mirror of
https://github.com/danswer-ai/danswer.git
synced 2025-05-02 16:00:34 +02:00
Partial Prompt Updates (#3880)
This commit is contained in:
Yuhong Sun
Evan Lohn
parent
9b6e51b4fe
commit
a067b32467
@ -12,12 +12,12 @@ from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer
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SubQuestionAnswerCheckUpdate,
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)
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from onyx.agents.agent_search.models import GraphConfig
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from onyx.agents.agent_search.shared_graph_utils.prompts import SUB_ANSWER_CHECK_PROMPT
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from onyx.agents.agent_search.shared_graph_utils.prompts import UNKNOWN_ANSWER
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from onyx.agents.agent_search.shared_graph_utils.utils import (
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get_langgraph_node_log_string,
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)
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from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
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from onyx.prompts.agent_search import SUB_ANSWER_CHECK_PROMPT
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from onyx.prompts.agent_search import UNKNOWN_ANSWER
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def check_sub_answer(
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@ -16,7 +16,6 @@ from onyx.agents.agent_search.models import GraphConfig
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from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
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build_sub_question_answer_prompt,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import NO_RECOVERED_DOCS
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from onyx.agents.agent_search.shared_graph_utils.utils import get_answer_citation_ids
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from onyx.agents.agent_search.shared_graph_utils.utils import (
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get_langgraph_node_log_string,
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@ -31,6 +30,7 @@ from onyx.chat.models import StreamStopInfo
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from onyx.chat.models import StreamStopReason
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from onyx.chat.models import StreamType
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from onyx.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
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from onyx.prompts.agent_search import NO_RECOVERED_DOCS
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from onyx.utils.logger import setup_logger
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logger = setup_logger()
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@ -30,16 +30,6 @@ from onyx.agents.agent_search.shared_graph_utils.models import InitialAgentResul
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from onyx.agents.agent_search.shared_graph_utils.operators import (
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dedup_inference_sections,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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INITIAL_ANSWER_PROMPT_W_SUB_QUESTIONS,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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INITIAL_ANSWER_PROMPT_WO_SUB_QUESTIONS,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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SUB_QUESTION_ANSWER_TEMPLATE,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import UNKNOWN_ANSWER
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from onyx.agents.agent_search.shared_graph_utils.utils import (
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dispatch_main_answer_stop_info,
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)
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@ -57,6 +47,16 @@ from onyx.chat.models import ExtendedToolResponse
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from onyx.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
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from onyx.configs.agent_configs import AGENT_MIN_ORIG_QUESTION_DOCS
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from onyx.context.search.models import InferenceSection
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from onyx.prompts.agent_search import (
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INITIAL_ANSWER_PROMPT_W_SUB_QUESTIONS,
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)
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from onyx.prompts.agent_search import (
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INITIAL_ANSWER_PROMPT_WO_SUB_QUESTIONS,
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)
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from onyx.prompts.agent_search import (
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SUB_QUESTION_ANSWER_TEMPLATE,
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)
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from onyx.prompts.agent_search import UNKNOWN_ANSWER
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from onyx.tools.tool_implementations.search.search_tool import yield_search_responses
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@ -24,17 +24,15 @@ from onyx.agents.agent_search.deep_search.main.states import (
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from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.models import (
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QuestionRetrievalResult,
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)
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from onyx.context.search.models import InferenceSection
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### States ###
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class SubQuestionRetrievalInput(CoreState):
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pass
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exploratory_search_results: list[InferenceSection]
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## Graph State
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class SubQuestionRetrievalState(
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# This includes the core state
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SubQuestionRetrievalInput,
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@ -48,8 +46,6 @@ class SubQuestionRetrievalState(
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base_raw_search_result: Annotated[list[QuestionRetrievalResult], add]
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## Graph Output State - presently not used
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## Graph Output State
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class SubQuestionRetrievalOutput(TypedDict):
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log_messages: list[str]
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@ -22,12 +22,6 @@ from onyx.agents.agent_search.models import GraphConfig
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from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
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build_history_prompt,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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INITIAL_QUESTION_DECOMPOSITION_PROMPT,
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)
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from onyx.agents.agent_search.shared_graph_utils.utils import dispatch_separated
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from onyx.agents.agent_search.shared_graph_utils.utils import (
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get_langgraph_node_log_string,
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@ -38,6 +32,15 @@ from onyx.chat.models import StreamStopReason
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from onyx.chat.models import StreamType
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from onyx.chat.models import SubQuestionPiece
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from onyx.configs.agent_configs import AGENT_NUM_DOCS_FOR_DECOMPOSITION
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from onyx.prompts.agent_search import (
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INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH,
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)
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from onyx.prompts.agent_search import (
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INITIAL_QUESTION_DECOMPOSITION_PROMPT,
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)
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from onyx.utils.logger import setup_logger
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logger = setup_logger()
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def decompose_orig_question(
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@ -63,6 +66,12 @@ def decompose_orig_question(
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# Initial search to inform decomposition. Just get top 3 fits
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if perform_initial_search_decomposition:
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# Due to unfortunate state representation in LangGraph, we need here to double check that the retrieval has
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# happened prior to this point, allowing silent failure here since it is not critical for decomposition in
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# all queries.
