mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-08-17 21:25:46 +02:00
William FH
@@ -235,7 +235,7 @@
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" # Call the chat bot\n",
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" chat_bot_response = my_chat_bot(messages)\n",
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" # Respond with an AI Message\n",
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" return {\"messages\":[AIMessage(content=chat_bot_response[\"content\"])]}"
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" return {\"messages\": [AIMessage(content=chat_bot_response[\"content\"])]}"
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]
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},
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{
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@@ -270,7 +270,7 @@
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" # Call the simulated user\n",
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" response = simulated_user.invoke({\"messages\": new_messages})\n",
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" # This response is an AI message - we need to flip this to be a human message\n",
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" return {\"messages\":[HumanMessage(content=response.content)]}"
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" return {\"messages\": [HumanMessage(content=response.content)]}"
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]
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},
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{
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@@ -331,6 +331,7 @@
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"class State(TypedDict):\n",
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" messages: Annotated[list, add_messages]\n",
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"\n",
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"\n",
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"graph_builder = StateGraph(State)\n",
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"graph_builder.add_node(\"user\", simulated_user_node)\n",
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"graph_builder.add_node(\"chat_bot\", chat_bot_node)\n",
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@@ -79,7 +79,7 @@
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"\n",
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"\n",
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"def info_chain(state):\n",
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" messages = get_messages_info(state['messages'])\n",
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" messages = get_messages_info(state[\"messages\"])\n",
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" response = llm_with_tool.invoke(messages)\n",
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" return {\"messages\": [response]}"
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]
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@@ -126,7 +126,7 @@
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"\n",
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"\n",
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"def prompt_gen_chain(state):\n",
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" messages = get_prompt_messages(state['messages'])\n",
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" messages = get_prompt_messages(state[\"messages\"])\n",
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" response = llm.invoke(messages)\n",
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" return {\"messages\": [response]}"
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]
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@@ -158,7 +158,7 @@
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"\n",
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"\n",
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"def get_state(state) -> Literal[\"add_tool_message\", \"info\", \"__end__\"]:\n",
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" messages = state['messages']\n",
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" messages = state[\"messages\"]\n",
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" if isinstance(messages[-1], AIMessage) and messages[-1].tool_calls:\n",
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" return \"add_tool_message\"\n",
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" elif not isinstance(messages[-1], HumanMessage):\n",
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@@ -190,9 +190,11 @@
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"from typing import Annotated\n",
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"from typing_extensions import TypedDict\n",
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"\n",
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"\n",
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"class State(TypedDict):\n",
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" messages: Annotated[list, add_messages]\n",
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"\n",
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"\n",
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"memory = MemorySaver()\n",
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"workflow = StateGraph(State)\n",
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"workflow.add_node(\"info\", info_chain)\n",
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@@ -201,9 +203,14 @@
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"\n",
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"@workflow.add_node\n",
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"def add_tool_message(state: State):\n",
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" return {\"messages\": [ToolMessage(\n",
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" content=\"Prompt generated!\", tool_call_id=state['messages'][-1].tool_calls[0][\"id\"]\n",
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" )]}\n",
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" return {\n",
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" \"messages\": [\n",
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" ToolMessage(\n",
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" content=\"Prompt generated!\",\n",
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" tool_call_id=state[\"messages\"][-1].tool_calls[0][\"id\"],\n",
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" )\n",
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" ]\n",
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" }\n",
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"\n",
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"\n",
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"workflow.add_conditional_edges(\"info\", get_state)\n",
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@@ -364,7 +371,7 @@
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" for output in graph.stream(\n",
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" {\"messages\": [HumanMessage(content=user)]}, config=config, stream_mode=\"updates\"\n",
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" ):\n",
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" last_message = next(iter(output.values()))['messages'][-1]\n",
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" last_message = next(iter(output.values()))[\"messages\"][-1]\n",
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" last_message.pretty_print()\n",
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"\n",
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" if output and \"prompt\" in output:\n",
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@@ -239,7 +239,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.1"
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"version": "3.12.2"
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}
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},
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"nbformat": 4,
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@@ -225,7 +225,14 @@
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"\n",
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"Define the (`fetch_user_flight_information`) tool to let the agent see the current user's flight information. Then define tools to search for flights and manage the passenger's bookings stored in the SQL database.\n",
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"\n",
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"We use `ensure_config` to pass in the `passenger_id` in via configurable parameters. The LLM never has to provide these explicitly, they are provided for a given invocation of the graph so that each user cannot access other passengers' booking information."
