embed-tree 0.1.2

Incremental hierarchical clustering tree over content embeddings

embed-tree

embed-tree turns content nodes into a browsable, labeled hierarchy.

The public model is intentionally small:

ContentNode(id=..., text=..., metadata={...})
BranchNode(id, label=None, children=[])
EmbedTree(embedder, config=None, state=None, labeler=None)

ContentNode.text is the string passed to the embedder. metadata is opaque user data returned by queries and preserved in exported branches.

Install

pip install embed-tree

Optional integrations:

pip install "embed-tree[openai]"
pip install "embed-tree[local]"
pip install "embed-tree[sql]"

Quick Start

from embed_tree import ContentNode, EmbedTree, TagSetEmbedder, TreeConfig

nodes = [
    ContentNode(id="doc-1", text="import pipeline docs", metadata={"tags": ["docs", "ingest"]}),
    ContentNode(id="doc-2", text="retry handling for ingestion", metadata={"tags": ["ingest"]}),
    ContentNode(id="doc-3", text="summary generation latency", metadata={"tags": ["analysis"]}),
    ContentNode(id="doc-4", text="schema mapping examples", metadata={"tags": ["docs", "schemas"]}),
]

tree = EmbedTree(
    embedder=TagSetEmbedder(["docs", "ingest", "analysis", "schemas"]),
    config=TreeConfig(max_branches=4, leaf_capacity=2),
)

tree.add_nodes(nodes)
tree.organize()  # rebalance the hierarchy, then label each branch

print(tree.show())
branch = tree.to_branch()

Use a real text embedder in production:

from embed_tree import ContentNode, EmbedTree, OpenAITextEmbedder

tree = EmbedTree(OpenAITextEmbedder(model="text-embedding-3-small", api_key="..."))
tree.add_node(
    ContentNode(
        id="doc-1",
        text="Some document summary",
        metadata={"source": "docs"},
    )
)

Core API

tree.add_node(ContentNode(...))      # -> id
tree.add_nodes([ContentNode(...)])   # -> list[id]
tree.add_branch(BranchNode(...))     # -> list[id], inserts all content leaves

tree.query("query text", k=10, exhaustive=False)
tree.remove(node_id)
tree.remove_batch([node_id])

tree.rebalance()
tree.label(labeler=None)
tree.organize(labeler=None) # rebalance + re-label

tree.to_branch(max_items=None)
tree.show(max_items=3)
len(tree)

BranchNode is the public tree shape. It can represent an input branch from a loader or the organized output from EmbedTree.to_branch().

For folder-based trees, FileSystemTreeLoader uses the file content MD5 as id. Its optional text_generator(path, raw_text) can derive the embed text from raw file text while preserving file identity. Its optional additional_metadata_derivers is a list of callables that derive metadata such as new_file_name from file content; derived dictionaries are merged in order, with later keys winning. FolderTreePersister moves existing files only when a node has a content MD5 as its id or explicit MD5 metadata and that MD5 exists under the current root. If no current file matches, path metadata can point to a source file to copy when its MD5 matches the same identity. If neither exists, missing_node_file controls the result: "skip" warns and skips by default, "create" writes a .txt snapshot containing text and metadata, and "raise" raises MissingNodeFileError. new_file_name can rename moved/copied files or snapshots.

EmbedTree has internal runtime nodes and content records which are not public API.

Persistence

Use a state loader that can save materialized state:

from embed_tree import EmbedTree, JsonTreeLoader

tree = EmbedTree(embedder, state=JsonTreeLoader("./tree.json"))

Development

uv sync --extra dev
uv run --extra dev pytest -q