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Update variables.py
Browse files- variables.py +11 -3
variables.py
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@@ -72,8 +72,8 @@ def process_tweets(df,df_users):
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return df_tweets
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@st.experimental_singleton(suppress_st_warning=True)
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def
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'''
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# Split tweets int chunks
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
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@@ -90,6 +90,14 @@ def embed_tweets(file,model,query,_prompt):
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docsearch = FAISS.from_texts(texts, emb)
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chain_type_kwargs = {"prompt": _prompt}
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chain = VectorDBQA.from_chain_type(
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ChatOpenAI(temperature=0),
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@@ -97,7 +105,7 @@ def embed_tweets(file,model,query,_prompt):
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vectorstore=docsearch,
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chain_type_kwargs=chain_type_kwargs,
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return_source_documents=True,
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k=
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)
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result = chain({"query": query})
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return df_tweets
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@st.experimental_singleton(suppress_st_warning=True)
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def create_vectorstore(file,model):
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'''Create FAISS vectorstore'''
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# Split tweets int chunks
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
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docsearch = FAISS.from_texts(texts, emb)
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return docsearch
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@st.experimental_singleton(suppress_st_warning=True)
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def embed_tweets(query,_prompt):
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'''Process file with latest tweets'''
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chain_type_kwargs = {"prompt": _prompt}
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chain = VectorDBQA.from_chain_type(
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ChatOpenAI(temperature=0),
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vectorstore=docsearch,
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chain_type_kwargs=chain_type_kwargs,
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return_source_documents=True,
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k=3
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)
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result = chain({"query": query})
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