From 0a38bbc750dad086e111237a3ee500d0eaf0074e Mon Sep 17 00:00:00 2001 From: Harrison Chase Date: Fri, 14 Apr 2023 07:54:57 -0700 Subject: [PATCH] updates to vectorstore memory (#2875) --- .../types/vectorstore_retriever_memory.ipynb | 117 ++++++++++++------ 1 file changed, 78 insertions(+), 39 deletions(-) diff --git a/docs/modules/memory/types/vectorstore_retriever_memory.ipynb b/docs/modules/memory/types/vectorstore_retriever_memory.ipynb index a7d54810..27fdc82f 100644 --- a/docs/modules/memory/types/vectorstore_retriever_memory.ipynb +++ b/docs/modules/memory/types/vectorstore_retriever_memory.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "id": "ff4be5f3", "metadata": {}, @@ -10,7 +9,9 @@ "\n", "`VectorStoreRetrieverMemory` stores memories in a VectorDB and queries the top-K most \"salient\" docs every time it is called.\n", "\n", - "This differs from most of the other Memory classes in that it doesn't explicitly track the order of interactions." + "This differs from most of the other Memory classes in that it doesn't explicitly track the order of interactions.\n", + "\n", + "In this case, the \"docs\" are previous conversation snippets. This can be useful to refer to relevant pieces of information that the AI was told earlier in the conversation." ] }, { @@ -26,7 +27,8 @@ "from langchain.embeddings.openai import OpenAIEmbeddings\n", "from langchain.llms import OpenAI\n", "from langchain.memory import VectorStoreRetrieverMemory\n", - "from langchain.chains import ConversationChain" + "from langchain.chains import ConversationChain\n", + "from langchain.prompts import PromptTemplate" ] }, { @@ -41,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 29, "id": "eef56f65", "metadata": { "tags": [] @@ -61,7 +63,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "8f4bdf92", "metadata": {}, @@ -73,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 30, "id": "e00d4938", "metadata": { "tags": [] @@ -86,14 +87,14 @@ "memory = VectorStoreRetrieverMemory(retriever=retriever)\n", "\n", "# When added to an agent, the memory object can save pertinent information from conversations or used tools\n", - "memory.save_context({\"input\": \"check the latest scores of the Warriors game\"}, {\"output\": \"the Warriors are up against the Astros 88 to 84\"})\n", - "memory.save_context({\"input\": \"I need help doing my taxes - what's the standard deduction this year?\"}, {\"output\": \"...\"})\n", - "memory.save_context({\"input\": \"What's the the time?\"}, {\"output\": f\"It's {datetime.now()}\"}) # " + "memory.save_context({\"input\": \"My favorite food is pizza\"}, {\"output\": \"thats good to know\"})\n", + "memory.save_context({\"input\": \"My favorite sport is soccer\"}, {\"output\": \"...\"})\n", + "memory.save_context({\"input\": \"I don't the Celtics\"}, {\"output\": \"ok\"}) # " ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 31, "id": "2fe28a28", "metadata": { "tags": [] @@ -103,7 +104,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "input: I need help doing my taxes - what's the standard deduction this year?\n", + "input: My favorite sport is soccer\n", "output: ...\n" ] } @@ -111,7 +112,7 @@ "source": [ "# Notice the first result returned is the memory pertaining to tax help, which the language model deems more semantically relevant\n", "# to a 1099 than the other documents, despite them both containing numbers.\n", - "print(memory.load_memory_variables({\"prompt\": \"What's a 1099?\"})[\"history\"])" + "print(memory.load_memory_variables({\"prompt\": \"what sport should i watch?\"})[\"history\"])" ] }, { @@ -125,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 32, "id": "ebd68c10", "metadata": { "tags": [] @@ -141,9 +142,13 @@ "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", + "Relevant pieces of previous conversation:\n", + "input: My favorite food is pizza\n", + "output: thats good to know\n", + "\n", + "(You do not need to use these pieces of information if not relevant)\n", + "\n", "Current conversation:\n", - "input: I need help doing my taxes - what's the standard deduction this year?\n", - "output: ...\n", "Human: Hi, my name is Perry, what's up?\n", "AI:\u001b[0m\n", "\n", @@ -153,18 +158,32 @@ { "data": { "text/plain": [ - "\" Hi Perry, my name is AI. I'm doing great, how about you? I understand you need help with your taxes. What specifically do you need help with?\"" + "\" Hi Perry, I'm doing well. How about you?\"" ] }, - "execution_count": 5, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "llm = OpenAI(temperature=0) # Can be any valid LLM\n", + "_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.