forked from Archives/langchain
Samantha/add conversation chain (#166)
Add MemoryChain and ConversationChain as chains that take a docstore in addition to the prompt, and use the docstore to stuff context into the prompt. This can be used to have an ongoing conversation with a chatbot. Probably needs a bit of refactoring for code quality Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>harrison/flexible_model_args
Samantha Whitmore
2 years ago
committed by
GitHub
parent
4334ffa6f9
commit
a408ed3ea3
@ -0,0 +1,283 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "ae046bff",
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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 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
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"\n",
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"Current conversation:\n",
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"\n",
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"Human: Hi there!\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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"' Hello! How are you today?'"
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]
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},
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"execution_count": 1,
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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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"from langchain import OpenAI, ConversationChain\n",
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"from langchain.chains.conversation.memory import ConversationSummaryMemory\n",
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"\n",
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"llm = OpenAI(temperature=0)\n",
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"conversation = ConversationChain(llm=llm, verbose=True)\n",
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"\n",
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"conversation.predict(input=\"Hi there!\")"
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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": "d8e2a6ff",
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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 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
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"\n",
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"Current conversation:\n",
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"\n",
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"Human: Hi there!\n",
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"AI: Hello! How are you today?\n",
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"Human: I'm doing well! Just having a conversation with an AI.\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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"\" That's great! What would you like to talk about?\""
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]
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},
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"execution_count": 2,
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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.predict(input=\"I'm doing well! Just having a conversation with an AI.\")"
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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": "15eda316",
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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 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
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"\n",
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"Current conversation:\n",
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"\n",
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"Human: Hi there!\n",
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"AI: Hello! How are you today?\n",
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"Human: I'm doing well! Just having a conversation with an AI.\n",
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"AI: That's great! What would you like to talk about?\n",
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"Human: Tell me about yourself.\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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"' I am an AI created to provide information and support to humans. I enjoy learning and exploring new things.'"
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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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"conversation.predict(input=\"Tell me about yourself.\")"
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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": "b7274f2c",
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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 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
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"\n",
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"Current conversation:\n",
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"\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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"\"\\n\\nI'm doing well, thank you for asking. I'm currently working on a project that I'm really excited about.\""
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]
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},
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"execution_count": 4,
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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_summary = ConversationChain(llm=llm, memory=ConversationSummaryMemory(llm=OpenAI()), verbose=True)\n",
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"conversation_with_summary.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": 5,
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"id": "a6b6b88f",
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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 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
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"\n",
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"Current conversation:\n",
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"\n",
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"The human greets the AI and asks how it is doing. The AI responds that it is doing well and is currently working on a project that it is excited about.\n",
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"Human: Tell me more about it!\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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"\"\\n\\nI'm working on a project that involves helping people to better understand and use artificial intelligence. I'm really excited about it because I think it has the potential to make a big difference in people's lives.\""
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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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"conversation_with_summary.predict(input=\"Tell me more about it!\")"
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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": "dad869fe",
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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 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
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"\n",
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"Current conversation:\n",
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"\n",
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"\n",
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"The human greets the AI and asks how it is doing. The AI responds that it is doing well and is currently working on a project that it is excited about - a project that involves helping people to better understand and use artificial intelligence.\n",
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"Human: Very cool -- what is the scope of the project?\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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"'\\n\\nThe project is still in the early stages, but the goal is to create a resource that will help people to understand artificial intelligence and how to use it effectively.'"
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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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"conversation_with_summary.predict(input=\"Very cool -- what is the scope of the project?\")"
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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": "0eb11bd0",
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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": "3.7.6"
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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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@ -0,0 +1 @@
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"""Chain that carries on a conversation from a prompt plus history."""
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@ -0,0 +1,61 @@
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"""Chain that carries on a conversation and calls an LLM."""
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from typing import Dict, List
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from pydantic import BaseModel, Extra, Field, root_validator
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from langchain.chains.base import Memory
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from langchain.chains.conversation.memory import ConversationBufferMemory
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from langchain.chains.conversation.prompt import PROMPT
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from langchain.chains.llm import LLMChain
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from langchain.prompts.base import BasePromptTemplate
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class ConversationChain(LLMChain, BaseModel):
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"""Chain to have a conversation and load context from memory.
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Example:
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.. code-block:: python
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from langchain import ConversationChain, OpenAI
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conversation = ConversationChain(llm=OpenAI())
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"""
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memory: Memory = Field(default_factory=ConversationBufferMemory)
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"""Default memory store."""
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prompt: BasePromptTemplate = PROMPT
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"""Default conversation prompt to use."""
