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import utils
import os
import openai
from llama_index import SimpleDirectoryReader
from llama_index import Document
from llama_index import VectorStoreIndex
from llama_index import ServiceContext
from llama_index.llms import OpenAI
from llama_index.embeddings import HuggingFaceEmbedding
from trulens_eval import Tru
from llama_index.memory import ChatMemoryBuffer
from utils import get_prebuilt_trulens_recorder
import time
openai.api_key = utils.get_openai_api_key()
documents = SimpleDirectoryReader(
input_files=["./raw_documents/HI_Knowledge_Base.pdf"]
).load_data()
document = Document(text="\n\n".join([doc.text for doc in documents]))
### gpt-4-1106-preview
### gpt-3.5-turbo-1106 / gpt-3.5-turbo
print("Initializing GPT 3.5 ..")
llm = OpenAI(model="gpt-3.5-turbo-1106", temperature=0.1)
print("Initializing bge-small-en-v1.5 embedding model ..")
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
print("Creating vector store ..")
service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model)
index = VectorStoreIndex.from_documents([document], service_context=service_context)
if False:
query_engine = index.as_query_engine(streaming=True)
else:
memory = ChatMemoryBuffer.from_defaults(token_limit=15000)
# chat_engine = index.as_query_engine(streaming=True)
chat_engine = index.as_chat_engine(
chat_mode="context",
memory=memory
)
while True:
input_str = input("[User]: ")
if input_str == "END":
break
# res = chat_engine.query(input_str)
res = chat_engine.stream_chat(input_str)
bot_response = ""
print("[Bot]: ", end="")
for s in res.response_gen:
bot_response += s
print(s, end="")
print("") |