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from fastapi import FastAPI
from pydantic import BaseModel
from typing import Optional
from llama_index.core import Document, ServiceContext
from llama_index.llms.llama_cpp import LlamaCPP
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.node_parser import SemanticSplitterNodeParser
import os
app = FastAPI()
# 🔹 Schéma d'entrée
class ChunkRequest(BaseModel):
text: str
source_id: Optional[str] = None
titre: Optional[str] = None
source: Optional[str] = None
type: Optional[str] = None
# 🔹 Endpoint principal
@app.post("/chunk")
async def chunk_text(data: ChunkRequest):
llm = LlamaCPP(
model_path="/models/mistral-7b-instruct.gguf",
temperature=0.1,
max_new_tokens=512,
context_window=2048,
generate_kwargs={"top_p": 0.95},
model_kwargs={"n_gpu_layers": 1},
)
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
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],
"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)}
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