Add some utils for getting traces from the langfuse client

[evangriffiths]

Example usage:

api_keys = APIKeys()
assert api_keys.bet_from_address == Web3.to_checksum_address(
    "0xe7aa88a1d044e5c987ecce55ae8d2b562a41b72d"  # prophetgpt4
)
start_time = datetime(2024, 9, 13)
langfuse = Langfuse(
    secret_key=api_keys.langfuse_secret_key.get_secret_value(),
    public_key=api_keys.langfuse_public_key,
    host=api_keys.langfuse_host,
)

traces = get_traces_for_agent(
    agent_name="DeployablePredictionProphetGPT4TurboFinalAgent",
    from_timestamp=start_time,
    has_output=True,
    client=langfuse,
)

bets: list[ResolvedBet] = OmenAgentMarket.get_resolved_bets_made_since(
    better_address=api_keys.bet_from_address,
    start_time=start_time,
    end_time=None,
)


for bet in bets:
    trace = get_trace_for_bet(bet, traces)
    market = trace_to_omen_agent_market(trace)
    answer = trace_to_answer(trace)
    trades = trace_to_trades(trace)

[coderabbitai]

Walkthrough

The changes introduce a new file, langfuse_client_utils.py, which contains utility functions for managing Langfuse traces within prediction markets. Key functionalities include fetching traces for specific agents, converting traces into structured data models, and retrieving relevant trades and answers. The implementation supports pagination and filtering, and ensures trace management aligns with bet creation times.

Changes

Files	Change Summary
prediction_market_agent_tooling/tools/langfuse_client_utils.py	- Added functions to fetch and manage Langfuse traces, including pagination and filtering. - Functions to convert traces into market data, answers, and trades. - Utility functions for datetime matching and trace retrieval based on bet creation.

---

Recent review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between deefeb5 and 42814c6.

Files selected for processing (1)

• prediction_market_agent_tooling/tools/langfuse_client_utils.py (1 hunks)

Additional comments not posted (6)

prediction_market_agent_tooling/tools/langfuse_client_utils.py (6)

16-47: LGTM!

The get_traces_for_agent function is well-implemented and follows best practices:

• It uses pagination to fetch traces efficiently.
• It filters the traces based on the provided parameters.
• The function logic is correct and easy to understand.
• The function is well-documented with a clear docstring.
• The function name and parameter names are descriptive.
• The function is modular and reusable.

Great job!

---

50-54: LGTM!

The trace_to_omen_agent_market function is well-implemented:

• It uses assertions to validate the trace input format.
• It extracts the relevant data from the trace input and validates it using the model_validate method.
• The function logic is correct and follows best practices.
• The function name is descriptive and clear.

Good work!

---

57-60: LGTM!

The trace_to_answer function is well-implemented:

• It uses assertions to validate the trace output format.
• It extracts the relevant data from the trace output and validates it using the model_validate method.
• The function logic is correct and follows best practices.
• The function name is descriptive and clear.

Nice work!

---

63-66: LGTM!

The trace_to_trades function is well-implemented:

• It uses assertions to validate the trace output format.
• It extracts the relevant data from the trace output and validates each trade using the model_validate method.
• The function logic is correct and follows best practices.
• The function name is descriptive and clear.

Good job!

---

69-77: LGTM!

The get_closest_datetime_from_list function is well-implemented:

• It handles the edge case when the list has only one datetime.
• It uses the min function with a custom key to efficiently find the closest datetime.
• The function logic is correct and follows best practices.
• The function name is descriptive and clear.
• The function is well-documented with a clear docstring.

Great work!

---

80-102: LGTM!

The get_trace_for_bet function is well-implemented:

• It filters the traces based on the market id of the bet to only consider relevant traces.
• It uses the get_closest_datetime_from_list function to efficiently find the index of the closest trace to the bet.
• The sanity check ensures that the closest trace is after the bet, which is a valid assumption.
• The function handles the case when no trace is found for the bet by returning None and logging a warning.
• The function logic is correct and follows best practices.
• The function name is descriptive and clear.

Excellent work!

---

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[kongzii]

Hmm, shall we change session_id to just the agent's name? Or we could add agent's name as a tag to filter it by that.

(not as part of this PR, just thinking that fetching all the traces won't be very efficient in future)

[kongzii]

In UI, they allow to filter by session id startswith and similar fancy stuff, it didn't cross my mind to check if they allow that in Python SDK as well 😮‍💨

[evangriffiths]

Yeah I was surprised by that too. I would have guessed that the sdk they expose would be the same as what they're using for the web backend, but I guess not!

Or we could add agent's name as a tag to filter it by that.

I was going to suggest this! Will make a ticket

[evangriffiths]

Ticket for us here: #417

Also made an issue on the langfuse github https://github.com/orgs/langfuse/discussions/3395

[kongzii]

Instead of getting traces, it might be more future-proof to get observations with name process_market, right now, it's the same thing (trace of trader agent === observation of process_market). But it could be changed in the future and one trace could contain many process_market observations: gnosis/prediction-market-agent#444 (comment).

However it works now, it's already implement and it's just "maybe" so good by me, just FYI.

[evangriffiths]

Just tried this (client.get_observations(name="process_market", ...)) but it returns an empty list - observations have the names of spans (in red), whereas process_market is the name of the trace.

So I've added the trace_name="process_market" arg. But atm the only traces containing agent names (e.g. DeployablePredictionProphetGPT4TurboFinalAgent) are already the "process_market" traces. But I guess as you say it's more future proof.

[kongzii]

But both trace and span are created using @observe()! That behaviour doesn't make sense! 😄

Thanks for trying and sorry for killing your time then 😢

[evangriffiths]

No worries - it might be something I'm doing wrong. But I'll leave it for now if that's okay :)