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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,88 +1,90 @@ | ||
| from relation_extraction.API_handler import APIHandler | ||
| import requests | ||
| from llama_cpp import Llama | ||
| import re | ||
| import os | ||
|
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| class LLMMessenger(APIHandler): | ||
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| def API_endpoint(): | ||
| return "" | ||
| return "http://knox-proxy01.srv.aau.dk/llama-api/llama" | ||
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| def send_request(request): | ||
| HEADERS = {"Access-Authorization": os.getenv("ACCESS_SECRET")} | ||
| response = requests.post(url=LLMMessenger.API_endpoint(), json=request, headers=HEADERS) | ||
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| # Put the location of to the GGUF model that you've download from HuggingFace here | ||
| model_path = "llama-2-7b-chat.Q2_K.gguf" | ||
| # # Put the location of to the GGUF model that you've download from HuggingFace here | ||
| # model_path = "./relation_extraction/multilingual/llama-2-7b-chat.Q2_K.gguf" | ||
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| # Create a llama model | ||
| #model = Llama(model_path=model_path, n_ctx=4092) | ||
| # # Create a llama model | ||
| # model = Llama(model_path=model_path, n_ctx=4096) | ||
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| # Prompt creation | ||
| system_message = """### Instruction ### | ||
| When given a sentence and the entity mentions in the sentence, you should perform relation extraction. This includes marking an entity mention as subject, marking another entity mention as object, and identifying the relation between the subject and object. You should only use entity mentions specified in the prompt. You should only use relations from the list of relations given in the context. | ||
| # prompt = f"""<s>[INST] <<SYS>> | ||
| # {request["system_message"]} | ||
| # <</SYS>> | ||
| # {request["user_message"]} [/INST]""" | ||
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| ### Context ### | ||
| List of relations: [location, birthPlace, deathPlace, owns, sibling, child, parent, title, employer, age, residence, headquarter, deathCause, member, foundedBy, religion] | ||
| ### Input Data ### | ||
| You should perform relation extraction when prompted with input on the following format: | ||
| "sentence", [comma_separated_list_of_entity_mentions] | ||
| ### Output Indicator ### | ||
| If no relation can be found in the sentence, or the entity mentions have not been specified in the user prompt, you should respond with "undefined". In all other cases, your output should be a list of triples on the following format: | ||
| <subject, relation, object> | ||
| """ | ||
| user_message = '"Casper and Rytter has the same mother", [Casper, Rytter]' | ||
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| prompt = f"""<s>[INST] <<SYS>> | ||
| {system_message} | ||
| <</SYS>> | ||
| {user_message} [/INST]""" | ||
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| # Model parameters | ||
| max_tokens = 4092 | ||
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| # Run the model | ||
| output = model(prompt, max_tokens=max_tokens, echo=True) | ||
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| # Print the model output | ||
| # print(output["choices"][0]["text"]) | ||
| # with open("LlamaResponse.txt", "w") as file: | ||
| # # Write content to the file | ||
| # file.write(output["choices"][0]["text"]) | ||
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| #response = requests.post(url=LLMMessenger.API_endpoint) | ||
| return output | ||
| # # Run the model | ||
| # output = model(prompt, max_tokens=request["max_tokens"], echo=True) | ||
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| return response | ||
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| def process_message(response): | ||
| print("Recieved response from Llama2...") | ||
| print(response) | ||
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| def costruct_prompt_message(data): | ||
| system_message = """### Instruction ### | ||
| When given a sentence and the entity mentions in the sentence, you should perform relation extraction. This includes marking an entity mention as subject, marking another entity mention as object, and identifying the relation between the subject and object. You should only use entity mentions specified in the prompt. You should only use relations from the list of relations given in the context. | ||
| ### Context ### | ||
| List of relations: [location, birthPlace, deathPlace, owns, sibling, child, parent, title, employer, age, residence, headquarter, deathCause, member, foundedBy, religion] | ||
| ### Input Data ### | ||
| You should perform relation extraction when prompted with input on the following format: | ||
| "sentence", [comma_separated_list_of_entity_mentions] | ||
| ### Output Indicator ### | ||
| If no relation can be found in the sentence, or the entity mentions have not been specified in the user prompt, you should respond with "undefined". In all other cases, your output should be a list of triples on the following format: | ||
| <subject, relation, object> | ||
| triples = [] | ||
| answer = re.split("/INST]", response["choices"][0]["text"])[1] | ||
| llama_triples = re.findall('<["\s\w\d,"]*,[\s\w\d]*,["\s\w\d,"]*>|\[["\s\w\d,"]*,[\s\w\d]*,["\s\w\d,"]*\]', answer) | ||
| for llama_triple in llama_triples: | ||
| triple = re.split('"', llama_triple.replace("<", "").replace(">", "").replace("]", "").replace("[", ""))[1:-1] | ||
