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| from llama_index import GPTPineconeIndex, LLMPredictor, ServiceContext | |
| import pinecone | |
| from langchain import OpenAI | |
| import os | |
| from llama_index.langchain_helpers.agents import IndexToolConfig, LlamaIndexTool, LlamaToolkit, create_llama_chat_agent | |
| from langchain.chains.conversation.memory import ConversationBufferMemory | |
| # logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) | |
| # logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) | |
| pinecone_key=os.environ['PINECONE_KEY'] | |
| def askQuestion(brain, question, prompt, temperature, maxTokens): | |
| temperature = float(temperature) | |
| finalQuestion = prompt+question | |
| print(brain, finalQuestion, temperature, maxTokens) | |
| Brain_Name = brain.lower() | |
| print(Brain_Name) | |
| pinecone.init(api_key=pinecone_key, | |
| environment="us-west4-gcp") | |
| pineconeindex = pinecone.Index(Brain_Name) | |
| index = GPTPineconeIndex([], pinecone_index=pineconeindex) | |
| # index = GPTSimpleVectorIndex.load_from_disk('index.json') | |
| # For Q-A set this value to 4, For Content-Genration set this value b/w 7-10. | |
| data_chunks = 5 | |
| # prompt query goes here | |
| # query="summarize in full detail the solution that dimetyd is providing, and previous email sequences which can be used as a context knowledge" | |
| query = finalQuestion | |
| # relevant info from brain goes here | |
| info = ["pdf"] | |
| llm_predictor = LLMPredictor(llm=OpenAI( | |
| temperature=temperature, model_name="text-davinci-003", max_tokens=maxTokens)) | |
| service_context_gpt4 = ServiceContext.from_defaults( | |
| llm_predictor=llm_predictor) | |
| response = index.query(query, service_context=service_context_gpt4, | |
| similarity_top_k=data_chunks, response_mode="compact") | |
| print(question) | |
| print(response) | |
| memory = ConversationBufferMemory(memory_key="chat_history") | |
| memory.chat_memory.add_user_message(question) | |
| memory.chat_memory.add_ai_message(response.response) | |
| return response, memory | |
| def getBrains(name): | |
| pinecone.init(api_key=pinecone_key, | |
| environment="us-west4-gcp") | |
| active_indexes = pinecone.list_indexes() | |
| print(active_indexes) | |
| name = name.lower() | |
| if name in active_indexes: | |
| return True | |
| else: | |
| return False | |
| def runAgent(memory, question, temperature, maxTokens): | |
| if (memory == False): | |
| return "Please Initiate the Chat first.." | |
| temperature = float(temperature) | |
| pinecone.init(api_key=pinecone_key, | |
| environment="us-west4-gcp") | |
| pineconeindex = pinecone.Index("dimetyd-test") | |
| index = GPTPineconeIndex([], pinecone_index=pineconeindex) | |
| # memory = ConversationBufferMemory(memory_key="chat_history") | |
| # print(memory.chat_memory) | |
| llm = OpenAI( | |
| temperature=temperature, model_name="text-davinci-003", max_tokens=maxTokens) | |
| tool_config = IndexToolConfig( | |
| index=index, | |
| name="Vector Index", | |
| description="Use this tool if you can't find the required Information in the previous message history", | |
| index_query_kwargs={"similarity_top_k": 4, "response_mode": "compact"}, | |
| tool_kwargs={"return_direct": True} | |
| ) | |
| toolkit = LlamaToolkit(index_configs=[tool_config]) | |
| agent_chain = create_llama_chat_agent( | |
| toolkit, llm, memory=memory, verbose=True) | |
| response = agent_chain.run(question) | |
| # print(memory.chat_memory) | |
| return response, memory | |