Source: DeepHub IMBA
This article is about 1100 words long, and it is recommended to read in 5 minutes.
In this article, we will explore some issues related to Langchain and consider some alternative frameworks.
Langchain has gained attention for its ability to simplify interactions with large language models (LLM). With its advanced API, it streamlines the process of integrating LLM into various applications.
However, while Langchain may initially seem like a convenient tool, it can sometimes feel more like a language maze rather than a straightforward solution. In this article, we will explore some issues related to Langchain and consider some alternative frameworks.
Inefficient Token Usage
One significant issue with Langchain is its token counting functionality, which is inefficient for small datasets. While some developers choose to create their own token counting functions, there are other solutions available to address this problem.
Alternative Solution: Tiktoken is a Python library developed by OpenAI to address token counting issues more efficiently. It provides a simple way to calculate tokens in a text string without needing to use 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 insufficient and often inaccurate documentation. Misleading documentation can lead to costly mistakes in development projects, and there are often 404 error pages. This may be related to Langchain’s rapid development; given the fast version iterations, the lag in documentation is understandable, but we can only hope for improvements soon.
Too Many Concepts Can Be Confusing, Excessive “Helper” Functions
Langchain’s codebase has been criticized for its many confusing concepts, making it difficult for developers to understand and use. One aspect of this issue is the presence of numerous “helper” functions, which upon closer inspection are essentially wrappers around standard Python functions. Developers may prefer a framework that provides clearer and more direct access to core functionalities without unnecessary intermediate functions.
For example, this is a simple split function:
Inconsistent Behavior and Hidden Details
LangChain has been criticized for hiding important details and exhibiting inconsistent behavior, which can lead to unexpected issues in production systems. An interesting aspect of the Langchain ConversationRetrievalChain involves the rephrasing of input questions. This rephrasing can sometimes be very broad, disrupting the natural flow of conversation and causing it to lose context.
Lack of Standard Interoperable Data Types
Another drawback of Langchain is the lack of a standard way to represent data. This inconsistency can hinder integration with other frameworks and tools, making it challenging to work within the broader machine learning tool ecosystem.
Some Alternative Choices
Are there better alternatives that offer easier usability, scalability, activity, and features?
LlamaIndex is a data framework that easily connects large language models to custom data sources. It can be used to store, query, and index data, and also provides various 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 various tools for querying and understanding text data.
Conclusion
This article only summarizes some issues encountered by Langchain users; not everyone who has used Langchain will encounter these, but it cannot be guaranteed that you won’t face them in the future, so it is important to be aware of these issues others have faced.
While Langchain is a powerful tool for beginners, as one learns and understands the framework more deeply, one should realize that there are more efficient and direct methods to handle advanced tasks. Langchain is excellent for getting started, but may not be suitable for production.
Editor: Wenjing
