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William Fu-Hinthorn
2024-02-19 21:12:27 -08:00
parent 2c208e3c50
commit 9027246db3
1 changed files with 435 additions and 203 deletions
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examples/lats/lats.ipynb
+435 -203
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@@ -61,7 +61,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 126,
"id": "5f43795e-353d-4a35-8dc9-c867b0d18568",
"metadata": {},
"outputs": [],
@@ -79,7 +79,7 @@
"# Optional: Configure tracing to visualize and debug the agent\n",
"_set_if_undefined(\"LANGCHAIN_API_KEY\")\n",
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_PROJECT\"] = \"LATS\"\n",
"os.environ[\"LANGCHAIN_PROJECT\"] = \"Reflexion\"\n",
"\n",
"_set_if_undefined(\"OPENAI_API_KEY\")\n",
"_set_if_undefined(\"TAVILY_API_KEY\")"
@@ -97,7 +97,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 127,
"id": "07f0c534-598b-4ce6-8ec3-eba1e9241a88",
"metadata": {},
"outputs": [],
@@ -111,7 +111,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 128,
"id": "1ff6248c-a7b7-4be1-9c5e-576a05d719ed",
"metadata": {},
"outputs": [],
@@ -130,7 +130,7 @@
},
{
"cell_type": "code",
"execution_count": 160,
"execution_count": 129,
"id": "72fc5363-f0f3-4362-8499-14eb583bd75b",
"metadata": {},
"outputs": [],
@@ -138,75 +138,92 @@
"import datetime\n",
"from typing import List\n",
"\n",
"from langchain.output_parsers.openai_tools import JsonOutputToolsParser\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompt_values import PromptValue\n",
"from langchain_core.prompt_values import ChatPromptValue\n",
"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
"from langchain_core.pydantic_v1 import BaseModel, Field, ValidationError\n",
"from langchain_core.runnables import RunnableConfig\n",
"from langchain_openai import ChatOpenAI\n",
"from langsmith import traceable\n",
"\n",
"prompt_template = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"You are a helpful assistant.\",\n",
" \"You are an AI assistant.\",\n",
" ),\n",
" (\"user\", \"{input}\"),\n",
" MessagesPlaceholder(variable_name=\"messages\", optional=True),\n",
" MessagesPlaceholder(variable_name=\"messages\"),\n",
" ]\n",
").partial(\n",
" time=lambda: datetime.datetime.now().isoformat(),\n",
")\n",
"\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo\")\n",
"initial_answer_chain = prompt_template | llm | StrOutputParser()\n",
"llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n",
"\n",
"\n",
"def expand(prompt_value: PromptValue, config):\n",
" n = config[\"configurable\"].get(\"n\", 5)\n",
" llm_res = llm.generate([prompt_value.to_messages()], n=n, temperature=1.0)\n",
" # Could consider scoring dupped values higher\n",
" dedupped = {gen.text: gen for gen in llm_res.generations[0]}\n",
" return [AIMessage(content=gen.text) for gen in dedupped.values()]\n",
"\n",
"\n",
"expansion_prompt_template = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"You are a helpful assistant. Generate an \"\n",
" \"improved response based on the provided reflections.\",\n",
" ),\n",
" (\"user\", \"{input}\"),\n",
" MessagesPlaceholder(variable_name=\"messages\", optional=True),\n",
" ]\n",
")\n",
"expansion_chain = expansion_prompt_template | expand"
"initial_answer_chain = prompt_template | llm"
]
},
{
"cell_type": "code",
"execution_count": 148,
"id": "8a215bc6-f3fc-4b4d-98b1-eb760e4c9199",
"execution_count": 130,
"id": "550bff9a-86aa-43ad-ad98-506e97c122d2",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.pydantic_v1 import BaseModel\n",
"# This generates N candidate values\n",
"# for a single input\n",
"def generate_candidates(messages: ChatPromptValue, config: RunnableConfig):\n",
" n = config[\"configurable\"].get(\"N\", 5)\n",
" chat_result = llm.generate([messages.to_messages()], n=n)\n",
" return [AIMessage(content=gen.text) for gen in chat_result.generations[0]]\n",
"\n",
"\n",
"class Reflection(BaseModel):\n",
" critique: str = Field(\n",
" description=\"~50-100 word critique of the current response, \"\n",
" \"outline parts that are missing, superfluous, or low quality.\"\n",
" )\n",
" is_finished: bool = Field(\n",
" description=\"Whether this response completely and accurately resolves the user's request.\"\n",
" )\n",
" score: int = Field(gte=1, lte=10, description=\"Score of the response.\")"
"expansion_chain = prompt_template | generate_candidates"
]
},
{
"cell_type": "code",
"execution_count": 164,
"execution_count": 133,
"id": "a6374041-c91b-44eb-b1a2-530d1579b2bc",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import create_structured_output_runnable\n",
"\n",
"\n",
"class Reflection(BaseModel):\n",
" reflections: str = Field(\n",
" description=\"The critique and reflections on the sufficiency, superfluency,\"\n",
" \" and general quality of the response\"\n",
" )\n",
" score: int = Field(\n",
" description=\"Score from 1-10 on the quality of the candidate response.\"\n",
" )\n",
" found_solution: bool = Field(\n",
" description=\"Whether the response has fully solved the question or task.\"\n",
" )\n",
"\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"Reflect and grade the assistant response to the user question below.\",\n",
" ),\n",
" MessagesPlaceholder(variable_name=\"input\"),\n",
" MessagesPlaceholder(variable_name=\"candidate\"),\n",
" (\n",
" \"system\",\n",
" \"Reflect on the assistant response above, critique, and score the response.\",\n",
" ),\n",
" ]\n",
")\n",
"reflection_chain = create_structured_output_runnable(output_schema=Reflection, llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"id": "c1219cd1-6cfb-4c83-8579-d3a003569150",
