semble

uv tool upgrade semble   # upgrade
uv cache clean semble    # for MCP users (restart your MCP client after)

For sandboxed or scripted environments, skip the prompts with --agent and, optionally, --type:

semble install --agent claude --type mcp subagent --yes

--agent accepts one or more agent ids (e.g. claude, codex, pi); --type accepts mcp, instructions, subagent, skill, or all (default: all but skill); --yes skips the confirmation prompt (requires --agent for a fully non-interactive run).

Semble reads .gitignore and .sembleignore files to determine which files to index. Both files use standard gitignore syntax and their patterns are merged. .sembleignore lets you add semble-specific rules without touching .gitignore. Rules are applied recursively, so a .sembleignore in a subdirectory applies to that subtree.

Excluding files: add patterns the same way you would in .gitignore:

# .sembleignore
generated/     # exclude generated dir
*.pb.go.       # exclude Go protobuf files

Including non-default extensions: prefix the extension pattern with ! to force-include files that semble wouldn’t index by default:

# .sembleignore
!*.proto       # include Protobuf files
!*.cob         # include COBOL files

Run semble file-types to see which extensions are indexed per content type. Extensions not listed (including data files such as .json and .csv) are not indexed by default and can be force-included with a ! pattern as above.

Semble also always skips a set of well-known non-source directories regardless of ignore files (e.g. node_modules/, .venv/, dist/, build/, __pycache__/, and similar).

semble savings shows how many tokens semble has saved across all your searches:

semble savings

  Semble Token Savings
  ════════════════════════════════════════════════════════════════════════

  Total saved:  ~714.2M tokens  (94%)
  Total calls:  14.3k
  Efficiency:  ███████████████████████░  94%

  By Period
  ────────────────────────────────────────────────────────────────────────
  Period             Calls           Saved  Ratio
  ────────────────────────────────────────────────────────────────────────
  Today                198    ~1.4M tokens  ███████████████████████░  95%
  Last 7 days        13.1k  ~707.2M tokens  ███████████████████████░  94%
  All time           14.3k  ~714.2M tokens  ███████████████████████░  94%

  By Call Type
  ────────────────────────────────────────────────────────────────────────
  #     Call type            Calls  Share
  ────────────────────────────────────────────────────────────────────────
  1.    search               14.1k  ████████████████    99%
  2.    find_related           205  █░░░░░░░░░░░░░░░     1%
  ════════════════════════════════════════════════════════════════════════

Savings are calculated as follows: for each call, semble records the total character count of the unique files containing returned chunks and the character count of the snippets returned. Estimated tokens saved is (file chars − snippet chars) / 4 (4 chars per token). This is a conservative estimate: the baseline is reading matched files in full, which is how coding agents often explore unfamiliar code.

By default, your Semble savings statistics and any saved indexes are stored in the OS cache folder (~/Library/Caches/semble/ on macOS, ~/.cache/semble/ on Linux, %LOCALAPPDATA%\semble\Cache\ on Windows). To override this location you can supply an environment variable SEMBLE_CACHE_LOCATION which should be the full path to the target cache location e.g. ~/my-folder/my-caches/semble.

Files larger than 1 MB are skipped during indexing to keep index builds lean. Skipped files are reported as a warning at index time. If you work with large generated or ingested documents, you can raise (or lower) this limit with the SEMBLE_MAX_FILE_BYTES environment variable (in bytes).

On first use, Semble also downloads the embedding model from Hugging Face and caches it in the standard Hugging Face cache (~/.cache/huggingface/ by default, or $HF_HOME if set); this only happens once and requires network access.

Use semble clear to remove cached data: semble clear index (saved indexes), semble clear savings (usage stats), semble clear orphans (indexes for repos no longer present on disk), or semble clear all (everything).

Semble can also be used as a Python library for programmatic access, useful when building custom tooling or integrating search directly into your own code.

from semble import ContentType, SembleIndex

# Index a local directory (code only, the default)
index = SembleIndex.from_path("./my-project")

# Index docs and prose (markdown, rst, etc.)
index = SembleIndex.from_path("./my-project", content=ContentType.DOCS)

# Index everything (code, docs, and config)
index = SembleIndex.from_path("./my-project", content=[ContentType.CODE, ContentType.DOCS, ContentType.CONFIG])

# Index code and docs together
index = SembleIndex.from_path("./my-project", content=[ContentType.CODE, ContentType.DOCS])

# Index a remote git repository
index = SembleIndex.from_git("https://github.com/MinishLab/model2vec")

# Merge indexes from several repos into one (chunk paths are prefixed with the repo name)
index = SembleIndex.merge([("./service-a", SembleIndex.from_path("./service-a")), ("./service-b", SembleIndex.from_path("./service-b"))])

# Search the index with a natural-language or code query
results = index.search("save model to disk", top_k=3)

# Find code similar to a specific result
related = index.find_related(results[0], top_k=3)

# Each result exposes the matched chunk
result = results[0]
result.chunk.file_path   # "model2vec/model.py"
result.chunk.start_line  # 127
result.chunk.end_line    # 150
result.chunk.content     # "def save_pretrained(self, path: PathLike, ..."

When many files need indexing, the CLI and MCP server chunk them in 4 worker processes (SEMBLE_INDEX_WORKERS sets the number, 0 turns them off). In the Python API they are off by default. To turn them on, set SEMBLE_INDEX_WORKERS and call Semble from inside an if __name__ == "__main__": block, since each worker re-imports your script.

• Adaptive weighting. Symbol-like queries (Foo::bar, _private, getUserById) get more lexical weight, while natural-language queries stay balanced between semantic and lexical retrievers.
• Definition boosts. A chunk that defines the queried symbol (a class, def, func, etc.) is ranked above chunks that merely reference it.
• Identifier stems. Query tokens are stemmed and matched against identifier stems in a chunk, giving an additional weight to chunks that contain them. For example, querying parse config boosts chunks containing parseConfig, ConfigParser, or config_parser.
• File coherence. When multiple chunks from the same file match the query, the file is boosted so the top result reflects broad file-level relevance rather than a single out-of-context chunk.
• Noise penalties. Test files, compat//legacy/ shims, example code, and .d.ts declaration stubs are down-ranked so canonical implementations surface first.