mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-10-01 22:15:11 +02:00
docs (examples): replace SqliteSaver with MemorySaver (#1271)
This commit is contained in:
Vadym Barda
@@ -32,7 +32,10 @@
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"scrolled": true
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},
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"outputs": [],
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"source": ["%%capture --no-stderr\n%pip install -U langgraph langchain-community langchain-openai scikit-learn"]
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"source": [
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"%%capture --no-stderr\n",
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"%pip install -U langgraph langchain-community langchain-openai scikit-learn"
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]
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},
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{
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"cell_type": "markdown",
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@@ -48,7 +51,15 @@
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"id": "3d1ef253-6b0c-4481-868c-e1fe84f2c8ff",
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"metadata": {},
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"outputs": [],
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"source": ["import requests\n\nurl = \"https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db\"\nresponse = requests.get(url)\n\nwith open(\"Chinook.db\", \"wb\") as file:\n file.write(response.content)"]
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"source": [
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"import requests\n",
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"\n",
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"url = \"https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db\"\n",
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"response = requests.get(url)\n",
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"\n",
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"with open(\"Chinook.db\", \"wb\") as file:\n",
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" file.write(response.content)"
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]
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},
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{
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"cell_type": "code",
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@@ -77,7 +88,12 @@
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"output_type": "execute_result"
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}
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],
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"source": ["from langchain_community.utilities import SQLDatabase\n\ndb = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\ndb.get_usable_table_names()"]
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"source": [
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"from langchain_community.utilities import SQLDatabase\n",
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"\n",
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"db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\n",
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"db.get_usable_table_names()"
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]
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},
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{
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"cell_type": "markdown",
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@@ -96,7 +112,11 @@
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"id": "d9ea4e80-30e6-4d46-b480-35f0be2fb055",
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"metadata": {},
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"outputs": [],
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"source": ["from langchain_openai import ChatOpenAI\n\nmodel = ChatOpenAI(temperature=0, streaming=True, model=\"gpt-4o\")"]
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"source": [
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"model = ChatOpenAI(temperature=0, streaming=True, model=\"gpt-4o\")"
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]
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},
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{
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"cell_type": "markdown",
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@@ -118,7 +138,9 @@
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"id": "ea958e9f-ab1f-49b5-bd85-16332055297c",
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"metadata": {},
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"outputs": [],
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"source": ["from langchain_core.messages import HumanMessage, SystemMessage"]
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"source": [
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"from langchain_core.messages import HumanMessage, SystemMessage"
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]
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},
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{
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"cell_type": "markdown",
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@@ -137,7 +159,12 @@
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"id": "975b039a",
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"metadata": {},
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"outputs": [],
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"source": ["# This tool is given to the agent to look up information about a customer\ndef get_customer_info(customer_id: int):\n \"\"\"Look up customer info given their ID. ALWAYS make sure you have the customer ID before invoking this.\"\"\"\n return db.run(f\"SELECT * FROM Customer WHERE CustomerID = {customer_id};\")"]
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"source": [
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"# This tool is given to the agent to look up information about a customer\n",
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"def get_customer_info(customer_id: int):\n",
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" \"\"\"Look up customer info given their ID. ALWAYS make sure you have the customer ID before invoking this.\"\"\"\n",
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" return db.run(f\"SELECT * FROM Customer WHERE CustomerID = {customer_id};\")"
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]
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},
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{
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"cell_type": "code",
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@@ -145,7 +172,20 @@
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"id": "1d5fa446",
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"metadata": {},
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"outputs": [],
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"source": ["customer_prompt = \"\"\"Your job is to help a user update their profile.\n\nYou only have certain tools you can use. These tools require specific input. If you don't know the required input, then ask the user for it.\n\nIf you are unable to help the user, you can \"\"\"\n\n\ndef get_customer_messages(messages):\n return [SystemMessage(content=customer_prompt)] + messages\n\n\ncustomer_chain = get_customer_messages | model.bind_tools([get_customer_info])"]
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"source": [
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"customer_prompt = \"\"\"Your job is to help a user update their profile.\n",
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"\n",
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"You only have certain tools you can use. These tools require specific input. If you don't know the required input, then ask the user for it.\n",
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"\n",
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"If you are unable to help the user, you can \"\"\"\n",
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"\n",
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"\n",
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"def get_customer_messages(messages):\n",
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" return [SystemMessage(content=customer_prompt)] + messages\n",
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"\n",
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"\n",
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"customer_chain = get_customer_messages | model.bind_tools([get_customer_info])"
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]
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},
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{
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"cell_type": "markdown",
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@@ -166,7 +206,19 @@
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"id": "a8604a3b-b484-4b2b-a914-4236cb98c524",
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"metadata": {},
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"outputs": [],
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"source": ["from langchain_community.vectorstores import SKLearnVectorStore\nfrom langchain_openai import OpenAIEmbeddings\n\nartists = db._execute(\"select * from Artist\")\nsongs = db._execute(\"select * from Track\")\nartist_retriever = SKLearnVectorStore.from_texts(\n [a[\"Name\"] for a in artists], OpenAIEmbeddings(), metadatas=artists\n).as_retriever()\nsong_retriever = SKLearnVectorStore.from_texts(\n [a[\"Name\"] for a in songs], OpenAIEmbeddings(), metadatas=songs\n).as_retriever()"]
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"source": [
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"from langchain_community.vectorstores import SKLearnVectorStore\n",
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"from langchain_openai import OpenAIEmbeddings\n",
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"\n",
