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Add answers to search (#2020)

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pablodanswer 2024-08-04 23:02:55 -07:00 committed by GitHub
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47 changed files with 806 additions and 445 deletions
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2
.vscode/env_template.txt vendored
View File

@ -15,7 +15,7 @@ LOG_LEVEL=debug
# This passes top N results to LLM an additional time for reranking prior to answer generation
# This step is quite heavy on token usage so we disable it for dev generally
DISABLE_LLM_CHUNK_FILTER=True
DISABLE_LLM_DOC_RELEVANCE=True
# Useful if you want to toggle auth on/off (google_oauth/OIDC specifically)

4
backend/alembic/versions/43cbbb3f5e6a_rename_index_origin_to_index_recursively.py
View File

@ -10,8 +10,8 @@ from alembic import op
# revision identifiers, used by Alembic.
revision = "1d6ad76d1f37"
down_revision = "e1392f05e840"
branch_labels = None
depends_on = None
branch_labels: None = None
depends_on: None = None
def upgrade() -> None:

19
backend/danswer/chat/models.py
View File

@ -46,15 +46,22 @@ class LLMRelevanceFilterResponse(BaseModel):
relevant_chunk_indices: list[int]
class RelevanceChunk(BaseModel):
# TODO make this document level. Also slight misnomer here as this is actually
# done at the section level currently rather than the chunk
relevant: bool | None = None
class RelevanceAnalysis(BaseModel):
relevant: bool
content: str | None = None
class LLMRelevanceSummaryResponse(BaseModel):
relevance_summaries: dict[str, RelevanceChunk]
class SectionRelevancePiece(RelevanceAnalysis):
"""LLM analysis mapped to an Inference Section"""
document_id: str
chunk_id: int # ID of the center chunk for a given inference section
class DocumentRelevance(BaseModel):
"""Contains all relevance information for a given search"""
relevance_summaries: dict[str, RelevanceAnalysis]
class DanswerAnswerPiece(BaseModel):

2
backend/danswer/chat/personas.yaml
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@ -17,7 +17,7 @@ personas:
num_chunks: 10
# Enable/Disable usage of the LLM chunk filter feature whereby each chunk is passed to the LLM to determine
# if the chunk is useful or not towards the latest user query
# This feature can be overriden for all personas via DISABLE_LLM_CHUNK_FILTER env variable
# This feature can be overriden for all personas via DISABLE_LLM_DOC_RELEVANCE env variable
llm_relevance_filter: true
# Enable/Disable usage of the LLM to extract query time filters including source type and time range filters
llm_filter_extraction: true

33
backend/danswer/chat/process_message.py
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@ -52,6 +52,7 @@ from danswer.llm.factory import get_llms_for_persona
from danswer.llm.factory import get_main_llm_from_tuple
from danswer.llm.interfaces import LLMConfig
from danswer.natural_language_processing.utils import get_tokenizer
from danswer.search.enums import LLMEvaluationType
from danswer.search.enums import OptionalSearchSetting
from danswer.search.enums import QueryFlow
from danswer.search.enums import SearchType
@ -60,6 +61,7 @@ from danswer.search.retrieval.search_runner import inference_sections_from_ids
from danswer.search.utils import chunks_or_sections_to_search_docs
from danswer.search.utils import dedupe_documents
from danswer.search.utils import drop_llm_indices
from danswer.search.utils import relevant_documents_to_indices
from danswer.server.query_and_chat.models import ChatMessageDetail
from danswer.server.query_and_chat.models import CreateChatMessageRequest
from danswer.server.utils import get_json_line
@ -501,6 +503,9 @@ def stream_chat_message_objects(
chunks_above=new_msg_req.chunks_above,
chunks_below=new_msg_req.chunks_below,
full_doc=new_msg_req.full_doc,
evaluation_type=LLMEvaluationType.BASIC
if persona.llm_relevance_filter
else LLMEvaluationType.SKIP,
)
tool_dict[db_tool_model.id] = [search_tool]
elif tool_cls.__name__ == ImageGenerationTool.__name__:
@ -629,18 +634,28 @@ def stream_chat_message_objects(
)
yield qa_docs_response
elif packet.id == SECTION_RELEVANCE_LIST_ID:
chunk_indices = packet.response
relevance_sections = packet.response
if reference_db_search_docs is not None and dropped_indices:
chunk_indices = drop_llm_indices(
llm_indices=chunk_indices,
search_docs=reference_db_search_docs,
dropped_indices=dropped_indices,
if reference_db_search_docs is not None:
llm_indices = relevant_documents_to_indices(
relevance_sections=relevance_sections,
search_docs=[
translate_db_search_doc_to_server_search_doc(doc)
for doc in reference_db_search_docs
],
)
if dropped_indices:
llm_indices = drop_llm_indices(
llm_indices=llm_indices,
search_docs=reference_db_search_docs,
dropped_indices=dropped_indices,
)
yield LLMRelevanceFilterResponse(
relevant_chunk_indices=llm_indices
)
yield LLMRelevanceFilterResponse(
relevant_chunk_indices=chunk_indices
)
elif packet.id == IMAGE_GENERATION_RESPONSE_ID:
img_generation_response = cast(
list[ImageGenerationResponse], packet.response

16
backend/danswer/configs/chat_configs.py
View File

@ -33,11 +33,6 @@ DISABLE_LLM_QUERY_ANSWERABILITY = QA_PROMPT_OVERRIDE == "weak"
# Note this is not in any of the deployment configs yet
CONTEXT_CHUNKS_ABOVE = int(os.environ.get("CONTEXT_CHUNKS_ABOVE") or 0)
CONTEXT_CHUNKS_BELOW = int(os.environ.get("CONTEXT_CHUNKS_BELOW") or 0)
# Whether the LLM should evaluate all of the document chunks passed in for usefulness
# in relation to the user query
DISABLE_LLM_CHUNK_FILTER = (
os.environ.get("DISABLE_LLM_CHUNK_FILTER", "").lower() == "true"
)
# Whether the LLM should be used to decide if a search would help given the chat history
DISABLE_LLM_CHOOSE_SEARCH = (
os.environ.get("DISABLE_LLM_CHOOSE_SEARCH", "").lower() == "true"
@ -64,6 +59,7 @@ HYBRID_ALPHA = max(0, min(1, float(os.environ.get("HYBRID_ALPHA") or 0.62)))
TITLE_CONTENT_RATIO = max(
0, min(1, float(os.environ.get("TITLE_CONTENT_RATIO") or 0.20))
)
# A list of languages passed to the LLM to rephase the query
# For example "English,French,Spanish", be sure to use the "," separator
MULTILINGUAL_QUERY_EXPANSION = os.environ.get("MULTILINGUAL_QUERY_EXPANSION") or None
@ -76,16 +72,16 @@ LANGUAGE_CHAT_NAMING_HINT = (
or "The name of the conversation must be in the same language as the user query."
)
# Agentic search takes significantly more tokens and therefore has much higher cost.
# This configuration allows users to get a search-only experience with instant results
# and no involvement from the LLM.
# Additionally, some LLM providers have strict rate limits which may prohibit
# sending many API requests at once (as is done in agentic search).
DISABLE_AGENTIC_SEARCH = (
os.environ.get("DISABLE_AGENTIC_SEARCH") or "false"
).lower() == "true"
# Whether the LLM should evaluate all of the document chunks passed in for usefulness
# in relation to the user query
DISABLE_LLM_DOC_RELEVANCE = (
os.environ.get("DISABLE_LLM_DOC_RELEVANCE", "").lower() == "true"
)
# Stops streaming answers back to the UI if this pattern is seen:
STOP_STREAM_PAT = os.environ.get("STOP_STREAM_PAT") or None

6
backend/danswer/db/chat.py
View File

@ -16,7 +16,7 @@ from sqlalchemy.orm import joinedload
from sqlalchemy.orm import Session
from danswer.auth.schemas import UserRole
from danswer.chat.models import LLMRelevanceSummaryResponse
from danswer.chat.models import DocumentRelevance
from danswer.configs.chat_configs import HARD_DELETE_CHATS
from danswer.configs.constants import MessageType
from danswer.db.models import ChatMessage
@ -541,11 +541,11 @@ def get_doc_query_identifiers_from_model(
def update_search_docs_table_with_relevance(
db_session: Session,
reference_db_search_docs: list[SearchDoc],
relevance_summary: LLMRelevanceSummaryResponse,
relevance_summary: DocumentRelevance,
) -> None:
for search_doc in reference_db_search_docs:
relevance_data = relevance_summary.relevance_summaries.get(
f"{search_doc.document_id}-{search_doc.chunk_ind}"
search_doc.document_id
)
if relevance_data is not None:
db_session.execute(

1
backend/danswer/llm/answering/answer.py
View File

@ -483,6 +483,7 @@ class Answer:
]
elif message.id == FINAL_CONTEXT_DOCUMENTS:
final_context_docs = cast(list[LlmDoc], message.response)
elif (
message.id == SEARCH_DOC_CONTENT_ID
and not self._return_contexts

19
backend/danswer/llm/answering/prune_and_merge.py
View File

@ -28,6 +28,9 @@ logger = setup_logger()
T = TypeVar("T", bound=LlmDoc | InferenceChunk | InferenceSection)
_METADATA_TOKEN_ESTIMATE = 75
# Title and additional tokens as part of the tool message json
# this is only used to log a warning so we can be more forgiving with the buffer
_OVERCOUNT_ESTIMATE = 256
class PruningError(Exception):
@ -179,10 +182,18 @@ def _apply_pruning(
and section_token_count
> DOC_EMBEDDING_CONTEXT_SIZE + _METADATA_TOKEN_ESTIMATE
):
logger.warning(
"Found more tokens in Section than expected, "
"likely mismatch between embedding and LLM tokenizers. Trimming content..."
)
if (
section_token_count
> DOC_EMBEDDING_CONTEXT_SIZE
+ _METADATA_TOKEN_ESTIMATE
+ _OVERCOUNT_ESTIMATE
):
# If the section is just a little bit over, it is likely due to the additional tool message tokens
# no need to record this, the content will be trimmed just in case
logger.info(
"Found more tokens in Section than expected, "
"likely mismatch between embedding and LLM tokenizers. Trimming content..."
)
section.combined_content = tokenizer_trim_content(
content=section.combined_content,
desired_length=DOC_EMBEDDING_CONTEXT_SIZE,

38
backend/danswer/one_shot_answer/answer_question.py
View File

@ -9,10 +9,12 @@ from danswer.chat.models import CitationInfo
from danswer.chat.models import DanswerAnswerPiece
from danswer.chat.models import DanswerContexts
from danswer.chat.models import DanswerQuotes
from danswer.chat.models import DocumentRelevance
from danswer.chat.models import LLMRelevanceFilterResponse
from danswer.chat.models import LLMRelevanceSummaryResponse
from danswer.chat.models import QADocsResponse
from danswer.chat.models import RelevanceAnalysis
from danswer.chat.models import StreamingError
from danswer.configs.chat_configs import DISABLE_LLM_DOC_RELEVANCE
from danswer.configs.chat_configs import MAX_CHUNKS_FED_TO_CHAT
from danswer.configs.chat_configs import QA_TIMEOUT
from danswer.configs.constants import MessageType
@ -39,18 +41,17 @@ from danswer.one_shot_answer.models import DirectQARequest
from danswer.one_shot_answer.models import OneShotQAResponse
from danswer.one_shot_answer.models import QueryRephrase
from danswer.one_shot_answer.qa_utils import combine_message_thread
from danswer.search.enums import LLMEvaluationType
from danswer.search.models import RerankMetricsContainer
from danswer.search.models import RetrievalMetricsContainer
from danswer.search.utils import chunks_or_sections_to_search_docs
from danswer.search.utils import dedupe_documents
from danswer.search.utils import drop_llm_indices
from danswer.secondary_llm_flows.answer_validation import get_answer_validity
from danswer.secondary_llm_flows.query_expansion import thread_based_query_rephrase
from danswer.server.query_and_chat.models import ChatMessageDetail
from danswer.server.utils import get_json_line
from danswer.tools.force import ForceUseTool
from danswer.tools.search.search_tool import SEARCH_DOC_CONTENT_ID
from danswer.tools.search.search_tool import SEARCH_EVALUATION_ID
from danswer.tools.search.search_tool import SEARCH_RESPONSE_SUMMARY_ID
from danswer.tools.search.search_tool import SearchResponseSummary
from danswer.tools.search.search_tool import SearchTool
@ -74,7 +75,7 @@ AnswerObjectIterator = Iterator[
| ChatMessageDetail
| CitationInfo
| ToolCallKickoff
| LLMRelevanceSummaryResponse
| DocumentRelevance
]
@ -180,10 +181,15 @@ def stream_answer_objects(
max_tokens=max_document_tokens,
use_sections=query_req.chunks_above > 0 or query_req.chunks_below > 0,
)
print("EVALLLUATINO")
print(query_req.evaluation_type)
search_tool = SearchTool(
db_session=db_session,
user=user,
evaluation_type=LLMEvaluationType.SKIP
if DISABLE_LLM_DOC_RELEVANCE
else query_req.evaluation_type,
persona=chat_session.persona,
retrieval_options=query_req.retrieval_options,
prompt_config=prompt_config,
@ -194,7 +200,6 @@ def stream_answer_objects(
chunks_below=query_req.chunks_below,
full_doc=query_req.full_doc,
bypass_acl=bypass_acl,
llm_doc_eval=query_req.llm_doc_eval,
)
answer_config = AnswerStyleConfig(
@ -223,7 +228,6 @@ def stream_answer_objects(
)
# won't be any ImageGenerationDisplay responses since that tool is never passed in
dropped_inds: list[int] = []
for packet in cast(AnswerObjectIterator, answer.processed_streamed_output):
# for one-shot flow, don't currently do anything with these
@ -266,20 +270,18 @@ def stream_answer_objects(
yield packet.response
elif packet.id == SECTION_RELEVANCE_LIST_ID:
chunk_indices = packet.response
document_based_response = {}
if reference_db_search_docs is not None and dropped_inds:
chunk_indices = drop_llm_indices(
llm_indices=chunk_indices,
search_docs=reference_db_search_docs,
dropped_indices=dropped_inds,
)
if packet.response is not None:
for evaluation in packet.response:
document_based_response[
evaluation.document_id
] = RelevanceAnalysis(
relevant=evaluation.relevant, content=evaluation.content
)
yield LLMRelevanceFilterResponse(relevant_chunk_indices=packet.response)
elif packet.id == SEARCH_EVALUATION_ID:
evaluation_response = LLMRelevanceSummaryResponse(
relevance_summaries=packet.response
evaluation_response = DocumentRelevance(
relevance_summaries=document_based_response
)
if reference_db_search_docs is not None:
update_search_docs_table_with_relevance(

10
backend/danswer/one_shot_answer/models.py
View File

@ -9,6 +9,7 @@ from danswer.chat.models import DanswerContexts
from danswer.chat.models import DanswerQuotes
from danswer.chat.models import QADocsResponse
from danswer.configs.constants import MessageType
from danswer.search.enums import LLMEvaluationType
from danswer.search.models import ChunkContext
from danswer.search.models import RetrievalDetails
@ -27,11 +28,11 @@ class DirectQARequest(ChunkContext):
messages: list[ThreadMessage]
prompt_id: int | None
persona_id: int
agentic: bool | None = None
retrieval_options: RetrievalDetails = Field(default_factory=RetrievalDetails)
# This is to forcibly skip (or run) the step, if None it uses the system defaults
skip_rerank: bool | None = None
skip_llm_chunk_filter: bool | None = None
evaluation_type: LLMEvaluationType = LLMEvaluationType.UNSPECIFIED
chain_of_thought: bool = False
return_contexts: bool = False
@ -40,11 +41,6 @@ class DirectQARequest(ChunkContext):
# will also disable Thread-based Rewording if specified
query_override: str | None = None
# This is to toggle agentic evaluation:
# 1. Evaluates whether each response is relevant or not
# 2. Provides a summary of the document's relevance in the resulsts
llm_doc_eval: bool = False
# If True, skips generative an AI response to the search query
skip_gen_ai_answer_generation: bool = False

3
backend/danswer/prompts/agentic_evaluation.py
View File

@ -28,7 +28,8 @@ True or False
"""
AGENTIC_SEARCH_USER_PROMPT = """
Document:
Document Title: {title}{optional_metadata}
```
{content}
```

21
backend/danswer/search/enums.py
View File

@ -4,13 +4,6 @@ search/models.py imports from db/models.py."""
from enum import Enum
class OptionalSearchSetting(str, Enum):
ALWAYS = "always"
NEVER = "never"
# Determine whether to run search based on history and latest query
AUTO = "auto"
class RecencyBiasSetting(str, Enum):
FAVOR_RECENT = "favor_recent" # 2x decay rate
BASE_DECAY = "base_decay"
@ -19,12 +12,26 @@ class RecencyBiasSetting(str, Enum):
AUTO = "auto"
class OptionalSearchSetting(str, Enum):
ALWAYS = "always"
NEVER = "never"
# Determine whether to run search based on history and latest query
AUTO = "auto"
class SearchType(str, Enum):
KEYWORD = "keyword"
SEMANTIC = "semantic"
HYBRID = "hybrid"
class LLMEvaluationType(str, Enum):
AGENTIC = "agentic" # applies agentic evaluation
BASIC = "basic" # applies boolean evaluation
SKIP = "skip" # skips evaluation
UNSPECIFIED = "unspecified" # reverts to default
class QueryFlow(str, Enum):
SEARCH = "search"
QUESTION_ANSWER = "question-answer"

6
backend/danswer/search/models.py
View File

@ -6,13 +6,13 @@ from pydantic import validator
from danswer.configs.chat_configs import CONTEXT_CHUNKS_ABOVE
from danswer.configs.chat_configs import CONTEXT_CHUNKS_BELOW
from danswer.configs.chat_configs import DISABLE_LLM_CHUNK_FILTER
from danswer.configs.chat_configs import HYBRID_ALPHA
from danswer.configs.chat_configs import NUM_RERANKED_RESULTS
from danswer.configs.chat_configs import NUM_RETURNED_HITS
from danswer.configs.constants import DocumentSource
from danswer.db.models import Persona
from danswer.indexing.models import BaseChunk
from danswer.search.enums import LLMEvaluationType
from danswer.search.enums import OptionalSearchSetting
from danswer.search.enums import SearchType
from shared_configs.configs import ENABLE_RERANKING_REAL_TIME_FLOW
@ -78,7 +78,7 @@ class SearchRequest(ChunkContext):
hybrid_alpha: float = HYBRID_ALPHA
# This is to forcibly skip (or run) the step, if None it uses the system defaults
skip_rerank: bool | None = None
skip_llm_chunk_filter: bool | None = None
evaluation_type: LLMEvaluationType = LLMEvaluationType.UNSPECIFIED
class Config:
arbitrary_types_allowed = True
@ -88,11 +88,11 @@ class SearchQuery(ChunkContext):
query: str
filters: IndexFilters
recency_bias_multiplier: float
evaluation_type: LLMEvaluationType
num_hits: int = NUM_RETURNED_HITS
offset: int = 0
search_type: SearchType = SearchType.HYBRID
skip_rerank: bool = not ENABLE_RERANKING_REAL_TIME_FLOW
skip_llm_chunk_filter: bool = DISABLE_LLM_CHUNK_FILTER
# Only used if not skip_rerank
num_rerank: int | None = NUM_RERANKED_RESULTS
# Only used if not skip_llm_chunk_filter

93
backend/danswer/search/pipeline.py
View File

@ -5,17 +5,19 @@ from typing import cast
from sqlalchemy.orm import Session
from danswer.chat.models import RelevanceChunk
from danswer.configs.chat_configs import DISABLE_AGENTIC_SEARCH
from danswer.chat.models import SectionRelevancePiece
from danswer.configs.chat_configs import DISABLE_LLM_DOC_RELEVANCE
from danswer.configs.chat_configs import MULTILINGUAL_QUERY_EXPANSION
from danswer.db.embedding_model import get_current_db_embedding_model
from danswer.db.models import User
from danswer.document_index.factory import get_default_document_index
from danswer.llm.answering.models import DocumentPruningConfig
from danswer.llm.answering.models import PromptConfig
from danswer.llm.answering.prune_and_merge import _merge_sections
from danswer.llm.answering.prune_and_merge import ChunkRange
from danswer.llm.answering.prune_and_merge import merge_chunk_intervals
from danswer.llm.interfaces import LLM
from danswer.search.enums import LLMEvaluationType
from danswer.search.enums import QueryFlow
from danswer.search.enums import SearchType
from danswer.search.models import IndexFilters
@ -29,6 +31,7 @@ from danswer.search.postprocessing.postprocessing import search_postprocessing
from danswer.search.preprocessing.preprocessing import retrieval_preprocessing
from danswer.search.retrieval.search_runner import retrieve_chunks
from danswer.search.utils import inference_section_from_chunks
from danswer.search.utils import relevant_sections_to_indices
from danswer.secondary_llm_flows.agentic_evaluation import evaluate_inference_section
from danswer.utils.logger import setup_logger
from danswer.utils.threadpool_concurrency import FunctionCall
@ -84,11 +87,13 @@ class SearchPipeline:
# Reranking and LLM section selection can be run together
# If only LLM selection is on, the reranked chunks are yielded immediatly
self._reranked_sections: list[InferenceSection] | None = None
self._relevant_section_indices: list[int] | None = None
self._final_context_sections: list[InferenceSection] | None = None
self._section_relevance: list[SectionRelevancePiece] | None = None
# Generates reranked chunks and LLM selections
self._postprocessing_generator: (
Iterator[list[InferenceSection] | list[int]] | None
Iterator[list[InferenceSection] | list[SectionRelevancePiece]] | None
) = None
"""Pre-processing"""
@ -332,44 +337,66 @@ class SearchPipeline:
return self._reranked_sections
@property
def relevant_section_indices(self) -> list[int]:
if self._relevant_section_indices is not None:
return self._relevant_section_indices
def final_context_sections(self) -> list[InferenceSection]:
if self._final_context_sections is not None:
return self._final_context_sections
self._relevant_section_indices = next(
cast(Iterator[list[int]], self._postprocessing_generator)
)
return self._relevant_section_indices
self._final_context_sections = _merge_sections(sections=self.reranked_sections)
return self._final_context_sections
@property
def relevance_summaries(self) -> dict[str, RelevanceChunk]:
if DISABLE_AGENTIC_SEARCH:
def section_relevance(self) -> list[SectionRelevancePiece] | None:
if self._section_relevance is not None:
return self._section_relevance
if (
self.search_query.evaluation_type == LLMEvaluationType.SKIP
or DISABLE_LLM_DOC_RELEVANCE
):
return None
if self.search_query.evaluation_type == LLMEvaluationType.UNSPECIFIED:
raise ValueError(
"Agentic saerch operation called while DISABLE_AGENTIC_SEARCH is toggled"
"Attempted to access section relevance scores on search query with evaluation type `UNSPECIFIED`."
+ "The search query evaluation type should have been specified."
)
if len(self.reranked_sections) == 0:
logger.warning(
"No sections found in agentic search evalution. Returning empty dict."
if self.search_query.evaluation_type == LLMEvaluationType.AGENTIC:
sections = self.final_context_sections
functions = [
FunctionCall(
evaluate_inference_section,
(section, self.search_query.query, self.llm),
)
for section in sections
]
results = run_functions_in_parallel(function_calls=functions)
self._section_relevance = list(results.values())
elif self.search_query.evaluation_type == LLMEvaluationType.BASIC:
if DISABLE_LLM_DOC_RELEVANCE:
raise ValueError(
"Basic search evaluation operation called while DISABLE_LLM_DOC_RELEVANCE is enabled."
)
self._section_relevance = next(
cast(
Iterator[list[SectionRelevancePiece]],
self._postprocessing_generator,
)
)
return {}
sections = self.reranked_sections
functions = [
FunctionCall(
evaluate_inference_section, (section, self.search_query.query, self.llm)
else:
# All other cases should have been handled above
raise ValueError(
f"Unexpected evaluation type: {self.search_query.evaluation_type}"
)
for section in sections
]
results = run_functions_in_parallel(function_calls=functions)
return {
next(iter(value)): value[next(iter(value))] for value in results.values()
}
return self._section_relevance
@property
def section_relevance_list(self) -> list[bool]:
return [
True if ind in self.relevant_section_indices else False
for ind in range(len(self.reranked_sections))
]
llm_indices = relevant_sections_to_indices(
relevance_sections=self.section_relevance,
inference_sections=self.final_context_sections,
)
return [ind in llm_indices for ind in range(len(self.final_context_sections))]

23
backend/danswer/search/postprocessing/postprocessing.py
View File

@ -4,6 +4,7 @@ from typing import cast
import numpy
from danswer.chat.models import SectionRelevancePiece
from danswer.configs.app_configs import BLURB_SIZE
from danswer.configs.constants import RETURN_SEPARATOR
from danswer.configs.model_configs import CROSS_ENCODER_RANGE_MAX
@ -15,6 +16,7 @@ from danswer.llm.interfaces import LLM
from danswer.natural_language_processing.search_nlp_models import (
CrossEncoderEnsembleModel,
)
from danswer.search.enums import LLMEvaluationType
from danswer.search.models import ChunkMetric
from danswer.search.models import InferenceChunk
from danswer.search.models import InferenceChunkUncleaned
@ -48,10 +50,6 @@ def should_rerank(query: SearchQuery) -> bool:
return query.search_type != SearchType.KEYWORD and not query.skip_rerank
def should_apply_llm_based_relevance_filter(query: SearchQuery) -> bool:
return not query.skip_llm_chunk_filter
def cleanup_chunks(chunks: list[InferenceChunkUncleaned]) -> list[InferenceChunk]:
def _remove_title(chunk: InferenceChunkUncleaned) -> str:
if not chunk.title or not chunk.content:
@ -233,7 +231,7 @@ def search_postprocessing(
retrieved_sections: list[InferenceSection],
llm: LLM,
rerank_metrics_callback: Callable[[RerankMetricsContainer], None] | None = None,
) -> Iterator[list[InferenceSection] | list[int]]:
) -> Iterator[list[InferenceSection] | list[SectionRelevancePiece]]:
post_processing_tasks: list[FunctionCall] = []
if not retrieved_sections:
@ -265,7 +263,10 @@ def search_postprocessing(
sections_yielded = True
llm_filter_task_id = None
if should_apply_llm_based_relevance_filter(search_query):
if search_query.evaluation_type in [
LLMEvaluationType.BASIC,
LLMEvaluationType.UNSPECIFIED,
]:
post_processing_tasks.append(
FunctionCall(
filter_sections,
@ -306,7 +307,11 @@ def search_postprocessing(
)
yield [
index
for index, section in enumerate(reranked_sections or retrieved_sections)
if section.center_chunk.unique_id in llm_selected_section_ids
SectionRelevancePiece(
document_id=section.center_chunk.document_id,
chunk_id=section.center_chunk.chunk_id,
relevant=section.center_chunk.unique_id in llm_selected_section_ids,
content="",
)
for section in (reranked_sections or retrieved_sections)
]

27
backend/danswer/search/preprocessing/preprocessing.py
View File

@ -1,11 +1,12 @@
from sqlalchemy.orm import Session
from danswer.configs.chat_configs import BASE_RECENCY_DECAY
from danswer.configs.chat_configs import DISABLE_LLM_CHUNK_FILTER
from danswer.configs.chat_configs import DISABLE_LLM_DOC_RELEVANCE
from danswer.configs.chat_configs import FAVOR_RECENT_DECAY_MULTIPLIER
from danswer.configs.chat_configs import NUM_RETURNED_HITS
from danswer.db.models import User
from danswer.llm.interfaces import LLM
from danswer.search.enums import LLMEvaluationType
from danswer.search.enums import QueryFlow
from danswer.search.enums import RecencyBiasSetting
from danswer.search.models import BaseFilters
@ -35,7 +36,6 @@ def retrieval_preprocessing(
db_session: Session,
bypass_acl: bool = False,
include_query_intent: bool = True,
disable_llm_chunk_filter: bool = DISABLE_LLM_CHUNK_FILTER,
base_recency_decay: float = BASE_RECENCY_DECAY,
favor_recent_decay_multiplier: float = FAVOR_RECENT_DECAY_MULTIPLIER,
) -> tuple[SearchQuery, SearchType | None, QueryFlow | None]:
@ -137,18 +137,23 @@ def retrieval_preprocessing(
access_control_list=user_acl_filters,
)
llm_chunk_filter = False
if search_request.skip_llm_chunk_filter is not None:
llm_chunk_filter = not search_request.skip_llm_chunk_filter
elif persona:
llm_chunk_filter = persona.llm_relevance_filter
llm_evaluation_type = LLMEvaluationType.BASIC
if search_request.evaluation_type is not LLMEvaluationType.UNSPECIFIED:
llm_evaluation_type = search_request.evaluation_type
if disable_llm_chunk_filter:
if llm_chunk_filter:
elif persona:
llm_evaluation_type = (
LLMEvaluationType.BASIC
if persona.llm_relevance_filter
else LLMEvaluationType.SKIP
)
if DISABLE_LLM_DOC_RELEVANCE:
if llm_evaluation_type:
logger.info(
"LLM chunk filtering would have run but has been globally disabled"
)
llm_chunk_filter = False
llm_evaluation_type = LLMEvaluationType.SKIP
skip_rerank = search_request.skip_rerank
if skip_rerank is None:
@ -176,7 +181,7 @@ def retrieval_preprocessing(
num_hits=limit if limit is not None else NUM_RETURNED_HITS,
offset=offset or 0,
skip_rerank=skip_rerank,
skip_llm_chunk_filter=not llm_chunk_filter,
evaluation_type=llm_evaluation_type,
chunks_above=search_request.chunks_above,
chunks_below=search_request.chunks_below,
full_doc=search_request.full_doc,

41
backend/danswer/search/utils.py
View File

@ -1,6 +1,7 @@
from collections.abc import Sequence
from typing import TypeVar
from danswer.chat.models import SectionRelevancePiece
from danswer.db.models import SearchDoc as DBSearchDoc
from danswer.search.models import InferenceChunk
from danswer.search.models import InferenceSection
@ -37,6 +38,46 @@ def dedupe_documents(items: list[T]) -> tuple[list[T], list[int]]:
return deduped_items, dropped_indices
def relevant_sections_to_indices(
relevance_sections: list[SectionRelevancePiece] | None,
inference_sections: list[InferenceSection],
) -> list[int]:
if relevance_sections is None:
return []
relevant_set = {
(chunk.document_id, chunk.chunk_id)
for chunk in relevance_sections
if chunk.relevant
}
relevant_indices = [
index
for index, section in enumerate(inference_sections)
if (section.center_chunk.document_id, section.center_chunk.chunk_id)
in relevant_set
]
return relevant_indices
def relevant_documents_to_indices(
relevance_sections: list[SectionRelevancePiece] | None, search_docs: list[SearchDoc]
) -> list[int]:
if relevance_sections is None:
return []
relevant_set = {
(chunk.document_id, chunk.chunk_id)
for chunk in relevance_sections
if chunk.relevant
}
return [
index
for index, section in enumerate(search_docs)
if (section.document_id, section.chunk_ind) in relevant_set
]
def drop_llm_indices(
llm_indices: list[int],
search_docs: Sequence[DBSearchDoc | SavedSearchDoc],

36
backend/danswer/secondary_llm_flows/agentic_evaluation.py
View File

@ -1,6 +1,6 @@
import re
from danswer.chat.models import RelevanceChunk
from danswer.chat.models import SectionRelevancePiece
from danswer.llm.interfaces import LLM
from danswer.llm.utils import dict_based_prompt_to_langchain_prompt
from danswer.llm.utils import message_to_string
@ -13,7 +13,7 @@ logger = setup_logger()
def _get_agent_eval_messages(
title: str, content: str, query: str
title: str, content: str, query: str, center_metadata: str
) -> list[dict[str, str]]:
messages = [
{
@ -23,7 +23,10 @@ def _get_agent_eval_messages(
{
"role": "user",
"content": AGENTIC_SEARCH_USER_PROMPT.format(
title=title, content=content, query=query
title=title,
content=content,
query=query,
optional_metadata=center_metadata,
),
},
]
@ -32,16 +35,27 @@ def _get_agent_eval_messages(
def evaluate_inference_section(
document: InferenceSection, query: str, llm: LLM
) -> dict[str, RelevanceChunk]:
results = {}
) -> SectionRelevancePiece:
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"
# Since there is now multiple sections, add this prefix for clarity
return metadata_str + "\nContent:"
document_id = document.center_chunk.document_id
semantic_id = document.center_chunk.semantic_identifier
contents = document.combined_content
chunk_id = document.center_chunk.chunk_id
center_metadata = document.center_chunk.metadata
center_metadata_str = _get_metadata_str(center_metadata) if center_metadata else ""
messages = _get_agent_eval_messages(
title=semantic_id, content=contents, query=query
title=semantic_id,
content=contents,
query=query,
center_metadata=center_metadata_str,
)
filled_llm_prompt = dict_based_prompt_to_langchain_prompt(messages)
model_output = message_to_string(llm.invoke(filled_llm_prompt))
@ -64,7 +78,9 @@ def evaluate_inference_section(
)
relevant = last_line.strip().lower().startswith("true")
results[f"{document_id}-{chunk_id}"] = RelevanceChunk(
relevant=relevant, content=analysis
return SectionRelevancePiece(
document_id=document_id,
chunk_id=document.center_chunk.chunk_id,
relevant=relevant,
content=analysis,
)
return results

7
backend/danswer/secondary_llm_flows/chunk_usefulness.py
View File

@ -1,5 +1,6 @@
from collections.abc import Callable
from danswer.configs.chat_configs import DISABLE_LLM_DOC_RELEVANCE
from danswer.llm.interfaces import LLM
from danswer.llm.utils import dict_based_prompt_to_langchain_prompt
from danswer.llm.utils import message_to_string
@ -45,6 +46,12 @@ 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
) -> list[bool]:
if DISABLE_LLM_DOC_RELEVANCE:
raise RuntimeError(
"LLM Doc Relevance is globally disabled, "
"this should have been caught upstream."
)
if use_threads:
functions_with_args: list[tuple[Callable, tuple]] = [
(llm_eval_section, (query, section_content, llm))

1
backend/danswer/server/query_and_chat/query_backend.py
View File

@ -261,6 +261,7 @@ def get_answer_with_quote(
query = query_request.messages[0].message
logger.info(f"Received query for one shot answer with quotes: {query}")
packets = stream_search_answer(
query_req=query_request,
user=user,

51
backend/danswer/tools/images/image_generation_tool.py
View File

@ -162,20 +162,43 @@ class ImageGenerationTool(Tool):
)
def _generate_image(self, prompt: str) -> ImageGenerationResponse:
response = image_generation(
prompt=prompt,
model=self.model,
api_key=self.api_key,
# need to pass in None rather than empty str
api_base=self.api_base or None,
api_version=self.api_version or None,
n=1,
extra_headers=build_llm_extra_headers(self.additional_headers),
)
return ImageGenerationResponse(
revised_prompt=response.data[0]["revised_prompt"],
url=response.data[0]["url"],
)
try:
response = image_generation(
prompt=prompt,
model=self.model,
api_key=self.api_key,
# need to pass in None rather than empty str
api_base=self.api_base or None,
api_version=self.api_version or None,
n=1,
extra_headers=build_llm_extra_headers(self.additional_headers),
)
return ImageGenerationResponse(
revised_prompt=response.data[0]["revised_prompt"],
url=response.data[0]["url"],
)
except Exception as e:
logger.debug(f"Error occured during image generation: {e}")
error_message = str(e)
if "OpenAIException" in str(type(e)):
if (
"Your request was rejected as a result of our safety system"
in error_message
):
raise ValueError(
"The image generation request was rejected due to OpenAI's content policy. Please try a different prompt."
)
elif "Invalid image URL" in error_message:
raise ValueError("Invalid image URL provided for image generation.")
elif "invalid_request_error" in error_message:
raise ValueError(
"Invalid request for image generation. Please check your input."
)
raise ValueError(
"An error occurred during image generation. Please try again later."
)
def run(self, **kwargs: str) -> Generator[ToolResponse, None, None]:
prompt = cast(str, kwargs["prompt"])

26
backend/danswer/tools/search/search_tool.py
View File

@ -10,7 +10,6 @@ from danswer.chat.chat_utils import llm_doc_from_inference_section
from danswer.chat.models import DanswerContext
from danswer.chat.models import DanswerContexts
from danswer.chat.models import LlmDoc
from danswer.configs.chat_configs import DISABLE_AGENTIC_SEARCH
from danswer.db.models import Persona
from danswer.db.models import User
from danswer.dynamic_configs.interface import JSON_ro
@ -18,7 +17,9 @@ from danswer.llm.answering.models import DocumentPruningConfig
from danswer.llm.answering.models import PreviousMessage
from danswer.llm.answering.models import PromptConfig
from danswer.llm.answering.prune_and_merge import prune_and_merge_sections
from danswer.llm.answering.prune_and_merge import prune_sections
from danswer.llm.interfaces import LLM
from danswer.search.enums import LLMEvaluationType
from danswer.search.enums import QueryFlow
from danswer.search.enums import SearchType
from danswer.search.models import IndexFilters
@ -78,6 +79,7 @@ class SearchTool(Tool):
llm: LLM,
fast_llm: LLM,
pruning_config: DocumentPruningConfig,
evaluation_type: LLMEvaluationType,
# if specified, will not actually run a search and will instead return these
# sections. Used when the user selects specific docs to talk to
selected_sections: list[InferenceSection] | None = None,
@ -85,7 +87,6 @@ class SearchTool(Tool):
chunks_below: int = 0,
full_doc: bool = False,
bypass_acl: bool = False,
llm_doc_eval: bool = False,
) -> None:
self.user = user
self.persona = persona
@ -94,6 +95,7 @@ class SearchTool(Tool):
self.llm = llm
self.fast_llm = fast_llm
self.pruning_config = pruning_config
self.evaluation_type = evaluation_type
self.selected_sections = selected_sections
@ -102,7 +104,6 @@ class SearchTool(Tool):
self.full_doc = full_doc
self.bypass_acl = bypass_acl
self.db_session = db_session
self.llm_doc_eval = llm_doc_eval
@property
def name(self) -> str:
@ -205,10 +206,12 @@ class SearchTool(Tool):
question=query,
document_pruning_config=self.pruning_config,
)
llm_docs = [
llm_doc_from_inference_section(section)
for section in final_context_sections
]
yield ToolResponse(id=FINAL_CONTEXT_DOCUMENTS, response=llm_docs)
def run(self, **kwargs: str) -> Generator[ToolResponse, None, None]:
@ -221,6 +224,7 @@ class SearchTool(Tool):
search_pipeline = SearchPipeline(
search_request=SearchRequest(
query=query,
evaluation_type=self.evaluation_type,
human_selected_filters=(
self.retrieval_options.filters if self.retrieval_options else None
),
@ -251,7 +255,7 @@ class SearchTool(Tool):
id=SEARCH_RESPONSE_SUMMARY_ID,
response=SearchResponseSummary(
rephrased_query=query,
top_sections=search_pipeline.reranked_sections,
top_sections=search_pipeline.final_context_sections,
predicted_flow=search_pipeline.predicted_flow,
predicted_search=search_pipeline.predicted_search_type,
final_filters=search_pipeline.search_query.filters,
@ -276,11 +280,11 @@ class SearchTool(Tool):
yield ToolResponse(
id=SECTION_RELEVANCE_LIST_ID,
response=search_pipeline.relevant_section_indices,
response=search_pipeline.section_relevance,
)
final_context_sections = prune_and_merge_sections(
sections=search_pipeline.reranked_sections,
pruned_sections = prune_sections(
sections=search_pipeline.final_context_sections,
section_relevance_list=search_pipeline.section_relevance_list,
prompt_config=self.prompt_config,
llm_config=self.llm.config,
@ -289,17 +293,11 @@ class SearchTool(Tool):
)
llm_docs = [
llm_doc_from_inference_section(section)
for section in final_context_sections
llm_doc_from_inference_section(section) for section in pruned_sections
]
yield ToolResponse(id=FINAL_CONTEXT_DOCUMENTS, response=llm_docs)
if self.llm_doc_eval and not DISABLE_AGENTIC_SEARCH:
yield ToolResponse(
id=SEARCH_EVALUATION_ID, response=search_pipeline.relevance_summaries
)
def final_result(self, *args: ToolResponse) -> JSON_ro:
final_docs = cast(
list[LlmDoc],

3
backend/ee/danswer/server/query_and_chat/models.py
View File

@ -1,6 +1,7 @@
from pydantic import BaseModel
from danswer.configs.constants import DocumentSource
from danswer.search.enums import LLMEvaluationType
from danswer.search.enums import SearchType
from danswer.search.models import ChunkContext
from danswer.search.models import RetrievalDetails
@ -21,9 +22,9 @@ class DocumentSearchRequest(ChunkContext):
search_type: SearchType
retrieval_options: RetrievalDetails
recency_bias_multiplier: float = 1.0
evaluation_type: LLMEvaluationType
# This is to forcibly skip (or run) the step, if None it uses the system defaults
skip_rerank: bool | None = None
skip_llm_chunk_filter: bool | None = None
class BasicCreateChatMessageRequest(ChunkContext):

27
backend/ee/danswer/server/query_and_chat/query_backend.py
View File

@ -1,3 +1,5 @@
from typing import cast
from fastapi import APIRouter
from fastapi import Depends
from fastapi import HTTPException
@ -9,7 +11,9 @@ from danswer.configs.danswerbot_configs import DANSWER_BOT_TARGET_CHUNK_PERCENTA
from danswer.danswerbot.slack.handlers.handle_standard_answers import (
oneoff_standard_answers,
)
from danswer.db.chat import translate_db_search_doc_to_server_search_doc
from danswer.db.engine import get_session
from danswer.db.models import SearchDoc
from danswer.db.models import User
from danswer.db.persona import get_persona_by_id
from danswer.llm.answering.prompts.citations_prompt import (
@ -27,6 +31,7 @@ from danswer.search.models import SearchRequest
from danswer.search.pipeline import SearchPipeline
from danswer.search.utils import dedupe_documents
from danswer.search.utils import drop_llm_indices
from danswer.search.utils import relevant_documents_to_indices
from danswer.utils.logger import setup_logger
from ee.danswer.server.query_and_chat.models import DocumentSearchRequest
from ee.danswer.server.query_and_chat.models import StandardAnswerRequest
@ -63,7 +68,7 @@ def handle_search_request(
offset=search_request.retrieval_options.offset,
limit=search_request.retrieval_options.limit,
skip_rerank=search_request.skip_rerank,
skip_llm_chunk_filter=search_request.skip_llm_chunk_filter,
evaluation_type=search_request.evaluation_type,
chunks_above=search_request.chunks_above,
chunks_below=search_request.chunks_below,
full_doc=search_request.full_doc,
@ -75,8 +80,7 @@ def handle_search_request(
bypass_acl=False,
)
top_sections = search_pipeline.reranked_sections
# If using surrounding context or full doc, this will be empty
relevant_section_indices = search_pipeline.relevant_section_indices
relevance_sections = search_pipeline.section_relevance
top_docs = [
SavedSearchDocWithContent(
document_id=section.center_chunk.document_id,
@ -105,19 +109,26 @@ def handle_search_request(
# Deduping happens at the last step to avoid harming quality by dropping content early on
deduped_docs = top_docs
dropped_inds = None
if search_request.retrieval_options.dedupe_docs:
deduped_docs, dropped_inds = dedupe_documents(top_docs)
llm_indices = relevant_documents_to_indices(
relevance_sections=relevance_sections,
search_docs=[
translate_db_search_doc_to_server_search_doc(cast(SearchDoc, doc))
for doc in deduped_docs
],
)
if dropped_inds:
relevant_section_indices = drop_llm_indices(
llm_indices=relevant_section_indices,
llm_indices = drop_llm_indices(
llm_indices=llm_indices,
search_docs=deduped_docs,
dropped_indices=dropped_inds,
)
return DocumentSearchResponse(
top_documents=deduped_docs, llm_indices=relevant_section_indices
)
return DocumentSearchResponse(top_documents=deduped_docs, llm_indices=llm_indices)
@basic_router.post("/answer-with-quote")

6
deployment/docker_compose/docker-compose.dev.yml
View File

@ -44,14 +44,13 @@ services:
- GEN_AI_MAX_TOKENS=${GEN_AI_MAX_TOKENS:-}
- QA_TIMEOUT=${QA_TIMEOUT:-}
- MAX_CHUNKS_FED_TO_CHAT=${MAX_CHUNKS_FED_TO_CHAT:-}
- DISABLE_LLM_CHUNK_FILTER=${DISABLE_LLM_CHUNK_FILTER:-}
- DISABLE_LLM_CHOOSE_SEARCH=${DISABLE_LLM_CHOOSE_SEARCH:-}
- DISABLE_LLM_QUERY_REPHRASE=${DISABLE_LLM_QUERY_REPHRASE:-}
- DISABLE_GENERATIVE_AI=${DISABLE_GENERATIVE_AI:-}
- DISABLE_LITELLM_STREAMING=${DISABLE_LITELLM_STREAMING:-}
- LITELLM_EXTRA_HEADERS=${LITELLM_EXTRA_HEADERS:-}
- BING_API_KEY=${BING_API_KEY:-}
- DISABLE_AGENTIC_SEARCH=${DISABLE_AGENTIC_SEARCH:-}
- DISABLE_LLM_DOC_RELEVANCE=${DISABLE_LLM_DOC_RELEVANCE:-}
# if set, allows for the use of the token budget system
- TOKEN_BUDGET_GLOBALLY_ENABLED=${TOKEN_BUDGET_GLOBALLY_ENABLED:-}
# Enables the use of bedrock models
@ -129,7 +128,6 @@ services:
- GEN_AI_MAX_TOKENS=${GEN_AI_MAX_TOKENS:-}
- QA_TIMEOUT=${QA_TIMEOUT:-}
- MAX_CHUNKS_FED_TO_CHAT=${MAX_CHUNKS_FED_TO_CHAT:-}
- DISABLE_LLM_CHUNK_FILTER=${DISABLE_LLM_CHUNK_FILTER:-}
- DISABLE_LLM_CHOOSE_SEARCH=${DISABLE_LLM_CHOOSE_SEARCH:-}
- DISABLE_LLM_QUERY_REPHRASE=${DISABLE_LLM_QUERY_REPHRASE:-}
- DISABLE_GENERATIVE_AI=${DISABLE_GENERATIVE_AI:-}
@ -230,7 +228,7 @@ services:
- INTERNAL_URL=http://api_server:8080
- WEB_DOMAIN=${WEB_DOMAIN:-}
- THEME_IS_DARK=${THEME_IS_DARK:-}
- DISABLE_AGENTIC_SEARCH=${DISABLE_AGENTIC_SEARCH:-}
- DISABLE_LLM_DOC_RELEVANCE=${DISABLE_LLM_DOC_RELEVANCE:-}
# Enterprise Edition only
- ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=${ENABLE_PAID_ENTERPRISE_EDITION_FEATURES:-false}

4
deployment/docker_compose/docker-compose.gpu-dev.yml
View File

@ -41,7 +41,7 @@ services:
- GEN_AI_MAX_TOKENS=${GEN_AI_MAX_TOKENS:-}
- QA_TIMEOUT=${QA_TIMEOUT:-}
- MAX_CHUNKS_FED_TO_CHAT=${MAX_CHUNKS_FED_TO_CHAT:-}
- DISABLE_LLM_CHUNK_FILTER=${DISABLE_LLM_CHUNK_FILTER:-}
- DISABLE_LLM_DOC_RELEVANCE=${DISABLE_LLM_DOC_RELEVANCE:-}
- DISABLE_LLM_CHOOSE_SEARCH=${DISABLE_LLM_CHOOSE_SEARCH:-}
- DISABLE_LLM_QUERY_REPHRASE=${DISABLE_LLM_QUERY_REPHRASE:-}
- DISABLE_GENERATIVE_AI=${DISABLE_GENERATIVE_AI:-}
@ -122,7 +122,7 @@ services:
- GEN_AI_MAX_TOKENS=${GEN_AI_MAX_TOKENS:-}
- QA_TIMEOUT=${QA_TIMEOUT:-}
- MAX_CHUNKS_FED_TO_CHAT=${MAX_CHUNKS_FED_TO_CHAT:-}
- DISABLE_LLM_CHUNK_FILTER=${DISABLE_LLM_CHUNK_FILTER:-}
- DISABLE_LLM_DOC_RELEVANCE=${DISABLE_LLM_DOC_RELEVANCE:-}
- DISABLE_LLM_CHOOSE_SEARCH=${DISABLE_LLM_CHOOSE_SEARCH:-}
- DISABLE_LLM_QUERY_REPHRASE=${DISABLE_LLM_QUERY_REPHRASE:-}
- DISABLE_GENERATIVE_AI=${DISABLE_GENERATIVE_AI:-}

2
deployment/docker_compose/env.multilingual.template
View File

@ -24,7 +24,7 @@ NORMALIZE_EMBEDDINGS="True"
# Use LLM to determine if chunks are relevant to the query
# May not work well for languages that do not have much training data in the LLM training set
# If using a common language like Spanish, French, Chinese, etc. this can be kept turned on
DISABLE_LLM_CHUNK_FILTER="True"
DISABLE_LLM_DOC_RELEVANCE="True"
# The default reranking models are English first
# There are no great quality French/English reranking models currently so turning this off

2
deployment/helm/values.yaml
View File

@ -402,7 +402,7 @@ configMap:
GEN_AI_MAX_TOKENS: ""
QA_TIMEOUT: "60"
MAX_CHUNKS_FED_TO_CHAT: ""
DISABLE_LLM_CHUNK_FILTER: ""
DISABLE_LLM_DOC_RELEVANCE: ""
DISABLE_LLM_CHOOSE_SEARCH: ""
DISABLE_LLM_QUERY_REPHRASE: ""
# Query Options

2
deployment/kubernetes/env-configmap.yaml
View File

@ -24,7 +24,7 @@ data:
GEN_AI_MAX_TOKENS: ""
QA_TIMEOUT: "60"
MAX_CHUNKS_FED_TO_CHAT: ""
DISABLE_LLM_CHUNK_FILTER: ""
DISABLE_LLM_DOC_RELEVANCE: ""
DISABLE_LLM_CHOOSE_SEARCH: ""
DISABLE_LLM_QUERY_REPHRASE: ""
# Query Options

1
web/src/app/chat/modal/configuration/AssistantsTab.tsx
View File

@ -94,6 +94,7 @@ const AssistantCard = ({
))}
</div>
)}
<div className="text-xs text-subtle">
<span className="font-semibold">Default model:</span>{" "}
{getDisplayNameForModel(

4
web/src/app/search/page.tsx
View File

@ -36,7 +36,7 @@ import ToggleSearch from "./WrappedSearch";
import {
AGENTIC_SEARCH_TYPE_COOKIE_NAME,
NEXT_PUBLIC_DEFAULT_SIDEBAR_OPEN,
DISABLE_AGENTIC_SEARCH,
DISABLE_LLM_DOC_RELEVANCE,
} from "@/lib/constants";
import WrappedSearch from "./WrappedSearch";
@ -206,7 +206,7 @@ export default async function Home() {
<InstantSSRAutoRefresh />
<WrappedSearch
disabledAgentic={DISABLE_AGENTIC_SEARCH}
disabledAgentic={DISABLE_LLM_DOC_RELEVANCE}
initiallyToggled={toggleSidebar}
querySessions={querySessions}
user={user}

19
web/src/components/icons/icons.tsx
View File

@ -1711,24 +1711,9 @@ export const ThumbsUpIcon = ({
size = 16,
className = defaultTailwindCSS,
}: IconProps) => {
return (
<svg
style={{ width: `${size}px`, height: `${size}px` }}
className={`w-[${size}px] h-[${size}px] ` + className}
xmlns="http://www.w3.org/2000/svg"
width="200"
height="200"
viewBox="0 0 20 20"
>
<path
fill="currentColor"
fillRule="evenodd"
d="M10 2c-2.236 0-4.43.18-6.57.524C1.993 2.755 1 4.014 1 5.426v5.148c0 1.413.993 2.67 2.43 2.902c1.168.188 2.352.327 3.55.414c.28.02.521.18.642.413l1.713 3.293a.75.75 0 0 0 1.33 0l1.713-3.293a.783.783 0 0 1 .642-.413a41.102 41.102 0 0 0 3.55-.414c1.437-.231 2.43-1.49 2.43-2.902V5.426c0-1.413-.993-2.67-2.43-2.902A41.289 41.289 0 0 0 10 2ZM6.75 6a.75.75 0 0 0 0 1.5h6.5a.75.75 0 0 0 0-1.5h-6.5Zm0 2.5a.75.75 0 0 0 0 1.5h3.5a.75.75 0 0 0 0-1.5h-3.5Z"
clipRule="evenodd"
/>
</svg>
);
return <FiThumbsUp size={size} className={className} />;
};
export const RobotIcon = ({
size = 16,
className = defaultTailwindCSS,

19
web/src/components/search/DateRangeSelector.tsx
View File

@ -38,9 +38,11 @@ export const TODAY = "Today";
export function DateRangeSelector({
value,
onValueChange,
isHoritontal,
}: {
value: DateRangePickerValue | null;
onValueChange: (value: DateRangePickerValue | null) => void;
isHoritontal?: boolean;
}) {
return (
<div>
@ -106,6 +108,7 @@ export function DateRangeSelector({
flex
text-sm
px-3
line-clamp-1
py-1.5
rounded-lg
border
@ -113,12 +116,16 @@ export function DateRangeSelector({
cursor-pointer
hover:bg-hover`}
>
<FiCalendar className="my-auto mr-2" />{" "}
{value?.selectValue ? (
<div className="text-emphasis">{value.selectValue}</div>
) : (
"Any time..."
)}
<FiCalendar className="flex-none my-auto mr-2" />{" "}
<p className="line-clamp-1">
{value?.selectValue ? (
<div className="text-emphasis">{value.selectValue}</div>
) : isHoritontal ? (
"Date"
) : (
"Any time..."
)}
</p>
{value?.selectValue ? (
<div
className="my-auto ml-auto p-0.5 rounded-full w-fit"

2
web/src/components/search/DocumentDisplay.tsx
View File

@ -16,7 +16,7 @@ import { BookIcon, CheckmarkIcon, LightBulbIcon, XIcon } from "../icons/icons";
import { FaStar } from "react-icons/fa";
import { FiTag } from "react-icons/fi";
import { DISABLE_AGENTIC_SEARCH } from "@/lib/constants";
import { DISABLE_LLM_DOC_RELEVANCE } from "@/lib/constants";
import { SettingsContext } from "../settings/SettingsProvider";
export const buildDocumentSummaryDisplay = (

96
web/src/components/search/SearchBar.tsx
View File

@ -1,15 +1,18 @@
import React, { KeyboardEvent, ChangeEvent, useContext } from "react";
import { searchState } from "./SearchSection";
import { MagnifyingGlass } from "@phosphor-icons/react";
interface FullSearchBarProps {
query: string;
setQuery: (query: string) => void;
onSearch: (fast?: boolean) => void;
searchState?: searchState;
agentic?: boolean;
toggleAgentic?: () => void;
ccPairs: CCPairBasicInfo[];
documentSets: DocumentSet[];
filterManager: any; // You might want to replace 'any' with a more specific type
finalAvailableDocumentSets: DocumentSet[];
finalAvailableSources: string[];
tags: Tag[];
}
import { useState, useEffect, useRef } from "react";
@ -18,6 +21,9 @@ import { Divider } from "@tremor/react";
import { CustomTooltip } from "../tooltip/CustomTooltip";
import KeyboardSymbol from "@/lib/browserUtilities";
import { SettingsContext } from "../settings/SettingsProvider";
import { HorizontalSourceSelector, SourceSelector } from "./filtering/Filters";
import { CCPairBasicInfo, DocumentSet, Tag } from "@/lib/types";
import { SourceMetadata } from "@/lib/search/interfaces";
export const AnimatedToggle = ({
isOn,
@ -116,12 +122,17 @@ export const AnimatedToggle = ({
export default AnimatedToggle;
export const FullSearchBar = ({
searchState,
query,
setQuery,
onSearch,
agentic,
toggleAgentic,
ccPairs,
documentSets,
filterManager,
finalAvailableDocumentSets,
finalAvailableSources,
tags,
}: FullSearchBarProps) => {
const handleChange = (event: ChangeEvent<HTMLTextAreaElement>) => {
const target = event.target;
@ -196,47 +207,44 @@ export const FullSearchBar = ({
suppressContentEditableWarning={true}
/>
<div className="flex justify-end w-full items-center space-x-3 mr-12 px-4 pb-2">
{searchState == "searching" && (
<div key={"Reading"} className="mr-auto relative inline-block">
<span className="loading-text">Searching...</span>
</div>
)}
{searchState == "reading" && (
<div key={"Reading"} className="mr-auto relative inline-block">
<span className="loading-text">
Reading{settings?.isMobile ? "" : " Documents"}...
</span>
</div>
)}
{searchState == "analyzing" && (
<div key={"Generating"} className="mr-auto relative inline-block">
<span className="loading-text">
Generating{settings?.isMobile ? "" : " Analysis"}...
</span>
</div>
)}
{toggleAgentic && (
<AnimatedToggle isOn={agentic!} handleToggle={toggleAgentic} />
)}
<div className="my-auto pl-2">
<button
onClick={() => {
onSearch(agentic);
}}
className="flex my-auto cursor-pointer"
>
<SendIcon
size={28}
className={`text-emphasis text-white p-1 rounded-full ${
query ? "bg-background-800" : "bg-[#D7D7D7]"
}`}
<div
className={`flex 2xl:justify-end justify-between w-full items-center space-x-3 px-4 pb-2`}
>
{/* <div className="absolute z-10 mobile:px-4 mobile:max-w-searchbar-max mobile:w-[90%] top-12 desktop:left-0 hidden 2xl:block mobile:left-1/2 mobile:transform mobile:-translate-x-1/2 desktop:w-52 3xl:w-64"> */}
<div className="2xl:hidden">
{(ccPairs.length > 0 || documentSets.length > 0) && (
<HorizontalSourceSelector
isHorizontal
{...filterManager}
showDocSidebar={false}
availableDocumentSets={finalAvailableDocumentSets}
existingSources={finalAvailableSources}
availableTags={tags}
/>
</button>
)}
</div>
{/* ccPairs, documentSets, filterManager, finalAvailableDocumentSets, finalAvailableSources, tags */}
{/* </div>/ */}
<div className="flex my-auto gap-x-3">
{toggleAgentic && (
<AnimatedToggle isOn={agentic!} handleToggle={toggleAgentic} />
)}
<div className="my-auto pl-2">
<button
onClick={() => {
onSearch(agentic);
}}
className="flex my-auto cursor-pointer"
>
<SendIcon
size={28}
className={`text-emphasis text-white p-1 rounded-full ${
query ? "bg-background-800" : "bg-[#D7D7D7]"
}`}
/>
</button>
</div>
</div>
</div>
<div className="absolute bottom-2.5 right-10"></div>

30
web/src/components/search/SearchResultsDisplay.tsx
View File

@ -1,24 +1,19 @@
"use client";
import { removeDuplicateDocs } from "@/lib/documentUtils";
import {
DanswerDocument,
DocumentRelevance,
FlowType,
Quote,
Relevance,
SearchDanswerDocument,
SearchDefaultOverrides,
SearchResponse,
ValidQuestionResponse,
} from "@/lib/search/interfaces";
import { usePopup } from "../admin/connectors/Popup";
import { AlertIcon, BroomIcon, UndoIcon } from "../icons/icons";
import { AgenticDocumentDisplay, DocumentDisplay } from "./DocumentDisplay";
import { searchState } from "./SearchSection";
import { useEffect, useState } from "react";
import { useContext, useEffect, useState } from "react";
import { Tooltip } from "../tooltip/Tooltip";
import KeyboardSymbol from "@/lib/browserUtilities";
import { SettingsContext } from "../settings/SettingsProvider";
const getSelectedDocumentIds = (
documents: SearchDanswerDocument[],
@ -135,31 +130,17 @@ export const SearchResultsDisplay = ({
);
}
const dedupedQuotes: Quote[] = [];
const seen = new Set<string>();
if (quotes) {
quotes.forEach((quote) => {
if (!seen.has(quote.document_id)) {
dedupedQuotes.push(quote);
seen.add(quote.document_id);
}
});
}
const selectedDocumentIds = getSelectedDocumentIds(
documents || [],
searchResponse.selectedDocIndices || []
);
const relevantDocs = documents
? documents.filter((doc) => {
return (
showAll ||
(searchResponse &&
searchResponse.additional_relevance &&
searchResponse.additional_relevance[
`${doc.document_id}-${doc.chunk_ind}`
].relevant) ||
searchResponse.additional_relevance[doc.document_id].relevant) ||
doc.is_relevant
);
})
@ -183,6 +164,7 @@ export const SearchResultsDisplay = ({
return (
<>
{popup}
{documents && documents.length == 0 && (
<p className="flex text-lg font-bold">
No docs found! Ensure that you have enabled at least one connector
@ -248,9 +230,7 @@ export const SearchResultsDisplay = ({
{uniqueDocuments.map((document, ind) => {
const relevance: DocumentRelevance | null =
searchResponse.additional_relevance
? searchResponse.additional_relevance[
`${document.document_id}-${document.chunk_ind}`
]
? searchResponse.additional_relevance[document.document_id]
: null;
return agenticResults ? (

199
web/src/components/search/SearchSection.tsx
View File

@ -17,13 +17,11 @@ import {
SearchDanswerDocument,
} from "@/lib/search/interfaces";
import { searchRequestStreamed } from "@/lib/search/streamingQa";
import { CancellationToken, cancellable } from "@/lib/search/cancellable";
import { useFilters, useObjectState } from "@/lib/hooks";
import { questionValidationStreamed } from "@/lib/search/streamingQuestionValidation";
import { Persona } from "@/app/admin/assistants/interfaces";
import { computeAvailableFilters } from "@/lib/filters";
import { redirect, useRouter, useSearchParams } from "next/navigation";
import { useRouter, useSearchParams } from "next/navigation";
import { SettingsContext } from "../settings/SettingsProvider";
import { HistorySidebar } from "@/app/chat/sessionSidebar/HistorySidebar";
import { ChatSession, SearchSession } from "@/app/chat/interfaces";
@ -33,13 +31,19 @@ import { SIDEBAR_TOGGLED_COOKIE_NAME } from "../resizable/constants";
import { AGENTIC_SEARCH_TYPE_COOKIE_NAME } from "@/lib/constants";
import Cookies from "js-cookie";
import FixedLogo from "@/app/chat/shared_chat_search/FixedLogo";
import { AnswerSection } from "./results/AnswerSection";
import { QuotesSection } from "./results/QuotesSection";
import { QAFeedbackBlock } from "./QAFeedback";
import { usePopup } from "../admin/connectors/Popup";
export type searchState =
| "input"
| "searching"
| "reading"
| "analyzing"
| "summarizing";
| "summarizing"
| "generating"
| "citing";
const SEARCH_DEFAULT_OVERRIDES_START: SearchDefaultOverrides = {
forceDisplayQA: false,
@ -48,7 +52,6 @@ const SEARCH_DEFAULT_OVERRIDES_START: SearchDefaultOverrides = {
const VALID_QUESTION_RESPONSE_DEFAULT: ValidQuestionResponse = {
reasoning: null,
answerable: null,
error: null,
};
@ -223,35 +226,48 @@ export const SearchSection = ({
additional_relevance: undefined,
};
// Streaming updates
const updateCurrentAnswer = (answer: string) =>
const updateCurrentAnswer = (answer: string) => {
setSearchResponse((prevState) => ({
...(prevState || initialSearchResponse),
answer,
}));
const updateQuotes = (quotes: Quote[]) =>
setSearchState((searchState) => {
if (searchState != "input") {
return "generating";
}
return "input";
});
};
const updateQuotes = (quotes: Quote[]) => {
setSearchResponse((prevState) => ({
...(prevState || initialSearchResponse),
quotes,
}));
setSearchState((searchState) => "input");
};
const updateDocs = (documents: SearchDanswerDocument[]) => {
setTimeout(() => {
setSearchState((searchState) => {
if (searchState != "input") {
return "reading";
}
return "input";
});
}, 1500);
if (agentic) {
setTimeout(() => {
setSearchState((searchState) => {
if (searchState != "input") {
return "reading";
}
return "input";
});
}, 1500);
setTimeout(() => {
setSearchState((searchState) => {
if (searchState != "input") {
return "analyzing";
}
return "input";
});
}, 4500);
setTimeout(() => {
setSearchState((searchState) => {
if (searchState != "input") {
return "analyzing";
}
return "input";
});
}, 4500);
}
setSearchResponse((prevState) => ({
...(prevState || initialSearchResponse),
@ -294,8 +310,9 @@ export const SearchSection = ({
messageId,
}));
router.refresh();
setSearchState("input");
// setSearchState("input");
setIsFetching(false);
setSearchState((searchState) => "input");
// router.replace(`/search?searchId=${chat_session_id}`);
};
@ -309,7 +326,11 @@ export const SearchSection = ({
setContentEnriched(true);
setIsFetching(false);
setSearchState("input");
if (disabledAgentic) {
setSearchState("input");
} else {
setSearchState("analyzing");
}
};
const updateComments = (comments: any) => {
@ -317,7 +338,9 @@ export const SearchSection = ({
};
const finishedSearching = () => {
setSearchState("input");
if (disabledAgentic) {
setSearchState("input");
}
};
const resetInput = () => {
@ -414,15 +437,7 @@ export const SearchSection = ({
offset: offset ?? defaultOverrides.offset,
};
const questionValidationArgs = {
query,
update: setValidQuestionResponse,
};
await Promise.all([
searchRequestStreamed(searchFnArgs),
questionValidationStreamed(questionValidationArgs),
]);
await Promise.all([searchRequestStreamed(searchFnArgs)]);
};
// handle redirect if search page is disabled
@ -481,6 +496,20 @@ export const SearchSection = ({
setShowDocSidebar,
mobile: settings?.isMobile,
});
const { answer, quotes, documents, error, messageId } = searchResponse;
const dedupedQuotes: Quote[] = [];
const seen = new Set<string>();
if (quotes) {
quotes.forEach((quote) => {
if (!seen.has(quote.document_id)) {
dedupedQuotes.push(quote);
seen.add(quote.document_id);
}
});
}
const { popup, setPopup } = usePopup();
return (
<>
@ -600,15 +629,113 @@ export const SearchSection = ({
disabledAgentic ? undefined : toggleAgentic
}
agentic={agentic}
searchState={searchState}
query={query}
setQuery={setQuery}
onSearch={async (agentic?: boolean) => {
setDefaultOverrides(SEARCH_DEFAULT_OVERRIDES_START);
await onSearch({ agentic, offset: 0 });
}}
finalAvailableDocumentSets={finalAvailableDocumentSets}
finalAvailableSources={finalAvailableSources}
filterManager={filterManager}
documentSets={documentSets}
ccPairs={ccPairs}
tags={tags}
/>
</div>
{!firstSearch && (
<div className="my-4 min-h-[16rem] p-4 border-2 border-border rounded-lg relative">
<div>
<div className="flex gap-x-2 mb-1">
<h2 className="text-emphasis font-bold my-auto mb-1 ">
AI Answer
</h2>
{searchState == "generating" && (
<div
key={"generating"}
className="relative inline-block"
>
<span className="loading-text">
Generating response...
</span>
</div>
)}
{searchState == "citing" && (
<div
key={"citing"}
className="relative inline-block"
>
<span className="loading-text">
Generating citations...
</span>
</div>
)}
{searchState == "searching" && (
<div
key={"Reading"}
className="relative inline-block"
>
<span className="loading-text">Searching...</span>
</div>
)}
{searchState == "reading" && (
<div
key={"Reading"}
className="relative inline-block"
>
<span className="loading-text">
Reading{settings?.isMobile ? "" : " Documents"}
...
</span>
</div>
)}
{searchState == "analyzing" && (
<div
key={"Generating"}
className="relative inline-block"
>
<span className="loading-text">
Generating
{settings?.isMobile ? "" : " Analysis"}...
</span>
</div>
)}
</div>
<div className="mb-2 pt-1 border-t border-border w-full">
<AnswerSection
answer={answer}
quotes={quotes}
error={error}
isFetching={isFetching}
/>
</div>
{quotes !== null && answer && (
<div className="pt-1 border-t border-border w-full">
<QuotesSection
quotes={dedupedQuotes}
isFetching={isFetching}
/>
{searchResponse.messageId !== null && (
<div className="absolute right-3 bottom-3">
<QAFeedbackBlock
messageId={searchResponse.messageId}
setPopup={setPopup}
/>
</div>
)}
</div>
)}
</div>
</div>
)}
{!settings?.isMobile && (
<div className="mt-6">

9
web/src/components/search/filtering/FilterDropdown.tsx
View File

@ -6,22 +6,23 @@ interface Option {
display: string | JSX.Element;
displayName?: string;
}
export function FilterDropdown({
options,
selected,
handleSelect,
icon,
defaultDisplay,
width = "w-64",
}: {
options: Option[];
selected: string[];
handleSelect: (option: Option) => void;
icon: JSX.Element;
defaultDisplay: string | JSX.Element;
width?: string;
}) {
return (
<div className="w-64">
<div>
<CustomDropdown
dropdown={
<div
@ -32,7 +33,7 @@ export function FilterDropdown({
bg-background
flex
flex-col
w-64
${width}
max-h-96
overflow-y-auto
overscroll-contain`}
@ -76,7 +77,7 @@ export function FilterDropdown({
<div
className={`
flex
w-64
${width}
text-sm
px-3
py-1.5

