AntropicModel: add prefill field for structured outputs, AmazonBedrock: New component format
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namastex888
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d9eb3decf1
2 changed files with 151 additions and 157 deletions
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@ -1,33 +1,20 @@
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from typing import Optional
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from langchain_community.chat_models.bedrock import BedrockChat
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from langchain_community.chat_models.bedrock import BedrockChat
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from langflow.base.constants import STREAM_INFO_TEXT
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from langflow.base.constants import STREAM_INFO_TEXT
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from langflow.base.models.model import LCModelComponent
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from langflow.base.models.model import LCModelComponent
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from langflow.field_typing import Text
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from langflow.field_typing import BaseLanguageModel, Text
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from langflow.inputs import BoolInput, DictInput, DropdownInput, StrInput
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from langflow.template import Output
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class AmazonBedrockComponent(LCModelComponent):
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class AmazonBedrockComponent(LCModelComponent):
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display_name: str = "Amazon Bedrock"
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display_name: str = "Amazon Bedrock"
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description: str = "Generate text using Amazon Bedrock LLMs."
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description: str = "Generate text using Amazon Bedrock LLMs."
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icon = "Amazon"
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icon = "Amazon"
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field_order = [
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inputs = [
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"model_id",
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StrInput(name="input_value", display_name="Input", input_types=["Text", "Data", "Prompt"]),
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"credentials_profile_name",
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DropdownInput(
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"region_name",
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name="model_id",
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"model_kwargs",
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display_name="Model Id",
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"endpoint_url",
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options=[
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"cache",
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"stream",
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"input_value",
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"system_message",
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]
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def build_config(self):
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return {
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"model_id": {
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"display_name": "Model Id",
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"options": [
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"amazon.titan-text-express-v1",
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"amazon.titan-text-express-v1",
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"amazon.titan-text-lite-v1",
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"amazon.titan-text-lite-v1",
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"amazon.titan-embed-text-v1",
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"amazon.titan-embed-text-v1",
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@ -49,40 +36,43 @@ class AmazonBedrockComponent(LCModelComponent):
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"mistral.mistral-7b-instruct-v0:2",
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"mistral.mistral-7b-instruct-v0:2",
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"mistral.mixtral-8x7b-instruct-v0:1",
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"mistral.mixtral-8x7b-instruct-v0:1",
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],
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],
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},
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value="anthropic.claude-instant-v1",
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"credentials_profile_name": {"display_name": "Credentials Profile Name"},
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),
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"endpoint_url": {"display_name": "Endpoint URL"},
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StrInput(name="credentials_profile_name", display_name="Credentials Profile Name"),
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"region_name": {"display_name": "Region Name"},
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StrInput(name="region_name", display_name="Region Name"),
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"model_kwargs": {
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DictInput(name="model_kwargs", display_name="Model Kwargs", advanced=True),
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"display_name": "Model Kwargs",
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StrInput(name="endpoint_url", display_name="Endpoint URL"),
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"advanced": True,
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BoolInput(name="cache", display_name="Cache"),
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},
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StrInput(
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"cache": {"display_name": "Cache"},
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name="system_message",
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"input_value": {"display_name": "Input", "input_types": ["Text", "Data", "Prompt"]},
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display_name="System Message",
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"system_message": {
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info="System message to pass to the model.",
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"display_name": "System Message",
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advanced=True,
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"info": "System message to pass to the model.",
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),
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"advanced": True,
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BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
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},
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]
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"stream": {
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outputs = [
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"display_name": "Stream",
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Output(display_name="Text", name="text_output", method="text_response"),
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"info": STREAM_INFO_TEXT,
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Output(display_name="Language Model", name="model_output", method="build_model"),
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"advanced": True,
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]
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},
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}
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def build(
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def text_response(self) -> Text:
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self,
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input_value = self.input_value
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input_value: Text,
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stream = self.stream
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system_message: Optional[str] = None,
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system_message = self.system_message
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model_id: str = "anthropic.claude-instant-v1",
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output = self.build_model()
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credentials_profile_name: Optional[str] = None,
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result = self.get_chat_result(output, stream, input_value, system_message)
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region_name: Optional[str] = None,
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self.status = result
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model_kwargs: Optional[dict] = None,
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return result
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endpoint_url: Optional[str] = None,
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cache: Optional[bool] = None,
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def build_model(self) -> BaseLanguageModel:
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stream: bool = False,
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model_id = self.model_id
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) -> Text:
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credentials_profile_name = self.credentials_profile_name
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region_name = self.region_name
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model_kwargs = self.model_kwargs
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endpoint_url = self.endpoint_url
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cache = self.cache
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stream = self.stream
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try:
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try:
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output = BedrockChat(
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output = BedrockChat(
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credentials_profile_name=credentials_profile_name,
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credentials_profile_name=credentials_profile_name,
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@ -92,8 +82,8 @@ class AmazonBedrockComponent(LCModelComponent):
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endpoint_url=endpoint_url,
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endpoint_url=endpoint_url,
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streaming=stream,
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streaming=stream,
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cache=cache,
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cache=cache,
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) # type: ignore
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)
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except Exception as e:
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except Exception as e:
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raise ValueError("Could not connect to AmazonBedrock API.") from e
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raise ValueError("Could not connect to AmazonBedrock API.") from e
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return output
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return self.get_chat_result(output, stream, input_value, system_message)
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@ -5,30 +5,31 @@ from pydantic.v1 import SecretStr
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from langflow.base.constants import STREAM_INFO_TEXT
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from langflow.base.constants import STREAM_INFO_TEXT
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from langflow.base.models.model import LCModelComponent
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from langflow.base.models.model import LCModelComponent
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from langflow.field_typing import Text
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from langflow.field_typing import BaseLanguageModel, Text
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from langflow.inputs import BoolInput, DropdownInput, FloatInput, IntInput, SecretStrInput, StrInput
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from langflow.template import Output
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class AnthropicLLM(LCModelComponent):
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class AnthropicModelComponent(LCModelComponent):
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display_name: str = "Anthropic"
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display_name = "Anthropic"
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description: str = "Generate text using Anthropic Chat&Completion LLMs."
