Go Summarize

Livestream: Retrieval Augmented Generation (RAG) with LangChain and KDB.AI

984 views|9 months ago
💫 Short Summary

The video discusses Retrieval Augmented Generation (RAG) and its application in enhancing large language models' capabilities through data retrieval and generation. It covers methods for improving chatbots and digital assistants' performance, including the use of guard rails and knowledge graphs. The importance of chunking data for optimal results, selecting the right embedding model, and setting up a Vector database is highlighted. The video also compares large language models and emphasizes the significance of setting up the schema and creating a table for similarity searches. Temperature adjustments in models affect response creativity, and future sessions on sentiment analysis with Vector databases are mentioned. The video concludes with details on upcoming sessions and encourages viewer participation in suggesting topics. Live streams continue on Wednesdays, and viewers are thanked for their participation.

✨ Highlights
📊 Transcript
Overview of Retrieval Augmented Generation (RAG) in LLMs and vector databases.
RAG extends the knowledge base of LLMs to private data, enabling conversational interactions with data.
Instructions provided on signing up for KDB AAI and accessing Python notebook for RAG.
RAG is described as an evolving space with different approaches, with a basic use case demonstrated.
Future sessions will explore various RAG approaches, with Ryan Sigler introducing RAG and explaining its functionality in the session.
Overview of Retrieval Augmented Generation (RAG) in utilizing large language models for answering questions on untrained data.
The two main steps in RAG are retrieval, where relevant data is extracted for the model, and generation, where the retrieved data is used to generate a response.
Chunking up data is necessary for large language models due to their limited context window.
Different methodologies can be applied for effective data chunking in RAG.
The process of embedding raw text into a machine-readable language using numbers allows for capturing contextual relationships between words.
The embedded data can be stored in a vector database for unique capabilities in retrieving relevant information through similarity searches.
Using similarity searches for every user prompt may result in irrelevant information being extracted, affecting the large language model.
While not recommended for production, this approach can be useful for understanding retrieval augmented generation.
Methods for improving the performance of chatbots and digital assistants.
Guard rails can provide preset responses to specific topics to prevent certain questions from being sent to the language model.
Knowledge graphs represent data as nodes and edges, providing more context to the language model for better responses.
Utilizing relevant nodes from the knowledge graph can enhance the understanding of user prompts.
These approaches offer ways to enhance the capabilities of AI chatbots and digital assistants.
Utilizing language models for response generation.
Naive approach and advanced methods with reasoning capabilities are discussed.
Advanced methods offer structure and guidance in determining next steps in the response pipeline.
Mention of upcoming sessions on optimizing RAG and performance.
Instructions on installing requirements, importing packages, and setting up API keys for connecting to KDB AI client and Vector database.
Comparison of large language models from OpenAI and Hugging Face, and the use of text loaders and chunkers for data processing.
Importance of chunk size for optimal results and maintaining context between chunks with chunk overlap.
Demonstrating the process of chunking data and analyzing the State of the Union address as a proof point for model training.
Highlighting the effectiveness of the model by showcasing the speech occurring after GPT was trained.
Overview of Embedding Raw Text into Vector Format.
Importance of selecting the right embedding model, such as OpenAI's embeddings, for text conversion.
Setting up a Vector database using KDB and defining the schema with columns like ID, text, and embeddings.
Mention of search metrics like Euclidean distance and cosine similarity for similarity search.
Discussion on the type of index used for the Vector database, like flat index, to create effective text embeddings.
The process of creating a vector database for similarity searches and utilizing large language models for retrieval and augmented generation.
Setting up a schema, creating a table, and populating it with chunks and an embedding model are initial steps in the process.
Retrieval involves sending relevant information to a large language model for further analysis and processing.
Augmented generation includes defining two large language models and creating chains for each model to pass in documents or chunks as prompts.
Running the pipeline involves querying similar chunks and analyzing the strength of the nation, concluding with the potential of the American people to turn crises into opportunities.
The Google flan T5 model focuses on short specific answers rather than broad questions.
Retrieval QA allows for specifying the number of related chunks sent to the language model, improving precision.
Adjusting temperature in models impacts creativity and hallucination in responses, with higher temperatures resulting in more creativity.
Lower temperatures yield more specific answers, ideal for academic applications.
Experimenting with temperature settings can alter response styles and outcomes.
Analysis and Response Models for Vector Databases
Importance of tailored responses for vector databases to effectively analyze data.
Need to protect certain aspects of a country and eliminate unnecessary resources after experiments.
Emphasis on measuring the impact of responses and upcoming sessions on sentiment analysis with vector databases.
Future sessions will focus on improving response performance.
Schedule for live streams
Sessions will be held on Wednesdays at 11:30 AM Eastern time and 4:30 PM UK time.
Viewers can suggest topics via LinkedIn.
Audience is thanked for joining and encouraged to have a great week.
The next session is eagerly anticipated.