docs(rag): add domain model and original research input

chunking/docs/adr/0007-hybrid-search.md

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+# ADR 0007: Hybrid Search (Dense + Sparse)
+
+## Status
+
+Accepted
+
+## Context
+
+Insurance queries often contain specific terms that require exact matching:
+- Policy numbers and IDs
+- Coverage names (e.g., "comprehensive", "third-party")
+- Legal phrases and terminology
+- Product codes and identifiers
+
+Pure dense vector search excels at semantic similarity but can dilute exact term matches. A query for "policy AC-12345" should match documents containing that exact string, not just semantically similar policy-related content.
+
+## Decision
+
+Implement **hybrid search** combining dense and sparse vectors:
+
+### Dense Vectors (Semantic)
+- `text-embedding-3-large` (online) or `nomic-embed-text` (offline)
+- Captures semantic meaning and intent
+
+### Sparse Vectors (Lexical)
+- **BM25** — classic term frequency scoring, fast, no model needed
+- Alternative: **SPLADE** — learned sparse embeddings with term expansion
+
+### Qdrant Configuration
+
+```python
+from qdrant_client.models import VectorParams, SparseVectorParams
+
+client.create_collection(
+    collection_name="insurance_docs",
+    vectors_config={
+        "openai": VectorParams(size=3072, distance=Distance.COSINE),
+        "nomic": VectorParams(size=768, distance=Distance.COSINE),
+    },
+    sparse_vectors_config={
+        "bm25": SparseVectorParams(
+            modifier=models.Modifier.IDF,  # Use IDF weighting
+        )
+    }
+)
+```
+
+### Query Fusion
+
+Use **Reciprocal Rank Fusion (RRF)** to combine dense and sparse results:
+
+```python
+def hybrid_search(query, top_k=20):
+    # Dense search
+    dense_results = client.search(
+        collection_name="insurance_docs",
+        query_vector=("openai", dense_embedding),
+        limit=top_k
+    )
+
+    # Sparse search
+    sparse_results = client.search(
+        collection_name="insurance_docs",
+        query_vector=("bm25", sparse_vector),
+        limit=top_k
+    )
+
+    # RRF fusion
+    return reciprocal_rank_fusion(dense_results, sparse_results)
+```
+
+### Why BM25 over SPLADE
+- No model required — simpler deployment
+- Faster indexing and query
+- Works offline without additional dependencies
+- SPLADE could be added later if term expansion proves valuable
+
+## Consequences
+
+### Positive
+- **Exact term matching** — policy numbers, IDs, specific terminology
+- **Semantic + lexical** — best of both worlds
+- **Improved precision** — especially for queries with specific terms
+- **No additional model** — BM25 is built into Qdrant
+
+### Negative
+- **Added complexity** — must maintain and query two vector types
+- **Fusion tuning** — RRF parameters may need adjustment
+- **Storage overhead** — sparse vectors add storage (typically smaller than dense)
+
+### Neutral
+- Need to implement RRF or similar fusion logic
+- May need different fusion weights for different query types
+
+## Implementation Notes
+
+- Use Qdrant's built-in BM25 sparse indexing
+- Implement RRF with tunable `k` parameter (default k=60)
+- Log dense vs sparse contributions to final ranking for debugging
+- Consider query routing: queries with numbers/IDs → boost sparse weight