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Feat: add fault-tolerant mechanism to RAPTOR (#11206)
### What problem does this PR solve? Add fault-tolerant mechanism to RAPTOR. ### Type of change - [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Yongteng Lei
154
rag/raptor.py
154
rag/raptor.py
@ -15,27 +15,35 @@
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#
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import logging
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import re
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import umap
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import numpy as np
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from sklearn.mixture import GaussianMixture
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import trio
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import umap
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from sklearn.mixture import GaussianMixture
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from api.db.services.task_service import has_canceled
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from common.connection_utils import timeout
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from common.exceptions import TaskCanceledException
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from common.token_utils import truncate
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from graphrag.utils import (
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get_llm_cache,
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chat_limiter,
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get_embed_cache,
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get_llm_cache,
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set_embed_cache,
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set_llm_cache,
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chat_limiter,
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)
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from common.token_utils import truncate
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class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
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def __init__(
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self, max_cluster, llm_model, embd_model, prompt, max_token=512, threshold=0.1
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self,
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max_cluster,
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llm_model,
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embd_model,
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prompt,
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max_token=512,
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threshold=0.1,
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max_errors=3,
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):
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self._max_cluster = max_cluster
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self._llm_model = llm_model
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@ -43,31 +51,35 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
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self._threshold = threshold
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self._prompt = prompt
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self._max_token = max_token
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self._max_errors = max(1, max_errors)
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self._error_count = 0
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@timeout(60*20)
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@timeout(60 * 20)
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async def _chat(self, system, history, gen_conf):
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response = await trio.to_thread.run_sync(
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lambda: get_llm_cache(self._llm_model.llm_name, system, history, gen_conf)
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)
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cached = await trio.to_thread.run_sync(lambda: get_llm_cache(self._llm_model.llm_name, system, history, gen_conf))
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if cached:
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return cached
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if response:
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return response
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response = await trio.to_thread.run_sync(
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lambda: self._llm_model.chat(system, history, gen_conf)
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)
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response = re.sub(r"^.*</think>", "", response, flags=re.DOTALL)
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if response.find("**ERROR**") >= 0:
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raise Exception(response)
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await trio.to_thread.run_sync(
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lambda: set_llm_cache(self._llm_model.llm_name, system, response, history, gen_conf)
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)
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return response
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last_exc = None
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for attempt in range(3):
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try:
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response = await trio.to_thread.run_sync(lambda: self._llm_model.chat(system, history, gen_conf))
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response = re.sub(r"^.*</think>", "", response, flags=re.DOTALL)
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if response.find("**ERROR**") >= 0:
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raise Exception(response)
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await trio.to_thread.run_sync(lambda: set_llm_cache(self._llm_model.llm_name, system, response, history, gen_conf))
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return response
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except Exception as exc:
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last_exc = exc
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logging.warning("RAPTOR LLM call failed on attempt %d/3: %s", attempt + 1, exc)
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if attempt < 2:
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await trio.sleep(1 + attempt)
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raise last_exc if last_exc else Exception("LLM chat failed without exception")
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@timeout(20)
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async def _embedding_encode(self, txt):
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response = await trio.to_thread.run_sync(
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lambda: get_embed_cache(self._embd_model.llm_name, txt)
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)
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response = await trio.to_thread.run_sync(lambda: get_embed_cache(self._embd_model.llm_name, txt))
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if response is not None:
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return response
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embds, _ = await trio.to_thread.run_sync(lambda: self._embd_model.encode([txt]))
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@ -82,7 +94,6 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
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n_clusters = np.arange(1, max_clusters)
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bics = []
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for n in n_clusters:
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if task_id:
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if has_canceled(task_id):
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logging.info(f"Task {task_id} cancelled during get optimal clusters.")
