File Attachments

Send documents, PDFs, and files to LLMs for analysis and processing.

Overview

Eden AI V3 LLM endpoints support file attachments, enabling you to:

Analyze PDF documents
Process text files
Extract data from structured documents
Summarize reports and papers
Answer questions about document content

File support varies by provider, with some supporting advanced document understanding and others focused on text extraction.

Supported File Formats

Format	Extension	OpenAI	Anthropic	Google	Use Cases
PDF	.pdf	✓	✓	✓	Reports, invoices, contracts
Text	.txt	✓	✓	✓	Logs, code, plain text
Word	.docx	✓	✓	✓	Documents, reports
Rich Text	.rtf	-	✓	-	Formatted documents

File Input Methods

V3 provides three ways to send files to LLMs:

1. File Upload (Recommended)

Upload files once, reference multiple times:

import requests

# Step 1: Upload the file
upload_url = "https://api.edenai.run/v3/upload"
upload_headers = {"Authorization": "Bearer YOUR_API_KEY"}

files = {"file": open("report.pdf", "rb")}
data = {"purpose": "llm-analysis"}

upload_response = requests.post(
    upload_url,
    headers=upload_headers,
    files=files,
    data=data
)

file_id = upload_response.json()["file_id"]
print(f"Uploaded file ID: {file_id}")

# Step 2: Use the file in LLM request
llm_url = "https://api.edenai.run/v3/llm/chat/completions"
llm_headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "openai/gpt-4o",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Summarize this document in 3 bullet points."
                },
                {
                    "type": "file",
                    "file": {"file_id": file_id}
                }
            ]
        }
    ]
}

response = requests.post(llm_url, headers=llm_headers, json=payload)
result = response.json()
print(result["choices"][0]["message"]["content"])

2. File URL

Use publicly accessible file URLs:

import requests

url = "https://api.edenai.run/v3/llm/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "anthropic/claude-sonnet-4-5",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Extract all key findings from this research paper."
                },
                {
                    "type": "file",
                    "file": {
                        "file_id": "https://example.com/research-paper.pdf"
                    }
                }
            ]
        }
    ]
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result["choices"][0]["message"]["content"])

3. Base64 File Data

Encode files directly in the request:

import base64
import requests

# Read and encode file
with open("contract.pdf", "rb") as f:
    file_data = base64.b64encode(f.read()).decode('utf-8')

# Create data URL
data_url = f"data:application/pdf;base64,{file_data}"

url = "https://api.edenai.run/v3/llm/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "google/gemini-1.5-pro",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Review this contract and highlight any concerning clauses."
                },
                {
                    "type": "file",
                    "file": {"file_data": data_url}
                }
            ]
        }
    ]
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result["choices"][0]["message"]["content"])

Common Use Cases

Document Summarization

Extract key points from long documents:

import requests

# Upload document
upload_response = requests.post(
    "https://api.edenai.run/v3/upload",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    files={"file": open("quarterly-report.pdf", "rb")}
)
file_id = upload_response.json()["file_id"]

# Request summary
url = "https://api.edenai.run/v3/llm/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "anthropic/claude-sonnet-4-5",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": """Provide a comprehensive summary with:
                    1. Executive summary (2-3 sentences)
                    2. Key metrics and numbers
                    3. Main highlights
                    4. Concerns or risks mentioned
                    5. Recommendations"""
                },
                {
                    "type": "file",
                    "file": {"file_id": file_id}
                }
            ]
        }
    ],
    "max_tokens": 1500
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result["choices"][0]["message"]["content"])

Question Answering on Documents

Ask specific questions about document content:

import requests

# Upload document once
upload_response = requests.post(
    "https://api.edenai.run/v3/upload",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    files={"file": open("policy-document.pdf", "rb")}
)
file_id = upload_response.json()["file_id"]

url = "https://api.edenai.run/v3/llm/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

# Ask multiple questions about the same document
questions = [
    "What is the refund policy?",
    "Are there any age restrictions?",
    "What payment methods are accepted?"
]

for question in questions:
    payload = {
        "model": "openai/gpt-4o",
        "messages": [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": question},
                    {"type": "file", "file": {"file_id": file_id}}
                ]
            }
        ],
        "temperature": 0.2  # Low for factual answers
    }

    print(f"\nQuestion: {question}")
    response = requests.post(url, headers=headers, json=payload)
    result = response.json()
    answer = result["choices"][0]["message"]["content"]

    print(f"Answer: {answer}\n")

