More Cleanup and Deduplication (#675)

backend/danswer/direct_qa/answer_question.py

@@ -1,4 +1,5 @@
 from collections.abc import Callable
+from collections.abc import Iterator
 
 from sqlalchemy.orm import Session
 
@@ -6,7 +7,10 @@ from danswer.configs.app_configs import DISABLE_GENERATIVE_AI
 from danswer.configs.app_configs import NUM_DOCUMENT_TOKENS_FED_TO_GENERATIVE_MODEL
 from danswer.configs.app_configs import QA_TIMEOUT
 from danswer.configs.constants import IGNORE_FOR_QA
+from danswer.db.feedback import create_query_event
 from danswer.db.models import User
+from danswer.direct_qa.interfaces import DanswerAnswerPiece
+from danswer.direct_qa.interfaces import StreamingError
 from danswer.direct_qa.llm_utils import get_default_qa_model
 from danswer.direct_qa.models import LLMMetricsContainer
 from danswer.direct_qa.qa_utils import get_usable_chunks
@@ -21,8 +25,11 @@ from danswer.secondary_llm_flows.answer_validation import get_answer_validity
 from danswer.secondary_llm_flows.extract_filters import extract_question_time_filters
 from danswer.server.models import QAResponse
 from danswer.server.models import QuestionRequest
+from danswer.server.models import RerankedRetrievalDocs
+from danswer.server.utils import get_json_line
 from danswer.utils.logger import setup_logger
 from danswer.utils.timing import log_function_time
+from danswer.utils.timing import log_generator_function_time
 
 logger = setup_logger()
 
@@ -169,3 +176,114 @@ def answer_qa_query(
         favor_recent=favor_recent,
         error_msg=error_msg,
     )
+
+
+@log_generator_function_time()
+def answer_qa_query_stream(
+    question: QuestionRequest,
+    user: User | None,
+    db_session: Session,
+    disable_generative_answer: bool = DISABLE_GENERATIVE_AI,
+) -> Iterator[str]:
+    logger.debug(
+        f"Received QA query ({question.search_type.value} search): {question.query}"
+    )
+    logger.debug(f"Query filters: {question.filters}")
+
+    answer_so_far: str = ""
+    query = question.query
+    offset_count = question.offset if question.offset is not None else 0
+
+    time_cutoff, favor_recent = extract_question_time_filters(question)
+    question.filters.time_cutoff = time_cutoff
+    question.favor_recent = favor_recent
+
+    ranked_chunks, unranked_chunks, query_event_id = danswer_search(
+        question=question,
+        user=user,
+        db_session=db_session,
+        document_index=get_default_document_index(),
+    )
+
+    # TODO retire this
+    predicted_search, predicted_flow = query_intent(query)
+
+    top_docs = chunks_to_search_docs(ranked_chunks)
+    unranked_top_docs = chunks_to_search_docs(unranked_chunks)
+
+    initial_response = RerankedRetrievalDocs(
+        top_documents=top_docs,
+        unranked_top_documents=unranked_top_docs,
+        # if generative AI is disabled, set flow as search so frontend
+        # doesn't ask the user if they want to run QA over more documents
+        predicted_flow=QueryFlow.SEARCH
+        if disable_generative_answer
+        else predicted_flow,
+        predicted_search=predicted_search,
+        time_cutoff=time_cutoff,
+        favor_recent=favor_recent,
+    ).dict()
+
+    logger.debug(f"Sending Initial Retrival Results: {initial_response}")
+    yield get_json_line(initial_response)
+
+    if not ranked_chunks:
+        logger.debug("No Documents Found")
+        return
+
+    if disable_generative_answer:
+        logger.debug("Skipping QA because generative AI is disabled")
+        return
+
+    try:
+        qa_model = get_default_qa_model()
+    except Exception as e:
+        logger.exception("Unable to get QA model")
+        error = StreamingError(error=str(e))
+        yield get_json_line(error.dict())
+        return
+
+    # remove chunks marked as not applicable for QA (e.g. Google Drive file
+    # types which can't be parsed). These chunks are useful to show in the
+    # search results, but not for QA.
+    filtered_ranked_chunks = [
+        chunk for chunk in ranked_chunks if not chunk.metadata.get(IGNORE_FOR_QA)
+    ]
+
+    # get all chunks that fit into the token limit
+    usable_chunks = get_usable_chunks(
+        chunks=filtered_ranked_chunks,
+        token_limit=NUM_DOCUMENT_TOKENS_FED_TO_GENERATIVE_MODEL,
+        offset=offset_count,
+    )
+    logger.debug(
+        f"Chunks fed to LLM: {[chunk.semantic_identifier for chunk in usable_chunks]}"
+    )
+
+    try:
+        for response_packet in qa_model.answer_question_stream(query, usable_chunks):
+            if response_packet is None:
+                continue
+            if (
+                isinstance(response_packet, DanswerAnswerPiece)
+                and response_packet.answer_piece
+            ):
+                answer_so_far = answer_so_far + response_packet.answer_piece
+            logger.debug(f"Sending packet: {response_packet}")
+            yield get_json_line(response_packet.dict())
+    except Exception:
+        # exception is logged in the answer_question method, no need to re-log
+        logger.exception("Failed to run QA")
+        error = StreamingError(error="The LLM failed to produce a useable response")
+        yield get_json_line(error.dict())
+
+    query_event_id = create_query_event(
+        query=query,
+        search_type=question.search_type,
+        llm_answer=answer_so_far,
+        retrieved_document_ids=[doc.document_id for doc in top_docs],
+        user_id=None if user is None else user.id,
+        db_session=db_session,
+    )
+
+    yield get_json_line({"query_event_id": query_event_id})

