docs: Add openGauss vector store documentation (#30742)

Hey LangChain community! 👋 Excited to propose official documentation for
our new openGauss integration that brings powerful vector capabilities
to the stack!

### What's Inside 📦  
1. **Full Integration Guide**  
Introducing
[langchain-opengauss](https://pypi.org/project/langchain-opengauss/) on
PyPI - your new toolkit for:
   🔍 Native hybrid search (vectors + metadata)  
   🚀 Production-grade connection pooling  
   🧩 Automatic schema management  

2. **Rigorous Testing Passed** ✅  
![Benchmark
Results](https://github.com/user-attachments/assets/ae3b21f7-aeea-4ae7-a142-f2aec57936a0)
   - 100% non-async test coverage  

ps: Current implementation resides in my personal repository:
https://github.com/mpb159753/langchain-opengauss, How can I transfer
process to langchain-ai org?? *Keen to hear your thoughts and make this
integration shine!* ✨

---------

Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>

docs/docs/integrations/vectorstores/opengauss.ipynb

@@ -0,0 +1,373 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "1957f5cb",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: openGauss\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef1f0986",
+   "metadata": {},
+   "source": [
+    "# openGauss VectorStore\n",
+    "\n",
+    "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.\n",
+    " vector store."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36fdc060",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "### Launch openGauss Container"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "```bash\n",
+    "docker run --name opengauss \\\n",
+    "  -d \\\n",
+    "  -e GS_PASSWORD='MyStrongPass@123' \\\n",
+    "  -p 8888:5432 \\\n",
+    "  opengauss/opengauss-server:latest\n",
+    "```"
+   ],
+   "id": "e006fdc593107ef5"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a51b3f07b83b8a1d",
+   "metadata": {},
+   "source": "### Install langchain-opengauss"
+  },
+  {
+   "cell_type": "raw",
+   "id": "ad030f666e228cc8",
+   "metadata": {},
+   "source": [
+    "```bash\n",
+    "pip install langchain-opengauss\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d14f2f5f8ab0df7",
+   "metadata": {},
+   "source": [
+    "**System Requirements**:\n",
+    "- openGauss ≥ 7.0.0\n",
+    "- Python ≥ 3.8\n",
+    "- psycopg2-binary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9695dee7",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "Using your openGauss Credentials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93df377e",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
+    "\n",
+    "<EmbeddingTabs/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc37144c-208d-4ab3-9f3a-0407a69fe052",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_opengauss import OpenGauss, OpenGaussSettings\n",
+    "\n",
+    "# Configure with schema validation\n",
+    "config = OpenGaussSettings(\n",
+    "    table_name=\"test_langchain\",\n",
+    "    embedding_dimension=384,\n",
+    "    index_type=\"HNSW\",\n",
+    "    distance_strategy=\"COSINE\",\n",
+    ")\n",
+    "vector_store = OpenGauss(embedding=embeddings, config=config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac6071d4",
+   "metadata": {},
+   "source": [
+    "## Manage vector store\n",
+    "\n",
+    "### Add items to vector store\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17f5efc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.documents import Document\n",
+    "\n",
+    "document_1 = Document(page_content=\"foo\", metadata={\"source\": \"https://example.com\"})\n",
+    "\n",
+    "document_2 = Document(page_content=\"bar\", metadata={\"source\": \"https://example.com\"})\n",
+    "\n",
+    "document_3 = Document(page_content=\"baz\", metadata={\"source\": \"https://example.com\"})\n",
+    "\n",
+    "documents = [document_1, document_2, document_3]\n",
+    "\n",
+    "vector_store.add_documents(documents=documents, ids=[\"1\", \"2\", \"3\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c738c3e0",
+   "metadata": {},
+   "source": "### Update items in vector store\n"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0aa8b71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "updated_document = Document(\n",
+    "    page_content=\"qux\", metadata={\"source\": \"https://another-example.com\"}\n",
+    ")\n",
+    "\n",
+    "# If the id is already exist, will update the document\n",
+    "vector_store.add_documents(document_id=\"1\", document=updated_document)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcf1b905",
+   "metadata": {},
+   "source": "### Delete items from vector store\n"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef61e188",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector_store.delete(ids=[\"3\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3620501",
+   "metadata": {},
+   "source": [
+    "## Query vector store\n",
+    "\n",
+    "Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.\n",
+    "\n",
+    "### Query directly\n",
+    "\n",
+    "Performing a simple similarity search can be done as follows:\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa0a16fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = vector_store.similarity_search(\n",
+    "    query=\"thud\", k=1, filter={\"source\": \"https://another-example.com\"}\n",
+    ")\n",
+    "for doc in results:\n",
+    "    print(f\"* {doc.page_content} [{doc.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ed9d733",
+   "metadata": {},
+   "source": "If you want to execute a similarity search and receive the corresponding scores you can run:\n"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5efd2eaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = vector_store.similarity_search_with_score(\n",