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"We the can [access the RunnableConfig](https://python.langchain.com/v0.2/docs/how_to/tool_configure/#inferring-by-parameter-type) for a given run to check the `passenger_id` of the user accessing this application. The LLM never has to provide these explicitly, they are provided for a given invocation of the graph so that each user cannot access other passengers' booking information.\n",
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"\n",
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"<div class=\"admonition warning\">\n",
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" <p class=\"admonition-title\">Compatibility</p>\n",
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" <p>\n",
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" This tutorial expects `langchain-core>=0.2.16` to use the injected RunnableConfig. Prior to that, you'd use `ensure_config` to collect the config from context.\n",
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" </p>\n",
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"</div> \n"
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]
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},
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{
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@@ -240,18 +247,17 @@
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"from typing import Optional\n",
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"\n",
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"import pytz\n",
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"from langchain_core.runnables import ensure_config\n",
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"from langchain_core.runnables import RunnableConfig\n",
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"\n",
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"\n",
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"@tool\n",
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"def fetch_user_flight_information() -> list[dict]:\n",
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"def fetch_user_flight_information(config: RunnableConfig) -> list[dict]:\n",
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" \"\"\"Fetch all tickets for the user along with corresponding flight information and seat assignments.\n",
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"\n",
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" Returns:\n",
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" A list of dictionaries where each dictionary contains the ticket details,\n",
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" associated flight details, and the seat assignments for each ticket belonging to the user.\n",
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" \"\"\"\n",
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" config = ensure_config() # Fetch from the context\n",
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" configuration = config.get(\"configurable\", {})\n",
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" passenger_id = configuration.get(\"passenger_id\", None)\n",
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" if not passenger_id:\n",
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@@ -328,9 +334,10 @@
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"\n",
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"\n",
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"@tool\n",
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"def update_ticket_to_new_flight(ticket_no: str, new_flight_id: int) -> str:\n",
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"def update_ticket_to_new_flight(\n",
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" ticket_no: str, new_flight_id: int, *, config: RunnableConfig\n",
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") -> str:\n",
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" \"\"\"Update the user's ticket to a new valid flight.\"\"\"\n",
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" config = ensure_config()\n",
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" configuration = config.get(\"configurable\", {})\n",
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" passenger_id = configuration.get(\"passenger_id\", None)\n",
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" if not passenger_id:\n",
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@@ -396,9 +403,8 @@
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"\n",
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"\n",