backend/danswer/direct_qa/llm_utils.py

@@ -1,5 +1,3 @@
-from openai.error import AuthenticationError
-
 from danswer.configs.app_configs import QA_TIMEOUT
 from danswer.direct_qa.interfaces import QAModel
 from danswer.direct_qa.qa_block import QABlock
@@ -12,25 +10,6 @@ from danswer.utils.logger import setup_logger
 logger = setup_logger()
 
 
-def check_model_api_key_is_valid(model_api_key: str) -> bool:
-    if not model_api_key:
-        return False
-
-    llm = get_default_llm(api_key=model_api_key, timeout=10)
-
-    # try for up to 2 timeouts (e.g. 10 seconds in total)
-    for _ in range(2):
-        try:
-            llm.invoke("Do not respond")
-            return True
-        except AuthenticationError:
-            return False
-        except Exception as e:
-            logger.warning(f"GenAI API key failed for the following reason: {e}")
-
-    return False
-
-
 # TODO introduce the prompt choice parameter
 def get_default_qa_handler(real_time_flow: bool = True) -> QAHandler:
     return (

backend/danswer/direct_qa/qa_utils.py

@@ -4,7 +4,6 @@ import re
 from collections.abc import Generator
 from collections.abc import Iterator
 from json.decoder import JSONDecodeError
-from typing import cast
 from typing import Optional
 from typing import Tuple
 
@@ -12,8 +11,6 @@ import regex
 
 from danswer.configs.app_configs import NUM_DOCUMENT_TOKENS_FED_TO_GENERATIVE_MODEL
 from danswer.configs.app_configs import QUOTE_ALLOWED_ERROR_PERCENT
-from danswer.configs.constants import GEN_AI_API_KEY_STORAGE_KEY
-from danswer.configs.model_configs import GEN_AI_API_KEY
 from danswer.direct_qa.interfaces import DanswerAnswer
 from danswer.direct_qa.interfaces import DanswerAnswerPiece
 from danswer.direct_qa.interfaces import DanswerQuote
@@ -21,8 +18,6 @@ from danswer.direct_qa.interfaces import DanswerQuotes
 from danswer.direct_qa.qa_prompts import ANSWER_PAT
 from danswer.direct_qa.qa_prompts import QUOTE_PAT
 from danswer.direct_qa.qa_prompts import UNCERTAINTY_PAT
-from danswer.dynamic_configs import get_dynamic_config_store
-from danswer.dynamic_configs.interface import ConfigNotFoundError
 from danswer.indexing.models import InferenceChunk
 from danswer.llm.utils import check_number_of_tokens
 from danswer.utils.logger import setup_logger
@@ -33,17 +28,6 @@ from danswer.utils.text_processing import shared_precompare_cleanup
 logger = setup_logger()
 
