Update Classes/Owiki_Class.py

Classes/Owiki_Class.py

@@ -1,104 +1,91 @@
-import os
-import google.generativeai as genai
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_community.vectorstores import FAISS
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
-from langchain.prompts import PromptTemplate
-import json
-import re
-from Classes.Helper_Class import DB_Retriever
-from typing import Optional
-
-os.environ["GOOGLE_API_KEY"] = "AIzaSyBoghqvvnMMS4bA61LjQkkPNdIRetqk438"
-genai.configure(api_key="AIzaSyBoghqvvnMMS4bA61LjQkkPNdIRetqk438")
-
-class OWiki:
-    def __init__(self,**kwargs):
-        temperature = kwargs['temperature']
-        self.summary = kwargs['summary_length']
-        model = kwargs["model"]
-        self.db_loc = kwargs["db_loc"]
-        self.llm = ChatGoogleGenerativeAI(model=model,
-                            temperature=temperature)
-        self.model_embedding = kwargs['model_embeddings']
-    
-
-    def get_summary_template(self):
-        prompt = """Generate a summary for the following conversational data in less than {summary} lines.\nText:\n{text}\n\nSummary:"""
-        prompt_template = PromptTemplate(template = prompt,input_variables=['summary','text'])
-        return prompt_template
-    
-    def create_sql_prompt_template(self,schemas):
-        prompt =  """Write an SQL query for the following questions whose schemas are as follows.\nSQL Schema:"""
-        for table_name,table_schema in schemas.items():
-            prompt+= f"Table Name: {table_name}, Schema : {table_schema}\n\n"
-        prompt+=  """\n\nQuestion:{question}\n\nAnswer:"""
-        prompt_template = PromptTemplate(template = prompt,input_variables=['question'])
-        return prompt_template
-
-    def create_prompt_for_OIC_bot(self):
-        template = """You are OIC(Oracle Integration Cloud) Bot.Follow chat instructions and answer the question based only on the following 
-        Chat_instructions:
-        1. Response must contain Question Explaination along with Potential Solution Headings.
-        2. Response must contain all possible Error Scenarios if applicable along with a Summary Heading containing breif summary at the end.
-
-        Context:
-        {context}
-        
-        Question: {question}
-        """
-        prompt = PromptTemplate.from_template(template)
-        return prompt
-    
-    def create_sql_agent(self,question,schemas):
-        prompt_template = self.create_sql_prompt_template(schemas)
-        chain = prompt_template | self.llm | StrOutputParser()
-        response = chain.invoke({"question":question})
-        response = self.format_llm_response(response)
-        return response
-    
-    def generate_summary(self,text):
-        prompt_template = self.get_summary_template()
-        chain = prompt_template | self.llm | StrOutputParser()
-        response = chain.invoke({"text":text,"summary":self.summary})
-        return response
-
-    def format_llm_response(self,text):
-        bold_pattern = r"\*\*(.*?)\*\*"
-        italic_pattern = r"\*(.*?)\*"
-        code_pattern = r"```(.*?)```"
-        text = text.replace('\n', '<br>')
-        formatted_text = re.sub(code_pattern,"<pre><code>\\1</code></pre>",text)
-        formatted_text = re.sub(bold_pattern, "<b>\\1</b>", formatted_text)
-        formatted_text = re.sub(italic_pattern, "<i>\\1</i>", formatted_text)
-        return formatted_text
-
-    def search_from_db(self, query : str, chat_history : Optional[str] ) -> str :
-        db = DB_Retriever(self.db_loc,self.model_embedding)
-        retriever = db.retrieve(query)
-        prompt = self.create_prompt_for_OIC_bot()
-        chat_history = self.generate_summary(chat_history)
-        retrieval_chain = (
-        {"context": retriever, "question": RunnablePassthrough()}
-        | prompt
-        | self.llm
-        | StrOutputParser()
-        )
-        response = retrieval_chain.invoke(query)
-        # response = self.format_llm_response(response)
-        return response
-    
-if __name__=="__main__":
-    with open("src/config.json",'r') as f:
-        hyperparameters = json.load(f)
-    a = OWiki(**hyperparameters)
-#     print(a.generate_summary("""User:What is ML?\nBot:Machine learning (ML) is a branch of 
-#  and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.
-
-# How does machine learning work?