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"@tool\n",
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"def cancel_ticket(ticket_no: str) -> str:\n",
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"def cancel_ticket(ticket_no: str, *, config: RunnableConfig) -> str:\n",
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" \"\"\"Cancel the user's ticket and remove it from the database.\"\"\"\n",
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" config = ensure_config()\n",
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" configuration = config.get(\"configurable\", {})\n",
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" passenger_id = configuration.get(\"passenger_id\", None)\n",
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" if not passenger_id:\n",
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@@ -4407,7 +4413,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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"version": "3.12.2"
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}
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},
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"nbformat": 4,
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@@ -141,15 +141,18 @@
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" print(\"----\")\n",
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" return \"Sunny!\"\n",
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"\n",
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"model = ChatAnthropic(model_name=\"claude-3-5-sonnet-20240620\").bind_tools([weather_search])\n",
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"\n",
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"model = ChatAnthropic(model_name=\"claude-3-5-sonnet-20240620\").bind_tools(\n",
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" [weather_search]\n",
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")\n",
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"\n",
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"\n",
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"class State(MessagesState):\n",
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" \"\"\"Simple state.\"\"\"\n",
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"\n",
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"\n",
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"def call_llm(state):\n",
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" return {\n",
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" \"messages\": [model.invoke(state['messages'])]\n",
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" }\n",
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" return {\"messages\": [model.invoke(state[\"messages\"])]}\n",
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"\n",
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"\n",
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"def human_review_node(state):\n",
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@@ -159,28 +162,30 @@
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"def run_tool(state):\n",
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" new_messages = []\n",
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" tools = {\"weather_search\": weather_search}\n",
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" tool_calls = state['messages'][-1].tool_calls\n",
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" tool_calls = state[\"messages\"][-1].tool_calls\n",
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" for tool_call in tool_calls:\n",
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" tool = tools[tool_call['name']]\n",
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" result = tool.invoke(tool_call['args'])\n",
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" new_messages.append({\n",
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" \"role\": \"tool\",\n",
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" \"name\": tool_call['name'],\n",
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" \"content\": result,\n",
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" \"tool_call_id\": tool_call['id']\n",
|
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" })\n",
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" tool = tools[tool_call[\"name\"]]\n",
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" result = tool.invoke(tool_call[\"args\"])\n",
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" new_messages.append(\n",
