import lancedb
import pandas as pd
import pyarrow as pa

uri = "data/sample-lancedb"
db = lancedb.connect(uri)
data = [
    {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
    {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
]
tbl = db.create_table("my_table", data=data)

######################
-Examples-
######################
Example 1:

Entity_types: [person, technology, mission, organization, location]
Text:
while Alex clenched his jaw, the buzz of frustration dull against the backdrop of Taylor's authoritarian certainty. It was this competitive undercurrent that kept him alert, the sense that his and Jordan's shared commitment to discovery was an unspoken rebellion against Cruz's narrowing vision of control and order.

Then Taylor did something unexpected. They paused beside Jordan and, for a moment, observed the device with something akin to reverence. �If this tech can be understood..." Taylor said, their voice quieter, "It could change the game for us. For all of us.�

The underlying dismissal earlier seemed to falter, replaced by a glimpse of reluctant respect for the gravity of what lay in their hands. Jordan looked up, and for a fleeting heartbeat, their eyes locked with Taylor's, a wordless clash of wills softening into an uneasy truce.

It was a small transformation, barely perceptible, but one that Alex noted with an inward nod. They had all been brought here by different paths
################
Output:
("entity"{tuple_delimiter}"Alex"{tuple_delimiter}"person"{tuple_delimiter}"Alex is a character who experiences frustration and is observant of the dynamics among other characters."){record_delimiter}
("entity"{tuple_delimiter}"Taylor"{tuple_delimiter}"person"{tuple_delimiter}"Taylor is portrayed with authoritarian certainty and shows a moment of reverence towards a device, indicating a change in perspective."){record_delimiter}
("entity"{tuple_delimiter}"Jordan"{tuple_delimiter}"person"{tuple_delimiter}"Jordan shares a commitment to discovery and has a significant interaction with Taylor regarding a device."){record_delimiter}
("entity"{tuple_delimiter}"Cruz"{tuple_delimiter}"person"{tuple_delimiter}"Cruz is associated with a vision of control and order, influencing the dynamics among other characters."){record_delimiter}
("entity"{tuple_delimiter}"The Device"{tuple_delimiter}"technology"{tuple_delimiter}"The Device is central to the story, with potential game-changing implications, and is revered by Taylor."){record_delimiter}
("relationship"{tuple_delimiter}"Alex"{tuple_delimiter}"Taylor"{tuple_delimiter}"Alex is affected by Taylor's authoritarian certainty and observes changes in Taylor's attitude towards the device."{tuple_delimiter}7){record_delimiter}
("relationship"{tuple_delimiter}"Alex"{tuple_delimiter}"Jordan"{tuple_delimiter}"Alex and Jordan share a commitment to discovery, which contrasts with Cruz's vision."{tuple_delimiter}6){record_delimiter}
("relationship"{tuple_delimiter}"Taylor"{tuple_delimiter}"Jordan"{tuple_delimiter}"Taylor and Jordan interact directly regarding the device, leading to a moment of mutual respect and an uneasy truce."{tuple_delimiter}8){record_delimiter}
("relationship"{tuple_delimiter}"Jordan"{tuple_delimiter}"Cruz"{tuple_delimiter}"Jordan's commitment to discovery is in rebellion against Cruz's vision of control and order."{tuple_delimiter}5){record_delimiter}
("relationship"{tuple_delimiter}"Taylor"{tuple_delimiter}"The Device"{tuple_delimiter}"Taylor shows reverence towards the device, indicating its importance and potential impact."{tuple_delimiter}9){completion_delimiter}
#############################
Example 2:

Entity_types: [person, technology, mission, organization, location]
Text:
They were no longer mere operatives; they had become guardians of a threshold, keepers of a message from a realm beyond stars and stripes. This elevation in their mission could not be shackled by regulations and established protocols�it demanded a new perspective, a new resolve.

Tension threaded through the dialogue of beeps and static as communications with Washington buzzed in the background. The team stood, a portentous air enveloping them. It was clear that the decisions they made in the ensuing hours could redefine humanity's place in the cosmos or condemn them to ignorance and potential peril.

Their connection to the stars solidified, the group moved to address the crystallizing warning, shifting from passive recipients to active participants. Mercer's latter instincts gained precedence� the team's mandate had evolved, no longer solely to observe and report but to interact and prepare. A metamorphosis had begun, and Operation: Dulce hummed with the newfound frequency of their daring, a tone set not by the earthly
#############
Output:
("entity"{tuple_delimiter}"Washington"{tuple_delimiter}"location"{tuple_delimiter}"Washington is a location where communications are being received, indicating its importance in the decision-making process."){record_delimiter}
("entity"{tuple_delimiter}"Operation: Dulce"{tuple_delimiter}"mission"{tuple_delimiter}"Operation: Dulce is described as a mission that has evolved to interact and prepare, indicating a significant shift in objectives and activities."){record_delimiter}
("entity"{tuple_delimiter}"The team"{tuple_delimiter}"organization"{tuple_delimiter}"The team is portrayed as a group of individuals who have transitioned from passive observers to active participants in a mission, showing a dynamic change in their role."){record_delimiter}
("relationship"{tuple_delimiter}"The team"{tuple_delimiter}"Washington"{tuple_delimiter}"The team receives communications from Washington, which influences their decision-making process."{tuple_delimiter}7){record_delimiter}
("relationship"{tuple_delimiter}"The team"{tuple_delimiter}"Operation: Dulce"{tuple_delimiter}"The team is directly involved in Operation: Dulce, executing its evolved objectives and activities."{tuple_delimiter}9){completion_delimiter}
#############################
Example 3:

Entity_types: [person, role, technology, organization, event, location, concept]
Text:
their voice slicing through the buzz of activity. "Control may be an illusion when facing an intelligence that literally writes its own rules," they stated stoically, casting a watchful eye over the flurry of data.

"It's like it's learning to communicate," offered Sam Rivera from a nearby interface, their youthful energy boding a mix of awe and anxiety. "This gives talking to strangers' a whole new meaning."

Alex surveyed his team�each face a study in concentration, determination, and not a small measure of trepidation. "This might well be our first contact," he acknowledged, "And we need to be ready for whatever answers back."

Together, they stood on the edge of the unknown, forging humanity's response to a message from the heavens. The ensuing silence was palpable�a collective introspection about their role in this grand cosmic play, one that could rewrite human history.

The encrypted dialogue continued to unfold, its intricate patterns showing an almost uncanny anticipation
#############
Output:
("entity"{tuple_delimiter}"Sam Rivera"{tuple_delimiter}"person"{tuple_delimiter}"Sam Rivera is a member of a team working on communicating with an unknown intelligence, showing a mix of awe and anxiety."){record_delimiter}
("entity"{tuple_delimiter}"Alex"{tuple_delimiter}"person"{tuple_delimiter}"Alex is the leader of a team attempting first contact with an unknown intelligence, acknowledging the significance of their task."){record_delimiter}
("entity"{tuple_delimiter}"Control"{tuple_delimiter}"concept"{tuple_delimiter}"Control refers to the ability to manage or govern, which is challenged by an intelligence that writes its own rules."){record_delimiter}
("entity"{tuple_delimiter}"Intelligence"{tuple_delimiter}"concept"{tuple_delimiter}"Intelligence here refers to an unknown entity capable of writing its own rules and learning to communicate."){record_delimiter}
("entity"{tuple_delimiter}"First Contact"{tuple_delimiter}"event"{tuple_delimiter}"First Contact is the potential initial communication between humanity and an unknown intelligence."){record_delimiter}
("entity"{tuple_delimiter}"Humanity's Response"{tuple_delimiter}"event"{tuple_delimiter}"Humanity's Response is the collective action taken by Alex's team in response to a message from an unknown intelligence."){record_delimiter}
("relationship"{tuple_delimiter}"Sam Rivera"{tuple_delimiter}"Intelligence"{tuple_delimiter}"Sam Rivera is directly involved in the process of learning to communicate with the unknown intelligence."{tuple_delimiter}9){record_delimiter}
("relationship"{tuple_delimiter}"Alex"{tuple_delimiter}"First Contact"{tuple_delimiter}"Alex leads the team that might be making the First Contact with the unknown intelligence."{tuple_delimiter}10){record_delimiter}
("relationship"{tuple_delimiter}"Alex"{tuple_delimiter}"Humanity's Response"{tuple_delimiter}"Alex and his team are the key figures in Humanity's Response to the unknown intelligence."{tuple_delimiter}8){record_delimiter}
("relationship"{tuple_delimiter}"Control"{tuple_delimiter}"Intelligence"{tuple_delimiter}"The concept of Control is challenged by the Intelligence that writes its own rules."{tuple_delimiter}7){completion_delimiter}
#############################```

