mirror of
https://github.com/langchain-ai/langgraph.git
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docs: remove extra line from generate function (#1392)
This commit is contained in:
Hassan Memon
@@ -20,7 +20,10 @@
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"id": "969fb438",
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"metadata": {},
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"outputs": [],
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"source": ["%%capture --no-stderr\n%pip install -U --quiet langchain-community tiktoken langchain-openai langchainhub chromadb langchain langgraph langchain-text-splitters"]
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"source": [
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"%%capture --no-stderr\n",
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"%pip install -U --quiet langchain-community tiktoken langchain-openai langchainhub chromadb langchain langgraph langchain-text-splitters"
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]
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},
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{
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"cell_type": "code",
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@@ -28,7 +31,22 @@
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"id": "e4958a8c",
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"metadata": {},
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"outputs": [],
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"source": ["import getpass\nimport os\n\n\ndef _set_env(key: str):\n if key not in os.environ:\n os.environ[key] = getpass.getpass(f\"{key}:\")\n\n\n_set_env(\"OPENAI_API_KEY\")\n\n# (Optional) For tracing\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n_set_env(\"LANGCHAIN_API_KEY\")"]
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"\n",
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"def _set_env(key: str):\n",
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" if key not in os.environ:\n",
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" os.environ[key] = getpass.getpass(f\"{key}:\")\n",
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"\n",
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"\n",
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"_set_env(\"OPENAI_API_KEY\")\n",
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"\n",
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"# (Optional) For tracing\n",
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"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"_set_env(\"LANGCHAIN_API_KEY\")"
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]
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},
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{
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"cell_type": "markdown",
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@@ -46,7 +64,34 @@
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"id": "e50c9efe-4abe-42fa-b35a-05eeeede9ec6",
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"metadata": {},
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"outputs": [],
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"source": ["from langchain_community.document_loaders import WebBaseLoader\nfrom langchain_community.vectorstores import Chroma\nfrom langchain_openai import OpenAIEmbeddings\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\n\nurls = [\n \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n]\n\ndocs = [WebBaseLoader(url).load() for url in urls]\ndocs_list = [item for sublist in docs for item in sublist]\n\ntext_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n chunk_size=100, chunk_overlap=50\n)\ndoc_splits = text_splitter.split_documents(docs_list)\n\n# Add to vectorDB\nvectorstore = Chroma.from_documents(\n documents=doc_splits,\n collection_name=\"rag-chroma\",\n embedding=OpenAIEmbeddings(),\n)\nretriever = vectorstore.as_retriever()"]
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"source": [
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"from langchain_community.document_loaders import WebBaseLoader\n",
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"from langchain_community.vectorstores import Chroma\n",
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"from langchain_openai import OpenAIEmbeddings\n",
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"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
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"\n",
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"urls = [\n",
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" \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n",
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" \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n",
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" \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n",
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"]\n",
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"\n",
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"docs = [WebBaseLoader(url).load() for url in urls]\n",
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"docs_list = [item for sublist in docs for item in sublist]\n",
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"\n",
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"text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
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" chunk_size=100, chunk_overlap=50\n",
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")\n",
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"doc_splits = text_splitter.split_documents(docs_list)\n",
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"\n",
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"# Add to vectorDB\n",
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"vectorstore = Chroma.from_documents(\n",
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" documents=doc_splits,\n",
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" collection_name=\"rag-chroma\",\n",
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" embedding=OpenAIEmbeddings(),\n",
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")\n",
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"retriever = vectorstore.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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@@ -62,7 +107,17 @@
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"id": "0b97bdd8-d7e3-444d-ac96-5ef4725f9048",
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"metadata": {},
