# Store the embeddings in a vector database
ids = []
for i in range(0, len(input_array)):
    ids.append(str(i))

chroma_client = chromadb.Client()
collection = chroma_client.create_collection("pdf_pages")
collection.add(
    embeddings=embeddings,
    ids=ids,
)
```

Finally, provide a query and run a search over the documents. This will return a list of sorted IDs representing the most similar pages to the query.

```python PYTHON
query = "Do the speakers come with an optical cable?"