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" {\n",
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" \"role\": \"tool\",\n",
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" \"name\": tool_call[\"name\"],\n",
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" \"content\": result,\n",
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" \"tool_call_id\": tool_call[\"id\"],\n",
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" }\n",
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" )\n",
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" return {\"messages\": new_messages}\n",
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"\n",
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"\n",
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"def route_after_llm(state) -> Literal[END, \"human_review_node\"]:\n",
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" if len(state['messages'][-1].tool_calls) == 0:\n",
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" if len(state[\"messages\"][-1].tool_calls) == 0:\n",
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" return END\n",
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" else:\n",
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" return \"human_review_node\"\n",
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"\n",
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"\n",
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"def route_after_human(state) -> Literal[\"run_tool\", \"call_llm\"]:\n",
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" if isinstance(state['messages'][-1], AIMessage):\n",
|
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" if isinstance(state[\"messages\"][-1], AIMessage):\n",
|
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" return \"run_tool\"\n",
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" else:\n",
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" return \"call_llm\"\n",
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@@ -460,35 +465,35 @@
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"print(\"Current State:\")\n",
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"print(state.values)\n",
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"print(\"\\nCurrent Tool Call ID:\")\n",
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"current_content = state.values['messages'][-1].content\n",
|
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"current_id = state.values['messages'][-1].id\n",
|
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"tool_call_id = state.values['messages'][-1].tool_calls[0]['id']\n",
|
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"current_content = state.values[\"messages\"][-1].content\n",
|
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"current_id = state.values[\"messages\"][-1].id\n",
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"tool_call_id = state.values[\"messages\"][-1].tool_calls[0][\"id\"]\n",
|
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"print(tool_call_id)\n",
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"\n",
|
||||
"# We now need to construct a replacement tool call.\n",
|
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"# We will change the argument to be `San Francisco, USA`\n",
|
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"# Note that we could change any number of arguments or tool names - it just has to be a valid one\n",
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"new_message = {\n",
|
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" \"role\": \"assistant\", \n",
|
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" \"role\": \"assistant\",\n",
|
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" \"content\": current_content,\n",
|
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" \"tool_calls\": [\n",
|
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" {\n",
|
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" \"id\": tool_call_id,\n",
|
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" \"name\": \"weather_search\",\n",
|
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" \"args\": {\"city\": \"San Francisco, USA\"}\n",
|
||||
" \"args\": {\"city\": \"San Francisco, USA\"},\n",
|
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" }\n",
|
||||
" ],\n",
|
||||
" # This is important - this needs to be the same as the message you replacing!\n",
|
||||
" # Otherwise, it will show up as a separate message\n",
|
||||
" \"id\": current_id\n",
|
||||
" \"id\": current_id,\n",
|
||||
"}\n",
|
||||
