使用 LangChain 构建 RAG 代理

export LANGSMITH_TRACING="true"
export LANGSMITH_API_KEY="..."

import getpass
import os

os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

import bs4
from langchain_community.document_loaders import WebBaseLoader

# Only keep post title, headers, and content from the full HTML.
bs4_strainer = bs4.SoupStrainer(class_=("post-title", "post-header", "post-content"))
loader = WebBaseLoader(
    web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
    bs_kwargs={"parse_only": bs4_strainer},
)
docs = loader.load()

assert len(docs) == 1
print(f"Total characters: {len(docs[0].page_content)}")

Total characters: 43131

print(docs[0].page_content[:500])

      LLM Powered Autonomous Agents

Date: June 23, 2023  |  Estimated Reading Time: 31 min  |  Author: Lilian Weng


Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.
Agent System Overview#
In

from langchain_text_splitters import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,  # chunk size (characters)
    chunk_overlap=200,  # chunk overlap (characters)
    add_start_index=True,  # track index in original document
)
all_splits = text_splitter.split_documents(docs)

print(f"Split blog post into {len(all_splits)} sub-documents.")

Split blog post into 66 sub-documents.

document_ids = vector_store.add_documents(documents=all_splits)

print(document_ids[:3])

['07c18af6-ad58-479a-bfb1-d508033f9c64', '9000bf8e-1993-446f-8d4d-f4e507ba4b8f', 'ba3b5d14-bed9-4f5f-88be-44c88aedc2e6']

from langchain.tools import tool

@tool(response_format="content_and_artifact")
def retrieve_context(query: str):
    """Retrieve information to help answer a query."""
    retrieved_docs = vector_store.similarity_search(query, k=2)
    serialized = "\n\n".join(
        (f"Source: {doc.metadata}\nContent: {doc.page_content}")
        for doc in retrieved_docs
    )
    return serialized, retrieved_docs

from langchain.agents import create_agent


tools = [retrieve_context]
# If desired, specify custom instructions
prompt = (
    "You have access to a tool that retrieves context from a blog post. "
    "Use the tool to help answer user queries."
)
agent = create_agent(model, tools, system_prompt=prompt)

query = (
    "What is the standard method for Task Decomposition?\n\n"
    "Once you get the answer, look up common extensions of that method."
)

for event in agent.stream(
    {"messages": [{"role": "user", "content": query}]},
    stream_mode="values",
):
    event["messages"][-1].pretty_print()

================================ Human Message =================================

What is the standard method for Task Decomposition?

Once you get the answer, look up common extensions of that method.
================================== Ai Message ==================================
Tool Calls:
  retrieve_context (call_d6AVxICMPQYwAKj9lgH4E337)
 Call ID: call_d6AVxICMPQYwAKj9lgH4E337
  Args:
    query: standard method for Task Decomposition
================================= Tool Message =================================
Name: retrieve_context

Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Task decomposition can be done...

Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Component One: Planning...
================================== Ai Message ==================================
Tool Calls:
  retrieve_context (call_0dbMOw7266jvETbXWn4JqWpR)
 Call ID: call_0dbMOw7266jvETbXWn4JqWpR
  Args:
    query: common extensions of the standard method for Task Decomposition
================================= Tool Message =================================
Name: retrieve_context

Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Task decomposition can be done...

Source: {'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
Content: Component One: Planning...
================================== Ai Message ==================================

The standard method for Task Decomposition often used is the Chain of Thought (CoT)...

from langchain.agents.middleware import dynamic_prompt, ModelRequest

@dynamic_prompt
def prompt_with_context(request: ModelRequest) -> str:
    """Inject context into state messages."""
    last_query = request.state["messages"][-1].text
    retrieved_docs = vector_store.similarity_search(last_query)

    docs_content = "\n\n".join(doc.page_content for doc in retrieved_docs)

    system_message = (
        "You are a helpful assistant. Use the following context in your response:"
        f"\n\n{docs_content}"
    )

    return system_message


agent = create_agent(model, tools=[], middleware=[prompt_with_context])

query = "What is task decomposition?"
for step in agent.stream(
    {"messages": [{"role": "user", "content": query}]},
    stream_mode="values",
):
    step["messages"][-1].pretty_print()

================================ Human Message =================================

What is task decomposition?
================================== Ai Message ==================================

Task decomposition is...