New version where the streaming streamlit app is working. Refactoring of the prompt mechanism. New, more efficient prompt with citations of the URL.
This commit is contained in:
parent
62559dcd0f
commit
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@ -11,3 +11,4 @@ index_cera2/
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index_cera2_distiluse/
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__pycache__/
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chromadbtest/
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rag.log
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2
PAD
2
PAD
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@ -65,3 +65,5 @@ pip install langchain
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pip install -U sentence-transformers
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pip install streamlit
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python -m streamlit run app.py
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26
app.py
26
app.py
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@ -3,14 +3,16 @@ from rag import RAG
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import re
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import logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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@st.cache_resource
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def init_rag():
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llm_model_path = '/Users/peportier/llm/a/a/zephyr-7b-beta.Q5_K_M.gguf'
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embed_model_name = 'intfloat/multilingual-e5-large'
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collection_name = 'cera'
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chromadb_path = './chromadb'
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rag = RAG(llm_model_path, embed_model_name, collection_name, chromadb_path)
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return rag
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llm_model_path = '/Users/peportier/llm/a/a/zephyr-7b-beta.Q5_K_M.gguf'
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embed_model_name = 'intfloat/multilingual-e5-large'
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collection_name = 'cera'
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chromadb_path = './chromadb'
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rag = RAG(llm_model_path, embed_model_name, collection_name, chromadb_path)
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rag = init_rag()
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st.title("CERA Chat")
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@ -37,10 +39,10 @@ if prompt := st.chat_input("Comment puis-je vous aider ?"):
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full_response += response
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message_placeholder.markdown(full_response + "▌")
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message_placeholder.markdown(full_response)
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url_pattern = r"URL :\n(https?://[^\s]+)"
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urls = re.findall(url_pattern, rag.chat_history[-1]['assistant'])
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markdown_urls = "\n".join([f"- {url}" for url in urls])
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logging.info(markdown_urls)
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#message_placeholder.markdown(markdown_urls)
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# url_pattern = r"URL :\n(https?://[^\s]+)"
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# urls = re.findall(url_pattern, rag.chat_history[-1]['assistant'])
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# markdown_urls = "\n".join([f"- {url}" for url in urls])
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# logging.info(f"URLs: \n{markdown_urls}")
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# message_placeholder.markdown(markdown_urls)
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st.session_state.messages.append({"role": "assistant", "content": full_response})
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@ -0,0 +1,21 @@
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from rag import RAG
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llm_model_path = '/Users/peportier/llm/a/a/zephyr-7b-beta.Q5_K_M.gguf'
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embed_model_name = 'intfloat/multilingual-e5-large'
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collection_name = 'cera'
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chromadb_path = './chromadb'
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rag = RAG(llm_model_path, embed_model_name, collection_name, chromadb_path)
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query1 = "Comment aider une entreprise qui rencontre des problèmes de trésorerie ?"
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ans1 = rag.chat(query1, stream=True)
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query2 = "Pouvez-vous m'en dire plus au sujet du deuxième point ?"
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ans2 = rag.chat(query2, stream=True)
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# Queries:
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#
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# Lorsque mon client est en télétravail, quels sont les risques couverts par son assurance habitation ?
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#
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# Quel est le risque de perte attaché à la détention de Parts Sociales ?
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#
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# Comment procéder pour déclarer un sinistre habitation ? ou Comment procéder pour déclarer un sinistre Visa Premier ?
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231
rag.py
231
rag.py
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@ -3,48 +3,103 @@ import chromadb
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from llama_cpp import Llama
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import copy
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import logging
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import re
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logging.basicConfig(filename='rag.log', level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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class RAG:
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def __init__(self, llm_model_path, embed_model_name, collection_name, chromadb_path):
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logging.info('INIT')
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self.chat_history = []
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self.rag_system_prompt = """
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Vous êtes un assistant IA qui répond à la question posée par l'utilisateur en utilisant un contexte répertorié ci-dessous dans la rubrique Contexte.
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Le contexte est formé de passages exraits du site web commercial de la Caisse d'Epargne Rhône-Alpes, une banque française régionale.
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Votre réponse ne doit pas mentionner des informations déjà présentes dans l'historique de la conversation qui est répertorié ci-dessous dans la rubrique Historique.
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Vous fournissez avec soin des réponses précises, factuelles, réfléchies et nuancées, et vous êtes doué pour le raisonnement.
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Toutes les informations factuelles que vous utilisez pour répondre proviennent exclusivement du contexte.
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Si vous ne pouvez pas répondre à la question sur la base des éléments du contexte, dites simplement que vous ne savez pas, n'essayez pas d'inventer une réponse.
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Vos réponses doivent être brèves.
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Vos utilisateurs savent que vos réponses sont brèves et qu'elles ne mentionnent que les éléments du contexte, il n'est pas nécessaire de le leur rappeler.
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Vous gardez le rôle d'assistant et vous ne générez jamais le texte '<|user|>'.
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Vous rédigez vos réponses en français au format markdown sous forme d'une liste composée de 1 à 7 éléments au maximum.
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Voici le format que doit prendre votre réponse :
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```
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Voici des éléments de réponse :
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1. Elément de réponse.
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2. Elément de réponse.
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3. Elément de réponse.
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4. ...
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```
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self.tag_system = '<|system|>'
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self.tag_user = '<|user|>'
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self.tag_assistant = '<|assistant|>'
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self.tag_end = '</s>'
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self.rag_prompt = """
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{tag_system}
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Objectif
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========
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----------------------------------------
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Historique :
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Vous êtes un assistant IA spécialiste des produits et services de la Caisse d'Epargne Rhône-Alpes, \
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une banque régionale française.
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Vous aidez un conseiller clientèle de la banque à mieux répondre aux besoins des clients.
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Vous fournissez avec soin des réponses précises et factuelles aux questions du conseiller.
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Utilisation du contexte
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=======================
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Vous répondez à la question posée par le conseiller en utilisant un contexte \
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formé de passages exraits du site web commercial de la banque.
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Votre réponse se base exclusivement sur les informations factuelles présentes dans le contexte.
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Si vous ne pouvez pas répondre à la question sur la base des éléments du contexte, \
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dites simplement que vous ne savez pas, n'essayez pas d'inventer une réponse.
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Voici le format d'un passage du contexte :
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```
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Titre :
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Le titre du passage
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Catégorie :
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La catégorie du passage
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URL :
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https://www.caisse-epargne.fr/rhone-alpes/url/du/passage
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Contenu :
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Le contenu du passage
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```
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Vos réponses doivent toujours citer l'URL des passages utilisés. \
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Assurez-vous que l'URL citée correspond exactement à celle du passage. \
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Ne générez pas de nouvelles URLs. \
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Les conseillers sont encouragés à vérifier les URLs citées.
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Format de réponse
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=================
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Formulez chaque réponse sous forme de recommandations directes et concises, \
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en utilisant le langage et les termes présents dans le contexte.
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Citez l'URL en fin de réponse ou immédiatement après la recommandation pertinente.
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Vous rédigez votre réponse en français sous forme d'une liste d'informations \
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synthétiques extraites du contexte et qui seront utiles au conseiller.
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Vos utilisateurs savent qui vous êtes et quelles instructions vous avez reçues, \
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il n'est pas nécessaire de le leur rapeler.
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Voici le format que doit suivre votre réponse :
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```
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Voici des informations qui pourront aider votre client :
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1. Utilisez [une solution spécifique du contexte] pour [traiter un aspect du problème]. Par exemple, [détail concret tiré du contexte]. Pour plus d'informations voir https://www.caisse-epargne.fr/rhone-alpes/url/du/passage
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2. Considérez [une autre solution du contexte], qui est particulièrement adaptée pour [un autre aspect du problème]. Par exemple, [autre détail concret du contexte]. Pour plus d'informations voir https://www.caisse-epargne.fr/rhone-alpes/url/du/passage
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```
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{tag_end}
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{history}
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----------------------------------------
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{tag_user}
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Contexte :
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==========
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{context}
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Question de l'utilisateur :
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===========================
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{query}
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{tag_end}
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{tag_assistant}
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Voici des informations qui pourront aider votre client :
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1.