"metadata": {},
"outputs": [],
@@ -217,7 +234,7 @@
"class Node:\n",
" def __init__(\n",
" self,\n",
" solution: str,\n",
" solution: AIMessage,\n",
" parent: Optional[Node] = None,\n",
" reflection: Optional[Reflection] = None,\n",
" ):\n",
@@ -227,54 +244,79 @@
" self.value = 0\n",
" self.visits = 0\n",
" self.reflection = reflection\n",
" self.is_finished = False\n",
" if reflection is None and reflection.is_finished:\n",
" self._mark_as_finished()\n",
" self._is_solved = reflection.found_solution\n",
" if self._is_solved:\n",
" self._mark_tree_as_solved()\n",
"\n",
" def upper_confidence_bound(self, exploration_weight: float = 1.0):\n",
" if self.parent is None:\n",
" raise ValueError(\"Cannot compute UTC for root node.\")\n",
" @property\n",
" def is_solved(self):\n",
" return self._is_solved\n",
"\n",
" def _mark_tree_as_solved(self):\n",
" parent = self.parent\n",
" while parent:\n",
" parent._is_solved = True\n",
" parent = parent.parent\n",
"\n",
" def add_reflection(self, reflection: Reflection):\n",
" if self.reflection is not None:\n",
" raise ValueError(\"Cannot overwrite exisitng reflection\")\n",
" self.reflection = reflection\n",
" if reflection.is_solved:\n",
" self._is_solved = reflection.is_solved\n",
" self._mark_tree_as_solved()\n",
"\n",
" def uct(self, exploration_weight=1.0):\n",
" if self.visits == 0:\n",
" return self.value\n",
" return (self.value / self.visits) + exploration_weight * math.sqrt(\n",
" math.log(self.parent.visits) / self.visits\n",
" )\n",
"\n",
" @property\n",
" def best_child(self):\n",
" if not self.children:\n",
" return None\n",
" return max(self.children, key=lambda child: child.uct())\n",
"\n",
" @property\n",
" def best_child_score(self):\n",
" def best_child_value(self):\n",
" if not self.children:\n",
" return None\n",
" return max(self.children, key=lambda child: child.value)\n",
"\n",
" @property\n",
" def depth_at_node(self):\n",
" if self.children:\n",
" return 1 + max([child.depth_at_node for child in self.children])\n",
" return 1\n",
"\n",
" def add_reflection(self, reflection: Reflection):\n",
" if self.reflection is not None:\n",
" raise ValueError(\"Cannot overwrite existing reflection\")\n",
" self.reflection = reflection\n",
" if reflection is None and reflection.is_finished:\n",
" self._mark_as_finished()\n",
"\n",
" def update(self, reward: float):\n",
" self.visits += 1\n",
" self.value += reward\n",
"\n",
" def _mark_as_finished(self):\n",
" self.is_finished = True\n",
" def get_messages(self) -> List[BaseMessage]:\n",
" messages = []\n",
" parent = self.parent\n",
" while parent:\n",
" parent.is_finished = True\n",
" parent = parent.parent"
" messages.extend(\n",
" [\n",
" HumanMessage(content=self.reflection),\n",
" self.solution,\n",
" ]\n",
" )\n",
" return messages[::-1] # root solution, reflection, child 1, ...\n",
"\n",
" @property\n",
" def max_depth(self) -> int:\n",
" if self.children:\n",
" return 1 + max([child.max_depth for child in self.children])\n",
" return 1"
]
},
{
"cell_type": "code",
"execution_count": 90,
"id": "152d2d40-11e2-4922-82d7-592bdd90b7d3",
"metadata": {},
"outputs": [],
"source": [
"# def get_score():\n",
"# (self.value / self.visits) + exploration_weight * math.sqrt(\n",
"# math.log(self.parent.visits) / self.visits\n",
"# )"
]
},
{
@@ -291,16 +333,6 @@
"c.. # context"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63ef2b7d-d73e-4073-b549-f37b96011145",
"metadata": {},
"outputs": [],
"source": [
"from "
]
},
{
"cell_type": "markdown",
"id": "d9c1ba9b-a083-443c-af82-fa116fc450de",
@@ -322,149 +354,349 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 138,
"id": "47b43e08-ee04-4121-a294-fbbd5a4e3995",
"metadata": {},
"outputs": [],
"source": [
"import operator\n",
"from collections import deque\n",
"\n",
"from typing_extensions import Annotated, TypedDict\n",
"\n",
"from langgraph.graph import StateGraph\n",
"\n",
"\n",
"class TreeState(TypedDict):\n",
" # The full tree\n",
" root: Node\n",
" # The rolled-out steps so far\n",
" input: str\n",
" solution: AIMessage\n",
"\n",
"\n",
"builder = StateGraph(TreeState)\n",
"\n",
"\n",
"def start(state: TreeState) -> dict:\n",
" user_input = [HumanMessage(content=state[\"input\"])]\n",
" res = initial_answer_chain.invoke({\"messages\": user_input})\n",
" reflection = reflection_chain.invoke({\"input\": user_input, \"candidate\": [res]})\n",
" root = Node(res, reflection)\n",
" return {\n",
" **state,\n",
" \"root\": root,\n",
" }\n",
"\n",
"\n",
"def expand(state: TreeState, config: RunnableConfig) -> dict:\n",
" root = state[\"root\"]\n",
" user_input = [HumanMessage(content=state[\"input\"])]\n",
" best_candidate: Node = root.best_child()\n",
" messages = user_input + best_candidate.get_messages()\n",
" # Generate N candidates from the single child candidate\n",
" new_candidates = expansion_chain.invoke({\"messages\": messages}, config)\n",
" # Reflect on each candidate\n",
" # For tasks with external validation, you'd add that here.\n",
" reflections = reflection_chain.batch(\n",
" [[{\"input\": user_input, \"candidate\": [msg]}] for msg in new_candidates],\n",
" config,\n",
" )\n",
" # Grow tree\n",
" child_nodes = [\n",
" Node(cand, reflection) for cand, reflection in zip(new_candidates, reflections)\n",
" ]\n",
" best_candidate.children.extend(child_nodes)\n",
" # We have already extended the tree directly, so we just return the state\n",
" return state\n",
"\n",
"\n",
"def select_solution(state: TreeState):\n",
" all_nodes = []\n",
" nodes = deque()\n",
" nodes.append(state[\"root\"])\n",
" while nodes:\n",
" node = nodes.popleft()\n",
