Merge pull request #15 from guardrails-ai/main

manifest and endpoint updates

README.md

@@ -1,17 +1,18 @@
-## Overview
+# Overview
 
 | Developed by | Tryolabs |
-| --- | --- |
 | Date of development | Feb 15, 2024 |
 | Validator type | Format |
-| Blog | - |
+| Blog |  |
 | License | Apache 2 |
 | Input/Output | Output |
 
 ## Description
+
+### Intended Use
 This validator checks if a text is related with a topic.
 
-## Requirements
+### Requirements
 
 * Dependencies:
 	- guardrails-ai>=0.4.0
@@ -22,15 +23,15 @@ This validator checks if a text is related with a topic.
 * Foundation model access keys:
 	- OPENAI_API_KEY
 
-# Installation
+## Installation
 
 ```bash
-guardrails hub install hub://tryolabs/restricttotopic
+$ guardrails hub install hub://tryolabs/restricttotopic
 ```
 
-# Usage Examples
+## Usage Examples
 
-## Validating string output via Python
+### Validating string output via Python
 
 In this example, we apply the validator to a string output generated by an LLM.
 
@@ -59,7 +60,7 @@ The Beatles were a charismatic English pop-rock band of the 1960s.
 """)  # Validator fails
 ```
 
-## Validating JSON output via Python
+### Validating JSON output via Python
 
 In this example, we apply the validator to a string field of a JSON output generated by an LLM.
 
@@ -123,6 +124,6 @@ Note:
 2. When invoking `guard.parse(...)`, ensure to pass the appropriate `metadata` dictionary that includes keys and values required by this validator. If `guard` is associated with multiple validators, combine all necessary metadata into a single dictionary.
 
 **Parameters**
-- **`value`** *(Any):* The input value to validate.
-- **`metadata`** *(dict):* A dictionary containing metadata required for validation. No additional metadata keys are needed for this validator.
+- **`value`** *(Any)*: The input value to validate.
+- **`metadata`** *(dict)*: A dictionary containing metadata required for validation. No additional metadata keys are needed for this validator.
 </ul>

app.py

@@ -0,0 +1,76 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Union
+from transformers import pipeline
+import torch
+import os
+
+app = FastAPI()
+
+env = os.environ.get("env", "dev")
+torch_device = "cuda" if env == "prod" else "cpu"
+
+# Initialize the zero-shot classification pipeline
+classifier = pipeline(
+    "zero-shot-classification",
+    model="facebook/bart-large-mnli",
+    device=torch.device(torch_device),
+    hypothesis_template="This example has to do with topic {}.",
+    multi_label=True,
+)
+
+class InferenceData(BaseModel):
+    name: str
+    shape: List[int]
+    data: Union[List[str], List[float]]
+    datatype: str
+
+class InputRequest(BaseModel):
+    text: str
+    candidate_topics: List[str]
+    zero_shot_threshold: float = 0.5
+
+class OutputResponse(BaseModel):
+    modelname: str
+    modelversion: str
+    outputs: List[InferenceData]
+
+@app.post("/validate", response_model=OutputResponse)
+def restrict_to_topic(input_request: InputRequest):
+    print('make request')
+
+    text = input_request.text
+    candidate_topics = input_request.candidate_topics
+    zero_shot_threshold = input_request.zero_shot_threshold
+
+
+    if text is None or candidate_topics is None:
+        raise HTTPException(status_code=400, detail="Invalid input format")
+
+    # Perform zero-shot classification
+    result = classifier(text, candidate_topics)
+    print("result: ", result)
+    topics = result["labels"]
+    scores = result["scores"]
+    found_topics = [topic for topic, score in zip(topics, scores) if score > zero_shot_threshold]
+
+    if not found_topics:
+        found_topics = ["No valid topic found."]
+
+    output_data = OutputResponse(
+        modelname="RestrictToTopicModel",
+        modelversion="1",
+        outputs=[
+            InferenceData(
+                name="results",
+                datatype="BYTES",
+                shape=[len(found_topics)],
+                data=found_topics
+            )
+        ]
+    )
+
+    return output_data
+
+# Run the app with uvicorn
+# Save this script as app.py and run with: uvicorn app:app --reload

inferless.yaml

@@ -0,0 +1,39 @@
+# Inferless config file (version: 1.0.0)
+version: 1.0.0
+
+name: Restrict-To-Topic
+import_source: GIT
+
+# you can choose the options between ONNX, TENSORFLOW, PYTORCH
+source_framework_type: PYTORCH
+
+configuration:
+  # if you want to use a custom runtime, add the runtime id below.
+  # you can find it by running `inferless r6897f8untime list` or create one with `inferless runtime upload` and update this file it by running `inferless runtime select --id <RUNTIME_ID>`.
+  custom_runtime_id: 035c210b-3425-43d7-be4f-f341fae13842
+  custom_runtime_version: '0'
+
+  # if you want to use a custom volume, add the volume id and name below,
+  # you can find it by running `inferless volume list` or create one with `inferless volume create -n {VOLUME_NAME}`
+  custom_volume_id: ''
+  custom_volume_name: ''
+
+  gpu_type: T4
+  inference_time: '180'
+  is_dedicated: false
+  is_serverless: false
+  max_replica: '1'
+  min_replica: '0'
+  scale_down_delay: '600'
+  region: region-1
+  vcpu: '1.5'
+  ram: '7'
+env:
+  # Add your environment variables here
+  # ENV: 'PROD'
+secrets:
+  # Add your secret ids here you can find it by running `inferless secrets list`
+  # - 65723205-ce21-4392-a10b-3tf00c58988c
+io_schema: true
+model_url: https://github.com/guardrails-ai/restricttotopic
+provider: GITHUB

input_schema.py

@@ -0,0 +1,20 @@
+INPUT_SCHEMA = {
+    "text": {
+        "example": ["Let's talk about Iphones made by Apple"],
+        "shape": [1],
+        "datatype": "STRING",
+        "required": True,
+    },
+    "candidate_topics": {
+        "example": ["Apple Iphone", "Samsung Galaxy"],
+        "shape": [-1],
+        "datatype": "STRING",
+        "required": True,
+    },
+    "zero_shot_threshold": {
+        "example": [0.5],
+        "shape": [1],
+        "datatype": "FP32",
+        "required": True,
+    },
+}

validator/main.py

@@ -5,6 +5,7 @@
 
 from dotenv import load_dotenv
 from guardrails.validator_base import (
+    ErrorSpan,
     FailResult,
     PassResult,
     ValidationResult,
@@ -332,14 +333,31 @@ def validate(
             elif topic in self._invalid_topics:
                 invalid_topics_found.append(topic)
 
+        error_spans = []
+
         # Require at least one valid topic and no invalid topics
         if invalid_topics_found:
+            for topic in invalid_topics_found:
+                error_spans.append(
+                    ErrorSpan(
+                        start=value.find(topic),
+                        end=value.find(topic) + len(topic),
+                        reason=f"Text contains invalid topic: {topic}",
+                    )
+                )
             return FailResult(
-                error_message=f"Invalid topics found: {invalid_topics_found}"
+                error_message=f"Invalid topics found: {invalid_topics_found}",
+                error_spans=error_spans
             )
         if not valid_topics_found:
-            return FailResult(error_message="No valid topic was found.")
-
+            return FailResult(
+                error_message="No valid topic was found.",
+                error_spans=[ErrorSpan(
+                    start=0,
+                    end=len(value),
+                    reason="No valid topic was found."
+                )]
+            )
         return PassResult()
 
     def _inference_local(self, model_input: Any) -> Any: