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Revert "docs: add reference to docs.ollama.com (#12800)" (#12803) 

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docs/api/authentication.mdx deleted 100644 → 0
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---
title: Authentication
---
No authentication is required when accessing Ollama's API locally via `http://localhost:11434`.
Authentication is required for the following:
* Running cloud models via ollama.com
* Publishing models
* Downloading private models
Ollama supports two authentication methods:
* **Signing in**: sign in from your local installation, and Ollama will automatically take care of authenticating requests to ollama.com when running commands
* **API keys**: API keys for programmatic access to ollama.com's API
## Signing in
To sign in to ollama.com from your local installation of Ollama, run:
```
ollama signin
```
Once signed in, Ollama will automatically authenticate commands as required:
```
ollama run gpt-oss:120b-cloud
```
Similarly, when accessing a local API endpoint that requires cloud access, Ollama will automatically authenticate the request:
```shell
curl http://localhost:11434/api/generate -d '{
"model": "gpt-oss:120b-cloud",
"prompt": "Why is the sky blue?"
}'
```
## API keys
For direct access to ollama.com's API served at `https://ollama.com/api`, authentication via API keys is required.
First, create an [API key](https://ollama.com/settings/keys), then set the `OLLAMA_API_KEY` environment variable:
```shell
export OLLAMA_API_KEY=your_api_key
```
Then use the API key in the Authorization header:
```shell
curl https://ollama.com/api/generate \
-H "Authorization: Bearer $OLLAMA_API_KEY" \
-d '{
"model": "gpt-oss:120b",
"prompt": "Why is the sky blue?",
"stream": false
}'
```
API keys don't currently expire, however you can revoke them at any time in your [API keys settings](https://ollama.com/settings/keys).
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---
title: Errors
---
## Status codes
Endpoints return appropriate HTTP status codes based on the success or failure of the request in the HTTP status line (e.g. `HTTP/1.1 200 OK` or `HTTP/1.1 400 Bad Request`). Common status codes are:
- `200`: Success
- `400`: Bad Request (missing parameters, invalid JSON, etc.)
- `404`: Not Found (model doesn't exist, etc.)
- `429`: Too Many Requests (e.g. when a rate limit is exceeded)
- `500`: Internal Server Error
- `502`: Bad Gateway (e.g. when a cloud model cannot be reached)
## Error messages
Errors are returned in the `application/json` format with the following structure, with the error message in the `error` property:
```json
{
"error": "the model failed to generate a response"
}
```
## Errors that occur while streaming
If an error occurs mid-stream, the error will be returned as an object in the `application/x-ndjson` format with an `error` property. Since the response has already started, the status code of the response will not be changed.
```json
{"model":"gemma3","created_at":"2025-10-26T17:21:21.196249Z","response":" Yes","done":false}
{"model":"gemma3","created_at":"2025-10-26T17:21:21.207235Z","response":".","done":false}
{"model":"gemma3","created_at":"2025-10-26T17:21:21.219166Z","response":"I","done":false}
{"model":"gemma3","created_at":"2025-10-26T17:21:21.231094Z","response":"can","done":false}
{"error":"an error was encountered while running the model"}
```
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---
title: "Introduction"
---
Ollama's API allows you to run and interact with models programatically.
## Get started
If you're just getting started, follow the [quickstart](/quickstart) documentation to get up and running with Ollama's API.
## Base URL
After installation, Ollama's API is served by default at:
```
http://localhost:11434/api
```
For running cloud models on **ollama.com**, the same API is available with the following base URL:
```
https://ollama.com/api
```
## Example request
Once Ollama is running, its API is automatically available and can be accessed via `curl`:
```shell
curl http://localhost:11434/api/generate -d '{
"model": "gemma3",
"prompt": "Why is the sky blue?"
}'
```
## Libraries
Ollama has official libraries for Python and JavaScript:
- [Python](https://github.com/ollama/ollama-python)
- [JavaScript](https://github.com/ollama/ollama-js)
Several community-maintained libraries are available for Ollama. For a full list, see the [Ollama GitHub repository](https://github.com/ollama/ollama?tab=readme-ov-file#libraries-1).
## Versioning
Ollama's API isn't strictly versioned, but the API is expected to be stable and backwards compatible. Deprecations are rare and will be announced in the [release notes](https://github.com/ollama/ollama/releases).
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docs/api/streaming.mdx deleted 100644 → 0
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---
title: Streaming
---
Certain API endpoints stream responses by default, such as `/api/generate`. These responses are provided in the newline-delimited JSON format (i.e. the `application/x-ndjson` content type). For example:
```json
{"model":"gemma3","created_at":"2025-10-26T17:15:24.097767Z","response":"That","done":false}
{"model":"gemma3","created_at":"2025-10-26T17:15:24.109172Z","response":"'","done":false}
{"model":"gemma3","created_at":"2025-10-26T17:15:24.121485Z","response":"s","done":false}
{"model":"gemma3","created_at":"2025-10-26T17:15:24.132802Z","response":" a","done":false}
{"model":"gemma3","created_at":"2025-10-26T17:15:24.143931Z","response":" fantastic","done":false}
{"model":"gemma3","created_at":"2025-10-26T17:15:24.155176Z","response":" question","done":false}
{"model":"gemma3","created_at":"2025-10-26T17:15:24.166576Z","response":"!","done":true, "done_reason": "stop"}
```
## Disabling streaming
Streaming can be disabled by providing `{"stream": false}` in the request body for any endpoint that support streaming. This will cause responses to be returned in the `application/json` format instead:
```json
{"model":"gemma3","created_at":"2025-10-26T17:15:24.166576Z","response":"That's a fantastic question!","done":true}
```
## When to use streaming vs non-streaming
**Streaming (default)**:
- Real-time response generation
- Lower perceived latency
- Better for long generations
**Non-streaming**:
- Simpler to process
- Better for short responses, or structured outputs
- Easier to handle in some applications
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---
title: Usage
---
Ollama's API responses include metrics that can be used for measuring performance and model usage:
* `total_duration`: How long the response took to generate
* `load_duration`: How long the model took to load
* `prompt_eval_count`: How many input tokens were processed
* `prompt_eval_duration`: How long it took to evaluate the prompt
* `eval_count`: How many output tokens were processes
* `eval_duration`: How long it took to generate the output tokens
All timing values are measured in nanoseconds.
## Example response
For endpoints that return usage metrics, the response body will include the usage fields. For example, a non-streaming call to `/api/generate` may return the following response:
```json
{
"model": "gemma3",
"created_at": "2025-10-17T23:14:07.414671Z",
"response": "Hello! How can I help you today?",
"done": true,
"done_reason": "stop",
"total_duration": 174560334,
"load_duration": 101397084,
"prompt_eval_count": 11,
"prompt_eval_duration": 13074791,
"eval_count": 18,
"eval_duration": 52479709
}
```
For endpoints that return **streaming responses**, usage fields are included as part of the final chunk, where `done` is `true`.
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---
title: Benchmark
---
Go benchmark tests that measure end-to-end performance of a running Ollama server. Run these tests to evaluate model inference performance on your hardware and measure the impact of code changes.
## When to use
Run these benchmarks when:
- Making changes to the model inference engine
- Modifying model loading/unloading logic
- Changing prompt processing or token generation code
- Implementing a new model architecture
- Testing performance across different hardware setups
## Prerequisites
- Ollama server running locally with `ollama serve` on `127.0.0.1:11434`
## Usage and Examples
<Note>
All commands must be run from the root directory of the Ollama project.
