Initial deployment with RAG model

RAG.py

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+import os
+import faiss
+import pickle
+import numpy as np
+import openai
+import tiktoken
+from dotenv import load_dotenv
+from openai import OpenAI
+from pathlib import Path
+from huggingface_hub import hf_hub_download
+
+
+# —— CONFIG ——
+load_dotenv()
+# Use the new OpenAI client
+client = OpenAI()
+EMBED_MODEL           = "text-embedding-3-small"
+CHAT_MODEL            = "o4-mini-2025-04-16"
+FAISS_INDEX_FILE      = "tindle_index.faiss"
+IDS_PKL               = "tindle_ids.pkl"
+CHUNKS_PKL            = "tindle_chunks.pkl"
+TOP_K                 = 10
+MAX_TOKENS_CONTEXT    = 4000
+SYSTEM_PROMPT = (
+    "Tu es un assistant expert en droit fiscal. "
+    "Fais d’abord appel aux passages fournis pour répondre. "
+    "Si ces passages sont insuffisants, utilise tes connaissances générales en le précisant clairement."
+)
+
+# —— CHARGEMENT DE L'INDEX ——
+
+# Télécharger les fichiers depuis le repo Hugging Face
+index_path = hf_hub_download(repo_id="Jordanche/fiscarag", filename=FAISS_INDEX_FILE)
+ids_path = hf_hub_download(repo_id="Jordanche/fiscarag", filename=IDS_PKL)
+chunks_path = hf_hub_download(repo_id="Jordanche/fiscarag", filename=CHUNKS_PKL)
+
+# Charger les fichiers
+index = faiss.read_index(index_path)
+with open(ids_path, "rb") as f:
+    ids = pickle.load(f)
+with open(chunks_path, "rb") as f:
+    chunks_dict = pickle.load(f)
+
+# —— TOKEN COUNTER ——
+enc = tiktoken.get_encoding("cl100k_base")
+def num_tokens(s: str) -> int:
+    return len(enc.encode(s))
+
+
+# —— FONCTIONS RAG ——
+
+def embed_question(question: str) -> list[float]:
+    resp = client.embeddings.create(
+        model=EMBED_MODEL,
+        input=[question]
+    )
+    # on récupère l'attribut .data, puis .embedding
+    return resp.data[0].embedding
+
+
+def retrieve_chunks(q_emb: list[float], k: int = TOP_K):
+    xq = np.array([q_emb], dtype="float32")
+    distances, indices = index.search(xq, k)
+    out = []
+    for dist, idx in zip(distances[0], indices[0]):
+        cid  = ids[idx]
+        meta = chunks_dict[cid]
+        out.append({
+            "score":  float(dist),
+            "id":     cid,
+            "offset": meta["offset"],
+            "text":   meta["text"]
+        })
+    return out
+
+
+def build_context(chunks, max_tokens=MAX_TOKENS_CONTEXT):
+    parts, tokens = [], 0
+    for c in sorted(chunks, key=lambda x: x["score"]):
+        piece = f"(Source: {c['id']}@{c['offset']}) {c['text']}"
+        nt = num_tokens(piece)
+        if tokens + nt > max_tokens:
+            break
+        parts.append(piece)
+        tokens += nt
+    return "\n\n".join(parts)
+
+
+def make_prompt(question: str, context: str):
+    return [
+      {"role": "system", "content": SYSTEM_PROMPT},
+      {"role": "user",   "content": f"Question: {question}\n\nContexte:\n{context}"}
+    ]
+
+
+def answer_question(question: str, k: int = TOP_K) -> str:
+    # 1) Embed
+    q_emb = embed_question(question)
+
+    # 2) Retrieve
+    top_chunks = retrieve_chunks(q_emb, k)
+
+    # 3) Assemble
+    context = build_context(top_chunks)
+
+    # 4) Prompt
+    messages = make_prompt(question, context)
+    # 5) Call LLM
+    resp = client.chat.completions.create(
+        model=CHAT_MODEL,
+        messages=messages    )
+    return resp.choices[0].message.content
+
+# —— EXEMPLE ——
+if __name__ == "__main__":
+    question = "Quels sont les délais pour la réhabilitation d'hôtels en outre-mer ?"
+    print(answer_question(question, k=10))

app.py

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+import streamlit as st
+from RAG import answer_question
+
+st.set_page_config(page_title="Tindle RAG Chat", page_icon="💬", layout="centered")
+
+st.title("💬 Tindle RAG Chat")
+st.markdown("Posez une question sur le droit fiscal, l'assistant RAG vous répondra en s'appuyant sur les sources indexées.")
+
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+
+# Affichage de l'historique du chat
+for msg in st.session_state["messages"]:
+    if msg["role"] == "user":
+        st.chat_message("user").markdown(msg["content"])
+    else:
+        st.chat_message("assistant").markdown(msg["content"])
+
+# Saisie utilisateur
+if prompt := st.chat_input("Votre question..."):
+    st.session_state["messages"].append({"role": "user", "content": prompt})
+    with st.spinner("Recherche de la réponse..."):
+        try:
+            response = answer_question(prompt)
+        except Exception as e:
+            response = f"Erreur lors de l'appel au RAG : {e}"
+    st.session_state["messages"].append({"role": "assistant", "content": response})
+    st.rerun()

requirements.txt

@@ -1,3 +1,8 @@
 altair
-pandas
-streamlit
+streamlit>=1.28.0
+openai>=1.0.0
+faiss-cpu>=1.7.4
+numpy>=1.24.0
+tiktoken>=0.5.0
+python-dotenv>=1.0.0
+pyarrow>=12.0.0