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https://github.com/hwchase17/langchain
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docs: add serialization guide (#23223)
This commit is contained in:
ccurme
GitHub
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commit
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@ -225,6 +225,8 @@ All of LangChain components can easily be extended to support your own versions.
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- [How to: create custom callback handlers](/docs/how_to/custom_callbacks)
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- [How to: create custom callback handlers](/docs/how_to/custom_callbacks)
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- [How to: define a custom tool](/docs/how_to/custom_tools)
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- [How to: define a custom tool](/docs/how_to/custom_tools)
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### Serialization
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- [How to: save and load LangChain objects](/docs/how_to/serialization)
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## Use cases
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## Use cases
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305
docs/docs/how_to/serialization.ipynb
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docs/docs/how_to/serialization.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ab3dc782-321e-4503-96ee-ac88a15e4b5e",
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"metadata": {},
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"source": [
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"# How to save and load LangChain objects\n",
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"\n",
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"LangChain classes implement standard methods for serialization. Serializing LangChain objects using these methods confer some advantages:\n",
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"\n",
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"- Secrets, such as API keys, are separated from other parameters and can be loaded back to the object on de-serialization;\n",
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"- 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.\n",
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"\n",
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"To save and load LangChain objects using this system, use the `dumpd`, `dumps`, `load`, and `loads` functions in the [load module](https://api.python.langchain.com/en/latest/core_api_reference.html#module-langchain_core.load) of `langchain-core`. These functions support JSON and JSON-serializable objects.\n",
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"\n",
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"All LangChain objects that inherit from [Serializable](https://api.python.langchain.com/en/latest/load/langchain_core.load.serializable.Serializable.html) are JSON-serializable. Examples include [messages](https://api.python.langchain.com/en/latest/core_api_reference.html#module-langchain_core.messages), [document objects](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) (e.g., as returned from [retrievers](/docs/concepts/#retrievers)), and most [Runnables](/docs/concepts/#langchain-expression-language-lcel), such as chat models, retrievers, and [chains](/docs/how_to/sequence) implemented with the LangChain Expression Language.\n",
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"\n",
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"Below we walk through an example with a simple [LLM chain](/docs/tutorials/llm_chain).\n",
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"\n",
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":::{.callout-caution}\n",
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"\n",
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"De-serialization using `load` and `loads` can instantiate any serializable LangChain object. Only use this feature with trusted inputs!\n",
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"\n",
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"De-serialization is a beta feature and is subject to change.\n",
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":::"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "f85d9e51-2a36-4f69-83b1-c716cd43f790",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.load import dumpd, dumps, load, loads\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", \"Translate the following into {language}:\"),\n",
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" (\"user\", \"{text}\"),\n",
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" ],\n",
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")\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", api_key=\"llm-api-key\")\n",
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"\n",
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"chain = prompt | llm"
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]
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},
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{
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"cell_type": "markdown",
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"id": "356ea99f-5cb5-4433-9a6c-2443d2be9ed3",
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"metadata": {},
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"source": [
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"## Saving objects\n",
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"\n",
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"### To json"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "26516764-d46b-4357-a6c6-bd8315bfa530",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\n",
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" \"lc\": 1,\n",
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" \"type\": \"constructor\",\n",
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" \"id\": [\n",
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" \"langchain\",\n",
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" \"schema\",\n",
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" \"runnable\",\n",
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" \"RunnableSequence\"\n",
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" ],\n",
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" \"kwargs\": {\n",
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" \"first\": {\n",
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" \"lc\": 1,\n",
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" \"type\": \"constructor\",\n",
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" \"id\": [\n",
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" \"langchain\",\n",
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" \"prompts\",\n",
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" \"chat\",\n",
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" \"ChatPromptTemplate\"\n",
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" ],\n",
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" \"kwargs\": {\n",
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" \"input_variables\": [\n",
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" \"language\",\n",
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" \"text\"\n",
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" ],\n",
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" \"messages\": [\n",
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" {\n",
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" \"lc\": 1,\n",
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" \"type\": \"constructor\",\n",
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" \n"
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]
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}
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],
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"source": [
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"string_representation = dumps(chain, pretty=True)\n",
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"print(string_representation[:500])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bd425716-545d-466b-a4e5-dc9952cfd72a",
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"metadata": {},
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"source": [
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"### To a json-serializable Python dict"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "6561a968-1741-4419-8c29-e705b9d0ef39",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'dict'>\n"
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]
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}
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],
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"source": [
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"dict_representation = dumpd(chain)\n",
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"\n",
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"print(type(dict_representation))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "711e986e-dd24-4839-9e38-c57903378a5f",
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"metadata": {},
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"source": [
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"### To disk"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f818378b-f4d6-43a7-895b-76cf7359b157",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"with open(\"/tmp/chain.json\", \"w\") as fp:\n",
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" json.dump(string_representation, fp)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1e621a32-ff5f-4627-ad59-88cacba73c6b",
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"metadata": {},
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"source": [
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"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:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "8225e150-000a-4fbc-9f3d-09568f4b560b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'openai_api_key': 'OPENAI_API_KEY'}"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain.last.lc_secrets"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6d090177-eb1c-4bfb-8c13-29286afe17d9",
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"metadata": {},
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"source": [
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"## Loading objects\n",
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"\n",
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"Specifying `secrets_map` in `load` and `loads` will load the corresponding secrets onto the de-serialized LangChain object.\n",
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"\n",
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"### From string"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "54a66267-5f3a-40a2-bfcc-8b44bb24c154",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = loads(string_representation, secrets_map={\"OPENAI_API_KEY\": \"llm-api-key\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5ed9aff1-92cc-44ba-b2ec-4d12f924fa03",
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"metadata": {},
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"source": [
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"### From dict"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "76979932-13de-4427-9f88-040fb05a6778",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = load(dict_representation, secrets_map={\"OPENAI_API_KEY\": \"llm-api-key\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7dd81a2a-5163-414d-ab42-f1c35e30471b",
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"metadata": {},
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"source": [
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"### From disk"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "033f62a7-3377-472a-be58-718baa6ab445",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"/tmp/chain.json\", \"r\") as fp:\n",
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" chain = loads(json.load(fp), secrets_map={\"OPENAI_API_KEY\": \"llm-api-key\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dc520fdb-035a-468f-a8a8-c3ffe8ed98eb",
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"metadata": {},
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"source": [
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"Note that we recover the API key specified at the start of the guide:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "566b2475-d9b4-432b-8c3b-27c2f183624e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'llm-api-key'"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain.last.openai_api_key.get_secret_value()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7b4cba53-e1d5-4979-927e-b5794a02afc3",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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Block a user