" all_nodes.extend(node.children)\n",
" for n in node.children:\n",
" nodes.append(n)\n",
" # TODO: Diff between value and reward?\n",
" best_node = max(all_nodes, key=lambda node: node.value)\n",
" return {**state, \"solution\": best_node.solution}\n",
"\n",
"\n",
"def should_loop(state: TreeState):\n",
" root = state[\"root\"]\n",
" if root.is_solved:\n",
" return \"select_solution\"\n",
" if root.max_depth() > 5:\n",
" return \"select_solution\"\n",
" return \"expand\"\n",
"\n",
"\n",
"builder.add_node(\"start\", start)\n",
"builder.add_node(\"expand\", expand)\n",
"builder.add_node(\"select_solution\", select_solution)\n",
"builder.set_entry_point(\"start\")\n",
"\n",
"\n",
"builder.add_conditional_edges(\n",
" \"start\",\n",
" # Either expand/rollout or finish\n",
" should_loop,\n",
")\n",
"builder.add_conditional_edges(\n",
" \"expand\",\n",
" # Either continue to rollout or finish\n",
" should_loop,\n",
")\n",
"\n",
"builder.set_finish_point(\"select_solution\")\n",
"graph = builder.compile()"
]
},
{
"cell_type": "code",
"execution_count": 140,
"id": "58d7a4af-a78b-409d-a042-73596546679f",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Invalid input type <class 'dict'>. Must be a PromptValue, str, or list of BaseMessages.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[140], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mwhat\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ms the capital of ninevah\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.pyenv/versions/3.11.2/lib/python3.11/site-packages/langgraph/pregel/__init__.py:579\u001b[0m, in \u001b[0;36mPregel.invoke\u001b[0;34m(self, input, config, output_keys, input_keys, **kwargs)\u001b[0m\n\u001b[1;32m 569\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m 570\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 571\u001b[0m \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 576\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 577\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]:\n\u001b[1;32m 578\u001b[0m latest: Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 579\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 580\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 581\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 582\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_keys\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 583\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 584\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 585\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 586\u001b[0m \u001b[43m \u001b[49m\u001b[43mlatest\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m latest\n",
"File \u001b[0;32m~/.pyenv/versions/3.11.2/lib/python3.11/site-packages/langgraph/pregel/__init__.py:615\u001b[0m, in \u001b[0;36mPregel.transform\u001b[0;34m(self, input, config, output_keys, input_keys, **kwargs)\u001b[0m\n\u001b[1;32m 606\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\n\u001b[1;32m 607\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 608\u001b[0m \u001b[38;5;28minput\u001b[39m: Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 613\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 614\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]]:\n\u001b[0;32m--> 615\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transform_stream_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 616\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 617\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transform\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 618\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 619\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 620\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 621\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 622\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 623\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01myield\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\n",
"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/runnables/base.py:1497\u001b[0m, in \u001b[0;36mRunnable._transform_stream_with_config\u001b[0;34m(self, input, transformer, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m 1495\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m-> 1497\u001b[0m chunk: Output \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(\u001b[38;5;28mnext\u001b[39m, iterator) \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m final_output_supported:\n",
"File \u001b[0;32m~/.pyenv/versions/3.11.2/lib/python3.11/site-packages/langgraph/pregel/__init__.py:355\u001b[0m, in \u001b[0;36mPregel._transform\u001b[0;34m(self, input, run_manager, config, input_keys, output_keys, interrupt)\u001b[0m\n\u001b[1;32m 348\u001b[0m done, inflight \u001b[38;5;241m=\u001b[39m concurrent\u001b[38;5;241m.\u001b[39mfutures\u001b[38;5;241m.\u001b[39mwait(\n\u001b[1;32m 349\u001b[0m futures,\n\u001b[1;32m 350\u001b[0m return_when\u001b[38;5;241m=\u001b[39mconcurrent\u001b[38;5;241m.\u001b[39mfutures\u001b[38;5;241m.\u001b[39mFIRST_EXCEPTION,\n\u001b[1;32m 351\u001b[0m timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstep_timeout,\n\u001b[1;32m 352\u001b[0m )\n\u001b[1;32m 354\u001b[0m \u001b[38;5;66;03m# interrupt on failure or timeout\u001b[39;00m\n\u001b[0;32m--> 355\u001b[0m \u001b[43m_interrupt_or_proceed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdone\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minflight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 357\u001b[0m \u001b[38;5;66;03m# apply writes to channels\u001b[39;00m\n\u001b[1;32m 358\u001b[0m _apply_writes(\n\u001b[1;32m 359\u001b[0m checkpoint, channels, pending_writes, config, step \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 360\u001b[0m )\n",
