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Source access control --------------------- `active_docs` is client-supplied and reached the retriever unchecked, and the retriever queries `WHERE source_id = <id>` with no owner predicate — so any caller could pass any source id to /stream or /api/answer and have another tenant's documents quoted back, while /api/sources/<id>/search correctly refused the same id. Gate it through `can_access`, the helper the guarded endpoints already use, and filter `self.source` down to the authorized set. Fails closed: no principal, or a check that errors, drops the source. Three sibling paths had the same gap: - workflow agent nodes: `AgentNodeConfig.sources` is written verbatim from client JSON at save time and nothing validated it, so a node could name any tenant's source. Gate against the workflow owner, so shared workflows keep reading their owner's sources like shared agents do. - /api/share: `_resolve_source_pg_id` resolved any id with no ownership predicate and baked it into the agent the share creates; /api/search then searched it. Authorize before attaching. - search_service: re-resolve the ids stored on an agent row instead of trusting them, so a row written by any future path with the same gap cannot be read back. Team grantees previously lost their source's retrieval config: the post-check read was still owner-scoped, so it missed and fell back to defaults (an `agentic_tool` source was bulk-prefetched for every grantee). Read unscoped after `can_access` passes. Retrieval --------- `PGVectorStore._ensure_table_exists` created an IVFFlat index on the empty table it had just created. IVFFlat computes centroids at build time, so those centroids were random, and combined with the `source_id` post-filter a source with hundreds of embedded chunks returned zero rows — retrieval reported no documents, the model answered from memory, and nothing was logged. Stop creating the index (exact search is correct and fast well past the sizes most deployments reach); raise `ivfflat.probes` to sqrt(lists) where an index still exists; and re-run a short indexed search exactly, since post-filtering means no index setting can guarantee a full result. `graphrag` had the same empty-table index with no fallback at all. Also: bound `chunks` to 0-500 on both the request and agent paths (0 still means "skip retrieval"), let a source's configured `retrieval.chunks` outrank the request body, and cap ClassicRAG's per-source floor at max(top_k, n_sources) so attaching sources cannot inflate the result set. Silent failures --------------- An empty retrieval was invisible to both the model and the client: the `source` event was suppressed when the list was empty, so "searched and found nothing" looked identical to "no source attached", and the prompt said nothing at all. Emit the event always, and tell the model when a search ran and returned nothing. A file that parses to nothing now fails ingest with a message naming the cause instead of storing an embedding of the empty string. `score_threshold` returns warnings when the active store or retriever cannot honour it. Prompt structure ---------------- Retrieved documents move from the system prompt into the user turn, with the injection guard restated next to them: they change every turn (defeating prefix caching), they are third-party text that should not carry system authority, and routing them through the query budget makes them truncatable rather than silently crowding it out. Documents are shed lowest-ranked-first before the question is touched. The six chat presets (3 tones x 2 retrieval modes) differed only in their Answering section; they are now composed from single-source fragments at load time, not through Jinja inheritance, which would have opened a file-read surface in the template sandbox and broken the tool-prefetch parser. Per-tool guidance moves out of the prompt into tool schemas, so it travels with the tool and cannot render when the tool is absent. A plain-text custom prompt is staged as a persona value inside the skeleton instead of replacing it wholesale — it used to silently lose the injection guard, platform block, memory and attachments, and its braces are now inert. Other fixes ----------- - agents/base: an oversized system prompt drove the query budget negative and dispatched a full-price request with an empty question; raise instead. - llm/anthropic: migrate off the retired Text Completions API. It flattened history to first+last message and ignored tools entirely. Adds the missing Anthropic handler, without which every tool call was silently dropped. - sources/upload: `sitemap` had no branch, so every sitemap ingest died on a TypeError; `validate_url` now rejects a falsy URL cleanly. - workflow nodes: retrieved documents never reached the node agent, so a classic node with a source and an ordinary prompt answered "I have no documents" while the run reported completed. - parser/bulk: copy the metadata dict, or every chunk reports the last chunk's token_count. - crawler_loader: carry the page title, or citations render the whole chunk body as the label.
158 lines
5.7 KiB
Python
158 lines
5.7 KiB
Python
"""Retrieved documents must survive the whole request path to the user turn.
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The document-placement change was originally verified by constructing a
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``ClassicAgent`` with ``retrieved_docs`` already populated, which skipped the
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seam that actually carries them: retrieval -> ``StreamProcessor`` ->
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``agent_kwargs`` -> ``BaseAgent._build_messages``. A break anywhere along it
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looks exactly like "the model ignored my source", so it is pinned here.
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"""
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from __future__ import annotations
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from unittest.mock import MagicMock, patch
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import pytest
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from application.agents.classic_agent import ClassicAgent
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from application.api.answer.services.stream_processor import StreamProcessor
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DOCS = [
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{"text": "Clause 4: reporting is due within 30 days.", "filename": "aml.pdf"},
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{"text": "Clause 9: records are kept for five years.", "filename": "aml.pdf"},
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]
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def _processor(**data) -> StreamProcessor:
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"""A processor with only the fields the retrieval seam touches."""