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"artists = db._execute(\"select * from Artist\")\n",
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"songs = db._execute(\"select * from Track\")\n",
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"artist_retriever = SKLearnVectorStore.from_texts(\n",
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" [a[\"Name\"] for a in artists], OpenAIEmbeddings(), metadatas=artists\n",
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").as_retriever()\n",
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"song_retriever = SKLearnVectorStore.from_texts(\n",
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" [a[\"Name\"] for a in songs], OpenAIEmbeddings(), metadatas=songs\n",
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").as_retriever()"
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]
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},
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{
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"cell_type": "markdown",
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@@ -182,7 +234,16 @@
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"id": "0a2a2b74",
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"metadata": {},
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"outputs": [],
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"source": ["def get_albums_by_artist(artist):\n \"\"\"Get albums by an artist (or similar artists).\"\"\"\n docs = artist_retriever.get_relevant_documents(artist)\n artist_ids = \", \".join([str(d.metadata[\"ArtistId\"]) for d in docs])\n return db.run(\n f\"SELECT Title, Name FROM Album LEFT JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Album.ArtistId in ({artist_ids});\",\n include_columns=True,\n )"]
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"source": [
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"def get_albums_by_artist(artist):\n",
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" \"\"\"Get albums by an artist (or similar artists).\"\"\"\n",
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" docs = artist_retriever.get_relevant_documents(artist)\n",
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" artist_ids = \", \".join([str(d.metadata[\"ArtistId\"]) for d in docs])\n",
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" return db.run(\n",
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" f\"SELECT Title, Name FROM Album LEFT JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Album.ArtistId in ({artist_ids});\",\n",
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" include_columns=True,\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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@@ -198,7 +259,16 @@
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"id": "da533f50",
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"metadata": {},
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"outputs": [],
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"source": ["def get_tracks_by_artist(artist):\n \"\"\"Get songs by an artist (or similar artists).\"\"\"\n docs = artist_retriever.invoke(artist)\n artist_ids = \", \".join([str(d.metadata[\"ArtistId\"]) for d in docs])\n return db.run(\n f\"SELECT Track.Name as SongName, Artist.Name as ArtistName FROM Album LEFT JOIN Artist ON Album.ArtistId = Artist.ArtistId LEFT JOIN Track ON Track.AlbumId = Album.AlbumId WHERE Album.ArtistId in ({artist_ids});\",\n include_columns=True,\n )"]
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"source": [
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"def get_tracks_by_artist(artist):\n",
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" \"\"\"Get songs by an artist (or similar artists).\"\"\"\n",
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" docs = artist_retriever.invoke(artist)\n",
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" artist_ids = \", \".join([str(d.metadata[\"ArtistId\"]) for d in docs])\n",
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" return db.run(\n",
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" f\"SELECT Track.Name as SongName, Artist.Name as ArtistName FROM Album LEFT JOIN Artist ON Album.ArtistId = Artist.ArtistId LEFT JOIN Track ON Track.AlbumId = Album.AlbumId WHERE Album.ArtistId in ({artist_ids});\",\n",
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" include_columns=True,\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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@@ -214,7 +284,11 @@
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"id": "b3c07010",
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"metadata": {},
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"outputs": [],
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"source": ["def check_for_songs(song_title):\n \"\"\"Check if a song exists by its name.\"\"\"\n return song_retriever.invoke(song_title)"]
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"source": [
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"def check_for_songs(song_title):\n",
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" \"\"\"Check if a song exists by its name.\"\"\"\n",
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" return song_retriever.invoke(song_title)"
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]
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},
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{
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"cell_type": "markdown",
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@@ -230,7 +304,23 @@
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"id": "72a14d5c",
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"metadata": {},
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"outputs": [],
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"source": ["song_system_message = \"\"\"Your job is to help a customer find any songs they are looking for. \n\nYou only have certain tools you can use. If a customer asks you to look something up that you don't know how, politely tell them what you can help with.\n\nWhen looking up artists and songs, sometimes the artist/song will not be found. In that case, the tools will return information \\\non similar songs and artists. This is intentional, it is not the tool messing up.\"\"\"\n\n\ndef get_song_messages(messages):\n return [SystemMessage(content=song_system_message)] + messages\n\n\nsong_recc_chain = get_song_messages | model.bind_tools(\n [get_albums_by_artist, get_tracks_by_artist, check_for_songs]\n)"]
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"source": [
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"song_system_message = \"\"\"Your job is to help a customer find any songs they are looking for. \n",
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"\n",
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"You only have certain tools you can use. If a customer asks you to look something up that you don't know how, politely tell them what you can help with.\n",
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"\n",
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"When looking up artists and songs, sometimes the artist/song will not be found. In that case, the tools will return information \\\n",
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"on similar songs and artists. This is intentional, it is not the tool messing up.\"\"\"\n",
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"\n",
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"\n",
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"def get_song_messages(messages):\n",
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" return [SystemMessage(content=song_system_message)] + messages\n",
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"\n",
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"\n",
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"song_recc_chain = get_song_messages | model.bind_tools(\n",
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" [get_albums_by_artist, get_tracks_by_artist, check_for_songs]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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@@ -249,7 +339,10 @@
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"output_type": "execute_result"
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}
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],
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"source": ["msgs = [HumanMessage(content=\"hi! can you help me find songs by amy whinehouse?\")]\nsong_recc_chain.invoke(msgs)"]
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"source": [
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"msgs = [HumanMessage(content=\"hi! can you help me find songs by amy whinehouse?\")]\n",
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"song_recc_chain.invoke(msgs)"
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]
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},
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{
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"cell_type": "markdown",
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@@ -267,7 +360,32 @@
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"id": "73e74268",
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"metadata": {},
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"outputs": [],
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"source": ["from langchain_core.messages import AIMessage, HumanMessage, SystemMessage\nfrom langchain_core.pydantic_v1 import BaseModel, Field\n\n\nclass Router(BaseModel):\n \"\"\"Call this if you are able to route the user to the appropriate representative.\"\"\"\n\n choice: str = Field(description=\"should be one of: music, customer\")\n\n\nsystem_message = \"\"\"Your job is to help as a customer service representative for a music store.\n\nYou should interact politely with customers to try to figure out how you can help. You can help in a few ways:\n\n- Updating user information: if a customer wants to update the information in the user database. Call the router with `customer`\n- Recommending music: if a customer wants to find some music or information about music. Call the router with `music`\n\nIf the user is asking or wants to ask about updating or accessing their information, send them to that route.\nIf the user is asking or wants to ask about music, send them to that route.\nOtherwise, respond.\"\"\"\n\n\ndef get_messages(messages):\n return [SystemMessage(content=system_message)] + messages"]
