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Unify, respect perms

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shamoon 2025-04-25 00:09:33 -07:00
parent ccfc7d98b1
commit 5f26139a5f
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3 changed files with 37 additions and 40 deletions
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52
src/paperless/ai/chat.py
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@ -1,20 +1,38 @@
import logging
from django.contrib.auth.models import User
from llama_index.core import VectorStoreIndex
from llama_index.core.query_engine import RetrieverQueryEngine
from documents.models import Document
from paperless.ai.client import AIClient
from paperless.ai.indexing import get_document_retriever
from paperless.ai.indexing import load_index
logger = logging.getLogger("paperless.ai.chat")
def chat_with_documents(prompt: str, user: User) -> str:
retriever = get_document_retriever(top_k=5)
def chat_with_documents(prompt: str, documents: list[Document]) -> str:
client = AIClient()
index = load_index()
doc_ids = [doc.pk for doc in documents]
# Filter only the node(s) that match the document IDs
nodes = [
node
for node in index.docstore.docs.values()
if node.metadata.get("document_id") in doc_ids
]
if len(nodes) == 0:
logger.warning("No nodes found for the given documents.")
return "Sorry, I couldn't find any content to answer your question."
local_index = VectorStoreIndex.from_documents(nodes)
retriever = local_index.as_retriever(
similarity_top_k=3 if len(documents) == 1 else 5,
)
query_engine = RetrieverQueryEngine.from_args(
retriever=retriever,
llm=client.llm,
@ -24,29 +42,3 @@ def chat_with_documents(prompt: str, user: User) -> str:
response = query_engine.query(prompt)
logger.debug("Document chat response: %s", response)
return str(response)
def chat_with_single_document(document, question: str, user):
index = load_index()
# Filter only the node(s) belonging to this doc
nodes = [
node
for node in index.docstore.docs.values()
if node.metadata.get("document_id") == str(document.id)
]
if not nodes:
raise Exception("This document is not indexed yet.")
local_index = VectorStoreIndex.from_documents(nodes)
client = AIClient()
engine = RetrieverQueryEngine.from_args(
retriever=local_index.as_retriever(similarity_top_k=3),
llm=client.llm,
)
response = engine.query(question)
return str(response)