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"""
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self.query_reformulate_system_prompt = """
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self.query_reformulate_prompt = """
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{tag_system}
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Instructions :
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==============
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Vous êtes un interprète conversationnel pour une conversation entre un utilisateur et \
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un assistant IA spécialiste des produits et services de la Caisse d'Epargne Rhône-Alpes, \
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une banque régionale française.
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L'utilisateur vous posera une question sans contexte. \
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L'utilisateur vous posera une question sans contexte.
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Vous devez reformuler la question pour prendre en compte le contexte de la conversation.
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Vous devez supposer que la question est liée aux produits et services de la Caisse d'Epargne Rhône-Alpes.
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Vous devez également consulter l'historique de la conversation ci-dessous lorsque vous reformulez la question. \
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Vous devez également consulter l'historique de la conversation ci-dessous lorsque vous reformulez la question.
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Par exemple, vous remplacerez les pronoms par les noms les plus probables dans l'historique de la conversation.
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Lorsque vous reformulez la question, accordez plus d'importance à la dernière question et \
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à la dernière réponse dans l'historique des conversations.
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@ -53,26 +108,36 @@ de sorte que l'échange le plus récent se trouve en haut de la page.
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Répondez en seulement une phrase avec la question reformulée.
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Historique de la conversation :
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===============================
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{history}
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{tag_end}
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{tag_user}
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Reformulez la question suivante : "{query}"
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{tag_end}
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{tag_assistant}
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Question reformulée : "
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"""
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self.prefix_assistant_prompt = '1. '
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self.embed_model = SentenceTransformer(embed_model_name)
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self.chroma_client = chromadb.PersistentClient(path=chromadb_path)
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self.collection = self.chroma_client.get_collection(name=collection_name)
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self.llm = Llama(model_path=llm_model_path, n_gpu_layers=1, use_mlock=True, n_ctx=4096)
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def answer(self, prompt, stream=False):
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def answer(self, prompt, stream):
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response = self.llm(prompt = prompt,
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temperature = 0.1,
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temperature = 0.7,
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mirostat_mode = 2,
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stream = stream,
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max_tokens = -1,
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stop = ['</s>', ' 8.', '\n\n', '<|user|>'])
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stop = [self.tag_end, self.tag_user])
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if stream:
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return response
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else: return response["choices"][0]["text"]
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def query_collection(self, query, n_results=3):
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logging.info(f"query_collection / query: \n{query}")
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query = 'query: ' + query
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query_embedding = self.embed_model.encode(query, normalize_embeddings=True)
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query_embedding = query_embedding.tolist()
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@ -80,6 +145,13 @@ Historique de la conversation :
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query_embeddings=[query_embedding],
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n_results=n_results,
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)
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ids_sources = ""
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for i in range(len(results["documents"][0])):
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id = results["ids"][0][i]
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ids_sources += id + " ; "
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logging.info(f"query_collection / sources: \n{ids_sources}")
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return results
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def format_passages(self, query_results):
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@ -107,70 +179,49 @@ Historique de la conversation :
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result = '\n'.join(result)
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return result
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def format_rag_prompt(self, query, context="", history=""):
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user_query = f"Question de l'utilisateur : \n{query}\n\n"
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assistant_answer = f"Réponse de l'assistant : \n 1. "
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self.chat_history.append({'user': user_query, 'assistant': assistant_answer})
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def answer_rag_prompt_streaming(self, prompt):
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logging.info(f"answer_rag_prompt_streaming: \n{prompt}")
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ans = self.answer(prompt, stream=True)