{"type": "error", "data": "Error Invoking LLM", "stack": "Traceback (most recent call last):\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\base\\base_llm.py\", line 57, in _invoke\n    output = await self._execute_llm(input, **kwargs)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\openai\\openai_chat_llm.py\", line 55, in _execute_llm\n    completion = await self.client.chat.completions.create(\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\openai\\resources\\chat\\completions.py\", line 1289, in create\n    return await self._post(\n           ^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\openai\\_base_client.py\", line 1805, in post\n    return await self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\openai\\_base_client.py\", line 1503, in request\n    return await self._request(\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\openai\\_base_client.py\", line 1599, in _request\n    raise self._make_status_error_from_response(err.response) from None\nopenai.RateLimitError: Error code: 429 - {'error': {'code': '429', 'message': 'Requests to the ChatCompletions_Create Operation under Azure OpenAI API version 2024-02-15-preview have exceeded token rate limit of your current OpenAI S0 pricing tier. Please retry after 48 seconds. Please go here: https://aka.ms/oai/quotaincrease if you would like to further increase the default rate limit.'}}\n", "source": "Error code: 429 - {'error': {'code': '429', 'message': 'Requests to the ChatCompletions_Create Operation under Azure OpenAI API version 2024-02-15-preview have exceeded token rate limit of your current OpenAI S0 pricing tier. Please retry after 48 seconds. Please go here: https://aka.ms/oai/quotaincrease if you would like to further increase the default rate limit.'}}", "details": {"input": "\n-Goal-\nGiven a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the text and all relationships among the identified entities.\n\n-Steps-\n1. Identify all entities. For each identified entity, extract the following information:\n- entity_name: Name of the entity, capitalized\n- entity_type: One of the following types: [person, location, organization, document, event, relationship, object]\n- entity_description: Comprehensive description of the entity's attributes and activities\nFormat each entity as (\"entity\"<|><entity_name><|><entity_type><|><entity_description>\n\n2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.\nFor each pair of related entities, extract the following information:\n- source_entity: name of the source entity, as identified in step 1\n- target_entity: name of the target entity, as identified in step 1\n- relationship_description: explanation as to why you think the source entity and the target entity are related to each other\n- relationship_strength: an integer score between 1 to 10, indicating strength of the relationship between the source entity and target entity\n\nFormat each relationship as (\"relationship\"<|><source_entity><|><target_entity><|><relationship_description><|><relationship_strength>)\n\n3. Return output in English as a single list of all the entities and relationships identified in steps 1 and 2. Use **##** as the list delimiter.\n\n4. When finished, output <|COMPLETE|>\n\n-Examples-\n######################\n\nExample 1:\n\nentity_types: [person, location, organization, document, event, relationship, object]\ntext:\n were sonorous and melancholy. Occasionally they were fantastic and cheerful. Clearly they reflected the thoughts which possessed him, but whether the music aided those thoughts, or whether the playing was simply the result of a whim or fancy was more than I could determine. I might have rebelled against these ex- asperating solos had it not been that he usually terminated them by playing in quick succession a whole series of my favourite airs as a slight com- pensation for the trial upon my patience. \nDuring the first week or so we had no callers, and I had begun to think that my companion was as friendless a man as I was myself. Presently, however, I found that he had many acquaintances, and those in the most different classes of society. There was one little sallow rat-faced, dark-eyed fel- low who was introduced to me as Mr. Lestrade, and who came three or four times in a single week. One morning a young girl called\n------------------------\noutput:\n**Entities:**\n\n(\"entity\"{\"tuple_delimiter\"}\"Mr. Lestrade\"{\"tuple_delimiter\"}\"person\"{\"tuple_delimiter\"}\"A little sallow rat-faced, dark-eyed fellow who visited three or four times in a single week.