Optimize vacancies vectorization
This commit is contained in:
parent
df33ce79bb
commit
0658f61f84
@ -9,7 +9,6 @@ dependencies = [
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"django>=5.2.7",
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"gunicorn>=23.0.0",
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"langchain-community>=0.4.1",
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"langchain-qdrant>=1.1.0",
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582
uv.lock
582
uv.lock
@ -1,95 +1,6 @@
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version = 1
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||||
|
||||
[[package]]
|
||||
name = "zstandard"
|
||||
version = "0.25.0"
|
||||
|
||||
@ -1,15 +1,33 @@
|
||||
import os
|
||||
import io
|
||||
import os
|
||||
import traceback
|
||||
from telegram import InlineKeyboardButton, InlineKeyboardMarkup, Update, ReplyKeyboardMarkup, KeyboardButton
|
||||
from telegram.ext import filters, ApplicationBuilder, MessageHandler, CommandHandler, ContextTypes
|
||||
from pypdf import PdfReader
|
||||
from vacancies.main.models import Customer, CustomerCV
|
||||
|
||||
from langchain.agents import create_agent
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
|
||||
from vacancies.main.vector_store import add_vectors, extract_features, get_next_vacancy
|
||||
from pypdf import PdfReader
|
||||
from telegram import (
|
||||
InlineKeyboardButton,
|
||||
InlineKeyboardMarkup,
|
||||
KeyboardButton,
|
||||
ReplyKeyboardMarkup,
|
||||
Update,
|
||||
)
|
||||
from telegram.ext import (
|
||||
ApplicationBuilder,
|
||||
CommandHandler,
|
||||
ContextTypes,
|
||||
MessageHandler,
|
||||
filters,
|
||||
)
|
||||
|
||||
from vacancies.conf.settings import DB_URI
|
||||
from vacancies.main.models import Customer, CustomerCV
|
||||
from vacancies.main.vector_store import (
|
||||
add_vectors,
|
||||
batch_extract_features,
|
||||
get_next_vacancy,
|
||||
)
|
||||
|
||||
SYSTEM_PROMPT = """
|
||||
Ты — карьерный копилот для ИТ. Ты можешь отвечать на любые вопросы по тематике карьеры.
|
||||
@ -107,7 +125,7 @@ async def handle_document(update: Update, context: ContextTypes.DEFAULT_TYPE):
|
||||
customer_cv, _ = await CustomerCV.objects.aupdate_or_create(customer=customer, defaults=dict(
|
||||
content=resume,
|
||||
))
|
||||
features = extract_features(customer_cv.content)
|
||||
features = batch_extract_features(customer_cv.content)[0]
|
||||
add_vectors(
|
||||
"cvs",
|
||||
customer_cv.id,
|
||||
|
||||
@ -1,16 +1,25 @@
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from datetime import datetime, timedelta
|
||||
from itertools import batched
|
||||
|
||||
import clickhouse_connect
|
||||
from django.core.management import BaseCommand
|
||||
from qdrant_client.models import OrderBy
|
||||
|
||||
from vacancies.conf.settings import CLICKHOUSE_HOST, CLICKHOUSE_PORT
|
||||
from vacancies.main.vector_store import add_vectors, extract_features, qdrant_client
|
||||
from vacancies.main.vector_store import (
|
||||
add_vectors,
|
||||
batch_extract_features,
|
||||
embed_features,
|
||||
qdrant_client,
|
||||
)
|
||||
|
||||
BATCH_SIZE = 50
|
||||
|
||||
clickhouse_client = clickhouse_connect.create_client(host=CLICKHOUSE_HOST, port=CLICKHOUSE_PORT)
|
||||
|
||||
query = """
|
||||
SELECT id, chat_username, telegram_id, message, timestamp
|
||||
SELECT DISTINCT ON (message) id, chat_username, telegram_id, message, timestamp
|
||||
FROM telegram_parser_chatmessage
|
||||
WHERE timestamp >= %(timestamp)s
|
||||
AND length(message) > 150
|
||||
@ -25,7 +34,7 @@ WHERE timestamp >= %(timestamp)s
|
||||
AND arrayCount(x -> position(lower(message), x) > 0, [
|
||||
'о себе', 'обо мне', 'умею', '#ищу', '#резюме', 'университет', 'колледж'
|
||||
]) = 0
|
||||
AND id NOT IN %(exist_points)s
|
||||
ORDER BY timestamp ASC
|
||||
"""
|
||||
|
||||
|
||||
@ -33,46 +42,19 @@ class Command(BaseCommand):
|
||||
help = "Collect vacancies from telegram messages"
|
||||
|
||||
def handle(self, *args, **options):
|
||||
next_page_offset = 0
|
||||
exist_points_ids = [-1]
|
||||
while next_page_offset is not None:
|
||||
