1. 数据准备

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

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=250, chunk_overlap=0
)
doc_splits = text_splitter.split_documents(docs_list)

from langchain_community.embeddings import ZhipuAIEmbeddings
embed = ZhipuAIEmbeddings(
    model="Embedding-3",
    api_key="your api key",
)

# Add to vectorDB
batch_size = 10
for i in range(0, len(doc_splits), batch_size):
    # 确保切片不会超出数组边界
    batch = doc_splits[i:min(i + batch_size, len(doc_splits))]
    
    vectorstore = Chroma.from_documents(
        documents=batch,
        collection_name="rag-chroma",
        embedding=embed,
        persist_directory="./chroma_db"
    )

retriever = vectorstore.as_retriever()

2. question_router llm模型

### Router

from typing import Literal

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

from pydantic import BaseModel, Field
# Data model
class RouteQuery(BaseModel):
    """Route a user query to the most relevant datasource."""

    datasource: Literal["vectorstore", "web_search"] = Field(
        ...,
        description="Given a user question choose to route it to web search or a vectorstore.",
    )


from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    temperature=0,
    model="GLM-4-plus",
    openai_api_key="your api key",
    openai_api_base="https://open.bigmodel.cn/api/paas/v4/"
)
structured_llm_router = llm.with_structured_output(RouteQuery)

# Prompt
system = """You are an expert at routing a user question to a vectorstore or web search.
The vectorstore contains documents related to agents, prompt engineering, and adversarial attacks.
Use the vectorstore for questions on these topics. Otherwise, use web-search."""
route_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        ("human", "{question}"),
    ]
)

question_router = route_prompt | structured_llm_router
print(
    question_router.invoke(
        {"question": "Who will the Bears draft first in the NFL draft?"}
    )
)
print(question_router.invoke({"question": "What are the types of agent memory?"}))

datasource='web_search'
datasource='vectorstore'

3. Retrieval Grader llm模型

# Data model
class GradeDocuments(BaseModel):
    """Binary score for relevance check on retrieved documents."""

    binary_score: str = Field(
        description="Documents are relevant to the question, 'yes' or 'no'"
    )


from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    temperature=0,
    model="GLM-4-plus",
    openai_api_key="your api key",
    openai_api_base="https://open.bigmodel.cn/api/paas/v4/"
)
structured_llm_grader = llm.with_structured_output(GradeDocuments)

# Prompt
system = """You are a grader assessing relevance of a retrieved document to a user question. \n 
    If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \n
    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \n
    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question."""
grade_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        ("human", "Retrieved document: \n\n {document} \n\n User question: {question}"),
    ]
)

retrieval_grader = grade_prompt | structured_llm_grader
question = "agent memory"
docs = retriever.invoke(question)
doc_txt = docs[1].page_content
print(retrieval_grader.invoke({"question": question, "document": doc_txt}))

binary_score='yes'

4. Generate llm 模型

### Generate

from langchain import hub
from langchain_core.output_parsers import StrOutputParser

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


from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    temperature=0,
    model="GLM-4-plus",
    openai_api_key="your api key",
    openai_api_base="https://open.bigmodel.cn/api/paas/v4/"
)

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


# Chain
rag_chain = prompt | llm | StrOutputParser()

# Run
generation = rag_chain.invoke({"context": docs, "question": question})
print(generation)

d:\soft\anaconda\envs\langchain\Lib\site-packages\langsmith\client.py:354: LangSmithMissingAPIKeyWarning: API key must be provided when using hosted LangSmith API
  warnings.warn(


In a LLM-powered autonomous agent system, memory is a crucial component. It includes various types of memory and utilizes techniques like Maximum Inner Product Search (MIPS) for efficient information retrieval. This memory system complements the LLM, which acts as the agent's brain, enabling the agent to perform complex tasks effectively.

