Reference implementation knowledgebase

This notebook stores favourite implementation repositories in one local LanceDB database. It keeps registered sources, indexed README/module/function/class/method records, and direct same-repo caller/callee links for examples an agent can inspect before writing similar code.

Library searches inspect the target workspace before looking for a version. A relevant installed direct dependency uses its exact version, downloads that public PyPI release only when it is missing, then parses notebooks for nbdev projects and Python files for other projects. The index reuses embeddings for unchanged file hashes and keeps only the newest three indexed versions per package.

Production contract

Reference knowledge is explicit and local. It registers repositories, ingests Python and notebook implementation records, preserves dependency and branch context, and returns structured examples through one query path.

Imports

Reference indexing uses filesystem scans, Python AST parsing, notebook reads, LanceDB storage, and sentence-transformers embeddings.

import warnings

Reference implementation knowledgebase

The reference knowledgebase stores favourite implementation repositories in one local LanceDB database. It keeps three tables: reference_repos for registered sources, reference_items for README/module/function/class/method records, and reference_edges for direct same-repo caller/callee links.

This feature requires both LanceDB and sentence-transformers. Ingestion writes a full sentence-transformers vector for every indexed item, and queries combine structured filters, LanceDB full-text search, and vector similarity over that searchable vector store. If either dependency or the configured embedding model is unavailable, reference ingestion/querying fails clearly instead of falling back to a weaker embedding.

The public workflow is:

reference_add(...) registers a local path or GitHub repository.
reference_ingest(...) clones with gh repo clone when possible, extracts Python and notebook symbols, and writes LanceDB rows.
reference_query(...) returns examples, dependency status, optional branch context, and increments each returned item’s returned_count.

Reference knowledge examples below use tiny local fixtures. They document the public API without touching the global user database.

Small executable examples appear after the reference API definitions so they can call the exported functions directly.

Registry and storage

The registry records where reference code came from. It keeps repository metadata separate from indexed implementation rows so re-ingesting one source can replace its records without disturbing the others.

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reference_home

def reference_home(
    path:NoneType=None
):

Return the global reference knowledge directory.

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reference_add

def reference_add(
    url:str, # Repository URL or local repository path
    name:str | None=None, # Stable registry name; defaults from URL/path
    version:str='HEAD', # Git ref to ingest
    package:str | None=None, # Import/distribution package name
    path:str | None=None, # Override reference home
):

Register or update one reference repository.

Retrieval and relevance

The search layer combines metadata filters, lexical search, vectors, and small domain rules. The rules matter because a query for a specific helper should not be promoted by an unrelated generic name.

Specific reference relevance

reference_query separates exact identifier intent from generic query fragments. A hit must cover the specific terms of a multi-concept request; otherwise it is reported as low confidence instead of being promoted by an incidental symbol component.

Repository extraction

Indexed rows retain enough source context to make a returned result useful without opening a whole repository. Python and notebook readers share the same normalized row format.

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reference_ingest

def reference_ingest(
    name:str | None=None, # Registry name to ingest
    all:bool=False, # Ingest all registered repositories
    path:str | None=None, # Override reference home
    force:bool=False, # Reingest even when resolved version appears unchanged
):

Clone registered references and index Python/notebook implementations.

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reference_list

def reference_list(
    path:str | None=None
):

Return registered reference repositories and indexed item counts.

Problem-solution memory

Each new pair needs at least four distinct namespaced tags from topic, sub-topic, library, problem-category, and solution-category. Records also keep the repository, commit, verification command, and verification time.

Queries keep tag filtering exact. Hits report matching tags and score components. A query with no exact match reports the miss, counts broader candidates, and suggests available tags instead of returning a loose match.

Problem-solution memory

Problem memory records a reusable observation beside the source-code index. It is separate from reference retrieval because an agent may need a prior decision even when no code example is relevant.

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problem_statement_add

def problem_statement_add(
    problem:str, # Reusable problem statement
    solution:str, # Reusable solution statement
    task:str='', # Task where this pair was observed
    project:str='.', # Project path or label
    evidence:str='', # Short evidence that the solution worked
    outcome:str='applied', # applied, partial, failed, skipped, etc.
    tags:NoneType=None, # At least four namespaced tags
    repository:str='', # Repository containing the verified evidence
    commit:str='', # Source commit for the evidence
    verification:str='', # Command that verified the solution
    verified_at:float | None=None, # Verification time as a Unix timestamp
    path:str | None=None, # Override reference home
):

Store or update one reusable problem-solution statement.

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problem_statement_query

def problem_statement_query(
    query:str, # New problem or task to compare against stored memories
    top_k:int=5, # Number of memories to return
    project:str | None=None, # Optional project filter
    tags:NoneType=None, # Optional tags; every requested tag must be present
    path:str | None=None, # Override reference home
):

Find reusable problem-solution statements for a new task.

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problem_statement_list

def problem_statement_list(
    path:str | None=None, limit:int=20
):

List recent reusable problem-solution statements.

Fastcore 2 migration verification runs this test before the later reference-query setup imports. Keep the demo-path helpers local to the test that uses them.

Small reference API examples:

Workspace-aware queries

The query path reads the target workspace before looking up implementation examples. That keeps a direct dependency on its installed version and prevents a local editable package from being replaced by a public download.

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reference_propose

def reference_propose(
    problem:str, # Observed problem in the library
    repo:str, # Indexed reference repository name
    version:str | None=None, # Optional indexed version
    symbol:str | None=None, # Optional affected symbol
    current_repo:str='.', # Workspace using the library
    path:str | None=None, # Override reference home
):

Prepare an upstream change proposal without creating a pull request.

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reference_query

def reference_query(
    query:str, # Natural-language implementation query
    top_k:int=3, # Number of implementation hits to return
    include_branch:bool=False, # Include direct same-repo callers and callees
    current_repo:str='.', # Workspace whose installed libraries should be reused
    repos:NoneType=None, # Optional repo name or comma-separated names to search
    path:str | None=None, # Override reference home
    kind:str | None=None, # Optional item kind filter
    package:str | None=None, # Optional package filter and auto-download target
    version:str | None=None, # Optional package version
    module:str | None=None, # Optional module filter
    symbol:str | None=None, # Optional symbol filter
    include_local:bool=True, # Include current repository reuse advice
    candidate_k:int | None=None, # Candidate pool size before final reranking
    explain:bool=True, # Include score breakdowns and why text
    allow_download:bool=True, # Download public source for a relevant package
):

Search reference code using the target workspace’s installed version.

example_home = reference_home(demo_path("reference-example-home"))
assert example_home.name == "reference-example-home"
remove_demo_path(example_home)

example_vector = _reference_vector("make page endpoint")
assert len(example_vector) > 100

Reference repositories are managed through the Python registry. The executable contract uses a local git repository so ingestion never depends on network access.

Retrieval Experiments

This small experiment compares four search surfaces on a tiny structured corpus:

tags_fts: LanceDB full-text search over tags plus symbol/doc text, with no vector similarity.
docstring: vector similarity over docstrings only.
signature_doc_return: vector similarity over the signature, docstring, and return statements.
full_body: vector similarity over the whole indexed source block.

The expected symbols mirror common fastcore lookups: store_attr, patch, and compose.

Retrieval experiments

The experiment compares searchable representations on a tiny fixed corpus. It is kept separate from the production query path so it can explain the tradeoff without changing query behavior.

def _experiment_returns(source):
    try: tree = ast.parse(source or "")
    except SyntaxError: return ""
    vals = []
    for node in ast.walk(tree):
        if isinstance(node, ast.Return) and node.value is not None:
            try: vals.append(ast.unparse(node.value))
            except Exception: pass
    return "\n".join(vals)

def _experiment_text(row, mode):
    if mode == "docstring": return row.get("docstring", "")
    if mode == "signature_doc_return":
        return "\n".join([row.get("signature", ""), row.get("docstring", ""), _experiment_returns(row.get("source", ""))])
    if mode == "full_body": return row.get("source", "")
    return " ".join([*(row.get("tags") or []), row.get("symbol", ""), row.get("signature", ""), row.get("docstring", "")])

def _experiment_table(home, rows, mode):
    table_rows = []
    for row in rows:
        text = _experiment_text(row, mode)
        table_rows.append({**row, "search_text": text, "vector": _reference_vector(text)})
    table = _reference_db(home).create_table(f"reference_experiment_{mode}", data=table_rows, mode="overwrite")
    try: table.create_fts_index("search_text", replace=True)
    except Exception: pass
    return table

def _experiment_search(table, query, mode, top_k=3):
    if mode == "tags_fts":
        rows = table.search(query, query_type="fts").limit(top_k).to_list()
    else:
        rows = table.search(_reference_vector(query)).metric("cosine").limit(top_k).to_list()
    return [row["symbol"] for row in rows]

experiment_home = demo_path("reference-experiment-home")
experiment_rows = [
    _item_row("mini", "v1", "mini", "function", "mini.basics", "store_attr", Path("mini.py"),
              "def store_attr(names=None, self=None, **attrs):\n    return _store_attr(self, **attrs)",
              signature="def store_attr(names=None, self=None, **attrs)",
              docstring="Store params named in comma-separated names from calling context into attrs in self."),
    _item_row("mini", "v1", "mini", "function", "mini.basics", "patch", Path("mini.py"),
              "def patch(f=None, *, cls_method=False):\n    return patch_to(cls_method=cls_method)(f)",
              signature="def patch(f=None, *, cls_method=False)",
              docstring="Decorator: add f to the first parameter's class based on type annotations."),
    _item_row("mini", "v1", "mini", "function", "mini.basics", "compose", Path("mini.py"),
              "def compose(*funcs):\n    def _inner(x):\n        for f in funcs: x = f(x)\n        return x\n    return _inner",
              signature="def compose(*funcs)",
              docstring="Create a function that composes all functions in funcs."),
    _item_row("mini", "v1", "mini", "function", "mini.foundation", "flatmap", Path("mini.py"),
              "def flatmap(f, xs):\n    return [y for x in xs for y in f(x)]",
              signature="def flatmap(f, xs)",
              docstring="Apply f to each element and flatten the results into a single list."),
]
experiment_queries = {
    "store constructor arguments as attributes": "store_attr",
    "patch a method onto a class": "patch",
    "compose functions into a pipeline": "compose",
}
experiment_results = {}
try:
    for mode in ("tags_fts", "docstring", "signature_doc_return", "full_body"):
        table = _experiment_table(str(experiment_home), experiment_rows, mode)
        experiment_results[mode] = {
            query: _experiment_search(table, query, mode, top_k=3)
            for query in experiment_queries
        }
finally:
    remove_demo_path(experiment_home)
experiment_results

experiment_accuracy = {
    mode: sum(hits and hits[0] == expected for query, expected in experiment_queries.items() for hits in [mode_results[query]])
    for mode, mode_results in experiment_results.items()
}
assert experiment_accuracy["signature_doc_return"] >= experiment_accuracy["docstring"]
assert experiment_accuracy["signature_doc_return"] >= experiment_accuracy["full_body"]
experiment_accuracy

Version-aware sources

A relevant direct dependency is indexed at the version the target workspace uses. Public PyPI downloads are only a fallback when that exact source is not already available.

Reusing local repositories

reference_discover stores reference roots and registers Git repositories at the root or one directory below it. Local names include a path hash, so two repositories with the same folder name remain separate.

Local repository discovery

Discovery only registers Git repositories at configured roots. Limiting the scan to roots and their direct children keeps a background index from unexpectedly walking an entire home directory.

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reference_discover

def reference_discover(
    roots:NoneType=None, # Comma-separated or iterable reference roots
    path:str | None=None, # Override reference home
    ingest:bool=True, # Index each discovered repository
    force:bool=False, # Re-ingest unchanged repositories
):

Register direct-child Git repositories from configured roots.

Workspace versions and upstream proposals

reference_query asks the target workspace’s virtual environment which distributions are installed. It only downloads public source for a relevant package, so returned code can match the version the project already uses. reference_propose writes review material only; it never creates a fork or pull request.

Small public examples

reference_home is deliberately simple. It makes the storage location explicit, which is useful when an agent needs an isolated database for a test or a workspace-specific index.

reference_home("demo-knowledge")

Query planning

_query_feature_plan makes the non-obvious parts of retrieval visible before any database work starts. It records the requested evidence, likely implementation kinds, and the search passes that will be applied.

_query_feature_plan("find a FastHTML route test")

The focused check keeps the public storage location and query-intent classification stable without requiring LanceDB, a model, or a repository fixture.

Lazy embedding dependency

Sentence-transformers is imported only when vector search is needed. That keeps registry and metadata commands usable without loading a model at module import time.