E-commerce product search with full-text search
+ + +## Prerequisites + +- **Python 3.10+** +- **A TiDB Cloud Serverless cluster**: Create a free cluster here: [tidbcloud.com ↗️](https://tidbcloud.com/?utm_source=github&utm_medium=referral&utm_campaign=pytidb_readme) + +## How to run + +**Step 1**: Clone the repository to local + +```bash +git clone https://github.com/pingcap/pytidb.git +cd pytidb/examples/fulltext_search/; +``` + +**Step 2**: Install the required packages and setup environment + +```bash +python -m venv .venv +source .venv/bin/activate +pip install -r reqs.txt +``` + +**Step 3**: Set up environment to connect to database + +Go to the [TiDB Cloud console](https://tidbcloud.com/), create a new cluster if you don't have one, and then get the connection parameters on the connection dialog. + +```bash +cat > .env <TiDB Hybrid Search Demo
+ + +## Prerequisites + +* Python 3.10+ +* TiDB database instance (👉 [Create a free TiDB Serverless Cluster](https://tidbcloud.com/free-trial)) +* OpenAI API key (Go to [OpenAI](https://platform.openai.com/api-keys) to get the API key) + +> **Note** +> +> Currently, full-text search is only available for the following product option and region: +> +> - TiDB Cloud Serverless: Frankfurt (eu-central-1), Singapore (ap-southeast-1) + +## How to run + +**Step 1**: Clone the repository + +```bash +git clone https://github.com/pingcap/pytidb.git +cd pytidb/examples/hybrid_search; +``` + +**Step 2**: Install the required packages and setup environment + +```bash +python -m venv .venv +source .venv/bin/activate +pip install -r reqs.txt +``` + +**Step 3**: Set up environment to connect to storage + +If you are using TiDB Cloud, you can find the connection parameters in the [TiDB Cloud console](https://tidbcloud.com/). + +```bash +cat > .env <
+
+
Pet image search via multimodal embeddings
+ + + +## Prerequisites + +- **Python 3.10+** +- **A TiDB Cloud Serverless cluster**: Create a free cluster here: [tidbcloud.com ↗️](https://tidbcloud.com/?utm_source=github&utm_medium=referral&utm_campaign=pytidb_readme) +- **Jina AI API Key**: Get your free API key at [jina.ai Embeddings ↗️](https://jina.ai/embeddings/) + +## How to run + +**Step 1**: Clone the repository to local + +```bash +git clone https://github.com/pingcap/pytidb.git +cd pytidb/examples/image_search/ +``` + +**Step 2**: Install the required packages + +```bash +python -m venv .venv +source .venv/bin/activate # Windows: .venv\Scripts\activate +pip install -r reqs.txt +``` + +**Step 3**: Set up environment variables + +Go to [TiDB Cloud console](https://tidbcloud.com/clusters) and get the connection parameters, then set up the environment variable like this: + +```bash +cat > .env <
+
\ No newline at end of file
diff --git a/src/ai/examples/memory-with-pytidb.md b/src/ai/examples/memory-with-pytidb.md
new file mode 100644
index 00000000..15a7a4db
--- /dev/null
+++ b/src/ai/examples/memory-with-pytidb.md
@@ -0,0 +1,100 @@
+---
+title: Memory
+description: "Implement conversation memory for chatbots and conversational AI applications."
+source_repo: "https://github.com/pingcap/pytidb/tree/main/examples/memory"
+---
+
+# Agent Memory Examples
+
+* Use `pytidb` to connect to TiDB
+
+## Prerequisites
+
+* Python 3.10+
+* TiDB server connection parameters, either local or TiDB Cloud
+* OpenAI API key
+
+
+## How to run
+
+**Step1**: Clone the repo
+
+```bash
+git clone https://github.com/pingcap/pytidb.git
+cd pytidb/examples/memory;
+```
+
+**Step2**: Install the required packages and setup environment
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+pip install -r reqs.txt
+```
+
+**Step3**: Set up environment to connect to storage
+
+You can find the connection string in the [TiDB Cloud console](https://tidbcloud.com/).
