Question and answer over multiple csv files in langchain – Langchain

The Solutions:

Solution 1: Using Vectorstore and RetrievalQAChain***

1. Load CSV files into a Vectorstore:

Use a library like Pinecone or Metal to create a vectorstore. Load the CSV files into the vectorstore, using a field in the CSV file as the document ID.

import pinecone

client = pinecone.Client()
vector_index = client.index.create("my-vector-index")

2. Create a RetrievalQAChain:

This chain will allow you to perform question answering over the vectorstore.

from langchain.retrieval import RetrievalQAChain
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone

chain = RetrievalQAChain(
    vectorstore=Pinecone(client, vector_index),
    embeddings=OpenAIEmbeddings(),
    max_results=10,
)

3. Ask questions:

You can now ask questions to the chain and retrieve the relevant documents.

query = "How many females are present?"
results = chain.run(query)

Solution 2: Using DirectoryLoader

To load multiple CSV files into Langchain and perform question answering over them, a DirectoryLoader can be utilized. This loader allows you to specify a directory containing the CSV files, and it will automatically load all the files within that directory.

from langchain.document_loaders import DirectoryLoader
from langchain.document_loaders.csv_loader import CSVLoader

# Define the directory containing the CSV files
data_directory = "../data/"

# Create a DirectoryLoader using the CSVLoader as the base loader
loader = DirectoryLoader(data_directory, glob="**/*.csv", loader_cls=CSVLoader)

# Load the documents from the directory
documents = loader.load()

Video Explanation:

The following video, titled "ChatCSV App: Chat with CSV files using LangChain and Llama 2 ...", provides additional insights and in-depth exploration related to the topics discussed in this post.

langchain #llama2 #llama #csv #chatcsv #chatbot #largelanguagemodels #generativeai #generativemodels ...