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Serialization
 Contents 
Saving a chain to disk
Loading a chain from disk
Saving components separately
Serialization#
This notebook covers how to serialize chains to and from disk. The serialization format we use is json or yaml. Currently, only some chains support this type of serialization. We will grow the number of supported chains over time.
Saving a chain to disk#
First, let’s go over how to save a chain to disk. This can be done with the .save method, and specifying a file path with a json or yaml extension.
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")
Let’s now take a look at what’s inside this saved file
!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,
"best_of": 1,
        "request_timeout": null,
        "logit_bias": {},
        "_type": "openai"
    },
    "output_key": "text",
    "_type": "llm_chain"
}
Loading a chain from disk#
We can load a chain from disk by using the load_chain method.
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'
Saving components separately#
In the above example, we can see that the prompt and llm configuration information is saved in the same json as the overall chain. Alternatively, we can split them up and save them separately. This is often useful to make the saved components more modular. In order to do this, we just need to specify llm_path instead of the llm component, and prompt_path instead of the prompt component.
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,
"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"
}
We can then load it in the same way
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'

Sequential Chains

Transformation Chain
 Contents
  
Saving a chain to disk
Loading a chain from disk
Saving components separately
By Harrison Chase