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Running Pipelines

Execute deployed pipelines via CLI, HTTP API, or programmatically.

Quick Reference

hayhooks pipeline run my_pipeline --param 'query="What is Haystack?"'

curl -X POST http://localhost:1416/my_pipeline/run \
  -H 'Content-Type: application/json' \
  -d '{"query":"What is Haystack?"}'

import requests

resp = requests.post(
    "http://localhost:1416/my_pipeline/run",
    json={"query": "What is Haystack?"}
)
print(resp.json())

import httpx
import asyncio

async def main():
    async with httpx.AsyncClient() as client:
        r = await client.post(
            "http://localhost:1416/my_pipeline/run",
            json={"query": "What is Haystack?"}
        )
        print(r.json())

asyncio.run(main())

curl -X POST http://localhost:1416/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "my_pipeline",
    "messages": [{"role": "user", "content": "What is Haystack?"}]
  }'

See Open WebUI Integration for setup details.

For more CLI examples, see CLI Commands.

File Uploads

CLI

# Single file
hayhooks pipeline run my_pipeline --file document.pdf --param 'query="Summarize"'

# Multiple files
hayhooks pipeline run my_pipeline --file doc1.pdf --file doc2.txt

# Directory
hayhooks pipeline run my_pipeline --dir ./documents

HTTP

curl -X POST http://localhost:1416/my_pipeline/run \
  -F 'files=@document.pdf' \
  -F 'query="Summarize this document"'

See File Upload Support for implementation details.

Python Integration

Requests

import requests

resp = requests.post(
    "http://localhost:1416/my_pipeline/run",
    json={"query": "What is Haystack?"}
)
print(resp.json())

Async (httpx)

import httpx
import asyncio

async def main():
    async with httpx.AsyncClient() as client:
        r = await client.post(
            "http://localhost:1416/my_pipeline/run",
            json={"query": "What is Haystack?"}
        )
        print(r.json())

asyncio.run(main())

Streaming

Implement run_chat_completion or run_chat_completion_async in your wrapper. See OpenAI Compatibility for details.

Error Handling & Retry Logic

import requests
from requests.exceptions import RequestException
import time

def run_with_retry(pipeline_name, params, max_retries=3):
    url = f"http://localhost:1416/{pipeline_name}/run"

    for attempt in range(max_retries):
        try:
            response = requests.post(url, json=params)
            response.raise_for_status()
            return response.json()
        except RequestException as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(2 ** attempt)  # Exponential backoff

Logging

Add logging to your pipeline wrappers:

from hayhooks import log

class PipelineWrapper(BasePipelineWrapper):
    def run_api(self, query: str) -> str:
        log.info(f"Processing query: {query}")
        result = self.pipeline.run({"prompt": {"query": query}})
        log.info("Pipeline completed")
        return result["llm"]["replies"][0]

See Logging Reference for details.

Next Steps