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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>
78 lines
2.6 KiB
Plaintext
78 lines
2.6 KiB
Plaintext
---
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title: Get Memory Settings
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description: "Retrieve the current memory backend configuration including the active provider (file store or Mem0), LLM and embedder settings, vector store configuration, and auto-learn status."
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api: GET /api/memory/settings
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baseUrl: http://localhost:8000
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layout: '@/layouts/APIEndpointLayout.astro'
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auth: bearer
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section: API Reference
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ogType: article
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keywords: ["memory settings", "backend config", "mem0 settings"]
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tags: ["api", "memory"]
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---
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## Overview
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Returns the current memory backend configuration, including the active backend, mem0 provider settings, and auto-learn status.
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## Response
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<ResponseField name="memory_backend" type="string">Active memory backend (`file` or `mem0`)</ResponseField>
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<ResponseField name="mem0_llm_provider" type="string">LLM provider for mem0 (`ollama`, `openai`, `anthropic`)</ResponseField>
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<ResponseField name="mem0_llm_model" type="string">LLM model name (e.g., `llama3.2`)</ResponseField>
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<ResponseField name="mem0_embedder_provider" type="string">Embedding provider (`ollama`, `openai`)</ResponseField>
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<ResponseField name="mem0_embedder_model" type="string">Embedding model name (e.g., `nomic-embed-text`)</ResponseField>
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<ResponseField name="mem0_vector_store" type="string">Vector store backend (`qdrant`)</ResponseField>
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<ResponseField name="mem0_ollama_base_url" type="string">Ollama server URL</ResponseField>
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<ResponseField name="mem0_auto_learn" type="boolean">Whether auto-learn is enabled</ResponseField>
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<RequestExample>
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<Tabs items={["cURL", "JavaScript", "Python"]}>
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<Tab title="cURL">
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```bash
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curl -X GET "http://localhost:8000/api/memory/settings" \
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-H "Authorization: Bearer <token>"
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```
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</Tab>
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<Tab title="JavaScript">
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```javascript
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const response = await fetch("http://localhost:8000/api/memory/settings", {
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headers: { "Authorization": "Bearer <token>" }
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});
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const data = await response.json();
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console.log(data);
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```
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</Tab>
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<Tab title="Python">
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```python
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import requests
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response = requests.get(
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"http://localhost:8000/api/memory/settings",
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headers={"Authorization": "Bearer <token>"}
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)
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print(response.json())
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```
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</Tab>
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</Tabs>
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</RequestExample>
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<ResponseExample>
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<Tabs items={["200"]}>
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<Tab title="200">
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```json
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{
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"memory_backend": "mem0",
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"mem0_llm_provider": "ollama",
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"mem0_llm_model": "llama3.2",
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"mem0_embedder_provider": "ollama",
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"mem0_embedder_model": "nomic-embed-text",
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"mem0_vector_store": "qdrant",
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"mem0_ollama_base_url": "http://localhost:11434",
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"mem0_auto_learn": true
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}
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```
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</Tab>
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</Tabs>
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</ResponseExample>
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