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"source": [
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"from langchain_core.messages import AIMessage, HumanMessage, SystemMessage\n",
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"from langchain_core.pydantic_v1 import BaseModel, Field\n",
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"\n",
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"\n",
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"class Router(BaseModel):\n",
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" \"\"\"Call this if you are able to route the user to the appropriate representative.\"\"\"\n",
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"\n",
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" choice: str = Field(description=\"should be one of: music, customer\")\n",
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"\n",
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"\n",
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"system_message = \"\"\"Your job is to help as a customer service representative for a music store.\n",
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"\n",
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"You should interact politely with customers to try to figure out how you can help. You can help in a few ways:\n",
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"\n",
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"- Updating user information: if a customer wants to update the information in the user database. Call the router with `customer`\n",
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"- Recommending music: if a customer wants to find some music or information about music. Call the router with `music`\n",
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"\n",
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"If the user is asking or wants to ask about updating or accessing their information, send them to that route.\n",
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"If the user is asking or wants to ask about music, send them to that route.\n",
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"Otherwise, respond.\"\"\"\n",
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"\n",
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"\n",
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"def get_messages(messages):\n",
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" return [SystemMessage(content=system_message)] + messages"
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]
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},
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{
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"cell_type": "code",
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@@ -275,7 +393,9 @@
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"id": "ddf27314",
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"metadata": {},
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"outputs": [],
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"source": ["chain = get_messages | model.bind_tools([Router])"]
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"source": [
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"chain = get_messages | model.bind_tools([Router])"
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]
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},
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{
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"cell_type": "code",
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@@ -294,7 +414,10 @@
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"output_type": "execute_result"
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}
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],
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"source": ["msgs = [HumanMessage(content=\"hi! can you help me find a good song?\")]\nchain.invoke(msgs)"]
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"source": [
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"msgs = [HumanMessage(content=\"hi! can you help me find a good song?\")]\n",
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"chain.invoke(msgs)"
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]
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},
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{
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"cell_type": "code",
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@@ -313,7 +436,10 @@
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"output_type": "execute_result"
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}
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],
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"source": ["msgs = [HumanMessage(content=\"hi! what's the email you have for me?\")]\nchain.invoke(msgs)"]
|
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"source": [
|
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"msgs = [HumanMessage(content=\"hi! what's the email you have for me?\")]\n",
|
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"chain.invoke(msgs)"
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]
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},
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{
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"cell_type": "code",
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@@ -321,7 +447,15 @@
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"id": "bd6ddd8b-7500-46a7-811d-3bcb937bda51",
|
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"metadata": {},
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"outputs": [],
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"source": ["from langchain_core.messages import AIMessage\n\n\ndef add_name(message, name):\n _dict = message.dict()\n _dict[\"name\"] = name\n return AIMessage(**_dict)"]
|
||||
"source": [
|
||||
"from langchain_core.messages import AIMessage\n",
|
||||
"\n",
|
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"\n",
|
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"def add_name(message, name):\n",
|
||||
" _dict = message.dict()\n",
|
||||
" _dict[\"name\"] = name\n",
|
||||
" return AIMessage(**_dict)"
|
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -329,7 +463,45 @@
|
||||
"id": "27494de5-8345-4c23-bc0e-81e0dd5d47d8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["import json\n\nfrom langgraph.graph import END, START\n\n\ndef _get_last_ai_message(messages):\n for m in messages[::-1]:\n if isinstance(m, AIMessage):\n return m\n return None\n\n\ndef _is_tool_call(msg):\n return hasattr(msg, \"additional_kwargs\") and \"tool_calls\" in msg.additional_kwargs\n\n\ndef _route(messages):\n last_message = messages[-1]\n if isinstance(last_message, AIMessage):\n if not last_message.tool_calls:\n return END\n else:\n if last_message.name == \"general\":\n if len(last_message.tool_calls) > 1:\n raise ValueError(\"Too many tools\")\n return last_message.tool_calls[0][\"args\"][\"choice\"]\n else:\n return \"tools\"\n last_m = _get_last_ai_message(messages)\n if last_m is None:\n return \"general\"\n if last_m.name == \"music\":\n return \"music\"\n elif last_m.name == \"customer\":\n return \"customer\"\n else:\n return \"general\""]
|
||||
"source": [
|
||||
"import json\n",
|
||||
"\n",
|
||||
"from langgraph.graph import END, START\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def _get_last_ai_message(messages):\n",
|
||||
" for m in messages[::-1]:\n",
|
||||
" if isinstance(m, AIMessage):\n",
|
||||
" return m\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def _is_tool_call(msg):\n",
|
||||
" return hasattr(msg, \"additional_kwargs\") and \"tool_calls\" in msg.additional_kwargs\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def _route(messages):\n",
|
||||
" last_message = messages[-1]\n",
|
||||
" if isinstance(last_message, AIMessage):\n",
|
||||
" if not last_message.tool_calls:\n",
|
||||
" return END\n",
|
||||
" else:\n",
|
||||
" if last_message.name == \"general\":\n",
|
||||
" if len(last_message.tool_calls) > 1:\n",
|
||||
" raise ValueError(\"Too many tools\")\n",
|
||||
" return last_message.tool_calls[0][\"args\"][\"choice\"]\n",
|
||||
" else:\n",
|
||||
" return \"tools\"\n",
|
||||
" last_m = _get_last_ai_message(messages)\n",
|
||||
" if last_m is None:\n",
|
||||
" return \"general\"\n",
|
||||
" if last_m.name == \"music\":\n",
|
||||
" return \"music\"\n",
|
||||
" elif last_m.name == \"customer\":\n",
|
||||
" return \"customer\"\n",
|
||||
" else:\n",
|
||||
" return \"general\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -337,7 +509,12 @@
|
||||
"id": "8aec704a-46fe-4fb3-bdee-11c3bbffc370",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["from langgraph.prebuilt import ToolNode\n\ntools = [get_albums_by_artist, get_tracks_by_artist, check_for_songs, get_customer_info]\ntool_node = ToolNode(tools)"]
|
||||
"source": [
|
||||
"from langgraph.prebuilt import ToolNode\n",
|
||||
"\n",
|
||||
"tools = [get_albums_by_artist, get_tracks_by_artist, check_for_songs, get_customer_info]\n",
|
||||
"tool_node = ToolNode(tools)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -345,7 +522,16 @@
|
||||
"id": "4d5b75c6-73e0-4922-a765-a15be63f869e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["def _filter_out_routes(messages):\n ms = []\n for m in messages:\n if _is_tool_call(m):\n if m.name == \"general\":\n continue\n ms.append(m)\n return ms"]
|
||||
"source": [
|
||||
"def _filter_out_routes(messages):\n",
|
||||
" ms = []\n",
|
||||
" for m in messages:\n",
|
||||
" if _is_tool_call(m):\n",
|
||||
" if m.name == \"general\":\n",
|
||||
" continue\n",
|
||||
" ms.append(m)\n",
|
||||
" return ms"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -353,7 +539,13 @@
|
||||
"id": "fd4dbf98-dbb3-411a-bad6-2bb334072aaf",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["from functools import partial\n\ngeneral_node = _filter_out_routes | chain | partial(add_name, name=\"general\")\nmusic_node = _filter_out_routes | song_recc_chain | partial(add_name, name=\"music\")\ncustomer_node = _filter_out_routes | customer_chain | partial(add_name, name=\"customer\")"]
