mirror of
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136 lines
4.5 KiB
Python
136 lines
4.5 KiB
Python
import json
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import openai
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from datetime import datetime
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from typing import Optional, cast
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from posthog.hogql import ast
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from posthog.hogql.parser import parse_select
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from posthog.hogql_queries.insights.paginators import HogQLHasMorePaginator
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from posthog.schema import HogQLQueryResponse
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from posthog.utils import get_instance_region
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from prometheus_client import Histogram
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from posthog.api.activity_log import ServerTimingsGathered
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from posthog.models import Team, User
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import structlog
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logger = structlog.get_logger(__name__)
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TOKENS_IN_PROMPT_HISTOGRAM = Histogram(
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"posthog_survey_summary_tokens_in_prompt_histogram",
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"histogram of the number of tokens in the prompt used to generate a survey summary",
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buckets=[
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0,
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10,
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50,
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100,
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500,
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1000,
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2000,
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3000,
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4000,
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5000,
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6000,
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7000,
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8000,
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10000,
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20000,
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30000,
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40000,
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50000,
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100000,
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128000,
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float("inf"),
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],
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)
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def prepare_data(query_response: HogQLQueryResponse) -> list[str]:
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response_values = []
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properties_list: list[dict] = [json.loads(x[1]) for x in query_response.results]
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for props in properties_list:
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response_values.extend([value for key, value in props.items() if key.startswith("$survey_response") and value])
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return response_values
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def summarize_survey_responses(
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survey_id: str, question_index: Optional[int], survey_start: datetime, survey_end: datetime, team: Team, user: User
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):
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timer = ServerTimingsGathered()
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with timer("prepare_query"):
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paginator = HogQLHasMorePaginator(limit=100, offset=0)
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q = parse_select(
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"""
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SELECT distinct_id, properties
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FROM events
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WHERE event == 'survey sent'
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AND properties.$survey_id = {survey_id}
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-- e.g. `$survey_response` or `$survey_response_2`
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AND trim(JSONExtractString(properties, {survey_response_property})) != ''
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AND timestamp >= {start_date}
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AND timestamp <= {end_date}
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""",
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{
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"survey_id": ast.Constant(value=survey_id),
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"survey_response_property": ast.Constant(
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value=f"$survey_response_{question_index}" if question_index else "$survey_response"
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),
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"start_date": ast.Constant(value=survey_start),
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"end_date": ast.Constant(value=survey_end),
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},
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)
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with timer("run_query"):
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query_response = paginator.execute_hogql_query(
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team=team,
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query_type="survey_response_list_query",
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query=cast(ast.SelectQuery, q),
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)
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with timer("llm_api_prep"):
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instance_region = get_instance_region() or "HOBBY"
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prepared_data = prepare_data(query_response)
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with timer("openai_completion"):
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result = openai.chat.completions.create(
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model="gpt-4o-mini", # allows 128k tokens
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temperature=0.7,
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messages=[
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{
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"role": "system",
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"content": """
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You are a product manager's assistant. You summarise survey responses from users for the product manager.
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You don't do any other tasks.
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""",
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},
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{
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"role": "user",
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"content": f"""the survey responses are {prepared_data}.""",
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},
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{
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"role": "user",
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"content": """
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generate a one or two paragraph summary of the survey response.
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only summarize, the goal is to identify real user pain points and needs
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use bullet points to identify the themes, and highlights of quotes to bring them to life
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we're trying to identify what to work on
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use as concise and simple language as is possible.
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generate no text other than the summary.
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the aim is to let people see themes in the responses received. return the text in github flavoured markdown format""",
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},
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],
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user=f"{instance_region}/{user.pk}",
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)
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usage = result.usage.prompt_tokens if result.usage else None
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if usage:
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TOKENS_IN_PROMPT_HISTOGRAM.observe(usage)
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content: str = result.choices[0].message.content or ""
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return {"content": content, "timings": timer.get_all_timings()}
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