\")\n\n\n**Relationships:**\n\n(\"relationship\"{\"tuple_delimiter\"}\"Mr. Lestrade\"{\"tuple_delimiter\"}\"companion\"{\"tuple_delimiter\"}\"Mr. Lestrade is an acquaintance of the narrator's companion.\"{\"tuple_delimiter\"}6)\n\n<|COMPLETE|>\n#############################\n\n\nExample 2:\n\nentity_types: [person, location, organization, document, event, relationship, object]\ntext:\n and spread out the documents upon his knees. Then he lit his pipe and sat for some time smoking and turning them over. \n\"You never heard me talk of Victor Trevor?\" he asked. \"He was the only friend I made during the two years I was at college. I was never a very socia- ble fellow, Watson, always rather fond of moping in my rooms and working out my own little meth- ods of thought, so that I never mixed much with the men of my year. Bar fencing and boxing I had few athletic tastes, and then my line of study was quite distinct from that of the other fellows, so that we had no points of contact at all. Trevor was the \n\n\nonly man I knew, and that only through the acci- dent of his bull terrier freezing on to my ankle one morning as I went down to chapel. \n\"It was a prosaic way of forming a friendship, but it was effective. I was laid by the heels\n------------------------\noutput:\n**Entities:**\n\n(\"entity\"{\"tuple_delimiter\"}\"Victor Trevor\"{\"tuple_delimiter\"}\"person\"{\"tuple_delimiter\"}\"Victor Trevor is the only friend the narrator made during his two years at college. Their friendship began when Trevor's bull terrier bit the narrator's ankle.\"}\n\n(\"entity\"{\"tuple_delimiter\"}\"Watson\"{\"tuple_delimiter\"}\"person\"{\"tuple_delimiter\"}\"Watson is the person being spoken to by the narrator. He is a companion and confidant of the narrator.\"}\n\n(\"entity\"{\"tuple_delimiter\"}\"college\"{\"tuple_delimiter\"}\"location\"{\"tuple_delimiter\"}\"The college is the place where the narrator spent two years and met Victor Trevor.\"}\n\n(\"entity\"{\"tuple_delimiter\"}\"chapel\"{\"tuple_delimiter\"}\"location\"{\"tuple_delimiter\"}\"The chapel is the location where the narrator was heading when he encountered Victor Trevor's bull terrier.\"}\n\n(\"entity\"{\"tuple_delimiter\"}\"bull terrier\"{\"tuple_delimiter\"}\"object\"{\"tuple_delimiter\"}\"The bull terrier is the dog belonging to Victor Trevor that bit the narrator's ankle, leading to their friendship.\"}\n\n**Relationships:**\n\n(\"relationship\"{\"tuple_delimiter\"}\"Victor Trevor\"{\"tuple_delimiter\"}\"bull terrier\"{\"tuple_delimiter\"}\"The bull terrier belongs to Victor Trevor and was the catalyst for the friendship between Victor Trevor and the narrator.\"{\"tuple_delimiter\"}8)\n\n(\"relationship\"{\"tuple_delimiter\"}\"Victor Trevor\"{\"tuple_delimiter\"}\"college\"{\"tuple_delimiter\"}\"Victor Trevor attended the same college as the narrator, where they became friends.\"{\"tuple_delimiter\"}7)\n\n(\"relationship\"{\"tuple_delimiter\"}\"narrator\"{\"tuple_delimiter\"}\"college\"{\"tuple_delimiter\"}\"The narrator spent two years at the college, where he met Victor Trevor.\"{\"tuple_delimiter\"}7)\n\n(\"relationship\"{\"tuple_delimiter\"}\"narrator\"{\"tuple_delimiter\"}\"Victor Trevor\"{\"tuple_delimiter\"}\"Victor Trevor is the only friend the narrator made during his two years at college.