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# %%
from utils import retrieve_collections, get_chroma_client
from load_model import load_embedding
#retrieve_collections()
client = get_chroma_client()
# %%
client.reset()
# %%
collections = tuple( [collection.name for collection in client.list_collections()] ) ##Keine Embedding function in der Collection angelegt...
ef = load_embedding("hkunlp/instructor-large")
collection="heikostest2"
client.create_collection(collection, embedding_function=ef, metadata={"loaded_docs":[]})
# %%
my_col = client.list_collections()
# %%
my_col.embedding_function
# %%
from langchain.vectorstores import Chroma
import load_model
from load_model import load_embedding
persist_directory = load_model.persist_directory
ef = load_embedding("hkunlp/instructor-large")
vectorstore = Chroma(
collection_name="papers",
embedding_function=ef,
persist_directory=persist_directory,
)
# %%
query = "What did the president say about Ketanji Brown Jackson"
docs = vectorstore.similarity_search(query)
# %%
docs
# %%
vectorstore.similarity_search_with_score(query) |