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docs: Re-write custom agent to show to write a tools agent (#15907)
Shows how to write a tools agent rather than a functions agent.
This commit is contained in:
Eugene Yurtsev
GitHub
parent
404abf139a
commit
e5672bc944
@ -19,7 +19,7 @@
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"\n",
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"This notebook goes through how to create your own custom agent.\n",
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"\n",
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"In this example, we will use OpenAI Function Calling to create this agent.\n",
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"In this example, we will use OpenAI Tool Calling to create this agent.\n",
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"**This is generally the most reliable way to create agents.**\n",
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"\n",
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"We will first create it WITHOUT memory, but we will then show how to add memory in.\n",
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@ -61,10 +61,21 @@
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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": "fbe32b5f",
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"execution_count": 2,
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"id": "490bab35-adbb-4b45-8d0d-232414121e97",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"3"
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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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"from langchain.agents import tool\n",
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"\n",
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@ -75,6 +86,16 @@
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" return len(word)\n",
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"\n",
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"\n",
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"get_word_length.invoke(\"abc\")"
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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": "c9821fb3-4449-49a0-a708-88a18d39e068",
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"metadata": {},
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"outputs": [],
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"source": [
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"tools = [get_word_length]"
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]
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},
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@ -91,7 +112,7 @@
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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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"execution_count": 4,
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"id": "aa4b50ea",
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"metadata": {},
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"outputs": [],
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@ -116,22 +137,24 @@
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"metadata": {},
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"source": [
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"## Bind tools to LLM\n",
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"How does the agent know what tools it can use?\n",
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"In this case we're relying on OpenAI function calling LLMs, which take functions as a separate argument and have been specifically trained to know when to invoke those functions.\n",
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"\n",
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"To pass in our tools to the agent, we just need to format them to the [OpenAI function format](https://openai.com/blog/function-calling-and-other-api-updates) and pass them to our model. (By `bind`-ing the functions, we're making sure that they're passed in each time the model is invoked.)"
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"How does the agent know what tools it can use?\n",
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"\n",
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"In this case we're relying on OpenAI tool calling LLMs, which take tools as a separate argument and have been specifically trained to know when to invoke those tools.\n",
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"\n",
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"To pass in our tools to the agent, we just need to format them to the [OpenAI tool format](https://platform.openai.com/docs/api-reference/chat/create) and pass them to our model. (By `bind`-ing the functions, we're making sure that they're passed in each time the model is invoked.)"
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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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"execution_count": 5,
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"id": "e82713b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.tools.convert_to_openai import format_tool_to_openai_function\n",
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"from langchain_community.tools.convert_to_openai import format_tool_to_openai_tool\n",
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"\n",
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"llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])"
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"llm_with_tools = llm.bind(tools=[format_tool_to_openai_tool(tool) for tool in tools])"
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]
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},
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{
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@ -146,30 +169,32 @@
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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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"execution_count": 6,
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"id": "925a8ca4",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents.format_scratchpad import format_to_openai_function_messages\n",
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"from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser\n",
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"from langchain.agents.format_scratchpad.openai_tools import (\n",
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" format_to_openai_tool_messages,\n",
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")\n",
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"from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser\n",
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"\n",
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"agent = (\n",
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" {\n",
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" \"input\": lambda x: x[\"input\"],\n",
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" \"agent_scratchpad\": lambda x: format_to_openai_function_messages(\n",
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" \"agent_scratchpad\": lambda x: format_to_openai_tool_messages(\n",
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" x[\"intermediate_steps\"]\n",
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" ),\n",
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" }\n",
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" | prompt\n",
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" | llm_with_tools\n",
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" | OpenAIFunctionsAgentOutputParser()\n",
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" | OpenAIToolsAgentOutputParser()\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": 8,
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"execution_count": 7,
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"id": "9af9734e",
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"metadata": {},
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"outputs": [],
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@ -181,7 +206,7 @@
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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": 8,
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"id": "653b1617",
