LangChain-24 Agengts 通过TavilySearch Agent实现检索内容并回答 AgentExecutor转换Search 借助Prompt Tools工具_tavilysearchresults

安装依赖

pip install -qU langchain-core langchain-openai
1

Prompt

# 从Hub加载Prompt，自己写加载进来也一样
# SYSTEM
#
# You are a helpful assistant
#
# PLACEHOLDER
#
# chat_history
# HUMAN
#
# {input}
#
# PLACEHOLDER
#
1
2
3
4
5
6
7
8
9
10
11
12
13
14

编写代码

from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.tools.retriever import create_retriever_tool
from langchain_openai import ChatOpenAI
from langchain.agents import create_openai_functions_agent
from langchain.agents import AgentExecutor
from langchain import hub


# 这里需要配置KEY 免费
search = TavilySearchResults()
# message1 = search.invoke("what is the weather in SF")
# print(f"message1: {message1}")

loader = WebBaseLoader("https://docs.smith.langchain.com/overview")
docs = loader.load()
documents = RecursiveCharacterTextSplitter(
    chunk_size=1000, chunk_overlap=200
).split_documents(docs)
vector = FAISS.from_documents(documents, OpenAIEmbeddings())
retriever = vector.as_retriever()
result1 = retriever.get_relevant_documents("how to upload a dataset")[0]
print(f"result1: {result1}")

# 转换为工具
retriever_tool = create_retriever_tool(
    retriever,
    "langsmith_search",
    "Search for information about LangSmith. For any questions about LangSmith, you must use this tool!",
)

# 定义工具
tools = [search, retriever_tool]
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

# 从Hub加载Prompt，自己写加载进来也一样
# SYSTEM
#
# You are a helpful assistant
#
# PLACEHOLDER
#
# chat_history
# HUMAN
#
# {input}
#
# PLACEHOLDER
#
# agent_scratchpad
prompt = hub.pull("hwchase17/openai-functions-agent")
print(f"prompt message: {prompt.messages}")

# 新建Agent
agent = create_openai_functions_agent(llm, tools, prompt)
# Agent执行器
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# 尝试执行
message2 = agent_executor.invoke({"input": "hi!"})
print(f"message2: {message2}")

# 执行任务
message3 = agent_executor.invoke({"input": "how can langsmith help with testing?"})
print(f"message3: {message3}")


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70

运行结果

➜ python3 test24.py
result1: page_content="about LangSmith:User Guide: Learn about the workflows LangSmith supports at each stage of the LLM application lifecycle.Setup: Learn how to create an account, obtain an API key, and configure your environment.Pricing: Learn about the pricing model for LangSmith.Self-Hosting: Learn about self-hosting options for LangSmith.Proxy: Learn about the proxy capabilities of LangSmith.Tracing: Learn about the tracing capabilities of LangSmith.Evaluation: Learn about the evaluation capabilities of LangSmith.Prompt Hub Learn about the Prompt Hub, a prompt management tool built into LangSmith.Additional Resources\u200bLangSmith Cookbook: A collection of tutorials and end-to-end walkthroughs using LangSmith.LangChain Python: Docs for the Python LangChain library.LangChain Python API Reference: documentation to review the core APIs of LangChain.LangChain JS: Docs for the TypeScript LangChain libraryDiscord: Join us on our Discord to discuss all things LangChain!Contact SalesIf you're interested in" metadata={'source': 'https://docs.smith.langchain.com/overview', 'title': 'LangSmith | 
声明：本文内容由网友自发贡献，不代表【wpsshop博客】立场，版权归原作者所有，本站不承担相应法律责任。如您发现有侵权的内容，请联系我们。转载请注明出处：https://www.wpsshop.cn/w/我家小花儿/article/detail/916506