66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
#!/usr/bin/env python
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# Load documents from a directory into the Qdrant vector store.
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#
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# Usage:
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# python load_documents.py /path/to/docs
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#
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# The script walks through the specified directory, reads all .txt files, splits them into chunks using
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# `RecursiveCharacterTextSplitter`, embeds the chunks with `OllamaEmbeddings`, and stores them in the
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# local Qdrant collection via the helper functions defined in :mod:`main`.
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import os
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import sys
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from pathlib import Path
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from uuid import uuid4
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from main import get_vector_store, get_embeddings, chunk_document
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def load_documents_from_dir(directory: str) -> int:
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"""Load all .txt files from *directory* into the vector store.
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Parameters
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----------
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directory: str
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Path to the directory containing text files.
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Returns
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-------
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int
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Number of documents (chunks) added to the store.
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"""
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directory_path = Path(directory)
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if not directory_path.is_dir():
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raise ValueError(f"{directory!r} is not a directory")
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vector_store = get_vector_store()
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embedding = get_embeddings()
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total_chunks = 0
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for file_path in directory_path.rglob("*.txt"):
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try:
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content = file_path.read_text(encoding="utf-8")
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except Exception as exc:
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print(f"Failed to read {file_path}: {exc}", file=sys.stderr)
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continue
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title = file_path.stem
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documents = chunk_document(content, title)
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ids = [str(uuid4()) for _ in documents]
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embeddings = embedding.embed_documents([doc.page_content for doc in documents])
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vector_store.add_documents(documents, ids=ids, embeddings=embeddings)
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total_chunks += len(documents)
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return total_chunks
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if __name__ == "__main__":
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if len(sys.argv) != 2:
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print("Usage: python load_documents.py <directory>")
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sys.exit(1)
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dir_path = sys.argv[1]
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try:
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added = load_documents_from_dir(dir_path)
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print(f"Added {added} chunk(s) to the knowledge base.")
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except Exception as exc:
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print(f"Error: {exc}", file=sys.stderr)
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sys.exit(1)
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