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[docs] Template moulding in docs/platform and index improvement (#3663)

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This commit is contained in:
Parth Sharma
2025-10-26 02:35:18 +05:30
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commit 61faf71064
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docs/docs.json
+40 -12
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@@ -24,7 +24,9 @@
{
"group": "Start Here",
"icon": "home",
"pages": ["introduction"]
"pages": [
"introduction"
]
}
]
},
@@ -69,11 +71,12 @@
},
{
"group": "Advanced Features",
"icon": "sparkles",
"icon": "bolt",
"pages": [
"platform/features/graph-memory",
"platform/features/graph-threshold",
"platform/features/advanced-retrieval",
"platform/advanced-memory-operations",
"platform/features/criteria-retrieval",
"platform/features/contextual-add",
"platform/features/custom-instructions"
@@ -103,7 +106,9 @@
{
"group": "Support & Troubleshooting",
"icon": "life-buoy",
"pages": ["platform/faqs", "platform/advanced-memory-operations"]
"pages": [
"platform/faqs"
]
},
{
"group": "Migration Guide",
@@ -274,7 +279,10 @@
{
"group": "Community & Support",
"icon": "users",
"pages": ["contributing/development", "contributing/documentation"]
"pages": [
"contributing/development",
"contributing/documentation"
]
}
]
},
@@ -298,7 +306,9 @@
{
"group": "Overview",
"icon": "lightbulb",
"pages": ["examples"]
"pages": [
"examples"
]
},
{
"group": "Getting Started Examples",
@@ -355,7 +365,10 @@
{
"group": "Cloud & Infrastructure",
"icon": "cloud",
"pages": ["examples/aws_example", "examples/aws_neptune_analytics_hybrid_store"]
"pages": [
"examples/aws_example",
"examples/aws_neptune_analytics_hybrid_store"
]
}
]
},
@@ -365,7 +378,9 @@
{
"group": "Overview",
"icon": "plug",
"pages": ["integrations"]
"pages": [
"integrations"
]
},
{
"group": "Agent Frameworks",
@@ -395,7 +410,9 @@
{
"group": "Cloud & Infrastructure",
"icon": "cloud",
"pages": ["integrations/aws-bedrock"]
"pages": [
"integrations/aws-bedrock"
]
},
{
"group": "Developer Tools",
@@ -417,7 +434,10 @@
{
"group": "Getting Started",
"icon": "rocket",
"pages": ["api-reference", "api-reference/organizations-projects"]
"pages": [
"api-reference",
"api-reference/organizations-projects"
]
},
{
"group": "Core Memory Operations",
@@ -495,12 +515,16 @@
{
"group": "Changelog",
"icon": "rocket",
"pages": ["changelog"]
"pages": [
"changelog"
]
},
{
"group": "Legacy Docs",
"icon": "archive",
"pages": ["v0x/introduction"]
"pages": [
"v0x/introduction"
]
}
],
"icon": "clock"
@@ -522,7 +546,11 @@
{
"group": "Getting Started",
"icon": "rocket",
"pages": ["v0x/introduction", "v0x/quickstart", "v0x/faqs"]
"pages": [
"v0x/introduction",
"v0x/quickstart",
"v0x/faqs"
]
},
{
"group": "Core Concepts",
docs/open-source/configuration.mdx
+131 -294
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@@ -1,336 +1,173 @@
---
title: "Configuration"
description: "Configure Mem0 with custom LLMs, vector stores, embedders, and rerankers for production deployments"
title: "Configure the OSS Stack"
description: "Wire up Mem0 OSS with your preferred LLM, vector store, embedder, and reranker."
icon: "sliders"
iconType: "solid"
---
Mem0 is highly configurable, allowing you to customize every component of your memory system. Choose from **51+ supported providers** across LLMs, vector databases, embedders, and rerankers.
# Configure Mem0 OSS Components
## Quick Start
<Info>
**Prerequisites**
- Python 3.10+ with `pip` available
- Running vector database (e.g., Qdrant, Postgres + pgvector) or access credentials for a managed store
- API keys for your chosen LLM, embedder, and reranker providers
</Info>
The simplest setup uses OpenAI defaults:
<Tip>
Start from the <Link href="/open-source/python-quickstart">Python quickstart</Link> if you still need the base CLI and repository.
</Tip>
```python
import os
from mem0 import Memory
## Install dependencies
os.environ["OPENAI_API_KEY"] = "your-api-key"
m = Memory()
<Tabs>
<Tab title="Python">
<Steps>
<Step title="Install Mem0 OSS">
```bash
pip install mem0ai
```
</Step>
<Step title="Add provider SDKs (example: Qdrant + OpenAI)">
```bash
pip install qdrant-client openai
```
</Step>
</Steps>
</Tab>
<Tab title="Docker Compose">
<Steps>
<Step title="Clone the repo and copy the compose file">
```bash
git clone https://github.com/mem0ai/mem0.git
cd mem0/examples/docker-compose
```
</Step>
<Step title="Install dependencies for local overrides">
```bash
pip install -r requirements.txt
```
</Step>
</Steps>
</Tab>
</Tabs>
For production or custom setups, configure specific components:
## Define your configuration
<Tabs>
<Tab title="Python">
<Steps>
<Step title="Create a configuration dictionary">
```python
from mem0 import Memory
config = {
"vector_store": {
"provider": "qdrant",
"config": {
"host": "localhost",
"port": 6333
}
"config": {"host": "localhost", "port": 6333},
},
"llm": {
"provider": "openai",
"config": {
"model": "gpt-4.1-nano-2025-04-14",
"temperature": 0.1
}
}
}
m = Memory.from_config(config)
```
## Configuration Components
Mem0 has four configurable components. Click any to see all supported providers and detailed configuration options.
<CardGroup cols={2}>
<Card title="LLMs" icon="message-bot" href="/components/llms/overview">
**17 providers** including OpenAI, Anthropic, Ollama, Groq, and more
Configure the language model for memory extraction and processing
</Card>
<Card title="Vector Databases" icon="hard-drive" href="/components/vectordbs/overview">
**25+ databases** including Qdrant, Chroma, Pinecone, Weaviate, and more
Choose where to store and retrieve memory embeddings
</Card>
<Card title="Embedding Models" icon="cube" href="/components/embedders/overview">
**9 providers** including OpenAI, HuggingFace, Ollama, and more
Select the model to convert memories into vector embeddings
</Card>
<Card title="Rerankers" icon="ranking-star" href="/components/rerankers/overview">
**4 models** including Cohere, Zero Entropy, and LLM-based
Improve search relevance by re-scoring retrieved memories
</Card>
</CardGroup>
## Configuration Recipes
### Production Setup with Qdrant
For production deployments, use a dedicated vector store:
<Steps>
<Step title="Start Qdrant">
```bash
docker pull qdrant/qdrant
docker run -p 6333:6333 -p 6334:6334 \
-v $(pwd)/qdrant_storage:/qdrant/storage:z \
qdrant/qdrant
````
</Step>
<Step title="Configure Mem0">
```python
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key"
config = {
"vector_store": {
"provider": "qdrant",
"config": {
"host": "localhost",
"port": 6333,
}
}
}
m = Memory.from_config(config)
````
</Step>
</Steps>
### Fully Local Setup
Run Mem0 completely offline with Ollama (no external APIs):
<Card title="Local Setup with Ollama" icon="server" href="/examples/mem0-with-ollama">
Step-by-step guide to run Mem0 with local LLM and embeddings
</Card>
### Multi-Cloud Setup
Mix providers from different clouds:
```python
config = {
"llm": {
"provider": "azure_openai",
"config": {
"api_key": "azure-key",
"deployment_name": "gpt-4.1-nano-2025-04-14"
}
},
"vector_store": {
"provider": "pinecone",
"config": {
"api_key": "pinecone-key",
"index_name": "mem0"
}
"config": {"model": "gpt-4.1-mini", "temperature": 0.1},
},
"embedder": {
"provider": "vertexai",
"config": {
"model": "textembedding-gecko@003"
}
}
}
```
### Graph Memory Setup
Enable relationship tracking with Neo4j:
```python
config = {
"graph_store": {
"provider": "neo4j",
"config": {
"url": "neo4j+s://your-instance.databases.neo4j.io",
"username": "neo4j",
"password": "your-password"
}
}
}
m = Memory.from_config(config)
```
<Card title="Graph Memory Guide" icon="diagram-project" href="/open-source/features/graph-memory">
Learn how to use graph memory for relationship-based retrieval
</Card>
## Advanced Configuration
### Custom Prompts
Override default prompts for memory processing:
```python
config = {
"custom_fact_extraction_prompt": """
Extract key facts from the conversation.
