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如何处理未生成任何查询的情况

有时，查询分析技术可能会允许生成任意数量的查询——包括零个查询！在这种情况下，我们的整体链需要在决定是否调用检索器之前，先检查查询分析的结果。

我们将为这个示例使用模拟数据。

设置​

安装依赖​

%pip install -qU langchain langchain-community langchain-openai langchain-chroma
Note: you may need to restart the kernel to use updated packages.

设置环境变量​

我们将在本示例中使用 OpenAI：

import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass()

# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

创建索引​

我们将基于虚假信息创建一个向量存储。

from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter

texts = ["Harrison worked at Kensho"]
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = Chroma.from_texts(
    texts,
    embeddings,
)
retriever = vectorstore.as_retriever()

查询分析​

我们将使用函数调用来结构化输出。然而，我们会配置大语言模型（LLM），使其无需调用代表搜索查询的函数（即使它决定不这样做）。随后，我们还将使用一个提示词来进行查询分析，明确指出何时应该以及不应该进行搜索。

from typing import Optional

from pydantic import BaseModel, Field


class Search(BaseModel):
    """Search over a database of job records."""

    query: str = Field(
        ...,
        description="Similarity search query applied to job record.",
    )
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

system = """You have the ability to issue search queries to get information to help answer user information.

You do not NEED to look things up. If you don't need to, then just respond normally."""
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        ("human", "{question}"),
    ]
)
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
structured_llm = llm.bind_tools([Search])
query_analyzer = {"question": RunnablePassthrough()} | prompt | structured_llm

我们可以看到，通过调用它，我们会收到一条消息，该消息有时（但并非总是）会返回工具调用。

query_analyzer.invoke("where did Harrison Work")
AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_korLZrh08PTRL94f4L7rFqdj', 'function': {'arguments': '{"query":"Harrison"}', 'name': 'Search'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 95, 'total_tokens': 109}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_483d39d857', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-ea94d376-37bf-4f80-abe6-e3b42b767ea0-0', tool_calls=[{'name': 'Search', 'args': {'query': 'Harrison'}, 'id': 'call_korLZrh08PTRL94f4L7rFqdj', 'type': 'tool_call'}], usage_metadata={'input_tokens': 95, 'output_tokens': 14, 'total_tokens': 109})
query_analyzer.invoke("hi!")
AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 93, 'total_tokens': 103}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_483d39d857', 'finish_reason': 'stop', 'logprobs': None}, id='run-ebdfc44a-455a-4ca6-be85-84559886b1e1-0', usage_metadata={'input_tokens': 93, 'output_tokens': 10, 'total_tokens': 103})

检索与查询分析​

那么我们将如何将其包含在链中呢？让我们看下面的例子。

from langchain_core.output_parsers.openai_tools import PydanticToolsParser
from langchain_core.runnables import chain

output_parser = PydanticToolsParser(tools=[Search])
@chain
def custom_chain(question):
    response = query_analyzer.invoke(question)
    if "tool_calls" in response.additional_kwargs:
        query = output_parser.invoke(response)
        docs = retriever.invoke(query[0].query)
        # Could add more logic - like another LLM call - here
        return docs
    else:
        return response
custom_chain.invoke("where did Harrison Work")
Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1
[Document(page_content='Harrison worked at Kensho')]
custom_chain.invoke("hi!")
AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 93, 'total_tokens': 103}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_483d39d857', 'finish_reason': 'stop', 'logprobs': None}, id='run-e87f058d-30c0-4075-8a89-a01b982d557e-0', usage_metadata={'input_tokens': 93, 'output_tokens': 10, 'total_tokens': 103})