|
||||
"source": [
|
||||
"from functools import partial\n",
|
||||
"\n",
|
||||
"general_node = _filter_out_routes | chain | partial(add_name, name=\"general\")\n",
|
||||
"music_node = _filter_out_routes | song_recc_chain | partial(add_name, name=\"music\")\n",
|
||||
"customer_node = _filter_out_routes | customer_chain | partial(add_name, name=\"customer\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -361,7 +553,33 @@
|
||||
"id": "dcade924",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["from langgraph.checkpoint.sqlite import SqliteSaver\n\nfrom langgraph.graph import MessageGraph\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")\ngraph = MessageGraph()\nnodes = {\n \"general\": \"general\",\n \"music\": \"music\",\n END: END,\n \"tools\": \"tools\",\n \"customer\": \"customer\",\n}\n# Define a new graph\nworkflow = MessageGraph()\nworkflow.add_node(\"general\", general_node)\nworkflow.add_node(\"music\", music_node)\nworkflow.add_node(\"customer\", customer_node)\nworkflow.add_node(\"tools\", tool_node)\nworkflow.add_conditional_edges(\"general\", _route, nodes)\nworkflow.add_conditional_edges(\"tools\", _route, nodes)\nworkflow.add_conditional_edges(\"music\", _route, nodes)\nworkflow.add_conditional_edges(\"customer\", _route, nodes)\nworkflow.add_conditional_edges(START, _route, nodes)\ngraph = workflow.compile()"]
|
||||
"source": [
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"\n",
|
||||
"from langgraph.graph import MessageGraph\n",
|
||||
"\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"graph = MessageGraph()\n",
|
||||
"nodes = {\n",
|
||||
" \"general\": \"general\",\n",
|
||||
" \"music\": \"music\",\n",
|
||||
" END: END,\n",
|
||||
" \"tools\": \"tools\",\n",
|
||||
" \"customer\": \"customer\",\n",
|
||||
"}\n",
|
||||
"# Define a new graph\n",
|
||||
"workflow = MessageGraph()\n",
|
||||
"workflow.add_node(\"general\", general_node)\n",
|
||||
"workflow.add_node(\"music\", music_node)\n",
|
||||
"workflow.add_node(\"customer\", customer_node)\n",
|
||||
"workflow.add_node(\"tools\", tool_node)\n",
|
||||
"workflow.add_conditional_edges(\"general\", _route, nodes)\n",
|
||||
"workflow.add_conditional_edges(\"tools\", _route, nodes)\n",
|
||||
"workflow.add_conditional_edges(\"music\", _route, nodes)\n",
|
||||
"workflow.add_conditional_edges(\"customer\", _route, nodes)\n",
|
||||
"workflow.add_conditional_edges(START, _route, nodes)\n",
|
||||
"graph = workflow.compile()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -370,7 +588,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdin",
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"User (q/Q to quit): what music do you have?\n"
|
||||
@@ -395,7 +613,7 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdin",
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"User (q/Q to quit): how about shakira?\n"
|
||||
@@ -446,7 +664,7 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdin",
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"User (q/Q to quit): hm cool\n"
|
||||
@@ -483,7 +701,7 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdin",
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"User (q/Q to quit): q\n"
|
||||
@@ -497,7 +715,27 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": ["import uuid\n\nfrom langchain_core.messages import HumanMessage\n\nfrom langgraph.graph.graph import START\n\nhistory = []\nwhile True:\n user = input(\"User (q/Q to quit): \")\n if user in {\"q\", \"Q\"}:\n print(\"AI: Byebye\")\n break\n history.append(HumanMessage(content=user))\n async for output in graph.astream(history):\n for key, value in output.items():\n print(f\"Output from node '{key}':\")\n print(\"---\")\n print(value)\n print(\"\\n---\\n\")"]
|
||||
"source": [
|
||||
"import uuid\n",
|
||||
"\n",
|
||||
"from langchain_core.messages import HumanMessage\n",
|
||||
"\n",
|
||||
"from langgraph.graph.graph import START\n",
|
||||
"\n",
|
||||
"history = []\n",
|
||||
"while True:\n",
|
||||
" user = input(\"User (q/Q to quit): \")\n",
|
||||
" if user in {\"q\", \"Q\"}:\n",
|
||||
" print(\"AI: Byebye\")\n",
|
||||
" break\n",
|
||||
" history.append(HumanMessage(content=user))\n",
|
||||
" async for output in graph.astream(history):\n",
|
||||
" for key, value in output.items():\n",
|
||||
" print(f\"Output from node '{key}':\")\n",
|
||||
" print(\"---\")\n",
|
||||
" print(value)\n",
|
||||
" print(\"\\n---\\n\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
@@ -176,10 +176,10 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import START, MessageGraph\n",
|
||||
"\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"workflow = MessageGraph()\n",
|
||||
"workflow.add_node(\"info\", chain)\n",
|
||||
"workflow.add_node(\"prompt\", prompt_gen_chain)\n",
|
||||
|
||||
@@ -154,7 +154,7 @@
|
||||
"id": "2dff2209-44c7-4e2c-b607-ba6675f9e45f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["from langgraph.checkpoint.sqlite import SqliteSaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(GraphState)\n\n# Define the nodes\nbuilder.add_node(\"generate\", generate) # generation solution\nbuilder.add_node(\"check_code\", code_check) # check code\n\n# Build graph\nbuilder.add_edge(START, \"generate\")\nbuilder.add_edge(\"generate\", \"check_code\")\nbuilder.add_conditional_edges(\n \"check_code\",\n decide_to_finish,\n {\n \"end\": END,\n \"generate\": \"generate\",\n },\n)\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")\ngraph = builder.compile(checkpointer=memory)"]
|
||||
"source": ["from langgraph.checkpoint.memory import MemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(GraphState)\n\n# Define the nodes\nbuilder.add_node(\"generate\", generate) # generation solution\nbuilder.add_node(\"check_code\", code_check) # check code\n\n# Build graph\nbuilder.add_edge(START, \"generate\")\nbuilder.add_edge(\"generate\", \"check_code\")\nbuilder.add_conditional_edges(\n \"check_code\",\n decide_to_finish,\n {\n \"end\": END,\n \"generate\": \"generate\",\n },\n)\n\nmemory = MemorySaver()\ngraph = builder.compile(checkpointer=memory)"]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -39,7 +39,10 @@
|
||||
"id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["%%capture --no-stderr\n%pip install --quiet -U langgraph langchain_openai"]
|
||||
"source": [
|
||||
"%%capture --no-stderr\n",
|
||||
"%pip install --quiet -U langgraph langchain_openai"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -55,7 +58,18 @@
|
||||
"id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["import getpass\nimport os\n\n\ndef _set_env(var: str):\n if not os.environ.get(var):\n os.environ[var] = getpass.getpass(f\"{var}: \")\n\n\n_set_env(\"OPENAI_API_KEY\")"]
|
||||
"source": [
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def _set_env(var: str):\n",
|
||||
" if not os.environ.get(var):\n",
|
||||
" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"_set_env(\"OPENAI_API_KEY\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -71,7 +85,10 @@
|
||||
"id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n_set_env(\"LANGCHAIN_API_KEY\")"]
|
||||
"source": [
|
||||
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
|
||||
"_set_env(\"LANGCHAIN_API_KEY\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -89,7 +106,22 @@
|
||||
"id": "6098e5cb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["from typing import Annotated\n\nfrom typing_extensions import TypedDict\n\nfrom langgraph.graph.message import add_messages\n\n# `add_messages`` essentially does this\n# (with more robust handling)\n# def add_messages(left: list, right: list):\n# return left + right\n\n\nclass State(TypedDict):\n messages: Annotated[list, add_messages]"]
|
||||
"source": [
|
||||
"from typing import Annotated\n",
|
||||
"\n",
|
||||
"from typing_extensions import TypedDict\n",
|
||||
"\n",
|
||||
"from langgraph.graph.message import add_messages\n",
|
||||
"\n",
|
||||
"# `add_messages`` essentially does this\n",
|
||||
"# (with more robust handling)\n",
|
||||
"# def add_messages(left: list, right: list):\n",
|
||||
"# return left + right\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class State(TypedDict):\n",
|
||||
" messages: Annotated[list, add_messages]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -109,7 +141,22 @@
|
||||
"id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["from langchain_core.tools import tool\n\n\n@tool\ndef search(query: str):\n \"\"\"Call to surf the web.\"\"\"\n # This is a placeholder for the actual implementation\n # Don't let the LLM know this though 😊\n return [\n \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n ]\n\n\ntools = [search]"]
|
||||
"source": [
|
||||
"from langchain_core.tools import tool\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@tool\n",
|
||||
"def search(query: str):\n",
|
||||
" \"\"\"Call to surf the web.\"\"\"\n",
|
||||
" # This is a placeholder for the actual implementation\n",
|
||||
" # Don't let the LLM know this though 😊\n",
|
||||
" return [\n",
|
||||
" \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n",
|
||||
" ]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"tools = [search]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -127,7 +174,11 @@
|
||||
"id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["from langgraph.prebuilt import ToolExecutor\n\ntool_executor = ToolExecutor(tools)"]
|
||||
"source": [
|
||||
"from langgraph.prebuilt import ToolExecutor\n",
|
||||
"\n",
|
||||
"tool_executor = ToolExecutor(tools)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -148,7 +199,11 @@
|
||||
"id": "892b54b9-75f0-4804-9ed0-88b5e5532989",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["from langchain_openai import ChatOpenAI\n\nmodel = ChatOpenAI(temperature=0)"]
|
||||
"source": [
|
||||
"from langchain_openai import ChatOpenAI\n",
|
||||
"\n",
|
||||
"model = ChatOpenAI(temperature=0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -166,7 +221,9 @@
|
||||
"id": "cd3cbae5-d92c-4559-a4aa-44721b80d107",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["model = model.bind_tools(tools)"]
|
||||
"source": [
|
||||
"model = model.bind_tools(tools)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -201,7 +258,53 @@
|
||||
