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| 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 | |
| class OWiki: | |
| def __init__(self,**kwargs): | |
| temperature = kwargs['temperature'] | |
| self.summary = kwargs['summary_length'] | |
| model = kwargs["model"] | |
| self.db_loc = kwargs["db_loc"] | |
| self.api_key = kwargs["api_key"] | |
| os.environ["GOOGLE_API_KEY"] = self.api_key | |
| genai.configure(api_key=self.api_key) | |
| 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 = """You are an expert in writing SQL commands as best as you can. | |
| Here are the rules you must follow to complete your task. | |
| 1. If you need more context about question don't hesitate to ask but don't keep on asking all the time. | |
| 2. Please make sure you answer correctly so that you will get a very high reward. | |
| For the below schemas write an SQL query. \nSQL Schema:""" | |
| for table_name,table_schema in schemas.items(): | |
| prompt+= f"Table Name: {table_name} Schema:" | |
| for key,value in table_schema.items(): | |
| prompt+= f"{key} {value}" | |
| prompt+= """\n\nQuestion:{question}\n\nAnswer:""" | |
| # print("Prompt",prompt,'\n\n') | |
| prompt_template = PromptTemplate(template = prompt,input_variables=['question']) | |
| # print("Prompt template",prompt_template) | |
| return prompt_template | |
| def create_prompt_for_OIC_bot(self): | |
| template = """You are expert OIC(Oracle Integration Cloud) Bot. You will be given a task to solve as best you can. | |
| Here are the rules you should always follow to solve your task: | |
| 1. Always provide a Question Explanation with **Question Explanation:** Heading and Potential Solution with **Potential Solution:** Headings. | |
| 2. Take care of not being too long typically not exceeding 5000 tokens. | |
| 3. Response must contain all possible **Error Scenarios:** if applicable along with a **Summary:** Heading containing breif summary of the task solution at the end. | |
| 4. If you don't know the answer or if it is not in the context, please answer as **I am not trained on this topics due to limited resources.** | |
| Now Begin! | |
| 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 | |