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Comment out Tavily tool for now due to CI
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python-recipes/agents/03_memory_agent.ipynb

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@@ -40,20 +40,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": null,
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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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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"%pip install -q langchain-openai langgraph-checkpoint langgraph-checkpoint-redis \"langchain-community>=0.2.11\" tavily-python langchain-redis pydantic ulid"
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]
@@ -85,7 +74,10 @@
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"\n",
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"\n",
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"_set_env(\"OPENAI_API_KEY\")\n",
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"_set_env(\"TAVILY_API_KEY\")"
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"\n",
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"# Uncomment this if you have a Tavily API key and want to\n",
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"# use the web search tool.\n",
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"# _set_env(\"TAVILY_API_KEY\")"
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]
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},
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{
@@ -134,20 +126,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": null,
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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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"True"
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]
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},
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"execution_count": 11,
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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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"outputs": [],
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"source": [
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"import os\n",
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"from redis import Redis\n",
@@ -335,18 +316,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": null,
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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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"16:49:44 redisvl.index.index INFO Index already exists, not overwriting.\n",
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"Long-term memory index ready\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"from redisvl.index import SearchIndex\n",
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"from redisvl.schema.schema import IndexSchema\n",
@@ -409,23 +381,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "OpenAI API key is required. Provide it in api_config or set the OPENAI_API_KEY environment variable.",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[14], line 13\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# If we have any memories that aren't associated with a user, we'll use this ID.\u001b[39;00m\n\u001b[1;32m 11\u001b[0m SYSTEM_USER_ID \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msystem\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 13\u001b[0m openai_embed \u001b[38;5;241m=\u001b[39m \u001b[43mOpenAITextVectorizer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtext-embedding-ada-002\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# Change this to MemoryStrategy.TOOLS to use function-calling to store and\u001b[39;00m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# retrieve memories.\u001b[39;00m\n\u001b[1;32m 17\u001b[0m memory_strategy \u001b[38;5;241m=\u001b[39m MemoryStrategy\u001b[38;5;241m.\u001b[39mMANUAL\n",
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"File \u001b[0;32m~/src/redis-ai-resources/env/lib/python3.11/site-packages/redisvl/utils/vectorize/text/openai.py:75\u001b[0m, in \u001b[0;36mOpenAITextVectorizer.__init__\u001b[0;34m(self, model, api_config, dtype, **kwargs)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(model\u001b[38;5;241m=\u001b[39mmodel, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;66;03m# Init clients\u001b[39;00m\n\u001b[0;32m---> 75\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_initialize_clients\u001b[49m\u001b[43m(\u001b[49m\u001b[43mapi_config\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 76\u001b[0m \u001b[38;5;66;03m# Set model dimensions after init\u001b[39;00m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_model_dims()\n",
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"File \u001b[0;32m~/src/redis-ai-resources/env/lib/python3.11/site-packages/redisvl/utils/vectorize/text/openai.py:100\u001b[0m, in \u001b[0;36mOpenAITextVectorizer._initialize_clients\u001b[0;34m(self, api_config, **kwargs)\u001b[0m\n\u001b[1;32m 96\u001b[0m api_key \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 97\u001b[0m api_config\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapi_key\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m api_config \u001b[38;5;28;01melse\u001b[39;00m os\u001b[38;5;241m.\u001b[39mgetenv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOPENAI_API_KEY\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 98\u001b[0m )\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m api_key:\n\u001b[0;32m--> 100\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 101\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOpenAI API key is required. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 102\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mProvide it in api_config or set the OPENAI_API_KEY\u001b[39m\u001b[38;5;130;01m\\\u001b[39;00m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;124m environment variable.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 104\u001b[0m )\n\u001b[1;32m 106\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_client \u001b[38;5;241m=\u001b[39m OpenAI(api_key\u001b[38;5;241m=\u001b[39mapi_key, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mapi_config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 107\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_aclient \u001b[38;5;241m=\u001b[39m AsyncOpenAI(api_key\u001b[38;5;241m=\u001b[39mapi_key, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mapi_config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
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"\u001b[0;31mValueError\u001b[0m: OpenAI API key is required. Provide it in api_config or set the OPENAI_API_KEY environment variable."
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]
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}
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],
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"outputs": [],
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"source": [
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"import logging\n",
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"\n",
@@ -1113,8 +1071,12 @@
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"llm = ChatOpenAI(model=\"gpt-4o\", temperature=0.7)\n",
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"\n",
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"\n",
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"web_search_tool = CachingTavilySearchResults(max_results=2)\n",
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"base_tools = [web_search_tool]\n",
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"# Uncomment these lines if you have a Tavily API key and want to use the web\n",
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"# search tool. The agent is much more useful with this tool.\n",
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"# web_search_tool = CachingTavilySearchResults(max_results=2)\n",
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"# base_tools = [web_search_tool]\n",
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"base_tools = []\n",
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"\n",
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"if memory_strategy == MemoryStrategy.TOOLS:\n",
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" tools = base_tools + [store_memory_tool, retrieve_memories_tool]\n",
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"elif memory_strategy == MemoryStrategy.MANUAL:\n",

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