Focus on: preferences, decisions, and context.
Output as a single sentence.
""",
"custom_update_memory_prompt": """
Update the existing memory with new information.
Preserve important context from the old memory.
"""
}
```
<CardGroup cols={2}>
<Card title="Custom Fact Extraction" icon="sparkles" href="/open-source/features/custom-fact-extraction-prompt">
Customize how memories are extracted from conversations
</Card>
<Card title="Custom Memory Updates" icon="pen" href="/open-source/features/custom-update-memory-prompt">
Control how existing memories are modified
</Card>
</CardGroup>
### Reranking for Better Search
Add reranking to improve search relevance:
```python
config = {
"rerank": {
"config": {"model": "textembedding-gecko@003"},
},
"reranker": {
"provider": "cohere",
"config": {
"model": "rerank-english-v3.0",
"top_k": 5
}
}
"config": {"model": "rerank-english-v3.0"},
},
}
memory = Memory.from_config(config)
```
</Step>
<Step title="Store secrets as environment variables">
```bash
export QDRANT_API_KEY="..."
export OPENAI_API_KEY="..."
export COHERE_API_KEY="..."
```
</Step>
</Steps>
</Tab>
<Tab title="config.yaml">
<Steps>
<Step title="Create a `config.yaml` file">
```yaml
vector_store:
provider: qdrant
config:
host: localhost
port: 6333
<Card title="Reranker-Enhanced Search" icon="arrow-up-arrow-down" href="/open-source/features/reranker-search">
Learn how reranking improves memory search accuracy
</Card>
llm:
provider: azure_openai
config:
api_key: ${AZURE_OPENAI_KEY}
deployment_name: gpt-4.1-mini
### History Database
Configure where operation history is stored:
embedder:
provider: ollama
config:
model: nomic-embed-text
reranker:
provider: zero_entropy
config:
api_key: ${ZERO_ENTROPY_KEY}
```
</Step>
<Step title="Load the config file at runtime">
```python
config = {
"history_db_path": "/custom/path/to/history.db"
}
```
from mem0 import Memory
## All Configuration Options
memory = Memory.from_config_file("config.yaml")
```
</Step>
</Steps>
</Tab>
</Tabs>
<Info icon="check">
Run `memory.add(["Remember my favorite cafe in Tokyo."], user_id="alex")` and then `memory.search("favorite cafe", user_id="alex")`. You should see the Qdrant collection populate and the reranker mark the memory as a top hit.
</Info>
## Tune component settings
<AccordionGroup>
<Accordion title="LLM Configuration">
| Parameter | Description | Provider |
|-----------------------|-----------------------------------------------|-------------------|
| `provider` | LLM provider (e.g., "openai", "anthropic") | All |
| `model` | Model to use | All |
| `temperature` | Temperature of the model (0.0-2.0) | All |
| `api_key` | API key to use | Most |
| `max_tokens` | Maximum tokens to generate | All |
| `top_p` | Nucleus sampling threshold | All |
| `top_k` | Top-k sampling parameter | Some |
| `ollama_base_url` | Base URL for Ollama API | Ollama |
| `openai_base_url` | Base URL for OpenAI API | OpenAI |
| `azure_kwargs` | Azure-specific initialization args | Azure OpenAI |
**See all 17 LLM providers:** [LLMs Overview](/components/llms/overview)
<Accordion title="Vector store collections">
Name collections explicitly in production (`collection_name`) to isolate tenants and enable per-tenant retention policies.
</Accordion>
<Accordion title="Vector Store Configuration">
Common parameters (provider-specific options vary):
| Parameter | Description | Example |
| ----------------- | -------------------------- | ----------- |
| `provider` | Vector store provider | "qdrant" |
| `host` | Host address | "localhost" |
| `port` | Port number | 6333 |
| `collection_name` | Collection/index name | "memories" |
| `api_key` | API key (for cloud stores) | "your-key" |
**See all 25+ vector stores:** [Vector Databases Overview](/components/vectordbs/overview)
<Accordion title="LLM extraction temperature">
Keep extraction temperatures ≤0.2 so advanced memories stay deterministic. Raise it only when you see missing facts.
