langgraph/examples/rag/langgraph_agentic_rag.ipynb

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! pip install langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph

LangGraph Retrieval Agent

Retrieval Agents are useful when we want to make decisions about whether to retrieve from an index.

To implement a retrieval agent, we simple need to give an LLM access to a retriever tool.

We can incorperate this into LangGraph.

Retriever

First, we index 3 blog posts.

In [3]:

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings

urls = [
    "https://lilianweng.github.io/posts/2023-06-23-agent/",
    "https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/",
    "https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/",
]

docs = [WebBaseLoader(url).load() for url in urls]
docs_list = [item for sublist in docs for item in sublist]

text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
    chunk_size=100, chunk_overlap=50
)
doc_splits = text_splitter.split_documents(docs_list)

# Add to vectorDB
vectorstore = Chroma.from_documents(
    documents=doc_splits,
    collection_name="rag-chroma",
    embedding=OpenAIEmbeddings(),
)
retriever = vectorstore.as_retriever()

Then we create a retriever tool.

In [4]:

from langchain.tools.retriever import create_retriever_tool

tool = create_retriever_tool(
    retriever,
    "retrieve_blog_posts",
    "Search and return information about Lilian Weng blog posts on LLM agents, prompt engineering, and adversarial attacks on LLMs.",
)

tools = [tool]

from langgraph.prebuilt import ToolExecutor

tool_executor = ToolExecutor(tools)

Agent state

We will defined a graph.

A state object that it passes around to each node.

Our state will be a list of messages.

Each node in our graph will append to it.

In [5]:

import operator
from typing import Annotated, Sequence, TypedDict

from langchain_core.messages import BaseMessage


class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], operator.add]

Nodes and Edges

We can lay out an agentic RAG graph like this:

• The state is a set of messages
• Each node will update (append to) state
• Conditional edges decide which node to visit next

In [38]:

import json
import operator
from typing import Annotated, Sequence, TypedDict

from langchain import hub
from langchain.output_parsers import PydanticOutputParser
from langchain.prompts import PromptTemplate
from langchain.tools.render import format_tool_to_openai_function
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_core.messages import BaseMessage, FunctionMessage
from langchain.output_parsers.openai_tools import PydanticToolsParser
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import ToolInvocation
from langchain_core.output_parsers import StrOutputParser

### Edges


def should_retrieve(state):
    """
    Decides whether the agent should retrieve more information or end the process.

    This function checks the last message in the state for a function call. If a function call is
    present, the process continues to retrieve information. Otherwise, it ends the process.

    Args:
        state (messages): The current state

    Returns:
        str: A decision to either "continue" the retrieval process or "end" it
    """
    
    print("---DECIDE TO RETRIEVE---")
    messages = state["messages"]
    last_message = messages[-1]
    
    # If there is no function call, then we finish
    if "function_call" not in last_message.additional_kwargs:
        print("---DECISION: DO NOT RETRIEVE / DONE---")
        return "end"
    # Otherwise there is a function call, so we continue
    else:
        print("---DECISION: RETRIEVE---")
        return "continue"


def grade_documents(state):
    """
    Determines whether the retrieved documents are relevant to the question.

    Args:
        state (messages): The current state

    Returns:
        str: A decision for whether the documents are relevant or not
    """

    print("---CHECK RELEVANCE---")

    # Data model
    class grade(BaseModel):
        """Binary score for relevance check."""

        binary_score: str = Field(description="Relevance score 'yes' or 'no'")

    # LLM
    model = ChatOpenAI(temperature=0, model="gpt-4-0125-preview", streaming=True)

    # Tool
    grade_tool_oai = convert_to_openai_tool(grade)

    # LLM with tool and enforce invocation
    llm_with_tool = model.bind(
        tools=[convert_to_openai_tool(grade_tool_oai)],
        tool_choice={"type": "function", "function": {"name": "grade"}},
    )

    # Parser
    parser_tool = PydanticToolsParser(tools=[grade])

    # Prompt
    prompt = PromptTemplate(
        template="""You are a grader assessing relevance of a retrieved document to a user question. \n 
        Here is the retrieved document: \n\n {context} \n\n
        Here is the user question: {question} \n
        If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \n
        Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.""",
        input_variables=["context", "question"],
    )

