# Source: https://docs.unstructured.io/examplecode/codesamples/oss/vector-database.md

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.unstructured.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Vector database ingestion

<Note> This sample code utilizes the [Unstructured Open Source](/open-source/introduction/overview "Open Source") Library. </Note>

In this guide, we demonstrate how to leverage Unstructured.IO, ChromaDB, and LangChain to summarize topics from the front page of CNN Lite. Utilizing the modern LLM stack, including Unstructured, Chroma, and LangChain, this workflow is streamlined to less than two dozen lines of code.

## Gather Links with Unstructured

First, we gather links from the CNN Lite homepage using the partition\_html function from Unstructured. When Unstructured partitions HTML pages, links are included in the metadata for each element, making link collection straightforward.

```python  theme={null}
from unstructured.partition.html import partition_html

cnn_lite_url = "https://lite.cnn.com/"
elements = partition_html(url=cnn_lite_url)
links = []

for element in elements:
    if element.metadata.link_urls:
        relative_link = element.metadata.link_urls[0][1:]
        if relative_link.startswith("2024"):
            links.append(f"{cnn_lite_url}{relative_link}")

```

## Ingest Individual Articles with UnstructuredURLLoader

With the links in hand, we preprocess individual news articles using UnstructuredURLLoader. This loader fetches content from the web and then uses the unstructured partition function to extract content and metadata. Here we preprocess HTML files, but it also works with other response types like application/pdf. The result is a list of LangChain Document objects.

```python  theme={null}
from langchain.document_loaders import UnstructuredURLLoader

loaders = UnstructuredURLLoader(urls=links, show_progress_bar=True)
docs = loaders.load()

```

## Load Documents into ChromaDB

The next step is to load the preprocessed documents into ChromaDB. This process involves vectorizing the documents using OpenAI embeddings and loading them into Chroma’s vector store. Once in Chroma, similarity search can be performed to retrieve documents related to specific topics.

```python  theme={null}
from langchain.vectorstores.chroma import Chroma
from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(docs, embeddings)
query_docs = vectorstore.similarity_search("Update on the coup in Niger.", k=1)

```

## Summarize the Documents

After retrieving relevant documents from Chroma, we summarize them using LangChain. The load\_summarization\_chain function allows for easy summarization, simply requiring the selection of an LLM and summarization chain.

```python  theme={null}
from langchain.chat_models import ChatOpenAI
from langchain.chains.summarize import load_summarize_chain

llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo-16k")
chain = load_summarize_chain(llm, chain_type="stuff")
chain.run(query_docs)

```

## Jupyter Notebook

To delve deeper into this example, you can access the full Jupyter Notebook here: [News of the Day Notebook](https://github.com/Unstructured-IO/unstructured/blob/main/examples/chroma-news-of-the-day/news-of-the-day.ipynb)