response = qdrant_client.scroll(
|
||||
collection_name="vacancies",
|
||||
limit=100_000,
|
||||
offset=next_page_offset,
|
||||
with_payload=False,
|
||||
with_vectors=False,
|
||||
timeout=30,
|
||||
)
|
||||
exist_points_ids.extend([point.id for point in response[0]])
|
||||
next_page_offset = response[1]
|
||||
exist_points_set = tuple(set(exist_points_ids))
|
||||
|
||||
response = qdrant_client.scroll(
|
||||
collection_name="vacancies",
|
||||
limit=1,
|
||||
order_by=OrderBy(
|
||||
key="timestamp",
|
||||
direction="desc",
|
||||
),
|
||||
)
|
||||
response = qdrant_client.scroll(collection_name="vacancies", limit=1, order_by=OrderBy(key="timestamp", direction="desc"))
|
||||
last_point_timestamp = datetime.now() - timedelta(days=30)
|
||||
if response:
|
||||
if response[0]:
|
||||
last_point_timestamp = response[0][0].payload["timestamp"]
|
||||
|
||||
result_rows = clickhouse_client.query(
|
||||
query,
|
||||
parameters={"timestamp": last_point_timestamp, "exist_points": exist_points_set},
|
||||
).result_rows
|
||||
result_rows = clickhouse_client.query(query, parameters={"timestamp": last_point_timestamp}).result_rows
|
||||
|
||||
for index, row in enumerate(result_rows):
|
||||
(id, chat_username, telegram_id, message, timestamp) = row
|
||||
link = f"https://t.me/{chat_username}/{telegram_id}"
|
||||
print(f"Processing {index+1}/{len(result_rows)} link: {link}")
|
||||
features = extract_features(message)
|
||||
add_vectors(
|
||||
"vacancies",
|
||||
id,
|
||||
features.model_dump(),
|
||||
{'content': message, 'features_json': features.model_dump(), "link": link, "timestamp": timestamp},
|
||||
)
|
||||
for index, rows in enumerate(batched(result_rows, BATCH_SIZE)):
|
||||
vacancies_features = batch_extract_features([row[3] for row in rows])
|
||||
|
||||
with ThreadPoolExecutor() as pool:
|
||||
vacancies_vectors = pool.map(embed_features, [vacancy_features.model_dump() for vacancy_features in vacancies_features])
|
||||
|
||||
for (id, chat_username, telegram_id, message, timestamp), vacancy_features, vacancy_vectors in zip(rows, vacancies_features, vacancies_vectors):
|
||||
payload = {'content': message, 'features_json': vacancy_features.model_dump(), "link": f"https://t.me/{chat_username}/{telegram_id}", "timestamp": timestamp}
|
||||
add_vectors("vacancies", id, vacancy_features.model_dump(), payload, vacancy_vectors)
|
||||
|
||||
@ -1,5 +1,4 @@
|
||||
from langchain_community.embeddings import DeepInfraEmbeddings
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
|
||||
from qdrant_client import QdrantClient, models
|
||||
from qdrant_client.models import Filter, HasIdCondition
|
||||
|
||||
@ -14,23 +13,13 @@ FEATURE_NAMES = [
|
||||
]
|
||||
|
||||
weights = {
|
||||
"job_title": 42,
|
||||
"employment_type": 5,
|
||||
"work_format": 5,
|
||||
"experience": 8,
|
||||
"position_level": 5,
|
||||
"industry": 1,
|
||||
"job_title": 70,
|
||||
"tech_stack": 10,
|
||||
"location": 4,
|
||||
"salary_range": 10,
|
||||
"languages": 4,
|
||||
"education": 2,
|
||||
"schedule": 2,
|
||||
"additional_requirements": 2,
|
||||
}
|
||||
|
||||
vectors_config = {
|
||||
name: models.VectorParams(size=4096, distance=models.Distance.COSINE) for name in FEATURE_NAMES
|
||||
name: models.VectorParams(size=3072, distance=models.Distance.COSINE) for name in FEATURE_NAMES
|
||||
}
|
||||
|
||||
if not qdrant_client.collection_exists("vacancies"):
|
||||
@ -49,12 +38,9 @@ if not qdrant_client.collection_exists("cvs"):
|
||||
vectors_config=vectors_config,
|
||||
)
|
||||
|
||||
embedding = DeepInfraEmbeddings(
|
||||
model_id="Qwen/Qwen3-Embedding-8B-batch",
|
||||
)
|
||||
embedding = OpenAIEmbeddings(model="text-embedding-3-large")
|
||||
|
||||
def _prepare_texts(features):
|
||||
"""Prepare texts for each feature from features dict."""
|
||||
texts = {}
|
||||
for name in FEATURE_NAMES:
|
||||
value = features.get(name)
|
||||
@ -66,31 +52,21 @@ def _prepare_texts(features):
|
||||
return texts
|
||||
|
||||
|
||||
def add_vectors(collection_name: str, _id: int, features: dict, payload: dict):
|
||||
"""Add vectors for a vacancy based on its features."""
