File Integration

curl -X POST 'https://api.memchat.io/integrations/files' \
  -H 'X-API-Key: YOUR_API_KEY' \
  -F 'file=@document.pdf' \
  -F 'category=learning' \
  -F 'extract_mode=comprehensive'

import requests

url = "https://api.memchat.io/integrations/files"
headers = {
    "X-API-Key": "YOUR_API_KEY"
}

files = {
    'file': open('document.pdf', 'rb')
}

data = {
    'category': 'learning',
    'extract_mode': 'comprehensive'
}

response = requests.post(url, headers=headers, files=files, data=data)
result = response.json()

const formData = new FormData();
formData.append('file', fileInput.files[0]);
formData.append('category', 'learning');
formData.append('extract_mode', 'comprehensive');

const response = await fetch('https://api.memchat.io/integrations/files', {
  method: 'POST',
  headers: {
    'X-API-Key': 'YOUR_API_KEY',
  },
  body: formData,
});

const result = await response.json();

{
  "integration_id": "file_int_456def",
  "filename": "research_paper.pdf",
  "file_size": 2458697,
  "status": "completed",
  "extraction_stats": {
    "total_pages": 15,
    "text_length": 45678,
    "chunks_created": 23,
    "memories_extracted": 12,
    "categories_detected": ["learning", "career", "interests"]
  },
  "memories": [
    {
      "memory_id": "mem_789ghi",
      "content": "The study demonstrates that machine learning algorithms can improve medical diagnosis accuracy by 23% when trained on diverse datasets.",
      "category": "learning",
      "importance": 0.87,
      "page_number": 3
    }
  ],
  "created_at": "2024-01-15T16:30:00Z",
  "completed_at": "2024-01-15T16:32:15Z"
}

POST

integrations

files

curl -X POST 'https://api.memchat.io/integrations/files' \
  -H 'X-API-Key: YOUR_API_KEY' \
  -F 'file=@document.pdf' \
  -F 'category=learning' \
  -F 'extract_mode=comprehensive'

import requests

url = "https://api.memchat.io/integrations/files"
headers = {
    "X-API-Key": "YOUR_API_KEY"
}

files = {
    'file': open('document.pdf', 'rb')
}

data = {
    'category': 'learning',
    'extract_mode': 'comprehensive'
}

response = requests.post(url, headers=headers, files=files, data=data)
result = response.json()

const formData = new FormData();
formData.append('file', fileInput.files[0]);
formData.append('category', 'learning');
formData.append('extract_mode', 'comprehensive');

const response = await fetch('https://api.memchat.io/integrations/files', {
  method: 'POST',
  headers: {
    'X-API-Key': 'YOUR_API_KEY',
  },
  body: formData,
});

const result = await response.json();

{
  "integration_id": "file_int_456def",
  "filename": "research_paper.pdf",
  "file_size": 2458697,
  "status": "completed",
  "extraction_stats": {
    "total_pages": 15,
    "text_length": 45678,
    "chunks_created": 23,
    "memories_extracted": 12,
    "categories_detected": ["learning", "career", "interests"]
  },
  "memories": [
    {
      "memory_id": "mem_789ghi",
      "content": "The study demonstrates that machine learning algorithms can improve medical diagnosis accuracy by 23% when trained on diverse datasets.",
      "category": "learning",
      "importance": 0.87,
      "page_number": 3
    }
  ],
  "created_at": "2024-01-15T16:30:00Z",
  "completed_at": "2024-01-15T16:32:15Z"
}

Upload documents to automatically extract and store memories from their content.

