Feature Guide Template

Use this when you introduce or deepen a single Mem0 capability (Graph Memory, Advanced Retrieval, etc.). Aim for crisp problem framing, a walkthrough of how the feature works, and practical configuration guidance with clear exits.

Reader Promise

• Understand the pain the feature solves and when to reach for it.
• See how to enable, configure, and observe the feature in action.
• Know the next conceptual deep dive and a hands-on example to try.

Start → Middle → End Pattern

1. Start: Why this feature exists

• Frontmatter stays outcome-driven: title, description, icon, optional badge (e.g., “Advanced”).
• Opening paragraph = two sentences: problem, then payoff. Keep energy high right from the start.
• Include an <Info> block titled “You’ll use this when…” with 3 bullets (user persona, workload, expected benefit).
• When reshaping legacy feature docs, carry over existing diagrams, tables, and gotchas. Organize them under these headings rather than replacing them unless the product has changed.
• If there’s a known caveat (pricing, performance), surface it early in a <Warning> so readers don’t get surprised later.
• Optional but encouraged: add a Mermaid diagram right after the intro to show how components connect; delete it if the story is obvious without visuals.
• Add a ## Configure access snippet (even if it’s “Confirm your Mem0 API key is already configured”) so contributors never forget to mention the baseline setup.

2. Middle: How it works

• Create three predictable sections:

1. Feature anatomy: Diagram or bullet list of moving parts. Use a table if you need to compare modes (platform vs OSS).
2. Configure it: Step-by-step enabling instructions with <CodeGroup> or JSON/YAML snippets. Follow each code block with a short explanation of why it matters.
3. See it in action: End-to-end example (often reusing operation snippets). Pair code with <Info icon="check"> for expected results and <Tip> for optimization hints.

• Insert <Note> blocks for cross-links (e.g., “Also available via REST endpoint /v1/...”).
• Keep the tone instructive but light. No long manifestos.

3. End: Evaluate and go deeper

• Add an ## Verify the feature is working section with bullets (metrics, logs, dashboards).
• Follow with ## Best practices or ## Tuning tips (3–4 bullets max).
• Close with the standard two-card CTA pair: left card = related concept or architecture page, right card = cookbook/application. Keep the comment reminder to double-check links.
• If providers differ meaningfully, summarize them in a final accordion (<AccordionGroup> with one <Accordion> per provider) so readers can expand what they need without scrolling walls of configuration.

Markdown Skeleton

---
title: Advanced Retrieval
description: Increase relevance with reranking, criteria filters, and context windows.
icon: "sparkles"
badge: "Advanced"
---

# Advanced Retrieval

Mem0’s advanced retrieval elevates search accuracy when basic keyword matches aren’t enough. Turn it on when you need precise context for high-stakes conversations.

<Info>
  **You’ll use this when…**
  - You need semantic ranking across long-running agents
  - Compliance requires tight control over returned memories
  - Personalization hinges on precise filters
</Info>

<Warning>
  Advanced retrieval currently applies to managed Platform projects only. Self-hosted users should rely on the OSS reranker configuration.
</Warning>

{/* Optional: remove if no diagram is needed */}
```mermaid
%% Diagram the moving parts (delete when you fill this out)
graph TD
A[Input] --> B[Feature]
B --> C[Output]

Feature anatomy

• Outline the moving parts (retriever, reranker, filters).
• Add a table comparing default vs advanced behavior.

Configure it

Python

client = Client(...)
client.memories.search(criteria={...})

TypeScript

const memories = await mem0.memories.search({ criteria: { ... } });

Explain which knobs matter (e.g., rerank_top_k, criteria, filters).

OSS users can mirror this by enabling the reranker in config.yaml. Link to the integration guide if relevant.

See it in action

Walk through a real request/response. Include sample payloads and highlight notable fields.

Expect the top memory to match the user persona you set earlier. If not, revisit your filters.

Provider setup

[Provider name]

Outline configuration or link to provider docs here.

Verify the feature is working

• Watch the dashboard analytics for retrieval latency changes.
• Check logs for reranker_applied: true.

Best practices

• Keep criteria minimal. Overfiltering hurts recall.
• Pair with Memory Filters for hybrid scoring.

[**Dive Into Memory Scoring**

Understand how Mem0 ranks memories under the hood.](https://docs.mem0.ai/core-concepts/memory-types)

[**Build a Research Copilot**

See advanced retrieval driving a full knowledge assistant.](https://docs.mem0.ai/cookbooks/operations/deep-research)

Stick to this outline. Keep the “why” up front, the “how” in the middle, and the “where to go next” crystal clear at the end.