 
-def get_gen_ai_api_key() -> str | None:
-    # first check if the key has been provided by the UI
-    try:
-        return cast(str, get_dynamic_config_store().load(GEN_AI_API_KEY_STORAGE_KEY))
-    except ConfigNotFoundError:
-        pass
-
-    # if not provided by the UI, fallback to the env variable
-    return GEN_AI_API_KEY
-
-
 def extract_answer_quotes_freeform(
     answer_raw: str,
 ) -> Tuple[Optional[str], Optional[list[str]]]:

backend/danswer/llm/chat_llm.py

@@ -1,22 +1,77 @@
+import abc
+from collections.abc import Iterator
+
 import litellm  # type:ignore
 from langchain.chat_models import ChatLiteLLM
+from langchain.chat_models.base import BaseChatModel
+from langchain.schema.language_model import LanguageModelInput
 
+from danswer.configs.app_configs import LOG_ALL_MODEL_INTERACTIONS
 from danswer.configs.model_configs import GEN_AI_API_ENDPOINT
 from danswer.configs.model_configs import GEN_AI_API_VERSION
 from danswer.configs.model_configs import GEN_AI_MAX_OUTPUT_TOKENS
 from danswer.configs.model_configs import GEN_AI_MODEL_PROVIDER
 from danswer.configs.model_configs import GEN_AI_MODEL_VERSION
 from danswer.configs.model_configs import GEN_AI_TEMPERATURE
-from danswer.llm.interfaces import LangChainChatLLM
+from danswer.llm.interfaces import LLM
+from danswer.llm.utils import message_generator_to_string_generator
 from danswer.llm.utils import should_be_verbose
+from danswer.utils.logger import setup_logger
 
 
+logger = setup_logger()
+
 # If a user configures a different model and it doesn't support all the same
 # parameters like frequency and presence, just ignore them
 litellm.drop_params = True
 litellm.telemetry = False
 
 
+class LangChainChatLLM(LLM, abc.ABC):
+    @property
+    @abc.abstractmethod
+    def llm(self) -> BaseChatModel:
+        raise NotImplementedError
+
+    def _log_model_config(self) -> None:
+        logger.debug(
+            f"Model Class: {self.llm.__class__.__name__}, Model Config: {self.llm.__dict__}"
+        )
+
+    @staticmethod
+    def _log_prompt(prompt: LanguageModelInput) -> None:
+        if isinstance(prompt, list):
+            for ind, msg in enumerate(prompt):
+                logger.debug(f"Message {ind}:\n{msg.content}")
+        if isinstance(prompt, str):
+            logger.debug(f"Prompt:\n{prompt}")
+
+    def invoke(self, prompt: LanguageModelInput) -> str:
+        self._log_model_config()
+        if LOG_ALL_MODEL_INTERACTIONS:
+            self._log_prompt(prompt)
+
+        model_raw = self.llm.invoke(prompt).content
+        if LOG_ALL_MODEL_INTERACTIONS:
+            logger.debug(f"Raw Model Output:\n{model_raw}")
+
+        return model_raw
+
+    def stream(self, prompt: LanguageModelInput) -> Iterator[str]:
+        self._log_model_config()
+        if LOG_ALL_MODEL_INTERACTIONS:
+            self._log_prompt(prompt)
+
+        output_tokens = []
+        for token in message_generator_to_string_generator(self.llm.stream(prompt)):
+            output_tokens.append(token)
+            yield token
+
+        full_output = "".join(output_tokens)
+        if LOG_ALL_MODEL_INTERACTIONS:
+            logger.debug(f"Raw Model Output:\n{full_output}")
+
+
 def _get_model_str(
     model_provider: str | None,
     model_version: str | None,

