mirror of
https://github.com/ParisNeo/lollms.git
synced 2025-04-16 06:56:33 +00:00
added rag deactivate mode
This commit is contained in:
Saifeddine ALOUI
parent
f186a2cdc3
commit
afad6aeb92
@ -1,5 +1,5 @@
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# =================== Lord Of Large Language Multimodal Systems Configuration file ===========================
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version: 132
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version: 133
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binding_name: null
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model_name: null
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model_variant: null
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@ -275,6 +275,8 @@ rag_activate_multi_hops: false #if true, we use multi hops algorithm to do multi
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rag_min_nb_tokens_in_chunk: 10 #this removed any useless junk ith less than x tokens
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rag_max_n_hops: 3 #We set the maximum number of hop in multi hops rag
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rag_deactivate: false # if you have a large context model, you can activate this to use your document as a whole
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contextual_summary: false #If activated this will completely replace the rag and instead will use contextual summary
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activate_skills_lib: false # Activate vectorizing previous conversations
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173
lollms/app.py
173
lollms/app.py
@ -1009,26 +1009,33 @@ class LollmsApplication(LoLLMsCom):
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if documentation=="":
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documentation=f"{self.separator_template}{self.start_header_id_template}Documentation:\n"
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if self.config.data_vectorization_build_keys_words:
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if discussion is None:
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discussion = self.recover_discussion(client_id)
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query = self.personality.fast_gen(f"{self.separator_template}{self.start_header_id_template}instruction: Read the discussion and rewrite the last prompt for someone who didn't read the entire discussion.\nDo not answer the prompt. Do not add explanations.{self.separator_template}{self.start_header_id_template}discussion:\n{discussion[-2048:]}{self.separator_template}{self.start_header_id_template}enhanced query: ", max_generation_size=256, show_progress=True)
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ASCIIColors.cyan(f"Query:{query}")
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if not self.config.rag_deactivate:
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if self.config.data_vectorization_build_keys_words:
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if discussion is None:
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discussion = self.recover_discussion(client_id)
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query = self.personality.fast_gen(f"{self.separator_template}{self.start_header_id_template}instruction: Read the discussion and rewrite the last prompt for someone who didn't read the entire discussion.\nDo not answer the prompt. Do not add explanations.{self.separator_template}{self.start_header_id_template}discussion:\n{discussion[-2048:]}{self.separator_template}{self.start_header_id_template}enhanced query: ", max_generation_size=256, show_progress=True)
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ASCIIColors.cyan(f"Query:{query}")
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else:
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query = current_message.content
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try:
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chunks:List[Chunk] = self.personality.persona_data_vectorizer.search(query, int(self.config.rag_n_chunks))
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for chunk in chunks:
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if self.config.data_vectorization_put_chunk_informations_into_context:
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documentation += f"{self.start_header_id_template}document chunk{self.end_header_id_template}\ndocument title: {chunk.doc.title}\nchunk content:\n{chunk.text}\n"
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else:
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documentation += f"{self.start_header_id_template}chunk{self.end_header_id_template}\n{chunk.text}\n"
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documentation += f"{self.separator_template}{self.start_header_id_template}important information: Use the documentation data to answer the user questions. If the data is not present in the documentation, please tell the user that the information he is asking for does not exist in the documentation section. It is strictly forbidden to give the user an answer without having actual proof from the documentation.\n"
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except Exception as ex:
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trace_exception(ex)
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self.warning("Couldn't add documentation to the context. Please verify the vector database")
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else:
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query = current_message.content
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try:
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chunks:List[Chunk] = self.personality.persona_data_vectorizer.search(query, int(self.config.rag_n_chunks))
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for chunk in chunks:
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if self.config.data_vectorization_put_chunk_informations_into_context:
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documentation += f"{self.start_header_id_template}document chunk{self.end_header_id_template}\ndocument title: {chunk.doc.title}\nchunk content:\n{chunk.text}\n"
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else:
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documentation += f"{self.start_header_id_template}chunk{self.end_header_id_template}\n{chunk.text}\n"
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documentation += f"{self.separator_template}{self.start_header_id_template}important information: Use the documentation data to answer the user questions. If the data is not present in the documentation, please tell the user that the information he is asking for does not exist in the documentation section. It is strictly forbidden to give the user an answer without having actual proof from the documentation.\n"
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except Exception as ex:
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trace_exception(ex)
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self.warning("Couldn't add documentation to the context. Please verify the vector database")
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docs = self.personality.persona_data_vectorizer.list_documents()
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for doc in docs:
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documentation += self.personality.persona_data_vectorizer.get_document(doc['title'])
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if not self.personality.ignore_discussion_documents_rag:
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query = None
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if len(self.active_rag_dbs) > 0 :
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@ -1127,67 +1134,73 @@ class LollmsApplication(LoLLMsCom):
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documentation += document_infos
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if (len(client.discussion.text_files) > 0) and client.discussion.vectorizer is not None:
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if discussion is None:
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discussion = self.recover_discussion(client_id)
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if not self.config.rag_deactivate:
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if discussion is None:
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discussion = self.recover_discussion(client_id)
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if documentation=="":
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documentation=f"{self.separator_template}".join([
