# Task ID: 11
# Title: Develop NLP similarity detection for Process Intelligence Engine
# Status: in_progress
# Dependencies: None
# Priority: high
# Description: Implement NLP-based similarity detection for comparing process steps

# Details:
Develop a system that can detect semantic similarity between process steps in different SOPs and PDDs. This will be used to identify redundant or similar processes across the organization.

# Test Strategy:
- Unit tests for similarity detection with known similar and dissimilar examples
- Integration tests with the Process Intelligence Engine

# Subtasks:

## 1. Research and select NLP libraries and embedding models [pending]
### Dependencies: None
### Priority: high
### Description: Evaluate and select appropriate NLP libraries and embedding models for semantic similarity detection
### Details:
Research and evaluate different NLP libraries and embedding models for semantic similarity detection. Consider factors such as accuracy, performance, ease of integration, and licensing.

### Research Results:
Research completed successfully with excellent results.

## Recommendations
**Library and Model Selection**: ["Use sentence-transformers (v2.7+) with a model like 'all-MiniLM-L6-v2' for fast, accurate embeddings", "For higher accuracy or technical domains, use 'roberta-base' or a domain-specific model via Hugging Face Transformers", 'Integrate spaCy for preprocessing and entity extraction']
**Similarity Computation Example (Python)**: from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('all-MiniLM-L6-v2')
steps = ["Log in to the system", "Authenticate user credentials"]
embeddings = model.encode(steps, convert_to_tensor=True)
similarity = util.pytorch_cos_sim(embeddings, embeddings[1])
print(f"Cosine similarity: {similarity.item():.4f}")
**Hybrid DSPy Integration Example**: import dspy
from sentence_transformers import SentenceTransformer, util

class TextSimilarityInput(dspy.Signature):
    """Compare two text passages for semantic similarity."""
    text_a = dspy.InputField(desc="First text passage to compare")
    text_b = dspy.InputField(desc="Second text passage to compare")

class TextSimilarityOutput(dspy.Signature):
    """Output indicating whether two text passages are semantically similar."""
    are_similar = dspy.OutputField(desc="Boolean indicating if the passages are semantically similar")
    explanation = dspy.OutputField(desc="Brief explanation of the similarity assessment")

class TextSimilarityModule(dspy.Module):
    """A DSPy module that uses both embedding similarity and LLM judgment to determine text similarity."""
    
    def __init__(self, threshold=0.8):
        super().__init__()
        self.threshold = threshold
        self.model = SentenceTransformer('all-MiniLM-L6-v2')
        self.llm_judge = dspy.ChainOfThought(TextSimilarityInput, TextSimilarityOutput)
    
    def forward(self, text_a, text_b):
        # First check via embeddings
        emb_a = self.model.encode(text_a, convert_to_tensor=True)
        emb_b = self.model.encode(text_b, convert_to_tensor=True)
        cos_sim = util.pytorch_cos_sim(emb_a, emb_b).item()
        
        # Clear decision based on vector similarity
        if cos_sim > self.threshold:
            return TextSimilarityOutput(
                are_similar=True,
                explanation=f"High cosine similarity of {cos_sim:.2f}, exceeding threshold of {self.threshold}."
            )
        elif cos_sim < (self.threshold - 0.2):
            return TextSimilarityOutput(
                are_similar=False,
                explanation=f"Low cosine similarity of {cos_sim:.2f}, well below threshold of {self.threshold}."
            )
        
        # Use LLM for ambiguous cases
        llm_result = self.llm_judge(text_a=text_a, text_b=text_b)
        return llm_result

# Example usage
if __name__ == "__main__":
    # Configure the DSPy LLM
    dspy.settings.configure(lm=dspy.OpenAI(model="gpt-4"))
    
    # Initialize the module
    similarity_module = TextSimilarityModule(threshold=0.8)
    
    # Test with examples
    examples = [
        ("Check if the file exists", "Verify that the file is present"),
        ("Download the dataset from the URL", "Train the model on the dataset")
    ]
    
    for ex_a, ex_b in examples:
        result = similarity_module(ex_a, ex_b)
        print(f"Text A: '{ex_a}'")
        print(f"Text B: '{ex_b}'")
        print(f"Similar: {result.are_similar}")
        print(f"Explanation: {result.explanation}")
        print("-" * 50)
**Batch Processing and Caching**: ['Encode all process steps in batches to leverage hardware acceleration', 'Store embeddings in a vector database (e.g., FAISS) for fast KNN search']
**Evaluation**: ['Label a sample set of similar/dissimilar step pairs from PDDs/SOPs', 'Compute precision, recall, and F1-score for similarity detection', 'Benchmark inference time and memory usage']

## Resources
sentence-transformers documentation (https://www.sbert.net/)
Hugging Face Transformers docs (https://huggingface.co/docs/transformers/)
DSPy documentation (https://github.com/stanfordnlp/dspy)
Ultimate Guide To Text Similarity With Python[1]
Top 10 Tools for Calculating Semantic Similarity[3]
NLP Techniques For Measuring Text Similarity - Restack[5]

## Sources
https://www.sbert.net/)",
https://huggingface.co/docs/transformers/)",
https://github.com/stanfordnlp/dspy)",

## 2. Define similarity metrics and thresholds for process steps [pending]
### Dependencies: 1
### Priority: medium
### Description: Define appropriate similarity metrics and thresholds for determining when process steps are similar

## 3. Implement embedding-based similarity detection [pending]
### Dependencies: 1, 2
### Priority: high
### Description: Implement the core similarity detection functionality using embeddings

## 4. Develop API for similarity queries [pending]
### Dependencies: 3
### Priority: medium
### Description: Create an API for querying similarity between process steps

## 5. Integrate with Process Intelligence Engine [pending]
### Dependencies: 4
### Priority: medium
### Description: Integrate the similarity detection with the Process Intelligence Engine

## 6. Implement caching for performance optimization [pending]
### Dependencies: 3
### Priority: low
### Description: Add caching mechanisms to improve performance for repeated similarity checks

## 7. Create visualization for similar processes [pending]
### Dependencies: 5
### Priority: low
### Description: Develop visualizations to show similar processes across the organization
