mirror of
https://github.com/danswer-ai/danswer.git
synced 2025-04-08 11:58:34 +02:00
Enable selection of long documents
This commit is contained in:
Weves
Chris Weaver
parent
4c9709ae4a
commit
e54ce779fd
@ -1,6 +1,7 @@
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import re
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from collections.abc import Callable
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from collections.abc import Iterator
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from datetime import datetime
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from functools import lru_cache
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from typing import cast
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@ -15,14 +16,19 @@ from danswer.chat.models import LlmDoc
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from danswer.configs.chat_configs import MULTILINGUAL_QUERY_EXPANSION
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from danswer.configs.chat_configs import NUM_DOCUMENT_TOKENS_FED_TO_GENERATIVE_MODEL
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from danswer.configs.chat_configs import STOP_STREAM_PAT
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from danswer.configs.constants import DocumentSource
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from danswer.configs.constants import IGNORE_FOR_QA
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from danswer.configs.model_configs import GEN_AI_HISTORY_CUTOFF
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from danswer.configs.model_configs import GEN_AI_MAX_INPUT_TOKENS
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from danswer.configs.model_configs import GEN_AI_MAX_OUTPUT_TOKENS
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from danswer.configs.model_configs import GEN_AI_MODEL_VERSION
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from danswer.configs.model_configs import GEN_AI_SINGLE_USER_MESSAGE_EXPECTED_MAX_TOKENS
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from danswer.db.chat import get_chat_messages_by_session
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from danswer.db.models import ChatMessage
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from danswer.db.models import Persona
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from danswer.db.models import Prompt
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from danswer.indexing.models import InferenceChunk
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from danswer.llm.utils import check_number_of_tokens
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from danswer.llm.utils import get_llm_max_tokens
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from danswer.prompts.chat_prompts import CHAT_USER_CONTEXT_FREE_PROMPT
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from danswer.prompts.chat_prompts import CHAT_USER_PROMPT
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from danswer.prompts.chat_prompts import CITATION_REMINDER
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@ -33,6 +39,12 @@ from danswer.prompts.constants import CODE_BLOCK_PAT
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from danswer.prompts.constants import TRIPLE_BACKTICK
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from danswer.prompts.direct_qa_prompts import LANGUAGE_HINT
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from danswer.prompts.prompt_utils import get_current_llm_day_time
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from danswer.prompts.token_counts import (
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CHAT_USER_PROMPT_WITH_CONTEXT_OVERHEAD_TOKEN_CNT,
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)
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from danswer.prompts.token_counts import CITATION_REMINDER_TOKEN_CNT
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from danswer.prompts.token_counts import CITATION_STATEMENT_TOKEN_CNT
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from danswer.prompts.token_counts import LANGUAGE_HINT_TOKEN_CNT
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# Maps connector enum string to a more natural language representation for the LLM
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# If not on the list, uses the original but slightly cleaned up, see below
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@ -50,19 +62,39 @@ def clean_up_source(source_str: str) -> str:
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return source_str.replace("_", " ").title()
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def build_context_str(
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def build_doc_context_str(
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semantic_identifier: str,
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source_type: DocumentSource,
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content: str,
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ind: int,
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include_metadata: bool = True,
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updated_at: datetime | None = None,
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) -> str:
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context_str = ""
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if include_metadata:
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context_str += f"DOCUMENT {ind}: {semantic_identifier}\n"
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context_str += f"Source: {clean_up_source(source_type)}\n"
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if updated_at:
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update_str = updated_at.strftime("%B %d, %Y %H:%M")
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context_str += f"Updated: {update_str}\n"
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context_str += f"{CODE_BLOCK_PAT.format(content.strip())}\n\n\n"
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return context_str
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def build_complete_context_str(
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context_docs: list[LlmDoc | InferenceChunk],
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include_metadata: bool = True,
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) -> str:
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context_str = ""
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for ind, doc in enumerate(context_docs, start=1):
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if include_metadata:
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context_str += f"DOCUMENT {ind}: {doc.semantic_identifier}\n"
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context_str += f"Source: {clean_up_source(doc.source_type)}\n"
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if doc.updated_at:
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update_str = doc.updated_at.strftime("%B %d, %Y %H:%M")
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context_str += f"Updated: {update_str}\n"
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context_str += f"{CODE_BLOCK_PAT.format(doc.content.strip())}\n\n\n"
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context_str += build_doc_context_str(
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semantic_identifier=doc.semantic_identifier,
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source_type=doc.source_type,
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content=doc.content,
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updated_at=doc.updated_at,
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ind=ind,
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include_metadata=include_metadata,
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)
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return context_str.strip()
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@ -71,7 +103,7 @@ def build_context_str(
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def build_chat_system_message(
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prompt: Prompt,
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context_exists: bool,
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llm_tokenizer: Callable,
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llm_tokenizer_encode_func: Callable,
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citation_line: str = REQUIRE_CITATION_STATEMENT,
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no_citation_line: str = NO_CITATION_STATEMENT,
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) -> tuple[SystemMessage | None, int]:
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@ -92,7 +124,7 @@ def build_chat_system_message(
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if not system_prompt:
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return None, 0
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token_count = len(llm_tokenizer(system_prompt))
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token_count = len(llm_tokenizer_encode_func(system_prompt))
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system_msg = SystemMessage(content=system_prompt)
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return system_msg, token_count
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@ -138,7 +170,7 @@ def build_chat_user_message(
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chat_message: ChatMessage,
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prompt: Prompt,
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context_docs: list[LlmDoc],
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llm_tokenizer: Callable,