+
+If you are using a local TiDB server, you can set up the environment variable like this:
+
+```bash
+cat > .env <
+
+
+ Demo Gallery
+
+ Explore hands-on demos showcasing how TiDB empowers AI applications.
+Get started quickly with TiDB Cloud Serverless to build your own AI-powered solutions.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ ⭐ Featured
+
+
+
+
+ 
+
+
+
+
+
+ Image Search
++ Build an image search application using multimodal embeddings for both text-to-image and image-to-image search. +
+
+ 
+
+
+
+
+
+ RAG
++ Build a RAG application that combines document retrieval with language generation. +
+
+
+ 💭
+
+
+
+ Memory
++ Implement conversation memory for chatbots and conversational AI applications. +
+🚀 Getting Started
+ +🔍 Search & Retrieval
+
+
+
+
+ 
+
+
+
+
+
+ Vector Search
++ Implement semantic search using vector embeddings to find similar content. +
+
+ 
+
+
+
+
+
+ Fulltext Search
++ Perform traditional text search using MySQL fulltext search capabilities. +
+
+ 
+
+
+
+
+
+ Hybrid Search
++ Combine vector search and fulltext search for more comprehensive results. +
+
+
+
+
+
+
+ Image Search
++ Build an image search application using multimodal embeddings for both text-to-image and image-to-image search. +
+🤖 AI Applications
+
+
+
+
+
+
+
+
+
+
+ RAG
++ Build a RAG application that combines document retrieval with language generation. +
+
+
+ 💭
+
+
+
+
+ Memory
++ Implement conversation memory for chatbots and conversational AI applications. +
+
+
+ 💬
+
+
+
+ Text2SQL
++ Convert natural language queries into SQL statements using AI models. +
+
+
+ Ready to build your AI application?
++ Start your AI journey with TiDB Cloud Serverless. Follow our quickstart guide to build your first AI-powered application in minutes, or explore specific examples for your use case. +
+ +RAG application built with PyTiDB
+ + +## Prerequisites + +- **Python 3.10+** +- **A TiDB Cloud Serverless cluster**: Create a free cluster here: [tidbcloud.com ↗️](https://tidbcloud.com/?utm_source=github&utm_medium=referral&utm_campaign=pytidb_readme) +- **Ollama**: You can install it from [Ollama ↗️](https://ollama.com/download) + +## How to run + +**Step 1**: Prepare the inference API + +Pull the embedding and LLM model via ollama CLI: + +```bash +ollama pull mxbai-embed-large +ollama pull gemma3:4b +ollama run gemma3:4b +``` + +Test the `/embed` and `/generate` endpoints to make sure they are running: + +```bash +curl http://localhost:11434/api/embed -d '{ + "model": "mxbai-embed-large", + "input": "Llamas are members of the camelid family" +}' +``` + +```bash +curl http://localhost:11434/api/generate -d '{ + "model": "gemma3:4b", + "prompt": "Hello, Who are you?" +}' +``` + +**Step 2**: Clone the repository to local + +```bash +git clone https://github.com/pingcap/pytidb.git +cd pytidb/examples/rag/; +``` + +**Step 3**: Install the required packages and setup environment + +```bash +python -m venv .venv +source .venv/bin/activate +pip install -r reqs.txt +``` + +**Step 4**: Set up environment to connect to database + +Go to [TiDB Cloud console](https://tidbcloud.com/clusters) and get the connection parameters, then set up the environment variable like this: + +```bash +cat > .env <Semantic search with vector embeddings
