From 85327d7bd1af890b9c75df9a352d2efafa326295 Mon Sep 17 00:00:00 2001 From: Sitam Meur <103279526+sitammeur@users.noreply.github.com> Date: Thu, 5 Feb 2026 07:23:03 +0000 Subject: [PATCH 1/2] add form automation agentic workflow --- form-filling-automation/.env.example | 2 + form-filling-automation/README.md | 69 + form-filling-automation/app.py | 461 ++ form-filling-automation/documents/fw9.pdf | Bin 0 -> 140815 bytes .../documents/user-identity.pdf | Bin 0 -> 193438 bytes form-filling-automation/flow.py | 349 ++ form-filling-automation/main.py | 63 + form-filling-automation/models.py | 205 + form-filling-automation/pyproject.toml | 16 + form-filling-automation/schemas/w9.yaml | 83 + form-filling-automation/tools.py | 139 + form-filling-automation/uv.lock | 3946 +++++++++++++++++ 12 files changed, 5333 insertions(+) create mode 100644 form-filling-automation/.env.example create mode 100644 form-filling-automation/README.md create mode 100644 form-filling-automation/app.py create mode 100644 form-filling-automation/documents/fw9.pdf create mode 100644 form-filling-automation/documents/user-identity.pdf create mode 100644 form-filling-automation/flow.py create mode 100644 form-filling-automation/main.py create mode 100644 form-filling-automation/models.py create mode 100644 form-filling-automation/pyproject.toml create mode 100644 form-filling-automation/schemas/w9.yaml create mode 100644 form-filling-automation/tools.py create mode 100644 form-filling-automation/uv.lock diff --git a/form-filling-automation/.env.example b/form-filling-automation/.env.example new file mode 100644 index 000000000..3adecdc3e --- /dev/null +++ b/form-filling-automation/.env.example @@ -0,0 +1,2 @@ +DATALAB_API_KEY= +OPENROUTER_API_KEY= \ No newline at end of file diff --git a/form-filling-automation/README.md b/form-filling-automation/README.md new file mode 100644 index 000000000..1efd0a2a9 --- /dev/null +++ b/form-filling-automation/README.md @@ -0,0 +1,69 @@ +# Automated Form Filling + +Automated multi-agent workflow for filling out forms: extract data from documents using OCR, map it to form fields, and generate filled PDFs. The pipeline uses a W-9 tax form as an example, which can be extended to accommodate other forms. + +We use: + +- [CrewAI](https://docs.crewai.com/) (agentic design) for multi-agent orchestration +- [Datalab](https://documentation.datalab.to/) (document conversion & form filling) for OCR and form filling +- [Streamlit](https://streamlit.io/) for an interactive UI +- [MiniMax-M2.1](https://openrouter.ai/minimax/minimax-m2.1) (via OpenRouter) as the LLM for the agents + +## Set Up + +Follow these steps one by one: + +### Create .env File + +Create a `.env` file in the root directory of your project with the following content: + +```env +OPENROUTER_API_KEY= +DATALAB_API_KEY= +``` + +### Install Dependencies + +```bash +uv sync +source .venv/bin/activate +``` + +On Windows (PowerShell): + +```powershell +uv sync +.venv\Scripts\activate +``` + +This installs all required dependencies (CrewAI, Datalab SDK, Streamlit, etc.). + +## Run CrewAI Workflow (CLI) + +To run the form-filling workflow from the command line (e.g. with the bundled W-9 example): + +```bash +python main.py +``` + +You can also use the workflow programmatically via `run_form_flow()` in `main.py`, passing paths to your source document, blank form PDF, and form schema (YAML). + +## Run Streamlit Interface + +To run the Streamlit interface: + +```bash +streamlit run app.py +``` + +This starts the web UI where you can upload documents, choose a form schema, and run the pipeline. Use the URL shown in the terminal (e.g. `http://localhost:8501`) to open the app in your browser. + +## 📬 Stay Updated with Our Newsletter! + +**Get a FREE Data Science eBook** 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. [Subscribe now!](https://join.dailydoseofds.com) + +[![Daily Dose of Data Science Newsletter](https://github.com/patchy631/ai-engineering/blob/main/resources/join_ddods.png)](https://join.dailydoseofds.com) + +## Contribution + +Contributions are welcome! Feel free to fork this repository and submit pull requests with your improvements. diff --git a/form-filling-automation/app.py b/form-filling-automation/app.py new file mode 100644 index 000000000..f24a8c2e3 --- /dev/null +++ b/form-filling-automation/app.py @@ -0,0 +1,461 @@ +"""Streamlit UI for Generic Form Automation Agent.""" + +import base64 +from datetime import datetime +import json +import os +import tempfile + +from crewai import Crew, Process +from crewai.events import ( + AgentExecutionCompletedEvent, + AgentExecutionErrorEvent, + AgentExecutionStartedEvent, + BaseEventListener, + CrewKickoffCompletedEvent, + CrewKickoffStartedEvent, +) +import streamlit as st + +from flow import ( + create_document_scanner_agent, + create_fill_form_task, + create_form_filler_agent, + create_form_transformer_agent, + create_scan_document_task, + create_transform_data_task, +) +from models import FormSchema, GenericFormData, list_available_schemas, load_schema + + +# ── CrewAI Event Listener ───────────────────────────────────────────────── +class StreamlitLogListener(BaseEventListener): + """Captures CrewAI events into a list for Streamlit display.""" + + def __init__(self): + self.logs: list[dict] = [] + super().