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Update app.py

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Saifeddine ALOUI 2024-12-03 09:55:52 +01:00 committed by GitHub
parent 58ab336aa3
commit 8cbbb988cd
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1 changed files with 15 additions and 15 deletions
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lollms/app.py
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@ -337,19 +337,20 @@ class LollmsApplication(LoLLMsCom):
from lollmsvectordb import VectorDatabase
from lollmsvectordb.text_document_loader import TextDocumentsLoader
from lollmsvectordb.lollms_tokenizers.tiktoken_tokenizer import TikTokenTokenizer
if self.lollms.config.rag_vectorizer=="semantic":
if self.config.rag_vectorizer=="semantic":
from lollmsvectordb.lollms_vectorizers.semantic_vectorizer import SemanticVectorizer
vectorizer = SemanticVectorizer(self.lollms.config.rag_vectorizer_model)
elif self.lollms.config.rag_vectorizer=="tfidf":
vectorizer = SemanticVectorizer(self.config.rag_vectorizer_model)
elif self.config.rag_vectorizer=="tfidf":
from lollmsvectordb.lollms_vectorizers.tfidf_vectorizer import TFIDFVectorizer
vectorizer = TFIDFVectorizer()
elif self.lollms.config.rag_vectorizer=="openai":
elif self.config.rag_vectorizer=="openai":
from lollmsvectordb.lollms_vectorizers.openai_vectorizer import OpenAIVectorizer
vectorizer = OpenAIVectorizer(self.lollms.config.rag_vectorizer_model, self.lollms.config.rag_vectorizer_openai_key)
vectorizer = OpenAIVectorizer(self.config.rag_vectorizer_model, self.config.rag_vectorizer_openai_key)
vdb = VectorDatabase(Path(parts[1])/f"{db_name}.sqlite", vectorizer, None if self.lollms.config.rag_vectorizer=="semantic" else self.model if self.model else TikTokenTokenizer(), n_neighbors=self.config.rag_n_chunks)
vdb = VectorDatabase(Path(parts[1])/f"{db_name}.sqlite", vectorizer, None if self.config.rag_vectorizer=="semantic" else self.model if self.model else TikTokenTokenizer(), n_neighbors=self.config.rag_n_chunks)
self.active_rag_dbs.append({"name":parts[0],"path":parts[1],"vectorizer":vdb})
except:
except Exception as ex:
trace_exception(ex)
ASCIIColors.error(f"Couldn't load "+str(Path(parts[1])/f"{db_name}.sqlite")+" consider revectorizing it")
def start_servers(self):
@ -966,21 +967,21 @@ class LollmsApplication(LoLLMsCom):
# boosting information
if self.config.positive_boost:
positive_boost=f"{self.separator_template}{self.start_header_id_template}important information: "+self.config.positive_boost+"\n"
positive_boost=f"{self.system_custom_header('important information')}"+self.config.positive_boost+"\n"
n_positive_boost = len(self.model.tokenize(positive_boost))
else:
positive_boost=""
n_positive_boost = 0
if self.config.negative_boost:
negative_boost=f"{self.separator_template}{self.start_header_id_template}important information: "+self.config.negative_boost+"\n"
negative_boost=f"{self.system_custom_header('important information')}"+self.config.negative_boost+"\n"
n_negative_boost = len(self.model.tokenize(negative_boost))
else:
negative_boost=""
n_negative_boost = 0
if self.config.fun_mode:
fun_mode=f"{self.separator_template}{self.start_header_id_template}important information: Fun mode activated. In this mode you must answer in a funny playful way. Do not be serious in your answers. Each answer needs to make the user laugh.\n"
fun_mode=f"{self.system_custom_header('important information')} Fun mode activated. In this mode you must answer in a funny playful way. Do not be serious in your answers. Each answer needs to make the user laugh.\n"
n_fun_mode = len(self.model.tokenize(positive_boost))
else:
fun_mode=""
@ -1091,11 +1092,11 @@ class LollmsApplication(LoLLMsCom):
if self.config.data_vectorization_build_keys_words:
self.personality.step_start("Building vector store query")
q = f"{self.separator_template}".join([
f"{self.start_header_id_template}instruction{self.end_header_id_template}Read the entire discussion and rewrite the last prompt for someone who hasn't read the discussion.",
f"{self.system_custom_header('instruction')}Read the entire discussion and rewrite the last prompt for someone who hasn't read the discussion.",
"Do not answer the prompt. Do not provide any explanations.",
f"{self.start_header_id_template}discussion{self.end_header_id_template}",
f"{self.system_custom_header('discussion')}",
f"{discussion[-2048:]}",
f"{self.start_header_id_template}enhanced_query{self.end_header_id_template}"
f"{self.ai_custom_header('enhanced_query')}"
])
query = self.personality.fast_gen(q, max_generation_size=256, show_progress=True, callback=self.personality.sink)
self.personality.step_end("Building vector store query")
@ -1105,7 +1106,7 @@ class LollmsApplication(LoLLMsCom):
query = current_message.content
if documentation=="":
documentation=f"{self.separator_template}".join([
f"{self.separator_template}{self.start_header_id_template}important information{self.end_header_id_template}",
f"{self.system_custom_header('important information')}",
"Always refer to the provided documentation to answer user questions accurately.",
"Absence of Information: If the required information is not available in the documentation, inform the user that the requested information is not present in the documentation section.",
"Strict Adherence to Documentation: It is strictly prohibited to provide answers without concrete evidence from the documentation.",
@ -1142,7 +1143,6 @@ class LollmsApplication(LoLLMsCom):
v.add_summaries(doc['path'],[{"context":query, "summary":summary}])
full_documentation += document_infos
documentation += self.personality.summarize_text(full_documentation, f"Extract information from the current text chunk and previous text chunks to answer the query. If there is no information about the query, just return an empty string.\n{self.system_custom_header('query')}{query}", callback=self.personality.sink)
else:
results = []
recovered_ids=[[] for _ in range(len(self.active_rag_dbs))]