"File \u001b[0;32m~/.pyenv/versions/3.11.2/lib/python3.11/site-packages/langgraph/pregel/__init__.py:698\u001b[0m, in \u001b[0;36m_interrupt_or_proceed\u001b[0;34m(done, inflight, step)\u001b[0m\n\u001b[1;32m 696\u001b[0m inflight\u001b[38;5;241m.\u001b[39mpop()\u001b[38;5;241m.\u001b[39mcancel()\n\u001b[1;32m 697\u001b[0m \u001b[38;5;66;03m# raise the exception\u001b[39;00m\n\u001b[0;32m--> 698\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m 699\u001b[0m \u001b[38;5;66;03m# TODO this is where retry of an entire step would happen\u001b[39;00m\n\u001b[1;32m 701\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inflight:\n\u001b[1;32m 702\u001b[0m \u001b[38;5;66;03m# if we got here means we timed out\u001b[39;00m\n",
"File \u001b[0;32m~/.pyenv/versions/3.11.2/lib/python3.11/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n",
"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/runnables/base.py:4064\u001b[0m, in \u001b[0;36mRunnableBindingBase.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 4058\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m 4059\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 4060\u001b[0m \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[1;32m 4061\u001b[0m config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 4062\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m 4063\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Output:\n\u001b[0;32m-> 4064\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbound\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4065\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4066\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4067\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4068\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/runnables/base.py:2053\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m 2051\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 2052\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[0;32m-> 2053\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2054\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2055\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[1;32m 2056\u001b[0m \u001b[43m \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2057\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2058\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2059\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2060\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m 2061\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/runnables/base.py:3507\u001b[0m, in \u001b[0;36mRunnableLambda.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 3505\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Invoke this runnable synchronously.\"\"\"\u001b[39;00m\n\u001b[1;32m 3506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunc\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 3507\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3508\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_invoke\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3509\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3510\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3511\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3512\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3513\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 3514\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 3515\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot invoke a coroutine function synchronously.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 3516\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse `ainvoke` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 3517\u001b[0m )\n",
"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/runnables/base.py:1246\u001b[0m, in \u001b[0;36mRunnable._call_with_config\u001b[0;34m(self, func, input, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m 1242\u001b[0m context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m 1243\u001b[0m context\u001b[38;5;241m.\u001b[39mrun(var_child_runnable_config\u001b[38;5;241m.\u001b[39mset, child_config)\n\u001b[1;32m 1244\u001b[0m output \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m 1245\u001b[0m Output,\n\u001b[0;32m-> 1246\u001b[0m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1247\u001b[0m \u001b[43m \u001b[49m\u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1248\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m 1249\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m 1250\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1251\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1252\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1253\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 1254\u001b[0m )\n\u001b[1;32m 1255\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 1256\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/runnables/config.py:326\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m 325\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 326\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/runnables/base.py:3383\u001b[0m, in \u001b[0;36mRunnableLambda._invoke\u001b[0;34m(self, input, run_manager, config, **kwargs)\u001b[0m\n\u001b[1;32m 3381\u001b[0m output \u001b[38;5;241m=\u001b[39m chunk\n\u001b[1;32m 3382\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3383\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3384\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 3385\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3386\u001b[0m \u001b[38;5;66;03m# If the output is a runnable, invoke it\u001b[39;00m\n\u001b[1;32m 3387\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, Runnable):\n",
