add agent chat pack

llama_hub/llama_packs/gradio_agent_chat/README.md

@@ -0,0 +1,70 @@
+# Gradio Chat With Your LlamaIndex Agent
+
+Create a LlamaIndex Agent (i.e., `BaseAgent`) and quickly chat with it using
+this pack's Gradio Chatbot interface.
+
+<p text-align="center">
+  <figure>
+    <img src="chat-with-your-agent-dark.png" width="75%" />
+    <figcaption>Dark mode</figcaption>
+  </figure>
+</p>
+<p text-align="center">
+  <figure>
+    <img src="chat-with-your-agent-light.png" width="75%" />
+    <figcaption>Light  mode</figcaption>
+  </figure>
+</p>
+
+## Usage
+
+You can download the pack to a `./gradio_agent_chat_pack` directory:
+
+`sample.py`
+```python
+from llama_index.llama_packs import download_llama_pack
+from llama_index.agent import OpenAIAgent
+from llama_index.llms import OpenAI
+from llama_index.tools import FunctionTool
+
+
+def add(a: int, b: int) -> int:
+    """Add two integers and returns the result integer"""
+    return a + b
+
+
+def multiply(a: int, b: int) -> int:
+    """Multiple two integers and returns the result integer"""
+    return a * b
+
+
+multiply_tool = FunctionTool.from_defaults(fn=multiply)
+add_tool = FunctionTool.from_defaults(fn=add)
+
+# Works with any BaseAgent
+agent = OpenAIAgent.from_tools(
+    tools=[multiply_tool, add_tool],
+    llm=OpenAI(model="gpt-3.5-turbo-1106"), 
+    verbose=True  # thoughts are displayed in the Gradio interface!
+)
+
+# download and install dependencies
+GradioAgentChatPack = download_llama_pack(
+  "GradioAgentChatPack", "./gradio_agent_chat_pack"
+)
+
+gradio_agent_chat_pack = GradioAgentChatPack(agent=agent)
+
+if __name__ == "__main__":
+    gradio_agent_chat_pack.run()
+```
+
+From here, you can use the pack, or inspect and modify the pack in `./gradio_agent_chat_pack`.
+
+The `run()` function is a light wrapper around `gr.demo.launch()`. To run the
+app directly, use in your terminal:
+
+```bash
+export OPENAI_API_KEY="sk-..."
+python ./gradio_agent_chat/sample.py
+```

llama_hub/llama_packs/gradio_agent_chat/__init__.py

@@ -0,0 +1,3 @@
+from llama_hub.llama_packs.gradio_agent_chat.base import GradioAgentChatPack
+
+__all__ = ["GradioAgentChatPack"]