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llm_tokenizer_encode_func: Callable,
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all_doc_useful: bool,
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user_prompt_template: str = CHAT_USER_PROMPT,
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context_free_template: str = CHAT_USER_CONTEXT_FREE_PROMPT,
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@ -156,11 +188,11 @@ def build_chat_user_message(
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else user_query
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)
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user_prompt = user_prompt.strip()
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token_count = len(llm_tokenizer(user_prompt))
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token_count = len(llm_tokenizer_encode_func(user_prompt))
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user_msg = HumanMessage(content=user_prompt)
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return user_msg, token_count
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context_docs_str = build_context_str(
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context_docs_str = build_complete_context_str(
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cast(list[LlmDoc | InferenceChunk], context_docs)
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)
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optional_ignore = "" if all_doc_useful else ignore_str
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@ -175,7 +207,7 @@ def build_chat_user_message(
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)
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user_prompt = user_prompt.strip()
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token_count = len(llm_tokenizer(user_prompt))
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token_count = len(llm_tokenizer_encode_func(user_prompt))
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user_msg = HumanMessage(content=user_prompt)
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return user_msg, token_count
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@ -357,16 +389,17 @@ def combine_message_chain(
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return "\n\n".join(message_strs)
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def find_last_index(
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lst: list[int], max_prompt_tokens: int = GEN_AI_MAX_INPUT_TOKENS
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) -> int:
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_PER_MESSAGE_TOKEN_BUFFER = 7
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def find_last_index(lst: list[int], max_prompt_tokens: int) -> int:
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"""From the back, find the index of the last element to include
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before the list exceeds the maximum"""
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running_sum = 0
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last_ind = 0
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for i in range(len(lst) - 1, -1, -1):
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running_sum += lst[i]
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running_sum += lst[i] + _PER_MESSAGE_TOKEN_BUFFER
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if running_sum > max_prompt_tokens:
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last_ind = i + 1
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break
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@ -382,14 +415,11 @@ def drop_messages_history_overflow(
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history_token_counts: list[int],
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final_msg: BaseMessage,
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final_msg_token_count: int,
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max_allowed_tokens: int | None,
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max_allowed_tokens: int,
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) -> list[BaseMessage]:
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"""As message history grows, messages need to be dropped starting from the furthest in the past.
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The System message should be kept if at all possible and the latest user input which is inserted in the
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prompt template must be included"""
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if max_allowed_tokens is None:
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max_allowed_tokens = GEN_AI_MAX_INPUT_TOKENS
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if len(history_msgs) != len(history_token_counts):
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# This should never happen
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raise ValueError("Need exactly 1 token count per message for tracking overflow")
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@ -508,3 +538,68 @@ def extract_citations_from_stream(
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yield DanswerAnswerPiece(answer_piece="[" + curr_segment)
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else:
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yield DanswerAnswerPiece(answer_piece=curr_segment)
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def get_prompt_tokens(prompt: Prompt) -> int:
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return (
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check_number_of_tokens(prompt.system_prompt)
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+ check_number_of_tokens(prompt.task_prompt)
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+ CHAT_USER_PROMPT_WITH_CONTEXT_OVERHEAD_TOKEN_CNT
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+ CITATION_STATEMENT_TOKEN_CNT
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+ CITATION_REMINDER_TOKEN_CNT
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+ (LANGUAGE_HINT_TOKEN_CNT if bool(MULTILINGUAL_QUERY_EXPANSION) else 0)
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)
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# buffer just to be safe so that we don't overflow the token limit due to
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# a small miscalculation
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_MISC_BUFFER = 40
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def compute_max_document_tokens(
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persona: Persona, actual_user_input: str | None = None
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) -> int:
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"""Estimates the number of tokens available for context documents. Formula is roughly:
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(
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model_context_window - reserved_output_tokens - prompt_tokens
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- (actual_user_input OR reserved_user_message_tokens) - buffer (just to be safe)
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)
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The actual_user_input is used at query time. If we are calculating this before knowing the exact input (e.g.
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if we're trying to determine if the user should be able to select another document) then we just set an
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arbitrary "upper bound".
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"""
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llm_name = GEN_AI_MODEL_VERSION
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if persona.llm_model_version_override:
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llm_name = persona.llm_model_version_override
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# if we can't find a number of tokens, just assume some common default
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model_full_context_window = get_llm_max_tokens(llm_name) or 4096
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if persona.prompts:
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prompt_tokens = get_prompt_tokens(persona.prompts[0])
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else:
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raise RuntimeError("Persona has no prompts - this should never happen")
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user_input_tokens = (
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check_number_of_tokens(actual_user_input)
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if actual_user_input is not None
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else GEN_AI_SINGLE_USER_MESSAGE_EXPECTED_MAX_TOKENS
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)
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return (
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model_full_context_window
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- GEN_AI_MAX_OUTPUT_TOKENS
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- prompt_tokens
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- user_input_tokens
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- _MISC_BUFFER
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)
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def compute_max_llm_input_tokens(persona: Persona) -> int:
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"""Maximum tokens allows in the input to the LLM (of any type)."""
