upd: llm models
This commit is contained in:
namastex888
parent
60806556b1
commit
5774a94898
2 changed files with 228 additions and 282 deletions
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@ -1,249 +1,229 @@
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from typing import Any, List, Optional
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import httpx
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from langchain_community.chat_models.ollama import ChatOllama
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from typing import Any, Dict, List, Optional
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from langchain_community.chat_models import ChatOllama
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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.field_typing import BaseLanguageModel, Text
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from langflow.template.field.base import Input, Output
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from langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, StrInput
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from langflow.template import Output
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class ChatOllamaComponent(LCModelComponent):
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display_name = "Ollama"
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description = "Generate text using Ollama Local LLMs."
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icon = "Ollama"
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def update_build_config(self, build_config: dict, field_value: Any, field_name: str | None = None):
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if field_name == "mirostat":
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if field_value == "Disabled":
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build_config["mirostat_eta"]["advanced"] = True
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build_config["mirostat_tau"]["advanced"] = True
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build_config["mirostat_eta"]["value"] = None
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build_config["mirostat_tau"]["value"] = None
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else:
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build_config["mirostat_eta"]["advanced"] = False
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build_config["mirostat_tau"]["advanced"] = False
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if field_value == "Mirostat 2.0":
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build_config["mirostat_eta"]["value"] = 0.2
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build_config["mirostat_tau"]["value"] = 10
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else:
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build_config["mirostat_eta"]["value"] = 0.1
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build_config["mirostat_tau"]["value"] = 5
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if field_name == "model":
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base_url_dict = build_config.get("base_url", {})
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base_url_load_from_db = base_url_dict.get("load_from_db", False)
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base_url_value = base_url_dict.get("value")
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if base_url_load_from_db:
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base_url_value = self.variables(base_url_value)
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elif not base_url_value:
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base_url_value = "http://localhost:11434"
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build_config["model"]["options"] = self.get_model(base_url_value + "/api/tags")
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if field_name == "keep_alive_flag":
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if field_value == "Keep":
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build_config["keep_alive"]["value"] = "-1"
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build_config["keep_alive"]["advanced"] = True
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elif field_value == "Immediately":
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build_config["keep_alive"]["value"] = "0"
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build_config["keep_alive"]["advanced"] = True
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else:
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build_config["keep_alive"]["advanced"] = False
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return build_config
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def get_model(self, url: str) -> List[str]:
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try:
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with httpx.Client() as client:
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response = client.get(url)
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response.raise_for_status()
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data = response.json()
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model_names = [model["name"] for model in data.get("models", [])]
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return model_names
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except Exception as e:
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raise ValueError("Could not retrieve models") from e
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inputs = [
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Input(
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StrInput(
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name="base_url",
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field_type=Optional[str],
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display_name="Base URL",
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info="Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.",
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value="http://localhost:11434",
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advanced=True,
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),
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Input(
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StrInput(
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name="model",
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field_type=str,
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display_name="Model Name",
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options=[], # This should be dynamically loaded if possible
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value="llama2",
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info="Refer to https://ollama.ai/library for more models.",
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real_time_refresh=True,
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refresh_button=True,
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),
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Input(
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FloatInput(
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name="temperature",
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display_name="Temperature",
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value=0.8,
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info="Controls the creativity of model responses.",
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),
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StrInput(
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name="format",
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display_name="Format",
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info="Specify the format of the output (e.g., json).",
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advanced=True,
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),
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DictInput(
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name="metadata",
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display_name="Metadata",
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info="Metadata to add to the run trace.",
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advanced=True,
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),
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DropdownInput(
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name="mirostat",
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field_type=str,
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display_name="Mirostat",
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options=["Disabled", "Mirostat", "Mirostat 2.0"],
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info="Enable/disable Mirostat sampling for controlling perplexity.",
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advanced=False,
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real_time_refresh=True,
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refresh_button=True,
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value="Disabled",
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advanced=True,
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),
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Input(
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FloatInput(
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name="mirostat_eta",
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field_type=Optional[float],
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display_name="Mirostat Eta",
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info="Learning rate for Mirostat algorithm.",
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info="Learning rate for Mirostat algorithm. (Default: 0.1)",
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advanced=True,
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real_time_refresh=True,
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value=None, # Default can vary based on mirostat status
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),
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Input(
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FloatInput(
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name="mirostat_tau",
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field_type=Optional[float],
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display_name="Mirostat Tau",
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info="Controls the balance between coherence and diversity of the output.",