5. Hallucination Grader

### Hallucination Grader


# Data model
class GradeHallucinations(BaseModel):
    """Binary score for hallucination present in generation answer."""

    binary_score: str = Field(
        description="Answer is grounded in the facts, 'yes' or 'no'"
    )


from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    temperature=0,
    model="GLM-4-plus",
    openai_api_key="your api key",
    openai_api_base="https://open.bigmodel.cn/api/paas/v4/"
)
structured_llm_grader = llm.with_structured_output(GradeHallucinations)

# Prompt
system = """You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \n 
     Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts."""
hallucination_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        ("human", "Set of facts: \n\n {documents} \n\n LLM generation: {generation}"),
    ]
)

hallucination_grader = hallucination_prompt | structured_llm_grader
hallucination_grader.invoke({"documents": docs, "generation": generation})

GradeHallucinations(binary_score='yes')

6. Answer Grader llm 模型

# Data model
class GradeAnswer(BaseModel):
    """Binary score to assess answer addresses question."""

    binary_score: str = Field(
        description="Answer addresses the question, 'yes' or 'no'"
    )


from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    temperature=0,
    model="GLM-4-plus",
    openai_api_key="your api key",
    openai_api_base="https://open.bigmodel.cn/api/paas/v4/"
)
structured_llm_grader = llm.with_structured_output(GradeAnswer)

# Prompt
system = """You are a grader assessing whether an answer addresses / resolves a question \n 
     Give a binary score 'yes' or 'no'. Yes' means that the answer resolves the question."""
answer_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        ("human", "User question: \n\n {question} \n\n LLM generation: {generation}"),
    ]
)

answer_grader = answer_prompt | structured_llm_grader
answer_grader.invoke({"question": question, "generation": generation})

GradeAnswer(binary_score='yes')

7. Question Re-writer llm 模型

# LLM
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    temperature=0,
    model="GLM-4-plus",
    openai_api_key="your api key",
    openai_api_base="https://open.bigmodel.cn/api/paas/v4/"
)

# Prompt
system = """You a question re-writer that converts an input question to a better version that is optimized \n 
     for vectorstore retrieval. Look at the input and try to reason about the underlying semantic intent / meaning."""
re_write_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        (
            "human",
            "Here is the initial question: \n\n {question} \n Formulate an improved question.",
        ),
    ]
)

question_rewriter = re_write_prompt | llm | StrOutputParser()
question_rewriter.invoke({"question": question})

'To optimize the initial question "agent memory" for vectorstore retrieval, we need to clarify the intent and provide more context. The term "agent memory" could refer to various concepts, such as memory in AI agents, memory management in software agents, or even human agents\' memory in certain contexts. \n\nImproved Question: "What are the key principles and mechanisms involved in memory management for AI agents?"\n\nThis version is more specific and provides clear context, making it easier for a vectorstore retrieval system to identify relevant information. It assumes the intent is to understand how memory is handled in the context of artificial intelligence agents. If the intent is different, please provide more context for further refinement.'

8. Websearch 工具

### Search
import os
from langchain_community.tools.tavily_search import TavilySearchResults
os.environ["TAVILY_API_KEY"] = "your api key"
web_search_tool = TavilySearchResults(k=3)

9. Graph中的State数据结构

from typing import List

from typing_extensions import TypedDict


class GraphState(TypedDict):
    """
    Represents the state of our graph.

    Attributes:
        question: question
        generation: LLM generation
        documents: list of documents
    """

    question: str
    generation: str
    documents: List[str]

10. Graph中的各个节点函数

from langchain.schema import Document


def retrieve(state):
    """
    Retrieve documents

    Args:
        state (dict): The current graph state

    Returns:
        state (dict): New key added to state, documents, that contains retrieved documents
    """
    print("---RETRIEVE---")
    question = state["question"]

    # Retrieval
    documents = retriever.invoke(question)
    return {"documents": documents, "question": question}


def generate(state):
    """
    Generate answer

    Args:
        state (dict): The current graph state

    Returns:
        state (dict): New key added to state, generation, that contains LLM generation
    """
    print("---GENERATE---")
    question = state["question"]
    documents = state["documents"]

