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**Description:** Added support for a Pandas DataFrame OutputParser with format instructions, along with unit tests and a demo notebook. Namely, we've added the ability to request data from a DataFrame, have the LLM parse the request, and then use that request to retrieve a well-formatted response. Within LangChain, it seamlessly integrates with language models like OpenAI's `text-davinci-003`, facilitating streamlined interaction using the format instructions (just like the other output parsers). This parser structures its requests as `<operation/column/row>[<optional_array_params>]`. The instructions detail permissible operations, valid columns, and array formats, ensuring clarity and adherence to the required format. For example: - When the LLM receives the input: "Retrieve the mean of `num_legs` from rows 1 to 3." - The provided format instructions guide the LLM to structure the request as: "mean:num_legs[1..3]". The parser processes this formatted request, leveraging the LLM's understanding to extract the mean of `num_legs` from rows 1 to 3 within the Pandas DataFrame. This integration allows users to communicate requests naturally, with the LLM transforming these instructions into structured commands understood by the `PandasDataFrameOutputParser`. The format instructions act as a bridge between natural language queries and precise DataFrame operations, optimizing communication and data retrieval. **Issue:** - https://github.com/langchain-ai/langchain/issues/11532 **Dependencies:** No additional dependencies :) **Tag maintainer:** @baskaryan **Twitter handle:** No need. :) --------- Co-authored-by: Wasee Alam <waseealam@protonmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> |
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