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https://github.com/hwchase17/langchain
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fix (#7947)
This commit is contained in:
Bagatur
GitHub
parent
7bb843477f
commit
f97535b33e
@ -80,12 +80,13 @@
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for extracting information in structured formats.\n",
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"Human: Use the given format to extract information from the following input:\n",
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"Human: Sally is 13\n",
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"Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
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" {'function_call': {'name': '_OutputFormatter', 'arguments': '{\\n \"output\": {\\n \"name\": \"Sally\",\\n \"age\": 13,\\n \"fav_food\": \"Unknown\"\\n }\\n}'}}\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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@ -93,7 +94,7 @@
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{
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"data": {
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"text/plain": [
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"{'name': 'Sally', 'age': 13}"
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"Person(name='Sally', age=13, fav_food='Unknown')"
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]
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},
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"execution_count": 3,
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@ -103,7 +104,7 @@
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],
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"source": [
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"# If we pass in a model explicitly, we need to make sure it supports the OpenAI function-calling API.\n",
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"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0613\", temperature=0)\n",
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"llm = ChatOpenAI(model=\"gpt-4\", temperature=0)\n",
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"\n",
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"prompt_msgs = [\n",
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" SystemMessage(\n",
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@ -141,12 +142,13 @@
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for extracting information in structured formats.\n",
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"Human: Use the given format to extract information from the following input:\n",
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"Human: Sally is 13, Joey just turned 12 and loves spinach. Caroline is 10 years older than Sally, so she's 23.\n",
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"Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
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" {'function_call': {'name': '_OutputFormatter', 'arguments': '{\\n \"output\": {\\n \"people\": [\\n {\\n \"name\": \"Sally\",\\n \"age\": 13,\\n \"fav_food\": \"\"\\n },\\n {\\n \"name\": \"Joey\",\\n \"age\": 12,\\n \"fav_food\": \"spinach\"\\n },\\n {\\n \"name\": \"Caroline\",\\n \"age\": 23,\\n \"fav_food\": \"\"\\n }\\n ]\\n }\\n}'}}\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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@ -154,9 +156,7 @@
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{
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"data": {
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"text/plain": [
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"{'people': [{'name': 'Sally', 'age': 13, 'fav_food': ''},\n",
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" {'name': 'Joey', 'age': 12, 'fav_food': 'spinach'},\n",
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" {'name': 'Caroline', 'age': 23, 'fav_food': ''}]}"
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"People(people=[Person(name='Sally', age=13, fav_food=''), Person(name='Joey', age=12, fav_food='spinach'), Person(name='Caroline', age=23, fav_food='')])"
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]
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},
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"execution_count": 4,
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@ -192,7 +192,7 @@
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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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"execution_count": 6,
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"id": "3484415e",
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"metadata": {},
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"outputs": [],
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@ -216,7 +216,7 @@
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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": 7,
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"id": "be9b76b3",
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"metadata": {},
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"outputs": [
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@ -226,12 +226,13 @@
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for extracting information in structured formats.\n",
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"Human: Use the given format to extract information from the following input:\n",
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"Human: Sally is 13\n",
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"Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
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" {'function_call': {'name': 'output_formatter', 'arguments': '{\\n \"name\": \"Sally\",\\n \"age\": 13\\n}'}}\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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@ -242,7 +243,7 @@
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"{'name': 'Sally', 'age': 13}"
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]
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},
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"execution_count": 6,
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -278,7 +279,7 @@
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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": 8,
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"id": "17f52508",
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"metadata": {},
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"outputs": [],
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@ -301,7 +302,7 @@
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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": 9,
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"id": "a4658ad8",
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"metadata": {},
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"outputs": [
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@ -311,12 +312,13 @@
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for recording entities\n",
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"Human: Make calls to the relevant function to record the entities in the following input:\n",
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"Human: Harry was a chubby brown beagle who loved chicken\n",
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"Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
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" {'function_call': {'name': 'RecordDog', 'arguments': '{\\n \"name\": \"Harry\",\\n \"color\": \"brown\",\\n \"fav_food\": \"chicken\"\\n}'}}\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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@ -327,7 +329,7 @@
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"RecordDog(name='Harry', color='brown', fav_food='chicken')"
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]
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},
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"execution_count": 8,
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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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@ -360,7 +362,7 @@
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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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"execution_count": 10,
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"id": "95ac5825",
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"metadata": {},
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"outputs": [
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@ -370,12 +372,13 @@
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for recording entities\n",
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"Human: Make calls to the relevant function to record the entities in the following input:\n",
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"Human: The most important thing to remember about Tommy, my 12 year old, is that he'll do anything for apple pie.\n",
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"Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
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" {'function_call': {'name': 'record_person', 'arguments': '{\\n \"name\": \"Tommy\",\\n \"age\": 12,\\n \"fav_food\": {\\n \"food\": \"apple pie\"\\n }\\n}'}}\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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@ -386,7 +389,7 @@
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"{'name': 'Tommy', 'age': 12, 'fav_food': {'food': 'apple pie'}}"
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]
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},
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"execution_count": 9,
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -431,7 +434,7 @@
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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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"execution_count": 11,
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"id": "8b0d11de",
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"metadata": {},
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"outputs": [
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@ -441,12 +444,13 @@
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for recording entities\n",
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"Human: Make calls to the relevant function to record the entities in the following input:\n",
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"Human: I can't find my dog Henry anywhere, he's a small brown beagle. Could you send a message about him?\n",
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"Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
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" {'function_call': {'name': 'record_dog', 'arguments': '{\\n \"name\": \"Henry\",\\n \"color\": \"brown\",\\n \"fav_food\": {\\n \"food\": null\\n }\\n}'}}\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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@ -458,7 +462,7 @@
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" 'arguments': {'name': 'Henry', 'color': 'brown', 'fav_food': {'food': None}}}"
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]
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},
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"execution_count": 10,
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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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@ -494,14 +498,6 @@
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"- [OpenAPI](/docs/modules/chains/additional/openapi_openai): take an OpenAPI spec and create + execute valid requests against the API, using OpenAI functions under the hood.\n",
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"- [QA with citations](/docs/modules/chains/additional/qa_citations): use OpenAI functions ability to extract citations from text."
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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": "93425c66",
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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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@ -102,7 +102,7 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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elif role == "assistant":
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# Fix for azure
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# Also OpenAI returns None for tool invocations
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content = _dict.get("content", "")
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content = _dict.get("content", "") or ""
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if _dict.get("function_call"):
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additional_kwargs = {"function_call": dict(_dict["function_call"])}
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else:
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