mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-08-31 12:19:58 +02:00
fixed some (#1784)
This commit is contained in:
Isaac Francisco
@@ -869,7 +869,7 @@
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"id": "cb4072c9-775e-4bf5-8a1a-fb822e6de9d7",
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"metadata": {},
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"source": [
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"For streaming events, in LangGraph you need to use `.astream_events` method on the `CompiledGraph`. In LangGraph Cloud this is done via passing `stream_mode=\"events\"`"
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"For streaming events, in LangGraph you need to use `.astream` method on the `CompiledGraph`. In LangGraph Cloud this is done via passing `stream_mode=\"events\"`"
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]
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},
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{
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@@ -882,89 +882,42 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "94b815e4-1dd2-4999-9e73-6e29836d9160",
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"execution_count": 22,
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"id": "fc692060",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"Tool Calls:\n",
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" get_weather (call_UPFCSk4cQTFAuET2WgAzq0el)\n",
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" Call ID: call_UPFCSk4cQTFAuET2WgAzq0el\n",
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" Args:\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"Tool Calls:\n",
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" (None)\n",
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" Call ID: None\n",
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" Args:\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"Invalid Tool Calls:\n",
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" None (None)\n",
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" Call ID: None\n",
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" Args:\n",
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" city\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"Invalid Tool Calls:\n",
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" None (None)\n",
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" Call ID: None\n",
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" Args:\n",
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" \":\"\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"Invalid Tool Calls:\n",
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" None (None)\n",
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" Call ID: None\n",
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" Args:\n",
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" sf\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"Invalid Tool Calls:\n",
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" None (None)\n",
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" Call ID: None\n",
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" Args:\n",
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" \"}\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"\n",
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"The\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"\n",
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" weather\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"\n",
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" in\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"\n",
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" San\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"\n",
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" Francisco\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"\n",
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" is\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"\n",
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" sunny\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"\n",
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"!\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"\n",
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" ☀\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n",
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"\n",
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"️\n",
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"============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n"
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"[{'name': 'get_weather', 'args': {}, 'id': 'call_G6boP6Hj21glqPTqtFdTllUd', 'type': 'tool_call'}]\n",
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"[{'name': 'get_weather', 'args': {}, 'id': 'call_G6boP6Hj21glqPTqtFdTllUd', 'type': 'tool_call'}]\n",
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"[{'name': 'get_weather', 'args': {}, 'id': 'call_G6boP6Hj21glqPTqtFdTllUd', 'type': 'tool_call'}]\n",
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"[{'name': 'get_weather', 'args': {'city': ''}, 'id': 'call_G6boP6Hj21glqPTqtFdTllUd', 'type': 'tool_call'}]\n",
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"[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_G6boP6Hj21glqPTqtFdTllUd', 'type': 'tool_call'}]\n",
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"[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_G6boP6Hj21glqPTqtFdTllUd', 'type': 'tool_call'}]\n",
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"It's always sunny in sf|The| weather| in| San| Francisco| is| currently| sunny|.|"
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]
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}
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],
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"source": [
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"from langchain_core.messages import AIMessageChunk\n",
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"\n",
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"inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n",
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"async for chunk in graph.astream_events(inputs, version=\"v2\"):\n",
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" if chunk[\"event\"] == \"on_chat_model_stream\":\n",
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" chunk[\"data\"][\"chunk\"].pretty_print()"
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"first = True\n",
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"async for msg, metadata in graph.astream(inputs, stream_mode=\"messages\"):\n",
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" if msg.content:\n",
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" print(msg.content, end=\"|\", flush=True)\n",
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"\n",
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" if isinstance(msg, AIMessageChunk):\n",
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" if first:\n",
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" gathered = msg\n",
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" first = False\n",
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" else:\n",
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" gathered = gathered + msg\n",
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"\n",
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" if msg.tool_call_chunks:\n",
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" print(gathered.tool_calls)"
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]
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},
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{
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@@ -117,7 +117,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 14,
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"id": "2cb38dd9-74d8-456d-9e39-4655f2bf3f37",
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"metadata": {},
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"outputs": [],
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@@ -157,7 +157,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 15,
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"id": "7254310e-7016-45f7-9795-6d52a1160086",
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"metadata": {},
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"outputs": [],
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@@ -177,88 +177,53 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "31fe94ab-80de-4729-843e-5a0fe1bb52c0",
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"execution_count": 25,
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"id": "ec461f66",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1. Books: A shelf is typically used to store books. These can be novels, textbooks, or reference books, and they come in various sizes and genres.\n",
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"\n",
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"2. Picture frames: Picture frames are commonly placed on shelves to display photographs or artwork. They can be made of wood, metal, or plastic and come in different shapes and sizes.\n",
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"\n",
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"3. Decorative items: Shelves often display decorative items such as vases, figurines, or candles. These items can add a personal touch to a room and enhance its aesthetic appeal."
