Merge pull request #66 from ks6088ts-labs/feature/issue-64_add-tools

add an agent app with LangGraph

docs/README.md

@@ -12,6 +12,8 @@
 - [Add message history (memory) > In-memory](https://python.langchain.com/v0.1/docs/expression_language/how_to/message_history/#in-memory)
 - [Tool calling agent](https://python.langchain.com/v0.1/docs/modules/agents/agent_types/tool_calling/)
 - [LangChain > Tools > Bing Search](https://python.langchain.com/v0.1/docs/integrations/tools/bing_search/)
+- [Tool Calling with LangChain](https://blog.langchain.dev/tool-calling-with-langchain/)
+- [langchain/cookbook/tool_call_messages.ipynb](https://github.com/langchain-ai/langchain/blob/master/cookbook/tool_call_messages.ipynb?ref=blog.langchain.dev)
 
 ## Backend
 

frontend/entrypoint.py

@@ -1,6 +1,7 @@
 import logging
 
 from frontend.solutions import (
+    agent_langgraph,
     azure_ai_vision,
     azure_storage,
     chat,
@@ -28,6 +29,7 @@ def start(
             SolutionType.DOCUMENT_INTELLIGENCE.value: document_intelligence.start,
             SolutionType.AZURE_STORAGE.value: azure_storage.start,
             SolutionType.AZURE_AI_VISION.value: azure_ai_vision.start,
+            SolutionType.AGENT_LANGGRAPH.value: agent_langgraph.start,
         }
         return solutions[solution_name.upper()](
             backend_url=backend_url,

frontend/solutions/agent_langgraph.py

@@ -0,0 +1,100 @@
+import logging
+import operator
+from collections.abc import Sequence
+from os import getenv
+from typing import Annotated, TypedDict
+
+import streamlit as st
+from dotenv import load_dotenv
+from langchain_core.messages import BaseMessage, HumanMessage, ToolMessage
+from langchain_core.runnables import ConfigurableField, RunnableLambda
+from langchain_openai import AzureChatOpenAI
+from langgraph.graph import END, StateGraph
+
+from frontend.solutions.internals.tools.examples import add, exponentiate, get_current_weather, multiply
+
+logger = logging.getLogger(__name__)
+load_dotenv("frontend.env")
+
+tools = [
+    multiply,
+    exponentiate,
+    add,
+    get_current_weather,
+]
+llm = AzureChatOpenAI(
+    api_key=getenv("AZURE_OPENAI_API_KEY"),
+    api_version=getenv("AZURE_OPENAI_API_VERSION"),
+    azure_endpoint=getenv("AZURE_OPENAI_ENDPOINT"),
+    azure_deployment=getenv("AZURE_OPENAI_GPT_MODEL"),
+).bind_tools(tools)
+
+llm_with_tools = llm.configurable_alternatives(
+    ConfigurableField(id="llm"),
+)
+
+
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[BaseMessage], operator.add]
+
+
+def should_continue(state):
+    return "continue" if state["messages"][-1].tool_calls else "end"
+
+
+def call_model(state, config):
+    return {"messages": [llm_with_tools.invoke(state["messages"], config=config)]}
+
+
+def call_tools(state):
+    def _invoke_tool(tool_call):
+        tool = {tool.name: tool for tool in tools}[tool_call["name"]]
+        return ToolMessage(tool.invoke(tool_call["args"]), tool_call_id=tool_call["id"])
+
+    tool_executor = RunnableLambda(_invoke_tool)
+    last_message = state["messages"][-1]
+    return {"messages": tool_executor.batch(last_message.tool_calls)}
+
+
+def create_graph():
+    workflow = StateGraph(AgentState)
+    workflow.add_node("agent", call_model)
+    workflow.add_node("action", call_tools)
+    workflow.set_entry_point("agent")
+    workflow.add_conditional_edges(
+        "agent",
+        should_continue,
+        {
+            "continue": "action",
+            "end": END,
+        },
+    )
+    workflow.add_edge("action", "agent")
+    return workflow.compile()
+
+
+def start(
+    backend_url: str,
+    log_level: int,
+):
+    logger.setLevel(log_level)
+    logger.debug(f"set log level to {log_level}")
+
+    st.title("ChatGPT-like clone")
+
+    graph = create_graph()
+
+    if prompt := st.chat_input("What is up?"):
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        with st.spinner("Analyzing..."):
+            with st.chat_message("assistant"):
+                response = graph.invoke(
+                    {
+                        "messages": [
+                            HumanMessage(prompt),
+                        ]
+                    }
+                )
+                st.json(response)

frontend/solutions/chat_langchain.py

@@ -22,15 +22,6 @@ def get_session_history(session_id: str) -> BaseChatMessageHistory:
     return store[session_id]
 
 
-def run_bing_search(
-    query: str,
-    k=1,
-) -> str:
-    from langchain_community.utilities import BingSearchAPIWrapper
-
-    return BingSearchAPIWrapper(k=k).run(query=query)
-
-
 def start(
     backend_url: str,
     log_level: int,

frontend/solutions/internals/tools/examples.py

@@ -0,0 +1,25 @@
+from langchain_core.tools import tool
+
+
+@tool
+def multiply(x: float, y: float) -> float:
+    """Multiply 'x' times 'y'."""
+    return x * y
+
+
+@tool
+def exponentiate(x: float, y: float) -> float:
+    """Raise 'x' to the 'y'."""
+    return x**y
+
+
+@tool
+def add(x: float, y: float) -> float:
+    """Add 'x' and 'y'."""
+    return x + y
+
+
+@tool
+def get_current_weather(city: str) -> str:
+    """Get the current weather in 'city'."""
+    return f"The weather in {city} is sunny."

frontend/solutions/types.py

@@ -9,3 +9,4 @@ class SolutionType(Enum):
     DOCUMENT_INTELLIGENCE = "DOCUMENT_INTELLIGENCE"
     AZURE_STORAGE = "AZURE_STORAGE"
     AZURE_AI_VISION = "AZURE_AI_VISION"
+    AGENT_LANGGRAPH = "AGENT_LANGGRAPH"

pyproject.toml

@@ -52,6 +52,7 @@ langchain = "^0.2.1"
 langchain-openai = "^0.1.8"
 langchain-community = "^0.2.1"
 langsmith = "^0.1.67"
+langgraph = "^0.0.60"
 
 
 [tool.poetry.group.azure-functions.dependencies]