Remove deprecated keyword extraction query methods
- Delete query_with_keywords function - Remove kg_query_with_keywords helper - Drop separate keyword extraction methods
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yangdx
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2 changed files with 0 additions and 256 deletions
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@ -72,7 +72,6 @@ from .operate import (
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merge_nodes_and_edges,
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kg_query,
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naive_query,
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query_with_keywords,
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_rebuild_knowledge_from_chunks,
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)
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from .constants import GRAPH_FIELD_SEP
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@ -1816,8 +1815,6 @@ class LightRAG:
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"""
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# If a custom model is provided in param, temporarily update global config
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global_config = asdict(self)
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# Save original query for vector search
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param.original_query = query
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if param.mode in ["local", "global", "hybrid", "mix"]:
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response = await kg_query(
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@ -1859,59 +1856,6 @@ class LightRAG:
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await self._query_done()
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return response
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# TODO: Deprecated, use user_prompt in QueryParam instead
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def query_with_separate_keyword_extraction(
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self, query: str, prompt: str, param: QueryParam = QueryParam()
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):
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"""
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Query with separate keyword extraction step.
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This method extracts keywords from the query first, then uses them for the query.
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Args:
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query: User query
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prompt: Additional prompt for the query
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param: Query parameters
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Returns:
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Query response
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"""
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loop = always_get_an_event_loop()
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return loop.run_until_complete(
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self.aquery_with_separate_keyword_extraction(query, prompt, param)
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)
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# TODO: Deprecated, use user_prompt in QueryParam instead
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async def aquery_with_separate_keyword_extraction(
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self, query: str, prompt: str, param: QueryParam = QueryParam()
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) -> str | AsyncIterator[str]:
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"""
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Async version of query_with_separate_keyword_extraction.
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Args:
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query: User query
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prompt: Additional prompt for the query
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param: Query parameters
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Returns:
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Query response or async iterator
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"""
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response = await query_with_keywords(
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query=query,
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prompt=prompt,
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param=param,
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knowledge_graph_inst=self.chunk_entity_relation_graph,
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entities_vdb=self.entities_vdb,
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relationships_vdb=self.relationships_vdb,
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chunks_vdb=self.chunks_vdb,
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text_chunks_db=self.text_chunks,
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global_config=asdict(self),
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hashing_kv=self.llm_response_cache,
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)
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await self._query_done()
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return response
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async def _query_done(self):
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await self.llm_response_cache.index_done_callback()
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@ -3187,203 +3187,3 @@ async def naive_query(
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)
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return response
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# TODO: Deprecated, use user_prompt in QueryParam instead
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async def kg_query_with_keywords(
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query: str,
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knowledge_graph_inst: BaseGraphStorage,
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entities_vdb: BaseVectorStorage,
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relationships_vdb: BaseVectorStorage,
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text_chunks_db: BaseKVStorage,
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query_param: QueryParam,
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global_config: dict[str, str],
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hashing_kv: BaseKVStorage | None = None,
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ll_keywords: list[str] = [],
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hl_keywords: list[str] = [],
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chunks_vdb: BaseVectorStorage | None = None,
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) -> str | AsyncIterator[str]:
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"""
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Refactored kg_query that does NOT extract keywords by itself.
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It expects hl_keywords and ll_keywords to be set in query_param, or defaults to empty.
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Then it uses those to build context and produce a final LLM response.
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"""
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if query_param.model_func:
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use_model_func = query_param.model_func
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else:
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use_model_func = global_config["llm_model_func"]
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# Apply higher priority (5) to query relation LLM function
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use_model_func = partial(use_model_func, _priority=5)
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args_hash = compute_args_hash(query_param.mode, query)
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cached_response, quantized, min_val, max_val = await handle_cache(
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hashing_kv, args_hash, query, query_param.mode, cache_type="query"
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)
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if cached_response is not None:
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return cached_response
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# If neither has any keywords, you could handle that logic here.
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if not hl_keywords and not ll_keywords:
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logger.warning(
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"No keywords found in query_param. Could default to global mode or fail."
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)
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return PROMPTS["fail_response"]
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if not ll_keywords and query_param.mode in ["local", "hybrid"]:
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logger.warning("low_level_keywords is empty, switching to global mode.")
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query_param.mode = "global"
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if not hl_keywords and query_param.mode in ["global", "hybrid"]:
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logger.warning("high_level_keywords is empty, switching to local mode.")