\"{\"tuple_delimiter\"}9)\n\n(\"relationship\"{\"tuple_delimiter\"}\"narrator\"{\"tuple_delimiter\"}\"chapel\"{\"tuple_delimiter\"}\"The narrator was heading to the chapel when he encountered Victor Trevor's bull terrier.\"{\"tuple_delimiter\"}6)\n\n(\"relationship\"{\"tuple_delimiter\"}\"narrator\"{\"tuple_delimiter\"}\"Watson\"{\"tuple_delimiter\"}\"Watson is the person being spoken to by the narrator, indicating a close relationship.\"{\"tuple_delimiter\"}8)\n\n**<|COMPLETE|>**\n#############################\n\n\n\n-Real Data-\n######################\nentity_types: [person, location, organization, document, event, relationship, object]\ntext: - lish Sir Robert in a fair position in life. Both po- lice and coroner took a lenient view of the trans- action, and beyond a mild censure for the delay in registering the lady's decease, the lucky owner got away scatheless from this strange incident in a career which has now outlived its shadows and promises to end in an honoured old age. \n\n\nThe Adventure of the Retired Colourman \n\n\nThe Adventure of the Retired Colourman \n\n\n\nout?' \n\n\nherlock Holmes was in a melancholy and philosophic mood that morning. His alert practical nature was subject to such reactions. \n'Did you see him?\" he asked. \n'You mean the old fellow who has just gone \n\"Precisely.\" \n\"Yes, I met him at the door. \" \n\"What did you think of him?\" \n\"A pathetic, futile, broken creature.\" \n\"Exactly, Watson. Pathetic and futile. But is not all life pathetic and futile? Is not his story a micro- cosm of the whole? We reach. We grasp. And what is left in our hands at the end? A shadow. Or worse than a shadow \u2014 misery.\" \n\"Is he one of your clients?\" \n\"Well, I suppose I may call him so. He has been sent on by the Yard. Just as medical men occasion- ally send their incurables to a quack. They argue that they can do nothing more, and that whatever\n######################\noutput:"}}
{"type": "error", "data": "Entity Extraction Error", "stack": "Traceback (most recent call last):\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\index\\graph\\extractors\\graph\\graph_extractor.py\", line 118, in __call__\n    result = await self._process_document(text, prompt_variables)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\index\\graph\\extractors\\graph\\graph_extractor.py\", line 146, in _process_document\n    response = await self._llm(\n               ^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\openai\\json_parsing_llm.py\", line 34, in __call__\n    result = await self._delegate(input, **kwargs)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\openai\\openai_token_replacing_llm.py\", line 37, in __call__\n    return await self._delegate(input, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\openai\\openai_history_tracking_llm.py\", line 33, in __call__\n    output = await self._delegate(input, **kwargs)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\base\\caching_llm.py\", line 104, in __call__\n    result = await self._delegate(input, **kwargs)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\base\\rate_limiting_llm.py\", line 177, in __call__\n    result, start = await execute_with_retry()\n                    ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\base\\rate_limiting_llm.py\", line 159, in execute_with_retry\n    async for attempt in retryer:\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\tenacity\\asyncio\\__init__.py\", line 166, in __anext__\n    do = await self.iter(retry_state=self._retry_state)\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\tenacity\\asyncio\\__init__.py\", line 153, in iter\n    result = await action(retry_state)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\tenacity\\_utils.py\", line 99, in inner\n    return call(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\tenacity\\__init__.py\", line 418, in exc_check\n    raise retry_exc.reraise()\n          ^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\tenacity\\__init__.py\", line 185, in reraise\n    raise self.last_attempt.result()\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Python311\\Lib\\concurrent\\futures\\_base.py\", line 449, in result\n    return self.