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Fix vector retrieval and enhance LLM vacancy ranking

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This commit is contained in:
estromenko 2025-10-31 21:39:36 +03:00
parent 08d6cddb67
commit cd1bb90167
1 changed files with 9 additions and 5 deletions
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14
vacancies/main/vector_store.py
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@ -106,15 +106,15 @@ def add_vectors(collection_name: str, _id: int, features: dict, payload: dict):
def search_similarities(query_filter: Filter, cv_id: int): def search_similarities(query_filter: Filter, cv_id: int):
vectors = client.retrieve( cv = client.retrieve(
collection_name="cvs", collection_name="cvs",
ids=[cv_id], ids=[cv_id],
with_vectors=True, with_vectors=True,
)[0].vector )[0]
max_similarities = {} max_similarities = {}
vacancies_content = {} vacancies_content = {}
for name, vec in vectors.items(): for name, vec in cv.vector.items():
if any(v != 0 for v in vec): if any(v != 0 for v in vec):
results = client.query_points( results = client.query_points(
collection_name="vacancies", collection_name="vacancies",
@ -140,11 +140,15 @@ def search_similarities(query_filter: Filter, cv_id: int):
total = sum(feature_sims[feature] * weights.get(feature, 1) for feature in feature_sims) total = sum(feature_sims[feature] * weights.get(feature, 1) for feature in feature_sims)
scored.append({"id": vid, "score": total, "content": vacancies_content[vid]["content"], "link": vacancies_content[vid]["link"]}) scored.append({"id": vid, "score": total, "content": vacancies_content[vid]["content"], "link": vacancies_content[vid]["link"]})
scored.sort(key=lambda x: x["score"], reverse=True)
prompt = f""" prompt = f"""
Среди вакансий ниже выбери одну наиболее релевантную и выведи ее индекс. Резюме: {cv.payload['content']}
Среди вакансий ниже выбери одну наиболее релевантную и выведи ее индекс(от 0 до 9).
Если среди вакансий нет подходящих, то верни -1. Если среди вакансий нет подходящих, то верни -1.
В ответе выведи только число. В ответе выведи только число.
{scored} {scored[:10]}
""" """
openai_client = ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal", temperature=0, seed=42, top_p=1) openai_client = ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal", temperature=0, seed=42, top_p=1)
response = openai_client.invoke(prompt) response = openai_client.invoke(prompt)