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ML-Learner / server /src /models /multi_rag_models.py

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0225ffc verified 5 months ago

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from typing import Any, List, Literal

from langgraph.graph.message import MessagesState
from pydantic import BaseModel


class State(MessagesState):
    vector_store_file_paths: List[str]

    require_db_search: bool

    queries: List[str]

    retreived_results: List[Any]

    refined_results: List[Any]

    relevance: Literal[
        "CORRECT",
        "AMBIGUOUS",
        "INCORRECT"
    ]

    web_search_results: List[Any]

    docs_feed_to_llm: str

    ai_response: str


class Orchastrator_output(BaseModel):
    require_db_search: bool


class Query_generation_output(BaseModel):
    queries: List[str]


class Relevance_output(BaseModel):
    relevance: Literal[
        "CORRECT",
        "AMBIGUOUS",
        "INCORRECT"
    ]


class WebSearchOutput(BaseModel):
    queries: List[str]

	from typing import Any, List, Literal

	from langgraph.graph.message import MessagesState
	from pydantic import BaseModel


	class State(MessagesState):
	vector_store_file_paths: List[str]

	require_db_search: bool

	queries: List[str]

	retreived_results: List[Any]

	refined_results: List[Any]

	relevance: Literal[
	"CORRECT",
	"AMBIGUOUS",
	"INCORRECT"
	]

	web_search_results: List[Any]

	docs_feed_to_llm: str

	ai_response: str


	class Orchastrator_output(BaseModel):
	require_db_search: bool


	class Query_generation_output(BaseModel):
	queries: List[str]


	class Relevance_output(BaseModel):
	relevance: Literal[
	"CORRECT",
	"AMBIGUOUS",
	"INCORRECT"
	]


	class WebSearchOutput(BaseModel):
	queries: List[str]