"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/runnables/config.py:326\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m 325\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 326\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[138], line 23\u001b[0m, in \u001b[0;36mstart\u001b[0;34m(state)\u001b[0m\n\u001b[1;32m 21\u001b[0m user_input \u001b[38;5;241m=\u001b[39m [HumanMessage(content\u001b[38;5;241m=\u001b[39mstate[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput\u001b[39m\u001b[38;5;124m\"\u001b[39m])]\n\u001b[1;32m 22\u001b[0m res \u001b[38;5;241m=\u001b[39m initial_answer_chain\u001b[38;5;241m.\u001b[39minvoke({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m: user_input})\n\u001b[0;32m---> 23\u001b[0m reflection \u001b[38;5;241m=\u001b[39m \u001b[43mreflection_chain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43muser_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcandidate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mres\u001b[49m\u001b[43m]\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 24\u001b[0m root \u001b[38;5;241m=\u001b[39m Node(res, reflection)\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m 26\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mstate,\n\u001b[1;32m 27\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mroot\u001b[39m\u001b[38;5;124m\"\u001b[39m: root,\n\u001b[1;32m 28\u001b[0m }\n",
"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/runnables/base.py:2053\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m 2051\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 2052\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[0;32m-> 2053\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2054\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2055\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[1;32m 2056\u001b[0m \u001b[43m \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2057\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2058\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2059\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2060\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m 2061\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/runnables/base.py:4064\u001b[0m, in \u001b[0;36mRunnableBindingBase.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 4058\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m 4059\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 4060\u001b[0m \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[1;32m 4061\u001b[0m config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 4062\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m 4063\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Output:\n\u001b[0;32m-> 4064\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbound\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4065\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4066\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4067\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4068\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/language_models/chat_models.py:167\u001b[0m, in \u001b[0;36mBaseChatModel.invoke\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28minput\u001b[39m: LanguageModelInput,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 162\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m BaseMessage:\n\u001b[1;32m 163\u001b[0m config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[1;32m 164\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[1;32m 165\u001b[0m ChatGeneration,\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate_prompt(\n\u001b[0;32m--> 167\u001b[0m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m],\n\u001b[1;32m 168\u001b[0m stop\u001b[38;5;241m=\u001b[39mstop,\n\u001b[1;32m 169\u001b[0m callbacks\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m 170\u001b[0m tags\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtags\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m 171\u001b[0m metadata\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m 172\u001b[0m run_name\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m 173\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 174\u001b[0m )\u001b[38;5;241m.\u001b[39mgenerations[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 175\u001b[0m )\u001b[38;5;241m.\u001b[39mmessage\n",
"File \u001b[0;32m~/code/lc/langchain/libs/core/langchain_core/language_models/chat_models.py:150\u001b[0m, in \u001b[0;36mBaseChatModel._convert_input\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ChatPromptValue(messages\u001b[38;5;241m=\u001b[39mconvert_to_messages(\u001b[38;5;28minput\u001b[39m))\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 151\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid input type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28minput\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 152\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMust be a PromptValue, str, or list of BaseMessages.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 153\u001b[0m )\n",
"\u001b[0;31mValueError\u001b[0m: Invalid input type <class 'dict'>. Must be a PromptValue, str, or list of BaseMessages."
