mirror of
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200 lines
8.3 KiB
Python
200 lines
8.3 KiB
Python
#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import random
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from copy import deepcopy
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import xxhash
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from agent.component.llm import LLMParam, LLM
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from api.db.joint_services.tenant_model_service import get_model_config_from_provider_instance
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from api.db.services.document_service import DocumentService
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from api.db.services.llm_service import LLMBundle
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from api.db.services.task_service import has_canceled
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from common.constants import LLMType
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from rag.advanced_rag.knowlege_compile.runner import (
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DOC_STRUCTURE_COMPILE_BATCH_CHUNKS,
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load_active_templates,
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resolve_template_ids_from_groups,
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run_structure_compile_over_batches,
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split_tree_templates,
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)
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from rag.flow.base import ProcessBase, ProcessParamBase
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class CompilerParam(ProcessParamBase, LLMParam):
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"""Parameters for the knowledge-Compiler flow component.
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Same LLM-backed shape as the Extractor, but instead of a single inline
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``knowledge_compilation`` config it drives compilation from one or more
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saved **compilation-template groups** (``compilation_template_group_ids``).
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Each group resolves to a set of templates, each of which carries its own
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structure-compilation config (kind, fields, synthesis, ...).
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"""
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def __init__(self):
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super().__init__()
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self.compilation_template_group_ids = []
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def check(self):
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super().check()
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self.check_empty(self.compilation_template_group_ids, "Compilation Template Groups")
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class Compiler(ProcessBase, LLM):
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component_name = "Compiler"
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def _compile_progress(self, prog=None, msg=""):
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"""Adapt the knowledge-compile ``callback`` protocol to the flow
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callback. Downstream compile helpers invoke the callback either as
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``callback(prog, msg)`` (positional) or ``callback(msg=...)``; the
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flow's ``self.callback`` expects ``(progress, message)``.
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"""
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self.callback(prog, msg)
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def _compile_language(self, kwargs: dict) -> str:
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language = kwargs.get("language") or getattr(self._canvas, "_language", None)
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if isinstance(language, str):
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language = language.strip()
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if not language and getattr(self._canvas, "_doc_id", None):
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config = DocumentService.get_chunking_config(self._canvas._doc_id) or {}
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language = config.get("language")
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if isinstance(language, str):
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language = language.strip()
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return language or "English"
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async def _invoke(self, **kwargs):
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self.set_output("output_format", "chunks")
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self.callback(random.randint(1, 5) / 100.0, "Start knowledge compilation.")
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# Collect the upstream chunk list (same contract as the Extractor).
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inputs = self.get_input_elements()
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chunks = []
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for _, v in inputs.items():
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val = v["value"]
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if isinstance(val, list):
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chunks = deepcopy(val)
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tenant_id = self._canvas.get_tenant_id()
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doc_id = self._canvas._doc_id
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kb_id = getattr(self._canvas, "_kb_id", None) or DocumentService.get_knowledgebase_id(doc_id)
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language = self._compile_language(kwargs)
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if not chunks:
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self.set_output("chunks", chunks)
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return
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for ck in chunks:
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ck["doc_id"] = doc_id
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ck["id"] = xxhash.xxh64((ck["text"] + str(ck["doc_id"])).encode("utf-8")).hexdigest()
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# Resolve the configured template groups to concrete, active
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# (non-artifact) structure-compilation templates.
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template_ids = resolve_template_ids_from_groups(self._param.compilation_template_group_ids, tenant_id)
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active_templates = load_active_templates(template_ids, tenant_id)
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if not active_templates:
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self.callback(msg="No active compilation templates resolved from the configured groups.")
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self.set_output("chunks", chunks)
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return
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# Per-template chat model: a template may pin its own ``llm_id``;
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# otherwise fall back to this component's configured chat model.
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llm_bundle_cache: dict[str, LLMBundle] = {}
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chat_mdl_by_tid: dict[str, LLMBundle] = {}
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filtered_templates: list[tuple[str, dict]] = []
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default_chat_mdl = None
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for template_id, parser_cfg in active_templates:
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tpl_llm_id = parser_cfg.get("llm_id") if isinstance(parser_cfg, dict) else None
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if isinstance(tpl_llm_id, str) and tpl_llm_id.strip():
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chat_llm_id = tpl_llm_id.strip()
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if chat_llm_id not in llm_bundle_cache:
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try:
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cfg = get_model_config_from_provider_instance(tenant_id, LLMType.CHAT, chat_llm_id)
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llm_bundle_cache[chat_llm_id] = LLMBundle(
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tenant_id,
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cfg,
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lang=language,
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max_retries=self._param.max_retries,
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retry_interval=self._param.delay_after_error,
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)
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except Exception:
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logging.exception(
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"Compiler: cannot resolve chat model %s for template %s; skipping",
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chat_llm_id,
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template_id,
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)
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continue
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chat_mdl_by_tid[template_id] = llm_bundle_cache[chat_llm_id]
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else:
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if default_chat_mdl is None:
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default_chat_mdl = LLMBundle(
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tenant_id,
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self.chat_mdl.model_config,
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lang=language,
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max_retries=self._param.max_retries,
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retry_interval=self._param.delay_after_error,
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)
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chat_mdl_by_tid[template_id] = default_chat_mdl
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filtered_templates.append((template_id, parser_cfg))
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if not filtered_templates:
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self.set_output("chunks", chunks)
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return
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active_templates = filtered_templates
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embedding_model = LLMBundle(
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tenant_id,
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LLMType.EMBEDDING,
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lang=language,
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max_retries=self._param.max_retries,
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retry_interval=self._param.delay_after_error,
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)
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tree_templates, non_tree_templates = split_tree_templates(active_templates)
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if tree_templates:
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# ``tree`` templates run RAPTOR over the whole document by
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# reloading vectors from the doc store; that path is owned by the
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# chunking task executor and isn't available from the flow.
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logging.warning(
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"Compiler: %d tree-kind template(s) are not supported in the flow pipeline; skipping",
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len(tree_templates),
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)
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self.callback(msg=f"Skipping {len(tree_templates)} tree-kind template(s) (unsupported in flow).")
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if non_tree_templates:
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task_id = getattr(self._canvas, "task_id", None)
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def _cancelled() -> bool:
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return bool(task_id) and has_canceled(task_id)
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async def _chunk_batches():
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for i in range(0, len(chunks), DOC_STRUCTURE_COMPILE_BATCH_CHUNKS):
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yield chunks[i:i + DOC_STRUCTURE_COMPILE_BATCH_CHUNKS]
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await run_structure_compile_over_batches(
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active_templates=non_tree_templates,
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chat_mdl_by_tid=chat_mdl_by_tid,
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embedding_model=embedding_model,
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tenant_id=tenant_id,
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kb_id=kb_id,
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doc_id=doc_id,
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language=language,
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chunk_batches=_chunk_batches(),
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progress_cb=self._compile_progress,
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cancel_check=_cancelled,
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)
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self.set_output("chunks", chunks)
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