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"1|.| Books| -| A| collection| of| written| or| printed| works| bound| together| and| typically| held| upright| on| a| shelf| for| easy| access| and| storage|.\n",
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"|2|.| Picture| frames| -| Decor|ative| frames| used| to| display| photographs| or| artwork| on| a| shelf|,| adding| a| personal| touch| to| the| space|.\n",
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"|3|.| Decor|ative| figur|ines| -| Small| sculptures| or| statues| that| are| placed| on| a| shelf| for| decorative| purposes|,| adding| visual| interest| and| personality| to| the| room|.|"
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]
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}
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],
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"source": [
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"async for event in agent.astream_events(\n",
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" {\"messages\": [(\"human\", \"what items are on the shelf?\")]}, version=\"v2\"\n",
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"):\n",
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" tags = event.get(\"tags\", [])\n",
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" if event[\"event\"] == \"on_chat_model_stream\" and \"tool_llm\" in tags:\n",
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" print(event[\"data\"][\"chunk\"].content, end=\"\", flush=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ebd8902e-935b-4724-8b5d-551b7674fd34",
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"metadata": {},
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"source": [
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"Let's inspect the last event to get the final list of messages from the agent"
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"from langchain_core.messages import HumanMessage\n",
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"\n",
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"inputs = [HumanMessage(content=\"what is the weather in sf\")]\n",
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"final_message = \"\"\n",
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"async for msg, metadata in agent.astream({\"messages\": [(\"human\", \"what items are on the shelf?\")]}, stream_mode=\"messages\"):\n",
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" # Stream all messages from the tool node\n",
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" if msg.content and not isinstance(msg,HumanMessage) and metadata['langgraph_node'] == 'tools' and not msg.name:\n",
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" print(msg.content, end=\"|\", flush=True)\n",
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" # Final message should come from our agent\n",
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" if msg.content and metadata['langgraph_node'] == \"agent\":\n",
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" final_message += msg.content"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "ca382c1f-b1c7-4c8a-bd9b-7a873b891b3e",
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"metadata": {},
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"outputs": [],
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"source": [
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"final_messages = event[\"data\"][\"output\"][\"messages\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "3fa7d768-5a84-475a-950e-fd351a44841b",
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"execution_count": 26,
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"id": "1b35d72f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"================================\u001b[1m Human Message \u001b[0m=================================\n",
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"\n",
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"what items are on the shelf?\n",
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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"Tool Calls:\n",
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" get_items (call_5CAMZ3asoLsZm9ocMbCOWxYQ)\n",
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" Call ID: call_5CAMZ3asoLsZm9ocMbCOWxYQ\n",
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" Args:\n",
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" place: shelf\n",
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"=================================\u001b[1m Tool Message \u001b[0m=================================\n",
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"Name: get_items\n",
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"\n",
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"1. Books - A collection of written or printed works bound together with covers. They can be fiction or non-fiction and come in various genres.\n",
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"\n",
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"2. Picture frames - A decorative border for a photograph or artwork, typically made of wood, metal, or plastic. Picture frames are used to display and protect a picture or painting.\n",
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"\n",