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input_key: str = "input" #: :meta private:
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output_key: str = "response" #: :meta private:
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buffer: str = "" #: :meta private:
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@property
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def input_keys(self) -> List[str]:
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"""Use this since so some prompt vars come from history."""
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return [self.input_key]
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@root_validator()
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def validate_prompt_input_variables(cls, values: Dict) -> Dict:
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"""Validate that prompt input variables are consistent."""
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memory_keys = values["memory"].dynamic_keys
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input_key = values["input_key"]
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if input_key in memory_keys:
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raise ValueError(
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f"The input key {input_key} was also found in the memory keys "
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f"({memory_keys}) - please provide keys that don't overlap."
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)
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prompt_variables = values["prompt"].input_variables
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expected_keys = memory_keys + [input_key]
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if set(expected_keys) != set(prompt_variables):
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raise ValueError(
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"Got unexpected prompt input variables. The prompt expects "
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f"{prompt_variables}, but got {memory_keys} as inputs from "
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f"memory, and {input_key} as the normal input key."
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)
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return values
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@ -0,0 +1,86 @@
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"""Memory modules for conversation prompts."""
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from typing import Any, Dict, List
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from pydantic import BaseModel, root_validator
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from langchain.chains.base import Memory
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from langchain.chains.conversation.prompt import SUMMARY_PROMPT
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from langchain.chains.llm import LLMChain
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from langchain.llms.base import LLM
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from langchain.prompts.base import BasePromptTemplate
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class ConversationBufferMemory(Memory, BaseModel):
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"""Buffer for storing conversation memory."""
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buffer: str = ""
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dynamic_key: str = "history" #: :meta private:
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@property
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def dynamic_keys(self) -> List[str]:
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"""Will always return list of dynamic keys.
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:meta private:
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"""
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return [self.dynamic_key]
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def _load_dynamic_keys(self, inputs: Dict[str, Any]) -> Dict[str, str]:
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"""Return history buffer."""
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return {self.dynamic_key: self.buffer}
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def _save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
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"""Save context from this conversation to buffer."""
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prompt_input_keys = list(set(inputs).difference(self.dynamic_keys))
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if len(prompt_input_keys) != 1:
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raise ValueError(f"One input key expected got {prompt_input_keys}")
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if len(outputs) != 1:
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raise ValueError(f"One output key expected, got {outputs.keys()}")
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human = "Human: " + inputs[prompt_input_keys[0]]
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ai = "AI: " + outputs[list(outputs.keys())[0]]
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self.buffer += "\n" + "\n".join([human, ai])
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class ConversationSummaryMemory(Memory, BaseModel):
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"""Conversation summarizer to memory."""
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buffer: str = ""
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llm: LLM
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prompt: BasePromptTemplate = SUMMARY_PROMPT
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dynamic_key: str = "history" #: :meta private:
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@property
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def dynamic_keys(self) -> List[str]:
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"""Will always return list of dynamic keys.
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:meta private:
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"""
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return [self.dynamic_key]
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def _load_dynamic_keys(self, inputs: Dict[str, Any]) -> Dict[str, str]:
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"""Return history buffer."""
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return {self.dynamic_key: self.buffer}
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@root_validator()
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def validate_prompt_input_variables(cls, values: Dict) -> Dict:
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"""Validate that prompt input variables are consistent."""
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prompt_variables = values["prompt"].input_variables
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expected_keys = {"summary", "new_lines"}
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if expected_keys != set(prompt_variables):
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raise ValueError(
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"Got unexpected prompt input variables. The prompt expects "
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f"{prompt_variables}, but it should have {expected_keys}."
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)
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return values
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def _save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
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"""Save context from this conversation to buffer."""
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prompt_input_keys = list(set(inputs).difference(self.dynamic_keys))
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if len(prompt_input_keys) != 1:
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raise ValueError(f"One input key expected got {prompt_input_keys}")
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if len(outputs) != 1:
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raise ValueError(f"One output key expected, got {outputs.keys()}")
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human = "Human: " + inputs[prompt_input_keys[0]]
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ai = "AI: " + list(outputs.values())[0]
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new_lines = "\n".join([human, ai])
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chain = LLMChain(llm=self.llm, prompt=self.prompt)
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self.buffer = chain.predict(summary=self.buffer, new_lines=new_lines)
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@ -0,0 +1,37 @@
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# flake8: noqa
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from langchain.prompts.prompt import PromptTemplate
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_DEFAULT_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. If the AI does not know the answer to a question, it truthfully says it does not know.
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Current conversation:
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{history}
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Human: {input}
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AI:"""
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PROMPT = PromptTemplate(
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input_variables=["history", "input"], template=_DEFAULT_TEMPLATE
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)
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_DEFAULT_SUMMARIZER_TEMPLATE = """Progressively summarize the lines of conversation provided, adding onto the previous summary returning a new summary.
