improve llm selection for simple reasoning pipeline
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b2089245f2
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@ -17,8 +17,7 @@ if machine == "x86_64":
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BINARY_REMOTE_NAME = f"frpc_{platform.system().lower()}_{machine.lower()}"
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EXTENSION = ".exe" if os.name == "nt" else ""
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BINARY_URL = (
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"some-endpoint.com"
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f"/kotaemon/tunneling/{VERSION}/{BINARY_REMOTE_NAME}{EXTENSION}"
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"some-endpoint.com" f"/kotaemon/tunneling/{VERSION}/{BINARY_REMOTE_NAME}{EXTENSION}"
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)
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BINARY_FILENAME = f"{BINARY_REMOTE_NAME}_v{VERSION}"
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@ -194,7 +194,6 @@ class ChatOpenAI(LCChatMixin, ChatLLM): # type: ignore
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class AzureChatOpenAI(LCChatMixin, ChatLLM): # type: ignore
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def __init__(
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self,
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azure_endpoint: str | None = None,
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@ -1,4 +1,5 @@
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"""Common components, some kind of config"""
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import logging
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from functools import cache
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from pathlib import Path
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@ -71,7 +72,7 @@ class ModelPool:
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}
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def options(self) -> dict:
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"""Present a list of models"""
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"""Present a dict of models"""
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return self._models
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def get_random_name(self) -> str:
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@ -1,5 +1,6 @@
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from __future__ import annotations
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import logging
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import shutil
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import warnings
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from collections import defaultdict
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@ -8,7 +9,7 @@ from hashlib import sha256
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from pathlib import Path
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from typing import Optional
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from ktem.components import embeddings, filestorage_path, llms
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from ktem.components import embeddings, filestorage_path
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from ktem.db.models import engine
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from llama_index.vector_stores import (
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FilterCondition,
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@ -25,10 +26,12 @@ from theflow.utils.modules import import_dotted_string
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from kotaemon.base import RetrievedDocument
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from kotaemon.indices import VectorIndexing, VectorRetrieval
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from kotaemon.indices.ingests import DocumentIngestor
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from kotaemon.indices.rankings import BaseReranking, LLMReranking
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from kotaemon.indices.rankings import BaseReranking
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from .base import BaseFileIndexIndexing, BaseFileIndexRetriever
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logger = logging.getLogger(__name__)
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@lru_cache
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def dev_settings():
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@ -67,7 +70,7 @@ class DocumentRetrievalPipeline(BaseFileIndexRetriever):
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vector_retrieval: VectorRetrieval = VectorRetrieval.withx(
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embedding=embeddings.get_default(),
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)
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reranker: BaseReranking = LLMReranking.withx(llm=llms.get_lowest_cost())
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reranker: BaseReranking
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get_extra_table: bool = False
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def run(
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@ -153,7 +156,23 @@ class DocumentRetrievalPipeline(BaseFileIndexRetriever):
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@classmethod
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def get_user_settings(cls) -> dict:
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from ktem.components import llms
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try:
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reranking_llm = llms.get_lowest_cost_name()
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reranking_llm_choices = list(llms.options().keys())
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except Exception as e:
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logger.error(e)
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reranking_llm = None
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reranking_llm_choices = []
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return {
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"reranking_llm": {
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"name": "LLM for reranking",
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"value": reranking_llm,
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"component": "dropdown",
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"choices": reranking_llm_choices,
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},
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"separate_embedding": {
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"name": "Use separate embedding",
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"value": False,
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@ -185,7 +204,7 @@ class DocumentRetrievalPipeline(BaseFileIndexRetriever):
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},
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"use_reranking": {
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"name": "Use reranking",
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"value": True,
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"value": False,
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"choices": [True, False],
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"component": "checkbox",
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},
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@ -199,7 +218,10 @@ class DocumentRetrievalPipeline(BaseFileIndexRetriever):
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settings: the settings of the app
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kwargs: other arguments
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"""
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retriever = cls(get_extra_table=user_settings["prioritize_table"])
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retriever = cls(
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get_extra_table=user_settings["prioritize_table"],
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reranker=user_settings["reranking_llm"],
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)
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if not user_settings["use_reranking"]:
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retriever.reranker = None # type: ignore
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@ -159,6 +159,7 @@ class AnswerWithContextPipeline(BaseComponent):
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qa_table_template: str = DEFAULT_QA_TABLE_PROMPT
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qa_chatbot_template: str = DEFAULT_QA_CHATBOT_PROMPT
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enable_citation: bool = False
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system_prompt: str = ""
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lang: str = "English" # support English and Japanese
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@ -200,7 +201,8 @@ class AnswerWithContextPipeline(BaseComponent):
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lang=self.lang,
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)
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if evidence:
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citation_task = None
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if evidence and self.enable_citation:
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citation_task = asyncio.create_task(
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self.citation_pipeline.ainvoke(context=evidence, question=question)
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)
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@ -226,7 +228,7 @@ class AnswerWithContextPipeline(BaseComponent):
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# retrieve the citation
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print("Waiting for citation task")
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if evidence:
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if citation_task is not None:
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citation = await citation_task
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else:
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citation = None
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@ -353,7 +355,15 @@ class FullQAPipeline(BaseReasoning):
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_id = cls.get_info()["id"]
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pipeline = FullQAPipeline(retrievers=retrievers)
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pipeline.answering_pipeline.llm = llms.get_highest_accuracy()
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pipeline.answering_pipeline.llm = llms[
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settings[f"reasoning.options.{_id}.main_llm"]
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]
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pipeline.answering_pipeline.citation_pipeline.llm = llms[
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settings[f"reasoning.options.{_id}.citation_llm"]
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]
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pipeline.answering_pipeline.enable_citation = settings[
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f"reasoning.options.{_id}.highlight_citation"
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]
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pipeline.answering_pipeline.lang = {"en": "English", "ja": "Japanese"}.get(
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settings["reasoning.lang"], "English"
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)
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@ -384,7 +394,7 @@ class FullQAPipeline(BaseReasoning):
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return {
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"highlight_citation": {
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"name": "Highlight Citation",
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"value": True,
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"value": False,
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"component": "checkbox",
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},
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"citation_llm": {
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