feat: implement QuestionBank CRUD with pagination and template query

- Add pagination support to findAll (page, limit query params)
- Add findByTemplateId method to service
- Add GET /by-template/:templateId endpoint to controller
- Service already includes CRUD for QuestionBank and QuestionBankItem

docs/design/feat-cross-doc-comparison.md

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+# Design: Cross-Document Comparison (Agentic Workflow)
+
+## 1. Background & Problem
+Users often need to compare multiple documents (e.g., "Compare the financial reports of Q1 and Q2" or "Differences between Product A and Product B specs").
+Standard RAG retrieves chunks based on semantic similarity to the query. While "Multi-Query" helps, standard RAG might:
+1.  Retrieve too many chunks from one document and miss the other.
+2.  Fail to align comparable attributes (e.g., comparing "revenue" in Doc A with "profit" in Doc B).
+3.  Produce a generic text answer instead of a structured comparison.
+
+## 2. Solution: Agentic Comparison Workflow
+We will implement a specialized workflow (or "Light Agent") that:
+1.  **Analyzes the Request**: Identifies the subjects to compare (e.g., "Q1 Report", "Q2 Report") and the dimensions (e.g., "Revenue", "Risks").
+2.  **Targeted Retrieval**:
+    -   Explicitly filters/searches for Doc A.
+    -   Explicitly filters/searches for Doc B.
+3.  **Structured Synthesis**: Generates the answer, potentially forcing a Markdown Table format for clarity.
+
+## 3. Technical Architecture
+
+### 3.1 Backend (`ComparisonService` or extension to `RagService`)
+-   **Intent Detection**: Modify `ChatService` or `RagService` to detect comparison intent (can utilize LLM or simple heuristics + keywords).
+-   **Planning**: If comparison is detected:
+    1.  Identify Target Files: Resolve file names/IDs from the query (e.g., "Q1" -> matches file "2024_Q1_Report.pdf").
+    2.  Dimension Extraction: What to compare? (e.g., "summary", "key metrics").
+    3.  Execution:
+        -   Run Search on File A with query "key metrics".
+        -   Run Search on File B with query "key metrics".
+        -   Combine context.
+-   **Prompting**: Use a prompt optimized for comparison (e.g., "Generate a comparison table...").
+
+### 3.2 Frontend (`ChatInterface`)
+-   **UI Trigger**: (Optional) specific "Compare" button, or just natural language.
+-   **Visuals**: Render the response standard markdown (which supports tables).
+-   **Source Attribution**: Ensure citations map back to the correct respective documents.
+
+## 4. Implementation Steps
+
+1.  **Intent & Entity Extraction (Simple Version)**:
+    -   In `RagService`, add a step `detectComparisonIntent(query)`.
+    -   Return `subjects: string[]` (approximate filenames) and `dimensions: string`.
+    
+2.  **Targeted Search**:
+    -   Use `elasticsearchService` to search *specifically* within the resolved file IDs (if we can map names to IDs).
+    -   Fall back to broad search if file mapping fails.
+
+3.  **Comparison Prompt**:
+    -   Update `rag.service.ts` to use a `comparisonPromise` if intent is detected.
+
+## 5. Risks & limitations
+-   **File Name Matching**: Mapping user spoken "Q1" to "2024_Q1_Report_Final.pdf" is hard without fuzzy matching or LLM resolution.
+    -   *Mitigation*: Use a lightweight LLM call or fuzzy search on the file list to resolve IDs.
+-   **Latency**: Two searches + entity resolution might add latency.
+    -   *Mitigation*: Run searches in parallel.
+
+## 6. MVP Scope
+-   Automated detection of "Compare A and B".
+-   Attempt to identify if A and B refer to specific files in the selected knowledge base.
+-   If identified, restrict search scopes accordingly (or boost them).
+-   Generate a table response.