如何让你的RAG应用程序返回来源
在问答应用中，向用户展示用于生成答案的来源通常很重要。最简单的方法是让链返回每次生成中检索到的文档。
我们将基于我们在RAG教程中构建的问答应用程序进行工作，该应用程序是在Lilian Weng的LLM驱动的自主代理博客文章基础上开发的。
我们将介绍两种方法：
我们还将展示如何将来源结构化为模型响应，以便模型可以报告其在生成答案时使用的具体来源。
设置
依赖项
我们将使用以下包：
%pip install --upgrade --quiet langchain langchain-community langchainhub beautifulsoup4
LangSmith
您使用LangChain构建的许多应用程序将包含多个步骤，涉及多次LLM调用。随着这些应用程序变得越来越复杂，能够检查您的链或代理内部究竟发生了什么变得至关重要。实现这一点的最佳方式是使用LangSmith。
请注意，LangSmith 不是必需的，但它很有帮助。如果您确实想使用 LangSmith，在您通过上述链接注册后，请确保设置您的环境变量以开始记录跟踪：
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()
组件
我们需要从LangChain的集成套件中选择三个组件。
一个聊天模型:
Select chat model:
pip install -qU langchain-openai
import getpass
import os
if not os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o-mini")
一个嵌入模型:
Select embeddings model:
pip install -qU langchain-openai
import getpass
import os
if not os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
以及一个向量存储：
Select vector store:
pip install -qU langchain-core
from langchain_core.vectorstores import InMemoryVectorStore
vector_store = InMemoryVectorStore(embeddings)
RAG 应用
让我们用我们在LLM驱动的自主代理博客文章中构建的资源，在RAG教程中重建问答应用。
首先我们索引我们的文档：
import bs4
from langchain import hub
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from typing_extensions import List, TypedDict
# Load and chunk contents of the blog
loader = WebBaseLoader(
web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
bs_kwargs=dict(
parse_only=bs4.SoupStrainer(
class_=("post-content", "post-title", "post-header")
)
),
)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
all_splits = text_splitter.split_documents(docs)
# Index chunks
_ = vector_store.add_documents(documents=all_splits)
接下来我们构建应用程序：
from langchain import hub
from langchain_core.documents import Document
from langgraph.graph import START, StateGraph
from typing_extensions import List, TypedDict
# Define prompt for question-answering
prompt = hub.pull("rlm/rag-prompt")
# Define state for application
class State(TypedDict):
question: str
context: List[Document]
answer: str
# Define application steps
def retrieve(state: State):
retrieved_docs = vector_store.similarity_search(state["question"])
return {"context": retrieved_docs}
def generate(state: State):
docs_content = "\n\n".join(doc.page_content for doc in state["context"])
messages = prompt.invoke({"question": state["question"], "context": docs_content})
response = llm.invoke(messages)
return {"answer": response.content}
# Compile application and test
graph_builder = StateGraph(State).add_sequence([retrieve, generate])
graph_builder.add_edge(START, "retrieve")
graph = graph_builder.compile()
from IPython.display import Image, display
display(Image(graph.get_graph().draw_mermaid_png()))
因为我们在应用程序的状态中跟踪检索到的上下文，所以在调用应用程序后可以访问它：
result = graph.invoke({"question": "What is Task Decomposition?"})
print(f'Context: {result["context"]}\n\n')
print(f'Answer: {result["answer"]}')
Context: [Document(id='c8471b37-07d8-4d51-856e-4b2c22bca88d', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.'), Document(id='acb7eb6f-f252-4353-aec2-f459135354ba', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\nTask decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.'), Document(id='4fae6668-7fec-4237-9b2d-78132f4f3f3f', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content='Resources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.'), Document(id='3c79dd86-595e-42e8-b64d-404780f9e2d9', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content="(3) Task execution: Expert models execute on the specific tasks and log results.\nInstruction:\n\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user's request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.")]
Answer: Task Decomposition is the process of breaking down a complex task into smaller, manageable steps to facilitate execution. This can be achieved through techniques like Chain of Thought, which encourages step-by-step reasoning, or Tree of Thoughts, which explores multiple reasoning paths for each step. It can be implemented using simple prompts, specific instructions, or human input to effectively tackle the original task.
在这里，"context" 包含了LLM在生成"answer"中的响应时使用的来源。
模型响应中的结构来源
到目前为止，我们只是简单地将从检索步骤返回的文档传播到最终响应中。但这可能无法说明模型在生成答案时依赖了哪些信息子集。下面，我们展示了如何将来源结构化为模型响应，使模型能够报告其答案所依赖的具体上下文。
扩展上述LangGraph实现非常简单。下面，我们做了一个简单的更改：我们使用模型的工具调用功能来生成结构化输出，包括答案和来源列表。响应的模式在下面的AnswerWithSources TypedDict中表示。
from typing import List
from typing_extensions import Annotated, TypedDict
# Desired schema for response
class AnswerWithSources(TypedDict):
"""An answer to the question, with sources."""
