
Most common position in a pipeline	Flexible
Mandatory run variables	“sources”: A list of OpenAPI specification sources, which can be file paths or `ByteStream` objects
Output variables	“functions”: A list of JSON OpenAI function calling definitions objects. For each path definition in OpenAPI specification, a corresponding OpenAI function calling definitions is generated. “openapi_specs”: A list of JSON/YAML objects with references resolved. Such OpenAPI spec (with references resolved) can, in turn, be used as input to OpenAPIServiceConnector.
API reference	Converters
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/openapi_functions.py

Overview

OpenAPIServiceToFunctions transforms OpenAPI service specifications into an OpenAI function calling format. It takes an OpenAPI specification, processes it to extract function definitions, and formats these definitions to be compatible with OpenAI's function calling JSON format.

OpenAPIServiceToFunctions is valuable when used together with OpenAPIServiceConnector component. It converts OpenAPI specifications into definitions suitable for OpenAI's function calls, allowing OpenAPIServiceConnector to handle input parameters for the OpenAPI specification and facilitate their use in REST API calls through OpenAPIServiceConnector.

To use OpenAPIServiceToFunctions, you need to install an optional jsonref dependency with:

pip install jsonref

OpenAPIServiceToFunctions component doesn’t have any init parameters.

Usage

On its own

This component is primarily meant to be used in pipelines. Using this component alone is useful when you want to convert OpenAPI specification into OpenAI's function call specification and then perhaps save it in a file and subsequently use it in function calling.

In a pipeline

In a pipeline context, OpenAPIServiceToFunctions is most valuable when used alongside OpenAPIServiceConnector. For instance, let’s consider integrating serper.dev search engine bridge into a pipeline. OpenAPIServiceToFunctions retrieves the OpenAPI specification of Serper from https://bit.ly/serper_dev_spec, converts this specification into a format that OpenAI's function calling mechanism can understand, and then seamlessly passes this translated specification as generation_kwargs for LLM function calling invocation.

📘
To run the following code snippet, note that you have to have your own Serper and OpenAI API keys.

import json
import requests

from typing import Dict, Any, List
from haystack import Pipeline
from haystack.components.generators.utils import print_streaming_chunk
from haystack.components.converters import OpenAPIServiceToFunctions, OutputAdapter
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.connectors import OpenAPIServiceConnector
from haystack.components.fetchers import LinkContentFetcher
from haystack.dataclasses import ChatMessage, ByteStream
from haystack.utils import Secret

def prepare_fc_params(openai_functions_schema: Dict[str, Any]) -> Dict[str, Any]:
    return {
        "tools": [{
            "type": "function",
            "function": openai_functions_schema
        }],
        "tool_choice": {
            "type": "function",
            "function": {"name": openai_functions_schema["name"]}
        }
    }

system_prompt = requests.get("https://bit.ly/serper_dev_system_prompt").text
serper_spec = requests.get("https://bit.ly/serper_dev_spec").text

pipe = Pipeline()
pipe.add_component("spec_to_functions", OpenAPIServiceToFunctions())
pipe.add_component("functions_llm", OpenAIChatGenerator(api_key=Secret.from_token(llm_api_key), model="gpt-3.5-turbo-0613"))
pipe.add_component("openapi_container", OpenAPIServiceConnector())
pipe.add_component("a1", OutputAdapter("{{functions[0] | prepare_fc}}", Dict[str, Any], {"prepare_fc": prepare_fc_params}))
pipe.add_component("a2", OutputAdapter("{{specs[0]}}", Dict[str, Any]))
pipe.add_component("a3", OutputAdapter("{{system_message + service_response}}", List[ChatMessage]))
pipe.add_component("llm", OpenAIChatGenerator(api_key=Secret.from_token(llm_api_key), model="gpt-4-1106-preview", streaming_callback=print_streaming_chunk))

pipe.connect("spec_to_functions.functions", "a1.functions")
pipe.connect("spec_to_functions.openapi_specs", "a2.specs")
pipe.connect("a1", "functions_llm.generation_kwargs")
pipe.connect("functions_llm.replies", "openapi_container.messages")
pipe.connect("a2", "openapi_container.service_openapi_spec")
pipe.connect("openapi_container.service_response", "a3.service_response")
pipe.connect("a3", "llm.messages")

user_prompt = "Why was Sam Altman ousted from OpenAI?"

result = pipe.run(data={"functions_llm": {"messages":[ChatMessage.from_system("Only do function calling"), ChatMessage.from_user(user_prompt)]}, 
                        "openapi_container": {"service_credentials": serper_dev_key},
                        "spec_to_functions": {"sources": [ByteStream.from_string(serper_spec)]},
                        "a3": {"system_message": [ChatMessage.from_system(system_prompt)]}})

>Sam Altman was ousted from OpenAI on November 17, 2023, following 
>a "deliberative review process" by the board of directors. The board concluded 
>that he was not "consistently candid in his communications". However, he 
>returned as CEO just days after his ouster.