"id": "3b541bb9-900c-40d0-964d-7b5dfee30667",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["from langchain_core.messages import ToolMessage\n\nfrom langgraph.prebuilt import ToolInvocation\n\n\n# Define the function that determines whether to continue or not\ndef should_continue(state):\n messages = state[\"messages\"]\n last_message = messages[-1]\n # If there is no function call, then we finish\n if not last_message.tool_calls:\n return \"end\"\n # Otherwise if there is, we continue\n else:\n return \"continue\"\n\n\n# Define the function that calls the model\ndef call_model(state):\n messages = state[\"messages\"]\n response = model.invoke(messages)\n # We return a list, because this will get added to the existing list\n return {\"messages\": [response]}\n\n\n# Define the function to execute tools\ndef call_tool(state):\n messages = state[\"messages\"]\n # Based on the continue condition\n # we know the last message involves a function call\n last_message = messages[-1]\n # We construct an ToolInvocation from the function_call\n tool_call = last_message.tool_calls[0]\n action = ToolInvocation(\n tool=tool_call[\"name\"],\n tool_input=tool_call[\"args\"],\n )\n # We call the tool_executor and get back a response\n response = tool_executor.invoke(action)\n # We use the response to create a ToolMessage\n tool_message = ToolMessage(\n content=str(response), name=action.tool, tool_call_id=tool_call[\"id\"]\n )\n # We return a list, because this will get added to the existing list\n return {\"messages\": [tool_message]}"]
|
||||
"source": [
|
||||
"from langchain_core.messages import ToolMessage\n",
|
||||
"\n",
|
||||
"from langgraph.prebuilt import ToolInvocation\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Define the function that determines whether to continue or not\n",
|
||||
"def should_continue(state):\n",
|
||||
" messages = state[\"messages\"]\n",
|
||||
" last_message = messages[-1]\n",
|
||||
" # If there is no function call, then we finish\n",
|
||||
" if not last_message.tool_calls:\n",
|
||||
" return \"end\"\n",
|
||||
" # Otherwise if there is, we continue\n",
|
||||
" else:\n",
|
||||
" return \"continue\"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Define the function that calls the model\n",
|
||||
"def call_model(state):\n",
|
||||
" messages = state[\"messages\"]\n",
|
||||
" response = model.invoke(messages)\n",
|
||||
" # We return a list, because this will get added to the existing list\n",
|
||||
" return {\"messages\": [response]}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Define the function to execute tools\n",
|
||||
"def call_tool(state):\n",
|
||||
" messages = state[\"messages\"]\n",
|
||||
" # Based on the continue condition\n",
|
||||
" # we know the last message involves a function call\n",
|
||||
" last_message = messages[-1]\n",
|
||||
" # We construct an ToolInvocation from the function_call\n",
|
||||
" tool_call = last_message.tool_calls[0]\n",
|
||||
" action = ToolInvocation(\n",
|
||||
" tool=tool_call[\"name\"],\n",
|
||||
" tool_input=tool_call[\"args\"],\n",
|
||||
" )\n",
|
||||
" # We call the tool_executor and get back a response\n",
|
||||
" response = tool_executor.invoke(action)\n",
|
||||
" # We use the response to create a ToolMessage\n",
|
||||
" tool_message = ToolMessage(\n",
|
||||
" content=str(response), name=action.tool, tool_call_id=tool_call[\"id\"]\n",
|
||||
" )\n",
|
||||
" # We return a list, because this will get added to the existing list\n",
|
||||
" return {\"messages\": [tool_message]}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -219,7 +322,45 @@
|
||||
"id": "812b4e70-4956-4415-8880-db48b3dcbad2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["from langgraph.graph import END, StateGraph, START\n\n# Define a new graph\nworkflow = StateGraph(State)\n\n# Define the two nodes we will cycle between\nworkflow.add_node(\"agent\", call_model)\nworkflow.add_node(\"action\", call_tool)\n\n# Set the entrypoint as `agent`\n# This means that this node is the first one called\nworkflow.add_edge(START, \"agent\")\n\n# We now add a conditional edge\nworkflow.add_conditional_edges(\n # First, we define the start node. We use `agent`.\n # This means these are the edges taken after the `agent` node is called.\n \"agent\",\n # Next, we pass in the function that will determine which node is called next.\n should_continue,\n # Finally we pass in a mapping.\n # The keys are strings, and the values are other nodes.\n # END is a special node marking that the graph should finish.\n # What will happen is we will call `should_continue`, and then the output of that\n # will be matched against the keys in this mapping.\n # Based on which one it matches, that node will then be called.\n {\n # If `tools`, then we call the tool node.\n \"continue\": \"action\",\n # Otherwise we finish.\n \"end\": END,\n },\n)\n\n# We now add a normal edge from `tools` to `agent`.\n# This means that after `tools` is called, `agent` node is called next.\nworkflow.add_edge(\"action\", \"agent\")"]
|
||||
"source": [
|
||||
"from langgraph.graph import END, StateGraph, START\n",
|
||||
"\n",
|
||||
"# Define a new graph\n",
|
||||
"workflow = StateGraph(State)\n",
|
||||
"\n",
|
||||
"# Define the two nodes we will cycle between\n",
|
||||
"workflow.add_node(\"agent\", call_model)\n",
|
||||
"workflow.add_node(\"action\", call_tool)\n",
|
||||
"\n",
|
||||
"# Set the entrypoint as `agent`\n",
|
||||
"# This means that this node is the first one called\n",
|
||||
"workflow.add_edge(START, \"agent\")\n",
|
||||
"\n",
|
||||
"# We now add a conditional edge\n",
|
||||
"workflow.add_conditional_edges(\n",
|
||||
" # First, we define the start node. We use `agent`.\n",
|
||||
" # This means these are the edges taken after the `agent` node is called.\n",
|
||||
" \"agent\",\n",
|
||||
" # Next, we pass in the function that will determine which node is called next.\n",
|
||||
" should_continue,\n",
|
||||
" # Finally we pass in a mapping.\n",
|
||||
" # The keys are strings, and the values are other nodes.\n",
|
||||
" # END is a special node marking that the graph should finish.\n",
|
||||
" # What will happen is we will call `should_continue`, and then the output of that\n",
|
||||
" # will be matched against the keys in this mapping.\n",
|
||||
" # Based on which one it matches, that node will then be called.\n",
|
||||
" {\n",
|
||||
" # If `tools`, then we call the tool node.\n",
|
||||
" \"continue\": \"action\",\n",
|
||||
" # Otherwise we finish.\n",
|
||||
" \"end\": END,\n",
|
||||
" },\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# We now add a normal edge from `tools` to `agent`.\n",
|
||||
"# This means that after `tools` is called, `agent` node is called next.\n",
|
||||
"workflow.add_edge(\"action\", \"agent\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -237,7 +378,11 @@
|
||||
"id": "6845ed6a-d155-4105-9160-28849877248b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["from langgraph.checkpoint.sqlite import SqliteSaver\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")"]
|
||||
"source": [
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"\n",
|
||||
"memory = MemorySaver()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -255,7 +400,12 @@
|
||||
"id": "79d29875-8aa8-434c-9f20-1c58346a6249",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["# Finally, we compile it!\n# This compiles it into a LangChain Runnable,\n# meaning you can use it as you would any other runnable\napp = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])"]
|
||||
"source": [
|
||||
"# Finally, we compile it!\n",
|
||||
"# This compiles it into a LangChain Runnable,\n",
|
||||
"# meaning you can use it as you would any other runnable\n",
|
||||
"app = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -282,7 +432,11 @@
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": ["from IPython.display import Image, display\n\ndisplay(Image(app.get_graph().draw_mermaid_png()))"]
|
||||
"source": [
|
||||
"from IPython.display import Image, display\n",
|
||||
"\n",
|
||||
"display(Image(app.get_graph().draw_mermaid_png()))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -313,7 +467,14 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": ["from langchain_core.messages import HumanMessage\n\nthread = {\"configurable\": {\"thread_id\": \"2\"}}\ninputs = [HumanMessage(content=\"hi! I'm bob\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"]
|
||||
"source": [
|
||||
"from langchain_core.messages import HumanMessage\n",
|
||||
"\n",
|
||||
"thread = {\"configurable\": {\"thread_id\": \"2\"}}\n",
|
||||
"inputs = [HumanMessage(content=\"hi! I'm bob\")]\n",
|
||||
"for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
|
||||
" event[\"messages\"][-1].pretty_print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -334,7 +495,11 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": ["inputs = [HumanMessage(content=\"What did I tell you my name was?\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"]
|
||||
"source": [
|
||||
"inputs = [HumanMessage(content=\"What did I tell you my name was?\")]\n",
|
||||
"for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
|
||||
" event[\"messages\"][-1].pretty_print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -358,7 +523,11 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": ["inputs = [HumanMessage(content=\"what's the weather in sf now?\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"]
|
||||
"source": [
|
||||
"inputs = [HumanMessage(content=\"what's the weather in sf now?\")]\n",
|
||||
"for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
|
||||
" event[\"messages\"][-1].pretty_print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -392,7 +561,10 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": ["for event in app.stream(None, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"]
|
||||
"source": [
|
||||
"for event in app.stream(None, thread, stream_mode=\"values\"):\n",
|
||||
" event[\"messages\"][-1].pretty_print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -427,7 +599,43 @@
|
||||
