mirror of
https://github.com/hwchase17/langchain.git
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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-benchmarks
This commit is contained in:
Dennis Antela Martinez
GitHub
parent
c6ab1bb3cb
commit
53c67e04d4
108
docs/modules/llms/integrations/aleph_alpha.ipynb
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108
docs/modules/llms/integrations/aleph_alpha.ipynb
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@ -0,0 +1,108 @@
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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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@ -2,6 +2,7 @@
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from typing import Dict, Type
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from langchain.llms.ai21 import AI21
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from langchain.llms.aleph_alpha import AlephAlpha
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from langchain.llms.anthropic import Anthropic
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from langchain.llms.base import BaseLLM
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from langchain.llms.cerebriumai import CerebriumAI
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@ -20,6 +21,7 @@ from langchain.llms.self_hosted_hugging_face import SelfHostedHuggingFaceLLM
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__all__ = [
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"Anthropic",
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"AlephAlpha",
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"CerebriumAI",
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"Cohere",
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"ForefrontAI",
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@ -39,6 +41,7 @@ __all__ = [
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type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
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"ai21": AI21,
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"aleph_alpha": AlephAlpha,
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"anthropic": Anthropic,
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"cerebriumai": CerebriumAI,
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"cohere": Cohere,
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236
langchain/llms/aleph_alpha.py
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236
langchain/llms/aleph_alpha.py
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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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128
poetry.lock
generated
128
poetry.lock
generated
@ -12,6 +12,21 @@ files = [
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{file = "absl_py-1.4.0-py3-none-any.whl", hash = "sha256:0d3fe606adfa4f7db64792dd4c7aee4ee0c38ab75dfd353b7a83ed3e957fcb47"},
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]
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[[package]]
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name = "aiodns"
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version = "3.0.0"
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description = "Simple DNS resolver for asyncio"
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category = "main"
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optional = false
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python-versions = "*"
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files = [
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{file = "aiodns-3.0.0-py3-none-any.whl", hash = "sha256:2b19bc5f97e5c936638d28e665923c093d8af2bf3aa88d35c43417fa25d136a2"},
|
||||
{file = "aiodns-3.0.0.tar.gz", hash = "sha256:946bdfabe743fceeeb093c8a010f5d1645f708a241be849e17edfb0e49e08cd6"},
|
||||
]
|
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[package.dependencies]
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pycares = ">=4.0.0"
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[[package]]
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name = "aiohttp"
