![logo](https://raw.githubusercontent.com/deepset-ai/haystack-integrations/main/logos/monsterapi.png)
Integration: MonsterAPI
Use open Language Models served by MonsterAPI
Table of Contents
Overview
MonsterAPI provides access to powerful language models designed for various text generation tasks. With the MonsterAPI integration, you can leverage these models within the Haystack framework for enhanced natural language processing capabilities.
To start using MonsterAPI, sign up for an API key here. This key provides access to the MonsterAPI, which supports rapid inference and customization through various parameters.
Usage
MonsterAPI’s API is OpenAI compatible, making it easy to use within Haystack via OpenAI Generators.
Using Generator
Here’s an example of using a model served via MonsterAPI to perform question answering on a web page. You need to set the environment variable MONSTER_API_KEY
and choose a
compatible model.
from haystack import Pipeline
from haystack.utils import Secret
from haystack.components.fetchers import LinkContentFetcher
from haystack.components.converters import HTMLToDocument
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator
fetcher = LinkContentFetcher()
converter = HTMLToDocument()
prompt_template = """
According to the contents of this website:
{% for document in documents %}
{{document.content}}
{% endfor %}
Answer the given question: {{query}}
Answer:
"""
prompt_builder = PromptBuilder(template=prompt_template)
llm = OpenAIGenerator(
api_key=Secret.from_env_var("MONSTER_API_KEY"),
api_base_url="https://llm.monsterapi.ai/v1/",
model="microsoft/Phi-3-mini-4k-instruct",
generation_kwargs = {"max_tokens": 256}
)
pipeline = Pipeline()
pipeline.add_component("fetcher", fetcher)
pipeline.add_component("converter", converter)
pipeline.add_component("prompt", prompt_builder)
pipeline.add_component("llm", llm)
pipeline.connect("fetcher.streams", "converter.sources")
pipeline.connect("converter.documents", "prompt.documents")
pipeline.connect("prompt.prompt", "llm.prompt")
result = pipeline.run({"fetcher": {"urls": ["https://developer.monsterapi.ai/docs/"]},
"prompt": {"query": "What are the features of MonsterAPI?"}})
print(result["llm"]["replies"][0])
Using ChatGenerator
Here’s an example of engaging in a multi-turn conversation with a MonsterAPI model. You need to set the environment variable MONSTER_API_KEY
and choose a
compatible model.
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.utils import Secret
generator = OpenAIChatGenerator(
api_key=Secret.from_env_var("MONSTER_API_KEY"),
api_base_url="https://llm.monsterapi.ai/v1/",
model="microsoft/Phi-3-mini-4k-instruct",
generation_kwargs = {"max_tokens": 256}
)
messages = []
while True:
msg = input("Enter your message or Q to exit\n ")
if msg=="Q":
break
messages.append(ChatMessage.from_user(msg))
response = generator.run(messages=messages)
assistant_resp = response['replies'][0]
print(assistant_resp.content)
messages.append(assistant_resp)