Improve slack flow

backend/alembic/versions/fcd135795f21_add_slack_bot_display_type.py

@@ -0,0 +1,39 @@
+"""Add slack bot display type
+
+Revision ID: fcd135795f21
+Revises: 0a2b51deb0b8
+Create Date: 2024-03-04 17:03:27.116284
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+# revision identifiers, used by Alembic.
+revision = "fcd135795f21"
+down_revision = "0a2b51deb0b8"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "slack_bot_config",
+        sa.Column(
+            "response_type",
+            sa.Enum(
+                "QUOTES",
+                "CITATIONS",
+                name="slackbotresponsetype",
+                native_enum=False,
+            ),
+            nullable=True,
+        ),
+    )
+    op.execute(
+        "UPDATE slack_bot_config SET response_type = 'QUOTES' WHERE response_type IS NULL"
+    )
+    op.alter_column("slack_bot_config", "response_type", nullable=False)
+
+
+def downgrade() -> None:
+    op.drop_column("slack_bot_config", "response_type")

backend/danswer/chat/chat_utils.py

@@ -1,7 +1,7 @@
 import re
 from collections.abc import Callable
 from collections.abc import Iterator
-from datetime import datetime
+from collections.abc import Sequence
 from functools import lru_cache
 from typing import cast
 
@@ -16,7 +16,6 @@ from danswer.chat.models import DanswerAnswerPiece
 from danswer.chat.models import LlmDoc
 from danswer.configs.chat_configs import MULTILINGUAL_QUERY_EXPANSION
 from danswer.configs.chat_configs import STOP_STREAM_PAT
-from danswer.configs.constants import DocumentSource
 from danswer.configs.constants import IGNORE_FOR_QA
 from danswer.configs.model_configs import DOC_EMBEDDING_CONTEXT_SIZE
 from danswer.configs.model_configs import GEN_AI_SINGLE_USER_MESSAGE_EXPECTED_MAX_TOKENS
@@ -34,13 +33,12 @@ from danswer.llm.utils import tokenizer_trim_content
 from danswer.prompts.chat_prompts import ADDITIONAL_INFO
 from danswer.prompts.chat_prompts import CHAT_USER_CONTEXT_FREE_PROMPT
 from danswer.prompts.chat_prompts import CHAT_USER_PROMPT
-from danswer.prompts.chat_prompts import CITATION_REMINDER
-from danswer.prompts.chat_prompts import DEFAULT_IGNORE_STATEMENT
 from danswer.prompts.chat_prompts import NO_CITATION_STATEMENT
 from danswer.prompts.chat_prompts import REQUIRE_CITATION_STATEMENT
-from danswer.prompts.constants import CODE_BLOCK_PAT
+from danswer.prompts.constants import DEFAULT_IGNORE_STATEMENT
 from danswer.prompts.constants import TRIPLE_BACKTICK
-from danswer.prompts.direct_qa_prompts import LANGUAGE_HINT
+from danswer.prompts.prompt_utils import build_complete_context_str
+from danswer.prompts.prompt_utils import build_task_prompt_reminders
 from danswer.prompts.prompt_utils import get_current_llm_day_time
 from danswer.prompts.token_counts import ADDITIONAL_INFO_TOKEN_CNT
 from danswer.prompts.token_counts import (
@@ -53,68 +51,6 @@ from danswer.utils.logger import setup_logger
 
 logger = setup_logger()
 
-# Maps connector enum string to a more natural language representation for the LLM
-# If not on the list, uses the original but slightly cleaned up, see below
-CONNECTOR_NAME_MAP = {
-    "web": "Website",
-    "requesttracker": "Request Tracker",
-    "github": "GitHub",
-    "file": "File Upload",
-}
-
-
-def clean_up_source(source_str: str) -> str:
-    if source_str in CONNECTOR_NAME_MAP:
-        return CONNECTOR_NAME_MAP[source_str]
-    return source_str.replace("_", " ").title()
-
-
-def build_doc_context_str(
-    semantic_identifier: str,
-    source_type: DocumentSource,
-    content: str,
-    metadata_dict: dict[str, str | list[str]],
-    updated_at: datetime | None,
-    ind: int,
-    include_metadata: bool = True,
-) -> str:
-    context_str = ""
-    if include_metadata:
-        context_str += f"DOCUMENT {ind}: {semantic_identifier}\n"
-        context_str += f"Source: {clean_up_source(source_type)}\n"
-
-        for k, v in metadata_dict.items():
-            if isinstance(v, list):
-                v_str = ", ".join(v)
-                context_str += f"{k.capitalize()}: {v_str}\n"
-            else:
-                context_str += f"{k.capitalize()}: {v}\n"
-
-        if updated_at:
-            update_str = updated_at.strftime("%B %d, %Y %H:%M")
-            context_str += f"Updated: {update_str}\n"
-    context_str += f"{CODE_BLOCK_PAT.format(content.strip())}\n\n\n"
-    return context_str
-
-
-def build_complete_context_str(
-    context_docs: list[LlmDoc | InferenceChunk],
-    include_metadata: bool = True,
-) -> str:
-    context_str = ""
-    for ind, doc in enumerate(context_docs, start=1):
-        context_str += build_doc_context_str(
-            semantic_identifier=doc.semantic_identifier,
-            source_type=doc.source_type,
-            content=doc.content,
-            metadata_dict=doc.metadata,
-            updated_at=doc.updated_at,
-            ind=ind,
-            include_metadata=include_metadata,
-        )
-
-    return context_str.strip()
-
 
 @lru_cache()
 def build_chat_system_message(
@@ -147,18 +83,6 @@ def build_chat_system_message(
     return system_msg, token_count
 
 
-def build_task_prompt_reminders(
-    prompt: Prompt,
-    use_language_hint: bool = bool(MULTILINGUAL_QUERY_EXPANSION),
-    citation_str: str = CITATION_REMINDER,
-    language_hint_str: str = LANGUAGE_HINT,
-) -> str:
-    base_task = prompt.task_prompt
-    citation_or_nothing = citation_str if prompt.include_citations else ""
-    language_hint_or_nothing = language_hint_str.lstrip() if use_language_hint else ""
-    return base_task + citation_or_nothing + language_hint_or_nothing
-
-
 def llm_doc_from_inference_chunk(inf_chunk: InferenceChunk) -> LlmDoc:
     return LlmDoc(
         document_id=inf_chunk.document_id,
@@ -172,7 +96,7 @@ def llm_doc_from_inference_chunk(inf_chunk: InferenceChunk) -> LlmDoc:
 
 
 def map_document_id_order(
-    chunks: list[InferenceChunk | LlmDoc], one_indexed: bool = True
+    chunks: Sequence[InferenceChunk | LlmDoc], one_indexed: bool = True
 ) -> dict[str, int]:
     order_mapping = {}
     current = 1 if one_indexed else 0
@@ -568,6 +492,63 @@ def extract_citations_from_stream(
             yield DanswerAnswerPiece(answer_piece=curr_segment)
 
