Upload LangChain_Panel_QA_App.py

app.py

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+# -*- coding: utf-8 -*-
+"""LangChain_Panel_QA_App.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Gt2-IOpWB3wRBFbNlMZ9LuZsr6REOvT9
+
+# LangChain QA Panel App
+
+This notebook shows how to make this app:
+"""
+
+!pip install langchain openai chromadb tiktoken pypdf panel
+
+import os
+from langchain.chains import RetrievalQA
+from langchain.llms import OpenAI
+from langchain.document_loaders import TextLoader
+from langchain.document_loaders import PyPDFLoader
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+import panel as pn
+import tempfile
+
+pn.extension('texteditor', template="bootstrap", sizing_mode='stretch_width')
+pn.state.template.param.update(
+    main_max_width="690px",
+    header_background="#F08080",
+)
+
+file_input = pn.widgets.FileInput(width=300)
+
+openaikey = pn.widgets.PasswordInput(
+    value="", placeholder="Enter your OpenAI API Key here...", width=300
+)
+prompt = pn.widgets.TextEditor(
+    value="", placeholder="Enter your questions here...", height=160, toolbar=False
+)
+run_button = pn.widgets.Button(name="Run!")
+
+select_k = pn.widgets.IntSlider(
+    name="Number of relevant chunks", start=1, end=5, step=1, value=2
+)
+select_chain_type = pn.widgets.RadioButtonGroup(
+    name='Chain type',
+    options=['stuff', 'map_reduce', "refine", "map_rerank"]
+)
+
+widgets = pn.Row(
+    pn.Column(prompt, run_button, margin=5),
+    pn.Card(
+        "Chain type:",
+        pn.Column(select_chain_type, select_k),
+        title="Advanced settings", margin=10
+    ), width=600
+)
+
+def qa(file, query, chain_type, k):
+    # load document
+    loader = PyPDFLoader(file)
+    documents = loader.load()
+    # split the documents into chunks
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    texts = text_splitter.split_documents(documents)
+    # select which embeddings we want to use
+    embeddings = OpenAIEmbeddings()
+    # create the vectorestore to use as the index
+    db = Chroma.from_documents(texts, embeddings)
+    # expose this index in a retriever interface
+    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": k})
+    # create a chain to answer questions
+    qa = RetrievalQA.from_chain_type(
+        llm=OpenAI(), chain_type=chain_type, retriever=retriever, return_source_documents=True)
+    result = qa({"query": query})
+    print(result['result'])
+    return result
+
+# result = qa("example.pdf", "what is the total number of AI publications?")
+
+convos = []  # store all panel objects in a list
+
+def qa_result(_):
+    os.environ["OPENAI_API_KEY"] = openaikey.value
+
+    # save pdf file to a temp file
+    if file_input.value is not None:
+        file_input.save("/.cache/temp.pdf")
+
+        prompt_text = prompt.value
+        if prompt_text:
+            result = qa(file="/.cache/temp.pdf", query=prompt_text, chain_type=select_chain_type.value, k=select_k.value)
+            convos.extend([
+                pn.Row(
+                    pn.panel("\U0001F60A", width=10),
+                    prompt_text,
+                    width=600
+                ),
+                pn.Row(
+                    pn.panel("\U0001F916", width=10),
+                    pn.Column(
+                        result["result"],
+                        "Relevant source text:",
+                        pn.pane.Markdown('\n--------------------------------------------------------------------\n'.join(doc.page_content for doc in result["source_documents"]))
+                    )
+                )
+            ])
+            #return convos
+    return pn.Column(*convos, margin=15, width=575, min_height=400)
+
+qa_interactive = pn.panel(
+    pn.bind(qa_result, run_button),
+    loading_indicator=True,
+)
+
+output = pn.WidgetBox('*Output will show up here:*', qa_interactive, width=630, scroll=True)
+
+# layout
+dashboard = pn.Column(
+    pn.pane.Markdown("""
+    ## \U0001F916 Question Answering with your PDF file
+
+    1) Upload a PDF. 2) Enter OpenAI API key. This costs $. Set up billing at [OpenAI](https://platform.openai.com/account). 3) Type a question and click "Run".
+
+    """),
+    pn.Row(file_input,openaikey),
+    output,
+    widgets
+
+)
+
+dashboard.servable()
+