mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +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
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "44c9933a",
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"metadata": {},
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"source": [
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"# Conversation Knowledge Graph Memory\n",
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"\n",
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"This type of memory uses a knowledge graph to recreate memory.\n",
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"\n",
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"Let's first walk through how to use the utilities"
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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": "f71f40ba",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.memory import ConversationKGMemory\n",
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"from langchain.llms import OpenAI"
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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": "2f4a3c85",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = OpenAI(temperature=0)\n",
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"memory = ConversationKGMemory(llm=llm)\n",
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"memory.save_context({\"input\": \"say hi to sam\"}, {\"output\": \"who is sam\"})\n",
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"memory.save_context({\"input\": \"sam is a friend\"}, {\"output\": \"okay\"})"
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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": "72283b4f",
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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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"{'history': 'On Sam: Sam is friend.'}"
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]
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},
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"execution_count": 3,
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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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"memory.load_memory_variables({\"input\": \"who is sam\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0c8ff11e",
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"metadata": {},
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"source": [
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"We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
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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": "44df43af",
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"metadata": {},
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"outputs": [],
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"source": [
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"memory = ConversationKGMemory(llm=llm, return_messages=True)\n",
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"memory.save_context({\"input\": \"say hi to sam\"}, {\"output\": \"who is sam\"})\n",
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"memory.save_context({\"input\": \"sam is a friend\"}, {\"output\": \"okay\"})"
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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": "4726b1c8",
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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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"{'history': [SystemMessage(content='On Sam: Sam is friend.', additional_kwargs={})]}"
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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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"memory.load_memory_variables({\"input\": \"who is sam\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dc956b0e",
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"metadata": {},
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"source": [
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"We can also more modularly get current entities from a new message (will use previous messages as context.)"
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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": "36331ca5",
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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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"['Sam']"
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]
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},
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"execution_count": 9,
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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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"memory.get_current_entities(\"what's Sams favorite color?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e8749134",
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"metadata": {},
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"source": [
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"We can also more modularly get knowledge triplets from a new message (will use previous messages as context.)"
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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": "b02d44db",
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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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"[KnowledgeTriple(subject='Sam', predicate='favorite color', object_='red')]"
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]
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},
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"execution_count": 10,
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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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"memory.get_knowledge_triplets(\"her favorite color is red\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f7a02ef3",
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"metadata": {},
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"source": [
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"## Using in a chain\n",
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"Let's now use this in a 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": 7,
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"id": "b462baf1",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = OpenAI(temperature=0)\n",
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"from langchain.prompts.prompt import PromptTemplate\n",
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"from langchain.chains import ConversationChain\n",
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"\n",
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"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",
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"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",
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"\n",
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"Relevant Information:\n",
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"\n",
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"{history}\n",
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"\n",
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"Conversation:\n",
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"Human: {input}\n",
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"AI:\"\"\"\n",
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"prompt = PromptTemplate(input_variables=[\"history\", \"input\"], template=template)\n",
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"conversation_with_kg = ConversationChain(\n",
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" llm=llm, verbose=True, prompt=prompt, memory=ConversationKGMemory(llm=llm)\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": 8,
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"id": "97efaf38",
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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 ConversationChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\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",
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"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",
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"\n",
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"Relevant Information:\n",
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"\n",
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"\n",
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"\n",
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"Conversation:\n",
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"Human: Hi, what's up?\n",
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"AI:\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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"\" 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?\""
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]
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},
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"execution_count": 8,
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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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"conversation_with_kg.predict(input=\"Hi, what's up?\")"
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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": "55b5bcad",
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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 ConversationChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\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",
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"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",
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"\n",
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"Relevant Information:\n",
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"\n",
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"\n",
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"\n",
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"Conversation:\n",
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"Human: My name is James and I'm helping Will. He's an engineer.\n",
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"AI:\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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"\" 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?\""
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]
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},
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"execution_count": 9,
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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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"conversation_with_kg.predict(\n",
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" input=\"My name is James and I'm helping Will. He's an engineer.\"\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": 10,
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"id": "9981e219",
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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 ConversationChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\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",
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"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",
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"\n",
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"Relevant Information:\n",
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"\n",
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"On Will: Will is an engineer.\n",
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"\n",
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"Conversation:\n",
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"Human: What do you know about Will?\n",
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"AI:\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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"' Will is an engineer.'"
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]
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},
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"execution_count": 10,
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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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"conversation_with_kg.predict(input=\"What do you know about Will?\")"
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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": "8c09a239",
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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": "3.9.1"
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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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