Merge pull request #349 from ggozad/fix/convert-urlparse

fix convert() misreading text content that starts with a URL

CHANGELOG.md

@@ -1,6 +1,17 @@
 # Changelog
 ## [Unreleased]
 
+### Fixed
+
+- **`create_document`, `update_document`, and rebuild (`RECHUNK` / full fallback) no longer misread URL-prefixed text as a URL to fetch.** These paths passed known-text content through `HaikuRAG.convert()`, which dispatches on `urlparse(source).scheme`; text whose first line was `https://...` (common for clipped web pages and notes) got handed to `httpx.get` and crashed with `httpx.InvalidURL` on embedded whitespace. Fixed by calling `converter.convert_text(...)` directly at those sites; `convert()` itself is unchanged for `create_document_from_source`.
+
+### Changed
+
+- **Skills share a single `HaikuRAG` client per invocation** via the new `haiku.skills>=0.15.0` `lifespan` hook. The skill's sub-agent opens one read-only client on entry, all tool calls reuse it, and it closes on exit — replacing the old pattern of open/close around every `search` / `list_documents` / `get_document` call.
+- **`max_searches` tracked on `RAGRunDeps.search_count`** instead of a module-level `ctx.run_id`-keyed dict. Eliminates a memory leak in long-running processes where old run ids were never evicted.
+- **Analysis sandbox persists variables across `execute_code` calls within one invocation.** Re-enables the incremental-exploration workflow (search in one call, process results in the next). Each new skill invocation constructs a fresh `Sandbox` via the analysis lifespan, so there is no cross-invocation leak.
+- **Skill state is scoped to the current invocation.** Lifespans now clear `citations`, `searches`, and (for analysis) `executions` at the start of each invocation, so state deltas sent to the AG-UI client reflect only the in-progress turn. `citation_index` is preserved across invocations so past-turn citation chunk ids remain resolvable, and `document_filter` is preserved as session-level config.
+
 ## [0.41.0] - 2026-04-20
 
 ### Added

docs/skills/analysis.md

@@ -37,10 +37,11 @@ class AnalysisState(BaseModel):
     searches: dict[str, list[SearchResult]] = {}
 ```
 
-- **document_filter** — SQL WHERE clause applied to `search` and `list_documents` calls.
-- **executions** — Each `execute_code` call appends a `CodeExecutionEntry` with code, stdout, stderr, and success status.
-- **citation_index** / **citations** — Same per-turn citation tracking as the RAG skill.
-- **searches** — Search results from both the `search` tool and sandbox-internal searches.
+- **document_filter** — SQL WHERE clause applied to `search` and `list_documents` calls. Persists across invocations as session-level configuration.
+- **executions** — Each `execute_code` call appends a `CodeExecutionEntry` with code, stdout, stderr, and success status. Cleared at the start of each invocation; mirrors the sandbox lifecycle (variables persist across calls within one invocation, a fresh sandbox is built per invocation).
+- **citation_index** — Citations indexed by chunk ID. Accumulates across invocations (same semantics as the RAG skill).
+- **citations** — Cleared at the start of each invocation; holds only the in-progress turn.
+- **searches** — Search results from both the `search` tool and sandbox-internal searches. Cleared at the start of each invocation.
 
 ## Usage with RAG Skill
 

docs/skills/rag.md

@@ -36,7 +36,7 @@ class RAGState(BaseModel):
     searches: dict[str, list[SearchResult]] = {}
 ```
 
-- **citation_index** — All citations indexed by chunk ID (deduplicated across turns).
-- **citations** — Per-turn lists of chunk IDs registered via the `cite` tool.
-- **document_filter** — SQL WHERE clause applied to `search` and `list_documents` calls. Set this to scope queries to specific documents.
-- **searches** — Search results keyed by query string.
+- **citation_index** — All citations indexed by chunk ID. Accumulates across invocations so historical turns' chunk IDs remain resolvable in the UI scrollback.
+- **citations** — Chunk IDs registered via the `cite` tool. Cleared at the start of each invocation; holds only the in-progress turn.
+- **document_filter** — SQL WHERE clause applied to `search` and `list_documents` calls. Persists across invocations as session-level configuration.
+- **searches** — Search results keyed by query string. Cleared at the start of each invocation.

haiku_rag_slim/haiku/rag/agents/analysis/sandbox.py

@@ -8,7 +8,7 @@ from pathlib import Path
 from typing import TYPE_CHECKING, Any, Literal
 
 import pydantic_monty
-from pydantic_monty import CallbackFile, MemoryFile, OSAccess
+from pydantic_monty import CallbackFile, MemoryFile, MontyRepl, OSAccess
 
 from haiku.rag.agents.analysis.dependencies import AnalysisContext
 from haiku.rag.config.models import AppConfig
@@ -44,11 +44,12 @@ class Sandbox:
     and resolved asynchronously on the host.
     Documents are exposed via a virtual filesystem at ``/documents/{id}/``.
 
-    Each ``execute()`` call runs in a fresh interpreter — variables do not
-    persist between calls.
+    The interpreter uses a REPL session — variables persist across
+    ``execute()`` calls within the same Sandbox instance.
 
         sandbox = Sandbox(db_path, config, context)
-        result = await sandbox.execute("print('hello')")
+        result = await sandbox.execute("x = await search('query')")
+        result = await sandbox.execute("print(x[0]['content'])")  # x persists
     """
 
     _db_path: Path
@@ -56,6 +57,8 @@ class Sandbox:
     _context: AnalysisContext
     _search_results: "list[SearchResult]"
     _items_cache: dict[str, str] | None
+    _repl: MontyRepl | None
+    _vfs: OSAccess | None
 
     def __init__(
         self,
@@ -68,6 +71,8 @@ class Sandbox:
         self._context = context
         self._search_results = []
         self._items_cache = None
+        self._repl = None
+        self._vfs = None
 
     def _build_external_functions(self) -> dict[str, Any]:
         """Build async external functions for the Monty interpreter."""
@@ -245,37 +250,46 @@ class Sandbox:
 
         return OSAccess(files)
 
