Building a knowledge base

A repository of documents of structured data 使用LangChain’s document loaders and vector stores 来建立你自己的知识库如果你已经有知识库,你不需要重建,你只需要

Connect it as a tool for agent in agentic RAG
Query it and supply the retrieved content as context to the LLM (2-Step RAG)

From retrieval to RAG

Retrieval pipeline

Document loaders,text splitters,Embedding models,vector stores,retrievers

RAG architectures

Architecture	Description	Control	Flexibility	Latency	Example Use Case
2-Step RAG	Retrieval always happens before generation. Simple and predictable	✅ High	❌ Low	⚡ Fast	FAQs, documentation bots
Agentic RAG	An LLM-powered agent decides when and how to retrieve during reasoning	❌ Low	✅ High	⏳ Variable	Research assistants with access to multiple tools
Hybrid	Combines characteristics of both approaches with validation steps	⚖️ Medium	⚖️ Medium	⏳ Variable	Domain-specific Q&A with quality validation

2-step RAG

Agentic RAG

由LLM来决定是否需要检索和如何检索信息

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import requests
from langchain.tools import tool
from langchain.chat_models import init_chat_model
from langchain.agents import create_agent


@tool
def fetch_url(url: str) -> str:
    """Fetch text content from a URL"""
    response = requests.get(url, timeout=10.0)
    response.raise_for_status()
    return response.text

system_prompt = """\
Use fetch_url when you need to fetch information from a web-page; quote relevant snippets.
"""

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=[fetch_url], # A tool for retrieval
    system_prompt=system_prompt,
)

Hybrid RAG

结合了前两者的优点,核心组件包括:

查询增强:修改question来提高检索质量,这可能包括重写不清晰的查询、生成多个变体，或使用其他上下文扩展查询。

检索验证:评估检索到的资料是否充足

答案验证:检查生成的答案准确性,完整性和于源文件的一致性

此架构适用于

存在模糊或描述不详的查询的应用场景
需要验证或质量控制步骤的系统
涉及多个数据源或迭代优化的工作流

Building a knowledge base#

From retrieval to RAG#

Retrieval pipeline#

RAG architectures#

2-step RAG#

Agentic RAG#

Hybrid RAG#