Per-article summarization and preference matching
This commit is contained in:
Adrian Rumpold
153
indexing.py
153
indexing.py
@@ -19,7 +19,7 @@ from scrape import JinaScraper
|
||||
|
||||
NUM_STORIES = 20
|
||||
USER_PREFERENCES = ["Machine Learning", "Linux", "Open-Source"]
|
||||
ENABLE_SLACK = False # Send updates to Slack, need to set SLACK_BOT_TOKEN env var
|
||||
ENABLE_SLACK = True # Send updates to Slack, need to set SLACK_BOT_TOKEN env var
|
||||
ENABLE_MLFLOW_TRACING = False # Use MLflow (at http://localhost:5000) for tracing
|
||||
|
||||
|
||||
@@ -41,12 +41,13 @@ class State(TypedDict):
|
||||
preferences: Iterable[str]
|
||||
context: list[langchain_core.documents.Document]
|
||||
answer: str
|
||||
summaries: list[dict]
|
||||
|
||||
|
||||
def retrieve(state: State):
|
||||
def retrieve(state: State, top_n: int = 5) -> State:
|
||||
# Search for relevant documents
|
||||
retrieved_docs = vector_store.similarity_search(
|
||||
"Categories: " + ", ".join(state["preferences"]), k=10
|
||||
"Categories: " + ", ".join(state["preferences"]), k=20
|
||||
)
|
||||
|
||||
# If you're using chunks, group them back into complete stories
|
||||
@@ -76,39 +77,126 @@ def retrieve(state: State):
|
||||
)
|
||||
complete_stories.append(complete_story)
|
||||
|
||||
return {"context": complete_stories[:5]} # Limit to top 5 stories
|
||||
return {"context": complete_stories[:top_n]}
|
||||
|
||||
|
||||
def generate(state: State):
|
||||
docs_content = "\n\n".join(doc.page_content for doc in state["context"])
|
||||
def generate_structured_summaries(state: State):
|
||||
"""Generate structured summaries for each story individually."""
|
||||
summaries = []
|
||||
|
||||
prompt = langchain.prompts.PromptTemplate(
|
||||
input_variables=["preferences", "context"],
|
||||
template=(
|
||||
"You are a helpful assistant that can provide updates on technology topics based on the topics a user has expressed interest in and additional context.\n\n"
|
||||
"Please respond in Markdown format and group your answers based on the categories of the items in the context.\n"
|
||||
"If applicable, add hyperlinks to the original source as part of the headline for each story.\n"
|
||||
"Limit your summaries to approximately 100 words per item.\n\n"
|
||||
"Preferences: {preferences}\n\n"
|
||||
"Context:\n{context}\n\n"
|
||||
"Answer:"
|
||||
),
|
||||
for doc in state["context"]:
|
||||
# Create a prompt for individual story summarization
|
||||
prompt = langchain.prompts.PromptTemplate(
|
||||
input_variables=["preferences", "title", "content", "source", "categories"],
|
||||
template=(
|
||||
"You are a helpful assistant that summarizes technology articles.\n\n"
|
||||
"User preferences: {preferences}\n\n"
|
||||
"Article title: {title}\n"
|
||||
"Article categories: {categories}\n"
|
||||
"Article content: {content}\n"
|
||||
"Source URL: {source}\n\n"
|
||||
"Use an informative but not too formal tone.\n"
|
||||
"Please provide:\n"
|
||||
"1. A concise summary (around 50 words) that highlights the key insights from the article.\n"
|
||||
"2. The single user preference that this article best matches (or 'Other' if none match well)\n\n"
|
||||
"Format your response as:\n"
|
||||
