langchain/docs/extras/modules/memory/adding_memory.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "00695447",
"metadata": {},
"source": [
"# How to add Memory to an LLMChain\n",
"\n",
"This notebook goes over how to use the Memory class with an LLMChain. For the purposes of this walkthrough, we will add the `ConversationBufferMemory` class, although this can be any memory class."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9f1aaf47",
"metadata": {},
"outputs": [],
"source": [
"from langchain.memory import ConversationBufferMemory\n",
"from langchain import OpenAI, LLMChain, PromptTemplate"
]
},
{
"cell_type": "markdown",
"id": "4b066ced",
"metadata": {},
"source": [
"The most important step is setting up the prompt correctly. In the below prompt, we have two input keys: one for the actual input, another for the input from the Memory class. Importantly, we make sure the keys in the PromptTemplate and the ConversationBufferMemory match up (`chat_history`)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e5501eda",
"metadata": {},
"outputs": [],
"source": [
"template = \"\"\"You are a chatbot having a conversation with a human.\n",
"\n",
"{chat_history}\n",
"Human: {human_input}\n",
"Chatbot:\"\"\"\n",
"\n",
"prompt = PromptTemplate(\n",
" input_variables=[\"chat_history\", \"human_input\"], template=template\n",
")\n",
"memory = ConversationBufferMemory(memory_key=\"chat_history\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f6566275",
"metadata": {},
"outputs": [],
"source": [
"llm_chain = LLMChain(\n",
" llm=OpenAI(),\n",
" prompt=prompt,\n",
" verbose=True,\n",
" memory=memory,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e2b189dc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
"\n",
"\n",
"Human: Hi there my friend\n",
"Chatbot:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"' Hi there, how are you doing today?'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llm_chain.predict(human_input=\"Hi there my friend\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a902729f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
"\n",
"\n",
"Human: Hi there my friend\n",
"AI: Hi there, how are you doing today?\n",
Update adding_memory.ipynb (#5806) just change "to" to "too" so it matches the above prompt <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
2023-06-07 05:10:53 +00:00
"Human: Not too bad - how are you?\n",
"Chatbot:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\" I'm doing great, thank you for asking!\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llm_chain.predict(human_input=\"Not too bad - how are you?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ae5309bb",
"metadata": {},
"outputs": [],
"source": []
}
],
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"language": "python",
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"language_info": {
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