fix: improve inline citation parsing bump:patch
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Tadashi
parent
f3a2a293f2
commit
f15abdbb23
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@ -342,11 +342,11 @@ class AnswerWithContextPipeline(BaseComponent):
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span_idx = span.get("idx", None)
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if span_idx is not None:
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to_highlight = f"【{span_idx + 1}】" + to_highlight
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to_highlight = f"【{span_idx}】" + to_highlight
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text += Render.highlight(
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to_highlight,
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elem_id=str(span_idx + 1) if span_idx is not None else None,
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elem_id=str(span_idx) if span_idx is not None else None,
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)
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if idx < len(ss) - 1:
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text += cur_doc.text[span["end"] : ss[idx + 1]["start"]]
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@ -1,6 +1,7 @@
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import re
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import threading
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from collections import defaultdict
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from dataclasses import dataclass
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from typing import Generator
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import numpy as np
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@ -8,7 +9,6 @@ import numpy as np
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from kotaemon.base import AIMessage, Document, HumanMessage, SystemMessage
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from kotaemon.llms import PromptTemplate
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from .citation import CiteEvidence
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from .citation_qa import CITATION_TIMEOUT, MAX_IMAGES, AnswerWithContextPipeline
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from .format_context import EVIDENCE_MODE_FIGURE
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from .utils import find_start_end_phrase
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@ -61,12 +61,27 @@ START_PHRASE: Fixed-size Chunker This is our baseline chunker
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END_PHRASE: this shows good retrieval quality.
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FINAL ANSWER
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An alternative to semantic chunking is fixed-size chunking. This traditional method involves splitting documents into chunks of a predetermined or user-specified size, regardless of semantic content, which is computationally efficient【1】. However, it may result in the fragmentation of semantically related content, thereby potentially degrading retrieval performance【2】.
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An alternative to semantic chunking is fixed-size chunking. This traditional method involves splitting documents into chunks of a predetermined or user-specified size, regardless of semantic content, which is computationally efficient【1】. However, it may result in the fragmentation of semantically related content, thereby potentially degrading retrieval performance【1】【2】.
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QUESTION: {question}\n
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ANSWER:
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""" # noqa
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START_ANSWER = "FINAL ANSWER"
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START_CITATION = "CITATION LIST"
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CITATION_PATTERN = r"citation【(\d+)】"
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START_ANSWER_PATTERN = "start_phrase:"
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END_ANSWER_PATTERN = "end_phrase:"
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@dataclass
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class InlineEvidence:
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"""List of evidences to support the answer."""
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start_phrase: str | None = None
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end_phrase: str | None = None
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idx: int | None = None
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class AnswerWithInlineCitation(AnswerWithContextPipeline):
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"""Answer the question based on the evidence with inline citation"""
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@ -85,15 +100,54 @@ class AnswerWithInlineCitation(AnswerWithContextPipeline):
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return prompt, evidence
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def answer_to_citations(self, answer):
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evidences = []
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def answer_to_citations(self, answer) -> list[InlineEvidence]:
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citations: list[InlineEvidence] = []
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lines = answer.split("\n")
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for line in lines:
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for keyword in ["START_PHRASE:", "END_PHRASE:"]:
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if line.startswith(keyword):
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evidences.append(line[len(keyword) :].strip())
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return CiteEvidence(evidences=evidences)
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current_evidence = None
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for line in lines:
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# check citation idx using regex
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match = re.match(CITATION_PATTERN, line.lower())
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if match:
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try:
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parsed_citation_idx = int(match.group(1))
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except ValueError:
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parsed_citation_idx = None
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# conclude the current evidence if exists
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if current_evidence:
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citations.append(current_evidence)
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current_evidence = None
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current_evidence = InlineEvidence(idx=parsed_citation_idx)
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else:
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for keyword in [START_ANSWER_PATTERN, END_ANSWER_PATTERN]:
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if line.lower().startswith(keyword):
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matched_phrase = line[len(keyword) :].strip()
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if not current_evidence:
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current_evidence = InlineEvidence(idx=None)
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if keyword == START_ANSWER_PATTERN:
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current_evidence.start_phrase = matched_phrase
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else:
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current_evidence.end_phrase = matched_phrase
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break
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if (
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current_evidence
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and current_evidence.end_phrase
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and current_evidence.start_phrase
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):
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citations.append(current_evidence)
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current_evidence = None
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if current_evidence:
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citations.append(current_evidence)
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return citations
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def replace_citation_with_link(self, answer: str):
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# Define the regex pattern to match 【number】
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@ -114,6 +168,8 @@ class AnswerWithInlineCitation(AnswerWithContextPipeline):
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),
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)
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answer = answer.replace(START_CITATION, "")
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return answer
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def stream( # type: ignore
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@ -178,8 +234,6 @@ class AnswerWithInlineCitation(AnswerWithContextPipeline):
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# append main prompt
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messages.append(HumanMessage(content=prompt))
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START_ANSWER = "FINAL ANSWER"
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start_of_answer = True
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final_answer = ""
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try:
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@ -187,12 +241,24 @@ class AnswerWithInlineCitation(AnswerWithContextPipeline):
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print("Trying LLM streaming")
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for out_msg in self.llm.stream(messages):
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if START_ANSWER in output:
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if not final_answer:
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try:
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left_over_answer = output.split(START_ANSWER)[1].lstrip()
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except IndexError:
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left_over_answer = ""
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if left_over_answer:
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out_msg.text = left_over_answer + out_msg.text
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final_answer += (
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out_msg.text.lstrip() if start_of_answer else out_msg.text
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out_msg.text.lstrip() if not final_answer else out_msg.text
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)
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start_of_answer = False
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yield Document(channel="chat", content=out_msg.text)
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# check for the edge case of citation list is repeated
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# with smaller LLMs
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if START_CITATION in out_msg.text:
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break
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output += out_msg.text
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logprobs += out_msg.logprobs
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except NotImplementedError:
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@ -235,10 +301,15 @@ class AnswerWithInlineCitation(AnswerWithContextPipeline):
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if not answer.metadata["citation"]:
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return spans
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evidences = answer.metadata["citation"].evidences
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evidences = answer.metadata["citation"]
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for e_id, evidence in enumerate(evidences):
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start_phrase, end_phrase = evidence.start_phrase, evidence.end_phrase
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evidence_idx = evidence.idx
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if evidence_idx is None:
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evidence_idx = e_id + 1
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for start_idx in range(0, len(evidences), 2):
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start_phrase, end_phrase = evidences[start_idx : start_idx + 2]
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best_match = None
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best_match_length = 0
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best_match_doc_idx = None
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@ -259,7 +330,7 @@ class AnswerWithInlineCitation(AnswerWithContextPipeline):
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{
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"start": best_match[0],
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"end": best_match[1],
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"idx": start_idx // 2, # implicitly set from the start_idx
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"idx": evidence_idx,
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}
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)
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return spans
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