Gathering detailed insights and metrics for ollama
Installations
npm install ollamaDeveloper Guide
BETA
Typescript
Yes
Module System
CommonJS, ESM
Node Version
23.6.0
NPM Version
10.9.2
Score
97.8
Supply Chain
99.6
Quality
92.2
Maintenance
100
Vulnerability
100
License
Releases
19
Contributors
26
Languages
2
TypeScript
JavaScript
TypeScript (97.75%)
JavaScript (2.25%)
Developer
Download Statistics
Total Downloads
2,791,061
Last Day
18,186
Last Week
101,379
Last Month
442,928
Last Year
2,788,785
GitHub Statistics
2,882 Stars
171 Commits
237 Forks
26 Watching
30 Branches
26 Contributors
Bundle Size
16.33 kB
Minified
5.40 kB
Minified + Gzipped
Maintainers
4
Package Meta Information
Latest Version
0.5.12
Package Id
ollama@0.5.12
Unpacked Size
102.77 kB
Size
20.08 kB
File Count
21
NPM Version
10.9.2
Node Version
23.6.0
Publised On
14 Jan 2025
Total Downloads
Cumulative downloads
Total Downloads
2,791,061
Last day
-23.4%
18,186
Compared to previous day
Last week
-23.4%
101,379
Compared to previous week
Last month
10.8%
442,928
Compared to previous month
Last year
122,430.1%
2,788,785
Compared to previous year
Daily Downloads
Weekly Downloads
Monthly Downloads
Yearly Downloads
Versions
Ollama JavaScript Library
The Ollama JavaScript library provides the easiest way to integrate your JavaScript project with Ollama.
Getting Started
npm i ollama
Usage
1import ollama from 'ollama' 2 3const response = await ollama.chat({ 4 model: 'llama3.1', 5 messages: [{ role: 'user', content: 'Why is the sky blue?' }], 6}) 7console.log(response.message.content)
Browser Usage
To use the library without node, import the browser module.
1import 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.
1import ollama from 'ollama' 2 3const message = { role: 'user', content: 'Why is the sky blue?' } 4const response = await ollama.chat({ model: 'llama3.1', messages: [message], stream: true }) 5for await (const part of response) { 6 process.stdout.write(part.message.content) 7}
Create
1import ollama from 'ollama' 2 3const modelfile = ` 4FROM llama3.1 5SYSTEM "You are mario from super mario bros." 6` 7await ollama.create({ model: 'example', modelfile: modelfile })
API
The Ollama JavaScript library's API is designed around the Ollama REST API
chat
1ollama.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 anAsyncGeneratoris returned.keep_alive<string | number>: (Optional) How long to keep the model loaded. A number (seconds) or a string with a duration unit suffix ("300ms", "1.5h", "2h45m", etc.)tools<Tool[]>: (Optional) A list of tool calls the model may make.options<Options>: (Optional) Options to configure the runtime.
-
Returns:
<ChatResponse>
generate
1ollama.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 anAsyncGeneratoris returned.keep_alive<string | number>: (Optional) How long to keep the model loaded. A number (seconds) or a string with a duration unit suffix ("300ms", "1.5h", "2h45m", etc.)options<Options>: (Optional) Options to configure the runtime.
- Returns:
<GenerateResponse>
pull
1ollama.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 anAsyncGeneratoris returned.
- Returns:
<ProgressResponse>
push
1ollama.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 anAsyncGeneratoris returned.
- Returns:
<ProgressResponse>
create
1ollama.create(request)
request<Object>: The request object containing create parameters.model<string>The name of the model to create.from<string>: The base model to derive from.stream<boolean>: (Optional) When true anAsyncGeneratoris returned.quantize<string>: Quanization precision level (q8_0,q4_K_M, etc.).template<string>: (Optional) The prompt template to use with the model.license<string|string[]>: (Optional) The license(s) associated with the model.system<string>: (Optional) The system prompt for the model.parameters<Record<string, unknown>>: (Optional) Additional model parameters as key-value pairs.messages<Message[]>: (Optional) Initial chat messages for the model.adapters<Record<string, string>>: (Optional) A key-value map of LoRA adapter configurations.
- Returns:
<ProgressResponse>
Note: The files parameter is not currently supported in ollama-js.
delete
1ollama.delete(request)
request<Object>: The request object containing delete parameters.model<string>The name of the model to delete.
- Returns:
<StatusResponse>
copy
1ollama.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
1ollama.list()
- Returns:
<ListResponse>
show
1ollama.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
1ollama.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. A number (seconds) or a string with a duration unit suffix ("300ms", "1.5h", "2h45m", etc.)options<Options>: (Optional) Options to configure the runtime.
- Returns:
<EmbedResponse>
ps
1ollama.ps()
- Returns:
<ListResponse>
abort
1ollama.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.
1import { Ollama } from 'ollama' 2 3const ollama = new Ollama({ host: 'http://127.0.0.1:11434' }) 4const response = await ollama.chat({ 5 model: 'llama3.1', 6 messages: [{ role: 'user', content: 'Why is the sky blue?' }], 7})
Building
To build the project files run:
1npm run build
No vulnerabilities found.
No security vulnerabilities found.