backend/danswer/llm/factory.py

@@ -1,10 +1,10 @@
 from danswer.configs.app_configs import QA_TIMEOUT
 from danswer.configs.model_configs import GEN_AI_MODEL_PROVIDER
-from danswer.direct_qa.qa_utils import get_gen_ai_api_key
+from danswer.llm.chat_llm import DefaultMultiLLM
 from danswer.llm.custom_llm import CustomModelServer
 from danswer.llm.gpt_4_all import DanswerGPT4All
 from danswer.llm.interfaces import LLM
-from danswer.llm.multi_llm import DefaultMultiLLM
+from danswer.llm.utils import get_gen_ai_api_key
 
 
 def get_default_llm(

backend/danswer/llm/interfaces.py

@@ -1,11 +1,8 @@
 import abc
 from collections.abc import Iterator
 
-from langchain.chat_models.base import BaseChatModel
 from langchain.schema.language_model import LanguageModelInput
 
-from danswer.configs.app_configs import LOG_ALL_MODEL_INTERACTIONS
-from danswer.llm.utils import message_generator_to_string_generator
 from danswer.utils.logger import setup_logger
 
 
@@ -28,48 +25,3 @@ class LLM(abc.ABC):
     @abc.abstractmethod
     def stream(self, prompt: LanguageModelInput) -> Iterator[str]:
         raise NotImplementedError
-
-
-class LangChainChatLLM(LLM, abc.ABC):
-    @property
-    @abc.abstractmethod
-    def llm(self) -> BaseChatModel:
-        raise NotImplementedError
-
-    def _log_model_config(self) -> None:
-        logger.debug(
-            f"Model Class: {self.llm.__class__.__name__}, Model Config: {self.llm.__dict__}"
-        )
-
-    @staticmethod
-    def _log_prompt(prompt: LanguageModelInput) -> None:
-        if isinstance(prompt, list):
-            for ind, msg in enumerate(prompt):
-                logger.debug(f"Message {ind}:\n{msg.content}")
-        if isinstance(prompt, str):
-            logger.debug(f"Prompt:\n{prompt}")
-
-    def invoke(self, prompt: LanguageModelInput) -> str:
-        self._log_model_config()
-        if LOG_ALL_MODEL_INTERACTIONS:
-            self._log_prompt(prompt)
-
-        model_raw = self.llm.invoke(prompt).content
-        if LOG_ALL_MODEL_INTERACTIONS:
-            logger.debug(f"Raw Model Output:\n{model_raw}")
-
-        return model_raw
-
-    def stream(self, prompt: LanguageModelInput) -> Iterator[str]:
-        self._log_model_config()
-        if LOG_ALL_MODEL_INTERACTIONS:
-            self._log_prompt(prompt)
-
-        output_tokens = []
-        for token in message_generator_to_string_generator(self.llm.stream(prompt)):
-            output_tokens.append(token)
-            yield token
-
-        full_output = "".join(output_tokens)
-        if LOG_ALL_MODEL_INTERACTIONS:
-            logger.debug(f"Raw Model Output:\n{full_output}")

backend/danswer/llm/utils.py

@@ -1,6 +1,7 @@
 from collections.abc import Callable
 from collections.abc import Iterator
 from typing import Any
+from typing import cast
 
 import tiktoken
 from langchain.prompts.base import StringPromptValue
@@ -14,9 +15,16 @@ from langchain.schema.messages import HumanMessage
 from langchain.schema.messages import SystemMessage
 
 from danswer.configs.app_configs import LOG_LEVEL
+from danswer.configs.constants import GEN_AI_API_KEY_STORAGE_KEY
 from danswer.configs.constants import MessageType
+from danswer.configs.model_configs import GEN_AI_API_KEY
 from danswer.db.models import ChatMessage
+from danswer.dynamic_configs import get_dynamic_config_store
+from danswer.dynamic_configs.interface import ConfigNotFoundError
+from danswer.llm.interfaces import LLM
+from danswer.utils.logger import setup_logger
 