 
+def reorganize_citations(
+    answer: str, citations: list[CitationInfo]
+) -> tuple[str, list[CitationInfo]]:
+    """For a complete, citation-aware response, we want to reorganize the citations so that
+    they are in the order of the documents that were used in the response. This just looks nicer / avoids
+    confusion ("Why is there [7] when only 2 documents are cited?")."""
+
+    # Regular expression to find all instances of [[x]](LINK)
+    pattern = r"\[\[(.*?)\]\]\((.*?)\)"
+
+    all_citation_matches = re.findall(pattern, answer)
+
+    new_citation_info: dict[int, CitationInfo] = {}
+    for citation_match in all_citation_matches:
+        try:
+            citation_num = int(citation_match[0])
+            if citation_num in new_citation_info:
+                continue
+
+            matching_citation = next(
+                iter([c for c in citations if c.citation_num == int(citation_num)]),
+                None,
+            )
+            if matching_citation is None:
+                continue
+
+            new_citation_info[citation_num] = CitationInfo(
+                citation_num=len(new_citation_info) + 1,
+                document_id=matching_citation.document_id,
+            )
+        except Exception:
+            pass
+
+    # Function to replace citations with their new number
+    def slack_link_format(match: re.Match) -> str:
+        link_text = match.group(1)
+        try:
+            citation_num = int(link_text)
+            if citation_num in new_citation_info:
+                link_text = new_citation_info[citation_num].citation_num
+        except Exception:
+            pass
+
+        link_url = match.group(2)
+        return f"[[{link_text}]]({link_url})"
+
+    # Substitute all matches in the input text
+    new_answer = re.sub(pattern, slack_link_format, answer)
+
+    # if any citations weren't parsable, just add them back to be safe
+    for citation in citations:
+        if citation.citation_num not in new_citation_info:
+            new_citation_info[citation.citation_num] = citation
+
+    return new_answer, list(new_citation_info.values())
+
+
 def get_prompt_tokens(prompt: Prompt) -> int:
     # Note: currently custom prompts do not allow datetime aware, only default prompts
     return (

backend/danswer/chat/process_message.py

@@ -7,7 +7,6 @@ from sqlalchemy.orm import Session
 
 from danswer.chat.chat_utils import build_chat_system_message
 from danswer.chat.chat_utils import build_chat_user_message
-from danswer.chat.chat_utils import build_doc_context_str
 from danswer.chat.chat_utils import compute_max_document_tokens
 from danswer.chat.chat_utils import compute_max_llm_input_tokens
 from danswer.chat.chat_utils import create_chat_chain
@@ -51,6 +50,7 @@ from danswer.llm.utils import get_default_llm_version
 from danswer.llm.utils import get_max_input_tokens
 from danswer.llm.utils import tokenizer_trim_content
 from danswer.llm.utils import translate_history_to_basemessages
+from danswer.prompts.prompt_utils import build_doc_context_str
 from danswer.search.models import OptionalSearchSetting
 from danswer.search.models import RetrievalDetails
 from danswer.search.request_preprocessing import retrieval_preprocessing

backend/danswer/configs/danswerbot_configs.py

@@ -52,6 +52,8 @@ ENABLE_DANSWERBOT_REFLEXION = (
 )
 # Currently not support chain of thought, probably will add back later
 DANSWER_BOT_DISABLE_COT = True
+# if set, will default DanswerBot to use quotes and reference documents
+DANSWER_BOT_USE_QUOTES = os.environ.get("DANSWER_BOT_USE_QUOTES", "").lower() == "true"
 
 # Maximum Questions Per Minute, Default Uncapped
 DANSWER_BOT_MAX_QPM = int(os.environ.get("DANSWER_BOT_MAX_QPM") or 0) or None

backend/danswer/danswerbot/slack/blocks.py

@@ -1,15 +1,20 @@
+import re
 from datetime import datetime
+from re import Match
 
 import pytz
 import timeago  # type: ignore
 from slack_sdk.models.blocks import ActionsBlock
 from slack_sdk.models.blocks import Block
 from slack_sdk.models.blocks import ButtonElement
+from slack_sdk.models.blocks import ContextBlock
 from slack_sdk.models.blocks import DividerBlock
 from slack_sdk.models.blocks import HeaderBlock
 from slack_sdk.models.blocks import Option
 from slack_sdk.models.blocks import RadioButtonsElement
 from slack_sdk.models.blocks import SectionBlock
+from slack_sdk.models.blocks.basic_components import MarkdownTextObject
+from slack_sdk.models.blocks.block_elements import ImageElement
 
 from danswer.chat.models import DanswerQuote
 from danswer.configs.app_configs import DISABLE_GENERATIVE_AI
@@ -22,6 +27,7 @@ from danswer.danswerbot.slack.constants import FOLLOWUP_BUTTON_ACTION_ID
 from danswer.danswerbot.slack.constants import FOLLOWUP_BUTTON_RESOLVED_ACTION_ID
 from danswer.danswerbot.slack.constants import IMMEDIATE_RESOLVED_BUTTON_ACTION_ID
 from danswer.danswerbot.slack.constants import LIKE_BLOCK_ACTION_ID
+from danswer.danswerbot.slack.icons import source_to_github_img_link
 from danswer.danswerbot.slack.utils import build_feedback_id
 from danswer.danswerbot.slack.utils import remove_slack_text_interactions
 from danswer.danswerbot.slack.utils import translate_vespa_highlight_to_slack
@@ -29,7 +35,35 @@ from danswer.search.models import SavedSearchDoc
 from danswer.utils.text_processing import decode_escapes
 from danswer.utils.text_processing import replace_whitespaces_w_space
 
-_MAX_BLURB_LEN = 75
+_MAX_BLURB_LEN = 45
+
+
+def _process_citations_for_slack(text: str) -> str:
+    """
+    Converts instances of [[x]](LINK) in the input text to Slack's link format <LINK|[x]>.
+
+    Args:
+    - text (str): The input string containing markdown links.
+
+    Returns:
+    - str: The string with markdown links converted to Slack format.
+    """
+    # Regular expression to find all instances of [[x]](LINK)
+    pattern = r"\[\[(.*?)\]\]\((.*?)\)"
+
+    # Function to replace each found instance with Slack's format
+    def slack_link_format(match: Match) -> str:
+        link_text = match.group(1)
+        link_url = match.group(2)
+        return f"<{link_url}|[{link_text}]>"
+
+    # Substitute all matches in the input text
+    return re.sub(pattern, slack_link_format, text)
+
+
+def clean_markdown_link_text(text: str) -> str:
+    # Remove any newlines within the text
+    return text.replace("\n", " ").strip()
 
 
 def build_qa_feedback_block(message_id: int) -> Block:
@@ -38,13 +72,12 @@ def build_qa_feedback_block(message_id: int) -> Block:
         elements=[
             ButtonElement(
                 action_id=LIKE_BLOCK_ACTION_ID,
-                text="👍",
+                text="👍 Helpful",
                 style="primary",
             ),
             ButtonElement(
                 action_id=DISLIKE_BLOCK_ACTION_ID,
-                text="👎",
+                text="👎 Not helpful",
-                style="danger",
             ),
         ],
     )
@@ -164,6 +197,80 @@ def build_documents_blocks(
     return section_blocks
 
