E wehewehe i kahi ʻōnaehana AI

ai_system = lc.AISystem( inoa = "my_ai_system", model=lc.Model(inoa="my_model", type="transformer", ala="/path/to/model"), prompt=lc.Prompt(inoa = "my_prompt", kikokikona = "He laʻana kaha kēia") )

Hoʻonohonoho i ka hoʻokō ʻana i ka pipeline data a me ka ʻōnaehana AI

af.schedule_task( task_id="my_task", dag_id="my_dag", pipeline=ʻikepili_pipeline, ʻōnaehana=ai_system )

Ma kēia hiʻohiʻona, wehewehe mākou i kahi pipeline data a me kahi ʻōnaehana AI e hoʻohana ana iā DSPy a me LangChain, a laila hoʻonohonoho i ka hoʻokō ʻana i kēia mau ʻāpana me ka Apache Airflow.

** 5-10 kiʻekiʻe loa i hoʻohana ʻia i nā kauoha/nā ʻāpana**

Eia nā 5-10 kiʻekiʻe loa i hoʻohana ʻia i nā kauoha / snippets no DSPy a me LangChain:

1. `dsp.Pipeline(inoa = "my_pipeline", nā kumu=[kumu], nā mea hana=[processor], sinks=[sink])`
2. `lc.Model(inoa="my_model", type="transformer", ala="/path/to/model")`
3. `lc.Prompt(inoa = "my_prompt", kikokikona = "He laʻana hoʻokō kēia")`
4. `dsp.Source(inoa="my_data_source", type="csv", ala="/path/to/data.csv")`
5. `dsp.Processor(inoa="my_data_processor", type="pandas", func=lambda x: x.dropna())`
6. `dsp.Sink(inoa="my_data_sink", type="csv", ala="/path/to/output.csv")`
7. `lc.AISystem(inoa = "my_ai_system", model=model, prompt=prompt)`
8. `af.schedule_task(task_id="my_task", dag_id="my_dag", pipeline=pipeline, system=system)`
9. `dsp.run_pipeline(pipeline)`
10. `lc.run_system(system)`

**Hōʻikeʻike**

Eia kahi hiʻohiʻona o ka DSPy a me LangChain frameworks me ka hoʻohana ʻana iā Chart.js:
```json
{
  "type": "pā",
  "ʻikepili": {
    "labels": ["DSPy", "LangChain"],
    "mau ʻikepili": [
      {
        "label": "Nā hiʻohiʻona",
        "ʻikepili": [10, 15],
        "ʻulaʻula": [
          "rgba(255, 99, 132, 0.2)",
          "rgba(54, 162, 235, 0.2)"
        ],
        "BorderColor": [
          "rgba(255, 99, 132, 1)",
          "rgba(54, 162, 235, 1)"
        ],
        "BorderWidth": 1
      }
    ]
  },
  "koho": {}
}

Hōʻike kēia hiʻohiʻona i ka helu o nā hiʻohiʻona ma DSPy a me LangChain.

Hoʻoholo

I ka hopena, ʻo DSPy a me LangChain ʻelua mau papa hana ikaika e hiki ai ke kūkulu i nā ʻōnaehana polokalamu paʻa a hiki ke hoʻonui ʻia i ka makahiki o AI. Ma ka hoʻohana ʻana i kēia mau papa hana, hiki i nā mea hoʻomohala ke hana i nā paipu LLM nui i maʻalahi e mālama, scalable, a kūpaʻa i nā hemahema. Me ke kōkua o ka orchestration a me nā mea hana automation e like me Apache Airflow a me Kubernetes, hiki i nā mea hoʻomohala ke hoʻonohonoho i ka hoʻokō ʻana i kēia mau pipelines a me nā ʻōnaehana, e maʻalahi ai ka hoʻokele ʻana i nā ʻōnaehana AI paʻakikī.

Ka hana e hiki mai ana

Loaʻa nā hana e hiki mai ana:

1. Hoʻomohala i nā hiʻohiʻona hou aku no DSPy a me LangChain.
2. Hoʻonui i ka hana a me ka scalability o DSPy a me LangChain.
3. Ke hoʻomohala nei i nā mea hoʻokani a me nā mea hana automation no DSPy a me LangChain.
4. Ke hana ʻana i nā hiʻohiʻona hou aʻe a me nā dashboards no DSPy a me LangChain.

References

1. Nā palapala DSPy: https://dspy.readthedocs.io/en/latest/
2. Nā palapala LangChain: https://langchain.readthedocs.io/en/latest/
3. ʻO Apache Airflow palapala: https://airflow.apache.org/docs/apache-airflow/stable/index.html
4. Nā palapala Kubernetes: https://kubernetes.io/docs/home/