序列化

本文档介绍了如何将链条序列化到磁盘和从磁盘反序列化。我们使用的序列化格式是json或yaml。目前，只有一些链条支持这种类型的序列化。随着时间的推移，我们将增加支持的链条数量。

将链条保存到磁盘

首先，让我们介绍如何将链条保存到磁盘。可以使用.save方法，并指定一个带有json或yaml扩展名的文件路径。

from langchain import PromptTemplate, OpenAI, LLMChain
template = """Question: {question}

Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)

llm_chain.save("llm_chain.json")

现在让我们来看看保存的文件中的内容。

!cat llm_chain.json

{
    "memory": null,
    "verbose": true,
    "prompt": {
        "input_variables": [
            "question"
        ],
        "output_parser": null,
        "template": "Question: {question}\n\nAnswer: Let's think step by step.",
        "template_format": "f-string"
    },
    "llm": {
        "model_name": "text-davinci-003",
        "temperature": 0.0,
        "max_tokens": 256,
        "top_p": 1,
        "frequency_penalty": 0,
        "presence_penalty": 0,
        "n": 1,
        "best_of": 1,
        "request_timeout": null,
        "logit_bias": {},
        "_type": "openai"
    },
    "output_key": "text",
    "_type": "llm_chain"
}

从磁盘加载链条

我们可以使用load_chain方法从磁盘加载链条。

from langchain.chains import load_chain

chain = load_chain("llm_chain.json")

chain.run("whats 2 + 2")

> Entering new LLMChain chain...
Prompt after formatting:
Question: whats 2 + 2

Answer: Let's think step by step.

> Finished chain.

' 2 + 2 = 4'

分别保存组件

在上面的例子中，我们可以看到提示和LLM配置信息与整个链条保存在同一个json中。或者，我们可以将它们分开保存。这通常有助于使保存的组件更加模块化。为了做到这一点，我们只需要指定llm_path而不是llm组件，并且指定prompt_path而不是prompt组件。

llm_chain.prompt.save("prompt.json")

!cat prompt.json

{
    "input_variables": [
        "question"
    ],
    "output_parser": null,
    "template": "Question: {question}\n\nAnswer: Let's think step by step.",
    "template_format": "f-string"
}

llm_chain.llm.save("llm.json")

!cat llm.json

{
    "model_name": "text-davinci-003",
    "temperature": 0.0,
    "max_tokens": 256,
    "top_p": 1,
    "frequency_penalty": 0,
    "presence_penalty": 0,
    "n": 1,
    "best_of": 1,
    "request_timeout": null,
    "logit_bias": {},
    "_type": "openai"
}

config = {
    "memory": None,
    "verbose": True,
    "prompt_path": "prompt.json",
    "llm_path": "llm.json",
    "output_key": "text",
    "_type": "llm_chain"
}
import json
with open("llm_chain_separate.json", "w") as f:
    json.dump(config, f, indent=2)

!cat llm_chain_separate.json

{
  "memory": null,
  "verbose": true,
  "prompt_path": "prompt.json",
  "llm_path": "llm.json",
  "output_key": "text",
  "_type": "llm_chain"
}

我们可以以相同的方式加载它。

chain = load_chain("llm_chain_separate.json")

chain.run("whats 2 + 2")

> Entering new LLMChain chain...
Prompt after formatting:
Question: whats 2 + 2

Answer: Let's think step by step.

> Finished chain.

' 2 + 2 = 4'