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176 changes: 173 additions & 3 deletions README.md
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<div align="center">

**[Wyvern](https://docs.wyvern.ai) is an open source Machine Learning platform for marketplaces**
**[Wyvern](https://docs.wyvern.ai) is a Real-time Machine Learning platform for marketplaces**

</div>

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</div>

<div align="center">
<a href="https://join.slack.com/t/wyvernai/shared_invite/zt-22nu62m6f-duK46NyhIINsdjKJct1hqg"><img src="https://badgen.net/badge/join/slack/blue?icon=slack"/></a>
<a href="https://github.com/Wyvern-AI/wyvern/blob/main/LICENSE"><img src="https://badgen.net/badge/License/Elv2/green?icon=github"/></a>
</div>

## Check Out [Wyvern's Official Doc](https://docs.wyvern.ai)
## What is Wyvern?

## Install Wyvern
Wyvern is a real-time machine learning platform for marketplaces:

- **Search and Discovery**: Wyvern specializes in bringing use cases like **recommendations and rankings** in-house.
- **Empower the Data Team**: Wyvern is tailored for your data team to independently build and deploy production-grade machine learning pipelines for the e-commerce and marketplace industry, reducing the engineering involvement in the entire process.
- **Orchestration for ML Pipelines**: Wyvern is agnostic to the solutions your pick for your feature sotre, model serving solution, or data warehouse. It automates the process of retrieving data from the feature store and passing data to the model service, as well as logging all the events. It abstracts all the engineering work above away from data scientists, with the goal of enabling data scientists to own the full ML stack, so that they can just focus on defining the request/response schemas of the API, the model, the features the model depends on, the business logic after the model, and finally training the models with the feedback data generated by the ML pipeline.

Our expalation of why we built Wyvern can be found [here](https://docs.wyvern.ai/why).

### Wyvern Architecture

![Wyvern Architecture](/docs/wyvern_architecture.png)

Overall, Wyvern gives you a framework to quickly define your real-time ML pipeline. There are a couple of important components as you can see in the architecture:

1. **Retrieval**: Wyvern can connect to and retrieve objects from your search index.
2. **Feature Module**: Wyvern has the built-in support for [feast](https://feast.dev/), an open source feature store. It also supports connecting to the feature store that you would like to use. Moreover, Wyvern provides interfaces for you to define your [batch features](/batch_feature) and [real-time features](/realtime_feature) easily, with the support of feature grouping, feature sharing, features for composite entities and request based features.
3. **Model Module**: Wyvern provides [the model interface](/model_service#define-the-whole-model) that allows you to define your own model in place or call your model service. It provides an interface to define features that your model depends on easily.
4. **Business Logic**: Wyvern makes defining your business logic easy after the model inference. For example, if your want to promote a specific brand of tshirt and move it to the top of the ranking result for the "tshirt" query.
5. **Observability and Event Logging**: All the events in your ML application, including real-time feature, model, business logic, product impression, as well your own custom events, are being automatically logged by Wyvern and data can be piped to your data warehouse. Refer to [Logging Events](/logging_events) for more information.
6. **Training Dataset**: Wyvern provides the feature store serving solution (currently integrated only with feast) to serve all data of the historical batch features and the real-time features that are logged in your data warehouse.

As Wyvern is open sourced, we will bring in more integrations with different feature stores, model serving solutions, search index for retrieval, observability tools, as well as integrations with more data warehouses.

## Quickstart

### Install Wyvern

```
pip install wyvern-ai
```

### Initiate Wyvern Application

Once Wyvern is installed, run this command to initialize your Wyvern project:

```
wyvern init name_of_your_project
```

Now that the `wyvern init` has set up your initial repository, you should see the following file structure in the generated repository:

```
├── pipelines
│ ├── __init__.py
│ ├── main.py
│ ├── product_ranking
│ │ ├── __init__.py
│ │ ├── models.py
│ │ ├── ranking_pipline.py
│ │ ├── realtime_features.py
│ │ ├── schemas.py
└── .gitignore
```

The generated template code is an [ML pipeline](https://docs.wyvern.ai/ml_pipeline) example for ranking products.

