From c98f559d4114a3ea8a50b275f495735fb949cc36 Mon Sep 17 00:00:00 2001 From: GautamR-Samagra Date: Wed, 2 Aug 2023 07:58:13 +0000 Subject: [PATCH] embed_chunk_page_number --- src/chunking/MPNet/local/Dockerfile | 4 +- .../MPNet/local/NEP_Final_English.pdf | Bin 1453912 -> 0 bytes src/chunking/MPNet/local/api.py | 34 +++++++- src/chunking/MPNet/local/model.py | 78 ++++++++++++------ 4 files changed, 89 insertions(+), 27 deletions(-) delete mode 100644 src/chunking/MPNet/local/NEP_Final_English.pdf diff --git a/src/chunking/MPNet/local/Dockerfile b/src/chunking/MPNet/local/Dockerfile index 3cea204..30f7e1f 100644 --- a/src/chunking/MPNet/local/Dockerfile +++ b/src/chunking/MPNet/local/Dockerfile @@ -10,6 +10,6 @@ RUN pip3 install -r requirements.txt # Copy the rest of the application code to the working directory COPY . /app/ -EXPOSE 8000 +EXPOSE 7035 # Set the entrypoint for the container -CMD ["hypercorn", "--bind", "0.0.0.0:8000", "api:app"] \ No newline at end of file +CMD 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zf9F=M%dsF-Ul9)QrHoxe46)9`>)zz|zT?*tRKJGECi%4rCjAt^DA$36CfV(XfXd%=geA|vZGsAasXomFYVJ(4lI8jFPa5LNvGsD9TSGdb+<_h_rNA&Ow2`N`P cEj= self.max_chunk_length: - chunks.append(curr_sentence) + chunks.append((curr_sentence, start_page, end_page)) curr_sentence = sentence + start_page = end_page = page_num else: curr_sentence = curr_sentence + ' ' + sentence - chunks.append(curr_sentence) + end_page = page_num + if start_page is None: + start_page = page_num + chunks.append((curr_sentence, start_page, end_page)) return chunks + # taken from https://medium.com/@npolovinkin/how-to-chunk-text-into-paragraphs-using-python-8ae66be38ea6 - def getChunksConsideringNeighbouringSimilarity(self,numberOfSentencesToCalculatedWeightedSum=10): + def getChunksConsideringNeighbouringSimilarity(self, numberOfSentencesToCalculatedWeightedSum=10): encoding_model = SentenceTransformer('all-mpnet-base-v2') if self.embedding is None: print('Document Encoding Process :-') + sentences_only = [sentence for sentence, _ in self.sentencesInDoc] self.embedding = encoding_model.encode( - self.sentencesInDoc, + sentences_only, show_progress_bar=True, batch_size=32, - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + device=torch.device("cuda" if torch.cuda.is_available() else "cpu") ) similarities = cosine_similarity(self.embedding) @@ -61,19 +69,40 @@ def getChunksConsideringNeighbouringSimilarity(self,numberOfSentencesToCalculate minimas = argrelextrema(activated_similarities, np.less, order=2)[0].reshape(-1) return self.getChunksWithMinimas(minimas) - def getChunksWithMinimas(self,minimas): + + def getChunksWithMinimas(self, minimas): chunks = [] chunk_start = 0 + chunk_text = '' + start_page = None + end_page = None + for minima in minimas: - mergedSentence = ' '.join(self.sentencesInDoc[chunk_start:minima]) + for i in range(chunk_start, minima): + sentence, page_num = self.sentencesInDoc[i] + chunk_text += ' ' + sentence + end_page = page_num + if start_page is None: + start_page = page_num + + chunks.append((chunk_text.strip(), start_page, end_page)) + chunk_text = '' + start_page = end_page = None chunk_start = minima - currSentenceSplits = textwrap.wrap(mergedSentence, self.max_chunk_length) - chunks.extend(currSentenceSplits) - if(chunk_start != len(self.sentencesInDoc) - 1): - chunks.extend(self.sentencesInDoc[chunk_start:]) + + if chunk_start != len(self.sentencesInDoc): + for i in range(chunk_start, len(self.sentencesInDoc)): + sentence, page_num = self.sentencesInDoc[i] + chunk_text += ' ' + sentence + end_page = page_num + if start_page is None: + start_page = page_num + chunks.append((chunk_text.strip(), start_page, end_page)) + return chunks + def rev_sigmoid(self,x:float)->float: return (1 / (1 + math.exp(0.5*x))) @@ -120,10 +149,13 @@ def __new__(cls, context): async def inference(self, request: ModelRequest): # Modify this function according to model requirements such that inputs and output remains the same - splitter = Splitter(request.text, 4 * 1024) + pages = request.text + splitter = Splitter(pages, 4 * 1024) chunks = splitter.getChunksConsideringNeighbouringSimilarity() - df = pd.DataFrame({'content': chunks}) - # Convert DataFrame to a CSV string + df = pd.DataFrame({'content': [content for content, _, _ in chunks], + 'start_page': [start_page for _, start_page, _ in chunks], + 'end_page': [end_page for _, _, end_page in chunks]}) + csv_string = df.to_csv(index=False) # Properly escape the CSV string