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query_param.mode = "local"
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ll_keywords_str = ", ".join(ll_keywords) if ll_keywords else ""
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hl_keywords_str = ", ".join(hl_keywords) if hl_keywords else ""
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context = await _build_query_context(
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query,
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ll_keywords_str,
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hl_keywords_str,
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knowledge_graph_inst,
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entities_vdb,
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relationships_vdb,
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text_chunks_db,
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query_param,
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chunks_vdb=chunks_vdb,
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)
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if not context:
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return PROMPTS["fail_response"]
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if query_param.only_need_context:
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return context
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# Process conversation history
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history_context = ""
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if query_param.conversation_history:
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history_context = get_conversation_turns(
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query_param.conversation_history, query_param.history_turns
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)
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sys_prompt_temp = PROMPTS["rag_response"]
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sys_prompt = sys_prompt_temp.format(
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context_data=context,
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response_type=query_param.response_type,
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history=history_context,
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)
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if query_param.only_need_prompt:
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return sys_prompt
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tokenizer: Tokenizer = global_config["tokenizer"]
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len_of_prompts = len(tokenizer.encode(query + sys_prompt))
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logger.debug(
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f"[kg_query_with_keywords] Sending to LLM: {len_of_prompts:,} tokens (Query: {len(tokenizer.encode(query))}, System: {len(tokenizer.encode(sys_prompt))})"
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)
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# 6. Generate response
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response = await use_model_func(
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query,
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system_prompt=sys_prompt,
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stream=query_param.stream,
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)
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# Clean up response content
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if isinstance(response, str) and len(response) > len(sys_prompt):
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response = (
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response.replace(sys_prompt, "")
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.replace("user", "")
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.replace("model", "")
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.replace(query, "")
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.replace("<system>", "")
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.replace("</system>", "")
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.strip()
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)
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if hashing_kv.global_config.get("enable_llm_cache"):
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await save_to_cache(
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hashing_kv,
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CacheData(
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args_hash=args_hash,
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content=response,
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prompt=query,
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mode=query_param.mode,
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cache_type="query",
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),
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)
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return response
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# TODO: Deprecated, use user_prompt in QueryParam instead
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async def query_with_keywords(
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query: str,
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prompt: str,
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param: QueryParam,
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knowledge_graph_inst: BaseGraphStorage,
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entities_vdb: BaseVectorStorage,
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relationships_vdb: BaseVectorStorage,
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chunks_vdb: BaseVectorStorage,
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text_chunks_db: BaseKVStorage,
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global_config: dict[str, str],
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hashing_kv: BaseKVStorage | None = None,
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) -> str | AsyncIterator[str]:
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"""
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Extract keywords from the query and then use them for retrieving information.
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1. Extracts high-level and low-level keywords from the query
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2. Formats the query with the extracted keywords and prompt
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3. Uses the appropriate query method based on param.mode
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Args:
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query: The user's query
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prompt: Additional prompt to prepend to the query
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param: Query parameters
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knowledge_graph_inst: Knowledge graph storage
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entities_vdb: Entities vector database
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relationships_vdb: Relationships vector database
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chunks_vdb: Document chunks vector database
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text_chunks_db: Text chunks storage
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global_config: Global configuration
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hashing_kv: Cache storage
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Returns:
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Query response or async iterator
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"""
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# Extract keywords
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hl_keywords, ll_keywords = await get_keywords_from_query(
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query=query,
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query_param=param,
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global_config=global_config,
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hashing_kv=hashing_kv,
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)
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# Create a new string with the prompt and the keywords
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keywords_str = ", ".join(ll_keywords + hl_keywords)
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formatted_question = (
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f"{prompt}\n\n### Keywords\n\n{keywords_str}\n\n### Query\n\n{query}"
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)
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# Use appropriate query method based on mode
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if param.mode in ["local", "global", "hybrid", "mix"]:
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return await kg_query_with_keywords(
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formatted_question,
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knowledge_graph_inst,
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entities_vdb,
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relationships_vdb,
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text_chunks_db,
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param,
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global_config,
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hashing_kv=hashing_kv,
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hl_keywords=hl_keywords,
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ll_keywords=ll_keywords,
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chunks_vdb=chunks_vdb,
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)
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elif param.mode == "naive":
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return await naive_query(
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formatted_question,
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chunks_vdb,
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text_chunks_db,
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param,
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global_config,
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hashing_kv=hashing_kv,
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)
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else:
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raise ValueError(f"Unknown mode {param.mode}")
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