__init__() + + def setup_listeners(self, crewai_event_bus): + @crewai_event_bus.on(CrewKickoffStartedEvent) + def on_crew_started(source, event): + self.logs.append( + { + "time": datetime.now().strftime("%H:%M:%S"), + "type": "crew", + "icon": "rocket", + "message": f"Crew '{event.crew_name}' started", + } + ) + + @crewai_event_bus.on(CrewKickoffCompletedEvent) + def on_crew_completed(source, event): + self.logs.append( + { + "time": datetime.now().strftime("%H:%M:%S"), + "type": "crew", + "icon": "check", + "message": f"Crew '{event.crew_name}' completed", + "detail": str(event.output) if event.output else None, + } + ) + + @crewai_event_bus.on(AgentExecutionStartedEvent) + def on_agent_started(source, event): + self.logs.append( + { + "time": datetime.now().strftime("%H:%M:%S"), + "type": "agent", + "icon": "brain", + "message": f"Agent '{event.agent.role}' started working", + } + ) + + @crewai_event_bus.on(AgentExecutionCompletedEvent) + def on_agent_completed(source, event): + self.logs.append( + { + "time": datetime.now().strftime("%H:%M:%S"), + "type": "agent", + "icon": "white_check_mark", + "message": f"Agent '{event.agent.role}' completed", + "detail": str(event.output) if event.output else None, + } + ) + + @crewai_event_bus.on(AgentExecutionErrorEvent) + def on_agent_error(source, event): + self.logs.append( + { + "time": datetime.now().strftime("%H:%M:%S"), + "type": "error", + "icon": "x", + "message": f"Agent error: {event.error}", + } + ) + + def clear(self): + self.logs.clear() + + def render(self): + """Render collected logs as formatted text for st.code / st.markdown.""" + lines = [] + for entry in self.logs: + ts = entry["time"] + msg = entry["message"] + lines.append(f"[{ts}] {msg}") + if entry.get("detail"): + for dl in entry["detail"].splitlines(): + lines.append(f" {dl}") + return "\n".join(lines) if lines else "(no events captured)" + + def display(self): + """Render logs inside a scrollable container in Streamlit.""" + text = self.render() + st.html( + f'
'
+            f"{text}
" + ) + + +st.set_page_config( + page_title="Form Automation Agent", + page_icon=":page_facing_up:", + layout="wide", +) + +# ── Header ────────────────────────────────────────────────────────────────── +st.markdown("# :page_facing_up: Form Automation Agent") +powered_by_html = """ +
+ Powered by + + and + +
+""" +st.markdown(powered_by_html, unsafe_allow_html=True) +st.divider() + + +# ── Helpers ───────────────────────────────────────────────────────────────── +def pdf_preview(pdf_bytes: bytes, height: int = 400): + """Render an inline PDF preview using a base64 iframe.""" + b64 = base64.b64encode(pdf_bytes).decode() + st.markdown( + f'', + unsafe_allow_html=True, + ) + + +def extract_json_from_text(text: str) -> dict: + """Extract JSON from text that may contain markdown code blocks.""" + import re + + # Try direct parse first + try: + return json.loads(text) + except json.JSONDecodeError: + pass + + # Try to find JSON in markdown code block + json_pattern = r"```(?:json)?\s*([\s\S]*?)```" + matches = re.findall(json_pattern, text) + for match in matches: + try: + return json.loads(match.strip()) + except json.JSONDecodeError: + continue + + # Try to find raw JSON object + brace_pattern = r"\{[\s\S]*\}" + matches = re.findall(brace_pattern, text) + for match in matches: + try: + return json.loads(match) + except json.JSONDecodeError: + continue + + return {} + + +# ── Sidebar ───────────────────────────────────────────────────────────────── +with st.sidebar: + st.markdown("### :gear: Form Schema") + + # Load available schemas + available_schemas = list_available_schemas("schemas") + + if available_schemas: + schema_options = {s["form_name"]: s["path"] for s in available_schemas} + selected_schema_name = st.selectbox( + "Select Form Type", + options=list(schema_options.keys()), + help="Choose the type of form to fill", + ) + selected_schema_path = schema_options[selected_schema_name] + + # Show schema details + try: + schema = load_schema(selected_schema_path) + with st.expander("Schema Details", expanded=False): + st.write(f"**Form Type:** {schema.form_type}") + st.write(f"**Fields:** {len(schema.fields)}") + st.write("**Field Names:**") + for f in schema.fields: + req = "required" if f.required else "optional" + st.write(f"- `{f.name}` ({req})") + except Exception as e: + st.error(f"Error loading schema: {e}") + schema = None + else: + st.warning("No schemas found in 'schemas/' directory") + schema = None + selected_schema_path = None + + # Allow custom schema upload + st.markdown("---") + custom_schema_file = st.file_uploader( + "Or upload custom schema (YAML)", + type=["yaml", "yml"], + key="custom_schema", + help="Upload a custom form schema file", + ) + if custom_schema_file: + try: + import yaml + + schema_data = yaml.safe_load(custom_schema_file.getvalue().decode("utf-8")) + schema = FormSchema.from_dict(schema_data) + st.success(f"Loaded custom schema: {schema.form_name}") + except Exception as e: + st.error(f"Invalid schema file: {e}") + schema = None + + st.markdown("---") + st.markdown("### :arrow_up: Documents") + + identity_file = st.file_uploader( + "Source document (PDF)", + type=["pdf"], + key="identity", + help="Upload your source document (e.g. identity document, data source).", + ) + if identity_file: + pdf_preview(identity_file.getvalue(), height=250) + + st.markdown("---") + st.markdown("### :clipboard: Blank Form") + + blank_form_file = st.file_uploader( + "Upload blank form (PDF)", + type=["pdf"], + key="blank_form", + help="Upload blank form PDF to be filled.", + ) + if blank_form_file: + pdf_preview(blank_form_file.getvalue(), height=250) + + st.markdown("---") + run_disabled = identity_file is None or blank_form_file is None or schema is None + run_clicked = st.button( + ":rocket: Run Agent", + disabled=run_disabled, + type="primary", + use_container_width=True, + help="Kicks