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"outputs": [],
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"source": ["from langchain.tools.retriever import create_retriever_tool\n\nretriever_tool = create_retriever_tool(\n retriever,\n \"retrieve_blog_posts\",\n \"Search and return information about Lilian Weng blog posts on LLM agents, prompt engineering, and adversarial attacks on LLMs.\",\n)\n\ntools = [retriever_tool]"]
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"source": [
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"from langchain.tools.retriever import create_retriever_tool\n",
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"\n",
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"retriever_tool = create_retriever_tool(\n",
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" retriever,\n",
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" \"retrieve_blog_posts\",\n",
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" \"Search and return information about Lilian Weng blog posts on LLM agents, prompt engineering, and adversarial attacks on LLMs.\",\n",
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")\n",
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"\n",
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"tools = [retriever_tool]"
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]
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},
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{
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"cell_type": "markdown",
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@@ -86,7 +141,19 @@
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"id": "0e378706-47d5-425a-8ba0-57b9acffbd0c",
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"metadata": {},
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"outputs": [],
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"source": ["from typing import Annotated, Sequence, TypedDict\n\nfrom langchain_core.messages import BaseMessage\n\nfrom langgraph.graph.message import add_messages\n\n\nclass AgentState(TypedDict):\n # The add_messages function defines how an update should be processed\n # Default is to replace. add_messages says \"append\"\n messages: Annotated[Sequence[BaseMessage], add_messages]"]
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"source": [
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"from typing import Annotated, Sequence, TypedDict\n",
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"\n",
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"from langchain_core.messages import BaseMessage\n",
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"\n",
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"from langgraph.graph.message import add_messages\n",
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"\n",
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"\n",
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"class AgentState(TypedDict):\n",
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" # The add_messages function defines how an update should be processed\n",
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" # Default is to replace. add_messages says \"append\"\n",
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" messages: Annotated[Sequence[BaseMessage], add_messages]"
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]
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},
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{
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"attachments": {
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@@ -129,7 +196,173 @@
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]
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}
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],
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"source": ["from typing import Annotated, Literal, Sequence, TypedDict\n\nfrom langchain import hub\nfrom langchain_core.messages import BaseMessage, HumanMessage\nfrom langchain_core.output_parsers import StrOutputParser\nfrom langchain_core.prompts import PromptTemplate\nfrom langchain_core.pydantic_v1 import BaseModel, Field\nfrom langchain_openai import ChatOpenAI\n\nfrom langgraph.prebuilt import tools_condition\n\n### Edges\n\n\ndef grade_documents(state) -> Literal[\"generate\", \"rewrite\"]:\n \"\"\"\n Determines whether the retrieved documents are relevant to the question.\n\n Args:\n state (messages): The current state\n\n Returns:\n str: A decision for whether the documents are relevant or not\n \"\"\"\n\n print(\"---CHECK RELEVANCE---\")\n\n # Data model\n class grade(BaseModel):\n \"\"\"Binary score for relevance check.\"\"\"\n\n binary_score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n\n # LLM\n model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n\n # LLM with tool and validation\n llm_with_tool = model.with_structured_output(grade)\n\n # Prompt\n prompt = PromptTemplate(\n template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n Here is the retrieved document: \\n\\n {context} \\n\\n\n Here is the user question: {question} \\n\n If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\n Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\",\n input_variables=[\"context\", \"question\"],\n )\n\n # Chain\n chain = prompt | llm_with_tool\n\n messages = state[\"messages\"]\n last_message = messages[-1]\n\n question = messages[0].content\n docs = last_message.content\n\n scored_result = chain.invoke({\"question\": question, \"context\": docs})\n\n score = scored_result.binary_score\n\n if score == \"yes\":\n print(\"---DECISION: DOCS RELEVANT---\")\n return \"generate\"\n\n else:\n print(\"---DECISION: DOCS NOT RELEVANT---\")\n print(score)\n return \"rewrite\"\n\n\n### Nodes\n\n\ndef agent(state):\n \"\"\"\n Invokes the agent model to generate a response based on the current state. Given\n the question, it will decide to retrieve using the retriever tool, or simply end.\n\n Args:\n state (messages): The current state\n\n Returns:\n dict: The updated state with the agent response appended to messages\n \"\"\"\n print(\"---CALL AGENT---\")\n messages = state[\"messages\"]\n model = ChatOpenAI(temperature=0, streaming=True, model=\"gpt-4-turbo\")\n model = model.bind_tools(tools)\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\ndef rewrite(state):\n \"\"\"\n Transform the query to produce a better question.