climateqa/engine/talk_to_data/vanna_class.py

from vanna.base import VannaBase
from pinecone import Pinecone
from climateqa.engine.embeddings import get_embeddings_function
import pandas as pd
import hashlib

class MyCustomVectorDB(VannaBase):
    
    """
    VectorDB class for storing and retrieving vectors from Pinecone.
    
    args : 
        config (dict) : Configuration dictionary containing the Pinecone API key and the index name :
            - pc_api_key (str) : Pinecone API key
            - index_name (str) : Pinecone index name
            - top_k (int) : Number of top results to return (default = 2)
            
    """
    
    def __init__(self,config):
        super().__init__(config = config)
        try : 
            self.api_key = config.get('pc_api_key')
            self.index_name = config.get('index_name')
        except : 
            raise Exception("Please provide the Pinecone API key and the index name")
        
        self.pc = Pinecone(api_key = self.api_key)
        self.index = self.pc.Index(self.index_name)
        self.top_k = config.get('top_k', 2)
        self.embeddings = get_embeddings_function()
        
        
    def check_embedding(self, id, namespace):
        fetched = self.index.fetch(ids = [id], namespace = namespace)
        if fetched['vectors'] == {}: 
            return False
        return True
    
    def generate_hash_id(self, data: str) -> str:
        """
        Generate a unique hash ID for the given data.
        
        Args:
            data (str): The input data to hash (e.g., a concatenated string of user attributes).
        
        Returns:
            str: A unique hash ID as a hexadecimal string.
        """
        
        data_bytes = data.encode('utf-8')
        hash_object = hashlib.sha256(data_bytes)
        hash_id = hash_object.hexdigest()
        
        return hash_id
    
    def add_ddl(self, ddl: str, **kwargs) -> str:
        id = self.generate_hash_id(ddl) + '_ddl'
        
        if self.check_embedding(id, 'ddl'):
            print(f"DDL having id {id} already exists")
            return id
        
        self.index.upsert(
            vectors = [(id, self.embeddings.embed_query(ddl), {'ddl': ddl})],
            namespace = 'ddl'
        )
        
        return id

    def add_documentation(self, doc: str, **kwargs) -> str:
        id = self.generate_hash_id(doc) + '_doc'
        
        if self.check_embedding(id, 'documentation'):
            print(f"Documentation having id {id} already exists")
            return id
        
        self.index.upsert(
            vectors = [(id, self.embeddings.embed_query(doc), {'doc': doc})],
            namespace = 'documentation'
        )
        
        return id

    def add_question_sql(self, question: str, sql: str, **kwargs) -> str:
        id = self.generate_hash_id(question) + '_sql'
        
        if self.check_embedding(id, 'question_sql'):
            print(f"Question-SQL pair having id {id} already exists")
            return id
        
        self.index.upsert(
            vectors = [(id, self.embeddings.embed_query(question + sql), {'question': question, 'sql': sql})],
            namespace = 'question_sql'
        )
        
        return id

    def get_related_ddl(self, question: str, **kwargs) -> list:
        res = self.index.query(
            vector=self.embeddings.embed_query(question),
            top_k=self.top_k,
            namespace='ddl',
            include_metadata=True
        )
        
        return [match['metadata']['ddl'] for match in res['matches']]

    def get_related_documentation(self, question: str, **kwargs) -> list:
        res = self.index.query(
            vector=self.embeddings.embed_query(question),
            top_k=self.top_k,
            namespace='documentation',
            include_metadata=True
        )
        
        return [match['metadata']['doc'] for match in res['matches']]

    def get_similar_question_sql(self, question: str, **kwargs) -> list:
        res = self.index.query(
            vector=self.embeddings.embed_query(question),
            top_k=self.top_k,
            namespace='question_sql',
            include_metadata=True
        )
        
        return [(match['metadata']['question'], match['metadata']['sql']) for match in res['matches']]

    def get_training_data(self, **kwargs) -> pd.DataFrame:
        
        list_of_data = []
        
        namespaces = ['ddl', 'documentation', 'question_sql']
        
        for namespace in namespaces:
            
            data = self.index.query(
            top_k=10000,
            namespace=namespace,
            include_metadata=True,
            include_values=False
            )
            
            for match in data['matches']:
                list_of_data.append(match['metadata'])
                
        return pd.DataFrame(list_of_data)
            


    def remove_training_data(self, id: str, **kwargs) -> bool:
        if id.endswith("_ddl"):
            self.Index.delete(ids=[id], namespace="_ddl")
            return True
        if id.endswith("_sql"):
            self.index.delete(ids=[id], namespace="_sql")
            return True
        
        if id.endswith("_doc"):
            self.Index.delete(ids=[id], namespace="_doc")
            return True
        
        return False
    
    def generate_embedding(self, text, **kwargs):
        # Implement the method here
        pass


    def get_sql_prompt(
            self,
            initial_prompt : str,
            question: str,
            question_sql_list: list,
            ddl_list: list,
            doc_list: list,
            **kwargs,
        ):
            """
            Example:
            ```python
            vn.get_sql_prompt(
                question="What are the top 10 customers by sales?",
                question_sql_list=[{"question": "What are the top 10 customers by sales?", "sql": "SELECT * FROM customers ORDER BY sales DESC LIMIT 10"}],
                ddl_list=["CREATE TABLE customers (id INT, name TEXT, sales DECIMAL)"],
                doc_list=["The customers table contains information about customers and their sales."],
            )

            ```

            This method is used to generate a prompt for the LLM to generate SQL.