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if not state.exploratory_search_results:
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logger.error("Initial search for decomposition failed")
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sample_doc_str = "\n\n".join(
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[
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doc.combined_content
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@ -10,17 +10,15 @@ from onyx.agents.agent_search.deep_search.main.states import (
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from onyx.agents.agent_search.deep_search.main.states import (
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SubQuestionResultsUpdate,
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)
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from onyx.context.search.models import InferenceSection
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### States ###
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class SubQuestionAnsweringInput(CoreState):
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pass
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exploratory_search_results: list[InferenceSection]
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## Graph State
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class SubQuestionAnsweringState(
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# This includes the core state
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SubQuestionAnsweringInput,
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@ -31,8 +29,6 @@ class SubQuestionAnsweringState(
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pass
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## Graph Output State - presently not used
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## Graph Output State
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class SubQuestionAnsweringOutput(TypedDict):
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log_messages: list[str]
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@ -10,14 +10,14 @@ from onyx.agents.agent_search.deep_search.main.states import (
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)
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from onyx.agents.agent_search.deep_search.main.states import MainState
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from onyx.agents.agent_search.models import GraphConfig
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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INITIAL_REFINED_ANSWER_COMPARISON_PROMPT,
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)
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from onyx.agents.agent_search.shared_graph_utils.utils import (
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get_langgraph_node_log_string,
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)
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from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
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from onyx.chat.models import RefinedAnswerImprovement
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from onyx.prompts.agent_search import (
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INITIAL_REFINED_ANSWER_COMPARISON_PROMPT,
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)
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def compare_answers(
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@ -20,9 +20,6 @@ from onyx.agents.agent_search.models import GraphConfig
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from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
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build_history_prompt,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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REFINEMENT_QUESTION_DECOMPOSITION_PROMPT,
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)
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from onyx.agents.agent_search.shared_graph_utils.utils import dispatch_separated
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from onyx.agents.agent_search.shared_graph_utils.utils import (
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format_entity_term_extraction,
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@ -32,6 +29,9 @@ from onyx.agents.agent_search.shared_graph_utils.utils import (
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)
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from onyx.agents.agent_search.shared_graph_utils.utils import make_question_id
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from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
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from onyx.prompts.agent_search import (
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REFINEMENT_QUESTION_DECOMPOSITION_PROMPT,
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)
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from onyx.tools.models import ToolCallKickoff
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@ -18,19 +18,14 @@ from onyx.agents.agent_search.shared_graph_utils.models import EntityExtractionR
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from onyx.agents.agent_search.shared_graph_utils.models import (
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EntityRelationshipTermExtraction,
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)
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from onyx.agents.agent_search.shared_graph_utils.models import Relationship
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from onyx.agents.agent_search.shared_graph_utils.models import Term
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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ENTITY_TERM_EXTRACTION_PROMPT,
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)
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from onyx.agents.agent_search.shared_graph_utils.utils import format_docs
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from onyx.agents.agent_search.shared_graph_utils.utils import (
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get_langgraph_node_log_string,
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)
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from onyx.configs.constants import NUM_EXPLORATORY_DOCS
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from onyx.prompts.agent_search import (
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ENTITY_TERM_EXTRACTION_PROMPT,
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)
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def extract_entities_terms(
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@ -28,16 +28,6 @@ from onyx.agents.agent_search.shared_graph_utils.models import RefinedAgentStats
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from onyx.agents.agent_search.shared_graph_utils.operators import (
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dedup_inference_sections,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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REFINED_ANSWER_PROMPT_W_SUB_QUESTIONS,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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REFINED_ANSWER_PROMPT_WO_SUB_QUESTIONS,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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SUB_QUESTION_ANSWER_TEMPLATE_REFINED,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import UNKNOWN_ANSWER
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from onyx.agents.agent_search.shared_graph_utils.utils import (
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dispatch_main_answer_stop_info,
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)
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@ -55,6 +45,16 @@ from onyx.chat.models import AgentAnswerPiece
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from onyx.chat.models import ExtendedToolResponse
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from onyx.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
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from onyx.configs.agent_configs import AGENT_MIN_ORIG_QUESTION_DOCS
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from onyx.prompts.agent_search import (
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REFINED_ANSWER_PROMPT_W_SUB_QUESTIONS,
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)
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from onyx.prompts.agent_search import (
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REFINED_ANSWER_PROMPT_WO_SUB_QUESTIONS,
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)
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from onyx.prompts.agent_search import (