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@ -101,7 +112,7 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
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layers = [(0, len(chunks))]
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start, end = 0, len(chunks)
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@timeout(60*20)
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@timeout(60 * 20)
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async def summarize(ck_idx: list[int]):
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nonlocal chunks
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@ -111,47 +122,50 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
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raise TaskCanceledException(f"Task {task_id} was cancelled")
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texts = [chunks[i][0] for i in ck_idx]
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len_per_chunk = int(
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(self._llm_model.max_length - self._max_token) / len(texts)
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)
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cluster_content = "\n".join(
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[truncate(t, max(1, len_per_chunk)) for t in texts]
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)
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async with chat_limiter:
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len_per_chunk = int((self._llm_model.max_length - self._max_token) / len(texts))
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cluster_content = "\n".join([truncate(t, max(1, len_per_chunk)) for t in texts])
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try:
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async with chat_limiter:
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if task_id and has_canceled(task_id):
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logging.info(f"Task {task_id} cancelled before RAPTOR LLM call.")
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raise TaskCanceledException(f"Task {task_id} was cancelled")
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if task_id and has_canceled(task_id):
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logging.info(f"Task {task_id} cancelled before RAPTOR LLM call.")
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raise TaskCanceledException(f"Task {task_id} was cancelled")
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cnt = await self._chat(
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"You're a helpful assistant.",
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[
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{
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"role": "user",
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"content": self._prompt.format(cluster_content=cluster_content),
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}
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],
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{"max_tokens": max(self._max_token, 512)}, # fix issue: #10235
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)
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cnt = re.sub(
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"(······\n由于长度的原因，回答被截断了，要继续吗？|For the content length reason, it stopped, continue?)",
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"",
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cnt,
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)
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logging.debug(f"SUM: {cnt}")
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cnt = await self._chat(
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"You're a helpful assistant.",
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[
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{
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"role": "user",
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"content": self._prompt.format(
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cluster_content=cluster_content
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),
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}
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],
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{"max_tokens": max(self._max_token, 512)}, # fix issue: #10235
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)
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cnt = re.sub(
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"(······\n由于长度的原因，回答被截断了，要继续吗？|For the content length reason, it stopped, continue?)",
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"",
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cnt,
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)
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logging.debug(f"SUM: {cnt}")
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if task_id and has_canceled(task_id):
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logging.info(f"Task {task_id} cancelled before RAPTOR embedding.")
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raise TaskCanceledException(f"Task {task_id} was cancelled")
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if task_id and has_canceled(task_id):
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logging.info(f"Task {task_id} cancelled before RAPTOR embedding.")
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raise TaskCanceledException(f"Task {task_id} was cancelled")
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embds = await self._embedding_encode(cnt)
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chunks.append((cnt, embds))
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embds = await self._embedding_encode(cnt)
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chunks.append((cnt, embds))
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except TaskCanceledException:
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raise
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except Exception as exc:
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self._error_count += 1
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warn_msg = f"[RAPTOR] Skip cluster ({len(ck_idx)} chunks) due to error: {exc}"
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logging.warning(warn_msg)
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if callback:
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callback(msg=warn_msg)
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if self._error_count >= self._max_errors:
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raise RuntimeError(f"RAPTOR aborted after {self._error_count} errors. Last error: {exc}") from exc
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labels = []
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while end - start > 1:
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if task_id:
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if has_canceled(task_id):
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logging.info(f"Task {task_id} cancelled during RAPTOR layer processing.")
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@ -161,11 +175,7 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
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if len(embeddings) == 2:
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await summarize([start, start + 1])
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if callback:
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callback(
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msg="Cluster one layer: {} -> {}".format(
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end - start, len(chunks) - end
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)
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)
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callback(msg="Cluster one layer: {} -> {}".format(end - start, len(chunks) - end))
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labels.extend([0, 0])
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layers.append((end, len(chunks)))
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start = end
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@ -199,17 +209,11 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
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nursery.start_soon(summarize, ck_idx)
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assert len(chunks) - end == n_clusters, "{} vs. {}".format(
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len(chunks) - end, n_clusters
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)
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assert len(chunks) - end == n_clusters, "{} vs. {}".format(len(chunks) - end, n_clusters)
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labels.extend(lbls)
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layers.append((end, len(chunks)))
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if callback:
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callback(
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msg="Cluster one layer: {} -> {}".format(
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end - start, len(chunks) - end
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)
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)
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callback(msg="Cluster one layer: {} -> {}".format(end - start, len(chunks) - end))
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start = end
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end = len(chunks)
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