scope-classifier 0.3.1

A scope classification library around your prompts

Scope Classifier

Given the specifications of an AI assistant and a user query, scope-classifier classifies the user query into one of the following five classes:

• Directly Supported: The query is clearly within the assistant's capabilities.
• Potentially Supported: The query could plausibly be handled by the assistant.
• Out of Scope: The query is outside the assistant's defined role.
• Restricted: The query cannot be handled due to a specific constraint.
• Chit Chat: The query is a social interaction not related to the assistant's function.

Available Models

Currently, the following models are supported:

Model	Parameters	Description	Accuracy	Required VRAM	Relative Speed
small (or edobobo/cernas-qwen3-1.7B-v0.4)	1.7B	A compact and fast model for general use.	85.3	8GB	1x
medium (or (mocked) organization/medium-model-7B)	7B	A larger model with better performance.	90.7	12GB	0.75x
large (or (mocked) organization/large-model-13B)	13B	The best performing model for complex tasks.	94.2	24GB	0.5x

Quickstart

Install scope-classifier:

pip install scope-classifier[vllm]

Then:

from scope_classifier import ScopeClassifier

# Initialize the classifier
classifier = ScopeClassifier(
    "vllm",  # backend (or use "hf" if you prefer not to use vllm)
    "small"  # model
)

# Define the AI assistant's scope
ai_service_description = (
    "You are a virtual assistant for a parcel delivery service. "
    "You can only answer questions about package tracking. "
    "Never respond to requests for refunds."
)

# Classify a user's query
user_query = "If the package hasn't arrived by tomorrow, can I get my money back?"
classification_output = classifier.classify(user_query, ai_service_description)

print(f"Scope: {classification_output.scope_class.value}")
if classification_output.evidences:
    print("Evidences:")
    for evidence in classification_output.evidences:
        print(f"  - {evidence}")

Installation

Internally, scope-classifier works by prompting local LLMs on your data. To do this, we currently support two possible backends:

1. HuggingFace (hf): Uses HuggingFace pipelines, ideal for offline processing
2. vLLM (vllm): Faster version using vLLM, strongly encouraged for better performance

While hf comes always bundled with scope-classifier, you need to install some more dependencies to use vllm:

pip install scope-classifier[vllm]

IMPORTANT: these two backends are local, meaning no external API call, no data leaving your network

Usage

Input Formats

The classify method is flexible and accepts various input formats for the conversation.

User query as a string

As shown in the Quick Start, you can pass the user query as a simple string.

classification_output = classifier.classify(
    "When is my package scheduled to arrive?",
    ai_service_description
)

User query as a dictionary

You can also provide the user query in a dictionary format, similar to OpenAI's API:

classification_output = classifier.classify(
    {"role": "user", "content": "When is my package scheduled to arrive?"},
    ai_service_description
)

Note that the role must be "user".

Conversation as a list of dictionaries

For multi-turn conversations, you can pass a list of messages:

conversation_history = [
    {"role": "user", "content": "I ordered a package, tracking number 1234567890"},
    {"role": "assistant", "content": "Great, the package is in transit. What would you like to know?"},
    {"role": "user", "content": "If it doesn't arrive tomorrow, can I get a refund?"},
]

classification_output = classifier.classify(conversation_history, ai_service_description)

The classifier will always consider the last user message for scope classification.

Batch Processing

You can classify multiple conversations at once using batch_classify.

Single AI Service Description

Classify multiple user queries against the same AI service description:

queries = [
    "If the package hasn't arrived by tomorrow, can I get my money back?",
    "When is the package expected to be delivered?"
]

classification_outputs = classifier.batch_classify(
    queries,
    ai_service_description=ai_service_description
)

for i, output in enumerate(classification_outputs):
    print(f"--- Query {i+1} ---")
    print(f"Scope: {output.scope_class.value}")
    if output.evidences:
        print("Evidences:")
        for evidence in output.evidences:
            print(f"  - {evidence}")

