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added new summary tools

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Saifeddine ALOUI 2024-03-24 15:39:35 +01:00
parent 2209934b94
commit 6d32a1bc42
1 changed files with 27 additions and 3 deletions
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30
lollms/personality.py
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@ -31,7 +31,7 @@ from lollms.types import MSG_TYPE
import json
from typing import Any, List, Optional, Type, Callable, Dict, Any, Union
import json
from safe_store import TextVectorizer, GenericDataLoader, VisualizationMethod, VectorizationMethod
from safe_store import TextVectorizer, GenericDataLoader, VisualizationMethod, VectorizationMethod, DocumentDecomposer
from functools import partial
import sys
from lollms.com import LoLLMsCom
@ -2184,13 +2184,26 @@ class APScript(StateMachine):
]),
max_generation_size=max_generation_size))
return translated
def summerize(self, chunks, summary_instruction="summerize", chunk_name="chunk", answer_start="", max_generation_size=3000, callback=None):
def summerize_text(self, text, summary_instruction="summerize", doc_name="chunk", answer_start="", max_generation_size=3000, max_summary_size=512, callback=None):
depth=0
tk = self.personality.model.tokenize(text)
while len(tk)>max_summary_size:
self.step_start(f"Comprerssing.. [depth {depth}]")
chunk_size = int(self.personality.config.ctx_size*0.6)
document_chunks = DocumentDecomposer.decompose_document(text, chunk_size, 0, self.personality.model.tokenize, self.personality.model.detokenize, True)
text = self.summerize_chunks(document_chunks,summary_instruction, doc_name, answer_start, max_generation_size, callback)
tk = self.personality.model.tokenize(text)
self.step_end(f"Comprerssing.. [depth {depth}]")
return text
def summerize_chunks(self, chunks, summary_instruction="summerize", doc_name="chunk", answer_start="", max_generation_size=3000, callback=None):
summeries = []
for i, chunk in enumerate(chunks):
self.step_start(f"Processing chunk : {i+1}/{len(chunks)}")
summary = f"```markdown\n{answer_start}"+ self.fast_gen(
"\n".join([
f"!@>Document_chunk: {chunk_name}:",
f"!@>Document_chunk: {doc_name}:",
f"{chunk}",
f"!@>instruction: {summary_instruction}",
f"!@>summary:",
@ -2273,6 +2286,17 @@ class APScript(StateMachine):
return self.personality.internet_search_with_vectorization(query, quick_search=quick_search)
def vectorize_and_query(self, text, query, max_chunk_size=512, overlap_size=20, internet_vectorization_nb_chunks=3):
vectorizer = TextVectorizer(VectorizationMethod.TFIDF_VECTORIZER, model = self.personality.model)
decomposer = DocumentDecomposer()
chunks = decomposer.decompose_document(text, max_chunk_size, overlap_size,self.personality.model.tokenize,self.personality.model.detokenize)
for i, chunk in enumerate(chunks):
vectorizer.add_document(f"chunk_{i}", self.personality.model.detokenize(chunk))
vectorizer.index()
docs, sorted_similarities, document_ids = vectorizer.recover_text(query, internet_vectorization_nb_chunks)
return docs, sorted_similarities
def step_start(self, step_text, callback: Callable[[str, MSG_TYPE, dict, list], bool]=None):
"""This triggers a step start