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Streamlit chatbot

StreamlitChatPack #

Bases: BaseLlamaPack

Streamlit chatbot pack.

Source code in llama-index-packs/llama-index-packs-streamlit-chatbot/llama_index/packs/streamlit_chatbot/base.py
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class StreamlitChatPack(BaseLlamaPack):
    """Streamlit chatbot pack."""

    def __init__(
        self,
        wikipedia_page: str = "Snowflake Inc.",
        run_from_main: bool = False,
        **kwargs: Any,
    ) -> None:
        """Init params."""
        if not run_from_main:
            raise ValueError(
                "Please run this llama-pack directly with "
                "`streamlit run [download_dir]/streamlit_chatbot/base.py`"
            )

        self.wikipedia_page = wikipedia_page

    def get_modules(self) -> Dict[str, Any]:
        """Get modules."""
        return {}

    def run(self, *args: Any, **kwargs: Any) -> Any:
        """Run the pipeline."""
        import streamlit as st
        from streamlit_pills import pills

        st.set_page_config(
            page_title=f"Chat with {self.wikipedia_page}'s Wikipedia page, powered by LlamaIndex",
            page_icon="๐Ÿฆ™",
            layout="centered",
            initial_sidebar_state="auto",
            menu_items=None,
        )

        if "messages" not in st.session_state:  # Initialize the chat messages history
            st.session_state["messages"] = [
                {"role": "assistant", "content": "Ask me a question about Snowflake!"}
            ]

        st.title(
            f"Chat with {self.wikipedia_page}'s Wikipedia page, powered by LlamaIndex ๐Ÿ’ฌ๐Ÿฆ™"
        )
        st.info(
            "This example is powered by the **[Llama Hub Wikipedia Loader](https://llamahub.ai/l/wikipedia)**. Use any of [Llama Hub's many loaders](https://llamahub.ai/) to retrieve and chat with your data via a Streamlit app.",
            icon="โ„น๏ธ",
        )

        def add_to_message_history(role, content):
            message = {"role": role, "content": str(content)}
            st.session_state["messages"].append(
                message
            )  # Add response to message history

        @st.cache_resource
        def load_index_data():
            loader = WikipediaReader()
            docs = loader.load_data(pages=[self.wikipedia_page])
            service_context = ServiceContext.from_defaults(
                llm=OpenAI(model="gpt-3.5-turbo", temperature=0.5)
            )
            return VectorStoreIndex.from_documents(
                docs, service_context=service_context
            )

        index = load_index_data()

        selected = pills(
            "Choose a question to get started or write your own below.",
            [
                "What is Snowflake?",
                "What company did Snowflake announce they would acquire in October 2023?",
                "What company did Snowflake acquire in March 2022?",
                "When did Snowflake IPO?",
            ],
            clearable=True,
            index=None,
        )

        if "chat_engine" not in st.session_state:  # Initialize the query engine
            st.session_state["chat_engine"] = index.as_chat_engine(
                chat_mode="context", verbose=True
            )

        for message in st.session_state["messages"]:  # Display the prior chat messages
            with st.chat_message(message["role"]):
                st.write(message["content"])

        # To avoid duplicated display of answered pill questions each rerun
        if selected and selected not in st.session_state.get(
            "displayed_pill_questions", set()
        ):
            st.session_state.setdefault("displayed_pill_questions", set()).add(selected)
            with st.chat_message("user"):
                st.write(selected)
            with st.chat_message("assistant"):
                response = st.session_state["chat_engine"].stream_chat(selected)
                response_str = ""
                response_container = st.empty()
                for token in response.response_gen:
                    response_str += token
                    response_container.write(response_str)
                add_to_message_history("user", selected)
                add_to_message_history("assistant", response)

        if prompt := st.chat_input(
            "Your question"
        ):  # Prompt for user input and save to chat history
            add_to_message_history("user", prompt)

            # Display the new question immediately after it is entered
            with st.chat_message("user"):
                st.write(prompt)

            # If last message is not from assistant, generate a new response
            # if st.session_state["messages"][-1]["role"] != "assistant":
            with st.chat_message("assistant"):
                response = st.session_state["chat_engine"].stream_chat(prompt)
                response_str = ""
                response_container = st.empty()
                for token in response.response_gen:
                    response_str += token
                    response_container.write(response_str)
                # st.write(response.response)
                add_to_message_history("assistant", response.response)

            # Save the state of the generator
            st.session_state["response_gen"] = response.response_gen

get_modules #

get_modules() -> Dict[str, Any]

Get modules.

