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https://github.com/yugabytedb-samples/yugabytedb-langchain-openai-shoe-store-search
Shoe Store Search Application written in Python/Flask, LangChain, OpenAI and YugabyteDB.
https://github.com/yugabytedb-samples/yugabytedb-langchain-openai-shoe-store-search
Last synced: about 7 hours ago
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Shoe Store Search Application written in Python/Flask, LangChain, OpenAI and YugabyteDB.
- Host: GitHub
- URL: https://github.com/yugabytedb-samples/yugabytedb-langchain-openai-shoe-store-search
- Owner: YugabyteDB-Samples
- Created: 2024-01-19T23:06:09.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-01-25T02:46:37.000Z (10 months ago)
- Last Synced: 2024-04-14T18:45:22.362Z (7 months ago)
- Language: Python
- Size: 9.77 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Shoe Store Search Tool with LangChain, OpenAI and YugabyteDB
This is a sample application written in Python that demonstrates how use LangChain to build applications with LLM integration. By using the [SQL](https://python.langchain.com/docs/use_cases/qa_structured/sql) chain capabilities of LangChain in conjunction with an [OpenAI](https://openai.com/) LLM, the application can query a [PostgreSQL-compatible](https://www.yugabyte.com/postgresql/postgresql-compatibility/) YugabyteDB database from natural language.
# Prerequisites
* Install Python3
* Install Docker
## Set up the application
Download the application and provide settings specific to your deployment:
1. Clone the repository.
```sh
git clone https://github.com/YugabyteDB-Samples/yugabytedb-langchain-openai-shoe-store-search.git
```
2. Install the application dependencies.
Dependencies can be installed in a virtual environment, or globally on your machine.
* Option 1 (recommended): Install Dependencies from *requirements.txt* in virtual environment
```sh
python3 -m venv yb-langchain-env
source yb-langchain-env/bin/activate
pip install -r requirements.txt
# NOTE: Users with M1 Mac machines should use requirements-m1.txt instead:
# pip install -r requirements-m1.txt
```
* Option 2: Install Dependencies Globally
```sh
pip install langchain
pip install psycopg2
# NOTE: Users with M1 Mac machines should install the psycopg2 binary instead:
# pip install psycopg2-binary
pip install langchain_openai
pip install langchain_experimental
pip install flask
pip install python-dotenv
```
3. Create an [OpenAI API Key](https://platform.openai.com/api-keys) and store it's value in a secure location. This will be used to connect the application to the LLM to generate SQL queries and an appropriate response from the database.
4. Configure the application environment variables in `{project_directory/.env}`.
# Get Started with YugabyteDB
YugabyteDB is a [PostgreSQL-compatible](https://www.yugabyte.com/postgresql/postgresql-compatibility/) distributed database.
Start a 3-node YugabyteDB cluster in Docker (or feel free to use another deployment option):
```sh
# NOTE: if the ~/yb_docker_data already exists on your machine, delete and re-create it
mkdir ~/yb_docker_data
docker network create custom-network
docker run -d --name yugabytedb-node1 --net custom-network \
-p 15433:15433 -p 7001:7000 -p 9001:9000 -p 5433:5433 \
-v ~/yb_docker_data/node1:/home/yugabyte/yb_data --restart unless-stopped \
yugabytedb/yugabyte:2.20.1.0-b97 \
bin/yugabyted start \
--base_dir=/home/yugabyte/yb_data --background=false
docker run -d --name yugabytedb-node2 --net custom-network \
-p 15434:15433 -p 7002:7000 -p 9002:9000 -p 5434:5433 \
-v ~/yb_docker_data/node2:/home/yugabyte/yb_data --restart unless-stopped \
yugabytedb/yugabyte:2.20.1.0-b97 \
bin/yugabyted start --join=yugabytedb-node1 \
--base_dir=/home/yugabyte/yb_data --background=false
docker run -d --name yugabytedb-node3 --net custom-network \
-p 15435:15433 -p 7003:7000 -p 9003:9000 -p 5435:5433 \
-v ~/yb_docker_data/node3:/home/yugabyte/yb_data --restart unless-stopped \
yugabytedb/yugabyte:2.20.1.0-b97 \
bin/yugabyted start --join=yugabytedb-node1 \
--base_dir=/home/yugabyte/yb_data --background=false
```
The database connectivity settings are provided in the `{project_dir}/.env` file and do not need to be changed if you started the cluster with the preceding command.
Navigate to the YugabyteDB UI to confirm that the database is up and running, at .
# Load the E-Commerce Schema and Seed Data
This application requires an e-commerce database with a product catalog and inventory information. This schema includes `products`, `users`, `purchases` and `product_inventory`. It also creates a read-only user role to prevent any destructive actions while querying the database directly from LangChain.
The `pg_trgm` PostgreSQL extension is installed to execute similarity searches on alphanumeric text.
1. Copy the schema to the first node's Docker container.
```sh
docker cp {project_dir}/sql/schema.sql yugabytedb-node1:/home
```
2. Copy the seed data file to the Docker container.
```sh
docker cp {project_dir}/sql/generated_data.sql yugabytedb-node1:/home
```
3. Execute the SQL files against the database.
```sh
docker exec -it yugabytedb-node1 bin/ysqlsh -h yugabytedb-node1 -c '\i /home/schema.sql'
docker exec -it yugabytedb-node1 bin/ysqlsh -h yugabytedb-node1 -c '\i /home/generated_data.sql'
```
# Start the Flask Server
The Flask server for this application exposes a REST endpoint which returns values from the database.
1. Start the server.
```sh
python app.py
```
```output
* Running on http://127.0.0.1:8080
```
2. Send a POST request to the `/queries` endpoint with a relevant prompt. For instance:
```sh
# What purchases have been made by user1?
# What colors do the Intelligent Racer come in?
# How many narrow shoes come in pink?
# Find me shoes that are in stock and available in size 15.
curl -X POST http://localhost:8080/queries -H "Content-Type: application/json" -d '{"user_prompt":"Find me shoes that are in stock and available in size 15."}'
```
```output
[
{
"color": [
"yellow",
"blue"
],
"description": null,
"name": "Efficient Jogger 1",
"price": "110.08",
"quantity": 22,
"width": [
"narrow",
"wide"
]
},
{
"color": [
"blue",
"yellow"
],
"description": null,
"name": "Efficient Jogger 8",
"price": "143.63",
"quantity": 85,
"width": [
"wide",
"narrow"
]
},
...
]
```