Natuurlijk! Hier is een gedetailleerd plan en configuratieadvies om een AutoGPT-agent te ontwikkelen die automatisch documenten verwerkt, belangrijke informatie extraheert en korte samenvattingen produceert, met aandacht voor vertrouwelijkheid: **1. Doel en Functionaliteiten Bepalen** - Verwerk documenten in PDF- en Word-formaat. - Extraheer belangrijke informatie (zoals datums, namen, kernpunten). - Maak korte, overzichtelijke samenvattingen. - Output in een tekstbestand. - Behandel documenten vertrouwelijk en beveiligd. **2. Technische Benodigdheden en Tools** - Programmeertaal: Python (voor compatibiliteit en ondersteuning). - Documentverwerking: - PDF: `PyPDF2`, `pdfplumber`, of `pdfminer.six` - Word: `python-docx` - Natuurlijke taalverwerking: - `spaCy` of `transformers` (bijv. BERT, GPT-modellen voor samenvatting) - Automatisering en integratie: - AutoGPT-framework (voor agent-automatisering) - Optioneel: `langchain` voor ketenbeheer - Bestandsbeheer en privacy: - Lokale opslag, geen cloud als vertrouwelijkheid prioriteit heeft. - Versleuteling van opgeslagen data (`cryptography` module). **3. Stappenplan voor Ontwikkeling** ***a. Documentinvoer en preprocessing*** - Ontwikkel een interface (bijv. command line of GUI) om documenten te uploaden. - Detecteer het documenttype (PDF of Word). - Laad en converteer documenten naar platte tekst: - Voor PDF: gebruik `pdfplumber` of `PyPDF2`. - Voor Word: gebruik `python-docx`. - Verwijder ruis (headers, footers, ongewenste tekens). ***b. Vertrouwelijkheid en beveiliging*** - Bewaar documenten en resultaten lokaal. - Versleutel documenten en outputbestanden met AES (bijv. via `cryptography`). - Zorg dat gegevens niet worden gedeeld of opgeslagen op externe servers tenzij versleuteld en noodzakelijk. ***c. Informatie-extractie*** - Gebruik NLP-tools zoals `spaCy` voor named entity recognition (NER) om belangrijke gegevens te identificeren. - Bijvoorbeeld: datums, locaties, personen. - Pas regels of ML-modellen toe om kernpunten te identificeren. - Filter irrelevante info en focus op kerninhoud. ***d. Samenvatting genereren*** - Gebruik een GPT-model of andere transformer-gebaseerde samenvattingstechnologie. - Bijvoorbeeld: OpenAI API (gpt-3.5-turbo of gpt-4) met instructie voor korte samenvatting. - Alternatief: lokale modellen via Hugging Face (bijvoorbeeld `bart-large-cnn`). - Configureer het model voor korte samenvattingen: - Prompt voorbeeld: “Maak een korte samenvatting van de volgende tekst, niet langer dan 3 zinnen.” ***e. Output naar tekstbestand*** - Sla de samenvatting en belangrijke informatie op in een tekstbestand (.txt). - Voeg eventueel metadata toe (bestandsnaam, datum, documenttype). ***f. Automatisering via AutoGPT*** - Configureer AutoGPT met prompts en tools: - Input: document pad - Processen: extractie → samenvatting → opslag - Stel de agent zo in dat hij vertrouwelijke gegevens niet naar externe servers stuurt, tenzij versleuteld. - Zorg dat de agent meerdere documenten kan verwerken in batch. **4. Optimalisatie en Testing** - Test met verschillende documenten om de nauwkeurigheid van extractie en samenvatting te controleren. - Pas parameters aan: - Lengte van samenvatting - Nauwkeurigheid van info-extractie - Implementeer foutafhandeling en logging. **5. Beveiliging en Privacy** - Gebruik lokale modellen en opslag. - Versleutel alle vertrouwelijke gegevens. - Beperk toegang tot de systeem. - Overweeg een beveiligde omgeving (bijv. een geïsoleerde server). **6. Documentatie en Gebruik** - Schrijf duidelijke handleidingen voor gebruik. - Zorg voor eenvoudige installatie en configuratie. - Documenteer veiligheidsmaatregelen. **Samenvatting** Met dit plan bouw je een veilige, geautomatiseerde agent die documenten in PDF- en Word-formaat verwerkt, belangrijke info extraheert, korte samenvattingen maakt en alles lokaal opslaat, waardoor vertrouwelijkheid gewaarborgd blijft. Gebruik Python en relevante NLP-tools, configureer AutoGPT voor geautomatiseerde workflows en zorg voor encryptie en beveiliging. Heeft u nog specifieke vragen of wensen voor extra functionaliteiten?