 
+def build_sources_blocks(
+    cited_documents: list[tuple[int, SavedSearchDoc]],
+    num_docs_to_display: int = DANSWER_BOT_NUM_DOCS_TO_DISPLAY,
+) -> list[Block]:
+    if not cited_documents:
+        return [
+            SectionBlock(
+                text="*Warning*: no sources were cited for this answer, so it may be unreliable 😔"
+            )
+        ]
+
+    seen_docs_identifiers = set()
+    section_blocks: list[Block] = [SectionBlock(text="*Sources:*")]
+    included_docs = 0
+    for citation_num, d in cited_documents:
+        if d.document_id in seen_docs_identifiers:
+            continue
+        seen_docs_identifiers.add(d.document_id)
+
+        doc_sem_id = d.semantic_identifier
+        if d.source_type == DocumentSource.SLACK.value:
+            # for legacy reasons, before the switch to how Slack semantic identifiers are constructed
+            if "#" not in doc_sem_id:
+                doc_sem_id = "#" + doc_sem_id
+
+        # this is needed to try and prevent the line from overflowing
+        # if it does overflow, the image gets placed above the title and it
+        # looks bad
+        doc_sem_id = (
+            doc_sem_id[:_MAX_BLURB_LEN] + "..."
+            if len(doc_sem_id) > _MAX_BLURB_LEN
+            else doc_sem_id
+        )
+
+        owner_str = f"By {d.primary_owners[0]}" if d.primary_owners else None
+        days_ago_str = (
+            timeago.format(d.updated_at, datetime.now(pytz.utc))
+            if d.updated_at
+            else None
+        )
+        final_metadata_str = " | ".join(
+            ([owner_str] if owner_str else [])
+            + ([days_ago_str] if days_ago_str else [])
+        )
+
+        document_title = clean_markdown_link_text(doc_sem_id)
+        img_link = source_to_github_img_link(d.source_type)
+
+        section_blocks.append(
+            ContextBlock(
+                elements=(
+                    [
+                        ImageElement(
+                            image_url=img_link,
+                            alt_text=f"{d.source_type.value} logo",
+                        )
+                    ]
+                    if img_link
+                    else []
+                )
+                + [
+                    MarkdownTextObject(
+                        text=f"*<{d.link}|[{citation_num}] {document_title}>*\n{final_metadata_str}"
+                    ),
+                ]
+            )
+        )
+
+        if included_docs >= num_docs_to_display:
+            break
+
+    return section_blocks
+
+
 def build_quotes_block(
     quotes: list[DanswerQuote],
 ) -> list[Block]:
@@ -214,6 +321,7 @@ def build_qa_response_blocks(
     time_cutoff: datetime | None,
     favor_recent: bool,
     skip_quotes: bool = False,
+    process_message_for_citations: bool = False,
     skip_ai_feedback: bool = False,
 ) -> list[Block]:
     if DISABLE_GENERATIVE_AI:
@@ -221,8 +329,6 @@ def build_qa_response_blocks(
 
     quotes_blocks: list[Block] = []
 
-    ai_answer_header = HeaderBlock(text="AI Answer")
-
     filter_block: Block | None = None
     if time_cutoff or favor_recent or source_filters:
         filter_text = "Filters: "
@@ -247,6 +353,8 @@ def build_qa_response_blocks(
         )
     else:
         answer_processed = decode_escapes(remove_slack_text_interactions(answer))
+        if process_message_for_citations:
+            answer_processed = _process_citations_for_slack(answer_processed)
         answer_block = SectionBlock(text=answer_processed)
         if quotes:
             quotes_blocks = build_quotes_block(quotes)
@@ -259,7 +367,7 @@ def build_qa_response_blocks(
                 )
             ]
 
-    response_blocks: list[Block] = [ai_answer_header]
+    response_blocks: list[Block] = []
 
     if filter_block is not None:
         response_blocks.append(filter_block)
@@ -271,7 +379,6 @@ def build_qa_response_blocks(
 
     if not skip_quotes:
         response_blocks.extend(quotes_blocks)
-    response_blocks.append(DividerBlock())
 
     return response_blocks
 

backend/danswer/danswerbot/slack/handlers/handle_message.py

@@ -9,6 +9,7 @@ from typing import TypeVar
 from retry import retry
 from slack_sdk import WebClient
 from slack_sdk.errors import SlackApiError
+from slack_sdk.models.blocks import DividerBlock
 from sqlalchemy.orm import Session
 
 from danswer.chat.chat_utils import compute_max_document_tokens
@@ -18,12 +19,14 @@ from danswer.configs.danswerbot_configs import DANSWER_BOT_DISABLE_DOCS_ONLY_ANS
 from danswer.configs.danswerbot_configs import DANSWER_BOT_DISPLAY_ERROR_MSGS
 from danswer.configs.danswerbot_configs import DANSWER_BOT_NUM_RETRIES
 from danswer.configs.danswerbot_configs import DANSWER_BOT_TARGET_CHUNK_PERCENTAGE
+from danswer.configs.danswerbot_configs import DANSWER_BOT_USE_QUOTES
 from danswer.configs.danswerbot_configs import DANSWER_REACT_EMOJI
 from danswer.configs.danswerbot_configs import DISABLE_DANSWER_BOT_FILTER_DETECT
 from danswer.configs.danswerbot_configs import ENABLE_DANSWERBOT_REFLEXION
 from danswer.danswerbot.slack.blocks import build_documents_blocks
 from danswer.danswerbot.slack.blocks import build_follow_up_block
 from danswer.danswerbot.slack.blocks import build_qa_response_blocks
+from danswer.danswerbot.slack.blocks import build_sources_blocks
 from danswer.danswerbot.slack.blocks import get_restate_blocks
 from danswer.danswerbot.slack.constants import SLACK_CHANNEL_ID
 from danswer.danswerbot.slack.models import SlackMessageInfo
@@ -35,6 +38,7 @@ from danswer.danswerbot.slack.utils import SlackRateLimiter
 from danswer.danswerbot.slack.utils import update_emote_react
 from danswer.db.engine import get_sqlalchemy_engine
 from danswer.db.models import SlackBotConfig
+from danswer.db.models import SlackBotResponseType
 from danswer.llm.utils import check_number_of_tokens
 from danswer.llm.utils import get_default_llm_version
 from danswer.llm.utils import get_max_input_tokens
@@ -137,6 +141,13 @@ def handle_message(
 
     should_respond_even_with_no_docs = persona.num_chunks == 0 if persona else False
 
+    # figure out if we want to use citations or quotes
+    use_citations = (
+        not DANSWER_BOT_USE_QUOTES
+        if channel_config is None
+        else channel_config.response_type == SlackBotResponseType.CITATIONS
+    )
+
     # List of user id to send message to, if None, send to everyone in channel
     send_to: list[str] | None = None
     respond_tag_only = False
@@ -259,6 +270,7 @@ def handle_message(
                 answer_generation_timeout=answer_generation_timeout,
                 enable_reflexion=reflexion,
                 bypass_acl=bypass_acl,
+                use_citations=use_citations,
             )
             if not answer.error_msg:
                 return answer
@@ -387,7 +399,10 @@ def handle_message(
         source_filters=retrieval_info.applied_source_filters,
         time_cutoff=retrieval_info.applied_time_cutoff,
         favor_recent=retrieval_info.recency_bias_multiplier > 1,
-        skip_quotes=persona is not None,  # currently Personas don't support quotes
+        # currently Personas don't support quotes
+        # if citations are enabled, also don't use quotes
+        skip_quotes=persona is not None or use_citations,
+        process_message_for_citations=use_citations,
     )
 