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SUB_QUESTION_ANSWER_TEMPLATE_REFINED,
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)
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from onyx.prompts.agent_search import UNKNOWN_ANSWER
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from onyx.tools.tool_implementations.search.search_tool import yield_search_responses
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@ -16,14 +16,14 @@ from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states impor
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QueryExpansionUpdate,
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)
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from onyx.agents.agent_search.models import GraphConfig
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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QUERY_REWRITING_PROMPT,
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)
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from onyx.agents.agent_search.shared_graph_utils.utils import dispatch_separated
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from onyx.agents.agent_search.shared_graph_utils.utils import (
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get_langgraph_node_log_string,
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)
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from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
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from onyx.prompts.agent_search import (
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QUERY_REWRITING_PROMPT,
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)
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def expand_queries(
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@ -13,7 +13,7 @@ from onyx.agents.agent_search.models import GraphConfig
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from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
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trim_prompt_piece,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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from onyx.prompts.agent_search import (
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DOCUMENT_VERIFICATION_PROMPT,
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)
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@ -7,8 +7,6 @@ from onyx.agents.agent_search.models import GraphConfig
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from onyx.agents.agent_search.shared_graph_utils.models import (
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AgentPromptEnrichmentComponents,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import HISTORY_FRAMING_PROMPT
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from onyx.agents.agent_search.shared_graph_utils.prompts import SUB_QUESTION_RAG_PROMPT
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from onyx.agents.agent_search.shared_graph_utils.utils import (
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get_persona_agent_prompt_expressions,
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)
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@ -20,6 +18,8 @@ from onyx.llm.interfaces import LLMConfig
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from onyx.llm.utils import get_max_input_tokens
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from onyx.natural_language_processing.utils import get_tokenizer
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from onyx.natural_language_processing.utils import tokenizer_trim_content
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from onyx.prompts.agent_search import HISTORY_FRAMING_PROMPT
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from onyx.prompts.agent_search import SUB_QUESTION_RAG_PROMPT
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from onyx.prompts.prompt_utils import build_date_time_string
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@ -24,15 +24,6 @@ from onyx.agents.agent_search.shared_graph_utils.models import (
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EntityRelationshipTermExtraction,
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)
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from onyx.agents.agent_search.shared_graph_utils.models import PersonaPromptExpressions
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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ASSISTANT_SYSTEM_PROMPT_DEFAULT,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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ASSISTANT_SYSTEM_PROMPT_PERSONA,
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)
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from onyx.agents.agent_search.shared_graph_utils.prompts import (
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HISTORY_CONTEXT_SUMMARY_PROMPT,
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)
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from onyx.chat.models import AnswerPacket
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from onyx.chat.models import AnswerStyleConfig
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from onyx.chat.models import CitationConfig
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@ -56,6 +47,15 @@ from onyx.db.engine import get_session_context_manager
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from onyx.db.persona import get_persona_by_id
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from onyx.db.persona import Persona
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from onyx.llm.interfaces import LLM
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from onyx.prompts.agent_search import (
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ASSISTANT_SYSTEM_PROMPT_DEFAULT,
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)
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from onyx.prompts.agent_search import (
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ASSISTANT_SYSTEM_PROMPT_PERSONA,
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)
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from onyx.prompts.agent_search import (
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HISTORY_CONTEXT_SUMMARY_PROMPT,
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)
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from onyx.tools.force import ForceUseTool
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from onyx.tools.tool_constructor import SearchToolConfig
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from onyx.tools.tool_implementations.search.search_tool import (
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@ -8,7 +8,7 @@ AGENT_DEFAULT_RERANKING_HITS = 10
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AGENT_DEFAULT_SUB_QUESTION_MAX_CONTEXT_HITS = 8
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AGENT_DEFAULT_NUM_DOCS_FOR_INITIAL_DECOMPOSITION = 3
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AGENT_DEFAULT_NUM_DOCS_FOR_REFINED_DECOMPOSITION = 5
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AGENT_DEFAULT_EXPLORATORY_SEARCH_RESULTS = 3
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AGENT_DEFAULT_EXPLORATORY_SEARCH_RESULTS = 5
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AGENT_DEFAULT_MIN_ORIG_QUESTION_DOCS = 3
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AGENT_DEFAULT_MAX_ANSWER_CONTEXT_DOCS = 10
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AGENT_DEFAULT_MAX_STATIC_HISTORY_WORD_LENGTH = 2000
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@ -59,7 +59,7 @@ AGENT_NUM_DOCS_FOR_REFINED_DECOMPOSITION = int(
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AGENT_EXPLORATORY_SEARCH_RESULTS = int(
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os.environ.get("AGENT_EXPLORATORY_SEARCH_RESULTS")
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or AGENT_DEFAULT_EXPLORATORY_SEARCH_RESULTS
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) # 3
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) # 5
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AGENT_MIN_ORIG_QUESTION_DOCS = int(
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os.environ.get("AGENT_MIN_ORIG_QUESTION_DOCS")
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@ -1,55 +1,53 @@
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# The prompts for the agentic framework. The order follows approximately the order
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# of the actions in the graph
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# Standards
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SEPARATOR_LINE = "-------"
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UNKNOWN_ANSWER = "I do not have enough information to answer this question."
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NO_RECOVERED_DOCS = "No relevant information recovered"
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DATE_PROMPT = """Today is {date}.\n\n"""
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DATE_PROMPT = "Today is {date}.\n\n"
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SUB_CHECK_YES = "yes"
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SUB_CHECK_NO = "no"
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# Framing/Support/Template Prompts
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HISTORY_FRAMING_PROMPT = """\n
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HISTORY_FRAMING_PROMPT = f"""
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For more context, here is the history of the conversation so far that preceded this question:
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\n-------\n
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{history}
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\n-------\n\n
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"""
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{SEPARATOR_LINE}
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{{history}}
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{SEPARATOR_LINE}
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""".strip()
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ASSISTANT_SYSTEM_PROMPT_DEFAULT = """
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You are an assistant for question-answering tasks."""
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ASSISTANT_SYSTEM_PROMPT_PERSONA = """
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ASSISTANT_SYSTEM_PROMPT_DEFAULT = (
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"""You are an assistant for question-answering tasks."""
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)
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ASSISTANT_SYSTEM_PROMPT_PERSONA = f"""
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You are an assistant for question-answering tasks. Here is more information about you:
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\n-------\n
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{persona_prompt}
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\n-------\n
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"""
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{SEPARATOR_LINE}
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||||
{{persona_prompt}}
|
||||
{SEPARATOR_LINE}
|
||||
""".strip()
|
||||
|
||||
|
||||
SUB_QUESTION_ANSWER_TEMPLATE = """\n
|
||||
Sub-Question: Q{sub_question_num}\n Sub-Question:\n - \n{sub_question}\n --\nAnswer:\n -\n {sub_answer}\n\n
|
||||
"""
|
||||
|
||||
SUB_QUESTION_ANSWER_TEMPLATE_REFINED = """\n
|
||||
Sub-Question: Q{sub_question_num}\n
|
||||
|
||||
SUB_QUESTION_ANSWER_TEMPLATE_REFINED = f"""
|
||||
Sub-Question: Q{{sub_question_num}}\n
|
||||
Type:
|
||||
\n----\n
|
||||
{sub_question_type}
|
||||
\n----\n
|
||||
{SEPARATOR_LINE}
|
||||
{{sub_question_type}}
|
||||
{SEPARATOR_LINE}
|
||||
Sub-Question:
|
||||
\n----\n
|
||||
{sub_question}
|
||||
\n----\n
|
||||
\nAnswer:
|
||||
\n----\n
|
||||
{sub_answer}
|
||||
\n----\n
|
||||
\n
|
||||
"""
|
||||
{SEPARATOR_LINE}
|
||||
{{sub_question}}
|
||||
{SEPARATOR_LINE}
|
||||
Answer:
|
||||
{SEPARATOR_LINE}
|
||||
{{sub_answer}}
|
||||
{SEPARATOR_LINE}
|
||||
""".strip()
|
||||
|
||||
|
||||
SUB_QUESTION_ANSWER_TEMPLATE_REFINED = """\n
|
||||
@ -58,230 +56,206 @@ Sub-Question: Q{sub_question_num}\n Type: {sub_question_type}\n Sub-Question:\n
|
||||
"""
|
||||
|
||||
|
||||
# Setap/Util Prompts
|
||||
|
||||
ENTITY_TERM_EXTRACTION_PROMPT = """ \n
|
||||
# Step/Utility Prompts
|
||||
ENTITY_TERM_EXTRACTION_PROMPT = f"""
|
||||
Based on the original question and some context retrieved from a dataset, please generate a list of
|
||||
entities (e.g. companies, organizations, industries, products, locations, etc.), terms and concepts
|
||||
(e.g. sales, revenue, etc.) that are relevant for the question, plus their relations to each other.
|
||||
|
||||
\n\n
|
||||
Here is the original question:
|
||||
\n-------\n
|
||||
{question}
|
||||
\n-------\n
|
||||
{SEPARATOR_LINE}
|
||||
{{question}}
|
||||
{SEPARATOR_LINE}
|
||||
|
||||
And here is the context retrieved:
|
||||
\n-------\n
|
||||
{context}
|
||||
\n-------\n
|
||||
{SEPARATOR_LINE}
|
||||
{{context}}
|
||||
{SEPARATOR_LINE}
|
||||
|
||||
Please format your answer as a json object in the following format:
|
||||
|
||||
{{"retrieved_entities_relationships": {{
|
||||
"entities": [{{
|
||||
"entity_name": <assign a name for the entity>,
|
||||
"entity_type": <specify a short type name for the entity, such as 'company', 'location',...>
|
||||
}}],
|
||||
"relationships": [{{
|
||||
"relationship_name": <assign a name for the relationship>,
|
||||
"relationship_type": <specify a short type name for the relationship, such as 'sales_to', 'is_location_of',...>,
|
||||
"relationship_entities": [<related entity name 1>, <related entity name 2>, ...]