"graph.update_state(\n",
|
||||
" # This is the config which represents this thread\n",
|
||||
" thread, \n",
|
||||
" thread,\n",
|
||||
" # This is the updated value we want to push\n",
|
||||
" {\"messages\": [new_message]}, \n",
|
||||
" {\"messages\": [new_message]},\n",
|
||||
" # We push this update acting as our human_review_node\n",
|
||||
" as_node=\"human_review_node\"\n",
|
||||
" as_node=\"human_review_node\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Let's now continue executing from here\n",
|
||||
@@ -595,26 +600,26 @@
|
||||
"print(\"Current State:\")\n",
|
||||
"print(state.values)\n",
|
||||
"print(\"\\nCurrent Tool Call ID:\")\n",
|
||||
"tool_call_id = state.values['messages'][-1].tool_calls[0]['id']\n",
|
||||
"tool_call_id = state.values[\"messages\"][-1].tool_calls[0][\"id\"]\n",
|
||||
"print(tool_call_id)\n",
|
||||
"\n",
|
||||
"# We now need to construct a replacement tool call.\n",
|
||||
"# We will change the argument to be `San Francisco, USA`\n",
|
||||
"# Note that we could change any number of arguments or tool names - it just has to be a valid one\n",
|
||||
"new_message = {\n",
|
||||
" \"role\": \"tool\", \n",
|
||||
" \"role\": \"tool\",\n",
|
||||
" # This is our natural language feedback\n",
|
||||
" \"content\": \"User requested changes: pass in the country as well\",\n",
|
||||
" \"name\": \"weather_search\",\n",
|
||||
" \"tool_call_id\": tool_call_id\n",
|
||||
" \"tool_call_id\": tool_call_id,\n",
|
||||
"}\n",
|
||||
"graph.update_state(\n",
|
||||
" # This is the config which represents this thread\n",
|
||||
" thread, \n",
|
||||
" thread,\n",
|
||||
" # This is the updated value we want to push\n",
|
||||
" {\"messages\": [new_message]}, \n",
|
||||
" {\"messages\": [new_message]},\n",
|
||||
" # We push this update acting as our human_review_node\n",
|
||||
" as_node=\"human_review_node\"\n",
|
||||
" as_node=\"human_review_node\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Let's now continue executing from here\n",
|
||||
|
||||
@@ -33,15 +33,19 @@
|
||||
"from langgraph.graph import StateGraph, START, END\n",
|
||||
"from typing import TypedDict\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class InputState(TypedDict):\n",
|
||||
" question: str\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class OutputState(TypedDict):\n",
|
||||
" answer: str\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def answer_node(state: InputState):\n",
|
||||
" return {\"answer\": \"bye\"}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"graph = StateGraph(input=InputState, output=OutputState)\n",
|
||||
"graph.add_node(answer_node)\n",
|
||||
"graph.add_edge(START, \"answer_node\")\n",
|
||||
|
||||
@@ -526,7 +526,7 @@
|
||||
" \"tasks\": tasks,\n",
|
||||
" }\n",
|
||||
" )\n",
|
||||
" return {\"messages\":[scheduled_tasks]}"
|
||||
" return {\"messages\": [scheduled_tasks]}"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -653,7 +653,7 @@
|
||||
" )\n",
|
||||
" ]\n",
|
||||
" else:\n",
|
||||
" return {\"messages\":response + [AIMessage(content=decision.action.response)]}\n",
|
||||
" return {\"messages\": response + [AIMessage(content=decision.action.response)]}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def select_recent_messages(state) -> dict:\n",
|
||||
@@ -726,9 +726,11 @@
|
||||
"from langgraph.graph.message import add_messages\n",
|
||||
"from typing import Annotated\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class State(TypedDict):\n",
|
||||
" messages: Annotated[list, add_messages]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"graph_builder = StateGraph(State)\n",
|
||||
"\n",
|
||||
"# 1. Define vertices\n",
|
||||
@@ -794,7 +796,9 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for step in chain.stream({\"messages\":[HumanMessage(content=\"What's the GDP of New York?\")]}):\n",
|
||||
"for step in chain.stream(\n",
|
||||
" {\"messages\": [HumanMessage(content=\"What's the GDP of New York?\")]}\n",
|
||||
"):\n",
|
||||
" print(step)\n",
|
||||
" print(\"---\")"
|
||||
]
|
||||
|
||||
@@ -328,9 +328,9 @@
|
||||
" \"set more_information_needed False and populate a blank string for the query.\"\n",
|
||||
" )\n",
|
||||
" input_messages = [system] + state[\"messages\"]\n",
|
||||
" response = llm.bind_tools(\n",
|
||||
" [QueryForTools], tool_choice=True\n",
|
||||
" ).invoke(input_messages)\n",
|
||||
" response = llm.bind_tools([QueryForTools], tool_choice=True).invoke(\n",
|
||||
" input_messages\n",
|
||||
" )\n",
|
||||
" query = response.tool_calls[0][\"args\"][\"query\"]\n",
|
||||
" tool_documents = vector_store.similarity_search(query)\n",
