util

Checkpointer

Source code in src/ursa/util/__init__.py

class Checkpointer:
    @classmethod
    def from_workspace(
        cls,
        workspace: Path,
        db_dir: str = "db",
        db_name: str = "checkpointer.db",
    ) -> SqliteSaver:
        (db_path := workspace / db_dir).mkdir(parents=True, exist_ok=True)
        conn = sqlite3.connect(str(db_path / db_name), check_same_thread=False)
        return SqliteSaver(conn)

    @classmethod
    def from_path(cls, db_path: Path) -> SqliteSaver:
        """Make checkpointer sqlite db.

        Args
        ====
        * db_path: The path to the SQLite database file (e.g. ./checkpoint.db) to be created.
        """

        db_path.parent.mkdir(parents=True, exist_ok=True)
        conn = sqlite3.connect(str(db_path), check_same_thread=False)
        return SqliteSaver(conn)

from_path(db_path) classmethod

Make checkpointer sqlite db.

Args

• db_path: The path to the SQLite database file (e.g. ./checkpoint.db) to be created.

Source code in src/ursa/util/__init__.py

@classmethod
def from_path(cls, db_path: Path) -> SqliteSaver:
    """Make checkpointer sqlite db.

    Args
    ====
    * db_path: The path to the SQLite database file (e.g. ./checkpoint.db) to be created.
    """

    db_path.parent.mkdir(parents=True, exist_ok=True)
    conn = sqlite3.connect(str(db_path), check_same_thread=False)
    return SqliteSaver(conn)

diff_renderer

DiffRenderer

Renderable diff—console.print(DiffRenderer(...))

Source code in src/ursa/util/diff_renderer.py

class DiffRenderer:
    """Renderable diff—`console.print(DiffRenderer(...))`"""

    def __init__(self, content: str, updated: str, filename: str):
        # total lines in each version
        self._old_total = len(content.splitlines())
        self._new_total = len(updated.splitlines())

        # number of digits in the largest count
        self._num_width = len(str(max(self._old_total, self._new_total))) + 2

        # get the diff
        self._diff_lines = list(
            difflib.unified_diff(
                content.splitlines(),
                updated.splitlines(),
                fromfile=f"{filename} (original)",
                tofile=f"{filename} (modified)",
                lineterm="",
            )
        )

        # get syntax style
        try:
            self._lexer_name = Syntax.guess_lexer(filename, updated)
        except Exception:
            self._lexer_name = "text"

    def __rich_console__(
        self, console: Console, opts: ConsoleOptions
    ) -> RenderResult:
        old_line = new_line = None
        width = console.width

        for raw in self._diff_lines:
            # grab line numbers from hunk header
            if m := _HUNK_RE.match(raw):
                old_line, new_line = map(int, m.groups())
                # build a marker
                n = self._num_width
                tick_col = "." * (n - 1)
                indent_ticks = f" {tick_col} {tick_col}"
                # pad to the indent width
                full_indent = indent_ticks.ljust(2 * n + 3)
                yield Text(
                    f"{full_indent}{raw}".ljust(width), style="white on grey30"
                )
                continue

            # skip header lines
            if raw.startswith(("---", "+++")):
                continue

            # split the line
            if raw.startswith("+"):
                style = _STYLE["add"]
                code = raw[1:]
            elif raw.startswith("-"):
                style = _STYLE["del"]
                code = raw[1:]
            else:
                style = _STYLE["ctx"]
                code = raw[1:] if raw.startswith(" ") else raw

            # compute line numbers
            if raw.startswith("+"):
                old_num, new_num = None, new_line
                new_line += 1
            elif raw.startswith("-"):
                old_num, new_num = old_line, None
                old_line += 1
            else:
                old_num, new_num = old_line, new_line
                old_line += 1
                new_line += 1

            old_str = str(old_num) if old_num is not None else " "
            new_str = str(new_num) if new_num is not None else " "

