Chroma Vector Store

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

%pip install llama-index-vector-stores-chroma

!pip install llama-index

Creating a Chroma Index

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

import os
import getpass

# os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai

openai.api_key = "sk-"

import chromadb

chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")

from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.chroma import ChromaVectorStore
from IPython.display import Markdown, display

from llama_index.core.schema import TextNode

nodes = [
    TextNode(
        text="The Shawshank Redemption",
        metadata={
            "author": "Stephen King",
            "theme": "Friendship",
            "year": 1994,
        },
    ),
    TextNode(
        text="The Godfather",
        metadata={
            "director": "Francis Ford Coppola",
            "theme": "Mafia",
            "year": 1972,
        },
    ),
    TextNode(
        text="Inception",
        metadata={
            "director": "Christopher Nolan",
            "theme": "Fiction",
            "year": 2010,
        },
    ),
    TextNode(
        text="To Kill a Mockingbird",
        metadata={
            "author": "Harper Lee",
            "theme": "Mafia",
            "year": 1960,
        },
    ),
    TextNode(
        text="1984",
        metadata={
            "author": "George Orwell",
            "theme": "Totalitarianism",
            "year": 1949,
        },
    ),
    TextNode(
        text="The Great Gatsby",
        metadata={
            "author": "F. Scott Fitzgerald",
            "theme": "The American Dream",
            "year": 1925,
        },
    ),
    TextNode(
        text="Harry Potter and the Sorcerer's Stone",
        metadata={
            "author": "J.K. Rowling",
            "theme": "Fiction",
            "year": 1997,
        },
    ),
]

from llama_index.core import StorageContext


vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex(nodes, storage_context=storage_context)

One Exact Match Filter

from llama_index.core.vector_stores import (
    MetadataFilter,
    MetadataFilters,
    FilterOperator,
)


filters = MetadataFilters(
    filters=[
        MetadataFilter(key="theme", operator=FilterOperator.EQ, value="Mafia"),
    ]
)

retriever = index.as_retriever(filters=filters)
retriever.retrieve("What is inception about?")

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"





[NodeWithScore(node=TextNode(id_='f343294f-4cd5-4f1c-acbf-19490aa95efb', embedding=None, metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia', 'year': 1972}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='79563896e320da86be371351f55d903acdcfb3229368a6622f6be6e929e8b7cc', text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.6215522669166147),
 NodeWithScore(node=TextNode(id_='7910d5cd-7871-46e5-b71a-0dae1797aee1', embedding=None, metadata={'author': 'Harper Lee', 'theme': 'Mafia', 'year': 1960}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='0a1875c24455356c77eedd8eddd39035ec622959b59d2296eff56d42019a0c00', text='To Kill a Mockingbird', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.5873631114046581)]

Multiple Exact Match Metadata Filters

from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters


filters = MetadataFilters(
    filters=[
        MetadataFilter(key="theme", value="Mafia"),
        MetadataFilter(key="year", value=1972),
    ]
)

retriever = index.as_retriever(filters=filters)
retriever.retrieve("What is inception about?")

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"





[NodeWithScore(node=TextNode(id_='f343294f-4cd5-4f1c-acbf-19490aa95efb', embedding=None, metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia', 'year': 1972}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='79563896e320da86be371351f55d903acdcfb3229368a6622f6be6e929e8b7cc', text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.6215522669166147)]

Multiple Metadata Filters with AND condition

from llama_index.core.vector_stores import FilterOperator, FilterCondition


filters = MetadataFilters(
    filters=[
        MetadataFilter(key="theme", value="Fiction"),
        MetadataFilter(key="year", value=1997, operator=FilterOperator.GT),
    ],
    condition=FilterCondition.AND,
)

retriever = index.as_retriever(filters=filters)
retriever.retrieve("Harry Potter?")

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"





[NodeWithScore(node=TextNode(id_='b71ce5e8-353e-42c6-94b3-d0a11370aaba', embedding=None, metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='110b4ab08da17685bdc3d53aecf6085a535dd00a43612eed991bce8074aa36a9', text='Inception', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.6250006485226994)]

Multiple Metadata Filters with OR condition

from llama_index.core.vector_stores import FilterOperator, FilterCondition


filters = MetadataFilters(
    filters=[
        MetadataFilter(key="theme", value="Fiction"),
        MetadataFilter(key="year", value=1997, operator=FilterOperator.GT),
    ],
    condition=FilterCondition.OR,
)

retriever = index.as_retriever(filters=filters)
retriever.retrieve("Harry Potter?")

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"





[NodeWithScore(node=TextNode(id_='6b0e9499-9f4d-4637-ab2a-460e5c870948', embedding=None, metadata={'author': 'J.K. Rowling', 'theme': 'Fiction', 'year': 1997}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='a2656c2bc96ed472bb0ed3ea81075042e9860987f3156428789d07079e019ed0', text="Harry Potter and the Sorcerer's Stone", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.7405548668973673),
 NodeWithScore(node=TextNode(id_='b71ce5e8-353e-42c6-94b3-d0a11370aaba', embedding=None, metadata={'director': 'Christopher Nolan', 'theme': 'Fiction', 'year': 2010}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='110b4ab08da17685bdc3d53aecf6085a535dd00a43612eed991bce8074aa36a9', text='Inception', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.6250006485226994)]