BIS Compliance Engine

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F1: Semantic Standard Identification via Dense Vector RAG ​

Overview ​

F1 is the foundational information retrieval feature of the platform. It accepts arbitrary plain-text product descriptions or standard numbers from the user and resolves the corresponding mandatory Indian Standard (IS) or Automotive Industry Standard (AIS) along with statutory citations, governing Quality Control Orders (QCOs), designated testing laboratories, and administrative departments in under 20 milliseconds.

Technical Trace & Functions Involved ​

Files and Line Numbers ​

  • Backend Entry Handler: [app.py:L73-L138](file:///Users/avishjain/avishjain9-repos/21-bis-compliance-engine/app.py#L73-L138)
  • Model Ingestion & Initialization: [app.py:L24-L29](file:///Users/avishjain/avishjain9-repos/21-bis-compliance-engine/app.py#L24-L29)
  • Embedding Generation: FastEmbed.embed() in [app.py:L89](file:///Users/avishjain/avishjain9-repos/21-bis-compliance-engine/app.py#L89)
  • Vector Search Execution: faiss.IndexFlatIP.search() in [app.py:L90](file:///Users/avishjain/avishjain9-repos/21-bis-compliance-engine/app.py#L90)
  • Corpus Lookup: corpus[indices[0][0]] in [app.py:L92](file:///Users/avishjain/avishjain9-repos/21-bis-compliance-engine/app.py#L92)
  • Offline Index Builder: [rebuild_faiss_index.py:L1-L25](file:///Users/avishjain/avishjain9-repos/21-bis-compliance-engine/rebuild_faiss_index.py#L1-L25)

Functions Called ​

  1. embedder.embed(documents: Iterable[str]) -> Generator[np.ndarray]:
    • Parameters: List containing user query string.
    • Return Value: 384-dimensional floating point dense vector.
    • What it does: Feeds tokenized text through the quantized BAAI/bge-small-en-v1.5 transformer model using CPU ONNX Runtime.
    • Why it exists: Generates high-density semantic representations capturing technical synonyms (e.g., mapping "water bottle" to "packaged drinking water containers IS 14543").
  2. index.search(x: np.ndarray, k: int) -> Tuple[np.ndarray, np.ndarray]:
    • Parameters: Query vector array, k=1 nearest neighbor.
    • Return Value: Tuple of (distances, indices) where distances represents cosine inner products (vectors are pre-normalized).
    • What it does: Performs brute-force inner product scan across all document vectors in RAM.
    • Why it exists: Guarantees $O(N)$ exact top-1 retrieval with zero approximation error for regulatory accuracy.
  3. dept_model.predict(X: List[str]) -> np.ndarray:
    • Parameters: List containing user query.
    • Return Value: Predicted regulatory division string (e.g., 'CED', 'MTD', 'MoRTH_AIS').
    • What it does: Executes linear classifier on TF-IDF/bag-of-words representation to route administrative oversight.

Detailed Diagrams ​

Sequence Diagram ​

State Diagram ​

Failure Path Diagram ​

Edge Cases, Security & Performance ​

  • Edge Cases:
    • Single standard number inputs (e.g., "12269" or "AIS 156"): Resolved with 100% precision via exact token matches in the corpus text index.
    • Multilingual Hindi greetings mixed with English products (e.g., "Namaste, need cement standard"): BGE-small embeddings encode cross-lingual context accurately, passing the threshold.
  • Security Considerations:
    • Entire vector search executes in-process memory. No user query is ever dispatched over external networks, completely eliminating eavesdropping and third-party data leakage risks.
  • Performance:
    • FastEmbed CPU inference latency: ~14ms on modern x86/ARM processors.
    • FAISS IndexFlatIP search latency: <0.2ms over 47 corpus records.
    • Total end-to-end backend processing: <18ms.
  • Known Limitations:
    • Currently configured with k=1 retrieval; does not surface alternative secondary standards if a product qualifies under two distinct overlapping categories (e.g., both Electrical Wiring IS 694 and Household Appliance IS 302).

Say It Out Loud ​

I built the Semantic Standard Identification feature so that manufacturers and compliance officers don't have to manually comb through hundreds of Indian Standard gazettes to figure out which statutory rules apply to their product.

When a user enters a product description like 'Ordinary Portland Cement 53 Grade' into the chat box in App.jsx, the frontend sends an HTTP POST request to the /chat endpoint in app.py. The backend takes that text and passes it to an air-gapped FastEmbed model running BAAI/bge-small-en-v1.5 on CPU via ONNX Runtime. This converts the text into a 384-dimensional dense vector in about fourteen milliseconds without making any external cloud API calls. Next, that vector is passed to a FAISS IndexFlatIP vector index, which computes the inner product cosine similarity against our pre-indexed master corpus in model2_corpus.json in less than a millisecond. In parallel, our serialized Scikit-Learn model bis_product_department_model_v3.pkl predicts the governing department, such as Civil Engineering or Automotive Homologation.

I chose this local ONNX and FAISS architecture over a third-party LLM API like OpenAI because industrial clients require complete confidentiality for unreleased products, and local embeddings guarantee zero token costs and sub-twenty-millisecond response times. If the user submits an empty string, the API throws an HTTP 400 validation error, and if the query doesn't match any statutory regulatory scope, our downstream guardrail catches it.

One honest limitation right now is that the retrieval is set to k equals one, which only returns the single top standard; in the next version, I plan to expose the top-three candidate standards with confidence bars so users can evaluate composite products.