admin
251 lines
8.3 KiB
Python
251 lines
8.3 KiB
Python
"""
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Summarizer Module
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-----------------
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Provides a client to summarize long transcripts via an LLM endpoint.
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Behavior:
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- Chunks transcript into 10,240-character segments.
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- Summarizes each chunk.
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- Summarizes the summaries into a final, detailed summary.
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Environment Variables:
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- SUMMARIZER_API_URL: (required) Base URL of the LLM API (e.g., http://localhost:8080)
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- SUMMARIZER_API_KEY: (optional) API key, if required
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- SUMMARIZER_MODEL: (optional) Model name (e.g., llama-3.1-8b-instruct)
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"""
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import os
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import json
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import logging
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from typing import Optional
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import httpx
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logger = logging.getLogger("scraibe.summarizer")
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class SummarizerError(Exception):
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"""Raised when the summarization API call fails."""
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pass
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class SummarizerClient:
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"""
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HTTP client for an OpenAI-compatible chat completions endpoint.
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Used to summarize long transcripts in chunks.
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"""
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CHUNK_SIZE = 10_240 # characters per chunk
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def __init__(
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self,
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api_url: Optional[str] = None,
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api_key: Optional[str] = None,
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model: Optional[str] = None,
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timeout: float = 600.0,
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):
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self.api_url = (api_url or os.getenv("SUMMARIZER_API_URL")).strip().rstrip("/")
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self.api_key = api_key or os.getenv("SUMMARIZER_API_KEY") or None
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self.model = model or os.getenv("SUMMARIZER_MODEL") or "llama-3.1-8b-instruct"
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self.timeout = timeout
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if not self.api_url:
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raise SummarizerError(
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"SUMMARIZER_API_URL is not set. "
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"Provide the summarization LLM URL via environment or constructor."
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)
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logger.info(
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"Initializing SummarizerClient: url=%s model=%s",
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self.api_url,
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self.model,
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)
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self._client = httpx.Client(
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base_url=self.api_url,
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timeout=self.timeout,
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follow_redirects=True,
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)
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def close(self):
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self._client.close()
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def __del__(self):
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try:
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self._client.close()
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except Exception:
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pass
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def summarize_transcript(self, transcript: str) -> str:
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"""
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Summarize a (possibly very long) transcript.
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Strategy:
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- Split transcript into chunks of CHUNK_SIZE characters.
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- Generate a detailed summary for each chunk.
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- Combine all chunk summaries and generate a final, concise but thorough summary.
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The final summary should make it clear:
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- What was discussed
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- Main issues
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- Outcomes / decisions
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- Next steps / action items
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"""
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if not transcript.strip():
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logger.warning("Empty transcript provided to summarize_transcript.")
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return "No transcript provided to summarize."
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logger.info(
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"Starting summarization for transcript length=%d chars",
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len(transcript),
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)
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# 1) Chunk the transcript
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chunks = self._chunk_text(transcript)
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logger.info("Split transcript into %d chunks.", len(chunks))
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# 2) Summarize each chunk
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chunk_summaries = []
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for i, chunk in enumerate(chunks):
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logger.info(
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"Summarizing chunk %d/%d (length=%d)",
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i + 1,
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len(chunks),
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len(chunk),
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)
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summary = self._summarize_chunk(chunk, i, len(chunks))
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chunk_summaries.append(summary)
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# 3) Combine and summarize summaries
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combined = "\n\n".join(chunk_summaries)
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logger.info(
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"Combining %d chunk summaries (total length=%d) for final summary.",
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len(chunk_summaries),
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len(combined),
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)
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final_summary = self._summarize_combined(combined)
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logger.info("Summarization completed.")
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return final_summary
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def _chunk_text(self, text: str) -> list[str]:
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"""Split text into chunks of CHUNK_SIZE characters."""
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chunks = []
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start = 0
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while start < len(text):
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end = start + self.CHUNK_SIZE
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if end >= len(text):
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chunks.append(text[start:])
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break
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# Try to break at a reasonable boundary (newline or space)
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break_pos = text.rfind("\n", start, end)
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if break_pos == -1:
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break_pos = text.rfind(" ", start, end)
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if break_pos == -1 or break_pos <= start:
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break_pos = end
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chunks.append(text[start:break_pos].strip())
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start = break_pos
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return chunks
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def _summarize_chunk(self, chunk: str, index: int, total: int) -> str:
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system_prompt = (
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"You are an expert legal and business meeting summarizer. "
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"You will receive a segment of a longer transcript. "
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"Provide a detailed, structured summary of this segment, focusing on: "
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"- Topics discussed\n"
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"- Key points and arguments\n"
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"- Decisions and agreements\n"
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"- Action items and responsibilities\n"
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"- Any risks, conflicts, or open issues\n\n"
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"Be concise but complete. Use bullet points when helpful. "
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"Do not add information that is not present in the transcript."
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)
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user_prompt = (
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f"This is segment {index + 1} of {total} from a longer conversation.\n\n"
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f"{chunk}"
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)
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return self._chat_completion(system_prompt, user_prompt)
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def _summarize_combined(self, combined_summaries: str) -> str:
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system_prompt = (
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"You are an expert legal and business meeting summarizer. "
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"You will receive several intermediate summaries of a longer conversation. "
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"Produce a single, comprehensive summary that makes it clear: "
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"- The overall purpose and context of the discussion\n"
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"- The main issues and topics addressed\n"
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"- Key arguments and positions (briefly)\n"
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"- Decisions and outcomes\n"
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"- Action items, responsibilities, and next steps\n"
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"- Any unresolved issues or risks\n\n"
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"The summary should be detailed enough that a reader who was not present "
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"can understand what happened and what is expected going forward. "
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"Use clear, concise language and bullet points where appropriate."
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)
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user_prompt = (
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"Here are the intermediate summaries from different parts of the same conversation:\n\n"
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f"{combined_summaries}"
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)
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return self._chat_completion(system_prompt, user_prompt)
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def _chat_completion(self, system_prompt: str, user_prompt: str) -> str:
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"""
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Call OpenAI-compatible /v1/chat/completions endpoint.
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"""
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payload = {
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"model": self.model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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"temperature": 0.3,
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}
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headers = {
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"Content-Type": "application/json",
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}
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if self.api_key:
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headers["Authorization"] = f"Bearer {self.api_key}"
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logger.info("Calling summarizer endpoint: /v1/chat/completions")
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resp = self._client.post(
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"/v1/chat/completions",
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json=payload,
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headers=headers,
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)
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logger.info("Summarizer response status: %d", resp.status_code)
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if resp.status_code >= 400:
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logger.error("Summarizer error response: %s", resp.text)
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raise SummarizerError(
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f"Summarizer API error {resp.status_code}: {resp.text}"
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)
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try:
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data = resp.json()
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except json.JSONDecodeError:
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logger.error("Failed to parse summarizer response as JSON.")
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raise SummarizerError(
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"Failed to parse summarizer response as JSON."
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)
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# Extract assistant message
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try:
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content = data["choices"][0]["message"]["content"]
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return content.strip()
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except (KeyError, IndexError, TypeError):
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logger.error(
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"Unexpected summarizer response format: %s",
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json.dumps(data, indent=2),
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)
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raise SummarizerError(
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"Unexpected summarizer response format: "
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f"{json.dumps(data, indent=2)}"
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)
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