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system_prompt = self.rag_system_prompt.format(history=history, context=context)
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prompt = ""
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prompt = f"<|system|>\n{system_prompt.strip()}</s>\n"
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prompt += f"<|user|>\n{query}</s>\n"
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prompt += f"<|assistant|>\n Voici des éléments de réponse : \n 1. "
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return prompt
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def remove_references(self, text):
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motif = r"\(Passage \d+\)"
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res = re.sub(motif, '', text)
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return res
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yield self.prefix_assistant_prompt
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for item in ans:
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item = item["choices"][0]["text"]
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self.chat_history[-1]['assistant'] += item
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yield item
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def answer_rag_prompt(self, query, query_results, stream=False):
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query_context = self.format_passages(query_results)
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def answer_rag_prompt_non_streaming(self, prompt):
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logging.info(f"answer_rag_prompt_non_streaming: \n{prompt}")
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ans = self.answer(prompt, stream=False)
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self.chat_history[-1]['assistant'] += ans
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ans = self.prefix_assistant_prompt + ans
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return ans
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def prepare_prompt(self, query, query_results):
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context = self.format_passages(query_results)
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history = ""
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for i in reversed(range(len(self.chat_history))):
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history += self.chat_history[i]["user"]
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history += self.remove_references(self.chat_history[i]["assistant"])
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history += "\n\n"
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prompt = self.format_rag_prompt(query, query_context, history)
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for i in range(len(self.chat_history)):
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history += f"<|user|>\n{self.chat_history[i]['user']}</s>\n"
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history += f"<|assistant|>\n{self.chat_history[i]['assistant']}</s>\n"
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logging.info(prompt)
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ans = self.answer(prompt, stream)
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if stream:
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yield ' 1. '
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for item in ans:
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item = item["choices"][0]["text"]
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self.chat_history[-1]['assistant'] += item
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yield item
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else:
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self.chat_history[-1]['assistant'] += ans
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ans = '1. ' + ans
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return ans
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def format_prompt_reformulate_query(self, query):
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system_prompt = self.query_reformulate_system_prompt
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for i in reversed(range(len(self.chat_history))):
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system_prompt += self.chat_history[i]["user"]
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system_prompt += self.chat_history[i]["assistant"]
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self.chat_history.append({'user': query, 'assistant': self.prefix_assistant_prompt})
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prompt = ""
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prompt = f"<|system|>\n{system_prompt.strip()}</s>\n"
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prompt += f"<|user|>\nPeux-tu reformuler la question suivante : \n \"{query}\"</s>\n"
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prompt += f"<|assistant|> Question reformulée : \n\""
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return prompt
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return self.rag_prompt.format(history=history, query=query, context=context,
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tag_user=self.tag_user, tag_system=self.tag_system,
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tag_assistant=self.tag_assistant, tag_end=self.tag_end)
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def reformulate_query(self, query):
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prompt = self.format_prompt_reformulate_query(query)
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logging.info(prompt)
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ans = self.answer(prompt)
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history = ""
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for i in reversed(range(len(self.chat_history))):
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history += f"Question de l'utilisateur :\n{self.chat_history[i]['user']}\n"
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history += f"Réponse de l'assistant :\n{self.chat_history[i]['assistant']}\n"
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prompt = self.query_reformulate_prompt.format(history=history, query=query,
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tag_user=self.tag_user, tag_system=self.tag_system,
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tag_assistant=self.tag_assistant, tag_end=self.tag_end)
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logging.info(f"reformulate_query: \n{prompt}")
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ans = self.answer(prompt, stream=False)
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last_quote_index = ans.rfind('"')
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if last_quote_index != -1:
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@ -183,11 +234,15 @@ Historique de la conversation :
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logging.info(f"La requête n'a pas pu être reformulée.")
