mirror of
https://github.com/langchain-ai/langgraph.git
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docs: add custom hooks for rendering jupyter notebooks (#2067)
This commit is contained in:
Vadym Barda
@@ -0,0 +1,246 @@
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import importlib
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import inspect
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import logging
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import os
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import re
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from typing import List, Literal, Optional
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from typing_extensions import TypedDict
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import nbformat
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from nbconvert.preprocessors import Preprocessor
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Base URL for all class documentation
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_LANGCHAIN_API_REFERENCE = "https://python.langchain.com/api_reference/"
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_LANGGRAPH_API_REFERENCE = "https://langchain-ai.github.io/langgraph/reference/"
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# (alias/re-exported modules, source module, class, docs namespace)
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MANUAL_API_REFERENCES_LANGGRAPH = [
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(
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["langgraph.prebuilt"],
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"langgraph.prebuilt.chat_agent_executor",
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"create_react_agent",
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"prebuilt",
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),
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(["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
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(
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["langgraph.prebuilt"],
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"langgraph.prebuilt.tool_node",
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"tools_condition",
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"prebuilt",
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),
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(
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["langgraph.prebuilt"],
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"langgraph.prebuilt.tool_node",
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"InjectedState",
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"prebuilt",
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),
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# Graph
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(["langgraph.graph"], "langgraph.graph.message", "add_messages", "graphs"),
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(["langgraph.graph"], "langgraph.graph.state", "StateGraph", "graphs"),
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(["langgraph.graph"], "langgraph.graph.state", "CompiledStateGraph", "graphs"),
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([], "langgraph.types", "StreamMode", "types"),
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(["langgraph.graph"], "langgraph.constants", "START", "constants"),
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(["langgraph.graph"], "langgraph.constants", "END", "constants"),
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(["langgraph.constants"], "langgraph.types", "Send", "types"),
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(["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
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([], "langgraph.types", "RetryPolicy", "types"),
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([], "langgraph.checkpoint.base", "Checkpoint", "checkpoints"),
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([], "langgraph.checkpoint.base", "CheckpointMetadata", "checkpoints"),
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([], "langgraph.checkpoint.base", "BaseCheckpointSaver", "checkpoints"),
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([], "langgraph.checkpoint.base", "SerializerProtocol", "checkpoints"),
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([], "langgraph.checkpoint.serde.jsonplus", "JsonPlusSerializer", "checkpoints"),
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([], "langgraph.checkpoint.memory", "MemorySaver", "checkpoints"),
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([], "langgraph.checkpoint.sqlite.aio", "AsyncSqliteSaver", "checkpoints"),
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([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
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([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
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([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
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]
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WELL_KNOWN_LANGGRAPH_OBJECTS = {
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(module_, class_): (source_module, namespace)
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for (modules, source_module, class_, namespace) in MANUAL_API_REFERENCES_LANGGRAPH
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for module_ in modules + [source_module]
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}
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def _make_regular_expression(pkg_prefix: str) -> re.Pattern:
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if not pkg_prefix.isidentifier():
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raise ValueError(f"Invalid package prefix: {pkg_prefix}")
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return re.compile(
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r"from\s+(" + pkg_prefix + "(?:_\w+)?(?:\.\w+)*?)\s+import\s+"
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r"((?:\w+(?:,\s*)?)*" # Match zero or more words separated by a comma+optional ws
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r"(?:\s*\(.*?\))?)", # Match optional parentheses block
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re.DOTALL, # Match newlines as well
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)
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# Regular expression to match langchain import lines
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_IMPORT_LANGCHAIN_RE = _make_regular_expression("langchain")
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_IMPORT_LANGGRAPH_RE = _make_regular_expression("langgraph")
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def _get_full_module_name(module_path, class_name) -> Optional[str]:
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"""Get full module name using inspect"""
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try:
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module = importlib.import_module(module_path)
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class_ = getattr(module, class_name)
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module = inspect.getmodule(class_)
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if module is None:
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# For constants, inspect.getmodule() might return None
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# In this case, we'll return the original module_path
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return module_path
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return module.__name__
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except AttributeError as e:
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logger.warning(f"Could not find module for {class_name}, {e}")
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return None
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except ImportError as e:
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logger.warning(f"Failed to load for class {class_name}, {e}")
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return None
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def _get_doc_title(data: str, file_name: str) -> str:
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try:
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return re.findall(r"^#\s*(.*)", data, re.MULTILINE)[0]
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except IndexError:
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pass
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# Parse the rst-style titles
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try:
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return re.findall(r"^(.*)\n=+\n", data, re.MULTILINE)[0]
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except IndexError:
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return file_name
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class ImportInformation(TypedDict):
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imported: str # imported class name
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source: str # module path
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docs: str # URL to the documentation
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title: str # Title of the document
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def _get_imports(
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code: str, doc_title: str, package_ecosystem: Literal["langchain", "langgraph"]
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) -> List[ImportInformation]:
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"""Get imports from the given code block.
