Building a CG&S Industry$-$Specific GenAI Advanced Multimodal RAG Chatbot $($in Python$)$:You are tasked with developing an advanced chatbot specifically tailored for the Consumer Goods & Services $($CG&S$)$ industry using Python. This chatbot will integrate powerful Generative AI models such as Gemini, GPT$,$ Mistral, and Claude, and must handle multimodal inputs, including PDFs$,$ text files, images, and table data. Follow the instructions below to ensure the solution is comprehensive and fully functional. Make sure not to use ChatGPT during the development of this solution.

$1.$ Data Preparation

Input Types: The system must support inputs in various formats: PDFs$,$ text files, images $($containing product information, reviews, etc.$),$ and structured table data. Ensure the model can process and retrieve meaningful insights from these multimodal inputs.

OCR for Images: Implement OCR $($Optical Character Recognition$)$ to extract textual information from images. This will allow the chatbot to handle image inputs seamlessly.

Knowledge Base Storage: Organize the inputs locally and ensure they are stored in a structured format for efficient retrieval.

$2.$ Vector Database Setup

Database Choice: Choose Faiss or Chroma to set up the vector database, which will store embeddings and handle semantic search for faster retrieval.

Indexing: Use pre$-$trained models $($available on Hugging Face$)$ to generate vector embeddings for the data. Ensure that embeddings for text, PDF$,$ image, and table data are indexed correctly to enable quick information retrieval.

$3.$ LLM Integration

Model Incorporation: Integrate the following models$$Gemini$,$ GPT$,$ Mistral, and Claude$$for generating intelligent, contextually relevant responses.

Multimodal Capabilities: Test and ensure that the models can handle multimodal inputs $($text$,$ images, PDFs$,$ tables$)$ efficiently.

Hyperparameter Tuning: Fine$-$tune these models via Hugging Face's API, adjusting parameters like learning rate, batch size, and embedding dimensions to maximize performance.

$4.$ Frameworks

LangChain: Utilize LangChain to link various components $($LLMs$,$ vector database, document retrieval system$).$ This will form the backbone of your multimodal chatbot pipeline.

LlamaIndex: Use LlamaIndex for efficient indexing and querying of the document repository, enabling fast and accurate searches within the data.

$5.$ Prompt Engineering

Chain of Thought Prompts: Develop prompts that encourage the models to generate structured, step$-$by$-$step reasoning responses to enhance coherence and relevance.

Multimodal Prompting: Ensure that your prompts are designed to support multimodal inputs, allowing the chatbot to process and respond to queries based on PDFs$,$ text, images, and tables.

$6.$ Evaluation Metrics

The solution must be evaluated comprehensively across various dimensions:

Completeness: Check whether the chatbot provides complete and thorough responses to user queries.

Coherence: Ensure the chatbot$$s responses are logically structured and easy to follow.

Relevance: The responses should be highly relevant to the user$$s queries, considering all input types.

Semantic Similarity: Use cosine similarity to measure how closely the response matches the query.

Correctness: Validate the factual accuracy of the information generated by the chatbot.

Context Precision & Recall: Evaluate how well the chatbot understands the query context and retrieves information with high precision.

Answer Ranking: Implement a ranking mechanism where the chatbot provides responses ordered by relevance and confidence score.

$7.$ Optimization

Similarity Search: Implement similarity search algorithms to optimize the chatbot$$s ability to retrieve the most contextually relevant information. This will help enhance the overall quality and relevance of responses.

Performance Monitoring: Continuously monitor the system for speed and efficiency, especially when dealing with large datasets, and make adjustments as necessary.

$8.$ Benchmarking

Model Performance Comparison: Benchmark the Gemini, GPT$,$ Mistral, and Claude models across various data types $($text$,$ images, PDFs$,$ and tables$)$ based on the evaluation metrics.

Scenario$-$Specific Comparison: Perform an advanced analysis, comparing model performance for different input types $($text vs$.$ multimodal$)$ and document the insights.

Visualization: Use graphs or charts to represent the comparison results for easier interpretation of the models' strengths and weaknesses.

$9.$ Deployment in Streamlit

UI Development: Create an intuitive and user$-$friendly interface using Streamlit. Ensure that the UI supports:

Real$-$time query input and instant response generation.

Query history tracking for users to revisit previous queries and responses.

Visual feedback for image, PDF$,$ and table queries.

Scalable and Secure Deployment: Deploy the chatbot on a cloud platform ensuring that it can handle multiple users efficiently. Implement security measures to protect data and user privacy.