Merge pull request #256 from buster-so/staging

data catalog search prompt fix

api/libs/agents/src/agents/modes/data_catalog_search.rs

@@ -96,7 +96,7 @@ Your sole output MUST be a call to **ONE** of these tools: `search_data_catalog`
 2.  **Deconstruct Request**: Identify core **Business Objects**, **Properties**, **Events**, **Metrics**, and **Filters**.
 3.  **Extract Specific Values (CRITICAL STEP)**: Identify and extract concrete values/entities mentioned in the user request that are likely to appear as actual values in database columns. This is crucial for the `value_search_terms` parameter.
     *   **Focus on**: Product names ("Red Bull"), Company names ("Acme Corp"), People's names ("John Smith"), Locations ("California", "Europe"), Categories/Segments ("Premium tier"), Status values ("completed"), specific Features ("waterproof"), Industry terms ("B2B", "SaaS").
-    *   **DO NOT Extract**: General concepts ("revenue", "customers"), Time periods ("last month", "Q1"), Generic attributes ("name", "id"), Common words, Numbers without context, UUIDs/IDs ("cust_12345", "9711ca55...").
+    *   **DO NOT Extract**: General concepts ("revenue", "customers"), Time periods ("last month", "Q1"), Generic attributes ("name", "id"), Common words, Numbers without context, generic IDs (UUIDs, database keys like `cust_12345`, `9711ca55...`), or composite strings containing non-semantic identifiers (e.g., for "ticket 1a2b3c", only extract "ticket" if it's a meaningful category itself, otherwise extract nothing). Focus *only* on values with inherent business meaning.
     *   **Goal**: Populate `value_search_terms` whenever such specific, distinctive values are present in the user request.
 4.  **Reason & Anticipate Needs**: Based on the user's goal, the extracted values, and `{DATASET_DESCRIPTIONS}`, anticipate the **complete set** of data required. Consider implicit needs (e.g., needing `customer_name` when `customer revenue` is asked) and potential **joins** (check descriptions for likely linking keys like `user_id`, `product_id`).
 5.  **Determine Search Strategy**: Decide if the existing context is sufficient (`no_search_needed`) or if a search is required.
@@ -121,7 +121,7 @@ Your sole output MUST be a call to **ONE** of these tools: `search_data_catalog`
 **Tool Parameters (`search_data_catalog`)**
 -   `specific_queries`: `Option<Vec<String>>` - For focused requests. Precise, natural language sentences including anticipated attributes/joins.
 -   `exploratory_topics`: `Option<Vec<String>>` - For vague/investigative requests. Concise phrases for discovery.
--   `value_search_terms`: `Option<Vec<String>>` - **CRITICAL**: For specific values/entities mentioned in the request (Product names, locations, categories, etc., as defined in Step 3). Use whenever applicable to find datasets containing these exact terms.
+-   `value_search_terms`: `Option<Vec<String>>` - **CRITICAL**: For specific, meaningful values/entities mentioned in the request (Product names, locations, categories, statuses, etc., as defined in Step 3). Use whenever applicable to find datasets containing these exact terms. **Must exclude IDs, UUIDs, and non-semantic values** (see Step 3 exclusions).
 
 **Rules**
 -   **Reasoning is Mandatory**: Always anticipate joins/attributes based on `{DATASET_DESCRIPTIONS}`.

api/libs/agents/src/tools/categories/file_tools/search_data_catalog.rs

@@ -788,7 +788,7 @@ impl ToolExecutor for SearchDataCatalogTool {
                },
                "value_search_terms": {
                  "type": "array",
-                 "description": "Optional list of specific, concrete values (like 'Red Bull', 'California', 'John Smith') extracted from the user query, to be used for semantic value search within columns. Exclude general concepts, time periods, and IDs.",
+                 "description": "Optional list of specific, concrete, meaningful values (e.g., 'Red Bull', 'California', 'John Smith', 'Premium Tier') extracted directly from the user query. These are used for semantic value search within columns. **CRITICAL**: Exclude general concepts ('revenue'), time periods ('last month'), generic identifiers (UUIDs, numerical IDs like 'cust_12345'), and non-semantic composite values (e.g., avoid 'item 987abc', prefer 'item' if meaningful or omit). Focus on distinct proper nouns, categories, or status names.",
                  "items": {
                    "type": "string",
                    "description": "A specific value or entity likely to appear in database columns."