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# Description `quivr-core` - Generate a fixture to simulate a model with function calling - Monkey patch `QuivrQARAG` stream - Tests function `quivr-api` - Fixes empty API responses - Fixes non function calling models --------- Co-authored-by: Stan Girard <girard.stanislas@gmail.com>
94 lines
2.5 KiB
Python
94 lines
2.5 KiB
Python
from datetime import datetime
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from typing import Any
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from uuid import UUID
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from langchain_core.documents import Document
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from langchain_core.messages import AIMessage, HumanMessage
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from langchain_core.pydantic_v1 import BaseModel as BaseModelV1
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from langchain_core.pydantic_v1 import Field as FieldV1
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from pydantic import BaseModel
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from typing_extensions import TypedDict
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class cited_answer(BaseModelV1):
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"""Answer the user question based only on the given sources, and cite the sources used."""
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answer: str = FieldV1(
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...,
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description="The answer to the user question, which is based only on the given sources.",
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)
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thoughts: str = FieldV1(
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...,
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description="""Description of the thought process, based only on the given sources.
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Cite the text as much as possible and give the document name it appears in. In the format : 'Doc_name states : cited_text'. Be the most
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procedural as possible. Write all the steps needed to find the answer until you find it.""",
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)
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citations: list[int] = FieldV1(
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...,
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description="The integer IDs of the SPECIFIC sources which justify the answer.",
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)
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followup_questions: list[str] = FieldV1(
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...,
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description="Generate up to 3 follow-up questions that could be asked based on the answer given or context provided.",
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)
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class ChatMessage(BaseModelV1):
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chat_id: UUID
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message_id: UUID
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brain_id: UUID
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msg: AIMessage | HumanMessage
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message_time: datetime
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metadata: dict[str, Any]
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class Source(BaseModel):
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name: str
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source_url: str
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type: str
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original_file_name: str
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citation: str
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class RawRAGChunkResponse(TypedDict):
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answer: dict[str, Any]
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docs: dict[str, Any]
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class RawRAGResponse(TypedDict):
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answer: dict[str, Any]
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docs: dict[str, Any]
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class RAGResponseMetadata(BaseModel):
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citations: list[int] | None = None
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thoughts: str | list[str] | None = None
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followup_questions: list[str] | None = None
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sources: list[Any] | None = None
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class ParsedRAGResponse(BaseModel):
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answer: str
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metadata: RAGResponseMetadata | None = None
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class ParsedRAGChunkResponse(BaseModel):
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answer: str
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metadata: RAGResponseMetadata
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last_chunk: bool = False
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class QuivrKnowledge(BaseModel):
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id: UUID
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brain_id: UUID
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file_name: str | None = None
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url: str | None = None
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extension: str = "txt"
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# NOTE: for compatibility issues with langchain <-> PydanticV1
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class SearchResult(BaseModelV1):
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chunk: Document
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distance: float
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