-    async def execute(self, code: str) -> SandboxResult:
-        """Execute Python code in the Monty interpreter."""
-        external_fns = self._build_external_functions()
-        vfs = await self._build_vfs()
-
-        input_names: list[str] = []
-        inputs: dict[str, Any] | None = None
-        if self._context.documents:
-            input_names.append("documents")
-            inputs = {
-                "documents": [
-                    {
-                        "id": d.id,
-                        "title": d.title,
-                        "uri": d.uri,
-                        "content": d.content,
-                    }
-                    for d in self._context.documents
-                ]
-            }
-
-        try:
-            monty = pydantic_monty.Monty(
-                code,
-                inputs=input_names,
+    async def _ensure_initialized(self) -> tuple[MontyRepl, OSAccess]:
+        """Initialize the REPL session and VFS on first use."""
+        if self._repl is None:
+            self._vfs = await self._build_vfs()
+            self._repl = MontyRepl(
+                limits={
+                    "max_duration_secs": self._config.analysis.code_timeout,
+                },
             )
-        except (
-            pydantic_monty.MontySyntaxError,
-            pydantic_monty.MontyRuntimeError,
-        ) as e:
-            return SandboxResult(stdout="", stderr=str(e), success=False)
+            if self._context.documents:
+                await pydantic_monty.run_repl_async(
+                    self._repl,
+                    "pass",
+                    inputs={
+                        "documents": [
+                            {
+                                "id": d.id,
+                                "title": d.title,
+                                "uri": d.uri,
+                                "content": d.content,
+                            }
+                            for d in self._context.documents
+                        ]
+                    },
+                    external_functions=self._build_external_functions(),
+                    os=self._vfs,
+                )
+        repl = self._repl
+        vfs = self._vfs
+        if repl is None or vfs is None:
+            raise RuntimeError("Sandbox initialization failed")
+        return repl, vfs
+
+    async def execute(self, code: str) -> SandboxResult:
+        """Execute Python code in the Monty REPL.
+
+        Variables persist across calls within the same Sandbox instance.
+        """
+        repl, vfs = await self._ensure_initialized()
+        external_fns = self._build_external_functions()
 
         stdout_lines: list[str] = []
 
@@ -283,20 +297,19 @@ class Sandbox:
             stdout_lines.append(text)
 
         max_chars = self._config.analysis.max_output_chars
-        limits: pydantic_monty.ResourceLimits = {
-            "max_duration_secs": self._config.analysis.code_timeout,
-        }
 
         try:
-            output = await pydantic_monty.run_monty_async(
-                monty,
-                inputs=inputs,
+            output = await pydantic_monty.run_repl_async(
+                repl,
+                code,
                 external_functions=external_fns,
-                limits=limits,
                 print_callback=print_callback,
                 os=vfs,
             )
-        except pydantic_monty.MontyRuntimeError as e:
+        except (
+            pydantic_monty.MontySyntaxError,
+            pydantic_monty.MontyRuntimeError,
+        ) as e:
             stdout = "".join(stdout_lines)
             if len(stdout) > max_chars:
                 stdout = stdout[:max_chars] + "\n... (output truncated)"

haiku_rag_slim/haiku/rag/client.py

@@ -495,7 +495,8 @@ class HaikuRAG:
         from haiku.rag.embeddings import embed_chunks
 
         # Convert → Chunk → Embed using primitives
-        docling_document = await self.convert(content, format=format)
+        converter = get_converter(self._config)
+        docling_document = await converter.convert_text(content, format=format)
         chunks = await self.chunk(docling_document)
         embedded_chunks = await embed_chunks(chunks, self._config)
 
@@ -1000,7 +1001,10 @@ class HaikuRAG:
         # Content provided without chunks - convert, chunk, and embed using primitives
         assert content is not None
         existing_doc.content = content
-        converted_docling = await self.convert(existing_doc.content)
+        converter = get_converter(self._config)
+        converted_docling = await converter.convert_text(
+            existing_doc.content, format="md"
+        )
         existing_doc.set_docling(converted_docling)
 
         new_chunks = await self.chunk(converted_docling)
@@ -1558,11 +1562,13 @@ class HaikuRAG:
         pending_docs: list[Document] = []
         pending_doc_ids: list[str] = []
 
+        converter = get_converter(self._config)
+
         for doc in documents:
             assert doc.id is not None
 
-            # Convert content to DoclingDocument
-            docling_document = await self.convert(doc.content)
+            # Convert stored markdown to DoclingDocument
+            docling_document = await converter.convert_text(doc.content, format="md")
 
             # Chunk and embed
             chunks = await self.chunk(docling_document)
@@ -1605,6 +1611,7 @@ class HaikuRAG:
         pending_chunks: list[Chunk] = []
         pending_docs: list[Document] = []
         pending_doc_ids: list[str] = []
+        converter = get_converter(self._config)
 
         for doc in documents:
             assert doc.id is not None
@@ -1643,7 +1650,7 @@ class HaikuRAG:
                     "Source missing for %s, re-embedding from content", doc.uri
                 )
 
-            docling_document = await self.convert(doc.content)
+            docling_document = await converter.convert_text(doc.content, format="md")
             chunks = await self.chunk(docling_document)
             embedded_chunks = await embed_chunks(chunks, self._config)
 