|
||||
texts = _prepare_texts(features)
|
||||
vectors = {}
|
||||
for name, text in texts.items():
|
||||
vectors[name] = [0.0] * 4096
|
||||
if text:
|
||||
vec = embedding.embed_query(text)
|
||||
vectors[name] = vec
|
||||
def embed_features(features):
|
||||
features = {key: value for key, value in features.items() if value}
|
||||
features_texts = _prepare_texts(features)
|
||||
names, texts = features_texts.keys(), features_texts.values()
|
||||
vectors = dict(zip(names, embedding.embed_documents(texts)))
|
||||
return vectors
|
||||
|
||||
|
||||
def add_vectors(collection_name: str, _id: int, features: dict, payload: dict, vectors):
|
||||
max_similarities = {}
|
||||
for name, vec in vectors.items():
|
||||
if any(v != 0 for v in vec):
|
||||
results = qdrant_client.query_points(
|
||||
collection_name="vacancies",
|
||||
query=vec,
|
||||
using=name,
|
||||
limit=100,
|
||||
)
|
||||
results = qdrant_client.query_points(collection_name="vacancies", query=vec, using=name, limit=100)
|
||||
for res in results.points:
|
||||
vid = res.id
|
||||
sim = res.score
|
||||
if vid not in max_similarities:
|
||||
max_similarities[vid] = {}
|
||||
max_similarities[vid][name] = sim
|
||||
max_similarities.setdefault(res.id, {})
|
||||
max_similarities[res.id][name] = res.score
|
||||
|
||||
scored = []
|
||||
for vid, feature_sims in max_similarities.items():
|
||||
@ -103,25 +79,14 @@ def add_vectors(collection_name: str, _id: int, features: dict, payload: dict):
|
||||
|
||||
qdrant_client.upsert(
|
||||
collection_name=collection_name,
|
||||
points=[
|
||||
models.PointStruct(
|
||||
id=_id,
|
||||
vector=vectors,
|
||||
payload=payload,
|
||||
)
|
||||
]
|
||||
points=[models.PointStruct(id=_id, vector=vectors, payload=payload)]
|
||||
)
|
||||
|
||||
|
||||
def search_similarities(query_filter: Filter, cv_id: int):
|
||||
cv = qdrant_client.retrieve(
|
||||
collection_name="cvs",
|
||||
ids=[cv_id],
|
||||
with_vectors=True,
|
||||
)[0]
|
||||
cv = qdrant_client.retrieve(collection_name="cvs", ids=[cv_id], with_vectors=True)[0]
|
||||
|
||||
max_similarities = {}
|
||||
vacancies_content = {}
|
||||
max_similarities, vacancies_content = {}, {}
|
||||
for name, vec in cv.vector.items():
|
||||
results = qdrant_client.query_points(
|
||||
collection_name="vacancies",
|
||||
@ -132,16 +97,13 @@ def search_similarities(query_filter: Filter, cv_id: int):
|
||||
query_filter=query_filter,
|
||||
)
|
||||
for res in results.points:
|
||||
vid = res.id
|
||||
sim = res.score
|
||||
if vid not in max_similarities:
|
||||
max_similarities[vid] = {}
|
||||
max_similarities[vid][name] = sim
|
||||
if vid not in vacancies_content:
|
||||
vacancies_content[vid] = {}
|
||||
vacancies_content[vid]["content"] = res.payload["content"]
|
||||
vacancies_content[vid]["features_json"] = res.payload["features_json"]
|
||||
vacancies_content[vid]["link"] = res.payload["link"]
|
||||
max_similarities.setdefault(res.id, {})
|
||||
vacancies_content.setdefault(res.id, {})
|
||||
|
||||
max_similarities[res.id][name] = res.score
|
||||
vacancies_content[res.id]["content"] = res.payload["content"]
|
||||
vacancies_content[res.id]["features_json"] = res.payload["features_json"]
|
||||
vacancies_content[res.id]["link"] = res.payload["link"]
|
||||
|
||||
scored = []
|
||||
for vid, feature_sims in max_similarities.items():
|
||||
@ -156,14 +118,13 @@ def search_similarities(query_filter: Filter, cv_id: int):
|
||||
})
|
||||
|
||||
scored.sort(key=lambda x: x["score"], reverse=True)
|
||||
import pprint
|
||||
pprint.pprint(scored[:5])
|
||||
|
||||
return scored[0]["id"], scored[0]["content"], scored[0]["link"]
|
||||
|
||||
|
||||
def extract_features(content: str) -> VacancyFeatures:
|
||||
prompt = f"""
|
||||
def batch_extract_features(contents: list[str]) -> list[VacancyFeatures]:
|
||||
prompts = [
|
||||
f"""
|
||||
Extract the following features from the job vacancy description. If a feature is not mentioned, set it to null.
|
||||
Features:
|
||||
- job_title: Должность (e.g., DevOps, Python программист)
|
||||
@ -182,9 +143,11 @@ def extract_features(content: str) -> VacancyFeatures:
|
||||
Vacancy content:
|
||||
{content}
|
||||
"""
|
||||
for content in contents
|
||||
]
|
||||
openai_client = ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal", temperature=0, seed=42, top_p=1)
|
||||
structured_llm = openai_client.with_structured_output(VacancyFeatures)
|
||||
response = structured_llm.invoke(prompt)
|
||||
response = structured_llm.batch(prompts)
|
||||
return response
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user