feat: Add Prompt component to prompts/__init__.py
This commit is contained in:
Rodrigo
parent
2996cb726d
commit
0d609b9376
6 changed files with 175 additions and 198 deletions
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@ -1,150 +1,115 @@
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from typing import Dict, List, Optional
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from langchain_openai.embeddings.base import OpenAIEmbeddings
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from pydantic.v1 import SecretStr
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from langflow.custom import CustomComponent
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from langflow.field_typing import Embeddings, NestedDict
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from langflow.base.models.model import LCModelComponent
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from langflow.field_typing import Embeddings
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from langflow.inputs import (
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BoolInput,
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DictInput,
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FloatInput,
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IntInput,
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SecretStrInput,
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StrInput,
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DropdownInput
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)
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from langflow.template import Output
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class OpenAIEmbeddingsComponent(CustomComponent):
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class OpenAIEmbeddingsComponent(LCModelComponent):
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display_name = "OpenAI Embeddings"
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description = "Generate embeddings using OpenAI models."
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icon = "OpenAI"
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inputs = [
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DictInput(
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name="default_headers",
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display_name="Default Headers",
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advanced=True,
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info="Default headers to use for the API request.",
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),
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DictInput(
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name="default_query",
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display_name="Default Query",
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advanced=True,
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info="Default query parameters to use for the API request.",
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),
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IntInput(name="chunk_size", display_name="Chunk Size", advanced=True, value=1000),
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StrInput(name="client", display_name="Client", advanced=True),
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StrInput(name="deployment", display_name="Deployment", advanced=True),
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IntInput(
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name="embedding_ctx_length",
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display_name="Embedding Context Length",
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advanced=True,
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value=1536
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),
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IntInput(name="max_retries", display_name="Max Retries", value=3, advanced=True),
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DropdownInput(
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name="model",
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display_name="Model",
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advanced=False,
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options=[
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"text-embedding-3-small",
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"text-embedding-3-large",
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"text-embedding-ada-002",
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],
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value="text-embedding-3-small"
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),
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DictInput(name="model_kwargs", display_name="Model Kwargs", advanced=True),
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SecretStrInput(
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name="openai_api_base", display_name="OpenAI API Base", advanced=True
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),
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SecretStrInput(name="openai_api_key", display_name="OpenAI API Key"),
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SecretStrInput(
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name="openai_api_type", display_name="OpenAI API Type", advanced=True
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),
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StrInput(
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name="openai_api_version", display_name="OpenAI API Version", advanced=True
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),
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StrInput(
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name="openai_organization",
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display_name="OpenAI Organization",
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advanced=True,
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),
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StrInput(name="openai_proxy", display_name="OpenAI Proxy", advanced=True),
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FloatInput(
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name="request_timeout", display_name="Request Timeout", advanced=True
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),
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BoolInput(
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name="show_progress_bar", display_name="Show Progress Bar", advanced=True
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),
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BoolInput(name="skip_empty", display_name="Skip Empty", advanced=True),
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StrInput(
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name="tiktoken_model_name",
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display_name="TikToken Model Name",
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advanced=True,
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),
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BoolInput(
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name="tiktoken_enable", display_name="TikToken Enable", advanced=True
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),
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]
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def build_config(self):
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return {
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"allowed_special": {
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"display_name": "Allowed Special",
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"advanced": True,
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"field_type": "str",
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"is_list": True,
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},
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"default_headers": {
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"display_name": "Default Headers",
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"advanced": True,
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"field_type": "dict",
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},
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"default_query": {
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"display_name": "Default Query",
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"advanced": True,
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"field_type": "NestedDict",
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},
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"disallowed_special": {
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"display_name": "Disallowed Special",
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"advanced": True,
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"field_type": "str",
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"is_list": True,
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},
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"chunk_size": {"display_name": "Chunk Size", "advanced": True},
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"client": {"display_name": "Client", "advanced": True},
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"deployment": {"display_name": "Deployment", "advanced": True},
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"embedding_ctx_length": {
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"display_name": "Embedding Context Length",
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"advanced": True,
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},
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"max_retries": {"display_name": "Max Retries", "advanced": True},
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"model": {
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"display_name": "Model",
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"advanced": False,
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"options": [
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"text-embedding-3-small",
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"text-embedding-3-large",
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"text-embedding-ada-002",
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],
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},
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"model_kwargs": {"display_name": "Model Kwargs", "advanced": True},
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"openai_api_base": {
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"display_name": "OpenAI API Base",
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"password": True,
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"advanced": True,
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},
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"openai_api_key": {"display_name": "OpenAI API Key", "password": True},
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"openai_api_type": {
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"display_name": "OpenAI API Type",
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"advanced": True,
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"password": True,
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},
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"openai_api_version": {
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"display_name": "OpenAI API Version",
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"advanced": True,
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},
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"openai_organization": {
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"display_name": "OpenAI Organization",
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"advanced": True,
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},
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"openai_proxy": {"display_name": "OpenAI Proxy", "advanced": True},
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"request_timeout": {"display_name": "Request Timeout", "advanced": True},
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"show_progress_bar": {
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"display_name": "Show Progress Bar",
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"advanced": True,
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},
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"skip_empty": {"display_name": "Skip Empty", "advanced": True},
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"tiktoken_model_name": {
