Observability for Predibase with Opik

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import os
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import getpass
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if "PREDIBASE_API_TOKEN" not in os.environ:
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    os.environ["PREDIBASE_API_TOKEN"] = getpass.getpass("Enter your Predibase API token: ")

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import os
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from langchain_community.llms import Predibase
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from opik.integrations.langchain import OpikTracer
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os.environ["OPIK_PROJECT_NAME"] = "predibase-integration-demo"
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# Create the Opik tracer
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opik_tracer = OpikTracer(tags=["predibase", "langchain"])
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# Create Predibase model
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model = Predibase(
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    model="mistral-7b",
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    predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"),
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)
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# Test the model with Opik tracing
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response = model.invoke(
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    "Can you recommend me a nice dry wine?",
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    config={
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        "temperature": 0.5,
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        "max_new_tokens": 1024,
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        "callbacks": [opik_tracer]
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    }
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)
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print(response)

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model = Predibase(
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    model="mistral-7b",
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    predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"),
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).with_config({"callbacks": [opik_tracer]})

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from langchain.chains import LLMChain, SimpleSequentialChain
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from langchain_core.prompts import PromptTemplate
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# Synopsis chain
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template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
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Title: {title}
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Playwright: This is a synopsis for the above play:"""
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prompt_template = PromptTemplate(input_variables=["title"], template=template)
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synopsis_chain = LLMChain(llm=model, prompt=prompt_template)
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# Review chain
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template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.
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Play Synopsis:
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{synopsis}
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Review from a New York Times play critic of the above play:"""
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prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)
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review_chain = LLMChain(llm=model, prompt=prompt_template)
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# Overall chain
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overall_chain = SimpleSequentialChain(
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    chains=[synopsis_chain, review_chain], verbose=True
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)
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# Run the chain with Opik tracing
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review = overall_chain.run("Tragedy at sunset on the beach", callbacks=[opik_tracer])
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print(review)

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traces = opik_tracer.created_traces()
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print("Collected trace IDs:", [trace.id for trace in traces])
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# Flush traces to ensure all data is logged
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opik_tracer.flush()

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fine_tuned_model = Predibase(
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    model="my-base-LLM",
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    predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"),
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    predibase_sdk_version=None,
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    adapter_id="my-finetuned-adapter-id",
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    adapter_version=1,
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    **{
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        "api_token": os.environ.get("HUGGING_FACE_HUB_TOKEN"),
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        "max_new_tokens": 5,
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    },
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)
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# Configure the Opik tracer
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fine_tuned_model = fine_tuned_model.with_config({"callbacks": [opik_tracer]})
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# Invoke the fine-tuned model
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response = fine_tuned_model.invoke(
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    "Can you help categorize the following emails into positive, negative, and neutral?",
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    **{"temperature": 0.5, "max_new_tokens": 1024},
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)
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print(response)
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# Final flush to ensure all traces are logged
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opik_tracer.flush()