bagoodex-search-v1

The model allows searching for answers to user queries on the internet. It supports two usage options:

1

Using six specialized API endpoints, each designed to search for only one specific type of information. These endpoints return structured responses, making them more suitable for integration into specialized services (e.g., a weather widget). Here are the types of information you can retrieve this way:

• Links

• Images

• Videos

• Weather details for a specified location

• Locations

• Knowledge about a topic, structured as a small knowledge base

See API references and examples on the subpages.

2

As a regular chat completion model (but searching on the internet): enter a query in the prompt and receive an internet-sourced answer, similar to asking a question on a search engine through a browser. See the parameters for this usage option in the API schema below. You'll also find an example of calling this API in code at the bottom of the page.

API Schema

Generate a conversational response using a language model.

Creates a chat completion using a language model, allowing interactive conversation by predicting the next response based on the given chat history. This is useful for AI-driven dialogue systems and virtual assistants.

post

/v1/chat/completions

Authorizations

Body

frequency_penaltynumber | nullable

logprobsboolean | nullable

top_logprobsnumber | nullable

max_tokensnumber · min: 1 · default: 512

ninteger | nullable

presence_penaltynumber | nullable

seedinteger · min: 1

streamboolean · default: false

temperaturenumber · max: 2

top_pnumber · min: 0.1 · max: 1

userstring

best_ofinteger | nullable

use_beam_searchboolean | nullable

top_kinteger | nullable

min_pnumber | nullable

repetition_penaltynumber | nullable

length_penaltynumber | nullable

early_stoppingboolean | nullable

ignore_eosboolean | nullable

min_tokensinteger | nullable

skip_special_tokensboolean | nullable

spaces_between_special_tokensany | nullable

echoboolean | nullable

If true, the new message will be prepended with the last message if they belong to the same role.

add_generation_promptboolean | nullable

If true, the generation prompt will be added to the chat template. This is a parameter used by chat template in tokenizer config of the model.

add_special_tokensboolean | nullable

If true, special tokens (e.g. BOS) will be added to the prompt on top of what is added by the chat template. For most models, the chat template takes care of adding the special tokens so this should be set to False (as is the default).

chat_templatestring | nullable

A Jinja template to use for this conversion. If this is not passed, the model's default chat template will be used instead.

include_stop_str_in_outputboolean | nullable

Whether to include the stop string in the output. This is only applied when the stop or stop_token_ids is set

guided_regexstring | nullable

If specified, the output will follow the regex pattern.

guided_grammarstring | nullable

If specified, the output will follow the context free grammar.

guided_whitespace_patternstring | nullable

If specified, will override the default whitespace pattern for guided json decoding.

modelundefined · enumrequired

Options: bagoodex/bagoodex-search-v1

guided_decoding_backendstring · enum | nullable

If specified, will override the default guided decoding backend of the server for this specific request. If set, must be either 'outlines' / 'lm-format-enforcer'

Options: outlines, lm-format-enforcer

logit_biasobject | nullable

stop_token_idsinteger[] | nullable

chat_template_kwargsobject | nullable

Additional kwargs to pass to the template renderer. Will be accessible by the chat template

guided_choicestring[] | nullable

documentsobject[] | nullable

stopany of

guided_jsonany of

If specified, the output will follow the JSON schema.

messagesobject[]required

response_formatone of

tool_choiceany ofDeprecated

toolsobject[]Deprecated

Responses

cURL

JavaScript

Python

HTTP

cURL

curl -L \
  --request POST \
  --url 'https://api.aimlapi.com/v1/chat/completions' \
  --header 'Authorization: Bearer JWT' \
  --header 'Content-Type: application/json' \
  --data '{
    "frequency_penalty": 1,
    "logprobs": true,
    "top_logprobs": 1,
    "max_tokens": 512,
    "n": 1,
    "presence_penalty": 1,
    "seed": 1,
    "stream": false,
    "temperature": 1,
    "top_p": 1,
    "user": "text",
    "best_of": 1,
    "use_beam_search": true,
    "top_k": 1,
    "min_p": 1,
    "repetition_penalty": 1,
    "length_penalty": 1,
    "early_stopping": true,
    "ignore_eos": true,
    "min_tokens": 1,
    "skip_special_tokens": true,
    "spaces_between_special_tokens": null,
    "echo": true,
    "add_generation_prompt": true,
    "add_special_tokens": true,
    "chat_template": "text",
    "include_stop_str_in_output": true,
    "guided_regex": "text",
    "guided_grammar": "text",
    "guided_whitespace_pattern": "text",
    "model": "bagoodex/bagoodex-search-v1",
    "guided_decoding_backend": "outlines",
    "logit_bias": {
      "ANY_ADDITIONAL_PROPERTY": 1
    },
    "stop_token_ids": [
      1
    ],
    "chat_template_kwargs": {
      "ANY_ADDITIONAL_PROPERTY": null
    },
    "guided_choice": [
      "text"
    ],
    "documents": [
      {
        "ANY_ADDITIONAL_PROPERTY": "text"
      }
    ],
    "stop": "text",
    "guided_json": "text",
    "messages": [
      {
        "content": "text",
        "role": "user"
      }
    ],
    "response_format": {
      "type": "text"
    },
    "tool_choice": "none",
    "tools": [
      {
        "type": "function",
        "function": {
          "description": "text",
          "name": "text",
          "parameters": null,
          "required": [
            "text"
          ]
        }
      }
    ]
  }'

201

201

No body

Example

Check how this call is made in the Python example:

import requests
from openai import OpenAI

# Insert your AIML API Key instead of <YOUR_API_KEY>:
API_KEY = '<YOUR_API_KEY>'
API_URL = 'https://api.aimlapi.com'

# Call the standart chat completion endpoint to get an ID
def complete_chat():
    client = OpenAI(
        base_url=API_URL,
        api_key=API_KEY,
    )    

    response = client.chat.completions.create(
        model="bagoodex/bagoodex-search-v1",
        messages=[
            {
                "role": "user",
                
                # Enter your query here
                "content": 'how to make a slingshot',
            },
        ],
    )
    
    
    print(response.choices[0].message.content)


# Run the function
complete_chat()

Response

To make a slingshot, you can follow the instructions provided in the two sources:

**Option 1: Make a Giant Slingshot**

* Start by cutting two 2x4's to a length of 40 inches each, which will be the main arms of the slingshot.
* Attach the arms to a base made of plywood using screws, and then add side braces to support the arms.
* Install an exercise band as the launching mechanism, making sure to tighten it to achieve the desired distance.
* Add a cross brace to keep the arms rigid and prevent them from spreading or caving in.

**Option 2: Make a Stick Slingshot**

* Find a sturdy, Y-shaped stick and break it down to the desired shape.
* Cut notches on the ends of the stick to hold the rubber bands in place.
* Create a pouch by folding a piece of fabric in half and then half again, and then cutting small holes for the rubber bands.        
* Thread the rubber bands through the holes and tie them securely to the stick using thread.
* Decorate the slingshot with coloured yarn or twine if desired.

You can choose to make either a giant slingshot or a stick slingshot, depending on your preference and the materials available.