mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
87e502c6bc
Co-authored-by: jacoblee93 <jacoblee93@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
357 lines
9.3 KiB
Plaintext
357 lines
9.3 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "44c9933a",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Conversation Knowledge Graph Memory\n",
|
|
"\n",
|
|
"This type of memory uses a knowledge graph to recreate memory.\n",
|
|
"\n",
|
|
"Let's first walk through how to use the utilities"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "f71f40ba",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.memory import ConversationKGMemory\n",
|
|
"from langchain.llms import OpenAI"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "2f4a3c85",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"llm = OpenAI(temperature=0)\n",
|
|
"memory = ConversationKGMemory(llm=llm)\n",
|
|
"memory.save_context({\"input\": \"say hi to sam\"}, {\"output\": \"who is sam\"})\n",
|
|
"memory.save_context({\"input\": \"sam is a friend\"}, {\"output\": \"okay\"})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"id": "72283b4f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'history': 'On Sam: Sam is friend.'}"
|
|
]
|
|
},
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"memory.load_memory_variables({\"input\": \"who is sam\"})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0c8ff11e",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "44df43af",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"memory = ConversationKGMemory(llm=llm, return_messages=True)\n",
|
|
"memory.save_context({\"input\": \"say hi to sam\"}, {\"output\": \"who is sam\"})\n",
|
|
"memory.save_context({\"input\": \"sam is a friend\"}, {\"output\": \"okay\"})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "4726b1c8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'history': [SystemMessage(content='On Sam: Sam is friend.', additional_kwargs={})]}"
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"memory.load_memory_variables({\"input\": \"who is sam\"})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "dc956b0e",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can also more modularly get current entities from a new message (will use previous messages as context.)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "36331ca5",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['Sam']"
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"memory.get_current_entities(\"what's Sams favorite color?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e8749134",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can also more modularly get knowledge triplets from a new message (will use previous messages as context.)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "b02d44db",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[KnowledgeTriple(subject='Sam', predicate='favorite color', object_='red')]"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"memory.get_knowledge_triplets(\"her favorite color is red\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f7a02ef3",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Using in a chain\n",
|
|
"Let's now use this in a chain!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "b462baf1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"llm = OpenAI(temperature=0)\n",
|
|
"from langchain.prompts.prompt import PromptTemplate\n",
|
|
"from langchain.chains import ConversationChain\n",
|
|
"\n",
|
|
"template = \"\"\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
|
|
"If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.\n",
|
|
"\n",
|
|
"Relevant Information:\n",
|
|
"\n",
|
|
"{history}\n",
|
|
"\n",
|
|
"Conversation:\n",
|
|
"Human: {input}\n",
|
|
"AI:\"\"\"\n",
|
|
"prompt = PromptTemplate(input_variables=[\"history\", \"input\"], template=template)\n",
|
|
"conversation_with_kg = ConversationChain(\n",
|
|
" llm=llm, verbose=True, prompt=prompt, memory=ConversationKGMemory(llm=llm)\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "97efaf38",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"\n",
|
|
"\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
|
|
"Prompt after formatting:\n",
|
|
"\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
|
|
"If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.\n",
|
|
"\n",
|
|
"Relevant Information:\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"Conversation:\n",
|
|
"Human: Hi, what's up?\n",
|
|
"AI:\u001b[0m\n",
|
|
"\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"\" Hi there! I'm doing great. I'm currently in the process of learning about the world around me. I'm learning about different cultures, languages, and customs. It's really fascinating! How about you?\""
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"conversation_with_kg.predict(input=\"Hi, what's up?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "55b5bcad",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"\n",
|
|
"\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
|
|
"Prompt after formatting:\n",
|
|
"\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
|
|
"If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.\n",
|
|
"\n",
|
|
"Relevant Information:\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"Conversation:\n",
|
|
"Human: My name is James and I'm helping Will. He's an engineer.\n",
|
|
"AI:\u001b[0m\n",
|
|
"\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"\" Hi James, it's nice to meet you. I'm an AI and I understand you're helping Will, the engineer. What kind of engineering does he do?\""
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"conversation_with_kg.predict(\n",
|
|
" input=\"My name is James and I'm helping Will. He's an engineer.\"\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "9981e219",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"\n",
|
|
"\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
|
|
"Prompt after formatting:\n",
|
|
"\u001b[32;1m\u001b[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. \n",
|
|
"If the AI does not know the answer to a question, it truthfully says it does not know. The AI ONLY uses information contained in the \"Relevant Information\" section and does not hallucinate.\n",
|
|
"\n",
|
|
"Relevant Information:\n",
|
|
"\n",
|
|
"On Will: Will is an engineer.\n",
|
|
"\n",
|
|
"Conversation:\n",
|
|
"Human: What do you know about Will?\n",
|
|
"AI:\u001b[0m\n",
|
|
"\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"' Will is an engineer.'"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"conversation_with_kg.predict(input=\"What do you know about Will?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "8c09a239",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|