docs: update README.

README.md

@@ -13,10 +13,10 @@
 | Metric | Target | **Our Score** |
 |---|---|---|
 | Hit Rate @3 | > 80% | **100%** (10/10) |
-| MRR @5 | > 0.7 | **1.000** |
+| MRR @5 | > 0.7 | **0.950** |
-| Avg Latency | < 5 s | **~19 ms** |
+| Avg Latency | < 5 s | **~18 ms** |
 
-All 10 public queries returned the expected standard at rank 1. Average query latency is 19 ms after the index warms up — 250× faster than the 5 s target.
+All 10 public queries returned the expected standard in the top 2 results. Average query latency is 19 ms after the index warms up — 250× faster than the 5 s target.
 
 ---
 
@@ -28,7 +28,7 @@ Indian Micro and Small Enterprises (MSEs) spend weeks manually searching BIS SP-
 2. **Get ranked BIS standards** with matched sections and relevance scores in milliseconds
 3. **Read AI explanations** of why each standard applies, generated by Groq LLM
 
-The system covers all **573 unique standards** across **25 building material categories** from BIS SP-21 (Summaries of Indian Standards for Building Materials).
+The system covers all **586 unique standards** across **25 building material categories** from BIS SP-21 (Summaries of Indian Standards for Building Materials).
 
 ---
 
@@ -39,8 +39,8 @@ The system covers all **573 unique standards** across **25 building material cat
 ```
 data/raw/dataset.pdf  (BIS SP-21, 929 pages)
   → src/parse_bis_pdf.py
-  → data/processed/standards.json          573 structured records  [committed]
+  → data/processed/standards.json          586 structured records  [committed]
-  → data/processed/standards_chunks.json   1,236 RAG-ready chunks  [committed]
+  → data/processed/standards_chunks.json   1,269 RAG-ready chunks  [committed]
   → inference.py --build
   → data/processed/embeddings.npy          dense vectors           [gitignored — rebuild locally]
   → data/processed/faiss.index             FAISS index             [gitignored — rebuild locally]
@@ -70,7 +70,7 @@ Browser / API Client
   - Pass 4: truncates next-standard content bleed at a second `1. Scope` marker
 - Each standard is further split by section with **50-word overlap** to prevent context loss at boundaries
 - Weak chunks (<30 words) are merged with their neighbour
-- Result: 1,236 chunks from 573 standards (avg 2.2 chunks/standard)
+- Result: 1,269 chunks from 586 standards (avg 2.2 chunks/standard)
 
 **Hybrid Retrieval** (`inference.py`):
 - **Dense**: FAISS `IndexFlatIP` with `all-MiniLM-L6-v2` embeddings (384-dim cosine similarity)
@@ -83,6 +83,7 @@ Browser / API Client
 - +0.25 if ≥60% of significant title words appear in the query (strong title match)
 - +0.20 if an exact IS ID from the query matches this standard
 - +0.35 / -0.40 grade discriminator: boosts/penalises OPC-grade standards (33/43/53) when query names a specific grade
+- +0.30 / -0.20 part-number discriminator: boosts matching Part N and penalises non-matching parts when query explicitly names a part number (handles "Part – 1", "PART2" etc.)
 - -0.15 penalty for very short chunks (<40 body words)
 
 **Post-grouping Part disambiguation**: when multiple parts of the same IS base number survive into the candidate set with identical titles, IDF-weighted discriminating keyword scores break the tie — rarer corpus terms (e.g. "lightweight") carry proportionally more weight.
@@ -95,7 +96,7 @@ Browser / API Client
 |---|---|
 | Persistent Python daemon | FAISS index load takes ~18 s cold. Spawn once at boot, queue all requests through a single process — zero cold start per query. |
 | `inference.py` never modified | Bridge pattern: `bridge/retrieve.py` imports `inference.py` as a module. Judges run `inference.py` directly; the web server uses the bridge. Both paths are identical. |
-| In-memory data | 573 standards + 1,236 chunks fit comfortably in RAM. No database dependency, no I/O per request. |
+| In-memory data | 586 standards + 1,269 chunks fit comfortably in RAM. No database dependency, no I/O per request. |
 | LLM fallbacks everywhere | Every Groq call is wrapped with a timeout (8 s) and a safe default return. `Promise.allSettled` for parallel calls. Server starts and retrieval works without a `GROQ_API_KEY`. |
 | Weighted BM25 document | Repeating title tokens ×4 makes exact IS-standard name queries dominant over body-text noise — critical for the BIS domain where standard names are precise. |
 
@@ -105,14 +106,13 @@ Browser / API Client
 
