---
title: Awadb Vector Store
 | Developer Documentation
---

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

```
%pip install llama-index-embeddings-huggingface
%pip install llama-index-vector-stores-awadb
```

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

## Creating an Awadb index

```
import logging
import sys


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

#### Load documents, build the VectorStoreIndex

```
from llama_index.core import (
    SimpleDirectoryReader,
    VectorStoreIndex,
    StorageContext,
)
from IPython.display import Markdown, display
import openai


openai.api_key = ""
```

```
INFO:numexpr.utils:Note: NumExpr detected 12 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
Note: NumExpr detected 12 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
INFO:numexpr.utils:NumExpr defaulting to 8 threads.
NumExpr defaulting to 8 threads.
```

#### Download Data

```
!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
```

#### Load Data

```
# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
```

```
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.vector_stores.awadb import AwaDBVectorStore


embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")


vector_store = AwaDBVectorStore()
storage_context = StorageContext.from_defaults(vector_store=vector_store)


index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, embed_model=embed_model
)
```

#### Query Index

```
# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
```

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

**Growing up, the author wrote short stories, experimented with programming on an IBM 1401, nagged his father to buy a TRS-80 computer, wrote simple games, a program to predict how high his model rockets would fly, and a word processor. He also studied philosophy in college, switched to AI, and worked on building the infrastructure of the web. He wrote essays and published them online, had dinners for a group of friends every Thursday night, painted, and bought a building in Cambridge.**

```
# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query(
    "What did the author do after his time at Y Combinator?"
)
```

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

**After his time at Y Combinator, the author wrote essays, worked on Lisp, and painted. He also visited his mother in Oregon and helped her get out of a nursing home.**