</Accordion>
<Accordion title="Embedder Configuration">
| Parameter | Description | Default |
|-------------|---------------------------------|------------------------------|
| `provider` | Embedding provider | "openai" |
| `model` | Embedding model to use | "text-embedding-3-small" |
| `api_key` | API key for embedding service | None |
**See all 9 embedder providers:** [Embedders Overview](/components/embedders/overview)
</Accordion>
<Accordion title="Reranker Configuration">
| Parameter | Description | Example |
|-------------|---------------------------------|------------------------------|
| `provider` | Reranker provider | "cohere" |
| `model` | Reranker model to use | "rerank-english-v3.0" |
| `top_k` | Number of results to return | 5 |
| `api_key` | API key for reranker service | "your-key" |
**See all reranker options:** [Rerankers Overview](/components/rerankers/overview)
</Accordion>
<Accordion title="Graph Store Configuration">
| Parameter | Description | Example |
|-------------|---------------------------------|------------------------------|
| `provider` | Graph store provider | "neo4j" |
| `url` | Connection URL | "neo4j+s://..." |
| `username` | Authentication username | "neo4j" |
| `password` | Authentication password | "your-password" |
**Learn more:** [Graph Memory Overview](/open-source/features/graph-memory)
</Accordion>
<Accordion title="General Configuration">
| Parameter | Description | Default |
|------------------|--------------------------------------|----------------------------|
| `history_db_path` | Path to the history database | "{mem0_dir}/history.db" |
| `custom_fact_extraction_prompt` | Custom prompt for memory extraction | None |
| `custom_update_memory_prompt` | Custom prompt for memory updates | None |
<Accordion title="Reranker depth">
Limit `top_k` to 10–20 results; sending more adds latency without meaningful gains.
</Accordion>
</AccordionGroup>
## Next Steps
<Warning>
Mixing managed and self-hosted components? Make sure every outbound provider call happens through a secure network path. Managed rerankers often require outbound internet even if your vector store is on-prem.
</Warning>
<CardGroup cols={3}>
<Card title="Python Quickstart" icon="python" href="/open-source/python-quickstart">
Get started with the Python SDK
</Card>
## Quick recovery
<Card title="Self-Hosting Features" icon="server" href="/open-source/features/overview">
Explore OSS-specific capabilities
</Card>
- Qdrant connection errors → confirm port `6333` is exposed and API key (if set) matches.
- Empty search results → verify the embedder model name; a mismatch causes dimension errors.
- `Unknown reranker` → update the SDK (`pip install --upgrade mem0ai`) to load the latest provider registry.
<Card title="Examples" icon="book" href="/examples">
See configuration examples in action
</Card>
<CardGroup cols={2}>
<Card
title="Pick Providers"
description="Review the LLM, vector store, embedder, and reranker catalogs."
icon="sitemap"
href="/components/llms/overview"
/>
<Card
title="Deploy with Docker Compose"
description="Follow the end-to-end OSS deployment walkthrough."
icon="server"
href="/examples/mem0-with-ollama"
/>
</CardGroup>
docs/platform/advanced-memory-operations.mdx
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docs/platform/features/platform-overview.mdx
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@@ -1,46 +1,55 @@
---
description: "See how Mem0 Platform features evolve from baseline filters to graph-powered retrieval."
icon: "sparkles"
title: Overview
---
Learn about the key features and capabilities that make Mem0 a powerful platform for memory management and retrieval.
Mem0 Platform features help managed deployments scale from basic filtering to graph-powered retrieval and data governance. Use this page to pick the right feature lane for your team.
## Core Features
<Info>
New to the platform? Start with the <Link href="/platform/quickstart">Platform quickstart</Link>, then dive into the journeys below.
</Info>
<CardGroup>
<Card title="Advanced Retrieval" icon="magnifying-glass" href="/platform/features/advanced-retrieval">
Superior search results using state-of-the-art algorithms, including keyword search, reranking, and filtering capabilities.
## Choose your path
<CardGroup cols={3}>
<Card title="Apply Essential Filters" icon="funnel" href="/platform/features/v2-memory-filters">
Control which memories surface with field-level filtering and async defaults.
</Card>
<Card title="Contextual Add" icon="square-plus" href="/platform/features/contextual-add">
Only send your latest conversation history - we automatically retrieve the rest and generate properly contextualized memories.
<Card title="Go Real-Time with Async" icon="bolt" href="/platform/features/async-client">
Stream add/search requests without blocking your agents.
</Card>
<Card title="Multimodal Support" icon="photo-film" href="/platform/features/multimodal-support">
Process and analyze various types of content including images.
