feat: Add langgraph chat with bedrock

app.py

@@ -1,64 +1,4 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
+from tdagent.grchat import gr_app
 
 if __name__ == "__main__":
-    demo.launch()
+    gr_app.launch()

pyproject.toml

@@ -13,19 +13,17 @@ requires-python = ">=3.10,<4"
 readme = "README.md"
 license = ""
 dependencies = [
+    "gradio[mcp]>=5.32.1",
     "huggingface-hub>=0.32.3",
+    "langchain-aws>=0.2.24",
+    "langgraph>=0.4.7",
 ]
 
 [project.scripts]
 
 
 [dependency-groups]
-dev = [
-    "mypy~=1.14",
- "ruff>=0.9,<1",
- "pre-commit~=3.4",
- "pip-audit>=2.9.0",
-]
+dev = ["mypy~=1.14", "ruff>=0.9,<1", "pre-commit~=3.4", "pip-audit>=2.9.0"]
 test = [
     "pytest>=7.4.4,<8",
     "pytest-cov>=4.1.0,<5",
@@ -67,7 +65,7 @@ disallow_untyped_decorators = true
 warn_redundant_casts = true
 warn_unused_ignores = true
 exclude = "docs/"
-plugins = []                       # ["numpy.typing.mypy_plugin", "pydantic.mypy"]
+plugins = ["pydantic.mypy"]        # ["numpy.typing.mypy_plugin"]
 
 [[tool.mypy.overrides]]
 module = "tests.*"

tdagent/grchat.py

@@ -0,0 +1,202 @@
+from __future__ import annotations
+
+from collections.abc import Mapping
+from types import MappingProxyType
+
+import boto3
+import botocore
+import botocore.exceptions
+import gradio as gr
+from langchain_aws import ChatBedrock
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+from langgraph.graph.graph import CompiledGraph
+from langgraph.prebuilt import create_react_agent
+
+
+#### Constants ####
+
+SYSTEM_MESSAGE = SystemMessage(
+    "You are a helpful assistant.",
+)
+
+GRADIO_ROLE_TO_LG_MESSAGE_TYPE = MappingProxyType(
+    {
+        "user": HumanMessage,
+        "assistant": AIMessage,
+    },
+)
+
+
+#### Shared variables ####
+
+llm_agent: CompiledGraph | None = None
+
+#### Utility functions ####
+
+
+def create_bedrock_llm(
+    bedrock_model_id: str,
+    aws_access_key: str,
+    aws_secret_key: str,
+    aws_session_token: str,
+    aws_region: str,
+) -> tuple[ChatBedrock | None, str]:
+    """Create a LangGraph Bedrock agent."""
+    boto3_config = {
+        "aws_access_key_id": aws_access_key,
+        "aws_secret_access_key": aws_secret_key,
+        "aws_session_token": aws_session_token if aws_session_token else None,
+        "region_name": aws_region,
+    }
+
+    # Verify credentials
+    try:
+        sts = boto3.client("sts", **boto3_config)
+        sts.get_caller_identity()
+    except botocore.exceptions.ClientError as err:
+        return None, str(err)
+
+    try:
+        bedrock_client = boto3.client("bedrock-runtime", **boto3_config)
+        llm = ChatBedrock(
+            model=bedrock_model_id,
+            client=bedrock_client,
+            model_kwargs={"temperature": 0.7},
+        )
+    except Exception as e:  # noqa: BLE001
+        return None, str(e)
+
+    return llm, ""
+
+
+#### UI functionality ####
+
+
+async def gr_connect_to_bedrock(
+    model_id: str,
+    access_key: str,
+    secret_key: str,
+    session_token: str,
+    region: str,
+) -> str:
+    """Initialize Bedrock agent."""
+    global llm_agent  # noqa: PLW0603
+
+    if not access_key or not secret_key:
+        return "❌ Please provide both Access Key ID and Secret Access Key"
+
+    llm, error = create_bedrock_llm(
+        model_id,
+        access_key.strip(),
+        secret_key.strip(),
+        session_token.strip(),
+        region,
+    )
+
+    if llm is None:
+        return f"❌ Connection failed: {error}"
+
+    llm_agent = create_react_agent(
+        model=llm,
+        tools=[],
+        prompt=SYSTEM_MESSAGE,
+    )
+
+    return "✅ Successfully connected to AWS Bedrock!"
+
+
+async def gr_chat_function(  # noqa: D103
+    message: str,
+    history: list[Mapping[str, str]],
+) -> str:
+    if llm_agent is None:
+        return "Please configure your credentials first."
+
+    messages = []
+    for hist_msg in history:
+        role = hist_msg["role"]
+        message_type = GRADIO_ROLE_TO_LG_MESSAGE_TYPE[role]
+        messages.append(message_type(content=hist_msg["content"]))
+
+    messages.append(HumanMessage(content=message))
+
+    llm_response = await llm_agent.ainvoke(
+        {
+            "messages": messages,
+        },
+    )
+
+    return llm_response["messages"][-1].content
+
+
+## UI components ##
+
+with gr.Blocks() as gr_app:
+    gr.Markdown("# 🔐 Secure Bedrock Chatbot")
+
+    # Credentials section (collapsible)
+    with gr.Accordion("🔑 Bedrock Configuration", open=True):
+        gr.Markdown(
+            "**Note**: Credentials are only stored in memory during your session.",
+        )
+        with gr.Row():
+            bedrock_model_id_textbox = gr.Textbox(
+                label="Bedrock Model Id",
+                value="eu.anthropic.claude-3-5-sonnet-20240620-v1:0",
+            )
+        with gr.Row():
+            aws_access_key_textbox = gr.Textbox(
+                label="AWS Access Key ID",
+                type="password",
+                placeholder="Enter your AWS Access Key ID",
+            )
+            aws_secret_key_textbox = gr.Textbox(
+                label="AWS Secret Access Key",
+                type="password",
+                placeholder="Enter your AWS Secret Access Key",
+            )
+        with gr.Row():
+            aws_session_token_textbox = gr.Textbox(
+                label="AWS Session Token",
+                type="password",
+                placeholder="Enter your AWS session token",
+            )
+        with gr.Row():
+            aws_region_dropdown = gr.Dropdown(
+                label="AWS Region",
+                choices=[
+                    "us-east-1",
+                    "us-west-2",
+                    "eu-west-1",
+                    "eu-central-1",
+                    "ap-southeast-1",
+                ],
+                value="eu-west-1",
+            )
+            connect_btn = gr.Button("🔌 Connect to Bedrock", variant="primary")
+
+        status_textbox = gr.Textbox(label="Connection Status", interactive=False)
+
+        connect_btn.click(
+            gr_connect_to_bedrock,
+            inputs=[
+                bedrock_model_id_textbox,
+                aws_access_key_textbox,
+                aws_secret_key_textbox,
+                aws_session_token_textbox,
+                aws_region_dropdown,
+            ],
+            outputs=[status_textbox],
+        )
+
+    chat_interface = gr.ChatInterface(
+        fn=gr_chat_function,
+        type="messages",
+        examples=[],
+        title="Agent with MCP Tools",
+        description="This is a simple agent that uses MCP tools.",
+    )
+
+
+if __name__ == "__main__":
+    gr_app.launch()