Solve below problem in python,make sure code is running do test with dummy data and don't take help of chatgpt$/$gemini they are not capable of giving rightcode

Problem :

Objective is to build a multimodal summarization and information generative system using LangChain and Google$$s Gemini LLM that can handle various content types like tables, images, PDFs$,$ video, and audio files. The system will generate and summarize content from these inputs by leveraging RAG architecture, where both the knowledge base and the input queries are vectorized for fast and relevant information generation.

$1.$ Task Overview: Build a multimodal pipeline using LangChain and Google's Gemini LLM to:

Generate relevant information from multimodal content sources like text, tables, images, PDFs$,$ videos, and audio.

Summarize these multimodal sources into concise, meaningful outputs.

Evaluate the response quality through specific evaluation metrics.

Ensure scalability, accuracy, and speed in the retrieval process.

$2.$ Data Preparation:

Input formats: Web$-$based articles, PDFs$,$ tables, images, audio, and video files containing rich content $($such as blog posts, infographics, and media$).$

The system will extract relevant textual content using OCR $($for images$),$ speech$-$to$-$text $($for audio$),$ and video summarization techniques.

Multimodal content will be indexed and stored in a vector database using embeddings $($e$.$g$.,$ from Hugging Face models$)$ to support efficient semantic search.

$3.$ Vector Database and Retrieval Mechanism:

Use Faiss or Chroma as the vector database for efficient information generative system from the knowledge base.

Each input $($whether text, image, or video$)$ will be converted into a vector embedding.

Queries will also be vectorized to generate the most relevant content chunks based on semantic similarity.

$4.$ LLM and RAG Pipeline Setup:

Google$$s Gemini LLM will be integrated with LangChain to generate the final summarized output.

LangChain will serve as the orchestrator between the vector database $($retrieval$),$ LLM $($generation$),$ and multimodal input processing pipelines.

The summarization process will first retrieve relevant content from the multimodal sources and then generate a coherent, concise response.

$5.$ Prompt Design for Summarization:

Develop a Chain of Thought prompting structure where the LLM will process each content type $($text$,$ image, table, video, audio$)$ in a structured, step$-$by$-$step manner.

The LLM will use these structured prompts to understand and summarize multimodal inputs.

$6.$ Response Evaluation Metrics: To evaluate the RAG pipeline and its summarization performance, you will measure:

Completeness: Does the summary include all key points from the original multimodal content?

Coherence: Is the summary logically structured and easy to understand?

Relevance: How relevant is the retrieved content to the original query?

Correctness: Is the factual information accurate?

Context Precision & Recall: How well does the summary capture and retrieve the relevant context from multimodal sources?

Semantic Similarity: How semantically similar is the summary to the source content?

Summarization Accuracy: Compare against baseline models using metrics like ROUGE, BLEU, and Perplexity.

$7.$ Implementation Steps:

Environment Setup: Install all necessary dependencies like google.generativeai, langchain, faiss, tesseract, and other multimodal processing libraries.

Data Loading: Use loaders from LangChain to handle web$-$based content, PDFs$,$ tables, images $($via OCR$),$ and audio $($via speech$-$to$-$text$).$

Multimodal Embedding: Use Hugging Face models for generating vector embeddings for each modality.

Querying the Vector Database: Upon receiving a user query, vectorize the query and retrieve relevant multimodal chunks from the database.

Summarization Chain: Use LLMChain in LangChain to generate a summary, combining relevant content from the different modalities.

Response Evaluation: Implement evaluation modules to measure the output on various metrics like ROUGE, BLEU, Perplexity, and context$-$specific measures like relevance and coherence.

$8.$ Example Use Case $($Prompt$)$:

User Query: "Summarize this article along with its embedded tables and images."

Input: A blog post URL with embedded images, tables, and infographics.

Response:

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"The article discusses the evolution of Large Language Models $($LLMs$),$ focusing on the importance of vectors, tokens, and embeddings. The accompanying table highlights key differences between tokenization strategies. The infographic illustrates the LLM architecture, showing how embeddings are processed within the network layers."

$9.$ Output & Feedback Loop:

Provide users with the ability to fine$-$tune the summarization by adjusting parameters like maximum length, top$-$k sampling, temperature, and other hyperparameters.

Enable a feedback loop where users can indicate whether the generated summary was helpful, triggering potential improvements in the model through retraining.