</Note>
Basic syntax:
```bash
go test -bench=. ./benchmark/... -m $MODEL_NAME
```
Required flags:
- `-bench=.`: Run all benchmarks
- `-m`: Model name to benchmark
Optional flags:
- `-count N`: Number of times to run the benchmark (useful for statistical analysis)
- `-timeout T`: Maximum time for the benchmark to run (e.g. "10m" for 10 minutes)
Common usage patterns:
Single benchmark run with a model specified:
```bash
go test -bench=. ./benchmark/... -m llama3.3
```
## Output metrics
The benchmark reports several key metrics:
- `gen_tok/s`: Generated tokens per second
- `prompt_tok/s`: Prompt processing tokens per second
- `ttft_ms`: Time to first token in milliseconds
- `load_ms`: Model load time in milliseconds
- `gen_tokens`: Total tokens generated
- `prompt_tokens`: Total prompt tokens processed
Each benchmark runs two scenarios:
- Cold start: Model is loaded from disk for each test
- Warm start: Model is pre-loaded in memory
Three prompt lengths are tested for each scenario:
- Short prompt (100 tokens)
- Medium prompt (500 tokens)
- Long prompt (1000 tokens)
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---
title: Embeddings
description: Generate text embeddings for semantic search, retrieval, and RAG.
---
Embeddings turn text into numeric vectors you can store in a vector database, search with cosine similarity, or use in RAG pipelines. The vector length depends on the model (typically 384–1024 dimensions).
## Recommended models
- [embeddinggemma](https://ollama.com/library/embeddinggemma)
- [qwen3-embedding](https://ollama.com/library/qwen3-embedding)
- [all-minilm](https://ollama.com/library/all-minilm)
## Generate embeddings
Use `/api/embed` with a single string.
<Tabs>
<Tab title="cURL">
```shell
curl -X POST http://localhost:11434/api/embed \
-H "Content-Type: application/json" \
-d '{
"model": "embeddinggemma",
"input": "The quick brown fox jumps over the lazy dog."
}'
```
</Tab>
<Tab title="Python">
```python
import ollama
single = ollama.embed(
model='embeddinggemma',
input='The quick brown fox jumps over the lazy dog.'
)
print(len(single['embeddings'][0])) # vector length
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
const single = await ollama.embed({
model: 'embeddinggemma',
input: 'The quick brown fox jumps over the lazy dog.',
})
console.log(single.embeddings[0].length) // vector length
```
</Tab>
</Tabs>
<Note>
The `/api/embed` endpoint returns L2‑normalized (unit‑length) vectors.
</Note>
## Generate a batch of embeddings
Pass an array of strings to `input`.
<Tabs>
<Tab title="cURL">
```shell
curl -X POST http://localhost:11434/api/embed \
-H "Content-Type: application/json" \
-d '{
"model": "embeddinggemma",
"input": [
"First sentence",
"Second sentence",
"Third sentence"
]
}'
```
</Tab>
<Tab title="Python">
```python
import ollama
batch = ollama.embed(
model='embeddinggemma',
input=[
'The quick brown fox jumps over the lazy dog.',
'The five boxing wizards jump quickly.',
'Jackdaws love my big sphinx of quartz.',
]
)
print(len(batch['embeddings'])) # number of vectors
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
const batch = await ollama.embed({
model: 'embeddinggemma',
input: [
'The quick brown fox jumps over the lazy dog.',
'The five boxing wizards jump quickly.',
'Jackdaws love my big sphinx of quartz.',
],
})
console.log(batch.embeddings.length) // number of vectors
```
</Tab>
</Tabs>
## Tips
- Use cosine similarity for most semantic search use cases.
- Use the same embedding model for both indexing and querying.
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---
title: Streaming
---
Streaming allows you to render text as it is produced by the model.
Streaming is enabled by default through the REST API, but disabled by default in the SDKs.
To enable streaming in the SDKs, set the `stream` parameter to `True`.
## Key streaming concepts
1. Chatting: Stream partial assistant messages. Each chunk includes the `content` so you can render messages as they arrive.
1. Thinking: Thinking-capable models emit a `thinking` field alongside regular content in each chunk. Detect this field in streaming chunks to show or hide reasoning traces before the final answer arrives.
1. Tool calling: Watch for streamed `tool_calls` in each chunk, execute the requested tool, and append tool outputs back into the conversation.
## Handling streamed chunks
<Note> It is necessary to accumulate the partial fields in order to maintain the history of the conversation. This is particularly important for tool calling where the thinking, tool call from the model, and the executed tool result must be passed back to the model in the next request. </Note>
<Tabs>
<Tab title="Python">
```python
from ollama import chat
stream = chat(
model='qwen3',
messages=[{'role': 'user', 'content': 'What is 17 × 23?'}],
stream=True,
)
in_thinking = False
content = ''
thinking = ''
for chunk in stream:
if chunk.message.thinking:
if not in_thinking:
in_thinking = True
print('Thinking:\n', end='', flush=True)
print(chunk.message.thinking, end='', flush=True)
# accumulate the partial thinking
thinking += chunk.message.thinking
elif chunk.message.content:
if in_thinking:
in_thinking = False
print('\n\nAnswer:\n', end='', flush=True)
print(chunk.message.content, end='', flush=True)
# accumulate the partial content
content += chunk.message.content
# append the accumulated fields to the messages for the next request
new_messages = [{ role: 'assistant', thinking: thinking, content: content }]
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
async function main() {
const stream = await ollama.chat({
model: 'qwen3',
messages: [{ role: 'user', content: 'What is 17 × 23?' }],
stream: true,
})
let inThinking = false
let content = ''
let thinking = ''
for await (const chunk of stream) {
if (chunk.message.thinking) {
if (!inThinking) {
inThinking = true
process.stdout.write('Thinking:\n')
}
process.stdout.write(chunk.message.thinking)
// accumulate the partial thinking
thinking += chunk.message.thinking
} else if (chunk.message.content) {
if (inThinking) {
inThinking = false
process.stdout.write('\n\nAnswer:\n')
}
process.stdout.write(chunk.message.content)
// accumulate the partial content
content += chunk.message.content
}
}
// append the accumulated fields to the messages for the next request
new_messages = [{ role: 'assistant', thinking: thinking, content: content }]
}
main().catch(console.error)
```
</Tab>
</Tabs>
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---
title: Structured Outputs
---
Structured outputs let you enforce a JSON schema on model responses so you can reliably extract structured data, describe images, or keep every reply consistent.
## Generating structured JSON
<Tabs>
<Tab title="cURL">
```shell
curl -X POST http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
"model": "gpt-oss",
"messages": [{"role": "user", "content": "Tell me about Canada in one line"}],
"stream": false,
"format": "json"
}'
```
</Tab>
<Tab title="Python">
```python
from ollama import chat
response = chat(
model='gpt-oss',
messages=[{'role': 'user', 'content': 'Tell me about Canada.'}],
format='json'
)
print(response.message.content)
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
const response = await ollama.chat({
model: 'gpt-oss',
messages: [{ role: 'user', content: 'Tell me about Canada.' }],
format: 'json'
})
console.log(response.message.content)
```
</Tab>
</Tabs>
## Generating structured JSON with a schema
Provide a JSON schema to the `format` field.
<Note>
It is ideal to also pass the JSON schema as a string in the prompt to ground the model's response.