"id": "5454f436-d56e-4499-9381-06192aca1b56",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["import json\nfrom typing import Optional\n\nfrom langchain_core.messages import AIMessage\n\n\n# Helper function to construct message asking for verification\ndef generate_verification_message(message: AIMessage) -> None:\n \"\"\"Generate \"verification message\" from message with tool calls.\"\"\"\n serialized_tool_calls = json.dumps(\n message.tool_calls,\n indent=2,\n )\n return AIMessage(\n content=(\n \"I plan to invoke the following tools, do you approve?\\n\\n\"\n \"Type 'y' if you do, anything else to stop.\\n\\n\"\n f\"{serialized_tool_calls}\"\n ),\n id=message.id,\n )\n\n\n# Helper function to stream output from the graph\ndef stream_app_catch_tool_calls(inputs, thread) -> Optional[AIMessage]:\n \"\"\"Stream app, catching tool calls.\"\"\"\n tool_call_message = None\n for event in app.stream(inputs, thread, stream_mode=\"values\"):\n message = event[\"messages\"][-1]\n if isinstance(message, AIMessage) and message.tool_calls:\n tool_call_message = message\n else:\n message.pretty_print()\n\n return tool_call_message"]
|
||||
"source": [
|
||||
"import json\n",
|
||||
"from typing import Optional\n",
|
||||
"\n",
|
||||
"from langchain_core.messages import AIMessage\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Helper function to construct message asking for verification\n",
|
||||
"def generate_verification_message(message: AIMessage) -> None:\n",
|
||||
" \"\"\"Generate \"verification message\" from message with tool calls.\"\"\"\n",
|
||||
" serialized_tool_calls = json.dumps(\n",
|
||||
" message.tool_calls,\n",
|
||||
" indent=2,\n",
|
||||
" )\n",
|
||||
" return AIMessage(\n",
|
||||
" content=(\n",
|
||||
" \"I plan to invoke the following tools, do you approve?\\n\\n\"\n",
|
||||
" \"Type 'y' if you do, anything else to stop.\\n\\n\"\n",
|
||||
" f\"{serialized_tool_calls}\"\n",
|
||||
" ),\n",
|
||||
" id=message.id,\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Helper function to stream output from the graph\n",
|
||||
"def stream_app_catch_tool_calls(inputs, thread) -> Optional[AIMessage]:\n",
|
||||
" \"\"\"Stream app, catching tool calls.\"\"\"\n",
|
||||
" tool_call_message = None\n",
|
||||
" for event in app.stream(inputs, thread, stream_mode=\"values\"):\n",
|
||||
" message = event[\"messages\"][-1]\n",
|
||||
" if isinstance(message, AIMessage) and message.tool_calls:\n",
|
||||
" tool_call_message = message\n",
|
||||
" else:\n",
|
||||
" message.pretty_print()\n",
|
||||
"\n",
|
||||
" return tool_call_message"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -514,7 +722,43 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": ["import uuid\n\nthread = {\"configurable\": {\"thread_id\": \"3\"}}\n\ntool_call_message = stream_app_catch_tool_calls(\n {\"messages\": [HumanMessage(\"what's the weather in sf now?\")]},\n thread,\n)\n\nwhile tool_call_message:\n verification_message = generate_verification_message(tool_call_message)\n verification_message.pretty_print()\n input_message = HumanMessage(input())\n if input_message.content == \"exit\":\n break\n input_message.pretty_print()\n\n # First we update the state with the verification message and the input message.\n # note that `generate_verification_message` sets the message ID to be the same\n # as the ID from the original tool call message. Updating the state with this\n # message will overwrite the previous tool call.\n snapshot = app.get_state(thread)\n snapshot.values[\"messages\"] += [verification_message, input_message]\n\n if input_message.content == \"y\":\n tool_call_message.id = str(uuid.uuid4())\n # If verified, we append the tool call message to the state\n # and resume execution.\n snapshot.values[\"messages\"] += [tool_call_message]\n app.update_state(thread, snapshot.values, as_node=\"agent\")\n else:\n # Otherwise, resume execution from the input message.\n app.update_state(thread, snapshot.values, as_node=\"__start__\")\n\n tool_call_message = stream_app_catch_tool_calls(None, thread)"]
|
||||
"source": [
|
||||
"import uuid\n",
|
||||
"\n",
|
||||
"thread = {\"configurable\": {\"thread_id\": \"3\"}}\n",
|
||||
"\n",
|
||||
"tool_call_message = stream_app_catch_tool_calls(\n",
|
||||
" {\"messages\": [HumanMessage(\"what's the weather in sf now?\")]},\n",
|
||||
" thread,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"while tool_call_message:\n",
|
||||
" verification_message = generate_verification_message(tool_call_message)\n",
|
||||
" verification_message.pretty_print()\n",
|
||||
" input_message = HumanMessage(input())\n",
|
||||
" if input_message.content == \"exit\":\n",
|
||||
" break\n",
|
||||
" input_message.pretty_print()\n",
|
||||
"\n",
|
||||
" # First we update the state with the verification message and the input message.\n",
|
||||
" # note that `generate_verification_message` sets the message ID to be the same\n",
|
||||
" # as the ID from the original tool call message. Updating the state with this\n",
|
||||
" # message will overwrite the previous tool call.\n",
|
||||
" snapshot = app.get_state(thread)\n",
|
||||
" snapshot.values[\"messages\"] += [verification_message, input_message]\n",
|
||||
"\n",
|
||||
" if input_message.content == \"y\":\n",
|
||||
" tool_call_message.id = str(uuid.uuid4())\n",
|
||||
" # If verified, we append the tool call message to the state\n",
|
||||
" # and resume execution.\n",
|
||||
" snapshot.values[\"messages\"] += [tool_call_message]\n",
|
||||
" app.update_state(thread, snapshot.values, as_node=\"agent\")\n",
|
||||
" else:\n",
|
||||
" # Otherwise, resume execution from the input message.\n",
|
||||
" app.update_state(thread, snapshot.values, as_node=\"__start__\")\n",
|
||||
"\n",
|
||||
" tool_call_message = stream_app_catch_tool_calls(None, thread)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -535,7 +779,34 @@
|
||||
"id": "03232f16-d6fe-46d0-afa0-a6f0d0bf16de",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["class State(TypedDict):\n messages: Annotated[list, add_messages]\n tool_call_message: Optional[AIMessage]\n\n\ndef call_model(state):\n messages = state[\"messages\"]\n if messages[-1].content == \"y\":\n return {\n \"messages\": [state[\"tool_call_message\"]],\n \"tool_call_message\": None,\n }\n else:\n response = model.invoke(messages)\n if response.tool_calls:\n verification_message = generate_verification_message(response)\n response.id = str(uuid.uuid4())\n return {\n \"messages\": [verification_message],\n \"tool_call_message\": response,\n }\n else:\n return {\n \"messages\": [response],\n \"tool_call_message\": None,\n }"]
|
||||
"source": [
|
||||
"class State(TypedDict):\n",
|
||||
" messages: Annotated[list, add_messages]\n",
|
||||
" tool_call_message: Optional[AIMessage]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def call_model(state):\n",
|
||||
" messages = state[\"messages\"]\n",
|
||||
" if messages[-1].content == \"y\":\n",
|
||||
" return {\n",
|
||||
" \"messages\": [state[\"tool_call_message\"]],\n",
|
||||
" \"tool_call_message\": None,\n",
|
||||
" }\n",
|
||||
" else:\n",
|
||||
" response = model.invoke(messages)\n",
|
||||
" if response.tool_calls:\n",
|
||||
" verification_message = generate_verification_message(response)\n",
|
||||
" response.id = str(uuid.uuid4())\n",
|
||||
" return {\n",
|
||||
" \"messages\": [verification_message],\n",
|
||||
" \"tool_call_message\": response,\n",
|
||||
" }\n",
|
||||
" else:\n",
|
||||
" return {\n",
|
||||
" \"messages\": [response],\n",
|
||||
" \"tool_call_message\": None,\n",
|
||||
" }"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -551,7 +822,27 @@
|
||||
"id": "502dc688-c926-407e-8759-8c9e39eb4257",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["workflow = StateGraph(State)\n\nworkflow.add_node(\"agent\", call_model)\nworkflow.add_node(\"action\", call_tool)\n\nworkflow.add_edge(START, \"agent\")\n\nworkflow.add_conditional_edges(\n \"agent\",\n should_continue,\n {\n \"continue\": \"action\",\n \"end\": END,\n },\n)\n\nworkflow.add_edge(\"action\", \"agent\")\n\napp = workflow.compile(checkpointer=memory)"]
|
||||
"source": [
|
||||
"workflow = StateGraph(State)\n",
|
||||
"\n",
|
||||
"workflow.add_node(\"agent\", call_model)\n",
|
||||
"workflow.add_node(\"action\", call_tool)\n",
|
||||
"\n",
|
||||
"workflow.add_edge(START, \"agent\")\n",
|
||||
"\n",
|
||||
"workflow.add_conditional_edges(\n",
|
||||
" \"agent\",\n",
|
||||
" should_continue,\n",
|
||||
" {\n",
|
||||
" \"continue\": \"action\",\n",
|
||||
" \"end\": END,\n",
|
||||
" },\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"workflow.add_edge(\"action\", \"agent\")\n",
|
||||
"\n",
|
||||
"app = workflow.compile(checkpointer=memory)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -584,7 +875,13 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": ["thread = {\"configurable\": {\"thread_id\": \"4\"}}\n\ninputs = [HumanMessage(content=\"what's the weather in sf?\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"]
|
||||
"source": [
|
||||
"thread = {\"configurable\": {\"thread_id\": \"4\"}}\n",
|
||||
"\n",
|
||||
"inputs = [HumanMessage(content=\"what's the weather in sf?\")]\n",
|
||||
"for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
|
||||
" event[\"messages\"][-1].pretty_print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -617,7 +914,11 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": ["inputs = [HumanMessage(content=\"can you specify sf in CA?\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"]
|
||||
"source": [
|
||||
"inputs = [HumanMessage(content=\"can you specify sf in CA?\")]\n",
|
||||
"for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
|
||||
" event[\"messages\"][-1].pretty_print()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -648,7 +949,11 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": ["inputs = [HumanMessage(content=\"y\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"]
|
||||
"source": [
|
||||
"inputs = [HumanMessage(content=\"y\")]\n",
|
||||
"for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
|
||||
" event[\"messages\"][-1].pretty_print()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
@@ -42,7 +42,6 @@
|
||||
"def answer_node(state: InputState):\n",
|
||||
" return {\"answer\": \"bye\"}\n",
|
||||
"\n",
|
||||
"check = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"graph = StateGraph(input=InputState, output=OutputState)\n",
|
||||
"graph.add_node(answer_node)\n",
|
||||
"graph.add_edge(START, \"answer_node\")\n",
|
||||
|
||||
+18
-18
@@ -848,7 +848,7 @@
|
||||
"\n",
|
||||
"We will see later that **checkpointing** is _much_ more powerful than simple chat memory - it lets you save and resume complex state at any time for error recovery, human-in-the-loop workflows, time travel interactions, and more. But before we get too ahead of ourselves, let's add checkpointing to enable multi-turn conversations.\n",
|
||||
"\n",
|
||||
"To get started, create a `SqliteSaver` checkpointer."
