Awesome
Ollama JavaScript Library
The Ollama JavaScript library provides the easiest way to integrate your JavaScript project with Ollama.
Getting Started
npm i ollama
Usage
import ollama from 'ollama'
const response = await ollama.chat({
model: 'llama3.1',
messages: [{ role: 'user', content: 'Why is the sky blue?' }],
})
console.log(response.message.content)
Browser Usage
To use the library without node, import the browser module.
import ollama from 'ollama/browser'
Streaming responses
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: 'llama3.1', messages: [message], stream: true })
for await (const part of response) {
process.stdout.write(part.message.content)
}
Create
import ollama from 'ollama'
const modelfile = `
FROM llama3.1
SYSTEM "You are mario from super mario bros."
`
await ollama.create({ model: 'example', modelfile: modelfile })
API
The Ollama JavaScript library's API is designed around the Ollama REST API
chat
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
).stream
<boolean>
: (Optional) When true anAsyncGenerator
is returned.keep_alive
<string | number>
: (Optional) How long to keep the model loaded.tools
<Tool[]>
: (Optional) A list of tool calls the model may make.options
<Options>
: (Optional) Options to configure the runtime.
-
Returns:
<ChatResponse>
generate
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.suffix
<string>
: (Optional) Suffix is the text that comes after the inserted text.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
).stream
<boolean>
: (Optional) When true anAsyncGenerator
is returned.keep_alive
<string | number>
: (Optional) How long to keep the model loaded.options
<Options>
: (Optional) Options to configure the runtime.
- Returns:
<GenerateResponse>
pull
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.stream
<boolean>
: (Optional) When true anAsyncGenerator
is returned.
- Returns:
<ProgressResponse>
push
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.stream
<boolean>
: (Optional) When true anAsyncGenerator
is returned.
- Returns:
<ProgressResponse>
create
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 anAsyncGenerator
is returned.
- Returns:
<ProgressResponse>
delete
ollama.delete(request)
request
<Object>
: The request object containing delete parameters.model
<string>
The name of the model to delete.
- Returns:
<StatusResponse>
copy
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.
- Returns:
<StatusResponse>
list
ollama.list()
- Returns:
<ListResponse>
show
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.
- Returns:
<ShowResponse>
embed
ollama.embed(request)
request
<Object>
: The request object containing embedding parameters.model
<string>
The name of the model used to generate the embeddings.input
<string> | <string[]>
: The input used to generate the embeddings.truncate
<boolean>
: (Optional) Truncate the input to fit the maximum context length supported by the model.keep_alive
<string | number>
: (Optional) How long to keep the model loaded.options
<Options>
: (Optional) Options to configure the runtime.
- Returns:
<EmbedResponse>
ps
ollama.ps()
- Returns:
<ListResponse>
abort
ollama.abort()
This method will abort all streamed generations currently running with the client instance. If there is a need to manage streams with timeouts, it is recommended to have one Ollama client per stream.
All asynchronous threads listening to streams (typically the for await (const part of response)
) will throw an AbortError
exception. See examples/abort/abort-all-requests.ts for an example.
Custom client
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://127.0.0.1:11434' })
const response = await ollama.chat({
model: 'llama3.1',
messages: [{ role: 'user', content: 'Why is the sky blue?' }],
})
Building
To build the project files run:
npm run build