mirror of
https://github.com/MODSetter/SurfSense.git
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f5ea337b75
This commit introduces Alison, an AI-powered classroom IT support assistant, as a new module within the SurfSense application. Key features of this implementation include: - A new LangGraph-based agent for conversational troubleshooting. - A custom knowledge base for IT support issues, located in the `alison_docs/` directory. - An extension of the RAG pipeline to use Alison's knowledge base. - Role-aware responses for professors and proctors. - A configuration toggle to enable or disable the Alison module. - Documentation for setting up and using Alison. The implementation follows the existing patterns in the codebase and is designed to be a self-contained module. Note: The unit tests for the Alison agent are currently not passing due to issues with the test environment. Further work is needed to get the tests to run correctly.
123 lines
4.2 KiB
Python
123 lines
4.2 KiB
Python
from sqlalchemy import func, select, text
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from sqlalchemy.orm import joinedload
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from app.config import config
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from app.db import Chunk, Document, DocumentType, SearchSpace, User
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class AlisonKnowledgeRetriever:
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def __init__(self, db_session):
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self.db_session = db_session
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async def hybrid_search(self, query_text: str, top_k: int) -> list:
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# Get the "alison" user and "Alison's Knowledge Base" search space
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result = await self.db_session.execute(select(User).where(User.email == "alison@surfsense.ai"))
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alison_user = await (await result.scalars()).first()
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if not alison_user:
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return []
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result = await self.db_session.execute(select(SearchSpace).where(SearchSpace.name == "Alison's Knowledge Base"))
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alison_search_space = await (await result.scalars()).first()
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if not alison_search_space:
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return []
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embedding_model = config.embedding_model_instance
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query_embedding = embedding_model.embed(query_text)
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k = 60
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n_results = top_k * 2
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tsvector = func.to_tsvector("english", Chunk.content)
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tsquery = func.plainto_tsquery("english", query_text)
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base_conditions = [
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SearchSpace.user_id == alison_user.id,
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Document.search_space_id == alison_search_space.id,
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]
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semantic_search_cte = (
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select(
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Chunk.id,
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func.rank()
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.over(order_by=Chunk.embedding.op("<=>")(query_embedding))
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.label("rank"),
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)
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.join(Document, Chunk.document_id == Document.id)
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.join(SearchSpace, Document.search_space_id == SearchSpace.id)
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.where(*base_conditions)
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)
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semantic_search_cte = (
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semantic_search_cte.order_by(Chunk.embedding.op("<=>")(query_embedding))
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.limit(n_results)
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.cte("semantic_search")
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)
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keyword_search_cte = (
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select(
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Chunk.id,
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func.rank()
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.over(order_by=func.ts_rank_cd(tsvector, tsquery).desc())
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.label("rank"),
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)
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.join(Document, Chunk.document_id == Document.id)
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.join(SearchSpace, Document.search_space_id == SearchSpace.id)
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.where(*base_conditions)
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.where(tsvector.op("@@")(tsquery))
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)
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keyword_search_cte = (
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keyword_search_cte.order_by(func.ts_rank_cd(tsvector, tsquery).desc())
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.limit(n_results)
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.cte("keyword_search")
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)
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final_query = (
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select(
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Chunk,
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(
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func.coalesce(1.0 / (k + semantic_search_cte.c.rank), 0.0)
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+ func.coalesce(1.0 / (k + keyword_search_cte.c.rank), 0.0)
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).label("score"),
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)
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.select_from(
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semantic_search_cte.outerjoin(
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keyword_search_cte,
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semantic_search_cte.c.id == keyword_search_cte.c.id,
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full=True,
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)
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)
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.join(
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Chunk,
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Chunk.id
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== func.coalesce(semantic_search_cte.c.id, keyword_search_cte.c.id),
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)
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.options(joinedload(Chunk.document))
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.order_by(text("score DESC"))
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.limit(top_k)
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)
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result = await self.db_session.execute(final_query)
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chunks_with_scores = (await result.all())
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if not chunks_with_scores:
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return []
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serialized_results = []
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for chunk, score in chunks_with_scores:
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serialized_results.append(
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{
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"chunk_id": chunk.id,
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"content": chunk.content,
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"score": float(score),
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"document": {
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"id": chunk.document.id,
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"title": chunk.document.title,
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"document_type": chunk.document.document_type.value
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if hasattr(chunk.document, "document_type")
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else None,
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"metadata": chunk.document.document_metadata,
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},
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}
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)
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return serialized_results
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