            # Syntax-highlight the code part
            syntax = Syntax(
                code, self._lexer_name, line_numbers=False, word_wrap=False
            )
            text_code: Text = syntax.highlight(code)
            if text_code.plain.endswith("\n"):
                text_code = text_code[:-1]
            # apply background
            text_code.stylize(style.bg)

            # line numbers + code
            nums = Text(
                f"{old_str:>{self._num_width}}{new_str:>{self._num_width}} ",
                style=f"white {style.bg}",
            )
            diff_mark = Text(style.prefix, style=f"bright_white {style.bg}")
            line_text = nums + diff_mark + text_code

            # pad to console width
            pad_len = width - line_text.cell_len
            if pad_len > 0:
                line_text.append(" " * pad_len, style=style.bg)

            yield line_text

helperFunctions

run_tool_calls(ai_msg, tools)

Parameters:

Name	Type	Description	Default
ai_msg	AIMessage	The LLM's AIMessage containing tool calls.	required
tools	ToolRegistry | Iterable[Runnable | Callable[..., Any]]	Either a dict {name: tool} or an iterable of tools (must have .name for mapping). Each tool can be a Runnable or a plain callable.	required

Returns:

Name	Type	Description
out	list[BaseMessage]	list[BaseMessage] to feed back to the model

Source code in src/ursa/util/helperFunctions.py

def run_tool_calls(
    ai_msg: AIMessage,
    tools: ToolRegistry | Iterable[Runnable | Callable[..., Any]],
) -> list[BaseMessage]:
    """
    Args:
        ai_msg: The LLM's AIMessage containing tool calls.
        tools: Either a dict {name: tool} or an iterable of tools (must have `.name`
               for mapping). Each tool can be a Runnable or a plain callable.

    Returns:
        out: list[BaseMessage] to feed back to the model
    """
    # Build a name->tool map
    if isinstance(tools, dict):
        registry: ToolRegistry = tools  # type: ignore
    else:
        registry = {}
        for t in tools:
            name = getattr(t, "name", None) or getattr(t, "__name__", None)
            if not name:
                raise ValueError(f"Tool {t!r} has no discoverable name.")
            registry[name] = t  # type: ignore

    calls = extract_tool_calls(ai_msg)

    if not calls:
        return []

    out: list[BaseMessage] = []
    for call in calls:
        name = call.get("name")
        args = call.get("args", {}) or {}
        call_id = call.get("id") or f"call_{uuid.uuid4().hex}"

        # 1) the AIMessage that generated the call
        out.append(ai_msg)

        # 2) the ToolMessage with the execution result (or error)
        if name not in registry:
            content = f"ERROR: unknown tool '{name}'."
        else:
            try:
                result = _invoke_tool(registry[name], args)
                content = _stringify_output(result)
            except Exception as e:
                content = f"ERROR: {type(e).__name__}: {e}"

        out.append(
            ToolMessage(content=content, tool_call_id=call_id, name=name)
        )

    return out

logo_generator

generate_logo_sync(*, problem_text, workspace, out_dir, filename=None, model='gpt-image-1', size=None, background='opaque', quality='high', n=1, overwrite=False, style='sticker', allow_text=False, palette=None, mode='logo', aspect='square', style_intensity='overt', console=None, image_model_provider='openai', image_provider_kwargs=None)

Generate an image. Default behavior matches previous versions (logo/sticker). To create a cinematic illustration, set mode='scene' and consider aspect='wide'.