    # RAG generation
    generation = rag_chain.invoke({"context": documents, "question": question})
    return {"documents": documents, "question": question, "generation": generation}


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

    Args:
        state (dict): The current graph state

    Returns:
        state (dict): Updates documents key with only filtered relevant documents
    """

    print("---CHECK DOCUMENT RELEVANCE TO QUESTION---")
    question = state["question"]
    documents = state["documents"]

    # Score each doc
    filtered_docs = []
    for d in documents:
        score = retrieval_grader.invoke(
            {"question": question, "document": d.page_content}
        )
        grade = score.binary_score
        if grade == "yes":
            print("---GRADE: DOCUMENT RELEVANT---")
            filtered_docs.append(d)
        else:
            print("---GRADE: DOCUMENT NOT RELEVANT---")
            continue
    return {"documents": filtered_docs, "question": question}


def transform_query(state):
    """
    Transform the query to produce a better question.

    Args:
        state (dict): The current graph state

    Returns:
        state (dict): Updates question key with a re-phrased question
    """

    print("---TRANSFORM QUERY---")
    question = state["question"]
    documents = state["documents"]

    # Re-write question
    better_question = question_rewriter.invoke({"question": question})
    return {"documents": documents, "question": better_question}


def web_search(state):
    """
    Web search based on the re-phrased question.

    Args:
        state (dict): The current graph state

    Returns:
        state (dict): Updates documents key with appended web results
    """

    print("---WEB SEARCH---")
    question = state["question"]

    # Web search
    docs = web_search_tool.invoke({"query": question})
    web_results = "\n".join([d["content"] for d in docs])
    web_results = Document(page_content=web_results)

    return {"documents": web_results, "question": question}


### Edges ###


def route_question(state):
    """
    Route question to web search or RAG.

    Args:
        state (dict): The current graph state

    Returns:
        str: Next node to call
    """

    print("---ROUTE QUESTION---")
    question = state["question"]
    source = question_router.invoke({"question": question})
    if source.datasource == "web_search":
        print("---ROUTE QUESTION TO WEB SEARCH---")
        return "web_search"
    elif source.datasource == "vectorstore":
        print("---ROUTE QUESTION TO RAG---")
        return "vectorstore"


def decide_to_generate(state):
    """
    Determines whether to generate an answer, or re-generate a question.

    Args:
        state (dict): The current graph state

    Returns:
        str: Binary decision for next node to call
    """

    print("---ASSESS GRADED DOCUMENTS---")
    state["question"]
    filtered_documents = state["documents"]

    if not filtered_documents:
        # All documents have been filtered check_relevance
        # We will re-generate a new query
        print(
            "---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---"
        )
        return "transform_query"
    else:
        # We have relevant documents, so generate answer
        print("---DECISION: GENERATE---")
        return "generate"


def grade_generation_v_documents_and_question(state):
    """
    Determines whether the generation is grounded in the document and answers question.

    Args:
        state (dict): The current graph state

    Returns:
        str: Decision for next node to call
    """

    print("---CHECK HALLUCINATIONS---")
    question = state["question"]
    documents = state["documents"]
    generation = state["generation"]

    score = hallucination_grader.invoke(
        {"documents": documents, "generation": generation}
    )
    grade = score.binary_score

    # Check hallucination
    if grade == "yes":
        print("---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---")
        # Check question-answering
        print("---GRADE GENERATION vs QUESTION---")
        score = answer_grader.invoke({"question": question, "generation": generation})
        grade = score.binary_score
        if grade == "yes":
            print("---DECISION: GENERATION ADDRESSES QUESTION---")
            return "useful"
        else:
            print("---DECISION: GENERATION DOES NOT ADDRESS QUESTION---")
            return "not useful"
    else:
        pprint("---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---")
        return "not supported"

11. Graph中的各条边

from langgraph.graph import END, StateGraph, START

workflow = StateGraph(GraphState)

# Define the nodes
workflow.add_node("web_search", web_search)  # web search
workflow.add_node("retrieve", retrieve)  # retrieve
workflow.add_node("grade_documents", grade_documents)  # grade documents
workflow.add_node("generate", generate)  # generatae
workflow.add_node("transform_query", transform_query)  # transform_query

