text stringlengths 3 1.68M | id stringlengths 13 169 | metadata dict | __index_level_0__ int64 0 2.21k |
|---|---|---|---|
# accelerate-aws-sagemaker
Examples showcasing AWS SageMaker integration of 🤗 Accelerate. Just give the `accelerate config` and do `accelerate launch` 🚀. It's as simple as that!
1. Set up the accelerate config by running `accelerate config --config_file accelerate_config.yaml` and answer the SageMaker questions.
2.... | notebooks/sagemaker/22_accelerate_sagemaker_examples/README.md/0 | {
"file_path": "notebooks/sagemaker/22_accelerate_sagemaker_examples/README.md",
"repo_id": "notebooks",
"token_count": 3628
} | 329 |
from typing import List
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
class WikipediaRetriever(BaseRetriever, WikipediaAPIWr... | langchain/libs/community/langchain_community/retrievers/wikipedia.py/0 | {
"file_path": "langchain/libs/community/langchain_community/retrievers/wikipedia.py",
"repo_id": "langchain",
"token_count": 202
} | 279 |
import { ChatOpenAI } from "@langchain/openai";
import { initializeAgentExecutorWithOptions } from "langchain/agents";
import { Calculator } from "langchain/tools/calculator";
import { SerpAPI } from "@langchain/community/tools/serpapi";
export const run = async () => {
process.env.LANGCHAIN_TRACING = "true";
cons... | langchainjs/examples/src/agents/chat_mrkl_with_tracing.ts/0 | {
"file_path": "langchainjs/examples/src/agents/chat_mrkl_with_tracing.ts",
"repo_id": "langchainjs",
"token_count": 400
} | 778 |
from typing import List
import datasets
from datasets.tasks import ImageClassification
from ..folder_based_builder import folder_based_builder
logger = datasets.utils.logging.get_logger(__name__)
class ImageFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""BuilderConfig for ImageFolder."""
... | datasets/src/datasets/packaged_modules/imagefolder/imagefolder.py/0 | {
"file_path": "datasets/src/datasets/packaged_modules/imagefolder/imagefolder.py",
"repo_id": "datasets",
"token_count": 883
} | 135 |
<jupyter_start><jupyter_text>Diffusion pour l'audio Dans ce *notebook*, nous allons jeter un bref coup d'œil à la génération d'audio avec des modèles de diffusion.Ce que vous allez apprendre :- Comment l'audio est représenté dans un ordinateur- Les méthodes de conversion entre les données audio brutes et les spectrogra... | diffusion-models-class/units/fr/unit4/diffusion_for_audio.ipynb/0 | {
"file_path": "diffusion-models-class/units/fr/unit4/diffusion_for_audio.ipynb",
"repo_id": "diffusion-models-class",
"token_count": 5905
} | 308 |
# Copyright 2024 Open AI and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py",
"repo_id": "diffusers",
"token_count": 5792
} | 241 |
# Ontotext GraphDB
>[Ontotext GraphDB](https://graphdb.ontotext.com/) is a graph database and knowledge discovery tool compliant with RDF and SPARQL.