<Card title="Unlock Graph Memory" icon="circle-nodes" href="/platform/features/graph-memory">
Layer relationships on top of vectors for richer recalls.
</Card>
<Card title="Memory Customization" icon="filter" href="/platform/features/selective-memory">
Customize and curate stored memories to focus on relevant information while excluding unnecessary data, enabling improved accuracy, privacy control, and resource efficiency.
<Card title="Boost Retrieval Quality" icon="sparkles" href="/platform/features/advanced-retrieval">
Combine metadata filtering, rerankers, and per-request toggles.
</Card>
<Card title="Custom Categories" icon="tags" href="/platform/features/custom-categories">
Create and manage custom categories to organize memories based on your specific needs and requirements.
<Card title="Manage Data Lifecycle" icon="database" href="/platform/features/direct-import">
Handle imports, exports, timestamps, and expirations at scale.
</Card>
<Card title="Custom Instructions" icon="list-check" href="/platform/features/custom-instructions">
Define specific guidelines for your project to ensure consistent handling of information and requirements.
</Card>
<Card title="Direct Import" icon="message-bot" href="/platform/features/direct-import">
Tailor the behavior of your Mem0 instance with custom prompts for specific use cases or domains.
</Card>
<Card title="Async Client" icon="bolt" href="/platform/features/async-client">
Asynchronous client for non-blocking operations and high concurrency applications.
</Card>
<Card title="Memory Export" icon="file-export" href="/platform/features/memory-export">
Export memories in structured formats using customizable Pydantic schemas.
</Card>
<Card title="Graph Memory" icon="circle-nodes" href="/platform/features/graph-memory">
Add memories in the form of nodes and edges in a graph database and search for related memories.
<Card title="Extend With Integrations" icon="plug" href="/platform/features/webhooks">
Wire webhook callbacks, feedback loops, and multi-agent chat.
</Card>
</CardGroup>
## Getting Help
<Tip>
Self-hosting instead? Jump to the <Link href="/open-source/features/overview">OSS feature overview</Link> for equivalent capabilities.
</Tip>
If you have any questions about these features or need assistance, our team is here to help:
## Keep going
<Snippet file="get-help.mdx" />
<CardGroup cols={2}>
<Card
title="Compare with Open Source"
description="See how managed features map to the OSS stack."
icon="server"
href="/platform/platform-vs-oss"
/>
<Card
title="Run the Quickstart"
description="Provision the workspace and ship your first advanced search."
icon="rocket"
href="/platform/quickstart"
/>
</CardGroup>
docs/templates/api_reference_template.mdx
Vendored
+1 -1
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@@ -124,7 +124,7 @@ const response = await fetch("https://api.mem0.ai/v1/memories", {
- [Build a Customer Support Agent](/cookbooks/customer-support-agent)
<!-- DEBUG: verify CTA targets -->
{/* DEBUG: verify CTA targets */}
<CardGroup cols={2}>
<Card
docs/templates/concept_guide_template.mdx
Vendored
+4 -4
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@@ -45,11 +45,11 @@ icon: "lightbulb"
- **[Term]** – [Short definition]
- **[Term]** – [Short definition]
<!-- Optional: delete if not needed -->
{/* Optional: delete if not needed */}
```mermaid
graph LR
A[Input] --> B[Concept]
B --> C[Outcome]
A[Input] */} B[Concept]
B */} C[Outcome]
```
## How does it work?
@@ -91,7 +91,7 @@ graph LR
- [Cookbook or integration demonstrating the concept]
- [Recording, demo, or sample repo]
<!-- DEBUG: verify CTA targets -->
{/* DEBUG: verify CTA targets */}
<CardGroup cols={2}>
<Card
docs/templates/feature_guide_template.mdx
Vendored
+3 -3
View File
@@ -62,12 +62,12 @@ Mem0’s advanced retrieval elevates search accuracy when basic keyword matches
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 -->
{/* 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]
A[Input] */} B[Feature]
B */} C[Output]
```
## Feature anatomy
docs/templates/integration_guide_template.mdx
Vendored
+4 -4
View File
@@ -45,11 +45,11 @@ Combine Mem0’s memory layer with [Partner] to [describe the joint outcome].
[Use only if access is gated or breaking changes exist. Delete when not needed.]