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"display_name": "TikToken Model Name",
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"advanced": True,
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},
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"tiktoken_enable": {"display_name": "TikToken Enable", "advanced": True},
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"dimensions": {
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"display_name": "Dimensions",
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"info": "The number of dimensions the resulting output embeddings should have. Only supported by certain models.",
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"advanced": True,
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},
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}
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def build(
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self,
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openai_api_key: str,
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default_headers: Optional[Dict[str, str]] = None,
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default_query: Optional[NestedDict] = {},
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allowed_special: List[str] = [],
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disallowed_special: List[str] = ["all"],
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chunk_size: int = 1000,
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deployment: str = "text-embedding-ada-002",
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embedding_ctx_length: int = 8191,
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max_retries: int = 6,
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model: str = "text-embedding-ada-002",
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model_kwargs: NestedDict = {},
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openai_api_base: Optional[str] = None,
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openai_api_type: Optional[str] = None,
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openai_api_version: Optional[str] = None,
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openai_organization: Optional[str] = None,
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openai_proxy: Optional[str] = None,
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request_timeout: Optional[float] = None,
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show_progress_bar: bool = False,
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skip_empty: bool = False,
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tiktoken_enable: bool = True,
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tiktoken_model_name: Optional[str] = None,
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dimensions: Optional[int] = None,
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) -> Embeddings:
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# This is to avoid errors with Vector Stores (e.g Chroma)
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if disallowed_special == ["all"]:
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disallowed_special = "all" # type: ignore
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if openai_api_key:
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api_key = SecretStr(openai_api_key)
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else:
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api_key = None
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outputs = [
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Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
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]
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def build_embeddings(self) -> Embeddings:
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return OpenAIEmbeddings(
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tiktoken_enabled=tiktoken_enable,
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default_headers=default_headers,
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default_query=default_query,
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allowed_special=set(allowed_special),
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tiktoken_enabled=self.tiktoken_enable,
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default_headers=self.default_headers,
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default_query=self.default_query,
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allowed_special="all",
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disallowed_special="all",
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chunk_size=chunk_size,
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deployment=deployment,
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embedding_ctx_length=embedding_ctx_length,
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max_retries=max_retries,
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model=model,
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model_kwargs=model_kwargs,
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base_url=openai_api_base,
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api_key=api_key,
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openai_api_type=openai_api_type,
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api_version=openai_api_version,
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organization=openai_organization,
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openai_proxy=openai_proxy,
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timeout=request_timeout,
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show_progress_bar=show_progress_bar,
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skip_empty=skip_empty,
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tiktoken_model_name=tiktoken_model_name,
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dimensions=dimensions,
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chunk_size=self.chunk_size,
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deployment=self.deployment,
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embedding_ctx_length=self.embedding_ctx_length,
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max_retries=self.max_retries,
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model=self.model,
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model_kwargs=self.model_kwargs,
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base_url=self.openai_api_base,
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api_key=self.openai_api_key,
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openai_api_type=self.openai_api_type,
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api_version=self.openai_api_version,
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organization=self.openai_organization,
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openai_proxy=self.openai_proxy,
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timeout=self.request_timeout,
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show_progress_bar=self.show_progress_bar,
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skip_empty=self.skip_empty,
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tiktoken_model_name=self.tiktoken_model_name,
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)
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@ -12,7 +12,7 @@ class ParseDataComponent(Component):
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inputs = [
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HandleInput(
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name="data", display_name="Data", info="The data to convert to text.", input_types=["Message", "Data"]
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name="data", display_name="Data", info="The data to convert to text.", input_types=["Data"]
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),
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MultilineInput(
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name="template",
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@ -27,7 +27,7 @@ class ParseDataComponent(Component):
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def parse_data_to_text(self) -> Text:
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data = self.data if isinstance(self.data, list) else [self.data]
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template = self.template or "Text: {text}"
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template = self.template
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result_string = data_to_text(template, data)
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self.status = result_string
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3
src/backend/base/langflow/components/prompts/__init__.py
Normal file
3
src/backend/base/langflow/components/prompts/__init__.py
Normal file
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@ -0,0 +1,3 @@
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from .Prompt import Prompt
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__all__ = ["Prompt"]
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@ -57,30 +57,30 @@ def __getattr__(name: str) -> Any:
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__all__ = [
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"NestedDict",
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"Data",
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"Tool",
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"PromptTemplate",
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"Chain",
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"AgentExecutor",
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"BaseChatMemory",
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"BaseLLM",
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"BaseLanguageModel",
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"BaseLLM",
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"BaseLoader",
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"BaseMemory",
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"BaseOutputParser",
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"BaseRetriever",
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"VectorStore",
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"Embeddings",
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"TextSplitter",
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"Document",
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"AgentExecutor",
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"Text",
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"Object",
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"Callable",
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"BasePromptTemplate",
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"BaseRetriever",
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"Callable",
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"Chain",
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"ChatPromptTemplate",
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"Prompt",
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"RangeSpec",
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"Input",
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"Code",
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"Data",
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"Document",
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"Embeddings",
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"Input",
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"NestedDict",
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"Object",
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"Prompt",
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"PromptTemplate",
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"RangeSpec",
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"Text",
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"TextSplitter",
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"Tool",
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"VectorStore",
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]
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@ -23,6 +23,11 @@ class HandleInput(BaseInputMixin, ListableInputMixin):
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field_type: Optional[SerializableFieldTypes] = FieldTypes.OTHER
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# class DataInput(HandleInput):
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# input_types: list[str] = ["Data"]
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# ! Let's add this?
