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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
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:"
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 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
|