mirror of
https://github.com/labring/FastGPT.git
synced 2026-04-27 02:08:10 +08:00
Archer
87b0bca30c
* cloud doc * doc refactor * doc move * seo * remove doc * yml * doc * fix: tsconfig * fix: tsconfig
181 lines
7.3 KiB
Plaintext
181 lines
7.3 KiB
Plaintext
---
|
|
title: Integrating Local Models with Ollama
|
|
description: Deploy your own models using Ollama
|
|
---
|
|
|
|
[Ollama](https://ollama.com/) is an open-source AI model deployment tool focused on simplifying the deployment and usage of large language models. It supports one-click download and running of various LLMs.
|
|
|
|
## Installing Ollama
|
|
|
|
Ollama supports multiple installation methods, but Docker is recommended. If you install Ollama directly on your host machine, you'll need to figure out how to let the FastGPT Docker container access Ollama on the host, which can be tricky.
|
|
|
|
### Docker Installation (Recommended)
|
|
|
|
Use Ollama's official Docker image for one-click installation and startup (make sure Docker is installed on your machine):
|
|
|
|
```bash
|
|
docker pull ollama/ollama
|
|
docker run --rm -d --name ollama -p 11434:11434 ollama/ollama
|
|
```
|
|
|
|
If your FastGPT is deployed in Docker, make sure the Ollama container is on the same network as FastGPT. Otherwise, FastGPT may not be able to access it:
|
|
|
|
```bash
|
|
docker run --rm -d --name ollama --network (your FastGPT container network) -p 11434:11434 ollama/ollama
|
|
```
|
|
|
|
### Host Installation
|
|
|
|
If you prefer not to use Docker, you can install directly on the host machine.
|
|
|
|
#### MacOS
|
|
|
|
If you're on macOS with Homebrew installed:
|
|
|
|
```bash
|
|
brew install ollama
|
|
ollama serve # Start the service after installation
|
|
```
|
|
|
|
#### Linux
|
|
|
|
On Linux, you can use a package manager. For Ubuntu:
|
|
|
|
```bash
|
|
curl https://ollama.com/install.sh | sh # Downloads and runs the official install script
|
|
ollama serve # Start the service after installation
|
|
```
|
|
|
|
#### Windows
|
|
|
|
On Windows, download the installer from the Ollama official website. Run the installer and follow the wizard. After installation, start the service in Command Prompt or PowerShell:
|
|
|
|
```bash
|
|
ollama serve # After installation, visit http://localhost:11434 in your browser to verify Ollama is running
|
|
```
|
|
|
|
#### Additional Notes
|
|
|
|
If you installed Ollama as a host application (not via Docker), make sure Ollama listens on 0.0.0.0.
|
|
|
|
##### 1. Linux
|
|
|
|
If Ollama runs as a systemd service, edit the service file with `sudo systemctl edit ollama.service`. Add `Environment="OLLAMA_HOST=0.0.0.0"` under the [Service] section. Save and exit, then run `sudo systemctl daemon-reload` and `sudo systemctl restart ollama` to apply.
|
|
|
|
##### 2. MacOS
|
|
|
|
Open a terminal and run `launchctl setenv ollama_host "0.0.0.0"`, then restart the Ollama application.
|
|
|
|
##### 3. Windows
|
|
|
|
Open "Edit system environment variables" from the Start menu or search bar. In "System Properties", click "Environment Variables". Under "System variables", click "New" and create a variable named OLLAMA_HOST with value 0.0.0.0. Click "OK" to save, then restart Ollama from the Start menu.
|
|
|
|
### Pull Model Images
|
|
|
|
After installing Ollama, no models are available locally -- you need to pull them:
|
|
|
|
```bash
|
|
# For Docker deployment, enter the container first: docker exec -it [Ollama container name] /bin/sh
|
|
ollama pull [model name]
|
|
```
|
|
|
|
|
|
|
|
### Test Communication
|
|
|
|
After installation, verify connectivity by entering the FastGPT container and trying to reach Ollama:
|
|
|
|
```bash
|
|
docker exec -it [FastGPT container name] /bin/sh
|
|
curl http://XXX.XXX.XXX.XXX:11434 # Container: "http://[container name]:[port]", Host: "http://[host IP]:[port]" (host IP cannot be localhost)
|
|
```
|
|
|
|
If you see that the Ollama service is running, communication is working.
