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use output parsers in agents (#2987)
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@@ -35,7 +35,7 @@
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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": 3,
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"id": "16c4dc59",
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"metadata": {},
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"outputs": [],
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@@ -45,7 +45,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 4,
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"id": "46b9489d",
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"metadata": {},
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"outputs": [
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@@ -72,7 +72,7 @@
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"'There are 891 rows in the dataframe.'"
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]
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},
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"execution_count": 12,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -83,7 +83,7 @@
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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": "a96309be",
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"metadata": {},
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"outputs": [
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@@ -110,7 +110,7 @@
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"'30 people have more than 3 siblings.'"
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]
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},
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -121,7 +121,7 @@
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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": "964a09f7",
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"metadata": {},
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"outputs": [
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@@ -143,7 +143,7 @@
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"Thought:\u001b[32;1m\u001b[1;3m I need to import the math library\n",
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"Action: python_repl_ast\n",
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"Action Input: import math\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mNone\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
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"Action: python_repl_ast\n",
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"Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
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@@ -160,7 +160,7 @@
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"'5.449689683556195'"
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]
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},
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"execution_count": 7,
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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