LangChain

即插即用#

此文档建立在工具检索的基础上，但从plugnplai中获取所有工具-一个人工智能插件目录。

设置环境#

导入所需的内容，等等。

安装plugnplai lib以从https://plugplai.com目录获取活动插件列表 (opens in a new tab)

pip install plugnplai -q

[notice] A new release of pip available: 22.3.1 -> 23.1.1
 
[notice] To update, run: pip install --upgrade pip
 
Note: you may need to restart the kernel to use updated packages.

from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
 
from langchain.prompts import StringPromptTemplate
 
from langchain import OpenAI, SerpAPIWrapper, LLMChain
 
from typing import List, Union
 
from langchain.schema import AgentAction, AgentFinish
 
from langchain.agents.agent_toolkits import NLAToolkit
 
from langchain.tools.plugin import AIPlugin
 
import re
 
import plugnplai

设置LLM#

llm = OpenAI(temperature=0)

设置插件#

加载和索引插件

# Get all plugins from plugnplai.com
 
urls = plugnplai.get_plugins()
 
 
 
# Get ChatGPT plugins - only ChatGPT verified plugins
 
urls = plugnplai.get_plugins(filter = 'ChatGPT')
 
 
 
# Get working plugins - only tested plugins (in progress)
 
urls = plugnplai.get_plugins(filter = 'working')
 
 
 
 
 
AI_PLUGINS = [AIPlugin.from_url(url + "/.well-known/ai-plugin.json") for url in urls]

工具检索#

我们将使用向量存储为每个工具描述创建嵌入。然后，对于传入的查询，我们可以为该查询创建嵌入，并进行相似性搜索以查找相关工具。

 
from langchain.vectorstores import FAISS
 
from langchain.embeddings import OpenAIEmbeddings
 
from langchain.schema import Document

 
embeddings = OpenAIEmbeddings()
 
docs = [
 
    Document(page_content=plugin.description_for_model, 
 
             metadata={"plugin_name": plugin.name_for_model}
 
            )
 
    for plugin in AI_PLUGINS
 
]
 
vector_store = FAISS.from_documents(docs, embeddings)
 
toolkits_dict = {plugin.name_for_model: 
 
                 NLAToolkit.from_llm_and_ai_plugin(llm, plugin) 
 
                 for plugin in AI_PLUGINS}

 
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
 
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
 
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
 
Attempting to load an OpenAPI 3.0.2 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
 
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
 
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
 
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
 
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
 
Attempting to load a Swagger 2.0 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.

 
retriever = vector_store.as_retriever()
 
 
 
def get_tools(query):
 
    # Get documents, which contain the Plugins to use
 
    docs = retriever.get_relevant_documents(query)
 
    # Get the toolkits, one for each plugin
 
    tool_kits = [toolkits_dict[d.metadata["plugin_name"]] for d in docs]
 
    # Get the tools: a separate NLAChain for each endpoint
 
    tools = []
 
    for tk in tool_kits:
 
        tools.extend(tk.nla_tools)
 
    return tools

我们现在可以测试这个检索器，看看它是否有效。

 
tools = get_tools("What could I do today with my kiddo")
 
[t.name for t in tools]

 
['Milo.askMilo',
 
 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.search_all_actions',
 
 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.preview_a_zap',
 
 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.get_configuration_link',
 
 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.list_exposed_actions',
 
 'SchoolDigger_API_V2.0.Autocomplete_GetSchools',
 
 'SchoolDigger_API_V2.0.Districts_GetAllDistricts2',
 
 'SchoolDigger_API_V2.0.Districts_GetDistrict2',
 
 'SchoolDigger_API_V2.0.Rankings_GetSchoolRank2',
 
 'SchoolDigger_API_V2.0.Rankings_GetRank_District',
 
 'SchoolDigger_API_V2.0.Schools_GetAllSchools20',
 
 'SchoolDigger_API_V2.0.Schools_GetSchool20',
 
 'Speak.translate',
 
 'Speak.explainPhrase',
 
 'Speak.explainTask']

 
tools = get_tools("what shirts can i buy?")
 