]
}
],
"source": [
"graph.invoke({\"input\": \"what's the capital of ninevah\"})"
]
},
{
"cell_type": "code",
"execution_count": 123,
"id": "bc7ba5b1-0f4e-40cb-a4d1-f86dc935742f",
"metadata": {},
"outputs": [],
"source": [
"# with langsmith.trace(\"mcts\", inputs={\"item\": item}) as trace:\n",
"# # Get the first output\n",
"# cur_func_impl = gen.func_impl(item[\"prompt\"], model, \"simple\")\n",
"# root = Node(cur_func_impl) # initial solution (for pass@1 metric)\n",
"# test_feedback.append(feedback)\n",
"# with langsmith.trace(\n",
"# \"self_reflection\", inputs={\"fun\": cur_func_impl}\n",
"# ) as trace:\n",
"# reflection = gen.self_reflection(cur_func_impl, feedback, model)\n",
"# trace.end(outputs={\"reflection\": reflection})\n",
"# reflections += [reflection]\n",
"# root.test_feedback = feedback\n",
"# root.reflection = reflection\n",
"# if is_leetcode:\n",
"# tests_i = item[\"visible_tests\"]\n",
"# else:\n",
"# tests_i = gen.internal_tests(item[\"prompt\"], test_model, 6)\n",
"\n",
"# for cur_iter in range(max_iters):\n",
"# # Selection\n",
"# with langsmith.trace(\n",
"# \"first_attempt\", inputs={\"item\": item}\n",
"# ) as trace:\n",
"# while cur_func_impl is None:\n",
"# cur_func_impl = gen.func_impl(item[\"prompt\"], model, \"simple\")\n",
"# trace.end(outputs={\"cur_func_impl\": cur_func_impl})\n",
"# root = Node(cur_func_impl) # initial solution (for pass@1 metric)\n",
"\n",
"# node = root\n",
"# trajectory = {\"solutions\": [], \"feedbacks\": []}\n",
"# # Lists for logging\n",
"# reflections = []\n",
"# implementations = []\n",
"# test_feedback = []\n",
"# is_solved = False\n",
"\n",
"# while node.children:\n",
"# node = node.best_child()\n",
"# trajectory[\"solutions\"].append(node.solution)\n",
"# # first attempt\n",
"\n",
"# # Expansion\n",
"# for _ in range(n):\n",
"# new_solution = None\n",
"# strategy = \"mcts\"\n",
"# prev_func_impl = node.solution\n",
"# feedback = node.test_feedback\n",
"# reflection = node.reflection\n",
"# acc_feedback, acc_reflection = gather_context_from_tree(node)\n",
"# new_solution = gen.func_impl(\n",
"# func_sig=item[\"prompt\"],\n",
"# model=model,\n",
"# strategy=strategy,\n",
"# prev_func_impl=prev_func_impl,\n",
"# feedback=feedback,\n",
"# self_reflection=reflection,\n",
"# acc_feedback=acc_feedback,\n",
"# acc_reflection=acc_reflection,\n",
"# implementations.append(cur_func_impl)\n",
"# assert isinstance(cur_func_impl, str)\n",
"# is_passing, feedback, _ = exe.execute(cur_func_impl, tests_i)\n",
"# trace.end(outputs={\"is_passing\": is_passing, \"feedback\": feedback})\n",
"# test_feedback.append(feedback)\n",
"# with langsmith.trace(\n",
"# \"self_reflection\", inputs={\"fun\": cur_func_impl}\n",
"# ) as trace:\n",
"# reflection = gen.self_reflection(cur_func_impl, feedback, model)\n",
"# trace.end(outputs={\"reflection\": reflection})\n",
"# reflections += [reflection]\n",
"# root.test_feedback = feedback\n",
"# root.reflection = reflection\n",
"\n",
"# for cur_iter in range(max_iters):\n",
"# # Selection\n",
"\n",
"# node = root\n",
"# trajectory = {\"solutions\": [], \"feedbacks\": []}\n",
"\n",
"# while node.children:\n",
"# node = node.best_child()\n",
"# trajectory[\"solutions\"].append(node.solution)\n",
"\n",
"# # Expansion\n",
"# for _ in range(n):\n",
"# new_solution = None\n",
"# strategy = \"mcts\"\n",
"# prev_func_impl = node.solution\n",
"# feedback = node.test_feedback\n",
