from langchain.vectorstores.chroma import Chroma
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
@app.route('/search',methods=['POST'])
def searchPost():
query = request.json['query']
try:
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
vectordb = Chroma(persist_directory=persist_directory,
embedding_function=embedding_function)
output = vectordb.similarity_search(query=query)
metadata_ids = [doc.metadata['id'] for doc in output]
return metadata_ids
except Exception as e:
print('error',e)
return 'error'
This is the way to query chromadb with langchain, If i add k= any number, the results are increasing
output = vectordb.similarity_search(query=query, k=40)
So how can I do pagination with langchain and chromadb?