     # Get the chunks fed to the LLM only, then fill with other docs
@@ -397,16 +412,33 @@ def handle_message(
         doc for idx, doc in enumerate(top_docs) if idx not in llm_doc_inds
     ]
     priority_ordered_docs = llm_docs + remaining_docs
-    document_blocks = (
+
-        build_documents_blocks(
+    document_blocks = []
+    citations_block = []
+    # if citations are enabled, only show cited documents
+    if use_citations:
+        citations = answer.citations or []
+        cited_docs = []
+        for citation in citations:
+            matching_doc = next(
+                (d for d in top_docs if d.document_id == citation.document_id),
+                None,
+            )
+            if matching_doc:
+                cited_docs.append((citation.citation_num, matching_doc))
+
+        cited_docs.sort()
+        citations_block = build_sources_blocks(cited_documents=cited_docs)
+    elif priority_ordered_docs:
+        document_blocks = build_documents_blocks(
             documents=priority_ordered_docs,
             message_id=answer.chat_message_id,
         )
-        if priority_ordered_docs
+        document_blocks = [DividerBlock()] + document_blocks
-        else []
-    )
 
-    all_blocks = restate_question_block + answer_blocks + document_blocks
+    all_blocks = (
+        restate_question_block + answer_blocks + citations_block + document_blocks
+    )
 
     if channel_conf and channel_conf.get("follow_up_tags") is not None:
         all_blocks.append(build_follow_up_block(message_id=answer.chat_message_id))

backend/danswer/danswerbot/slack/icons.py

@@ -0,0 +1,58 @@
+from danswer.configs.constants import DocumentSource
+
+
+def source_to_github_img_link(source: DocumentSource) -> str | None:
+    # TODO: store these images somewhere better
+    if source == DocumentSource.WEB.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/improve-slack-flow/backend/slackbot_images/Web.png"
+    if source == DocumentSource.FILE.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/improve-slack-flow/backend/slackbot_images/File.png"
+    if source == DocumentSource.GOOGLE_SITES.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/GoogleSites.png"
+    if source == DocumentSource.SLACK.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Slack.png"
+    if source == DocumentSource.GMAIL.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Gmail.png"
+    if source == DocumentSource.GOOGLE_DRIVE.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/GoogleDrive.png"
+    if source == DocumentSource.GITHUB.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Github.png"
+    if source == DocumentSource.GITLAB.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Gitlab.png"
+    if source == DocumentSource.CONFLUENCE.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/improve-slack-flow/backend/slackbot_images/Confluence.png"
+    if source == DocumentSource.JIRA.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/improve-slack-flow/backend/slackbot_images/Jira.png"
+    if source == DocumentSource.NOTION.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Notion.png"
+    if source == DocumentSource.ZENDESK.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/improve-slack-flow/backend/slackbot_images/Zendesk.png"
+    if source == DocumentSource.GONG.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Gong.png"
+    if source == DocumentSource.LINEAR.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Linear.png"
+    if source == DocumentSource.PRODUCTBOARD.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Productboard.webp"
+    if source == DocumentSource.SLAB.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/SlabLogo.png"
+    if source == DocumentSource.ZULIP.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Zulip.png"
+    if source == DocumentSource.GURU.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/improve-slack-flow/backend/slackbot_images/Guru.png"
+    if source == DocumentSource.HUBSPOT.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/HubSpot.png"
+    if source == DocumentSource.DOCUMENT360.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Document360.png"
+    if source == DocumentSource.BOOKSTACK.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Bookstack.png"
+    if source == DocumentSource.LOOPIO.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Loopio.png"
+    if source == DocumentSource.SHAREPOINT.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Sharepoint.png"
+    if source == DocumentSource.REQUESTTRACKER.value:
+        # just use file icon for now
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/improve-slack-flow/backend/slackbot_images/File.png"
+    if source == DocumentSource.INGESTION_API.value:
+        return "https://raw.githubusercontent.com/danswer-ai/danswer/improve-slack-flow/backend/slackbot_images/File.png"
+
+    return "https://raw.githubusercontent.com/danswer-ai/danswer/improve-slack-flow/backend/slackbot_images/File.png"

backend/danswer/danswerbot/slack/utils.py

@@ -346,8 +346,12 @@ def read_slack_thread(
             if len(blocks) <= 1:
                 continue
 
-            # The useful block is the second one after the header block that says AI Answer
+            # For the old flow, the useful block is the second one after the header block that says AI Answer
-            message = reply["blocks"][1]["text"]["text"]
+            if reply["blocks"][0]["text"]["text"] == "AI Answer":
+                message = reply["blocks"][1]["text"]["text"]
+            else:
+                # for the new flow, the answer is the first block
+                message = reply["blocks"][0]["text"]["text"]
 
             if message.startswith("_Filters"):
                 if len(blocks) <= 2:

backend/danswer/db/models.py

@@ -811,6 +811,11 @@ class ChannelConfig(TypedDict):
     follow_up_tags: NotRequired[list[str]]
 
 
+class SlackBotResponseType(str, PyEnum):
+    QUOTES = "quotes"
+    CITATIONS = "citations"
+
+
 class SlackBotConfig(Base):
     __tablename__ = "slack_bot_config"
 
@@ -822,6 +827,9 @@ class SlackBotConfig(Base):
     channel_config: Mapped[ChannelConfig] = mapped_column(
         postgresql.JSONB(), nullable=False
     )
+    response_type: Mapped[SlackBotResponseType] = mapped_column(
+        Enum(SlackBotResponseType, native_enum=False), nullable=False
+    )
 
     persona: Mapped[Persona | None] = relationship("Persona")
 

backend/danswer/db/slack_bot_config.py

@@ -11,6 +11,7 @@ from danswer.db.models import ChannelConfig
 from danswer.db.models import Persona
 from danswer.db.models import Persona__DocumentSet
 from danswer.db.models import SlackBotConfig
+from danswer.db.models import SlackBotResponseType
 from danswer.search.models import RecencyBiasSetting
 
 
@@ -72,11 +73,13 @@ def create_slack_bot_persona(
 def insert_slack_bot_config(
     persona_id: int | None,
     channel_config: ChannelConfig,
+    response_type: SlackBotResponseType,
     db_session: Session,
 ) -> SlackBotConfig:
     slack_bot_config = SlackBotConfig(
         persona_id=persona_id,
         channel_config=channel_config,
+        response_type=response_type,
     )
     db_session.add(slack_bot_config)
     db_session.commit()
@@ -88,6 +91,7 @@ def update_slack_bot_config(
     slack_bot_config_id: int,
     persona_id: int | None,
     channel_config: ChannelConfig,
+    response_type: SlackBotResponseType,
     db_session: Session,
 ) -> SlackBotConfig:
     slack_bot_config = db_session.scalar(
@@ -105,6 +109,7 @@ def update_slack_bot_config(
     # will encounter `violates foreign key constraint` errors
     slack_bot_config.persona_id = persona_id
     slack_bot_config.channel_config = channel_config
+    slack_bot_config.response_type = response_type
 
     # if the persona has changed, then clean up the old persona
     if persona_id != existing_persona_id and existing_persona_id:

backend/danswer/one_shot_answer/answer_question.py

@@ -3,10 +3,18 @@ from collections.abc import Callable
 from collections.abc import Iterator
 from typing import cast
 