## Dependencies
Install the [rdflib](https://github.com/RDFLib/rdflib) package with
```bash
pip install rdflib==7.0.0
```
## Graph QA Chain
Connect your GraphDB Data... | langchain/docs/docs/integrations/providers/ontotext_graphdb.mdx/0 | {
"file_path": "langchain/docs/docs/integrations/providers/ontotext_graphdb.mdx",
"repo_id": "langchain",
"token_count": 187
} | 154 |
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.tools.azure_cv import AzureCVToolSpec
def test_class():
names_of_base_classes = [b.__name__ for b in AzureCVToolSpec.__mro__]
assert BaseToolSpec.__name__ in names_of_base_classes
| llama_index/llama-index-integrations/tools/llama-index-tools-azure-cv/tests/test_tools_azure_cv.py/0 | {
"file_path": "llama_index/llama-index-integrations/tools/llama-index-tools-azure-cv/tests/test_tools_azure_cv.py",
"repo_id": "llama_index",
"token_count": 96
} | 1,477 |
from llama_index.core.llms.base import BaseLLM
from llama_index.llms.azure_openai import AzureOpenAI
def test_text_inference_embedding_class():
names_of_base_classes = [b.__name__ for b in AzureOpenAI.__mro__]
assert BaseLLM.__name__ in names_of_base_classes
| llama_index/llama-index-integrations/llms/llama-index-llms-azure-openai/tests/test_llms_azure_openai.py/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-azure-openai/tests/test_llms_azure_openai.py",
"repo_id": "llama_index",
"token_count": 101
} | 1,303 |
from langchain_community.vectorstores.docarray.hnsw import DocArrayHnswSearch
from langchain_community.vectorstores.docarray.in_memory import DocArrayInMemorySearch
__all__ = [
"DocArrayHnswSearch",
"DocArrayInMemorySearch",
]
| langchain/libs/langchain/langchain/vectorstores/docarray/__init__.py/0 | {
"file_path": "langchain/libs/langchain/langchain/vectorstores/docarray/__init__.py",
"repo_id": "langchain",
"token_count": 76
} | 602 |
from fastapi import APIRouter
from app.api.assistants import router as assistants_router
from app.api.runs import router as runs_router
from app.api.threads import router as threads_router
router = APIRouter()
@router.get("/ok")
async def ok():
return {"ok": True}
router.include_router(
assistants_router,... | opengpts/backend/app/api/__init__.py/0 | {
"file_path": "opengpts/backend/app/api/__init__.py",
"repo_id": "opengpts",
"token_count": 208
} | 1,984 |
from langchain.chains.query_constructor.base import load_query_constructor_runnable
__all__ = ["load_query_constructor_runnable"]
| langchain/libs/langchain/langchain/chains/query_constructor/__init__.py/0 | {
"file_path": "langchain/libs/langchain/langchain/chains/query_constructor/__init__.py",
"repo_id": "langchain",
"token_count": 42
} | 464 |
python_sources()
| llama_index/llama-index-integrations/readers/llama-index-readers-google/llama_index/readers/google/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-google/llama_index/readers/google/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,376 |
from string import Formatter
from typing import List
from langchain.schema import Document
document_template = """
PASSAGE: {page_content}
METADATA: {metadata}
"""
def combine_documents(documents: List[Document]) -> str:
"""
Combine a list of documents into a single string that might be passed further down
... | langchain/templates/self-query-qdrant/self_query_qdrant/helper.py/0 | {
"file_path": "langchain/templates/self-query-qdrant/self_query_qdrant/helper.py",
"repo_id": "langchain",
"token_count": 242
} | 712 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | transformers/src/transformers/models/vitmatte/configuration_vitmatte.py/0 | {
"file_path": "transformers/src/transformers/models/vitmatte/configuration_vitmatte.py",
"repo_id": "transformers",
"token_count": 2359
} | 748 |
from langchain_community.vectorstores.redis.filters import (
RedisFilter,
RedisFilterExpression,
RedisFilterField,
RedisFilterOperator,
RedisNum,
RedisTag,
RedisText,
check_operator_misuse,
)
__all__ = [
"RedisFilterOperator",
"RedisFilter",
"RedisFilterField",
"check_op... | langchain/libs/langchain/langchain/vectorstores/redis/filters.py/0 | {
"file_path": "langchain/libs/langchain/langchain/vectorstores/redis/filters.py",
"repo_id": "langchain",
"token_count": 180
} | 630 |
import { BufferMemory } from "langchain/memory";
import {
ChatPromptTemplate,
MessagesPlaceholder,
} from "@langchain/core/prompts";
import { RunnableSequence } from "@langchain/core/runnables";
import { ChatAnthropic } from "@langchain/anthropic";
const model = new ChatAnthropic();
