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Code Issues Packages Projects Releases Wiki Activity

Rebase search changes to EE APIs (#55)

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This commit is contained in:
Yuhong Sun
2024-03-26 15:58:04 -07:00
committed by Chris Weaver
parent ef4f06a375
commit 7b16cb9562
1 changed files with 20 additions and 53 deletions
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69
backend/ee/danswer/server/query_and_chat/query_backend.py
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@@ -3,27 +3,22 @@ from fastapi import Depends
from sqlalchemy.orm import Session from sqlalchemy.orm import Session
from danswer.auth.users import current_user from danswer.auth.users import current_user
from danswer.chat.chat_utils import compute_max_document_tokens
from danswer.configs.chat_configs import DISABLE_LLM_CHUNK_FILTER from danswer.configs.chat_configs import DISABLE_LLM_CHUNK_FILTER
from danswer.configs.chat_configs import NUM_RETURNED_HITS
from danswer.configs.danswerbot_configs import DANSWER_BOT_TARGET_CHUNK_PERCENTAGE from danswer.configs.danswerbot_configs import DANSWER_BOT_TARGET_CHUNK_PERCENTAGE
from danswer.db.chat import get_persona_by_id from danswer.db.chat import get_persona_by_id
from danswer.db.embedding_model import get_current_db_embedding_model
from danswer.db.engine import get_session from danswer.db.engine import get_session
from danswer.db.models import User from danswer.db.models import User
from danswer.document_index.factory import get_default_document_index from danswer.llm.answering.prompts.citations_prompt import compute_max_document_tokens
from danswer.llm.utils import get_default_llm_version from danswer.llm.utils import get_default_llm_version
from danswer.llm.utils import get_max_input_tokens from danswer.llm.utils import get_max_input_tokens
from danswer.one_shot_answer.answer_question import get_search_answer from danswer.one_shot_answer.answer_question import get_search_answer
from danswer.one_shot_answer.models import DirectQARequest from danswer.one_shot_answer.models import DirectQARequest
from danswer.one_shot_answer.models import OneShotQAResponse from danswer.one_shot_answer.models import OneShotQAResponse
from danswer.search.access_filters import build_access_filters_for_user
from danswer.search.models import IndexFilters
from danswer.search.models import SavedSearchDoc from danswer.search.models import SavedSearchDoc
from danswer.search.models import SearchQuery from danswer.search.models import SearchRequest
from danswer.search.models import SearchResponse from danswer.search.models import SearchResponse
from danswer.search.search_runner import chunks_to_search_docs from danswer.search.pipeline import SearchPipeline
from danswer.search.search_runner import full_chunk_search from danswer.search.utils import chunks_to_search_docs
from danswer.server.query_and_chat.models import DocumentSearchRequest from danswer.server.query_and_chat.models import DocumentSearchRequest
from danswer.utils.logger import setup_logger from danswer.utils.logger import setup_logger
@@ -42,59 +37,31 @@ def handle_search_request(
) -> SearchResponse: ) -> SearchResponse:
"""Simple search endpoint, does not create a new message or records in the DB""" """Simple search endpoint, does not create a new message or records in the DB"""
query = search_request.message query = search_request.message
filters = search_request.retrieval_options.filters
logger.info(f"Received document search query: {query}") logger.info(f"Received document search query: {query}")
user_acl_filters = build_access_filters_for_user(user, db_session) search_pipeline = SearchPipeline(
final_filters = IndexFilters( search_request=SearchRequest(
source_type=filters.source_type if filters else None,
document_set=filters.document_set if filters else None,
time_cutoff=filters.time_cutoff if filters else None,
tags=filters.tags if filters else None,
access_control_list=user_acl_filters,
)
limit = (
search_request.retrieval_options.limit
if search_request.retrieval_options.limit is not None
else NUM_RETURNED_HITS
)
offset = (
search_request.retrieval_options.offset
if search_request.retrieval_options.offset is not None
else 0
)
search_query = SearchQuery(
query=query, query=query,
search_type=search_request.search_type, search_type=search_request.search_type,
filters=final_filters, human_selected_filters=search_request.retrieval_options.filters,
recency_bias_multiplier=search_request.recency_bias_multiplier, enable_auto_detect_filters=search_request.retrieval_options.enable_auto_detect_filters,
num_hits=limit, persona=None, # For simplicity, default settings should be good for this search
offset=offset, offset=search_request.retrieval_options.offset,
skip_rerank=search_request.skip_rerank, limit=search_request.retrieval_options.limit,
skip_llm_chunk_filter=disable_llm_chunk_filter, ),
user=user,
db_session=db_session,
bypass_acl=False,
) )
top_chunks = search_pipeline.reranked_docs
db_embedding_model = get_current_db_embedding_model(db_session) relevant_chunk_indices = search_pipeline.relevant_chunk_indicies
document_index = get_default_document_index(
primary_index_name=db_embedding_model.index_name, secondary_index_name=None
)
top_chunks, llm_selection = full_chunk_search(
query=search_query, document_index=document_index, db_session=db_session
)
top_docs = chunks_to_search_docs(top_chunks) top_docs = chunks_to_search_docs(top_chunks)
llm_selection_indices = [
index for index, value in enumerate(llm_selection) if value
]
# No need to save the docs for this API # No need to save the docs for this API
fake_saved_docs = [SavedSearchDoc.from_search_doc(doc) for doc in top_docs] fake_saved_docs = [SavedSearchDoc.from_search_doc(doc) for doc in top_docs]
return SearchResponse( return SearchResponse(
top_documents=fake_saved_docs, llm_indices=llm_selection_indices top_documents=fake_saved_docs, llm_indices=relevant_chunk_indices
) )