ollama_embeddings

Available in: Self-Managed

Ollama connectors are currently only available on BYOC GCP clusters.

When Redpanda Connect runs a data pipeline with a Ollama processor in it, Redpanda Cloud deploys a GPU-powered instance for the exclusive use of that pipeline. As pricing is based on resource consumption, this can have cost implications.

Generates vector embeddings from text, using the Ollama API.

  • Common

  • Advanced

# Common config fields, showing default values
label: ""
ollama_embeddings:
  model: nomic-embed-text # No default (required)
  text: "" # No default (optional)
  runner:
    context_size: 0 # No default (optional)
    batch_size: 0 # No default (optional)
  server_address: http://127.0.0.1:11434 # No default (optional)
# All config fields, showing default values
label: ""
ollama_embeddings:
  model: nomic-embed-text # No default (required)
  text: "" # No default (optional)
  runner:
    context_size: 0 # No default (optional)
    batch_size: 0 # No default (optional)
    gpu_layers: 0 # No default (optional)
    threads: 0 # No default (optional)
    use_mmap: false # No default (optional)
    use_mlock: false # No default (optional)
  server_address: http://127.0.0.1:11434 # No default (optional)
  cache_directory: /opt/cache/connect/ollama # No default (optional)
  download_url: "" # No default (optional)

This processor sends text to your chosen Ollama large language model (LLM) and creates vector embeddings, using the Ollama API. Vector embeddings are long arrays of numbers that represent values or objects, in this case text.

By default, the processor starts and runs a locally installed Ollama server. Alternatively, to use an already running Ollama server, add your server details to the server_address field. You can download and install Ollama from the Ollama website.

For more information, see the Ollama documentation.

Fields

cache_directory

If server_address is not set - the directory to download the ollama binary and use as a model cache.

Type: string

# Examples:
cache_directory: /opt/cache/connect/ollama

download_url

If server_address is not set - the URL to download the ollama binary from. Defaults to the offical Ollama GitHub release for this platform.

Type: string

model

The name of the Ollama LLM to use. For a full list of models, see the Ollama website.

Type: string

# Examples:
model: nomic-embed-text
model: mxbai-embed-large
model: snowflake-artic-embed
model: all-minilm

runner

Options for the model runner that are used when the model is first loaded into memory.

Type: object

runner.batch_size

The maximum number of requests to process in parallel.

Type: int

runner.context_size

Sets the size of the context window used to generate the next token. Using a larger context window uses more memory and takes longer to processor.

Type: int

runner.gpu_layers

This option allows offloading some layers to the GPU for computation. This generally results in increased performance. By default, the runtime decides the number of layers dynamically.

Type: int

runner.threads

Set the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has. By default, the runtime decides the optimal number of threads.

Type: int

runner.use_mmap

Map the model into memory. This is only support on unix systems and allows loading only the necessary parts of the model as needed.

Type: bool

server_address

The address of the Ollama server to use. Leave the field blank and the processor starts and runs a local Ollama server or specify the address of your own local or remote server.

Type: string

# Examples:
server_address: http://127.0.0.1:11434

text

The text you want to create vector embeddings for. By default, the processor submits the entire payload as a string. This field supports interpolation functions.

Type: string