query: Chat with a LLM through Ollama

In rollama: Communicate with 'Ollama' to Run Large Language Models Locally

Chat with a LLM through Ollama

Description

Chat with a LLM through Ollama

Usage

query(
  q,
  model = NULL,
  screen = TRUE,
  server = NULL,
  images = NULL,
  model_params = NULL,
  output = c("response", "text", "list", "data.frame", "httr2_response", "httr2_request"),
  format = NULL,
  template = NULL,
  verbose = getOption("rollama_verbose", default = interactive())
)

chat(
  q,
  model = NULL,
  screen = TRUE,
  server = NULL,
  images = NULL,
  model_params = NULL,
  template = NULL,
  verbose = getOption("rollama_verbose", default = interactive())
)

Arguments

q	the question as a character string or a conversation object.
model	which model(s) to use. See https://ollama.com/library for options. Default is "llama3.1". Set option(rollama_model = "modelname") to change default for the current session. See pull_model for more details.
screen	Logical. Should the answer be printed to the screen.
server	URL to one or several Ollama servers (not the API). Defaults to "http://localhost:11434".
images	path(s) to images (for multimodal models such as llava).
model_params	a named list of additional model parameters listed in the documentation for the Modelfile such as temperature. Use a seed and set the temperature to zero to get reproducible results (see examples).
output	what the function should return. Possible values are "response", "text", "list", "data.frame", "httr2_response" or "httr2_request" see details.
format	the format to return a response in. Currently the only accepted value is "json".
template	the prompt template to use (overrides what is defined in the Modelfile).
verbose	Whether to print status messages to the Console (TRUE/FALSE). The default is to have status messages in interactive sessions. Can be changed with options(rollama_verbose = FALSE).

Details

query sends a single question to the API, without knowledge about previous questions (only the config message is relevant). chat treats new messages as part of the same conversation until new_chat is called.

To make the output reproducible, you can set a seed with options(rollama_seed = 42). As long as the seed stays the same, the models will give the same answer, changing the seed leads to a different answer.

For the output of query, there are a couple of options:

• response: the response of the Ollama server

• text: only the answer as a character vector

• data.frame: a data.frame containing model and response

• list: a list containing the prompt to Ollama and the response

• httr2_response: the response of the Ollama server including HTML headers in the httr2 response format

• httr2_request: httr2_request objects in a list, in case you want to run them with httr2::req_perform(), httr2::req_perform_sequential(), or httr2::req_perform_parallel() yourself.

Value

list of objects set in output parameter.

Examples

#' # ask a single question
query("why is the sky blue?")

# hold a conversation
chat("why is the sky blue?")
chat("and how do you know that?")

# save the response to an object and extract the answer
resp <- query(q = "why is the sky blue?")
answer <- resp[[1]]$message$content

# or just get the answer directly
answer <- query(q = "why is the sky blue?", output = "text")

# ask question about images (to a multimodal model)
images <- c("https://avatars.githubusercontent.com/u/23524101?v=4", # remote
            "/path/to/your/image.jpg") # or local images supported
query(q = "describe these images",
      model = "llava",
      images = images[1]) # just using the first path as the second is not real

# set custom options for the model at runtime (rather than in create_model())
query("why is the sky blue?",
      model_params = list(
        num_keep = 5,
        seed = 42,
        num_predict = 100,
        top_k = 20,
        top_p = 0.9,
        min_p = 0.0,
        tfs_z = 0.5,
        typical_p = 0.7,
        repeat_last_n = 33,
        temperature = 0.8,
        repeat_penalty = 1.2,
        presence_penalty = 1.5,
        frequency_penalty = 1.0,
        mirostat = 1,
        mirostat_tau = 0.8,
        mirostat_eta = 0.6,
        penalize_newline = TRUE,
        numa = FALSE,
        num_ctx = 1024,
        num_batch = 2,
        num_gpu = 0,
        main_gpu = 0,
        low_vram = FALSE,
        vocab_only = FALSE,
        use_mmap = TRUE,
        use_mlock = FALSE,
        num_thread = 8
      ))

# use a seed to get reproducible results
query("why is the sky blue?", model_params = list(seed = 42))

# to set a seed for the whole session you can use
options(rollama_seed = 42)

# this might be interesting if you want to turn off the GPU and load the
# model into the system memory (slower, but most people have more RAM than
# VRAM, which might be interesting for larger models)
query("why is the sky blue?",
       model_params = list(num_gpu = 0))

# Asking the same question to multiple models is also supported
query("why is the sky blue?", model = c("llama3.1", "orca-mini"))

# And if you have multiple Ollama servers in your network, you can send
# requests to them in parallel
if (ping_ollama(c("http://localhost:11434/",
                  "http://192.168.2.45:11434/"))) { # check if servers are running
  query("why is the sky blue?", model = c("llama3.1", "orca-mini"),
        server = c("http://localhost:11434/",
                   "http://192.168.2.45:11434/"))
}

rollama documentation built on April 3, 2025, 7:48 p.m.