End-to-end LLM project for equity research analysis using Langchain, OpenAI, and Streamlit.
Build an end-to-end LLM project for equity research analysis using Langchain, OpenAI, and Streamlit. Create a news research tool that retrieves answers from news articles and provides summaries.
00:00:00 Build an end-to-end LLM project for equity research analysis using Langchain, OpenAI, and Streamlit. Create a news research tool that retrieves answers from news articles and provides summaries. Overcome limitations of using Chat GPT by building a knowledge base and using relevant chunks of articles.
š This video explains the process of building an end-to-end LLM project for equity research analysis using Langchain, OpenAI, and Streamlit.
š¦ The project involves creating a news research tool that can retrieve answers and summaries based on a given set of news article URLs.
š” 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.
00:10:43 LLM Project | End to End LLM Project Using Langchain, OpenAI in Finance Domain. This video explains the architecture of the project, including the use of Vector databases, web scraping, and chatbots for question answering.
Vector databases help in performing faster searches.
Building a project in streamlit and using POC for testing.
Architecture involves database injection system and chatbot.
00:21:28 In this video, the speaker explains how to split text into smaller chunks using different separators in order to optimize language models. They demonstrate the use of text splitter classes in Langchain and provide examples with different chunk sizes.
š Text splitting is necessary to reduce the token size limit in LLM projects.
š Merging smaller chunks of text helps optimize efficiency in LLM projects.
š Overlap between chunks allows for better contextual understanding in LLM projects.
00:32:16 Learn how to create embeddings and store them in a lightweight vector database like Phase for faster search in a finance project using Langchain and OpenAI.
š The video discusses the process of creating chunks from a given text using recursive text splitter.
š” The video introduces the concept of using a lightweight in-memory Vector database called Phase for efficient search on vectors.
š The video demonstrates how to convert text into vectors using the Sentence Transformer library and perform similarity search with Phase index.
00:43:01 This video demonstrates the use of Langchain and OpenAI in an end-to-end LLM project in the finance domain. It covers semantic search, retrieval QA, and different methods for handling token limit.
š Semantic search captures the context or meaning of a sentence to provide similar sentences.
š Langchain is a library used for storing and retrieving vectors for question-answering tasks.
āļø The retrieval QA method using Langchain involves storing vectors in a vector database and asking questions to retrieve relevant chunks.
00:53:44 LLM Project using Langchain and OpenAI to analyze finance documents. Use prompts to retrieve relevant information and summarize answers. Final answer is the Tiago sng price.
š The video demonstrates how to use Langchain and OpenAI to create an end-to-end LLM project in the finance domain.
š The project involves loading and splitting data, generating embeddings, and creating a face index for efficient retrieval.
āļø The speaker emphasizes the importance of understanding the fundamentals and assembling the individual project components.
01:04:29 LLM Project using Langchain, OpenAI in Finance Domain. Code loads data, splits into chunks, builds embeddings, and creates a QA chain for answering questions. A UI is provided for user interaction. Real-life use case for equity research analysts.
āļø The video demonstrates the process of using Langchain and OpenAI in a finance project.
š The project involves loading data, splitting it into chunks, building embeddings, and creating an index for retrieval.
š The tool allows users to ask questions and receive answers based on the loaded data, with sources provided.
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