forked from Archives/langchain
add aleph alpha llm (#1207)
Integrate Aleph Alpha's client into Langchain to provide access to the luminous models - more info on latest benchmarks here: https://www.aleph-alpha.com/luminous-performance-benchmarkssearx-search-suffix
Dennis Antela Martinez
1 year ago
committed by
GitHub
parent
c6ab1bb3cb
commit
53c67e04d4
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "9597802c",
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"metadata": {},
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"source": [
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"# Aleph Alpha\n",
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"This example goes over how to use LangChain to interact with Aleph Alpha models"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "6fb585dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import AlephAlpha\n",
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"from langchain import PromptTemplate, LLMChain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "f81a230d",
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"metadata": {},
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"outputs": [],
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"source": [
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"template = \"\"\"Q: {question}\n",
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"\n",
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"A:\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "f0d26e48",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = AlephAlpha(model=\"luminous-extended\", maximum_tokens=20, stop_sequences=[\"Q:\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "6811d621",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "3058e63f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"' Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.\\n'"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"question = \"What is AI?\"\n",
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"\n",
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"llm_chain.run(question)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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},
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"vscode": {
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"interpreter": {
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"hash": "2d002ec47225e662695b764370d7966aa11eeb4302edc2f497bbf96d49c8f899"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,236 @@
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"""Wrapper around Aleph Alpha APIs."""
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from typing import Any, Dict, List, Optional, Sequence
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from pydantic import BaseModel, Extra, root_validator
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from langchain.llms.base import LLM
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from langchain.llms.utils import enforce_stop_tokens
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from langchain.utils import get_from_dict_or_env
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class AlephAlpha(LLM, BaseModel):
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"""Wrapper around Aleph Alpha large language models.
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To use, you should have the ``aleph_alpha_client`` python package installed, and the
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environment variable ``ALEPH_ALPHA_API_KEY`` set with your API key, or pass
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it as a named parameter to the constructor.
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Parameters are explained more in depth here:
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https://github.com/Aleph-Alpha/aleph-alpha-client/blob/c14b7dd2b4325c7da0d6a119f6e76385800e097b/aleph_alpha_client/completion.py#L10
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Example:
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.. code-block:: python
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from langchain.llms import AlephAlpha
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alpeh_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key")
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"""
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client: Any #: :meta private:
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model: Optional[str] = "luminous-base"
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"""Model name to use."""
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maximum_tokens: int = 64
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"""The maximum number of tokens to be generated."""
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temperature: float = 0.0
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"""A non-negative float that tunes the degree of randomness in generation."""
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top_k: int = 0
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"""Number of most likely tokens to consider at each step."""
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top_p: float = 0.0
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"""Total probability mass of tokens to consider at each step."""
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presence_penalty: float = 0.0
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"""Penalizes repeated tokens."""
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frequency_penalty: float = 0.0
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"""Penalizes repeated tokens according to frequency."""
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repetition_penalties_include_prompt: Optional[bool] = False
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"""Flag deciding whether presence penalty or frequency penalty are
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updated from the prompt."""
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use_multiplicative_presence_penalty: Optional[bool] = False
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"""Flag deciding whether presence penalty is applied
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multiplicatively (True) or additively (False)."""
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penalty_bias: Optional[str] = None
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"""Penalty bias for the completion."""
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penalty_exceptions: Optional[List[str]] = None
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"""List of strings that may be generated without penalty,
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regardless of other penalty settings"""
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penalty_exceptions_include_stop_sequences: Optional[bool] = None
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"""Should stop_sequences be included in penalty_exceptions."""
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best_of: Optional[int] = None
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"""returns the one with the "best of" results
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(highest log probability per token)
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"""
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n: int = 1
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"""How many completions to generate for each prompt."""
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logit_bias: Optional[Dict[int, float]] = None
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"""The logit bias allows to influence the likelihood of generating tokens."""
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log_probs: Optional[int] = None
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"""Number of top log probabilities to be returned for each generated token."""
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tokens: Optional[bool] = False
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"""return tokens of completion."""
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disable_optimizations: Optional[bool] = False
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minimum_tokens: Optional[int] = 0
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"""Generate at least this number of tokens."""
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echo: bool = False
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"""Echo the prompt in the completion."""
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use_multiplicative_frequency_penalty: bool = False
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sequence_penalty: float = 0.0
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sequence_penalty_min_length: int = 2
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use_multiplicative_sequence_penalty: bool = False
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completion_bias_inclusion: Optional[Sequence[str]] = None
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completion_bias_inclusion_first_token_only: bool = False
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completion_bias_exclusion: Optional[Sequence[str]] = None
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completion_bias_exclusion_first_token_only: bool = False
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"""Only consider the first token for the completion_bias_exclusion."""
