start

examples/langgraph.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53b9dce4-e4ae-4bdb-b752-0f04350a2e3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.agents import create_openai_functions_agent\n",
+    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain import hub\n",
+    "from langchain.agents import AgentExecutor, create_openai_functions_agent\n",
+    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_core.runnables import RunnablePassthrough, RunnableLambda\n",
+    "from permchain.langgraph import Actor, DecisionPoint, Graph\n",
+    "\n",
+    "tools = [TavilySearchResults(max_results=1)]\n",
+    "\n",
+    "# Get the prompt to use - you can modify this!\n",
+    "prompt = hub.pull(\"hwchase17/openai-functions-agent\")\n",
+    "\n",
+    "# Choose the LLM that will drive the agent\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-1106\")\n",
+    "\n",
+    "# Construct the OpenAI Functions agent\n",
+    "agent_runnable = create_openai_functions_agent(llm, tools, prompt)\n",
+    "\n",
+    "from langchain_core.agents import AgentFinish\n",
+    "# Define decision-making logic\n",
+    "def should_continue(data):\n",
+    "    # Logic to decide whether to continue in the loop or exit\n",
+    "    if isinstance(data['agent_outcome'], AgentFinish):\n",
+    "        return \"exit\"\n",
+    "    else:\n",
+    "        return \"continue\"\n",
+    "    \n",
+    "def execute_tools(data):\n",
+    "    agent_action = data.pop('agent_outcome')\n",
+    "    observation = {t.name: t for t in tools}[agent_action.tool].invoke(agent_action.tool_input)\n",
+    "    data['intermediate_steps'].append((agent_action, observation))\n",
+    "    return data\n",
+    "    \n",
+    "    \n",
+    "\n",
+    "# Define agents\n",
+    "agent = RunnablePassthrough.assign(\n",
+    "    agent_outcome = agent_runnable\n",
+    ")\n",
+    "\n",
+    "# Define a new graph\n",
+    "workflow = Graph()\n",
+    "decision_point = DecisionPoint(\"exit?\", should_continue)\n",
+    "llm_agent = Actor(\"agent\", agent)\n",
+    "tool_actor = Actor(\"tools\", RunnableLambda(execute_tools))\n",
+    "\n",
+    "# Register actors\n",
+    "workflow.register(llm_agent)\n",
+    "workflow.register(tool_actor)\n",
+    "workflow.register(decision_point)\n",
+    "\n",
+    "# Define connections with conditional logic\n",
+    "workflow.connect(llm_agent, decision_point)\n",
+    "workflow.branch(decision_point, tool_actor, condition=\"continue\")\n",
+    "workflow.branch(decision_point, None, condition=\"exit\")  # Exit the workflow\n",
+    "workflow.connect(tool_actor, llm_agent)\n",
+    "\n",
+    "# Define entry point and execute the graph\n",
+    "workflow.set_entry_point(llm_agent)\n",
+    "chain = workflow.compile()\n",
+    "\n",
+    "chain.invoke({\"input\": \"what is the weather in sf\", \"intermediate_steps\": []})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2515edbc-a9b6-42ce-bb5e-f1f2503d1bb4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

permchain/langgraph/__init__.py

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+from langchain_core.runnables import Runnable, RunnableMap
+from typing import Callable, Union, Optional
+from permchain import Channel, Pregel
+
+class Actor:
+
+    def __init__(self, name: str, runnable: Runnable):
+        self.name = name
+        self.runnable = runnable
+
+
+class DecisionPoint:
+
+    def __init__(self, name: str, callable: Callable):
+        self.name = name
+        self.callable = callable
+
+class End:
+    name = "end"
+
+
+def branch(data, condition, mapping):
+    result = condition(data)
+    return Channel.write_to(mapping[result])
+
+class Graph:
+
+    def __init__(self):
+        self.nodes = {"end": End()}
+        self.connections = {}
+        self.branches = {}
+        self.entry_point: Optional[str] = None
+        self.finish_points = set()
+
+    def register(self, node: Union[Actor, DecisionPoint]):
+        if node.name in self.nodes:
+            raise ValueError(f"Actor `{node.name}` already present.")
+        self.nodes[node.name] = node
+
+    def connect(self, start: Actor, end: Union[Actor, DecisionPoint]):
+        if start.name not in self.nodes:
+            raise ValueError(f"Need to register `{start.name}` first")
+        if end.name not in self.nodes:
+            raise ValueError(f"Need to register `{end.name}` first")
+        if start.name in self.connections:
+            raise ValueError(f"Already found path for {start.name}")
+        self.connections[start.name] = end.name
+
+    def branch(self, start: DecisionPoint, end: Optional[Actor], condition: str):
+        end = end or End()
+        if start.name not in self.nodes:
+            raise ValueError(f"Need to register `{start.name}` first")
+        if end.name not in self.nodes:
+            raise ValueError(f"Need to register `{end.name}` first")
+        if start.name not in self.branches:
+            self.branches[start.name] = {}
+        if condition in self.branches[start.name]:
+            raise ValueError(f"Already found a condition for {start.name} and {condition}")
+        self.branches[start.name][condition] = end.name
+
+    def set_entry_point(self, node: Union[DecisionPoint, Actor]):
+        if node.name not in self.nodes:
+            raise ValueError(f"Need to register `{node.name}` first")
+        self.entry_point = node.name
+
+    def set_finish_point(self, node: Actor):
+        if node.name not in self.nodes:
+            raise ValueError(f"Need to register `{node.name}` first")
+        self.finish_points |= node.name
+
+    def compile(self):
+        # Validate all nodes have an entry point
+        all_nodes = set(self.nodes)
+        all_entry_points = set(self.connections).union(self.branches).union(self.finish_points)
+        branch_ends = set()
+        for v in self.branches.values():
+            branch_ends.update(v.values())
+        all_finish_points = set(self.connections.values()).union(branch_ends).union({self.entry_point})
+        # If a node is not a finish point, then it is missing an entry point
+        missing_entry = all_nodes.difference(all_finish_points)
+        if missing_entry:
+            raise ValueError(f"Some nodes are missing entry points: {missing_entry}")
+        # If a node is not an entry point, then it is missing a finish point
+        missing_finish = all_nodes.difference(all_entry_points).difference({"end"})
+        if missing_finish:
+            raise ValueError(f"Some nodes are missing finish points: {missing_finish}")
+        chains = {
+            start: Channel.subscribe_to(start) | self.nodes[start].runnable | Channel.write_to(end)
+            for start, end in self.connections.items()
+        }
+
+        decisions = {
+            start: Channel.subscribe_to(start) | (lambda x: branch(x, self.nodes[start].callable, mapping))
+            for start, mapping in self.branches.items()
+        }
+        endings = {
+            end: Channel.subscribe_to(end) | self.nodes[end].runnable | Channel.write_to("end")
+            for end in self.finish_points
+        }
+        app = Pregel(
+            chains = {**chains, **decisions, **endings},
+            input=self.entry_point,
+            output="end"
+        )
+        return app
+
+
+
+
+
+
+
+
+
+