How to save and load LangChain objects

LangChain classes implement standard methods for serialization. Serializing LangChain objects using these methods confer some advantages:

• Secrets, such as API keys, are separated from other parameters and can be loaded back to the object on de-serialization;
• De-serialization is kept compatible across package versions, so objects that were serialized with one version of LangChain can be properly de-serialized with another.

To save and load LangChain objects using this system, use the dumpd, dumps, load, and loads functions in the load module of langchain-core. These functions support JSON and JSON-serializable objects.

All LangChain objects that inherit from Serializable are JSON-serializable. Examples include messages, document objects (e.g., as returned from retrievers), and most Runnables, such as chat models, retrievers, and chains implemented with the LangChain Expression Language.

Below we walk through an example with a simple LLM chain.

caution

De-serialization using load and loads can instantiate any serializable LangChain object. Only use this feature with trusted inputs!

De-serialization is a beta feature and is subject to change.

from langchain_core.load import dumpd, dumps, load, loads
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "Translate the following into {language}:"),
        ("user", "{text}"),
    ],
)

llm = ChatOpenAI(model="gpt-3.5-turbo-0125", api_key="llm-api-key")

chain = prompt | llm

API Reference:dumpd | dumps | load | loads | ChatPromptTemplate | ChatOpenAI

Saving objects

To json

string_representation = dumps(chain, pretty=True)
print(string_representation[:500])

{
  "lc": 1,
  "type": "constructor",
  "id": [
    "langchain",
    "schema",
    "runnable",
    "RunnableSequence"
  ],
  "kwargs": {
    "first": {
      "lc": 1,
      "type": "constructor",
      "id": [
        "langchain",
        "prompts",
        "chat",
        "ChatPromptTemplate"
      ],
      "kwargs": {
        "input_variables": [
          "language",
          "text"
        ],
        "messages": [
          {
            "lc": 1,
            "type": "constructor",

To a json-serializable Python dict

dict_representation = dumpd(chain)

print(type(dict_representation))

<class 'dict'>

To disk

import json

with open("/tmp/chain.json", "w") as fp:
    json.dump(string_representation, fp)

Note that the API key is withheld from the serialized representations. Parameters that are considered secret are specified by the .lc_secrets attribute of the LangChain object:

chain.last.lc_secrets

{'openai_api_key': 'OPENAI_API_KEY'}

Loading objects

Specifying secrets_map in load and loads will load the corresponding secrets onto the de-serialized LangChain object.

From string

chain = loads(string_representation, secrets_map={"OPENAI_API_KEY": "llm-api-key"})

From dict

chain = load(dict_representation, secrets_map={"OPENAI_API_KEY": "llm-api-key"})

From disk

with open("/tmp/chain.json", "r") as fp:
    chain = loads(json.load(fp), secrets_map={"OPENAI_API_KEY": "llm-api-key"})

Note that we recover the API key specified at the start of the guide:

chain.last.openai_api_key.get_secret_value()

'llm-api-key'