mirror of
https://github.com/hwchase17/langchain.git
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9ac442624c
This PR: - Increases `qdrant-client` version to 1.0.4 - Introduces custom content and metadata keys (as requested in #1087) - Moves all the `QdrantClient` parameters into the method parameters to simplify code completion
377 lines
8.7 KiB
Plaintext
377 lines
8.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "cbe47c3a",
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"metadata": {},
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"source": [
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"# Serialization\n",
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"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.\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e4a8a447",
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"metadata": {},
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"source": [
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"## Saving a chain to disk\n",
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"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."
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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": 1,
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"id": "26e28451",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain import PromptTemplate, OpenAI, LLMChain\n",
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
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"llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)\n"
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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": "bfa18e1f",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain.save(\"llm_chain.json\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ea82665d",
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"metadata": {},
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"source": [
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"Let's now take a look at what's inside this saved file"
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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": "0fd33328",
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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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"{\r\n",
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" \"memory\": null,\r\n",
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" \"verbose\": true,\r\n",
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" \"prompt\": {\r\n",
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" \"input_variables\": [\r\n",
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" \"question\"\r\n",
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" ],\r\n",
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" \"output_parser\": null,\r\n",
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" \"template\": \"Question: {question}\\n\\nAnswer: Let's think step by step.\",\r\n",
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" \"template_format\": \"f-string\"\r\n",
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" },\r\n",
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" \"llm\": {\r\n",
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" \"model_name\": \"text-davinci-003\",\r\n",
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" \"temperature\": 0.0,\r\n",
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" \"max_tokens\": 256,\r\n",
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" \"top_p\": 1,\r\n",
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" \"frequency_penalty\": 0,\r\n",
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" \"presence_penalty\": 0,\r\n",
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" \"n\": 1,\r\n",
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" \"best_of\": 1,\r\n",
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" \"request_timeout\": null,\r\n",
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" \"logit_bias\": {},\r\n",
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" \"_type\": \"openai\"\r\n",
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" },\r\n",
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" \"output_key\": \"text\",\r\n",
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" \"_type\": \"llm_chain\"\r\n",
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"}"
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]
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}
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],
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"source": [
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"!cat llm_chain.json"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2012c724",
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"metadata": {},
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"source": [
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"## Loading a chain from disk\n",
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"We can load a chain from disk by using the `load_chain` method."
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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": "342a1974",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import load_chain"
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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": "394b7da8",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = load_chain(\"llm_chain.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": 6,
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"id": "20d99787",
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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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"\n",
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"\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
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"Prompt after formatting:\n",
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"\u001B[32;1m\u001B[1;3mQuestion: whats 2 + 2\n",
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"\n",
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"Answer: Let's think step by step.\u001B[0m\n",
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"\n",
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"\u001B[1m> Finished chain.\u001B[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"' 2 + 2 = 4'"
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]
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},
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"execution_count": 6,
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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.run(\"whats 2 + 2\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "14449679",
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"metadata": {},
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"source": [
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"## Saving components separately\n",
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"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."
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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": "50ec35ab",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain.prompt.save(\"prompt.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": 8,
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"id": "c48b39aa",
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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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"{\r\n",
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" \"input_variables\": [\r\n",
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" \"question\"\r\n",
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" ],\r\n",
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" \"output_parser\": null,\r\n",
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" \"template\": \"Question: {question}\\n\\nAnswer: Let's think step by step.\",\r\n",
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" \"template_format\": \"f-string\"\r\n",
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"}"
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]
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}
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],
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"source": [
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"!cat prompt.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": 9,
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"id": "13c92944",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain.llm.save(\"llm.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": 10,
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"id": "1b815f89",
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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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"{\r\n",
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" \"model_name\": \"text-davinci-003\",\r\n",
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" \"temperature\": 0.0,\r\n",
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" \"max_tokens\": 256,\r\n",
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" \"top_p\": 1,\r\n",
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" \"frequency_penalty\": 0,\r\n",
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" \"presence_penalty\": 0,\r\n",
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" \"n\": 1,\r\n",
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" \"best_of\": 1,\r\n",
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" \"request_timeout\": null,\r\n",
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" \"logit_bias\": {},\r\n",
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" \"_type\": \"openai\"\r\n",
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"}"
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]
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}
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],
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"source": [
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"!cat llm.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": 11,
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"id": "7e6aa9ab",
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"metadata": {},
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"outputs": [],
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"source": [
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"config = {\n",
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" \"memory\": None,\n",
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" \"verbose\": True,\n",
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" \"prompt_path\": \"prompt.json\",\n",
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" \"llm_path\": \"llm.json\",\n",
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" \"output_key\": \"text\",\n",
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" \"_type\": \"llm_chain\"\n",
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"}\n",
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"import json\n",
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"with open(\"llm_chain_separate.json\", \"w\") as f:\n",
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" json.dump(config, f, indent=2)"
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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": "8e959ca6",
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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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"{\r\n",
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" \"memory\": null,\r\n",
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" \"verbose\": true,\r\n",
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" \"prompt_path\": \"prompt.json\",\r\n",
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" \"llm_path\": \"llm.json\",\r\n",
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" \"output_key\": \"text\",\r\n",
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" \"_type\": \"llm_chain\"\r\n",
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"}"
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]
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}
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],
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"source": [
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"!cat llm_chain_separate.json"
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]
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},
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{
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"cell_type": "markdown",
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"id": "662731c0",
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"metadata": {},
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"source": [
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"We can then load it in the same way"
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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": 13,
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"id": "d69ceb93",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = load_chain(\"llm_chain_separate.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": 15,
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"id": "a99d61b9",
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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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"\n",
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"\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
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"Prompt after formatting:\n",
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"\u001B[32;1m\u001B[1;3mQuestion: whats 2 + 2\n",
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"\n",
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"Answer: Let's think step by step.\u001B[0m\n",
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"\n",
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"\u001B[1m> Finished chain.\u001B[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"' 2 + 2 = 4'"
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]
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},
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"execution_count": 15,
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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.run(\"whats 2 + 2\")"
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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": "822b7c12",
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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": "3.10.9"
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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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