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version = "3.8.3"
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@ -121,6 +136,21 @@ yarl = ">=1.0,<2.0"
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[package.extras]
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speedups = ["Brotli", "aiodns", "cchardet"]
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|
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[[package]]
|
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name = "aiohttp-retry"
|
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version = "2.8.3"
|
||||
description = "Simple retry client for aiohttp"
|
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category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "aiohttp_retry-2.8.3-py3-none-any.whl", hash = "sha256:3aeeead8f6afe48272db93ced9440cf4eda8b6fd7ee2abb25357b7eb28525b45"},
|
||||
{file = "aiohttp_retry-2.8.3.tar.gz", hash = "sha256:9a8e637e31682ad36e1ff9f8bcba912fcfc7d7041722bc901a4b948da4d71ea9"},
|
||||
]
|
||||
|
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[package.dependencies]
|
||||
aiohttp = "*"
|
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||||
[[package]]
|
||||
name = "aiosignal"
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||||
version = "1.3.1"
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@ -148,6 +178,32 @@ files = [
|
||||
{file = "alabaster-0.7.13.tar.gz", hash = "sha256:a27a4a084d5e690e16e01e03ad2b2e552c61a65469419b907243193de1a84ae2"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "aleph-alpha-client"
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||||
version = "2.15.0"
|
||||
description = "python client to interact with Aleph Alpha api endpoints"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "aleph-alpha-client-2.15.0.tar.gz", hash = "sha256:b50329572c319fbf0d17fab3e34a46c15f164931f40ff6eb2379c8276d4728fa"},
|
||||
{file = "aleph_alpha_client-2.15.0-py3-none-any.whl", hash = "sha256:1cc0e17cd5f4a578f10e3cc22379f9e6765fed112108bcd84d5e3a77a094c58f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
aiodns = ">=3.0.0"
|
||||
aiohttp = ">=3.8.3"
|
||||
aiohttp-retry = ">=2.8.3"
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requests = ">=2.28"
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tokenizers = ">=0.13.2"
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urllib3 = ">=1.26"
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|
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[package.extras]
|
||||
dev = ["black", "ipykernel", "mypy", "nbconvert", "pytest", "pytest-aiohttp", "pytest-cov", "pytest-dotenv", "pytest-httpserver", "types-requests"]
|
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docs = ["sphinx", "sphinx-rtd-theme"]
|
||||
test = ["pytest", "pytest-aiohttp", "pytest-cov", "pytest-dotenv", "pytest-httpserver"]
|
||||
types = ["mypy", "types-requests"]
|
||||
|
||||
[[package]]
|
||||
name = "anthropic"
|
||||
version = "0.2.2"
|
||||
@ -4066,6 +4122,74 @@ files = [
|
||||
[package.dependencies]
|
||||
pyasn1 = ">=0.4.6,<0.5.0"
|
||||
|
||||
[[package]]
|
||||
name = "pycares"
|
||||
version = "4.3.0"
|
||||
description = "Python interface for c-ares"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "pycares-4.3.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:19c9cdd3322d422931982939773e453e491dfc5c0b2e23d7266959315c7a0824"},
|
||||
{file = "pycares-4.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9e56e9cdf46a092970dc4b75bbabddea9f480be5eeadc3fcae3eb5c6807c4136"},
|
||||
{file = "pycares-4.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1c75a6241c79b935048272cb77df498da64b8defc8c4b29fdf9870e43ba4cbb4"},
|
||||
{file = "pycares-4.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:24d8654fac3742791b8bef59d1fbb3e19ae6a5c48876a6d98659f7c66ee546c4"},
|
||||
{file = "pycares-4.3.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ebf50b049a245880f1aa16a6f72c4408e0a65b49ea1d3bf13383a44a2cabd2bf"},
|
||||
{file = "pycares-4.3.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:84daf560962763c0359fd79c750ef480f0fda40c08b57765088dbe362e8dc452"},
|
||||
{file = "pycares-4.3.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:978d10da7ee74b9979c494afa8b646411119ad0186a29c7f13c72bb4295630c6"},