off multi-agent workflow: Scan -> Transform -> Fill.", + ) + +# ── Idle state ────────────────────────────────────────────────────────────── +if not run_clicked: + if schema is None: + st.warning("Please select or upload a form schema to continue.") + else: + st.info( + f"Ready to fill **{schema.form_name}**. " + "Upload your source document and blank form in sidebar, " + "then click **Run Agent** to start." + ) + st.stop() + +# ── Workflow execution ────────────────────────────────────────────────────── +tmp_dir = tempfile.mkdtemp(prefix="form_agent_") + +identity_path = os.path.join(tmp_dir, identity_file.name) +with open(identity_path, "wb") as f: + f.write(identity_file.getvalue()) + +blank_form_path = os.path.join(tmp_dir, blank_form_file.name) +with open(blank_form_path, "wb") as f: + f.write(blank_form_file.getvalue()) + +output_filename = f"{schema.form_type}_completed.pdf" +output_path = os.path.join(tmp_dir, output_filename) + +# Create the event listener once — it auto-registers with the CrewAI event bus +log_listener = StreamlitLogListener() + +steps_container = st.container() +extracted_text: str = "" +form_data: GenericFormData | None = None +failed = False + +# ── Step 1: Scan document ─────────────────────────────────────────────────── +with steps_container: + with st.status( + ":mag: **Step 1/3** — Scanning source document...", expanded=True + ) as s1: + try: + log_listener.clear() + + scanner_agent = create_document_scanner_agent() + scan_task = create_scan_document_task(scanner_agent) + + crew = Crew( + agents=[scanner_agent], + tasks=[scan_task], + process=Process.sequential, + verbose=True, + ) + result = crew.kickoff(inputs={"document_file": identity_path}) + + extracted_text = result.raw if result.raw else "" + if not extracted_text: + raise ValueError("No text extracted from document") + + log_listener.display() + s1.update( + label=":white_check_mark: **Step 1/3** — Document scanned", + state="complete", + expanded=False, + ) + except Exception as exc: + log_listener.display() + s1.update( + label=":x: **Step 1/3** — Scan failed", + state="error", + expanded=True, + ) + st.error(f"Document scan error: {exc}") + failed = True + +# ── Step 2: Transform data ────────────────────────────────────────────────── +if not failed: + with steps_container: + with st.status( + f":arrows_counterclockwise: **Step 2/3** — Transforming data for {schema.form_name}...", + expanded=True, + ) as s2: + try: + log_listener.clear() + + transformer_agent = create_form_transformer_agent(schema) + transform_task = create_transform_data_task(transformer_agent, schema) + + crew = Crew( + agents=[transformer_agent], + tasks=[transform_task], + process=Process.sequential, + verbose=True, + ) + result = crew.kickoff(inputs={"extracted_text": extracted_text}) + + # Parse result into GenericFormData (handles bool -> string conversion) + raw_output = result.raw if result.raw else "{}" + parsed = extract_json_from_text(raw_output) + form_data = GenericFormData.from_dict(parsed) + + log_listener.display() + s2.update( + label=":white_check_mark: **Step 2/3** — Data transformed", + state="complete", + expanded=False, + ) + except Exception as exc: + log_listener.display() + s2.update( + label=":x: **Step 2/3** — Transform failed", + state="error", + expanded=True, + ) + st.error(f"Data transform error: {exc}") + failed = True + +# ── Step 3: Fill form ─────────────────────────────────────────────────────── +if not failed: + with steps_container: + with st.status( + f":pencil2: **Step 3/3** — Filling {schema.form_name} PDF...", expanded=True + ) as s3: + try: + log_listener.clear() + + filler_agent = create_form_filler_agent(schema) + fill_task = create_fill_form_task(filler_agent, schema) + + crew = Crew( + agents=[filler_agent], + tasks=[fill_task], + process=Process.sequential, + verbose=True, + ) + crew.kickoff( + inputs={ + "form_data": json.dumps(form_data.to_tool_format(), indent=2), + "blank_form_path": blank_form_path, + "output_path": output_path, + } + ) + + log_listener.display() + s3.update( + label=":white_check_mark: **Step 3/3** — Form filled", + state="complete", + expanded=False, + ) + except Exception as exc: + log_listener.display() + s3.update( + label=":x: **Step 3/3** — Fill failed", + state="error", + expanded=True, + ) + st.error(f"Form fill error: {exc}") + failed = True + +if failed: + st.stop() + +# ── Results ───────────────────────────────────────────────────────────────── +st.balloons() +st.divider() + +col_data, col_result = st.columns([3, 2], gap="large") + +with col_data: + st.markdown(f"### :bar_chart: Extracted Data for {schema.form_name}") + if form_data: + st.json(form_data.to_tool_format()) + else: + st.info("No extracted data available.") + +with col_result: + st.markdown("### :white_check_mark: Completed Form") + if os.path.exists(output_path): + with open(output_path, "rb") as f: + pdf_bytes = f.read() + st.success(f"{schema.form_name} filled successfully!") + pdf_preview(pdf_bytes) + st.download_button( + label=":arrow_down: Download Completed PDF", + data=pdf_bytes, + file_name=output_filename, + mime="application/pdf", + use_container_width=True, + ) + else: + st.warning("Completed PDF not found at expected output path.") diff --git 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import Agent, Crew, Process, Task +from crewai.flow.flow import Flow, listen, start +from crewai.llm import LLM +from dotenv import load_dotenv + +from models import FormFillingFlowState, FormSchema, GenericFormData +from tools import DocumentScanTool, FormFillTool + +load_dotenv() + + +# ============================================================================= +# LLM Configuration +# ============================================================================= +llm = LLM( + model="openrouter/minimax/minimax-m2.1", + api_key=os.environ.get("OPENROUTER_API_KEY"), +) + + +# ============================================================================= +# Agent Factory Functions (Dynamic based on schema) +# ============================================================================= +def create_document_scanner_agent() -> Agent: + """Create the document scanner agent.""" + return Agent( + role="Document Scanner Specialist", + goal="Extract all information from uploaded documents.", + backstory=( + "You are an expert document analyst with years of experience in OCR " + "and data extraction. You specialize in reading documents. " + "You are meticulous about accuracy and can identify even poorly scanned text." + ), + tools=[DocumentScanTool()], + llm=llm, + ) + + +def create_form_transformer_agent(schema: FormSchema) -> Agent: + """Create the form data transformer agent based on schema.""" + field_instructions = schema.to_extraction_prompt() + + return Agent( + role="Form Data Transformer", + goal=( + f"Transform extracted data into the precise form field format for {schema.form_name}. " + "Map document data to the correct field names with descriptions and properly formatted values." + ), + backstory=( + f"You are a documentation specialist with deep expertise in {schema.form_name}. " + "You understand exactly how data from source documents maps to each field on the form. " + "You ensure data is properly formatted and that information is correctly structured.\n\n" + f"The form has these fields:\n{field_instructions}" + ), + llm=llm, + ) + + +def create_form_filler_agent(schema: FormSchema) -> Agent: + """Create the form filler agent based on schema.""" + return Agent( + role="Form Filler", + goal=( + f"Accurately map extracted personal data to the correct {schema.form_name} fields " + "and generate a completed PDF." + ), + backstory=( + "You are a precision-focused form filler. " + "You excel at matching extracted data to appropriate fields " + "and ensuring forms are filled correctly." + ), + tools=[FormFillTool()], + llm=llm, + ) + + +# ============================================================================= +# Task Factory Functions (Dynamic based on schema) +# ============================================================================= +def create_scan_document_task(agent: Agent) -> Task: + """Create the document scanning task.""" + return Task( + description=( + "Scan and extract ALL information from the document " + "document file: {document_file}\n\n" + "Be thorough and accurate. Return all text content found." + ), + expected_output="All extracted text content from the document.", + agent=agent, + ) + + +def create_transform_data_task(agent: Agent, schema: FormSchema) -> Task: + """Create the data transformation task based on schema.""" + field_instructions = schema.to_extraction_prompt() + + return Task( + description=( + f"Transform the extracted document data into {schema.form_name} field format.\n\n" + "Input Data:\n{extracted_text}\n\n" + f"Map the data to these form fields:\n{field_instructions}\n\n" + "You MUST create a JSON output where each field has:\n" + "- 'value': The actual data to fill in the field\n" + "- 'description': A description of what the field represents\n\n" + "Example format:\n" + '{"name": {"value": "JOHN DOE", "description": "Name of entity/individual"}, ' + '"address": {"value": "123 Main St", "description": "Street address"}}\n\n' + "IMPORTANT:\n" + "- Only include fields that have values from the source document\n" + "- Skip fields that don't have corresponding data\n" + "- For SSN/EIN split into separate fields (ssn_1, ssn_2, ssn_3), split the digits appropriately\n" + "- Strip hyphens from SSN/EIN values - use digits only" + ), + expected_output=( + "A JSON object with form fields mapped correctly. Each field must have " + "'value' and 'description' keys." + ), + agent=agent, + ) + + +def create_fill_form_task(agent: Agent, schema: FormSchema) -> Task: + """Create the form filling task based on schema.""" + return Task( + description=( + f"Fill out the {schema.form_name} using the provided form data.\n\n" + "Blank Form Path: {blank_form_path}\n\n" + "Form Data:\n{form_data}\n\n" + "Output Path: {output_path}\n\n" + f"Context for form filling: {schema.context}\n\n" + "CRITICAL INSTRUCTIONS:\n" + "1. The form_filler tool uses AI-powered semantic field matching.\n" + "2. Pass the field_data EXACTLY as provided above - use the semantic field names.\n" + "3. Each field MUST be a dict with 'value' and 'description' keys.\n" + "4. Do NOT map to PDF internal field names - use semantic names.\n" + "5. The tool automatically matches semantic fields to PDF form fields.