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"metadata": {},
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"outputs": [
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@ -193,10 +218,10 @@
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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;3m\n",
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"Invoking: `get_word_length` with `{'word': 'educa'}`\n",
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"Invoking: `get_word_length` with `{'word': 'eudca'}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3m5\u001b[0m\u001b[32;1m\u001b[1;3mThere are 5 letters in the word \"educa\".\u001b[0m\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3m5\u001b[0m\u001b[32;1m\u001b[1;3mThere are 5 letters in the word \"eudca\".\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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@ -204,17 +229,21 @@
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{
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"data": {
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"text/plain": [
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"{'input': 'How many letters in the word educa',\n",
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" 'output': 'There are 5 letters in the word \"educa\".'}"
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"[{'actions': [OpenAIToolAgentAction(tool='get_word_length', tool_input={'word': 'eudca'}, log=\"\\nInvoking: `get_word_length` with `{'word': 'eudca'}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_U9SR78eT398r9UbzID2N9LXh', 'function': {'arguments': '{\\n \"word\": \"eudca\"\\n}', 'name': 'get_word_length'}, 'type': 'function'}]})], tool_call_id='call_U9SR78eT398r9UbzID2N9LXh')],\n",
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" 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_U9SR78eT398r9UbzID2N9LXh', 'function': {'arguments': '{\\n \"word\": \"eudca\"\\n}', 'name': 'get_word_length'}, 'type': 'function'}]})]},\n",
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" {'steps': [AgentStep(action=OpenAIToolAgentAction(tool='get_word_length', tool_input={'word': 'eudca'}, log=\"\\nInvoking: `get_word_length` with `{'word': 'eudca'}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_U9SR78eT398r9UbzID2N9LXh', 'function': {'arguments': '{\\n \"word\": \"eudca\"\\n}', 'name': 'get_word_length'}, 'type': 'function'}]})], tool_call_id='call_U9SR78eT398r9UbzID2N9LXh'), observation=5)],\n",
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" 'messages': [FunctionMessage(content='5', name='get_word_length')]},\n",
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" {'output': 'There are 5 letters in the word \"eudca\".',\n",
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" 'messages': [AIMessage(content='There are 5 letters in the word \"eudca\".')]}]"
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]
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},
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"execution_count": 14,
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"execution_count": 8,
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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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"agent_executor.invoke({\"input\": \"How many letters in the word educa\"})"
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"list(agent_executor.stream({\"input\": \"How many letters in the word eudca\"}))"
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]
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},
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{
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@ -227,7 +256,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 9,
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"id": "60f5dc19",
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"metadata": {},
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"outputs": [
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@ -237,7 +266,7 @@
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"AIMessage(content='There are 6 letters in the word \"educa\".')"
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]
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},
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"execution_count": 15,
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -270,7 +299,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 10,
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"id": "169006d5",
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"metadata": {},
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"outputs": [],
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@ -301,7 +330,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 11,
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"id": "8c03f36c",
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"metadata": {},
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"outputs": [],
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@ -321,7 +350,7 @@
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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": 12,
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"id": "5429d97f",
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"metadata": {},
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"outputs": [],
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@ -329,14 +358,14 @@
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"agent = (\n",
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" {\n",
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" \"input\": lambda x: x[\"input\"],\n",
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" \"agent_scratchpad\": lambda x: format_to_openai_function_messages(\n",
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" \"agent_scratchpad\": lambda x: format_to_openai_tool_messages(\n",
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" x[\"intermediate_steps\"]\n",
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" ),\n",
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" \"chat_history\": lambda x: x[\"chat_history\"],\n",
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" }\n",
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" | prompt\n",
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" | llm_with_tools\n",
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" | OpenAIFunctionsAgentOutputParser()\n",
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" | OpenAIToolsAgentOutputParser()\n",
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")\n",
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"agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
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]
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@ -351,7 +380,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 13,
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"id": "9d9da346",
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"metadata": {},
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"outputs": [
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@ -386,7 +415,7 @@
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" 'output': 'No, \"educa\" is not a real word in English.'}"
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]
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},
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"execution_count": 19,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -402,14 +431,6 @@
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")\n",
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"agent_executor.invoke({\"input\": \"is that a real word?\", \"chat_history\": chat_history})"
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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": "f21bcd99",
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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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@ -428,7 +449,7 @@
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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": "3.10.1"
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"version": "3.11.4"
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},
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"vscode": {
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"interpreter": {
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