Context Vault - MemContext

Context Vault lets your app add company or project knowledge once, then retrieve the right source passages and extracted facts whenever your AI system needs context. Use it for support bots, internal copilots, onboarding agents, sales assistants, or any workflow where an AI system needs to answer from trusted workspace documents.

Why Context Vault Exists

Normal memory is best for short, durable facts like preferences, decisions, and project context. Company knowledge is different. It usually lives in PDFs, Markdown files, help centers, contracts, onboarding docs, tickets, and long internal notes. Context Vault is the AI context pipeline for that kind of knowledge. It lets you add long-form content, turn it into searchable context, and retrieve the right pieces when your AI application needs to answer. Use Context Vault when you want to:

Let a support bot answer from product documentation
Let an internal copilot search handbooks, policies, and SOPs
Let a legal or finance assistant retrieve exact clauses from PDFs
Let an AI workflow combine source passages with extracted facts

Do not use Context Vault for:

Small user preferences that should be saved as personal memories
Temporary chat messages that do not need to be remembered
Private tenant data unless you put each tenant in its own workspace or scope

What You Can Add

Context Vault accepts:

PDFs
Markdown and text files
DOCX files
CSV files
Images and scanned documents
Public file URLs
Documentation or website URLs
Already extracted text or Markdown

For files that need OCR, MemContext extracts readable text automatically. For documentation URLs, MemContext fetches clean page content that is easier to search than raw HTML.

How The AI Context Pipeline Works

When you add a document, MemContext prepares it for retrieval in three layers:

Source document: the original file, URL, or text stays connected to the workspace document.
Document chunks: the content is split into meaningful sections so your AI can receive the exact source passage it needs.
Extracted memories: important facts are saved separately as atomic memories and linked back to the chunks they came from.

This is why Context Vault can return both the original source context and the clean extracted knowledge. Your app can pass both to an LLM, show citations, or let users inspect the original document.

Add A Document With The SDK

import { MemContextClient } from "memcontext-sdk";

const client = new MemContextClient({
  apiKey: process.env.MEMCONTEXT_API_KEY!,
});

await client.ingestContextVaultDocument({
  workspaceId: "8b3b8f4c-...",
  title: "Product docs",
  uri: "https://docs.example.com",
  sourceType: "url",
  crawlSubpages: true,
});

Upload a local file:

await client.uploadContextVaultDocument({
  workspaceId: "8b3b8f4c-...",
  title: "Employee handbook",
  file,
  filename: "handbook.pdf",
  sourceType: "pdf",
});

Add A Document With The API

Upload A File

Use this when your client has the file bytes.

curl -X POST https://api.memcontext.in/api/company-brain/documents/upload \
  -H "X-API-Key: mc_your_key" \
  -F "workspaceId=8b3b8f4c-..." \
  -F "title=Employee Handbook" \
  -F "sourceType=pdf" \
  -F "file=@handbook.pdf"

Add A Public File URL

Use this when the file is already available at a public URL.

curl -X POST https://api.memcontext.in/api/company-brain/documents \
  -H "Content-Type: application/json" \
  -H "X-API-Key: mc_your_key" \
  -d '{
    "workspaceId": "8b3b8f4c-...",
    "title": "Security Whitepaper",
    "sourceType": "pdf",
    "uri": "https://example.com/security-whitepaper.pdf",
    "scope": "security"
  }'

Add A Documentation URL

Use this for documentation sites, help centers, or public web pages.

curl -X POST https://api.memcontext.in/api/company-brain/documents \
  -H "Content-Type: application/json" \
  -H "X-API-Key: mc_your_key" \
  -d '{
    "workspaceId": "8b3b8f4c-...",
    "title": "Product Docs",
    "uri": "https://docs.example.com",
    "sourceType": "url",
    "crawlSubpages": true,
    "priorityPageLimit": 15,
    "subpageTarget": ["api", "guides"]
  }'

For docs sites that expose llms.txt, MemContext uses the page map to discover and rank useful pages, then ingests only the selected priority pages. priorityPageLimit defaults to 15 and is capped at 25.