|
|
|
|
## Integrating Ollama with FastGPT
|
|
|
|
### 1. Check Available Models
|
|
|
|
First, check which models Ollama has:
|
|
|
|
```bash
|
|
# For Docker-deployed Ollama: docker exec -it [Ollama container name] /bin/sh
|
|
ollama ls
|
|
```
|
|
|
|
|
|
|
|
### 2. AI Proxy Integration
|
|
|
|
If you're using FastGPT's default configuration from [here](/docs/self-host/deploy/docker), AI Proxy is enabled by default.
|
|
|
|
|
|
|
|
Make sure your FastGPT can access the Ollama container. If not, refer to the [installation section](#installing-ollama) above -- check whether the host isn't listening on 0.0.0.0 or the containers aren't on the same network.
|
|
|
|
|
|
|
|
In FastGPT, go to Account -> Model Providers -> Model Configuration -> Add Model. Make sure the model ID matches the model name in OneAPI. See details [here](/docs/self-host/config/model/intro).
|
|
|
|
|
|
|
|
|
|
|
|
Run FastGPT, then go to Account -> Model Providers -> Model Channels -> Add Channel. Select Ollama as the channel type, add your pulled model, and fill in the proxy address. For container-deployed Ollama, the address is http://address:port. Note: container deployment uses "http://[container name]:[port]", host installation uses "http://[host IP]:[port]" (host IP cannot be localhost).
|
|
|
|
|
|
|
|
Create an app in the workspace and select the model you added. The model name shown is the alias you set. Note: the same model cannot be added multiple times -- the system uses the alias from the most recent addition.
|
|
|
|
|
|
|
|
### 3. OneAPI Integration
|
|
|
|
If you want to use OneAPI, pull the OneAPI image and run it on the same network as FastGPT:
|
|
|
|
```bash
|
|
# Pull the OneAPI image
|
|
docker pull intel/oneapi-hpckit
|
|
|
|
# Run the container on the FastGPT network
|
|
docker run -it --network [FastGPT network] --name container_name intel/oneapi-hpckit /bin/bash
|
|
```
|
|
|
|
In the OneAPI page, add a new channel with type Ollama. Enter your Ollama model name (must match exactly), then fill in the Ollama proxy address below -- default is http://address:port, without /v1. Test the channel after adding. This example uses Docker-deployed Ollama; for host-installed Ollama, use http://[host IP]:[port].
|
|
|
|
|
|
|
|
After adding the channel, click Token -> Add Token, fill in the name, and configure as needed.
|
|
|
|
|
|
|
|
Edit the FastGPT docker-compose.yml file: comment out AI Proxy, set OPENAI_BASE_URL to your OneAPI address (default http://address:port/v1 -- /v1 is required), and set KEY to your OneAPI token.
|
|
|
|
|
|
|
|
Then [jump to section 5](#5-model-addition-and-usage) to add and use models.
|
|
|
|
### 4. Direct Integration
|
|
|
|
If you don't want to use AI Proxy or OneAPI, you can connect directly. Edit the FastGPT docker-compose.yml: comment out AI Proxy code, set OPENAI_BASE_URL to your Ollama address (default http://address:port/v1 -- /v1 is required), and set KEY to any value (Ollama has no authentication by default; if you've enabled it, use the correct key). Everything else is the same as the OneAPI approach -- just add your model in FastGPT. This example uses Docker-deployed Ollama; for host-installed Ollama, use http://[host IP]:[port].
|
|
|
|
|
|
|
|
After completing the setup, [click here](#5-model-addition-and-usage) to add and use models.
|
|
|
|
### 5. Model Addition and Usage
|
|
|
|
In FastGPT, go to Account -> Model Providers -> Model Configuration -> Add Model. Make sure the model ID matches the model name in OneAPI.
|
|
|
|
|
|
|
|
|
|
|
|
Create an app in the workspace and select the model you added. The model name shown is the alias you set. Note: the same model cannot be added multiple times -- the system uses the alias from the most recent addition.
|
|
|
|
|
|
|
|
### 6. Additional Notes
|
|
|
|
For the Ollama proxy addresses above: host-installed Ollama uses "http://[host IP]:[port]", container-deployed Ollama uses "http://[container name]:[port]".
|