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The Ollama JavaScript library provides the easiest way to integrate your JavaScript project with Ollama.
npm i ollama
import ollama from 'ollama'
const response = await ollama.chat({
model: 'llama2',
messages: [{ role: 'user', content: 'Why is the sky blue?' }],
})
console.log(response.message.content)
Response streaming can be enabled by setting stream: true
, modifying function calls to return an AsyncGenerator
where each part is an object in the stream.
import ollama from 'ollama'
const message = { role: 'user', content: 'Why is the sky blue?' }
const response = await ollama.chat({ model: 'llama2', messages: [message], stream: true })
for await (const part of response) {
process.stdout.write(part.message.content)
}
import ollama from 'ollama'
const modelfile = `
FROM llama2
SYSTEM "You are mario from super mario bros."
`
await ollama.create({ model: 'example', modelfile: modelfile })
The Ollama JavaScript library's API is designed around the Ollama REST API
ollama.chat(request)
request
<Object>
: The request object containing chat parameters.
model
<string>
The name of the model to use for the chat.messages
<Message[]>
: Array of message objects representing the chat history.
role
<string>
: The role of the message sender ('user', 'system', or 'assistant').content
<string>
: The content of the message.images
<Uint8Array[] | string[]>
: (Optional) Images to be included in the message, either as Uint8Array or base64 encoded strings.format
<string>
: (Optional) Set the expected format of the response (json
).options
<Options>
: (Optional) Options to configure the runtime.stream
<boolean>
: (Optional) When true an AsyncGenerator
is returned.Returns: <ChatResponse>
ollama.generate(request)
request
<Object>
: The request object containing generate parameters.
model
<string>
The name of the model to use for the chat.prompt
<string>
: The prompt to send to the model.system
<string>
: (Optional) Override the model system prompt.template
<string>
: (Optional) Override the model template.raw
<boolean>
: (Optional) Bypass the prompt template and pass the prompt directly to the model.images
<Uint8Array[] | string[]>
: (Optional) Images to be included, either as Uint8Array or base64 encoded strings.format
<string>
: (Optional) Set the expected format of the response (json
).options
<Options>
: (Optional) Options to configure the runtime.stream
<boolean>
: (Optional) When true an AsyncGenerator
is returned.<GenerateResponse>
ollama.pull(request)
request
<Object>
: The request object containing pull parameters.
model
<string>
The name of the model to pull.insecure
<boolean>
: (Optional) Pull from servers whose identity cannot be verified.username
<string>
: (Optional) Username of the user pulling the model.password
<string>
: (Optional) Password of the user pulling the model.stream
<boolean>
: (Optional) When true an AsyncGenerator
is returned.<ProgressResponse>
ollama.push(request)
request
<Object>
: The request object containing push parameters.
model
<string>
The name of the model to push.insecure
<boolean>
: (Optional) Push to servers whose identity cannot be verified.username
<string>
: (Optional) Username of the user pushing the model.password
<string>
: (Optional) Password of the user pushing the model.stream
<boolean>
: (Optional) When true an AsyncGenerator
is returned.<ProgressResponse>
ollama.create(request)
request
<Object>
: The request object containing create parameters.
model
<string>
The name of the model to create.path
<string>
: (Optional) The path to the Modelfile of the model to create.modelfile
<string>
: (Optional) The content of the Modelfile to create.stream
<boolean>
: (Optional) When true an AsyncGenerator
is returned.<ProgressResponse>
ollama.delete(request)
request
<Object>
: The request object containing delete parameters.
model
<string>
The name of the model to delete.<StatusResponse>
ollama.copy(request)
request
<Object>
: The request object containing copy parameters.
source
<string>
The name of the model to copy from.destination
<string>
The name of the model to copy to.<StatusResponse>
ollama.list()
<ListResponse>
ollama.show(request)
request
<Object>
: The request object containing show parameters.
model
<string>
The name of the model to show.system
<string>
: (Optional) Override the model system prompt returned.template
<string>
: (Optional) Override the model template returned.options
<Options>
: (Optional) Options to configure the runtime.<ShowResponse>
ollama.embeddings(request)
request
<Object>
: The request object containing embedding parameters.
model
<string>
The name of the model used to generate the embeddings.prompt
<string>
: The prompt used to generate the embedding.options
<Options>
: (Optional) Options to configure the runtime.<EmbeddingsResponse>
A custom client can be created with the following fields:
host
<string>
: (Optional) The Ollama host address. Default: "http://127.0.0.1:11434"
.fetch
<Object>
: (Optional) The fetch library used to make requests to the Ollama host.import { Ollama } from 'ollama'
const ollama = new Ollama({ host: 'http://localhost:11434' })
const response = await ollama.chat({
model: 'llama2',
messages: [{ role: 'user', content: 'Why is the sky blue?' }],
})
To build the project files run:
npm run build
FAQs
Ollama Javascript library
The npm package ollama receives a total of 73,831 weekly downloads. As such, ollama popularity was classified as popular.
We found that ollama demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 4 open source maintainers collaborating on the project.
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