Google Gemini components for building AI agents.
Changelog
6 versions
v1.1.0
Latest
- Added MakeApiCall component for performing arbitrary authorized API calls to the Google Gemini API.
v1.0.4
- Updated langchain npm dependencies.
v1.0.3
- Added validation for required fields in components.
v1.0.2
- Fixed an error `JSON schema is invalid` that occurred when the AiAgent used a tool with an empty parameters object.
v1.0.1
- TransformTextToJSON: improved error handling for invalid JSON schema input.
v1.0.0
- First version.
Authentication
| Type | Details |
|---|---|
| API Key |
API Key (text)
Log into your Google Console account and create an API key for the Gemini API.
|
Actions
7
Generate Embeddings
Generate embeddings for text data. The text is split into chunks and embedding is returned for each chunk.
The returned embeddings is an array of the form:
TIP: use the JSONata modifier to convert the embeddings array into custom formats. For convenience, the component also returns the first vector in the embeddings array which is useful when querying vector databases to find relevant chunks.
The returned embeddings is an array of the form:
[{ "index": 0, "text": "chunk1", "vector": [1.1, 1.2, 1.3] }].TIP: use the JSONata modifier to convert the embeddings array into custom formats. For convenience, the component also returns the first vector in the embeddings array which is useful when querying vector databases to find relevant chunks.
Generate Embeddings From File
Generate embeddings for a text file. The text is split into parts, each part is split into chunks and embedding is returned for each chunk. The component emits embeddings array for each file part (1MB).
The returned embeddings is an array of the form:
TIP: use the JSONata modifier to convert the embeddings array into custom formats.
The returned embeddings is an array of the form:
[{ "index": 0, "text": "chunk1", "vector": [1.1, 1.2, 1.3] }].TIP: use the JSONata modifier to convert the embeddings array into custom formats.