askbit-faq-retriever

AskBit FAQ Retriever

A fast, interpretable FAQ retriever using bit vector encoding of SBERT sentence embeddings combined with a binary KNN classifier. This repository hosts a model artifact from the AskBit project.

📚 This model was created as part of an educational journey exploring efficient semantic FAQ matching with bitwise vector representations and KNN classification.

• 🔢 Uses SBERT (all-MiniLM-L6-v2) to embed question-answer pairs as dense semantic vectors.
• 🧠 Converts dense embeddings into binarized bit vectors for fast similarity search.
• ⚡ Uses a K-Nearest Neighbors classifier with Hamming distance over bit vectors.
• 💡 Fully open source, efficient, and suitable for lightweight semantic FAQ retrieval.
• 🗂️ Model file: model.pkl
• 📄 Training data file: faq.json

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📁 Files in This Repository

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🧠 How It Works

Semantic Bit Vector Encoding (SbertBitEncoder)

• Uses the Sentence-BERT model (all-MiniLM-L6-v2) to generate dense semantic embeddings of entire question-answer pairs.
• Embeddings capture meaningful sentence-level semantics, enabling effective retrieval beyond simple word overlap.
• Each dense embedding vector is binarized by thresholding (e.g., bits set to 1 if value > 0) to produce a compact, fixed-length bit vector.
• Both the FAQ entries and queries are encoded this way, ensuring semantic similarity maps to bitwise proximity.

Binary K-Nearest Neighbors Classifier (FAQClassifier)

• Implements a KNN classifier using Hamming distance as the similarity metric on bit vectors.
• Learns to associate bit-encoded queries with their corresponding answers.
• Supports retrieving the best matching answer or top-k candidates with similarity scores.

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🚀 Usage Example

import pickle
import numpy as np

# Load the trained model artifact
with open("model.pkl", "rb") as f:
    model = pickle.load(f)

# Bit vector input: binarized SBERT embeddings (e.g., 384-bit vector)
query_vec = np.array([1, 0, 1, 1, 0, ..., 0])  # Must match training bit vector format

# Predict (get best matching answer)
answer = model.predict(query_vec)
print("Predicted answer:", answer)

⚠️ Important: Ensure you encode new queries with the same SBERT bit-vector encoder used at training for consistent results.

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📦 Dependencies

Install dependencies with:

pip install -r requirements.txt

Main dependencies:

• sentence-transformers
• scikit-learn
• numpy
• yake
• spacy (for optional text preprocessing)

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📚 Related Project

This model is part of the AskBit project on GitHub:

• ✅ Full source code with CLI and training scripts
• ✅ Debug and inspect bit vectors and retrieval results
• ✅ Lightweight, interpretable semantic FAQ search

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📜 License

MIT License — free to use, modify, or contribute.

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🤝 Contributing

This model is intended for learning and experimentation. Feel free to fork, improve, or build upon it!

Model trained and shared by @Shanvit