"PREFERENCE: [preference name or 'Other']\n"
|
||||
"SUMMARY: [your summary here]\n"
|
||||
),
|
||||
)
|
||||
|
||||
messages = prompt.invoke(
|
||||
{
|
||||
"preferences": ", ".join(state["preferences"]),
|
||||
"title": doc.metadata.get("title", "Unknown Title"),
|
||||
"content": doc.page_content[:5000], # Limit content length for LLM
|
||||
"source": doc.metadata.get("source", ""),
|
||||
"categories": ", ".join(doc.metadata.get("categories", [])),
|
||||
}
|
||||
)
|
||||
|
||||
response = llm.invoke(messages).content
|
||||
|
||||
# Parse the LLM response to extract preference and summary
|
||||
response_text = response if isinstance(response, str) else str(response)
|
||||
lines = response_text.strip().split("\n")
|
||||
matched_preference = "Other"
|
||||
summary_text = response_text
|
||||
|
||||
for line in lines:
|
||||
if line.startswith("PREFERENCE:"):
|
||||
matched_preference = line.replace("PREFERENCE:", "").strip()
|
||||
elif line.startswith("SUMMARY:"):
|
||||
summary_text = line.replace("SUMMARY:", "").strip()
|
||||
|
||||
# If we didn't find the structured format, use the whole response as summary
|
||||
if not any(line.startswith("SUMMARY:") for line in lines):
|
||||
summary_text = response_text.strip()
|
||||
|
||||
summaries.append(
|
||||
{
|
||||
"title": doc.metadata.get("title", "Unknown Title"),
|
||||
"summary": summary_text,
|
||||
"source_url": doc.metadata.get("source", ""),
|
||||
"categories": doc.metadata.get("categories", []),
|
||||
"story_id": doc.metadata.get("story_id"),
|
||||
"matched_preference": matched_preference,
|
||||
}
|
||||
)
|
||||
|
||||
return {"summaries": summaries}
|
||||
|
||||
|
||||
def group_stories_by_preference(
|
||||
summaries: list[dict], preferences: list[str]
|
||||
) -> dict[str, list[dict]]:
|
||||
"""Group stories by their matched preferences in the order of user preferences."""
|
||||
preference_groups = {}
|
||||
|
||||
# Group stories by the LLM-determined preference matching
|
||||
for summary in summaries:
|
||||
matched_preference = summary.get("matched_preference", "Other")
|
||||
|
||||
if matched_preference not in preference_groups:
|
||||
preference_groups[matched_preference] = []
|
||||
preference_groups[matched_preference].append(summary)
|
||||
|
||||
# Create ordered groups based on user preferences
|
||||
ordered_groups = {}
|
||||
|
||||
# Add groups for user preferences in order
|
||||
for preference in preferences:
|
||||
if preference in preference_groups:
|
||||
ordered_groups[preference] = preference_groups[preference]
|
||||
|
||||
# Add "Other" group at the end if it exists
|
||||
if "Other" in preference_groups:
|
||||
ordered_groups["Other"] = preference_groups["Other"]
|
||||
|
||||
return ordered_groups
|
||||
|
||||
|
||||
def create_slack_blocks(summaries: list[dict], preferences: list[str]) -> list[dict]:
|
||||
"""Convert structured summaries into Slack block format grouped by user preferences."""
|
||||
grouped_stories = group_stories_by_preference(summaries, preferences)
|
||||
return slack.format_slack_blocks(grouped_stories)
|
||||
|
||||
|
||||
def run_structured_query(
|
||||
preferences: Iterable[str],
|
||||
) -> list[dict]:
|
||||
"""Run query and return structured summary data."""