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"3. Candles - A cylinder of wax with a central wick that is lit to produce light or fragrance. Candles are often used for decoration, ambiance, or religious ceremonies.\n",
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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"\n",
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"The items on the shelf are:\n",
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"1. Books\n",
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"2. Picture frames\n",
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"3. Candles\n"
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]
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"data": {
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"text/plain": [
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"'The items on the shelf are:\\n1. Books\\n2. Picture frames\\n3. Decorative figurines'"
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]
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},
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"execution_count": 26,
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"metadata": {},
|
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"for message in final_messages:\n",
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" message.pretty_print()"
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"final_message"
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]
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},
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{
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@@ -266,7 +231,7 @@
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"id": "d7f9457c-5665-4cd5-9a99-d54c84270616",
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"metadata": {},
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"source": [
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"You can see that the content of the `ToolMessage` is the same as the output we streamed above"
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"You can see that the content of the final message is the same as the output we streamed above"
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]
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}
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],
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@@ -73,7 +73,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"id": "5e62618d-0e0c-483c-acd3-40a26e61894a",
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"metadata": {},
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"outputs": [],
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@@ -120,7 +120,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 3,
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"id": "8c7339d2-1835-4b5a-a99c-a60e150280af",
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"metadata": {},
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"outputs": [],
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@@ -183,7 +183,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 4,
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"id": "2ab6d079-ba06-48ba-abe5-e72df24407af",
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"metadata": {},
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"outputs": [
|
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@@ -214,7 +214,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"id": "84d65cbe-4cfe-44f8-b49e-b37632887c91",
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"metadata": {},
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"outputs": [],
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@@ -243,30 +243,24 @@
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{
|
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"cell_type": "code",
|
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"execution_count": 7,
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"id": "a37c3a5f-5a43-46db-940e-c583df776520",
|
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"id": "68ac2c7f",
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"name": "stdout",
|
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"output_type": "stream",
|
||||
"text": [
|
||||
"Well| folks|,| looks| like| we|'ve| got| some| cloudy| skies| in| the| Big| Apple| today|.| So| grab| your| umbrella| just| in| case|,| and| don|'t| let| those| clouds| rain| on| your| parade|!|"
|
||||
"Well| folks|,| let| me| tell| you|,| the| weather| in| San| Francisco| is| always| sunny|!| That|'s| right|,| you| can| expect| clear| skies| and| plenty| of| sunshine| when| you|'re| in| the| City| by| the| Bay|.| So| grab| your| sunglasses| and| get| ready| to| enjoy| some| beautiful| weather| in| San| Francisco|!|"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"inputs = {\"messages\": [(\"human\", \"what's the weather in nyc?\")]}\n",
|
||||
"async for event in app.astream_events(inputs, version=\"v2\"):\n",
|
||||
" kind = event[\"event\"]\n",
|
||||
" tags = event.get(\"tags\", [])\n",
|
||||
" # filter on the langgraph node name\n",
|
||||
" if kind == \"on_chat_model_stream\" and event[\"metadata\"].get(\"langgraph_node\") == \"final\":\n",
|
||||
" data = event[\"data\"]\n",
|
||||
" if data[\"chunk\"].content:\n",
