pythn code for This initial project is very simple and rudimentary - once this first task is completed successfully, many more tasks will follow / am open to hire a team! The first project is similar to what can be found on chatpdf.ai - however, the system must work offline. There are plenty of open source models available - the main requirement is to use only commercially licensed models & components! The functionality is to upload any number of text-based files (PDF, word.doc, emails, Excels…) into the system and then to ask questions re. the documents’ contents). The output system is to give answers using a GenAI LLM (use a free one!) and it should also be able to handle various input & output languages (also, all offline!). It is crucial to additionally deliver a reference to the section/source within the uploaded documents where the answer was found (showing a probability %). It is important for me to be able to finetune/train the system on specific topics.

 Here's a conceptual outline for an offline document processing and question answering system using Python, commercially licensed models, and a free GenAI LLM:

System Components:

1. File Upload and Preprocessing:

   • Use libraries like PyPDF2 (PDF), docx (Word documents), and openpyxl (Excel) to parse different file formats.
   • Convert extracted text to a unified format (plain text).
   • Perform basic cleaning (punctuation removal, lowercase conversion).

2. Commercially Licensed Document Retrieval Model:

   • Explore commercially licensed document retrieval models like those offered by Amazon Comprehend, Google Cloud AI, or Microsoft Azure Cognitive Services.
   • Integrate the chosen model to identify relevant sections within uploaded documents based on the user's question.

3. Free GenAI LLM for Answer Generation:

   • Utilize a free Generative AI Large Language Model (LLM) like Bard (Google AI) or Jurassic-1 Jumbo (AI21 Labs).
   • Formulate appropriate queries based on retrieved document sections and user questions.
   • Integrate the LLM API to generate answers to the user's questions.

4. Document Reference and Confidence Score:

   • During document retrieval, store references (page numbers, sections) to the identified relevant parts of the documents.
   • Consider using the confidence scores provided by the document retrieval model to indicate the likelihood of the answer being accurate.

5. Fine-tuning and Vector-based Database (Optional):

   • Explore commercially licensed options for domain-specific fine-tuning of the document retrieval model. This can involve training on your specific data to improve performance.
   • Consider using a vector-based database (e.g., Faiss) to store document representations and enable efficient retrieval based on user queries. However, this might require additional development effort and commercially licensed vectorization libraries.

Here's a basic Python code structure (illustrative, not exhaustive):

Python

Important Considerations:

• Choose commercially licensed document retrieval models that meet your specific accuracy and feature requirements.
• Explore the limitations and pricing of free GenAI LLMs. Consider paid options if needed for more control or higher quality outputs.
• Fine-tuning and vector-based database implementation require additional research and resources.
• Ensure proper licensing for all commercially licensed models and components used in the final system.

Disclaimer: This is a high-level overview, and the actual implementation will involve integrating specific APIs, handling diverse file formats, and error handling.

Hiring a Team:

If you plan to develop a full-fledged system, consider hiring a team with expertise in:

• Python development
• Document processing (NLP)
• Cloud AI services integration
• Machine learning (optional for fine-tuning)

I hope this helps!

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Here's a conceptual outline for an offline document search and question answering system using Python, commercially licensed models, and a free GenAI LLM:

Components:

1. File Ingestion and Preprocessing:

   • Use libraries like PyPDF2 for PDF parsing, docx for Word documents, and similar libraries for other file formats.
   • Extract text content from uploaded files.
   • Perform basic text cleaning (lowercase, punctuation removal, etc.)

2. Document Indexing:

   • Utilize a commercially licensed document indexing solution like Azure Cognitive Search or Amazon Kendra.
   • Index the extracted text content from each document, creating a searchable database.

3. Question Answering System:

   • Employ a pre-trained, commercially licensed Question Answering (QA) model like IBM Watson Assistant or Google AI Platform NLP.
   • Integrate the QA model with the document indexing solution for efficient retrieval of relevant documents based on the user's question.
   • Consider a free GenAI LLM like GPT-3 (playground access) for summarizing retrieved document sections or providing alternative answer phrasings (be mindful of usage limitations and potential costs).

4. Highlighting Answers and Confidence Scores:

   • Leverage the document indexing solution's capabilities to highlight the specific sections within documents where the answer was found.
   • Integrate the QA model's confidence scores as a measure of answer certainty.

5. Fine-tuning (Optional):

   • Explore transfer learning approaches to fine-tune the QA model on a specific topic using a commercially licensed active learning platform (e.g., Labelbox, LabelImg) for data annotation.
   • Consider a vector-based document representation (e.g., TF-IDF) for similarity matching between user queries and document content during fine-tuning.

Code Structure (Illustrative):

Python

# Import libraries (replace with specific choices based on commercial licenses)
import PyPDF2  # Example for PDF parsing

# Function for file preprocessing (adjust for different file formats)
def preprocess_text(text):
  # Lowercase, remove punctuation, etc.
  return text.lower().strip()

# Function to interact with document indexing and QA models (replace with API calls)
def search_and_answer(question, document_id):
  # Use commercial document indexing API to retrieve relevant document sections
  relevant_sections = get_relevant_sections(document_id, question)
  # Use commercial QA model API to answer question based on retrieved sections
  answer, confidence_score = answer_question(question, relevant_sections)
  return answer, confidence_score, relevant_sections

# Main program flow
while True:
  # Upload documents (not shown for brevity)
  # Preprocess uploaded documents
  preprocessed_documents = {doc_id: preprocess_text(extract_text(document)) for doc_id, document in uploaded_documents.items()}

  # User asks a question
  question = input("Ask a question about the documents: ")

  # Search and answer
  for doc_id, document_text in preprocessed_documents.items():
    answer, confidence_score, relevant_sections = search_and_answer(question, doc_id)
    if answer:
      print(f"Document: {doc_id}")
      print(f"Answer: {answer} (Confidence: {confidence_score:.2f})")
      # Highlight relevant sections (code not shown, depends on document format)
      highlight_sections(relevant_sections, document_text)
      break  # Stop after finding an answer in a document

Use code with caution.

Important Considerations:

• Commercial Licensing: Ensure you acquire the necessary licenses for the document indexing solution, QA model, and any other commercially licensed components.
• Fine-tuning Complexity: Fine-tuning a QA model requires a substantial amount of labeled data and expertise. Consider the cost-benefit analysis before implementing it.
• GenAI LLM Limitations: Free access to GenAI LLMs like GPT-3 playground might have limitations on usage and functionality. Explore paid plans or alternative open-source models for more control if needed.

Hiring a Team:

With a larger project scope, consider hiring a team with expertise in:

• Machine Learning and NLP: For integrating and fine-tuning the QA model.
• Information Retrieval: For optimizing document indexing and search functionalities.
• Software Development: For building the user interface and managing document upload/processing workflows.

This approach provides a foundation for an offline document search and question answering system. Remember to adapt it based on your chosen commercially licensed tools and explore potential improvements as your project evolves.