+ + +## Prerequisites + +- **Python 3.10+** +- **A TiDB Cloud Serverless cluster**: Create a free cluster here: [tidbcloud.com ↗️](https://tidbcloud.com/?utm_source=github&utm_medium=referral&utm_campaign=pytidb_readme) +- **Ollama**: You can install it from [Ollama ↗️](https://ollama.com/download) + +## How to run + +**Step 1**: Start the embedding service with Ollama + +Pull the embedding model: + +```bash +ollama pull mxbai-embed-large +``` + +Test the embedding service to make sure it is running: + +```bash +curl http://localhost:11434/api/embed -d '{ + "model": "mxbai-embed-large", + "input": "Llamas are members of the camelid family" +}' +``` + +**Step 2**: Clone the repository to local + +```bash +git clone https://github.com/pingcap/pytidb.git +cd pytidb/examples/vector_search/ +``` + +**Step 3**: Install the required packages and set up the environment + +```bash +python -m venv .venv +source .venv/bin/activate +pip install -r reqs.txt +``` + +**Step 4**: Set up environment to connect to TiDB + +Go to [TiDB Cloud console](https://tidbcloud.com/clusters) and get the connection parameters, then set up the environment variable like this: + +```bash +cat > .env <
+
\ No newline at end of file
diff --git a/src/templates/demo_page_template.j2 b/src/templates/demo_page_template.j2
new file mode 100644
index 00000000..fd772e66
--- /dev/null
+++ b/src/templates/demo_page_template.j2
@@ -0,0 +1,19 @@
+---
+title: {{ demo.title }}
+description: "{{ demo.description }}"
+source_repo: "{{ demo.doc_link | replace('/README.md', '') }}"
+---
+
+{{ content | safe }}
+
+---
+
+## Related Resources
+
+- **Source Code**: [View on GitHub]({{ demo.doc_link | replace('/README.md', '') }})
+- **Category**: {{ demo.category | title }}
+{% if demo.description %}
+- **Description**: {{ demo.description }}
+{% endif %}
+
+[🏠 Back to Demo Gallery](../index.md){ .md-button .md-button--primary }
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
index 835ed14a..9b1ae93a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -231,20 +231,28 @@ name = "docs"
version = "0.1.0"
source = { virtual = "." }
dependencies = [
+ { name = "click" },
+ { name = "jinja2" },
{ name = "mkdocs" },
{ name = "mkdocs-jupyter" },
{ name = "mkdocs-material" },
{ name = "mkdocs-redirects" },
{ name = "mkdocstrings", extra = ["python"] },
+ { name = "pyyaml" },
+ { name = "requests" },
]
[package.metadata]
requires-dist = [
+ { name = "click", specifier = ">=8.0.0" },
+ { name = "jinja2", specifier = ">=3.1.0" },
{ name = "mkdocs", specifier = ">=1.6.1" },
{ name = "mkdocs-jupyter", specifier = ">=0.25.1" },
{ name = "mkdocs-material", specifier = ">=9.6.12" },
{ name = "mkdocs-redirects", specifier = ">=1.2.2" },
{ name = "mkdocstrings", extras = ["python"], specifier = ">=0.29.1" },
+ { name = "pyyaml", specifier = ">=6.0" },
+ { name = "requests", specifier = ">=2.31.0" },
]
[[package]]
+
+
+ {{ config.title }}
++ {{ config.description | safe }} +
+
+
+
+
+ {%- for category in categories %}
+ {%- if category.demos %}
+ {%- set category_demos = [] %}
+ {%- for demo_id in category.demos %}
+ {%- set demo = demos | selectattr('id', 'equalto', demo_id) | first %}
+ {%- if demo %}{% set _ = category_demos.append(demo) %}{% endif %}
+ {%- endfor %}
+
+
+ {%- endif %}
+ {%- endfor %}
+
+ {{ category.title }}
+
+ {%- for demo in category_demos %}
+
+
+
+ {%- if demo.cover_image %}
+ 
+ {%- elif demo.icon %}
+
+ {{ demo.icon }}
+ {%- endif %}
+
+
+
+ {%- endfor %}
+ {{ demo.title }}
++ {{ demo.description }} +
+
+
+ {{ config.cta.title }}
++ {{ config.cta.description }} +
+ +