import chromadb from chromadb.config import Settings client = chromadb.Client(Settings(chroma_db_impl="duckdb+parquet", persist_directory="db/" ))

collection = client.create_collection(name="Students") student_info = """ Alexandra Thompson, a 19-year-old computer science sophomore with a 3.7 GPA, is a member of the programming and chess clubs who enjoys pizza, swimming, and hiking in her free time in hopes of working at a tech company after graduating from the University of Washington. """ club_info = """ The university chess club provides an outlet for students to come together and enjoy playing the classic strategy game of chess. Members of all skill levels are welcome, from beginners learning the rules to experienced tournament players. The club typically meets a few times per week to play casual games, participate in tournaments, analyze famous chess matches, and improve members' skills. """ university_info = """ The University of Washington, founded in 1861 in Seattle, is a public research university with over 45,000 students across three campuses in Seattle, Tacoma, and Bothell. As the flagship institution of the six public universities in Washington state, UW encompasses over 500 buildings and 20 million square feet of space, including one of the largest library systems in the world. """ collection.add( documents = [student_info, club_info, university_info], metadatas = [{"source": "student info"},{"source": "club info"},{'source':'university info'}], ids = ["id1", "id2", "id3"] )

results = collection.query( query_texts=["What is the student name?"], n_results=2 ) results

from chromadb.utils import embedding_functions openai_ef = embedding_functions.OpenAIEmbeddingFunction( model_name="text-embedding-ada-002" ) students_embeddings = openai_ef([student_info, club_info, university_info])

print(students_embeddings)

collection2 = client.get_or_create_collection(name="Students2") collection2.add( embeddings = students_embeddings, documents = [student_info, club_info, university_info], metadatas = [{"source": "student info"},{"source": "club info"},{'source':'university info'}], ids = ["id1", "id2", "id3"] )

collection2 = client.get_or_create_collection(name="Students2",embedding_function=openai_ef) collection2.add( documents = [student_info, club_info, university_info], metadatas = [{"source": "student info"},{"source": "club info"},{'source':'university info'}], ids = ["id1", "id2", "id3"] )

results = collection2.query( query_texts=["What is the student name?"], n_results=2 ) results

import time time.sleep(60)

collection2.update( ids=["id1"], documents=["Kristiane Carina, a 19-year-old computer science sophomore with a 3.7 GPA"], metadatas=[{"source": "student info"}], )

results = collection2.query( query_texts=["What is the student name?"], n_results=2 ) results

collection2.delete(ids = ['id1']) results = collection2.query( query_texts=["What is the student name?"], n_results=2 ) results

vector_collections = client.create_collection("vectordb") vector_collections.add( documents=["This is Chroma DB CheatSheet", "This is Chroma DB Documentation", "This document Chroma JS API Docs"], metadatas=[{"source": "Chroma Cheatsheet"}, {"source": "Chroma Doc"}, {'source':'JS API Doc'}], ids=["id1", "id2", "id3"] )

vector_collections.count()

vector_collections.get()

vector_collections.modify(name="chroma_info") # list all collections client.list_collections()

vector_collections_new = client.get_collection(name="chroma_info") client.delete_collection(name="chroma_info")

client.list_collections()

client.reset()

client.list_collections()