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llm_name = GEN_AI_MODEL_VERSION
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if persona.llm_model_version_override:
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llm_name = persona.llm_model_version_override
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model_full_context_window = get_llm_max_tokens(llm_name) or 4096
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return model_full_context_window - GEN_AI_MAX_OUTPUT_TOKENS - _MISC_BUFFER
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@ -7,6 +7,9 @@ from sqlalchemy.orm import Session
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from danswer.chat.chat_utils import build_chat_system_message
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from danswer.chat.chat_utils import build_chat_user_message
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from danswer.chat.chat_utils import build_doc_context_str
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from danswer.chat.chat_utils import compute_max_document_tokens
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from danswer.chat.chat_utils import compute_max_llm_input_tokens
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from danswer.chat.chat_utils import create_chat_chain
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from danswer.chat.chat_utils import drop_messages_history_overflow
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from danswer.chat.chat_utils import extract_citations_from_stream
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@ -42,8 +45,8 @@ from danswer.indexing.models import InferenceChunk
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from danswer.llm.exceptions import GenAIDisabledException
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from danswer.llm.factory import get_default_llm
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from danswer.llm.interfaces import LLM
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from danswer.llm.utils import get_default_llm_token_encode
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from danswer.llm.utils import get_llm_max_tokens
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from danswer.llm.utils import get_default_llm_tokenizer
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from danswer.llm.utils import tokenizer_trim_content
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from danswer.llm.utils import translate_history_to_basemessages
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from danswer.search.models import OptionalSearchSetting
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from danswer.search.models import RetrievalDetails
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@ -68,7 +71,7 @@ def generate_ai_chat_response(
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context_docs: list[LlmDoc],
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doc_id_to_rank_map: dict[str, int],
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llm: LLM | None,
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llm_tokenizer: Callable,
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llm_tokenizer_encode_func: Callable,
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all_doc_useful: bool,
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) -> Iterator[DanswerAnswerPiece | CitationInfo | StreamingError]:
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if llm is None:
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@ -88,7 +91,7 @@ def generate_ai_chat_response(
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system_message_or_none, system_tokens = build_chat_system_message(
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prompt=query_message.prompt,
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context_exists=context_exists,
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llm_tokenizer=llm_tokenizer,
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llm_tokenizer_encode_func=llm_tokenizer_encode_func,
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)
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history_basemessages, history_token_counts = translate_history_to_basemessages(
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@ -101,7 +104,7 @@ def generate_ai_chat_response(
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chat_message=query_message,
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prompt=query_message.prompt,
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context_docs=context_docs,
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llm_tokenizer=llm_tokenizer,
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llm_tokenizer_encode_func=llm_tokenizer_encode_func,
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all_doc_useful=all_doc_useful,
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)
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@ -112,9 +115,7 @@ def generate_ai_chat_response(
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history_token_counts=history_token_counts,
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final_msg=user_message,
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final_msg_token_count=user_tokens,
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max_allowed_tokens=get_llm_max_tokens(persona.llm_model_version_override)
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if persona.llm_model_version_override
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else None,
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max_allowed_tokens=compute_max_llm_input_tokens(persona),
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)
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# Good Debug/Breakpoint
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@ -195,7 +196,10 @@ def stream_chat_message(
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except GenAIDisabledException:
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llm = None
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llm_tokenizer = get_default_llm_token_encode()
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llm_tokenizer = get_default_llm_tokenizer()
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llm_tokenizer_encode_func = cast(
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Callable[[str], list[int]], llm_tokenizer.encode
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)
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embedding_model = get_current_db_embedding_model(db_session)
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document_index = get_default_document_index(
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@ -223,7 +227,7 @@ def stream_chat_message(
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parent_message=parent_message,
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prompt_id=prompt_id,
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message=message_text,
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token_count=len(llm_tokenizer(message_text)),
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token_count=len(llm_tokenizer_encode_func(message_text)),
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message_type=MessageType.USER,
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db_session=db_session,
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commit=False,
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@ -271,6 +275,66 @@ def stream_chat_message(
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doc_identifiers=identifier_tuples,
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document_index=document_index,
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)
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# truncate the last document if it exceeds the token limit
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max_document_tokens = compute_max_document_tokens(
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persona, actual_user_input=message_text
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)
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tokens_per_doc = [
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len(
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llm_tokenizer_encode_func(
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build_doc_context_str(
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semantic_identifier=llm_doc.semantic_identifier,
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source_type=llm_doc.source_type,
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content=llm_doc.content,
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updated_at=llm_doc.updated_at,
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ind=ind,
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)
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)
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)
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for ind, llm_doc in enumerate(llm_docs)
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]
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final_doc_ind = None
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total_tokens = 0
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for ind, tokens in enumerate(tokens_per_doc):
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total_tokens += tokens
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if total_tokens > max_document_tokens:
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final_doc_ind = ind
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break
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if final_doc_ind is not None:
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# only allow the final document to get truncated
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# if more than that, then the user message is too long
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if final_doc_ind != len(tokens_per_doc) - 1:
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yield get_json_line(
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StreamingError(
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error="LLM context window exceeded. Please de-select some documents or shorten your query."
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).dict()
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)
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return
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final_doc_desired_length = tokens_per_doc[final_doc_ind] - (
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total_tokens - max_document_tokens
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)
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# 75 tokens is a reasonable over-estimate of the metadata and title
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final_doc_content_length = final_doc_desired_length - 75
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# this could occur if we only have space for the title / metadata
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# not ideal, but it's the most reasonable thing to do
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# NOTE: the frontend prevents documents from being selected if
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# less than 75 tokens are available to try and avoid this situation
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# from occuring in the first place
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if final_doc_content_length <= 0:
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logger.error(
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f"Final doc ({llm_docs[final_doc_ind].semantic_identifier}) content "
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"length is less than 0. Removing this doc from the final prompt."
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)
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llm_docs.pop()
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else:
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llm_docs[final_doc_ind].content = tokenizer_trim_content(
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content=llm_docs[final_doc_ind].content,
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desired_length=final_doc_content_length,
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tokenizer=llm_tokenizer,
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)
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doc_id_to_rank_map = map_document_id_order(
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cast(list[InferenceChunk | LlmDoc], llm_docs)
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)
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@ -423,7 +487,7 @@ def stream_chat_message(
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context_docs=llm_docs,
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doc_id_to_rank_map=doc_id_to_rank_map,
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llm=llm,
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llm_tokenizer=llm_tokenizer,
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llm_tokenizer_encode_func=llm_tokenizer_encode_func,
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all_doc_useful=reference_doc_ids is not None,
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)
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@ -467,7 +531,7 @@ def stream_chat_message(
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# Saving Gen AI answer and responding with message info
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gen_ai_response_message = partial_response(
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message=llm_output,
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token_count=len(llm_tokenizer(llm_output)),
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token_count=len(llm_tokenizer_encode_func(llm_output)),
|
||||
citations=db_citations,
|
||||
error=error,
|
||||
)
|
||||
|
||||
@ -104,4 +104,10 @@ GEN_AI_MAX_INPUT_TOKENS = int(os.environ.get("GEN_AI_MAX_INPUT_TOKENS") or 3000)
|
||||
# History for secondary LLM flows, not primary chat flow, generally we don't need to
|
||||
# include as much as possible as this just bumps up the cost unnecessarily
|
||||
GEN_AI_HISTORY_CUTOFF = int(0.5 * GEN_AI_MAX_INPUT_TOKENS)