|
||||
{file = "pycares-4.3.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:326c5b9d7fe52eb3d243f5ead58d5c0011884226d961df8360a34618c38c7515"},
|
||||
{file = "pycares-4.3.0-cp310-cp310-win32.whl", hash = "sha256:da7c7089ae617317d2cbe38baefd3821387b3bfef7b3ee5b797b871cb1257974"},
|
||||
{file = "pycares-4.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:7106dc683db30e1d851283b7b9df7a5ea4964d6bdd000d918d91d4b1f9bed329"},
|
||||
{file = "pycares-4.3.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:4e7a24ecef0b1933f2a3fdbf328d1b529a76cda113f8364fa0742e5b3bd76566"},
|
||||
{file = "pycares-4.3.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:e7abccc2aa4771c06994e4d9ed596453061e2b8846f887d9c98a64ccdaf4790a"},
|
||||
{file = "pycares-4.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:531fed46c5ed798a914c3207be4ae7b297c4d09e4183d3cf8fd9ee59a55d5080"},
|
||||
{file = "pycares-4.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2c9335175af0c64a1e0ba67bdd349eb62d4eea0ad02c235ccdf0d535fd20f323"},
|
||||
{file = "pycares-4.3.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c5f0e95535027d2dcd51e780410632b0d3ed7e9e5ceb25dc0fe937f2c2960079"},
|
||||
{file = "pycares-4.3.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:3692179ce5fb96908ba342e1e5303608d0c976f0d5d4619fa9d3d6d9d5a9a1b4"},
|
||||
{file = "pycares-4.3.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:5c4cb6cc7fe8e0606d30b60367f59fe26d1472e88555d61e202db70dea5c8edb"},
|
||||
{file = "pycares-4.3.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:3215445396c74103e2054e6b349d9e85883ceda2006d0039fc2d58c9b11818a2"},
|
||||
{file = "pycares-4.3.0-cp311-cp311-win32.whl", hash = "sha256:6a0c0c3a0adf490bba9dbb37dbd07ec81e4a6584f095036ac34f06a633710ffe"},
|
||||
{file = "pycares-4.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:995cb37cc39bd40ca87bb16555a0f7724f3be30d9f9059a4caab2fde45b1b903"},
|
||||
{file = "pycares-4.3.0-cp36-cp36m-win32.whl", hash = "sha256:4c9187be72449c975c11daa1d94d7ddcc494f8a4c37a6c18f977cd7024a531d9"},
|
||||
{file = "pycares-4.3.0-cp36-cp36m-win_amd64.whl", hash = "sha256:d7405ba10a2903a58b8b0faedcb54994c9ee002ad01963587fabf93e7e479783"},
|
||||
{file = "pycares-4.3.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:40aaa12081495f879f11f4cfc95edfec1ea14711188563102f9e33fe98728fac"},
|
||||
{file = "pycares-4.3.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4972cac24b66c5997f3a3e2cb608e408066d80103d443e36d626a88a287b9ae7"},
|
||||
{file = "pycares-4.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:35886dba7aa5b73affca8729aeb5a1f5e94d3d9a764adb1b7e75bafca44eeca5"},
|
||||
{file = "pycares-4.3.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5cea6e1f3be016f155d60f27f16c1074d58b4d6e123228fdbc3326d076016af8"},
|
||||
{file = "pycares-4.3.0-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:3a9fd2665b053afb39226ac6f8137a60910ca7729358456df2fb94866f4297de"},
|
||||
{file = "pycares-4.3.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:e8e9195f869120e44e0aa0a6098bb5c19947f4753054365891f592e6f9eab3ef"},
|
||||
{file = "pycares-4.3.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:674486ecf2afb25ee219171b07cdaba481a1aaa2dabb155779c7be9ded03eaa9"},
|
||||
{file = "pycares-4.3.0-cp37-cp37m-win32.whl", hash = "sha256:1b6cd3161851499b6894d1e23bfd633e7b775472f5af35ae35409c4a47a2d45e"},
|
||||
{file = "pycares-4.3.0-cp37-cp37m-win_amd64.whl", hash = "sha256:710120c97b9afdba443564350c3f5f72fd9aae74d95b73dc062ca8ac3d7f36d7"},
|
||||
{file = "pycares-4.3.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:9103649bd29d84bc6bcfaf09def9c0592bbc766018fad19d76d09989608b915d"},
|
||||
{file = "pycares-4.3.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:c072dbaf73cb5434279578dc35322867d8d5df053e14fdcdcc589994ba4804ae"},
|
||||
{file = "pycares-4.3.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:008531733f9c7a976b59c7760a3672b191159fd69ae76c01ca051f20b5e44164"},
|
||||
{file = "pycares-4.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2aae02d97d77dcff840ab55f86cb8b99bf644acbca17e1edb7048408b9782088"},
|
||||
