set up for mvp

app.py

@@ -0,0 +1,98 @@
+import gradio as gr
+
+from langchain.document_loaders import OnlinePDFLoader
+
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.prompts import PromptTemplate
+
+text_splitter = CharacterTextSplitter(chunk_size=350, chunk_overlap=0)
+
+from langchain.llms import HuggingFaceHub
+flan_ul2 = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature":0.1, "max_new_tokens":300})
+
+from langchain.embeddings import HuggingFaceHubEmbeddings
+embeddings = HuggingFaceHubEmbeddings()
+
+from langchain.vectorstores import Chroma
+
+from langchain.chains import RetrievalQA
+def loading_pdf():
+    return "Loading..."
+def pdf_changes(pdf_doc):
+    loader = OnlinePDFLoader(pdf_doc.name)
+    documents = loader.load()
+    texts = text_splitter.split_documents(documents)
+    db = Chroma.from_documents(texts, embeddings)
+    retriever = db.as_retriever()
+    
+    prompt_template = """You have been given a pdf or pdfs. You must search these pdfs. 
+    If you don't know the answer, just say that you don't know, don't try to make up an answer.
+    Only answer the question.
+    
+    {context}
+    
+    Question: {question}
+    Answer:"""
+    PROMPT = PromptTemplate(
+        template=prompt_template, input_variables=["context", "question"]
+    )
+    chain_type_kwargs = {"prompt": PROMPT}
+    global qa 
+    qa = RetrievalQA.from_chain_type(
+        llm=flan_ul2, 
+        chain_type="stuff", 
+        retriever=retriever, 
+        return_source_documents=True,
+        chain_type_kwargs=chain_type_kwargs,
+    )
+    return "Ready"
+
+def add_text(history, text):
+    history = history + [(text, None)]
+    return history, ""
+
+def bot(history):
+    response = infer(history[-1][0])
+    history[-1][1] = response['result']
+    return history
+
+def infer(question):
+    
+    query = question
+    result = qa({"query": query})
+
+    return result
+
+css="""
+#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
+"""
+
+title = """
+<div style="text-align: center;max-width: 700px;">
+    <h1>Chat with PDF</h1>
+    <p style="text-align: center;">Upload a .PDF from your computer, click the "Load PDF to LangChain" button, <br />
+    when everything is ready, you can start asking questions about the pdf ;)</p>
+</div>
+"""
+
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.HTML(title)
+        
+        with gr.Column():
+            pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file")
+            with gr.Row():
+                langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
+                load_pdf = gr.Button("Load pdf to langchain")
+        
+        chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
+        with gr.Row():
+            question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
+    load_pdf.click(loading_pdf, None, langchain_status, queue=False)    
+    load_pdf.click(pdf_changes, pdf_doc, langchain_status, queue=False)
+    question.submit(add_text, [chatbot, question], [chatbot, question]).then(
+        bot, chatbot, chatbot
+    )
+
+demo.launch()

ingest.py

@@ -24,14 +24,14 @@ query_engine = index.as_query_engine()
 # Define the query text
 query_text = "How does the weather affect crop growth?"
 
-data = query_engine.query(query)
+data = query_engine.query(query_text)
 
 # Preprocess the query text
-query_builder = QueryBuilder(service_context)
-query = query_builder.build_query(query_text)
+# query_builder = QueryBuilder(service_context)
+# query = query_builder.build_query(query_text)
 
-# Search for similar documents or retrieve relevant information
-results = index.search(query)
+# # Search for similar documents or retrieve relevant information
+# results = index.search(query)
 
 # Process the search results
 for result in results: