diff --git a/backend/danswer/prompts/llm_chunk_filter.py b/backend/danswer/prompts/llm_chunk_filter.py index fe0b8d398d..9f55683691 100644 --- a/backend/danswer/prompts/llm_chunk_filter.py +++ b/backend/danswer/prompts/llm_chunk_filter.py @@ -11,7 +11,13 @@ it must contain information that is USEFUL for answering the query. If the section contains ANY useful information, that is good enough, \ it does not need to fully answer the every part of the user query. + +Title: {{title}} + +{{optional_metadata}} + Reference Section: + ``` {{chunk_text}} ``` diff --git a/backend/danswer/search/postprocessing/postprocessing.py b/backend/danswer/search/postprocessing/postprocessing.py index 5333ca3fd2..fdc2e5f4c3 100644 --- a/backend/danswer/search/postprocessing/postprocessing.py +++ b/backend/danswer/search/postprocessing/postprocessing.py @@ -212,11 +212,17 @@ def filter_sections( section.center_chunk.content if use_chunk else section.combined_content for section in sections_to_filter ] + metadata_list = [section.center_chunk.metadata for section in sections_to_filter] + titles = [ + section.center_chunk.semantic_identifier for section in sections_to_filter + ] llm_chunk_selection = llm_batch_eval_sections( query=query.query, section_contents=contents, llm=llm, + titles=titles, + metadata_list=metadata_list, ) return [ diff --git a/backend/danswer/secondary_llm_flows/chunk_usefulness.py b/backend/danswer/secondary_llm_flows/chunk_usefulness.py index 7dd0b93ec2..74e16311a4 100644 --- a/backend/danswer/secondary_llm_flows/chunk_usefulness.py +++ b/backend/danswer/secondary_llm_flows/chunk_usefulness.py @@ -12,17 +12,33 @@ from danswer.utils.threadpool_concurrency import run_functions_tuples_in_paralle logger = setup_logger() -def llm_eval_section(query: str, section_content: str, llm: LLM) -> bool: +def llm_eval_section( + query: str, + section_content: str, + llm: LLM, + title: str, + metadata: dict[str, str | list[str]], +) -> bool: + def _get_metadata_str(metadata: dict[str, str | list[str]]) -> str: + metadata_str = "\n\nMetadata:\n" + for key, value in metadata.items(): + value_str = ", ".join(value) if isinstance(value, list) else value + metadata_str += f"{key} - {value_str}\n" + return metadata_str + "\nContent:" + def _get_usefulness_messages() -> list[dict[str, str]]: + metadata_str = _get_metadata_str(metadata) if metadata else "" messages = [ { "role": "user", "content": SECTION_FILTER_PROMPT.format( - chunk_text=section_content, user_query=query + title=title, + chunk_text=section_content, + user_query=query, + optional_metadata=metadata_str, ), }, ] - return messages def _extract_usefulness(model_output: str) -> bool: @@ -34,9 +50,6 @@ def llm_eval_section(query: str, section_content: str, llm: LLM) -> bool: messages = _get_usefulness_messages() filled_llm_prompt = dict_based_prompt_to_langchain_prompt(messages) - # When running in a batch, it takes as long as the longest thread - # And when running a large batch, one may fail and take the whole timeout - # instead cap it to 5 seconds model_output = message_to_string(llm.invoke(filled_llm_prompt)) logger.debug(model_output) @@ -44,7 +57,12 @@ def llm_eval_section(query: str, section_content: str, llm: LLM) -> bool: def llm_batch_eval_sections( - query: str, section_contents: list[str], llm: LLM, use_threads: bool = True + query: str, + section_contents: list[str], + llm: LLM, + titles: list[str], + metadata_list: list[dict[str, str | list[str]]], + use_threads: bool = True, ) -> list[bool]: if DISABLE_LLM_DOC_RELEVANCE: raise RuntimeError( @@ -54,8 +72,10 @@ def llm_batch_eval_sections( if use_threads: functions_with_args: list[tuple[Callable, tuple]] = [ - (llm_eval_section, (query, section_content, llm)) - for section_content in section_contents + (llm_eval_section, (query, section_content, llm, title, metadata)) + for section_content, title, metadata in zip( + section_contents, titles, metadata_list + ) ] logger.debug( @@ -70,6 +90,8 @@ def llm_batch_eval_sections( else: return [ - llm_eval_section(query, section_content, llm) - for section_content in section_contents + llm_eval_section(query, section_content, llm, title, metadata) + for section_content, title, metadata in zip( + section_contents, titles, metadata_list + ) ] diff --git a/web/src/components/search/SearchResultsDisplay.tsx b/web/src/components/search/SearchResultsDisplay.tsx index 2953bb900b..4dea7478cf 100644 --- a/web/src/components/search/SearchResultsDisplay.tsx +++ b/web/src/components/search/SearchResultsDisplay.tsx @@ -14,6 +14,7 @@ import { useContext, useEffect, useState } from "react"; import { Tooltip } from "../tooltip/Tooltip"; import KeyboardSymbol from "@/lib/browserUtilities"; import { SettingsContext } from "../settings/SettingsProvider"; +import { DISABLE_LLM_DOC_RELEVANCE } from "@/lib/constants"; const getSelectedDocumentIds = ( documents: SearchDanswerDocument[], @@ -140,6 +141,7 @@ export const SearchResultsDisplay = ({ showAll || (searchResponse && searchResponse.additional_relevance && + searchResponse.additional_relevance[doc.document_id] && searchResponse.additional_relevance[doc.document_id].relevant) || doc.is_relevant ); @@ -175,46 +177,48 @@ export const SearchResultsDisplay = ({

Results

- {(contentEnriched || searchResponse.additional_relevance) && ( - -
- - - )} +
+ + {!sweep + ? agenticResults + ? "Show All" + : "Focus" + : agenticResults + ? "Focus" + : "Show All"} + + + {!sweep ? ( + + ) : ( + + )} + +
+ + + )}
{agenticResults && diff --git a/web/src/components/search/SearchSection.tsx b/web/src/components/search/SearchSection.tsx index ac9ee074cc..c2cba40b3b 100644 --- a/web/src/components/search/SearchSection.tsx +++ b/web/src/components/search/SearchSection.tsx @@ -170,11 +170,12 @@ export const SearchSection = ({ if (existingSearchIdRaw == null) { return; } - function extractFirstUserMessage( - chatSession: SearchSession + function extractFirstMessageByType( + chatSession: SearchSession, + messageType: "user" | "assistant" ): string | null { const userMessage = chatSession?.messages.find( - (msg) => msg.message_type === "user" + (msg) => msg.message_type === messageType ); return userMessage ? userMessage.message : null; } @@ -184,14 +185,18 @@ export const SearchSection = ({ `/api/query/search-session/${existingSearchessionId}` ); const searchSession = (await response.json()) as SearchSession; - const message = extractFirstUserMessage(searchSession); + const userMessage = extractFirstMessageByType(searchSession, "user"); + const assistantMessage = extractFirstMessageByType( + searchSession, + "assistant" + ); - if (message) { - setQuery(message); + if (userMessage) { + setQuery(userMessage); const danswerDocs: SearchResponse = { documents: searchSession.documents, suggestedSearchType: null, - answer: null, + answer: assistantMessage || "Search response not found", quotes: null, selectedDocIndices: null, error: null,