Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

Each retriever has its own strengths and weaknesses, and the choice of retriever depends on the specific use case and requirements. For example, the purpose of an Arxiv retriever is to retrieve scientific articles from the Arxiv.org archive.

LangFlow and Flowise are UIs that allow chaining LangChain components in an executable flowchart by dragging sidebar components onto the canvas and connecting them together to create your pipeline.

We can track the token usage in OpenAI models by hooking into the OpenAI callback:

Document loaders have a load() method that loads data from the configured source and returns it as documents. They may also have a lazy_load() method for loading data into memory as and when they are needed.

An agent is an autonomous software entity that is capable of taking actions to accomplish goals and tasks.

Retrieval can be improved by contextual compression, a technique where retrieved documents are compressed, and irrelevant information is filtered out.

In LangChain, we can also extract information from the conversation as facts and store these by integrating a knowledge graph as the memory.

LangChain excels at chaining LLMs together using agents to delegate actions to models. Its use cases emphasize prompt optimization and context-aware information retrieval/generation; however, with its Pythonic highly modular interface and its huge collection of tools, it is the number-one tool to implement complex business logic.

LlamaHub and LangChainHub provide open libraries of reusable elements to build sophisticated LLM systems