|
||||
"To get started, create a `MemorySaver` checkpointer."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -858,9 +858,9 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")"
|
||||
"memory = MemorySaver()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -868,7 +868,7 @@
|
||||
"id": "08d3d11a-1b42-4cbb-8e11-2a4294263d90",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Notice** that we've specified `:memory` as the Sqlite DB path. This is convenient for our tutorial (it saves it all in-memory). In a production application, you would likely change this to connect to your own DB and/or use one of the other checkpointer classes.\n",
|
||||
"**Notice** we're using an in-memory checkpointer. This is convenient for our tutorial (it saves it all in-memory). In a production application, you would likely change this to use `SqliteSaver` or `PostgresSaver` and connect to your own DB.\n",
|
||||
"\n",
|
||||
"Next define the graph. Now that you've already built your own `BasicToolNode`, we'll replace it with LangGraph's prebuilt `ToolNode` and `tools_condition`, since these do some nice things like parallel API execution. Apart from that, the following is all copied from Part 2."
|
||||
]
|
||||
@@ -1199,7 +1199,7 @@
|
||||
"from langchain_core.messages import BaseMessage\n",
|
||||
"from typing_extensions import TypedDict\n",
|
||||
"\n",
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import StateGraph\n",
|
||||
"from langgraph.graph.message import add_messages\n",
|
||||
"from langgraph.prebuilt import ToolNode\n",
|
||||
@@ -1277,12 +1277,12 @@
|
||||
"from langchain_core.messages import BaseMessage\n",
|
||||
"from typing_extensions import TypedDict\n",
|
||||
"\n",
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import StateGraph, START\n",
|
||||
"from langgraph.graph.message import add_messages\n",
|
||||
"from langgraph.prebuilt import ToolNode, tools_condition\n",
|
||||
"\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class State(TypedDict):\n",
|
||||
@@ -1508,7 +1508,7 @@
|
||||
"from langchain_core.messages import BaseMessage\n",
|
||||
"from typing_extensions import TypedDict\n",
|
||||
"\n",
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import StateGraph\n",
|
||||
"from langgraph.graph.message import add_messages\n",
|
||||
"from langgraph.prebuilt import ToolNode\n",
|
||||
@@ -1543,7 +1543,7 @@
|
||||
"graph_builder.add_edge(\"tools\", \"chatbot\")\n",
|
||||
"graph_builder.set_entry_point(\"chatbot\")\n",
|
||||
"\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"graph = graph_builder.compile(\n",
|
||||
" checkpointer=memory,\n",
|
||||
" # This is new!\n",
|
||||
@@ -1593,7 +1593,7 @@
|
||||
"from langchain_core.messages import BaseMessage\n",
|
||||
"from typing_extensions import TypedDict\n",
|
||||
"\n",
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import StateGraph, START\n",
|
||||
"from langgraph.graph.message import add_messages\n",
|
||||
"from langgraph.prebuilt import ToolNode, tools_condition\n",
|
||||
@@ -1627,7 +1627,7 @@
|
||||
")\n",
|
||||
"graph_builder.add_edge(\"tools\", \"chatbot\")\n",
|
||||
"graph_builder.add_edge(START, \"chatbot\")\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"graph = graph_builder.compile(\n",
|
||||
" checkpointer=memory,\n",
|
||||
" # This is new!\n",
|
||||
@@ -2092,7 +2092,7 @@
|
||||
"from langchain_core.messages import BaseMessage\n",
|
||||
"from typing_extensions import TypedDict\n",
|
||||
"\n",
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import StateGraph, START\n",
|
||||
"from langgraph.graph.message import add_messages\n",
|
||||
"from langgraph.prebuilt import ToolNode, tools_condition\n",
|
||||
@@ -2289,7 +2289,7 @@
|
||||
"graph_builder.add_edge(\"tools\", \"chatbot\")\n",
|
||||
"graph_builder.add_edge(\"human\", \"chatbot\")\n",
|
||||
"graph_builder.add_edge(START, \"chatbot\")\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"graph = graph_builder.compile(\n",
|
||||
" checkpointer=memory,\n",
|
||||
" # We interrupt before 'human' here instead.\n",
|
||||
@@ -2539,7 +2539,7 @@
|
||||
"from langchain_core.pydantic_v1 import BaseModel\n",
|
||||
"from typing_extensions import TypedDict\n",
|
||||
"\n",
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import StateGraph\n",
|
||||
"from langgraph.graph.message import add_messages\n",
|
||||
"from langgraph.prebuilt import ToolNode, tools_condition\n",
|
||||
@@ -2626,7 +2626,7 @@
|
||||
"graph_builder.add_edge(\"tools\", \"chatbot\")\n",
|
||||
"graph_builder.add_edge(\"human\", \"chatbot\")\n",
|
||||
"graph_builder.set_entry_point(\"chatbot\")\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"graph = graph_builder.compile(\n",
|
||||
" checkpointer=memory,\n",
|
||||
" interrupt_before=[\"human\"],\n",
|
||||
@@ -2665,11 +2665,11 @@
|
||||
"\n",
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_community.tools.tavily_search import TavilySearchResults\n",
|
||||
"from langchain_core.messages import AIMessage, BaseMessage, ToolMessage\n",
|
||||
"from langchain_core.messages import AIMessage, ToolMessage\n",
|
||||
"from langchain_core.pydantic_v1 import BaseModel\n",
|
||||
"from typing_extensions import TypedDict\n",
|
||||
"\n",
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import StateGraph, START\n",
|
||||
"from langgraph.graph.message import add_messages\n",
|
||||
"from langgraph.prebuilt import ToolNode, tools_condition\n",
|
||||
@@ -2756,7 +2756,7 @@
|
||||
"graph_builder.add_edge(\"tools\", \"chatbot\")\n",
|
||||
"graph_builder.add_edge(\"human\", \"chatbot\")\n",
|
||||
"graph_builder.add_edge(START, \"chatbot\")\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"graph = graph_builder.compile(\n",
|
||||
" checkpointer=memory,\n",
|
||||
" interrupt_before=[\"human\"],\n",
|
||||
|
||||
@@ -105,10 +105,10 @@
|
||||
"\n",
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.messages import SystemMessage, RemoveMessage\n",
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import MessagesState, StateGraph, START, END\n",
|
||||
"\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# We will add a `summary` attribute (in addition to `messages` key,\n",
|
||||
|
||||
@@ -112,11 +112,11 @@
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.tools import tool\n",
|
||||
"\n",
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import MessagesState, StateGraph, START\n",
|
||||
"from langgraph.prebuilt import ToolNode\n",
|
||||
"\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@tool\n",
|
||||
|
||||
@@ -103,11 +103,11 @@
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.tools import tool\n",
|
||||
"\n",
