Custom documents

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import requests
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BASE_URL = "https://alfa.boosted.ai/client"
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API_KEY = "YOUR_API_KEY_HERE"
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headers = {"x-api-key": API_KEY}  # No Content-Type for multipart uploads
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def upload_custom_documents(file_paths, base_path=""):
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    """
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    Upload one or more custom documents for use with agents.
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    Args:
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        file_paths (list): List of paths to files you want to upload
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        base_path (str, optional): Folder path where files should be stored
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    Returns:
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        dict: Response with uploaded document details
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    """
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    url = f"{BASE_URL}/custom-documents/add-documents"
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    # Add optional base_path parameter if provided
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    if base_path:
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        url += f"?base_path={base_path}"
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    # Prepare files for multipart upload
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    files = []
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    for path in file_paths:
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        file_name = path.split("/")[-1]
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        files.append(("files", (file_name, open(path, "rb"))))
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    # Make the request without Content-Type header (it's set automatically)
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    response = requests.post(
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        url,
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        headers={"x-api-key": API_KEY},
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        files=files
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    )
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    # Close all file handles
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    for _, (_, file_obj) in files:
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        file_obj.close()
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    if response.status_code == 202:
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        return response.json()
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    else:
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        print(f"Error uploading documents: {response.status_code}, {response.text}")
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        return None
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# Example usage
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file_paths = [
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    "/path/to/quarterly_report.pdf",
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    "/path/to/market_analysis.docx"
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]
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upload_result = upload_custom_documents(file_paths, "financial_reports")
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if upload_result and upload_result.get("success"):
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    print(f"Successfully uploaded {len(upload_result.get('added_listings', []))} documents")
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    for doc in upload_result.get("added_listings", []):
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        print(f"Document ID: {doc['file_id']}")
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        print(f"Name: {doc['name']}")
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        print(f"Path: {doc['full_path']}")
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        print(f"Size: {doc['size']} bytes")
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        print("---")

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def list_custom_documents():
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    """
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    List all custom documents in your workspace.
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    Returns:
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        list: List of document objects with their details
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    """
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    url = f"{BASE_URL}/custom-documents"
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    response = requests.get(url, headers=headers)
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    if response.status_code == 200:
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        data = response.json()
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        return data.get("documents", [])
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    else:
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        print(f"Error listing documents: {response.status_code}, {response.text}")
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        return []
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# Get and display all custom documents
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documents = list_custom_documents()
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print(f"Found {len(documents)} custom documents:")
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for doc in documents:
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    print(f"ID: {doc['file_id']}")
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    print(f"Name: {doc['name']}")
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    print(f"Path: {doc['full_path']}")
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    print(f"Type: {doc['type']}")
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    print(f"Upload time: {doc['upload_time']}")
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    print("---")

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def create_agent_with_document(prompt, document_id):
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    """
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    Create an agent that references a custom document.
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    Args:
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        prompt (str): The prompt with {doc} placeholder
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        document_id (str): The ID of the custom document to reference
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    Returns:
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        str: The agent ID if successful, empty string otherwise
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    """
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    url = f"{BASE_URL}/agent/create-agent"
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    # Create the request payload with the document reference
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    payload = {
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        "prompt": prompt,
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        "args": {
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            "doc": {
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                "id": document_id,
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                "type": "custom_document"
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            }
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        }
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    }
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    response = requests.post(url, headers=headers, json=payload)
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    if response.status_code == 201:
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        data = response.json()
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        agent_id = data.get("agent_id")
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        print(f"Agent created with ID: {agent_id}")
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        return agent_id
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    else:
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        print(f"Error creating agent: {response.status_code}, {response.text}")
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        return ""
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# Example: Create an agent to analyze a quarterly report
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document_id = "6fcc0ae7-809b-498c-b35b-4ced1114438b"  # ID from a previous upload
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prompt = "Read the custom document {doc} and summarize its key points. Format the summary to be easy to read."
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agent_id = create_agent_with_document(prompt, document_id)