haiku_rag_slim/haiku/rag/skills/_deps.py

@@ -0,0 +1,79 @@
+from collections.abc import AsyncIterator
+from contextlib import asynccontextmanager
+from dataclasses import dataclass
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+from haiku.rag.config.models import AppConfig
+from haiku.skills.state import SkillRunDeps
+
+if TYPE_CHECKING:
+    from haiku.rag.agents.analysis.sandbox import Sandbox
+    from haiku.rag.client import HaikuRAG
+
+
+@dataclass
+class RAGRunDeps(SkillRunDeps):
+    rag: "HaikuRAG | None" = None
+    search_count: int = 0
+
+
+@dataclass
+class AnalysisRunDeps(RAGRunDeps):
+    sandbox: "Sandbox | None" = None
+
+
+def _reset_invocation_state(state: Any) -> None:
+    """Clear state fields scoped to a single invocation.
+
+    Keeps ``citation_index`` (accumulates resolved citations across the session
+    for lookup) and ``document_filter`` (session-level). Clears ``citations``,
+    ``searches``, and (for analysis) ``executions``.
+    """
+    if state is None:
+        return
+    citations = getattr(state, "citations", None)
+    if citations is not None:
+        citations.clear()
+    searches = getattr(state, "searches", None)
+    if searches is not None:
+        searches.clear()
+    executions = getattr(state, "executions", None)
+    if executions is not None:
+        executions.clear()
+
+
+def make_rag_lifespan(db_path: Path, config: AppConfig):
+    @asynccontextmanager
+    async def lifespan(deps: RAGRunDeps) -> AsyncIterator[None]:
+        from haiku.rag.client import HaikuRAG
+
+        async with HaikuRAG(db_path, config=config, read_only=True) as rag:
+            deps.rag = rag
+            deps.search_count = 0
+            _reset_invocation_state(deps.state)
+            yield
+
+    return lifespan
+
+
+def make_analysis_lifespan(db_path: Path, config: AppConfig):
+    @asynccontextmanager
+    async def lifespan(deps: AnalysisRunDeps) -> AsyncIterator[None]:
+        from haiku.rag.agents.analysis.dependencies import AnalysisContext
+        from haiku.rag.agents.analysis.sandbox import Sandbox
+        from haiku.rag.client import HaikuRAG
+
+        doc_filter = getattr(deps.state, "document_filter", None)
+        async with HaikuRAG(db_path, config=config, read_only=True) as rag:
+            deps.rag = rag
+            deps.search_count = 0
+            deps.sandbox = Sandbox(
+                db_path=db_path,
+                config=config,
+                context=AnalysisContext(filter=doc_filter),
+            )
+            _reset_invocation_state(deps.state)
+            yield
+
+    return lifespan

haiku_rag_slim/haiku/rag/skills/_tools.py

@@ -5,9 +5,10 @@ from pydantic import BaseModel
 from pydantic_ai import RunContext
 
 from haiku.rag.agents.research.models import Citation
+from haiku.rag.client import HaikuRAG
 from haiku.rag.config.models import AppConfig
+from haiku.rag.skills._deps import RAGRunDeps
 from haiku.rag.store.models.chunk import SearchResult
-from haiku.skills.state import SkillRunDeps
 
 
 class CodeExecutionEntry(BaseModel):
@@ -18,22 +19,13 @@ class CodeExecutionEntry(BaseModel):
 
 
 async def skill_search(
-    db_path: Path,
-    config: AppConfig,
+    rag: HaikuRAG,
     query: str,
     limit: int | None = None,
     document_filter: str | None = None,
 ) -> tuple[str, list[SearchResult]]:
-    from haiku.rag.client import HaikuRAG
-
-    async with HaikuRAG(db_path, config=config, read_only=True) as rag:
-        results = await rag.search(
-            query,
-            limit=limit,
-            filter=document_filter,
-        )
-        results = await rag.expand_context(results)
-
+    results = await rag.search(query, limit=limit, filter=document_filter)
+    results = await rag.expand_context(results)
     formatted = "\n\n---\n\n".join(
         r.format_for_agent(rank=i + 1, total=len(results))
         for i, r in enumerate(results)
@@ -42,55 +34,55 @@ async def skill_search(
 
 
 async def skill_list_documents(
-    db_path: Path,
-    config: AppConfig,
+    rag: HaikuRAG,
     filter: str | None = None,
 ) -> list[dict[str, Any]]:
-    from haiku.rag.client import HaikuRAG
-
-    async with HaikuRAG(db_path, config=config, read_only=True) as rag:
-        documents = await rag.list_documents(filter=filter)
-        return [
-            {
-                "id": doc.id,
-                "title": doc.title,
-                "uri": doc.uri,
-                "metadata": doc.metadata,
-                "created_at": str(doc.created_at),
-                "updated_at": str(doc.updated_at),
-            }
-            for doc in documents
-        ]
+    documents = await rag.list_documents(filter=filter)
+    return [
+        {
+            "id": doc.id,
+            "title": doc.title,
+            "uri": doc.uri,
+            "metadata": doc.metadata,
+            "created_at": str(doc.created_at),
+            "updated_at": str(doc.updated_at),
+        }
+        for doc in documents
+    ]
 
 
 async def skill_get_document(
-    db_path: Path,
-    config: AppConfig,
+    rag: HaikuRAG,
     query: str,
 ) -> dict[str, Any] | None:
-    from haiku.rag.client import HaikuRAG
-
-    async with HaikuRAG(db_path, config=config, read_only=True) as rag:
-        document = await rag.resolve_document(query)
-        if document is None:
-            return None
-        return {
-            "id": document.id,
-            "content": document.content,
-            "title": document.title,
-            "uri": document.uri,
-            "metadata": document.metadata,
-            "created_at": str(document.created_at),
-            "updated_at": str(document.updated_at),
-        }
+    document = await rag.resolve_document(query)
+    if document is None:
+        return None
+    return {
+        "id": document.id,
+        "content": document.content,
+        "title": document.title,
+        "uri": document.uri,
+        "metadata": document.metadata,
+        "created_at": str(document.created_at),
+        "updated_at": str(document.updated_at),
+    }
 
 
-def _get_state(ctx: RunContext[SkillRunDeps], state_type: type[BaseModel]) -> Any:
+def _get_state(ctx: RunContext[RAGRunDeps], state_type: type[BaseModel]) -> Any:
     if ctx.deps and ctx.deps.state and isinstance(ctx.deps.state, state_type):
         return ctx.deps.state
     return None
 
 
+def _require_rag(ctx: RunContext[RAGRunDeps]) -> HaikuRAG:
+    if ctx.deps is None or ctx.deps.rag is None:
+        raise RuntimeError(
+            "RAGRunDeps.rag is not set — skill lifespan must run before tools."
+        )
+    return ctx.deps.rag
+
+
 def _register_citations(state: Any, citations: "list[Citation]") -> None:
     """Add citations to the index and record the turn's chunk IDs."""
     chunk_ids = []
@@ -174,10 +166,9 @@ def create_skill_tools(
 
     if "search" in tool_names:
         max_searches = config.qa.max_searches
-        search_counts: dict[str, int] = {}
 
         async def search(
-            ctx: RunContext[SkillRunDeps], query: str, limit: int | None = None
+            ctx: RunContext[RAGRunDeps], query: str, limit: int | None = None
         ) -> str:
             """Search the knowledge base using hybrid search (vector + full-text).
 