Data Extraction

Extract structured data from documents:

import requests
import json

# Upload invoice
upload_response = requests.post(
    "https://api.edenai.run/v3/upload",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    files={"file": open("invoice.pdf", "rb")}
)
file_id = upload_response.json()["file_id"]

url = "https://api.edenai.run/v3/llm/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "openai/gpt-4o",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": """Extract the following from this invoice as JSON:
                    {
                      "invoice_number": "",
                      "date": "",
                      "vendor": "",
                      "total_amount": 0.0,
                      "currency": "",
                      "line_items": [
                        {"description": "", "quantity": 0, "unit_price": 0.0, "total": 0.0}
                      ],
                      "tax_amount": 0.0
                    }"""
                },
                {
                    "type": "file",
                    "file": {"file_id": file_id}
                }
            ]
        }
    ],
    "temperature": 0.1  # Very low for accurate extraction
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
extracted_data = result["choices"][0]["message"]["content"]

# Parse extracted JSON
invoice_data = json.loads(extracted_data)
print("Invoice Number:", invoice_data["invoice_number"])
print("Total Amount:", invoice_data["total_amount"])
print("Line Items:", len(invoice_data["line_items"]))

Contract Analysis

Review legal documents and contracts:

import requests

upload_response = requests.post(
    "https://api.edenai.run/v3/upload",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    files={"file": open("contract.pdf", "rb")}
)
file_id = upload_response.json()["file_id"]

url = "https://api.edenai.run/v3/llm/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "anthropic/claude-opus-4-5",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": """Analyze this contract and provide:
                    1. Contract type and parties involved
                    2. Key terms and obligations
                    3. Payment terms and schedule
                    4. Termination clauses
                    5. Liability and indemnification
                    6. Potential risks or concerning clauses
                    7. Missing standard clauses
                    8. Overall assessment"""
                },
                {
                    "type": "file",
                    "file": {"file_id": file_id}
                }
            ]
        }
    ],
    "max_tokens": 2000,
    "temperature": 0.3
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result["choices"][0]["message"]["content"])

Code Review

Analyze code files and provide feedback:

import requests

# Upload code file
upload_response = requests.post(
    "https://api.edenai.run/v3/upload",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    files={"file": open("app.py", "rb")}
)
file_id = upload_response.json()["file_id"]

url = "https://api.edenai.run/v3/llm/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "openai/gpt-4o",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": """Review this code and provide:
                    1. Code quality assessment
                    2. Potential bugs or issues
                    3. Security vulnerabilities
                    4. Performance improvements
                    5. Best practice violations
                    6. Suggested refactoring"""
                },
                {
                    "type": "file",
                    "file": {"file_id": file_id}
                }
            ]
        }
    ],
    "max_tokens": 1500
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result["choices"][0]["message"]["content"])

Provider Capabilities

OpenAI (GPT-4o, GPT-4-turbo)

Strengths:

Fast document processing
Good for structured extraction
Reliable with common formats
Strong multi-page PDF handling

Limitations:

Max file size: 512 MB
Best for text-heavy documents

Example:

"model": "openai/gpt-4o"

Anthropic (Claude 3 Family)

Strengths:

Excellent reasoning about documents
Superior for complex analysis
Great for legal/technical documents
Detailed, thoughtful responses

Limitations:

Max file size: 10 MB (per file)
Slightly slower than OpenAI

Example:

"model": "anthropic/claude-sonnet-4-5"

Google (Gemini 1.5)

Strengths:

Massive context window (2GB+ files)
Best for very large documents
Fast processing (Flash variant)
Multi-document analysis

Limitations:

May be less detailed on complex reasoning

Example:

"model": "google/gemini-1.5-pro"

File Management Best Practices

Upload Once, Use Multiple Times

Files uploaded to /v3/upload persist for 7 days:

import requests
# Upload once
upload_response = requests.post(
    "https://api.edenai.run/v3/upload",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    files={"file": open("large-report.pdf", "rb")}
)
file_id = upload_response.json()["file_id"]

