📚 This video explains the process of building an end-to-end LLM project for equity research analysis using Langchain, OpenAI, and Streamlit.
🏦 The project involves creating a news research tool that can retrieve answers and summaries based on a given set of news article URLs.
💡 The tool addresses the challenges of copy-pasting articles, finding relevant information, and the word limit of chat GPT by using a knowledge base and smartly selecting chunks of text to optimize costs.
Vector databases help in performing faster searches.
Building a project in streamlit and using POC for testing.
Architecture involves database injection system and chatbot.
📚 Text splitting is necessary to reduce the token size limit in LLM projects.
🔗 Merging smaller chunks of text helps optimize efficiency in LLM projects.
🌐 Overlap between chunks allows for better contextual understanding in LLM projects.
📊 The video discusses the process of creating chunks from a given text using recursive text splitter.
💡 The video introduces the concept of using a lightweight in-memory Vector database called Phase for efficient search on vectors.
🔍 The video demonstrates how to convert text into vectors using the Sentence Transformer library and perform similarity search with Phase index.
🔍 Semantic search captures the context or meaning of a sentence to provide similar sentences.
📚 Langchain is a library used for storing and retrieving vectors for question-answering tasks.
⚙️ The retrieval QA method using Langchain involves storing vectors in a vector database and asking questions to retrieve relevant chunks.
🔍 The video demonstrates how to use Langchain and OpenAI to create an end-to-end LLM project in the finance domain.
📚 The project involves loading and splitting data, generating embeddings, and creating a face index for efficient retrieval.
⚙️ The speaker emphasizes the importance of understanding the fundamentals and assembling the individual project components.
⚙️ The video demonstrates the process of using Langchain and OpenAI in a finance project.
📚 The project involves loading data, splitting it into chunks, building embeddings, and creating an index for retrieval.
🔑 The tool allows users to ask questions and receive answers based on the loaded data, with sources provided.
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