Source code in src/ursa/util/logo_generator.py

def generate_logo_sync(
    *,
    problem_text: str,
    workspace: str,
    out_dir: str | Path,
    filename: str | None = None,
    model: str = "gpt-image-1",
    size: str | None = None,
    background: str = "opaque",
    quality: str = "high",
    n: int = 1,
    overwrite: bool = False,
    style: str = "sticker",
    allow_text: bool = False,
    palette: str | None = None,
    mode: str = "logo",
    aspect: str = "square",
    style_intensity: str = "overt",
    console: Optional[Console] = None,
    image_model_provider: str = "openai",
    image_provider_kwargs: Optional[dict] = None,
) -> Path:
    """
    Generate an image. Default behavior matches previous versions (logo/sticker).
    To create a cinematic illustration, set mode='scene' and consider aspect='wide'.
    """
    out_dir = Path(out_dir)
    out_dir.mkdir(parents=True, exist_ok=True)

    prompt, style_slug = _craft_logo_prompt(
        problem_text,
        workspace,
        style=style,
        allow_text=allow_text,
        palette=palette,
        mode=mode,
        style_intensity=style_intensity,
    )

    # Pretty console output
    extra_title = f"[bold magenta]mode: {mode}[/bold magenta] [dim]•[/dim] aspect: {aspect}"
    _render_prompt_panel(
        console=console,
        style_slug=style_slug,
        workspace=workspace,
        prompt=prompt,
        extra_title=extra_title,
    )

    main_path, alt_paths = _compose_filenames(out_dir, style_slug, filename, n)
    if main_path.exists() and not overwrite:
        return main_path

    # this is how we'll pass through a vision model and provider/url/endpoint
    client_kwargs = {}
    if image_provider_kwargs:
        # Only pass through safe/known kwargs
        for k in ("api_key", "base_url", "organization"):
            if k in image_provider_kwargs and image_provider_kwargs[k]:
                client_kwargs[k] = image_provider_kwargs[k]
    client = OpenAI(**client_kwargs)

    final_size = _normalize_size(size, aspect, mode)
    # Scenes tend to look odd with transparent backgrounds; force opaque.
    final_background = "opaque" if mode == "scene" else background

    kwargs = dict(
        model=model,
        prompt=prompt,
        size=final_size,
        n=n,
        quality=quality,
        background=final_background,
    )
    try:
        resp = client.images.generate(**kwargs)
    except Exception:
        # Some models ignore/forbid background=; retry without it
        kwargs.pop("background", None)
        resp = client.images.generate(**kwargs)

    main_path.write_bytes(base64.b64decode(resp.data[0].b64_json))
    for i, item in enumerate(resp.data[1:], start=0):
        if i < len(alt_paths):
            alt_paths[i].write_bytes(base64.b64decode(item.b64_json))
    return main_path

memory_logger

AgentMemory

Simple wrapper around a persistent Chroma vector-store for agent-conversation memory.

Parameters

path : str | Path | None Where to keep the on-disk Chroma DB. If None, a folder called agent_memory_db is created in the package’s base directory. collection_name : str Name of the Chroma collection. embedding_model : | None the embedding model

Notes

• Requires langchain-chroma, and chromadb.

Source code in src/ursa/util/memory_logger.py

class AgentMemory:
    """
    Simple wrapper around a persistent Chroma vector-store for agent-conversation memory.

    Parameters
    ----------
    path : str | Path | None
        Where to keep the on-disk Chroma DB.  If *None*, a folder called
        ``agent_memory_db`` is created in the package’s base directory.
    collection_name : str
        Name of the Chroma collection.
    embedding_model : <TODO> | None
        the embedding model

    Notes
    -----
    * Requires `langchain-chroma`, and `chromadb`.
    """

    @classmethod
    def get_db_path(cls, path: Optional[str | Path]) -> Path:
        match path:
            case None:
                return Path.home() / ".cache" / "ursa" / "rag" / "db"
            case str():
                return Path(path)
            case Path():
                return path
            case _:
                raise TypeError(
                    f"Type of path is `{type(path)}` "
                    "but `Optional[str | Path]` was expected."
                )

    def __init__(
        self,
        embedding_model,
        path: Optional[str | Path] = None,
        collection_name: str = "agent_memory",
    ) -> None:
        self.path = self.get_db_path(path)
        self.collection_name = collection_name
        self.path.mkdir(parents=True, exist_ok=True)
        self.embeddings = embedding_model

