> ## Documentation Index
> Fetch the complete documentation index at: https://nvd-54.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# OpenGauss VectorStore 集成

> Integrate with the OpenGauss VectorStore using LangChain Python.

This notebook covers how to get started with the openGauss VectorStore. [openGauss](https://opengauss.org/en/) is a high-performance relational database with native vector storage and retrieval capabilities. This integration enables ACID-compliant vector operations within LangChain applications, combining traditional SQL functionality with modern AI-driven similarity search.
vector store.

## 设置

### Launch openGauss Container

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
docker run --name opengauss \
  -d \
  -e GS_PASSWORD='MyStrongPass@123' \
  -p 8888:5432 \
  opengauss/opengauss-server:latest
```

### Install langchain-opengauss

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install langchain-opengauss
```

**System Requirements**:

* openGauss ≥ 7.0.0
* Python ≥ 3.8
* psycopg2-binary

### 凭证

Using your openGauss Credentials

## 初始化

<EmbeddingTabs />

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_opengauss import OpenGauss, OpenGaussSettings

# Configure with schema validation
config = OpenGaussSettings(
    table_name="test_langchain",
    embedding_dimension=384,
    index_type="HNSW",
    distance_strategy="COSINE",
)
vector_store = OpenGauss(embedding=embeddings, config=config)
```

## 管理向量存储

### 向向量存储添加项目

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_core.documents import Document

document_1 = Document(page_content="foo", metadata={"source": "https://example.com"})

document_2 = Document(page_content="bar", metadata={"source": "https://example.com"})

document_3 = Document(page_content="baz", metadata={"source": "https://example.com"})

documents = [document_1, document_2, document_3]

vector_store.add_documents(documents=documents, ids=["1", "2", "3"])
```

### Update items in vector store

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
updated_document = Document(
    page_content="qux", metadata={"source": "https://another-example.com"}
)

# If the id is already exist, will update the document
vector_store.add_documents(document_id="1", document=updated_document)
```

### 从向量存储删除项目

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
vector_store.delete(ids=["3"])
```

## 查询向量存储

一旦创建了向量存储并添加了相关文档，您很可能希望在链或智能体运行期间对其进行查询。

### 直接查询

可以按以下方式执行简单的相似度搜索：

* TODO: Edit and then run code cell to generate output

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
results = vector_store.similarity_search(
    query="thud", k=1, filter={"source": "https://another-example.com"}
)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")
```

如果您想执行相似度搜索并获取对应分数，可以运行：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
results = vector_store.similarity_search_with_score(
    query="thud", k=1, filter={"source": "https://example.com"}
)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
```

### 转换为检索器进行查询

您还可以将向量存储转换为检索器，以便在链中更方便地使用。

* TODO: Edit and then run code cell to generate output

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
retriever = vector_store.as_retriever(search_type="mmr", search_kwargs={"k": 1})
retriever.invoke("thud")
```

## 用于检索增强生成

有关如何将此向量存储用于检索增强生成 (RAG) 的指南，请参阅以下部分：

* [Tutorials](/oss/python/langchain/rag)
* [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)
* [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval/)

## 配置

### Connection settings

| Parameter             | Default                 | Description                                                                                                                                                                                                                                                         |
| --------------------- | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `host`                | localhost               | Database server address                                                                                                                                                                                                                                             |
| `port`                | 8888                    | Database connection port                                                                                                                                                                                                                                            |
| `user`                | gaussdb                 | Database username                                                                                                                                                                                                                                                   |
| `password`            | -                       | Complex password string                                                                                                                                                                                                                                             |
| `database`            | postgres                | Default database name                                                                                                                                                                                                                                               |
| `min_connections`     | 1                       | Connection pool minimum size                                                                                                                                                                                                                                        |
| `max_connections`     | 5                       | Connection pool maximum size                                                                                                                                                                                                                                        |
| `table_name`          | langchain\_docs         | Name of the table for storing vector data and metadata                                                                                                                                                                                                              |
| `index_type`          | IndexType.HNSW          | Vector index algorithm type. Options: HNSW or IVFFLAT\nDefault is HNSW.                                                                                                                                                                                             |
| `vector_type`         | VectorType.vector       | Type of vector representation to use. Default is Vector.                                                                                                                                                                                                            |
| `distance_strategy`   | DistanceStrategy.COSINE | Vector similarity metric to use for retrieval. Options: euclidean (L2 distance), cosine (angular distance, ideal for text embeddings), manhattan (L1 distance for sparse data), negative\_inner\_product (dot product for normalized vectors).\n Default is cosine. |
| `embedding_dimension` | 1536                    | Dimensionality of the vector embeddings.                                                                                                                                                                                                                            |

### Supported combinations

| Vector Type | Dimensions | Index Types  | Supported Distance Strategies          |
| ----------- | ---------- | ------------ | -------------------------------------- |
| vector      | ≤2000      | HNSW/IVFFLAT | COSINE/EUCLIDEAN/MANHATTAN/INNER\_PROD |

## Performance optimization

### Index tuning guidelines

**HNSW Parameters**:

* `m`: 16-100 (balance between recall and memory)
* `ef_construction`: 64-1000 (must be > 2\*m)

**IVFFLAT Recommendations**:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import math

lists = min(
    int(math.sqrt(total_rows)) if total_rows > 1e6 else int(total_rows / 1000),
    2000,  # openGauss maximum
)
```

### Connection pooling

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
OpenGaussSettings(min_connections=3, max_connections=20)
```

## Limitations

* `bit` and `sparsevec` vector types currently in development
* Maximum vector dimensions: 2000 for `vector` type

***

***

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