|
||||
}}],
|
||||
"terms": [{{
|
||||
"term_name": <assign a name for the term>,
|
||||
"term_type": <specify a short type name for the term, such as 'revenue', 'market_share',...>,
|
||||
"term_similar_to": <list terms that are similar to this term>
|
||||
}}]
|
||||
{{
|
||||
"retrieved_entities_relationships": {{
|
||||
"entities": [
|
||||
{{
|
||||
"entity_name": "<assign a name for the entity>",
|
||||
"entity_type": "<specify a short type name for the entity, such as 'company', 'location',...>"
|
||||
}}
|
||||
],
|
||||
"relationships": [
|
||||
{{
|
||||
"relationship_name": "<assign a name for the relationship>",
|
||||
"relationship_type": "<specify a short type name for the relationship, such as 'sales_to', 'is_location_of',...>",
|
||||
"relationship_entities": ["<related entity name 1>", "<related entity name 2>", "..."]
|
||||
}}
|
||||
],
|
||||
"terms": [
|
||||
{{
|
||||
"term_name": "<assign a name for the term>",
|
||||
"term_type": "<specify a short type name for the term, such as 'revenue', 'market_share',...>",
|
||||
"term_similar_to": ["<list terms that are similar to this term>"]
|
||||
}}
|
||||
]
|
||||
}}
|
||||
}}
|
||||
}}
|
||||
"""
|
||||
""".strip()
|
||||
|
||||
HISTORY_CONTEXT_SUMMARY_PROMPT = """\n
|
||||
{persona_specification}
|
||||
|
||||
Your task now is to summarize the key parts of the history of a conversation between a user and an agent. The
|
||||
summary has two purposes:
|
||||
1) providing the suitable context for a new question, and
|
||||
2) To capture the key information that was discussed and that the user may have a follow-up question about.
|
||||
\n-------\n
|
||||
{question}
|
||||
\n-------\n
|
||||
HISTORY_CONTEXT_SUMMARY_PROMPT = (
|
||||
"{persona_specification}\n\n"
|
||||
"Your task now is to summarize the key parts of the history of a conversation between a user and an agent."
|
||||
" The summary has two purposes:\n"
|
||||
" 1) providing the suitable context for a new question, and\n"
|
||||
" 2) To capture the key information that was discussed and that the user may have a follow-up question about.\n\n"
|
||||
"Here is the question:\n"
|
||||
f"{SEPARATOR_LINE}\n"
|
||||
"{question}\n"
|
||||
f"{SEPARATOR_LINE}\n\n"
|
||||
"And here is the history:\n"
|
||||
f"{SEPARATOR_LINE}\n"
|
||||
"{history}\n"
|
||||
f"{SEPARATOR_LINE}\n\n"
|
||||
"Please provide a summarized context from the history so that the question makes sense and can"
|
||||
" - with suitable extra information - be answered.\n\n"
|
||||
"Do not use more than three or four sentences.\n\n"
|
||||
"History summary:"
|
||||
).strip()
|
||||
|
||||
And here is the history:
|
||||
\n-------\n
|
||||
{history}
|
||||
\n-------\n
|
||||
|
||||
Please provide a summarized context from the history so that the question makes sense and can - with
|
||||
suitable extra information - be answered.
|
||||
|
||||
Please do not use more than three or four sentences.
|
||||
|
||||
History summary:
|
||||
"""
|
||||
|
||||
# INITIAL PHASE
|
||||
# Sub-question
|
||||
# Intentionally left a copy in case we want to modify this one differently
|
||||
INITIAL_QUESTION_DECOMPOSITION_PROMPT = (
|
||||
"Decompose the initial user question into no more than 3 appropriate sub-questions that help to answer the"
|
||||
" original question. The purpose for this decomposition may be to:\n"
|
||||
" 1) isolate individual entities (i.e., 'compare sales of company A and company B' ->"
|
||||
" ['what are sales for company A', 'what are sales for company B'])\n"
|
||||
" 2) clarify or disambiguate ambiguous terms (i.e., 'what is our success with company A' ->"
|
||||
" ['what are our sales with company A','what is our market share with company A',"
|
||||
" 'is company A a reference customer for us', etc.])\n"
|
||||
" 3) if a term or a metric is essentially clear, but it could relate to various components of an entity and you"
|
||||
" are generally familiar with the entity, then you can decompose the question into sub-questions that are more"
|
||||
" specific to components (i.e., 'what do we do to improve scalability of product X', 'what do we to to improve"
|
||||
" scalability of product X', 'what do we do to improve stability of product X', ...])\n"
|
||||
" 4) research an area that could really help to answer the question.\n\n"
|
||||
"Here is the initial question to decompose:\n"
|
||||
f"{SEPARATOR_LINE}\n"
|
||||
"{question}\n"
|
||||
f"{SEPARATOR_LINE}\n\n"
|
||||
"{history}\n\n"
|
||||
"Please formulate your answer as a newline-separated list of questions like so:\n"
|
||||
" <sub-question>\n"
|
||||
" <sub-question>\n"
|
||||
" <sub-question>\n"
|
||||
" ...\n\n"
|
||||
"Answer:"
|
||||
).strip()
|
||||
|
||||
|