+logger = setup_logger()
 
 _LLM_TOKENIZER: Callable[[str], Any] | None = None
 
@@ -107,7 +115,7 @@ def should_be_verbose() -> bool:
 def check_number_of_tokens(
     text: str, encode_fn: Callable[[str], list] | None = None
 ) -> int:
-    """Get's the number of tokens in the provided text, using the provided encoding
+    """Gets the number of tokens in the provided text, using the provided encoding
     function. If none is provided, default to the tiktoken encoder used by GPT-3.5
     and GPT-4.
     """
@@ -116,3 +124,26 @@ def check_number_of_tokens(
         encode_fn = tiktoken.get_encoding("cl100k_base").encode
 
     return len(encode_fn(text))
+
+
+def get_gen_ai_api_key() -> str | None:
+    # first check if the key has been provided by the UI
+    try:
+        return cast(str, get_dynamic_config_store().load(GEN_AI_API_KEY_STORAGE_KEY))
+    except ConfigNotFoundError:
+        pass
+
+    # if not provided by the UI, fallback to the env variable
+    return GEN_AI_API_KEY
+
+
+def test_llm(llm: LLM) -> bool:
+    # try for up to 2 timeouts (e.g. 10 seconds in total)
+    for _ in range(2):
+        try:
+            llm.invoke("Do not respond")
+            return True
+        except Exception as e:
+            logger.warning(f"GenAI API key failed for the following reason: {e}")
+
+    return False

backend/danswer/server/manage.py

@@ -23,12 +23,13 @@ from danswer.db.feedback import fetch_docs_ranked_by_boost
 from danswer.db.feedback import update_document_boost
 from danswer.db.feedback import update_document_hidden
 from danswer.db.models import User
-from danswer.direct_qa.llm_utils import check_model_api_key_is_valid
 from danswer.direct_qa.llm_utils import get_default_qa_model
-from danswer.direct_qa.qa_utils import get_gen_ai_api_key
 from danswer.document_index.factory import get_default_document_index
 from danswer.dynamic_configs import get_dynamic_config_store
 from danswer.dynamic_configs.interface import ConfigNotFoundError
+from danswer.llm.factory import get_default_llm
+from danswer.llm.utils import get_gen_ai_api_key
+from danswer.llm.utils import test_llm
 from danswer.server.models import ApiKey
 from danswer.server.models import BoostDoc
 from danswer.server.models import BoostUpdateRequest
@@ -132,7 +133,8 @@ def validate_existing_genai_api_key(
         raise HTTPException(status_code=404, detail="Key not found")
 
     try:
-        is_valid = check_model_api_key_is_valid(genai_api_key)
+        llm = get_default_llm(api_key=genai_api_key, timeout=10)
+        is_valid = test_llm(llm)
     except ValueError:
         # this is the case where they aren't using an OpenAI-based model
         is_valid = True
@@ -168,9 +170,15 @@ def store_genai_api_key(
     _: User = Depends(current_admin_user),
 ) -> None:
     try:
-        is_valid = check_model_api_key_is_valid(request.api_key)
+        if not request.api_key:
+            raise HTTPException(400, "No API key provided")
+
+        llm = get_default_llm(api_key=request.api_key, timeout=10)
+        is_valid = test_llm(llm)
+
         if not is_valid:
             raise HTTPException(400, "Invalid API key provided")
+
         get_dynamic_config_store().store(GEN_AI_API_KEY_STORAGE_KEY, request.api_key)
     except RuntimeError as e:
         raise HTTPException(400, str(e))

