23 lines
906 B
Python
23 lines
906 B
Python
from langchain.document_loaders import TextLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.vectorstores import Chroma
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from langchain.embeddings import LlamaCppEmbeddings
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from sys import argv
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from constants import PERSIST_DIRECTORY
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from constants import CHROMA_SETTINGS
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def main():
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# Load document and split in chunks
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loader = TextLoader(argv[1], encoding="utf8")
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documents = loader.load()
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
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texts = text_splitter.split_documents(documents)
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# Create embeddings
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llama = LlamaCppEmbeddings(model_path="./models/ggml-model-q4_0.bin")
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# Create and store locally vectorstore
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db = Chroma.from_documents(texts, llama, persist_directory=PERSIST_DIRECTORY, client_settings=CHROMA_SETTINGS)
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db.persist()
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db = None
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if __name__ == "__main__":
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main() |