</Note>
<Tabs>
<Tab title="cURL">
```shell
curl -X POST http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
"model": "gpt-oss",
"messages": [{"role": "user", "content": "Tell me about Canada."}],
"stream": false,
"format": {
"type": "object",
"properties": {
"name": {"type": "string"},
"capital": {"type": "string"},
"languages": {
"type": "array",
"items": {"type": "string"}
}
},
"required": ["name", "capital", "languages"]
}
}'
```
</Tab>
<Tab title="Python">
Use Pydantic models and pass `model_json_schema()` to `format`, then validate the response:
```python
from ollama import chat
from pydantic import BaseModel
class Country(BaseModel):
name: str
capital: str
languages: list[str]
response = chat(
model='gpt-oss',
messages=[{'role': 'user', 'content': 'Tell me about Canada.'}],
format=Country.model_json_schema(),
)
country = Country.model_validate_json(response.message.content)
print(country)
```
</Tab>
<Tab title="JavaScript">
Serialize a Zod schema with `zodToJsonSchema()` and parse the structured response:
```javascript
import ollama from 'ollama'
import { z } from 'zod'
import { zodToJsonSchema } from 'zod-to-json-schema'
const Country = z.object({
name: z.string(),
capital: z.string(),
languages: z.array(z.string()),
})
const response = await ollama.chat({
model: 'gpt-oss',
messages: [{ role: 'user', content: 'Tell me about Canada.' }],
format: zodToJsonSchema(Country),
})
const country = Country.parse(JSON.parse(response.message.content))
console.log(country)
```
</Tab>
</Tabs>
## Example: Extract structured data
Define the objects you want returned and let the model populate the fields:
```python
from ollama import chat
from pydantic import BaseModel
class Pet(BaseModel):
name: str
animal: str
age: int
color: str | None
favorite_toy: str | None
class PetList(BaseModel):
pets: list[Pet]
response = chat(
model='gpt-oss',
messages=[{'role': 'user', 'content': 'I have two cats named Luna and Loki...'}],
format=PetList.model_json_schema(),
)
pets = PetList.model_validate_json(response.message.content)
print(pets)
```
## Example: Vision with structured outputs
Vision models accept the same `format` parameter, enabling deterministic descriptions of images:
```python
from ollama import chat
from pydantic import BaseModel
from typing import Literal, Optional
class Object(BaseModel):
name: str
confidence: float
attributes: str
class ImageDescription(BaseModel):
summary: str
objects: list[Object]
scene: str
colors: list[str]
time_of_day: Literal['Morning', 'Afternoon', 'Evening', 'Night']
setting: Literal['Indoor', 'Outdoor', 'Unknown']
text_content: Optional[str] = None
response = chat(
model='gemma3',
messages=[{
'role': 'user',
'content': 'Describe this photo and list the objects you detect.',
'images': ['path/to/image.jpg'],
}],
format=ImageDescription.model_json_schema(),
options={'temperature': 0},
)
image_description = ImageDescription.model_validate_json(response.message.content)
print(image_description)
```
## Tips for reliable structured outputs
- Define schemas with Pydantic (Python) or Zod (JavaScript) so they can be reused for validation.
- Lower the temperature (e.g., set it to `0`) for more deterministic completions.
- Structured outputs work through the OpenAI-compatible API via `response_format`
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---
title: Thinking
---
Thinking-capable models emit a `thinking` field that separates their reasoning trace from the final answer.
Use this capability to audit model steps, animate the model *thinking* in a UI, or hide the trace entirely when you only need the final response.
## Supported models
- [Qwen 3](https://ollama.com/library/qwen3)
- [GPT-OSS](https://ollama.com/library/gpt-oss) *(use `think` levels: `low`, `medium`, `high` — the trace cannot be fully disabled)*
- [DeepSeek-v3.1](https://ollama.com/library/deepseek-v3.1)
- [DeepSeek R1](https://ollama.com/library/deepseek-r1)
- Browse the latest additions under [thinking models](https://ollama.com/search?c=thinking)
## Enable thinking in API calls
Set the `think` field on chat or generate requests. Most models accept booleans (`true`/`false`).
GPT-OSS instead expects one of `low`, `medium`, or `high` to tune the trace length.
The `message.thinking` (chat endpoint) or `thinking` (generate endpoint) field contains the reasoning trace while `message.content` / `response` holds the final answer.
<Tabs>
<Tab title="cURL">
```shell
curl http://localhost:11434/api/chat -d '{
"model": "qwen3",
"messages": [{
"role": "user",
"content": "How many letter r are in strawberry?"
}],
"think": true,
"stream": false
}'
```
</Tab>
<Tab title="Python">
```python
from ollama import chat
response = chat(
model='qwen3',
messages=[{'role': 'user', 'content': 'How many letter r are in strawberry?'}],
think=True,
stream=False,
)
print('Thinking:\n', response.message.thinking)
print('Answer:\n', response.message.content)
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
const response = await ollama.chat({
model: 'deepseek-r1',
messages: [{ role: 'user', content: 'How many letter r are in strawberry?' }],
think: true,
stream: false,
})
console.log('Thinking:\n', response.message.thinking)
console.log('Answer:\n', response.message.content)
```
</Tab>
</Tabs>
<Note>
GPT-OSS requires `think` to be set to `"low"`, `"medium"`, or `"high"`. Passing `true`/`false` is ignored for that model.
</Note>
## Stream the reasoning trace
Thinking streams interleave reasoning tokens before answer tokens. Detect the first `thinking` chunk to render a "thinking" section, then switch to the final reply once `message.content` arrives.
<Tabs>
<Tab title="Python">
```python
from ollama import chat
stream = chat(
model='qwen3',
messages=[{'role': 'user', 'content': 'What is 17 × 23?'}],
think=True,
stream=True,
)
in_thinking = False
for chunk in stream:
if chunk.message.thinking and not in_thinking:
in_thinking = True
print('Thinking:\n', end='')
if chunk.message.thinking:
print(chunk.message.thinking, end='')
elif chunk.message.content:
if in_thinking:
print('\n\nAnswer:\n', end='')
in_thinking = False
print(chunk.message.content, end='')
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
async function main() {
const stream = await ollama.chat({
model: 'qwen3',
messages: [{ role: 'user', content: 'What is 17 × 23?' }],
think: true,
stream: true,
})
let inThinking = false
for await (const chunk of stream) {
if (chunk.message.thinking && !inThinking) {
inThinking = true
process.stdout.write('Thinking:\n')
}
if (chunk.message.thinking) {
process.stdout.write(chunk.message.thinking)
} else if (chunk.message.content) {
if (inThinking) {
process.stdout.write('\n\nAnswer:\n')
inThinking = false
}
process.stdout.write(chunk.message.content)
}
}
}
main()
```
</Tab>
</Tabs>
## CLI quick reference
- Enable thinking for a single run: `ollama run deepseek-r1 --think "Where should I visit in Lisbon?"`
- Disable thinking: `ollama run deepseek-r1 --think=false "Summarize this article"`
- Hide the trace while still using a thinking model: `ollama run deepseek-r1 --hidethinking "Is 9.9 bigger or 9.11?"`
- Inside interactive sessions, toggle with `/set think` or `/set nothink`.
- GPT-OSS only accepts levels: `ollama run gpt-oss --think=low "Draft a headline"` (replace `low` with `medium` or `high` as needed).
