Exposing completions for use in an LLM flow involves making the model’s output accessible to other components or systems in your application. This typically requires capturing the completion result, formatting it for downstream use, and integrating it into workflows via APIs, function calls, or event triggers. For example, you might design a function that calls an LLM API, processes the response, and returns it in a structured format like JSON, which other parts of your code can consume. The key is to ensure the output is reliably passed to subsequent steps, whether for decision-making, data storage, or user interaction.
A common approach is to wrap the LLM API call in a reusable function or service. Suppose you’re using OpenAI’s API: after sending a prompt, you could extract the completion text, validate it, and pass it to a database or another service. For instance, a Python function might look like this:
def generate_response(prompt):
response = openai.Completion.create(model="gpt-3.5-turbo", prompt=prompt)
completion_text = response.choices[0].text.strip()
return {"result": completion_text, "status": "success"}
This function returns a structured dictionary, making it easy for other code to handle errors or route the output. You could also expose this via a REST API endpoint using frameworks like Flask or FastAPI, allowing external systems to request completions and integrate them into their workflows. Webhooks or message queues (e.g., RabbitMQ) can further decouple the LLM call from downstream processes, enabling asynchronous handling.
Considerations for robustness include error handling (e.g., retries for API failures), logging completions for debugging, and ensuring outputs align with expected formats. For example, if your flow requires the completion to be a valid email address, add validation steps before passing it to an email-sending service. You might also cache frequent requests to reduce latency or costs. By designing modular components—such as separate modules for API calls, validation, and integration—you create a flexible system where completions can be adapted for multiple use cases, like chatbots, data enrichment, or automated document generation.