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import os
import tempfile
import streamlit as st
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import EmbeddingsFilter
from langchain_community.document_loaders import Docx2txtLoader, PyPDFLoader, TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import DocArrayInMemorySearch
from langchain_text_splitters import RecursiveCharacterTextSplitter
@st.cache_resource(ttl="1h")
def configure_retriever(files, use_compression=False):
# Read documents
docs = []
temp_dir = tempfile.TemporaryDirectory()
for file in files:
temp_filepath = os.path.join(temp_dir.name, file.name)
with open(temp_filepath, "wb") as f:
f.write(file.getvalue())
_, extension = os.path.splitext(temp_filepath)
# Load the file using the appropriate loader
if extension == ".pdf":
loader = PyPDFLoader(temp_filepath)
elif extension == ".docx":
loader = Docx2txtLoader(temp_filepath)
elif extension == ".txt":
loader = TextLoader(temp_filepath)
else:
st.write("This document format is not supported!")
return None
docs.extend(loader.load())
# Split documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
# Create embeddings and store in vectordb
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectordb = DocArrayInMemorySearch.from_documents(splits, embeddings)
# Define retriever
retriever = vectordb.as_retriever(
search_type="mmr", search_kwargs={"k": 2, "fetch_k": 4}
)
if not use_compression:
return retriever
embeddings_filter = EmbeddingsFilter(
embeddings=embeddings, similarity_threshold=0.76
)
return ContextualCompressionRetriever(
base_compressor=embeddings_filter, base_retriever=retriever
)
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