\n" + "6. Pass the context parameter to help with semantic matching." + ), + expected_output="Confirmation of successful form filling with the path to the completed form.", + agent=agent, + ) + + +# ============================================================================= +# Generic Form Filling Flow +# ============================================================================= +class FormFillingFlow(Flow[FormFillingFlowState]): + """ + Generic Flow: Document Scanner → Form Data Transformer → Form Filler + + Works with any form type based on the provided FormSchema. + """ + + @start() + def initialize(self): + """Validate inputs and initialize workflow.""" + if not self.state.document_file: + self.state.errors.append("No document file provided") + return "failed" + + if not self.state.blank_form_path: + self.state.errors.append("No blank form path provided") + return "failed" + + if not self.state.form_schema: + self.state.errors.append("No form schema provided") + return "failed" + + if not self.state.output_path: + self.state.output_path = self.state.blank_form_path.replace( + ".pdf", "_completed.pdf" + ) + + self.state.status = "initialized" + return "ok" + + @listen(initialize) + def extract_document_data(self, prev): + """Extract data from document using OCR.""" + if prev == "failed": + return "failed" + + scanner_agent = create_document_scanner_agent() + scan_task = create_scan_document_task(scanner_agent) + + crew = Crew( + agents=[scanner_agent], + tasks=[scan_task], + process=Process.sequential, + verbose=True, + ) + result = crew.kickoff(inputs={"document_file": self.state.document_file}) + + # Store raw extracted text + self.state.extracted_text = result.raw if result.raw else "" + + if not self.state.extracted_text: + self.state.errors.append("No text extracted from document") + return "failed" + + self.state.status = "data_extracted" + return "ok" + + @listen(extract_document_data) + def transform_to_form_data(self, prev): + """Transform extracted text to form field format using schema.""" + if prev == "failed": + return "failed" + + if not self.state.extracted_text: + self.state.errors.append("No extracted text available to transform") + return "failed" + + schema = self.state.form_schema + transformer_agent = create_form_transformer_agent(schema) + transform_task = create_transform_data_task(transformer_agent, schema) + + crew = Crew( + agents=[transformer_agent], + tasks=[transform_task], + process=Process.sequential, + verbose=True, + ) + result = crew.kickoff(inputs={"extracted_text": self.state.extracted_text}) + + # Parse the result into GenericFormData (handles bool -> string conversion) + try: + raw_output = result.raw if result.raw else "{}" + # Try to extract JSON from the response + parsed = self._extract_json(raw_output) + + if isinstance(parsed, dict): + self.state.form_data = GenericFormData.from_dict(parsed) + else: + self.state.errors.append("Invalid form data format returned") + return "failed" + + except json.JSONDecodeError as e: + self.state.errors.append(f"Failed to parse form data: {e}") + return "failed" + + self.state.status = "form_data_ready" + return "ok" + + @listen(transform_to_form_data) + def fill_form(self, prev): + """Fill the form using the transformed data.""" + if prev == "failed": + return "failed" + + if not self.state.form_data: + self.state.errors.append("Missing form data") + return "failed" + + schema = self.state.form_schema + filler_agent = create_form_filler_agent(schema) + fill_task = create_fill_form_task(filler_agent, schema) + + crew = Crew( + agents=[filler_agent], + tasks=[fill_task], + process=Process.sequential, + verbose=True, + ) + crew.kickoff( + inputs={ + "form_data": json.dumps( + self.state.form_data.to_tool_format(), indent=2 + ), + "blank_form_path": self.state.blank_form_path, + "output_path": self.state.output_path, + } + ) + + self.state.completed_form_path = self.state.output_path + self.state.status = "completed" + return "ok" + + @listen(fill_form) + def finalize(self, prev): + """Return final results with state data for validation.""" + if prev == "failed" or self.state.errors: + return {"success": False, "errors": self.state.errors} + + # Log state for validation + print("\n" + "=" * 60) + print("FLOW STATE OUTPUT") + print("=" * 60) + print(f"\nStatus: {self.state.status}") + print( + f"Form Type: {self.state.form_schema.form_type if self.state.form_schema else 'N/A'}" + ) + + if self.state.form_data: + print( + f"\nForm Data (field_data):\n{json.dumps(self.state.form_data.to_tool_format(), indent=2)}" + ) + + print("=" * 60 + "\n") + + return { + "success": True, + "status": self.state.status, + "form_type": ( + self.state.form_schema.form_type if self.state.form_schema else None + ), + "form_data": ( + self.state.form_data.to_tool_format() if self.state.form_data else None + ), + "output_path": self.state.completed_form_path, + } + + def _extract_json(self, text: str) -> dict: + """Extract JSON from text that may contain markdown code blocks.""" + # Try direct parse first + try: + return json.loads(text) + except json.JSONDecodeError: + pass + + # Try to find JSON in markdown code block + json_pattern = r"```(?:json)?\s*([\s\S]*?)```" + matches = re.findall(json_pattern, text) + for match in matches: + try: + return json.loads(match.strip()) + except json.JSONDecodeError: + continue + + # Try to find raw JSON object + brace_pattern = r"\{[\s\S]*\}" + matches = re.findall(brace_pattern, text) + for match in matches: + try: + return json.loads(match) + except json.JSONDecodeError: + continue + + raise json.JSONDecodeError("No valid JSON found", text, 0) diff --git a/form-filling-automation/main.py b/form-filling-automation/main.py new file mode 100644 index 000000000..b6fb16b20 --- /dev/null +++ b/form-filling-automation/main.py @@ -0,0 +1,63 @@ +"""Main entry point for the generic form filling workflow.""" + +from typing import Any, Dict, Optional + +from flow import FormFillingFlow +from models import load_schema + + +def run_form_flow( + document_file: str, + blank_form_path: str, + schema_path: str, + output_path: Optional[str] = None, +) -> Dict[str, Any]: + """ + Run the generic form filling workflow. + + Args: + document_file: Path to source document file (e.g., identity document) + blank_form_path: Path to the blank form PDF to fill + schema_path: Path to the form schema YAML/JSON file + output_path: Optional path for the completed form + + Returns: + Dictionary with workflow results including: + - success: bool + - status: str + - form_type: str + - form_data: dict (the field_data used for filling) + - output_path: str (path to completed PDF) + - errors: list (if failed) + """ + # Load the form schema + schema = load_schema(schema_path) + + # Create and configure the flow + flow = FormFillingFlow() + flow.state.document_file = document_file + flow.state.blank_form_path = blank_form_path + flow.state.form_schema = schema + flow.state.output_path = output_path or "" + + return flow.kickoff() + + +# Backward compatibility alias +run_tax_form_flow = run_form_flow + + +if __name__ == "__main__": + # Example usage with W-9 form + document_file = "documents/user-identity.pdf" + blank_form_path = "documents/fw9.pdf" + schema_path = "schemas/w9.yaml" + output_path = "output/fw9_completed.pdf" + + result = run_form_flow( + document_file=document_file, + blank_form_path=blank_form_path, + schema_path=schema_path, + output_path=output_path, + ) + print(f"\nResult: {result}") diff --git a/form-filling-automation/models.py b/form-filling-automation/models.py new file mode 100644 index 000000000..1972ec9ba --- /dev/null +++ b/form-filling-automation/models.py @@ -0,0 +1,205 @@ +"""Pydantic Models for Structured Data - Generic Form Support""" + +from pathlib import Path +from typing import Any, Dict, List, Optional + +from pydantic import BaseModel, Field +import yaml + + +# ============================================================================= +# Generic Form Schema Models +# ============================================================================= +class FormField(BaseModel): + """Single field definition for any form schema.""" + + name: str = Field(..., description="Field key (e.g., 'ssn_1', 'name')") + description: str = Field(..., description="What to extract/fill for this field") + required: bool = Field(default=True, description="Whether field is required") + validation_pattern: Optional[str] = Field( + default=None, description="Optional regex pattern for validation" + ) + + +class FormSchema(BaseModel): + """Generic form schema - loaded from YAML/JSON config.""" + + form_type: str = Field( + ..., description="Form identifier (e.g., 'w9', '1099', 'i9')" + ) + form_name: str = Field(..., description="Human-readable form name") + context: str = Field( + ..., description="Description/context for the form filling API" + ) + fields: List[FormField] = Field(..., description="List of form fields") + + @classmethod + def from_yaml(cls, path: str) -> "FormSchema": + """Load form schema from a YAML file.""" + with open(path, "r", encoding="utf-8") as f: + data = yaml.safe_load(f) + return cls(**data) + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> "FormSchema": + """Load form schema from a dictionary.""" + return cls(**data) + + def get_field_names(self) -> List[str]: + """Get list of all field names.""" + return [f.name for f in self.fields] + + def get_required_fields(self) -> List[str]: + """Get list of required field names.""" + return [f.name for f in self.fields if f.required] + + def get_field_descriptions(self) -> Dict[str, str]: + """Get mapping of field names to descriptions.""" + return {f.name: f.description for f in self.fields} + + def to_extraction_prompt(self) -> str: + """Generate field extraction instructions for agents.""" + lines = [] + for f in self.fields: + req = "(required)" if f.required else "(optional)" + lines.append(f"- {f.name}: {f.description} {req}") + return "\n".join(lines) + + +class GenericFormData(BaseModel): + """Generic extracted/transformed form data - works with any form schema.""" + + field_data: Dict[str, Dict[str, str]] = Field( + default_factory=dict, + description="Form data: {field_name: {'value': str, 'description': str}}", + ) + + def to_tool_format(self) -> Dict[str, Dict[str, str]]: + """Convert to format expected by TaxFormFillTool.""" + return self.field_data + + def get_value(self, field_name: str) -> Optional[str]: + """Get value for a specific field.""" + if field_name in self.field_data: + return self.field_data[field_name].get("value") + return None + + def set_field(self, name: str, value: str, description: str) -> None: + """Set a field value with description.""" + self.field_data[name] = {"value": value, "description": description} + + @staticmethod + def _to_string(value: Any) -> str: + """Convert any value to string, handling booleans specially.""" + if isinstance(value, bool): + return "Yes" if value else "No" + if value is None: + return "" + return str(value) + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> "GenericFormData": + """Create from a dictionary, converting non-string values to strings.""" + normalized = {} + for field_name, field_info in data.items(): + if isinstance(field_info, dict): + normalized[field_name] = { + "value": cls._to_string(field_info.get("value", "")), + "description": cls._to_string(field_info.get("description", "")), + } + return cls(field_data=normalized) + + def validate_against_schema(self, schema: FormSchema) -> List[str]: + """Validate form data against schema, return list of errors.""" + errors = [] + for field in schema.fields: + if field.required: + if field.name not in self.field_data: + errors.append(f"Missing required field: {field.name}") + elif not self.field_data[field.name].get("value"): + errors.append(f"Empty value for required field: {field.name}") + return errors + + +# ============================================================================= +# Flow