[t.name for t in tools]

 
['Open_AI_Klarna_product_Api.productsUsingGET',
 
 'Milo.askMilo',
 
 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.search_all_actions',
 
 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.preview_a_zap',
 
 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.get_configuration_link',
 
 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.list_exposed_actions',
 
 'SchoolDigger_API_V2.0.Autocomplete_GetSchools',
 
 'SchoolDigger_API_V2.0.Districts_GetAllDistricts2',
 
 'SchoolDigger_API_V2.0.Districts_GetDistrict2',
 
 'SchoolDigger_API_V2.0.Rankings_GetSchoolRank2',
 
 'SchoolDigger_API_V2.0.Rankings_GetRank_District',
 
 'SchoolDigger_API_V2.0.Schools_GetAllSchools20',
 
 'SchoolDigger_API_V2.0.Schools_GetSchool20']

提示模板 Prompt Template

提示模板非常标准，因为实际上我们在实际提示模板中并没有改变太多逻辑，而是只是更改了检索方式。

 
# Set up the base template
 
template = """Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:
 
 
 
{tools}
 
 
 
Use the following format:
 
 
 
Question: the input question you must answer
 
Thought: you should always think about what to do
 
Action: the action to take, should be one of [{tool_names}]
 
Action Input: the input to the action
 
Observation: the result of the action
 
... (this Thought/Action/Action Input/Observation can repeat N times)
 
Thought: I now know the final answer
 
Final Answer: the final answer to the original input question
 
 
 
Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Arg"s
 
 
 
Question: {input}
 
{agent_scratchpad}"""

自定义提示模板现在有一个tools_getter的概念，我们在input上调用它来选择要使用的工具。

 
from typing import Callable
 
# Set up a prompt template
 
class CustomPromptTemplate(StringPromptTemplate):
 
    # The template to use
 
    template: str
 
    ############## NEW ######################
 
    # The list of tools available
 
    tools_getter: Callable
 
    
 
    def format(self, \*\*kwargs) -> str:
 
        # Get the intermediate steps (AgentAction, Observation tuples)
 
        # Format them in a particular way
 
        intermediate_steps = kwargs.pop("intermediate_steps")
 
        thoughts = ""
 
        for action, observation in intermediate_steps:
 
            thoughts += action.log
 
            thoughts += f"Observation: {observation}Thought: "
 
        # Set the agent_scratchpad variable to that value
 
        kwargs["agent_scratchpad"] = thoughts
 
        ############## NEW ######################
 
        tools = self.tools_getter(kwargs["input"])
 
        # Create a tools variable from the list of tools provided
 
        kwargs["tools"] = "".join([f"{tool.name}: {tool.description}" for tool in tools])
 
        # Create a list of tool names for the tools provided
 
        kwargs["tool_names"] = ", ".join([tool.name for tool in tools])
 
        return self.template.format(\*\*kwargs)

 
prompt = CustomPromptTemplate(
 
    template=template,
 
    tools_getter=get_tools,
 
    # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
 
    # This includes the `intermediate_steps` variable because that is needed
 
    input_variables=["input", "intermediate_steps"]
 
)

输出解析器#

由于我们不改变输出格式，所以输出解析器与上个文档没有变化。

class CustomOutputParser(AgentOutputParser):
 
    
 
    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
 
        # Check if agent should finish
 
        if "Final Answer:" in llm_output:
 
            return AgentFinish(
 
                # Return values is generally always a dictionary with a single `output` key
 
                # It is not recommended to try anything else at the moment :)
 
                return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
 
                log=llm_output,
 
            )
 
        # Parse out the action and action input
 
        regex = r"Action\s\*\d\*\s\*:(.\*?)Action\s\*\d\*\s\*Input\s\*\d\*\s\*:[\s]\*(.\*)"
 
        match = re.search(regex, llm_output, re.DOTALL)
 
        if not match:
 
            raise ValueError(f"Could not parse LLM output: `{llm_output}`")
 
        action = match.group(1).strip()
 
        action_input = match.group(2)
 
        # Return the action and action input
 
        return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)

output_parser = CustomOutputParser()

设置LLM停止序列和代理#

与上个文档相同

llm = OpenAI(temperature=0)

# LLM chain consisting of the LLM and a prompt
 
llm_chain = LLMChain(llm=llm, prompt=prompt)

tool_names = [tool.name for tool in tools]
 
agent = LLMSingleActionAgent(
 
    llm_chain=llm_chain, 
 
    output_parser=output_parser,
 
    stop=["Observation:"], 
 
    allowed_tools=tool_names
 
)

使用代理#

现在我们可以使用它了！

agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)

agent_executor.run("what shirts can i buy?")

> Entering new AgentExecutor chain...
 
Thought: I need to find a product API
 
Action: Open_AI_Klarna_product_Api.productsUsingGET
 
Action Input: shirts
 
 
 
Observation:I found 10 shirts from the API response. They range in price from $9.99 to $450.00 and come in a variety of materials, colors, and patterns. I now know what shirts I can buy
 
Final Answer: Arg, I found 10 shirts from the API response. They range in price from $9.99 to $450.00 and come in a variety of materials, colors, and patterns.
 
 
 
> Finished chain.

'Arg, I found 10 shirts from the API response. They range in price from $9.99 to $450.00 and come in a variety of materials, colors, and patterns.'

Custom Agent with Plugin Retrieval Sales Agent with Context