backend/danswer/server/search_backend.py

@@ -1,5 +1,3 @@
-from collections.abc import Generator
-
 from fastapi import APIRouter
 from fastapi import Depends
 from fastapi import HTTPException
@@ -9,30 +7,19 @@ from sqlalchemy.orm import Session
 
 from danswer.auth.users import current_admin_user
 from danswer.auth.users import current_user
-from danswer.configs.app_configs import DISABLE_GENERATIVE_AI
-from danswer.configs.app_configs import NUM_DOCUMENT_TOKENS_FED_TO_GENERATIVE_MODEL
-from danswer.configs.constants import IGNORE_FOR_QA
 from danswer.db.engine import get_session
 from danswer.db.feedback import create_doc_retrieval_feedback
-from danswer.db.feedback import create_query_event
 from danswer.db.feedback import update_query_event_feedback
 from danswer.db.models import User
 from danswer.direct_qa.answer_question import answer_qa_query
-from danswer.direct_qa.interfaces import DanswerAnswerPiece
+from danswer.direct_qa.answer_question import answer_qa_query_stream
-from danswer.direct_qa.interfaces import StreamingError
-from danswer.direct_qa.llm_utils import get_default_qa_model
-from danswer.direct_qa.qa_utils import get_usable_chunks
 from danswer.document_index.factory import get_default_document_index
 from danswer.document_index.vespa.index import VespaIndex
 from danswer.search.access_filters import build_access_filters_for_user
-from danswer.search.danswer_helper import query_intent
 from danswer.search.danswer_helper import recommend_search_flow
 from danswer.search.models import IndexFilters
-from danswer.search.models import QueryFlow
-from danswer.search.models import SearchQuery
 from danswer.search.search_runner import chunks_to_search_docs
 from danswer.search.search_runner import danswer_search
-from danswer.search.search_runner import search_chunks
 from danswer.secondary_llm_flows.extract_filters import extract_question_time_filters
 from danswer.secondary_llm_flows.query_validation import get_query_answerability
 from danswer.secondary_llm_flows.query_validation import stream_query_answerability
@@ -41,13 +28,10 @@ from danswer.server.models import QAFeedbackRequest
 from danswer.server.models import QAResponse
 from danswer.server.models import QueryValidationResponse
 from danswer.server.models import QuestionRequest
-from danswer.server.models import RerankedRetrievalDocs
 from danswer.server.models import SearchDoc
 from danswer.server.models import SearchFeedbackRequest
 from danswer.server.models import SearchResponse
-from danswer.server.utils import get_json_line
 from danswer.utils.logger import setup_logger
-from danswer.utils.timing import log_generator_function_time
 
 logger = setup_logger()
 
