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| import numpy as np | |
| import pandas as pd | |
| from rank_bm25 import BM25Okapi | |
| from llm import OpenAILLM | |
| def cosine_similarity(vector1, vector2): | |
| return np.dot(vector1, vector2) / (np.linalg.norm(vector1) * np.linalg.norm(vector2)) | |
| class QuestionAnswerer: | |
| def __init__(self, docs, embedding_model, llm=OpenAILLM('gpt-3.5-turbo'), cross_encoder=None): | |
| self.docs = docs | |
| self.bm25 = BM25Okapi([c.split(" ") for c in self.docs.chunks.values[1:]]) | |
| self.embedding_model = embedding_model | |
| self.llm = llm | |
| self.cross_encoder = cross_encoder | |
| def sim_search(self, query, n=10, use_hyde=False, use_dot_product=False): | |
| if use_hyde: | |
| generated_doc = self._get_generated_doc(query) | |
| print("generated document (hyde): \n", generated_doc) | |
| embedding = self.embedding_model.encode(generated_doc) | |
| else: | |
| embedding = self.embedding_model.encode(query) | |
| if use_dot_product: | |
| similarities = self.docs['embeddings'].apply(lambda x: np.dot(x, embedding)) | |
| else: | |
| similarities = self.docs['embeddings'].apply(lambda x: cosine_similarity(x, embedding)) | |
| self.docs['similarities'] = similarities | |
| return self.docs.sort_values('similarities', ascending=False).head(n) | |
| def sim_search_rerank(self, query, n=10, sim_search_n=100, **kwargs): | |
| search_results = self.sim_search(query, n=sim_search_n, use_hyde=False, **kwargs) | |
| reranked_results = self.rerank(search_results, query) | |
| return reranked_results[:n] | |
| def sim_search_bm25(self, query, n=10): | |
| tokenized_query = query.split(" ") | |
| doc_scores = self.bm25.get_scores(tokenized_query) | |
| self.docs['bm25'] = np.insert(doc_scores, 0, 0) #hack because I have to remove the first item, because I cannot split it | |
| result = self.docs.sort_values('bm25', ascending=False)[:n] | |
| return result | |
| def _create_prompt(self, context, question): | |
| return f""" | |
| Context information is below. | |
| --------------------- | |
| {context} | |
| --------------------- | |
| Given the context information and not prior knowledge, answer the query. | |
| Query: {question} | |
| Answer: \ | |
| """ | |
| def _get_generated_doc(self, question): | |
| prompt = f"""Write a guideline section in German answering the question below | |
| --------------------- | |
| Question: {question} | |
| --------------------- | |
| Answer: \ | |
| """ | |
| system_prompt = "You are an experienced radiologist answering medical questions. You answer only in German." | |
| return self.llm.get_response(system_prompt, prompt) | |
| def rerank(self, docs, query): | |
| inp = [[query, doc.chunks] for doc in docs.itertuples()] | |
| cross_scores = self.cross_encoder.predict(inp) if self.cross_encoder else [] | |
| docs['cross_score'] = cross_scores | |
| return docs.sort_values('cross_score', ascending=False) | |
| def answer_question(self, question, n=3, use_hyde=False, use_reranker=False, use_bm25=False): | |
| if use_reranker and use_hyde: | |
| print('reranking together with hyde is not supported yet') | |
| if use_reranker: | |
| search_results = self.sim_search_rerank(question, n=n) | |
| if use_bm25: | |
| search_results = self.sim_search_bm25(question, n=n) | |
| else: | |
| search_results = self.sim_search(question, n=n, use_hyde=use_hyde) | |
| context = "\n\n".join(search_results['chunks']) | |
| prompt = self._create_prompt(context, question) | |
| system_prompt = "You are a helpful assistant answering questions in German. You answer only in German. If you do not know an answer you say it. You do not fabricate answers." | |
| return self.llm.get_response(system_prompt, prompt) | |