mirror of
https://github.com/pocketpaw/pocketpaw.git
synced 2026-05-18 16:06:22 +00:00
4bb7313829
Consolidate documentation from the separate pocketpaw-web repo into the main pocketpaw repo. This keeps docs and code in sync so PRs can update both atomically. - Remove docs/ from .gitignore - Remove docs' own .git (was pocketpaw/pocketpaw-web) - Add .github/workflows/deploy-docs.yml (builds from docs/ subdirectory) - Track all 120+ MDX pages, config, landing page, and public assets The separate pocketpaw-web repo can now be archived. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
125 lines
3.7 KiB
Plaintext
125 lines
3.7 KiB
Plaintext
---
|
|
title: Update Memory Settings
|
|
description: "Update PocketPaw's memory backend configuration: switch between file store and Mem0, configure LLM and embedder providers, set vector store options, and toggle auto-learning."
|
|
api: POST /api/memory/settings
|
|
baseUrl: http://localhost:8000
|
|
layout: '@/layouts/APIEndpointLayout.astro'
|
|
auth: bearer
|
|
section: API Reference
|
|
ogType: article
|
|
keywords: ["update memory settings", "memory configuration", "backend switch"]
|
|
tags: ["api", "memory"]
|
|
---
|
|
|
|
## Overview
|
|
|
|
Updates the memory backend configuration. Changes take effect immediately — the memory manager is reloaded with the new settings.
|
|
|
|
## Request Body
|
|
|
|
<ParamTable type="body">
|
|
<Param name="memory_backend" type="string" enum={["file", "mem0"]}>
|
|
Memory backend to use. `file` is the default file-based store, `mem0` enables semantic memory with auto-learn.
|
|
</Param>
|
|
<Param name="mem0_llm_provider" type="string" enum={["ollama", "openai", "anthropic"]}>
|
|
LLM provider for mem0's extraction and summarization.
|
|
</Param>
|
|
<Param name="mem0_llm_model" type="string">
|
|
LLM model name (e.g., `llama3.2`, `gpt-4o-mini`).
|
|
</Param>
|
|
<Param name="mem0_embedder_provider" type="string" enum={["ollama", "openai"]}>
|
|
Embedding provider for semantic search.
|
|
</Param>
|
|
<Param name="mem0_embedder_model" type="string">
|
|
Embedding model name (e.g., `nomic-embed-text`, `text-embedding-3-small`).
|
|
</Param>
|
|
<Param name="mem0_vector_store" type="string" enum={["qdrant"]}>
|
|
Vector store backend.
|
|
</Param>
|
|
<Param name="mem0_ollama_base_url" type="string">
|
|
Ollama server URL (default: `http://localhost:11434`).
|
|
</Param>
|
|
<Param name="mem0_auto_learn" type="boolean">
|
|
Enable automatic memory extraction from conversations.
|
|
</Param>
|
|
</ParamTable>
|
|
|
|
## Response
|
|
|
|
<ResponseField name="status" type="string">`"ok"` on success</ResponseField>
|
|
|
|
<RequestExample>
|
|
<Tabs items={["cURL", "JavaScript", "Python"]}>
|
|
<Tab title="cURL">
|
|
```bash
|
|
curl -X POST "http://localhost:8000/api/memory/settings" \
|
|
-H "Authorization: Bearer <token>" \
|
|
-H "Content-Type: application/json" \
|
|
-d '{
|
|
"memory_backend": "mem0",
|
|
"mem0_llm_provider": "ollama",
|
|
"mem0_llm_model": "llama3.2",
|
|
"mem0_embedder_provider": "ollama",
|
|
"mem0_embedder_model": "nomic-embed-text",
|
|
"mem0_vector_store": "qdrant",
|
|
"mem0_auto_learn": true
|
|
}'
|
|
```
|
|
</Tab>
|
|
<Tab title="JavaScript">
|
|
```javascript
|
|
const response = await fetch("http://localhost:8000/api/memory/settings", {
|
|
method: "POST",
|
|
headers: {
|
|
"Authorization": "Bearer <token>",
|
|
"Content-Type": "application/json"
|
|
},
|
|
body: JSON.stringify({
|
|
memory_backend: "mem0",
|
|
mem0_llm_provider: "ollama",
|
|
mem0_llm_model: "llama3.2",
|
|
mem0_embedder_provider: "ollama",
|
|
mem0_embedder_model: "nomic-embed-text",
|
|
mem0_vector_store: "qdrant",
|
|
mem0_auto_learn: true
|
|
})
|
|
});
|
|
const data = await response.json();
|
|
console.log(data);
|
|
```
|
|
</Tab>
|
|
<Tab title="Python">
|
|
```python
|
|
import requests
|
|
|
|
response = requests.post(
|
|
"http://localhost:8000/api/memory/settings",
|
|
headers={"Authorization": "Bearer <token>"},
|
|
json={
|
|
"memory_backend": "mem0",
|
|
"mem0_llm_provider": "ollama",
|
|
"mem0_llm_model": "llama3.2",
|
|
"mem0_embedder_provider": "ollama",
|
|
"mem0_embedder_model": "nomic-embed-text",
|
|
"mem0_vector_store": "qdrant",
|
|
"mem0_auto_learn": True
|
|
}
|
|
)
|
|
print(response.json())
|
|
```
|
|
</Tab>
|
|
</Tabs>
|
|
</RequestExample>
|
|
|
|
<ResponseExample>
|
|
<Tabs items={["200"]}>
|
|
<Tab title="200">
|
|
```json
|
|
{
|
|
"status": "ok"
|
|
}
|
|
```
|
|
</Tab>
|
|
</Tabs>
|
|
</ResponseExample>
|