Of course. Here is a detailed plan and configuration guidance to build and optimize an AutoGPT agent for confidential document processing and summarization. ### **Core Concept & Architecture** This agent will be an automated workflow that: 1. **Watches** a designated secure input folder for new documents. 2. **Processes** each document: reading text, extracting key information. 3. **Analyzes** the content to generate a concise, structured summary. 4. **Saves** the summary to a secure output location. 5. **Archives** or deletes the original document based on your retention policy. We'll build this using a scripted approach with Python, as a pure AutoGPT instance might be overkill and less controllable for this specific, repetitive task. However, we will use the same principles and tools that power AutoGPT. --- ### **Phase 1: Technology Stack & Setup** #### **1. Core Programming Language & Environment** * **Python 3.8+**: The standard for AI/ML tasks. Use a virtual environment (e.g., `venv` or `conda`) to manage dependencies. * **IDE**: VS Code, PyCharm, or Jupyter Notebook for development. #### **2. Document Processing Libraries (Key Dependencies)** * **PyPDF2** or **PyMuPDF (fitz)**: For extracting text from PDFs. PyMuPDF is faster and more robust with complex layouts. * **python-docx**: For extracting text from `.docx` files. * **Unstructured IO**: A powerful library that cleans and pre-processes documents from various formats, handling tables, headers, etc. Highly recommended. ```bash pip install pymupdf python-docx unstructured ``` #### **3. Natural Language Processing (NLP) & Summarization** * **Hugging Face `transformers`**: To use state-of-the-art summarization models. * **Model Choice**: Use a pre-trained model like `facebook/bart-large-cnn` or `google/pegasus-xsum`. These are specifically fine-tuned for summarization and will give high-quality, brief outputs. * **SpaCy**: Excellent for advanced information extraction (Named Entity Recognition - NER - to find names, dates, organizations) if your summaries require structured data. ```bash pip install transformers torch spacy python -m spacy download en_core_web_sm # or a larger model for better accuracy ``` #### **4. Security & Confidentiality (Crucial)** * **Local Execution**: The **gold standard** for confidentiality. All processing happens on your local machine or a private server you control. **Do not use external APIs** (like OpenAI's API) for processing the document text unless they offer a guaranteed private, compliant endpoint and you have reviewed their data policy. * **File Encryption**: Use libraries like `cryptography` to encrypt files at rest if they are stored on a server. ```bash pip install cryptography ``` * **Secure File Handling**: Implement strict folder permissions for the input and output directories. --- ### **Phase 2: Agent Development Plan** #### **Step 1: Document Ingestion** Create a function that monitors a directory (`./input_docs/`) and can process both PDF and Word files. ```python import os from pathlib import Path import fitz # PyMuPDF import docx def extract_text_from_pdf(file_path): doc = fitz.open(file_path) text = "" for page in doc: text += page.get_text() return text def extract_text_from_docx(file_path): doc = docx.Document(file_path) text = "\n".join([paragraph.text for paragraph in doc.paragraphs]) return text def load_document(file_path): file_extension = Path(file_path).suffix.lower() if file_extension == '.pdf': return extract_text_from_pdf(file_path) elif file_extension in ('.docx', '.doc'): return extract_text_from_docx(file_path) else: raise ValueError(f"Unsupported file format: {file_extension}") ``` #### **Step 2: Text Preprocessing** Clean the extracted text to improve summarization quality. ```python def preprocess_text(text): # Remove excessive whitespace, special characters, etc. import re text = re.sub(r'\s+', ' ', text) # Replace multiple spaces/newlines with a single space text = text.strip() return text ``` #### **Step 3: summarization Engine** Implement the Hugging Face model for summarization. ```python from transformers import pipeline # Load the model once at startup summarizer = pipeline("summarization", model="facebook/bart-large-cnn") def generate_summary(text, max_length=130, min_length=30): """ Generate a brief summary of the input text. Adjust max_length and min_length for desired summary brevity. """ # Model has a max input limit. We need to chunk long documents. if len(text.split()) > 1024: # Simple