ZKnowledgeAgent / printvectordb.py
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deploy: initial clean deployment with lfs tracked database
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from app.services.vector_store import load_vector_store
from app.services.embedding_service import get_embedding_model
from langchain_text_splitters import RecursiveCharacterTextSplitter
embedding_model = get_embedding_model()
vector_store = load_vector_store(embedding_model)
data = vector_store.get()
for i, doc in enumerate(data["documents"]):
print("\n====================")
print(f"CHUNK {i}")
print("====================")
print(doc)