Multiple AI Service Descriptions

You can also provide a different AI service description for each conversation:

ai_service_descriptions = [
    "You are a virtual assistant for Postal Service. You only answer questions about package tracking. Never respond to refund requests.",
    "You are a virtual assistant for a Courier. You answer questions about package tracking. Never respond to refund requests."
]

classification_outputs = classifier.batch_classify(
    queries,
    ai_service_descriptions=ai_service_descriptions
)

# Process outputs...

AI Service Description

The AI service description can be provided in two ways:

1. As a single string: This is a straightforward way to describe the assistant's purpose and constraints.
2. As a structured object: For more detailed specifications and better performance, you can provide a dictionary or an object of type scope_classifier.modeling.AIServiceDescription. This is the recommended approach.

The AIServiceDescription object has the following fields:

• identity_role (str): The identity, role, and objectives of the AI Service.
• context (str): The context in which the AI Service operates (e.g., company, sector).
• functionalities (str): The functionalities provided by the AI Service.
• knowledge_scope (str): The scope of knowledge and expertise of the AI Service.
• conduct_guidelines (str): Conduct guidelines and principles followed by the AI Service.
• constraints (str): Constraints and limitations of the AI Service.
• fallback (str): Fallback mechanisms for out-of-scope requests.
• website_url (str, optional): The URL of the AI Service's website.

Using the structured format allows the underlying model to better understand the nuances of your AI assistant's scope and, therefore, perform better.

Example Usage

The examples/simple.py script demonstrates a basic use of the ScopeClassifier. It provides an example of how to classify a user query based on the AI service description using command-line arguments.

python examples/simple.py vllm small

The script will print the classification results and evidences extracted from the AI description:

# scope: Restricted
# evidences:
  * evidence: Non rispondere mai a richieste di rimborso.
# time: X.XX seconds

Model Serving

scope-classifier comes with built-in support for serving. For better performance, it consists of two components:

1. A vLLM serving engine that runs the model
2. A FastAPI server that provides the end-to-end API interface, mapping input data to prompts, invoking the vLLM serving engine and returning the response to the user

All of this is setup via the scope-classifier serve command:

# install the necessary packages
pip install scope-classifier[serve]

# start everything
scope-classifier serve small --port 8000

Once the server is running, you can interact with it as follows:

1. Direct HTTP Requests

Send requests to the /api/n/scope-classifier/classify (or /api/in/scope-classifier/batch-classify) endpoint using cURL or any HTTP client:

curl -X 'POST' \
  'http://localhost:8000/api/in/scope-classifier/classify' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "conversation": "If the package doesn''t arrive by tomorrow, can I get my money back?",
  "ai_service_description": "You are a virtual assistant for a parcel delivery service. You can only answer questions about package tracking. Never respond to requests for refunds."
}'

Response:

{
  "evidences": [
    "Never respond to requests for refunds."
  ],
  "scope_class": "Restricted",
  "time_taken": 0.23,
  "model": "small"
}

2. Python SDK

scope-classifier comes with built-in SDKs to invoke the server directly from Python. These SDKs support both synchronous and asynchronous invocations:

Synchronous API client:

from scope_classifier import ScopeClassifier

classifier = ScopeClassifier(
    "api",  # backend
    "http://localhost:8000"  # API URL
)

result = classifier.classify(
    "If the package doesn't arrive by tomorrow, can I get my money back?",
    "You are a virtual assistant for a parcel delivery service. You can only answer questions about package tracking. Never respond to requests for refunds."
)

print(f"Scope: {result.scope_class.value}")

Asynchronous API client:

For async usage, use the AsyncScopeClassifier with the async backend and await the classify calls:

from scope_classifier import AsyncScopeClassifier

classifier = AsyncScopeClassifier(
    "async",  # backend
    "http://localhost:8000"  # API URL
)

result = await classifier.classify(
    "If the package doesn't arrive by tomorrow, can I get my money back?",
    "You are a virtual assistant for a parcel delivery service. You can only answer questions about package tracking. Never respond to requests for refunds."
)

print(f"Scope: {result.scope_class.value}")

License

This project is licensed under the Apache 2.0 License.