chunking strategy: split into chunks of 1000 words chunks = [text[i:i+1000] for i in range(0, len(text), 1000)] summaries = [] for chunk in chunks: summary = summarizer(chunk, max_length=max_length, min_length=min_length, do_sample=False) summaries.append(summary[0]['summary_text']) combined_summary = " ".join(summaries) # Summarize the combined summary of chunks for final coherence final_summary = summarizer(combined_summary, max_length=max_length, min_length=min_length, do_sample=False) return final_summary[0]['summary_text'] else: summary = summarizer(text, max_length=max_length, min_length=min_length, do_sample=False) return summary[0]['summary_text'] ``` #### **Step 4: Output and Logging** Save the summary to a text file and move the processed document. ```python def save_summary(summary_text, original_filename, output_dir="./summaries/"): Path(output_dir).mkdir(exist_ok=True) output_filename = f"{Path(original_filename).stem}_summary.txt" output_path = Path(output_dir) / output_filename with open(output_path, 'w', encoding='utf-8') as f: f.write(summary_text) print(f"Summary saved to: {output_path}") return output_path # Optional: Move processed file to an archive folder def secure_delete(file_path): # For true confidentiality, overwrite file before deletion. # Use a library like `secure_delete` for this. os.remove(file_path) ``` #### **Step 5: Main Orchestration Loop** Tie everything together into an automated agent. ```python import time INPUT_DIR = "./secure_input/" ARCHIVE_DIR = "./processed_archive/" def main_loop(): Path(INPUT_DIR).mkdir(exist_ok=True) Path(ARCHIVE_DIR).mkdir(exist_ok=True) print("Agent started. Watching for documents...") while True: for filename in os.listdir(INPUT_DIR): file_path = os.path.join(INPUT_DIR, filename) if os.path.isfile(file_path): print(f"Processing: {filename}") try: # 1. Extract text = load_document(file_path) # 2. Preprocess clean_text = preprocess_text(text) # 3. Summarize summary = generate_summary(clean_text) # 4. Save save_summary(summary, filename) # 5. Archive original (or delete) os.rename(file_path, os.path.join(ARCHIVE_DIR, filename)) # secure_delete(file_path) # Uncomment for secure deletion instead of archiving except Exception as e: print(f"Error processing {filename}: {e}") time.sleep(10) # Check for new files every 10 seconds if __name__ == "__main__": main_loop() ``` --- ### **Phase 3: Optimization & Configuration Guidance** 1. **Performance:** * **GPU Acceleration:** If you have an NVIDIA GPU, install `CUDA` and `cuDNN`. The `transformers` library will automatically use `torch` with GPU support, making summarization drastically faster. * **Model Choice:** Smaller models like `sshleifer/distilbart-cnn-12-6` are faster but slightly less accurate. Test different models for your use case. 2. **Summary Quality:** * **Prompt Engineering:** Instead of just the raw text, you can prepend a instruction to the model: `"summarize the following document in three bullet points: ..."`. This often yields better, more structured results. * **Fine-Tuning:** For the highest accuracy on your specific project's documents (e.g., legal, medical, technical), you could fine-tune the summarization model on a dataset of your documents and their ideal summaries. This is an advanced but highly effective step. 3. **Security Hardening:** * **User Permissions:** Run the agent under a dedicated user account with read/write permissions *only* to the necessary folders. * **Network Isolation:** Run the agent on a machine with no internet access if possible. Download all models (`summarizer = pipeline(..., device_map="auto")`) beforehand. * **Audit Logging:** Log all actions (file processed, summary saved, errors) to a secure file for auditing purposes. 4. **Deployment:** * **Script:** Run the Python script directly on a desktop or server. * **Docker:** Containerize the application for easy, consistent deployment and isolation. * **System Service (Linux):** Configure the script to run as a `systemd` service so it starts automatically on boot and restarts on failure. ### **Final Recommendation** Start by building and testing the core functions (text extraction, summarization) in a Jupyter notebook. Once you are satisfied with the quality of the summaries, integrate them into the orchestration loop. **Remember:** The key to confidentiality is **local processing**. By using the Hugging Face `transformers` library offline on your own hardware, you ensure the document data never leaves your control.