__get_result()\n           ^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Python311\\Lib\\concurrent\\futures\\_base.py\", line 401, in __get_result\n    raise self._exception\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\base\\rate_limiting_llm.py\", line 165, in execute_with_retry\n    return await do_attempt(), start\n           ^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\base\\rate_limiting_llm.py\", line 151, in do_attempt\n    await sleep_for(sleep_time)\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\base\\rate_limiting_llm.py\", line 147, in do_attempt\n    return await self._delegate(input, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\base\\base_llm.py\", line 53, in __call__\n    return await self._invoke(input, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\base\\base_llm.py\", line 57, in _invoke\n    output = await self._execute_llm(input, **kwargs)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\llm\\openai\\openai_chat_llm.py\", line 55, in _execute_llm\n    completion = await self.client.chat.completions.create(\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\openai\\resources\\chat\\completions.py\", line 1289, in create\n    return await self._post(\n           ^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\openai\\_base_client.py\", line 1805, in post\n    return await self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\openai\\_base_client.py\", line 1503, in request\n    return await self._request(\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\openai\\_base_client.py\", line 1599, in _request\n    raise self._make_status_error_from_response(err.response) from None\nopenai.RateLimitError: Error code: 429 - {'error': {'code': '429', 'message': 'Requests to the ChatCompletions_Create Operation under Azure OpenAI API version 2024-02-15-preview have exceeded token rate limit of your current OpenAI S0 pricing tier. Please retry after 52 seconds. Please go here: https://aka.ms/oai/quotaincrease if you would like to further increase the default rate limit.'}}\n", "source": "Error code: 429 - {'error': {'code': '429', 'message': 'Requests to the ChatCompletions_Create Operation under Azure OpenAI API version 2024-02-15-preview have exceeded token rate limit of your current OpenAI S0 pricing tier. Please retry after 52 seconds. Please go here: https://aka.ms/oai/quotaincrease if you would like to further increase the default rate limit.'}}", "details": {"doc_index": 0, "text": "ly premeditated, then the means of covering it are coolly premeditated also. I hope, therefore, that we are in the presence of a serious misconception.\" \n\"But there is so much to explain.\" \n\"Well, we shall set about explaining it. When once your point of view is changed, the very thing which was so damning becomes a clue to the truth. For example, there is this revolver. Miss Dunbar disclaims all knowledge of it. On our new theory she is speaking truth when she says so. There- fore, it was placed in her wardrobe. Who placed it there? Someone who wished to incriminate her. Was not that person the actual criminal? You see how we come at once upon a most fruitful line of inquiry.\" \nWe were compelled to spend the night at Winchester, as the formalities had not yet been completed, but next morning, in the company of Mr. Joyce Cummings, the rising barrister who was entrusted with the defence, we were allowed to see the young lady in her cell. I had expected from all that we had heard to see a beautiful woman, but I can never forget the effect which Miss Dunbar pro- duced upon me. It was no wonder that even the masterful millionaire had found in her something more powerful than himself \u2014 something which could control and guide him. One felt, too, as one looked at the strong, clear-cut, and yet sensitive face, that even should she be capable"}}

