Update README.md

More improvements added

README.md

@@ -82,19 +82,37 @@ The prompt includes a few-shot example, demonstrating how the model should respo
 Instead of chain.run(), we use chain.invoke({}). This approach can be more flexible and allows for passing parameters in a structured manner if needed later.
 
 ## Improvements
-- Multi-File Support:
+- **Multi-File Support:**
   - Extend the script to handle multiple PDFs at once.
   - Aggregate or differentiate embeddings by metadata, ensuring queries can target specific documents or sections.
-- Model Agnosticism:
+- **Model Agnosticism:**
   - Easily switch embeddings or language models.
   - Try different Sentence Transformers models or local LLMs like LLaMA or Falcon.
-- User Interface:
-  - Add a simple command-line interface or a web UI (e.g., Streamlit or Gradio) for a more user-friendly querying experience.
-- Caching & Persistence:
+- **Caching & Persistence:**
   - Store FAISS indexes on disk for instant reloads without re-embedding.
   - Implement caching of embeddings and query results to speed up repeated queries.
-- Advanced Prompt Engineering:
-  - Experiment with different few-shot examples, system messages, and instructions to improve answer quality and formatting.
+- **Advanced Prompt Engineering:**
+  - Experiment with different few-shot examples, chain-of-thought prompting, system messages, and instructions to improve answer quality and formatting.
+- **Chunking Strategies:**
+  - Implement advanced chunking strategies:
+  - Use semantic chunking to divide text based on meaning or coherence rather than fixed sizes.
+  - Include options for overlapping chunks to improve retrieval precision.
+  - Integrate hierarchical chunking to preserve context across sections (e.g., chapters, headings, subheadings).
+- **Improved Retrieval Techniques:**
+  - Leverage Approximate Nearest Neighbor (ANN) algorithms to accelerate similarity search.
+  - Integrate with advanced vector databases (e.g., Pinecone, Weaviate, Milvus) for efficient and scalable retrieval.
+  - Use hybrid retrieval models, combining vector similarity with traditional keyword-based retrieval for better query coverage.
+- **Cross-Encoder Reranker:**
+  - Introduce a cross-encoder reranker to improve the quality of retrieved results:
+  - Apply a fine-tuned cross-encoder model to rerank top candidates from the initial vector search.
+  - Use a pre-trained or task-specific cross-encoder (e.g., models from Hugging Face like cross-encoder/ms-marco-TinyBERT-L-6).
+  - Improve relevance by jointly encoding the query and candidate passages, allowing contextual alignment and a more accurate similarity score.
+  - Dynamically adjust the balance between retrieval speed and reranking quality by tuning the number of top candidates to rerank.
+- **Graph-Based Retrieval Augmentation:**
+  - Adopt GraphRAG approaches:
+  - Represent documents and queries as nodes in a graph for relational context.
+  - Use graph-based algorithms to enhance retrieval by modeling relationships (e.g., citations, semantic links).
+  - Introduce parent document retrievers that prioritize and rank content based on its originating document or source reliability.
 
 
 With AskMyPDF, harness the power of LLMs and embeddings to transform your PDFs into a fully interactive, queryable knowledge source.