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class PromptInput(BaseInputMixin, ListableInputMixin):
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field_type: Optional[SerializableFieldTypes] = FieldTypes.PROMPT
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@ -38,29 +43,29 @@ class StrInput(BaseInputMixin, ListableInputMixin, DatabaseLoadMixin): # noqa:
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class TextInput(StrInput):
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input_types: list[str] = ["Data", "Message", "Text"]
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@field_validator("value")
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@classmethod
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def validate_value(cls, v: Any, _info):
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value = None
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if isinstance(v, str):
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value = v
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elif isinstance(v, Message):
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value = v.text
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elif isinstance(v, Data):
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if v.text_key in v.data:
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value = v.data[v.text_key]
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else:
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keys = ", ".join(v.data.keys())
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input_name = _info.data["name"]
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raise ValueError(
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f"The input to '{input_name}' must contain the key '{v.text_key}'."
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f"You can set `text_key` to one of the following keys: {keys} or set the value using another Component."
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)
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else:
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raise ValueError(f"Invalid input type {type(v)}")
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if isinstance(value, str):
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return value
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raise ValueError(f"Invalid value type {type(value)}")
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# @field_validator("value")
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# @classmethod
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# def validate_value(cls, v: Any, _info):
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# value = None
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# if isinstance(v, str):
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# value = v
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# elif isinstance(v, Message):
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# value = v.text
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# elif isinstance(v, Data):
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# if v.text_key in v.data:
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# value = v.data[v.text_key]
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# else:
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# keys = ", ".join(v.data.keys())
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# input_name = _info.data["name"]
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# raise ValueError(
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# f"The input to '{input_name}' must contain the key '{v.text_key}'."
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# f"You can set `text_key` to one of the following keys: {keys} or set the value using another Component."
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# )
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# else:
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# raise ValueError(f"Invalid input type {type(v)}")
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# if isinstance(value, str):
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# return value
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# raise ValueError(f"Invalid value type {type(value)}")
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class MultilineInput(BaseInputMixin):
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@ -108,16 +113,17 @@ class FileInput(BaseInputMixin, ListableInputMixin, FileMixin):
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InputTypes = Union[
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StrInput,
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SecretStrInput,
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IntInput,
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FloatInput,
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BoolInput,
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NestedDictInput,
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# DataInput, # ! Let's add this
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DictInput,
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DropdownInput,
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FileInput,
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PromptInput,
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MultilineInput,
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FloatInput,
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HandleInput,
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IntInput,
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MultilineInput,
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NestedDictInput,
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PromptInput,
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SecretStrInput,
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StrInput,
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]
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@ -247,7 +247,6 @@ export const nodeColors: { [char: string]: string } = {
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models: "#ab11ab",
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model_specs: "#6344BE",
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chains: "#FE7500",
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Document: "#7AAE42",
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list: "#9AAE42",
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agents: "#903BBE",
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tools: "#FF3434",
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@ -267,14 +266,18 @@ export const nodeColors: { [char: string]: string } = {
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experimental: "#E6277A",
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langchain_utilities: "#31A3CC",
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output_parsers: "#E6A627",
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str: "#4367BF",
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Text: "#4367BF",
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retrievers: "#e6b25a",
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unknown: "#9CA3AF",
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// custom_components: "#ab11ab",
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Data: "#9CA3AF",
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Message: "#4367BF",
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BaseLanguageModel: "#ab11ab",
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retrievers: "#e6b25a",
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//
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str: "#2563eb",
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Text: "#2563eb",
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unknown: "#9CA3AF",
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Document: "#65a30d",
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Data: "#dc2626",
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Message: "#4f46e5",
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Prompt: "#7c3aed",
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Embeddings: "#10b981",
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BaseLanguageModel: "#c026d3",
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};
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export const nodeNames: { [char: string]: string } = {
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