<Note>Thinking is enabled by default in the CLI and API for supported models.</Note>
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---
title: Vision
---
Vision models accept images alongside text so the model can describe, classify, and answer questions about what it sees.
## Quick start
```shell
ollama run gemma3 ./image.png whats in this image?
```
## Usage with Ollama's API
Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the REST API expects base64-encoded image data.
<Tabs>
<Tab title="cURL">
```shell
# 1. Download a sample image
curl -L -o test.jpg "https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg"
# 2. Encode the image
IMG=$(base64 < test.jpg | tr -d '\n')
# 3. Send it to Ollama
curl -X POST http://localhost:11434/api/chat \
-H "Content-Type: application/json" \
-d '{
"model": "gemma3",
"messages": [{
"role": "user",
"content": "What is in this image?",
"images": ["'"$IMG"'"]
}],
"stream": false
}'
"
```
</Tab>
<Tab title="Python">
```python
from ollama import chat
# from pathlib import Path
# Pass in the path to the image
path = input('Please enter the path to the image: ')
# You can also pass in base64 encoded image data
# img = base64.b64encode(Path(path).read_bytes()).decode()
# or the raw bytes
# img = Path(path).read_bytes()
response = chat(
model='gemma3',
messages=[
{
'role': 'user',
'content': 'What is in this image? Be concise.',
'images': [path],
}
],
)
print(response.message.content)
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
const imagePath = '/absolute/path/to/image.jpg'
const response = await ollama.chat({
model: 'gemma3',
messages: [
{ role: 'user', content: 'What is in this image?', images: [imagePath] }
],
stream: false,
})
console.log(response.message.content)
```
</Tab>
</Tabs>
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---
title: Web search
---
Ollama's web search API can be used to augment models with the latest information to reduce hallucinations and improve accuracy.
Web search is provided as a REST API with deeper tool integrations in the Python and JavaScript libraries. This also enables models like OpenAI’s gpt-oss models to conduct long-running research tasks.
## Authentication
For access to Ollama's web search API, create an [API key](https://ollama.com/settings/keys). A free Ollama account is required.
## Web search API
Performs a web search for a single query and returns relevant results.
### Request
`POST https://ollama.com/api/web_search`
- `query` (string, required): the search query string
- `max_results` (integer, optional): maximum results to return (default 5, max 10)
### Response
Returns an object containing:
- `results` (array): array of search result objects, each containing:
- `title` (string): the title of the web page
- `url` (string): the URL of the web page
- `content` (string): relevant content snippet from the web page
### Examples
<Note>
Ensure OLLAMA_API_KEY is set or it must be passed in the Authorization header.
</Note>
#### cURL Request
```bash
curl https://ollama.com/api/web_search \
--header "Authorization: Bearer $OLLAMA_API_KEY" \
-d '{
"query":"what is ollama?"
}'
```
**Response**
```json
{
"results": [
{
"title": "Ollama",
"url": "https://ollama.com/",
"content": "Cloud models are now available..."
},
{
"title": "What is Ollama? Introduction to the AI model management tool",
"url": "https://www.hostinger.com/tutorials/what-is-ollama",
"content": "Ariffud M. 6min Read..."
},
{
"title": "Ollama Explained: Transforming AI Accessibility and Language ...",
"url": "https://www.geeksforgeeks.org/artificial-intelligence/ollama-explained-transforming-ai-accessibility-and-language-processing/",
"content": "Data Science Data Science Projects Data Analysis..."
}
]
}
```
#### Python library
```python
import ollama
response = ollama.web_search("What is Ollama?")
print(response)
```
**Example output**
```python
results = [
{
"title": "Ollama",
"url": "https://ollama.com/",
"content": "Cloud models are now available in Ollama..."
},
{
"title": "What is Ollama? Features, Pricing, and Use Cases - Walturn",
"url": "https://www.walturn.com/insights/what-is-ollama-features-pricing-and-use-cases",
"content": "Our services..."
},
{
"title": "Complete Ollama Guide: Installation, Usage & Code Examples",
"url": "https://collabnix.com/complete-ollama-guide-installation-usage-code-examples",
"content": "Join our Discord Server..."
}
]
```
More Ollama [Python example](https://github.com/ollama/ollama-python/blob/main/examples/web-search.py)
#### JavaScript Library
```tsx
import { Ollama } from "ollama";
const client = new Ollama();
const results = await client.webSearch({ query: "what is ollama?" });
console.log(JSON.stringify(results, null, 2));
```
**Example output**
```json
{
"results": [
{
"title": "Ollama",
"url": "https://ollama.com/",
"content": "Cloud models are now available..."
},
{
"title": "What is Ollama? Introduction to the AI model management tool",
"url": "https://www.hostinger.com/tutorials/what-is-ollama",
"content": "Ollama is an open-source tool..."
},
{
"title": "Ollama Explained: Transforming AI Accessibility and Language Processing",
"url": "https://www.geeksforgeeks.org/artificial-intelligence/ollama-explained-transforming-ai-accessibility-and-language-processing/",
"content": "Ollama is a groundbreaking..."
}
]
}
```
More Ollama [JavaScript example](https://github.com/ollama/ollama-js/blob/main/examples/websearch/websearch-tools.ts)
## Web fetch API
Fetches a single web page by URL and returns its content.
### Request
`POST https://ollama.com/api/web_fetch`
- `url` (string, required): the URL to fetch
### Response
Returns an object containing:
- `title` (string): the title of the web page
- `content` (string): the main content of the web page
- `links` (array): array of links found on the page
### Examples
#### cURL Request
```python
curl --request POST \
--url https://ollama.com/api/web_fetch \
--header "Authorization: Bearer $OLLAMA_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
"url": "ollama.com"
}'
```
**Response**
```json
{
"title": "Ollama",
"content": "[Cloud models](https://ollama.com/blog/cloud-models) are now available in Ollama...",
"links": [
"http://ollama.com/",
"http://ollama.com/models",
"https://github.com/ollama/ollama"
]
```
#### Python SDK
```python
from ollama import web_fetch
result = web_fetch('https://ollama.com')
print(result)
```
**Result**
```python
WebFetchResponse(
title='Ollama',
content='[Cloud models](https://ollama.com/blog/cloud-models) are now available in Ollama\n\n**Chat & build
with open models**\n\n[Download](https://ollama.com/download) [Explore
models](https://ollama.com/models)\n\nAvailable for macOS, Windows, and Linux',
links=['https://ollama.com/', 'https://ollama.com/models', 'https://github.com/ollama/ollama']
)
```
#### JavaScript SDK
```tsx
import { Ollama } from "ollama";
const client = new Ollama();
const fetchResult = await client.webFetch({ url: "https://ollama.com" });
console.log(JSON.stringify(fetchResult, null, 2));
```
**Result**
```json
{
"title": "Ollama",
"content": "[Cloud models](https://ollama.com/blog/cloud-models) are now available in Ollama...",
"links": [
"https://ollama.com/",
"https://ollama.com/models",
"https://github.com/ollama/ollama"
]
}
```
## Building a search agent
Use Ollama’s web search API as a tool to build a mini search agent.
This example uses Alibaba’s Qwen 3 model with 4B parameters.