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return query
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def chat(self, query):
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def chat(self, query, stream=True):
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if len(self.chat_history) > 0:
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query = self.reformulate_query(query)
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query_results = self.query_collection(query)
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ans = self.answer_rag_prompt(query, query_results, stream=True)
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prompt = self.prepare_prompt(query, query_results)
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if stream:
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ans = self.answer_rag_prompt_streaming(prompt)
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else:
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ans = self.answer_rag_prompt_non_streaming(prompt)
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return ans
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def reset_history(self):
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@ -2,10 +2,21 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"id": "612c8bdb-83a8-4882-96a5-513ac7aedd7b",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/Users/peportier/miniforge3/envs/RAG_ENV/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
||||
" from .autonotebook import tqdm as notebook_tqdm\n",
|
||||
"/Users/peportier/miniforge3/envs/RAG_ENV/lib/python3.9/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
|
||||
" _torch_pytree._register_pytree_node(\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import importlib\n",
|
||||
"import rag\n",
|
||||
|
|
@ -16,10 +27,400 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 2,
|
||||
"id": "98130049-a4de-4532-8454-3df1a13094e7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2024-01-04 09:15:27,599 - INFO - Load pretrained SentenceTransformer: intfloat/multilingual-e5-large\n",
|
||||
"/Users/peportier/miniforge3/envs/RAG_ENV/lib/python3.9/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
|
||||
" _torch_pytree._register_pytree_node(\n",
|
||||
"2024-01-04 09:15:31,253 - INFO - Use pytorch device: cpu\n",
|
||||
"2024-01-04 09:15:31,257 - INFO - Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
|
||||
"llama_model_loader: loaded meta data with 21 key-value pairs and 291 tensors from /Users/peportier/llm/a/a/zephyr-7b-beta.Q5_K_M.gguf (version GGUF V3 (latest))\n",
|
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"llama_model_loader: - tensor 0: token_embd.weight q5_K [ 4096, 32000, 1, 1 ]\n",
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"llama_model_loader: - tensor 1: blk.0.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
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"llama_model_loader: - tensor 287: blk.31.attn_output.weight q5_K [ 4096, 4096, 1, 1 ]\n",
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"llama_model_loader: - tensor 288: blk.31.attn_q.weight q5_K [ 4096, 4096, 1, 1 ]\n",
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|
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"llama_model_loader: - tensor 290: output_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
|
||||
"llama_model_loader: - kv 0: general.architecture str = llama\n",
|
||||
"llama_model_loader: - kv 1: general.name str = huggingfaceh4_zephyr-7b-beta\n",
|
||||
"llama_model_loader: - kv 2: llama.context_length u32 = 32768\n",
|
||||
"llama_model_loader: - kv 3: llama.embedding_length u32 = 4096\n",
|
||||
"llama_model_loader: - kv 4: llama.block_count u32 = 32\n",
|
||||
"llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336\n",
|
||||
"llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128\n",
|
||||
"llama_model_loader: - kv 7: llama.attention.head_count u32 = 32\n",
|
||||
"llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8\n",
|
||||
"llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010\n",
|
||||
"llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000\n",
|
||||
"llama_model_loader: - kv 11: general.file_type u32 = 17\n",
|
||||
"llama_model_loader: - kv 12: tokenizer.ggml.model str = llama\n",
|
||||
"llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = [\"<unk>\", \"<s>\", \"</s>\", \"<0x00>\", \"<...\n",
|
||||
"llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...\n",
|
||||
"llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...\n",
|
||||
"llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1\n",
|
||||
"llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2\n",
|
||||
"llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0\n",