|
||||
" if hack_remove_tool_condition:\n",
|
||||
|
||||
@@ -329,6 +329,7 @@
|
||||
"\n",
|
||||
"tools = [get_context, cite_context_sources]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Define the function that calls the model\n",
|
||||
"def call_model(state, config):\n",
|
||||
" messages = state[\"messages\"]\n",
|
||||
|
||||
@@ -72,12 +72,12 @@
|
||||
"# Node to retrieve documents\n",
|
||||
"def retrieve_documents(state: QueryOutputState) -> DocumentOutputState:\n",
|
||||
" # Replace this with real logic\n",
|
||||
" return {\"docs\": [state['query']] * 2}\n",
|
||||
" return {\"docs\": [state[\"query\"]] * 2}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Node to generate answer\n",
|
||||
"def generate(state: GenerateInputState) -> OverallState:\n",
|
||||
" return {\"answer\": \"\\n\\n\".join(state['docs'] + [state['question']])}\n",
|
||||
" return {\"answer\": \"\\n\\n\".join(state[\"docs\"] + [state[\"question\"]])}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"graph = StateGraph(OverallState)\n",
|
||||
|
||||
@@ -587,7 +587,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.9"
|
||||
"version": "3.12.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -630,7 +630,7 @@
|
||||
" upsert=True,\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" await self.db[\"checkpoint_writes\"].bulk_write(operations)\n"
|
||||
" await self.db[\"checkpoint_writes\"].bulk_write(operations)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -685,7 +685,9 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with MongoDBSaver.from_conn_info(host=\"localhost\", port=27017, db_name=\"checkpoints\") as checkpointer:\n",
|
||||
"with MongoDBSaver.from_conn_info(\n",
|
||||
" host=\"localhost\", port=27017, db_name=\"checkpoints\"\n",
|
||||
") as checkpointer:\n",
|
||||
" graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n",
|
||||
" config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
|
||||
" res = graph.invoke({\"messages\": [(\"human\", \"what's the weather in sf\")]}, config)\n",
|
||||
@@ -796,10 +798,14 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"async with AsyncMongoDBSaver.from_conn_info(host=\"localhost\", port=27017, db_name=\"checkpoints\") as checkpointer:\n",
|
||||
"async with AsyncMongoDBSaver.from_conn_info(\n",
|
||||
" host=\"localhost\", port=27017, db_name=\"checkpoints\"\n",
|
||||
") as checkpointer:\n",
|
||||
" graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n",
|
||||
" config = {\"configurable\": {\"thread_id\": \"2\"}}\n",
|
||||
" res = await graph.ainvoke({\"messages\": [(\"human\", \"what's the weather in nyc\")]}, config)\n",
|
||||
" res = await graph.ainvoke(\n",
|
||||
" {\"messages\": [(\"human\", \"what's the weather in nyc\")]}, config\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" latest_checkpoint = await checkpointer.aget(config)\n",
|
||||
" latest_checkpoint_tuple = await checkpointer.aget_tuple(config)\n",
|
||||
|
||||
@@ -134,7 +134,7 @@
|
||||
"source": [
|
||||
"from psycopg.rows import dict_row\n",
|
||||
"\n",
|
||||
"connection_kwargs ={\n",
|
||||
"connection_kwargs = {\n",
|
||||
" \"autocommit\": True,\n",
|
||||
" \"prepare_threshold\": 0,\n",
|
||||
"}"
|
||||
@@ -165,7 +165,7 @@
|
||||
" # Example configuration\n",
|
||||
" conninfo=DB_URI,\n",
|
||||
" max_size=20,\n",
|
||||
" kwargs=connection_kwargs\n",
|
||||
" kwargs=connection_kwargs,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"with pool.connection() as conn:\n",
|
||||
@@ -393,7 +393,7 @@
|
||||
" # Example configuration\n",
|
||||
" conninfo=DB_URI,\n",
|
||||
" max_size=20,\n",
|
||||
" kwargs=connection_kwargs\n",
|
||||
" kwargs=connection_kwargs,\n",
|
||||
") as pool, pool.connection() as conn:\n",
|
||||
" checkpointer = AsyncPostgresSaver(conn)\n",
|
||||
"\n",
|
||||
|
||||
@@ -530,7 +530,9 @@
|
||||
"\n",
|
||||
" @classmethod\n",
|
||||
" @asynccontextmanager\n",
|
||||
" async def from_conn_info(cls, *, host: str, port: int, db: int) -> AsyncIterator[\"AsyncRedisSaver\"]:\n",
|
||||
" async def from_conn_info(\n",
|
||||
" cls, *, host: str, port: int, db: int\n",
|
||||
" ) -> AsyncIterator[\"AsyncRedisSaver\"]:\n",
|
||||
" conn = None\n",
|
||||
" try:\n",
|
||||
" conn = AsyncRedis(host=host, port=port, db=db)\n",
|
||||
@@ -887,10 +889,14 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"async with AsyncRedisSaver.from_conn_info(host=\"localhost\", port=6379, db=0) as checkpointer:\n",
|
||||
"async with AsyncRedisSaver.from_conn_info(\n",
|
||||
" host=\"localhost\", port=6379, db=0\n",
|
||||
") as checkpointer:\n",
