watsonx longchain example

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=[])