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f"{self.separator_template}{self.start_header_id_template}important information{self.end_header_id_template}Utilize Documentation Data: Always refer to the provided documentation to answer user questions accurately.",
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"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.",
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"Strict Adherence to Documentation: It is strictly prohibited to provide answers without concrete evidence from the documentation.",
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"Cite Your Sources: After providing an answer, include the full path to the document where the information was found.",
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f"{self.start_header_id_template}Documentation{self.end_header_id_template}"])
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documentation += f"{self.separator_template}"
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if documentation=="":
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documentation=f"{self.separator_template}".join([
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f"{self.separator_template}{self.start_header_id_template}important information{self.end_header_id_template}Utilize Documentation Data: Always refer to the provided documentation to answer user questions accurately.",
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"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.",
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"Strict Adherence to Documentation: It is strictly prohibited to provide answers without concrete evidence from the documentation.",
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"Cite Your Sources: After providing an answer, include the full path to the document where the information was found.",
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f"{self.start_header_id_template}Documentation{self.end_header_id_template}"])
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documentation += f"{self.separator_template}"
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if query is None:
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if self.config.data_vectorization_build_keys_words:
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self.personality.step_start("Building vector store query")
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query = self.personality.fast_gen(f"{self.separator_template}{self.start_header_id_template}instruction: Read the discussion and rewrite the last prompt for someone who didn't read the entire discussion.\nDo not answer the prompt. Do not add explanations.{self.separator_template}{self.start_header_id_template}discussion:\n{discussion[-2048:]}{self.separator_template}{self.start_header_id_template}enhanced query: ", max_generation_size=256, show_progress=True, callback=self.personality.sink)
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self.personality.step_end("Building vector store query")
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ASCIIColors.cyan(f"Query: {query}")
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if query is None:
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if self.config.data_vectorization_build_keys_words:
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self.personality.step_start("Building vector store query")
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query = self.personality.fast_gen(f"{self.separator_template}{self.start_header_id_template}instruction: Read the discussion and rewrite the last prompt for someone who didn't read the entire discussion.\nDo not answer the prompt. Do not add explanations.{self.separator_template}{self.start_header_id_template}discussion:\n{discussion[-2048:]}{self.separator_template}{self.start_header_id_template}enhanced query: ", max_generation_size=256, show_progress=True, callback=self.personality.sink)
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self.personality.step_end("Building vector store query")
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ASCIIColors.cyan(f"Query: {query}")
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else:
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query = current_message.content
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full_documentation=""
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if self.config.contextual_summary:
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v = client.discussion.vectorizer
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docs = v.list_documents()
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for doc in docs:
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document=v.get_document(document_path = doc["path"])
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self.personality.step_start(f"Summeryzing document {doc['path']}")
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summary = self.personality.summarize_text(document, f"Extract information from the following text chunk to answer this request. If there is no information about the query, just return an empty string.\n{self.system_custom_header('query')}{query}", callback=self.personality.sink)
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self.personality.step_end(f"Summeryzing document {doc['path']}")
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document_infos = f"{self.separator_template}".join([
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self.system_custom_header('document contextual summary'),
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f"source_document_title:{doc['title']}",
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f"source_document_path:{doc['path']}",
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f"content:\n{summary}\n"
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])
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documentation_entries.append({
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"document_title":doc['title'],
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"document_path":doc['path'],
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"chunk_content":summary,
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"chunk_size":0,
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"distance":0,
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})
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if summary!="":
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v.add_summaries(doc['path'],[{"context":query, "summary":summary}])
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full_documentation += document_infos
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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)
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else:
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query = current_message.content
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try:
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chunks:List[Chunk] = client.discussion.vectorizer.search(query, int(self.config.rag_n_chunks))
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for chunk in chunks:
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if self.config.data_vectorization_put_chunk_informations_into_context:
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documentation += f"{self.start_header_id_template}document chunk{self.end_header_id_template}\ndocument title: {chunk.doc.title}\nchunk content:\n{chunk.text}\n"
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else:
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documentation += f"{self.start_header_id_template}chunk{self.end_header_id_template}\n{chunk.text}\n"
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full_documentation=""
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if self.config.contextual_summary:
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v = client.discussion.vectorizer
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docs = v.list_documents()
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for doc in docs:
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document=v.get_document(document_path = doc["path"])
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self.personality.step_start(f"Summeryzing document {doc['path']}")
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summary = self.personality.summarize_text(document, f"Extract information from the following text chunk to answer this request. If there is no information about the query, just return an empty string.\n{self.system_custom_header('query')}{query}", callback=self.personality.sink)
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self.personality.step_end(f"Summeryzing document {doc['path']}")
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document_infos = f"{self.separator_template}".join([