```bash
ollama pull qwen3:4b
```
```python
from ollama import chat, web_fetch, web_search
available_tools = {'web_search': web_search, 'web_fetch': web_fetch}
messages = [{'role': 'user', 'content': "what is ollama's new engine"}]
while True:
response = chat(
model='qwen3:4b',
messages=messages,
tools=[web_search, web_fetch],
think=True
)
if response.message.thinking:
print('Thinking: ', response.message.thinking)
if response.message.content:
print('Content: ', response.message.content)
messages.append(response.message)
if response.message.tool_calls:
print('Tool calls: ', response.message.tool_calls)
for tool_call in response.message.tool_calls:
function_to_call = available_tools.get(tool_call.function.name)
if function_to_call:
args = tool_call.function.arguments
result = function_to_call(**args)
print('Result: ', str(result)[:200]+'...')
# Result is truncated for limited context lengths
messages.append({'role': 'tool', 'content': str(result)[:2000 * 4], 'tool_name': tool_call.function.name})
else:
messages.append({'role': 'tool', 'content': f'Tool {tool_call.function.name} not found', 'tool_name': tool_call.function.name})
else:
break
```
**Result**
```
Thinking: Okay, the user is asking about Ollama's new engine. I need to figure out what they're referring to. Ollama is a company that develops large language models, so maybe they've released a new model or an updated version of their existing engine....
Tool calls: [ToolCall(function=Function(name='web_search', arguments={'max_results': 3, 'query': 'Ollama new engine'}))]
Result: results=[WebSearchResult(content='# New model scheduling\n\n## September 23, 2025\n\nOllama now includes a significantly improved model scheduling system. Ahead of running a model, Ollama’s new engine
Thinking: Okay, the user asked about Ollama's new engine. Let me look at the search results.
First result is from September 23, 2025, talking about new model scheduling. It mentions improved memory management, reduced crashes, better GPU utilization, and multi-GPU performance. Examples show speed improvements and accurate memory reporting. Supported models include gemma3, llama4, qwen3, etc...
Content: Ollama has introduced two key updates to its engine, both released in 2025:
1. **Enhanced Model Scheduling (September 23, 2025)**
- **Precision Memory Management**: Exact memory allocation reduces out-of-memory crashes and optimizes GPU utilization.
- **Performance Gains**: Examples show significant speed improvements (e.g., 85.54 tokens/s vs 52.02 tokens/s) and full GPU layer utilization.
- **Multi-GPU Support**: Improved efficiency across multiple GPUs, with accurate memory reporting via tools like `nvidia-smi`.
- **Supported Models**: Includes `gemma3`, `llama4`, `qwen3`, `mistral-small3.2`, and more.
2. **Multimodal Engine (May 15, 2025)**
- **Vision Support**: First-class support for vision models, including `llama4:scout` (109B parameters), `gemma3`, `qwen2.5vl`, and `mistral-small3.1`.
- **Multimodal Tasks**: Examples include identifying animals in multiple images, answering location-based questions from videos, and document scanning.
These updates highlight Ollama's focus on efficiency, performance, and expanded capabilities for both text and vision tasks.
```
### Context length and agents
Web search results can return thousands of tokens. It is recommended to increase the context length of the model to at least ~32000 tokens. Search agents work best with full context length. [Ollama's cloud models](https://docs.ollama.com/cloud) run at the full context length.
## MCP Server
You can enable web search in any MCP client through the [Python MCP server](https://github.com/ollama/ollama-python/blob/main/examples/web-search-mcp.py).
### Cline
Ollama's web search can be integrated with Cline easily using the MCP server configuration.
`Manage MCP Servers` > `Configure MCP Servers` > Add the following configuration:
```json
{
"mcpServers": {
"web_search_and_fetch": {
"type": "stdio",
"command": "uv",
"args": ["run", "path/to/web-search-mcp.py"],
"env": { "OLLAMA_API_KEY": "your_api_key_here" }
}
}
}
```
![Cline MCP Configuration](/images/cline-mcp.png)
### Codex
Ollama works well with OpenAI's Codex tool.
Add the following configuration to `~/.codex/config.toml`
```python
[mcp_servers.web_search]
command = "uv"
args = ["run", "path/to/web-search-mcp.py"]
env = { "OLLAMA_API_KEY" = "your_api_key_here" }
```
![Codex MCP Configuration](/images/codex-mcp.png)
### Goose
Ollama can integrate with Goose via its MCP feature.
![Goose MCP Configuration 1](/images/goose-mcp-1.png)
![Goose MCP Configuration 2](/images/goose-mcp-2.png)
### Other integrations
Ollama can be integrated into most of the tools available either through direct integration of Ollama's API, Python / JavaScript libraries, OpenAI compatible API, and MCP server integration.
docs/cli.mdx deleted 100644 → 0
View file @ 934dd9e1
---
title: CLI Reference
---
### Run a model
```
ollama run gemma3
```
#### Multiline input
For multiline input, you can wrap text with `"""`:
```
>>> """Hello,
... world!
... """
I'm a basic program that prints the famous "Hello, world!" message to the console.
```
#### Multimodal models
```
ollama run gemma3 "What's in this image? /Users/jmorgan/Desktop/smile.png"
```
### Download a model
```
ollama pull gemma3
```
### Remove a model
```
ollama rm gemma3
```
### List models
```
ollama ls
```
### Sign in to Ollama
```
ollama signin
```
### Sign out of Ollama
```
ollama signout
```
### Create a customized model
First, create a `Modelfile`
```
FROM gemma3
SYSTEM """You are a happy cat."""
```
Then run `ollama create`:
```
ollama create -f Modelfile
```
### List running models
```
ollama ps
```
### Stop a running model
```
ollama stop gemma3
```
### Start Ollama
```
ollama serve
```
To view a list of environment variables that can be set run `ollama serve --help`
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---
title: Cloud
sidebarTitle: Cloud
---
<Info>Ollama's cloud is currently in preview.</Info>
## Cloud Models
Ollama's cloud models are a new kind of model in Ollama that can run without a powerful GPU. Instead, cloud models are automatically offloaded to Ollama's cloud service while offering the same capabilities as local models, making it possible to keep using your local tools while running larger models that wouldn't fit on a personal computer.
Ollama currently supports the following cloud models, with more coming soon:
- `deepseek-v3.1:671b-cloud`
- `gpt-oss:20b-cloud`
- `gpt-oss:120b-cloud`
- `kimi-k2:1t-cloud`
- `qwen3-coder:480b-cloud`
- `glm-4.6:cloud`
### Running Cloud models
Ollama's cloud models require an account on [ollama.com](https://ollama.com). To sign in or create an account, run:
```
ollama signin
```
<Tabs>
<Tab title="CLI">
To run a cloud model, open the terminal and run:
```
ollama run gpt-oss:120b-cloud
```
</Tab>
<Tab title="Python">
First, pull a cloud model so it can be accessed:
```
ollama pull gpt-oss:120b-cloud
```
Next, install [Ollama's Python library](https://github.com/ollama/ollama-python):
```
pip install ollama
```
Next, create and run a simple Python script:
```python
from ollama import Client
client = Client()
messages = [
{
'role': 'user',
'content': 'Why is the sky blue?',
},
]
for part in client.chat('gpt-oss:120b-cloud', messages=messages, stream=True):
print(part['message']['content'], end='', flush=True)
```
</Tab>
<Tab title="JavaScript">
First, pull a cloud model so it can be accessed:
```
ollama pull gpt-oss:120b-cloud
```
Next, install [Ollama's JavaScript library](https://github.com/ollama/ollama-js):
```
npm i ollama
```
Then use the library to run a cloud model:
```typescript
import { Ollama } from "ollama";
const ollama = new Ollama();
const response = await ollama.chat({
model: "gpt-oss:120b-cloud",
messages: [{ role: "user", content: "Explain quantum computing" }],
stream: true,
});
for await (const part of response) {
process.stdout.write(part.message.content);
}
```
</Tab>
<Tab title="cURL">
First, pull a cloud model so it can be accessed:
```
ollama pull gpt-oss:120b-cloud
```
Run the following cURL command to run the command via Ollama's API:
```
curl http://localhost:11434/api/chat -d '{
"model": "gpt-oss:120b-cloud",
"messages": [{
"role": "user",
"content": "Why is the sky blue?"
