Programmatically Interacting with LLMS
๐ 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.
๐ 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
, andAIMessage
/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
andRecursiveCharacterTextSplitter
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.