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self.system_custom_header('document contextual summary'),
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f"source_document_title:{doc['title']}",
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f"source_document_path:{doc['path']}",
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f"content:\n{summary}\n"
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])
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documentation_entries.append({
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"document_title":doc['title'],
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"document_path":doc['path'],
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"chunk_content":summary,
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"chunk_size":0,
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"distance":0,
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})
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if summary!="":
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v.add_summaries(doc['path'],[{"context":query, "summary":summary}])
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full_documentation += document_infos
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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)
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documentation += f"{self.separator_template}{self.start_header_id_template}important information: Use the documentation data to answer the user questions. If the data is not present in the documentation, please tell the user that the information he is asking for does not exist in the documentation section. It is strictly forbidden to give the user an answer without having actual proof from the documentation.\n"
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except Exception as ex:
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trace_exception(ex)
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self.warning("Couldn't add documentation to the context. Please verify the vector database")
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else:
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try:
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chunks:List[Chunk] = client.discussion.vectorizer.search(query, int(self.config.rag_n_chunks))
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for chunk in chunks:
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if self.config.data_vectorization_put_chunk_informations_into_context:
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documentation += f"{self.start_header_id_template}document chunk{self.end_header_id_template}\ndocument title: {chunk.doc.title}\nchunk content:\n{chunk.text}\n"
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else:
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documentation += f"{self.start_header_id_template}chunk{self.end_header_id_template}\n{chunk.text}\n"
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documentation += f"{self.separator_template}{self.start_header_id_template}important information: Use the documentation data to answer the user questions. If the data is not present in the documentation, please tell the user that the information he is asking for does not exist in the documentation section. It is strictly forbidden to give the user an answer without having actual proof from the documentation.\n"
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except Exception as ex:
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trace_exception(ex)
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self.warning("Couldn't add documentation to the context. Please verify the vector database")
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docs = client.discussion.vectorizer.list_documents()
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for doc in docs:
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documentation += client.discussion.vectorizer.get_document(doc['title'])
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# Check if there is discussion knowledge to add to the prompt
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if self.config.activate_skills_lib:
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try:
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@ -1239,6 +1252,16 @@ class LollmsApplication(LoLLMsCom):
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if len(documentation)>0:
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tokens_documentation = self.model.tokenize(documentation)
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n_doc_tk = len(tokens_documentation)
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self.info(f"The documentation consumes {n_doc_tk} tokens")
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if n_doc_tk>3*self.config.ctx_size/4:
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ASCIIColors.warning("The documentation is bigger than 3/4 of the context ")
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self.warning("The documentation is bigger than 3/4 of the context ")
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if n_doc_tk>=self.config.ctx_size-512:
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ASCIIColors.warning("The documentation is too big for the context")
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self.warning("The documentation is too big for the context it'll be cropped")
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documentation = self.model.detokenize(tokens_documentation[:(self.config.ctx_size-512)])
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n_doc_tk = self.config.ctx_size-512
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else:
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tokens_documentation = []
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n_doc_tk = 0
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@ -1,5 +1,5 @@
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# =================== Lord Of Large Language Multimodal Systems Configuration file ===========================
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version: 132
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version: 133
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binding_name: null
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model_name: null
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model_variant: null
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@ -275,6 +275,8 @@ rag_activate_multi_hops: false #if true, we use multi hops algorithm to do multi
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rag_min_nb_tokens_in_chunk: 10 #this removed any useless junk ith less than x tokens
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rag_max_n_hops: 3 #We set the maximum number of hop in multi hops rag
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rag_deactivate: false # if you have a large context model, you can activate this to use your document as a whole
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contextual_summary: false #If activated this will completely replace the rag and instead will use contextual summary
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activate_skills_lib: false # Activate vectorizing previous conversations
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@ -2052,7 +2052,7 @@ class StateMachine:
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try:
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func(command, full_context, callback, context_state, client)
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except:# retrocompatibility
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func(command, full_context)
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func(command, full_context, client)
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return
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default_func = current_state.get("default")
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@ -55,7 +55,7 @@ def add_events(sio:socketio):
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'js', 'json', 'log', 'lua', 'map', 'md', 'mov', 'mp3', 'mp4', 'pas', 'pdf',
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'php', 'png', 'ppt', 'pptx', 'ps1', 'py', 'rb', 'rtf', 's', 'se', 'sh', 'sln', 'snippet',
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'snippets', 'sql', 'svg', 'sym', 'tif', 'tiff', 'ts', 'txt', 'wav', 'webp',
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'xlsx', 'xls', 'xml', 'yaml', 'yml'
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'xlsx', 'xls', 'xml', 'yaml', 'yml', "vue"
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}
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def allowed_file(filename):
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