@@ -187,9 +178,8 @@ def create_skill_tools(
                 query: The search query.
                 limit: Maximum number of results.
             """
-            rid = ctx.run_id or ""
-            search_counts[rid] = search_counts.get(rid, 0) + 1
-            if search_counts[rid] > max_searches:
+            ctx.deps.search_count += 1
+            if ctx.deps.search_count > max_searches:
                 return (
                     "Search limit reached. Answer the question using "
                     "the results you already have."
@@ -197,8 +187,7 @@ def create_skill_tools(
 
             state = _get_state(ctx, state_type)
             formatted, results = await skill_search(
-                db_path,
-                config,
+                _require_rag(ctx),
                 query,
                 limit=limit,
                 document_filter=state.document_filter if state else None,
@@ -212,55 +201,52 @@ def create_skill_tools(
     if "list_documents" in tool_names:
 
         async def list_documents(
-            ctx: RunContext[SkillRunDeps],
+            ctx: RunContext[RAGRunDeps],
         ) -> list[dict[str, Any]]:
             """List all documents in the knowledge base."""
             state = _get_state(ctx, state_type)
-            result = await skill_list_documents(
-                db_path,
-                config,
+            return await skill_list_documents(
+                _require_rag(ctx),
                 filter=state.document_filter if state else None,
             )
-            return result
 
         tools["list_documents"] = list_documents
 
     if "get_document" in tool_names:
 
         async def get_document(
-            ctx: RunContext[SkillRunDeps], query: str
+            ctx: RunContext[RAGRunDeps], query: str
         ) -> dict[str, Any] | None:
             """Retrieve a document by ID, title, or URI.
 
             Args:
                 query: Document ID, title, or URI to look up.
             """
-            return await skill_get_document(db_path, config, query)
+            return await skill_get_document(_require_rag(ctx), query)
 
         tools["get_document"] = get_document
 
     if "execute_code" in tool_names:
+        from haiku.rag.skills._deps import AnalysisRunDeps
 
-        async def execute_code(ctx: RunContext[SkillRunDeps], code: str) -> str:
+        async def execute_code(ctx: RunContext[AnalysisRunDeps], code: str) -> str:
             """Execute Python code in a sandboxed interpreter.
 
             The code has access to search(), list_documents(), llm() functions
             and a virtual filesystem at /documents/ with document content and
             structure (metadata.json, content.txt, items.jsonl per document).
 
-            Use print() to output results. Each call runs in a fresh
-            interpreter — variables do not persist between calls.
+            Use print() to output results. Variables persist between calls
+            within the same skill invocation.
 
             Args:
                 code: Python code to execute.
             """
-            from haiku.rag.agents.analysis.dependencies import AnalysisContext
-            from haiku.rag.agents.analysis.sandbox import Sandbox
-
-            state = _get_state(ctx, state_type)
-            doc_filter = state.document_filter if state else None
-            context = AnalysisContext(filter=doc_filter)
-            sandbox = Sandbox(db_path=db_path, config=config, context=context)
+            if ctx.deps is None or ctx.deps.sandbox is None:
+                raise RuntimeError(
+                    "AnalysisRunDeps.sandbox is not set — skill lifespan must run before execute_code."
+                )
+            sandbox = ctx.deps.sandbox
             result = await sandbox.execute(code)
 
             state = _get_state(ctx, state_type)
@@ -291,7 +277,7 @@ def create_skill_tools(
 
     if "cite" in tool_names:
 
-        async def cite(ctx: RunContext[SkillRunDeps], chunk_ids: list[str]) -> str:
+        async def cite(ctx: RunContext[RAGRunDeps], chunk_ids: list[str]) -> str:
             """Register chunk IDs as citations for your answer.
 
             Call this after searching, with the chunk_id values from search

haiku_rag_slim/haiku/rag/skills/analysis.py

@@ -60,6 +60,7 @@ def create_skill(
         config: haiku.rag AppConfig instance. If None, uses get_config().
     """
     from haiku.rag.config import get_config
+    from haiku.rag.skills._deps import AnalysisRunDeps, make_analysis_lifespan
     from haiku.rag.skills._tools import create_skill_extras, create_skill_tools
 
     if config is None:
@@ -93,4 +94,6 @@ def create_skill(
         extras=extras,
         state_type=STATE_TYPE,
         state_namespace=STATE_NAMESPACE,
+        deps_type=AnalysisRunDeps,
+        lifespan=make_analysis_lifespan(db_path, config),
     )

haiku_rag_slim/haiku/rag/skills/rag-analysis/SKILL.md

@@ -15,7 +15,7 @@ You solve complex analytical questions by writing and executing Python code agai
 ## Tools
 
 ### execute_code
-Execute Python code in a sandboxed interpreter. Each call runs in a fresh interpreter — write self-contained code. Use `print()` to output results.
+Execute Python code in a sandboxed interpreter. Variables persist between calls — you can build state incrementally. Use `print()` to output results.
 
 Inside the code, these functions are available (use `await`):
 - `await search(query, limit=10)` → list of dicts with keys: chunk_id, content, document_id, document_title, document_uri, score, page_numbers, headings, doc_item_refs, labels
@@ -93,7 +93,7 @@ Search results include `doc_item_refs` (e.g. `["#/texts/48", "#/tables/0"]`) tha
 
