Add dev container and clean up repo

.devcontainer/devcontainer.json

@@ -0,0 +1,25 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the
+// README at: https://github.com/devcontainers/templates/tree/main/src/python
+{
+	"name": "Python 3",
+	// Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  // Note: Steamship currently requires Python 3.8
+	"image": "mcr.microsoft.com/devcontainers/python:0-3.8",
+
+	// Features to add to the dev container. More info: https://containers.dev/features.
+	// "features": {},
+
+	// Configure tool-specific properties.
+	"customizations": {
+		// Configure properties specific to VS Code.
+		"vscode": {
+			"settings": {},
+			"extensions": [
+				"streetsidesoftware.code-spell-checker"
+			]
+		}
+	},
+
+	// Use 'postCreateCommand' to run commands after the container is created.
+	"postCreateCommand": "pip3 install -r requirements.txt"
+}

.vscode/launch.json

@@ -0,0 +1,16 @@
+{
+  "version": "0.1.0",
+  "configurations": [
+    {
+      "name": "Run",
+      "type": "python",
+      "request": "launch",
+      "program": "${workspaceRoot}/main.py",
+      "console": "integratedTerminal",
+      "justMyCode": true,
+      "env": {
+        "PYTHONPATH": "${workspaceRoot}/src"
+      }
+    }
+  ]
+}

README.md

@@ -1,25 +1,17 @@
-# Tutorial: Telegram chatbot with LangChain 
+# : Deploying Multi-Modal LangChain Agents
 
-This project contains the necessary scaffolding to deploy LangChain conversation agents with memory and connect them to Telegram.
+This project contains the necessary scaffolding to deploy LangChain conversation agents with memory and connect them to
+Telegram.
 
-These 4 steps should get you online. If not, shoot me a message on [Discord](https://steamship.com/discord). Happy to help you out. 
+These 4 steps should get you online. If not, shoot me a message on [Discord](https://steamship.com/discord). Happy to
+help you out.
 
+Let's go:
 
-Let's go: 
+> Step 1: Just copy and paste your LangChain agent into `src/chatbot/get_agent`
 
-> Step 1: Just copy paste your LangChain conversation LLMChain into `src/chatbot/get_chatbot`
 
+> Step 2: Pip-install the latest `steamship_langchain`: `pip install --upgrade steamship_langchain`
 
-> Step 2: Add your telegram bot access token under `BOT_TOKEN` in `src/chatbot.py`. More info [here](docs/register-telegram-bot.md)
 
-
-> Step 3: Pip install the latest `steamship_langchain`: `pip install --upgrade steamship_langchain`
-
-
-> Step 4: Run `python deploy.py`
-
-
-## Variations 
-
-Examples of this package: 
-* Gym Bro with long-term memory: https://github.com/steamship-packages/langchain-telegram-chatbot/tree/ec/gym-bro
+> Step 3: Run `python deploy.py`

deploy.py

@@ -15,7 +15,7 @@
     package_handle=manifest.handle,
     version=manifest.version,
     instance_handle=f"{manifest.handle}-{manifest.version.replace('.', '-')}",
-    config={"bot_token": "5629695237:AAFwmYgYRIV1tyPSBEhdYhuQMPVFu_dliAA"},
+    config={"bot_token": "6140681319:AAFqNDOs68qROhCxUO8qOhR8V0IEr5k5vb8"},
 )
 
 bot.wait_for_init()

