Spaces:
Paused
Paused
File size: 9,238 Bytes
e7a5765 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 |
import os
import pickle
import logging
from typing import List, Optional, Dict, Any, Iterable, Tuple
import requests
import faiss
import json
from llama_index.core import VectorStoreIndex
from llama_index.core.schema import TextNode
from llama_index.vector_stores.faiss import FaissVectorStore
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from sentence_transformers.util import cos_sim
# === Logger configuration ===
logger = logging.getLogger("RAGEngine")
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
formatter = logging.Formatter("[%(asctime)s] %(levelname)s - %(message)s")
handler.setFormatter(formatter)
if not logger.handlers:
logger.addHandler(handler)
MAX_TOKENS = 64
DEFAULT_STOPS = ["### Réponse:", "\n\n", "###"]
class OllamaClient:
def __init__(self, model: str, host: Optional[str] = None, timeout: int = 300):
self.model = model
self.host = host or os.getenv("OLLAMA_HOST", "http://localhost:11434")
self.timeout = timeout
self._gen_url = self.host.rstrip("/") + "/api/generate"
def generate(self, prompt: str, stop: Optional[List[str]] = None,
max_tokens: Optional[int] = None, stream: bool = False,
options: Optional[Dict[str, Any]] = None, raw: bool = False) -> str | Iterable[str]:
payload: Dict[str, Any] = {
"model": self.model,
"prompt": prompt,
"stream": stream,
}
if raw:
payload["raw"] = True
if stop:
payload["stop"] = stop
if max_tokens is not None:
payload["num_predict"] = int(max_tokens)
# ❌ Pas d'options envoyées pour laisser Ollama choisir ses defaults
logger.debug(f"POST {self._gen_url} (stream={stream})")
if stream:
with requests.post(self._gen_url, json=payload, stream=True, timeout=self.timeout) as r:
r.raise_for_status()
for line in r.iter_lines(decode_unicode=True):
if not line:
continue
try:
data = json.loads(line)
except Exception:
continue
if "response" in data and not data.get("done"):
yield data["response"]
if data.get("done"):
break
return
r = requests.post(self._gen_url, json=payload, timeout=self.timeout)
r.raise_for_status()
data = r.json()
return data.get("response", "")
class RAGEngine:
def __init__(self, model_name: str, vector_path: str, index_path: str,
model_threads: int = 4, ollama_host: Optional[str] = None,
ollama_opts: Optional[Dict[str, Any]] = None):
logger.info(f"🔎 rag_model_ollama source: {__file__}")
logger.info("📦 Initialisation du moteur RAG (Ollama)...")
# ❌ Pas d'options Ollama stockées
self.llm = OllamaClient(model=model_name, host=ollama_host)
self.embed_model = HuggingFaceEmbedding(model_name="intfloat/multilingual-e5-base")
logger.info(f"📂 Chargement des données vectorielles depuis {vector_path}")
with open(vector_path, "rb") as f:
chunk_texts: List[str] = pickle.load(f)
nodes = [TextNode(text=chunk) for chunk in chunk_texts]
faiss_index = faiss.read_index(index_path)
vector_store = FaissVectorStore(faiss_index=faiss_index)
self.index = VectorStoreIndex(nodes=nodes, embed_model=self.embed_model, vector_store=vector_store)
logger.info("✅ Moteur RAG (Ollama) initialisé avec succès.")
# Warmup pour charger le modèle
try:
logger.info("⚡ Warmup du modèle Ollama...")