</Warning>
<!-- Optional architecture diagram -->
{/* Optional architecture diagram */}
```mermaid
graph LR
A[Mem0] --> B[Connector]
B --> C[Partner workflow]
A[Mem0] */} B[Connector]
B */} C[Partner workflow]
```
## Configure credentials
@@ -170,7 +170,7 @@ partner.registerTool("recallPreferences", async (userId: string) => {
- **[Issue]** — `[Fix or link to partner docs]`
- **[Issue]** — `[Fix or link to Mem0 troubleshooting guide]`
<!-- DEBUG: verify CTA targets -->
{/* DEBUG: verify CTA targets */}
<CardGroup cols={2}>
<Card
docs/templates/migration_guide_template.mdx
Vendored
+4 -4
View File
@@ -55,12 +55,12 @@ releaseDate: "[YYYY-MM-DD]" # Optional
- [Date]: [Milestone]
- [Date]: [Milestone]
<!-- Optional: delete if not needed -->
{/* Optional: delete if not needed */}
```mermaid
graph LR
A[Plan] --> B[Migrate]
B --> C[Validate]
C --> D[Roll back if needed]
A[Plan] */} B[Migrate]
B */} C[Validate]
C */} D[Roll back if needed]
```
## Plan
docs/templates/operation_guide_template.mdx
Vendored
+1 -1
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@@ -141,7 +141,7 @@ const response = await memory.add(payload);
- [Link to complementary operation]
- [Link to troubleshooting playbook section]
<!-- DEBUG: verify CTA targets -->
{/* DEBUG: verify CTA targets */}
<CardGroup cols={2}>
<Card
docs/templates/parameters_reference_template.mdx
Vendored
+1 -1
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@@ -129,7 +129,7 @@ These snippets confirm the method returns the new `memory_id` for follow-up oper
- **`400 Missing user_id`** — Provide either `user_id` or `agent_id` in the payload.
- **`422 Metadata too large`** — Reduce metadata size below 2KB (OSS hard limit).
<!-- DEBUG: verify CTA targets -->
{/* DEBUG: verify CTA targets */}
<CardGroup cols={2}>
<Card
docs/templates/quickstart_template.mdx
Vendored
+6 -6
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@@ -43,13 +43,13 @@ estimatedTime: "[~X minutes]"
[Optional: cross-link to OSS or platform alternative if applicable. Delete if unused.]
</Tip>
<!-- Optional: delete if not needed -->
{/* Optional: delete if not needed */}
```mermaid
graph LR
A[Install] --> B[Configure keys]
B --> C[Add memory]
C --> D[Search]
D --> E[Delete]
A[Install] */} B[Configure keys]
B */} C[Add memory]
C */} D[Search]
D */} E[Delete]
```
## Install dependencies
@@ -212,7 +212,7 @@ await memory.deleteAll({ userId: "alex" });
- `[Error message]` → `[One-line fix or link to troubleshooting guide]`
- `[Second error]` → `[How to resolve]`
<!-- DEBUG: verify CTA targets -->
{/* DEBUG: verify CTA targets */}
<CardGroup cols={2}>
<Card
docs/templates/release_notes_template.mdx
Vendored
+1 -1
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@@ -89,7 +89,7 @@ tags: ["platform", "oss"] # Optional filters
- [Contributor or team] — `[Short thank-you message].`
<!-- DEBUG: verify CTA targets -->
{/* DEBUG: verify CTA targets */}
<CardGroup cols={2}>
<Card
docs/templates/section_overview_template.mdx
Vendored
+5 -5
View File
@@ -36,12 +36,12 @@ icon: "compass"
Start with [Quickstart link] if you’re new, then choose a deeper topic below.
</Info>
<!-- Optional: delete if not needed -->
{/* Optional: delete if not needed */}
```mermaid
graph LR
A[Get set up] --> B[Learn concepts]
B --> C[Build workflows]
C --> D[Support & scale]
A[Get set up] */} B[Learn concepts]
B */} C[Build workflows]
C */} D[Support & scale]
```
## Choose your path
@@ -73,7 +73,7 @@ graph LR
## Keep going
<!-- DEBUG: verify CTA targets -->
{/* DEBUG: verify CTA targets */}
<CardGroup cols={2}>
<Card
docs/templates/troubleshooting_playbook_template.mdx
Vendored
+1 -1
View File
@@ -96,7 +96,7 @@ Run this check:
- [Feature or integration doc]
- [Runbook or SLO doc]
<!-- DEBUG: verify CTA targets -->
{/* DEBUG: verify CTA targets */}
<CardGroup cols={2}>
<Card