Class: Langchain::LLM::Ollama
Overview
Interface to Ollama API. Available models: ollama.ai/library
Usage:
llm = Langchain::LLM::Ollama.new(url: ENV["OLLAMA_URL"], default_options: {})
Constant Summary collapse
- DEFAULTS =
{ temperature: 0.0, completion_model: "llama3.1", embedding_model: "llama3.1", chat_model: "llama3.1" }.freeze
- EMBEDDING_SIZES =
{ codellama: 4_096, "dolphin-mixtral": 4_096, llama2: 4_096, llama3: 4_096, "llama3.1": 4_096, llava: 4_096, mistral: 4_096, "mistral-openorca": 4_096, mixtral: 4_096, tinydolphin: 2_048 }.freeze
Instance Attribute Summary collapse
-
#defaults ⇒ Object
readonly
Returns the value of attribute defaults.
-
#url ⇒ Object
readonly
Returns the value of attribute url.
Instance Method Summary collapse
-
#chat(messages:, model: nil, **params, &block) ⇒ Langchain::LLM::OllamaResponse
Generate a chat completion.
-
#complete(prompt:, model: defaults[:completion_model], images: nil, format: nil, system: nil, template: nil, context: nil, raw: nil, mirostat: nil, mirostat_eta: nil, mirostat_tau: nil, num_ctx: nil, num_gqa: nil, num_gpu: nil, num_thread: nil, repeat_last_n: nil, repeat_penalty: nil, temperature: defaults[:temperature], seed: nil, stop: nil, tfs_z: nil, num_predict: nil, top_k: nil, top_p: nil, stop_sequences: nil, &block) ⇒ Langchain::LLM::OllamaResponse
Generate the completion for a given prompt.
-
#default_dimensions ⇒ Integer
Returns the # of vector dimensions for the embeddings.
-
#embed(text:, model: , mirostat: nil, mirostat_eta: nil, mirostat_tau: nil, num_ctx: nil, num_gqa: nil, num_gpu: nil, num_thread: nil, repeat_last_n: nil, repeat_penalty: nil, temperature: , seed: nil, stop: nil, tfs_z: nil, num_predict: nil, top_k: nil, top_p: nil) ⇒ Langchain::LLM::OllamaResponse
Generate an embedding for a given text.
-
#initialize(url: "http://localhost:11434", api_key: nil, default_options: {}) ⇒ Ollama
constructor
Initialize the Ollama client.
-
#summarize(text:) ⇒ String
Generate a summary for a given text.
Methods inherited from Base
#chat_parameters, #default_dimension
Methods included from DependencyHelper
Constructor Details
#initialize(url: "http://localhost:11434", api_key: nil, default_options: {}) ⇒ Ollama
Initialize the Ollama client
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# File 'lib/langchain/llm/ollama.rb', line 38 def initialize(url: "http://localhost:11434", api_key: nil, default_options: {}) depends_on "faraday" @url = url @api_key = api_key @defaults = DEFAULTS.merge() chat_parameters.update( model: {default: @defaults[:chat_model]}, temperature: {default: @defaults[:temperature]}, template: {}, stream: {default: false}, response_format: {default: @defaults[:response_format]} ) chat_parameters.remap(response_format: :format) end |
Instance Attribute Details
#defaults ⇒ Object (readonly)
Returns the value of attribute defaults.
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# File 'lib/langchain/llm/ollama.rb', line 11 def defaults @defaults end |
#url ⇒ Object (readonly)
Returns the value of attribute url.