curl -X POST 'https://api.memchat.io/integrations/files' \
  -H 'X-API-Key: YOUR_API_KEY' \
  -F 'file=@document.pdf' \
  -F 'category=learning' \
  -F 'extract_mode=comprehensive'

import requests

url = "https://api.memchat.io/integrations/files"
headers = {
    "X-API-Key": "YOUR_API_KEY"
}

files = {
    'file': open('document.pdf', 'rb')
}

data = {
    'category': 'learning',
    'extract_mode': 'comprehensive'
}

response = requests.post(url, headers=headers, files=files, data=data)
result = response.json()

const formData = new FormData();
formData.append('file', fileInput.files[0]);
formData.append('category', 'learning');
formData.append('extract_mode', 'comprehensive');

const response = await fetch('https://api.memchat.io/integrations/files', {
  method: 'POST',
  headers: {
    'X-API-Key': 'YOUR_API_KEY',
  },
  body: formData,
});

const result = await response.json();

Request Parameters

file

required

Document file to process. Supported formats: PDF, DOCX, TXT, MD, HTML

string

Default category for extracted memories. One of: identity, career, interests, relationships, health, finance, learning, travel, productivity, private

string

default:"smart"

Extraction strategy. Options: comprehensive, smart, highlights, summary

number

default:"1000"

Maximum size of text chunks for processing (characters)

number

default:"200"

Overlap between chunks to maintain context

boolean

default:"true"

Whether to automatically categorize extracted memories

object

Additional metadata for the document

Show Metadata Properties

string

Document title

string

Document author

string

Source or origin of the document

array

Custom tags for the document

string

When the document was created

Response

string

Unique identifier for the file integration

string

Name of the uploaded file

number

Size of the uploaded file in bytes

string

Processing status: uploaded, processing, completed, error

object

Statistics about the extraction process

Show Extraction Statistics

number

Number of pages in the document

number

Total characters extracted

number

Number of text chunks created

number

Number of memories extracted

array

Categories detected in the content

array

List of extracted memories (if processing is complete)

Show Memory Properties

string

Unique identifier for the extracted memory

string

Extracted text content

string

Assigned category

number

Calculated importance score

number

Source page number in document

string

ISO 8601 timestamp when upload was initiated

string

ISO 8601 timestamp when processing completed

{
  "integration_id": "file_int_456def",
  "filename": "research_paper.pdf",
  "file_size": 2458697,
  "status": "completed",
  "extraction_stats": {
    "total_pages": 15,
    "text_length": 45678,
    "chunks_created": 23,
    "memories_extracted": 12,
    "categories_detected": ["learning", "career", "interests"]
  },
  "memories": [
    {
      "memory_id": "mem_789ghi",
      "content": "The study demonstrates that machine learning algorithms can improve medical diagnosis accuracy by 23% when trained on diverse datasets.",
      "category": "learning",
      "importance": 0.87,
      "page_number": 3
    }
  ],
  "created_at": "2024-01-15T16:30:00Z",
  "completed_at": "2024-01-15T16:32:15Z"
}

Extraction Modes

Comprehensive

Extracts memories from all significant text passages, creating detailed memories for each section.

Smart

Uses AI to identify the most important passages and concepts, creating focused memories.

Highlights

Extracts only key insights, findings, and actionable information.

Summary

Creates a single comprehensive memory summarizing the entire document.

Supported File Types

PDF

Text extraction from standard PDFs
OCR for scanned documents
Preserves page structure and formatting

Word Documents (DOCX)

Full text extraction
Maintains document structure
Extracts embedded content

Text Files (TXT, MD)

Direct text processing
Markdown formatting recognition
Fast processing

Web Pages (HTML)

Content extraction from HTML
Removes navigation and ads
Focuses on main content

Error Responses

object

Invalid file or parameters

{
  "error": "Unsupported file type",
  "code": "INVALID_FILE_TYPE",
  "supported_types": ["pdf", "docx", "txt", "md", "html"]
}

object

File too large

{
  "error": "File size exceeds limit",
  "code": "FILE_TOO_LARGE",
  "max_size_mb": 50,
  "file_size_mb": 75
}

Getting Started

Memory Management

User Profiles

Integrations

API Keys

Request Parameters

Response

Extraction Modes

Supported File Types

Error Responses

​Request Parameters

​Response

​Extraction Modes

​Supported File Types

​Error Responses

Request Parameters

Response

Extraction Modes

Supported File Types

Error Responses