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https://github.com/ParisNeo/lollms.git
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upgraded summerization code
This commit is contained in:
Saifeddine ALOUI
parent
6dbcc79b91
commit
f8720878dd
@ -16,6 +16,7 @@ from lollms.binding import LLMBinding, BindingType
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from lollms.utilities import PromptReshaper, PackageManager, discussion_path_to_url, process_ai_output
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from lollms.com import NotificationType, NotificationDisplayType
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from lollms.client_session import Session, Client
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import pkg_resources
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from pathlib import Path
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from PIL import Image
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@ -28,7 +29,7 @@ import subprocess
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import yaml
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from ascii_colors import ASCIIColors
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import time
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from lollms.types import MSG_TYPE
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from lollms.types import MSG_TYPE, SUMMARY_MODE
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import json
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from typing import Any, List, Optional, Type, Callable, Dict, Any, Union
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import json
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@ -2228,16 +2229,26 @@ class APScript(StateMachine):
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max_generation_size=3000,
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max_summary_size=512,
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callback=None,
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chunk_summary_post_processing=None
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chunk_summary_post_processing=None,
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summary_mode=SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL
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):
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depth=0
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tk = self.personality.model.tokenize(text)
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prev_len = len(tk)
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while len(tk)>max_summary_size:
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document_chunks=None
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while len(tk)>max_summary_size and (document_chunks is None or len(document_chunks)>1):
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self.step_start(f"Comprerssing {doc_name}... [depth {depth+1}]")
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chunk_size = int(self.personality.config.ctx_size*0.6)
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document_chunks = DocumentDecomposer.decompose_document(text, chunk_size, 0, self.personality.model.tokenize, self.personality.model.detokenize, True)
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text = self.summerize_chunks(document_chunks,summary_instruction, doc_name, answer_start, max_generation_size, callback, chunk_summary_post_processing=chunk_summary_post_processing)
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text = self.summerize_chunks(
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document_chunks,
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summary_instruction,
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doc_name,
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answer_start,
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max_generation_size,
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callback,
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chunk_summary_post_processing=chunk_summary_post_processing,
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summary_mode=summary_mode)
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tk = self.personality.model.tokenize(text)
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tk = self.personality.model.tokenize(text)
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dtk_ln=prev_len-len(tk)
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@ -2259,7 +2270,8 @@ class APScript(StateMachine):
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max_generation_size=3000,
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max_summary_size=512,
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callback=None,
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chunk_summary_post_processing=None
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chunk_summary_post_processing=None,
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summary_mode=SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL
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):
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depth=0
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tk = self.personality.model.tokenize(text)
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@ -2268,7 +2280,16 @@ class APScript(StateMachine):
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self.step_start(f"Comprerssing... [depth {depth+1}]")
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chunk_size = int(self.personality.config.ctx_size*0.6)
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document_chunks = DocumentDecomposer.decompose_document(text, chunk_size, 0, self.personality.model.tokenize, self.personality.model.detokenize, True)
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text = self.summerize_chunks(document_chunks, data_extraction_instruction, doc_name, answer_start, max_generation_size, callback, chunk_summary_post_processing=chunk_summary_post_processing)
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text = self.summerize_chunks(
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document_chunks,
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data_extraction_instruction,
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doc_name,
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answer_start,
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max_generation_size,
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callback,
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chunk_summary_post_processing=chunk_summary_post_processing,
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summary_mode=summary_mode
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)
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tk = self.personality.model.tokenize(text)
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dtk_ln=prev_len-len(tk)
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prev_len = len(tk)
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@ -2278,8 +2299,14 @@ class APScript(StateMachine):
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if dtk_ln<=10: # it is not sumlmarizing
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break
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self.step_start(f"Rewriting ...")
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text = self.summerize_chunks([text],
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final_task_instruction, doc_name, answer_start, max_generation_size, callback, chunk_summary_post_processing=chunk_summary_post_processing)
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text = self.summerize_chunks(
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[text],
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final_task_instruction,
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doc_name, answer_start,
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max_generation_size,
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callback,
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chunk_summary_post_processing=chunk_summary_post_processing
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)
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self.step_end(f"Rewriting ...")
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return text
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@ -2292,27 +2319,49 @@ class APScript(StateMachine):
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answer_start="",
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max_generation_size=3000,
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callback=None,
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chunk_summary_post_processing=None
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chunk_summary_post_processing=None,
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summary_mode=SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL
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):
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summeries = []
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for i, chunk in enumerate(chunks):
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self.step_start(f" Summary of {doc_name} - Processing chunk : {i+1}/{len(chunks)}")
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summary = f"{answer_start}"+ self.fast_gen(
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"\n".join([
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f"!@>Document_chunk: {doc_name}:",
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f"{chunk}",
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f"!@>instruction: {summary_instruction}",
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f"Answer directly with the summary with no extra comments.",
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f"!@>summary:",
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f"{answer_start}"
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]),
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max_generation_size=max_generation_size,
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callback=callback)
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if chunk_summary_post_processing:
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summary = chunk_summary_post_processing(summary)
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summeries.append(summary)
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self.step_end(f" Summary of {doc_name} - Processing chunk : {i+1}/{len(chunks)}")
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return "\n".join(summeries)
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if summary_mode==SUMMARY_MODE.SUMMARY_MODE_SEQUENCIAL:
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summary = ""
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for i, chunk in enumerate(chunks):
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self.step_start(f" Summary of {doc_name} - Processing chunk : {i+1}/{len(chunks)}")
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summary = f"{answer_start}"+ self.fast_gen(
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"\n".join([
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f"!@>Document_chunk: {doc_name}:",
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f"{summary}",
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f"{chunk}",
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f"!@>instruction: {summary_instruction}",
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f"Answer directly with the summary with no extra comments.",
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f"!@>summary:",
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f"{answer_start}"
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]),
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max_generation_size=max_generation_size,
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callback=callback)
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if chunk_summary_post_processing:
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summary = chunk_summary_post_processing(summary)
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self.step_end(f" Summary of {doc_name} - Processing chunk : {i+1}/{len(chunks)}")
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return summary
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else:
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summeries = []
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for i, chunk in enumerate(chunks):
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self.step_start(f" Summary of {doc_name} - Processing chunk : {i+1}/{len(chunks)}")
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summary = f"{answer_start}"+ self.fast_gen(
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"\n".join([
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f"!@>Document_chunk: {doc_name}:",
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f"{chunk}",
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f"!@>instruction: {summary_instruction}",
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f"Answer directly with the summary with no extra comments.",
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f"!@>summary:",
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f"{answer_start}"
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]),
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max_generation_size=max_generation_size,
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callback=callback)
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if chunk_summary_post_processing:
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summary = chunk_summary_post_processing(summary)
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summeries.append(summary)
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self.step_end(f" Summary of {doc_name} - Processing chunk : {i+1}/{len(chunks)}")
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return "\n".join(summeries)
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def sequencial_chunks_summary(
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self,
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@ -1,5 +1,4 @@
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from enum import Enum
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class MSG_TYPE(Enum):
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# Messaging
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MSG_TYPE_CHUNK = 0 # A chunk of a message (used for classical chat)
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@ -60,4 +59,9 @@ class GenerationPresets:
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"""
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Default preset with moderate temperature, top_k, and top_p.
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"""
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return {'temperature': 0.5, 'top_k': 20, 'top_p': 0.85}
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return {'temperature': 0.5, 'top_k': 20, 'top_p': 0.85}
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class SUMMARY_MODE(Enum):
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SUMMARY_MODE_SEQUENCIAL = 0
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SUMMARY_MODE_HIERARCHICAL = 0
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