Add Text Or Markdown Directly

Use this when your app already has clean text.

curl -X POST https://api.memcontext.in/api/company-brain/documents \
  -H "Content-Type: application/json" \
  -H "X-API-Key: mc_your_key" \
  -d '{
    "workspaceId": "8b3b8f4c-...",
    "title": "Leave Policy",
    "sourceType": "markdown",
    "content": "# Leave Policy\nEmployees get 18 paid leave days..."
  }'

Check Document Status

After a document is accepted, list documents to see its current status and progress.

const docs = await client.listContextVaultDocuments("8b3b8f4c-...");

curl "https://api.memcontext.in/api/company-brain/documents?workspaceId=8b3b8f4c-..." \
  -H "X-API-Key: mc_your_key"

Common statuses are pending, processing, retrying, completed, failed, and cancelled.

Search Workspace Knowledge

Context Vault search has three modes:

Mode	What it returns
`documents`	Source passages from uploaded files and URLs
`memories`	Extracted atomic facts with evidence
`hybrid`	Both document passages and extracted memory facts

Use documents when your AI needs exact source text, such as contract clauses, policy language, or troubleshooting steps. Use memories when your AI needs concise facts, such as “the refund window is 14 days” or “Scope is the tenant isolation key.” Use hybrid for most production AI apps. It gives the model both precise source passages and distilled facts, which usually improves answer quality and makes citations easier to show.

const results = await client.searchContextVault({
  workspaceId: "8b3b8f4c-...",
  query: "What is the leave policy?",
  mode: "hybrid",
});

curl "https://api.memcontext.in/api/company-brain/search?workspaceId=8b3b8f4c-...&query=leave%20policy&mode=hybrid" \
  -H "X-API-Key: mc_your_key"

Response Shape

Hybrid search returns document chunks and extracted memories separately:

{
  "mode": "hybrid",
  "found": 2,
  "chunks": [
    {
      "id": "chunk_...",
      "sourceId": "doc_...",
      "title": "Employee Handbook",
      "sourceType": "pdf",
      "sectionPath": "Leave Policy",
      "chunkIndex": 12,
      "content": "Employees receive 18 paid leave days per year...",
      "relevance": 0.91
    }
  ],
  "memories": [
    {
      "id": "mem_...",
      "content": "Employees receive 18 paid leave days per year.",
      "category": "fact",
      "evidence": [
        {
          "sourceId": "doc_...",
          "chunkId": "chunk_...",
          "title": "Employee Handbook",
          "sectionPath": "Leave Policy",
          "chunkIndex": 12,
          "quote": "Employees receive 18 paid leave days per year..."
        }
      ]
    }
  ]
}

For AI answer generation, pass chunks[] as source-document context and memories[] as a separate memory-context block. This keeps citations and high-level facts easy to handle in your own app.

Why Search Quality Improves

Context Vault does more than match keywords. It first gathers candidate results from document chunks and extracted memories. For document searches, MemContext then uses a re-ranking model on the top candidate passages, currently up to 30 candidates, so the most useful source text appears higher. This helps with questions where the best answer is not an exact keyword match.

Query	Good result should surface
”What obligations continue after termination?”	Termination, return, payment, and confidentiality terms
”Is the contractor an employee?”	Independent contractor and liability sections
”How should I isolate tenants?”	Scope and workspace isolation guidance

For best results, ask complete questions and include names, section numbers, or domain terms when they matter.

Browse Extracted Memories

List all extracted workspace memories:

const page = await client.listContextVaultMemories({
  workspaceId: "8b3b8f4c-...",
  limit: 20,
});

List memories from one document:

const extracted = await client.listContextVaultDocumentMemories(
  "8b3b8f4c-...",
  "document-id",
);

Load citations for one extracted memory:

const evidence = await client.listContextVaultMemoryEvidence(
  "8b3b8f4c-...",
  "memory-id",
);

Delete Or Cancel

Cancel a document that is still running:

await client.cancelContextVaultDocument("document-id");

Delete a document:

await client.deleteContextVaultDocument("document-id");

When a document is deleted, memories that only came from that document are removed from future retrieval. Memories supported by other documents remain available.

Supported Sources

Start with:

Markdown
TXT
PDF
DOCX
CSV
PNG, JPG/JPEG, WebP, and TIFF
Public documentation or website URLs

XLSX, PPTX, and external workspace connectors should be added after the core document workflow is stable.

​Why Context Vault Exists

​What You Can Add

​How The AI Context Pipeline Works

​Add A Document With The SDK

​Add A Document With The API

​Upload A File

​Add A Public File URL

​Add A Documentation URL

​Add Text Or Markdown Directly

​Check Document Status

​Search Workspace Knowledge

​Response Shape

​Why Search Quality Improves

​Browse Extracted Memories

​Delete Or Cancel

​Supported Sources