| import logging | |
| import sys | |
| import gradio as gr | |
| import asyncio | |
| import nest_asyncio | |
| logging.basicConfig(stream=sys.stdout, level=logging.INFO) | |
| logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) | |
| from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext | |
| from llama_index.llms import HuggingFaceLLM | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from g4f import Provider, models | |
| from langchain.llms.base import LLM | |
| from llama_index.llms import LangChainLLM | |
| from langchain_g4f import G4FLLM | |
| nest_asyncio.apply() | |
| documents = SimpleDirectoryReader('data').load_data() | |
| embed_model = HuggingFaceEmbeddings( | |
| model_name="sentence-transformers/all-mpnet-base-v2" | |
| ) | |
| llm= LLM = G4FLLM( | |
| model=models.gpt_35_turbo, | |
| provider=Provider.Acytoo, | |
| ) | |
| llm = LangChainLLM(llm=llm) | |
| service_context = ServiceContext.from_defaults(chunk_size=512, llm=llm, embed_model=embed_model) | |
| index = VectorStoreIndex.from_documents(documents, service_context=service_context) | |
| async def main(query): | |
| query_engine = index.as_query_engine() | |
| response = query_engine.query(query) | |
| print(response) | |
| return response | |
| iface = gr.Interface(fn=main, inputs="text", outputs="text") | |
| iface.launch() | |