}],
"stream": false
}'
```
</Tab>
</Tabs>
## Cloud API access
Cloud models can also be accessed directly on ollama.com's API. In this mode, ollama.com acts as a remote Ollama host.
### Authentication
For direct access to ollama.com's API, first create an [API key](https://ollama.com/settings/keys).
Then, set the `OLLAMA_API_KEY` environment variable to your API key.
```
export OLLAMA_API_KEY=your_api_key
```
### Listing models
For models available directly via Ollama's API, models can be listed via:
```
curl https://ollama.com/api/tags
```
### Generating a response
<Tabs>
<Tab title="Python">
First, install [Ollama's Python library](https://github.com/ollama/ollama-python)
```
pip install ollama
```
Then make a request
```python
import os
from ollama import Client
client = Client(
host="https://ollama.com",
headers={'Authorization': 'Bearer ' + os.environ.get('OLLAMA_API_KEY')}
)
messages = [
{
'role': 'user',
'content': 'Why is the sky blue?',
},
]
for part in client.chat('gpt-oss:120b', messages=messages, stream=True):
print(part['message']['content'], end='', flush=True)
```
</Tab>
<Tab title="JavaScript">
First, install [Ollama's JavaScript library](https://github.com/ollama/ollama-js):
```
npm i ollama
```
Next, make a request to the model:
```typescript
import { Ollama } from "ollama";
const ollama = new Ollama({
host: "https://ollama.com",
headers: {
Authorization: "Bearer " + process.env.OLLAMA_API_KEY,
},
});
const response = await ollama.chat({
model: "gpt-oss:120b",
messages: [{ role: "user", content: "Explain quantum computing" }],
stream: true,
});
for await (const part of response) {
process.stdout.write(part.message.content);
}
```
</Tab>
<Tab title="cURL">
Generate a response via Ollama's chat API:
```
curl https://ollama.com/api/chat \
-H "Authorization: Bearer $OLLAMA_API_KEY" \
-d '{
"model": "gpt-oss:120b",
"messages": [{
"role": "user",
"content": "Why is the sky blue?"
}],
"stream": false
}'
```
</Tab>
</Tabs>
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---
title: Context length
---
Context length is the maximum number of tokens that the model has access to in memory.
<Note>
The default context length in Ollama is 4096 tokens.
</Note>
Tasks which require large context like web search, agents, and coding tools should be set to at least 32000 tokens.
## Setting context length
Setting a larger context length will increase the amount of memory required to run a model. Ensure you have enough VRAM available to increase the context length.
Cloud models are set to their maximum context length by default.
### App
Change the slider in the Ollama app under settings to your desired context length.
![Context length in Ollama app](./images/ollama-settings.png)
### CLI
If editing the context length for Ollama is not possible, the context length can also be updated when serving Ollama.
```
OLLAMA_CONTEXT_LENGTH=32000 ollama serve
```
### Check allocated context length and model offloading
For best performance, use the maximum context length for a model, and avoid offloading the model to CPU. Verify the split under `PROCESSOR` using `ollama ps`.
```
ollama ps
```
```
NAME ID SIZE PROCESSOR CONTEXT UNTIL
gemma3:latest a2af6cc3eb7f 6.6 GB 100% GPU 65536 2 minutes from now
```
docs/docker.mdx deleted 100644 → 0
View file @ 934dd9e1
## CPU only
```shell
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
```
## Nvidia GPU
Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation).
### Install with Apt
1. Configure the repository
```shell
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
| sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
curl -fsSL https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
| sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \
| sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
```
2. Install the NVIDIA Container Toolkit packages
```shell
sudo apt-get install -y nvidia-container-toolkit
```
### Install with Yum or Dnf
1. Configure the repository
```shell
curl -fsSL https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo \
| sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo
```
2. Install the NVIDIA Container Toolkit packages
```shell
sudo yum install -y nvidia-container-toolkit
```
### Configure Docker to use Nvidia driver
```shell
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
```
### Start the container
```shell
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
```
<Note>
If you're running on an NVIDIA JetPack system, Ollama can't automatically discover the correct JetPack version.
Pass the environment variable `JETSON_JETPACK=5` or `JETSON_JETPACK=6` to the container to select version 5 or 6.
</Note>
## AMD GPU
To run Ollama using Docker with AMD GPUs, use the `rocm` tag and the following command:
```shell
docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama:rocm
```
## Run model locally
Now you can run a model:
```shell
docker exec -it ollama ollama run llama3.2
```
## Try different models
More models can be found on the [Ollama library](https://ollama.com/library).
docs/docs.json deleted 100644 → 0
View file @ 934dd9e1
{
"$schema": "https://mintlify.com/docs.json",
"name": "Ollama",
"colors": {
"primary": "#000",
"light": "#b5b5b5",
"dark": "#000"
},
"favicon": "/images/favicon.png",
"logo": {
"light": "/images/logo.png",
"dark": "/images/logo-dark.png",
"href": "https://ollama.com"
},
"theme": "maple",
"background": {
"color": {
"light": "#ffffff",
"dark": "#000000"
}
},
"fonts": {
"family": "system-ui",
"heading": {
"family": "system-ui"
},
"body": {
"family": "system-ui"
}
},
"styling": {
"codeblocks": "system"
},
"contextual": {
"options": ["copy"]
},
"navbar": {
"links": [
{
"label": "Sign in",
"href": "https://ollama.com/signin"
}
],
"primary": {
"type": "button",
"label": "Download",
"href": "https://ollama.com/download"
}
},
"api": {
"playground": {
"display": "simple"
},
"examples": {
"languages": ["curl"]
}
},
"redirects": [
{
"source": "/openai",
"destination": "/api/openai"
}
],
"navigation": {
"tabs": [
{
"tab": "Documentation",
"groups": [
{
"group": "Get started",
"pages": [
"index",
"quickstart",
"/cloud"
]
},
{
"group": "Capabilities",
"pages": [
"/capabilities/streaming",
"/capabilities/thinking",
"/capabilities/structured-outputs",
"/capabilities/vision",
"/capabilities/embeddings",
"/capabilities/tool-calling",
"/capabilities/web-search"
]
},
{
"group": "Integrations",
"pages": [
"/integrations/vscode",
"/integrations/jetbrains",
"/integrations/codex",
"/integrations/cline",
"/integrations/droid",
"/integrations/goose",
"/integrations/zed",
"/integrations/roo-code",
"/integrations/n8n",
"/integrations/xcode"
]
},
{
"group": "More information",
"pages": [
"/cli",
"/modelfile",
"/context-length",
"/linux",
"/docker",
"/faq",
"/gpu",
"/troubleshooting"
]
}
]
},
{
"tab": "API Reference",
"openapi": "openapi.yaml",
"groups": [
{
"group": "API Reference",
"pages": [
"/api/index",
"/api/authentication",
"/api/streaming",
"/api/usage",
"/api/errors",
"/api/openai-compatibility"
]
},
{
"group": "Endpoints",
"pages": [
"POST /api/generate",
"POST /api/chat",
"POST /api/embed",
"GET /api/tags",
"GET /api/ps",
"POST /api/show",
"POST /api/create",
"POST /api/copy",
"POST /api/pull",
"POST /api/push",
"DELETE /api/delete",
"GET /api/version"
]
}
]
}
]
}
}
docs/faq.mdx deleted 100644 → 0
View file @ 934dd9e1
---
title: FAQ
---
## How can I upgrade Ollama?
Ollama on macOS and Windows will automatically download updates. Click on the taskbar or menubar item and then click "Restart to update" to apply the update. Updates can also be installed by downloading the latest version [manually](https://ollama.com/download/).
