ragleap-tools

Standalone, dependency-light tool implementations for LLM tool-calling. Exposes OpenAI/Gemini-style function-calling schemas.

pip install ragleap-tools

What this is (and isn't)

ragleap-tools provides Tool objects - a name, a description, a JSON Schema for parameters, and a safe handler function. It does not own a tool-calling execution loop (deciding when to call a tool, running it, feeding the result back to the model) - that's ragleap-agents' job, per the project roadmap's own split. Wire these tools into your own tool-calling code, or into ragleap-agents once that ships.

Quickstart

from ragleap_tools import STATELESS_TOOLS, CALCULATOR_TOOL

# Give these to your LLM provider's tools= parameter:
openai_tools = [t.to_openai_schema() for t in STATELESS_TOOLS]
gemini_tools = [t.to_gemini_schema() for t in STATELESS_TOOLS]

# When the model calls one, invoke the handler yourself:
result = CALCULATOR_TOOL.call(expression="2 + 2 * sqrt(16)")
print(result.success, result.result)  # True 10.0

The 12 stateless tools

No configuration needed - import and use directly.

• CALCULATOR_TOOL - safe arithmetic (AST-based whitelist, never eval())
• CURRENT_DATETIME_TOOL, ADD_TO_DATE_TOOL, DATE_DIFFERENCE_TOOL - date/time math
• CONVERT_LENGTH_TOOL, CONVERT_WEIGHT_TOOL, CONVERT_TEMPERATURE_TOOL - unit conversion
• PARSE_JSON_TOOL, PARSE_CSV_TOOL - structured data parsing
• REGEX_EXTRACT_TOOL, WORD_COUNT_TOOL, TEXT_CASE_TRANSFORM_TOOL - text utilities

File operations (sandboxed, needs configuration)

from ragleap_tools import FileOpsConfig, make_file_tools

config = FileOpsConfig(root_dir="/path/to/a/safe/directory")
read_tool, write_tool, list_tool = make_file_tools(config)

Every operation is confined to root_dir - both ../ path traversal and symlink-based escapes are rejected (verified via real security tests, not just documented), not just naive string-prefix checking. There is no unsandboxed mode.

Document ingestion and search (optional, needs ragleap-rag)

pip install ragleap-tools[ingest]

from ragleap import RagLeap, ProviderConfig, EmbeddingConfig
from ragleap_tools import IngestConfig, make_ingest_tool, SearchConfig, make_search_tool

rag = RagLeap(database_url="...", primary=ProviderConfig(...), embedder=EmbeddingConfig(...))
ingest_tool = make_ingest_tool(IngestConfig(rag=rag))
search_tool = make_search_tool(SearchConfig(rag=rag))

ingest_document wraps ragleap-rag's already-tested ingest_text() - no new ingestion logic, just a tool schema on top of the real 28-format-capable pipeline. As of v0.1.1, it stores the filename as metadata ({"filename": filename}), enabling the per-document search below - v0.1.0 did not pass any metadata, which silently made per-document filtering impossible.

search_documents wraps ragleap-rag's already-tested retrieve() for hybrid vector+keyword search over previously ingested documents. Chunk dicts are returned unmodified - this tool doesn't assume ragleap-rag's exact field set. Pass an optional filename= to scope the search to a single document previously ingested via ingest_document:

result = search_tool.call(query="what was the Q3 revenue?", filename="q3-report.pdf")

ragleap-rag owns the actual ingestion and retrieval logic; these are thin adapters, same pattern ragleap-graph uses for its own optional ragleap-rag dependency.

Web search (BYOK, no extra dependencies)

from ragleap_tools import WebSearchConfig, TavilySearchProvider, make_web_search_tool

provider = TavilySearchProvider(api_key="...")  # or SerperSearchProvider(api_key="...")
search_tool = make_web_search_tool(WebSearchConfig(provider=provider))

result = search_tool.call(query="latest pgvector release", num_results=5)
# result.result == {"results": [{"title": ..., "url": ..., "snippet": ...}, ...], "count": 5}

Bring your own key: you construct the provider you want with your own api_key. There is no default provider and no environment-variable fallback. Uses only the standard library (urllib.request), so ragleap-tools still has zero required dependencies. SearchProvider is a small abstract class - implement search(query, num_results) to plug in any other search engine.

num_results is chosen by the model and costs your API quota, so it is clamped to 1-20.

Honest limitations: request shapes for both reference providers were checked against their current public docs, but neither has been called against a live account - treat them as best-effort until confirmed by someone with a real key. Results are text from arbitrary third-party pages, so treat them as untrusted input: this tool does not screen them for prompt injection. There is no caching, rate limiting or deduplication.

Deliberately out of scope

Each of these needs its own security-focused design pass, not a rushed inclusion here:

• Code execution - a real sandboxing/resource-limit design decision, not something to bolt on alongside a calculator.
• HTTP fetch - letting an LLM request arbitrary URLs carries real SSRF risk, same care level as code execution.
• Database/business-system connectors (SQL, CRM, payment processors, etc.) - some of what this ecosystem already has elsewhere (e.g. a live payment processor) would be a materially different risk if exposed to LLM tool-calling without deliberate guardrails (dry-run modes, confirmation steps, scoped permissions).

Status

v0.2.0. 85 tests, all passing, including real security verification for the two risk-sensitive tools (calculator's code-injection rejection, file ops' path-traversal and symlink-escape rejection) - not just documented as safe, actually tested against real attack vectors.

License

MIT