    # Chain
    chain = prompt | llm_with_tool | parser_tool

    messages = state["messages"]
    last_message = messages[-1]

    question = messages[0].content
    docs = last_message.content
    
    score = chain.invoke(
        {"question": question, 
         "context": docs}
    )
    
    grade = score[0].binary_score

    if grade == "yes":
        print("---DECISION: DOCS RELEVANT---")
        return "yes"

    else:
        print("---DECISION: DOCS NOT RELEVANT---")
        print(score.binary_score)
        return "no"


### Nodes


def agent(state):
    """
    Invokes the agent model to generate a response based on the current state. Given
    the question, it will decide to retrieve using the retriever tool, or simply end.

    Args:
        state (messages): The current state

    Returns:
        dict: The updated state with the agent response apended to messages
    """
    print("---CALL AGENT---")
    messages = state["messages"]
    model = ChatOpenAI(temperature=0, streaming=True, model="gpt-4-0125-preview")
    functions = [format_tool_to_openai_function(t) for t in tools]
    model = model.bind_functions(functions)
    response = model.invoke(messages)
    # We return a list, because this will get added to the existing list
    return {"messages": [response]}

def retrieve(state):
    """
    Uses tool to execute retrieval.

    Args:
        state (messages): The current state

    Returns:
        dict: The updated state with retrieved docs
    """
    print("---EXECUTE RETRIEVAL---")
    messages = state["messages"]
    # Based on the continue condition
    # we know the last message involves a function call
    last_message = messages[-1]
    # We construct an ToolInvocation from the function_call
    action = ToolInvocation(
        tool=last_message.additional_kwargs["function_call"]["name"],
        tool_input=json.loads(
            last_message.additional_kwargs["function_call"]["arguments"]
        ),
    )
    # We call the tool_executor and get back a response
    response = tool_executor.invoke(action)
    function_message = FunctionMessage(content=str(response), name=action.tool)

    # We return a list, because this will get added to the existing list
    return {"messages": [function_message]}

def rewrite(state):
    """
    Transform the query to produce a better question.
    
    Args:
        state (messages): The current state
    
    Returns:
        dict: The updated state with re-phrased question
    """
    
    print("---TRANSFORM QUERY---")
    messages = state["messages"]
    question = messages[0].content

    msg = HumanMessage(
        content=f""" \n 
    Look at the input and try to reason about the underlying semantic intent / meaning. \n 
    Here is the initial question:
    \n ------- \n
    {question} 
    \n ------- \n
    Formulate an improved question: """,
    )

    # Grader
    model = ChatOpenAI(temperature=0, model="gpt-4-0125-preview", streaming=True)
    response = model.invoke(msg)
    return {"messages": [response]}

def generate(state):
    """
    Generate answer

    Args:
        state (messages): The current state

    Returns:
         dict: The updated state with re-phrased question
    """
    print("---GENERATE---")
    messages = state["messages"]
    question = messages[0].content
    last_message = messages[-1]

    question = messages[0].content
    docs = last_message.content

    # Prompt
    prompt = hub.pull("rlm/rag-prompt")

    # LLM
    llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True)

    # Post-processing
    def format_docs(docs):
        return "\n\n".join(doc.page_content for doc in docs)

    # Chain
    rag_chain = prompt | llm | StrOutputParser()

    # Run
    response = rag_chain.invoke({"context": docs, "question": question})
    return {"messages": [response]}

Graph

• Start with an agent, call_model
• Agent make a decision to call a function
• If so, then action to call tool (retriever)
• Then call agent with the tool output added to messages (state)

In [39]:

from langgraph.graph import END, StateGraph

# Define a new graph
workflow = StateGraph(AgentState)

# Define the nodes we will cycle between
workflow.add_node("agent", agent)  # agent
workflow.add_node("retrieve", retrieve)  # retrieval
workflow.add_node("rewrite", rewrite)  # retrieval
workflow.add_node("generate", generate)  # retrieval