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import MessagesState, StateGraph, START\n",
|
||||
"from langgraph.prebuilt import ToolNode\n",
|
||||
"\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@tool\n",
|
||||
@@ -234,11 +234,11 @@
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.tools import tool\n",
|
||||
"\n",
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import MessagesState, StateGraph, START\n",
|
||||
"from langgraph.prebuilt import ToolNode\n",
|
||||
"\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@tool\n",
|
||||
|
||||
@@ -17,11 +17,11 @@
|
||||
"Example:\n",
|
||||
"```python\n",
|
||||
"from langgraph.graph import StateGraph\n",
|
||||
"from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"\n",
|
||||
"builder = StateGraph(....)\n",
|
||||
"# ... define the graph\n",
|
||||
"memory = AsyncSqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"graph = builder.compile(checkpointer=memory)\n",
|
||||
"...\n",
|
||||
"```\n",
|
||||
@@ -361,9 +361,9 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")"
|
||||
"memory = MemorySaver()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -246,7 +246,7 @@
|
||||
"id": "6845ed6a-d155-4105-9160-28849877248b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["from langgraph.checkpoint.sqlite import SqliteSaver\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")"]
|
||||
"source": ["from langgraph.checkpoint.memory import MemorySaver\n\nmemory = MemorySaver()"]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
|
||||
@@ -574,7 +574,7 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import START, END, StateGraph\n",
|
||||
"from langgraph.prebuilt import tools_condition\n",
|
||||
"from IPython.display import Image, display\n",
|
||||
@@ -597,7 +597,7 @@
|
||||
"builder.add_edge(\"tools\", \"assistant\")\n",
|
||||
"\n",
|
||||
"# The checkpointer lets the graph persist its state\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"react_graph = builder.compile(checkpointer=memory)\n",
|
||||
"\n",
|
||||
"# Show\n",
|
||||
|
||||
@@ -253,7 +253,7 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"from IPython.display import Image, display\n",
|
||||
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
|
||||
"from langgraph.checkpoint.memory import MemorySaver\n",
|
||||
"from langgraph.graph import END, START, StateGraph\n",
|
||||
"from langgraph.prebuilt import tools_condition\n",
|
||||
"\n",
|
||||
@@ -275,7 +275,7 @@
|
||||
"builder.add_edge(\"tools\", \"assistant\")\n",
|
||||
"\n",
|
||||
"# The checkpointer lets the graph persist its state\n",
|
||||
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
|
||||
"memory = MemorySaver()\n",
|
||||
"react_graph = builder.compile(checkpointer=memory)\n",
|
||||
"\n",
|
||||
"# Show\n",
|
||||
|
||||
@@ -598,7 +598,7 @@
|
||||
"id": "e6e73e85-1232-4848-beba-3139ac7d0a64",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["from langgraph.checkpoint.sqlite import SqliteSaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(State)\nbuilder.add_node(\"draft\", draft_solver)\nbuilder.add_edge(START, \"draft\")\nbuilder.add_node(\"retrieve\", retrieve_examples)\nbuilder.add_node(\"solve\", solver)\nbuilder.add_node(\"evaluate\", evaluate)\n# Add connectivity\nbuilder.add_edge(\"draft\", \"retrieve\")\nbuilder.add_edge(\"retrieve\", \"solve\")\nbuilder.add_edge(\"solve\", \"evaluate\")\n\n\ndef control_edge(state: State):\n if state.get(\"status\") == \"success\":\n return END\n return \"solve\"\n\n\nbuilder.add_conditional_edges(\"evaluate\", control_edge, {END: END, \"solve\": \"solve\"})\n\n\ncheckpointer = SqliteSaver.from_conn_string(\":memory:\")\ngraph = builder.compile(checkpointer=checkpointer)"]
|
||||
"source": ["from langgraph.checkpoint.memory import MemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(State)\nbuilder.add_node(\"draft\", draft_solver)\nbuilder.add_edge(START, \"draft\")\nbuilder.add_node(\"retrieve\", retrieve_examples)\nbuilder.add_node(\"solve\", solver)\nbuilder.add_node(\"evaluate\", evaluate)\n# Add connectivity\nbuilder.add_edge(\"draft\", \"retrieve\")\nbuilder.add_edge(\"retrieve\", \"solve\")\nbuilder.add_edge(\"solve\", \"evaluate\")\n\n\ndef control_edge(state: State):\n if state.get(\"status\") == \"success\":\n return END\n return \"solve\"\n\n\nbuilder.add_conditional_edges(\"evaluate\", control_edge, {END: END, \"solve\": \"solve\"})\n\n\ncheckpointer = MemorySaver()\ngraph = builder.compile(checkpointer=checkpointer)"]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -809,7 +809,7 @@
|
||||
"id": "3c6456ba-363c-4133-8631-6dabb042b6ce",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": ["# This is all the same as before\nfrom langgraph.checkpoint.sqlite import SqliteSaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(State)\nprompt = hub.pull(\"wfh/usaco-draft-solver\")\nllm = ChatAnthropic(model=\"claude-3-opus-20240229\", max_tokens_to_sample=4000)\n\ndraft_solver = Solver(llm, prompt.partial(examples=\"\"))\nbuilder.add_node(\"draft\", draft_solver)\nbuilder.add_edge(START, \"draft\")\nbuilder.add_node(\"retrieve\", retrieve_examples)\nsolver = Solver(llm, prompt)\nbuilder.add_node(\"solve\", solver)\nbuilder.add_node(\"evaluate\", evaluate)\nbuilder.add_edge(\"draft\", \"retrieve\")\nbuilder.add_edge(\"retrieve\", \"solve\")\nbuilder.add_edge(\"solve\", \"evaluate\")\n\n\ndef control_edge(state: State):\n if state.get(\"status\") == \"success\":\n return END\n return \"solve\"\n\n\nbuilder.add_conditional_edges(\"evaluate\", control_edge, {END: END, \"solve\": \"solve\"})\ncheckpointer = SqliteSaver.from_conn_string(\":memory:\")"]
|
||||
"source": ["# This is all the same as before\nfrom langgraph.checkpoint.memory import MemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(State)\nprompt = hub.pull(\"wfh/usaco-draft-solver\")\nllm = ChatAnthropic(model=\"claude-3-opus-20240229\", max_tokens_to_sample=4000)\n\ndraft_solver = Solver(llm, prompt.partial(examples=\"\"))\nbuilder.add_node(\"draft\", draft_solver)\nbuilder.add_edge(START, \"draft\")\nbuilder.add_node(\"retrieve\", retrieve_examples)\nsolver = Solver(llm, prompt)\nbuilder.add_node(\"solve\", solver)\nbuilder.add_node(\"evaluate\", evaluate)\nbuilder.add_edge(\"draft\", \"retrieve\")\nbuilder.add_edge(\"retrieve\", \"solve\")\nbuilder.add_edge(\"solve\", \"evaluate\")\n\n\ndef control_edge(state: State):\n if state.get(\"status\") == \"success\":\n return END\n return \"solve\"\n\n\nbuilder.add_conditional_edges(\"evaluate\", control_edge, {END: END, \"solve\": \"solve\"})\ncheckpointer = MemorySaver()"]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
|
||||
Reference in New Issue
Block a user