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Chat Memory\n**One sentence:** Store conversation history with automatic compression and memory extraction.  \n**When to use:** Chatbots, conversational AI, session management  \n**API:** `POST /v1/messages`, `GET /v1/messages/sessions/{id}`  \n**Addons:** None (standalone)  \n**[Quick start →](../quickstart/chat-memory.md) | [Deep dive →](../guides/messages-management.md)**\n\n### 3. Document Intelligence\n**One sentence:** Extract structured information from PDFs and documents with intelligent analysis.  \n**When to use:** Document extraction, classification, contract analysis, research papers  \n**API:** `POST /v1/document`, `GET /v1/document/status/{id}`  \n**Optional integration:** Vector Memory (for search), Knowledge Graphs (for relationships)  \n**[Quick start →](../quickstart/document-memory.md) | [Deep dive →](../guides/document-processing.md)**\n\n### 4. AI Model Proxy\n**One sentence:** Unified API to call multiple LLM providers with usage tracking.  \n**When to use:** Multi-model applications, cost tracking, fallback logic  \n**API:** `/v1/ai/openai/*`, `/v1/ai/anthropic/*`, `/v1/ai/usage`  \n**Addons:** None (standalone)  \n**[API reference →](../apis/index.yaml)**\n\n## Advanced Products (Addons)\n\n### 5. Knowledge Graphs\n**One sentence:** Extract entities and relationships from memories into a queryable graph.  \n**Requires:** Vector Memory OR Chat Memory  \n**When to use:** Need to understand relationships, not just similarity  \n**API:** `memory_policy`, `POST /v1/graphql`, `POST /v1/schemas`  \n**[Quick start →](../quickstart/structured-data-memory.md) | [Deep dive →](../guides/graph-generation.md)**\n\n### 5. Graph-Aware Search\n**One sentence:** Domain-tuned embeddings that filter by structured dimensions (language, topic, etc.)  \n**Requires:** Vector Memory  \n**When to use:** Baseline search isn't precise enough for your domain  \n**API:** `enable_holographic`, `holographic_config`, `/v1/frequencies`  \n**[Decision tree →](../guides/graph-aware-embeddings.md#do-you-need-graph-aware-embeddings) | [Deep dive →](../guides/graph-aware-embeddings.md)**\n\n### 6. Document Intelligence\n**One sentence:** Extract structured information from PDFs/docs and create searchable memories.  \n**Requires:** Vector Memory OR Chat Memory (creates memories)  \n**When to use:** PDF Q&A, document analysis, knowledge extraction  \n**API:** `POST /v1/document`, `GET /v1/document/status/{id}`  \n**[Quick start →](../quickstart/document-memory.md) | [Deep dive →](../guides/document-processing.md)**\n\n## Cross-Product Features\n\n### 7. Sync & Portability\n**One sentence:** Keep local/edge memory in sync with cloud and export/import memories.  \n**Works with:** All memory products  \n**API:** `/v1/sync/*`, `/v1/omo/*`  \n**[Deep dive →](../guides/portability-and-sync.md)**\n\n---\n\n## Common Product Bundles\n\n### Conversational AI Stack\n**Products:** Chat Memory + Vector Memory  \n**Use case:** Chatbot that remembers user preferences across sessions  \n**Example:** Store chat in sessions, extract long-term facts to Vector Memory\n\n### Document Q&A System\n**Products:** Document Intelligence + Vector Memory + Knowledge Graphs  \n**Use case:** \"Ask questions about uploaded docs\"  \n**Example:** Upload PDFs → Extract entities/relationships → Search with graph context\n\n### Code Search Platform\n**Products:** Vector Memory + Graph-Aware Search (cosqa schema)  \n**Use case:** Find code snippets by natural language  \n**Example:** \"How do I sort arrays in Python?\" returns only Python methods\n\n### Multi-Model Agent Platform\n**Products:** AI Proxy + Chat Memory + Vector Memory  \n**Use case:** Agent that remembers context and calls multiple LLMs  \n**Example:** Track costs across OpenAI/Anthropic, compress chat history, search memory\n\n---\n\n## Decision Flow\n\n**Start here:** What are you building?\n\n1. **\"Just need semantic search\"** → Vector Memory (standalone)\n2. **\"Chatbot with memory\"** → Chat Memory (standalone) OR Chat + Vector (if cross-session recall needed)\n3. **\"Document Q&A\"** → Document Intelligence + Vector Memory\n4. **\"Need relationships, not just similarity\"** → Vector Memory + Knowledge Graphs\n5. **\"Search isn't precise enough\"** → Vector Memory + Graph-Aware Search (evaluate first)\n6. **\"Multi-model app with memory\"** → AI Proxy + Vector/Chat Memory\n\n**Next:** See [Capability Matrix](./capability-matrix.md) for endpoint-level mapping.\n","lang":"markdown"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"2.-add-product-labels-to-existing-guides"},"children":["2. 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Cross-Link Products in Guides","id":"5.-cross-link-products-in-guides","depth":3},{"value":"Implementation Plan","id":"implementation-plan","depth":2},{"value":"Phase 1: Product Overview (1-2 hours)","id":"phase-1-product-overview-1-2-hours","depth":3},{"value":"Phase 2: Product Badges (30 min)","id":"phase-2-product-badges-30-min","depth":3},{"value":"Phase 3: Decision Support (1 hour)","id":"phase-3-decision-support-1-hour","depth":3},{"value":"Phase 4: Polish (30 min)","id":"phase-4-polish-30-min","depth":3},{"value":"Benefits","id":"benefits","depth":2},{"value":"For Developers","id":"for-developers","depth":3},{"value":"For Sales/Marketing","id":"for-sales/marketing","depth":3},{"value":"For Support","id":"for-support","depth":3},{"value":"Open Questions","id":"open-questions","depth":2},{"value":"Alternative: Keep Current Structure","id":"alternative-keep-current-structure","depth":2}],"frontmatter":{"seo":{"title":"Product-First Documentation Structure"}},"lastModified":"2026-04-22T01:40:48.000Z","pagePropGetterError":{"message":"","name":""}},"slug":"/internal/planning/product-structure-proposal","userData":{"isAuthenticated":false,"teams":["anonymous"]}}