 ## Important
 
-- Each `execute_code` call runs in a fresh interpreter — write self-contained code blocks
+- Variables persist between `execute_code` calls — you can search in one call and process results in the next
 - Use `print()` to output results — the output is your only feedback
 - Always execute code to answer questions — don't just describe what code would do
 - Use `await` for all async functions inside execute_code (search, list_documents, llm)

haiku_rag_slim/haiku/rag/skills/rag.py

@@ -75,6 +75,7 @@ def create_skill(
         config: haiku.rag AppConfig instance. If None, uses get_config().
     """
     from haiku.rag.config import get_config
+    from haiku.rag.skills._deps import RAGRunDeps, make_rag_lifespan
     from haiku.rag.skills._tools import create_skill_extras, create_skill_tools
 
     if config is None:
@@ -103,4 +104,6 @@ def create_skill(
         extras=extras,
         state_type=STATE_TYPE,
         state_namespace=STATE_NAMESPACE,
+        deps_type=RAGRunDeps,
+        lifespan=make_rag_lifespan(db_path, config),
     )

haiku_rag_slim/pyproject.toml

@@ -23,7 +23,7 @@ classifiers = [
 
 dependencies = [
     "docling-core>=2.71.0,<2.72",
-    "haiku.skills>=0.14.0",
+    "haiku.skills>=0.15.0",
     "httpx>=0.28.1",
     "jinja2>=3.1.0",
     "jsonpatch>=1.33",

tests/skills/conftest.py

@@ -7,15 +7,20 @@ from pydantic_ai import RunContext
 from haiku.rag.client import HaikuRAG
 from haiku.rag.config.models import AppConfig
 from haiku.rag.embeddings import EmbedderWrapper
-from haiku.skills.state import SkillRunDeps
+from haiku.rag.skills._deps import AnalysisRunDeps, RAGRunDeps
 
 VECTOR_DIM = 2560
 
 
-def _make_ctx(state=None):
-    """Create a mock RunContext with SkillRunDeps."""
+def _make_ctx(state=None, rag=None, sandbox=None):
+    """Create a mock RunContext with RAGRunDeps (or AnalysisRunDeps when state is AnalysisState)."""
+    from haiku.rag.skills.analysis import AnalysisState
+
     ctx = MagicMock(spec=RunContext)
-    ctx.deps = SkillRunDeps(state=state)
+    if isinstance(state, AnalysisState) or sandbox is not None:
+        ctx.deps = AnalysisRunDeps(state=state, rag=rag, sandbox=sandbox)
+    else:
+        ctx.deps = RAGRunDeps(state=state, rag=rag)
     return ctx
 
 
@@ -68,3 +73,26 @@ async def rag_db(temp_db_path):
             uri="test://ml-basics",
         )
     return temp_db_path
+
+
+@pytest.fixture
+async def rag_client(rag_db):
+    """Yield an open read-only HaikuRAG client on the sample db."""
+    async with HaikuRAG(rag_db, read_only=True) as rag:
+        yield rag
+
+
+@pytest.fixture
+def sandbox_factory(rag_db, test_app_config):
+    """Build Sandbox instances bound to the sample db, optionally with a doc filter."""
+    from haiku.rag.agents.analysis.dependencies import AnalysisContext
+    from haiku.rag.agents.analysis.sandbox import Sandbox
+
+    def _make(filter: str | None = None) -> Sandbox:
+        return Sandbox(
+            db_path=rag_db,
+            config=test_app_config,
+            context=AnalysisContext(filter=filter),
+        )
+
+    return _make

tests/skills/test_analysis.py

@@ -113,73 +113,75 @@ class TestDomainPreambleInAnalysisSkillInstructions:
 
 
 class TestExecuteCodeTool:
-    async def test_execute_code_returns_output(self, rag_db):
+    async def test_execute_code_returns_output(self, rag_db, sandbox_factory):
         from haiku.rag.skills.analysis import create_skill
 
         skill = create_skill(db_path=rag_db)
         execute_code = _get_tool(skill, "execute_code")
         state = AnalysisState()
-        ctx = _make_ctx(state)
+        ctx = _make_ctx(state, sandbox=sandbox_factory())
         result = await execute_code(ctx, code="print('hello')")
         assert "hello" in result
 
-    async def test_execute_code_updates_state(self, rag_db):
+    async def test_execute_code_updates_state(self, rag_db, sandbox_factory):
         from haiku.rag.skills.analysis import create_skill
 
         skill = create_skill(db_path=rag_db)
         execute_code = _get_tool(skill, "execute_code")
         state = AnalysisState()
-        ctx = _make_ctx(state)
+        ctx = _make_ctx(state, sandbox=sandbox_factory())
         await execute_code(ctx, code="print('hello')")
         assert len(state.executions) == 1
         assert state.executions[0].code == "print('hello')"
         assert state.executions[0].success is True
         assert "hello" in state.executions[0].stdout
 
-    async def test_execute_code_reports_errors(self, rag_db):
+    async def test_execute_code_reports_errors(self, rag_db, sandbox_factory):
         from haiku.rag.skills.analysis import create_skill
 
         skill = create_skill(db_path=rag_db)
         execute_code = _get_tool(skill, "execute_code")
         state = AnalysisState()
-        ctx = _make_ctx(state)
+        ctx = _make_ctx(state, sandbox=sandbox_factory())
         result = await execute_code(ctx, code="x = 1/0")
         assert "Error" in result
         assert "ZeroDivisionError" in result
         assert state.executions[0].success is False
 
-    async def test_execute_code_applies_document_filter(self, rag_db):
+    async def test_execute_code_applies_document_filter(self, rag_db, sandbox_factory):
         from haiku.rag.skills.analysis import create_skill
 
         skill = create_skill(db_path=rag_db)
         execute_code = _get_tool(skill, "execute_code")
         state = AnalysisState(document_filter="title = 'AI Overview'")
-        ctx = _make_ctx(state)
+        ctx = _make_ctx(state, sandbox=sandbox_factory(filter=state.document_filter))
         result = await execute_code(
             ctx, code="docs = await list_documents()\nprint(len(docs))"
         )
         assert "1" in result
 
-    async def test_execute_code_accumulates_search_results(self, rag_db):
+    async def test_execute_code_accumulates_search_results(
+        self, rag_db, sandbox_factory
+    ):
         from haiku.rag.skills.analysis import create_skill
 
         skill = create_skill(db_path=rag_db)
         execute_code = _get_tool(skill, "execute_code")
         state = AnalysisState()
-        ctx = _make_ctx(state)
+        ctx = _make_ctx(state, sandbox=sandbox_factory())
         await execute_code(
             ctx, code="results = await search('intelligence')\nprint(len(results))"
         )
         assert "_sandbox" in state.searches
         assert len(state.searches["_sandbox"]) > 0
 