​{"type": "error", "data": "Error executing verb \"cluster_graph\" in create_base_entity_graph: Columns must be same length as key", "stack": "Traceback (most recent call last):\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\datashaper\\workflow\\workflow.py\", line 410, in _execute_verb\n    result = node.verb.func(**verb_args)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\index\\verbs\\graph\\clustering\\cluster_graph.py\", line 102, in cluster_graph\n    output_df[[level_to, to]] = pd.DataFrame(\n    ~~~~~~~~~^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\pandas\\core\\frame.py\", line 4299, in __setitem__\n    self._setitem_array(key, value)\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\pandas\\core\\frame.py\", line 4341, in _setitem_array\n    check_key_length(self.columns, key, value)\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\pandas\\core\\indexers\\utils.py\", line 390, in check_key_length\n    raise ValueError(\"Columns must be same length as key\")\nValueError: Columns must be same length as key\n", "source": "Columns must be same length as key", "details": null}
{"type": "error", "data": "Error running pipeline!", "stack": "Traceback (most recent call last):\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\index\\run.py\", line 323, in run_pipeline\n    result = await workflow.run(context, callbacks)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\datashaper\\workflow\\workflow.py\", line 369, in run\n    timing = await self._execute_verb(node, context, callbacks)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\datashaper\\workflow\\workflow.py\", line 410, in _execute_verb\n    result = node.verb.func(**verb_args)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphrag\\index\\verbs\\graph\\clustering\\cluster_graph.py\", line 102, in cluster_graph\n    output_df[[level_to, to]] = pd.DataFrame(\n    ~~~~~~~~~^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\pandas\\core\\frame.py\", line 4299, in __setitem__\n    self._setitem_array(key, value)\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\pandas\\core\\frame.py\", line 4341, in _setitem_array\n    check_key_length(self.columns, key, value)\n  File \"C:\\Users\\steventruitt\\source\\repos\\graphrag\\graphragtest\\Lib\\site-packages\\pandas\\core\\indexers\\utils.py\", line 390, in check_key_length\n    raise ValueError(\"Columns must be same length as key\")\nValueError: Columns must be same length as key\n", "source": "Columns must be same length as key", "details": null}