|
||||
" graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n",
|
||||
" config = {\"configurable\": {\"thread_id\": \"2\"}}\n",
|
||||
" res = await graph.ainvoke({\"messages\": [(\"human\", \"what's the weather in nyc\")]}, config)\n",
|
||||
" res = await graph.ainvoke(\n",
|
||||
" {\"messages\": [(\"human\", \"what's the weather in nyc\")]}, config\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" latest_checkpoint = await checkpointer.aget(config)\n",
|
||||
" latest_checkpoint_tuple = await checkpointer.aget_tuple(config)\n",
|
||||
|
||||
@@ -269,7 +269,7 @@
|
||||
"class State(TypedDict):\n",
|
||||
" messages: Annotated[list, add_messages]\n",
|
||||
"\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"async def generation_node(state: Sequence[BaseMessage]):\n",
|
||||
" return await generate.ainvoke({\"messages\": state})\n",
|
||||
"\n",
|
||||
|
||||
@@ -392,6 +392,7 @@
|
||||
"class State(TypedDict):\n",
|
||||
" messages: Annotated[list, add_messages]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"MAX_ITERATIONS = 5\n",
|
||||
"builder = StateGraph(State)\n",
|
||||
"builder.add_node(\"draft\", first_responder.respond)\n",
|
||||
|
||||
@@ -68,7 +68,9 @@
|
||||
" # It's completely optional, but useful if you have many functions with similar names\n",
|
||||
" gen = RunnableGenerator(my_generator).with_config(\n",
|
||||
" tags=[\"should_stream\"],\n",
|
||||
" callbacks=config.get(\"callbacks\", []) # <-- Propagate callbacks (Python <= 3.10)\n",
|
||||
" callbacks=config.get(\n",
|
||||
" \"callbacks\", []\n",
|
||||
" ), # <-- Propagate callbacks (Python <= 3.10)\n",
|
||||
" )\n",
|
||||
" async for message in gen.astream(state):\n",
|
||||
" messages.append(message)\n",
|
||||
|
||||
@@ -169,9 +169,7 @@
|
||||
"from langchain_core.output_parsers import JsonOutputParser\n",
|
||||
"\n",
|
||||
"# JSON\n",
|
||||
"llm = ChatOllama(model=\"llama3.1\", \n",
|
||||
" format=\"json\", \n",
|
||||
" temperature=0)\n",
|
||||
"llm = ChatOllama(model=\"llama3.1\", format=\"json\", temperature=0)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"prompt = PromptTemplate(\n",
|
||||
@@ -210,6 +208,7 @@
|
||||
"from IPython.display import Image, display\n",
|
||||
"from langgraph.graph import START, END, StateGraph\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class GraphState(TypedDict):\n",
|
||||
" \"\"\"\n",
|
||||
" Represents the state of our graph.\n",
|
||||
@@ -381,21 +380,22 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import uuid \n",
|
||||
"import uuid\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def predict_custom_agent_answer(example: dict):\n",
|
||||
" \n",
|
||||
" config = {\"configurable\": {\"thread_id\": str(uuid.uuid4())}}\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" state_dict = custom_graph.invoke(\n",
|
||||
" {\"question\": example[\"input\"], \"steps\": []}, config\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" return {\"response\": state_dict[\"generation\"], \"steps\": state_dict[\"steps\"]}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"example = {\"input\": \"What are the types of agent memory?\"}\n",
|
||||
"#response = predict_custom_agent_answer(example)\n",
|
||||
"#response"
|
||||
"# response = predict_custom_agent_answer(example)\n",
|
||||
"# response"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -544,6 +544,7 @@
|
||||
" \"generate_answer\",\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def check_trajectory_custom(root_run: Run, example: Example) -> dict:\n",
|
||||
" \"\"\"\n",
|
||||
" Check if all expected tools are called in exact order and without any additional tool calls.\n",
|
||||
|
||||
@@ -134,6 +134,7 @@
|
||||
" for d in web_results\n",
|
||||
" ]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Tool list\n",
|
||||
"tools = [retrieve_documents, web_search]"
|
||||
]
|
||||
@@ -152,9 +153,11 @@
|
||||
"from langgraph.graph.message import AnyMessage, add_messages\n",
|
||||
"from typing_extensions import TypedDict\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class State(TypedDict):\n",
|
||||
" messages: Annotated[list[AnyMessage], add_messages]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class Assistant:\n",
|
||||
" def __init__(self, runnable: Runnable):\n",
|
||||
" \"\"\"\n",
|
||||
@@ -291,6 +294,7 @@
|
||||
"source": [
|
||||
"import uuid\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def predict_react_agent_answer(example: dict):\n",
|
||||
" \"\"\"Use this for answer evaluation\"\"\"\n",
|
||||
"\n",
|
||||
|
||||
Reference in New Issue
Block a user