State Model +# ============================================================================= +class FormFillingFlowState(BaseModel): + """State management for the generic form filling flow.""" + + # Input paths + document_file: str = "" + blank_form_path: str = "" + output_path: str = "" + + # Schema (loaded from config) + form_schema: Optional[FormSchema] = None + + # Extracted and transformed data + extracted_text: str = "" # Raw OCR output + form_data: Optional[GenericFormData] = None # Transformed field data + + # Output + completed_form_path: str = "" + status: str = "pending" + errors: List[str] = Field(default_factory=list) + + +# ============================================================================= +# Helper Functions +# ============================================================================= +def load_schema(schema_path: str) -> FormSchema: + """Load a form schema from file path.""" + path = Path(schema_path) + if not path.exists(): + raise FileNotFoundError(f"Schema file not found: {schema_path}") + + if path.suffix in (".yaml", ".yml"): + return FormSchema.from_yaml(schema_path) + elif path.suffix == ".json": + import json + + with open(schema_path, "r", encoding="utf-8") as f: + data = json.load(f) + return FormSchema.from_dict(data) + else: + raise ValueError(f"Unsupported schema format: {path.suffix}") + + +def list_available_schemas(schemas_dir: str = "schemas") -> List[Dict[str, str]]: + """List all available form schemas in the schemas directory.""" + schemas = [] + schemas_path = Path(schemas_dir) + + if not schemas_path.exists(): + return schemas + + for file in schemas_path.glob("*.yaml"): + try: + schema = FormSchema.from_yaml(str(file)) + schemas.append( + { + "file": file.name, + "path": str(file), + "form_type": schema.form_type, + "form_name": schema.form_name, + } + ) + except Exception: + continue + + for file in schemas_path.glob("*.yml"): + try: + schema = FormSchema.from_yaml(str(file)) + schemas.append( + { + "file": file.name, + "path": str(file), + "form_type": schema.form_type, + "form_name": schema.form_name, + } + ) + except Exception: + continue + + return schemas diff --git a/form-filling-automation/pyproject.toml b/form-filling-automation/pyproject.toml new file mode 100644 index 000000000..51a2f2965 --- /dev/null +++ b/form-filling-automation/pyproject.toml @@ -0,0 +1,16 @@ +[project] +name = "form-filling-automation" +version = "0.1.0" +description = "Multi-Agent Workflow for automating form filling" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "crewai[anthropic,google-genai,tools]>=0.108.0", + "datalab-python-sdk>=0.2.1", + "litellm>=1.81.3", + "nest-asyncio>=1.6.0", + "pydantic>=2.0.0", + "python-dotenv>=1.2.1", + "pyyaml>=6.0", + "streamlit>=1.45.0", +] diff --git a/form-filling-automation/schemas/w9.yaml b/form-filling-automation/schemas/w9.yaml new file mode 100644 index 000000000..56e569562 --- /dev/null +++ b/form-filling-automation/schemas/w9.yaml @@ -0,0 +1,83 @@ +form_type: w9 +form_name: "W-9 Request for Taxpayer Identification Number" +context: "This is a W-9 tax form used by US persons (citizens, residents, or entities) to provide their correct Taxpayer Identification Number (TIN) to withholding agents or payers." +fields: + - name: name + description: "Name of entity/individual (Line 1)" + required: true + + - name: business_name + description: "Business name/disregarded entity name, if different from above (Line 2)" + required: false + + - name: individual_sole_proprietor + description: "Individual/sole proprietor or single-member LLC checkbox for federal tax classification (Line 3a)" + required: false + + - name: c_corporation + description: "C Corporation checkbox for federal tax classification (Line 3b)" + required: false + + - name: s_corporation + description: "S Corporation checkbox for federal tax classification (Line 3c)" + required: false + + - name: partnership + description: "Partnership checkbox for federal tax classification (Line 3d)" + required: false + + - name: trust_estate + description: "Trust/estate checkbox for federal tax classification (Line 3e)" + required: false + + - name: llc + description: "LLC checkbox with tax classification code (Line 3f)" + required: false + + - name: other + description: "Other federal tax classification (Line 3g)" + required: false + + - name: exempt_payee_code + description: "Exempt payee code if applicable (Line 4)" + required: false + + - name: fatca_exemption_code + description: "Exemption from FATCA reporting code if applicable (Line 4)" + required: false + + - name: address + description: "Address (number, street, and apt. or suite no.) (Line 5)" + required: true + + - name: city_state_zip + description: "City, state, and ZIP code (Line 6)" + required: true + + - name: requester_name_address + description: "Requester's name and address (optional) (Line 7)" + required: false + + - name: account_numbers + description: "List account number(s) here (optional) (Line 8)" + required: false + + - name: ssn_1 + description: "Social security number first 3 digits (Part I - box 1)" + required: false + + - name: ssn_2 + description: "Social security number middle 2 digits (Part I - box 2)" + required: false + + - name: ssn_3 + description: "Social security number last 4 digits (Part I - box 3)" + required: false + + - name: ein_1 + description: "Employer identification number first 2 digits (Part I - box 1)" + required: false + + - name: ein_2 + description: "Employer identification number last 7 digits (Part I - box 2)" + required: false diff --git