            Args:
                question (str): The question to generate SQL for.
                question_sql_list (list): A list of questions and their corresponding SQL statements.
                ddl_list (list): A list of DDL statements.
                doc_list (list): A list of documentation.

            Returns:
                any: The prompt for the LLM to generate SQL.
            """

            if initial_prompt is None:
                initial_prompt = f"You are a {self.dialect} expert. " + \
                "Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. "

            initial_prompt = self.add_ddl_to_prompt(
                initial_prompt, ddl_list, max_tokens=self.max_tokens
            )

            if self.static_documentation != "":
                doc_list.append(self.static_documentation)

            initial_prompt = self.add_documentation_to_prompt(
                initial_prompt, doc_list, max_tokens=self.max_tokens
            )

            # initial_prompt = self.add_sql_to_prompt(
            #     initial_prompt, question_sql_list, max_tokens=self.max_tokens
            # )


            initial_prompt += (
                "===Response Guidelines \n"
                "1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. \n"
                "2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql \n"
                "3. If the provided context is insufficient, please give a sql query based on your knowledge and the context provided. \n"
                "4. Please use the most relevant table(s). \n"
                "5. If the question has been asked and answered before, please repeat the answer exactly as it was given before. \n"
                f"6. Ensure that the output SQL is {self.dialect}-compliant and executable, and free of syntax errors. \n"
                f"7. Add a description of the table in the result of the sql query, if relevant. \n"
                "8 Make sure to include the relevant KPI in the SQL query. The query should return impactfull data \n"
                # f"8. If a set of latitude,longitude is provided, make a intermediate query to find the nearest value in the table and replace the coordinates in the sql query. \n"
                # "7. Add a description of the table in the result of the sql query."
                # "7. If the question is about a specific latitude, longitude, query an interval of 0.3 and keep only the first set of coordinate. \n"
                # "7. Table names should be included in the result of the sql query. Use for example Mean_winter_temperature AS table_name in the query \n"
            )


            message_log = [self.system_message(initial_prompt)]

            for example in question_sql_list:
                if example is None:
                    print("example is None")
                else:
                    if example is not None and "question" in example and "sql" in example:
                        message_log.append(self.user_message(example["question"]))
                        message_log.append(self.assistant_message(example["sql"]))

            message_log.append(self.user_message(question))

            return message_log
        

# def get_sql_prompt(
#         self,
#         initial_prompt : str,
#         question: str,
#         question_sql_list: list,
#         ddl_list: list,
#         doc_list: list,
#         **kwargs,
#     ):
#         """
#         Example:
#         ```python
#         vn.get_sql_prompt(
#             question="What are the top 10 customers by sales?",
#             question_sql_list=[{"question": "What are the top 10 customers by sales?", "sql": "SELECT * FROM customers ORDER BY sales DESC LIMIT 10"}],
#             ddl_list=["CREATE TABLE customers (id INT, name TEXT, sales DECIMAL)"],
#             doc_list=["The customers table contains information about customers and their sales."],
#         )

#         ```

#         This method is used to generate a prompt for the LLM to generate SQL.

#         Args:
#             question (str): The question to generate SQL for.
#             question_sql_list (list): A list of questions and their corresponding SQL statements.
#             ddl_list (list): A list of DDL statements.
#             doc_list (list): A list of documentation.

#         Returns:
#             any: The prompt for the LLM to generate SQL.
#         """

#         if initial_prompt is None:
#             initial_prompt = f"You are a {self.dialect} expert. " + \
#             "Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. "

#         initial_prompt = self.add_ddl_to_prompt(
#             initial_prompt, ddl_list, max_tokens=self.max_tokens
#         )

#         if self.static_documentation != "":
#             doc_list.append(self.static_documentation)

#         initial_prompt = self.add_documentation_to_prompt(
#             initial_prompt, doc_list, max_tokens=self.max_tokens
#         )

#         initial_prompt += (
#             "===Response Guidelines \n"
#             "1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. \n"
#             "2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql \n"
#             "3. If the provided context is insufficient, please explain why it can't be generated. \n"
#             "4. Please use the most relevant table(s). \n"
#             "5. If the question has been asked and answered before, please repeat the answer exactly as it was given before. \n"
#             f"6. Ensure that the output SQL is {self.dialect}-compliant and executable, and free of syntax errors. \n"
#         )

#         message_log = [self.system_message(initial_prompt)]

#         for example in question_sql_list:
#             if example is None:
#                 print("example is None")
#             else:
#                 if example is not None and "question" in example and "sql" in example:
#                     message_log.append(self.user_message(example["question"]))
#                     message_log.append(self.assistant_message(example["sql"]))

#         message_log.append(self.user_message(question))

#         return message_log