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# File 'lib/langchain/llm/ollama.rb', line 11 def url @url end |
Instance Method Details
#chat(messages:, model: nil, **params, &block) ⇒ Langchain::LLM::OllamaResponse
Generate a chat completion
Example:
final_resp = ollama.chat(messages:) { |resp| print resp.chat_completion }
final_resp.total_tokens
The message object has the following fields:
role: the role of the message, either system, user or assistant
content: the content of the message
images (optional): a list of images to include in the message (for multimodal models such as llava)
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# File 'lib/langchain/llm/ollama.rb', line 177 def chat(messages:, model: nil, **params, &block) parameters = chat_parameters.to_params(params.merge(messages:, model:, stream: block_given?)) # rubocop:disable Performance/BlockGivenWithExplicitBlock responses_stream = [] client.post("api/chat", parameters) do |req| req..on_data = json_responses_chunk_handler do |parsed_chunk| responses_stream << parsed_chunk block&.call(OllamaResponse.new(parsed_chunk, model: parameters[:model])) end end generate_final_chat_completion_response(responses_stream, parameters[:model]) end |
#complete(prompt:, model: defaults[:completion_model], images: nil, format: nil, system: nil, template: nil, context: nil, raw: nil, mirostat: nil, mirostat_eta: nil, mirostat_tau: nil, num_ctx: nil, num_gqa: nil, num_gpu: nil, num_thread: nil, repeat_last_n: nil, repeat_penalty: nil, temperature: defaults[:temperature], seed: nil, stop: nil, tfs_z: nil, num_predict: nil, top_k: nil, top_p: nil, stop_sequences: nil, &block) ⇒ Langchain::LLM::OllamaResponse
Generate the completion for a given prompt
Example:
final_resp = ollama.complete(prompt:) { |resp| print resp.completion }
final_resp.total_tokens
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# File 'lib/langchain/llm/ollama.rb', line 78 def complete( prompt:, model: defaults[:completion_model], images: nil, format: nil, system: nil, template: nil, context: nil, raw: nil, mirostat: nil, mirostat_eta: nil, mirostat_tau: nil, num_ctx: nil, num_gqa: nil, num_gpu: nil, num_thread: nil, repeat_last_n: nil, repeat_penalty: nil, temperature: defaults[:temperature], seed: nil, stop: nil, tfs_z: nil, num_predict: nil, top_k: nil, top_p: nil, stop_sequences: nil, &block ) if stop_sequences stop = stop_sequences end parameters = { prompt: prompt, model: model, images: images, format: format, system: system, template: template, context: context, stream: block_given?, # rubocop:disable Performance/BlockGivenWithExplicitBlock raw: raw }.compact llm_parameters = { mirostat: mirostat, mirostat_eta: mirostat_eta, mirostat_tau: mirostat_tau, num_ctx: num_ctx, num_gqa: num_gqa, num_gpu: num_gpu, num_thread: num_thread, repeat_last_n: repeat_last_n, repeat_penalty: repeat_penalty, temperature: temperature, seed: seed, stop: stop, tfs_z: tfs_z, num_predict: num_predict, top_k: top_k, top_p: top_p } parameters[:options] = llm_parameters.compact responses_stream = [] client.post("api/generate", parameters) do |req| req..on_data = json_responses_chunk_handler do |parsed_chunk| responses_stream << parsed_chunk block&.call(OllamaResponse.new(parsed_chunk, model: parameters[:model])) end end generate_final_completion_response(responses_stream, parameters[:model]) end |
#default_dimensions ⇒ Integer
Returns the # of vector dimensions for the embeddings
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# File 'lib/langchain/llm/ollama.rb', line 55 def default_dimensions # since Ollama can run multiple models, look it up or generate an embedding and return the size @default_dimensions ||= EMBEDDING_SIZES.fetch(defaults[:embedding_model].to_sym) do (text: "test")..size end end |
#embed(text:, model: , mirostat: nil, mirostat_eta: nil, mirostat_tau: nil, num_ctx: nil, num_gqa: nil, num_gpu: nil, num_thread: nil, repeat_last_n: nil, repeat_penalty: nil, temperature: , seed: nil, stop: nil, tfs_z: nil, num_predict: nil, top_k: nil, top_p: nil) ⇒ Langchain::LLM::OllamaResponse
Generate an embedding for a given text
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# File 'lib/langchain/llm/ollama.rb', line 200 def ( text:, model: defaults[:embedding_model], mirostat: nil, mirostat_eta: nil, mirostat_tau: nil, num_ctx: nil, num_gqa: nil, num_gpu: nil, num_thread: nil, repeat_last_n: nil, repeat_penalty: nil, temperature: defaults[:temperature], seed: nil, stop: nil, tfs_z: nil, num_predict: nil, top_k: nil, top_p: nil ) parameters = { model: model, input: Array(text) }.compact llm_parameters = { mirostat: mirostat, mirostat_eta: mirostat_eta, mirostat_tau: mirostat_tau, num_ctx: num_ctx, num_gqa: num_gqa, num_gpu: num_gpu, num_thread: num_thread, repeat_last_n: repeat_last_n, repeat_penalty: repeat_penalty, temperature: temperature, seed: seed, stop: stop, tfs_z: tfs_z, num_predict: num_predict, top_k: top_k, top_p: top_p } parameters[:options] = llm_parameters.compact response = client.post("api/embed") do |req| req.body = parameters end OllamaResponse.new(response.body, model: parameters[:model]) end |
#summarize(text:) ⇒ String
Generate a summary for a given text
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# File 'lib/langchain/llm/ollama.rb', line 257 def summarize(text:) prompt_template = Langchain::Prompt.load_from_path( file_path: Langchain.root.join("langchain/llm/prompts/ollama/summarize_template.yaml") ) prompt = prompt_template.format(text: text) complete(prompt: prompt) end |