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45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
import logging
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from llama_index.core import VectorStoreIndex
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from llama_index.core.query_engine import RetrieverQueryEngine
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from documents.models import Document
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from paperless.ai.client import AIClient
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from paperless.ai.indexing import load_index
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logger = logging.getLogger("paperless.ai.chat")
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def chat_with_documents(prompt: str, documents: list[Document]) -> str:
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client = AIClient()
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index = load_index()
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doc_ids = [doc.pk for doc in documents]
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# Filter only the node(s) that match the document IDs
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nodes = [
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node
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for node in index.docstore.docs.values()
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if node.metadata.get("document_id") in doc_ids
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]
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if len(nodes) == 0:
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logger.warning("No nodes found for the given documents.")
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return "Sorry, I couldn't find any content to answer your question."
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local_index = VectorStoreIndex.from_documents(nodes)
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retriever = local_index.as_retriever(
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similarity_top_k=3 if len(documents) == 1 else 5,
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)
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query_engine = RetrieverQueryEngine.from_args(
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retriever=retriever,
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llm=client.llm,
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)
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logger.debug("Document chat prompt: %s", prompt)
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response = query_engine.query(prompt)
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logger.debug("Document chat response: %s", response)
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return str(response)
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