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contextual_control_threshold: Optional[float] = None
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"""If set to None, attention control parameters only apply to those tokens that have
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explicitly been set in the request.
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If set to a non-None value, control parameters are also applied to similar tokens.
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"""
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control_log_additive: Optional[bool] = True
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"""True: apply control by adding the log(control_factor) to attention scores.
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False: (attention_scores - - attention_scores.min(-1)) * control_factor
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"""
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repetition_penalties_include_completion: bool = True
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"""Flag deciding whether presence penalty or frequency penalty
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are updated from the completion."""
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raw_completion: bool = False
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"""Force the raw completion of the model to be returned."""
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aleph_alpha_api_key: Optional[str] = None
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"""API key for Aleph Alpha API."""
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stop_sequences: Optional[List[str]] = None
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"""Stop sequences to use."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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aleph_alpha_api_key = get_from_dict_or_env(
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values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY"
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)
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try:
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import aleph_alpha_client
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values["client"] = aleph_alpha_client.Client(token=aleph_alpha_api_key)
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except ImportError:
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raise ValueError(
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"Could not import aleph_alpha_client python package. "
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"Please it install it with `pip install aleph_alpha_client`."
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)
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return values
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling the Aleph Alpha API."""
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return {
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"maximum_tokens": self.maximum_tokens,
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"temperature": self.temperature,
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"top_k": self.top_k,
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"top_p": self.top_p,
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"presence_penalty": self.presence_penalty,
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"frequency_penalty": self.frequency_penalty,
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"n": self.n,
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"repetition_penalties_include_prompt": self.repetition_penalties_include_prompt, # noqa: E501
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"use_multiplicative_presence_penalty": self.use_multiplicative_presence_penalty, # noqa: E501
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"penalty_bias": self.penalty_bias,
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"penalty_exceptions": self.penalty_exceptions,
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"penalty_exceptions_include_stop_sequences": self.penalty_exceptions_include_stop_sequences, # noqa: E501
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"best_of": self.best_of,
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"logit_bias": self.logit_bias,
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"log_probs": self.log_probs,
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"tokens": self.tokens,
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"disable_optimizations": self.disable_optimizations,
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"minimum_tokens": self.minimum_tokens,
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"echo": self.echo,
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"use_multiplicative_frequency_penalty": self.use_multiplicative_frequency_penalty, # noqa: E501
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"sequence_penalty": self.sequence_penalty,
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"sequence_penalty_min_length": self.sequence_penalty_min_length,
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"use_multiplicative_sequence_penalty": self.use_multiplicative_sequence_penalty, # noqa: E501
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"completion_bias_inclusion": self.completion_bias_inclusion,
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"completion_bias_inclusion_first_token_only": self.completion_bias_inclusion_first_token_only, # noqa: E501
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"completion_bias_exclusion": self.completion_bias_exclusion,
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"completion_bias_exclusion_first_token_only": self.completion_bias_exclusion_first_token_only, # noqa: E501
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"contextual_control_threshold": self.contextual_control_threshold,
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"control_log_additive": self.control_log_additive,
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"repetition_penalties_include_completion": self.repetition_penalties_include_completion, # noqa: E501
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"raw_completion": self.raw_completion,
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}
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {**{"model": self.model}, **self._default_params}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "alpeh_alpha"
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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"""Call out to Aleph Alpha's completion endpoint.
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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response = alpeh_alpha("Tell me a joke.")
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"""
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from aleph_alpha_client import CompletionRequest, Prompt
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params = self._default_params
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if self.stop_sequences is not None and stop is not None:
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raise ValueError(
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"stop sequences found in both the input and default params."
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)
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elif self.stop_sequences is not None:
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params["stop_sequences"] = self.stop_sequences
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else:
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params["stop_sequences"] = stop
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request = CompletionRequest(prompt=Prompt.from_text(prompt), **params)
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response = self.client.complete(model=self.model, request=request)
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text = response.completions[0].completion
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# If stop tokens are provided, Aleph Alpha's endpoint returns them.
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# In order to make this consistent with other endpoints, we strip them.
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if stop is not None or self.stop_sequences is not None:
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text = enforce_stop_tokens(text, params["stop_sequences"])
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return text
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@ -0,0 +1,10 @@
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"""Test Aleph Alpha API wrapper."""
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from langchain.llms.aleph_alpha import AlephAlpha
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def test_aleph_alpha_call() -> None:
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"""Test valid call to cohere."""
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llm = AlephAlpha(maximum_tokens=10)
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output = llm("Say foo:")
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assert isinstance(output, str)
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