從 RetrievalQA 遷移
RetrievalQA 鏈使用檢索增強生成技術，對資料來源執行自然語言問答。
切換到 LCEL 實作的一些優點是
- 更易於自訂。提示和文件格式化方式等詳細資訊只能透過
RetrievalQA鏈中的特定參數進行配置。 - 更輕鬆地返回來源文件。
- 支援可執行方法，例如串流和非同步操作。
現在讓我們並排查看它們。我們將使用以下擷取程式碼將 Lilian Weng 關於自主代理的部落格文章載入到本機向量儲存庫中
共用設定
對於這兩個版本，我們都需要使用 WebBaseLoader 文件載入器載入資料，使用 RecursiveCharacterTextSplitter 分割資料，並將其新增到記憶體中的 FAISS 向量儲存庫。
我們還將實例化一個聊天模型以供使用。
%pip install --upgrade --quiet langchain-community langchain langchain-openai faiss-cpu beautifulsoup4
import os
from getpass import getpass
if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = getpass()
# Load docs
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import FAISS
from langchain_openai.chat_models import ChatOpenAI
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
data = loader.load()
# Split
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
all_splits = text_splitter.split_documents(data)
# Store splits
vectorstore = FAISS.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())
# LLM
llm = ChatOpenAI()
傳統
詳細資訊
from langchain import hub
from langchain.chains import RetrievalQA
# See full prompt at https://smith.langchain.com/hub/rlm/rag-prompt
prompt = hub.pull("rlm/rag-prompt")
qa_chain = RetrievalQA.from_llm(
llm, retriever=vectorstore.as_retriever(), prompt=prompt
)
qa_chain("What are autonomous agents?")
API 參考：hub | RetrievalQA
{'query': 'What are autonomous agents?',
'result': 'Autonomous agents are LLM-empowered agents capable of handling autonomous design, planning, and performance of complex scientific experiments. These agents can browse the Internet, read documentation, execute code, call robotics experimentation APIs, and leverage other LLMs. They can generate reasoning steps, such as developing a novel anticancer drug, based on requested tasks.'}
LCEL
詳細資訊
from langchain import hub
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
# See full prompt at https://smith.langchain.com/hub/rlm/rag-prompt
prompt = hub.pull("rlm/rag-prompt")
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
qa_chain = (
{
"context": vectorstore.as_retriever() | format_docs,
"question": RunnablePassthrough(),
}
| prompt
| llm
| StrOutputParser()
)
qa_chain.invoke("What are autonomous agents?")
'Autonomous agents are agents empowered by large language models (LLMs) that can handle autonomous design, planning, and performance of complex tasks such as scientific experiments. These agents can use tools to browse the Internet, read documentation, execute code, call robotics experimentation APIs, and leverage other LLMs for their tasks. The model can come up with reasoning steps when given a specific task, such as developing a novel anticancer drug.'
LCEL 實作公開了圍繞檢索、格式化文件以及將它們透過提示傳遞到 LLM 的內部機制，但它更冗長。您可以自訂並將此組合邏輯包裝在輔助函數中，或使用更高等級的 create_retrieval_chain 和 create_stuff_documents_chain 輔助方法
from langchain import hub
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
# See full prompt at https://smith.langchain.com/hub/langchain-ai/retrieval-qa-chat
retrieval_qa_chat_prompt = hub.pull("langchain-ai/retrieval-qa-chat")
combine_docs_chain = create_stuff_documents_chain(llm, retrieval_qa_chat_prompt)
rag_chain = create_retrieval_chain(vectorstore.as_retriever(), combine_docs_chain)
rag_chain.invoke({"input": "What are autonomous agents?"})
{'input': 'What are autonomous agents?',
'context': [Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log", 'description': '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.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Boiko et al. (2023) also looked into LLM-empowered agents for scientific discovery, to handle autonomous design, planning, and performance of complex scientific experiments. This agent can use tools to browse the Internet, read documentation, execute code, call robotics experimentation APIs and leverage other LLMs.\nFor example, when requested to "develop a novel anticancer drug", the model came up with the following reasoning steps:'),
Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log", 'description': '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.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Weng, Lilian. (Jun 2023). “LLM-powered Autonomous Agents”. Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/.'),
Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log", 'description': '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.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, 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#'),
Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log", 'description': '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.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Or\n@article{weng2023agent,\n title = "LLM-powered Autonomous Agents",\n author = "Weng, Lilian",\n journal = "lilianweng.github.io",\n year = "2023",\n month = "Jun",\n url = "https://lilianweng.github.io/posts/2023-06-23-agent/"\n}\nReferences#\n[1] Wei et al. “Chain of thought prompting elicits reasoning in large language models.” NeurIPS 2022\n[2] Yao et al. “Tree of Thoughts: Dliberate Problem Solving with Large Language Models.” arXiv preprint arXiv:2305.10601 (2023).')],
'answer': 'Autonomous agents are entities capable of operating independently to perform tasks or make decisions without direct human intervention. In the context provided, autonomous agents empowered by Large Language Models (LLMs) are used for scientific discovery, including tasks like autonomous design, planning, and executing complex scientific experiments.'}
下一步
查看 LCEL 概念文件，以獲取有關 LangChain 運算式語言的更多背景資訊。