-    async def test_execute_code_vfs_write_denied(self, rag_db):
+    async def test_execute_code_vfs_write_denied(self, rag_db, sandbox_factory):
         from haiku.rag.skills.analysis import create_skill
 
         skill = create_skill(db_path=rag_db)
         execute_code = _get_tool(skill, "execute_code")
         state = AnalysisState()
-        ctx = _make_ctx(state)
+        ctx = _make_ctx(state, sandbox=sandbox_factory())
         result = await execute_code(
             ctx,
             code=(
@@ -192,3 +194,109 @@ class TestExecuteCodeTool:
         )
         assert "Error" in result
         assert "read-only" in result
+
+    async def test_execute_code_variables_persist_within_invocation(
+        self, rag_db, sandbox_factory
+    ):
+        """Same sandbox across two calls → vars persist (one skill invocation)."""
+        from haiku.rag.skills.analysis import create_skill
+
+        skill = create_skill(db_path=rag_db)
+        execute_code = _get_tool(skill, "execute_code")
+        state = AnalysisState()
+        ctx = _make_ctx(state, sandbox=sandbox_factory())
+
+        await execute_code(ctx, code="x = 42")
+        result = await execute_code(ctx, code="print(x * 2)")
+        assert "84" in result
+
+    async def test_execute_code_isolated_across_invocations(
+        self, rag_db, sandbox_factory
+    ):
+        """Different Sandbox instances → no cross-invocation leak."""
+        from haiku.rag.skills.analysis import create_skill
+
+        skill = create_skill(db_path=rag_db)
+        execute_code = _get_tool(skill, "execute_code")
+
+        ctx1 = _make_ctx(AnalysisState(), sandbox=sandbox_factory())
+        await execute_code(ctx1, code="secret = 'do not leak'")
+
+        ctx2 = _make_ctx(AnalysisState(), sandbox=sandbox_factory())
+        result = await execute_code(ctx2, code="print(secret)")
+        assert not result.startswith("do not leak")
+        assert "Error" in result or "NameError" in result
+
+
+class TestAnalysisLifespan:
+    async def test_opens_client_and_sandbox_per_invocation(self, rag_db):
+        from haiku.rag.agents.analysis.sandbox import Sandbox
+        from haiku.rag.skills._deps import AnalysisRunDeps, make_analysis_lifespan
+
+        config = AppConfig()
+        lifespan = make_analysis_lifespan(rag_db, config)
+        deps = AnalysisRunDeps()
+        async with lifespan(deps):
+            assert deps.rag is not None
+            assert deps.rag.is_read_only
+            assert deps.search_count == 0
+            assert isinstance(deps.sandbox, Sandbox)
+            docs = await deps.rag.list_documents()
+            assert len(docs) == 2
+
+    async def test_lifespan_reads_document_filter_from_state(self, rag_db):
+        from haiku.rag.skills._deps import AnalysisRunDeps, make_analysis_lifespan
+        from haiku.rag.skills.analysis import AnalysisState
+
+        config = AppConfig()
+        lifespan = make_analysis_lifespan(rag_db, config)
+        state = AnalysisState(document_filter="title = 'AI Overview'")
+        deps = AnalysisRunDeps(state=state)
+        async with lifespan(deps):
+            assert deps.sandbox is not None
+            assert deps.sandbox._context.filter == "title = 'AI Overview'"
+
+    async def test_skill_has_lifespan_and_deps_type(
+        self, test_app_config, temp_db_path
+    ):
+        from haiku.rag.skills._deps import AnalysisRunDeps
+        from haiku.rag.skills.analysis import create_skill
+
+        skill = create_skill(config=test_app_config, db_path=temp_db_path)
+        assert skill.deps_type is AnalysisRunDeps
+        assert skill.lifespan is not None
+
+    async def test_lifespan_clears_executions_citations_searches(self, rag_db):
+        from haiku.rag.agents.research.models import Citation
+        from haiku.rag.skills._deps import AnalysisRunDeps, make_analysis_lifespan
+        from haiku.rag.skills._tools import CodeExecutionEntry
+        from haiku.rag.skills.analysis import AnalysisState
+
+        config = AppConfig()
+        lifespan = make_analysis_lifespan(rag_db, config)
+
+        state = AnalysisState(
+            document_filter="title = 'AI Overview'",
+            executions=[CodeExecutionEntry(code="prior", stdout="", success=True)],
+            citation_index={
+                "c1": Citation(
+                    index=1,
+                    chunk_id="c1",
+                    document_id="d1",
+                    document_title="t",
+                    document_uri="u",
+                    content="x",
+                    page_numbers=[],
+                    headings=[],
+                )
+            },
+            citations=[["c1"]],
+            searches={"prior": []},
+        )
+        deps = AnalysisRunDeps(state=state)
+        async with lifespan(deps):
+            assert state.executions == []
+            assert state.citations == []
+            assert state.searches == {}
+            assert "c1" in state.citation_index
+            assert state.document_filter == "title = 'AI Overview'"

tests/skills/test_rag.py

@@ -157,50 +157,50 @@ class TestSkillExtras:
 
 
 class TestSearchTool:
-    async def test_search_returns_formatted_string(self, rag_db):
+    async def test_search_returns_formatted_string(self, rag_db, rag_client):
         from haiku.rag.skills.rag import create_skill
 
         skill = create_skill(db_path=rag_db)
         search = _get_tool(skill, "search")
-        ctx = _make_ctx()
+        ctx = _make_ctx(rag=rag_client)
         result = await search(ctx, query="artificial intelligence")
         assert isinstance(result, str)
         assert len(result) > 0
 
-    async def test_search_updates_state(self, rag_db):
+    async def test_search_updates_state(self, rag_db, rag_client):
         from haiku.rag.skills.rag import RAGState, create_skill
 
         skill = create_skill(db_path=rag_db)
         search = _get_tool(skill, "search")
         state = RAGState()
-        ctx = _make_ctx(state)
+        ctx = _make_ctx(state, rag=rag_client)
         await search(ctx, query="artificial intelligence")
         assert "artificial intelligence" in state.searches
         results = state.searches["artificial intelligence"]
         assert len(results) > 0
         assert isinstance(results[0], SearchResult)
 