"# reflection = node.reflection\n",
"# acc_feedback, acc_reflection = gather_context_from_tree(node)\n",
"# with langsmith.trace(\n",
"# f\"expansion-{_}\", inputs={\"func_sig\": item[\"prompt\"], \"model\": model}\n",
"# ) as trace:\n",
"# while new_solution is None:\n",
"\n",
"# new_solution = gen.func_impl(\n",
"# # func_sig=item[\"prompt\"],\n",
"# # model=model,\n",
"# # strategy=strategy,\n",
"# prev_func_impl=prev_func_impl,\n",
"# feedback=feedback,\n",
"# self_reflection=reflection,\n",
"# # is this the stuff that's unique?\n",
"# acc_feedback=acc_feedback,\n",
"# acc_reflection=acc_reflection,\n",
"# )\n",
"\n",
"# combined_context = \"\\nPrevious Trial\\n\\n\" + new_solution\n",
"\n",
"# child = Node(\n",
"# new_solution,\n",
"# parent=node,\n",
"# context=combined_context,\n",
"# depth=node.depth + 1,\n",
"# )\n",
"# node.children.append(child)\n",
"\n",
"# combined_context = \"\\nPrevious Trial\\n\\n\" + new_solution\n",
"\n",
"# child = Node(\n",
"# new_solution,\n",
"# parent=node,\n",
"# context=combined_context,\n",
"# depth=node.depth + 1,\n",
"# )\n",
"# node.children.append(child)\n",
"\n",
"# # Simulation\n",
"# reward_real = 0\n",
"# for child in node.children:\n",
"# reflection = gen.self_reflection(\n",
"# child.solution, feedback_internal, model\n",
"# )\n",
"# reflections.append(reflection)\n",
"# child.reflection = reflection\n",
"# child.test_feedback = feedback_internal\n",
"# child.context += (\n",
"# \"\\n\\nPrevious Trial\\n\\n\"\n",
"# + child.solution\n",
"# + \"\\n\\nTest results: \\n\"\n",
"# + feedback_internal\n",
"# + \"\\n\\nSelf-reflection: \"\n",
"# + reflection\n",
"# )\n",
"\n",
"# if \"Tested passed:\" in feedback_internal:\n",
"# # Split at \"Tests failed:\" and get the part before it (which contains the passed tests)\n",
"# passed_section = feedback_internal.split(\"Tests failed:\")[0]\n",
"# # Split at \"Tested passed:\" and get the part after it, then count the non-empty lines\n",
"# reward_internal = len(\n",
"# [\n",
"# line\n",
"# for line in passed_section.split(\"Tested passed:\")[\n",
"# 1\n",
"# ].splitlines()\n",
"# if line.strip() != \"\"\n",
"# ]\n",
"# # Simulation\n",
"# reward_real = 0\n",
"# for child in node.children:\n",
"# is_passing_internal, feedback_internal, _ = exe.execute(\n",
"# child.solution, tests_i\n",
"# )\n",
"# reward_internal = reward_internal / len(tests_i)\n",
"# else:\n",
"# reward_internal = 0\n",
"# if is_passing_internal or cur_iter == max_iters - 1:\n",
"# is_passing = exe.evaluate(\n",
"# item[\"entry_point\"],\n",
"# child.solution,\n",
"# item[\"test\"],\n",
"# timeout=10,\n",
"# )\n",
"# if is_passing:\n",
"# item[\"solution\"] = child.solution\n",
"# is_solved = True\n",
"# reward_real = 1\n",
"# if not is_passing_internal:\n",
"# reflection = gen.self_reflection(\n",
"# child.solution, feedback_internal, model\n",
"# )\n",
"# reflections.append(reflection)\n",
"# child.reflection = reflection\n",
"# child.test_feedback = feedback_internal\n",
"# child.context += (\n",
"# \"\\n\\nPrevious Trial\\n\\n\"\n",
"# + child.solution\n",
"# + \"\\n\\nTest results: \\n\"\n",
"# + feedback_internal\n",
"# + \"\\n\\nSelf-reflection: \"\n",
"# + reflection\n",
"# )\n",
"# else:\n",
"# child.context += (\n",
"# \"\\n\\nPrevious Trial\\n\\n\"\n",
"# + child.solution\n",
"# + \"\\n\\nTest results: \\n\"\n",
"# + feedback_internal\n",
"# )\n",
