ValueError: Got unknown type S when using GPT-4 with LangChain for Summarization

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05-10-2024

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"ValueError: Got unknown type S" - Demystifying LangChain & GPT-4 Summarization Errors

When working with powerful tools like LangChain and GPT-4 for text summarization, encountering a "ValueError: Got unknown type S" can be frustrating. This error often signifies a mismatch between the data structure LangChain expects and what your code is providing. This article dives into the common causes of this error, offers solutions, and guides you towards error-free summarization with LangChain and GPT-4.

Scenario: Imagine you're building a system to summarize news articles using LangChain and GPT-4. Your code looks something like this:

from langchain.llms import OpenAI
from langchain.chains import  SummarizeChain
from langchain.document_loaders import TextLoader

# Load the news article
loader = TextLoader('news_article.txt')
docs = loader.load()

# Initialize OpenAI LLM
llm = OpenAI(temperature=0.7)

# Create the SummarizeChain
chain = SummarizeChain(llm=llm, chain_type="map_reduce")

# Summarize the article
summary = chain.run(docs)
print(summary)

However, you get the error "ValueError: Got unknown type S." What's going on?

Understanding the Error:

The "ValueError: Got unknown type S" arises when LangChain encounters a data structure it doesn't recognize. Specifically, the SummarizeChain expects a list of "Documents" as input, where each document represents a piece of text. However, your code might be passing something else, like a single string, a dictionary, or a structure not designed for LangChain's document processing.

Troubleshooting & Solutions:

1. Check Your docs Variable:

   • Single String: If docs is a single string containing your article, you need to wrap it in a list of documents:

     docs = [TextLoader('news_article.txt').load()]
     

   • Dictionary or Other Structures: Ensure you're converting your data into a list of LangChain Document objects. You might need to use functions like Document.from_text to convert your data.

2. Review chain_type:

   • The SummarizeChain uses various chain_type options like "map_reduce," "stuff," or "map_rewrite." Some of these may be stricter with input formats. Try experimenting with different options to see if one works better.

3. Inspect the loader Output:

   • Print the value of docs before passing it to the SummarizeChain to understand its structure and how it differs from what LangChain expects. This helps pinpoint the issue.

4. Use a Different Loader:

   • LangChain offers different DocumentLoader classes for various data formats (e.g., PDFs, web pages, etc.). If your input isn't plain text, use a suitable loader.

Code Examples for Common Scenarios:

Example 1: Summarizing a Single Text File:

from langchain.llms import OpenAI
from langchain.chains import SummarizeChain
from langchain.document_loaders import TextLoader

# Load the news article
loader = TextLoader('news_article.txt')
docs = loader.load()

# Initialize OpenAI LLM
llm = OpenAI(temperature=0.7)

# Create the SummarizeChain
chain = SummarizeChain(llm=llm, chain_type="map_reduce")

# Summarize the article
summary = chain.run(docs)
print(summary)

Example 2: Summarizing Multiple Text Files:

from langchain.llms import OpenAI
from langchain.chains import SummarizeChain
from langchain.document_loaders import DirectoryLoader

# Load all text files in a directory
loader = DirectoryLoader("articles", glob="*.txt")
docs = loader.load()

# Initialize OpenAI LLM
llm = OpenAI(temperature=0.7)

# Create the SummarizeChain
chain = SummarizeChain(llm=llm, chain_type="map_reduce")

# Summarize the articles
summary = chain.run(docs)
print(summary)

Conclusion:

"ValueError: Got unknown type S" is a common hurdle when working with LangChain and GPT-4 for summarization. By understanding the error's origin and applying these troubleshooting steps, you can overcome this error and successfully leverage these powerful tools for text summarization.

Further Resources:

• LangChain Documentation: https://langchain.readthedocs.io/en/latest/
• OpenAI API Documentation: https://platform.openai.com/docs/api-reference