Docs Cloud Develop Redpanda Connect Components Processors ollama_chat 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. Common Advanced # Common config fields, showing default values label: "" ollama_chat: model: llama3.1 # No default (required) prompt: "" # No default (optional) response_format: text max_tokens: 0 # No default (optional) temperature: 0 # 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_chat: model: llama3.1 # No default (required) prompt: "" # No default (optional) system_prompt: "" # 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) 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. 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 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 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 system_prompt The system prompt to submit to the Ollama LLM. This field supports interpolation functions. Type: string response_format The format of the response that 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 . 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 temperature The temperature of the model. Increasing the temperature makes the model answer more creatively. Type: int 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 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 top_k Reduces the probability of generating nonsense. A higher value, for example 100, will give more diverse answers. A lower value, for example 10, will be more conservative. Type: int top_p Works together with top-k. A higher value, for example 0.95, will lead to more diverse text. A lower value, for example 0.5, will generate more focused and conservative text. Type: float 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 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 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 stop Sets the stop sequences to use. When this pattern is encountered the LLM stops generating text and returns the final response. Type: array 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 processor. Type: int runner.batch_size The maximum number of requests to process in parallel. 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 runner.use_mlock Lock the model in memory, preventing it from being swapped out when memory-mapped. This option can improve performance but reduces some of the advantages of memory-mapping because it uses more RAM to run and can slow down load times as the model loads into RAM. 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 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 Back to top × Simple online edits For simple changes, such as fixing a typo, you can edit the content directly on GitHub. 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