ollama_chat
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 responses to messages in a chat conversation using the Ollama API and external tools.
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Common
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Advanced
# Common configuration fields, showing default values
label: ""
ollama_chat:
model: llama3.1 # No default (required)
prompt: "" # No default (optional)
image: 'root = this.image.decode("base64")' # Decode base64 encoded image. No default (optional)
response_format: text
max_tokens: 0 # No default (optional)
temperature: 0 # No default (optional)
save_prompt_metadata: false
history: "" # No default (optional)
tools: [] # No default (required)
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 configuration fields, showing default values
label: ""
ollama_chat:
model: llama3.1 # No default (required)
prompt: "" # No default (optional)
system_prompt: "" # No default (optional)
image: 'root = this.image.decode("base64")' # Decode base64 encoded image. No default (optional)
response_format: text
max_tokens: 0 # No default (optional)
temperature: 0 # No default (optional)
num_keep: 0 # No default (optional)
seed: 42 # No default (optional)
top_k: 0 # No default (optional)
top_p: 0 # No default (optional)
repeat_penalty: 0 # No default (optional)
presence_penalty: 0 # No default (optional)
frequency_penalty: 0 # No default (optional)
stop: [] # No default (optional)
save_prompt_metadata: false
history: "" # No default (optional)
max_tool_calls: 3
tools: [] # No default (required)
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 prompts to your chosen Ollama large language model (LLM) and generates text from the responses using the Ollama API and external tools.
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 and examples.
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 official Ollama GitHub release for this platform.
Type: string
frequency_penalty
Positive values penalize new tokens based on the frequency of their appearance in the text so far. This decreases the model’s likelihood to repeat the same line verbatim.
Type: float
history
Include historical messages in a chat request. You must use a Bloblang query to create an array of objects in the form of [{"role": "", "content":""}]
where:
-
role
is the sender of the original messages, eithersystem
,user
,assistant
, ortool
. -
content
is the text of the original messages.
Type: string
image
An optional image to submit along with the prompt
value. The result is a byte array.
Type: string
# Examples:
image: root = this.image.decode("base64") # decode base64 encoded image
max_tokens
The maximum number of tokens to predict and output. Limiting the amount of output means that requests are processed faster and have a fixed limit on the cost.
Type: int
max_tool_calls
The maximum number of sequential calls you can make to external tools to retrieve additional information to answer a prompt.
Type: int
Default: 3
model
The name of the Ollama LLM to use. For a full list of models, see the Ollama website.
Type: string
# Examples:
model: llama3.1
model: gemma2
model: qwen2
model: phi3
num_keep
Specify the number of tokens from the initial prompt to retain when the model resets its internal context. By default, this value is set to 4
. Use -1
to retain all tokens from the initial prompt.
Type: int
presence_penalty
Positive values penalize new tokens if they have appeared in the text so far. This increases the model’s likelihood to talk about new topics.
Type: float
prompt
The prompt you want to generate a response for. By default, the processor submits the entire payload as a string. This field supports interpolation functions.
Type: string
repeat_penalty
Sets how strongly to penalize repetitions. A higher value, for example 1.5, will penalize repetitions more strongly. A lower value, for example 0.9, will be more lenient.
Type: float
response_format
The format of the response the Ollama model generates. If specifying JSON output, then the prompt
should specify that the output should be in JSON as well.
Type: string
Default: text
Options: text
, json
runner
Options for the model runner that are used when the model is first loaded into memory.
Type: object
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 process.
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
save_prompt_metadata
Set to true
to save the prompt value to a metadata field (@prompt
) on the corresponding output message. If you use the system_prompt
field, its value is also saved to an @system_prompt
metadata field on each output message.
Type: bool
Default: false
seed
Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt.
Type: int
# Examples:
seed: 42
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
stop[]
Sets the stop sequences to use. When this pattern is encountered, the LLM stops generating text and returns the final response.
Type: array
system_prompt
The system prompt to submit to the Ollama LLM. This field supports interpolation functions.
Type: string
temperature
The temperature of the model. Increasing the temperature makes the model answer more creatively.
Type: int
tools[]
The external tools the LLM can invoke, such as functions, APIs, or web browsing. You can build a series of processors that include definitions of these tools, and the specified LLM can choose when to invoke them to help answer a prompt. For more information, see examples.
Type: object
Default: []
tools[].description
A description of this tool, the LLM uses this to decide if the tool should be used.
Type: string
tools[].parameters.properties.enum[]
Specifies that this parameter is an enum and only these specific values should be used.
Type: array
Default: []
Examples
Use Llava to analyze an image
This example fetches image URLs from stdin and has a multimodal LLM describe the image.
input:
stdin:
scanner:
lines: {}
pipeline:
processors:
- http:
verb: GET
url: "${!content().string()}"
- ollama_chat:
model: llava
prompt: "Describe the following image"
image: "root = content()"
output:
stdout:
codec: lines
Use subpipelines as tool calls
This example allows llama3.2 to execute a subpipeline as a tool call to get more data.
input:
generate:
count: 1
mapping: |
root = "What is the weather like in Chicago?"
pipeline:
processors:
- ollama_chat:
model: llama3.2
prompt: "${!content().string()}"
tools:
- name: GetWeather
description: "Retrieve the weather for a specific city"
parameters:
required: ["city"]
properties:
city:
type: string
description: the city to lookup the weather for
processors:
- http:
verb: GET
url: 'https://wttr.in/${!this.city}?T'
headers:
# Spoof curl user-ageent to get a plaintext text
User-Agent: curl/8.11.1
output:
stdout: {}