Chroma Multi-Modal Demo with LlamaIndex

Chroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.

</a> <a href="https://github.com/chroma-core/chroma/blob/master/LICENSE" target="_blank">

</a>

Chroma is fully-typed, fully-tested and fully-documented.

Install Chroma with:

pip install chromadb

Chroma runs in various modes. See below for examples of each integrated with LangChain.

in-memory - in a python script or jupyter notebook
in-memory with persistence - in a script or notebook and save/load to disk
in a docker container - as a server running your local machine or in the cloud

Like any other database, you can:

.add
.get
.update
.upsert
.delete
.peek
and .query runs the similarity search.

View full docs at docs.

Basic Example

In this basic example, we take the a Paul Graham essay, split it into chunks, embed it using an open-source embedding model, load it into Chroma, and then query it.

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

python

%pip install llama-index-vector-stores-qdrant
%pip install llama-index-embeddings-huggingface
%pip install llama-index-vector-stores-chroma

python

!pip install llama-index

Creating a Chroma Index

python

!pip install llama-index chromadb --quiet
!pip install chromadb==0.4.17
!pip install sentence-transformers
!pip install pydantic==1.10.11
!pip install open-clip-torch

python

# import
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from IPython.display import Markdown, display
import chromadb

python

# set up OpenAI
import os
import openai

OPENAI_API_KEY = ""
openai.api_key = OPENAI_API_KEY
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

Download Images and Texts from Wikipedia

python

import requests


def get_wikipedia_images(title):
    response = requests.get(
        "https://en.wikipedia.org/w/api.php",
        params={
            "action": "query",
            "format": "json",
            "titles": title,
            "prop": "imageinfo",
            "iiprop": "url|dimensions|mime",
            "generator": "images",
            "gimlimit": "50",
        },
    ).json()
    image_urls = []
    for page in response["query"]["pages"].values():
        if page["imageinfo"][0]["url"].endswith(".jpg") or page["imageinfo"][
            0
        ]["url"].endswith(".png"):
            image_urls.append(page["imageinfo"][0]["url"])
    return image_urls

python

from pathlib import Path
import urllib.request

image_uuid = 0
MAX_IMAGES_PER_WIKI = 20

wiki_titles = {
    "Tesla Model X",
    "Pablo Picasso",
    "Rivian",
    "The Lord of the Rings",
    "The Matrix",
    "The Simpsons",
}

data_path = Path("mixed_wiki")
if not data_path.exists():
    Path.mkdir(data_path)

for title in wiki_titles:
    response = requests.get(
        "https://en.wikipedia.org/w/api.php",
        params={
            "action": "query",
            "format": "json",
            "titles": title,
            "prop": "extracts",
            "explaintext": True,
        },
    ).json()
    page = next(iter(response["query"]["pages"].values()))
    wiki_text = page["extract"]

    with open(data_path / f"{title}.txt", "w") as fp:
        fp.write(wiki_text)

    images_per_wiki = 0
    try:
        # page_py = wikipedia.page(title)
        list_img_urls = get_wikipedia_images(title)
        # print(list_img_urls)

        for url in list_img_urls:
            if url.endswith(".jpg") or url.endswith(".png"):
                image_uuid += 1
                # image_file_name = title + "_" + url.split("/")[-1]

                urllib.request.urlretrieve(
                    url, data_path / f"{image_uuid}.jpg"
                )
                images_per_wiki += 1
                # Limit the number of images downloaded per wiki page to 15
                if images_per_wiki > MAX_IMAGES_PER_WIKI:
                    break
    except:
        print(str(Exception("No images found for Wikipedia page: ")) + title)
        continue

Set the embedding model

python

from chromadb.utils.embedding_functions import OpenCLIPEmbeddingFunction

# set defalut text and image embedding functions
embedding_function = OpenCLIPEmbeddingFunction()

python

from llama_index.core.indices import MultiModalVectorStoreIndex
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import SimpleDirectoryReader, StorageContext
from chromadb.utils.data_loaders import ImageLoader

image_loader = ImageLoader()

# create client and a new collection
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection(
    "multimodal_collection",
    embedding_function=embedding_function,
    data_loader=image_loader,
)


# load documents
documents = SimpleDirectoryReader("./mixed_wiki/").load_data()

# set up ChromaVectorStore and load in data
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents,
    storage_context=storage_context,
)

python

retriever = index.as_retriever(similarity_top_k=50)
retrieval_results = retriever.retrieve("Picasso famous paintings")

python

# print(retrieval_results)
from llama_index.core.schema import ImageNode
from llama_index.core.response.notebook_utils import (
    display_source_node,
    display_image_uris,
)


image_results = []
MAX_RES = 5
cnt = 0
for r in retrieval_results:
    if isinstance(r.node, ImageNode):
        image_results.append(r.node.metadata["file_path"])
    else:
        if cnt < MAX_RES:
            display_source_node(r)
        cnt += 1

display_image_uris(image_results, [3, 3], top_k=2)

Chroma Multi-Modal Demo with LlamaIndex

Basic Example

Creating a Chroma Index

Download Images and Texts from Wikipedia

Set the embedding model

Build Chroma Multi-Modal Index with LlamaIndex

Retrieve results from Multi-Modal Index