# Use in multiple requests over the next 7 days
for question in questions:
    payload = {
        "model": "openai/gpt-4o",
        "messages": [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": question},
                    {"type": "file", "file": {"file_id": file_id}}
                ]
            }
        ]
    }
    response = requests.post(url, headers=headers, json=payload)
    result = response.json()
    print(result["choices"][0]["message"]["content"])

File Size Optimization

For large files, consider preprocessing:

import requests
from PyPDF2 import PdfReader, PdfWriter

def extract_relevant_pages(input_pdf, pages_range):
    """Extract specific pages to reduce file size."""
    reader = PdfReader(input_pdf)
    writer = PdfWriter()

    for page_num in pages_range:
        writer.add_page(reader.pages[page_num])

    output_pdf = "extracted_pages.pdf"
    with open(output_pdf, "wb") as f:
        writer.write(f)

    return output_pdf

# Extract only pages 1-5 from a 100-page document
small_pdf = extract_relevant_pages("large-report.pdf", range(0, 5))

# Upload the smaller file
upload_response = requests.post(
    "https://api.edenai.run/v3/upload",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    files={"file": open(small_pdf, "rb")}
)

Handling Expiration

Track and refresh expired files:

from datetime import datetime, timedelta
import requests

class FileManager:
    def __init__(self, api_key):
        self.api_key = api_key
        self.files = {}  # {local_path: {file_id, expires_at}}

    def get_file_id(self, local_path):
        """Get file ID, re-uploading if expired."""
        if local_path in self.files:
            file_info = self.files[local_path]
            expires_at = datetime.fromisoformat(file_info["expires_at"])

            if datetime.now() < expires_at:
                return file_info["file_id"]

        # Upload new/expired file
        response = requests.post(
            "https://api.edenai.run/v3/upload",
            headers={"Authorization": f"Bearer {self.api_key}"},
            files={"file": open(local_path, "rb")}
        )
        result = response.json()

        self.files[local_path] = {
            "file_id": result["file_id"],
            "expires_at": result["expires_at"]
        }

        return result["file_id"]

# Usage
manager = FileManager("YOUR_API_KEY")
file_id = manager.get_file_id("document.pdf")  # Handles re-upload if needed

Advanced Patterns

Multi-Document Analysis

Analyze multiple documents together:

import requests

# Upload multiple documents
file_ids = []
for doc in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]:
    response = requests.post(
        "https://api.edenai.run/v3/upload",
        headers={"Authorization": "Bearer YOUR_API_KEY"},
        files={"file": open(doc, "rb")}
    )
    file_ids.append(response.json()["file_id"])

# Analyze all together
url = "https://api.edenai.run/v3/llm/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "google/gemini-1.5-pro",  # Large context window
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Compare these three documents and identify: 1) Common themes, 2) Discrepancies, 3) Unique points in each"
                }
            ] + [
                {"type": "file", "file": {"file_id": fid}}
                for fid in file_ids
            ]
        }
    ],
    "max_tokens": 2000
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result["choices"][0]["message"]["content"])

Conversational Document Analysis

Build multi-turn conversations about documents:

import requests

# Upload document
upload_response = requests.post(
    "https://api.edenai.run/v3/upload",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    files={"file": open("report.pdf", "rb")}
)
file_id = upload_response.json()["file_id"]

url = "https://api.edenai.run/v3/llm/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

# Conversation history
messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "What are the main conclusions of this report?"},
            {"type": "file", "file": {"file_id": file_id}}
        ]
    }
]

# First question
response = requests.post(
    url,
    headers=headers,
    json={"model": "anthropic/claude-sonnet-4-5", "messages": messages}
)

# Collect assistant's response
result = response.json()
assistant_response = result["choices"][0]["message"]["content"]

# Add to history
messages.append({"role": "assistant", "content": assistant_response})

# Follow-up question (no need to send file again)
messages.append({
    "role": "user",
    "content": "What data supports these conclusions?"
})

# Second request uses conversation context
response = requests.post(
    url,
    headers=headers,
    json={"model": "anthropic/claude-sonnet-4-5", "messages": messages}
)
result = response.json()
print(result["choices"][0]["message"]["content"])

Error Handling

Common File Errors

File too large:

{
  "error": {
    "code": "file_too_large",
    "message": "File size exceeds maximum allowed (512 MB for this provider)"
  }
}

Unsupported format:

{
  "error": {
    "code": "unsupported_file_type",
    "message": "File type .xlsx is not supported for this provider"
  }
}