On Linux, re-run the install script:
```shell
curl -fsSL https://ollama.com/install.sh | sh
```
## How can I view the logs?
Review the [Troubleshooting](./troubleshooting.md) docs for more about using logs.
## Is my GPU compatible with Ollama?
Please refer to the [GPU docs](./gpu.md).
## How can I specify the context window size?
By default, Ollama uses a context window size of 2048 tokens.
This can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context window to 8K, use:
```shell
OLLAMA_CONTEXT_LENGTH=8192 ollama serve
```
To change this when using `ollama run`, use `/set parameter`:
```shell
/set parameter num_ctx 4096
```
When using the API, specify the `num_ctx` parameter:
```shell
curl http://localhost:11434/api/generate -d '{
"model": "llama3.2",
"prompt": "Why is the sky blue?",
"options": {
"num_ctx": 4096
}
}'
```
## How can I tell if my model was loaded onto the GPU?
Use the `ollama ps` command to see what models are currently loaded into memory.
```shell
ollama ps
```
<Info>
**Output**: ``` NAME ID SIZE PROCESSOR UNTIL llama3:70b bcfb190ca3a7 42 GB
100% GPU 4 minutes from now ```
</Info>
The `Processor` column will show which memory the model was loaded in to:
- `100% GPU` means the model was loaded entirely into the GPU
- `100% CPU` means the model was loaded entirely in system memory
- `48%/52% CPU/GPU` means the model was loaded partially onto both the GPU and into system memory
## How do I configure Ollama server?
Ollama server can be configured with environment variables.
### Setting environment variables on Mac
If Ollama is run as a macOS application, environment variables should be set using `launchctl`:
1. For each environment variable, call `launchctl setenv`.
```bash
launchctl setenv OLLAMA_HOST "0.0.0.0:11434"
```
2. Restart Ollama application.
### Setting environment variables on Linux
If Ollama is run as a systemd service, environment variables should be set using `systemctl`:
1. Edit the systemd service by calling `systemctl edit ollama.service`. This will open an editor.
2. For each environment variable, add a line `Environment` under section `[Service]`:
```ini
[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
```
3. Save and exit.
4. Reload `systemd` and restart Ollama:
```shell
systemctl daemon-reload
systemctl restart ollama
```
### Setting environment variables on Windows
On Windows, Ollama inherits your user and system environment variables.
1. First Quit Ollama by clicking on it in the task bar.
2. Start the Settings (Windows 11) or Control Panel (Windows 10) application and search for _environment variables_.
3. Click on _Edit environment variables for your account_.
4. Edit or create a new variable for your user account for `OLLAMA_HOST`, `OLLAMA_MODELS`, etc.
5. Click OK/Apply to save.
6. Start the Ollama application from the Windows Start menu.
## How do I use Ollama behind a proxy?
Ollama pulls models from the Internet and may require a proxy server to access the models. Use `HTTPS_PROXY` to redirect outbound requests through the proxy. Ensure the proxy certificate is installed as a system certificate. Refer to the section above for how to use environment variables on your platform.
<Note>
Avoid setting `HTTP_PROXY`. Ollama does not use HTTP for model pulls, only
HTTPS. Setting `HTTP_PROXY` may interrupt client connections to the server.
</Note>
### How do I use Ollama behind a proxy in Docker?
The Ollama Docker container image can be configured to use a proxy by passing `-e HTTPS_PROXY=https://proxy.example.com` when starting the container.
Alternatively, the Docker daemon can be configured to use a proxy. Instructions are available for Docker Desktop on [macOS](https://docs.docker.com/desktop/settings/mac/#proxies), [Windows](https://docs.docker.com/desktop/settings/windows/#proxies), and [Linux](https://docs.docker.com/desktop/settings/linux/#proxies), and Docker [daemon with systemd](https://docs.docker.com/config/daemon/systemd/#httphttps-proxy).
Ensure the certificate is installed as a system certificate when using HTTPS. This may require a new Docker image when using a self-signed certificate.
```dockerfile
FROM ollama/ollama
COPY my-ca.pem /usr/local/share/ca-certificates/my-ca.crt
RUN update-ca-certificates
```
Build and run this image:
```shell
docker build -t ollama-with-ca .
docker run -d -e HTTPS_PROXY=https://my.proxy.example.com -p 11434:11434 ollama-with-ca
```
## Does Ollama send my prompts and answers back to ollama.com?
No. Ollama runs locally, and conversation data does not leave your machine.
## How can I expose Ollama on my network?
Ollama binds 127.0.0.1 port 11434 by default. Change the bind address with the `OLLAMA_HOST` environment variable.
Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
## How can I use Ollama with a proxy server?
Ollama runs an HTTP server and can be exposed using a proxy server such as Nginx. To do so, configure the proxy to forward requests and optionally set required headers (if not exposing Ollama on the network). For example, with Nginx:
```nginx
server {
listen 80;
server_name example.com; # Replace with your domain or IP
location / {
proxy_pass http://localhost:11434;
proxy_set_header Host localhost:11434;
}
}
```
## How can I use Ollama with ngrok?
Ollama can be accessed using a range of tools for tunneling tools. For example with Ngrok:
```shell
ngrok http 11434 --host-header="localhost:11434"
```
## How can I use Ollama with Cloudflare Tunnel?
To use Ollama with Cloudflare Tunnel, use the `--url` and `--http-host-header` flags:
```shell
cloudflared tunnel --url http://localhost:11434 --http-host-header="localhost:11434"
```
## How can I allow additional web origins to access Ollama?
Ollama allows cross-origin requests from `127.0.0.1` and `0.0.0.0` by default. Additional origins can be configured with `OLLAMA_ORIGINS`.
For browser extensions, you'll need to explicitly allow the extension's origin pattern. Set `OLLAMA_ORIGINS` to include `chrome-extension://*`, `moz-extension://*`, and `safari-web-extension://*` if you wish to allow all browser extensions access, or specific extensions as needed:
```
# Allow all Chrome, Firefox, and Safari extensions
OLLAMA_ORIGINS=chrome-extension://*,moz-extension://*,safari-web-extension://* ollama serve
```
Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
## Where are models stored?
- macOS: `~/.ollama/models`
- Linux: `/usr/share/ollama/.ollama/models`
- Windows: `C:\Users\%username%\.ollama\models`
### How do I set them to a different location?
If a different directory needs to be used, set the environment variable `OLLAMA_MODELS` to the chosen directory.
<Note>
On Linux using the standard installer, the `ollama` user needs read and write access to the specified directory. To assign the directory to the `ollama` user run `sudo chown -R ollama:ollama <directory>`.
