---
title: DashVector Reader
 | LlamaIndex OSS Documentation
---

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

```
%pip install llama-index-readers-dashvector
```

```
!pip install llama-index
```

```
import logging
import sys
import os


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

```
api_key = os.environ["DASHVECTOR_API_KEY"]
```

```
from llama_index.readers.dashvector import DashVectorReader


reader = DashVectorReader(api_key=api_key)
```

```
import numpy as np


# the query_vector is an embedding representation of your query_vector
query_vector = [n1, n2, n3, ...]
```

```
# NOTE: Required args are index_name, id_to_text_map, vector.
# In addition, we can pass through the metadata filter that meet the SQL syntax.
# See the Python client: https://pypi.org/project/dashvector/ for more details.
documents = reader.load_data(
    collection_name="quickstart",
    topk=3,
    vector=query_vector,
    filter="key = 'value'",
    output_fields=["key1", "key2"],
)
```

### Create index

```
from llama_index.core import ListIndex
from IPython.display import Markdown, display


index = ListIndex.from_documents(documents)
```

```
# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query("<query_text>")
```

```
display(Markdown(f"<b>{response}</b>"))
```