|
||||
# This is used when computing how much context space is available for documents
|
||||
# ahead of time in order to let the user know if they can "select" more documents
|
||||
# It represents a maximum "expected" number of input tokens from the latest user
|
||||
# message. At query time, we don't actually enforce this - we will only throw an
|
||||
# error if the total # of tokens exceeds the max input tokens.
|
||||
GEN_AI_SINGLE_USER_MESSAGE_EXPECTED_MAX_TOKENS = 512
|
||||
GEN_AI_TEMPERATURE = float(os.environ.get("GEN_AI_TEMPERATURE") or 0)
|
||||
|
||||
@ -35,7 +35,7 @@ _LLM_TOKENIZER: Any = None
|
||||
_LLM_TOKENIZER_ENCODE: Callable[[str], Any] | None = None
|
||||
|
||||
|
||||
def get_default_llm_tokenizer() -> Any:
|
||||
def get_default_llm_tokenizer() -> Encoding:
|
||||
"""Currently only supports the OpenAI default tokenizer: tiktoken"""
|
||||
global _LLM_TOKENIZER
|
||||
if _LLM_TOKENIZER is None:
|
||||
@ -56,16 +56,26 @@ def get_default_llm_token_encode() -> Callable[[str], Any]:
|
||||
return _LLM_TOKENIZER_ENCODE
|
||||
|
||||
|
||||
def tokenizer_trim_content(
|
||||
content: str, desired_length: int, tokenizer: Encoding
|
||||
) -> str:
|
||||
tokenizer = get_default_llm_tokenizer()
|
||||
tokens = tokenizer.encode(content)
|
||||
if len(tokens) > desired_length:
|
||||
content = tokenizer.decode(tokens[:desired_length])
|
||||
return content
|
||||
|
||||
|
||||
def tokenizer_trim_chunks(
|
||||
chunks: list[InferenceChunk], max_chunk_toks: int = DOC_EMBEDDING_CONTEXT_SIZE
|
||||
) -> list[InferenceChunk]:
|
||||
tokenizer = get_default_llm_tokenizer()
|
||||
new_chunks = copy(chunks)
|
||||
for ind, chunk in enumerate(new_chunks):
|
||||
tokens = tokenizer.encode(chunk.content)
|
||||
if len(tokens) > max_chunk_toks:
|
||||
new_content = tokenizer_trim_content(chunk.content, max_chunk_toks, tokenizer)
|
||||
if len(new_content) != len(chunk.content):
|
||||
new_chunk = copy(chunk)
|
||||
new_chunk.content = tokenizer.decode(tokens[:max_chunk_toks])
|
||||
new_chunk.content = new_content
|
||||
new_chunks[ind] = new_chunk
|
||||
return new_chunks
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ from collections.abc import Callable
|
||||
from collections.abc import Iterator
|
||||
from typing import cast
|
||||
|
||||
from danswer.chat.chat_utils import build_context_str
|
||||
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
|
||||
@ -145,7 +145,7 @@ class SingleMessageQAHandler(QAHandler):
|
||||
) -> str:
|
||||
context_block = ""
|
||||
if context_chunks:
|
||||
context_docs_str = build_context_str(
|
||||
context_docs_str = build_complete_context_str(
|
||||
cast(list[LlmDoc | InferenceChunk], context_chunks)
|
||||
)
|
||||
context_block = CONTEXT_BLOCK.format(context_docs_str=context_docs_str)
|
||||
@ -194,7 +194,7 @@ class SingleMessageScratchpadHandler(QAHandler):
|
||||
def build_prompt(
|
||||
self, query: str, history_str: str, context_chunks: list[InferenceChunk]
|
||||
) -> str:
|
||||
context_docs_str = build_context_str(
|
||||
context_docs_str = build_complete_context_str(
|
||||
cast(list[LlmDoc | InferenceChunk], context_chunks)
|
||||
)
|
||||
|
||||
|
||||
24
backend/danswer/prompts/token_counts.py
Normal file
24
backend/danswer/prompts/token_counts.py
Normal file
@ -0,0 +1,24 @@
|
||||
from danswer.llm.utils import check_number_of_tokens
|
||||
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.direct_qa_prompts import LANGUAGE_HINT
|
||||
|
||||
|
||||
# tokens outside of the actual persona's "user_prompt" that make up the end
|
||||
# user message
|
||||
CHAT_USER_PROMPT_WITH_CONTEXT_OVERHEAD_TOKEN_CNT = check_number_of_tokens(
|
||||
CHAT_USER_PROMPT.format(
|
||||
context_docs_str="",
|
||||
task_prompt="",
|
||||
user_query="",
|
||||
optional_ignore_statement=DEFAULT_IGNORE_STATEMENT,
|
||||
)
|
||||
)
|
||||
|
||||
CITATION_STATEMENT_TOKEN_CNT = check_number_of_tokens(REQUIRE_CITATION_STATEMENT)
|
||||
|
||||
CITATION_REMINDER_TOKEN_CNT = check_number_of_tokens(CITATION_REMINDER)
|
||||
|
||||
LANGUAGE_HINT_TOKEN_CNT = check_number_of_tokens(LANGUAGE_HINT)
|
||||
@ -5,6 +5,7 @@ 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
|
||||
@ -47,12 +48,22 @@ def get_document_info(
|
||||
|
||||
contents = [chunk.content for chunk in inference_chunks]
|
||||
|
||||
combined = "\n".join(contents)
|
||||
combined_contents = "\n".join(contents)
|
||||
|
||||
# get actual document context used for LLM
|
||||
first_chunk = inference_chunks[0]
|
||||
