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mrT23
2023-09-06 09:43:23 +03:00
parent bc95cf5b8e
commit 8962c9cf8a
2 changed files with 24 additions and 20 deletions
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2
pr_agent/settings/configuration.toml
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@ -100,7 +100,7 @@ polling_interval_seconds = 30
[pr_similar_issue]
skip_comments = false
force_update_dataset = false
max_issues_to_scan = 1000
max_issues_to_scan = 500
[pinecone]
# fill and place in .secrets.toml

42
pr_agent/tools/pr_similar_issue.py
View File

@ -176,24 +176,24 @@ class PRSimilarIssue:
level=IssueLevel.ISSUE)
)
corpus.append(issue_record)
if comments:
for j, comment in enumerate(comments):
comment_body = comment.body
num_words_comment = len(comment_body.split())
if num_words_comment < 10 or not isinstance(comment_body, str):
continue
if comments:
for j, comment in enumerate(comments):
comment_body = comment.body
num_words_comment = len(comment_body.split())
if num_words_comment < 10 or not isinstance(comment_body, str):
continue
if len(issue_str) < 8000 or \
self.token_handler.count_tokens(comment_body) < MAX_TOKENS[MODEL]:
comment_record = Record(
id=issue_key + ".comment_" + str(j + 1),
text=comment_body,
metadata=Metadata(repo=repo_name_for_index,
username=username, # use issue username for all comments
created_at=created_at,
level=IssueLevel.COMMENT)
)
corpus.append(comment_record)
if len(comment_body) < 8000 or \
self.token_handler.count_tokens(comment_body) < MAX_TOKENS[MODEL]:
comment_record = Record(
id=issue_key + ".comment_" + str(j + 1),
text=comment_body,
metadata=Metadata(repo=repo_name_for_index,
username=username, # use issue username for all comments
created_at=created_at,
level=IssueLevel.COMMENT)
)
corpus.append(comment_record)
df = pd.DataFrame(corpus.dict()["documents"])
logging.info('Done')
@ -205,9 +205,13 @@ class PRSimilarIssue:
embeds = [record['embedding'] for record in res['data']]
except:
embeds = []
logging.error('Failed to embed entire list, embedding one by one...')
for i, text in enumerate(list_to_encode):
res = openai.Embedding.create(input=[text], engine=MODEL)
embeds.append(res['data'][0]['embedding'])
try:
res = openai.Embedding.create(input=[text], engine=MODEL)
embeds.append(res['data'][0]['embedding'])
except:
embeds.append([0] * 1536)
df["values"] = embeds
meta = DatasetMetadata.empty()
meta.dense_model.dimension = len(embeds[0])