mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-19 13:23:35 +00:00
Minor grammar fixes for memory docs to improve readability (#303)
Nothing of substance was changed. I simply corrected a few minor errors that could slow down the reader. Co-authored-by: Hunter Gerlach <hunter@huntergerlach.com>
This commit is contained in:
Hunter Gerlach
GitHub
parent
9d08384d5f
commit
9ee6115deb
@ -1,333 +1,333 @@
|
|||||||
{
|
{
|
||||||
"cells": [
|
"cells": [
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "d31df93e",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"# Memory\n",
|
|
||||||
"So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of \"memory\" so that it may remember information about its previous interactions. The most clear and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of \"short-term memory\". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of \"long-term memory\". For more concrete ideas on the later, see this [awesome paper](https://memprompt.com/).\n",
|
|
||||||
"\n",
|
|
||||||
"LangChain provides several specially created chains just for this purpose. This notebook walk throughs using one of those chains (the `ConversationChain`) with two different types of memory."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "d051c1da",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"### ConversationChain with default memory\n",
|
|
||||||
"By default, the `ConversationChain` has a simple type of memory which remebers all previes inputs/outputs and adds them to the context that is passed. Let's take a look at using this chain (setting `verbose=True` so we can see the prompt)."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 1,
|
|
||||||
"id": "ae046bff",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
{
|
||||||
"name": "stdout",
|
"cell_type": "markdown",
|
||||||
"output_type": "stream",
|
"id": "d31df93e",
|
||||||
"text": [
|
"metadata": {},
|
||||||
"\n",
|
"source": [
|
||||||
"\n",
|
"# Memory\n",
|
||||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
"So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of \"memory\" so that it may remember information about its previous interactions. The clearest and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of \"short-term memory\". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of \"long-term memory\". For more concrete ideas on the latter, see this [awesome paper](https://memprompt.com/).\n",
|
||||||
"Prompt after formatting:\n",
|
"\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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
|
"LangChain provides several specially created chains just for this purpose. This notebook walks through using one of those chains (the `ConversationChain`) with two different types of memory."
|
||||||
"\n",
|
]
|
||||||
"Current conversation:\n",
|
|
||||||
"\n",
|
|
||||||
"Human: Hi there!\n",
|
|
||||||
"AI:\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
||||||
]
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"cell_type": "markdown",
|
||||||
"text/plain": [
|
"id": "d051c1da",
|
||||||
"' Hello! How are you today?'"
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### ConversationChain with default memory\n",
|
||||||
|
"By default, the `ConversationChain` has a simple type of memory that remembers all previous inputs/outputs and adds them to the context that is passed. Let's take a look at using this chain (setting `verbose=True` so we can see the prompt)."
|
||||||
]
|
]
|
||||||
},
|
|
||||||
"execution_count": 1,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"from langchain import OpenAI, ConversationChain\n",
|
|
||||||
"\n",
|
|
||||||
"llm = OpenAI(temperature=0)\n",
|
|
||||||
"conversation = ConversationChain(llm=llm, verbose=True)\n",
|
|
||||||
"\n",
|
|
||||||
"conversation.predict(input=\"Hi there!\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 2,
|
|
||||||
"id": "d8e2a6ff",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
|
|
||||||
"\n",
|
|
||||||
"Current conversation:\n",
|
|
||||||
"\n",
|
|
||||||
"Human: Hi there!\n",
|
|
||||||
"AI: Hello! How are you today?\n",
|
|
||||||
"Human: I'm doing well! Just having a conversation with an AI.\n",
|
|
||||||
"AI:\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
||||||
]
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"cell_type": "code",
|
||||||
"text/plain": [
|
"execution_count": 1,
|
||||||
"\" That's great! What would you like to talk about?\""
|
"id": "ae046bff",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Entering new 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
|
||||||
|
"\n",
|
||||||
|
"Current conversation:\n",
|
||||||
|
"\n",
|
||||||
|
"Human: Hi there!\n",
|
||||||
|
"AI:\u001b[0m\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"' Hello! How are you today?'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 1,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"from langchain import OpenAI, ConversationChain\n",
|
||||||
|
"\n",
|
||||||
|
"llm = OpenAI(temperature=0)\n",
|
||||||
|
"conversation = ConversationChain(llm=llm, verbose=True)\n",
|
||||||
|
"\n",
|
||||||
|
"conversation.predict(input=\"Hi there!\")"
|
||||||
]
|
]
|
||||||
},
|
|
||||||
"execution_count": 2,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"conversation.predict(input=\"I'm doing well! Just having a conversation with an AI.\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 3,
|
|
||||||
"id": "15eda316",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
|
|
||||||
"\n",
|
|
||||||
"Current conversation:\n",
|
|
||||||
"\n",
|
|
||||||
"Human: Hi there!\n",
|
|
||||||
"AI: Hello! How are you today?\n",
|
|
||||||
"Human: I'm doing well! Just having a conversation with an AI.\n",
|
|
||||||
"AI: That's great! What would you like to talk about?\n",
|
|
||||||
"Human: Tell me about yourself.\n",
|
|
||||||
"AI:\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
||||||
]
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"cell_type": "code",
|
||||||
"text/plain": [
|
"execution_count": 2,
|
||||||
"' I am an AI created to provide information and support to humans. I enjoy learning and exploring new things.'"
