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fix term weight issue (#3306)

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### What problem does this PR solve?

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
This commit is contained in:
Kevin Hu 2024-11-08 18:25:23 +08:00 committed by GitHub
parent 74d1eeb4d3
commit 004487cca0
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GPG Key ID: B5690EEEBB952194
2 changed files with 20 additions and 12 deletions
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28
rag/benchmark.py
View File

@ -34,12 +34,13 @@ from tqdm import tqdm
class Benchmark: class Benchmark:
def __init__(self, kb_id): def __init__(self, kb_id):
e, kb = KnowledgebaseService.get_by_id(kb_id) e, self.kb = KnowledgebaseService.get_by_id(kb_id)
self.similarity_threshold = kb.similarity_threshold self.similarity_threshold = self.kb.similarity_threshold
self.vector_similarity_weight = kb.vector_similarity_weight self.vector_similarity_weight = self.kb.vector_similarity_weight
self.embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id, lang=kb.language) self.embd_mdl = LLMBundle(self.kb.tenant_id, LLMType.EMBEDDING, llm_name=self.kb.embd_id, lang=self.kb.language)
def _get_benchmarks(self, query, dataset_idxnm, count=16): def _get_benchmarks(self, query, dataset_idxnm, count=16):
req = {"question": query, "size": count, "vector": True, "similarity": self.similarity_threshold} req = {"question": query, "size": count, "vector": True, "similarity": self.similarity_threshold}
sres = retrievaler.search(req, search.index_name(dataset_idxnm), self.embd_mdl) sres = retrievaler.search(req, search.index_name(dataset_idxnm), self.embd_mdl)
return sres return sres
@ -48,11 +49,15 @@ class Benchmark:
run = defaultdict(dict) run = defaultdict(dict)
query_list = list(qrels.keys()) query_list = list(qrels.keys())
for query in query_list: for query in query_list:
sres = self._get_benchmarks(query, dataset_idxnm)
sim, _, _ = retrievaler.rerank(sres, query, 1 - self.vector_similarity_weight, ranks = retrievaler.retrieval(query, self.embd_mdl, dataset_idxnm.replace("ragflow_", ""),
self.vector_similarity_weight) [self.kb.id], 0, 30,
for index, id in enumerate(sres.ids): 0.0, self.vector_similarity_weight)
run[query][id] = sim[index] for c in ranks["chunks"]:
if "vector" in c:
del c["vector"]
run[query][c["chunk_id"]] = c["similarity"]
return run return run
def embedding(self, docs, batch_size=16): def embedding(self, docs, batch_size=16):
@ -99,7 +104,8 @@ class Benchmark:
query = data.iloc[i]['query'] query = data.iloc[i]['query']
for rel, text in zip(data.iloc[i]['passages']['is_selected'], data.iloc[i]['passages']['passage_text']): for rel, text in zip(data.iloc[i]['passages']['is_selected'], data.iloc[i]['passages']['passage_text']):
d = { d = {
"id": get_uuid() "id": get_uuid(),
"kb_id": self.kb.id
} }
tokenize(d, text, "english") tokenize(d, text, "english")
docs.append(d) docs.append(d)
@ -208,6 +214,8 @@ class Benchmark:
scores = sorted(scores, key=lambda kk: kk[1]) scores = sorted(scores, key=lambda kk: kk[1])
for score in scores[:10]: for score in scores[:10]:
f.write('- text: ' + str(texts[score[0]]) + '\t qrel: ' + str(score[1]) + '\n') f.write('- text: ' + str(texts[score[0]]) + '\t qrel: ' + str(score[1]) + '\n')
json.dump(qrels, open(os.path.join(file_path, dataset + '.qrels.json'), "w+"), indent=2)
json.dump(run, open(os.path.join(file_path, dataset + '.run.json'), "w+"), indent=2)
print(os.path.join(file_path, dataset + '_result.md'), 'Saved!') print(os.path.join(file_path, dataset + '_result.md'), 'Saved!')
def __call__(self, dataset, file_path, miracl_corpus=''): def __call__(self, dataset, file_path, miracl_corpus=''):

4
rag/nlp/search.py
View File

@ -211,8 +211,8 @@ class Dealer:
continue continue
if not isinstance(v, type("")): if not isinstance(v, type("")):
m[n] = str(m[n]) m[n] = str(m[n])
if n.find("tks") > 0: #if n.find("tks") > 0:
m[n] = rmSpace(m[n]) # m[n] = rmSpace(m[n])
if m: if m:
res[d["id"]] = m res[d["id"]] = m