File not found:

{
  "error": {
    "code": "file_not_found",
    "message": "File with ID 550e8400-e29b-41d4-a716-446655440000 not found or expired"
  }
}

Robust Error Handling

import requests
import os

def process_document_safe(file_path, prompt):
    """Process document with comprehensive error handling."""
    # Check file exists
    if not os.path.exists(file_path):
        raise FileNotFoundError(f"File not found: {file_path}")

    # Check file size
    file_size_mb = os.path.getsize(file_path) / (1024 * 1024)
    if file_size_mb > 500:
        raise ValueError(f"File too large: {file_size_mb:.1f} MB (max 500 MB)")

    try:
        # Upload
        upload_response = requests.post(
            "https://api.edenai.run/v3/upload",
            headers={"Authorization": "Bearer YOUR_API_KEY"},
            files={"file": open(file_path, "rb")},
            timeout=60
        )
        upload_response.raise_for_status()
        file_id = upload_response.json()["file_id"]

        # Process
        llm_response = requests.post(
            "https://api.edenai.run/v3/llm/chat/completions",
            headers={
                "Authorization": "Bearer YOUR_API_KEY",
                "Content-Type": "application/json"
            },
            json={
                "model": "openai/gpt-4o",
                "messages": [
                    {
                        "role": "user",
                        "content": [
                            {"type": "text", "text": prompt},
                            {"type": "file", "file": {"file_id": file_id}}
                        ]
                    }
                ]
            },
            timeout=120
        )
        llm_response.raise_for_status()

        return llm_response.json()

    except requests.exceptions.Timeout:
        print("Request timeout. Try with a smaller file or simpler prompt.")
    except requests.exceptions.HTTPError as e:
        if e.response.status_code == 413:
            print("File too large for provider. Try splitting the document.")
        elif e.response.status_code == 422:
            print("Invalid file format:", e.response.json())
        else:
            print(f"HTTP error: {e}")
    except Exception as e:
        print(f"Unexpected error: {e}")

# Usage
response = process_document_safe("report.pdf", "Summarize this document")

Next Steps

Working with Media Files - Complete media guide
Vision Capabilities - Image analysis
Upload Files - File management
Chat Completions - Core LLM features
Streaming Responses - Handle SSE streams

V3 Documentation

File attachments

File Attachments

Overview

Supported File Formats

File Input Methods

1. File Upload (Recommended)

2. File URL

3. Base64 File Data

Common Use Cases

Document Summarization

Question Answering on Documents

Data Extraction

Contract Analysis

Code Review

Provider Capabilities

OpenAI (GPT-4o, GPT-4-turbo)

Anthropic (Claude 3 Family)

Google (Gemini 1.5)

File Management Best Practices

Upload Once, Use Multiple Times

File Size Optimization

Handling Expiration

Advanced Patterns

Multi-Document Analysis

Conversational Document Analysis

Error Handling

Common File Errors

Robust Error Handling

Next Steps

V3 Documentation

​File Attachments

​Overview

​Supported File Formats

​File Input Methods

​1. File Upload (Recommended)

​2. File URL

​3. Base64 File Data

​Common Use Cases

​Document Summarization

​Question Answering on Documents

​Data Extraction

​Contract Analysis

​Code Review

​Provider Capabilities

​OpenAI (GPT-4o, GPT-4-turbo)

​Anthropic (Claude 3 Family)

​Google (Gemini 1.5)

​File Management Best Practices

​Upload Once, Use Multiple Times

​File Size Optimization

​Handling Expiration

​Advanced Patterns

​Multi-Document Analysis

​Conversational Document Analysis

​Error Handling

​Common File Errors

​Robust Error Handling

​Next Steps

File Attachments

Overview

Supported File Formats

File Input Methods

1. File Upload (Recommended)

2. File URL

3. Base64 File Data

Common Use Cases

Document Summarization

Question Answering on Documents

Data Extraction

Contract Analysis

Code Review

Provider Capabilities

OpenAI (GPT-4o, GPT-4-turbo)

Anthropic (Claude 3 Family)

Google (Gemini 1.5)

File Management Best Practices

Upload Once, Use Multiple Times

File Size Optimization

Handling Expiration

Advanced Patterns

Multi-Document Analysis

Conversational Document Analysis

Error Handling

Common File Errors

Robust Error Handling

Next Steps