| import openai | |
| from langchain.chains.summarize import load_summarize_chain | |
| import os | |
| import json | |
| from pprint import pprint | |
| import pinecone | |
| import time | |
| from langchain.chat_models import AzureChatOpenAI | |
| from langchain.vectorstores import Pinecone | |
| from langchain.chains import RetrievalQA, LLMChain | |
| from langchain.memory import ConversationBufferWindowMemory | |
| from typing import Optional | |
| import pandas as pd | |
| from langchain.embeddings import OpenAIEmbeddings | |
| from Utility_New import MasterLLM, intent_recognition, entity_recognition_main, entity_recognition_sub, describe, compare, RAG, sentiment | |
| openai.api_type = "azure" | |
| openai.api_base = "https://di-sandbox-gpt4.openai.azure.com/" | |
| openai.api_version = "2023-07-01-preview" | |
| openai.api_key = "69ec3919a7314784be9c4f7414286fba" | |
| os.environ['OPENAI_API_KEY']=openai.api_key | |
| os.environ['OPENAI_API_BASE'] = openai.api_base | |
| os.environ['OPENAI_API_VERSION'] = openai.api_version | |
| os.environ['OPENAI_API_TYPE'] = openai.api_type | |
| PINECONE_API_KEY = '49e9d57f-ca7b-45d8-9fe5-b02db54b2dc7' | |
| pinecone.init( | |
| api_key=os.environ.get('PINECONE_API_KEY') or '49e9d57f-ca7b-45d8-9fe5-b02db54b2dc7', | |
| environment=os.environ.get('PINECONE_ENVIRONMENT') or 'gcp-starter' | |
| ) | |
| index_name = 'rag-sbc' | |
| embedding_model = OpenAIEmbeddings(openai_api_key = os.environ.get('OPENAI_API_KEY'), | |
| deployment="text-embedding-ada-002", | |
| model="text-embedding-ada-002", | |
| openai_api_base=os.environ.get('OPENAI_API_BASE'), | |
| openai_api_type=os.environ.get('OPENAI_API_TYPE')) | |
| index = pinecone.Index(index_name) | |
| vectorstore = Pinecone(index, embedding_model.embed_query, 'text') | |
| llm = AzureChatOpenAI(deployment_name = "GPT4_32k", model_name = "gpt-4-32k", temperature=0) | |
| conversational_memory = ConversationBufferWindowMemory( | |
| memory_key='chat_history', | |
| k=5, | |
| return_messages=True) | |
| session_memory = {} | |
| entities = {} | |
| data_json = pd.read_csv('Combined_data_final_V2.csv') | |
| data = pd.read_csv('Combined_data_SBC.csv') | |
| data.drop(columns=['ID'],inplace=True) | |
| result = {} | |
| for main_key, group_df in data.groupby('License Name'): | |
| group_dict = group_df.to_dict(orient='records') | |
| result[main_key] = group_dict | |
| json_result = json.dumps(result, indent=2) | |
| json_result = json_result.replace("\\n","") | |
| json_result = json_result.replace("\\u2002","") | |
| json_result = json_result.replace("\n","") | |
| json_result = json_result.replace("/","") | |
| data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x) | |
| LicenseName = data['License Name'].to_list() | |
| License_Service = data.groupby('License Name')['Service Name'].apply(list).reset_index(name='Service Name') | |
| def final_output(UserPrompt): | |
| final_json = {} | |
| intent = intent_recognition(UserPrompt) | |
| print('Intent') | |
| print(intent) | |
| print() | |
| oldPrompt = UserPrompt | |
| if len(list(entities.keys())) == 0: | |
| PrevEntity = [] | |
| else: | |
| PrevEntity = entities[list(entities.keys())[-1]] | |
| Prev_Entity = [i.strip() for i in PrevEntity] | |
| Prev_Entity = list(set(Prev_Entity)) | |
| print('Prev_Entity') | |
| print(Prev_Entity) | |
| print() | |
| UserPrompt, Prev_Entity = sentiment(UserPrompt, Prev_Entity) | |
| print('New Prev Entity') | |
| print(Prev_Entity) | |
| print('UserPrompt') | |
| print(UserPrompt) | |
| print() | |
| entity_main = entity_recognition_main(UserPrompt, LicenseName) | |
| entity_main = list(set(entity_main)) | |
| print('Main Entity') | |
| print(entity_main) | |
| print() | |
| entity_sub = entity_recognition_sub(UserPrompt, entity_main,License_Service) | |
| print('Sub Entity') | |
| print(entity_sub) | |
| print() | |
| entities[oldPrompt] = entity_main | |
| for i in Prev_Entity: | |
| entity_main.append(i) | |
| entity = entity_main | |
| entity = list(set(entity)) | |
| print('Full Entity') | |
| print(entity) | |
| print() | |
| try: | |
| json_data = json.loads(json_result) | |
| filtered_data = {key: json_data[key] for key in entity} | |
| filtered_json = json.dumps(filtered_data, indent=2) | |
| final_json = json.loads(filtered_json) | |
| except: | |
| final_json = {} | |
| c = 0 | |
| if intent in ['Transfer']: | |
| print(intent) | |
| print() | |
| output = "I have shared our last conversation on your WhatsApp" | |
| return output, intent | |
| elif len(entity)>0: | |
| for i in entity: | |
| if i in LicenseName: | |
| c+=1 | |
| if c > 0 and c == len(entity): | |
| if intent in ['Describe'] and len(entity) > 0: | |
| print('Describe') | |
| prompt, prompt_template = describe(entity, final_json, UserPrompt) | |
| chain = LLMChain(llm=llm, prompt=prompt_template,memory = conversational_memory) | |
| output = chain({'context':prompt})['text'] | |
| print(output) | |
| elif intent in ['Compare'] and len(entity) > 0: | |
| print('Compare') | |
| prompt,prompt_template = compare(entity, final_json, UserPrompt) | |
| chain = LLMChain(llm=llm, prompt=prompt_template,memory = conversational_memory) | |
| output = chain({'context':prompt})['text'] | |
| print(output) | |
| else: | |
| print("RAG1") | |
| prompt, prompt_template = RAG(UserPrompt,entity) | |
| qa = RetrievalQA.from_chain_type(llm=llm, | |
| chain_type="stuff", | |
| retriever=vectorstore.as_retriever(), | |
| memory = conversational_memory, | |
| chain_type_kwargs={"prompt": prompt_template}) | |
| output = qa.run(prompt) | |
| print(output) | |
| else: | |
| print("RAG2") | |
| prompt, prompt_template = RAG(UserPrompt,entity) | |
| qa = RetrievalQA.from_chain_type(llm=llm, | |
| chain_type="stuff", | |
| retriever=vectorstore.as_retriever(), | |
| memory = conversational_memory, | |
| chain_type_kwargs={"prompt": prompt_template}) | |
| output = qa.run(prompt) | |
| print(output) | |
| else: | |
| print("RAG1") | |
| prompt, prompt_template = RAG(UserPrompt,entity) | |
| qa = RetrievalQA.from_chain_type(llm=llm, | |
| chain_type="stuff", | |
| retriever=vectorstore.as_retriever(), | |
| memory = conversational_memory, | |
| chain_type_kwargs={"prompt": prompt_template}) | |
| output = qa.run(prompt) | |
| print(output) | |
| session_memory[oldPrompt] = output | |
| return output, intent | |