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info="Controls the balance between coherence and diversity of the output. (Default: 5.0)",
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advanced=True,
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real_time_refresh=True,
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value=None, # Default can vary based on mirostat status
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),
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Input(
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name="temperature",
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field_type=float,
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display_name="Temperature",
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info="Controls the creativity of model responses.",
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value=0.8,
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IntInput(
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name="num_ctx",
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display_name="Context Window Size",
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info="Size of the context window for generating tokens. (Default: 2048)",
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advanced=True,
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),
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Input(name="input_value", type=str, display_name="Input", input_types=["Text", "Data", "Prompt"]),
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Input(name="stream", type=bool, display_name="Stream", info=STREAM_INFO_TEXT, value=False),
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Input(
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IntInput(
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name="num_gpu",
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display_name="Number of GPUs",
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info="Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)",
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advanced=True,
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),
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IntInput(
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name="num_thread",
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display_name="Number of Threads",
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info="Number of threads to use during computation. (Default: detected for optimal performance)",
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advanced=True,
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),
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IntInput(
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name="repeat_last_n",
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display_name="Repeat Last N",
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info="How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)",
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advanced=True,
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),
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FloatInput(
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name="repeat_penalty",
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display_name="Repeat Penalty",
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info="Penalty for repetitions in generated text. (Default: 1.1)",
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advanced=True,
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),
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FloatInput(
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name="tfs_z",
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display_name="TFS Z",
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info="Tail free sampling value. (Default: 1)",
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advanced=True,
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),
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IntInput(
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name="timeout",
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display_name="Timeout",
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info="Timeout for the request stream.",
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advanced=True,
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),
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IntInput(
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name="top_k",
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display_name="Top K",
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info="Limits token selection to top K. (Default: 40)",
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advanced=True,
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),
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FloatInput(
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name="top_p",
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display_name="Top P",
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info="Works together with top-k. (Default: 0.9)",
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advanced=True,
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),
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BoolInput(
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name="verbose",
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display_name="Verbose",
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info="Whether to print out response text.",
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),
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StrInput(
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name="tags",
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display_name="Tags",
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info="Comma-separated list of tags to add to the run trace.",
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advanced=True,
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),
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StrInput(
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name="stop",
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display_name="Stop Tokens",
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info="Comma-separated list of tokens to signal the model to stop generating text.",
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advanced=True,
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),
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StrInput(
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name="system",
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display_name="System",
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info="System to use for generating text.",
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advanced=True,
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),
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StrInput(
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name="template",
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display_name="Template",
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info="Template to use for generating text.",
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advanced=True,
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),
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StrInput(
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name="input_value",
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display_name="Input",
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input_types=["Text", "Data", "Prompt"],
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),
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BoolInput(
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name="stream",
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display_name="Stream",
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info=STREAM_INFO_TEXT,
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),
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StrInput(
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name="system_message",
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field_type=Optional[str],
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display_name="System Message",
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info="System message to pass to the model.",
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advanced=True,
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value=None,
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),
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Input(
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name="headers",
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field_type=dict,
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display_name="Headers",
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info="Additional headers to send with the request.",
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advanced=True,
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),
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Input(
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name="keep_alive_flag",
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field_type=str,
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display_params=["Keep", "Immediately", "Minute", "Hour", "sec"],
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display_name="Unload interval",
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info="Controls how the model unload interval is managed.",
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real_time_refresh=True,
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refresh_button=True,
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),
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Input(
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name="keep_alive",
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field_type=int,
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display_name="Interval",
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info="How long the model will stay loaded into memory.",
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value=None,
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),
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]
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outputs = [
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Output(display_name="Text", name="text_output", method="text_response"),
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Output(display_name="Language Model", name="model_output", method="model_response"),