+from langchain.schema.messages import BaseMessage
+from langchain.schema.messages import HumanMessage
 from sqlalchemy.orm import Session
 
+from danswer.chat.chat_utils import build_chat_system_message
 from danswer.chat.chat_utils import compute_max_document_tokens
+from danswer.chat.chat_utils import extract_citations_from_stream
 from danswer.chat.chat_utils import get_chunks_for_qa
+from danswer.chat.chat_utils import llm_doc_from_inference_chunk
+from danswer.chat.chat_utils import map_document_id_order
+from danswer.chat.chat_utils import reorganize_citations
+from danswer.chat.models import CitationInfo
 from danswer.chat.models import DanswerAnswerPiece
 from danswer.chat.models import DanswerContext
 from danswer.chat.models import DanswerContexts
@@ -26,16 +34,23 @@ from danswer.db.chat import get_persona_by_id
 from danswer.db.chat import get_prompt_by_id
 from danswer.db.chat import translate_db_message_to_chat_message_detail
 from danswer.db.embedding_model import get_current_db_embedding_model
+from danswer.db.models import Prompt
 from danswer.db.models import User
 from danswer.document_index.factory import get_default_document_index
 from danswer.indexing.models import InferenceChunk
+from danswer.llm.factory import get_default_llm
 from danswer.llm.utils import get_default_llm_token_encode
+from danswer.llm.utils import get_default_llm_tokenizer
 from danswer.one_shot_answer.factory import get_question_answer_model
 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.models import ThreadMessage
 from danswer.one_shot_answer.qa_block import no_gen_ai_response
 from danswer.one_shot_answer.qa_utils import combine_message_thread
+from danswer.prompts.direct_qa_prompts import CITATIONS_PROMPT
+from danswer.prompts.prompt_utils import build_complete_context_str
+from danswer.prompts.prompt_utils import build_task_prompt_reminders
 from danswer.search.models import RerankMetricsContainer
 from danswer.search.models import RetrievalMetricsContainer
 from danswer.search.models import SavedSearchDoc
@@ -51,6 +66,118 @@ from danswer.utils.timing import log_generator_function_time
 
 logger = setup_logger()
 
+AnswerObjectIterator = Iterator[
+    QueryRephrase
+    | QADocsResponse
+    | LLMRelevanceFilterResponse
+    | DanswerAnswerPiece
+    | DanswerQuotes
+    | DanswerContexts
+    | StreamingError
+    | ChatMessageDetail
+    | CitationInfo
+]
+
+
+def quote_based_qa(
+    prompt: Prompt,
+    query_message: ThreadMessage,
+    history_str: str,
+    context_chunks: list[InferenceChunk],
+    llm_override: str | None,
+    timeout: int,
+    use_chain_of_thought: bool,
+    return_contexts: bool,
+    llm_metrics_callback: Callable[[LLMMetricsContainer], None] | None = None,
+) -> AnswerObjectIterator:
+    qa_model = get_question_answer_model(
+        prompt=prompt,
+        timeout=timeout,
+        chain_of_thought=use_chain_of_thought,
+        llm_version=llm_override,
+    )
+
+    full_prompt_str = (
+        qa_model.build_prompt(
+            query=query_message.message,
+            history_str=history_str,
+            context_chunks=context_chunks,
+        )
+        if qa_model is not None
+        else "Gen AI Disabled"
+    )
+
+    response_packets = (
+        qa_model.answer_question_stream(
+            prompt=full_prompt_str,
+            llm_context_docs=context_chunks,
+            metrics_callback=llm_metrics_callback,
+        )
+        if qa_model is not None
+        else no_gen_ai_response()
+    )
+
+    if qa_model is not None and return_contexts:
+        contexts = DanswerContexts(
+            contexts=[
+                DanswerContext(
+                    content=context_chunk.content,
+                    document_id=context_chunk.document_id,
+                    semantic_identifier=context_chunk.semantic_identifier,
+                    blurb=context_chunk.semantic_identifier,
+                )
+                for context_chunk in context_chunks
+            ]
+        )
+
+        response_packets = itertools.chain(response_packets, [contexts])
+
+    yield from response_packets
+
+
+def citation_based_qa(
+    prompt: Prompt,
+    query_message: ThreadMessage,
+    history_str: str,
+    context_chunks: list[InferenceChunk],
+    llm_override: str | None,
+    timeout: int,
+) -> AnswerObjectIterator:
+    llm_tokenizer = get_default_llm_tokenizer()
+
+    system_prompt_or_none, _ = build_chat_system_message(
+        prompt=prompt,
+        context_exists=True,
+        llm_tokenizer_encode_func=llm_tokenizer.encode,
+    )
+
+    task_prompt_with_reminder = build_task_prompt_reminders(prompt)
+
+    context_docs_str = build_complete_context_str(context_chunks)
+    user_message = HumanMessage(
+        content=CITATIONS_PROMPT.format(
+            task_prompt=task_prompt_with_reminder,
+            user_query=query_message.message,
+            history_block=history_str,
+            context_docs_str=context_docs_str,
+        )
+    )
+
+    llm = get_default_llm(
+        timeout=timeout,
+        gen_ai_model_version_override=llm_override,
+    )
+
+    llm_prompt: list[BaseMessage] = [user_message]
+    if system_prompt_or_none is not None:
+        llm_prompt = [system_prompt_or_none] + llm_prompt
+
+    llm_docs = [llm_doc_from_inference_chunk(chunk) for chunk in context_chunks]
+    doc_id_to_rank_map = map_document_id_order(llm_docs)
+
+    tokens = llm.stream(llm_prompt)
+    yield from extract_citations_from_stream(tokens, llm_docs, doc_id_to_rank_map)
+
 
 def stream_answer_objects(
     query_req: DirectQARequest,
@@ -66,20 +193,12 @@ def stream_answer_objects(
     default_chunk_size: int = DOC_EMBEDDING_CONTEXT_SIZE,
     timeout: int = QA_TIMEOUT,
     bypass_acl: bool = False,
+    use_citations: bool = False,
     retrieval_metrics_callback: Callable[[RetrievalMetricsContainer], None]
     | None = None,
     rerank_metrics_callback: Callable[[RerankMetricsContainer], None] | None = None,
     llm_metrics_callback: Callable[[LLMMetricsContainer], None] | None = None,
-) -> Iterator[
+) -> AnswerObjectIterator:
-    QueryRephrase
-    | QADocsResponse
-    | LLMRelevanceFilterResponse
-    | DanswerAnswerPiece
-    | DanswerQuotes
-    | DanswerContexts
-    | StreamingError
-    | ChatMessageDetail
-]:
     """Streams in order:
     1. [always] Retrieved documents, stops flow if nothing is found
     2. [conditional] LLM selected chunk indices if LLM chunk filtering is turned on
@@ -216,63 +335,51 @@ def stream_answer_objects(
             persona_id=query_req.persona_id, user_id=user_id, db_session=db_session
         )
         llm_override = persona.llm_model_version_override
-
+    if prompt is None:
-    qa_model = get_question_answer_model(
+        if not chat_session.persona.prompts:
-        prompt=prompt,
+            raise RuntimeError(
-        timeout=timeout,
+                "Persona does not have any prompts - this should never happen"
-        chain_of_thought=query_req.chain_of_thought,
+            )
-        llm_version=llm_override,
+        prompt = chat_session.persona.prompts[0]
-    )
-
-    full_prompt_str = (
-        qa_model.build_prompt(
-            query=query_msg.message, history_str=history_str, context_chunks=llm_chunks
-        )
-        if qa_model is not None
-        else "Gen AI Disabled"
-    )
 
