Please build a CG&S industry$-$specific Generative ai chatbot in python using streamlit interface where users can query about P&G products, product details, and reviews, here$$s a structured approach:

$1.$ Knowledge Base Setup

Data Sources:

PDF$,$ Text, and Images: Collect relevant PDFs$,$ text documents, and images related to P&G products.

Vector Database:

Use Faiss or Chroma for storing and retrieving vectorized information. Choose based on retrieval speed and compatibility with the data types.

$2.$ LLM Selection & Tuning

Models:

Evaluate Gemini, GPT$,$ Mistral, and Claude.

Focus on models with multimodal capabilities if image processing is required.

Hyperparameter Tuning:

Use Hugging Face API for fine$-$tuning the embedding model.

Adjust parameters like learning rate, batch size, and epochs for optimal performance.

Embedding Model:

Ensure the chosen model supports chat completion and integrates well with the vector database.

$3.$ Framework

LangChain:

Utilize LangChain to manage the flow of conversation, connect to LLMs$,$ and perform prompt engineering.

LlamaIndex:

Use LlamaIndex for indexing and retrieving documents efficiently.

$4.$ Prompt Engineering

Chain of Thought Prompting:

Implement Chain of Thought prompting to enhance reasoning capabilities, ensuring that the chatbot can handle complex queries logically and step$-$by$-$step.

$5.$ Evaluation Metrics

Completeness: Ensure all necessary information is retrieved and presented.

Coherence: Maintain logical flow in the chatbot$$s responses.

Relevance: Ensure the information is relevant to the query.

Semantic Similarity: Evaluate how closely the chatbot$$s response matches the intended meaning of the query.

Correctness: Verify the factual accuracy of the responses.

Context Precision & Recall: Measure how well the chatbot captures and recalls context from previous interactions.

$6.$ Optimization

Similarity Search:

Optimize the retrieval process by refining similarity search algorithms to improve the relevance of retrieved information.

$7.$ Deployment

Streamlit:

Deploy the chatbot on Streamlit for a user$-$friendly interface. Streamlit$$s flexibility allows for easy integration with Python$-$based models and frameworks.

Steps Overview:

Setup Knowledge Base: Store product$-$related PDFs$,$ text, and images.

Select LLM & Tune: Choose the best$-$suited model, tune hyperparameters.

Framework Integration: Implement using LangChain and LlamaIndex.

Prompt Engineering: Apply Chain of Thought prompts.

Evaluate & Optimize: Use defined metrics and optimize similarity search.

Deploy on Streamlit: Make the chatbot accessible and interactive.

This structure should guide you in creating an effective and responsive CG&S industry$-$specific chatbot.