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| from wandb.sdk.data_types.trace_tree import Trace | |
| def wandb_trace(rag_option, prompt, completion, result, generation_info, llm_output, chain, 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), | |
| metadata = {"chunk_overlap": "" if (rag_option == RAG_OFF) else config["chunk_overlap"], | |
| "chunk_size": "" if (rag_option == RAG_OFF) else config["chunk_size"], | |
| } if (str(err_msg) == "") else {}, | |
| inputs = {"rag_option": rag_option, | |
| "prompt": prompt, | |
| "chain_prompt": (str(chain.prompt) if (rag_option == RAG_OFF) else | |
| str(chain.combine_documents_chain.llm_chain.prompt)), | |
| "source_documents": "" if (rag_option == RAG_OFF) else str([doc.metadata["source"] for doc in completion["source_documents"]]), | |
| } if (str(err_msg) == "") else {}, | |
| outputs = {"result": result, | |
| "generation_info": str(generation_info), | |
| "llm_output": str(llm_output), | |
| "completion": str(completion), | |
| } if (str(err_msg) == "") else {}, | |
| model_dict = {"client": (str(chain.llm.client) if (rag_option == RAG_OFF) else | |
| str(chain.combine_documents_chain.llm_chain.llm.client)), | |
| "model_name": (str(chain.llm.model_name) if (rag_option == RAG_OFF) else | |
| str(chain.combine_documents_chain.llm_chain.llm.model_name)), | |
| "temperature": (str(chain.llm.temperature) if (rag_option == RAG_OFF) else | |
| str(chain.combine_documents_chain.llm_chain.llm.temperature)), | |
| "retriever": ("" if (rag_option == 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() |