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4792259 | 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 | import hashlib
import json
from pathlib import Path
from typing import List, Tuple
import torch
import torch.nn.functional as F
from rag.config import Settings
from rag.data import Doc
from rag.logging_utils import get_logger
logger = get_logger(__name__)
def last_token_pool(last_hidden_states, attention_mask):
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def _fingerprint(docs: List[Doc], settings: Settings) -> str:
h = hashlib.sha256()
h.update(settings.embed_model_id.encode("utf-8"))
h.update(str(settings.embed_max_len).encode("utf-8"))
for d in docs:
h.update(d.doc_name.encode("utf-8"))
h.update(d.company.encode("utf-8"))
h.update(d.text.encode("utf-8"))
return h.hexdigest()
def ensure_index_dir(settings: Settings):
Path(settings.index_dir).mkdir(parents=True, exist_ok=True)
@torch.no_grad()
def build_or_load_doc_embeddings(
docs: List[Doc],
embed_tokenizer,
embed_model,
settings: Settings,
) -> Tuple[torch.Tensor, str]:
"""
Returns (doc_embeddings [N, D] on CPU, fingerprint)
Caches to data/index/doc_embeds.pt
"""
ensure_index_dir(settings)
fp = _fingerprint(docs, settings)
cache_file = settings.doc_embeds_file()
meta_file = settings.doc_meta_file()
if cache_file.exists() and meta_file.exists():
try:
meta = json.loads(meta_file.read_text(encoding="utf-8"))
if meta.get("fingerprint") == fp:
logger.info("Loading cached doc embeddings: %s", str(cache_file))
payload = torch.load(cache_file, map_location="cpu")
return payload["embeddings"], fp
except Exception as e:
logger.warning("Failed to load cache, rebuilding. Reason: %s", e)
logger.info("Building doc embeddings cache (%d docs)...", len(docs))
doc_texts = [d.text for d in docs]
embs = []
for i in range(0, len(doc_texts), settings.embed_batch_size):
batch = doc_texts[i : i + settings.embed_batch_size]
d_inputs = embed_tokenizer(
batch,
max_length=settings.embed_max_len,
padding=True,
truncation=True,
return_tensors="pt",
).to(embed_model.device)
d_outputs = embed_model(**d_inputs)
batch_emb = last_token_pool(d_outputs.last_hidden_state, d_inputs["attention_mask"])
batch_emb = F.normalize(batch_emb, p=2, dim=1)
embs.append(batch_emb.detach().to("cpu"))
doc_embs = torch.cat(embs, dim=0)
torch.save({"embeddings": doc_embs}, cache_file)
meta_file.write_text(json.dumps({"fingerprint": fp, "n_docs": len(docs)}, indent=2), encoding="utf-8")
logger.info("Saved embeddings cache: %s", str(cache_file))
return doc_embs, fp
@torch.no_grad()
def embed_query(query: str, embed_tokenizer, embed_model, settings: Settings) -> torch.Tensor:
query_text = (
"Instruct: Given a user query, retrieve relevant passages that answer the query.\n"
f"Query: {query}"
)
q_inputs = embed_tokenizer(
[query_text],
max_length=settings.embed_max_len,
padding=True,
truncation=True,
return_tensors="pt",
).to(embed_model.device)
q_outputs = embed_model(**q_inputs)
q_emb = last_token_pool(q_outputs.last_hidden_state, q_inputs["attention_mask"])
q_emb = F.normalize(q_emb, p=2, dim=1)
return q_emb.detach().to("cpu") # keep retrieval ops on CPU
def topk_retrieval(q_emb_cpu: torch.Tensor, doc_embs_cpu: torch.Tensor, k: int) -> List[int]:
# q_emb: [1, D], doc_embs: [N, D]
scores = (q_emb_cpu @ doc_embs_cpu.T).squeeze(0)
k = min(k, scores.shape[0])
return torch.topk(scores, k=k).indices.tolist()
@torch.no_grad()
def rerank(
query: str,
candidate_docs: List[Doc],
rerank_tokenizer,
rerank_model,
settings: Settings,
k: int,
) -> Tuple[List[int], torch.Tensor]:
pairs = [[query, d.text] for d in candidate_docs]
r_inputs = rerank_tokenizer(
pairs,
padding=True,
truncation=True,
return_tensors="pt",
max_length=settings.rerank_max_len,
).to(rerank_model.device)
r_scores = rerank_model(**r_inputs, return_dict=True).logits.view(-1).float().detach().to("cpu")
k = min(k, len(candidate_docs))
top_idx = torch.topk(r_scores, k=k).indices.tolist()
return top_idx, r_scores
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