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feat: add LLM output for the AI ML component to use ChatOpenAI base model #3230

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merged 4 commits into from
Aug 7, 2024
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feat: add LLM output for the AI ML component to use ChatOpenAI base model #3230

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f160979
fix: add LLM output for the AI ML component, using ChatOpenAI base mo…
vasconceloscezar Aug 7, 2024
ef12dd0
[autofix.ci] apply automated fixes
autofix-ci[bot] Aug 7, 2024
2e7cc5d
fix: docs
vasconceloscezar Aug 7, 2024
c1cd252
fix: unused variables
vasconceloscezar Aug 7, 2024
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206 changes: 92 additions & 114 deletions src/backend/base/langflow/components/models/AIMLModel.py
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Original file line number Diff line number Diff line change
@@ -1,148 +1,126 @@
import json
import httpx
from langflow.base.models.aiml_constants import AIML_CHAT_MODELS
from langflow.custom.custom_component.component import Component
import operator
from functools import reduce

from langflow.inputs.inputs import FloatInput, IntInput, MessageInput, SecretStrInput
from langflow.schema.message import Message
from langflow.template.field.base import Output
from loguru import logger
from langflow.field_typing.range_spec import RangeSpec
from langchain_openai import ChatOpenAI
from pydantic.v1 import SecretStr

from langflow.base.models.aiml_constants import AIML_CHAT_MODELS
from langflow.base.models.model import LCModelComponent
from langflow.field_typing import LanguageModel
from langflow.inputs import (
BoolInput,
DictInput,
DropdownInput,
FloatInput,
IntInput,
SecretStrInput,
StrInput,
)


class AIMLModelComponent(Component):
display_name = "AI/ML API"
description = "Generates text using the AI/ML API"
icon = "AI/ML"
chat_completion_url = "https://api.aimlapi.com/v1/chat/completions"

outputs = [
Output(display_name="Text", name="text_output", method="make_request"),
]
class AIMLModelComponent(LCModelComponent):
display_name = "AIML"
description = "Generates text using AIML LLMs."
icon = "AIML"
name = "AIMLModel"
documentation = "https://docs.aimlapi.com/api-reference/examples"

inputs = [
DropdownInput(
name="model_name",
display_name="Model Name",
options=AIML_CHAT_MODELS,
required=True,
),
SecretStrInput(
name="aiml_api_key",
display_name="AI/ML API Key",
value="AIML_API_KEY",
),
MessageInput(name="input_value", display_name="Input", required=True),
inputs = LCModelComponent._base_inputs + [
IntInput(
name="max_tokens",
display_name="Max Tokens",
advanced=True,
info="The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
range_spec=RangeSpec(min=0, max=128000),
),
StrInput(
name="stop_tokens",
display_name="Stop Tokens",
info="Comma-separated list of tokens to signal the model to stop generating text.",
DictInput(name="model_kwargs", display_name="Model Kwargs", advanced=True),
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Hi @vasconceloscezar thanks for the improvements. Sorry, I was out of town last week or would have taken a look then.

You may be more familiar with the AI/ML API than I am, so just wanted to ask some clarifying questions here.

IIUC, the AI/ML API only supports certain parameters that are common to all supported models (https://docs.aimlapi.com/api-reference/parameters). Does allowing model_kwargs here give the user the false(?) impression that model-specific args are allowed?

BoolInput(
name="json_mode",
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I see this error using this change out of the box:

/Users/jordan.frazier/Documents/Langflow/langflow/.venv/lib/python3.11/site-packages/langchain_core/utils/utils.py:234: UserWarning: WARNING! json_mode is not default parameter.
                json_mode was transferred to model_kwargs.
                Please confirm that json_mode is what you intended.
  warnings.warn(
[08/12/24 09:44:42] ERROR    2024-08-12 09:44:42 - ERROR    - base - Completions.create() got an unexpected keyword argument 'json_mode'                                                               base.py:687
  • I believe json_mode is set differently using ChatOpenAI.
  • Did you test this and it worked for you? Just want to make sure I didn't accidentally change anything prior to testing.

display_name="JSON Mode",
advanced=True,
info="If True, it will output JSON regardless of passing a schema.",
),
IntInput(
name="top_k",
display_name="Top K",
info="Limits token selection to top K. (Default: 40)",
DictInput(
name="output_schema",
is_list=True,
display_name="Schema",
advanced=True,
info="The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
),
FloatInput(
name="top_p",
display_name="Top P",
info="Works together with top-k. (Default: 0.9)",
advanced=True,
DropdownInput(
name="model_name",
display_name="Model Name",
advanced=False,
options=AIML_CHAT_MODELS,
value=AIML_CHAT_MODELS[0],
),
FloatInput(
name="repeat_penalty",
display_name="Repeat Penalty",
info="Penalty for repetitions in generated text. (Default: 1.1)",
StrInput(
name="aiml_api_base",
display_name="AIML API Base",
advanced=True,
info="The base URL of the OpenAI API. Defaults to https://api.aimlapi.com . You can change this to use other APIs like JinaChat, LocalAI e Prem.",
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I believe the intent of this component was to only hit the AI/ML API, so would lean towards removing this parameter. If users want to use a different base, we can add another component for that or use the generic OpenAI component. (I'm glad using the base_url here works using the aiml base - I tried with the OpenAIEmbeddings class and had issues).