|
||||
"llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 2\n",
|
||||
"llama_model_loader: - kv 20: general.quantization_version u32 = 2\n",
|
||||
"llama_model_loader: - type f32: 65 tensors\n",
|
||||
"llama_model_loader: - type q5_K: 193 tensors\n",
|
||||
"llama_model_loader: - type q6_K: 33 tensors\n",
|
||||
"llm_load_vocab: special tokens definition check successful ( 259/32000 ).\n",
|
||||
"llm_load_print_meta: format = GGUF V3 (latest)\n",
|
||||
"llm_load_print_meta: arch = llama\n",
|
||||
"llm_load_print_meta: vocab type = SPM\n",
|
||||
"llm_load_print_meta: n_vocab = 32000\n",
|
||||
"llm_load_print_meta: n_merges = 0\n",
|
||||
"llm_load_print_meta: n_ctx_train = 32768\n",
|
||||
"llm_load_print_meta: n_embd = 4096\n",
|
||||
"llm_load_print_meta: n_head = 32\n",
|
||||
"llm_load_print_meta: n_head_kv = 8\n",
|
||||
"llm_load_print_meta: n_layer = 32\n",
|
||||
"llm_load_print_meta: n_rot = 128\n",
|
||||
"llm_load_print_meta: n_gqa = 4\n",
|
||||
"llm_load_print_meta: f_norm_eps = 0.0e+00\n",
|
||||
"llm_load_print_meta: f_norm_rms_eps = 1.0e-05\n",
|
||||
"llm_load_print_meta: f_clamp_kqv = 0.0e+00\n",
|
||||
"llm_load_print_meta: f_max_alibi_bias = 0.0e+00\n",
|
||||
"llm_load_print_meta: n_ff = 14336\n",
|
||||
"llm_load_print_meta: rope scaling = linear\n",
|
||||
"llm_load_print_meta: freq_base_train = 10000.0\n",
|
||||
"llm_load_print_meta: freq_scale_train = 1\n",
|
||||
"llm_load_print_meta: n_yarn_orig_ctx = 32768\n",
|
||||
"llm_load_print_meta: rope_finetuned = unknown\n",
|
||||
"llm_load_print_meta: model type = 7B\n",
|
||||
"llm_load_print_meta: model ftype = mostly Q5_K - Medium\n",
|
||||
"llm_load_print_meta: model params = 7.24 B\n",
|
||||
"llm_load_print_meta: model size = 4.78 GiB (5.67 BPW) \n",
|
||||
"llm_load_print_meta: general.name = huggingfaceh4_zephyr-7b-beta\n",
|
||||
"llm_load_print_meta: BOS token = 1 '<s>'\n",
|
||||
"llm_load_print_meta: EOS token = 2 '</s>'\n",
|
||||
"llm_load_print_meta: UNK token = 0 '<unk>'\n",
|
||||
"llm_load_print_meta: PAD token = 2 '</s>'\n",
|
||||
"llm_load_print_meta: LF token = 13 '<0x0A>'\n",
|
||||
"llm_load_tensors: ggml ctx size = 0.11 MiB\n",
|
||||
"llm_load_tensors: mem required = 4893.10 MiB\n",
|
||||
"...................................................................................................\n",
|
||||
"llama_new_context_with_model: n_ctx = 4096\n",
|
||||
"llama_new_context_with_model: freq_base = 10000.0\n",
|
||||
"llama_new_context_with_model: freq_scale = 1\n",
|
||||
"llama_new_context_with_model: kv self size = 512.00 MiB\n",
|
||||
"llama_build_graph: non-view tensors processed: 740/740\n",
|
||||
"ggml_metal_init: allocating\n",
|
||||
"ggml_metal_init: found device: Apple M2 Max\n",
|
||||
"ggml_metal_init: picking default device: Apple M2 Max\n",
|
||||
"ggml_metal_init: default.metallib not found, loading from source\n",
|
||||
"ggml_metal_init: loading '/Users/peportier/miniforge3/envs/RAG_ENV/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n",
|
||||
"ggml_metal_init: GPU name: Apple M2 Max\n",
|
||||
"ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)\n",
|
||||
"ggml_metal_init: hasUnifiedMemory = true\n",
|
||||
"ggml_metal_init: recommendedMaxWorkingSetSize = 49152.00 MiB\n",
|
||||
"ggml_metal_init: maxTransferRate = built-in GPU\n",
|
||||
"llama_new_context_with_model: compute buffer total size = 291.07 MiB\n",
|
||||
"llama_new_context_with_model: max tensor size = 102.54 MiB\n",
|
||||
"ggml_metal_add_buffer: allocated 'data ' buffer, size = 4893.70 MiB, ( 4894.33 / 49152.00)\n",
|
||||
"ggml_metal_add_buffer: allocated 'kv ' buffer, size = 512.02 MiB, ( 5406.34 / 49152.00)\n",
|
||||
"ggml_metal_add_buffer: allocated 'alloc ' buffer, size = 288.02 MiB, ( 5694.36 / 49152.00)\n",
|
||||
"AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"llm_model_path = '/Users/peportier/llm/a/a/zephyr-7b-beta.Q5_K_M.gguf'\n",
|
||||
"embed_model_name = 'intfloat/multilingual-e5-large'\n",
|
||||
|
|
@ -29,6 +430,35 @@
|
|||
"rag = RAG(llm_model_path, embed_model_name, collection_name, chromadb_path)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "b12ed9e5-cacc-4f9b-a6b9-38ccda00764f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Batches: 100%|████████████████████████████████████| 1/1 [00:00<00:00, 2.19it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"query1 = \"Comment la Caisse d'Epargne Rhône-Alpes peut-elle aider une entreprise qui rencontre des problèmes de trésorerie ?\"\n",
|
||||
"res1 = rag.chat(query1, stream=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "086aef56-223f-4d3b-a1f0-9d251095e9f9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"res1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
|
|
|||
Loading…
Reference in New Issue