answer: str
sources: Annotated[
List[str],
...,
"List of sources (author + year) used to answer the question",
]
class State(TypedDict):
question: str
context: List[Document]
answer: AnswerWithSources
def generate(state: State):
docs_content = "\n\n".join(doc.page_content for doc in state["context"])
messages = prompt.invoke({"question": state["question"], "context": docs_content})
structured_llm = llm.with_structured_output(AnswerWithSources)
response = structured_llm.invoke(messages)
return {"answer": response}
graph_builder = StateGraph(State).add_sequence([retrieve, generate])
graph_builder.add_edge(START, "retrieve")
graph = graph_builder.compile()
import json
result = graph.invoke({"question": "What is Chain of Thought?"})
print(json.dumps(result["answer"], indent=2))
{
"answer": "Chain of Thought (CoT) is a prompting technique that enhances model performance by instructing it to think step by step, allowing the decomposition of complex tasks into smaller, manageable steps. This method not only aids in task execution but also provides insights into the model's reasoning process. CoT has become a standard approach in improving how language models handle intricate problem-solving tasks.",
"sources": [
"Wei et al. 2022"
]
}
tip
查看 LangSmith 跟踪。
对话式RAG
第2部分的RAG教程实现了一种不同的架构，其中RAG流程中的步骤通过连续的消息对象来表示。这利用了聊天模型的额外工具调用功能，并且更自然地适应了“来回”对话的用户体验。
在该教程中（以及下文），我们将检索到的文档作为工件传播到工具消息上。这使得提取检索到的文档变得容易。为了方便起见，我们在状态中添加了它们作为额外的键。
请注意，我们将工具的响应格式定义为 "content_and_artifact"：
from langchain_core.tools import tool
@tool(response_format="content_and_artifact")
def retrieve(query: str):
"""Retrieve information related to a query."""
retrieved_docs = vector_store.similarity_search(query, k=2)
serialized = "\n\n".join(
(f"Source: {doc.metadata}\n" f"Content: {doc.page_content}")
for doc in retrieved_docs
)
return serialized, retrieved_docs
API Reference:tool
我们现在可以构建和编译与RAG教程第2部分中完全相同的应用程序，但有两处更改：
- 我们添加了一个
context键的状态来存储检索到的文档； - 在
generate步骤中，我们提取检索到的文档并将它们填充到状态中。
这些变化在下面突出显示。
from langchain_core.messages import SystemMessage
from langgraph.graph import END, MessagesState, StateGraph
from langgraph.prebuilt import ToolNode, tools_condition
class State(MessagesState):
context: List[Document]
# Step 1: Generate an AIMessage that may include a tool-call to be sent.
def query_or_respond(state: State):
"""Generate tool call for retrieval or respond."""
llm_with_tools = llm.bind_tools([retrieve])
response = llm_with_tools.invoke(state["messages"])
# MessagesState appends messages to state instead of overwriting
return {"messages": [response]}
# Step 2: Execute the retrieval.
tools = ToolNode([retrieve])
# Step 3: Generate a response using the retrieved content.
def generate(state: MessagesState):
"""Generate answer."""
# Get generated ToolMessages
recent_tool_messages = []
for message in reversed(state["messages"]):
if message.type == "tool":
recent_tool_messages.append(message)
else:
break
tool_messages = recent_tool_messages[::-1]
# Format into prompt
docs_content = "\n\n".join(doc.content for doc in tool_messages)
system_message_content = (
"You are an assistant for question-answering tasks. "
"Use the following pieces of retrieved context to answer "
"the question. If you don't know the answer, say that you "
"don't know. Use three sentences maximum and keep the "
"answer concise."
"\n\n"
f"{docs_content}"
)
conversation_messages = [
message
for message in state["messages"]
if message.type in ("human", "system")
or (message.type == "ai" and not message.tool_calls)
]
prompt = [SystemMessage(system_message_content)] + conversation_messages
# Run
response = llm.invoke(prompt)
context = []
for tool_message in tool_messages:
context.extend(tool_message.artifact)
return {"messages": [response], "context": context}
我们可以像以前一样编译应用程序：
graph_builder = StateGraph(MessagesState)
graph_builder.add_node(query_or_respond)
graph_builder.add_node(tools)
graph_builder.add_node(generate)
graph_builder.set_entry_point("query_or_respond")
graph_builder.add_conditional_edges(
"query_or_respond",
tools_condition,
{END: END, "tools": "tools"},
)
graph_builder.add_edge("tools", "generate")
graph_builder.add_edge("generate", END)
graph = graph_builder.compile()
display(Image(graph.get_graph().draw_mermaid_png()))
调用我们的应用程序时，我们看到从应用程序状态中可以访问检索到的Document对象。
input_message = "What is Task Decomposition?"
for step in graph.stream(
{"messages": [{"role": "user", "content": input_message}]},
stream_mode="values",
):
step["messages"][-1].pretty_print()
================================[1m Human Message [0m=================================
What is Task Decomposition?
==================================[1m Ai Message [0m==================================
Tool Calls:
retrieve (call_oA0XZ5hF70X0oW4ccNUFCFxX)
Call ID: call_oA0XZ5hF70X0oW4ccNUFCFxX
Args:
query: Task Decomposition
=================================[1m Tool Message [0m=================================
Name: retrieve
Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Fig. 1. Overview of a LLM-powered autonomous agent system.
Component One: Planning#
A complicated task usually involves many steps. An agent needs to know what they are and plan ahead.
Task Decomposition#
Chain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.
Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.
Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.
==================================[1m Ai Message [0m==================================
Task Decomposition is the process of breaking down a complicated task into smaller, manageable steps. It often utilizes techniques like Chain of Thought (CoT) prompting, which encourages models to think step by step, enhancing performance on complex tasks. This approach helps clarify the model's reasoning and makes it easier to tackle difficult problems.
step["context"]
[Document(id='c8471b37-07d8-4d51-856e-4b2c22bca88d', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.'),
Document(id='acb7eb6f-f252-4353-aec2-f459135354ba', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}, page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\nTask decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.')]
tip
查看 LangSmith 跟踪。