     # Create the first User query message
     new_user_message = create_new_chat_message(
         chat_session_id=chat_session.id,
         parent_message=root_message,
         prompt_id=query_req.prompt_id,
-        message=full_prompt_str,
+        message=query_msg.message,
-        token_count=len(llm_tokenizer(full_prompt_str)),
+        token_count=len(llm_tokenizer(query_msg.message)),
         message_type=MessageType.USER,
         db_session=db_session,
         commit=True,
     )
 
-    response_packets = (
+    if use_citations:
-        qa_model.answer_question_stream(
+        qa_stream = citation_based_qa(
-            prompt=full_prompt_str,
+            prompt=prompt,
-            llm_context_docs=llm_chunks,
+            query_message=query_msg,
-            metrics_callback=llm_metrics_callback,
+            history_str=history_str,
+            context_chunks=llm_chunks,
+            llm_override=llm_override,
+            timeout=timeout,
         )
-        if qa_model is not None
+    else:
-        else no_gen_ai_response()
+        qa_stream = quote_based_qa(
-    )
+            prompt=prompt,
-
+            query_message=query_msg,
-    if qa_model is not None and query_req.return_contexts:
+            history_str=history_str,
-        contexts = DanswerContexts(
+            context_chunks=llm_chunks,
-            contexts=[
+            llm_override=llm_override,
-                DanswerContext(
+            timeout=timeout,
-                    content=context_doc.content,
+            use_chain_of_thought=False,
-                    document_id=context_doc.document_id,
+            return_contexts=False,
-                    semantic_identifier=context_doc.semantic_identifier,
+            llm_metrics_callback=llm_metrics_callback,
-                    blurb=context_doc.semantic_identifier,
-                )
-                for context_doc in llm_chunks
-            ]
         )
 
-        response_packets = itertools.chain(response_packets, [contexts])
-
     # Capture outputs and errors
     llm_output = ""
     error: str | None = None
-    for packet in response_packets:
+    for packet in qa_stream:
         logger.debug(packet)
 
         if isinstance(packet, DanswerAnswerPiece):
@@ -333,6 +440,7 @@ def get_search_answer(
     answer_generation_timeout: int = QA_TIMEOUT,
     enable_reflexion: bool = False,
     bypass_acl: bool = False,
+    use_citations: bool = False,
     retrieval_metrics_callback: Callable[[RetrievalMetricsContainer], None]
     | None = None,
     rerank_metrics_callback: Callable[[RerankMetricsContainer], None] | None = None,
@@ -348,6 +456,7 @@ def get_search_answer(
         max_history_tokens=max_history_tokens,
         db_session=db_session,
         bypass_acl=bypass_acl,
+        use_citations=use_citations,
         timeout=answer_generation_timeout,
         retrieval_metrics_callback=retrieval_metrics_callback,
         rerank_metrics_callback=rerank_metrics_callback,
@@ -366,6 +475,11 @@ def get_search_answer(
             qa_response.llm_chunks_indices = packet.relevant_chunk_indices
         elif isinstance(packet, DanswerQuotes):
             qa_response.quotes = packet
+        elif isinstance(packet, CitationInfo):
+            if qa_response.citations:
+                qa_response.citations.append(packet)
+            else:
+                qa_response.citations = [packet]
         elif isinstance(packet, DanswerContexts):
             qa_response.contexts = packet
         elif isinstance(packet, StreamingError):
@@ -384,4 +498,10 @@ def get_search_answer(
         else:
             qa_response.answer_valid = True
 
+    if use_citations and qa_response.answer and qa_response.citations:
+        # Reorganize citation nums to be in the same order as the answer
+        qa_response.answer, qa_response.citations = reorganize_citations(
+            qa_response.answer, qa_response.citations
+        )
+
     return qa_response

backend/danswer/one_shot_answer/models.py

@@ -4,6 +4,7 @@ from pydantic import BaseModel
 from pydantic import Field
 from pydantic import root_validator
 
+from danswer.chat.models import CitationInfo
 from danswer.chat.models import DanswerContexts
 from danswer.chat.models import DanswerQuotes
 from danswer.chat.models import QADocsResponse
@@ -51,6 +52,7 @@ class OneShotQAResponse(BaseModel):
     answer: str | None = None
     rephrase: str | None = None
     quotes: DanswerQuotes | None = None
+    citations: list[CitationInfo] | None = None
     docs: QADocsResponse | None = None
     llm_chunks_indices: list[int] | None = None
     error_msg: str | None = None

backend/danswer/one_shot_answer/qa_block.py

@@ -4,7 +4,6 @@ from collections.abc import Callable
 from collections.abc import Iterator
 from typing import cast
 
-from danswer.chat.chat_utils import build_complete_context_str
 from danswer.chat.models import AnswerQuestionStreamReturn
 from danswer.chat.models import DanswerAnswer
 from danswer.chat.models import DanswerAnswerPiece
@@ -33,6 +32,7 @@ from danswer.prompts.direct_qa_prompts import PARAMATERIZED_PROMPT_WITHOUT_CONTE
 from danswer.prompts.direct_qa_prompts import WEAK_LLM_PROMPT
 from danswer.prompts.direct_qa_prompts import WEAK_MODEL_SYSTEM_PROMPT
 from danswer.prompts.direct_qa_prompts import WEAK_MODEL_TASK_PROMPT
+from danswer.prompts.prompt_utils import build_complete_context_str
 from danswer.utils.logger import setup_logger
 from danswer.utils.text_processing import clean_up_code_blocks
 from danswer.utils.text_processing import escape_newlines

backend/danswer/prompts/chat_prompts.py

@@ -17,8 +17,6 @@ Remember to provide inline citations in the format [1], [2], [3], etc.
 ADDITIONAL_INFO = "\n\nAdditional Information:\n\t- {datetime_info}."
 
