Embedding Tools¶

Configuration File: embedding_tools.json Tool Type: Local Tools Count: 6

This page contains all tools defined in the embedding_tools.json configuration file.

Available Tools¶

embedding_database_add (Type: EmbeddingDatabase)¶

Add new documents to an existing embedding database. Generates embeddings for new documents using…

embedding_database_add tool specification

Tool Information:

  • Name: embedding_database_add

  • Type: EmbeddingDatabase

  • Description: Add new documents to an existing embedding database. Generates embeddings for new documents using the same model as the original database and appends them to the existing FAISS index.

Parameters:

  • action (string) (optional) Action to add documents to existing database

  • database_name (string) (required) Name of the existing database to add documents to

  • documents (array) (required) List of new document texts to embed and add

  • metadata (array) (optional) Optional metadata for each new document (same length as documents)

Example Usage:

query = {
    "name": "embedding_database_add",
    "arguments": {
        "database_name": "example_value",
        "documents": ["item1", "item2"]
    }
}
result = tu.run(query)

embedding_database_create (Type: EmbeddingDatabase)¶

Create a new embedding database from a collection of documents. Generates embeddings using OpenAI…

embedding_database_create tool specification

Tool Information:

  • Name: embedding_database_create

  • Type: EmbeddingDatabase

  • Description: Create a new embedding database from a collection of documents. Generates embeddings using OpenAI or Azure OpenAI models and stores them in a searchable database with FAISS vector index and SQLite metadata storage.

Parameters:

  • action (string) (optional) Action to create database from documents

  • database_name (string) (required) Name for the new database (must be unique)

  • documents (array) (required) List of document texts to embed and store

  • metadata (array) (optional) Optional metadata for each document (same length as documents)

  • model (string) (optional) OpenAI/Azure OpenAI embedding model to use

  • description (string) (optional) Optional description for the database

Example Usage:

query = {
    "name": "embedding_database_create",
    "arguments": {
        "database_name": "example_value",
        "documents": ["item1", "item2"]
    }
}
result = tu.run(query)

embedding_database_load (Type: EmbeddingDatabase)¶

Load an existing embedding database from a local path or external source. Allows importing databa…

embedding_database_load tool specification

Tool Information:

  • Name: embedding_database_load

  • Type: EmbeddingDatabase

  • Description: Load an existing embedding database from a local path or external source. Allows importing databases created elsewhere or backed up databases into the current ToolUniverse instance.

Parameters:

  • action (string) (optional) Action to load database from external source

  • database_path (string) (required) Path to the existing database directory or file

  • database_name (string) (required) Local name to assign to the loaded database

  • overwrite (boolean) (optional) Whether to overwrite existing database with same name

Example Usage:

query = {
    "name": "embedding_database_load",
    "arguments": {
        "database_path": "example_value",
        "database_name": "example_value"
    }
}
result = tu.run(query)

embedding_database_search (Type: EmbeddingDatabase)¶

Search for semantically similar documents in an embedding database. Uses OpenAI embeddings to con…

embedding_database_search tool specification

Tool Information:

  • Name: embedding_database_search

  • Type: EmbeddingDatabase

  • Description: Search for semantically similar documents in an embedding database. Uses OpenAI embeddings to convert query text to vectors and performs similarity search using FAISS with optional metadata filtering.

Parameters:

  • action (string) (optional) Action to search the database

  • database_name (string) (required) Name of the database to search in

  • query (string) (required) Query text to find similar documents for

  • top_k (integer) (optional) Number of most similar documents to return

  • filters (object) (optional) Optional metadata filters to apply to search results

Example Usage:

query = {
    "name": "embedding_database_search",
    "arguments": {
        "database_name": "example_value",
        "query": "example_value"
    }
}
result = tu.run(query)

embedding_sync_download (Type: EmbeddingSync)¶

Download an embedding database from HuggingFace Hub to local storage. Allows accessing databases …

embedding_sync_download tool specification

Tool Information:

  • Name: embedding_sync_download

  • Type: EmbeddingSync

  • Description: Download an embedding database from HuggingFace Hub to local storage. Allows accessing databases shared by others or your own backups.

Parameters:

  • action (string) (optional) Action to download database from HuggingFace

  • repository (string) (required) HuggingFace repository to download from (format: username/repo-name)

  • local_name (string) (optional) Local name for the downloaded database (optional, defaults to repo name)

  • overwrite (boolean) (optional) Whether to overwrite existing local database with same name

Example Usage:

query = {
    "name": "embedding_sync_download",
    "arguments": {
        "repository": "example_value"
    }
}
result = tu.run(query)

embedding_sync_upload (Type: EmbeddingSync)¶

Upload a local embedding database to HuggingFace Hub for sharing and collaboration. Creates a dat…

embedding_sync_upload tool specification

Tool Information:

  • Name: embedding_sync_upload

  • Type: EmbeddingSync

  • Description: Upload a local embedding database to HuggingFace Hub for sharing and collaboration. Creates a dataset repository with the database files and metadata.

Parameters:

  • action (string) (optional) Action to upload database to HuggingFace

  • database_name (string) (required) Name of the local database to upload

  • repository (string) (required) HuggingFace repository name (format: username/repo-name)

  • description (string) (optional) Description for the HuggingFace dataset

  • private (boolean) (optional) Whether to create a private repository

  • commit_message (string) (optional) Commit message for the upload

Example Usage:

query = {
    "name": "embedding_sync_upload",
    "arguments": {
        "database_name": "example_value",
        "repository": "example_value"
    }
}
result = tu.run(query)