llama_hub/llama_packs/gradio_agent_chat/base.py

@@ -0,0 +1,149 @@
+from typing import Dict, Any, List, Tuple
+
+from llama_index.llama_pack.base import BaseLlamaPack
+from llama_index.agent.types import BaseAgent
+
+from io import StringIO
+import sys
+
+
+class Capturing(list):
+    """To capture the stdout from `BaseAgent.stream_chat` with `verbose=True`. Taken from
+    https://stackoverflow.com/questions/16571150/\
+        how-to-capture-stdout-output-from-a-python-function-call
+    """
+
+    def __enter__(self):
+        self._stdout = sys.stdout
+        sys.stdout = self._stringio = StringIO()
+        return self
+
+    def __exit__(self, *args):
+        self.extend(self._stringio.getvalue().splitlines())
+        del self._stringio  # free up some memory
+        sys.stdout = self._stdout
+
+
+class GradioAgentChatPack(BaseLlamaPack):
+    """Gradio chatbot to chat with your own Agent."""
+
+    def __init__(
+        self,
+        agent: BaseAgent,
+        **kwargs: Any,
+    ) -> None:
+        """Init params."""
+        try:
+            from ansi2html import Ansi2HTMLConverter
+        except ImportError:
+            raise ImportError("Please install ansi2html via `pip install ansi2html`")
+
+        self.agent = agent
+        self.thoughts = ""
+        self.conv = Ansi2HTMLConverter()
+
+    def get_modules(self) -> Dict[str, Any]:
+        """Get modules."""
+        return {"agent": self.agent}
+
+    def _handle_user_message(self, user_message, history):
+        """Handle the user submitted message. Clear message box, and append
+        to the history."""
+        return "", history + [(user_message, "")]
+
+    def _generate_response(
+        self, chat_history: List[Tuple[str, str]]
+    ) -> Tuple[str, List[Tuple[str, str]]]:
+        """Generate the response from agent, and capture the stdout of the
+        ReActAgent's thoughts.
+        """
+        with Capturing() as output:
+            response = self.agent.stream_chat(chat_history[-1][0])
+        ansi = "\n========\n".join(output)
+        html_output = self.conv.convert(ansi)
+        for token in response.response_gen:
+            chat_history[-1][1] += token
+            yield chat_history, str(html_output)
+
+    def _reset_chat(self) -> Tuple[str, str]:
+        """Reset the agent's chat history. And clear all dialogue boxes."""
+        # clear agent history
+        self.agent.reset()
+        return "", "", ""  # clear textboxes
+
+    def run(self, *args: Any, **kwargs: Any) -> Any:
+        """Run the pipeline."""
+        import gradio as gr
+        from gradio.themes.utils import fonts, colors, sizes
+
+        llama_theme = gr.themes.Soft(
+            primary_hue=colors.purple,
+            secondary_hue=colors.pink,
+            neutral_hue=colors.gray,
+            spacing_size=sizes.spacing_md,
+            radius_size=sizes.radius_md,
+            text_size=sizes.text_lg,
+            font=(
+                fonts.GoogleFont("Quicksand"),
+                "ui-sans-serif",
+                "sans-serif",
+            ),
+            font_mono=(
+                fonts.GoogleFont("IBM Plex Mono"),
+                "ui-monospace",
+                "monospace",
+            ),
+        )
+        llama_theme.set(
+            body_background_fill="#FFFFFF",
+            body_background_fill_dark="#000000",
+            button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)",
+            button_primary_background_fill_hover="linear-gradient(90deg, *primary_200, *secondary_300)",
+            button_primary_text_color="white",
+            button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *secondary_800)",
+            slider_color="*secondary_300",
+            slider_color_dark="*secondary_600",
+            block_title_text_weight="600",
+            block_border_width="3px",
+            block_shadow="*shadow_drop_lg",
+            button_shadow="*shadow_drop_lg",
+            button_large_padding="32px",
+        )
+
+        demo = gr.Blocks(
+            theme=llama_theme,
+            css="#box { height: 420px; overflow-y: scroll !important} #logo { align-self: right }",
+        )
+        with demo:
+            with gr.Row():
+                gr.Markdown(
+                    "# Gradio Chat With Your Agent Powered by LlamaIndex and LlamaHub 🦙\n"
+                    "This Gradio app allows you to chat with your own agent (`BaseAgent`).\n"
+                )
+                gr.Markdown(
+                    "[![Alt text](https://d3ddy8balm3goa.cloudfront.net/other/llama-index-light-transparent-sm-font.svg)](https://llamaindex.ai)",
+                    elem_id="logo",
+                )
+            with gr.Row():
+                chat_window = gr.Chatbot(
+                    label="Message History",
+                    scale=3,
+                )
+                console = gr.HTML(elem_id="box")
+            with gr.Row():
+                message = gr.Textbox(label="Write A Message", scale=4)
+                clear = gr.ClearButton()
+
+            message.submit(
+                self._handle_user_message,
+                [message, chat_window],
+                [message, chat_window],
+                queue=False,
+            ).then(
+                self._generate_response,
+                chat_window,
+                [chat_window, console],
+            )
+            clear.click(self._reset_chat, None, [message, chat_window, console])
+
+        demo.launch(server_name="0.0.0.0", server_port=8080)

llama_hub/llama_packs/gradio_agent_chat/requirements.txt

@@ -0,0 +1,3 @@
+llama-hub
+gradio
+ansi2html

llama_hub/llama_packs/gradio_react_agent_chatbot/README.md

@@ -16,7 +16,7 @@ additionally the agent's thoughts are captured in an `HTML` Block.
 You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:
 
 ```bash
-llamaindex-cli download-llamapack GradioReactAgentPack --download-dir ./gradio_react_agent_chatbot
+llamaindex-cli download-llamapack GradioReActAgentPack --download-dir ./gradio_react_agent_chatbot
 ```
 
 You can then inspect the files at `./gradio_react_agent_chatbot` and use them as a template for your own project!

llama_hub/llama_packs/gradio_react_agent_chatbot/__init__.py

@@ -0,0 +1,3 @@
+from llama_hub.llama_packs.gradio_react_agent_chatbot.base import GradioReActAgentPack
+
+__all__ = ["GradioReActAgentPack"]

llama_hub/llama_packs/library.json

@@ -64,6 +64,12 @@
     "author": "andrei-fajardo",
     "keywords": ["gradio", "react-agent", "chatbot", "tools"] 
   },
+  "GradioAgentChatPack": {
+    "id": "llama_packs/gradio_agent_chat",
+    "author": "andrei-fajardo",
+    "keywords": ["gradio", "agent", "chatbot", "tools"] 
+  },
+  }
   "WeaviateSubQuestionPack": {
     "id": "llama_packs/sub_question_weaviate",
     "author": "erika-cardenas",