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Output(display_name="Language Model", name="model_output", method="build_model"),
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]
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def text_response(self) -> Text:
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input_value = self.input_value
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stream = self.stream
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system_message = self.system_message
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output = self.model_response()
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output = self.build_model()
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result = self.get_chat_result(output, stream, input_value, system_message)
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self.status = result
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return result
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def model_response(self) -> BaseLanguageModel:
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base_url = self.base_url or "http://localhost:11434"
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model = self.model
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mirostat = self.mirostat or "Disabled"
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mirostat_eta = self.mirostat_eta
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mirostat_tau = self.mirostat_tau
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repeat_last_n = self.repeat_last_n
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verbose = self.verbose
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keep_alive = self.keep_alive
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keep_alive_flag = self.keep_alive_flag or "Keep"
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num_ctx = self.num_ctx
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num_gpu = self.num_gpu
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_format = self.format
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metadata = self.metadata
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num_thread = self.num_thread
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repeat_penalty = self.repeat_penalty
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stop = self.stop
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system = self.system
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tags = self.tags
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temperature = self.temperature
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template = self.template
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tfs_z = self.tfs_z
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timeout = self.timeout
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top_k = self.top_k
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top_p = self.top_p
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headers = self.headers
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def build_model(self) -> BaseLanguageModel:
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# Mapping mirostat settings to their corresponding values
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mirostat_options = {"Mirostat": 1, "Mirostat 2.0": 2}
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if keep_alive_flag == "Minute":
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keep_alive_instance = f"{keep_alive}m"
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elif keep_alive_flag == "Hour":
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keep_alive_instance = f"{keep_alive}h"
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elif keep_alive_flag == "sec":
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keep_alive_instance = f"{keep_alive}s"
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elif keep_alive_flag == "Keep":
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keep_alive_instance = "-1"
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elif keep_alive_flag == "Immediately":
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keep_alive_instance = "0"
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# Default to 0 for 'Disabled'
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mirostat_value = mirostat_options.get(self.mirostat, 0) # type: ignore
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# Set mirostat_eta and mirostat_tau to None if mirostat is disabled
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if mirostat_value == 0:
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mirostat_eta = None
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mirostat_tau = None
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else:
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keep_alive_instance = "Invalid option"
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mirostat_instance = 0
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if mirostat == "disable":
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mirostat_instance = 0
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mirostat_eta = self.mirostat_eta
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mirostat_tau = self.mirostat_tau
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# Mapping system settings to their corresponding values
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llm_params = {
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"base_url": base_url,
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"model": model,
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"mirostat": mirostat_instance,
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"keep_alive": keep_alive_instance,
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"format": _format,
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"metadata": metadata,
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"tags": tags,
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"base_url": self.base_url,
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"model": self.model,
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"mirostat": mirostat_value,
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"format": self.format,
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"metadata": self.metadata,
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"tags": self.tags.split(",") if self.tags else None,
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"mirostat_eta": mirostat_eta,
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"mirostat_tau": mirostat_tau,
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"num_ctx": num_ctx,
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"num_gpu": num_gpu,
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"num_thread": num_thread,
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"repeat_last_n": repeat_last_n,
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"repeat_penalty": repeat_penalty,
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"temperature": temperature,
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"stop": stop,
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"system": system,
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"template": template,
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"tfs_z": tfs_z,
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"timeout": timeout,
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"top_k": top_k,
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"top_p": top_p,
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"verbose": verbose,
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"headers": headers,
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"num_ctx": self.num_ctx or None,
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"num_gpu": self.num_gpu or None,
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"num_thread": self.num_thread or None,
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"repeat_last_n": self.repeat_last_n or None,
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"repeat_penalty": self.repeat_penalty or None,
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"temperature": self.temperature or None,
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"stop": self.stop.split(",") if self.stop else None,
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"system": self.system,
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"template": self.template,
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"tfs_z": self.tfs_z or None,
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"timeout": self.timeout or None,
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"top_k": self.top_k or None,
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"top_p": self.top_p or None,
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"verbose": self.verbose,
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}
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# Remove parameters with None values
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llm_params = {k: v for k, v in llm_params.items() if v is not None}
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try:
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output = ChatOllama(**llm_params)
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output = ChatOllama(**llm_params) # type: ignore
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except Exception as e:
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raise ValueError("Could not initialize Ollama LLM.") from e
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return output
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return output
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@ -1,112 +1,77 @@
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from typing import Optional
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from langchain_google_vertexai import ChatVertexAI
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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.field_typing import Text
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from langflow.field_typing import BaseLanguageModel, Text
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from langflow.inputs import BoolInput, FloatInput, IntInput, StrInput
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from langflow.template import Output
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class ChatVertexAIComponent(LCModelComponent):
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display_name = "Vertex AI"
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description = "Generate text using Vertex AI LLMs."