-    async def test_search_applies_document_filter_from_state(self, rag_db):
+    async def test_search_applies_document_filter_from_state(self, rag_db, rag_client):
         from haiku.rag.skills.rag import RAGState, create_skill
 
         skill = create_skill(db_path=rag_db)
         search = _get_tool(skill, "search")
         state = RAGState(document_filter="title = 'AI Overview'")
-        ctx = _make_ctx(state)
+        ctx = _make_ctx(state, rag=rag_client)
         result = await search(ctx, query="artificial intelligence")
         assert "AI Overview" in result
         assert "ML Basics" not in result
 
-    async def test_search_without_state(self, rag_db):
+    async def test_search_without_state(self, rag_db, rag_client):
         from haiku.rag.skills.rag import create_skill
 
         skill = create_skill(db_path=rag_db)
         search = _get_tool(skill, "search")
-        ctx = _make_ctx(state=None)
+        ctx = _make_ctx(state=None, rag=rag_client)
         result = await search(ctx, query="artificial intelligence")
         assert isinstance(result, str)
 
-    async def test_search_rate_limited(self, rag_db):
+    async def test_search_rate_limited(self, rag_db, rag_client):
         from haiku.rag.skills.rag import RAGState, create_skill
 
         config = AppConfig()
@@ -208,69 +208,71 @@ class TestSearchTool:
         skill = create_skill(db_path=rag_db, config=config)
         search = _get_tool(skill, "search")
         state = RAGState()
-        ctx = _make_ctx(state)
-        ctx.run_id = "test-run"
+        ctx = _make_ctx(state, rag=rag_client)
 
         await search(ctx, query="first")
         await search(ctx, query="second")
         result = await search(ctx, query="third")
         assert "Search limit reached" in result
+        assert ctx.deps.search_count == 3
         assert len(state.searches) == 2
 
 
 class TestListDocumentsTool:
-    async def test_list_documents_returns_results(self, rag_db):
+    async def test_list_documents_returns_results(self, rag_db, rag_client):
         from haiku.rag.skills.rag import create_skill
 
         skill = create_skill(db_path=rag_db)
         list_docs = _get_tool(skill, "list_documents")
-        ctx = _make_ctx()
+        ctx = _make_ctx(rag=rag_client)
         results = await list_docs(ctx)
         assert isinstance(results, list)
         assert len(results) == 2
 
-    async def test_list_documents_applies_document_filter_from_state(self, rag_db):
+    async def test_list_documents_applies_document_filter_from_state(
+        self, rag_db, rag_client
+    ):
         from haiku.rag.skills.rag import RAGState, create_skill
 
         skill = create_skill(db_path=rag_db)
         list_docs = _get_tool(skill, "list_documents")
         state = RAGState(document_filter="title = 'AI Overview'")
-        ctx = _make_ctx(state)
+        ctx = _make_ctx(state, rag=rag_client)
         results = await list_docs(ctx)
         assert len(results) == 1
         assert results[0]["title"] == "AI Overview"
 
 
 class TestGetDocumentTool:
-    async def test_get_document_by_title(self, rag_db):
+    async def test_get_document_by_title(self, rag_db, rag_client):
         from haiku.rag.skills.rag import create_skill
 
         skill = create_skill(db_path=rag_db)
         get_doc = _get_tool(skill, "get_document")
-        ctx = _make_ctx()
+        ctx = _make_ctx(rag=rag_client)
         result = await get_doc(ctx, query="AI Overview")
         assert result is not None
         assert result["title"] == "AI Overview"
 
-    async def test_get_document_not_found(self, rag_db):
+    async def test_get_document_not_found(self, rag_db, rag_client):
         from haiku.rag.skills.rag import create_skill
 
         skill = create_skill(db_path=rag_db)
         get_doc = _get_tool(skill, "get_document")
-        ctx = _make_ctx()
+        ctx = _make_ctx(rag=rag_client)
         result = await get_doc(ctx, query="nonexistent document xyz")
         assert result is None
 
 
 class TestCiteTool:
-    async def test_cite_registers_citations(self, rag_db):
+    async def test_cite_registers_citations(self, rag_db, rag_client):
         from haiku.rag.skills.rag import RAGState, create_skill
 
         skill = create_skill(db_path=rag_db)
         search = _get_tool(skill, "search")
         cite = _get_tool(skill, "cite")
         state = RAGState()
-        ctx = _make_ctx(state)
+        ctx = _make_ctx(state, rag=rag_client)
 
         await search(ctx, query="artificial intelligence")
         chunk_ids = [
@@ -286,14 +288,14 @@ class TestCiteTool:
         assert len(state.citations[0]) == 2
         assert all(cid in state.citation_index for cid in chunk_ids)
 
-    async def test_cite_deduplicates_in_index(self, rag_db):
+    async def test_cite_deduplicates_in_index(self, rag_db, rag_client):
         from haiku.rag.skills.rag import RAGState, create_skill
 
         skill = create_skill(db_path=rag_db)
         search = _get_tool(skill, "search")
         cite = _get_tool(skill, "cite")
         state = RAGState()
-        ctx = _make_ctx(state)
+        ctx = _make_ctx(state, rag=rag_client)
 
