satyam001 commited on
Commit
9306c69
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1 Parent(s): d1631d1

Update app.py

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Files changed (1) hide show
  1. app.py +17 -9
app.py CHANGED
@@ -10,8 +10,11 @@ from langchain.llms import OpenAI
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  from langchain.chains.question_answering import load_qa_chain
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  from langchain.callbacks import get_openai_callback
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  import os
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-
 
 
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  # Sidebar contents
 
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  with st.sidebar:
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  st.title('πŸ€—πŸ’¬ LLM Chat App')
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  st.markdown('''
@@ -28,7 +31,7 @@ with st.sidebar:
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  # load_dotenv()
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- # os.environ['OPEN_AI_APIKEY'] = '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'
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  def main():
@@ -62,9 +65,12 @@ def main():
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  VectorStore = pickle.load(f)
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  # st.write('Embeddings Loaded from the Disk')s
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  else:
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- embeddings = OpenAIEmbeddings(
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- openai_api_key='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')
 
 
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  VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
 
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  with open(f"{store_name}.pkl", "wb") as f:
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  pickle.dump(VectorStore, f)
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@@ -77,12 +83,14 @@ def main():
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  if query:
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  docs = VectorStore.similarity_search(query=query, k=3)
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-
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- llm = OpenAI()
 
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  chain = load_qa_chain(llm=llm, chain_type="stuff")
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- with get_openai_callback() as cb:
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- response = chain.run(input_documents=docs, question=query)
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- print(cb)
 
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  st.write(response)
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  from langchain.chains.question_answering import load_qa_chain
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  from langchain.callbacks import get_openai_callback
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  import os
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+ from langchain.vectorstores import Chroma
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+ from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
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+ from langchain.embeddings import HuggingFaceHubEmbeddings
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  # Sidebar contents
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+ from langchain.llms import HuggingFaceHub
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  with st.sidebar:
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  st.title('πŸ€—πŸ’¬ LLM Chat App')
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  st.markdown('''
 
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  # load_dotenv()
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+ os.environ['OPEN_AI_APIKEY'] = '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'
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  def main():
 
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  VectorStore = pickle.load(f)
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  # st.write('Embeddings Loaded from the Disk')s
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  else:
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+ # embeddings = OpenAIEmbeddings(
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+ # openai_api_key='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')
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+ # embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
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+ embeddings = HuggingFaceHubEmbeddings()
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  VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
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+ # VectorStore=Chroma.from_documents(chunks, embeddings)
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  with open(f"{store_name}.pkl", "wb") as f:
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  pickle.dump(VectorStore, f)
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  if query:
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  docs = VectorStore.similarity_search(query=query, k=3)
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+ llm = HuggingFaceHub(repo_id='OpenAssistant/oasst-sft-1-pythia-12b',
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+ token='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', model_kwargs={"temperature": 0.1, "max_new_tokens": 250})
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+ # llm = OpenAI()
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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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+ # with get_openai_callback() as cb:
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+ # response = chain.run(input_documents=docs, question=query)
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+ # print(cb)
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  st.write(response)
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