{
    "total_runtime": 10167.02763581276,
    "num_documents": 1,
    "input_load_time": 0,
    "workflows": {
        "create_base_text_units": {
            "overall": 2.7237725257873535,
            "0_orderby": 0.0060007572174072266,
            "1_zip": 0.006998538970947266,
            "2_aggregate_override": 0.008006811141967773,
            "3_chunk": 2.4501953125,
            "4_select": 0.006997585296630859,
            "5_unroll": 0.01601433753967285,
            "6_rename": 0.008991479873657227,
            "7_genid": 0.11474370956420898,
            "8_unzip": 0.007005214691162109,
            "9_copy": 0.007086038589477539,
            "10_filter": 0.08634305000305176
        },
        "create_base_extracted_entities": {
            "overall": 10163.398600578308,
            "0_entity_extract": 10163.042577505112,
            "1_merge_graphs": 0.3440265655517578
        },
        "create_summarized_entities": {
            "overall": 0.0209963321685791,
            "0_summarize_descriptions": 0.012000083923339844
        }
    }
}

https://microsoft.github.io/graphrag/posts/get_started/

 and OPENAI_KEY, python -m graphrag.index --init --root ./ragtest

/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).
  from pandas.core import (
2024-07-03 13:11:26.122415: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Traceback (most recent call last):
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/runpy.py", line 187, in _run_module_as_main
    mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/runpy.py", line 146, in _get_module_details
    return _get_module_details(pkg_main_name, error)
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/runpy.py", line 110, in _get_module_details
    __import__(pkg_name)
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/graphrag/index/__init__.py", line 40, in <module>
    from .run import run_pipeline, run_pipeline_with_config
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/graphrag/index/run.py", line 59, in <module>
    from .verbs import *  # noqa
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/graphrag/index/verbs/__init__.py", line 9, in <module>
    from .graph import (
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/graphrag/index/verbs/graph/__init__.py", line 10, in <module>
    from .layout import layout_graph
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/graphrag/index/verbs/graph/layout/__init__.py", line 6, in <module>
    from .layout_graph import layout_graph
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/graphrag/index/verbs/graph/layout/layout_graph.py", line 13, in <module>
    from graphrag.index.graph.visualization import GraphLayout
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/graphrag/index/graph/visualization/__init__.py", line 6, in <module>
    from .compute_umap_positions import compute_umap_positions, get_zero_positions
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/graphrag/index/graph/visualization/compute_umap_positions.py", line 6, in <module>
    import graspologic as gc
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/graspologic/__init__.py", line 4, in <module>
    import graspologic.align
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/graspologic/align/__init__.py", line 5, in <module>
    from .seedless_procrustes import SeedlessProcrustes
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/graspologic/align/seedless_procrustes.py", line 7, in <module>
    import ot
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/ot/__init__.py", line 21, in <module>
    from . import lp
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/ot/lp/__init__.py", line 20, in <module>
    from .dmmot import dmmot_monge_1dgrid_loss, dmmot_monge_1dgrid_optimize
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/ot/lp/dmmot.py", line 12, in <module>
    from ..backend import get_backend
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/ot/backend.py", line 145, in <module>
    import tensorflow as tf
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/tensorflow/__init__.py", line 470, in <module>
    _keras._load()
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/tensorflow/python/util/lazy_loader.py", line 41, in _load
    module = importlib.import_module(self.__name__)
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/__init__.py", line 21, in <module>
    from keras import models
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/models/__init__.py", line 18, in <module>
    from keras.engine.functional import Functional
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/engine/functional.py", line 26, in <module>
    from keras import backend
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/backend/__init__.py", line 3, in <module>
    from keras.backend import experimental
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/backend/experimental/__init__.py", line 3, in <module>
    from keras.src.backend import disable_tf_random_generator
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/src/__init__.py", line 21, in <module>
    from keras.src import applications
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/src/applications/__init__.py", line 18, in <module>
    from keras.src.applications.convnext import ConvNeXtBase
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/src/applications/convnext.py", line 28, in <module>
    from keras.src import backend
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/src/backend.py", line 35, in <module>
    from keras.src.engine import keras_tensor
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/src/engine/keras_tensor.py", line 19, in <module>
    from keras.src.utils import object_identity
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/src/utils/__init__.py", line 20, in <module>
    from keras.src.saving.serialization_lib import deserialize_keras_object
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/src/saving/serialization_lib.py", line 28, in <module>
    from keras.src.saving.legacy.saved_model.utils import in_tf_saved_model_scope
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/src/saving/legacy/saved_model/utils.py", line 30, in <module>
    from keras.src.utils.layer_utils import CallFunctionSpec
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/src/utils/layer_utils.py", line 26, in <module>
    from keras.src import initializers
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/src/initializers/__init__.py", line 23, in <module>
    from keras.src.initializers import initializers_v1
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/keras/src/initializers/initializers_v1.py", line 32, in <module>
    keras_export(v1=["keras.initializers.Zeros", "keras.initializers.zeros"])(
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/tensorflow/python/util/tf_export.py", line 348, in __call__
    self.set_attr(undecorated_func, api_names_attr, self._names)
  File "/Users/ankushsingal/miniconda3/envs/snakes/lib/python3.10/site-packages/tensorflow/python/util/tf_export.py", line 363, in set_attr
    raise SymbolAlreadyExposedError(
tensorflow.python.util.tf_export.SymbolAlreadyExposedError: Symbol Zeros is already exposed as ().

  File "graphrag/index/input/csv.py", line 38, in load_file
    data = pd.read_csv(buffer, encoding=config.encoding or "latin-1")
                                        ^^^^^^^^^^^^^^^
AttributeError: 'InputConfig' object has no attribute 'encoding'

/XXX/graphrag/.venv/lib/python3.10/site-packages/numpy/core/fromnume
ric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in 
a future version. Please use 'DataFrame.transpose' instead.