|
"id": "d8e2a6ff",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Entering new 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
|
||||||
|
"\n",
|
||||||
|
"Current conversation:\n",
|
||||||
|
"\n",
|
||||||
|
"Human: Hi there!\n",
|
||||||
|
"AI: Hello! How are you today?\n",
|
||||||
|
"Human: I'm doing well! Just having a conversation with an AI.\n",
|
||||||
|
"AI:\u001b[0m\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"\" That's great! What would you like to talk about?\""
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 2,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"conversation.predict(input=\"I'm doing well! Just having a conversation with an AI.\")"
|
||||||
]
|
]
|
||||||
},
|
|
||||||
"execution_count": 3,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"conversation.predict(input=\"Tell me about yourself.\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "4fad9448",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"### ConversationChain with ConversationSummaryMemory\n",
|
|
||||||
"Now lets take a look at using a slightly more complex type of memory - `ConversationSummaryMemory`. This type of memory creates a summary of the conversation over time. This can be useful for condensing information from the conversation over time.\n",
|
|
||||||
"\n",
|
|
||||||
"Let's walk through an example, again setting `verbose=True` so we can see the prompt."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 4,
|
|
||||||
"id": "f60a2fe8",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from langchain.chains.conversation.memory import ConversationSummaryMemory"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 5,
|
|
||||||
"id": "b7274f2c",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
|
|
||||||
"\n",
|
|
||||||
"Current conversation:\n",
|
|
||||||
"\n",
|
|
||||||
"Human: Hi, what's up?\n",
|
|
||||||
"AI:\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
||||||
]
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"cell_type": "code",
|
||||||
"text/plain": [
|
"execution_count": 3,
|
||||||
"\"\\n\\nI'm doing well, thank you for asking. I'm currently working on a project that I'm really excited about.\""
|
"id": "15eda316",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Entering new 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
|
||||||
|
"\n",
|
||||||
|
"Current conversation:\n",
|
||||||
|
"\n",
|
||||||
|
"Human: Hi there!\n",
|
||||||
|
"AI: Hello! How are you today?\n",
|
||||||
|
"Human: I'm doing well! Just having a conversation with an AI.\n",
|
||||||
|
"AI: That's great! What would you like to talk about?\n",
|
||||||
|
"Human: Tell me about yourself.\n",
|
||||||
|
"AI:\u001b[0m\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"' I am an AI created to provide information and support to humans. I enjoy learning and exploring new things.'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 3,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"conversation.predict(input=\"Tell me about yourself.\")"
|
||||||
]
|
]
|
||||||
},
|
|
||||||
"execution_count": 5,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"conversation_with_summary = ConversationChain(llm=llm, memory=ConversationSummaryMemory(llm=OpenAI()), verbose=True)\n",
|
|
||||||
"conversation_with_summary.predict(input=\"Hi, what's up?\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 6,
|
|
||||||
"id": "a6b6b88f",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
|
|
||||||
"\n",
|
|
||||||
"Current conversation:\n",
|
|
||||||
"\n",
|
|
||||||
"The human and artificial intelligence are talking. The human asked the AI what it is doing, and the AI said that it is working on a project that it is excited about.\n",
|
|
||||||
"Human: Tell me more about it!\n",
|
|
||||||
"AI:\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
||||||
]
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"cell_type": "markdown",
|
||||||
"text/plain": [
|
"id": "4fad9448",
|
||||||
"\"\\n\\nI'm working on a project that I'm really excited about. It's a lot of work, but I think it's going to be really great when it's finished. I can't wait to show it to you!\""
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### ConversationChain with ConversationSummaryMemory\n",
|
||||||
|
"Now let's take a look at using a slightly more complex type of memory - `ConversationSummaryMemory`. This type of memory creates a summary of the conversation over time. This can be useful for condensing information from the conversation over time.\n",
|
||||||
|
"\n",
|
||||||
|
"Let's walk through an example, again setting `verbose=True` so we can see the prompt."