||||
## Sub-question
|
||||
INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH = (
|
||||
"Decompose the initial user question into no more than 3 appropriate sub-questions that help to answer the"
|
||||
" original question. The purpose for this decomposition may be to:\n"
|
||||
" 1) isolate individual entities (i.e., 'compare sales of company A and company B' ->"
|
||||
" ['what are sales for company A', 'what are sales for company B'])\n"
|
||||
" 2) clarify or disambiguate ambiguous terms (i.e., 'what is our success with company A' ->"
|
||||
" ['what are our sales with company A','what is our market share with company A',"
|
||||
" 'is company A a reference customer for us', etc.])\n"
|
||||
" 3) if a term or a metric is essentially clear, but it could relate to various components of an entity and you"
|
||||
" are generally familiar with the entity, then you can decompose the question into sub-questions that are more"
|
||||
" specific to components (i.e., 'what do we do to improve scalability of product X', 'what do we to to improve"
|
||||
" scalability of product X', 'what do we do to improve stability of product X', ...])\n"
|
||||
" 4) research an area that could really help to answer the question.\n\n"
|
||||
"To give you some context, you will see below also some documents that may relate to the question. Please only"
|
||||
" use this information to learn what the question is approximately asking about, but do not focus on the details"
|
||||
" to construct the sub-questions! Also, some of the entities, relationships and terms that are in the dataset may"
|
||||
" not be in these few documents, so DO NOT focussed too much on the documents when constructing the sub-questions!"
|
||||
" Decomposition and disambiguations are most important!\n\n"
|
||||
"Here are the sample docs to give you some context:\n"
|
||||
f"{SEPARATOR_LINE}\n"
|
||||
"{sample_doc_str}\n"
|
||||
f"{SEPARATOR_LINE}\n\n"
|
||||
"And here is the initial question to decompose:\n"
|
||||
f"{SEPARATOR_LINE}\n"
|
||||
"{question}\n"
|
||||
f"{SEPARATOR_LINE}\n\n"
|
||||
"{history}\n\n"
|
||||
"Please formulate your answer as a newline-separated list of questions like so:\n"
|
||||
" <sub-question>\n"
|
||||
" <sub-question>\n"
|
||||
" <sub-question>\n"
|
||||
" ...\n\n"
|
||||
"Answer:"
|
||||
).strip()
|
||||
|
||||
INITIAL_QUESTION_DECOMPOSITION_PROMPT = """
|
||||
If you think it is helpful, please decompose an initial user question into no more than 3 appropriate sub-questions that help to
|
||||
answer the original question. The purpose for this decomposition may be to
|
||||
1) isolate individual entities (i.e., 'compare sales of company A and company B' -> ['what are sales for company A',
|
||||
'what are sales for company B')]
|
||||
2) clarify or disambiguate ambiguous terms (i.e., 'what is our success with company A' -> ['what are our sales with company A',
|
||||
'what is our market share with company A', 'is company A a reference customer for us', etc.])
|
||||
3) if a term or a metric is essentially clear, but it could relate to various components of an entity and you are generally
|
||||
familiar with the entity, then you can decompose the question into sub-questions that are more specific to components
|
||||
(i.e., 'what do we do to improve scalability of product X', 'what do we to to improve scalability of product X',
|
||||
'what do we do to improve stability of product X', ...])
|
||||
4) research an area that could really help to answer the question. (But clarifications or disambiguations are more important.)
|
||||
|
||||
If you think that a decomposition is not needed or helpful, please just return an empty string. That is ok too.
|
||||
|
||||
Here is the initial question:
|
||||
\n-------\n
|
||||
{question}
|
||||
\n-------\n
|
||||
{history}
|
||||
|
||||
Please formulate your answer as a newline-separated list of questions like so:
|
||||
<sub-question>
|
||||
<sub-question>
|
||||
<sub-question>
|
||||
|
||||
Answer:"""
|
||||
|
||||
|
||||
INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH = """
|
||||
If you think it is helpful, please decompose an initial user question into no more than 3 appropriate sub-questions that help to
|
||||
answer the original question. The purpose for this decomposition may be to
|
||||
1) isolate individual entities (i.e., 'compare sales of company A and company B' -> ['what are sales for company A',
|
||||
'what are sales for company B')]
|
||||
2) clarify or disambiguate ambiguous terms (i.e., 'what is our success with company A' -> ['what are our sales with company A',
|
||||
'what is our market share with company A', 'is company A a reference customer for us', etc.])