tokenizer_encode = get_default_llm_token_encode()
|
||||
full_context_str = build_doc_context_str(
|
||||
semantic_identifier=first_chunk.semantic_identifier,
|
||||
source_type=first_chunk.source_type,
|
||||
content=combined_contents,
|
||||
updated_at=first_chunk.updated_at,
|
||||
ind=0,
|
||||
)
|
||||
|
||||
return DocumentInfo(
|
||||
num_chunks=len(inference_chunks), num_tokens=len(tokenizer_encode(combined))
|
||||
num_chunks=len(inference_chunks),
|
||||
num_tokens=len(tokenizer_encode(full_context_str)),
|
||||
)
|
||||
|
||||
|
||||
|
||||
@ -2,9 +2,11 @@ from fastapi import APIRouter
|
||||
from fastapi import Depends
|
||||
from fastapi import HTTPException
|
||||
from fastapi.responses import StreamingResponse
|
||||
from pydantic import BaseModel
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from danswer.auth.users import current_user
|
||||
from danswer.chat.chat_utils import compute_max_document_tokens
|
||||
from danswer.chat.chat_utils import create_chat_chain
|
||||
from danswer.chat.process_message import stream_chat_message
|
||||
from danswer.db.chat import create_chat_session
|
||||
@ -13,6 +15,7 @@ from danswer.db.chat import get_chat_message
|
||||
from danswer.db.chat import get_chat_messages_by_session
|
||||
from danswer.db.chat import get_chat_session_by_id
|
||||
from danswer.db.chat import get_chat_sessions_by_user
|
||||
from danswer.db.chat import get_persona_by_id
|
||||
from danswer.db.chat import set_as_latest_chat_message
|
||||
from danswer.db.chat import translate_db_message_to_chat_message_detail
|
||||
from danswer.db.chat import update_chat_session
|
||||
@ -244,3 +247,27 @@ def create_search_feedback(
|
||||
document_index=document_index,
|
||||
db_session=db_session,
|
||||
)
|
||||
|
||||
|
||||
class MaxSelectedDocumentTokens(BaseModel):
|
||||
max_tokens: int
|
||||
|
||||
|
||||
@router.get("/max-selected-document-tokens")
|
||||
def get_max_document_tokens(
|
||||
persona_id: int,
|
||||
user: User | None = Depends(current_user),
|
||||
db_session: Session = Depends(get_session),
|
||||
) -> MaxSelectedDocumentTokens:
|
||||
try:
|
||||
persona = get_persona_by_id(
|
||||
persona_id=persona_id,
|
||||
user_id=user.id if user else None,
|
||||
db_session=db_session,
|
||||
)
|
||||
except ValueError:
|
||||
raise HTTPException(status_code=404, detail="Persona not found")
|
||||
|
||||
return MaxSelectedDocumentTokens(
|
||||
max_tokens=compute_max_document_tokens(persona),
|
||||
)
|
||||
|
||||
@ -40,8 +40,8 @@ import { ResizableSection } from "@/components/resizable/ResizableSection";
|
||||
import { DanswerInitializingLoader } from "@/components/DanswerInitializingLoader";
|
||||
import { ChatIntro } from "./ChatIntro";
|
||||
import { HEADER_PADDING } from "@/lib/constants";
|
||||
import { getSourcesForPersona } from "@/lib/sources";
|
||||
import { computeAvailableFilters } from "@/lib/filters";
|
||||
import { useDocumentSelection } from "./useDocumentSelection";
|
||||
|
||||
const MAX_INPUT_HEIGHT = 200;
|
||||
|
||||
@ -138,9 +138,6 @@ export const Chat = ({
|
||||
selectedMessageForDocDisplay
|
||||
)
|
||||
: { aiMessage: null };
|
||||
const [selectedDocuments, setSelectedDocuments] = useState<DanswerDocument[]>(
|
||||
[]
|
||||
);
|
||||
|
||||
const [selectedPersona, setSelectedPersona] = useState<Persona | undefined>(
|
||||
existingChatSessionPersonaId !== undefined
|
||||
@ -165,6 +162,30 @@ export const Chat = ({
|
||||
}
|
||||
}, [defaultSelectedPersonaId]);
|
||||
|
||||
const [
|
||||
selectedDocuments,
|
||||
toggleDocumentSelection,
|
||||
clearSelectedDocuments,
|
||||
selectedDocumentTokens,
|
||||
] = useDocumentSelection();
|
||||
// just choose a conservative default, this will be updated in the
|
||||
// background on initial load / on persona change
|
||||
const [maxTokens, setMaxTokens] = useState<number>(4096);
|
||||
// fetch # of allowed document tokens for the selected Persona
|
||||
useEffect(() => {
|
||||
async function fetchMaxTokens() {
|
||||
const response = await fetch(
|
||||
`/api/chat/max-selected-document-tokens?persona_id=${livePersona.id}`
|
||||
);
|
||||
if (response.ok) {
|
||||
const maxTokens = (await response.json()).max_tokens as number;
|
||||
setMaxTokens(maxTokens);
|
||||
}
|
||||
}
|
||||
|
||||
fetchMaxTokens();
|
||||
}, [livePersona]);
|
||||
|
||||
const filterManager = useFilters();
|
||||
const [finalAvailableSources, finalAvailableDocumentSets] =
|
||||
computeAvailableFilters({
|
||||
@ -725,7 +746,10 @@ export const Chat = ({
