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+---
+sidebar_label: LlamaIndex
+slug: /llamaindex
+---
+
+# Integrate seekdb vector search with LlamaIndex
+
+seekdb supports vector data storage, vector indexing, and embedding-based vector search. You can store vectorized data in seekdb for further search.
+
+LlamaIndex is a framework for building context-augmented generative AI applications by using large language models (LLMs), including proxies and workflows. It provides a wealth of capabilities such as data connectors, data indexes, proxies, observability/assessment integration, and workflows.
+
+This topic demonstrates how to integrate the vector search feature of seekdb with the Tongyi Qianwen (Qwen) API and LlamaIndex for Document Question Answering (DQA).
+
+## Prerequisites
+
+* You have deployed the seekdb database.
+  * Your environment has a database and account with read and write privileges.
+  * You can set the `ob_vector_memory_limit_percentage` parameter to enable vector search. We recommend keeping the default value of `0` (adaptive mode). For more precise configuration settings, see the relevant configuration documentation.
+* You have installed Python 3.9 or later.
+* You have installed the required dependencies:
+
+    ```shell
+    python3 -m pip install llama-index-vector-stores-oceanbase llama-index
+    python3 -m pip install llama-index-embeddings-dashscope
+    python3 -m pip install llama-index-llms-dashscope
+    ```
+
+* You have obtained the Qwen API key.
+
+## Step 1: Obtain the database connection information
+
+Contact the seekdb database deployment personnel or administrator to obtain the database connection string. For example:
+
+```sql
+obclient -h$host -P$port -u$user_name -p$password -D$database_name
+```
+
+**Parameters:**
+
+* `$host`: The IP address for connecting to the seekdb database.
+* `$port`: The port for connecting to the seekdb database. The default value is `2881`, which can be customized during deployment.
+* `$database_name`: The name of the database to access.
+
+    <main id="notice" type='notice'>
+        <h4>Notice</h4>
+        <p>The user connecting to the database must have the <code>CREATE</code>, <code>INSERT</code>, <code>DROP</code>, and <code>SELECT</code> privileges on the database.</p>
+    </main>
+
+* `$user_name`: The database account, in the format of `username`.
+* `$password`: The password for the account.
+
+For more information about the connection string, see [Connect to OceanBase Database by using OBClient](https://en.oceanbase.com/docs/common-oceanbase-database-10000000001971649).
+
+## Step 2: Build your AI assistant
+
+### Set the environment variable for the Qwen API key
+
+Create a [Qwen API key](https://www.alibabacloud.com/help/en/model-studio/get-api-key?spm=a2c63.l28256.help-menu-2400256.d_2.47db1b76nM44Ut) and [configure it in the environment variables](https://www.alibabacloud.com/help/en/model-studio/configure-api-key-through-environment-variables?spm=a2c63.p38356.help-menu-2400256.d_2_0_1.56069f6b3m576u).
+
+```shell
+export DASHSCOPE_API_KEY="YOUR_DASHSCOPE_API_KEY"
+```
+
+### Download the sample data
+
+```shell
+mkdir -p '/root/llamaindex/paul_graham/'
+wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O '/root/llamaindex/paul_graham/paul_graham_essay.txt'
+```
+
+### Load the data text
+
+```python
+import os
+from pyobvector import ObVecClient
+from llama_index.core import Settings
+from llama_index.embeddings.dashscope import DashScopeEmbedding
+from llama_index.core import (
+SimpleDirectoryReader,
+load_index_from_storage,
+VectorStoreIndex,
+StorageContext,
+)
+from llama_index.vector_stores.oceanbase import OceanBaseVectorStore
+from llama_index.llms.dashscope import DashScope, DashScopeGenerationModels
+#set ob client
+client = ObVecClient(uri="127.0.0.1:2881", user="root@test",password="",db_name="test")
+# Global Settings
+Settings.embed_model = DashScopeEmbedding()
+# config llm model
+dashscope_llm = DashScope(
+    model_name=DashScopeGenerationModels.QWEN_MAX,
+    api_key=os.environ.get("DASHSCOPE_API_KEY", ""),
+)
+# load documents
+documents = SimpleDirectoryReader("/root/llamaindex/paul_graham/").load_data()
+oceanbase = OceanBaseVectorStore(
+    client=client,
+    dim=1536,
+    drop_old=True,
+    normalize=True,
+)
+
+storage_context = StorageContext.from_defaults(vector_store=oceanbase)
+index = VectorStoreIndex.from_documents(
+    documents, storage_context=storage_context
+)
+```
+
+## Vector search
+
+This step shows how to query `"What did the author do growing up?"` from the document `paul_graham_essay.txt`.
+
+```shell
+# set Logging to DEBUG for more detailed outputs
+query_engine = index.as_query_engine(llm=dashscope_llm)
+res = query_engine.query("What did the author do growing up?")
+res.response
+```
+
+Expected result:
+
+```python
+'Growing up, the author worked on writing and programming outside of school. In terms of writing, he wrote short stories, which he now considers to be awful, as they had very little plot and focused mainly on characters with strong feelings. For programming, he started in 9th grade by trying to write programs on an IBM 1401 at his school, using an early version of Fortran. Later, after getting a TRS-80 microcomputer, he began to write more practical programs, including simple games, a program to predict the flight height of model rockets, and a word processor that his father used for writing.'
+```