Update README.md

README.md

@@ -1,5 +1,143 @@
----
-    library_name: transformers
-    tags: []
-    ---
-    
+import argparse
+
+from openai import OpenAI
+
+DEFAULT_QUESTION = """CREATE TABLE entity_a (
+    id INTEGER,
+    group_id INTEGER,
+    org_id INTEGER,
+    code VARCHAR(100),
+    name VARCHAR(255),
+    attr_1 VARCHAR(255),
+    attr_2 VARCHAR(255),
+    attr_3 TEXT
+);
+
+CREATE TABLE entity_b (
+    id INTEGER,
+    group_id INTEGER,
+    entity_a_id INTEGER,
+    time_key INTEGER,
+    metric_name VARCHAR(255),
+    metric_code VARCHAR(100),
+    metric_value REAL,
+    metric_unit VARCHAR(100)
+);
+ENTITIES = {
+    "metric": {
+        "metric_code": "METRIC_X",
+        "metric_unit": "UNIT_A"
+    },
+    "entity_a_field": {
+        "attr_1": [],
+        "attr_2": [],
+        "attr_3": [],
+        "id": []
+    },
+    "time_key": [year],
+Query:
+
+}
+
+
+"""
+
+
+class MyModel(object):
+    def __init__(self, model_name: str, api_key: str):
+        self.model_name = model_name
+        self.client = OpenAI(base_url=""", api_key=api_key)
+
+    def get_prompt(
+        self,
+        question: str,
+    ) -> list[dict[str, str]]:
+        return [
+            {
+                "role": "system",
+                "content": """
+You are a problem solving model working on task_description XML block:
+<task_description>You are a specialized Text-to-SQL assistant in the banking domain. Your objective is to translate natural language questions into valid SQLite queries using the provided schema and banking business logic.
+
+### Input:
+- Schema: Table definitions in SQL DDL format.
+- Relationships: Key linking logic between tables (system_data.branch_id = branch.id).
+- Data Content Context:
+    Indicator_Categories:
+      Group_A:
+        description: Primary metrics – inbound type
+        rule:
+          - metric_code LIKE 'MET_A_%'
+    
+      Group_B:
+        description: Primary metrics – outbound type
+        rule:
+          - metric_code LIKE 'MET_B_%'
+    
+      Group_C:
+        description: Stock / snapshot metrics
+        rule:
+          - metric_code LIKE 'MET_C_%'
+    
+      Group_D:
+        description: Exposure / obligation related metrics
+        rule:
+          - metric_code LIKE 'MET_D_%'
+          - metric_code LIKE 'MET_D_TOTAL_%'
+          - metric_code = 'MET_D_SPECIAL'
+    
+      Group_E:
+        description: Resource mobilization metrics
+        rule:
+          - metric_code LIKE 'MET_E_%'
+    
+      Group_F:
+        description: Ratio & efficiency indicators
+        rule:
+  - Unit Logic: {Which dmain} data is stored in 'Triệu VND'. If the Question mentions 'Tỷ', multiply the value by 1000.
+- Entities: Extracted key information including data_code, year, and branch filtering criteria.
+
+### Rules:
+1. ALWAYS perform an INNER JOIN between system_data and branch on system_data.branch_id = branch.id.
+2. ALWAYS SELECT system_data.data_code, system_data.year, system_data.branch_id, branch.name, system_data.value.
+3. Use exact Vietnamese accents for location values.
+4. Use LIKE '%keyword%' for text matching.
+5. Use UPPERCASE for SQL keywords.
+6. Output ONLY the SQL query. No explanations or markdown blocks.</task_description>
+You will be given a single task in the question XML block
+Solve only the task in question block.
+Generate only the answer, do not generate anything else
+""",
+            },
+            {
+                "role": "user",
+                "content": f"""
+
+Now for the real task, solve the task in question block.
+Generate only the solution, do not generate anything else
+<question>{question}</question>
+""",
+            },
+        ]
+
+    def invoke(self, question: str) -> str:
+        chat_response = self.client.chat.completions.create(
+            model=self.model_name,
+            messages=self.get_prompt(question),
+            temperature=0,
+            reasoning_effort="none",
+        )
+        return chat_response.choices[0].message.content
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--question", type=str, default=DEFAULT_QUESTION, required=False)
+    parser.add_argument("--api-key", type=str, default="", required=False)
+    parser.add_argument("--model", type=str, default="model", required=False)
+
+    args = parser.parse_args()
+
+    client = MyModel(model_name=args.model, api_key=args.api_key)
+
+    print(client.invoke(args.question))