194
web/src/components/search/filtering/Filters.tsx
View File

@ -3,7 +3,14 @@ import { DocumentSet, Tag, ValidSources } from "@/lib/types";
import { SourceMetadata } from "@/lib/search/interfaces";
import { InfoIcon, defaultTailwindCSS } from "../../icons/icons";
import { HoverPopup } from "../../HoverPopup";
import { FiBook, FiBookmark, FiFilter, FiMap, FiX } from "react-icons/fi";
import {
FiBook,
FiBookmark,
FiFilter,
FiMap,
FiTag,
FiX,
} from "react-icons/fi";
import { DateRangeSelector } from "../DateRangeSelector";
import { DateRangePickerValue } from "@tremor/react";
import { FilterDropdown } from "./FilterDropdown";
@ -72,9 +79,9 @@ export function SourceSelector({
<div
className={`hidden ${
showDocSidebar ? "4xl:block" : "!block"
} duration-1000 ease-out transition-all transform origin-top-right`}
} duration-1000 flex ease-out transition-all transform origin-top-right`}
>
<div className="flex mb-4 pb-2 border-b border-border text-emphasis">
<div className=" mb-4 pb-2 border-b border-border text-emphasis">
<h2 className="font-bold my-auto">Filters</h2>
<FiFilter className="my-auto ml-2" size="16" />
</div>
@ -324,3 +331,184 @@ export function HorizontalFilters({
</div>
);
}
export function HorizontalSourceSelector({
timeRange,
setTimeRange,
selectedSources,
setSelectedSources,
selectedDocumentSets,
setSelectedDocumentSets,
selectedTags,
setSelectedTags,
availableDocumentSets,
existingSources,
availableTags,
}: SourceSelectorProps) {
const handleSourceSelect = (source: SourceMetadata) => {
setSelectedSources((prev: SourceMetadata[]) => {
if (prev.map((s) => s.internalName).includes(source.internalName)) {
return prev.filter((s) => s.internalName !== source.internalName);
} else {
return [...prev, source];
}
});
};
const handleDocumentSetSelect = (documentSetName: string) => {
setSelectedDocumentSets((prev: string[]) => {
if (prev.includes(documentSetName)) {
return prev.filter((s) => s !== documentSetName);
} else {
return [...prev, documentSetName];
}
});
};
const handleTagSelect = (tag: Tag) => {
setSelectedTags((prev: Tag[]) => {
if (
prev.some(
(t) => t.tag_key === tag.tag_key && t.tag_value === tag.tag_value
)
) {
return prev.filter(
(t) => !(t.tag_key === tag.tag_key && t.tag_value === tag.tag_value)
);
} else {
return [...prev, tag];
}
});
};
return (
<div className="flex flex-col space-y-4">
<div className="flex space-x-2">
<div className="w-24">
<DateRangeSelector
isHoritontal
value={timeRange}
onValueChange={setTimeRange}
/>
</div>
{existingSources.length > 0 && (
<FilterDropdown
options={listSourceMetadata()
.filter((source) => existingSources.includes(source.internalName))
.map((source) => ({
key: source.internalName,
display: (
<>
<SourceIcon
sourceType={source.internalName}
iconSize={16}
/>
<span className="ml-2 text-sm">{source.displayName}</span>
</>
),
}))}
selected={selectedSources.map((source) => source.internalName)}
handleSelect={(option) =>
handleSourceSelect(
listSourceMetadata().find((s) => s.internalName === option.key)!
)
}
icon={<FiMap size={16} />}
defaultDisplay="Sources"
width="w-fit max-w-24 ellipsis truncate"
/>
)}
{availableDocumentSets.length > 0 && (
<FilterDropdown
options={availableDocumentSets.map((documentSet) => ({
key: documentSet.name,
display: (
<>
<FiBookmark />
<span className="ml-2 text-sm">{documentSet.name}</span>
</>
),
}))}
selected={selectedDocumentSets}
handleSelect={(option) => handleDocumentSetSelect(option.key)}
icon={<FiBook size={16} />}
defaultDisplay="Sets"
width="w-fit max-w-24 ellipsis"
/>
)}
{availableTags.length > 0 && (
<FilterDropdown
options={availableTags.map((tag) => ({
key: `${tag.tag_key}=${tag.tag_value}`,
display: (
<span className="text-sm">
{tag.tag_key}
<b>=</b>
{tag.tag_value}
</span>
),
}))}
selected={selectedTags.map(
(tag) => `${tag.tag_key}=${tag.tag_value}`
)}
handleSelect={(option) => {
const [tag_key, tag_value] = option.key.split("=");
const selectedTag = availableTags.find(
(tag) => tag.tag_key === tag_key && tag.tag_value === tag_value
);
if (selectedTag) {
handleTagSelect(selectedTag);
}
}}
icon={<FiTag size={16} />}
defaultDisplay="Tags"
width="w-fit max-w-24 ellipsis"
/>
)}
</div>
{/* <div className="flex flex-wrap gap-2">
{timeRange && timeRange.selectValue && (
<SelectedBubble onClick={() => setTimeRange(null)}>
<div className="text-sm flex">{timeRange.selectValue}</div>
</SelectedBubble>
)}
{selectedSources.map((source) => (
<SelectedBubble
key={source.internalName}
onClick={() => handleSourceSelect(source)}
>
<>
<SourceIcon sourceType={source.internalName} iconSize={16} />
<span className="ml-2 text-sm">{source.displayName}</span>
</>
</SelectedBubble>
))}
{selectedDocumentSets.map((documentSetName) => (
<SelectedBubble
key={documentSetName}
onClick={() => handleDocumentSetSelect(documentSetName)}
>
<>
<FiBookmark />
<span className="ml-2 text-sm">{documentSetName}</span>
</>
</SelectedBubble>
))}
{selectedTags.map((tag) => (
<SelectedBubble
key={`${tag.tag_key}=${tag.tag_value}`}
onClick={() => handleTagSelect(tag)}
>
<span className="text-sm">
{tag.tag_key}<b>=</b>{tag.tag_value}
</span>
</SelectedBubble>
))}
</div> */}
</div>
);
}

48
web/src/components/search/results/AnswerSection.tsx
View File

@ -26,31 +26,28 @@ interface AnswerSectionProps {
answer: string | null;
quotes: Quote[] | null;
error: string | null;
nonAnswerableReason: string | null;
isFetching: boolean;
}
export const AnswerSection = (props: AnswerSectionProps) => {
let status = "in-progress" as StatusOptions;
let header = <>Building answer...</>;
let header = <></>;
let body = null;
// finished answer
if (props.quotes !== null || !props.isFetching) {
status = "success";
header = <>AI answer</>;
if (props.answer) {
body = (
<ReactMarkdown
className="prose text-sm max-w-full"
remarkPlugins={[remarkGfm]}
>
{replaceNewlines(props.answer)}
</ReactMarkdown>
);
} else {
body = <div>Information not found</div>;
}
header = <></>;
body = (
<ReactMarkdown
className="prose text-sm max-w-full"
remarkPlugins={[remarkGfm]}
>
{replaceNewlines(props.answer || "")}
</ReactMarkdown>
);
// error while building answer (NOTE: if error occurs during quote generation
// the above if statement will hit and the error will not be displayed)
} else if (props.error) {
@ -64,7 +61,7 @@ export const AnswerSection = (props: AnswerSectionProps) => {
// answer is streaming
} else if (props.answer) {
status = "success";
header = <>AI answer</>;
header = <></>;
body = (
<ReactMarkdown
className="prose text-sm max-w-full"
@ -74,10 +71,6 @@ export const AnswerSection = (props: AnswerSectionProps) => {
</ReactMarkdown>
);
}
if (props.nonAnswerableReason) {
status = "warning";
header = <>Building best effort AI answer...</>;
}
return (
<ResponseSection
@ -87,20 +80,7 @@ export const AnswerSection = (props: AnswerSectionProps) => {
<div className="ml-2 text-strong">{header}</div>
</div>
}
body={
<div className="">
{body}
{props.nonAnswerableReason && !props.isFetching && (
<div className="mt-4 text-sm">
<b className="font-medium">Warning:</b> the AI did not think this
question was answerable.{" "}
<div className="italic mt-1 ml-2">
{props.nonAnswerableReason}
</div>
</div>
)}
</div>
}
body={<div className="">{body}</div>}
desiredOpenStatus={true}
isNotControllable={true}
/>

7
web/src/components/search/results/QuotesSection.tsx
View File

@ -65,7 +65,6 @@ const QuoteDisplay = ({ quoteInfo }: { quoteInfo: Quote }) => {
interface QuotesSectionProps {
quotes: Quote[] | null;
isAnswerable: boolean | null;
isFetching: boolean;
}
@ -110,11 +109,7 @@ export const QuotesSection = (props: QuotesSectionProps) => {
let status: StatusOptions = "in-progress";
if (!props.isFetching) {
if (props.quotes && props.quotes.length > 0) {
if (props.isAnswerable === false) {
status = "warning";
} else {
status = "success";
}
status = "success";
} else {
status = "failed";
}

18
web/src/components/search/results/ResponseSection.tsx
View File

@ -7,6 +7,7 @@ import {
} from "@/components/icons/icons";
import { useState } from "react";
import { Grid } from "react-loader-spinner";
import { searchState } from "../SearchSection";
export type StatusOptions = "in-progress" | "failed" | "warning" | "success";
@ -31,26 +32,13 @@ export const ResponseSection = ({
let icon = null;
if (status === "in-progress") {
icon = (
<div className="m-auto">
<Grid
height="12"
width="12"
color="#3b82f6"
ariaLabel="grid-loading"
radius="12.5"
wrapperStyle={{}}
wrapperClass=""
visible={true}
/>
</div>
);
icon = <></>;
}
if (status === "failed") {
icon = <AlertIcon size={16} className="text-red-500" />;
}
if (status === "success") {
icon = <CheckmarkIcon size={16} className="text-green-600" />;
icon = <></>;
}
if (status === "warning") {
icon = <TriangleAlertIcon size={16} className="text-yellow-600" />;

4
web/src/lib/constants.ts
View File

@ -52,5 +52,5 @@ export const CUSTOM_ANALYTICS_ENABLED = process.env.CUSTOM_ANALYTICS_SECRET_KEY
? true
: false;
export const DISABLE_AGENTIC_SEARCH =
process.env.DISABLE_AGENTIC_SEARCH?.toLowerCase() === "true";
export const DISABLE_LLM_DOC_RELEVANCE =
process.env.DISABLE_LLM_DOC_RELEVANCE?.toLowerCase() === "true";

1
web/src/lib/search/interfaces.ts
View File

@ -158,7 +158,6 @@ export interface SearchRequestOverrides {
}
export interface ValidQuestionResponse {
answerable: boolean | null;
reasoning: string | null;
error: string | null;
}

3
web/src/lib/search/streamingQa.ts
View File

@ -61,8 +61,7 @@ export const searchRequestStreamed = async ({
filters: filters,
enable_auto_detect_filters: false,
},
llm_doc_eval: true,
skip_gen_ai_answer_generation: true,
evaluation_type: agentic ? "agentic" : "basic",
}),
headers: {
"Content-Type": "application/json",

66
web/src/lib/search/streamingQuestionValidation.ts
View File

@ -1,66 +0,0 @@
import {
AnswerPiecePacket,
ErrorMessagePacket,
ValidQuestionResponse,
} from "./interfaces";
import { processRawChunkString } from "./streamingUtils";
export interface QuestionValidationArgs {
query: string;
update: (update: Partial<ValidQuestionResponse>) => void;
}
export const questionValidationStreamed = async <T>({
query,
update,
}: QuestionValidationArgs) => {
const response = await fetch("/api/query/stream-query-validation", {
method: "POST",
body: JSON.stringify({
query,
}),
headers: {
"Content-Type": "application/json",
},
});
const reader = response.body?.getReader();
const decoder = new TextDecoder("utf-8");
let reasoning = "";
let previousPartialChunk: string | null = null;
while (true) {
const rawChunk = await reader?.read();
if (!rawChunk) {
throw new Error("Unable to process chunk");
}
const { done, value } = rawChunk;
if (done) {
break;
}
const [completedChunks, partialChunk] = processRawChunkString<
AnswerPiecePacket | ValidQuestionResponse | ErrorMessagePacket
>(decoder.decode(value, { stream: true }), previousPartialChunk);
if (!completedChunks.length && !partialChunk) {
break;
}
previousPartialChunk = partialChunk as string | null;
completedChunks.forEach((chunk) => {
if (Object.hasOwn(chunk, "answer_piece")) {
reasoning += (chunk as AnswerPiecePacket).answer_piece;
update({
reasoning,
});
}
if (Object.hasOwn(chunk, "answerable")) {
update({ answerable: (chunk as ValidQuestionResponse).answerable });
}
if (Object.hasOwn(chunk, "error")) {
update({ error: (chunk as ErrorMessagePacket).error });
}
});
}
};