 ```
 SpecForge/
-├── inference.py                         # Entry point for judges — do not modify
+├── inference.py                         # Entry point for judges
 ├── requirements.txt                     # All Python dependencies
-├── scripts/
+├── eval_script.py                       # Provided evaluation script (Hit@3, MRR@5, latency)
-│   └── eval_script.py                   # Provided evaluation script (Hit@3, MRR@5, latency)
 ├── data/
 │   └── processed/
-│       ├── standards.json               # 573 parsed standards (committed)
+│       ├── standards.json               # 586 parsed standards (committed)
-│       ├── standards_chunks.json        # 1,236 RAG chunks (committed)
+│       ├── standards_chunks.json        # 1,269 RAG chunks (committed)
 │       ├── public_test_set.json         # 10 public evaluation queries
 │       └── retrieval_results.json       # Our results on public test set
 ├── src/
@@ -211,7 +211,7 @@ source .venv/bin/activate
 python inference.py --build
 ```
 
-Encodes 1,236 chunks, writes `embeddings.npy` + `faiss.index` to `data/processed/`. Takes **~2 min on CPU**. Subsequent starts load from cache — no rebuild needed unless chunks change.
+Encodes 1,269 chunks, writes `embeddings.npy` + `faiss.index` to `data/processed/`. Takes **~2 min on CPU**. Subsequent starts load from cache — no rebuild needed unless chunks change.
 
 ### Step 4 — Node.js dependencies
 
@@ -341,7 +341,7 @@ python inference.py \
   --output data/processed/retrieval_results.json
 
 # Step 2: score
-python scripts/eval_script.py \
+python eval_script.py \
   --results data/processed/retrieval_results.json
 ```
 
@@ -350,8 +350,8 @@ Targets and our results on the public set:
 | Metric | Formula | Target | Achieved |
 |---|---|---|---|
 | Hit Rate @3 | correct queries where expected std in top-3 / total | > 80% | **100%** |
-| MRR @5 | Σ(1/rank_i) / N | > 0.7 | **1.000** |
+| MRR @5 | Σ(1/rank_i) / N | > 0.7 | **0.950** |
-| Avg Latency | total_time / num_queries | < 5 s | **~0.019 s** |
+| Avg Latency | total_time / num_queries | < 5 s | **~0.018 s** |
 
 ---
 
@@ -422,7 +422,7 @@ All 25 material categories sorted alphabetically.
 ### `GET /api/stats`
 
 ```json
-{ "standards": 573, "chunks": 1236, "categories": 25 }
+{ "standards": 586, "chunks": 1269, "categories": 25 }
 ```
 
 ---
@@ -433,6 +433,7 @@ All 25 material categories sorted alphabetically.
 |---|---|
 | **Hybrid RAG retrieval** | FAISS (dense, 60%) + BM25 (sparse, 40%) fused and re-ranked |
 | **Re-ranking** | Keyword overlap, title match, exact IS-ID match, short-chunk penalty |
+| **Part-number disambiguation** | Explicit Part N in query boosts matching part ±0.30, penalises siblings ±0.20; handles em-dash/PART2 variants |
 | **AI explanations** | Groq `llama-3.1-8b-instant` — parallel, fallback-safe |
 | **Query rewriting** | LLM expands natural language to IS-standard vocabulary (optional) |
 | **Chunk-grounded QA** | Question answered from the most relevant chunk of a specific standard |