|
||||
graph_builder = langgraph.graph.StateGraph(State).add_sequence(
|
||||
[retrieve, generate_structured_summaries]
|
||||
)
|
||||
|
||||
messages = prompt.invoke(
|
||||
{"preferences": state["preferences"], "context": docs_content}
|
||||
)
|
||||
response = llm.invoke(messages)
|
||||
return {"answer": response.content}
|
||||
|
||||
|
||||
def run_query(preferences: Iterable[str]) -> str:
|
||||
graph_builder = langgraph.graph.StateGraph(State).add_sequence([retrieve, generate])
|
||||
graph_builder.add_edge(langgraph.graph.START, "retrieve")
|
||||
graph = graph_builder.compile()
|
||||
|
||||
response = graph.invoke(State(preferences=preferences, context=[], answer=""))
|
||||
return response["answer"]
|
||||
response = graph.invoke(
|
||||
State(preferences=preferences, context=[], answer="", summaries=[])
|
||||
)
|
||||
summaries = response["summaries"]
|
||||
return summaries
|
||||
|
||||
|
||||
def get_existing_story_ids() -> set[str]:
|
||||
@@ -235,10 +323,11 @@ async def main():
|
||||
print("No new stories to process")
|
||||
|
||||
# 4. Query
|
||||
answer = run_query(USER_PREFERENCES)
|
||||
print(answer)
|
||||
summaries = run_structured_query(USER_PREFERENCES)
|
||||
if ENABLE_SLACK:
|
||||
slack.send_message(channel="#ragpull-demo", text=answer)
|
||||
blocks = create_slack_blocks(summaries, USER_PREFERENCES)
|
||||
slack.send_message(channel="#ragpull-demo", blocks=blocks)
|
||||
print(summaries)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
91
slack.py
91
slack.py
@@ -1,52 +1,81 @@
|
||||
import logging
|
||||
import os
|
||||
|
||||
from langchain_core.documents import Document
|
||||
from slack_sdk import WebClient
|
||||
from slack_sdk.errors import SlackApiError
|
||||
|
||||
|
||||
def prepare_message_blocks(stories: list[Document]) -> list:
|
||||
blocks = []
|
||||
for story in stories:
|
||||
block = [
|
||||
{
|
||||
"type": "header",
|
||||
"text": {"type": "plain_text", "text": story.metadata["title"]},
|
||||
def format_story(story: dict) -> list:
|
||||
title_text = (
|
||||
f"<{story['source_url']}|{story['title']}>"
|
||||
if story["source_url"]
|
||||
else story["title"]
|
||||
)
|
||||
return [
|
||||
{
|
||||
"type": "section",
|
||||
"text": {
|
||||
"type": "mrkdwn",
|
||||
"text": f"*{title_text}*",
|
||||
},
|
||||
{
|
||||
"type": "context",
|
||||
"elements": [
|
||||
{
|
||||
"type": "plain_text",
|
||||
"text": f"Categories: {', '.join(story.metadata.get('categories', []))}",
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
"type": "context",
|
||||
"elements": [
|
||||
{
|
||||
"type": "plain_text",
|
||||
"text": f"Categories: {', '.join(story['categories'])}"
|
||||
if story["categories"]
|
||||
else "No categories",
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"type": "section",
|
||||
"text": {
|
||||
"type": "mrkdwn",
|
||||
"text": story["summary"],
|
||||
},
|
||||
{
|
||||
"type": "context",
|
||||
"elements": [
|
||||
{
|
||||
"type": "plain_text",
|
||||
"text": f"Posted on: {story.metadata['created_at']}",
|
||||
}
|
||||
],
|
||||
},
|
||||
{"type": "section", "text": {"type": "mrkdwn", "text": story.page_content}},
|
||||
]
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
def format_slack_blocks(grouped_stories: dict[str, list[dict]]) -> list[dict]:
|
||||
"""Format grouped stories into Slack block format."""
|
||||
blocks = []
|
||||
|
||||
# Header block
|
||||
blocks.append(
|
||||
{"type": "header", "text": {"type": "plain_text", "text": "🚀 Tech Updates"}}
|
||||
)
|
||||
|
||||
# Add stories for each group
|
||||
for group_name, stories in grouped_stories.items():
|
||||
# Group section header
|
||||
section_title = (
|
||||
"*Other Stories*" if group_name == "Other" else f"*{group_name}*"
|
||||
)
|
||||
blocks.append(
|
||||
{"type": "section", "text": {"type": "mrkdwn", "text": section_title}}
|
||||
)
|
||||
|
||||
for story in stories:
|
||||
blocks.extend(format_story(story))
|
||||
|
||||
# Add divider after each group (except the last one)
|
||||
blocks.append({"type": "divider"})
|
||||
|
||||
blocks.append(block)
|
||||
return blocks
|
||||
|
||||
|
||||
def send_message(channel: str, text: str) -> None:
|
||||
def send_message(channel: str, blocks: list) -> None:
|
||||
client = WebClient(token=os.environ["SLACK_BOT_TOKEN"])
|
||||
|
||||
try:
|
||||
response = client.chat_postMessage(
|
||||
channel=channel,
|
||||
username="HN Ragandy",
|
||||
text=text,
|
||||
text="Tech updates",
|
||||
blocks=blocks,
|
||||
unfurl_links=False,
|
||||
)
|
||||
response.validate()
|
||||
|
||||
Reference in New Issue
Block a user