# Build graph
workflow.add_conditional_edges(
    START,
    route_question,
    {
        "web_search": "web_search",
        "vectorstore": "retrieve",
    },
)
workflow.add_edge("web_search", "generate")
workflow.add_edge("retrieve", "grade_documents")
workflow.add_conditional_edges(
    "grade_documents",
    decide_to_generate,
    {
        "transform_query": "transform_query",
        "generate": "generate",
    },
)
workflow.add_edge("transform_query", "retrieve")
workflow.add_conditional_edges(
    "generate",
    grade_generation_v_documents_and_question,
    {
        "not supported": "generate",
        "useful": END,
        "not useful": "transform_query",
    },
)

# Compile
app = workflow.compile()

12. Graph可视化

from IPython.display import Image, display

try:
    display(Image(app.get_graph(xray=True).draw_mermaid_png()))
except Exception:
    # This requires some extra dependencies and is optional
    pass

13. 不同实例的运行结果

第一个实例

from pprint import pprint

# Run
inputs = {
    "question": "What player at the Bears expected to draft first in the 2024 NFL draft?"
}
for output in app.stream(inputs):
    for key, value in output.items():
        # Node
        pprint(f"Node '{key}':")
        # Optional: print full state at each node
        # pprint.pprint(value["keys"], indent=2, width=80, depth=None)
    pprint("\n---\n")

# Final generation
pprint(value["generation"])

输出：

---ROUTE QUESTION---
---ROUTE QUESTION TO WEB SEARCH---
---WEB SEARCH---
"Node 'web_search':"
'\n---\n'
---GENERATE---
---CHECK HALLUCINATIONS---
---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---
---GRADE GENERATION vs QUESTION---
---DECISION: GENERATION ADDRESSES QUESTION---
"Node 'generate':"
'\n---\n'
('The Chicago Bears were expected to draft Caleb Williams first in the 2024 '
 'NFL Draft. Williams, a quarterback from USC, was widely considered the top '
 'prospect after winning the Heisman Trophy in 2022. The Bears indeed selected '
 'him with the No. 1 overall pick.')

第二个实例

# Run
inputs = {"question": "What is the agent memory?"}
for output in app.stream(inputs):
    for key, value in output.items():
        # Node
        pprint(f"Node '{key}':")
        # Optional: print full state at each node
        # pprint.pprint(value["keys"], indent=2, width=80, depth=None)
    pprint("\n---\n")

# Final generation
pprint(value["generation"])

输出：

---ROUTE QUESTION---
---ROUTE QUESTION TO RAG---
---RETRIEVE---
"Node 'retrieve':"
'\n---\n'
---CHECK DOCUMENT RELEVANCE TO QUESTION---
---GRADE: DOCUMENT RELEVANT---
---GRADE: DOCUMENT RELEVANT---
---GRADE: DOCUMENT RELEVANT---
---GRADE: DOCUMENT RELEVANT---
---ASSESS GRADED DOCUMENTS---
---DECISION: GENERATE---
"Node 'grade_documents':"
'\n---\n'
---GENERATE---
---CHECK HALLUCINATIONS---
---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---
---GRADE GENERATION vs QUESTION---
---DECISION: GENERATION ADDRESSES QUESTION---
"Node 'generate':"
'\n---\n'
('The agent memory in a LLM-powered autonomous agent system is a key component '
 "that complements the LLM, which functions as the agent's brain. It includes "
 'various types of memory and utilizes techniques like Maximum Inner Product '
 'Search (MIPS) to enhance its functionality. This memory system aids the '
 'agent in retaining and retrieving information crucial for decision-making '
 'and task execution.')

官网中的问题是：

inputs = {"question": "What are the types of agent memory?"}

但用智谱的模型会限制死循环， 具体修改方法可以参考这篇 文章

langgraph官网链接：https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag/#use-graph

如果有任何问题，欢迎在评论区提问。