 
-DEFAULT_IGNORE_STATEMENT = " Ignore any context documents that are not relevant."
-
 CHAT_USER_PROMPT = f"""
 Refer to the following context documents when responding to me.{{optional_ignore_statement}}
 CONTEXT:

backend/danswer/prompts/constants.py

@@ -12,3 +12,18 @@ QUOTE_PAT = "Quote:"
 QUOTES_PAT_PLURAL = "Quotes:"
 INVALID_PAT = "Invalid:"
 SOURCES_KEY = "sources"
+
+DEFAULT_IGNORE_STATEMENT = " Ignore any context documents that are not relevant."
+
+REQUIRE_CITATION_STATEMENT = """
+Cite relevant statements INLINE using the format [1], [2], [3], etc to reference the document number, \
+DO NOT provide a reference section at the end and DO NOT provide any links following the citations.
+""".rstrip()
+
+NO_CITATION_STATEMENT = """
+Do not provide any citations even if there are examples in the chat history.
+""".rstrip()
+
+CITATION_REMINDER = """
+Remember to provide inline citations in the format [1], [2], [3], etc.
+"""

backend/danswer/prompts/direct_qa_prompts.py

@@ -2,6 +2,7 @@
 # It is used also for the one shot direct QA flow
 import json
 
+from danswer.prompts.constants import DEFAULT_IGNORE_STATEMENT
 from danswer.prompts.constants import FINAL_QUERY_PAT
 from danswer.prompts.constants import GENERAL_SEP_PAT
 from danswer.prompts.constants import QUESTION_PAT
@@ -96,6 +97,22 @@ SAMPLE RESPONSE:
 """.strip()
 
 
+# similar to the chat flow, but with the option of including a
+# "conversation history" block
+CITATIONS_PROMPT = f"""
+Refer to the following context documents when responding to me.{DEFAULT_IGNORE_STATEMENT}
+CONTEXT:
+{GENERAL_SEP_PAT}
+{{context_docs_str}}
+{GENERAL_SEP_PAT}
+
+{{history_block}}{{task_prompt}}
+
+{QUESTION_PAT.upper()}
+{{user_query}}
+"""
+
+
 # For weak LLM which only takes one chunk and cannot output json
 # Also not requiring quotes as it tends to not work
 WEAK_LLM_PROMPT = f"""

backend/danswer/prompts/prompt_utils.py

@@ -1,5 +1,15 @@
+from collections.abc import Sequence
 from datetime import datetime
 
+from danswer.chat.models import LlmDoc
+from danswer.configs.chat_configs import MULTILINGUAL_QUERY_EXPANSION
+from danswer.configs.constants import DocumentSource
+from danswer.db.models import Prompt
+from danswer.indexing.models import InferenceChunk
+from danswer.prompts.chat_prompts import CITATION_REMINDER
+from danswer.prompts.constants import CODE_BLOCK_PAT
+from danswer.prompts.direct_qa_prompts import LANGUAGE_HINT
+
 
 def get_current_llm_day_time() -> str:
     current_datetime = datetime.now()
@@ -7,3 +17,78 @@ def get_current_llm_day_time() -> str:
     formatted_datetime = current_datetime.strftime("%B %d, %Y %H:%M")
     day_of_week = current_datetime.strftime("%A")
     return f"The current day and time is {day_of_week} {formatted_datetime}"
+
+
+def build_task_prompt_reminders(
+    prompt: Prompt,
+    use_language_hint: bool = bool(MULTILINGUAL_QUERY_EXPANSION),
+    citation_str: str = CITATION_REMINDER,
+    language_hint_str: str = LANGUAGE_HINT,
+) -> str:
+    base_task = prompt.task_prompt
+    citation_or_nothing = citation_str if prompt.include_citations else ""
+    language_hint_or_nothing = language_hint_str.lstrip() if use_language_hint else ""
+    return base_task + citation_or_nothing + language_hint_or_nothing
+
+
+# Maps connector enum string to a more natural language representation for the LLM
+# If not on the list, uses the original but slightly cleaned up, see below
+CONNECTOR_NAME_MAP = {
+    "web": "Website",
+    "requesttracker": "Request Tracker",
+    "github": "GitHub",
+    "file": "File Upload",
+}
+
+
+def clean_up_source(source_str: str) -> str:
+    if source_str in CONNECTOR_NAME_MAP:
+        return CONNECTOR_NAME_MAP[source_str]
+    return source_str.replace("_", " ").title()
+
+
+def build_doc_context_str(
+    semantic_identifier: str,
+    source_type: DocumentSource,
+    content: str,
+    metadata_dict: dict[str, str | list[str]],
+    updated_at: datetime | None,
+    ind: int,
+    include_metadata: bool = True,
+) -> str:
+    context_str = ""
+    if include_metadata:
+        context_str += f"DOCUMENT {ind}: {semantic_identifier}\n"
+        context_str += f"Source: {clean_up_source(source_type)}\n"
+
+        for k, v in metadata_dict.items():
+            if isinstance(v, list):
+                v_str = ", ".join(v)
+                context_str += f"{k.capitalize()}: {v_str}\n"
+            else:
+                context_str += f"{k.capitalize()}: {v}\n"
+
+        if updated_at:
+            update_str = updated_at.strftime("%B %d, %Y %H:%M")
+            context_str += f"Updated: {update_str}\n"
+    context_str += f"{CODE_BLOCK_PAT.format(content.strip())}\n\n\n"
+    return context_str
+
+
+def build_complete_context_str(
+    context_docs: Sequence[LlmDoc | InferenceChunk],
+    include_metadata: bool = True,
+) -> str:
+    context_str = ""
+    for ind, doc in enumerate(context_docs, start=1):
+        context_str += build_doc_context_str(
+            semantic_identifier=doc.semantic_identifier,
+            source_type=doc.source_type,
+            content=doc.content,
+            metadata_dict=doc.metadata,
+            updated_at=doc.updated_at,
+            ind=ind,
+            include_metadata=include_metadata,
+        )
+
+    return context_str.strip()

backend/danswer/prompts/token_counts.py

@@ -2,8 +2,8 @@ from danswer.llm.utils import check_number_of_tokens
 from danswer.prompts.chat_prompts import ADDITIONAL_INFO
 from danswer.prompts.chat_prompts import CHAT_USER_PROMPT
 from danswer.prompts.chat_prompts import CITATION_REMINDER
-from danswer.prompts.chat_prompts import DEFAULT_IGNORE_STATEMENT
 from danswer.prompts.chat_prompts import REQUIRE_CITATION_STATEMENT
+from danswer.prompts.constants import DEFAULT_IGNORE_STATEMENT
 from danswer.prompts.direct_qa_prompts import LANGUAGE_HINT
 from danswer.prompts.prompt_utils import get_current_llm_day_time
 

backend/danswer/server/documents/document.py

@@ -5,12 +5,12 @@ from fastapi import Query
 from sqlalchemy.orm import Session
 
 from danswer.auth.users import current_user
-from danswer.chat.chat_utils import build_doc_context_str
 from danswer.db.embedding_model import get_current_db_embedding_model
 from danswer.db.engine import get_session
 from danswer.db.models import User
 from danswer.document_index.factory import get_default_document_index
 from danswer.llm.utils import get_default_llm_token_encode
+from danswer.prompts.prompt_utils import build_doc_context_str
 from danswer.search.access_filters import build_access_filters_for_user
 from danswer.search.models import IndexFilters
 from danswer.server.documents.models import ChunkInfo

backend/danswer/server/manage/models.py

@@ -9,6 +9,8 @@ from danswer.configs.constants import AuthType
 from danswer.danswerbot.slack.config import VALID_SLACK_FILTERS
 from danswer.db.models import AllowedAnswerFilters
 from danswer.db.models import ChannelConfig
+from danswer.db.models import SlackBotConfig as SlackBotConfigModel
+from danswer.db.models import SlackBotResponseType
 from danswer.server.features.persona.models import PersonaSnapshot
 
 
@@ -81,6 +83,7 @@ class SlackBotConfigCreationRequest(BaseModel):
     answer_filters: list[AllowedAnswerFilters] = []
     # list of user emails
     follow_up_tags: list[str] | None = None
+    response_type: SlackBotResponseType
 
