| from langchain.vectorstores import FAISS | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.document_loaders import TextLoader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| def build_vector_store(doc_path="model_docs/tsnet_manual.txt"): | |
| loader = TextLoader(doc_path) | |
| docs = loader.load() | |
| splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50) | |
| chunks = splitter.split_documents(docs) | |
| embedding = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") | |
| return FAISS.from_documents(chunks, embedding) | |