|
||||
<DocumentSidebar
|
||||
selectedMessage={aiMessage}
|
||||
selectedDocuments={selectedDocuments}
|
||||
setSelectedDocuments={setSelectedDocuments}
|
||||
toggleDocumentSelection={toggleDocumentSelection}
|
||||
clearSelectedDocuments={clearSelectedDocuments}
|
||||
selectedDocumentTokens={selectedDocumentTokens}
|
||||
maxTokens={maxTokens}
|
||||
isLoading={isFetchingChatMessages}
|
||||
/>
|
||||
</ResizableSection>
|
||||
|
||||
@ -4,7 +4,6 @@ import { PopupSpec } from "@/components/admin/connectors/Popup";
|
||||
import { DocumentFeedbackBlock } from "@/components/search/DocumentFeedbackBlock";
|
||||
import { DocumentUpdatedAtBadge } from "@/components/search/DocumentUpdatedAtBadge";
|
||||
import { DanswerDocument } from "@/lib/search/interfaces";
|
||||
import { useState } from "react";
|
||||
import { FiInfo, FiRadio } from "react-icons/fi";
|
||||
import { DocumentSelector } from "./DocumentSelector";
|
||||
import {
|
||||
@ -19,6 +18,7 @@ interface DocumentDisplayProps {
|
||||
isSelected: boolean;
|
||||
handleSelect: (documentId: string) => void;
|
||||
setPopup: (popupSpec: PopupSpec | null) => void;
|
||||
tokenLimitReached: boolean;
|
||||
}
|
||||
|
||||
export function ChatDocumentDisplay({
|
||||
@ -28,27 +28,19 @@ export function ChatDocumentDisplay({
|
||||
isSelected,
|
||||
handleSelect,
|
||||
setPopup,
|
||||
tokenLimitReached,
|
||||
}: DocumentDisplayProps) {
|
||||
const [isHovered, setIsHovered] = useState(false);
|
||||
|
||||
// Consider reintroducing null scored docs in the future
|
||||
if (document.score === null) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return (
|
||||
<div
|
||||
key={document.semantic_identifier}
|
||||
className="text-sm px-3"
|
||||
onMouseEnter={() => {
|
||||
setIsHovered(true);
|
||||
}}
|
||||
onMouseLeave={() => setIsHovered(false)}
|
||||
>
|
||||
<div className="flex relative w-full overflow-x-hidden">
|
||||
<div key={document.semantic_identifier} className="text-sm px-3">
|
||||
<div className="flex relative w-full overflow-y-visible">
|
||||
<a
|
||||
className={
|
||||
"rounded-lg flex font-bold flex-shrink truncate " +
|
||||
"rounded-lg flex font-bold flex-shrink truncate " +
|
||||
(document.link ? "" : "pointer-events-none")
|
||||
}
|
||||
href={document.link}
|
||||
@ -102,6 +94,7 @@ export function ChatDocumentDisplay({
|
||||
<DocumentSelector
|
||||
isSelected={isSelected}
|
||||
handleSelect={() => handleSelect(document.document_id)}
|
||||
isDisabled={tokenLimitReached && !isSelected}
|
||||
/>
|
||||
</div>
|
||||
<div>
|
||||
|
||||
@ -1,23 +1,66 @@
|
||||
import { HoverPopup } from "@/components/HoverPopup";
|
||||
import { useState } from "react";
|
||||
|
||||
export function DocumentSelector({
|
||||
isSelected,
|
||||
handleSelect,
|
||||
isDisabled,
|
||||
}: {
|
||||
isSelected: boolean;
|
||||
handleSelect: () => void;
|
||||
isDisabled?: boolean;
|
||||
}) {
|
||||
return (
|
||||
<div
|
||||
className="ml-auto flex cursor-pointer select-none"
|
||||
onClick={handleSelect}
|
||||
>
|
||||
<p className="mr-2 my-auto">Select</p>
|
||||
<input
|
||||
className="my-auto"
|
||||
type="checkbox"
|
||||
checked={isSelected}
|
||||
// dummy function to prevent warning
|
||||
onChange={() => null}
|
||||
/>
|
||||
</div>
|
||||
);
|
||||
const [popupDisabled, setPopupDisabled] = useState(false);
|
||||
|
||||
function onClick() {
|
||||
if (!isDisabled) {
|
||||
setPopupDisabled(true);
|
||||
handleSelect();
|
||||
// re-enable popup after 1 second so that we don't show the popup immediately upon the
|
||||
// user de-selecting a document
|
||||
setTimeout(() => {
|
||||
setPopupDisabled(false);
|
||||
}, 1000);
|
||||
}
|
||||
}
|
||||
|
||||
function Main() {
|
||||
return (
|
||||
<div
|
||||
className={
|
||||
"ml-auto flex select-none " + (!isDisabled ? " cursor-pointer" : "")
|
||||
}
|
||||
onClick={onClick}
|
||||
>
|
||||
<p className="mr-2 my-auto">Select</p>
|
||||
<input
|
||||
className="my-auto"
|
||||
type="checkbox"
|
||||
checked={isSelected}
|
||||
// dummy function to prevent warning
|
||||
onChange={() => null}
|
||||
disabled={isDisabled}
|
||||
/>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
if (isDisabled && !popupDisabled) {
|
||||
return (
|
||||
<div className="ml-auto">
|
||||
<HoverPopup
|
||||
mainContent={Main()}
|
||||
popupContent={
|
||||
<div className="w-48">
|
||||
LLM context limit reached 😔 If you want to chat with this
|
||||
document, please de-select others to free up space.