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description = "Generate text using Anthropic Chat&Completion LLMs with prefill support."
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icon = "Anthropic"
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icon = "Anthropic"
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field_order = [
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inputs = [
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"model",
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StrInput(
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"anthropic_api_key",
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name="input_value",
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"max_tokens",
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display_name="Input",
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"temperature",
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input_types=["Text", "Data", "Prompt", "Message"]),
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"anthropic_api_url",
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IntInput(
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"input_value",
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name="max_tokens",
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"system_message",
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display_name="Max Tokens",
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"stream",
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advanced=True,
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]
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info="The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
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),
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def build_config(self):
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DropdownInput(
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return {
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name="model",
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"model": {
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display_name="Model Name",
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"display_name": "Model Name",
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options=[
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"options": [
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"claude-3-opus-20240229",
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"claude-3-opus-20240229",
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"claude-3-sonnet-20240229",
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"claude-3-sonnet-20240229",
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"claude-3-haiku-20240307",
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"claude-3-haiku-20240307",
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@ -37,63 +38,65 @@ class AnthropicLLM(LCModelComponent):
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"claude-instant-1.2",
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"claude-instant-1.2",
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"claude-instant-1",
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"claude-instant-1",
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],
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],
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"info": "https://python.langchain.com/docs/integrations/chat/anthropic",
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info="https://python.langchain.com/docs/integrations/chat/anthropic",
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"required": True,
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value="claude-3-opus-20240229",
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"value": "claude-3-opus-20240229",
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),
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},
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SecretStrInput(
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"anthropic_api_key": {
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name="anthropic_api_key",
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"display_name": "Anthropic API Key",
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display_name="Anthropic API Key",
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"required": True,
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info="Your Anthropic API key.",
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"password": True,
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),
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"info": "Your Anthropic API key.",
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FloatInput(name="temperature", display_name="Temperature", value=0.1),
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},
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StrInput(
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"max_tokens": {
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name="anthropic_api_url",
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"display_name": "Max Tokens",
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display_name="Anthropic API URL",
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"advanced": True,
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advanced=True,
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"info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
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info="Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
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},
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),
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"temperature": {
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BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
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"display_name": "Temperature",
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StrInput(
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"field_type": "float",
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name="system_message",
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"value": 0.1,
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display_name="System Message",
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},
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info="System message to pass to the model.",
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"anthropic_api_url": {
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advanced=True,
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"display_name": "Anthropic API URL",
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),
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"advanced": True,
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StrInput(
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"info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
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name="prefill",
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},
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display_name="Prefill",
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"code": {"show": False},
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info="Prefill text to guide the model's response.",
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"input_value": {"display_name": "Input", "input_types": ["Text", "Data", "Prompt"]},
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advanced=True,
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"stream": {
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),
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"display_name": "Stream",
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]
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"advanced": True,
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outputs = [
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"info": STREAM_INFO_TEXT,
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Output(display_name="Text", name="text_output", method="text_response"),
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},
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Output(display_name="Language Model", name="model_output", method="build_model"),
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"system_message": {
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]
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"display_name": "System Message",
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"advanced": True,
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"info": "System message to pass to the model.",
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},
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}
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def build(
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def text_response(self) -> Text:
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self,
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input_value = self.input_value
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model: str,
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stream = self.stream
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input_value: Text,
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system_message = self.system_message
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system_message: Optional[str] = None,
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prefill = self.prefill
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anthropic_api_key: Optional[str] = None,
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output = self.build_model()
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max_tokens: Optional[int] = 1000,
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messages = [
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temperature: Optional[float] = None,
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("system", system_message),
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anthropic_api_url: Optional[str] = None,
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("human", input_value),
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stream: bool = False,
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("assistant", prefill),
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) -> Text:
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]
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# Set default API endpoint if not provided
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result = output.invoke(messages)
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if not anthropic_api_url:
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self.status = prefill + result.content
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anthropic_api_url = "https://api.anthropic.com"
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return prefill + result.content
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def build_model(self) -> BaseLanguageModel:
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model = self.model
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anthropic_api_key = self.anthropic_api_key
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max_tokens = self.max_tokens
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temperature = self.temperature
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anthropic_api_url = self.anthropic_api_url or "https://api.anthropic.com"
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try:
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try:
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output = ChatAnthropic(
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output = ChatAnthropic(
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model_name=model,
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model=model,
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anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None),
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anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None),
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max_tokens_to_sample=max_tokens, # type: ignore
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max_tokens_to_sample=max_tokens, # type: ignore
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temperature=temperature,
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temperature=temperature,
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@ -102,4 +105,5 @@ class AnthropicLLM(LCModelComponent):
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except Exception as e:
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except Exception as e:
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raise ValueError("Could not connect to Anthropic API.") from e
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raise ValueError("Could not connect to Anthropic API.") from e
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return self.get_chat_result(output, stream, input_value, system_message)
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return output
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