kcostya / llm_bits

Collection of notes based on LLM courses

Large Language Models

Summary

This is a collection of notes based on DeepLearning.Ai Large Language Models (LLM) courses.

Part 1: ChatGPT Prompt Engineering for Developers

• Principles
  • Write clear and specific instructions
  • Give model time to "think"
• Iterative prompt development
• Capabilities: Summarizing, Inferring, Transforming, Expanding
• Building a chatbot

Part 2: LangChain for LLM Application Development

• Memory types
  • ConversationBufferMemory: allows for storing of messages and then extracts the messages in a variable.
  • ConversationBufferWindowMemory: keeps a list of the interactions in the conversation over time, but uses only the last K interactions.
  • ConversationTokenBufferMemory: keeps a list of recent interactions in memory, and uses token length rather than number of interactions to determine when to flush interactions.
  • ConversationSummaryMemory: creates a summary of the conversation over time.
  • Additional memory types
    • Vector data memory: stores text in a vector database and retrieves the most relevant blocks of text.
    • Entity memories: details about specific entities.

It is possible to combine the memory types (e.g. Conversation memory + Entity memories). You can also store conversations in conventional databases (such as key-value store or SQL).

To install the OpenAI Python library:

!pip install openai

The library needs to be configured with your account's secret key, which is available on the website.

You can either set it as the OPENAI_API_KEY environment variable before using the library:

!export OPENAI_API_KEY='sk-...'

Or, set openai.api_key to its value:

import openai
openai.api_key = "sk-..."