| Name | firstbatch JSON |
| Version |
0.1.73
JSON |
| download |
| home_page | |
| Summary | FirstBatch SDK for integrating user embeddings to your project. Add real-time personalization to your AI application without user data. |
| upload_time | 2023-11-26 18:32:12 |
| maintainer | |
| docs_url | None |
| author | andthattoo |
| requires_python | >=3.9,<3.13 |
| license | MIT |
| keywords |
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| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| coveralls test coverage |
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# FirstBatch SDK
The FirstBatch SDK provides an interface for integrating vector databases and powering personalized AI experiences in your application.
## Key Features
- Seamlessly manage user sessions with persistent IDs or temporary sessions
- Send signal actions like likes, clicks, etc. to update user embeddings in real-time
- Fetch personalized batches of data tailored to each user's embeddings
- Support for multiple vector database integrations: Pinecone, Weaviate, etc.
- Built-in algorithms for common personalization use cases
- Easy configuration with Python classes and environment variables
## Getting Started
### Prerequisites
- Python 3.9+
- API keys for FirstBatch and your chosen vector database
### Installation
```
pip install firstbatch
```
## Basic Usage
1. **Initialize VectorDB of your choice**
```python
api_key = os.environ["PINECONE_API_KEY"]
env = os.environ["PINECONE_ENV"]
pinecone.init(api_key=api_key, environment=env)
index = pinecone.Index("your_index_name")
# Init FirstBatch
config = Config(batch_size=20)
personalized = FirstBatch(api_key=os.environ["FIRSTBATCH_API_KEY"], config=config)
personalized.add_vdb("my_db", Pinecone(index, embedding_size=1536))
```
### Personalization
2. **Create a session with an Algorithm suiting your needs**
```python
session = personalized.session(algorithm=AlgorithmLabel.AI_AGENTS, vdbid="my_db")
```
3. **Make recommendations**
```python
ids, batch = personalized.batch(session)
```
4. **Let users add signals to shape their embeddings**
```python
user_pick = 0 # User liked the first content from the previous batch.
personalized.add_signal(session, UserAction(Signal.LIKE), ids[user_pick])
```
## Support
For any issues or queries contact `support@firstbatch.xyz`.
## Resources
- [User Embedding Guide](https://firstbatch.gitbook.io/user-embeddings/)
- [SDK Documentation](https://firstbatch.gitbook.io/firstbatch-sdk/)
Feel free to dive into the technicalities and leverage FirstBatch SDK for highly personalized user experiences.
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"description": "# FirstBatch SDK\n\nThe FirstBatch SDK provides an interface for integrating vector databases and powering personalized AI experiences in your application.\n\n## Key Features\n\n- Seamlessly manage user sessions with persistent IDs or temporary sessions\n- Send signal actions like likes, clicks, etc. to update user embeddings in real-time\n- Fetch personalized batches of data tailored to each user's embeddings \n- Support for multiple vector database integrations: Pinecone, Weaviate, etc.\n- Built-in algorithms for common personalization use cases\n- Easy configuration with Python classes and environment variables\n\n## Getting Started\n\n### Prerequisites\n\n- Python 3.9+\n- API keys for FirstBatch and your chosen vector database\n\n### Installation\n\n```\npip install firstbatch\n```\n\n## Basic Usage\n\n1. **Initialize VectorDB of your choice**\n ```python\n api_key = os.environ[\"PINECONE_API_KEY\"]\n env = os.environ[\"PINECONE_ENV\"]\n\n pinecone.init(api_key=api_key, environment=env)\n index = pinecone.Index(\"your_index_name\")\n \n # Init FirstBatch\n config = Config(batch_size=20)\n personalized = FirstBatch(api_key=os.environ[\"FIRSTBATCH_API_KEY\"], config=config)\n \n personalized.add_vdb(\"my_db\", Pinecone(index, embedding_size=1536))\n ```\n\n### Personalization\n\n2. **Create a session with an Algorithm suiting your needs**\n ```python \n session = personalized.session(algorithm=AlgorithmLabel.AI_AGENTS, vdbid=\"my_db\")\n ```\n\n3. **Make recommendations**\n ```python\n ids, batch = personalized.batch(session)\n ```\n4. **Let users add signals to shape their embeddings**\n ```python\n user_pick = 0 # User liked the first content from the previous batch.\n personalized.add_signal(session, UserAction(Signal.LIKE), ids[user_pick])\n ```\n\n## Support\n\nFor any issues or queries contact `support@firstbatch.xyz`.\n\n \n## Resources\n\n- [User Embedding Guide](https://firstbatch.gitbook.io/user-embeddings/)\n- [SDK Documentation](https://firstbatch.gitbook.io/firstbatch-sdk/)\n\nFeel free to dive into the technicalities and leverage FirstBatch SDK for highly personalized user experiences.\n",
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