rick instructions for bulk insert

Author: humbertoyusta

flexus_simple_bots/rick/rick_bot.py

@@ -36,13 +36,14 @@
     fi_erp.ERP_TABLE_META_TOOL,
     fi_erp.ERP_TABLE_DATA_TOOL,
     fi_erp.ERP_TABLE_CRUD_TOOL,
+    fi_erp.ERP_CSV_IMPORT_TOOL,
     fi_mongo_store.MONGO_STORE_TOOL,
     fi_crm_automations.CRM_AUTOMATION_TOOL,
 ]
 
 
 async def rick_main_loop(fclient: ckit_client.FlexusClient, rcx: ckit_bot_exec.RobotContext) -> None:
-    dbname = f"{rcx.persona.ws_id}__{rcx.persona.persona_id}"
+    dbname = f"{rcx.persona.persona_id}_db"
     mongo_conn_str = await ckit_mongo.mongo_fetch_creds(fclient, rcx.persona.persona_id)
     mongo = AsyncMongoClient(mongo_conn_str)
     mydb = mongo[dbname]
@@ -91,6 +92,10 @@ async def toolcall_erp_data(toolcall: ckit_cloudtool.FCloudtoolCall, model_produ
     async def toolcall_erp_crud(toolcall: ckit_cloudtool.FCloudtoolCall, model_produced_args: Dict[str, Any]) -> str:
         return await erp_integration.handle_erp_crud(toolcall, model_produced_args)
 
+    @rcx.on_tool_call(fi_erp.ERP_CSV_IMPORT_TOOL.name)
+    async def toolcall_erp_csv_import(toolcall: ckit_cloudtool.FCloudtoolCall, model_produced_args: Dict[str, Any]) -> str:
+        return await erp_integration.handle_csv_import(toolcall, model_produced_args)
+
     @rcx.on_tool_call(fi_mongo_store.MONGO_STORE_TOOL.name)
     async def toolcall_mongo_store(toolcall: ckit_cloudtool.FCloudtoolCall, model_produced_args: Dict[str, Any]) -> str:
         return await fi_mongo_store.handle_mongo_store(rcx.workdir, mongo_collection, toolcall, model_produced_args)

flexus_simple_bots/rick/rick_prompts.py

@@ -35,6 +35,93 @@
   <button type="submit">Submit</button>
 </form>
 ```
+
+## Bulk Importing Records from CSV
+
+When a user wants to import records (e.g., contacts) from a CSV, follow this process:
+
+### Step 1: Get the CSV File
+
+Ask the user to upload their CSV file. They can attach it to the chat, and you will access it via mongo_store.
+
+### Step 2: Analyze CSV and Target Table
+
+1. Read the CSV (headers + sample rows) from Mongo
+2. Call erp_table_meta() to retrieve the full schema of the target table (e.g., crm_contact)
+3. Identify standard fields and the JSON details field for custom data
+
+### Step 3: Propose Field Mapping
+
+Create an intelligent mapping from CSV → table fields:
+
+1. Match columns by name similarity
+2. Propose transformations where needed (e.g., split full name, normalize phone/email, parse dates)
+3. Map unmatched CSV columns into the appropriate *_details JSON field
+4. Suggest an upsert key for deduplication (e.g., contact_email) if possible
+
+Present the mapping to the user in a clear format:
+```
+CSV Column → Target Field (Transformation)
+-----------------------------------------
+Email → contact_email (lowercase, trim)
+Full Name → contact_first_name + contact_last_name (split on first space)
+Phone → contact_phone (format: remove non-digits)
+Company → contact_details.company (custom field)
+Source → contact_details.source (custom field)
+
+Upsert key: contact_email (will update existing contacts with same email)
+```
+
+### Step 4: Validate and Adjust
+
+Ask the user to confirm or modify, field mappings, transformations, upsert behavior, validation rules
+
+### Step 5: Generate Python Script to Normalize the CSV
+Use python_execute() only to transform the uploaded file into a clean CSV whose columns exactly match the ERP table. Read from the Mongo attachment and write a new CSV:
+
+```python
+import pandas as pd
+
+SOURCE_FILE = "attachments/solar_root/leads_rows.csv"
+TARGET_TABLE = "crm_contact"
+OUTPUT_FILE = f"{{TARGET_TABLE}}_import.csv"
+
+df = pd.read_csv(SOURCE_FILE)
+records = []
+for _, row in df.iterrows():
+    full_name = str(row.get("Full Name", "")).strip()
+    parts = full_name.split(" ", 1)
+    first_name = parts[0] if parts else ""
+    last_name = parts[1] if len(parts) > 1 else ""
+    record = {{
+        "contact_first_name": first_name,
+        "contact_last_name": last_name,
+        "contact_email": str(row.get("Email", "")).strip().lower(),
+        "contact_phone": str(row.get("Phone", "")).strip(),
+        "contact_details": {{
+            "company": str(row.get("Company", "")).strip(),
+            "source": "csv_import"
+        }}
+    }}
+    records.append(record)
+
+normalized = pd.DataFrame(records)
+normalized.to_csv(OUTPUT_FILE, index=False)
+print(f"Saved {{OUTPUT_FILE}} with {{len(normalized)}} rows")
+```
+
+python_execute automatically uploads generated files back to Mongo under their filenames (e.g., `crm_contact_import.csv`), so you can reference them with mongo_store or the new import tool.
+
+### Step 6: Review the Normalized File
+1. Use `mongo_store(op="cat", args={{"path": "crm_contact_import.csv"}})` to show the first rows
+2. Confirm every column matches the ERP schema (no extras, correct casing) and the upsert key looks good
+3. Share stats (row count, notable transforms) with the user
+
+### Step 7: Import with `erp_csv_import`
+
+Use erp_csv_import() to import the cleaned CSV.
+
+After import, offer to create follow-up tasks or automations for the new contacts.
 """
 
 rick_prompt_default = f"""