const prompt = ChatPromptTempla... | langchainjs/examples/src/guides/expression_language/cookbook_memory.ts/0 | {
"file_path": "langchainjs/examples/src/guides/expression_language/cookbook_memory.ts",
"repo_id": "langchainjs",
"token_count": 701
} | 778 |
// Licensed to the LF AI & Data foundation under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use th... | milvus/internal/core/src/storage/PayloadStream.h/0 | {
"file_path": "milvus/internal/core/src/storage/PayloadStream.h",
"repo_id": "milvus",
"token_count": 784
} | 1,808 |
// Licensed to the LF AI & Data foundation under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use th... | milvus/internal/core/src/query/GroupByOperator.cpp/0 | {
"file_path": "milvus/internal/core/src/query/GroupByOperator.cpp",
"repo_id": "milvus",
"token_count": 4754
} | 1,759 |
# coding=utf-8
# Copyright 2022 The OFA-Sys Team Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/... | transformers/src/transformers/models/chinese_clip/convert_chinese_clip_original_pytorch_to_hf.py/0 | {
"file_path": "transformers/src/transformers/models/chinese_clip/convert_chinese_clip_original_pytorch_to_hf.py",
"repo_id": "transformers",
"token_count": 1990
} | 575 |
import multiprocessing
import numbers
import random
import pytest
import pandas as pd
from time import sleep
from base.client_base import TestcaseBase
from utils.util_log import test_log as log
from common import common_func as cf
from common import common_type as ct
from common.common_type import CaseLabel, CheckTas... | milvus/tests/python_client/rate_limit/test_rate_limit.py/0 | {
"file_path": "milvus/tests/python_client/rate_limit/test_rate_limit.py",
"repo_id": "milvus",
"token_count": 2143
} | 2,041 |
<jupyter_start><jupyter_text>Fine Tuning with Function CallingIn this notebook, we walk through how to fine-tune gpt-3.5-turbo with function calls. The primary use case here is structured data extraction. Our main focus is distilling GPT-4 outputs to help improve gpt-3.5-turbo function calling capabilities.We will walk... | llama_index/docs/examples/finetuning/openai_fine_tuning_functions.ipynb/0 | {
"file_path": "llama_index/docs/examples/finetuning/openai_fine_tuning_functions.ipynb",
"repo_id": "llama_index",
"token_count": 3807
} | 1,103 |
---
sidebar_position: 1
---
# Defining custom tools
One option for creating a tool that runs custom code is to use a `DynamicTool`.
The `DynamicTool` and `DynamicStructuredTool` classes takes as input a name, a description, and a function.
Importantly, the name and the description will be used by the language model ... | langchainjs/docs/core_docs/docs/modules/agents/tools/dynamic.mdx/0 | {
"file_path": "langchainjs/docs/core_docs/docs/modules/agents/tools/dynamic.mdx",
"repo_id": "langchainjs",
"token_count": 388
} | 802 |
def __getattr__(name: str = "") -> None:
"""Raise an error on import since is deprecated."""
raise ImportError(
"This module has been moved to langchain-experimental. "
"For more details: https://github.com/langchain-ai/langchain/discussions/11352."
"To access this code, install it with ... | langchain/libs/langchain/langchain/chains/llm_symbolic_math/__init__.py/0 | {
"file_path": "langchain/libs/langchain/langchain/chains/llm_symbolic_math/__init__.py",
"repo_id": "langchain",
"token_count": 175
} | 493 |
python_tests()
| llama_index/llama-index-packs/llama-index-packs-amazon-product-extraction/tests/BUILD/0 | {
"file_path": "llama_index/llama-index-packs/llama-index-packs-amazon-product-extraction/tests/BUILD",
"repo_id": "llama_index",
"token_count": 5
} | 1,642 |
python_sources()
| llama_index/llama-index-integrations/readers/llama-index-readers-whatsapp/llama_index/readers/whatsapp/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-whatsapp/llama_index/readers/whatsapp/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,542 |
package main
import (
"fmt"
"os"
"sort"
"github.com/spf13/viper"
"go.uber.org/zap"
"github.com/milvus-io/milvus/pkg/log"
)
func ShowYaml(filepath string) {
reader := viper.New()
reader.SetConfigFile(filepath)
if err := reader.ReadInConfig(); err != nil {
log.Warn("read config failed", zap.Error(err))
o... | milvus/cmd/tools/config/printer.go/0 | {
"file_path": "milvus/cmd/tools/config/printer.go",
"repo_id": "milvus",
"token_count": 217
} | 1,753 |
python_tests()
| llama_index/llama-index-packs/llama-index-packs-infer-retrieve-rerank/tests/BUILD/0 | {
"file_path": "llama_index/llama-index-packs/llama-index-packs-infer-retrieve-rerank/tests/BUILD",
"repo_id": "llama_index",
"token_count": 5
} | 1,571 |
"""KG-based data structures."""