|
||||
" # Empty content in the context of OpenAI or Anthropic usually means\n",
|
||||
" # that the model is asking for a tool to be invoked.\n",
|
||||
" # So we only print non-empty content\n",
|
||||
" print(data[\"chunk\"].content, end=\"|\", flush=True)"
|
||||
"from langchain_core.messages import HumanMessage\n",
|
||||
"\n",
|
||||
"inputs = [HumanMessage(content=\"what is the weather in sf\")]\n",
|
||||
"async for msg, metadata in app.astream({\"messages\": inputs}, stream_mode=\"messages\"):\n",
|
||||
" if msg.content and not isinstance(msg,HumanMessage) and metadata['langgraph_node'] == 'final':\n",
|
||||
" print(msg.content, end=\"|\", flush=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -87,7 +87,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": 7,
|
||||
"id": "d59234f9-173e-469d-a725-c13e0979663e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -139,7 +139,13 @@
|
||||
"\n",
|
||||
" if delta.content:\n",
|
||||
" response_content += delta.content\n",
|
||||
" llm_run_manager.on_llm_new_token(delta.content)\n",
|
||||
" # note: we're wrapping the response in ChatGenerationChunk so that we can stream this back using stream_mode=\"messages\"\n",
|
||||
" chunk = ChatGenerationChunk(\n",
|
||||
" message=AIMessageChunk(\n",
|
||||
" content=delta.content,\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" llm_run_manager.on_llm_new_token(delta.content, chunk=chunk)\n",
|
||||
"\n",
|
||||
" if delta.tool_calls:\n",
|
||||
" # note: for simplicity we're only handling a single tool call here\n",
|
||||
@@ -147,7 +153,7 @@
|
||||
" tool_call_function_name = delta.tool_calls[0].function.name\n",
|
||||
" tool_call_id = delta.tool_calls[0].id\n",
|
||||
"\n",
|
||||
" # note: we're wrapping the tools calls in ChatGenerationChunk so that the events from .astream_events in the graph can render tool calls correctly\n",
|
||||
" # note: we're wrapping the tools calls in ChatGenerationChunk so that we can stream this back using stream_mode=\"messages\"\n",
|
||||
" tool_call_chunk = ChatGenerationChunk(\n",
|
||||
" message=AIMessageChunk(\n",
|
||||
" content=\"\",\n",
|
||||
@@ -189,7 +195,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 8,
|
||||
"id": "b756ea32",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -239,7 +245,7 @@
|
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},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 9,
|
||||
"id": "228260be-1f9a-4195-80e0-9604f8a5dba6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -281,47 +287,42 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "45c96a79-4147-42e3-89fd-d942b2b49f6c",
|
||||
"execution_count": 10,
|
||||
"id": "d6ed3df5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': 'call_6XZimd7lxnCgoLK1ZM3iAR6S', 'function': {'arguments': '', 'name': 'get_items'}, 'type': 'function'}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [{'name': 'get_items', 'args': {}, 'id': 'call_6XZimd7lxnCgoLK1ZM3iAR6S', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': [{'name': 'get_items', 'args': '', 'id': 'call_6XZimd7lxnCgoLK1ZM3iAR6S', 'index': 0, 'type': 'tool_call_chunk'}]}\n",
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '{\"', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [{'name': '', 'args': {}, 'id': None, 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '{\"', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]}\n",
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': 'place', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': 'place', 'id': None, 'error': None, 'type': 'invalid_tool_call'}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': 'place', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]}\n",
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '\":\"', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': '\":\"', 'id': None, 'error': None, 'type': 'invalid_tool_call'}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '\":\"', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]}\n",
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': 'bed', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': 'bed', 'id': None, 'error': None, 'type': 'invalid_tool_call'}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': 'bed', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]}\n",
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': 'room', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': 'room', 'id': None, 'error': None, 'type': 'invalid_tool_call'}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': 'room', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]}\n",
|
||||
"LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '\"}', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': '\"}', 'id': None, 'error': None, 'type': 'invalid_tool_call'}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '\"}', 'id': None, 'index': 0, 'type': 'tool_call_chunk'}]}\n",
|
||||
