# 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.