|
||||||
]
|
]
|
||||||
},
|
|
||||||
"execution_count": 6,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"conversation_with_summary.predict(input=\"Tell me more about it!\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 7,
|
|
||||||
"id": "dad869fe",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
|
|
||||||
"\n",
|
|
||||||
"Current conversation:\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"The human and artificial intelligence are talking. The human asked the AI what it is doing, and the AI said that it is working on a project that it is excited about. The AI said that the project is a lot of work, but it is going to be great when it is finished.\n",
|
|
||||||
"Human: Very cool -- what is the scope of the project?\n",
|
|
||||||
"AI:\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
||||||
]
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"cell_type": "code",
|
||||||
"text/plain": [
|
"execution_count": 4,
|
||||||
"'\\n\\nThe project is quite large in scope. It involves a lot of data analysis and work with artificial intelligence algorithms.'"
|
"id": "f60a2fe8",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"from langchain.chains.conversation.memory import ConversationSummaryMemory"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"execution_count": 7,
|
{
|
||||||
"metadata": {},
|
"cell_type": "code",
|
||||||
"output_type": "execute_result"
|
"execution_count": 5,
|
||||||
|
"id": "b7274f2c",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Entering new 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
|
||||||
|
"\n",
|
||||||
|
"Current conversation:\n",
|
||||||
|
"\n",
|
||||||
|
"Human: Hi, what's up?\n",
|
||||||
|
"AI:\u001b[0m\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"\"\\n\\nI'm doing well, thank you for asking. I'm currently working on a project that I'm really excited about.\""
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 5,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"conversation_with_summary = ConversationChain(llm=llm, memory=ConversationSummaryMemory(llm=OpenAI()), verbose=True)\n",
|
||||||
|
"conversation_with_summary.predict(input=\"Hi, what's up?\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 6,
|
||||||
|
"id": "a6b6b88f",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Entering new 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
|
||||||
|
"\n",
|
||||||
|
"Current conversation:\n",
|
||||||
|
"\n",
|
||||||
|
"The human and artificial intelligence are talking. The human asked the AI what it is doing, and the AI said that it is working on a project that it is excited about.\n",
|
||||||
|
"Human: Tell me more about it!\n",
|
||||||
|
"AI:\u001b[0m\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"\"\\n\\nI'm working on a project that I'm really excited about. It's a lot of work, but I think it's going to be really great when it's finished. I can't wait to show it to you!\""
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 6,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"conversation_with_summary.predict(input=\"Tell me more about it!\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 7,
|
||||||
|
"id": "dad869fe",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Entering new 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. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
|
||||||
|
"\n",
|
||||||
|
"Current conversation:\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"The human and artificial intelligence are talking. The human asked the AI what it is doing, and the AI said that it is working on a project that it is excited about. The AI said that the project is a lot of work, but it is going to be great when it is finished.\n",
|
||||||
|
"Human: Very cool -- what is the scope of the project?\n",
|
||||||
|
"AI:\u001b[0m\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"'\\n\\nThe project is quite large in scope. It involves a lot of data analysis and work with artificial intelligence algorithms.'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 7,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"conversation_with_summary.predict(input=\"Very cool -- what is the scope of the project?\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "5c8735cc",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### More Resources on Memory\n",
|
||||||
|
"\n",
|
||||||
|
"This just scratches the surface of what you can do with memory. For more examples on things like how to implement custom memory classes, how to add memory to a custom LLM chain and how to use memory with an agent, please see the [How-To: Memory](../../examples/memory) section. For even more advanced ideas on memory (which will hopefully be included in LangChain soon!) see the [MemPrompt](https://memprompt.com/) paper."
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "436dda66",
|
||||||
|
"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"
|
||||||
}
|
}
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"conversation_with_summary.predict(input=\"Very cool -- what is the scope of the project?\")"
|
|
||||||
]
|
|
||||||
},
|
},
|
||||||
{
|
"nbformat": 4,
|
||||||
"cell_type": "markdown",
|
"nbformat_minor": 5
|
||||||
"id": "5c8735cc",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"### More Resources on Memory\n",
|
|
||||||
"\n",
|
|
||||||
"This just scratches the surface of what you can do with memory. For more examples on things like how to implement custom memory classes, how to add memory to a custom LLM chain and how to use memory with and agent, please see the [How-To: Memory](../../examples/memory) section. For even more advanced ideas on memory (which will hopefully be included in LangChain soon!) see the [MemPrompt](https://memprompt.com/) paper."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "436dda66",
|
|
||||||
"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
|
|
||||||
}
|
}
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user