for _ in self._complete_stream("Bonjour", max_tokens=8, raw=False):
pass
except Exception as e:
logger.warning(f"Warmup échoué : {e}")
def _complete_stream(self, prompt: str, stop: Optional[List[str]] = None,
max_tokens: int = MAX_TOKENS, raw: bool = False):
return self.llm.generate(prompt=prompt, stop=stop, max_tokens=max_tokens,
stream=True, raw=raw)
def _complete(self, prompt: str, stop: Optional[List[str]] = None,
max_tokens: int = 128, raw: bool = False) -> str:
text = self.llm.generate(prompt=prompt, stop=stop, max_tokens=max_tokens,
stream=False, raw=raw)
return (text or "").strip()
def _is_greeting(self, text: str) -> bool:
s = text.lower().strip()
return s in {"bonjour", "salut", "hello", "bonsoir", "hi", "coucou", "yo"} or len(s.split()) <= 2
def _decide_mode(self, scores: List[float], tau: float = 0.32, is_greeting: bool = False) -> str:
if is_greeting:
return "llm"
top = scores[0] if scores else 0.0
return "rag" if top >= tau else "llm"
def get_adaptive_top_k(self, question: str) -> int:
q = question.lower()
if len(q.split()) <= 7:
top_k = 8
elif any(w in q for w in ["liste", "résume", "quels sont", "explique", "comment"]):
top_k = 10
else:
top_k = 8
logger.info(f"🔢 top_k déterminé automatiquement : {top_k}")
return top_k
def rerank_nodes(self, question: str, retrieved_nodes, top_k: int = 3) -> Tuple[List[float], List[TextNode]]:
logger.info(f"🔍 Re-ranking des {len(retrieved_nodes)} chunks pour : « {question} »")
q_emb = self.embed_model.get_query_embedding(question)
scored_nodes: List[Tuple[float, TextNode]] = []
for node in retrieved_nodes:
chunk_emb = self.embed_model.get_text_embedding(node.get_content())
score = cos_sim(q_emb, chunk_emb).item()
scored_nodes.append((score, node))
ranked = sorted(scored_nodes, key=lambda x: x[0], reverse=True)
top = ranked[:top_k]
return [s for s, _ in top], [n for _, n in top]
def retrieve_context(self, question: str, top_k: int = 3) -> Tuple[str, List[TextNode], List[float]]:
retriever = self.index.as_retriever(similarity_top_k=top_k)
retrieved_nodes = retriever.retrieve(question)
scores, nodes = self.rerank_nodes(question, retrieved_nodes, top_k)
context = "\n\n".join(n.get_content()[:500] for n in nodes)
return context, nodes, scores
def ask(self, question: str, allow_fallback: bool = False) -> str:
"""Génération non-stream"""
logger.info(f"💬 [Non-stream] Question reçue : {question}")
is_hello = self._is_greeting(question)
context, scores = "", []
if not is_hello:
top_k = self.get_adaptive_top_k(question)
context, _, scores = self.retrieve_context(question, top_k)
mode = self._decide_mode(scores, tau=0.32, is_greeting=is_hello)
logger.info(f"🧭 Mode choisi (non-stream) : {mode}")
if mode == "rag":
prompt = (
"Instruction: Réponds uniquement à partir du contexte. "
"Si la réponse n'est pas déductible, réponds exactement: \"Information non présente dans le contexte.\""
f"\n\nContexte :\n{context}\n\nQuestion : {question}\nRéponse :"
)
return self._complete(prompt, stop=DEFAULT_STOPS, raw=False)
prompt_llm = (
"Réponds brièvement et précisément en français.\n"
f"Question : {question}\nRéponse :"
)
return self._complete(prompt_llm, stop=DEFAULT_STOPS, raw=False)
def ask_stream(self, question: str, allow_fallback: bool = False) -> Iterable[str]:
logger.info(f"💬 [Stream] Question reçue : {question}")
is_hello = self._is_greeting(question)
context, scores = "", []
if not is_hello:
top_k = self.get_adaptive_top_k(question)
context, _, scores = self.retrieve_context(question, top_k)
mode = self._decide_mode(scores, tau=0.32, is_greeting=is_hello)
logger.info(f"🧭 Mode choisi (stream) : {mode}")
if mode == "rag":
prompt = (
"Instruction: Réponds uniquement à partir du contexte. "
"Si la réponse n'est pas déductible, réponds exactement: \"Information non présente dans le contexte.\""
f"\n\nContexte :\n{context}\n\nQuestion : {question}\nRéponse :"
)
logger.info("📡 Début streaming (RAG)...")
for token in self._complete_stream(prompt, stop=DEFAULT_STOPS, raw=False):
yield token
logger.info("📡 Fin streaming (RAG).")
return
prompt_llm = (
"Réponds brièvement et précisément en français.\n"
f"Question : {question}\nRéponse :"
)
logger.info("📡 Début streaming (LLM pur)...")
for token in self._complete_stream(prompt_llm, stop=DEFAULT_STOPS, raw=False):
yield token
logger.info("📡 Fin streaming (LLM pur).")
|