"LLM token {'content': 'In', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' the', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' bedroom', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ',', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' you', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' have', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' socks', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ',', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' shoes', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ',', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' and', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' dust', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': ' b', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': 'unn', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': 'ies', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n",
|
||||
"LLM token {'content': '.', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n"
|
||||
"[{'name': 'get_items', 'args': {}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'get_items', 'args': {}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'get_items', 'args': {}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'get_items', 'args': {'place': ''}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'get_items', 'args': {'place': 'bed'}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'get_items', 'args': {'place': 'bedroom'}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'get_items', 'args': {'place': 'bedroom'}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
|
||||
"In| the| bedroom|,| you| have| socks|,| shoes|,| and| some| dust| b|unn|ies|.|"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"async for event in graph.astream_events(\n",
|
||||
" {\"messages\": [{\"role\": \"user\", \"content\": \"what's in the bedroom\"}]}, version=\"v2\"\n",
|
||||
"):\n",
|
||||
" tags = event.get(\"tags\", [])\n",
|
||||
" if event[\"event\"] == \"on_chat_model_stream\" and \"agent_llm\" in tags:\n",
|
||||
" print(\"LLM token\", event[\"data\"][\"chunk\"].dict())"
|
||||
"from langchain_core.messages import AIMessageChunk\n",
|
||||
"\n",
|
||||
"first = True\n",
|
||||
"async for msg, metadata in graph.astream({\"messages\": [{\"role\": \"user\", \"content\": \"what's in the bedroom\"}]}, stream_mode=\"messages\"):\n",
|
||||
" if msg.content:\n",
|
||||
" print(msg.content, end=\"|\", flush=True)\n",
|
||||
"\n",
|
||||
" if isinstance(msg, AIMessageChunk):\n",
|
||||
" if first:\n",
|
||||
" gathered = msg\n",
|
||||
" first = False\n",
|
||||
" else:\n",
|
||||
" gathered = gathered + msg\n",
|
||||
"\n",
|
||||
" if msg.tool_call_chunks:\n",
|
||||
" print(gathered.tool_calls)"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -7,7 +7,7 @@
|
||||
"source": [
|
||||
"# How to stream LLM tokens from your graph\n",
|
||||
"\n",
|
||||
"In this example we will stream tokens from the language model powering an agent. We will use a ReAct agent as an example. The main thing to bear in mind here is that using [async nodes](../async) typically offers the best behavior for this, since we will be using the `astream_events` method.\n",
|
||||
"In this example we will stream tokens from the language model powering an agent. We will use a ReAct agent as an example.\n",
|
||||
"\n",
|
||||
"This how-to guide closely follows the others in this directory, so we will call out differences with the **STREAMING** tag below (if you just want to search for those).\n",
|
||||
"\n",
|
||||
@@ -22,7 +22,7 @@
|
||||
" <p class=\"admonition-title\">Note on Python < 3.11</p>\n",
|
||||
" <p>\n",
|
||||
" When using python 3.8, 3.9, or 3.10, please ensure you manually pass the RunnableConfig through to the llm when invoking it like so: <code>llm.ainvoke(..., config)</code>.\n",
|
||||
" The <a href=\"https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events\">astream_events</a> method collects all events from your nested code using a streaming tracer passed as a callback. In 3.11 and above, this is automatically handled via <a href=\"https://docs.python.org/3/library/contextvars.html\">contextvar</a>'s; prior to 3.11, <a href=\"https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task\">asyncio's tasks</a> lacked proper contextvar support, meaning that the callbacks will only propagate if you manually pass the config through. We do this in the <code>call_model</code> method below.\n",
|
||||
" The stream method collects all events from your nested code using a streaming tracer passed as a callback. In 3.11 and above, this is automatically handled via <a href=\"https://docs.python.org/3/library/contextvars.html\">contextvar</a>'s; prior to 3.11, <a href=\"https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task\">asyncio's tasks</a> lacked proper contextvar support, meaning that the callbacks will only propagate if you manually pass the config through. We do this in the <code>call_model</code> method below.\n",
|
||||
" </p>\n",
|
||||
"</div> "
|
||||
]
|
||||
@@ -108,10 +108,19 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 1,
|
||||
"id": "17ef4967",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/Users/isaachershenson/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from typing import Annotated\n",
|
||||
"\n",
|
||||
@@ -143,7 +152,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 2,
|
||||
"id": "9a8bc61e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -172,7 +181,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 3,
|
||||
"id": "4d6ac180",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -200,7 +209,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 4,
|
||||
"id": "42c0af37",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -222,7 +231,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 5,