In [40]:

# Call agent node to decide to retrieve or not
workflow.set_entry_point("agent")

# Decide whether to retrieve
workflow.add_conditional_edges(
    "agent",
    # Assess agent decision
    should_retrieve,
    {
        # Call tool node
        "continue": "retrieve",
        "end": END,
    },
)

# Edges taken after the `action` node is called.
workflow.add_conditional_edges(
    "retrieve",
    # Assess agent decision
    grade_documents,
    {
        "yes": "generate",
        "no": "rewrite",  
    },
)
workflow.add_edge("generate", END)
workflow.add_edge("rewrite", "agent")

# Compile
app = workflow.compile()

In [41]:

import pprint
from langchain_core.messages import HumanMessage

inputs = {
    "messages": [
        HumanMessage(
            content="What does Lilian Weng say about the types of agent memory?"
        )
    ]
}
for output in app.stream(inputs):
    for key, value in output.items():
        pprint.pprint(f"Output from node '{key}':")
        pprint.pprint("---")
        pprint.pprint(value, indent=2, width=80, depth=None)
    pprint.pprint("\n---\n")

---CALL AGENT---
"Output from node 'agent':"
'---'
{ 'messages': [ AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{"query":"types of agent memory"}', 'name': 'retrieve_blog_posts'}})]}
'\n---\n'
---DECIDE TO RETRIEVE---
---DECISION: RETRIEVE---
---EXECUTE RETRIEVAL---
"Output from node 'retrieve':"
'---'
{ 'messages': [ FunctionMessage(content='Table of Contents\n\n\n\nAgent System Overview\n\nComponent One: Planning\n\nTask Decomposition\n\nSelf-Reflection\n\n\nComponent Two: Memory\n\nTypes of Memory\n\nMaximum Inner Product Search (MIPS)\n\n\nComponent Three: Tool Use\n\nCase Studies\n\nScientific Discovery Agent\n\nGenerative Agents Simulation\n\nProof-of-Concept Examples\n\n\nChallenges\n\nCitation\n\nReferences\n\nPlanning\n\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\n\n\nMemory\n\nMemory\n\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\n\n\nTool use\n\nThe design of generative agents combines LLM with memory, planning and reflection mechanisms to enable agents to behave conditioned on past experience, as well as to interact with other agents.', name='retrieve_blog_posts')]}
'\n---\n'
---CHECK RELEVANCE---
---DECISION: DOCS RELEVANT---
---GENERATE---
"Output from node 'generate':"
'---'
{ 'messages': [ 'Lilian Weng mentions two types of agent memory: short-term '
                'memory and long-term memory. Short-term memory is used for '
                'in-context learning, while long-term memory allows the agent '
                'to retain and recall information over extended periods.']}
'\n---\n'
"Output from node '__end__':"
'---'
{ 'messages': [ HumanMessage(content='What does Lilian Weng say about the types of agent memory?'),
                AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{"query":"types of agent memory"}', 'name': 'retrieve_blog_posts'}}),
                FunctionMessage(content='Table of Contents\n\n\n\nAgent System Overview\n\nComponent One: Planning\n\nTask Decomposition\n\nSelf-Reflection\n\n\nComponent Two: Memory\n\nTypes of Memory\n\nMaximum Inner Product Search (MIPS)\n\n\nComponent Three: Tool Use\n\nCase Studies\n\nScientific Discovery Agent\n\nGenerative Agents Simulation\n\nProof-of-Concept Examples\n\n\nChallenges\n\nCitation\n\nReferences\n\nPlanning\n\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\n\n\nMemory\n\nMemory\n\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\n\n\nTool use\n\nThe design of generative agents combines LLM with memory, planning and reflection mechanisms to enable agents to behave conditioned on past experience, as well as to interact with other agents.', name='retrieve_blog_posts'),
                'Lilian Weng mentions two types of agent memory: short-term '
                'memory and long-term memory. Short-term memory is used for '
                'in-context learning, while long-term memory allows the agent '
                'to retain and recall information over extended periods.']}
'\n---\n'

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