</Note>
Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
## How can I use Ollama in Visual Studio Code?
There is already a large collection of plugins available for VSCode as well as other editors that leverage Ollama. See the list of [extensions & plugins](https://github.com/ollama/ollama#extensions--plugins) at the bottom of the main repository readme.
## How do I use Ollama with GPU acceleration in Docker?
The Ollama Docker container can be configured with GPU acceleration in Linux or Windows (with WSL2). This requires the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit). See [ollama/ollama](https://hub.docker.com/r/ollama/ollama) for more details.
GPU acceleration is not available for Docker Desktop in macOS due to the lack of GPU passthrough and emulation.
## Why is networking slow in WSL2 on Windows 10?
This can impact both installing Ollama, as well as downloading models.
Open `Control Panel > Networking and Internet > View network status and tasks` and click on `Change adapter settings` on the left panel. Find the `vEthernel (WSL)` adapter, right click and select `Properties`.
Click on `Configure` and open the `Advanced` tab. Search through each of the properties until you find `Large Send Offload Version 2 (IPv4)` and `Large Send Offload Version 2 (IPv6)`. _Disable_ both of these
properties.
## How can I preload a model into Ollama to get faster response times?
If you are using the API you can preload a model by sending the Ollama server an empty request. This works with both the `/api/generate` and `/api/chat` API endpoints.
To preload the mistral model using the generate endpoint, use:
```shell
curl http://localhost:11434/api/generate -d '{"model": "mistral"}'
```
To use the chat completions endpoint, use:
```shell
curl http://localhost:11434/api/chat -d '{"model": "mistral"}'
```
To preload a model using the CLI, use the command:
```shell
ollama run llama3.2 ""
```
## How do I keep a model loaded in memory or make it unload immediately?
By default models are kept in memory for 5 minutes before being unloaded. This allows for quicker response times if you're making numerous requests to the LLM. If you want to immediately unload a model from memory, use the `ollama stop` command:
```shell
ollama stop llama3.2
```
If you're using the API, use the `keep_alive` parameter with the `/api/generate` and `/api/chat` endpoints to set the amount of time that a model stays in memory. The `keep_alive` parameter can be set to:
- a duration string (such as "10m" or "24h")
- a number in seconds (such as 3600)
- any negative number which will keep the model loaded in memory (e.g. -1 or "-1m")
- '0' which will unload the model immediately after generating a response
For example, to preload a model and leave it in memory use:
```shell
curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": -1}'
```
To unload the model and free up memory use:
```shell
curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": 0}'
```
Alternatively, you can change the amount of time all models are loaded into memory by setting the `OLLAMA_KEEP_ALIVE` environment variable when starting the Ollama server. The `OLLAMA_KEEP_ALIVE` variable uses the same parameter types as the `keep_alive` parameter types mentioned above. Refer to the section explaining [how to configure the Ollama server](#how-do-i-configure-ollama-server) to correctly set the environment variable.
The `keep_alive` API parameter with the `/api/generate` and `/api/chat` API endpoints will override the `OLLAMA_KEEP_ALIVE` setting.
## How do I manage the maximum number of requests the Ollama server can queue?
If too many requests are sent to the server, it will respond with a 503 error indicating the server is overloaded. You can adjust how many requests may be queue by setting `OLLAMA_MAX_QUEUE`.
## How does Ollama handle concurrent requests?
Ollama supports two levels of concurrent processing. If your system has sufficient available memory (system memory when using CPU inference, or VRAM for GPU inference) then multiple models can be loaded at the same time. For a given model, if there is sufficient available memory when the model is loaded, it is configured to allow parallel request processing.
If there is insufficient available memory to load a new model request while one or more models are already loaded, all new requests will be queued until the new model can be loaded. As prior models become idle, one or more will be unloaded to make room for the new model. Queued requests will be processed in order. When using GPU inference new models must be able to completely fit in VRAM to allow concurrent model loads.
Parallel request processing for a given model results in increasing the context size by the number of parallel requests. For example, a 2K context with 4 parallel requests will result in an 8K context and additional memory allocation.
The following server settings may be used to adjust how Ollama handles concurrent requests on most platforms:
- `OLLAMA_MAX_LOADED_MODELS` - The maximum number of models that can be loaded concurrently provided they fit in available memory. The default is 3 \* the number of GPUs or 3 for CPU inference.
- `OLLAMA_NUM_PARALLEL` - The maximum number of parallel requests each model will process at the same time. The default will auto-select either 4 or 1 based on available memory.
- `OLLAMA_MAX_QUEUE` - The maximum number of requests Ollama will queue when busy before rejecting additional requests. The default is 512
Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting. Once ROCm v6.2 is available, Windows Radeon will follow the defaults above. You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPUs VRAM.
## How does Ollama load models on multiple GPUs?
When loading a new model, Ollama evaluates the required VRAM for the model against what is currently available. If the model will entirely fit on any single GPU, Ollama will load the model on that GPU. This typically provides the best performance as it reduces the amount of data transferring across the PCI bus during inference. If the model does not fit entirely on one GPU, then it will be spread across all the available GPUs.
## How can I enable Flash Attention?
Flash Attention is a feature of most modern models that can significantly reduce memory usage as the context size grows. To enable Flash Attention, set the `OLLAMA_FLASH_ATTENTION` environment variable to `1` when starting the Ollama server.
## How can I set the quantization type for the K/V cache?
The K/V context cache can be quantized to significantly reduce memory usage when Flash Attention is enabled.
To use quantized K/V cache with Ollama you can set the following environment variable:
- `OLLAMA_KV_CACHE_TYPE` - The quantization type for the K/V cache. Default is `f16`.
<Note>
Currently this is a global option - meaning all models will run with the
specified quantization type.
</Note>
The currently available K/V cache quantization types are:
- `f16` - high precision and memory usage (default).
- `q8_0` - 8-bit quantization, uses approximately 1/2 the memory of `f16` with a very small loss in precision, this usually has no noticeable impact on the model's quality (recommended if not using f16).
- `q4_0` - 4-bit quantization, uses approximately 1/4 the memory of `f16` with a small-medium loss in precision that may be more noticeable at higher context sizes.
How much the cache quantization impacts the model's response quality will depend on the model and the task. Models that have a high GQA count (e.g. Qwen2) may see a larger impact on precision from quantization than models with a low GQA count.
You may need to experiment with different quantization types to find the best balance between memory usage and quality.
## Where can I find my Ollama Public Key?
Your **Ollama Public Key** is the public part of the key pair that lets your local Ollama instance talk to [ollama.com](https://ollama.com).
You'll need it to:
* Push models to Ollama
* Pull private models from Ollama to your machine
* Run models hosted in [Ollama Cloud](https://ollama.com/cloud)
### How to Add the Key
* **Sign-in via the Settings page** in the **Mac** and **Windows App**
* **Sign‑in via CLI**
```shell
ollama signin
```
* **Manually copy & paste** the key on the **Ollama Keys** page:
[https://ollama.com/settings/keys](https://ollama.com/settings/keys)
### Where the Ollama Public Key lives
| OS | Path to `id_ed25519.pub` |
| :- | :- |
| macOS | `~/.ollama/id_ed25519.pub` |
| Linux | `/usr/share/ollama/.ollama/id_ed25519.pub` |
| Windows | `C:\Users\<username>\.ollama\id_ed25519.pub` |
<Note>
Replace &lt;username&gt; with your actual Windows user name.
</Note>
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