Source code in llama-index-packs/llama-index-packs-streamlit-chatbot/llama_index/packs/streamlit_chatbot/base.py
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def get_modules(self) -> Dict[str, Any]:
    """Get modules."""
    return {}

run #

run(*args: Any, **kwargs: Any) -> Any

Run the pipeline.

Source code in llama-index-packs/llama-index-packs-streamlit-chatbot/llama_index/packs/streamlit_chatbot/base.py
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def run(self, *args: Any, **kwargs: Any) -> Any:
    """Run the pipeline."""
    import streamlit as st
    from streamlit_pills import pills

    st.set_page_config(
        page_title=f"Chat with {self.wikipedia_page}'s Wikipedia page, powered by LlamaIndex",
        page_icon="๐Ÿฆ™",
        layout="centered",
        initial_sidebar_state="auto",
        menu_items=None,
    )

    if "messages" not in st.session_state:  # Initialize the chat messages history
        st.session_state["messages"] = [
            {"role": "assistant", "content": "Ask me a question about Snowflake!"}
        ]

    st.title(
        f"Chat with {self.wikipedia_page}'s Wikipedia page, powered by LlamaIndex ๐Ÿ’ฌ๐Ÿฆ™"
    )
    st.info(
        "This example is powered by the **[Llama Hub Wikipedia Loader](https://llamahub.ai/l/wikipedia)**. Use any of [Llama Hub's many loaders](https://llamahub.ai/) to retrieve and chat with your data via a Streamlit app.",
        icon="โ„น๏ธ",
    )

    def add_to_message_history(role, content):
        message = {"role": role, "content": str(content)}
        st.session_state["messages"].append(
            message
        )  # Add response to message history

    @st.cache_resource
    def load_index_data():
        loader = WikipediaReader()
        docs = loader.load_data(pages=[self.wikipedia_page])
        service_context = ServiceContext.from_defaults(
            llm=OpenAI(model="gpt-3.5-turbo", temperature=0.5)
        )
        return VectorStoreIndex.from_documents(
            docs, service_context=service_context
        )

    index = load_index_data()

    selected = pills(
        "Choose a question to get started or write your own below.",
        [
            "What is Snowflake?",
            "What company did Snowflake announce they would acquire in October 2023?",
            "What company did Snowflake acquire in March 2022?",
            "When did Snowflake IPO?",
        ],
        clearable=True,
        index=None,
    )

    if "chat_engine" not in st.session_state:  # Initialize the query engine
        st.session_state["chat_engine"] = index.as_chat_engine(
            chat_mode="context", verbose=True
        )

    for message in st.session_state["messages"]:  # Display the prior chat messages
        with st.chat_message(message["role"]):
            st.write(message["content"])

    # To avoid duplicated display of answered pill questions each rerun
    if selected and selected not in st.session_state.get(
        "displayed_pill_questions", set()
    ):
        st.session_state.setdefault("displayed_pill_questions", set()).add(selected)
        with st.chat_message("user"):
            st.write(selected)
        with st.chat_message("assistant"):
            response = st.session_state["chat_engine"].stream_chat(selected)
            response_str = ""
            response_container = st.empty()
            for token in response.response_gen:
                response_str += token
                response_container.write(response_str)
            add_to_message_history("user", selected)
            add_to_message_history("assistant", response)

    if prompt := st.chat_input(
        "Your question"
    ):  # Prompt for user input and save to chat history
        add_to_message_history("user", prompt)

        # Display the new question immediately after it is entered
        with st.chat_message("user"):
            st.write(prompt)

        # If last message is not from assistant, generate a new response
        # if st.session_state["messages"][-1]["role"] != "assistant":
        with st.chat_message("assistant"):
            response = st.session_state["chat_engine"].stream_chat(prompt)
            response_str = ""
            response_container = st.empty()
            for token in response.response_gen:
                response_str += token
                response_container.write(response_str)
            # st.write(response.response)
            add_to_message_history("assistant", response.response)

        # Save the state of the generator
        st.session_state["response_gen"] = response.response_gen