@@ -190,144 +174,10 @@ def stream_direct_qa(
     user: User | None = Depends(current_user),
     db_session: Session = Depends(get_session),
 ) -> StreamingResponse:
-    send_packet_debug_msg = "Sending Packet: {}"
+    packets = answer_qa_query_stream(
-    top_documents_key = "top_documents"
+        question=question, user=user, db_session=db_session
-    unranked_top_docs_key = "unranked_top_documents"
-    predicted_flow_key = "predicted_flow"
-    predicted_search_key = "predicted_search"
-    query_event_id_key = "query_event_id"
-
-    logger.debug(
-        f"Received QA query ({question.search_type.value} search): {question.query}"
     )
-    logger.debug(f"Query filters: {question.filters}")
+    return StreamingResponse(packets, media_type="application/json")
-
-    @log_generator_function_time()
-    def stream_qa_portions(
-        disable_generative_answer: bool = DISABLE_GENERATIVE_AI,
-    ) -> Generator[str, None, None]:
-        answer_so_far: str = ""
-        query = question.query
-        offset_count = question.offset if question.offset is not None else 0
-
-        time_cutoff, favor_recent = extract_question_time_filters(question)
-        question.filters.time_cutoff = time_cutoff  # not used but just in case
-        filters = question.filters
-
-        user_acl_filters = build_access_filters_for_user(user, db_session)
-        final_filters = IndexFilters(
-            source_type=filters.source_type,
-            document_set=filters.document_set,
-            time_cutoff=time_cutoff,
-            access_control_list=user_acl_filters,
-        )
-
-        search_query = SearchQuery(
-            query=query,
-            search_type=question.search_type,
-            filters=final_filters,
-            favor_recent=favor_recent,
-        )
-
-        ranked_chunks, unranked_chunks = search_chunks(
-            query=search_query, document_index=get_default_document_index()
-        )
-
-        # TODO retire this
-        predicted_search, predicted_flow = query_intent(query)
-
-        if not ranked_chunks:
-            logger.debug("No Documents Found")
-            empty_docs_result = {
-                top_documents_key: None,
-                unranked_top_docs_key: None,
-                predicted_flow_key: predicted_flow,
-                predicted_search_key: predicted_search,
-            }
-            logger.debug(send_packet_debug_msg.format(empty_docs_result))
-            yield get_json_line(empty_docs_result)
-            return
-
-        top_docs = chunks_to_search_docs(ranked_chunks)
-        unranked_top_docs = chunks_to_search_docs(unranked_chunks)
-        initial_response = RerankedRetrievalDocs(
-            top_documents=top_docs,
-            unranked_top_documents=unranked_top_docs,
-            # if generative AI is disabled, set flow as search so frontend
-            # doesn't ask the user if they want to run QA over more documents
-            predicted_flow=QueryFlow.SEARCH
-            if disable_generative_answer
-            else predicted_flow,
-            predicted_search=predicted_search,
-            time_cutoff=time_cutoff,
-            favor_recent=favor_recent,
-        ).dict()
-
-        logger.debug(send_packet_debug_msg.format(initial_response))
-        yield get_json_line(initial_response)
-
-        if disable_generative_answer:
-            logger.debug("Skipping QA because generative AI is disabled")
-            return
-
-        try:
-            qa_model = get_default_qa_model()
-        except Exception as e:
-            logger.exception("Unable to get QA model")
-            error = StreamingError(error=str(e))
-            yield get_json_line(error.dict())
-            return
-
-        # remove chunks marked as not applicable for QA (e.g. Google Drive file
-        # types which can't be parsed). These chunks are useful to show in the
-        # search results, but not for QA.
-        filtered_ranked_chunks = [
-            chunk for chunk in ranked_chunks if not chunk.metadata.get(IGNORE_FOR_QA)
-        ]
-
-        # get all chunks that fit into the token limit
-        usable_chunks = get_usable_chunks(
-            chunks=filtered_ranked_chunks,
-            token_limit=NUM_DOCUMENT_TOKENS_FED_TO_GENERATIVE_MODEL,
-            offset=offset_count,
-        )
-        logger.debug(
-            f"Chunks fed to LLM: {[chunk.semantic_identifier for chunk in usable_chunks]}"
-        )
-
-        try:
-            for response_packet in qa_model.answer_question_stream(
-                query, usable_chunks
-            ):
-                if response_packet is None:
-                    continue
-                if (
-                    isinstance(response_packet, DanswerAnswerPiece)
-                    and response_packet.answer_piece
-                ):
-                    answer_so_far = answer_so_far + response_packet.answer_piece
-                logger.debug(f"Sending packet: {response_packet}")
-                yield get_json_line(response_packet.dict())
-        except Exception:
-            # exception is logged in the answer_question method, no need to re-log
-            logger.exception("Failed to run QA")
-            yield get_json_line(
-                {"error": "The LLM failed to produce a useable response"}
-            )
-
-        query_event_id = create_query_event(
-            query=query,
-            search_type=question.search_type,
-            llm_answer=answer_so_far,
-            retrieved_document_ids=[doc.document_id for doc in top_docs],
-            user_id=None if user is None else user.id,
-            db_session=db_session,
-        )
-
-        yield get_json_line({query_event_id_key: query_event_id})
-        return
-
-    return StreamingResponse(stream_qa_portions(), media_type="application/json")
 
 
 @router.post("/query-feedback")