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Why overlapping while converting into chunks: There are a few reasons to use overlapping chunks when processing text: Capture Context: Overlapping chunks ensure that sentences don't get split at awkward points, preserving context between chunks. This can be important for tasks like information retrieval or question answering. Reduce Boundary Issues: When searching for specific phrases, overlapping chunks can avoid missing matches that fall on chunk boundaries. Improve Retrieval: Overlaps allow for more flexibility in retrieving relevant information, especially when dealing with complex or ambiguous queries. How to know 50 words are overlapped: There are a few ways to determine the number of overlapping words between chunks: Maintain a Counter: During chunking, keep track of the number of words processed so far. When creating a new chunk, compare this counter with the previous chunk's ending position to calculate the overlap. Use Fixed Overlap Size: Define a fixed number of words for overlap (e.g., 50 words) and adjust chunk boundaries accordingly. Character-Level Processing: If you're working with character-level models, simply count the number of overlapping characters between chunks. What are indexing vectors: Indexing vectors are dense numerical representations of text documents or terms. These vectors encode the meaning and relationships within the text using techniques like Word2Vec or GloVe. Where to store indexing vectors: Indexing vectors can be stored in various ways depending on the application: In-Memory Storage: For smaller datasets and real-time applications, vectors can be kept in memory for fast access. Database Storage: For larger datasets, efficient databases like FAISS or Annoy can store and manage vectors for retrieval tasks. Distributed Storage: Large-scale systems might use distributed file systems like HDFS or cloud storage solutions like Amazon S3 for vector storage. How to split text or pdf text into chunks: There are multiple ways to split text or PDF documents into chunks: Fixed-Size Chunks: Divide the text into chunks of a predetermined size (e.g., 500 words). This is simple but might not be optimal for capturing context. Sentence-Based Chunks: Split the text at sentence boundaries. This ensures that each chunk represents a complete thought. Word-Based Chunks: Split the text based on word boundaries. This offers more granular control but might break up context. Sliding Window: Use a sliding window approach with a fixed chunk size and defined overlap to create overlapping chunks as mentioned earlier. Large Language Models (LLMs): RAG, Ollama, Cloudea: RAG (Retrieval-Augmented Generation): This is a technique for combining retrieval systems with large language models (LLMs) to improve the factual accuracy and informativeness of the LLM's outputs. Ollama: This is a Python library that provides access to various LLMs, including open-source models like LaMDA or Jurassic-1 Jumbo. It simplifies interacting with LLMs and building RAG applications. Cloudea: This term is not commonly used in the context of LLMs or text processing. It might be a misspelling of "CloudML" (Google Cloud Machine Learning Engine) or a reference to a specific, less popular service. Asked Gemini google model

@@shankarpentyala1660 Super Thanks 👍

yes

@@aryansalge4508 will that answer any kind of query related to tabular data ?....like mean, median, correlation, unique categories, groupby ?

@@tharunps8048 try PandasAi