### Run Wyvern Application

#### Pre-requisite

- Redis service
Wyvern uses redis as its index and online feature store. By default, Wyvern connects to the localhost:6379

Your can run this command to install and spin up local redis

```
wyvern redis
```

#### Run Wyvern

Now cd into the repository that was generated and run:

```
wyvern run
```

This spins up a service at http://0.0.0.0:5001 and the default ranking API schema could be found at [http://0.0.0.0:5001/redoc](http://0.0.0.0:5001/redoc#operation/MyRankingPipeline_api_v1_ranking_post).

#### Make A Request

Assuming you have 24 products and you would like to rank them. Here's a curl request:

```
curl --location 'http://0.0.0.0:5001/api/v1/ranking' \
--header 'Content-Type: application/json' \
--data '{
"request_id": "test_request_id",
"query": {"query": "candle"},
"candidates": [
{"product_id": "p1"},
{"product_id": "p2"},
{"product_id": "p3"},
{"product_id": "p4"},
{"product_id": "p5"},
{"product_id": "p6"},
{"product_id": "p7"},
{"product_id": "p8"},
{"product_id": "p9"},
{"product_id": "p10"},
{"product_id": "p11"},
{"product_id": "p12"},
{"product_id": "p13"},
{"product_id": "p14"},
{"product_id": "p15"},
{"product_id": "p16"},
{"product_id": "p17"},
{"product_id": "p18"},
{"product_id": "p19"},
{"product_id": "p20"},
{"product_id": "p21"},
{"product_id": "p22"},
{"product_id": "p23"},
{"product_id": "p24"}
],
"user_page_size": 8,
"user_page": 0,
"candidate_page_size": 24,
"candidate_page": 0
}'
```

This request sends 24 products retrieved from search to Wyvern and ask Wyvern to return the 8 products (in descending order) to show on the first page (`"user_page": 0`).

You should see a response with the products being ordered descendingly by their ranking score.

<details>
<summary>Click to see a response example</summary>

```
{
"ranked_candidates": [
{
"candidate_id": "p9",
"ranked_score": 43.13991415724884
},
{
"candidate_id": "p18",
"ranked_score": 42.314880208313376
},
{
"candidate_id": "p17",
"ranked_score": 41.62010469362527
},
{
"candidate_id": "p3",
"ranked_score": 40.48391586690772
},
{
"candidate_id": "p19",
"ranked_score": 39.82504624652922
},
{
"candidate_id": "p24",
"ranked_score": 39.10042317690844
},
{
"candidate_id": "p11",
"ranked_score": 38.670359237541945
},
{
"candidate_id": "p21",
"ranked_score": 37.27313489135458
}
]
}
```