|
||||
"id": "2bbdd3bc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -263,13 +272,13 @@
|
||||
"<div class=\"admonition note\">\n",
|
||||
" <p class=\"admonition-title\">Manual Callback Propagation</p>\n",
|
||||
" <p>\n",
|
||||
" Note that in <code>call_model(state: State, config: RunnableConfig):</code> below, we a) accept the <a href=\"https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig\">RunnableConfig</a> in the node and b) pass this in as the second arg for <code>llm.ainvoke(..., config)</code>. This is optional for python 3.11 and later. If you ever have a problem where the LLM tokens are not streamed when using `astream_events` and you are using an older version of python, it's worth checking to ensure that the callbacks are manually propagated.</p>\n",
|
||||
" Note that in <code>call_model(state: State, config: RunnableConfig):</code> below, we a) accept the <a href=\"https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig\">RunnableConfig</a> in the node and b) pass this in as the second arg for <code>llm.ainvoke(..., config)</code>. This is optional for python 3.11 and later.</p>\n",
|
||||
"</div> "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 12,
|
||||
"id": "3b541bb9-900c-40d0-964d-7b5dfee30667",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -315,7 +324,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 13,
|
||||
"id": "813ae66c-3b58-4283-a02a-36da72a2ab90",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -350,7 +359,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 14,
|
||||
"id": "72785b66",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -385,45 +394,45 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "cfd140f0-a5a6-4697-8115-322242f197b5",
|
||||
"execution_count": 16,
|
||||
"id": "96050fba",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"--\n",
|
||||
"Starting tool: search with inputs: {'query': 'weather in San Francisco'}\n",
|
||||
"Done tool: search\n",
|
||||
"Tool output was: content=['Cloudy with a chance of hail.'] name='search' tool_call_id='call_iqZnBxj7nJBwgixD3Mr1r9kT'\n",
|
||||
"--\n",
|
||||
"The| weather| in| San| Francisco| is| currently| cloudy| with| a| chance| of| hail|.|"
|
||||
"[{'name': 'search', 'args': {}, 'id': 'call_i5H0dF2CNHcaEuHvugnSc7Kv', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'search', 'args': {}, 'id': 'call_i5H0dF2CNHcaEuHvugnSc7Kv', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'search', 'args': {}, 'id': 'call_i5H0dF2CNHcaEuHvugnSc7Kv', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'search', 'args': {'query': ''}, 'id': 'call_i5H0dF2CNHcaEuHvugnSc7Kv', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'search', 'args': {'query': 'weather'}, 'id': 'call_i5H0dF2CNHcaEuHvugnSc7Kv', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'search', 'args': {'query': 'weather in'}, 'id': 'call_i5H0dF2CNHcaEuHvugnSc7Kv', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'search', 'args': {'query': 'weather in San'}, 'id': 'call_i5H0dF2CNHcaEuHvugnSc7Kv', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'call_i5H0dF2CNHcaEuHvugnSc7Kv', 'type': 'tool_call'}]\n",
|
||||
"[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'call_i5H0dF2CNHcaEuHvugnSc7Kv', 'type': 'tool_call'}]\n",
|
||||
"[\"Cloudy with a chance of hail.\"]|The| weather| in| San| Francisco| is| currently| cloudy| with| a| chance| of| hail|.|"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_core.messages import HumanMessage\n",
|
||||
"from langchain_core.messages import AIMessageChunk, HumanMessage\n",
|
||||
"\n",
|
||||
"inputs = [HumanMessage(content=\"what is the weather in sf\")]\n",
|
||||
"async for event in app.astream_events({\"messages\": inputs}, version=\"v1\"):\n",
|
||||
" kind = event[\"event\"]\n",
|
||||
" if kind == \"on_chat_model_stream\":\n",
|
||||
" content = event[\"data\"][\"chunk\"].content\n",
|
||||
" if content:\n",
|
||||
" # Empty content in the context of OpenAI or Anthropic usually means\n",
|
||||
" # that the model is asking for a tool to be invoked.\n",
|
||||
" # So we only print non-empty content\n",
|
||||
" print(content, end=\"|\")\n",
|
||||
" elif kind == \"on_tool_start\":\n",
|
||||
" print(\"--\")\n",
|
||||
" print(\n",
|
||||
" f\"Starting tool: {event['name']} with inputs: {event['data'].get('input')}\"\n",
|
||||
" )\n",
|
||||
" elif kind == \"on_tool_end\":\n",
|
||||
" print(f\"Done tool: {event['name']}\")\n",
|
||||
" print(f\"Tool output was: {event['data'].get('output')}\")\n",
|
||||
" print(\"--\")"
|
||||
"first = True\n",
|
||||
"async for msg, metadata in app.astream({\"messages\": inputs}, stream_mode=\"messages\"):\n",
|
||||
" if msg.content and not isinstance(msg,HumanMessage):\n",
|
||||
" print(msg.content, end=\"|\", flush=True)\n",
|
||||
"\n",
|
||||
" if isinstance(msg, AIMessageChunk):\n",
|
||||
" if first:\n",
|
||||
" gathered = msg\n",
|
||||
" first = False\n",
|
||||
" else:\n",
|
||||
" gathered = gathered + msg\n",
|
||||
"\n",
|
||||
" if msg.tool_call_chunks:\n",
|
||||
" print(gathered.tool_calls)"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -443,7 +452,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.9"
|
||||
"version": "3.9.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Reference in New Issue
Block a user