Update app.py

app.py

@@ -1,16 +1,16 @@
 from fastapi import FastAPI
 from pydantic import BaseModel
-from typing import Optional  # ✅ à AJOUTER ici
+from typing import Optional
 
 from llama_index.core import Document, ServiceContext
 from llama_index.llms.openai import OpenAI
 from llama_index.core.node_parser import SemanticSplitterNodeParser
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 import os
 
-
 app = FastAPI()
 
-# Requête d'entrée
+# 🔹 Schéma d'entrée
 class ChunkRequest(BaseModel):
     text: str
     source_id: Optional[str] = None
@@ -18,21 +18,36 @@ class ChunkRequest(BaseModel):
     source: Optional[str] = None
     type: Optional[str] = None
 
-
-# Réponse
+# 🔹 Endpoint principal
 @app.post("/chunk")
 async def chunk_text(data: ChunkRequest):
+    # Modèle LLM (OpenRouter - Llama 4 Maverick)
     llm = OpenAI(
         model="meta-llama/llama-4-maverick:free",
         api_base="https://openrouter.ai/api/v1",
         api_key=os.getenv("OPENROUTER_API_KEY")
     )
 
-    service_context = ServiceContext.from_defaults(llm=llm)
+    # 🔹 Embedding open source gratuit
+    embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+
+    # 🔹 Service Context avec LLM + embeddings
+    service_context = ServiceContext.from_defaults(
+        llm=llm,
+        embed_model=embed_model
+    )
 
     try:
         parser = SemanticSplitterNodeParser.from_defaults(service_context=service_context)
         nodes = parser.get_nodes_from_documents([Document(text=data.text)])
-        return {"chunks": [node.text for node in nodes]}
+
+        return {
+            "chunks": [node.text for node in nodes],
+            "metadatas": [node.metadata for node in nodes],
+            "source_id": data.source_id,
+            "titre": data.titre,
+            "source": data.source,
+            "type": data.type
+        }
     except Exception as e:
         return {"error": str(e)}