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Programmatically Interacting with LLMS

๐ŸŒˆ Summary

The article discusses the author's exploration of using large language models (LLMs) for various tasks, including single-shot prompting, multi-message chat, Retrieval-Augmented Generation (RAG), and tool usage. The author compares the implementations using the ollama-js, openai-js, and LangChain libraries with both ollama and openai backends.

๐Ÿ™‹ Q&A

[01] Prompting

1. What are the key differences between the prompting implementations using ollama-js, openai-js, and LangChain?

  • The ollama-js and openai-js implementations provide a more straightforward code interface, but the LangChain abstractions can help smooth over differences between the different providers.
  • LangChain introduces additional concepts and classes that may have a steeper learning curve, but can make it easier to retarget the code to different providers.

2. What are the benefits of using the LangChain approach for prompting?

  • The LangChain approach allows you to separate the business logic from the specific compute environment, making it easier to swap out the backend provider.
  • LangChain provides higher-level concepts like the ChatPromptTemplate, StringOutputParser, and others that can simplify the prompting implementation.

[02] Chatting

1. How does the chatting implementation differ between the ollama-js, openai-js, and LangChain approaches?

  • The ollama-js and openai-js implementations require more manual bookkeeping to keep track of the conversation context and pass it to the LLM.
  • The LangChain approach abstracts away much of this boilerplate using concepts like ChatPromptTemplate, ChatMessageHistory, and AIMessage/HumanMessage.

2. What are the advantages of the LangChain chatting implementation?

  • The LangChain chatting implementation provides a more structured and reusable way to manage the conversation context and flow.
  • It also offers the flexibility to use different backend implementations for the conversation history storage and retrieval.

[03] Retrieval-Augmented Generation (RAG)

1. How does the RAG implementation differ between the ollama-js and LangChain approaches?

  • The ollama-js approach involves manually importing the data, indexing it using an embedding model, and then querying the index to retrieve relevant documents.
  • The LangChain approach leverages higher-level concepts like VectorStore and RecursiveCharacterTextSplitter to simplify the data import and indexing process.

2. What are the benefits of the LangChain RAG implementation?

  • The LangChain RAG implementation provides more abstraction and reusable components for tasks like data retrieval, indexing, and querying.
  • This can help reduce the amount of boilerplate code and make it easier to experiment with different data sources and indexing strategies.

[04] Tools

1. How do the tool usage implementations differ between the ollama-js, openai-js, and LangChain approaches?

  • The ollama-js and openai-js approaches require manually defining the tool prompts and handling the JSON-formatted responses.
  • The LangChain approach provides a more structured way to define and use tools, including the ability to leverage pre-built tools like the Wikipedia lookup.

2. What are the advantages of the LangChain tool usage implementation?

  • The LangChain tool usage implementation abstracts away much of the low-level handling of the tool prompts and responses, making it easier to integrate external tools and services.
  • It also provides a more extensible and reusable framework for building tool-based applications on top of LLMs.
Shared by Daniel Chen ยท
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