from llama_index.core.indices.knowledge_graph.base import (
KnowledgeGraphIndex,
)
from llama_index.core.indices.knowledge_graph.retrievers import (
KGTableRetriever,
KnowledgeGraphRAGRetriever,
)
__all__ = [
"KnowledgeGraphIndex",
"KGTableRetriever",
"Knowledge... | llama_index/llama-index-core/llama_index/core/indices/knowledge_graph/__init__.py/0 | {
"file_path": "llama_index/llama-index-core/llama_index/core/indices/knowledge_graph/__init__.py",
"repo_id": "llama_index",
"token_count": 129
} | 1,263 |
import { Redis } from "ioredis";
import { OpenAIEmbeddings } from "@langchain/openai";
import { CacheBackedEmbeddings } from "langchain/embeddings/cache_backed";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { Text... | langchainjs/examples/src/embeddings/cache_backed_redis.ts/0 | {
"file_path": "langchainjs/examples/src/embeddings/cache_backed_redis.ts",
"repo_id": "langchainjs",
"token_count": 772
} | 813 |
"""Test tree summarize."""
from typing import Any, List, Sequence
from unittest.mock import Mock, patch
import pytest
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.indices.prompt_helper import PromptHelper
from llama_index.core.llms.mock import MockLLM
from llama_index.core.prompts.base... | llama_index/llama-index-core/tests/indices/response/test_tree_summarize.py/0 | {
"file_path": "llama_index/llama-index-core/tests/indices/response/test_tree_summarize.py",
"repo_id": "llama_index",
"token_count": 1922
} | 1,232 |
# Prerequisites:
# 1. Create a Dropbox app.
# 2. Give the app these scope permissions: `files.metadata.read`
# and `files.content.read`.
# 3. Generate access token: https://www.dropbox.com/developers/apps/create.
# 4. `pip install dropbox` (requires `pip install unstructured[pdf]` for PDF filetype).
import os
impo... | langchain/libs/community/langchain_community/document_loaders/dropbox.py/0 | {
"file_path": "langchain/libs/community/langchain_community/document_loaders/dropbox.py",
"repo_id": "langchain",
"token_count": 2791
} | 249 |
<jupyter_start><jupyter_text>Embaas[embaas](https://embaas.io) is a fully managed NLP API service that offers features like embedding generation, document text extraction, document to embeddings and more. You can choose a [variety of pre-trained models](https://embaas.io/docs/models/embeddings).In this tutorial, we wil... | langchain/docs/docs/integrations/text_embedding/embaas.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/text_embedding/embaas.ipynb",
"repo_id": "langchain",
"token_count": 492
} | 162 |
import * as url from "node:url";
import * as path from "node:path";
import * as fs from "node:fs/promises";
import { test, expect } from "@jest/globals";
import { Document } from "@langchain/core/documents";
import { ChatGPTLoader } from "../fs/chatgpt.js";
test("Test ChatGPT loader from blob to load all documents", a... | langchainjs/langchain/src/document_loaders/tests/chatgpt-blob.test.ts/0 | {
"file_path": "langchainjs/langchain/src/document_loaders/tests/chatgpt-blob.test.ts",
"repo_id": "langchainjs",
"token_count": 1013
} | 903 |
import 'isomorphic-fetch';
/* eslint-disable */
// tslint:disable
/**
* FastAPI
*
*
* OpenAPI spec version: 0.1.0
*
*
* NOTE: This class is auto generated by OpenAPI Generator+.
