copass-pydantic-ai 1.4.4

Pydantic AI tool adapters for Copass — drop-in discover/interpret/search tools for Pydantic AI agents

copass-pydantic-ai

Copass retrieval as Pydantic AI tools. The LLM picks discover (menu of relevant items) or search (synthesized answer) — you don't write the tool-calling loop. interpret is exposed for back-compat but legacy; prefer search for drill-in.

Prerequisites

Install the Copass CLI and bootstrap your account:

npm install -g @copass/cli
copass login                             # email OTP
copass setup                             # creates a sandbox, writes .olane/refs.json
copass apikey create --name my-app       # prints an olk_... key — shown once, save it

Output	Use as
olk_... key printed by copass apikey create	api_key on CopassRetrievalClient (typically via COPASS_API_KEY env)
sandbox_id in ./.olane/refs.json	sandbox_id on copass_tools (typically via COPASS_SANDBOX_ID env)

Ingest some content so retrieval has something to return:

copass ingest path/to/file.md

Install

pip install copass-pydantic-ai pydantic-ai

Requires Python 3.10+.

Quickstart

import os
from pydantic_ai import Agent
from copass_pydantic_ai import CopassRetrievalClient, copass_tools

# COPASS_API_KEY is the olk_... token from `copass apikey create`.
# COPASS_SANDBOX_ID is from .olane/refs.json (written by `copass setup`).
client = CopassRetrievalClient(
    api_url=os.environ.get("COPASS_API_URL", "https://ai.copass.id"),
    api_key=os.environ["COPASS_API_KEY"],
)
discover, interpret, search = copass_tools(
    client=client,
    sandbox_id=os.environ["COPASS_SANDBOX_ID"],
)

agent = Agent(
    "anthropic:claude-opus-4-7",
    tools=[discover, interpret, search],
)
result = await agent.run("what do we know about checkout retry behavior?")
print(result.output)

If it worked, the answer cites concepts from whatever you ingested. Run twice with a shared window (see below) — the second call won't re-surface items the agent already used.

Why this, not the raw API

• LLM chooses the retrieval shape. Three tools; the model picks the right one per turn.
• Pydantic AI-native. Type hints become the schema; docstrings become descriptions. No decorator dance.
• Trimmed responses. Tools return only what the model needs — no sandbox/query echoes.

Tools

Tool	When the LLM calls it
discover	"What's relevant?" — ranked menu of pointers
search	"Tell me about X" / "Answer this." — synthesized answer (canonical drill-in)
interpret	Legacy — brief pinned to canonical_ids. Prefer search for drill-in.

Window-aware retrieval

Pass any object with a get_turns() method:

class MyWindow:
    def __init__(self):
        self.turns: list[dict[str, str]] = []
    def get_turns(self) -> list[dict[str, str]]:
        return self.turns

window = MyWindow()
discover, interpret, search = copass_tools(
    client=client,
    sandbox_id=project_refs["sandbox_id"],
    window=window,
)

Every retrieval call forwards window.get_turns() as history so the server excludes already-seen content.

Low-level client

If you don't want the Pydantic AI wrapping, CopassRetrievalClient is a minimal async httpx client you can use directly:

menu = await client.discover("sb_...", query="...")
brief = await client.interpret("sb_...", query="...", items=[["cid1", "cid2"]])
answer = await client.search("sb_...", query="...")

Related

• @copass/ai-sdk, @copass/langchain, @copass/mastra — same shape for TypeScript frameworks
• @copass/mcp — standalone MCP server for Claude Code / Desktop / Cursor

License

MIT