Corrigir ainda ...

4.1_RAG_chroma_pdf_qa.py

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+"""
+Data Scientist.: Dr. Eddy Giusepe Chirinos Isidro
+
+Link de estudo --> https://sophiamyang.medium.com/building-a-retrieval-augmented-generation-chatbot-d567a24fcd14
+"""
+import os
+import tempfile
+
+import panel as pn
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.llms import OpenAI
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Chroma
+
+
+import panel as pn
+from panel.chat import ChatInterface
+pn.extension("perspective")
+
+
+def initialize_chain():
+    if key_input.value:
+        os.environ["OPENAI_API_KEY"] = key_input.value
+
+    selections = (pdf_input.value, k_slider.value, chain_select.value)
+    if selections in pn.state.cache:
+        return pn.state.cache[selections]
+
+    chat_input.placeholder = "Ask questions here!"
+
+    # load document
+    with tempfile.NamedTemporaryFile("wb", delete=False) as f:
+        f.write(pdf_input.value)
+    file_name = f.name
+    loader = PyPDFLoader(file_name)
+    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_slider.value}
+    )
+    # create a chain to answer questions
+    qa = RetrievalQA.from_chain_type(
+        llm=OpenAI(),
+        chain_type=chain_select.value,
+        retriever=retriever,
+        return_source_documents=True,
+        verbose=True,
+    )
+    return qa
+
+
+async def respond(contents, user, chat_interface):
+    if not pdf_input.value:
+        chat_interface.send(
+            {"user": "System", "value": "Please first upload a PDF!"}, respond=False
+        )
+        return
+    elif chat_interface.active == 0:
+        chat_interface.active = 1
+        chat_interface.active_widget.placeholder = "Ask questions here!"
+        yield {"user": "OpenAI", "value": "Let's chat about the PDF!"}
+        return
+
+    qa = initialize_chain()
+    response = qa({"query": contents})
+    answers = pn.Column(response["result"])
+    answers.append(pn.layout.Divider())
+    for doc in response["source_documents"][::-1]:
+        answers.append(f"**Page {doc.metadata['page']}**:")
+        answers.append(f"```\n{doc.page_content}\n```")
+    yield {"user": "OpenAI", "value": answers}
+
+
+pdf_input = pn.widgets.FileInput(accept=".pdf", value="", height=50)
+key_input = pn.widgets.PasswordInput(
+    name="OpenAI Key",
+    placeholder="sk-...",
+)
+k_slider = pn.widgets.IntSlider(
+    name="Number of Relevant Chunks", start=1, end=5, step=1, value=2
+)
+chain_select = pn.widgets.RadioButtonGroup(
+    name="Chain Type", options=["stuff", "map_reduce", "refine", "map_rerank"]
+)
+chat_input = pn.widgets.TextInput(placeholder="First, upload a PDF!")
+chat_interface = pn.chat.ChatInterface(
+    callback=respond, sizing_mode="stretch_width", widgets=[pdf_input, chat_input]
+)
+chat_interface.send(
+    {"user": "System", "value": "Please first upload a PDF and click send!"},
+    respond=False,
+)
+template = pn.template.BootstrapTemplate(
+    sidebar=[key_input, k_slider, chain_select], main=[chat_interface]
+)
+template.servable()