Publishes JSON data to a Chroma VectorDB. The Incoming data must be in single JSON per Line format, each with two keys: 'text' and 'metadata'. The text must be a string, while metadata must be a map with strings for values. Any additional fields will be ignored. If the collection name specified does not exist, the Processor will automatically create the collection.
chroma, vector, vectordb, embeddings, ai, artificial intelligence, ml, machine learning, text, LLM
In the list below, the names of required properties appear in bold. Any other properties (not in bold) are considered optional. The table also indicates any default values, and whether a property supports the NiFi Expression Language.
| Display Name | API Name | Default Value | Description |
|---|---|---|---|
| Store Document Text | Store Document Text | true | Specifies whether or not the text of the document should be stored in Chroma. If so, both the document's text and its embedding will be stored. If not, only the vector/embedding will be stored. |
| Distance Method | Distance Method | cosine | If the specified collection does not exist, it will be created using this Distance Method. If the collection exists, this property will be ignored. |
| Document ID Field Name | Document ID Field Name | Specifies the name of the field in the 'metadata' element of each document where the document's ID can be found.
If not specified, an ID will be generated based on the FlowFile's filename and a one-up number. Supports Expression Language: true (will be evaluated using flow file attributes and Environment variables) | |
| Connection Strategy | Connection Strategy | Remote Chroma Server | Specifies how to connect to the Chroma server |
| Directory | Directory | ./chroma | The Directory that Chroma should use to persist data |
| Hostname | Hostname | localhost | The hostname to connect to in order to communicate with Chroma |
| Port | Port | 8000 | The port that the Chroma server is listening on |
| Transport Protocol | Transport Protocol | https | Specifies whether connections should be made over http or https |
| Authentication Strategy | Authentication Strategy | Token Authentication | Specifies how to authenticate to Chroma server |
| Authentication Token | Authentication Token | The token to use for authenticating to Chroma server Sensitive Property: true | |
| Username | Username | The username to use for authenticating to Chroma server | |
| Password | Password | The password to use for authenticating to Chroma server Sensitive Property: true | |
| Collection Name | Collection Name | nifi | The name of the Chroma Collection Supports Expression Language: true (will be evaluated using flow file attributes and Environment variables) |
| Embedding Function | Embedding Function | ONNX all-MiniLM-L6-v2 Model | Specifies which embedding function should be used in order to create embeddings from incoming Documents |
| HuggingFace Model Name | HuggingFace Model Name | sentence-transformers/all-MiniLM-L6-v2 | The name of the HuggingFace model to use |
| HuggingFace API Key | HuggingFace API Key | The API Key for interacting with HuggingFace Sensitive Property: true | |
| OpenAI API Key | OpenAI API Key | The API Key for interacting with OpenAI Sensitive Property: true | |
| OpenAI Model Name | OpenAI Model Name | text-embedding-ada-002 | The name of the OpenAI model to use |
| OpenAI Organization ID | OpenAI Organization ID | The OpenAI Organization ID | |
| OpenAI API Base Path | OpenAI API Base Path | The API Base to use for interacting with OpenAI. This is used for interacting with different deployments, such as an Azure deployment. | |
| OpenAI API Deployment Type | OpenAI API Deployment Type | The type of the OpenAI API Deployment. This is used for interacting with different deployments, such as an Azure deployment. | |
| OpenAI API Version | OpenAI API Version | The OpenAI API Version. This is used for interacting with different deployments, such as an Azure deployment. | |
| Sentence Transformer Model Name | Sentence Transformer Model Name | all-MiniLM-L6-v2 | The name of the Sentence Transformer model to use |
| Sentence Transformer Device Type | Sentence Transformer Device Type | The type of device to use for performing the embeddings using the Sentence Transformer, such as 'cpu', 'cuda', 'mps', 'cuda:0', etc. If not specified, a GPU will be used if possible, otherwise a CPU. |