> ## Documentation Index
> Fetch the complete documentation index at: https://supermemory-vorflux-slack-workspace-override-consent.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# SuperRAG (Managed RAG as a service)
> Supermemory provides a managed RAG solution - extraction, indexing, storing, and retrieval.
Supermemory doesn't just store your content—it transforms it into optimized, searchable knowledge. Every upload goes through an intelligent pipeline that extracts, chunks, and indexes content in the ideal way for its type.
## Automatic Content Intelligence
When you add content, Supermemory:
1. **Detects the content type** — PDF, code, markdown, images, video, etc.
2. **Extracts content optimally** — Uses type-specific extraction (OCR for images, transcription for audio)
3. **Chunks intelligently** — Applies the right chunking strategy for the content type
4. **Generates embeddings** — Creates vector representations for semantic search
5. **Builds relationships** — Connects new knowledge to existing memories
```typescript theme={null}
// Just upload — Supermemory handles the rest
await client.documents.uploadFile({
file: fs.createReadStream('technical-documentation.pdf'),
metadata: JSON.stringify({ title: "Technical Documentation" })
});
```
No chunking strategies to configure. No embedding models to choose. It just works.
***
## Ingesting as pure SuperRAG (`taskType: "superrag"`)
By default, every `client.add()` call runs on the **memory** path (`taskType: "memory"`): Supermemory chunks and embeds the content for retrieval, *and* runs it through the memory pipeline — extracting facts, updating the profile, and linking it into the knowledge graph.
If you're ingesting content that's purely reference material — documentation, a large PDF, a knowledge base article — and you don't need Supermemory to derive personal facts or update a profile from it, set `taskType: "superrag"`. It skips the memory pipeline entirely and only does the chunk → embed → index work needed to make the content searchable.
```typescript TypeScript theme={null}
await client.add({
content: "...", // e.g. a long internal wiki page
containerTag: "docs_kb",
taskType: "superrag",
});
```
```python Python theme={null}
client.add(
content="...",
container_tag="docs_kb",
task_type="superrag",
)
```
```bash cURL theme={null}
curl -X POST "https://api.supermemory.ai/v3/documents" \
-H "Authorization: Bearer $SUPERMEMORY_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"content": "...",
"containerTag": "docs_kb",
"taskType": "superrag"
}'
```
| | `taskType: "memory"` (default) | `taskType: "superrag"` |
| -------------------------------------------- | ------------------------------ | -------------------------------------------------------- |
| Chunking, embedding, indexing | ✅ | ✅ — searchable immediately via `searchMode: "documents"` |
| Fact extraction into memories | ✅ | ❌ skipped |
| Profile (`static`/`dynamic`/buckets) updates | ✅ | ❌ skipped |
| Graph linking (updates/extends/derives) | ✅ | ❌ skipped |
| Price per ingested token | Full rate | **5x cheaper** |
`taskType: "superrag"` is a **5x discount on ingested tokens** — `sm_superrag_text`/`sm_superrag_rich` are priced at 20% of `sm_tokens_text`/`sm_tokens_rich`. See [Billing → Memory vs SuperRAG tokens](/overview/billing#memory-vs-superrag-tokens) for the exact rates.
Content ingested as `superrag` is retrievable via document search (`searchMode: "documents"`), but it will **never** surface as a memory, contribute to a user's profile, or connect into the knowledge graph. Use it for reference material you want searchable, not for anything that should shape what Supermemory knows about a user — that still needs the default `taskType: "memory"`.
When you're searching over a mix of both, `searchMode: "hybrid"` (below) is what pulls memory-path facts and superrag-path document chunks into one result set. More ingestion guidance: [Rules of supermemory → Ingest with SuperRag when you just need search](/concepts/rules#ingest-with-superrag-when-you-just-need-search).
***
## Smart Chunking by Content Type
Different content types need different chunking strategies. Supermemory applies the optimal approach automatically:
### Documents (PDF, DOCX)
PDFs and documents are chunked by **semantic sections** — headers, paragraphs, and logical boundaries. This preserves context better than arbitrary character splits.
```
├── Executive Summary (chunk 1)
├── Introduction (chunk 2)
├── Section 1: Architecture
│ ├── Overview (chunk 3)
│ └── Components (chunk 4)
└── Conclusion (chunk 5)
```
### Code
Code is chunked using [code-chunk](https://github.com/supermemoryai/code-chunk), our open-source library that understands AST (Abstract Syntax Tree) boundaries:
* Functions and methods stay intact
* Classes are chunked by method
* Import statements grouped separately
* Comments attached to their code blocks
```typescript theme={null}
// A 500-line file becomes meaningful chunks:
// - Imports + type definitions
// - Each function as a separate chunk
// - Class methods individually indexed
```
This means searching for "authentication middleware" finds the actual function, not a random slice of code.
### Web Pages
URLs are fetched, cleaned of navigation/ads, and chunked by article structure — headings, paragraphs, lists.
### Markdown
Chunked by heading hierarchy, preserving the document structure.
See [Content Types](/concepts/content-types) for the full list of supported formats.
***
## Hybrid Memory + RAG
Supermemory combines the best of both approaches in every search:
* Finds similar document chunks
* Great for knowledge retrieval
* Stateless — same results for everyone
* Extracts and tracks user facts
* Understands temporal context
* Personalizes results per user
With `searchMode: "hybrid"` (the default), you get both:
```typescript theme={null}
const results = await client.search({
q: "how do I deploy the app?",
containerTag: "user_123",
searchMode: "hybrid"
});
// Returns:
// - Deployment docs from your knowledge base (RAG)
// - User's previous deployment preferences (Memory)
// - Their specific environment configs (Memory)
```
***
## Search Optimization
Two flags give you fine-grained control over result quality:
### Reranking
Re-scores results using a cross-encoder model for better relevance:
```typescript theme={null}
const results = await client.search({
q: "complex technical question",
rerank: true // +~100ms, significantly better ranking
});
```
**When to use:** Complex queries, technical documentation, when precision matters more than speed.
### Query Rewriting
Expands your query to capture more relevant results:
```typescript theme={null}
const results = await client.search({
q: "how to auth",
rewriteQuery: true // Expands to "authentication login oauth jwt..."
});
```
**When to use:** Short queries, user-facing search, when recall matters.
***
## Why It's "Super"
| Traditional RAG | SUPER RAG |
| ------------------------ | -------------------------- |
| Manual chunking config | Automatic per content type |
| One-size-fits-all splits | AST-aware code chunking |
| Just document retrieval | Hybrid memory + documents |
| Static embeddings | Relationship-aware graph |
| Generic search | Rerank + query rewriting |
You focus on building your product. Supermemory handles the RAG complexity.
***
## Next Steps
All supported formats and how they're processed
The full processing pipeline
When to use each approach
Search parameters and optimization
Exact meter rates for memory vs SuperRAG tokens
`taskType` and other ingestion parameters