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https://github.com/QuivrHQ/quivr.git
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481f24f5be
# Description - Introduce `LLMEndpoint` class: wrapper around a `BaseChatLLM` to load OpenAI compatible models - Add `brain.search(...)` function to retrieve - Test with test-coverage: - Added ability to load brain `from langchain.Document` - Configured mypy and poetry lock in `.pre-commit.yaml` # Test coverage  --------- Co-authored-by: aminediro <aminedirhoussi@gmail.com>
97 lines
2.6 KiB
Python
97 lines
2.6 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.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 GetChatHistoryOutput(BaseModel):
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chat_id: UUID
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message_id: UUID
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user_message: str
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message_time: datetime
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assistant: str | None = None
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prompt_title: str | None = None
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brain_name: str | None = None
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brain_id: UUID | None = None # string because UUID is not JSON serializable
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metadata: dict | None = None
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thumbs: bool | None = None
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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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score: float
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