Using Memobase with Ollama
Memobase supports any OpenAI-compatible LLM provider as its backend. This tutorial demonstrates how to use Ollama to run a local LLM for both the Memobase server and your chat application.
Setup
1. Configure Ollama
- Install Ollama on your local machine.
- Verify the installation by running
ollama -v
. - Pull a model to use. For this example, we’ll use
qwen2.5:7b
.
2. Configure Memobase
To use a local LLM provider with the Memobase server, you need to modify your config.yaml
file.
config.yaml
.Set the following fields to point to your local Ollama instance:
Note: Since the Memobase server runs in a Docker container, we use host.docker.internal
to allow it to access the Ollama server running on your local machine at port 11434
.
Code Breakdown
This example uses Memobase’s OpenAI Memory Patch for a clear demonstration.
Client Initialization
First, we set up the OpenAI client to point to our local Ollama server and then apply the Memobase memory patch.
After patching, your OpenAI client is now stateful. It will automatically manage memory for each user, recalling information from past conversations.
Chat Function
Next, we create a chat function that uses the patched client. The key is to pass a user_id
to trigger the memory functionality.
Testing the Memory
Now, you can test the difference between a stateless and a stateful conversation:
For the complete code, see the full example on GitHub.