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EXAMPLE
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Current summary:
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The human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good.
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New lines of conversation:
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Human: Why do you think artificial intelligence is a force for good?
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AI: Because artificial intelligence will help humans reach their full potential.
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New summary:
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The human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential.
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END OF EXAMPLE
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Current summary:
|
||||
{summary}
|
||||
|
||||
New lines of conversation:
|
||||
{new_lines}
|
||||
|
||||
New summary:"""
|
||||
SUMMARY_PROMPT = PromptTemplate(
|
||||
input_variables=["summary", "new_lines"], template=_DEFAULT_SUMMARIZER_TEMPLATE
|
||||
)
|
||||
@ -1,4 +1,5 @@
|
||||
"""Wrappers on top of docstores."""
|
||||
from langchain.docstore.in_memory import InMemoryDocstore
|
||||
from langchain.docstore.wikipedia import Wikipedia
|
||||
|
||||
__all__ = ["Wikipedia"]
|
||||
__all__ = ["InMemoryDocstore", "Wikipedia"]
|
||||
|
||||
@ -0,0 +1,68 @@
|
||||
"""Test conversation chain and memory."""
|
||||
import pytest
|
||||
|
||||
from langchain.chains.base import Memory
|
||||
from langchain.chains.conversation.base import ConversationChain
|
||||
from langchain.chains.conversation.memory import (
|
||||
ConversationBufferMemory,
|
||||
ConversationSummaryMemory,
|
||||
)
|
||||
from langchain.prompts.prompt import PromptTemplate
|
||||
from tests.unit_tests.llms.fake_llm import FakeLLM
|
||||
|
||||
|
||||
def test_conversation_chain_works() -> None:
|
||||
"""Test that conversation chain works in basic setting."""
|
||||
llm = FakeLLM()
|
||||
prompt = PromptTemplate(input_variables=["foo", "bar"], template="{foo} {bar}")
|
||||
memory = ConversationBufferMemory(dynamic_key="foo")
|
||||
chain = ConversationChain(llm=llm, prompt=prompt, memory=memory, input_key="bar")
|
||||
chain.run("foo")
|
||||
|
||||
|
||||
def test_conversation_chain_errors_bad_prompt() -> None:
|
||||
"""Test that conversation chain works in basic setting."""
|
||||
llm = FakeLLM()
|
||||
prompt = PromptTemplate(input_variables=[], template="nothing here")
|
||||
with pytest.raises(ValueError):
|
||||
ConversationChain(llm=llm, prompt=prompt)
|
||||
|
||||
|
||||
def test_conversation_chain_errors_bad_variable() -> None:
|
||||
"""Test that conversation chain works in basic setting."""
|
||||
llm = FakeLLM()
|
||||
prompt = PromptTemplate(input_variables=["foo"], template="{foo}")
|
||||
memory = ConversationBufferMemory(dynamic_key="foo")
|
||||
with pytest.raises(ValueError):
|
||||
ConversationChain(llm=llm, prompt=prompt, memory=memory, input_key="foo")
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"memory",
|
||||
[
|
||||
ConversationBufferMemory(dynamic_key="baz"),
|
||||
ConversationSummaryMemory(llm=FakeLLM(), dynamic_key="baz"),
|
||||
],
|
||||
)
|
||||
def test_conversation_memory(memory: Memory) -> None:
|
||||
"""Test basic conversation memory functionality."""
|
||||
# This is a good input because the input is not the same as baz.
|
||||
good_inputs = {"foo": "bar", "baz": "foo"}
|
||||
# This is a good output because these is one variable.
|
||||
good_outputs = {"bar": "foo"}
|
||||
memory._save_context(good_inputs, good_outputs)
|
||||
# This is a bad input because there are two variables that aren't the same as baz.
|
||||
bad_inputs = {"foo": "bar", "foo1": "bar"}
|
||||
with pytest.raises(ValueError):
|
||||
memory._save_context(bad_inputs, good_outputs)
|
||||
# This is a bad input because the only variable is the same as baz.
|
||||
bad_inputs = {"baz": "bar"}
|
||||
with pytest.raises(ValueError):
|
||||
memory._save_context(bad_inputs, good_outputs)
|
||||
# This is a bad output because it is empty.
|
||||
with pytest.raises(ValueError):
|
||||
memory._save_context(good_inputs, {})
|
||||
# This is a bad output because there are two keys.
|
||||
bad_outputs = {"foo": "bar", "foo1": "bar"}
|
||||
with pytest.raises(ValueError):
|
||||
memory._save_context(good_inputs, bad_outputs)
|
||||
Loading…
Reference in New Issue