Spaces:
Sleeping
Sleeping
File size: 4,622 Bytes
35c5459 9facab9 35c5459 9facab9 35c5459 9facab9 35c5459 9facab9 35c5459 |
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 |
import gradio as gr
import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
# --- minimal core (in-memory only) ---
MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
_model = SentenceTransformer(MODEL_NAME)
_dim = int(_model.encode(["_probe_"], convert_to_numpy=True).shape[1]) # 384
_index = faiss.IndexFlatIP(_dim) # cosine via L2-normalized IP
_ids, _texts, _metas = [], [], []
def _normalize(v: np.ndarray) -> np.ndarray:
n = np.linalg.norm(v, axis=1, keepdims=True) + 1e-12
return (v / n).astype("float32")
def _chunk(text: str, size: int, overlap: int):
t = " ".join((text or "").split())
n = len(t); s = 0; out = []
if overlap >= size: overlap = max(size - 1, 0)
while s < n:
e = min(s + size, n)
out.append((t[s:e], s, e))
if e == n: break
s = max(e - overlap, 0)
return out
def reset():
global _index, _ids, _texts, _metas
_index = faiss.IndexFlatIP(_dim)
_ids, _texts, _metas = [], [], []
return gr.update(value="Index reset."), gr.update(value=0)
def load_sample():
docs = [
("a", "PySpark scales ETL across clusters.", {"tag":"spark"}),
("b", "FAISS powers fast vector similarity search used in retrieval.", {"tag":"faiss"})
]
return "\n".join([d[1] for d in docs])
def ingest(docs_text, size, overlap):
if not docs_text.strip():
return "Provide at least one line of text.", len(_ids)
# one document per line
lines = [ln.strip() for ln in docs_text.splitlines() if ln.strip()]
rows = []
for i, ln in enumerate(lines):
pid = f"doc-{len(_ids)}-{i}"
for ctext, s, e in _chunk(ln, size, overlap):
rows.append((f"{pid}::offset:{s}-{e}", ctext, {"parent_id": pid, "start": s, "end": e}))
if not rows:
return "No chunks produced.", len(_ids)
vecs = _normalize(_model.encode([r[1] for r in rows], convert_to_numpy=True))
_index.add(vecs)
for rid, txt, meta in rows:
_ids.append(rid); _texts.append(txt); _metas.append(meta)
return f"Ingested docs={len(lines)} chunks={len(rows)}", len(_ids)
def answer(q, k, max_context_chars):
if _index.ntotal == 0:
return {"answer": "Index is empty. Ingest first.", "matches": []}
qv = _normalize(_model.encode([q], convert_to_numpy=True))
D, I = _index.search(qv, int(k))
matches = []
for i, s in zip(I[0].tolist(), D[0].tolist()):
if i < 0:
continue
matches.append({
"id": _ids[i],
"score": float(s),
"text": _texts[i],
"meta": _metas[i]
})
if not matches:
out = "No relevant context."
else:
# 👇 only use the top match for the answer
top = matches[0]["text"]
out = f"Based on retrieved context:\n- {top}"
return {"answer": out, "matches": matches}
with gr.Blocks(title="RAG-as-a-Service") as demo:
gr.Markdown("### RAG-as-a-Service - Gradio\nIn-memory FAISS + MiniLM\n; one-line-per-doc ingest\n; quick answers.")
with gr.Row():
with gr.Column():
docs = gr.Textbox(label="Documents (one per line)", lines=6, placeholder="One document per line…")
with gr.Row():
chunk_size = gr.Slider(64, 1024, value=256, step=16, label="Chunk size")
overlap = gr.Slider(0, 256, value=32, step=8, label="Overlap")
with gr.Row():
ingest_btn = gr.Button("Ingest")
sample_btn = gr.Button("Load sample")
reset_btn = gr.Button("Reset")
ingest_status = gr.Textbox(label="Ingest status", interactive=False)
index_size = gr.Number(label="Index size", interactive=False, value=0)
with gr.Column():
q = gr.Textbox(label="Query", placeholder="Ask something...")
k = gr.Slider(1, 10, value=5, step=1, label="Top-K")
max_chars = gr.Slider(200, 4000, value=1000, step=100, label="Max context chars")
run = gr.Button("Answer")
out = gr.JSON(label="Answer + matches")
ingest_btn.click(
ingest,
[docs, chunk_size, overlap],
[ingest_status, index_size],
api_name="ingest" # exposes POST /api/ingest
)
sample_btn.click(load_sample, None, docs)
reset_btn.click(
reset,
None,
[ingest_status, index_size],
api_name="reset" # exposes POST /api/reset (optional)
)
run.click(
answer,
[q, k, max_chars],
out,
api_name="answer" # exposes POST /api/answer
)
if __name__ == "__main__":
demo.launch(share=True)
|