"# child.reflection = \"\"\n",
"# child.test_feedback = feedback_internal\n",
"\n",
"# if \"Tested passed:\" in feedback_internal:\n",
"# # Split at \"Tests failed:\" and get the part before it (which contains the passed tests)\n",
"# passed_section = feedback_internal.split(\"Tests failed:\")[0]\n",
"# # Split at \"Tested passed:\" and get the part after it, then count the non-empty lines\n",
"# reward_internal = len(\n",
"# [\n",
"# line\n",
"# for line in passed_section.split(\"Tested passed:\")[\n",
"# 1\n",
"# ].splitlines()\n",
"# if line.strip() != \"\"\n",
"# ]\n",
"# )\n",
"# reward_internal = reward_internal / len(tests_i)\n",
"# else:\n",
"# reward_internal = 0\n",
"# if is_passing_internal or cur_iter == max_iters - 1:\n",
"# is_passing = exe.evaluate(\n",
"# item[\"entry_point\"],\n",
"# child.solution,\n",
"# item[\"test\"],\n",
"# timeout=10,\n",
"# )\n",
"# if is_passing:\n",
"# item[\"solution\"] = child.solution\n",
"# is_solved = True\n",
"# reward_real = 1\n",
"# break\n",
"\n",
"# if is_solved:\n",
"# break\n",
"\n",
"# if is_solved:\n",
"# break\n",
"# reward = reward_internal + reward_real\n",
"# child.update(reward)\n",
"# print(reward_internal)\n",
"# print(reward_real)\n",
"# reward = reward_internal + reward_real\n",
"# child.update(reward)\n",
"\n",
"# # Backpropagation\n",
"# temp = child\n",
"# while temp.parent:\n",
"# temp = temp.parent\n",
"# temp.update(reward)\n",
"# # Backpropagation\n",
"# temp = child\n",
"# while temp.parent:\n",
"# temp = temp.parent\n",
"# temp.update(reward)\n",
"\n",
"# # Choose the best solution after all iterations\n",
"# best_solution = root.best_child_value().solution\n",
"# item[\"solution\"] = best_solution\n",
"# reflections.append(\"MCTS reflections\")\n",
"# implementations.append(best_solution)"
]
},
{
"cell_type": "code",
"execution_count": 144,
"id": "8ebb3a4f-d81e-4238-9c60-8004cedef454",
"metadata": {},
"outputs": [],
"source": [
"# from datasets import load_dataset\n",
"# # Choose the best solution after all iterations\n",
"# if is_solved:\n",
"# best_solution = item[\"solution\"]\n",
"# else:\n",
"# best_solution = root.best_child_value().solution\n",
"# item[\"solution\"] = best_solution\n",
"\n",
"# dataset = load_dataset(\"deepmind/code_contests\", split=\"valid\")\n",
"# example = dataset[70]"
"# is_passing, cur_feedback, _ = exe.execute(new_solution, tests_i)\n",
"# test_feedback.append(cur_feedback)\n",
"# is_passing = exe.evaluate(\n",
"# item[\"entry_point\"], best_solution, item[\"test\"], timeout=10\n",
"# )\n",
"# if is_passing:\n",
"# num_success += 1\n",
"\n",
"# reflections.append(\"MCTS reflections\")\n",
"# implementations.append(best_solution)\n",
"\n",
"# item[\"is_solved\"] = is_passing\n",
"# item[\"reflections\"] = reflections\n",
"# item[\"implementations\"] = implementations\n",
"# item[\"test_feedback\"] = test_feedback\n",
"# item[\"acc\"] = round(num_success / (idx + 1), 2)\n",
"# write_jsonl(log_path, [item], append=True)\n",
"\n",
"# print_v(f\"completed {idx+1}/{num_items}: acc = {round(num_success/(idx+1), 2)}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f4525b9-f63e-449d-9816-e37ca726a522",
"id": "bd8c6cc2-9b2d-4e46-8344-68d6606b3b74",
"metadata": {},
"outputs": [],
"source": []
"source": [
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(\"deepmind/code_contests\", split=\"valid\")\n",
"dataset[\"validation\"]"
]
}
],
"metadata": {