| from configs.model_config import * | |
| from langchain.embeddings.huggingface import HuggingFaceEmbeddings | |
| import nltk | |
| from vectorstores import MyFAISS | |
| from chains.local_doc_qa import load_file | |
| nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path | |
| if __name__ == "__main__": | |
| filepath = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), | |
| "knowledge_base", "samples", "content", "test.txt") | |
| embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[EMBEDDING_MODEL], | |
| model_kwargs={'device': EMBEDDING_DEVICE}) | |
| docs = load_file(filepath, using_zh_title_enhance=True) | |
| vector_store = MyFAISS.from_documents(docs, embeddings) | |
| query = "指令提示技术有什么示例" | |
| search_result = vector_store.similarity_search(query) | |
| print(search_result) | |
| pass | |