sample_name / ingest.py
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Create ingest.py
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import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.vectorstores import Chroma
from langchain.document_loaders import PyPDFLoader
model_name = "BAAI/bge-large-en"
model_kwargs = {"device": "cpu"}
encode_kwargs = {"normalize_embeddings": False}
embeddings = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
)
loader = PyPDFLoader("dino-types.pdf")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
texts = text_splitter.split_documents(documents)
# print(texts[0])
vector_store = Chroma.from_documents(
texts,
embeddings,
collection_metadata={"hnsw:space": "cosine"},
persist_directory="stores/dino_cosine",
)