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@ -16,7 +16,7 @@
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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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"execution_count": 1,
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"id": "ccc8ff98",
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"metadata": {},
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"outputs": [],
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@ -98,7 +98,7 @@
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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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"execution_count": 5,
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"id": "4f4aa234-9746-47d8-bec7-d76081ac3ef6",
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"metadata": {
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"tags": []
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@ -111,9 +111,17 @@
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mAction:\n",
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"```\n",
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"{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"Hello Erica, how can I assist you today?\"\n",
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"}\n",
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"```\n",
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"\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"Hi Erica! How can I assist you today?\n"
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"Hello Erica, how can I assist you today?\n"
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]
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}
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],
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@ -274,10 +282,119 @@
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"print(response)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "42473442",
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"metadata": {},
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"source": [
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"## Adding in memory\n",
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"\n",
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"Here is how you add in memory to this agent"
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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": "b5a0dd2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import MessagesPlaceholder\n",
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"from langchain.memory import ConversationBufferMemory"
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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": "91b9288f",
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"metadata": {},
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"outputs": [],
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"source": [
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"chat_history = MessagesPlaceholder(variable_name=\"chat_history\")\n",
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"memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)"
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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": "dba9e0d9",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_chain = initialize_agent(\n",
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" tools, \n",
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" llm, \n",
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" agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, \n",
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" verbose=True, \n",
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" memory=memory, \n",
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" agent_kwargs = {\n",
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" \"memory_prompts\": [chat_history],\n",
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" \"input_variables\": [\"input\", \"agent_scratchpad\", \"chat_history\"]\n",
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" }\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": 9,
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"id": "a9509461",
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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 AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mAction:\n",
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"```\n",
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"{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"Hi Erica! How can I assist you today?\"\n",
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"}\n",
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"```\n",
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"\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
|
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"Hi Erica! How can I assist you today?\n"
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]
|
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}
|
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],
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"source": [
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"response = await agent_chain.arun(input=\"Hi I'm Erica.\")\n",
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"print(response)"
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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": "412cedd2",
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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 AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mYour name is Erica.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
|
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"Your name is Erica.\n"
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|
]
|
|
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|
|
}
|
|
|
|
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],
|
|
|
|
|
"source": [
|
|
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|
|
"response = await agent_chain.arun(input=\"whats my name?\")\n",
|
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|
|
"print(response)"
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|
]
|
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|
|
},
|
|
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|
|
{
|
|
|
|
|
"cell_type": "code",
|
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|
|
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"execution_count": null,
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"id": "ebd7ae33-f67d-4378-ac79-9d91e0c8f53a",
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"id": "9af1a713",
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"metadata": {},
|
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"outputs": [],
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"source": []
|
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|
@ -299,7 +416,7 @@
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|
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"name": "python",
|
|
|
|
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"nbconvert_exporter": "python",
|
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|
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
|
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"version": "3.9.1"
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}
|
|
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|
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},
|
|
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"nbformat": 4,
|
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