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Args:
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code: Python code block from which to extract imports
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doc_title: Title of the document
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package_ecosystem: "langchain" or "langgraph". The two live in different
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repositories and have separate documentation sites.
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Returns:
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List of import information for the given code block
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"""
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imports = []
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if package_ecosystem == "langchain":
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pattern = _IMPORT_LANGCHAIN_RE
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elif package_ecosystem == "langgraph":
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pattern = _IMPORT_LANGGRAPH_RE
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else:
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raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
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for import_match in pattern.finditer(code):
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module = import_match.group(1)
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if "pydantic_v1" in module:
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continue
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imports_str = (
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import_match.group(2).replace("(\n", "").replace("\n)", "")
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) # Handle newlines within parentheses
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# remove any newline and spaces, then split by comma
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imported_classes = [
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imp.strip()
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for imp in re.split(r",\s*", imports_str.replace("\n", ""))
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if imp.strip()
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]
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for class_name in imported_classes:
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module_path = _get_full_module_name(module, class_name)
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if not module_path:
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continue
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if len(module_path.split(".")) < 2:
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continue
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if package_ecosystem == "langchain":
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pkg = module_path.split(".")[0].replace("langchain_", "")
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top_level_mod = module_path.split(".")[1]
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url = (
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_LANGCHAIN_API_REFERENCE
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+ pkg
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+ "/"
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+ top_level_mod
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+ "/"
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+ module_path
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+ "."
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+ class_name
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+ ".html"
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)
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elif package_ecosystem == "langgraph":
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if (module, class_name) not in WELL_KNOWN_LANGGRAPH_OBJECTS:
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# Likely not documented yet
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continue
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source_module, namespace = WELL_KNOWN_LANGGRAPH_OBJECTS[
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(module, class_name)
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]
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url = (
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_LANGGRAPH_API_REFERENCE
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+ namespace
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+ "/#"
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+ source_module
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+ "."
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+ class_name
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)
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else:
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raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
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# Add the import information to our list
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imports.append(
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{
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"imported": class_name,
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"source": module,
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"docs": url,
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"title": doc_title,
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}
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)
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return imports
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class ImportPreprocessor(Preprocessor):
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"""A preprocessor to replace imports in each Python code cell with links to their
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documentation and append the import info in a comment."""
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def preprocess(self, nb, resources):
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self.all_imports = []
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file_name = os.path.basename(resources.get("metadata", {}).get("name", ""))
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_DOC_TITLE = _get_doc_title(nb.cells[0].source, file_name)
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cells = []
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for cell in nb.cells:
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if cell.cell_type == "code":
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cells.append(cell)
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imports = _get_imports(
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cell.source, _DOC_TITLE, "langchain"
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) + _get_imports(cell.source, _DOC_TITLE, "langgraph")
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if not imports:
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continue
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cells.append(
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nbformat.v4.new_markdown_cell(
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source=f"""
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<div>
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<b>API Reference:</b>
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{' | '.join(f'<a href="{imp["docs"]}">{imp["imported"]}</a>' for imp in imports)}
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</div>
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"""
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)
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)
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else:
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cells.append(cell)
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nb.cells = cells
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return nb, resources
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