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

chunking/docs/adr/0001-single-collection-with-metadata.md

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+# ADR 0001: Single Collection with Metadata Filtering
+
+## Status
+
+Accepted
+
+## Context
+
+The current architecture uses ~30 collections in ChromaDB, each representing an insurance domain. The system must classify incoming queries to a single collection before retrieval. Misclassification leads to irrelevant results with no recovery path.
+
+The current hybrid classifier (keyword-first cascade with similarity + LLM) has:
+- No measurable accuracy metrics
+- No visibility into keyword coverage
+- Degraded performance on ambiguous queries
+- Similarity score domination when signals conflict
+
+Insurance domains are semantically similar — many questions could plausibly belong to multiple domains (e.g., "claim denial appeal" could be car, health, fire, etc.).
+
+## Decision
+
+Migrate to a **single collection** in Qdrant with rich metadata on each chunk:
+- `domain`: car | health | fire | life | travel | ...
+- `document_type`: policy | claim | faq | terms | ...
+- `language`: fa | en
+- `source_file`: filename
+- `chunk_index`: position in source document
+
+Queries will use metadata filtering to narrow search space when domain is known, or search the entire collection when ambiguous.
+
+## Consequences
+
+### Positive
+- **Eliminates classification failure mode** — no single point of failure at the routing stage
+- **Graceful degradation** — ambiguous queries search broader, still retrieve relevant results
+- **Simpler pipeline** — one collection to manage, one index to optimize
+- **Qdrant excels at filtered search** — metadata filtering happens before vector search, maintaining performance
+- **Easier to add new domains** — just add documents with new domain metadata, no collection creation
+
+### Negative
+- **Larger index to search** — all domains in one collection
+- **Requires disciplined metadata** — incorrect or missing metadata will cause retrieval failures
+- **Potential for cross-domain noise** — irrelevant domains could appear in results if filtering isn't precise
+
+### Neutral
+- Reranking becomes more important to sort results across domains
+- Embedding quality becomes more critical — must distinguish semantically similar domains
+
+## Alternatives Considered
+
+1. **Keep 30 collections + improve classifier** — adds metrics but retains single point of failure
+2. **Hybrid: few collections (3-5) + metadata** — reduces but doesn't eliminate classification problem
+3. **Multi-collection search with reranking** — more robust but slower and more complex