updating README.md

README.md

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-# Multi-Agent Open-Domain QnA with Cross-Source Reranking  
-
----
-
-## **Introduction**  
-
-The objective of this project is to develop a multi-agent open-domain question-answering (ODQA) system capable of retrieving and synthesizing information from diverse sources. These sources include web searches, large language models (LLMs) such as **Llama 3**, and vision models for multi-modal retrieval. Leveraging datasets like **KILT**, **Natural Questions**, **HotspotQA**, **TriviaQA**, and **ELI5**, the system incorporates a **cross-source reranking model** to improve the selection of the most accurate answers. This project emphasizes scalability and reliability by addressing both context-free and context-based scenarios, even when confronted with an increasing volume of irrelevant documents.
-
----
-
-## **Project Overview**  
-
-- **Pipeline Development**: Created a multi-agent ODQA pipeline integrating specialized retrieval agents.  
-- **Source Diversity**: Utilized web searches, LLMs, and vision models for retrieving information.  
-- **Cross-Source Reranking**: Applied methods such as Reciprocal Rank Fusion (RRF) to enhance answer accuracy.  
-- **Scalability Evaluation**: Tested the system on datasets with varying ratios of relevant and irrelevant documents.  
-
 ---
-
-## **Pipeline**  
-
-### **Dataset Construction**  
-
-- **Mini-Wiki Collection**:  
-   A condensed version of the Wikipedia dump was created by selecting a subset of documents relevant to the validation sets of **Natural Questions**, **HotspotQA**, **TriviaQA**, and **ELI5**.  
-
-- **Document Ratio Variants**:  
-   To evaluate retrieval scalability, multiple datasets with different relevant-to-irrelevant document ratios were constructed:  
-   - **1:0**: Contains only 1000 relevant documents for 1000 queries.  
-   - **1:1**: Contains 1000 relevant documents and 1000 irrelevant documents.  
-   - **1:2**: Contains 1000 relevant documents and 2000 irrelevant documents.  
-
----
-
-## **Retrieval Models**  
-
-To ensure robust and efficient retrieval, the project combined sparse and dense methods:  
-
-### **Sparse Retrieval Models**  
-
-1. **TF-IDF**:  
-   Measures the importance of terms in a document relative to the entire dataset.  
-   - Effective for small datasets.  
-   - Serves as a lightweight and interpretable baseline.  
-
-2. **BM25**:  
-   - Extends TF-IDF with term frequency normalization and length penalization.  
-   - Handles query-document term overlap better than TF-IDF.  
-
-3. **Bag of Words (BOW)**:  
-   - A simple vector-space model using term frequency vectors.  
-   - Acts as a baseline for comparison with more advanced methods.  
-
----
-
-### **Dense Retrieval Models**  
-
-1. **Text Embeddings (all-MiniLM-L6-v2)**:  
-   - A pre-trained sentence-transformer for generating compact, high-quality embeddings.  
-   - Captures semantic relationships between queries and documents.  
-   - Lightweight and suitable for large-scale datasets.  
-
-2. **Vision Embeddings (ViT)**:  
-   - Generates embeddings for image-based data, enabling multi-modal information retrieval.  
-   - Complements text-based retrieval for answering questions requiring visual context.  
-
+title: MultiAgent-QnA-ChatBot
+emoji: 🏢
+colorFrom: indigo
+colorTo: green
+sdk: gradio
+pinned: false
 ---
 
-## **Agents for Context Generation**  
-
-### **Query Modification Agent**  
-Refines user queries to optimize them for retrieval, ensuring that they are better suited for identifying relevant documents.  
-
-### **Keyword Extraction Agent**  
-Extracts key terms from the query and passes them to a **Wiki Agent**, which uses n-grams to retrieve relevant Wikipedia pages.  
-
-### **Llama 3 Agent**  
-Synthesizes context directly related to the user query, enriching the system’s ability to answer complex questions.  
-
----
-
-## **Post-Retrieval Process**  
-
-1. **Top-Ranked Document as Context**  
-   The highest-ranked document was used directly as context for QnA tasks.  
-
-2. **Iterative Use of Ranked Documents**  
-   Explored answers using documents ranked in descending order of relevance.  
-
-3. **Rank Fusion (RRF)**  
-   Combined rankings from multiple retrieval methods (e.g., BM25, TF-IDF, MiniLM) to improve robustness and accuracy.  
-
----
-
-## **Results and Evaluation**  
-
-### **Retrieval Model Scores**  
-
-| **Method**        | **Query Type** | **Ranking Scores** |  
-|--------------------|----------------|---------------------|  
-| **BOW**           | Modified       | 13.82 - 33.39      |  
-| **BM25**          | Modified       | 736.74 - 785.09    |  
-| **TF-IDF**        | Modified       | 730.61 - 788.87    |  
-| **Vision**         | Modified       | 0.03 - 5.08        |  
-| **MiniLM (Open)** | Modified       | 827.92 - 849.79    |  
-
-### **Question Answering Model Scores**  
-
-- **ROUGE Score**: Demonstrated improvements with RRF across most datasets.  
-- **Cosine Similarity Score**: Highlighted semantic alignment in dense methods.  
-- **BERT F1 Score**: Dense embeddings outperformed sparse methods.  
-
----
-
-## **Analysis**  
-
-1. **Sparse Models**:  
-   - Sparse methods like **BM25** and **TF-IDF** performed well on context-free datasets but struggled with context-based tasks.  
-   - **BOW** and **Vision** models were ineffective, worsening LLM performance compared to zero-shot baselines.  
-
-2. **Dense Models**:  
-   - Dense retrieval methods showed significant improvements in relevance and answer accuracy, especially when combining results with RRF.  
-
-3. **Cross-Source Reranking**:  
-   - RRF combining **BM25**, **TF-IDF**, and **MiniLM** yielded the best results.  
-   - Using LLMs as rerankers was less reliable, with a bias toward zero-shot outputs.  
-
----
-
-## **Conclusion**  
-
-The multi-agent ODQA system successfully integrates sparse and dense retrieval methods, leveraging RRF for cross-source reranking. Dense methods and generative agents like **Llama 3** significantly enhance the system’s capability in open-domain settings. Future work can focus on improving multi-modal integration and reducing biases in LLM-based reranking.  
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