\n", + "\n", + "Relevant pieces of previous conversation:\n", + "{history}\n", + "\n", + "(You do not need to use these pieces of information if not relevant)\n", + "\n", + "Current conversation:\n", + "Human: {input}\n", + "AI:\"\"\"\n", + "PROMPT = PromptTemplate(\n", + " input_variables=[\"history\", \"input\"], template=_DEFAULT_TEMPLATE\n", + ")\n", "conversation_with_summary = ConversationChain(\n", " llm=llm, \n", + " prompt=PROMPT,\n", " # We set a very low max_token_limit for the purposes of testing.\n", " memory=memory,\n", " verbose=True\n", @@ -174,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 33, "id": "86207a61", "metadata": { "tags": [] @@ -190,10 +209,14 @@ "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", + "Relevant pieces of previous conversation:\n", + "input: My favorite sport is soccer\n", + "output: ...\n", + "\n", + "(You do not need to use these pieces of information if not relevant)\n", + "\n", "Current conversation:\n", - "input: check the latest scores of the Warriors game\n", - "output: the Warriors are up against the Astros 88 to 84\n", - "Human: If the Cavaliers were to face off against the Warriers or the Astros, who would they most stand a chance to beat?\n", + "Human: what's my favorite sport?\n", "AI:\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" @@ -202,22 +225,22 @@ { "data": { "text/plain": [ - "\" It's hard to say without knowing the current form of the teams. However, based on the current scores, it looks like the Cavaliers would have a better chance of beating the Astros than the Warriors.\"" + "' You told me earlier that your favorite sport is soccer.'" ] }, - "execution_count": 6, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Here, the basketball related content is surfaced\n", - "conversation_with_summary.predict(input=\"If the Cavaliers were to face off against the Warriers or the Astros, who would they most stand a chance to beat?\")" + "conversation_with_summary.predict(input=\"what's my favorite sport?\")" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 34, "id": "8c669db1", "metadata": { "tags": [] @@ -233,10 +256,14 @@ "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", + "Relevant pieces of previous conversation:\n", + "input: My favorite food is pizza\n", + "output: thats good to know\n", + "\n", + "(You do not need to use these pieces of information if not relevant)\n", + "\n", "Current conversation:\n", - "input: What's the the time?\n", - "output: It's 2023-04-13 09:18:55.623736\n", - "Human: What day is it tomorrow?\n", + "Human: Whats my favorite food\n", "AI:\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" @@ -245,10 +272,10 @@ { "data": { "text/plain": [ - "' Tomorrow is 2023-04-14.'" + "' You said your favorite food is pizza.'" ] }, - "execution_count": 7, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -256,12 +283,12 @@ "source": [ "# Even though the language model is stateless, since relavent memory is fetched, it can \"reason\" about the time.\n", "# Timestamping memories and data is useful in general to let the agent determine temporal relevance\n", - "conversation_with_summary.predict(input=\"What day is it tomorrow?\")" + "conversation_with_summary.predict(input=\"Whats my favorite food\")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 35, "id": "8c09a239", "metadata": { "tags": [] @@ -277,10 +304,14 @@ "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", + "Relevant pieces of previous conversation:\n", "input: Hi, my name is Perry, what's up?\n", - "response: Hi Perry, my name is AI. I'm doing great, how about you? I understand you need help with your taxes. What specifically do you need help with?\n", - "Human: What's your name?\n", + "response: Hi Perry, I'm doing well. How about you?\n", + "\n", + "(You do not need to use these pieces of information if not relevant)\n", + "\n", + "Current conversation:\n", + "Human: What's my name?\n", "AI:\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" @@ -289,10 +320,10 @@ { "data": { "text/plain": [ - "\" My name is AI. It's nice to meet you, Perry.\"" + "' Your name is Perry.'" ] }, - "execution_count": 8, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -301,8 +332,16 @@ "# The memories from the conversation are automatically stored,\n", "# since this query best matches the introduction chat above,\n", "# the agent is able to 'remember' the user's name.\n", - "conversation_with_summary.predict(input=\"What's your name?\")" + "conversation_with_summary.predict(input=\"What's my name?\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df27c7dc", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -321,7 +360,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.2" + "version": "3.9.1" } }, "nbformat": 4,