|
||||
</div>
|
||||
}
|
||||
direction="left-top"
|
||||
/>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
return Main();
|
||||
}
|
||||
|
||||
@ -2,12 +2,13 @@ import { DanswerDocument } from "@/lib/search/interfaces";
|
||||
import { Text } from "@tremor/react";
|
||||
import { ChatDocumentDisplay } from "./ChatDocumentDisplay";
|
||||
import { usePopup } from "@/components/admin/connectors/Popup";
|
||||
import { FiFileText } from "react-icons/fi";
|
||||
import { FiAlertTriangle, FiFileText } from "react-icons/fi";
|
||||
import { SelectedDocumentDisplay } from "./SelectedDocumentDisplay";
|
||||
import { removeDuplicateDocs } from "@/lib/documentUtils";
|
||||
import { BasicSelectable } from "@/components/BasicClickable";
|
||||
import { Message, RetrievalType } from "../interfaces";
|
||||
import { HEADER_PADDING } from "@/lib/constants";
|
||||
import { HoverPopup } from "@/components/HoverPopup";
|
||||
|
||||
function SectionHeader({
|
||||
name,
|
||||
@ -27,12 +28,18 @@ function SectionHeader({
|
||||
export function DocumentSidebar({
|
||||
selectedMessage,
|
||||
selectedDocuments,
|
||||
setSelectedDocuments,
|
||||
toggleDocumentSelection,
|
||||
clearSelectedDocuments,
|
||||
selectedDocumentTokens,
|
||||
maxTokens,
|
||||
isLoading,
|
||||
}: {
|
||||
selectedMessage: Message | null;
|
||||
selectedDocuments: DanswerDocument[] | null;
|
||||
setSelectedDocuments: (documents: DanswerDocument[]) => void;
|
||||
toggleDocumentSelection: (document: DanswerDocument) => void;
|
||||
clearSelectedDocuments: () => void;
|
||||
selectedDocumentTokens: number;
|
||||
maxTokens: number;
|
||||
isLoading: boolean;
|
||||
}) {
|
||||
const { popup, setPopup } = usePopup();
|
||||
@ -44,6 +51,13 @@ export function DocumentSidebar({
|
||||
|
||||
const currentDocuments = selectedMessage?.documents || null;
|
||||
const dedupedDocuments = removeDuplicateDocs(currentDocuments || []);
|
||||
|
||||
// NOTE: do not allow selection if less than 75 tokens are left
|
||||
// this is to prevent the case where they are able to select the doc
|
||||
// but it basically is unused since it's truncated right at the very
|
||||
// start of the document (since title + metadata + misc overhead) takes up
|
||||
// space
|
||||
const tokenLimitReached = selectedDocumentTokens > maxTokens - 75;
|
||||
return (
|
||||
<div
|
||||
className={`
|
||||
@ -72,7 +86,7 @@ export function DocumentSidebar({
|
||||
</div>
|
||||
|
||||
{currentDocuments ? (
|
||||
<div className="overflow-y-auto dark-scrollbar overflow-x-hidden flex flex-col">
|
||||
<div className="overflow-y-auto dark-scrollbar flex flex-col">
|
||||
<div>
|
||||
{dedupedDocuments.length > 0 ? (
|
||||
dedupedDocuments.map((document, ind) => (
|
||||
@ -93,21 +107,13 @@ export function DocumentSidebar({
|
||||
document.document_id
|
||||
)}
|
||||
handleSelect={(documentId) => {
|
||||
if (selectedDocumentIds.includes(documentId)) {
|
||||
setSelectedDocuments(
|
||||
selectedDocuments!.filter(
|
||||
(document) => document.document_id !== documentId
|
||||
)
|
||||
);
|
||||
} else {
|
||||
setSelectedDocuments([
|
||||
...selectedDocuments!,
|
||||
currentDocuments.find(
|
||||
(document) => document.document_id === documentId
|
||||
)!,
|
||||
]);
|
||||
}
|
||||
toggleDocumentSelection(
|
||||
dedupedDocuments.find(
|
||||
(document) => document.document_id === documentId
|
||||
)!
|
||||
);
|
||||
}}
|
||||
tokenLimitReached={tokenLimitReached}
|
||||
/>
|
||||
</div>
|
||||
))
|
||||
@ -132,15 +138,48 @@ export function DocumentSidebar({
|
||||
|
||||
<div className="text-sm mb-4 border-t border-border pt-4 overflow-y-hidden flex flex-col">
|
||||
<div className="flex border-b border-border px-3">
|
||||
<div>
|
||||
<div className="flex">
|
||||
<SectionHeader name="Selected Documents" icon={FiFileText} />
|
||||
|
||||
{tokenLimitReached && (
|
||||
<div className="ml-2 my-auto">
|
||||
<div className="mb-2">
|
||||
<HoverPopup
|
||||
mainContent={
|
||||
<FiAlertTriangle
|
||||
className="text-alert my-auto"
|
||||
size="16"
|
||||
/>
|
||||
}
|
||||
popupContent={
|
||||
<Text className="w-40">
|
||||
Over LLM context length by:{" "}
|
||||
<i>{selectedDocumentTokens - maxTokens} tokens</i>
|
||||
<br />
|
||||
<br />
|
||||
{selectedDocuments && selectedDocuments.length > 0 && (
|
||||
<>
|
||||
Truncating: "
|
||||
<i>
|
||||
{
|
||||
selectedDocuments[selectedDocuments.length - 1]
|
||||
.semantic_identifier
|
||||
}
|
||||
</i>
|
||||
"
|
||||
</>
|
||||
)}
|
||||
</Text>
|
||||
}
|
||||
direction="left"
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{selectedDocuments && selectedDocuments.length > 0 && (
|
||||
<div
|
||||
className="ml-auto my-auto"
|
||||
onClick={() => setSelectedDocuments([])}
|
||||
>
|
||||
<div className="ml-auto my-auto" onClick={clearSelectedDocuments}>
|
||||
<BasicSelectable selected={false}>De-Select All</BasicSelectable>
|
||||
</div>
|
||||
)}
|
||||
@ -153,10 +192,10 @@ export function DocumentSidebar({
|
||||
key={document.document_id}
|
||||
document={document}
|
||||
handleDeselect={(documentId) => {
|
||||
setSelectedDocuments(
|
||||
selectedDocuments!.filter(
|
||||
(document) => document.document_id !== documentId
|
||||
)
|
||||
toggleDocumentSelection(
|
||||
dedupedDocuments.find(
|
||||
(document) => document.document_id === documentId
|
||||
)!
