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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
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