\n\n Args:\n state (messages): The current state\n\n Returns:\n dict: The updated state with re-phrased question\n \"\"\"\n\n print(\"---TRANSFORM QUERY---\")\n messages = state[\"messages\"]\n question = messages[0].content\n\n msg = [\n HumanMessage(\n content=f\"\"\" \\n \n Look at the input and try to reason about the underlying semantic intent / meaning. \\n \n Here is the initial question:\n \\n ------- \\n\n {question} \n \\n ------- \\n\n Formulate an improved question: \"\"\",\n )\n ]\n\n # Grader\n model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n response = model.invoke(msg)\n return {\"messages\": [response]}\n\n\ndef generate(state):\n \"\"\"\n Generate answer\n\n Args:\n state (messages): The current state\n\n Returns:\n dict: The updated state with re-phrased question\n \"\"\"\n print(\"---GENERATE---\")\n messages = state[\"messages\"]\n question = messages[0].content\n last_message = messages[-1]\n\n question = messages[0].content\n docs = last_message.content\n\n # Prompt\n prompt = hub.pull(\"rlm/rag-prompt\")\n\n # LLM\n llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, streaming=True)\n\n # Post-processing\n def format_docs(docs):\n return \"\\n\\n\".join(doc.page_content for doc in docs)\n\n # Chain\n rag_chain = prompt | llm | StrOutputParser()\n\n # Run\n response = rag_chain.invoke({\"context\": docs, \"question\": question})\n return {\"messages\": [response]}\n\n\nprint(\"*\" * 20 + \"Prompt[rlm/rag-prompt]\" + \"*\" * 20)\nprompt = hub.pull(\"rlm/rag-prompt\").pretty_print() # Show what the prompt looks like"]
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"source": [
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"from typing import Annotated, Literal, Sequence, TypedDict\n",
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"\n",
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"from langchain import hub\n",
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"from langchain_core.messages import BaseMessage, HumanMessage\n",
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"from langchain_core.output_parsers import StrOutputParser\n",
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"from langchain_core.prompts import PromptTemplate\n",
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"from langchain_core.pydantic_v1 import BaseModel, Field\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"from langgraph.prebuilt import tools_condition\n",
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"\n",
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"### Edges\n",
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"\n",
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"\n",
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"def grade_documents(state) -> Literal[\"generate\", \"rewrite\"]:\n",
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" \"\"\"\n",
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" Determines whether the retrieved documents are relevant to the question.\n",
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"\n",
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" Args:\n",
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" state (messages): The current state\n",
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"\n",
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" Returns:\n",
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" str: A decision for whether the documents are relevant or not\n",
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" \"\"\"\n",
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"\n",
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" print(\"---CHECK RELEVANCE---\")\n",
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"\n",
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" # Data model\n",
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" class grade(BaseModel):\n",
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" \"\"\"Binary score for relevance check.\"\"\"\n",
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"\n",
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" binary_score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n",
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"\n",
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" # LLM\n",
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" model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
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"\n",
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" # LLM with tool and validation\n",
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" llm_with_tool = model.with_structured_output(grade)\n",
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"\n",
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" # Prompt\n",
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" prompt = PromptTemplate(\n",
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" template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
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" Here is the retrieved document: \\n\\n {context} \\n\\n\n",
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" Here is the user question: {question} \\n\n",
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" If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\n",
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" Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\",\n",
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" input_variables=[\"context\", \"question\"],\n",
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" )\n",
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"\n",
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" # Chain\n",
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" chain = prompt | llm_with_tool\n",
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"\n",
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" messages = state[\"messages\"]\n",
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" last_message = messages[-1]\n",
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"\n",
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" question = messages[0].content\n",
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" docs = last_message.content\n",
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"\n",
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" scored_result = chain.invoke({\"question\": question, \"context\": docs})\n",
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"\n",
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" score = scored_result.binary_score\n",
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"\n",
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" if score == \"yes\":\n",
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" print(\"---DECISION: DOCS RELEVANT---\")\n",
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" return \"generate\"\n",
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"\n",