),
FloatInput(
name="temperature",
display_name="Temperature",
value=0.2,
info="Controls the creativity of model responses.",
SecretStrInput(
name="api_key",
display_name="AIML API Key",
info="The AIML API Key to use for the OpenAI model.",
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Shouldn't be specific to the OpenAI model

advanced=False,
value="AIML_API_KEY",
),
StrInput(
name="system_message",
display_name="System Message",
info="System message to pass to the model.",
FloatInput(name="temperature", display_name="Temperature", value=0.1),
IntInput(
name="seed",
display_name="Seed",
info="The seed controls the reproducibility of the job.",
advanced=True,
value=1,
),
]

def make_request(self) -> Message:
api_key = SecretStr(self.aiml_api_key) if self.aiml_api_key else None

headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key.get_secret_value()}" if api_key else "",
}

messages = []
if self.system_message:
messages.append({"role": "system", "content": self.system_message})
def build_model(self) -> LanguageModel: # type: ignore[type-var]
output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})
aiml_api_key = self.api_key
temperature = self.temperature
model_name: str = self.model_name
max_tokens = self.max_tokens
model_kwargs = self.model_kwargs or {}
aiml_api_base = self.aiml_api_base or "https://api.aimlapi.com"
json_mode = bool(output_schema_dict) or self.json_mode

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src/backend/base/langflow/components/models/AIMLModel.py:89:9: F841 Local variable `json_mode` is assigned to but never used
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Seems like output_schema exists only to determine whether to set json_mode. I do have the concern that even if fixed, the AI/ML API wouldn't support this (or, only would for specific models, which may be confusing for users).

seed = self.seed

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src/backend/base/langflow/components/models/AIMLModel.py:90:9: F841 Local variable `seed` is assigned to but never used

if self.input_value:
if isinstance(self.input_value, Message):
# Though we aren't using langchain here, the helper method is useful
message = self.input_value.to_lc_message()
if message.type == "human":
messages.append({"role": "user", "content": message.content})
else:
raise ValueError(f"Expected user message, saw: {message.type}")
else:
raise TypeError(f"Expected Message type, saw: {type(self.input_value)}")
if isinstance(aiml_api_key, SecretStr):
openai_api_key = aiml_api_key.get_secret_value()
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nit: var name should stay aiml_api_key

else:
raise ValueError("Please provide an input value")
openai_api_key = aiml_api_key

payload = {
"model": self.model_name,
"messages": messages,
"max_tokens": self.max_tokens or None,
"temperature": self.temperature or 0.2,
"top_k": self.top_k or 40,
"top_p": self.top_p or 0.9,
"repeat_penalty": self.repeat_penalty or 1.1,
"stop_tokens": self.stop_tokens or None,
}
model = ChatOpenAI(
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Was curious about this usage - does using OpenAI's implementation have any compatibility issues using other models?

model=model_name,
temperature=temperature,
api_key=openai_api_key,
base_url=aiml_api_base,
max_tokens=max_tokens or None,
**model_kwargs,
)

try:
response = httpx.post(self.chat_completion_url, headers=headers, json=payload)
try:
response.raise_for_status()
result_data = response.json()
choice = result_data["choices"][0]
result = choice["message"]["content"]
except httpx.HTTPStatusError as http_err:
logger.error(f"HTTP error occurred: {http_err}")
raise http_err
except httpx.RequestError as req_err:
logger.error(f"Request error occurred: {req_err}")
raise req_err
except json.JSONDecodeError:
logger.warning("Failed to decode JSON, response text: {response.text}")
result = response.text
except KeyError as key_err:
logger.warning(f"Key error: {key_err}, response content: {result_data}")
raise key_err
return model # type: ignore

def _get_exception_message(self, e: Exception):
"""
Get a message from an OpenAI exception.

self.status = result
except httpx.TimeoutException:
return Message(text="Request timed out.")
except Exception as exc:
logger.error(f"Error: {exc}")
raise
Args:
exception (Exception): The exception to get the message from.

return Message(text=result)
Returns:
str: The message from the exception.
"""
try:
from openai.error import BadRequestError
except ImportError:
return None
if isinstance(e, BadRequestError):
message = e.json_body.get("error", {}).get("message", "") # type: ignore
if message:
return message
return None
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