        # If a DB already exists, load it; otherwise defer creation until `build_index`.
        self.vectorstore: Optional[Chroma] = None
        if any(self.path.iterdir()):
            self.vectorstore = Chroma(
                collection_name=self.collection_name,
                embedding_function=self.embeddings,
                persist_directory=str(self.path),
            )

    # --------------------------------------------------------------------- #
    # ❶ Build & index a brand-new database                                   #
    # --------------------------------------------------------------------- #
    def build_index(
        self,
        chunks: Sequence[str],
        metadatas: Optional[Sequence[dict[str, Any]]] = None,
    ) -> None:
        """
        Create a fresh vector store from ``chunks``.  Existing data (if any)
        are overwritten.

        Parameters
        ----------
        chunks : Sequence[str]
            Text snippets (already chunked) to embed.
        metadatas : Sequence[dict] | None
            Optional metadata dict for each chunk, same length as ``chunks``.
        """
        docs = [
            Document(
                page_content=text, metadata=metadatas[i] if metadatas else {}
            )
            for i, text in enumerate(chunks)
        ]

        # Create (or overwrite) the collection
        self.vectorstore = Chroma.from_documents(
            documents=docs,
            embedding=self.embeddings,
            collection_name=self.collection_name,
            persist_directory=str(self.path),
        )

    # --------------------------------------------------------------------- #
    # ❷ Add new chunks and re-index                                          #
    # --------------------------------------------------------------------- #
    def add_memories(
        self,
        new_chunks: Sequence[str],
        metadatas: Optional[Sequence[dict[str, Any]]] = None,
    ) -> None:
        """
        Append new text chunks to the existing store (must call `build_index`
        first if the DB is empty).

        Raises
        ------
        RuntimeError
            If the vector store is not yet initialised.
        """
        if self.vectorstore is None:
            self.build_index(new_chunks, metadatas)
            print("----- Vector store initialised -----")

        docs = []
        for i, text in enumerate(new_chunks):
            if len(text) > 0:  # only add non-empty documents
                docs.append(
                    Document(
                        page_content=text,
                        metadata=metadatas[i] if metadatas else {},
                    )
                )
        self.vectorstore.add_documents(docs)

    # --------------------------------------------------------------------- #
    # ❸ Retrieve relevant chunks (RAG query)                                 #
    # --------------------------------------------------------------------- #
    def retrieve(
        self,
        query: str,
        k: int = 4,
        with_scores: bool = False,
        **search_kwargs,
    ):
        """
        Return the *k* most similar chunks for `query`.

        Parameters
        ----------
        query : str
            Natural-language question or statement.
        k : int
            How many results to return.
        with_scores : bool
            If True, also return similarity scores.
        **search_kwargs
            Extra kwargs forwarded to Chroma’s ``similarity_search*`` helpers.

        Returns
        -------
        list[Document] | list[tuple[Document, float]]
        """
        if self.vectorstore is None:
            return ["None"]

        if with_scores:
            return self.vectorstore.similarity_search_with_score(
                query, k=k, **search_kwargs
            )
        return self.vectorstore.similarity_search(query, k=k, **search_kwargs)

add_memories(new_chunks, metadatas=None)

Append new text chunks to the existing store (must call build_index first if the DB is empty).

Raises

RuntimeError If the vector store is not yet initialised.

Source code in src/ursa/util/memory_logger.py

def add_memories(
    self,
    new_chunks: Sequence[str],
    metadatas: Optional[Sequence[dict[str, Any]]] = None,
) -> None:
    """
    Append new text chunks to the existing store (must call `build_index`
    first if the DB is empty).

