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koishi-plugin-sns-embed

v0.1.0

Published

Koishi plugin for SNS message dump and vectorized embedding

Readme

koishi-plugin-sns-embed

npm

This is a plugin for Koishi that stroages SNS message records and create vectorized embeddings.

⚠️WARNING! This plugin will automatically embed SNS messages to vector database! Please make sure you have enough money in your account!


Features | Todo List

  • [✅] Dump all SNS messages to database. SQLite as default, MySQL supported.
  • [❌] More platform adapter support. Only Onebot is tested now.
  • [✅] Store more Metadata for SNS messages so that we can use them in complicated RAG callback rules.
  • [✅] Store MultiModal messages (Image, Video, Audio, etc.) to koishi assets. Local storage as default, S3 compatible or others not tested yet.
  • [❌] More OSS provider support. Alibaba Cloud, Tencent Cloud, Cloudflare R2, etc.
  • [❌] Use ASR model, VLM model, optical flow, etc. for MultiModal semantic understanding. Create vectorized embeddings for MultiModal messages. This is for further RAG callback.
  • [✅] Create vectorized embeddings with OpenAI compatible API for SNS messages. Only Zhipu API is tested now.
  • [✅] Store vectorized embeddings to vector database. Only Milvus is supported now.
  • [❌] Create more RAG callback rules and NLP models workflow for message reply. Prompt engineering nessitates in this step.
  • [❌] Cluster deployment parameters for Database, Vector Database, Object Storage, etc.

Configuration Parameters

| Category | Field | Description | |--------------------|-------------------|---------------------------------------------------------| | Database | table_name | Table name for SQL Database. | | Milvus | db_name | DB name for Milvus. Only set as "default" can create collection automatically. Otherwise, you need to create collection manually. | | Milvus | collection_name | Collection name for Milvus. | | Milvus | consistency_level | Consistency level for Milvus. Select one of "Strong", "Bounded Staleness", "Session", "Eventually". This parameter is only effective in cluster deployment. | | Milvus/Embedding | embeddingDimension | Embedding dimension for vectorized embeddings and vector database. | | Embedding | model | Embedding model name. | | Embedding | apiUrl | API URL for embedding model. Including endpoint. | | Embedding | apiKey | API Key for embedding model. |

Example Configuration

| Field | Parameters | |-------------|--------------------------------------------------| | table_name | sns_record | | db_name | default | | collection_name | sns_record | | consistency_level | Eventually | | embeddingDimension| 1536 | | model | embedding-3 | | apiUrl | https://open.bigmodel.cn/api/paas/v4/embeddings | | apiKey | your_api_key_here |