"""Document Analyzer Agent — analyzes uploaded PDF/DOCX documents based on a user prompt.
Features:
- Works with Bindu A2A FilePart messages
- Supports PDF and DOCX
- Prompt-driven analysis
- Multi-file support
"""
from bindu.penguin.bindufy import bindufy
from agno.agent import Agent
from agno.models.openrouter import OpenRouter
from dotenv import load_dotenv
import os
import io
import base64
from pypdf import PdfReader
from docx import Document
load_dotenv()
# Define LLM agent
agent = Agent(
instructions = """
You are an advanced document analysis assistant.
Your job is to analyze uploaded documents and answer the user's prompt
based ONLY on the document content.
Guidelines:
- Carefully read the document text
- Extract relevant insights requested in the prompt
- Be structured and clear
- If the prompt asks for research insights, provide:
- methodology
- research gap
- key findings
- conclusions
- If the prompt asks for summary, provide concise bullet points
- Do not hallucinate information outside the document
""",
model = OpenRouter(
id = "arcee-ai/trinity-large-preview:free",
api_key=os.getenv("OPENROUTER_API_KEY"),
),
)
# Document Parsing
def extract_text_from_pdf(file_bytes):
"""Extract text from pdf bytes"""
try:
reader = PdfReader(io.BytesIO(file_bytes))
except Exception as e:
raise ValueError(f"Invalid PDF file: {str(e)}")
text = []
for page in reader.pages:
try:
page_text = page.extract_text()
if page_text:
text.append(page_text)
except Exception:
continue
return "\n".join(text)
def extract_text_from_docx(file_bytes):
"""Extract text from docx bytes"""
doc = Document(io.BytesIO(file_bytes))
return "\n".join([p.text for p in doc.paragraphs])
def extract_document_text(file_bytes, mime_type):
"""Parse document according to their mime type"""
if mime_type == "application/pdf":
return extract_text_from_pdf(file_bytes)
if mime_type in [
"application/vnd.openxmlformats-officedocument.wordprocessingml.document"
]:
return extract_text_from_docx(file_bytes)
raise ValueError(f"Unsupported file type: {mime_type}")
# FilePart processing
def get_file_bytes(part):
"""Extract file bytes from FilePart"""
file_info = part["file"]
if "bytes" in file_info:
data = file_info["bytes"]
elif "data" in file_info:
data = file_info["data"]
else:
raise ValueError("Unsupported file part format")
if isinstance(data, str):
return base64.b64decode(data)
return data
# Handler
def handler(messages: list[dict]):
"""
Receives task.history — a list of A2A Message objects.
Each message has: role, parts[], kind, messageId, contextId, taskId
Each part has: kind="text"|"file", and either text or file.bytes+mimeType
"""
if not messages:
return "No messages received."
prompt = ""
extracted_docs = []
for msg in messages:
role = msg.get("role")
if role is not None and role != "user":
continue
parts = msg.get("parts") or []
for part in parts:
if part.get("kind") == "text":
prompt = part.get("text", "")
elif part.get("kind") == "file":
try:
file_info = part.get("file", {})
b64_data = file_info.get("bytes") or file_info.get("data")
mime_type = file_info.get("mimeType", "")
if not b64_data:
raise ValueError("No file data found")
file_bytes = (
base64.b64decode(b64_data)
if isinstance(b64_data, str)
else b64_data
)
doc_text = extract_document_text(file_bytes, mime_type)
extracted_docs.append(doc_text)
except Exception as e:
extracted_docs.append(f"Error processing file: {str(e)}")
if not extracted_docs:
return "No valid document found in the messages."
combined_document = "\n\n".join(extracted_docs)
result = agent.run(input=f"""
User Prompt:
{prompt}
Document Content:
{combined_document}
Provide analysis based on the prompt.
""")
return result
# Bindu config
config = {
"author" : "vyomrohila@gmail.com",
"name" : "document_analyzer_agent",
"description": "AI agent that analyzes uploaded PDF or DOCX documents based on a user prompt.",
"deployment": {
"url": "http://localhost:3773",
"expose": True,
"cors_origins": ["http://localhost:5173"],
},
"skills": ["skills/document-processing"],
"enable_system_message": False,
}
if __name__ == "__main__":
bindufy(config, handler)
