diff --git a/otel/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/watsonx-langchain.py b/otel/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/watsonx-langchain.py index 196680e..a59a6e7 100644 --- a/otel/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/watsonx-langchain.py +++ b/otel/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/watsonx-langchain.py @@ -13,6 +13,9 @@ # from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as WatsonMLGenParams from ibm_watsonx_ai.metanames import GenTextParamsMetaNames # from ibm_watsonx_ai.foundation_models import ModelInference +from ibm_watsonx_ai.foundation_models import Model as WatsonAIModel +from ibm_watsonx_ai.foundation_models.extensions.langchain import WatsonxLLM as WatsonxLLM_AI +from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as WatsonMLGenParams from langchain.llms.openai import OpenAI from langchain.agents import load_tools @@ -30,29 +33,29 @@ load_dotenv(find_dotenv()) -# Traceloop.init(api_endpoint=os.environ["OTLP_EXPORTER_HTTP"], -# # api_key=os.environ["TRACELOOP_API_KEY"], -# app_name=os.environ["SVC_NAME"], -# ) +Traceloop.init(api_endpoint=os.environ["OTLP_EXPORTER_HTTP"], + # api_key=os.environ["TRACELOOP_API_KEY"], + app_name=os.environ["SVC_NAME"], + ) """ only need 2 lines code to instrument Langchain LLM """ -from otel_lib.instrumentor import LangChainHandlerInstrumentor as SimplifiedLangChainHandlerInstrumentor -from opentelemetry.sdk._logs import LoggingHandler -tracer_provider, metric_provider, logger_provider = SimplifiedLangChainHandlerInstrumentor().instrument( - otlp_endpoint=os.environ["OTLP_EXPORTER"], - # otlp_endpoint=os.environ["OTLP_EXPORTER_GRPC"], - # metric_endpoint=os.environ["OTEL_METRICS_EXPORTER"], - # log_endpoint=os.environ["OTEL_LOG_EXPORTER"], - service_name=os.environ["SVC_NAME"], - insecure = True, - ) +# from otel_lib.instrumentor import LangChainHandlerInstrumentor as SimplifiedLangChainHandlerInstrumentor +# from opentelemetry.sdk._logs import LoggingHandler +# tracer_provider, metric_provider, logger_provider = SimplifiedLangChainHandlerInstrumentor().instrument( +# otlp_endpoint=os.environ["OTLP_EXPORTER"], +# # otlp_endpoint=os.environ["OTLP_EXPORTER_GRPC"], +# # metric_endpoint=os.environ["OTEL_METRICS_EXPORTER"], +# # log_endpoint=os.environ["OTEL_LOG_EXPORTER"], +# service_name=os.environ["SVC_NAME"], +# insecure = True, +# ) """======================================================= """ -handler = LoggingHandler(level=logging.DEBUG,logger_provider=logger_provider) +# handler = LoggingHandler(level=logging.DEBUG,logger_provider=logger_provider) # Create different namespaced loggers logger = logging.getLogger("mylog_test") -logger.addHandler(handler) +# logger.addHandler(handler) logger.setLevel(logging.DEBUG) # os.environ["WATSONX_APIKEY"] = os.getenv("IAM_API_KEY") @@ -82,40 +85,68 @@ # params=watson_ml_parameters, # ) -api_key = os.getenv("IBM_GENAI_KEY", None) -api_url = "https://bam-api.res.ibm.com" -creds = Credentials(api_key, api_endpoint=api_url) - -genai_parameters = GenaiGenerateParams( - decoding_method="sample", # Literal['greedy', 'sample'] - max_new_tokens=300, - min_new_tokens=10, - top_p=1, - top_k=50, - temperature=0.05, - time_limit=30000, - # length_penalty={"decay_factor": 2.5, "start_index": 