End-to-end LLM project for equity research analysis using Langchain, OpenAI, and Streamlit.

📚 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.