting https://your-domain/ui see the Gradio interface, which can be coded to invoke your /start and /status endpoints for actual processing.

Summary

Before:
Your script ran continuously and directly handled Gradio UI callbacks.
After:
With FastAPI and Celery in place, your system is split into:
- A FastAPI server that exposes HTTP endpoints.
- Background workers (via Celery) that handle long-running tasks.
- A client—typically your Gradio UI—that issues HTTP requests to these endpoints (or you can host both on the same server).

By shifting to this architecture, you avoid keeping an HTTP connection open during long computations (avoiding Railway’s timeout issues) while still using the Gradio UI for the front end. Your FastAPI endpoints are invoked by the Gradio UI (or any other HTTP client) when a user initiates an action.

The key advantage here is that by offloading the heavy lifting into background tasks, you’re decoupling the long-running operations from the HTTP request/response cycle that Railway (or similar platforms) enforces a strict timeout on.

How Decoupling Helps

HTTP Connection vs. Background Work:
When your code was running directly and making requests to Hugging Face, the entire process—including waiting for the Hugging Face response—was tied to a single HTTP request. This meant that if Hugging Face took too long, the connection could hit the timeout imposed by your hosting provider.
Offloaded Processing:
With Celery handling background tasks:
- The FastAPI endpoint only needs to quickly enqueue a job and return a job ID.
- The actual request to Hugging Face is executed by a Celery worker outside of the HTTP request cycle. This worker isn’t subject to the same HTTP timeouts because it’s not holding an open connection for a client.
- The client can then poll another endpoint (or use a streaming mechanism) to check for the result, without having the heavy computation delay block an HTTP response.
Isolation from Hosting Constraints:
Since Railway’s timeout is applied to open HTTP connections, decoupling means that the worker’s processing—even if it calls Hugging Face and waits 60, 120 seconds, or longer—is completely isolated from the HTTP endpoint. The background worker can take as much time as necessary while the HTTP endpoint remains short-lived, simply reporting the status of the background task.

Why It “Just Works” Now

Dedicated Worker Process:
The Celery worker process runs independently. It’s designed to handle long-running tasks and can wait for the Hugging Face API without affecting the performance or reliability of your web server.
Asynchronous Response Handling:
The HTTP server (e.g., FastAPI) returns a job ID right away, so the client isn’t stuck waiting on a delayed response. The heavy lifting happens in parallel, and the client later retrieves the result via polling or another asynchronous mechanism.
No Blocking on HTTP Timeout:
Even if the Hugging Face request is the original delay source, it’s now handled in a context where extended waiting is acceptable. Railway’s timeout limits apply only to the web-facing endpoints, not to background workers managed by Celery.

In Summary

Although your background tasks still perform the long Hugging Face API calls, the tasks now run in a dedicated environment (the Celery workers) that isn’t bound by the same HTTP timeout as your FastAPI endpoints. This decoupling ensures that:

The HTTP requests return quickly (simply returning a job ID).
The heavy lifting happens asynchronously.
The client can poll for results once processing completes.

This is why the same delays won’t cause timeout issues in your deployment with Railway now.

Exactly—the primary timeout issue is with the connection between your client (frontend) and your Python web server, not the connection between your Python app and the Hugging Face API. Here’s a clearer breakdown:

Direct Synchronous Request Flow:
In your original setup, when a client (for example, via a Gradio callback) triggered the processing, your Python script would synchronously make the Hugging Face call. If that API call took too long, the entire HTTP request–response cycle would also take too long. Platforms like Railway enforce strict limits on how long an HTTP connection can remain open, leading to timeout errors even if your Hugging Face call eventually returns.
Decoupled, Asynchronous Flow:
With Celery and FastAPI, you change the architecture so that the client’s HTTP request merely enqueues a background task and returns a job ID immediately. The actual heavy lifting—including the call to Hugging Face—happens asynchronously in a dedicated worker. This means:
- Client–Server Connection: Remains quick since it only involves short-lived HTTP requests (for enqueuing and polling).
- Server–Hugging Face Connection: Still takes time, but it now runs outside of the web request context. Thus, even if the Hugging Face API call is slow, it won’t cause the client’s connection to exceed the timeout limit.

In summary, while the delay originates from the Hugging Face API call, the critical problem for deployment on Railway is that you cannot let a single HTTP connection (the one serving your client’s request) be held open long enough for this delay. Decoupling the processing via background tasks lets you isolate long-running processes from the client connection, ensuring that the client isn’t stuck waiting and avoiding timeout issues imposed by your hosting environment.