+    "    query=\"thud\", k=1, filter={\"source\": \"https://example.com\"}\n",
+    ")\n",
+    "for doc, score in results:\n",
+    "    print(f\"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c235cdc",
+   "metadata": {},
+   "source": [
+    "### Query by turning into retriever\n",
+    "\n",
+    "You can also transform the vector store into a retriever for easier usage in your chains.\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3460093",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = vector_store.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 1})\n",
+    "retriever.invoke(\"thud\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "901c75dc",
+   "metadata": {},
+   "source": [
+    "## Usage for retrieval-augmented generation\n",
+    "\n",
+    "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
+    "\n",
+    "- [Tutorials](/docs/tutorials/)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "069f1b5f",
+   "metadata": {},
+   "source": [
+    "## Configuration\n",
+    "\n",
+    "### Connection Settings\n",
+    "| Parameter           | Default                 | Description                                            |\n",
+    "|---------------------|-------------------------|--------------------------------------------------------|\n",
+    "| `host`              | localhost               | Database server address                                |\n",
+    "| `port`              | 8888                    | Database connection port                               |\n",
+    "| `user`              | gaussdb                 | Database username                                      |\n",
+    "| `password`          | -                       | Complex password string                                |\n",
+    "| `database`          | postgres                | Default database name                                  |\n",
+    "| `min_connections`   | 1                       | Connection pool minimum size                           |\n",
+    "| `max_connections`   | 5                       | Connection pool maximum size                           |\n",
+    "| `table_name`        | langchain_docs          | Name of the table for storing vector data and metadata |\n",
+    "| `index_type`        | IndexType.HNSW          |Vector index algorithm type. Options: HNSW or IVFFLAT\\nDefault is HNSW.|\n",
+    "| `vector_type`       | VectorType.vector       |Type of vector representation to use. Default is Vector.|\n",
+    "| `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.|\n",
+    "|`embedding_dimension`| 1536                    |Dimensionality of the vector embeddings.|\n",
+    "\n",
+    "### Supported Combinations\n",
+    "\n",
+    "| Vector Type | Dimensions | Index Types  | Supported Distance Strategies         |\n",
+    "|-------------|------------|--------------|---------------------------------------|\n",
+    "| vector      | ≤2000      | HNSW/IVFFLAT | COSINE/EUCLIDEAN/MANHATTAN/INNER_PROD |\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a7b7b7c4f5a03e1",
+   "metadata": {},
+   "source": [
+    "## Performance Optimization\n",
+    "\n",
+    "### Index Tuning Guidelines\n",
+    "**HNSW Parameters**:\n",
+    "- `m`: 16-100 (balance between recall and memory)\n",
+    "- `ef_construction`: 64-1000 (must be > 2*m)\n",
+    "\n",
+    "**IVFFLAT Recommendations**:\n",
+    "```python\n",
+    "import math\n",
+    "\n",
+    "lists = min(\n",
+    "    int(math.sqrt(total_rows)) if total_rows > 1e6 else int(total_rows / 1000),\n",
+    "    2000,  # openGauss maximum\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "### Connection Pooling\n",
+    "```python\n",
+    "OpenGaussSettings(min_connections=3, max_connections=20)\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b581b499ffed641",
+   "metadata": {},
+   "source": [
+    "## Limitations\n",
+    "- `bit` and `sparsevec` vector types currently in development\n",
+    "- Maximum vector dimensions: 2000 for `vector` type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a27244f",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://python.langchain.com/api_reference/en/latest/vectorstores/opengauss.OpenGuass.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/scripts/vectorstore_feat_table.py

@@ -140,6 +140,17 @@ def get_vectorstore_table():
             "Local/Cloud": "Local",
             "IDs in add Documents": True,
         },
+        "openGauss": {
+            "Delete by ID": True,
+            "Filtering": True,
+            "similarity_search_by_vector": True,
+            "similarity_search_with_score": True,
+            "asearch": False,
+            "Passes Standard Tests": True,
+            "Multi Tenancy": False,
+            "Local/Cloud": "Local",
+            "IDs in add Documents": True,
+        },
         "QdrantVectorStore": {
             "Delete by ID": True,
             "Filtering": True,

docs/src/theme/FeatureTables.js

@@ -1130,6 +1130,19 @@ const FEATURE_TABLES = {
                 local: true,
                 idsInAddDocuments: false,
             },
+            {
+                name: "openGauss",
+                link: "openGauss",
+                deleteById: true,
+                filtering: true,
+                searchByVector: true,
+                searchWithScore: true,
+                async: false,
+                passesStandardTests: true,
+                multiTenancy: false,
+                local: true,
+                idsInAddDocuments: true,
+            },
             {
                 name: "PGVector",
                 link: "pgvector",