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" else:\n",
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" print(\"---DECISION: DOCS NOT RELEVANT---\")\n",
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" print(score)\n",
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" return \"rewrite\"\n",
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"\n",
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"\n",
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"### Nodes\n",
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"\n",
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"\n",
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"def agent(state):\n",
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" \"\"\"\n",
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" Invokes the agent model to generate a response based on the current state. Given\n",
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" the question, it will decide to retrieve using the retriever tool, or simply end.\n",
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"\n",
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" Args:\n",
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" state (messages): The current state\n",
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"\n",
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" Returns:\n",
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" dict: The updated state with the agent response appended to messages\n",
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" \"\"\"\n",
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" print(\"---CALL AGENT---\")\n",
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" messages = state[\"messages\"]\n",
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" model = ChatOpenAI(temperature=0, streaming=True, model=\"gpt-4-turbo\")\n",
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" model = model.bind_tools(tools)\n",
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" response = model.invoke(messages)\n",
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" # We return a list, because this will get added to the existing list\n",
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" return {\"messages\": [response]}\n",
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"\n",
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"\n",
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"def rewrite(state):\n",
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" \"\"\"\n",
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" Transform the query to produce a better question.\n",
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"\n",
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" Args:\n",
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" state (messages): The current state\n",
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"\n",
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" Returns:\n",
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" dict: The updated state with re-phrased question\n",
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" \"\"\"\n",
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"\n",
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" print(\"---TRANSFORM QUERY---\")\n",
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" messages = state[\"messages\"]\n",
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" question = messages[0].content\n",
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"\n",
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" msg = [\n",
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" HumanMessage(\n",
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" content=f\"\"\" \\n \n",
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" Look at the input and try to reason about the underlying semantic intent / meaning. \\n \n",
|
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" Here is the initial question:\n",
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" \\n ------- \\n\n",
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" {question} \n",
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" \\n ------- \\n\n",
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" Formulate an improved question: \"\"\",\n",
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" )\n",
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" ]\n",
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"\n",
|
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" # Grader\n",
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" model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
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" response = model.invoke(msg)\n",
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" return {\"messages\": [response]}\n",
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"\n",
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"\n",
|
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"def generate(state):\n",
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" \"\"\"\n",
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" Generate answer\n",
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"\n",
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" Args:\n",
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" state (messages): The current state\n",
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"\n",
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" Returns:\n",
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" dict: The updated state with re-phrased question\n",
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" \"\"\"\n",
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" print(\"---GENERATE---\")\n",
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" messages = state[\"messages\"]\n",
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" question = messages[0].content\n",
|
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" last_message = messages[-1]\n",
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"\n",
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" docs = last_message.content\n",
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"\n",
|
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" # Prompt\n",
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" prompt = hub.pull(\"rlm/rag-prompt\")\n",
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"\n",
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" # LLM\n",
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" llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, streaming=True)\n",