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icon = "VertexAI"
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field_order = [
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"credentials",
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"project",
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"examples",
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"location",
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"max_output_tokens",
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"model_name",
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"temperature",
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"top_k",
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"top_p",
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"verbose",
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"input_value",
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"system_message",
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"stream",
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inputs = [
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StrInput(name="input_value", display_name="Input", input_types=["Text", "Data", "Prompt"]),
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StrInput(
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name="credentials",
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display_name="Credentials",
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info="Path to the JSON file containing the credentials.",
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file_types=[".json"],
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advanced=True,
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),
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StrInput(name="project", display_name="Project", info="The project ID."),
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StrInput(
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name="examples",
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display_name="Examples",
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info="Examples to pass to the model.",
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multiline=True,
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advanced=True,
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),
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StrInput(name="location", display_name="Location", value="us-central1", advanced=True),
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IntInput(
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name="max_output_tokens",
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display_name="Max Output Tokens",
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value=128,
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advanced=True,
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),
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StrInput(name="model_name", display_name="Model Name", value="gemini-1.5-pro"),
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FloatInput(name="temperature", display_name="Temperature", value=0.0),
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IntInput(name="top_k", display_name="Top K", value=40, advanced=True),
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FloatInput(name="top_p", display_name="Top P", value=0.95, advanced=True),
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BoolInput(name="verbose", display_name="Verbose", value=False, advanced=True),
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BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
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StrInput(
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name="system_message",
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display_name="System Message",
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info="System message to pass to the model.",
|
||||
advanced=True,
|
||||
),
|
||||
]
|
||||
outputs = [
|
||||
Output(display_name="Text", name="text_output", method="text_response"),
|
||||
Output(display_name="Language Model", name="model_output", method="build_model"),
|
||||
]
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"credentials": {
|
||||
"display_name": "Credentials",
|
||||
"field_type": "file",
|
||||
"file_types": [".json"],
|
||||
"file_path": None,
|
||||
},
|
||||
"examples": {
|
||||
"display_name": "Examples",
|
||||
"multiline": True,
|
||||
},
|
||||
"location": {
|
||||
"display_name": "Location",
|
||||
"value": "us-central1",
|
||||
},
|
||||
"max_output_tokens": {
|
||||
"display_name": "Max Output Tokens",
|
||||
"value": 128,
|
||||
"advanced": True,
|
||||
},
|
||||
"model_name": {
|
||||
"display_name": "Model Name",
|
||||
"value": "chat-bison",
|
||||
},
|
||||
"project": {
|
||||
"display_name": "Project",
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"value": 0.0,
|
||||
},
|
||||
"top_k": {
|
||||
"display_name": "Top K",
|
||||
"value": 40,
|
||||
"advanced": True,
|
||||
},
|
||||
"top_p": {
|
||||
"display_name": "Top P",
|
||||
"value": 0.95,
|
||||
"advanced": True,
|
||||
},
|
||||
"verbose": {
|
||||
"display_name": "Verbose",
|
||||
"value": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Data", "Prompt"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
"advanced": True,
|
||||
},
|
||||
"system_message": {
|
||||
"display_name": "System Message",
|
||||
"info": "System message to pass to the model.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
def text_response(self) -> Text:
|
||||
input_value = self.input_value
|
||||
stream = self.stream
|
||||
system_message = self.system_message
|
||||
output = self.build_model()
|
||||
result = self.get_chat_result(output, stream, input_value, system_message)
|
||||
self.status = result
|
||||
return result
|
||||
|
||||
def build_model(self) -> BaseLanguageModel:
|
||||
credentials = self.credentials
|
||||
location = self.location
|
||||
max_output_tokens = self.max_output_tokens
|
||||
model_name = self.model_name
|
||||
project = self.project
|
||||
temperature = self.temperature
|
||||
top_k = self.top_k
|
||||
top_p = self.top_p
|
||||
verbose = self.verbose
|
||||
|
||||
def build(
|
||||
self,
|
||||
input_value: Text,
|
||||
credentials: Optional[str],
|
||||
project: str,
|
||||
location: str = "us-central1",
|
||||
max_output_tokens: int = 128,
|
||||
model_name: str = "chat-bison",
|
||||
temperature: float = 0.0,
|
||||
top_k: int = 40,
|
||||
top_p: float = 0.95,
|
||||
verbose: bool = False,
|
||||
stream: bool = False,
|
||||
system_message: Optional[str] = None,
|
||||
) -> Text:
|
||||
try:
|
||||
from langchain_google_vertexai import ChatVertexAI # type: ignore
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"To use the ChatVertexAI model, you need to install the langchain-google-vertexai package."
|
||||
)
|
||||
output = ChatVertexAI(
|
||||
credentials=credentials,
|
||||
location=location,
|
||||
|
|
@ -119,4 +84,5 @@ class ChatVertexAIComponent(LCModelComponent):
|
|||
verbose=verbose,
|
||||
)
|
||||
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
return output
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue