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import os, wandb
from wandb.sdk.data_types.trace_tree import Trace
WANDB_API_KEY = os.environ["WANDB_API_KEY"]
def trace_wandb(config,
is_rag_off,
prompt,
completion,
result,
chain,
cb,
err_msg,
start_time_ms,
end_time_ms):
wandb.init(project = "openai-llm-rag")
trace = Trace(
kind = "chain",
name = "" if (chain == None) else type(chain).__name__,
status_code = "success" if (str(err_msg) == "") else "error",
status_message = str(err_msg),
inputs = {"is_rag": not is_rag_off,
"prompt": prompt,
"chain_prompt": (str(chain.prompt) if (is_rag_off) else
str(chain.combine_documents_chain.llm_chain.prompt)),
"source_documents": "" if (is_rag_off) else str([doc.metadata["source"] for doc in completion["source_documents"]]),
} if (str(err_msg) == "") else {},
outputs = {"result": result,
"cb": str(cb),
"completion": str(completion),
} if (str(err_msg) == "") else {},
model_dict = {"client": (str(chain.llm.client) if (is_rag_off) else
str(chain.combine_documents_chain.llm_chain.llm.client)),
"model_name": (str(chain.llm.model_name) if (is_rag_off) else
str(chain.combine_documents_chain.llm_chain.llm.model_name)),
"temperature": (str(chain.llm.temperature) if (is_rag_off) else
str(chain.combine_documents_chain.llm_chain.llm.temperature)),
"retriever": ("" if (is_rag_off) else str(chain.retriever)),
} if (str(err_msg) == "") else {},
start_time_ms = start_time_ms,
end_time_ms = end_time_ms
)
trace.log("evaluation")
wandb.finish() |