Langchain has attracted attention for its ability to simplify the interaction aspects of large language models (llm). Its high-level API simplifies the process of integrating llm into various applications.
But Langchain may seem like a handy tool at first glance, but it is sometimes more of a language maze than a straightforward solution. In this article, we will explore some issues related to Langchain and consider some alternative frameworks.
Inefficient token usage
An important issue with Langchain is its token counting functionality, which is inefficient for small data sets. While some developers choose to create their own token counting functions, there are other solutions to this problem.
Alternative solution: Tiktoken is a Python library developed by OpenAI to solve the token counting problem more efficiently. It provides an easy way to count tokens in text strings without using a framework like Langchain for this specific task.
Documentation issues
Documentation is the cornerstone of any framework's usability, and Langchain has been criticized for its inadequate and often inaccurate documentation. Misleading documentation can lead to costly mistakes in development projects, and 404 error pages are also common. This may be related to the rapid development of Langchain. As a rapid version iteration, the delay of the document is understandable. I can only say that I hope to improve it as soon as possible in the future.
Too many concepts are easy to confuse, too many "auxiliary" functions
Langchain’s codebase has been criticized for being confusing with many concepts, making it difficult for developers to understand and use it. One aspect of this problem is the large number of "helper" functions that, upon closer inspection, reveal that they are essentially wrappers around standard Python functions. Developers may prefer frameworks that provide clearer and direct access to core functionality without the need for complex intermediary functionality.
For example, this is a simple division function:
Behaves inconsistently and hides details
LangChain has been criticized for hiding important details and inconsistent behavior, which can lead to unexpected problems in production systems. An interesting aspect of the example Langchain ConversationRetrievalChain involves the rephrasing of the input question. This repetitive phrasing can sometimes be so extensive that it disrupts the natural flow of the conversation and takes it out of context.
Lack of standard interoperable data types
Another disadvantage of Langchain is the lack of a standard way to represent data. This lack of consistency can hinder integration with other frameworks and tools, making it challenging to work within the broader ecosystem of machine learning tools.
some alternatives
Is there a better alternative that offers easier use, scalability, activity and features.
LlamaIndex is a data framework that makes it easy to connect large language models to custom data sources. It can be used to store, query, and index data, and also provides a variety of data visualization and analysis tools.
Deepset Haystack is another open source framework for building search and question answering applications using large language models. It is based on Hugging Face Transformers and provides a variety of tools for querying and understanding text data.
Summarize
This article only summarizes some problems encountered by Langchain users in use. Not all people who have used Langchain will encounter them, but there is no guarantee that you will not encounter them in the future, so you should still pay attention to these problems encountered by others.
While Langchain is a powerful tool for beginners, as you learn and understand the framework, you should realize that there are more efficient and straightforward ways to handle advanced tasks. Langchain is great for getting started, but not necessarily for production.
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Author:Woyera