|
||||
);
|
||||
}}
|
||||
/>
|
||||
|
||||
@ -49,7 +49,7 @@ export default async function Page({
|
||||
| AuthTypeMetadata
|
||||
| FullEmbeddingModelResponse
|
||||
| null
|
||||
)[] = [null, null, null, null, null, null, null, null];
|
||||
)[] = [null, null, null, null, null, null, null, null, null];
|
||||
try {
|
||||
results = await Promise.all(tasks);
|
||||
} catch (e) {
|
||||
|
||||
69
web/src/app/chat/useDocumentSelection.ts
Normal file
69
web/src/app/chat/useDocumentSelection.ts
Normal file
@ -0,0 +1,69 @@
|
||||
import { DanswerDocument } from "@/lib/search/interfaces";
|
||||
import { useState } from "react";
|
||||
|
||||
interface DocumentInfo {
|
||||
num_chunks: number;
|
||||
num_tokens: number;
|
||||
}
|
||||
|
||||
async function fetchDocumentLength(documentId: string) {
|
||||
const response = await fetch(
|
||||
`/api/document/document-size-info?document_id=${documentId}`
|
||||
);
|
||||
if (!response.ok) {
|
||||
return 0;
|
||||
}
|
||||
const data = (await response.json()) as DocumentInfo;
|
||||
return data.num_tokens;
|
||||
}
|
||||
|
||||
export function useDocumentSelection(): [
|
||||
DanswerDocument[],
|
||||
(document: DanswerDocument) => void,
|
||||
() => void,
|
||||
number,
|
||||
] {
|
||||
const [selectedDocuments, setSelectedDocuments] = useState<DanswerDocument[]>(
|
||||
[]
|
||||
);
|
||||
const [totalTokens, setTotalTokens] = useState(0);
|
||||
const selectedDocumentIds = selectedDocuments.map(
|
||||
(document) => document.document_id
|
||||
);
|
||||
const documentIdToLength = new Map<string, number>();
|
||||
|
||||
function toggleDocumentSelection(document: DanswerDocument) {
|
||||
const documentId = document.document_id;
|
||||
const isAdding = !selectedDocumentIds.includes(documentId);
|
||||
if (!isAdding) {
|
||||
setSelectedDocuments(
|
||||
selectedDocuments.filter(
|
||||
(document) => document.document_id !== documentId
|
||||
)
|
||||
);
|
||||
} else {
|
||||
setSelectedDocuments([...selectedDocuments, document]);
|
||||
}
|
||||
if (documentIdToLength.has(documentId)) {
|
||||
const length = documentIdToLength.get(documentId)!;
|
||||
setTotalTokens(isAdding ? totalTokens + length : totalTokens - length);
|
||||
} else {
|
||||
fetchDocumentLength(documentId).then((length) => {
|
||||
documentIdToLength.set(documentId, length);
|
||||
setTotalTokens(isAdding ? totalTokens + length : totalTokens - length);
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
function clearDocuments() {
|
||||
setSelectedDocuments([]);
|
||||
setTotalTokens(0);
|
||||
}
|
||||
|
||||
return [
|
||||
selectedDocuments,
|
||||
toggleDocumentSelection,
|
||||
clearDocuments,
|
||||
totalTokens,
|
||||
];
|
||||
}
|
||||
@ -4,7 +4,7 @@ interface HoverPopupProps {
|
||||
mainContent: string | JSX.Element;
|
||||
popupContent: string | JSX.Element;
|
||||
classNameModifications?: string;
|
||||
direction?: "left" | "bottom" | "top";
|
||||
direction?: "left" | "left-top" | "bottom" | "top";
|
||||
style?: "basic" | "dark";
|
||||
}
|
||||
|
||||
@ -18,9 +18,15 @@ export const HoverPopup = ({
|
||||
const [hovered, setHovered] = useState(false);
|
||||
|
||||
let popupDirectionClass;
|
||||
let popupStyle = {};
|
||||
switch (direction) {
|
||||
case "left":
|
||||
popupDirectionClass = "top-0 left-0 transform translate-x-[-110%]";
|
||||
popupDirectionClass = "top-0 left-0 transform";
|
||||
popupStyle = { transform: "translateX(calc(-100% - 5px))" };
|
||||
break;
|
||||
case "left-top":
|
||||
popupDirectionClass = "bottom-0 left-0";
|
||||
popupStyle = { transform: "translate(calc(-100% - 5px), 0)" };
|
||||
break;
|
||||
case "bottom":
|
||||
popupDirectionClass = "top-0 left-0 mt-6 pt-2";
|
||||
@ -39,7 +45,10 @@ export const HoverPopup = ({
|
||||
onMouseLeave={() => setHovered(false)}
|
||||
>
|
||||
{hovered && (
|
||||
<div className={`absolute ${popupDirectionClass} z-30`}>
|
||||
<div
|
||||
className={`absolute ${popupDirectionClass} z-30`}
|
||||
style={popupStyle}
|
||||
>
|
||||
<div
|
||||
className={
|
||||
`px-3 py-2 rounded bg-background border border-border` +
|
||||
|
||||
@ -59,6 +59,7 @@ module.exports = {
|
||||
},
|
||||
error: "#ef4444", // red-500
|
||||
success: "#059669", // emerald-600
|
||||
alert: "#f59e0b", // amber-600
|
||||
user: "#fb7185", // yellow-400
|
||||
ai: "#60a5fa", // blue-400
|
||||
// light mode
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user