2023-05-05 19:56:31 +00:00
|
|
|
import os
|
|
|
|
|
|
|
|
|
|
# Uncomment to specify your OpenAI API key here (local testing only, not in production!), or add corresponding environment variable (recommended)
|
|
|
|
|
# os.environ['OPENAI_API_KEY']= ""
|
|
|
|
|
|
|
|
|
|
from llama_index import LLMPredictor, PromptHelper, ServiceContext
|
|
|
|
|
from langchain.llms.openai import OpenAI
|
|
|
|
|
from llama_index import StorageContext, load_index_from_storage
|
|
|
|
|
|
2023-05-06 17:15:22 +00:00
|
|
|
base_path = os.environ.get('OPENAI_API_BASE', 'http://localhost:8080/v1')
|
2023-05-05 19:56:31 +00:00
|
|
|
|
|
|
|
|
# This example uses text-davinci-003 by default; feel free to change if desired
|
2023-05-06 22:58:30 +00:00
|
|
|
llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo", openai_api_base=base_path))
|
2023-05-05 19:56:31 +00:00
|
|
|
|
|
|
|
|
# Configure prompt parameters and initialise helper
|
|
|
|
|
max_input_size = 1024
|
|
|
|
|
num_output = 256
|
|
|
|
|
max_chunk_overlap = 20
|
|
|
|
|
|
|
|
|
|
prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)
|
|
|
|
|
|
|
|
|
|
# Load documents from the 'data' directory
|
|
|
|
|
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
|
|
|
|
|
|
|
|
|
|
# rebuild storage context
|
|
|
|
|
storage_context = StorageContext.from_defaults(persist_dir='./storage')
|
|
|
|
|
|
|
|
|
|
# load index
|
|
|
|
|
index = load_index_from_storage(storage_context, service_context=service_context, )
|
|
|
|
|
|
|
|
|
|
query_engine = index.as_query_engine()
|
|
|
|
|
response = query_engine.query("XXXXXX your question here XXXXX")
|
|
|
|
|
print(response)
|