         await search(ctx, query="artificial intelligence")
         chunk_ids = [
@@ -316,3 +318,75 @@ class TestCiteTool:
         ctx = _make_ctx(state=None)
         result = await cite(ctx, chunk_ids=["nonexistent"])
         assert "No state" in result
+
+
+class TestLifespan:
+    async def test_opens_one_client_per_invocation(self, rag_db):
+        """Lifespan opens one HaikuRAG client, available on ctx.deps.rag throughout."""
+        from haiku.rag.skills._deps import RAGRunDeps, make_rag_lifespan
+
+        config = AppConfig()
+        lifespan = make_rag_lifespan(rag_db, config)
+        deps = RAGRunDeps()
+        async with lifespan(deps):
+            assert deps.rag is not None
+            assert deps.rag.is_read_only
+            assert deps.search_count == 0
+            docs = await deps.rag.list_documents()
+            assert len(docs) == 2
+        # after exit the client has been closed; field still references it
+        assert deps.rag is not None
+
+    async def test_search_count_resets_per_invocation(self, rag_db):
+        from haiku.rag.skills._deps import RAGRunDeps, make_rag_lifespan
+
+        config = AppConfig()
+        lifespan = make_rag_lifespan(rag_db, config)
+
+        deps = RAGRunDeps(search_count=42)
+        async with lifespan(deps):
+            assert deps.search_count == 0
+
+        deps2 = RAGRunDeps(search_count=5)
+        async with lifespan(deps2):
+            assert deps2.search_count == 0
+
+    def test_skill_has_lifespan_and_deps_type(self, test_app_config, temp_db_path):
+        from haiku.rag.skills._deps import RAGRunDeps
+        from haiku.rag.skills.rag import create_skill
+
+        skill = create_skill(config=test_app_config, db_path=temp_db_path)
+        assert skill.deps_type is RAGRunDeps
+        assert skill.lifespan is not None
+
+    async def test_lifespan_clears_citations_and_searches_but_keeps_index(self, rag_db):
+        from haiku.rag.agents.research.models import Citation
+        from haiku.rag.skills._deps import RAGRunDeps, make_rag_lifespan
+        from haiku.rag.skills.rag import RAGState
+
+        config = AppConfig()
+        lifespan = make_rag_lifespan(rag_db, config)
+
+        state = RAGState(
+            document_filter="title = 'AI Overview'",
+            citation_index={
+                "c1": Citation(
+                    index=1,
+                    chunk_id="c1",
+                    document_id="d1",
+                    document_title="t",
+                    document_uri="u",
+                    content="x",
+                    page_numbers=[],
+                    headings=[],
+                )
+            },
+            citations=[["c1"]],
+            searches={"prior": []},
+        )
+        deps = RAGRunDeps(state=state)
+        async with lifespan(deps):
+            assert state.citations == []
+            assert state.searches == {}
+            assert "c1" in state.citation_index  # preserved for cross-turn lookup
+            assert state.document_filter == "title = 'AI Overview'"

tests/test_client.py

@@ -1544,3 +1544,78 @@ async def test_sql_injection_is_blocked_with_escaping(temp_db_path):
             filter=f"title = '{injection_payload}'"
         )
         assert len(docs_unescaped) == 2  # SQL injection succeeds without escaping
+
+
+# =============================================================================
+# URL-prefixed content regression tests
+# =============================================================================
+
+
+def _patch_embed_chunks(monkeypatch):
+    async def fake_embed_chunks(chunks, config):
+        for chunk in chunks:
+            chunk.embedding = [0.0] * 2560
+        return chunks
+
+    monkeypatch.setattr("haiku.rag.embeddings.embed_chunks", fake_embed_chunks)
+
+
+async def test_create_document_with_url_prefixed_content(temp_db_path, monkeypatch):
+    """Text whose first line is a URL must be stored as text, not fetched."""
+    _patch_embed_chunks(monkeypatch)
+
+    async with HaikuRAG(temp_db_path, create=True) as client:
+        content = "https://example.com/foo\n\n# Heading\n\nBody text here."
+        doc = await client.create_document(content=content, uri="test://url-prefixed")
+
+        assert doc.id is not None
+        assert "example.com" in doc.content
+        assert "Heading" in doc.content
+
+
+async def test_update_document_with_url_prefixed_content(temp_db_path, monkeypatch):
+    """update_document(content=...) with URL-prefixed text must not fetch it."""
+    _patch_embed_chunks(monkeypatch)
+
+    async with HaikuRAG(temp_db_path, create=True) as client:
+        doc = await client.create_document(
+            content="initial body", uri="test://update-url"
+        )
+        assert doc.id is not None
+
+        url_prefixed = "https://example.com/bar\n\n# New heading\n\nReplacement body."
+        updated = await client.update_document(doc.id, content=url_prefixed)
+
+        assert "example.com" in updated.content
+        assert "New heading" in updated.content
+
+
+async def test_rebuild_rechunk_with_url_prefixed_stored_content(
+    temp_db_path, monkeypatch
+):
+    """RECHUNK rebuild must handle stored markdown whose first line is a URL."""
+    from haiku.rag.client import RebuildMode
+
+    _patch_embed_chunks(monkeypatch)
+
+    async with HaikuRAG(temp_db_path, create=True) as client:
+        doc = await client.create_document(
+            content="plain seed content", uri="file:///nonexistent/path.txt"
+        )
+        assert doc.id is not None
+
+        # Overwrite stored content to simulate markdown that starts with a URL,
+        # bypassing the (also-affected) create_document path so this test
+        # specifically exercises the rebuild path.
+        doc.content = "https://example.com/baz\n\n# Stored\n\nStored body text."
+        await client.document_repository.update(doc)
+
+        processed_ids = [
+            doc_id async for doc_id in client.rebuild_database(mode=RebuildMode.RECHUNK)
+        ]
+        assert doc.id in processed_ids
+
+        doc_after = await client.document_repository.get_by_id(doc.id)
+        assert doc_after is not None
+        assert "example.com" in doc_after.content
+        assert "Stored" in doc_after.content

uv.lock

@@ -1570,7 +1570,7 @@ requires-dist = [
     { name = "cohere", marker = "extra == 'cohere'", specifier = ">=5.21.1" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.84.0" },
     { name = "docling-core", specifier = ">=2.71.0,<2.72" },
-    { name = "haiku-skills", specifier = ">=0.14.0" },
+    { name = "haiku-skills", specifier = ">=0.15.0" },
     { name = "httpx", specifier = ">=0.28.1" },
     { name = "jinja2", specifier = ">=3.1.0" },
     { name = "jsonpatch", specifier = ">=1.33" },
@@ -1604,7 +1604,7 @@ provides-extras = ["docling", "voyageai", "mxbai", "cohere", "zeroentropy", "jin
 
 [[package]]
 name = "haiku-skills"
-version = "0.14.0"
+version = "0.15.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "ag-ui-protocol" },
@@ -1614,9 +1614,9 @@ dependencies = [
     { name = "pyyaml" },
     { name = "skills-ref" },
 ]
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 ]
 
 [[package]]