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rag-qa-system

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feat: Implement RAG-based question answering system with FastAPI and Streamlit UI, including document ingestion and QA endpoints

3d7632a 9 months ago

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from pydantic import BaseModel, Field
from typing import List, Optional


class UploadResponse(BaseModel):
    job_id: str
    status: str


class JobStatus(BaseModel):
    id: str
    filename: str
    status: str
    error: Optional[str]


class QARequest(BaseModel):
    question: str = Field(..., min_length=3, max_length=500)
    top_k: int = Field(4, ge=1, le=10)


class ContextChunk(BaseModel):
    score: float
    source: str
    doc_id: str
    chunk: str


class QAResponse(BaseModel):
    answer: str
    contexts: List[ContextChunk]
    latency_ms: int

	from pydantic import BaseModel, Field
	from typing import List, Optional


	class UploadResponse(BaseModel):
	job_id: str
	status: str


	class JobStatus(BaseModel):
	id: str
	filename: str
	status: str
	error: Optional[str]


	class QARequest(BaseModel):
	question: str = Field(..., min_length=3, max_length=500)
	top_k: int = Field(4, ge=1, le=10)


	class ContextChunk(BaseModel):
	score: float
	source: str
	doc_id: str
	chunk: str


	class QAResponse(BaseModel):
	answer: str
	contexts: List[ContextChunk]
	latency_ms: int