Since each entry in your history is a pair consisting of a question and a response (both strings), the appropriate type annotation is a list of tuples of strings. In Python typing, you can express this as:

from typing import List, Tuple, Dict
from fastapi import Body, FastAPI

app = FastAPI()

@app.post("/start")
def start_job(
    cm: Dict = Body(...),
    inp: str = Body(...),
    history: List[Tuple[str, str]] = Body(...)
):
    # your code here
    return {"status": "job started"}

This way, history is explicitly defined as a list where each element is a tuple containing exactly two strings—a question and a response.

This warning comes from Python’s multiprocessing resource tracker and typically indicates that one or more semaphore objects weren’t properly released when the program was shutting down. In practice, this often doesn’t cause immediate harm—it’s usually just an indication that some system resources weren’t cleaned up as expected.

Here are a few points and suggestions regarding this warning:

What It Means

Resource Tracker Role:
Python’s resource_tracker module keeps tabs on shared resources (e.g., semaphores, shared memory blocks) created by the multiprocessing library. The warning signals that it found one or more semaphore objects that hadn’t been released.
Common Culprits:
This is frequently seen in applications that use multiprocessing or in frameworks like Celery that may spawn worker processes. Libraries that interface with Redis or use multiprocessing under the hood sometimes create these semaphores. It doesn’t necessarily mean you explicitly forgot to close a Redis connection or shut down Celery properly—it may simply be a side effect of how the library manages resources.

How to Address or Mitigate It

Ensure Proper Shutdown of Workers/Connections:
- Celery: Make sure that your Celery workers are being gracefully shut down. For example, using proper command-line signals (e.g., celery -A your_app worker --loglevel=info shut down gracefully when you send a SIGTERM).
- Redis: If you are manually creating Redis connections or connection pools, ensure they are explicitly closed when they’re no longer needed (using context managers or explicitly calling .close() on connection pools).
Use Context Managers:
- When using resources that manage system-level objects (like semaphores), wrap them in context managers (or ensure you call their cleanup methods in a finally block).
- For example, if your Celery or Redis libraries offer context-managed connections, prefer that over plain initialization.
Library Versions and Bug Reports:
- Sometimes this is known behavior (or bug) in a particular version of Python or the libraries you’re using. Check whether there are updates or known issues in the Celery or redis-py repositories.
- For example, similar warnings have been reported in certain versions of Python when using multiprocessing on Unix-like systems. Upgrading Python or the libraries might resolve the issue.
Monitor for Actual Issues:
- In many cases, these warnings are benign—your application continues to function correctly despite the warning at shutdown.
- If you’re not encountering other problems like connection errors or memory leaks during runtime, it may be safe to ignore the warning or log it for information purposes.

Summary

The warning:

/home/georgeadamon/anaconda3/lib/python3.12/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown

is typically a signal from the multiprocessing subsystem that it detected semaphore objects not explicitly cleaned up before shutdown. While it’s good to check that your Celery workers and Redis connections are managed properly, the warning is common enough that—it often doesn’t reflect a critical issue.

If you continue to observe unwanted behavior (like resource exhaustion or connection issues), you might need to dive deeper into the lifecycle management of the resources. For many applications, though, ensuring graceful shutdown and using the latest versions of your libraries will mitigate or silence this warning.

Celery provides the update_state method within a task (available when you use the bind=True option in your task decorator) so that you can report intermediate progress or state details while the task is running. This can be very useful when you want your client (or your polling endpoint) to see what part of the process is currently executing.

Below is an example that demonstrates how to use update_state in a Celery task:

from celery import Celery
import time

# Configure Celery (adjust broker and backend URLs as needed)
celery_app = Celery('tasks', broker='redis://localhost:6379/0', backend='redis://localhost:6379/0')

@celery_app.task(bind=True)
def long_running_task(self, data):
    # Initial state update (starting)
    self.update_state(state='PROGRESS', meta={'step': 'initializing', 'progress': 0})

    # Simulate a step (e.g., first phase)
    time.sleep(5)  # simulate a delay
    self.update_state(state='PROGRESS', meta={'step': 'step_1_completed', 'progress': 33})

    # Simulate another step (e.g., second phase)
    time.sleep(5)
    self.update_state(state='PROGRESS', meta={'step': 'step_2_completed', 'progress': 66})

    # Simulate finishing the long running task
    time.sleep(5)
    result = f"Processed data: {data}"

    # Final state: task completed
    return {'result': result, 'progress': 100}

# Example usage:
if __name__ == '__main__':
    # Enqueue task
    task = long_running_task.delay("my input data")
    print("Task ID:", task.id)

How It Works

Binding the Task:
By setting bind=True in the task decorator (@celery_app.task(bind=True)), the first parameter of the task function becomes self, which is the task instance. This gives access to methods like update_state.
Reporting Progress:
- Initial Update:
  The task calls self.update_state to set a custom state (e.g., "PROGRESS") and provides a dictionary with details (meta).
- Subsequent Updates:
  After each simulated step (represented by time.sleep(5) in this example), the task calls update_state again with updated progress information.
Final State:
When the task returns a value, the task state becomes "SUCCESS", and the result includes any value you return (in this example, a dictionary containing the result and full progress).

