Chain of table process is missing records if I process the data with 400+ records.

[mraguth]

https://github.com/run-llama/llama-hub/blob/main/llama_hub/llama_packs/tables/chain_of_table/chain_of_table.ipynb

from llama_index.prompts import PromptTemplate
from llama_index.query_pipeline import QueryPipeline

prompt_str = """\
Here's a serialized table.

{serialized_table}

Given this table please answer the question: {question}
Answer: """
prompt = PromptTemplate(prompt_str)
prompt_c = prompt.as_query_component(partial={"serialized_table": serialize_table(df)})
qp = QueryPipeline(chain=[prompt_c, llm])
response = qp.run("Which televised ABC game had the greatest attendance?")
print(str(response))

[dosubot[bot]]

Based on the information you've provided and the context from the LlamaIndex repository, it seems like the issue you're encountering might be due to limitations in handling large datasets efficiently or accurately. The chain of table process in LlamaIndex is marked as beta, indicating that there may still be issues or inefficiencies in processing large or complex datasets. This could be due to several reasons, such as memory constraints, limitations in the underlying algorithms, or inefficiencies in the data processing pipeline.

Here are some potential approaches to resolve this issue:

1. Optimization and Efficiency Improvements: Review and optimize the implementation of the chain of table process. This could involve optimizing the underlying algorithms, improving memory management, or streamlining the data processing pipeline to handle larger datasets more efficiently.

2. Batch Processing: If the issue is related to processing large datasets in a single operation, consider breaking the dataset into smaller batches and processing each batch separately. This can help manage memory usage and improve performance.

3. Increase Resources: If the issue is related to resource constraints, consider increasing the available resources (e.g., memory, processing power) to accommodate the processing of larger datasets.

4. Debugging and Profiling: Use debugging and profiling tools to identify bottlenecks or inefficiencies in the data processing pipeline. This can help pinpoint specific areas that need optimization or improvement.

The specific implementation of the chain of table process in the LlamaIndex codebase involves a series of steps to dynamically construct and execute a sequence of operations on a table to answer a given question. This process is encapsulated within the ChainOfTableQueryEngine class, which utilizes a custom query engine to run a "chain of thought" query engine approach. You can find more details about this implementation in the source code.

Unfortunately, I wasn't able to find specific information about the maximum number of records that the chain of table process in LlamaIndex can handle efficiently without missing any records, or the exact error or issue that occurs when the chain of table process in LlamaIndex tries to process datasets with 400+ records. If you could provide more details about the error messages or exceptions you're encountering, it would be helpful in diagnosing and addressing the issue.

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

Sources

• llama-index-packs/llama-index-packs-tables/examples/chain_of_table.ipynb
• llama-index-packs/llama-index-packs-tables/llama_index/packs/tables/chain_of_table/base.py
• llama-index-legacy/llama_index/legacy/vector_stores/docarray/in_memory.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.