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Update app.py
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app.py
CHANGED
@@ -6,8 +6,8 @@ from PyPDF2 import PdfReader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.vectorstores import chroma
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from langchain.chains.retrieval_qa.base import RetrievalQA
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from langchain_community.llms import huggingface_hub
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from langchain.document_loaders.pdf import PyMuPDFLoader
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#from transformers import AutoTokenizer, AutoModelForCausalLM
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@@ -68,12 +68,12 @@ def main():
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llm = huggingface_hub.HuggingFaceHub(repo_id="google/flan-t5-small",
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model_kwargs={"temperature":1.0, "max_length":256})
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docs = vector_store.similarity_search(query=query, k=3)
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retriever=vector_store.as_retriever()
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chain = RetrievalQA.from_chain_type(llm=llm,chain_type="stuff",retriever=retriever)
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response = chain.run(chain)
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st.write(response)
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.vectorstores import chroma
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#from langchain.chains.retrieval_qa.base import RetrievalQA
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from langchain.chains.question_answering import load_qa_chain
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from langchain_community.llms import huggingface_hub
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from langchain.document_loaders.pdf import PyMuPDFLoader
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#from transformers import AutoTokenizer, AutoModelForCausalLM
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llm = huggingface_hub.HuggingFaceHub(repo_id="google/flan-t5-small",
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model_kwargs={"temperature":1.0, "max_length":256})
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docs = vector_store.similarity_search(query=query, k=3)
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chain = load_qa_chain(llm=llm, chain_type="stuff")
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response = chain.run(input_documents=docs, question=query)
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#retriever=vector_store.as_retriever()
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#chain = RetrievalQA.from_chain_type(llm=llm,chain_type="stuff",retriever=retriever)
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#response = chain.run(chain)
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st.write(response)
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