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feat: rag csv support

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This commit is contained in:
Timothy J. Baek 2024-01-07 09:05:52 -08:00
parent d6a1bf1406
commit d4b2578f6e
2 changed files with 11 additions and 6 deletions
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11
backend/apps/rag/main.py
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@ -13,7 +13,12 @@ import os, shutil
# from chromadb.utils import embedding_functions
from langchain_community.document_loaders import WebBaseLoader, TextLoader, PyPDFLoader
from langchain_community.document_loaders import (
WebBaseLoader,
TextLoader,
PyPDFLoader,
CSVLoader,
)
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain.chains import RetrievalQA
@ -129,7 +134,7 @@ def store_doc(
):
# "https://www.gutenberg.org/files/1727/1727-h/1727-h.htm"
if file.content_type not in ["application/pdf", "text/plain"]:
if file.content_type not in ["application/pdf", "text/plain", "text/csv"]:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ERROR_MESSAGES.FILE_NOT_SUPPORTED,
@ -152,6 +157,8 @@ def store_doc(
loader = PyPDFLoader(file_path)
elif file.content_type == "text/plain":
loader = TextLoader(file_path)
elif file.content_type == "text/csv":
loader = CSVLoader(file_path)
data = loader.load()
result = store_data_in_vector_db(data, collection_name)

6
src/lib/components/chat/MessageInput.svelte
View file

@ -122,9 +122,8 @@
const file = inputFiles[0];
if (['image/gif', 'image/jpeg', 'image/png'].includes(file['type'])) {
reader.readAsDataURL(file);
} else if (['application/pdf', 'text/plain'].includes(file['type'])) {
} else if (['application/pdf', 'text/plain', 'text/csv'].includes(file['type'])) {
console.log(file);
// const hash = (await calculateSHA256(file)).substring(0, 63);
const res = await uploadDocToVectorDB(localStorage.token, '', file);
if (res) {
@ -241,9 +240,8 @@
const file = inputFiles[0];
if (['image/gif', 'image/jpeg', 'image/png'].includes(file['type'])) {
reader.readAsDataURL(file);
} else if (['application/pdf', 'text/plain'].includes(file['type'])) {
} else if (['application/pdf', 'text/plain', 'text/csv'].includes(file['type'])) {
console.log(file);
// const hash = (await calculateSHA256(file)).substring(0, 63);
const res = await uploadDocToVectorDB(localStorage.token, '', file);
if (res) {