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sp = StreamProcessor.__new__(StreamProcessor)
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sp.data = {"question": "Summarize current context", **data}
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sp.agent_id = None
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sp.agent_config = {"prompt_id": "default", "agent_type": "classic"}
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sp.source = {"active_docs": "src-1"}
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sp.all_sources = [{"id": "src-1", "retrieval": None}]
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sp.retriever_config = {
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"retriever_name": "classic",
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"chunks": 2,
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"doc_token_limit": 50000,
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}
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sp.retrieved_docs = []
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return sp
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@pytest.mark.unit
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class TestRetrievalReachesTheAgent:
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def test_prefetch_populates_retrieved_docs(self):
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sp = _processor()
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retriever = MagicMock()
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retriever.search.return_value = DOCS
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retriever.chunks = 2
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retriever.doc_token_limit = 50000
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with patch.object(sp, "create_retriever", return_value=retriever):
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docs_together, docs = sp.pre_fetch_docs("Summarize current context")
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assert docs == DOCS
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assert docs_together and "Clause 4" in docs_together
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# This is the attribute agent_kwargs forwards; empty here means the
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# model silently answers with no source material.
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assert sp.retrieved_docs == DOCS
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def test_no_active_docs_retrieves_nothing(self):
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"""The signature of a request that forgot to attach its source."""
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sp = _processor()
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sp.source = {}
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sp.all_sources = []
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docs_together, docs = sp.pre_fetch_docs("Summarize current context")
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assert docs is None and docs_together is None
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assert sp.retrieved_docs == []
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@pytest.mark.unit
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class TestDocumentsLandInTheUserTurn:
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def _agent(self, **kwargs):
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with patch("application.llm.llm_creator.LLMCreator.create_llm"), patch(
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"application.llm.handlers.handler_creator.LLMHandlerCreator.create_handler"
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):
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return ClassicAgent(
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endpoint="stream",
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llm_name="openai",
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model_id="gpt-4o",
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api_key="k",
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prompt="SYSTEM",
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decoded_token={"sub": "u"},
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tool_executor=MagicMock(),
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**kwargs,
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)
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def test_documents_reach_the_user_turn(self):
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agent = self._agent(retrieved_docs=DOCS)
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messages = agent._build_messages("SYSTEM", "Summarize current context")
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system, user = messages[0]["content"], messages[-1]["content"]
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assert "Clause 4" not in system, "documents must not sit in the system prompt"
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assert "<documents>" in user and "Clause 4" in user and "Clause 9" in user
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assert user.rstrip().endswith("Summarize current context")
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def test_empty_retrieval_leaves_the_question_alone(self):
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agent = self._agent(retrieved_docs=[])
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user = agent._build_messages("SYSTEM", "Summarize current context")[-1]["content"]
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assert user == "Summarize current context"
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@pytest.mark.unit
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class TestChunksPrecedence:
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"""A source that tuned its own top-k outranks the request body.
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The agentless path took ``chunks`` straight from the request, unbounded, so
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a client could both override an owner's tuning and ask for any number.
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"""
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def _sp(self, request_chunks=None, source_chunks=None):
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from application.storage.db.source_config import RetrievalConfig
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sp = _processor()
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sp._agent_data = None
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sp.model_id = "gpt-4o"
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sp.model_user_id = None
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sp.agent_key = None
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if request_chunks is not None:
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sp.data["chunks"] = request_chunks
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retrieval = (
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RetrievalConfig(chunks=source_chunks) if source_chunks else RetrievalConfig()
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)
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sp.all_sources = [{"id": "src-1", "retrieval": retrieval}]
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sp._configure_retriever()
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return sp.retriever_config["chunks"]
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def test_request_applies_when_source_is_unconfigured(self):
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assert self._sp(request_chunks="7") == 7
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def test_configured_source_beats_the_request(self):
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assert self._sp(request_chunks="100", source_chunks=5) == 5
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@pytest.mark.parametrize(
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"sent,expected",
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[
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("0", 0), # 0 means "suppress retrieval" — must survive clamping
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("-5", 0),
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("100000", 500),
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("501", 500),
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("abc", 2),
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],
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)
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def test_request_chunks_is_clamped(self, sent, expected):
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assert self._sp(request_chunks=sent) == expected
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def test_agent_chunks_is_clamped_too(self):
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"""The agent path was unbounded even after the request path was fixed."""
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sp = _processor()
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sp._agent_data = {"chunks": 100000}
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sp.model_id = "gpt-4o"
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sp.model_user_id = None
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sp.agent_key = None
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sp.all_sources = []
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sp._configure_retriever()
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assert sp.retriever_config["chunks"] == 500
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