how to achieve all the answers for all the rows present in the excel using LLM?

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# Iterate over the sorted file paths and create a loader for each file loaders = [CSVLoader(file_path=file_path, metadata_columns=['cricket'], encoding="utf-8") for file_path in csv_files_sorted] # Optional: If you need to combine the data from all loaders documents = [] for loader in loaders: data = loader.load() documents.extend(data) # Instantiate the OpenAIEmbeddings class openai = OpenAIEmbeddings() # Create a FAISS vector store from the embeddings vectorstore = FAISS.from_documents(documents, openai) retriever = vector_store.as_retriever(search_kwargs={"k": 5}) # Define your prompt template prompt_template = """Use the following pieces of information to answer the user's question. If you don't know the answer, just say that you don't know, don't try to make up an answer. Context: {context} Question: {question} Only return the helpful answer below and nothing else. If no context, then no answer. Helpful Answer:""" # Answer a question related to 'Cricket' category = 'engie' qa_chain = RetrievalQA.from_chain_type(llm=OpenAI(temperature=0.2), chain_type="stuff", retriever=retriever, return_source_documents=True) # Format the prompt using the template context = "" question = "what for it strives?" formatted_prompt = prompt_template.format(context=context, question=question) # Pass the formatted prompt to the RetrievalQA function llm_response = qa_chain(formatted_prompt) process_llm_response(llm_response)

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Hey @nithinreddyyyyyy! Great to see you diving into the depths of LangChain again. How's the coding journey treating you lately?

Based on your request, it seems you want to directly embed data from an Excel spreadsheet into a vector store, bypassing the retriever step in the RetrievalQAChain method. This approach aims to ensure that no information is overlooked, similar to the benefits of the RAG (Retrieval-Augmented Generation) model.

To achieve this, you would need to replace the CSVLoader with an ExcelLoader. However, the LangChain framework does not currently provide an ExcelLoader. You would need to create a custom ExcelLoader that can load data from an Excel spreadsheet.

Here is a simple example of how you might implement an ExcelLoader:

import pandas as pd
from langchain_community.document_loaders import DocumentLoader

class ExcelLoader(DocumentLoader):
    def __init__(self, filepath):
        self.filepath = filepath

    def load(self):
        df = pd.read_excel(self.filepath)
        return df.to_dict('records')

Then, you can use this ExcelLoader in place of the CSVLoader:

from langchain.indexes import VectorstoreIndexCreator
from langchain_community.vectorstores import FAISS
from your_module import ExcelLoader  # assuming you saved the ExcelLoader in a module named 'your_module'

loader = ExcelLoader("/path/to/your/excel/file.xlsx")

docs = loader.load()
index_creator = VectorstoreIndexCreator(vectorstore_cls=FAISS)

index = index_creator.from_documents(docs)

index.vectorstore.save_local("excel_data")

This will load the data from the Excel spreadsheet, convert it into a list of dictionaries (where each dictionary represents a row in the spreadsheet), and then create a vector store from these documents. The vector store is then saved locally.

Regarding your question about the LangChain framework supporting direct embedding of data into a vector store without using a retriever, the answer is yes. This is evident from the add_texts method in the FAISS class. This method allows you to directly add texts to the vector store, which are then embedded and stored. The method returns a list of IDs from adding the texts into the vector store.

Here is the relevant code:

def add_texts(
    self,
    texts: Iterable[str],
    metadatas: Optional[List[dict]] = None,
    ids: Optional[List[str]] = None,
    **kwargs: Any,
) -> List[str]:
    """Run more texts through the embeddings and add to the vectorstore.

    Args:
        texts: Iterable of strings to add to the vectorstore.
        metadatas: Optional list of metadatas associated with the texts.
        ids: Optional list of unique IDs.

    Returns:
        List of ids from adding the texts into the vectorstore.
    """
    texts = list(texts)
    embeddings = self._embed_documents(texts)
    return self.__add(texts, embeddings, metadatas=metadatas, ids=ids)

I hope this helps! If you have any further questions, feel free to ask.

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langchain-ai / langchain

how to achieve all the answers for all the rows present in the excel using LLM? #17731

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