{file = "pycares-4.3.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:257953ae6d400a934fd9193aeb20990ac84a78648bdf5978e998bd007a4045cd"},
|
||||
{file = "pycares-4.3.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:c28d481efae26936ec08cb6beea305f4b145503b152cf2c4dc68cc4ad9644f0e"},
|
||||
{file = "pycares-4.3.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:976249b39037dbfb709ccf7e1c40d2785905a0065536385d501b94570cfed96d"},
|
||||
{file = "pycares-4.3.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:98568c30cfab6b327d94ae1acdf85bbba4cffd415980804985d34ca07e6f4791"},
|
||||
{file = "pycares-4.3.0-cp38-cp38-win32.whl", hash = "sha256:a2f3c4f49f43162f7e684419d9834c2c8ec165e54cb8dc47aa9dc0c2132701c0"},
|
||||
{file = "pycares-4.3.0-cp38-cp38-win_amd64.whl", hash = "sha256:1730ef93e33e4682fbbf0e7fb19df2ed9822779d17de8ea6e20d5b0d71c1d2be"},
|
||||
{file = "pycares-4.3.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:5a26b3f1684557025da26ce65d076619890c82b95e38cc7284ce51c3539a1ce8"},
|
||||
{file = "pycares-4.3.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:86112cce01655b9f63c5e53b74722084e88e784a7a8ad138d373440337c591c9"},
|
||||
{file = "pycares-4.3.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c01465a191dc78e923884bb45cd63c7e012623e520cf7ed67e542413ee334804"},
|
||||
{file = "pycares-4.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9fd5d6012f3ee8c8038cbfe16e988bbd17b2f21eea86650874bf63757ee6161"},
|
||||
{file = "pycares-4.3.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:aa36b8ea91eae20b5c7205f3e6654423f066af24a1df02b274770a96cbcafaa7"},
|
||||
{file = "pycares-4.3.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:61019151130557c1788cae52e4f2f388a7520c9d92574f3a0d61c974c6740db0"},
|
||||
{file = "pycares-4.3.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:231962bb46274c52632469a1e686fab065dbd106dbef586de4f7fb101e297587"},
|
||||
{file = "pycares-4.3.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:6c979512fa51c7ccef5204fe10ed4e5c44c2bce5f335fe98a3e423f1672bd7d4"},
|
||||
{file = "pycares-4.3.0-cp39-cp39-win32.whl", hash = "sha256:655cf0df862ce3847a60e1a106dafa2ba2c14e6636bac49e874347acdc7312dc"},
|
||||
{file = "pycares-4.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:36f2251ad0f99a5ce13df45c94c3161d9734c9e9fa2b9b4cc163b853ca170dc5"},
|
||||
{file = "pycares-4.3.0.tar.gz", hash = "sha256:c542696f6dac978e9d99192384745a65f80a7d9450501151e4a7563e06010d45"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
cffi = ">=1.5.0"
|
||||
|
||||
[package.extras]
|
||||
idna = ["idna (>=2.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "pycodestyle"
|
||||
version = "2.10.0"
|
||||
@ -6177,7 +6301,7 @@ name = "tokenizers"
|
||||
version = "0.13.2"
|
||||
description = "Fast and Customizable Tokenizers"
|
||||
category = "main"
|
||||
optional = true
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "tokenizers-0.13.2-cp310-cp310-macosx_10_11_x86_64.whl", hash = "sha256:a6f36b1b499233bb4443b5e57e20630c5e02fba61109632f5e00dab970440157"},
|
||||
@ -7068,4 +7192,4 @@ llms = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "manifes
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "7997201f64373247d8799baed84a5ad11ab3d92e26cc2114b26e734cfb9664a4"
|
||||
content-hash = "2f916a8467f87cb850664b564c317dab569c9fee490e05308ac85427ef3abadc"
|
||||
|
||||
@ -48,6 +48,7 @@ sentence-transformers = {version = "^2", optional = true}
|
||||
aiohttp = "^3.8.3"
|
||||
pypdf = {version = "^3.4.0", optional = true}
|
||||
networkx = {version="^2.6.3", optional = true}
|
||||
aleph-alpha-client = "^2.15.0"
|
||||
|
||||
[tool.poetry.group.docs.dependencies]
|
||||
autodoc_pydantic = "^1.8.0"
|
||||
|
||||
10
tests/integration_tests/llms/test_aleph_alpha.py
Normal file
10
tests/integration_tests/llms/test_aleph_alpha.py
Normal file
@ -0,0 +1,10 @@
|
||||
"""Test Aleph Alpha API wrapper."""
|
||||
|
||||
from langchain.llms.aleph_alpha import AlephAlpha
|
||||
|
||||
|
||||
def test_aleph_alpha_call() -> None:
|
||||
"""Test valid call to cohere."""
|
||||
llm = AlephAlpha(maximum_tokens=10)
|
||||
output = llm("Say foo:")
|
||||
assert isinstance(output, str)
|
||||
Loading…
Reference in New Issue
Block a user