|
||||
3) if a term or a metric is essentially clear, but it could relate to various components of an entity and you are generally
|
||||
familiar with the entity, then you can decompose the question into sub-questions that are more specific to components
|
||||
(i.e., 'what do we do to improve scalability of product X', 'what do we to to improve scalability of product X',
|
||||
'what do we do to improve stability of product X', ...])
|
||||
4) research an area that could really help to answer the question. (But clarifications or disambiguations are more important.)
|
||||
|
||||
Here are some other rules:
|
||||
|
||||
1) To give you some context, you will see below also some documents that relate to the question. Please only
|
||||
use this information to learn what the question is approximately asking about, but do not focus on the details
|
||||
to construct the sub-questions! Also, some of the entities, relationships and terms that are in the dataset may
|
||||
not be in these few documents, so DO NOT focussed too much on the documents when constructing the sub-questions! Decomposition and
|
||||
disambiguations are most important!
|
||||
2) If you think that a decomposition is not needed or helpful, please just return an empty string. That is very much ok too.
|
||||
|
||||
Here are the sample docs to give you some context:
|
||||
\n-------\n
|
||||
{sample_doc_str}
|
||||
\n-------\n
|
||||
|
||||
And here is the initial question that you should think about decomposing:
|
||||
\n-------\n
|
||||
{question}
|
||||
\n-------\n
|
||||
|
||||
{history}
|
||||
|
||||
Please formulate your answer as a newline-separated list of questions like so:
|
||||
<sub-question>
|
||||
<sub-question>
|
||||
<sub-question>
|
||||
...
|
||||
|
||||
Answer:"""
|
||||
|
||||
# Retrieval
|
||||
QUERY_REWRITING_PROMPT = (
|
||||
"Please convert the initial user question into a 2-3 more appropriate short and pointed search queries for"
|
||||
" retrieval from a document store. Particularly, try to think about resolving ambiguities and make the search"
|
||||
" queries more specific, enabling the system to search more broadly.\n"
|
||||
"Also, try to make the search queries not redundant, i.e. not too similar!\n\n"
|
||||
"Here is the initial question:\n"
|
||||
f"{SEPARATOR_LINE}\n"
|
||||
"{question}\n"
|
||||
f"{SEPARATOR_LINE}\n\n"
|
||||
"Formulate the queries separated by newlines (Do not say 'Query 1: ...', just write the querytext) as follows:\n"
|
||||
"<query 1>\n"
|
||||
"<query 2>\n"
|
||||
"...\n\n"
|
||||
"Queries:"
|
||||
)
|
||||
|
||||
QUERY_REWRITING_PROMPT = """ \n
|
||||
Please convert an initial user question into a 2-3 more appropriate short and pointed search queries for retrivel from a
|
||||
document store. Particularly, try to think about resolving ambiguities and make the search queries more specific,
|
||||
enabling the system to search more broadly.
|
||||
Also, try to make the search queries not redundant, i.e. not too similar! \n\n
|
||||
Here is the initial question:
|
||||
\n-------\n
|
||||
{question}
|
||||
\n-------\n
|
||||
Formulate the queries separated by newlines (Do not say 'Query 1: ...', just write the querytext) as follows:
|
||||
<query 1>
|
||||
<query 2>
|
||||
...
|
||||
queries: """
|
||||
|
||||
DOCUMENT_VERIFICATION_PROMPT = """
|
||||
You are supposed to judge whether a document text contains data or information that is potentially relevant
|
||||
for a question. It does not have to be fully relevant, but check whether it has some information that
|
||||
would help - possibly in conjunction with other documents - to address the question.
|
||||
DOCUMENT_VERIFICATION_PROMPT = (
|
||||
"Determine whether the following document text contains data or information that is potentially relevant "
|
||||
"for a question. It does not have to be fully relevant, but check whether it has some information that "
|
||||
"would help - possibly in conjunction with other documents - to address the question.\n\n"
|
||||
"Be careful that you do not use a document where you are not sure whether the text applies to the objects "
|
||||
"or entities that are relevant for the question. For example, a book about chess could have long passage "
|
||||
"discussing the psychology of chess without - within the passage - mentioning chess. If now a question "
|
||||
"is asked about the psychology of football, one could be tempted to use the document as it does discuss "
|
||||
"psychology in sports. However, it is NOT about football and should not be deemed relevant. Please "
|
||||
"consider this logic.\n\n"
|
||||
"DOCUMENT TEXT:\n"
|
||||
f"{SEPARATOR_LINE}\n"
|
||||
"{document_content}\n"
|
||||
f"{SEPARATOR_LINE}\n\n"
|
||||
"Do you think that this document text is useful and relevant to answer the following question?\n\n"
|
||||
"QUESTION:\n"
|
||||
f"{SEPARATOR_LINE}\n"
|
||||
"{question}\n"
|
||||
f"{SEPARATOR_LINE}\n\n"
|
||||
"Please answer with exactly and only a 'yes' or 'no':\n\n"
|
||||
"Answer:"
|
||||
).strip()
|
||||
|
||||
Be careful that you do not use a document where you are not sure whether the text applies to the objects
|
||||
or entities that are relevant for the question. For example, a book about chess could have long passage
|
||||
discussing the psychology of chess without - within the passage - mentioning chess. If now a question
|
||||
is asked about the psychology of football, one could be tempted to use the document as it does discuss
|
||||
psychology in sports. However, it is NOT about football and should not be deemed relevant. Please
|
||||
consider this logic.
|
||||
|
||||
Here is a document text that you can take as a fact:
|
||||
|
||||
DOCUMENT INFORMATION:
|
||||
\n-------\n
|
||||
{document_content}
|
||||
\n-------\n
|
||||
|
||||
Do you think that this document text is useful and relevant to answer the following question?
|
||||
|
||||
QUESTION:
|
||||
\n-------\n
|
||||
{question}
|
||||
\n-------\n
|
||||
|
||||
Please answer with 'yes' or 'no':
|
||||
|
||||
Answer:
|
||||
|
||||
"""
|
||||
|
||||
# Sub-Question Anser Generation
|
||||
|
||||
SUB_QUESTION_RAG_PROMPT = (
|
||||
""" \n
|
||||
{date_prompt}
|
||||
Use the context provided below - and only the
|
||||
provided context - to answer the given question. (Note that the answer is in service of answering a broader
|
||||
question, given below as 'motivation'.)
|
||||
|
||||
Again, only use the provided context and do not use your internal knowledge! If you cannot answer the
|
||||
question based on the context, say """
|
||||
+ f'"{UNKNOWN_ANSWER}"'
|
||||
+ """. It is a matter of life and death that you do NOT
|
||||
use your internal knowledge, just the provided information!
|
||||
|
||||
Make sure that you keep all relevant information, specifically as it concerns to the ultimate goal.
|
||||
(But keep other details as well.)
|
||||
|
||||
It is critical that you provide inline citations in the format [[D1]](), [[D2]](), [[D3]](), etc!
|
||||
It is important that the citation is close to the information it supports.
|
||||
Proper citations are very important to the user!\n\n\n
|
||||
|
||||
For your general information, here is the ultimate motivation:
|
||||
\n--\n
|
||||
{original_question}
|
||||
\n--\n
|
||||
\n
|
||||
And here is the actual question I want you to answer based on the context above (with the motivation in mind):
|
||||
\n--\n {question} \n--\n
|
||||
|
||||
Here is the context:
|
||||
\n\n\n--\n {context} \n--\n
|
||||
Please keep your answer brief and concise, and focus on facts and data.
|
||||
|
||||
Answer:
|
||||
"""
|
||||
)
|
||||
"Use the context provided below - and only the provided context - to answer the given question. "
|
||||
"(Note that the answer is in service of answering a broader question, given below as 'motivation'.)\n\n"
|
||||
"Again, only use the provided context and do not use your internal knowledge! If you cannot answer the "
|
||||
f'question based on the context, say "{UNKNOWN_ANSWER}". It is a matter of life and death that you do NOT '
|
||||
"use your internal knowledge, just the provided information!\n\n"
|
||||
"Make sure that you keep all relevant information, specifically as it concerns to the ultimate goal. "
|
||||
"(But keep other details as well.)\n\n"
|
||||
"It is critical that you provide inline citations in the format [[D1]](), [[D2]](), [[D3]](), etc! "
|
||||
"It is important that the citation is close to the information it supports. "
|
||||
"Proper citations are very important to the user!\n\n"
|
||||
"For your general information, here is the ultimate motivation:\n"
|
||||
f"{SEPARATOR_LINE}\n"
|
||||
"{original_question}\n"
|
||||
f"{SEPARATOR_LINE}\n\n"
|
||||
"And here is the actual question I want you to answer based on the context above (with the motivation in mind):\n"
|
||||
f"{SEPARATOR_LINE}\n"
|
||||
"{question}\n"
|
||||
f"{SEPARATOR_LINE}\n\n"
|
||||
"Here is the context:\n"
|
||||
f"{SEPARATOR_LINE}\n"
|
||||
"{context}\n"
|
||||
f"{SEPARATOR_LINE}\n\n"
|
||||
"Please keep your answer brief and concise, and focus on facts and data.\n\n"
|
||||
"Answer:"
|
||||
).strip()
|
||||
|
||||
|
||||
SUB_ANSWER_CHECK_PROMPT = (
|
||||
@ -301,8 +275,8 @@ Does the suggested answer address the question? Please answer with """
|
||||
+ f'"{SUB_CHECK_YES}" or "{SUB_CHECK_NO}".'
|
||||
)
|
||||
|
||||
# Initial Answer Generation
|
||||
|
||||
# Initial Answer Generation
|
||||
INITIAL_ANSWER_PROMPT_W_SUB_QUESTIONS = (
|
||||
""" \n
|
||||
{persona_specification}
|
||||
Loading…
x
Reference in New Issue
Block a user