Need Guidance on Splitting and Sequencing Code for Language (Coding Language example C#) Conversion using langchain Framework

[ArnWEB]

Checked other resources

• I added a very descriptive title to this question.
• I searched the LangChain documentation with the integrated search.
• I used the GitHub search to find a similar question and didn't find it.

Commit to Help

• I commit to help with one of those options 👆

Example Code

NA

Description

Context:
I'm currently working on a project that involves converting .NET Framework C# code to .NET WebAPI using Langchain as the framework for communication with OpenAI. Our main challenge lies in dealing with large chunks of code exceeding the maximum token limit of 4000 tokens imposed by the model. While we've successfully employed techniques like chunking and using a refinement chain to process the code, we're encountering difficulties in maintaining the sequence of code lines throughout the conversion process.

Problem:
The primary issue arises when we split the code into multiple chunks to stay within the token limit. While we can ensure each chunk remains under the token limit, maintaining the sequence of code lines becomes challenging. Unlike summarization tasks where the output can be condensed, in code conversion tasks, the converted code should ideally remain the same length or longer than the input code.

Approach:
To address this issue, we've implemented a refinement chain where we feed the current output from the language model along with the next chunk as the next input. However, this approach isn't fully solving our problem, as the output from the model tends to reach its limit after a certain number of iterations. Since the converted code must match or exceed the length of the input code, we're struggling to find an effective solution that maintains both the sequence and length of the code during conversion.

Request for Guidance:
I'm seeking guidance from the community on how to effectively split and sequence large chunks of code for conversion while ensuring that the converted code remains coherent and maintains the original sequence of code lines. Any insights, suggestions, or alternative approaches to tackle this challenge would be greatly appreciated.

Thank you in advance for your assistance!

System Info

Using latest version of langchain

[dosubot[bot]]

🤖

Hey there @ArnWEB! Great to see you diving into another intriguing challenge with LangChain. How's the coding journey been treating you lately?

Thank you for your detailed explanation of the problem. It seems like you're dealing with a complex issue of maintaining the sequence of code lines during the conversion process while staying within the token limit.

As you've already implemented a refinement chain, I understand that you're looking for a more effective solution. While LangChain does not provide built-in methods or classes specifically for handling the sequencing of code lines after splitting the code into chunks, I can suggest a potential approach that might help.

You could consider implementing a custom sequencing mechanism that keeps track of the order of the chunks and their corresponding outputs. This could be a simple mapping between the chunk index and its output, stored in a data structure like a list or a dictionary. After all chunks have been processed, you can then reconstruct the entire converted code by iterating over this data structure in the correct order.

Here's a simplified example of what this might look like:

from langchain.text_splitter import TextSplitter

# Initialize a TextSplitter instance
text_splitter = TextSplitter(chunk_size=4000, chunk_overlap=200)

# Split the text into chunks
chunks = text_splitter.split_text(your_text)

# Initialize a dictionary to store the outputs
outputs = {}

# Process each chunk
for i, chunk in enumerate(chunks):
    # Convert the chunk using your language model
    output = convert_chunk(chunk)
    
    # Store the output in the dictionary with the chunk index as the key
    outputs[i] = output

# Reconstruct the entire converted code
converted_code = "".join(outputs[i] for i in sorted(outputs.keys()))

In this example, convert_chunk(chunk) is a placeholder for the actual function or method you're using to convert each chunk of code. Please replace it with your actual conversion function or method.

This approach should help you maintain the sequence of code lines during the conversion process. However, it's important to note that this is a simplified example and you might need to adjust it to fit your specific use case, especially when dealing with overlaps between chunks.

I hope this helps! If you have further questions or need more clarification, feel free to ask.

Sources

• libs/langchain/langchain/text_splitter.py

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[ArnWEB]

@dosu-bot we've implemented the Recursive Character Splitter technique to split the code into manageable chunks. However, appending the code with each chunk exacerbates the token limit issue, as the output code remains similar in size to the input code throughout the conversion iterations. This approach fails to effectively reduce the token count per iteration, hindering our ability to process large codebases efficiently.

[dosubot[bot]]

🤖

Based on the context provided, it seems you're already on the right track by using a technique to split your code into manageable chunks. However, the challenge lies in maintaining the coherence and sequence of the code lines during the conversion process.

In LangChain, there's a class called TokenTextSplitter that might be helpful in your case. This class is designed to split the text into tokens using a model tokenizer. It has a split_text method that splits the text into chunks of tokens. The Tokenizer object is initialized with the chunk_overlap and tokens_per_chunk parameters, which are set to the chunk_overlap and chunk_size attributes of the TokenTextSplitter object, respectively.

Here's a sample code snippet demonstrating how to use the TokenTextSplitter class:

from langchain.text_splitter import TokenTextSplitter

# Initialize the TokenTextSplitter object
splitter = TokenTextSplitter(chunk_size=4000, chunk_overlap=200)

# Split the .NET Framework C# code into chunks of tokens
chunks = splitter.split_text(csharp_code)

# Convert the chunks of tokens back into .NET WebAPI code
webapi_code = [splitter._tokenizer.decode(chunk) for chunk in chunks]

In this code snippet, csharp_code is the .NET Framework C# code that you want to split and sequence. The split_text method splits the csharp_code into chunks of tokens, and the decode method converts the chunks of tokens back into .NET WebAPI code. The webapi_code variable is a list of strings, where each string is a chunk of .NET WebAPI code.

Please note that the TokenTextSplitter class uses the tiktoken Python package to tokenize the text. You can find more details about this class in the libs/langchain/langchain/text_splitter.py file.

Regarding the tokenization of large chunks of code, there's a function split_text_on_tokens that seems to perform a similar task. This function splits a given text into chunks based on the number of tokens specified per chunk. You can find more details about this function in the libs/langchain/tests/unit_tests/test_text_splitter.py file.

I hope this information helps you in effectively splitting and sequencing large chunks of .NET Framework C# code for conversion to .NET WebAPI using LangChain. If you have further questions or need more clarification, feel free to ask.

Sources

• libs/langchain/langchain/text_splitter.py
• libs/langchain/tests/unit_tests/test_text_splitter.py

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[ghost]

Hi, have you found anything yet that works perfectly related to this topic. Would like to know more about how you solved it as i am also kinda working on something like this