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Update app.py
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app.py
CHANGED
@@ -9,6 +9,7 @@ from llama_index.embeddings.openai import OpenAIEmbedding
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from llama_index.vector_stores.faiss import FaissVectorStore
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from llama_index.core.ingestion import IngestionPipeline
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from langchain_community.vectorstores import FAISS as LangChainFAISS
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from langchain.chains import create_retrieval_chain
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from langchain.chains.combine_documents import create_stuff_documents_chain
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from langchain_core.prompts import ChatPromptTemplate
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@@ -43,16 +44,17 @@ if uploaded_file:
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data.to_csv(temp_file.name, index=False, encoding="utf-8")
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temp_file.flush()
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-
# Verify the temporary file
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st.write("Temporary file path:", temp_file_path)
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with open(temp_file_path, "r") as f:
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# Tabs for LangChain and LlamaIndex
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tab1, tab2 = st.tabs(["LangChain", "LlamaIndex"])
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# LangChain Tab with
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with tab1:
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st.subheader("LangChain Query")
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try:
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@@ -68,11 +70,15 @@ if uploaded_file:
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if documents:
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st.text(documents[0]["page_content"])
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# Create FAISS VectorStore
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langchain_index = faiss.IndexFlatL2(EMBED_DIMENSION)
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langchain_vector_store = LangChainFAISS(
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embedding_function=OpenAIEmbeddings(),
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index=langchain_index,
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)
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langchain_vector_store.add_documents(documents)
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from llama_index.vector_stores.faiss import FaissVectorStore
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from llama_index.core.ingestion import IngestionPipeline
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from langchain_community.vectorstores import FAISS as LangChainFAISS
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from langchain_community.docstore.in_memory import InMemoryDocstore
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from langchain.chains import create_retrieval_chain
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from langchain.chains.combine_documents import create_stuff_documents_chain
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from langchain_core.prompts import ChatPromptTemplate
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data.to_csv(temp_file.name, index=False, encoding="utf-8")
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temp_file.flush()
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# Debugging: Verify the temporary file (Display partially)
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st.write("Temporary file path:", temp_file_path)
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with open(temp_file_path, "r") as f:
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content = f.read()
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st.write("Partial file content (first 500 characters):")
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st.text(content[:500])
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# Tabs for LangChain and LlamaIndex
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tab1, tab2 = st.tabs(["LangChain", "LlamaIndex"])
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# LangChain Tab with Proper FAISS Initialization
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with tab1:
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st.subheader("LangChain Query")
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try:
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if documents:
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st.text(documents[0]["page_content"])
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# Create FAISS VectorStore with proper arguments
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langchain_index = faiss.IndexFlatL2(EMBED_DIMENSION)
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docstore = InMemoryDocstore() # Create an in-memory docstore
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index_to_docstore_id = {} # Mapping of index to document ID
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langchain_vector_store = LangChainFAISS(
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embedding_function=OpenAIEmbeddings(),
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index=langchain_index,
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docstore=docstore,
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index_to_docstore_id=index_to_docstore_id,
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)
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langchain_vector_store.add_documents(documents)
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