a/form-filling-automation/tools.py b/form-filling-automation/tools.py new file mode 100644 index 000000000..274e4f8b9 --- /dev/null +++ b/form-filling-automation/tools.py @@ -0,0 +1,139 @@ +"""Tools powered by Datalab SDK for document scanning and form filling.""" + +import os +from pathlib import Path +from typing import Any, Dict, Type + +from crewai.tools import BaseTool +from datalab_sdk import ConvertOptions, DatalabClient, FormFillingOptions +from dotenv import load_dotenv +import nest_asyncio +from pydantic import BaseModel, Field + +# Allow nested event loops +nest_asyncio.apply() + +load_dotenv() + + +class DocumentScanInput(BaseModel): + file_path: str = Field(..., description="Path to user identity document file") + + +class FormFillInput(BaseModel): + form_path: str = Field(..., description="Path to the blank form PDF") + field_data: Dict[str, Any] = Field(..., description="Field data to fill") + output_path: str = Field(..., description="Path to save the completed form") + context: str = Field( + default="Generic form", + description="Context/description of the form for semantic field matching", + ) + + +class DocumentScanTool(BaseTool): + name: str = "document_scanner" + description: str = "Scans user identity document and extracts text using OCR." + args_schema: Type[BaseModel] = DocumentScanInput + + def _run(self, file_path: str) -> str: + # Validate file exists + if not Path(file_path).exists(): + return f"Error: File not found: {file_path}" + + # Validate API key + api_key = os.environ.get("DATALAB_API_KEY") + if not api_key: + return "Error: DATALAB_API_KEY environment variable not set" + + try: + client = DatalabClient(api_key=api_key) + options = ConvertOptions(output_format="markdown", mode="accurate") + result = client.convert(file_path, options=options) + + if result.success: + if not result.markdown or result.markdown.strip() == "": + return ( + "Error: OCR returned empty content - document may be unreadable" + ) + return result.markdown + + return f"Error: {getattr(result, 'error', 'Unknown OCR error')}" + + except ConnectionError as e: + return f"Error: Failed to connect to Datalab API: {e}" + except TimeoutError as e: + return f"Error: Datalab API request timed out: {e}" + except Exception as e: + return f"Error: Unexpected error during document scan: {e}" + + +class FormFillTool(BaseTool): + name: str = "form_filler" + description: str = ( + "Fills PDF form with field data and saves completed form. Accepts dynamic context for any form type." + ) + args_schema: Type[BaseModel] = FormFillInput + + def _run( + self, + form_path: str, + field_data: Dict[str, Any], + output_path: str, + context: str = "Generic form", + ) -> str: + # Validate form file exists + if not Path(form_path).exists(): + return f"Error: Form file not found: {form_path}" + + # Validate field_data is not empty + if not field_data: + return "Error: No field data provided to fill the form" + + # Validate API key + api_key = os.environ.get("DATALAB_API_KEY") + if not api_key: + return "Error: DATALAB_API_KEY environment variable not set" + + try: + client = DatalabClient(api_key=api_key) + options = FormFillingOptions( + field_data=field_data, + confidence_threshold=0.7, + context=context, # Dynamic context from schema + skip_cache=True, + ) + result = client.fill(form_path, options=options) + + if result.success: + # Ensure output directory exists + output_dir = Path(output_path).parent + output_dir.mkdir(parents=True, exist_ok=True) + + # Verify directory was created + if not output_dir.exists(): + return f"Error: Failed to create output directory: {output_dir}" + + result.save_output(output_path) + + # Verify output file was created + if not Path(output_path).exists(): + return f"Error: Form was processed but output file was not created: {output_path}" + + # Build diagnostic message + filled = getattr(result, "fields_filled", []) + not_found = getattr(result, "fields_not_found", []) + msg = f"Form filled successfully: {output_path}\n" + msg += f"Fields filled ({len(filled)}): {filled}\n" + msg += f"Fields not found ({len(not_found)}): {not_found}" + return msg + + return f"Error: {getattr(result, 'error', 'Unknown form filling error')}" + + except ConnectionError as e: + return f"Error: Failed to connect to Datalab API: {e}" + except TimeoutError as e: + return f"Error: Datalab API request timed out: {e}" + except PermissionError as e: + return f"Error: Permission 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extract_json_from_text(text: str) -> dict: extracted_text = result.raw if result.raw else "" if not extracted_text: raise ValueError("No text extracted from document") + if extracted_text.startswith("Error:"): + raise ValueError(extracted_text) log_listener.display() s1.update( diff --git a/form-filling-automation/flow.py b/form-filling-automation/flow.py index 085ba5837..3ba0dfb77 100644 --- a/form-filling-automation/flow.py +++ b/form-filling-automation/flow.py @@ -23,8 +23,8 @@ # LLM Configuration # ============================================================================= llm = LLM( - model="openrouter/minimax/minimax-m2.1", - api_key=os.environ.get("OPENROUTER_API_KEY"), + model="gemini/gemini-2.5-flash", + api_key=os.environ.get("GOOGLE_API_KEY"), ) @@ -208,6 +208,10 @@ def extract_document_data(self, prev): self.state.errors.append("No text extracted from document") return "failed" + if self.state.extracted_text.startswith("Error:"): + self.state.errors.append(self.state.extracted_text) + return "failed" + self.state.status = "data_extracted" return "ok"

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