     @validator("answer_filters", pre=True)
     def validate_filters(cls, value: list[str]) -> list[str]:
@@ -104,6 +107,22 @@ class SlackBotConfig(BaseModel):
     id: int
     persona: PersonaSnapshot | None
     channel_config: ChannelConfig
+    response_type: SlackBotResponseType
+
+    @classmethod
+    def from_model(
+        cls, slack_bot_config_model: SlackBotConfigModel
+    ) -> "SlackBotConfig":
+        return cls(
+            id=slack_bot_config_model.id,
+            persona=(
+                PersonaSnapshot.from_model(slack_bot_config_model.persona)
+                if slack_bot_config_model.persona
+                else None
+            ),
+            channel_config=slack_bot_config_model.channel_config,
+            response_type=slack_bot_config_model.response_type,
+        )
 
 
 class ModelVersionResponse(BaseModel):

backend/danswer/server/manage/slack_bot.py

@@ -19,7 +19,6 @@ from danswer.db.slack_bot_config import insert_slack_bot_config
 from danswer.db.slack_bot_config import remove_slack_bot_config
 from danswer.db.slack_bot_config import update_slack_bot_config
 from danswer.dynamic_configs.interface import ConfigNotFoundError
-from danswer.server.features.persona.models import PersonaSnapshot
 from danswer.server.manage.models import SlackBotConfig
 from danswer.server.manage.models import SlackBotConfigCreationRequest
 from danswer.server.manage.models import SlackBotTokens
@@ -108,17 +107,10 @@ def create_slack_bot_config(
     slack_bot_config_model = insert_slack_bot_config(
         persona_id=persona_id,
         channel_config=channel_config,
+        response_type=slack_bot_config_creation_request.response_type,
         db_session=db_session,
     )
-    return SlackBotConfig(
+    return SlackBotConfig.from_model(slack_bot_config_model)
-        id=slack_bot_config_model.id,
-        persona=(
-            PersonaSnapshot.from_model(slack_bot_config_model.persona)
-            if slack_bot_config_model.persona
-            else None
-        ),
-        channel_config=slack_bot_config_model.channel_config,
-    )
 
 
 @router.patch("/admin/slack-bot/config/{slack_bot_config_id}")
@@ -170,17 +162,10 @@ def patch_slack_bot_config(
         slack_bot_config_id=slack_bot_config_id,
         persona_id=persona_id,
         channel_config=channel_config,
+        response_type=slack_bot_config_creation_request.response_type,
         db_session=db_session,
     )
-    return SlackBotConfig(
+    return SlackBotConfig.from_model(slack_bot_config_model)
-        id=slack_bot_config_model.id,
-        persona=(
-            PersonaSnapshot.from_model(slack_bot_config_model.persona)
-            if slack_bot_config_model.persona
-            else None
-        ),
-        channel_config=slack_bot_config_model.channel_config,
-    )
 
 
 @router.delete("/admin/slack-bot/config/{slack_bot_config_id}")
@@ -201,15 +186,7 @@ def list_slack_bot_configs(
 ) -> list[SlackBotConfig]:
     slack_bot_config_models = fetch_slack_bot_configs(db_session=db_session)
     return [
-        SlackBotConfig(
+        SlackBotConfig.from_model(slack_bot_config_model)
-            id=slack_bot_config_model.id,
-            persona=(
-                PersonaSnapshot.from_model(slack_bot_config_model.persona)
-                if slack_bot_config_model.persona
-                else None
-            ),
-            channel_config=slack_bot_config_model.channel_config,
-        )
         for slack_bot_config_model in slack_bot_config_models
     ]
 

backend/slackbot_images/README.md

@@ -0,0 +1,3 @@
+
+This folder contains images needed by the Danswer Slack Bot. When possible, we use the images
+within `web/public`, but sometimes those images do not work for the Slack Bot. 

web/src/app/admin/bot/SlackBotConfigCreationForm.tsx

@@ -90,9 +90,13 @@ export const SlackBotCreationForm = ({
               !isPersonaASlackBotPersona(existingSlackBotConfig.persona)
                 ? existingSlackBotConfig.persona.id
                 : null,
+            response_type: existingSlackBotConfig?.response_type || "citations",
           }}
           validationSchema={Yup.object().shape({
             channel_names: Yup.array().of(Yup.string()),
+            response_type: Yup.string()
+              .oneOf(["quotes", "citations"])
+              .required(),
             answer_validity_check_enabled: Yup.boolean().required(),
             questionmark_prefilter_enabled: Yup.boolean().required(),
             respond_tag_only: Yup.boolean().required(),
@@ -171,6 +175,33 @@ export const SlackBotCreationForm = ({
                     </div>
                   }
                 />
+
+                <SelectorFormField
+                  name="response_type"
+                  label="Response Format"
+                  subtext={
+                    <>
+                      If set to Citations, DanswerBot will respond with a direct
+                      answer with inline citations. It will also provide links
+                      to these cited documents below the answer. When in doubt,
+                      choose this option.
+                      <br />
+                      <br />
+                      If set to Quotes, DanswerBot will respond with a direct
+                      answer as well as with quotes pulled from the context
+                      documents to support that answer. DanswerBot will also
+                      give a list of relevant documents. Choose this option if
+                      you want a very detailed response AND/OR a list of
+                      relevant documents would be useful just in case the LLM
+                      missed anything.
+                    </>
+                  }
+                  options={[
+                    { name: "Citations", value: "citations" },
+                    { name: "Quotes", value: "quotes" },
+                  ]}
+                />
+
                 <Divider />
 
                 <SectionHeader>When should DanswerBot respond?</SectionHeader>

web/src/app/admin/bot/lib.ts

@@ -1,4 +1,8 @@
-import { ChannelConfig, SlackBotTokens } from "@/lib/types";
+import {
+  ChannelConfig,
+  SlackBotResponseType,
+  SlackBotTokens,
+} from "@/lib/types";
 import { Persona } from "../personas/interfaces";
 
 interface SlackBotConfigCreationRequest {
@@ -12,6 +16,7 @@ interface SlackBotConfigCreationRequest {
   respond_team_member_list: string[];
   follow_up_tags?: string[];
   usePersona: boolean;
+  response_type: SlackBotResponseType;
 }
 
 const buildFiltersFromCreationRequest = (
@@ -40,6 +45,7 @@ const buildRequestBodyFromCreationRequest = (
     ...(creationRequest.usePersona
       ? { persona_id: creationRequest.persona_id }
       : { document_sets: creationRequest.document_sets }),
+    response_type: creationRequest.response_type,
   });
 };
 

web/src/components/admin/connectors/Field.tsx

@@ -231,7 +231,7 @@ interface SelectorFormFieldProps {
   name: string;
   label?: string;
   options: StringOrNumberOption[];
-  subtext?: string;
+  subtext?: string | JSX.Element;
   includeDefault?: boolean;
 }
 

web/src/lib/types.ts

@@ -370,10 +370,13 @@ export interface ChannelConfig {
   follow_up_tags?: string[];
 }
 
+export type SlackBotResponseType = "quotes" | "citations";
+
 export interface SlackBotConfig {
   id: number;
   persona: Persona | null;
   channel_config: ChannelConfig;
+  response_type: SlackBotResponseType;
 }
 
 export interface SlackBotTokens {