    Raises
    ------
    RuntimeError
        If the vector store is not yet initialised.
    """
    if self.vectorstore is None:
        self.build_index(new_chunks, metadatas)
        print("----- Vector store initialised -----")

    docs = []
    for i, text in enumerate(new_chunks):
        if len(text) > 0:  # only add non-empty documents
            docs.append(
                Document(
                    page_content=text,
                    metadata=metadatas[i] if metadatas else {},
                )
            )
    self.vectorstore.add_documents(docs)

build_index(chunks, metadatas=None)

Create a fresh vector store from chunks. Existing data (if any) are overwritten.

Parameters

chunks : Sequence[str] Text snippets (already chunked) to embed. metadatas : Sequence[dict] | None Optional metadata dict for each chunk, same length as chunks.

Source code in src/ursa/util/memory_logger.py

def build_index(
    self,
    chunks: Sequence[str],
    metadatas: Optional[Sequence[dict[str, Any]]] = None,
) -> None:
    """
    Create a fresh vector store from ``chunks``.  Existing data (if any)
    are overwritten.

    Parameters
    ----------
    chunks : Sequence[str]
        Text snippets (already chunked) to embed.
    metadatas : Sequence[dict] | None
        Optional metadata dict for each chunk, same length as ``chunks``.
    """
    docs = [
        Document(
            page_content=text, metadata=metadatas[i] if metadatas else {}
        )
        for i, text in enumerate(chunks)
    ]

    # Create (or overwrite) the collection
    self.vectorstore = Chroma.from_documents(
        documents=docs,
        embedding=self.embeddings,
        collection_name=self.collection_name,
        persist_directory=str(self.path),
    )

retrieve(query, k=4, with_scores=False, **search_kwargs)

Return the k most similar chunks for query.

Parameters

query : str Natural-language question or statement. k : int How many results to return. with_scores : bool If True, also return similarity scores. **search_kwargs Extra kwargs forwarded to Chroma’s similarity_search* helpers.

Returns

list[Document] | list[tuple[Document, float]]

Source code in src/ursa/util/memory_logger.py

def retrieve(
    self,
    query: str,
    k: int = 4,
    with_scores: bool = False,
    **search_kwargs,
):
    """
    Return the *k* most similar chunks for `query`.

    Parameters
    ----------
    query : str
        Natural-language question or statement.
    k : int
        How many results to return.
    with_scores : bool
        If True, also return similarity scores.
    **search_kwargs
        Extra kwargs forwarded to Chroma’s ``similarity_search*`` helpers.

    Returns
    -------
    list[Document] | list[tuple[Document, float]]
    """
    if self.vectorstore is None:
        return ["None"]

    if with_scores:
        return self.vectorstore.similarity_search_with_score(
            query, k=k, **search_kwargs
        )
    return self.vectorstore.similarity_search(query, k=k, **search_kwargs)

delete_database(path=None)

Simple wrapper around a persistent Chroma vector-store for agent-conversation memory.

Parameters

path : str | Path | None Where the on-disk Chroma DB is for deleting. If None, a folder called agent_memory_db is created in the package’s base directory.

Source code in src/ursa/util/memory_logger.py

def delete_database(path: Optional[str | Path] = None):
    """
    Simple wrapper around a persistent Chroma vector-store for agent-conversation memory.

    Parameters
    ----------
    path : str | Path | None
        Where the on-disk Chroma DB is for deleting.  If *None*, a folder called
        ``agent_memory_db`` is created in the package’s base directory.
    """
    db_path = AgentMemory.get_db_path(path)
    if os.path.exists(db_path):
        shutil.rmtree(db_path)
        print(f"Database: {db_path} has been deleted.")
    else:
        print("No database found to delete.")

parse

extract_json(text)

Extract a JSON object or array from text that might contain markdown or other content.

The function attempts three strategies

1. Extract JSON from a markdown code block labeled as JSON.
2. Extract JSON from any markdown code block.
3. Use bracket matching to extract a JSON substring starting with '{' or '['.

Returns:

Type	Description
list[dict]	A Python object parsed from the JSON string (dict or list).

Raises:

Type	Description
ValueError	If no valid JSON is found.

Source code in src/ursa/util/parse.py

def extract_json(text: str) -> list[dict]:
    """
    Extract a JSON object or array from text that might contain markdown or other content.

    The function attempts three strategies:
        1. Extract JSON from a markdown code block labeled as JSON.
        2. Extract JSON from any markdown code block.
        3. Use bracket matching to extract a JSON substring starting with '{' or '['.

    Returns:
        A Python object parsed from the JSON string (dict or list).

    Raises:
        ValueError: If no valid JSON is found.
    """
    # Approach 1: Look for a markdown code block specifically labeled as JSON.
    labeled_block = re.search(
        r"```json\s*([\[{].*?[\]}])\s*```", text, re.DOTALL
    )
    if labeled_block:
        json_str = labeled_block.group(1).strip()
        try:
            return json.loads(json_str)
        except json.JSONDecodeError:
            # Fall back to the next approach if parsing fails.
            pass

    # Approach 2: Look for any code block delimited by triple backticks.
    generic_block = re.search(r"```(.*?)```", text, re.DOTALL)
    if generic_block:
        json_str = generic_block.group(1).strip()
        if json_str.startswith("{") or json_str.startswith("["):
            try:
                return json.loads(json_str)
            except json.JSONDecodeError:
                pass

    # Approach 3: Attempt to extract JSON using bracket matching.
    # Find the first occurrence of either '{' or '['.
    first_obj = text.find("{")
    first_arr = text.find("[")
    if first_obj == -1 and first_arr == -1:
        raise ValueError("No JSON object or array found in the text.")

    # Determine which bracket comes first.
    if first_obj == -1:
        start = first_arr
        open_bracket = "["
        close_bracket = "]"
    elif first_arr == -1:
        start = first_obj
        open_bracket = "{"
        close_bracket = "}"
    else:
        if first_obj < first_arr:
            start = first_obj
            open_bracket = "{"
            close_bracket = "}"
        else:
            start = first_arr
            open_bracket = "["
            close_bracket = "]"

    # Bracket matching: find the matching closing bracket.
    depth = 0
    end = None
    for i in range(start, len(text)):
        if text[i] == open_bracket:
            depth += 1
        elif text[i] == close_bracket:
            depth -= 1
            if depth == 0:
                end = i
                break

    if end is None:
        raise ValueError(
            "Could not find matching closing bracket for JSON content."
        )

    json_str = text[start : end + 1]
    try:
        return json.loads(json_str)
    except json.JSONDecodeError as e:
        raise ValueError("Extracted content is not valid JSON.") from e

extract_main_text_only(html, *, max_chars=250000)

Returns plain text with navigation/ads/scripts removed. Prefers trafilatura -> jusText -> BS4 paragraphs.

Source code in src/ursa/util/parse.py

def extract_main_text_only(html: str, *, max_chars: int = 250_000) -> str:
    """
    Returns plain text with navigation/ads/scripts removed.
    Prefers trafilatura -> jusText -> BS4 paragraphs.
    """
    # 1) Trafilatura
    # You can tune config: with_metadata, include_comments, include_images, favor_recall, etc.
    cfg = trafilatura.settings.use_config()
    cfg.set("DEFAULT", "include_comments", "false")
    cfg.set("DEFAULT", "include_tables", "false")
    cfg.set("DEFAULT", "favor_recall", "false")  # be stricter; less noise
    try:
        # If you fetched HTML already, use extract() on string; otherwise, fetch_url(url)
        txt = trafilatura.extract(
            html,
            config=cfg,
            include_comments=False,
            include_tables=False,
            favor_recall=False,
        )
        if txt and txt.strip():
            txt = _normalize_ws(txt)
            txt = _dedupe_lines(txt)
            return txt[:max_chars]
    except Exception:
        pass

    # 2) jusText
    try:
        paragraphs = justext.justext(html, justext.get_stoplist("English"))
        body_paras = [p.text for p in paragraphs if not p.is_boilerplate]
        if body_paras:
            txt = _normalize_ws("\n\n".join(body_paras))
            txt = _dedupe_lines(txt)
            return txt[:max_chars]
    except Exception:
        pass

    # 4) last-resort: BS4 paragraphs/headings only
    from bs4 import BeautifulSoup

    soup = BeautifulSoup(html, "html.parser")
    for tag in soup([
        "script",
        "style",
        "noscript",
        "header",
        "footer",
        "nav",
        "form",
        "aside",
    ]):
        tag.decompose()
    chunks = []
    for el in soup.find_all(["h1", "h2", "h3", "p", "li", "figcaption"]):
        t = el.get_text(" ", strip=True)
        if t:
            chunks.append(t)
    txt = _normalize_ws("\n\n".join(chunks))
    txt = _dedupe_lines(txt)
    return txt[:max_chars]

read_text_file(path)

Reads in a file at a given path into a string

Parameters:

Name	Type	Description	Default
path	str	string filename, with path, to read in	required

Source code in src/ursa/util/parse.py

def read_text_file(path: str) -> str:
    """
    Reads in a file at a given path into a string

    Args:
        path: string filename, with path, to read in
    """
    with open(path, "r", encoding="utf-8") as file:
        file_contents = file.read()
    return file_contents

resolve_pdf_from_osti_record(rec, *, headers=None, unpaywall_email=None, timeout=25)

Returns (pdf_url, landing_used, note) - pdf_url: direct downloadable PDF URL if found (or a strong candidate) - landing_used: landing page URL we parsed (if any) - note: brief trace of how we found it

Source code in src/ursa/util/parse.py

def resolve_pdf_from_osti_record(
    rec: dict[str, Any],
    *,
    headers: Optional[dict[str, str]] = None,
    unpaywall_email: Optional[str] = None,
    timeout: int = 25,
) -> tuple[Optional[str], Optional[str], str]:
    """
    Returns (pdf_url, landing_used, note)
      - pdf_url: direct downloadable PDF URL if found (or a strong candidate)
      - landing_used: landing page URL we parsed (if any)
      - note: brief trace of how we found it
    """
    headers = headers or {"User-Agent": "Mozilla/5.0"}
    note_parts: list[str] = []

    links = rec.get("links", []) or []
    # doi = rec.get("doi")

    # 1) Try 'fulltext' first (OSTI purl)
    fulltext = None
    for link in links:
        if link.get("rel") == "fulltext":
            fulltext = link.get("href")
            break

    if fulltext:
        note_parts.append("Tried links[fulltext] purl")
        try:
            # Follow redirects; stream to peek headers without loading whole body
            r = requests.get(
                fulltext,
                headers=headers,
                timeout=timeout,
                allow_redirects=True,
                stream=True,
            )
            r.raise_for_status()

            if _is_pdf_response(r):
                note_parts.append("fulltext resolved directly to PDF")
                return (r.url, None, " | ".join(note_parts))

            # Not a PDF: parse page HTML for meta or obvious PDF anchors
            # (If server sent binary but CT lied, _is_pdf_response would have caught via CD or ext)
            r.close()
            soup = _get_soup(fulltext, timeout=timeout, headers=headers)
            candidate = _find_pdf_on_landing(soup, fulltext)
            if candidate:
                note_parts.append(
                    "found PDF via meta/anchor on fulltext landing"
                )
                return (candidate, fulltext, " | ".join(note_parts))
        except Exception as e:
            note_parts.append(f"fulltext failed: {e}")

    # 2) Try DOE PAGES landing (citation_doe_pages)
    doe_pages = None
    for link in links:
        if link.get("rel") == "citation_doe_pages":
            doe_pages = link.get("href")
            break

    if doe_pages:
        note_parts.append("Tried links[citation_doe_pages] landing")
        try:
            soup = _get_soup(doe_pages, timeout=timeout, headers=headers)
            candidate = _find_pdf_on_landing(soup, doe_pages)
            if candidate:
                # Candidate may itself be a landing—check if it serves PDF
                try:
                    r2 = requests.get(
                        candidate,
                        headers=headers,
                        timeout=timeout,
                        allow_redirects=True,
                        stream=True,
                    )
                    r2.raise_for_status()
                    if _is_pdf_response(r2):
                        note_parts.append("citation_doe_pages → direct PDF")
                        return (r2.url, doe_pages, " | ".join(note_parts))
                    r2.close()
                except Exception:
                    pass
                # If not clearly PDF, still return as a candidate (agent will fetch & parse)
                note_parts.append(
                    "citation_doe_pages → PDF-like candidate (not confirmed by headers)"
                )
                return (candidate, doe_pages, " | ".join(note_parts))
        except Exception as e:
            note_parts.append(f"citation_doe_pages failed: {e}")

    # # 3) Optional: DOI → Unpaywall OA
    # if doi and unpaywall_email:
    #     note_parts.append("Tried Unpaywall via DOI")
    #     pdf_from_ua = _resolve_pdf_via_unpaywall(doi, unpaywall_email)
    #     if pdf_from_ua:
    #         # May be direct PDF or landing; the caller will validate headers during download
    #         note_parts.append("Unpaywall returned candidate")
    #         return (pdf_from_ua, None, " | ".join(note_parts))

    # 4) Give up
    note_parts.append("No PDF found")
    return (None, None, " | ".join(note_parts))

plan_renderer

render_plan_steps_rich(plan_steps, highlight_index=None)

Pretty table for a list of plan steps (strings or dicts), with an optional highlighted row.

Source code in src/ursa/util/plan_renderer.py

def render_plan_steps_rich(plan_steps, highlight_index: int | None = None):
    """Pretty table for a list of plan steps (strings or dicts), with an optional highlighted row."""

    console = get_console()

    if not plan_steps:
        return

    table = Table(
        title="Planned Steps",
        box=box.ROUNDED,
        show_lines=False,
        header_style="bold magenta",
        expand=True,
        row_styles=None,  # we'll control per-row styles manually
    )
    table.add_column("#", style="bold cyan", no_wrap=True)
    table.add_column("Name", style="bold", overflow="fold")
    table.add_column("Description", overflow="fold")
    table.add_column("Outputs", overflow="fold")
    table.add_column("Criteria", overflow="fold")
    table.add_column("Code?", justify="center", no_wrap=True)

    def bullets(items):
        if not items:
            return ""
        return "\n".join(f"• {x}" for x in items)

    def code_badge(needs_code: bool):
        return Text.from_markup(
            ":hammer_and_wrench: [bold green]Yes[/]"
            if needs_code
            else "[bold red]No[/]"
        )

    for i, step in enumerate(plan_steps, 1):
        # build cells
        if isinstance(step, dict):
            name = (
                step.get("name")
                or step.get("title")
                or step.get("id")
                or f"Step {i}"
            )
            desc = step.get("description") or ""
            outs = bullets(
                step.get("expected_outputs") or step.get("artifacts")
            )
            crit = bullets(step.get("success_criteria"))
            needs_code = bool(step.get("requires_code"))
        else:
            name, desc, outs, crit, needs_code = (
                f"Step {i}",
                str(step),
                "",
                "",
                False,
            )

        # style logic
        row_style = None
        idx0 = i - 1
        step_label = str(i)

        if highlight_index is not None:
            if idx0 < highlight_index:
                row_style = "dim"
            elif idx0 == highlight_index:
                row_style = "bold white on grey50"  # light gray
                # row_style = "bold black on bright_green"
                step_label = f"▶ {i}"  # pointer on current row

        table.add_row(
            step_label,
            str(name),
            str(desc),
            outs,
            crit,
            code_badge(needs_code),
            style=row_style,
        )

    console.print(table)