</details>

## More Documentations

- [How Wyvern ML Pipeline Works](https://docs.wyvern.ai/ml_pipeline)
- [Wyvern Feature Engineering](https://docs.wyvern.ai/feature_engineering_overview)
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74 changes: 38 additions & 36 deletions wyvern/config.py
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Expand Up @@ -11,42 +11,44 @@ class Settings(BaseSettings):
in production for secrets you do not wish to save in code
Attributes:
ENVIRONMENT: The environment the service is running in.
PROJECT_NAME: The name of the project
REDIS_HOST: The host of the redis instance
REDIS_PORT: The port of the redis instance
WYVERN_API_KEY: The API key for the Wyvern API
WYVERN_BASE_URL: The base url of the Wyvern API
WYVERN_ONLINE_FEATURES_PATH: The path to the online features endpoint
WYVERN_HISTORICAL_FEATURES_PATH: The path to the historical features endpoint
WYVERN_FEATURE_STORE_URL: The url of the Wyvern feature store
SNOWFLAKE_ACCOUNT: The account name of the Snowflake instance
SNOWFLAKE_USER: The username of the Snowflake instance
SNOWFLAKE_PASSWORD: The password of the Snowflake instance
SNOWFLAKE_ROLE: The role of the Snowflake instance
SNOWFLAKE_WAREHOUSE: The warehouse of the Snowflake instance
SNOWFLAKE_DATABASE: The database of the Snowflake instance
SNOWFLAKE_OFFLINE_STORE_SCHEMA: The schema of the Snowflake instance
AWS_ACCESS_KEY_ID: The access key id for the AWS instance
AWS_SECRET_ACCESS_KEY: The secret access key for the AWS instance
AWS_REGION_NAME: The region name for the AWS instance
FEATURE_STORE_TIMEOUT: The timeout for the feature store
SERVER_TIMEOUT: The timeout for the server
REDIS_BATCH_SIZE: The batch size for the redis instance
WYVERN_INDEX_VERSION: The version of the Wyvern index
MODELBIT_BATCH_SIZE: The batch size for the modelbit
EXPERIMENTATION_ENABLED: Whether experimentation is enabled
EXPERIMENTATION_PROVIDER: The experimentation provider
EPPO_API_KEY: The API key for EPPO (an experimentation provider)
FEATURE_STORE_ENABLED: Whether the feature store is enabled
EVENT_LOGGING_ENABLED: Whether event logging is enabled
ENVIRONMENT: The environment the service is running in. Default to `development`.
PROJECT_NAME: The name of the project. Default to `default`.
REDIS_HOST: The host of the redis instance. Default to `localhost`.
REDIS_PORT: The port of the redis instance. Default to `6379`.
WYVERN_API_KEY: The API key for the Wyvern API. Default to `""`, empty string.
WYVERN_BASE_URL: The base url of the Wyvern API. Default to `https://api.wyvern.ai`
WYVERN_ONLINE_FEATURES_PATH:
The path to the online features endpoint. Default to `/feature/get-online-features`.
WYVERN_HISTORICAL_FEATURES_PATH:
The path to the historical features endpoint. Default to `/feature/get-historical-features`.
WYVERN_FEATURE_STORE_URL: The url of the Wyvern feature store. Default to `https://api.wyvern.ai`.
SNOWFLAKE_ACCOUNT: The account name of the Snowflake instance. Default to `""`, empty string.
SNOWFLAKE_USER: The username of the Snowflake instance. Default to `""`, empty string.
SNOWFLAKE_PASSWORD: The password of the Snowflake instance. Default to `""`, empty string.
SNOWFLAKE_ROLE: The role of the Snowflake instance. Default to `""`, empty string.
SNOWFLAKE_WAREHOUSE: The warehouse of the Snowflake instance. Default to `""`, empty string.
SNOWFLAKE_DATABASE: The database of the Snowflake instance. Default to `""`, empty string.
SNOWFLAKE_OFFLINE_STORE_SCHEMA: The schema of the Snowflake instance. Default to `PUBLIC`.
AWS_ACCESS_KEY_ID: The access key id for the AWS instance. Default to `""`, empty string.
AWS_SECRET_ACCESS_KEY: The secret access key for the AWS instance. Default to `""`, empty string.
AWS_REGION_NAME: The region name for the AWS instance. Default to `us-east-1`.
FEATURE_STORE_TIMEOUT: The timeout for the feature store. Default to `60` seconds.
SERVER_TIMEOUT: The timeout for the server. Default to `60` seconds.
REDIS_BATCH_SIZE: The batch size for the redis instance. Default to `100`.
WYVERN_INDEX_VERSION: The version of the Wyvern index. Default to `1`.
MODELBIT_BATCH_SIZE: The batch size for the modelbit. Default to `30`.
EXPERIMENTATION_ENABLED: Whether experimentation is enabled. Default to `False`.
EXPERIMENTATION_PROVIDER: The experimentation provider. Default to `ExperimentationProvider.EPPO.value`.
EPPO_API_KEY: The API key for EPPO (an experimentation provider). Default to `""`, empty string.
FEATURE_STORE_ENABLED: Whether the feature store is enabled. Default to `True`.
EVENT_LOGGING_ENABLED: Whether event logging is enabled. Default to `True`.
"""

ENVIRONMENT: str = "development"
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