| if len(triple) == 3: | ||
| triple_object = {} | ||
| for i, entry in enumerate(triple): | ||
| triple_object[i.__str__()] = entry.strip(' ,') | ||
| triples.append(triple_object) | ||
| return triples | ||
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| def check_validity_of_response(sentence, response, relations): | ||
| triples = [] | ||
| valid_entity_mentions = [em["name"] for em in sentence["entityMentions"]] | ||
| for triple in response: | ||
| if triple["0"] in valid_entity_mentions and triple["1"] in relations and triple["2"] in valid_entity_mentions: # 0 = subject, 1 = predicate, and 2 = object | ||
| triples.append([[em["iri"] for em in sentence["entityMentions"] if em["name"] == triple["0"]][0], f'http://dbpedia.org/ontology/{triple["1"]}', [em["iri"] for em in sentence["entityMentions"] if em["name"] == triple["2"]][0]]) | ||
| return triples | ||
|
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| def prompt_llm(data, split_relations, relations): | ||
| triples = [] | ||
| system_message = f"""### Instruction ### | ||
| When given a sentence in either danish or english and the entity mentions in the sentence, you should find triples by performing relation extraction. This includes marking an entity mention as subject, marking another entity mention as object, and identifying the relation between the subject and object. You should only use entity mentions specified in the prompt. You should only use relations from the list of relations given in the context. You should provide reasoning for why each of the triples you find is correct. | ||
| S | ||
| ### Context ### | ||
| List of relations: [{", ".join(split_relations)}] | ||
| Here is a transcript with you. You are called Llama. | ||
| User: Sentence: "Aalborg is in Denmark" Entity mentions: ["Aalborg", "Denmark"] | ||
| Llama: The relation "is in" is not in the list of relations but "location" is in the list of relations. "Aalborg is in Denmark" implies that Aalborg is located in Denmark. Therefore, the triple <"Aalborg", location, "Denmark"> is correct. | ||
| User: Sentence: "Peter has a subscription to Pure Gym" Entity mentions: ["Peter", "Pure Gym"] | ||
| Llama: The relation "subscription" is not in the list of relations, but "member" is in the list of relations. "Peter has a subscription to Pure Gym" implies that Peter is a member of Pure Gym. Therefore, the triple <"Peter", member, "Pure Gym"> is correct. | ||
| User: Sentence: "Martin Eberhard and Marc Tarpenning are the original founders of Tesla" Entity mentions: ["Martin Eberhard", "Marc Tarpenning", "Tesla"] | ||
| Llama: The sentence states that Tesla was founded by both Martin Eberhard and Marc Tarpenning. The relation "foundedBy" is in the list of relations. Therefore, the two triples <"Tesla", foundedBy, "Martin Eberhard"> and <"Tesla", foundedBy, "Marc Tarpenning"> are correct. | ||
| User: Sentence: "Sofie was born in Kolding" Entity mentions: ["Sofie", "Kolding"] | ||
| Llama: The relation "born in" is not in the list of relations. But "born in" implies a place of birth, and "birthPlace" is in the list of relations. Therefore, the triple <"Sofie", birthPlace, "Kolding"> is correct. | ||
| User: Sentence: "Frederik is the father of Christian" Entity mentions: ["Frederik", "Christian"] | ||
| Llama: The relation "father" is not in the list of relations. However, a father is a parent and "parent" is in the list of relations. Therefore, the triple <"Frederik", parent, "Christian"> is correct. | ||
| ### Output Indicator ### | ||
| Before answering with a triple, you should explain why it is correct. If no relation can be found in the sentence, or the entity mentions have not been specified in the user prompt, you should respond with “undefined”. In all other cases, your output should be triples on the format <subject, relation, object> and an explanation for each triple. | ||
| """ | ||
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| request = {"system_message": system_message, "user_message": ""} | ||
| request = {"system_message": system_message, "user_message": "", "max_tokens": 4096} | ||
|
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| for file in data: | ||
| for sentence in file["sentences"]: | ||
| user_message = f'"{sentence["sentence"]}", [' | ||
| user_message = f'Sentence: "{sentence["sentence"]}" Entity mentions: [' | ||
| for em in sentence["entityMentions"]: | ||
| user_message += f"{em['name']}, " | ||
| user_message = user_message[:-2] + ']' #Remove comma and space after last entity mention | ||
| user_message += f'"{em["name"]}", ' | ||
| user_message = user_message[:-2] + ']' #Remove comma and space after last entity mention in message | ||
| request["user_message"] = user_message | ||
| response = LLMMessenger.send_request(request) | ||
| LLMMessenger.process_message(response) | ||
| process_response = LLMMessenger.process_message(response) | ||
| triples = LLMMessenger.check_validity_of_response(sentence, process_response, relations) | ||
| return triples | ||
|
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,7 +1,9 @@ | ||
| from relation_extraction.NaiveMVP.main import handle_relation_post_request | ||
| from relation_extraction.multilingual.main import begin_relation_extraction | ||
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| class RelationExtractor(): | ||
| @classmethod | ||
| def begin_extraction(self, data): | ||
| handle_relation_post_request(data) | ||
| begin_relation_extraction(data) |
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