-#  (link resides outside ibm.com) breaks out the learning system of a machine learning algorithm into three main parts.\nUser:How to integrate with Oracle\nUser:Explain what have you explained above\nBot:"""))
-#     print("*"*100)
-    # hyperparameters  = {"User":" id INTEGER PRIMARY KEY AUTOINCREMENT, name TEXT NOT NULL, email TEXT UNIQUE","User1":" id1 INTEGER PRIMARY KEY AUTOINCREMENT, name TEXT NOT NULL, email TEXT UNIQUE"}
-    # print(a.create_sql_agent("Filter out common values in table 1 and 2 based on id",**hyperparameters))
-    print(a.search_from_db("What is Machine Learning","You can answer out of context as well"))
+import os
+import google.generativeai as genai
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_community.vectorstores import FAISS
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain.prompts import PromptTemplate
+import json
+import re
+from Classes.Helper_Class import DB_Retriever
+from typing import Optional
+
+os.environ["GOOGLE_API_KEY"] = "AIzaSyBoghqvvnMMS4bA61LjQkkPNdIRetqk438"
+genai.configure(api_key="AIzaSyBoghqvvnMMS4bA61LjQkkPNdIRetqk438")
+
+class OWiki:
+    def __init__(self,**kwargs):
+        temperature = kwargs['temperature']
+        self.summary = kwargs['summary_length']
+        model = kwargs["model"]
+        self.db_loc = kwargs["db_loc"]
+        self.llm = ChatGoogleGenerativeAI(model=model,
+                            temperature=temperature)
+        self.model_embedding = kwargs['model_embeddings']
+    
+
+    def get_summary_template(self):
+        prompt = """Generate a summary for the following conversational data in less than {summary} lines.\nText:\n{text}\n\nSummary:"""
+        prompt_template = PromptTemplate(template = prompt,input_variables=['summary','text'])
+        return prompt_template
+    
+    def create_sql_prompt_template(self,schemas):
+        prompt =  """Write an SQL query for the following questions whose schemas are as follows.\nSQL Schema:"""
+        for table_name,table_schema in schemas.items():
+            prompt+= f"Table Name: {table_name}, Schema : {table_schema}\n\n"
+        prompt+=  """\n\nQuestion:{question}\n\nAnswer:"""
+        prompt_template = PromptTemplate(template = prompt,input_variables=['question'])
+        return prompt_template
+
+    def create_prompt_for_OIC_bot(self):
+        template = """You are OIC(Oracle Integration Cloud) Bot.Follow chat instructions and answer the question based only on the following 
+        Chat_instructions:
+        1. Response must contain Question Explaination along with Potential Solution Headings.
+        2. Response must contain all possible Error Scenarios if applicable along with a Summary Heading containing breif summary at the end.
+
+        Context:
+        {context}
+        
+        Question: {question}
+        """
+        prompt = PromptTemplate.from_template(template)
+        return prompt
+    
+    def create_sql_agent(self,question,schemas):
+        prompt_template = self.create_sql_prompt_template(schemas)
+        chain = prompt_template | self.llm | StrOutputParser()
+        response = chain.invoke({"question":question})
+        response = self.format_llm_response(response)
+        return response
+    
+    def generate_summary(self,text):
+        prompt_template = self.get_summary_template()
+        chain = prompt_template | self.llm | StrOutputParser()
+        response = chain.invoke({"text":text,"summary":self.summary})
+        return response
+
+    def format_llm_response(self,text):
+        bold_pattern = r"\*\*(.*?)\*\*"
+        italic_pattern = r"\*(.*?)\*"
+        code_pattern = r"```(.*?)```"
+        text = text.replace('\n', '<br>')
+        formatted_text = re.sub(code_pattern,"<pre><code>\\1</code></pre>",text)
+        formatted_text = re.sub(bold_pattern, "<b>\\1</b>", formatted_text)
+        formatted_text = re.sub(italic_pattern, "<i>\\1</i>", formatted_text)
+        return formatted_text
+
+    def search_from_db(self, query : str, chat_history : Optional[str] ) -> str :
+        db = DB_Retriever(self.db_loc,self.model_embedding)
+        retriever = db.retrieve(query)
+        prompt = self.create_prompt_for_OIC_bot()
+        chat_history = self.generate_summary(chat_history)
+        retrieval_chain = (
+        {"context": retriever, "question": RunnablePassthrough()}
+        | prompt
+        | self.llm
+        | StrOutputParser()
+        )
+        response = retrieval_chain.invoke(query)
+        response = self.format_llm_response(response)
+        return response
+