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"\n",
|
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" # Post-processing\n",
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" def format_docs(docs):\n",
|
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" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
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"\n",
|
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" # Chain\n",
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" rag_chain = prompt | llm | StrOutputParser()\n",
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"\n",
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" # Run\n",
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" response = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
|
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" return {\"messages\": [response]}\n",
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"\n",
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"\n",
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"print(\"*\" * 20 + \"Prompt[rlm/rag-prompt]\" + \"*\" * 20)\n",
|
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"prompt = hub.pull(\"rlm/rag-prompt\").pretty_print() # Show what the prompt looks like"
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]
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},
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{
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"cell_type": "markdown",
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@@ -150,7 +383,48 @@
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"id": "8718a37f-83c2-4f16-9850-e61e0f49c3d4",
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"metadata": {},
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"outputs": [],
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"source": ["from langgraph.graph import END, StateGraph, START\nfrom langgraph.prebuilt import ToolNode\n\n# Define a new graph\nworkflow = StateGraph(AgentState)\n\n# Define the nodes we will cycle between\nworkflow.add_node(\"agent\", agent) # agent\nretrieve = ToolNode([retriever_tool])\nworkflow.add_node(\"retrieve\", retrieve) # retrieval\nworkflow.add_node(\"rewrite\", rewrite) # Re-writing the question\nworkflow.add_node(\n \"generate\", generate\n) # Generating a response after we know the documents are relevant\n# Call agent node to decide to retrieve or not\nworkflow.add_edge(START, \"agent\")\n\n# Decide whether to retrieve\nworkflow.add_conditional_edges(\n \"agent\",\n # Assess agent decision\n tools_condition,\n {\n # Translate the condition outputs to nodes in our graph\n \"tools\": \"retrieve\",\n END: END,\n },\n)\n\n# Edges taken after the `action` node is called.\nworkflow.add_conditional_edges(\n \"retrieve\",\n # Assess agent decision\n grade_documents,\n)\nworkflow.add_edge(\"generate\", END)\nworkflow.add_edge(\"rewrite\", \"agent\")\n\n# Compile\ngraph = workflow.compile()"]
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"source": [
|
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"from langgraph.graph import END, StateGraph, START\n",
|
||||
"from langgraph.prebuilt import ToolNode\n",
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"\n",
|
||||
"# Define a new graph\n",
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"workflow = StateGraph(AgentState)\n",
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"\n",
|
||||
"# Define the nodes we will cycle between\n",
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"workflow.add_node(\"agent\", agent) # agent\n",
|
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"retrieve = ToolNode([retriever_tool])\n",
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"workflow.add_node(\"retrieve\", retrieve) # retrieval\n",
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"workflow.add_node(\"rewrite\", rewrite) # Re-writing the question\n",
|
||||
"workflow.add_node(\n",
|
||||
" \"generate\", generate\n",
|
||||
") # Generating a response after we know the documents are relevant\n",
|
||||
"# Call agent node to decide to retrieve or not\n",
|
||||
"workflow.add_edge(START, \"agent\")\n",
|
||||
"\n",
|
||||
"# Decide whether to retrieve\n",
|
||||
"workflow.add_conditional_edges(\n",
|
||||
" \"agent\",\n",
|
||||
" # Assess agent decision\n",
|
||||
" tools_condition,\n",
|
||||
" {\n",
|
||||
" # Translate the condition outputs to nodes in our graph\n",
|
||||
" \"tools\": \"retrieve\",\n",
|
||||
" END: END,\n",
|
||||
" },\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Edges taken after the `action` node is called.\n",
|
||||
"workflow.add_conditional_edges(\n",
|
||||
" \"retrieve\",\n",
|
||||
" # Assess agent decision\n",
|
||||
" grade_documents,\n",
|
||||
")\n",
|
||||
"workflow.add_edge(\"generate\", END)\n",
|
||||
"workflow.add_edge(\"rewrite\", \"agent\")\n",
|
||||
"\n",
|
||||
"# Compile\n",
|
||||
"graph = workflow.compile()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -169,7 +443,15 @@
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": ["from IPython.display import Image, display\n\ntry:\n display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass"]
|
||||
"source": [
|
||||
"from IPython.display import Image, display\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\n",
|
||||
"except Exception:\n",
|
||||
" # This requires some extra dependencies and is optional\n",
|
||||
" pass"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -203,7 +485,21 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": ["import pprint\n\ninputs = {\n \"messages\": [\n (\"user\", \"What does Lilian Weng say about the types of agent memory?\"),\n ]\n}\nfor output in graph.stream(inputs):\n for key, value in output.items():\n pprint.pprint(f\"Output from node '{key}':\")\n pprint.pprint(\"---\")\n pprint.pprint(value, indent=2, width=80, depth=None)\n pprint.pprint(\"\\n---\\n\")"]
|
||||
"source": [
|
||||
"import pprint\n",
|
||||
"\n",
|
||||
"inputs = {\n",
|
||||
" \"messages\": [\n",
|
||||
" (\"user\", \"What does Lilian Weng say about the types of agent memory?\"),\n",
|
||||
" ]\n",
|
||||
"}\n",
|
||||
"for output in graph.stream(inputs):\n",
|
||||
" for key, value in output.items():\n",
|
||||
" pprint.pprint(f\"Output from node '{key}':\")\n",
|
||||
" pprint.pprint(\"---\")\n",
|
||||
" pprint.pprint(value, indent=2, width=80, depth=None)\n",
|
||||
" pprint.pprint(\"\\n---\\n\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -211,7 +507,7 @@
|
||||
"id": "189333cc-5d34-4869-9f9b-741210e1096f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [""]
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
Reference in New Issue
Block a user