5}, - # repetition_penalty=1.2, - truncate_input_tokens=2048, - # random_seed=33, - stop_sequences=["fail", "stop1"], - return_options={ - "input_text": True, - "generated_tokens": True, - "input_tokens": True, - "token_logprobs": True, - "token_ranks": False, - "top_n_tokens": False - }, -) +os.environ["WATSONX_APIKEY"] = os.getenv("IAM_API_KEY") +apikey=os.getenv("IAM_API_KEY") +project_id=os.getenv("PROJECT_ID") +watson_ml_url="https://us-south.ml.cloud.ibm.com" + + +# api_key = os.getenv("IBM_GENAI_KEY", None) +# api_url = "https://bam-api.res.ibm.com" +# creds = Credentials(api_key, api_endpoint=api_url) + +# genai_parameters = GenaiGenerateParams( +# decoding_method="sample", # Literal['greedy', 'sample'] +# max_new_tokens=300, +# min_new_tokens=10, +# top_p=1, +# top_k=50, +# temperature=0.05, +# time_limit=30000, +# # length_penalty={"decay_factor": 2.5, "start_index": 5}, +# # repetition_penalty=1.2, +# truncate_input_tokens=2048, +# # random_seed=33, +# stop_sequences=["fail", "stop1"], +# return_options={ +# "input_text": True, +# "generated_tokens": True, +# "input_tokens": True, +# "token_logprobs": True, +# "token_ranks": False, +# "top_n_tokens": False +# }, +# ) -watsonx_genai_llm = LangChainInterface( - # model="google/flan-t5-xxl", - # model="meta-llama/llama-2-70b", - model = "ibm/granite-13b-chat-v1", - params=genai_parameters, - credentials=creds +watson_ml_parameters = { + WatsonMLGenParams.DECODING_METHOD: "sample", + WatsonMLGenParams.MAX_NEW_TOKENS: 30, + WatsonMLGenParams.MIN_NEW_TOKENS: 1, + WatsonMLGenParams.TEMPERATURE: 0.5, + WatsonMLGenParams.TOP_K: 50, + WatsonMLGenParams.TOP_P: 1, +} + +model = WatsonAIModel( + model_id="google/flan-ul2", + credentials={ + "apikey": apikey, + "url": watson_ml_url + }, + params=watson_ml_parameters, + project_id=project_id, ) +watsonx_ai_llm = WatsonxLLM_AI(model=model) +# watsonx_ml_llm = WatsonxLLM_ML(model=model) + +# watsonx_genai_llm = LangChainInterface( +# # model="google/flan-t5-xxl", +# # model="meta-llama/llama-2-70b", +# # model = "ibm/granite-13b-chat-v1", +# model="google/flan-ul2", +# params=genai_parameters, +# credentials=creds +# ) # openai_llm = OpenAI( @@ -152,7 +183,7 @@ def langchain_watson_genai_llm_chain(): HumanMessage(content=f"tell me what is the most famous dish in {RandomCountryName()}?"), ] first_prompt_template = ChatPromptTemplate.from_messages(first_prompt_messages) - first_chain = LLMChain(llm=watsonx_genai_llm, prompt=first_prompt_template, output_key="target") + first_chain = LLMChain(llm=watsonx_ai_llm, prompt=first_prompt_template, output_key="target") logger.info("first chain set", extra={"action": "set llm chain", "chain name": "first chain"}) second_prompt_messages = [ @@ -161,7 +192,7 @@ def langchain_watson_genai_llm_chain(): HumanMessagePromptTemplate.from_template("pls provide the recipe for dish {target}\n "), ] second_prompt_template = ChatPromptTemplate.from_messages(second_prompt_messages) - second_chain = LLMChain(llm=watsonx_genai_llm, prompt=second_prompt_template) + second_chain = LLMChain(llm=watsonx_ai_llm, prompt=second_prompt_template) logger.info("second chain set", extra={"action": "set llm chain", "chain name": "second chain"}) workflow = SequentialChain(chains=[first_chain, second_chain], input_variables=[])