main.py

@@ -0,0 +1,78 @@
+import logging
+import sys
+from functools import partial
+from typing import List
+
+from steamship.experimental.transports.chat import ChatMessage
+
+from api import LangChainTelegramChatbot
+
+sys.path.insert(0, "src")
+from steamship import Steamship, SteamshipError
+from steamship.cli.ship_spinner import ship_spinner
+from termcolor import colored
+
+
+def show_results(response_messages: List[ChatMessage]):
+    print(colored("\nResults: ", "blue", attrs=["bold"]))
+    for message in response_messages:
+        if message.mime_type and message.mime_type.startswith("image"):
+            print(message.url, end="\n\n")
+        else:
+            print(message.text, end="\n\n")
+
+
+class LoggingDisabled:
+    """Context manager that turns off logging within context."""
+
+    def __enter__(self):
+        logging.disable(logging.CRITICAL)
+
+    def __exit__(self, exit_type, exit_value, exit_traceback):
+        logging.disable(logging.NOTSET)
+
+
+def main():
+    Steamship()
+
+    with Steamship.temporary_workspace() as client:
+        run = partial(run_agent, agent=LangChainTelegramChatbot(client=client, config={"bot_token": "test"}))
+        print(f"Starting Agent...")
+
+        print(
+            f"If you make code changes, you will need to restart this client. Press CTRL+C to exit at any time.\n"
+        )
+
+        count = 1
+
+        while True:
+            print(f"----- Agent Run {count} -----")
+            prompt = input(colored(f"Prompt: ", "blue"))
+            run(
+                # client,
+                prompt=prompt,
+            )
+            count += 1
+
+
+def run_agent(agent, prompt: str, as_api: bool = False) -> None:
+    # For Debugging
+    if not agent.is_verbose_logging_enabled():  # display progress when verbose is False
+        print("Running: ", end="")
+        with ship_spinner():
+            response = agent.create_response(incoming_message=ChatMessage(text=prompt, chat_id="123"))
+    else:
+        response = agent.create_response(incoming_message=ChatMessage(text=prompt, chat_id="123"))
+
+    show_results(response)
+
+
+if __name__ == "__main__":
+    # when running locally, we can use print statements to capture logs / info.
+    # as a result, we will disable python logging to run. this will keep the output cleaner.
+    with LoggingDisabled():
+        try:
+            main()
+        except SteamshipError as e:
+            print(colored("Aborting! ", "red"), end="")
+            print(f"There was an error encountered when running: {e}")

requirements.txt

@@ -1,3 +1,4 @@
 steamship_langchain==0.0.20rc2
 pytimeparse
-steamship==2.16.10rc1
+steamship==2.16.10rc1
+termcolor

src/agent/base.py

@@ -0,0 +1,93 @@
+"""Define your LangChain chatbot."""
+import re
+from abc import abstractmethod
+from typing import List, Optional
+
+from langchain.agents import AgentExecutor
+from steamship import Block
+from steamship.experimental.package_starters.telegram_bot import TelegramBot
+from steamship.experimental.transports.chat import ChatMessage
+from steamship.invocable import post
+
+from agent.utils import is_valid_uuid, make_image_public, UUID_PATTERN
+
+
+class LangChainAgentBot(TelegramBot):
+
+    @abstractmethod
+    def get_agent(self, chat_id: str) -> AgentExecutor:
+        raise NotImplementedError()
+
+    def is_verbose_logging_enabled(self):
+        return True
+
+    @post("send_message")
+    def send_message(self, message: str, chat_id: str) -> str:
+        """Send a message to Telegram.
+
+        Note: This is a private endpoint that requires authentication."""
+        self.telegram_transport.send([ChatMessage(text=message, chat_id=chat_id)])
+        return "ok"
+
+    def _invoke_later(self, delay_ms: int, message: str, chat_id: str):
+        self.invoke_later(
+            "send_message",
+            delay_ms=delay_ms,
+            arguments={
+                "message": message,
+                "chat_id": chat_id,
+            },
+        )
+
+    def create_response(
+            self, incoming_message: ChatMessage
+    ) -> Optional[List[ChatMessage]]:
+        """Use the LLM to prepare the next response by appending the user input to the file and then generating."""
+        if incoming_message.text == "/start":
+            return [
+                ChatMessage(
+                    text="New conversation started.",
+                    chat_id=incoming_message.get_chat_id(),
+                )
+            ]
+
+        conversation = self.get_agent(
+            chat_id=incoming_message.get_chat_id(),
+        )
+        response = conversation.run(input=incoming_message.text)
+        response = UUID_PATTERN.split(response)
+        response = [re.sub(r"^\W+", "", el) for el in response]
+        return self.agent_output_to_chat_messages(
+            chat_id=incoming_message.get_chat_id(), agent_output=response
+        )
+
+    def agent_output_to_chat_messages(
+            self, chat_id: str, agent_output: List[str]
+    ) -> List[ChatMessage]:
+        """Transform the output of the Multi-Modal Agent into a list of ChatMessage objects.
+
+        The response of a Multi-Modal Agent contains one or more:
+        - parseable UUIDs, representing a block containing binary data, or:
+        - Text
+
+        This method inspects each string and creates a ChatMessage of the appropriate type.
+        """
+        ret = []
+        for part_response in agent_output:
+            if is_valid_uuid(part_response):
+                block = Block.get(self.client, _id=part_response)
+                message = ChatMessage.from_block(
+                    block,
+                    chat_id=chat_id,
+                )
+                message.url = make_image_public(self.client, block)
+
+            else:
+                message = ChatMessage(
+                    client=self.client,
+                    chat_id=chat_id,
+                    text=part_response,
+                )
+
+            ret.append(message)
+        return ret