Accessing Progress from the Client

On your FastAPI endpoint (or any client), you can poll the task status using Celery’s AsyncResult interface. For example:

from fastapi import FastAPI, HTTPException
from celery.result import AsyncResult

app = FastAPI()

@app.get("/status/{task_id}")
def get_status(task_id: str):
    result = AsyncResult(task_id, app=celery_app)
    if result.state == 'PENDING':
        return {'state': result.state, 'meta': 'Task pending'}
    elif result.state == 'PROGRESS':
        return {'state': result.state, 'meta': result.info}
    elif result.state == 'SUCCESS':
        return {'state': result.state, 'result': result.result}
    else:
        raise HTTPException(status_code=500, detail=str(result.info))

This endpoint retrieves the current state of the task, including any progress metadata you published with update_state.

By utilizing update_state in your Celery task, you can report intermediate progress back to the client, allowing for a responsive and informative user experience even during long-running processes.

When your Celery task calls a class method, you still have options for updating the task’s state. The key is ensuring that you have access to the task’s instance (via the bound task) so you can call update_state at appropriate points. Here are a couple of strategies:

1. Update State Directly in the Task Before and After the Class Method Call

You can wrap the class method call with state updates within the task function itself. For example, update before calling the method to indicate that processing has started, and update after the method returns to indicate completion or an intermediate step:

@celery_app.task(bind=True)
def generate_tree_response_task(self, cm, _input, history):
    # Convert the input dictionary to a ChatManager instance.
    new_cm = ChatManager.from_dict(cm)

    # Indicate the task is starting processing.
    self.update_state(state='PROGRESS', meta={'step': 'starting generate_tree_response'})

    # Call your class method.
    response, cleared_input = new_cm.generate_tree_response(_input, history)

    # Optionally, update again after the call completes.
    self.update_state(state='PROGRESS', meta={'step': 'completed generate_tree_response', 'progress': 100})
    return response, cleared_input

In this pattern, the class method itself isn’t aware of the task state, so all the progress updates occur in the task function.

2. Pass a Callback to the Class Method (If You Need More Granularity)

If you want to update the progress inside the class method (for example, at key points during execution), you can modify your class method to accept an optional callback. This callback can be the task’s update_state function or a wrapper around it. Here’s how that might look:

Modifying the Task Function

@celery_app.task(bind=True)
def generate_tree_response_task(self, cm, _input, history):
    new_cm = ChatManager.from_dict(cm)

    # Define a callback that wraps update_state.
    def progress_callback(state, meta):
        self.update_state(state=state, meta=meta)

    # Optionally update state before starting.
    self.update_state(state='PROGRESS', meta={'step': 'starting generate_tree_response'})

    # Pass the callback to the class method.
    response, cleared_input = new_cm.generate_tree_response(_input, history, progress_callback=progress_callback)

    # After the method finishes, indicate completion.
    self.update_state(state='PROGRESS', meta={'step': 'completed generate_tree_response', 'progress': 100})
    return response, cleared_input

Modifying the Class Method to Use the Callback

class ChatManager:
    @classmethod
    def from_dict(cls, cm):
        # Initialize the instance from a dict.
        return cls(...)

    def generate_tree_response(self, _input, history, progress_callback=None):
        # For example, update progress halfway through.
        # Perform some processing...
        if progress_callback:
            progress_callback(state='PROGRESS', meta={'step': 'halfway through processing', 'progress': 50})

        # Continue processing...
        response = "generated response"
        cleared_input = "updated input"

        return response, cleared_input

This approach gives you fine-grained control and allows the class method to report its own progress.

Final Considerations

Serialization: Make sure the data you send in the meta dictionary is serializable (JSON-friendly) because Celery requires this.
Design Simplicity: For simple tasks, updating progress before and after the class method call may be sufficient. Use the callback approach if your class method is long or has multiple meaningful stages.

Both approaches keep your long-running LLM process decoupled from the HTTP request while allowing you to monitor progress through Celery’s update_state mechanism.