* https://github.com/karlvr/openapi-generator-plus
* Do not edit the class manually.
*/
export const defaultFetch = fetch;
import {... | chroma/clients/js/src/generated/runtime.ts/0 | {
"file_path": "chroma/clients/js/src/generated/runtime.ts",
"repo_id": "chroma",
"token_count": 448
} | 33 |
from typing import Optional
from llama_index.legacy.storage.docstore.keyval_docstore import KVDocumentStore
from llama_index.legacy.storage.docstore.types import DEFAULT_BATCH_SIZE
from llama_index.legacy.storage.kvstore.dynamodb_kvstore import DynamoDBKVStore
class DynamoDBDocumentStore(KVDocumentStore):
def __... | llama_index/llama-index-legacy/llama_index/legacy/storage/docstore/dynamodb_docstore.py/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/storage/docstore/dynamodb_docstore.py",
"repo_id": "llama_index",
"token_count": 374
} | 1,523 |
import unittest
from typing import Callable
from datasets import Dataset, load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl.extras.dataset_formatting import get_formatting_func_from_dataset
from trl.models.utils import ChatMlSpecialTokens, setup_chat_format
class DatasetFormattingTe... | trl/tests/test_dataset_formatting.py/0 | {
"file_path": "trl/tests/test_dataset_formatting.py",
"repo_id": "trl",
"token_count": 3064
} | 871 |
/* eslint-disable no-plusplus */
/* eslint-disable prefer-template */
/* eslint-disable prefer-arrow-callback */
/* eslint-disable no-var */
/* eslint-disable vars-on-top */
/* eslint-disable no-param-reassign */
/* eslint-disable import/no-extraneous-dependencies */
/**
* This is copied from @vespaiach/axios-fetch-a... | langchainjs/langchain/src/util/axios-fetch-adapter.js/0 | {
"file_path": "langchainjs/langchain/src/util/axios-fetch-adapter.js",
"repo_id": "langchainjs",
"token_count": 4730
} | 945 |
// Copyright (C) 2019-2020 Zilliz. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable l... | milvus/pkg/util/hardware/container_linux.go/0 | {
"file_path": "milvus/pkg/util/hardware/container_linux.go",
"repo_id": "milvus",
"token_count": 1227
} | 1,836 |
from llama_index.readers.pdb.base import PdbAbstractReader
__all__ = ["PdbAbstractReader"]
| llama_index/llama-index-integrations/readers/llama-index-readers-pdb/llama_index/readers/pdb/__init__.py/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-pdb/llama_index/readers/pdb/__init__.py",
"repo_id": "llama_index",
"token_count": 31
} | 1,502 |
"""Hierarchical node parser."""
from typing import Any, Dict, List, Optional, Sequence
from llama_index.legacy.bridge.pydantic import Field
from llama_index.legacy.callbacks.base import CallbackManager
from llama_index.legacy.callbacks.schema import CBEventType, EventPayload
from llama_index.legacy.node_parser.interf... | llama_index/llama-index-legacy/llama_index/legacy/node_parser/relational/hierarchical.py/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/node_parser/relational/hierarchical.py",
"repo_id": "llama_index",
"token_count": 3376
} | 1,580 |
export * from "./chat_histories.js";
export * from "./vectorstores.js";
export * from "./caches.js";
| langchainjs/libs/langchain-redis/src/index.ts/0 | {
"file_path": "langchainjs/libs/langchain-redis/src/index.ts",
"repo_id": "langchainjs",
"token_count": 35
} | 1,020 |
export { Client } from "./client.js";
export type {
Dataset,
Example,
TracerSession,
Run,
Feedback,
} from "./schemas.js";
export { RunTree, type RunTreeConfig } from "./run_trees.js";
// Update using yarn bump-version
export const __version__ = "0.1.1";
| langsmith-sdk/js/src/index.ts/0 | {
"file_path": "langsmith-sdk/js/src/index.ts",
"repo_id": "langsmith-sdk",
"token_count": 97
} | 1,017 |
# coding=utf-8
# Copyright 2023 Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy... | transformers/src/transformers/models/ernie_m/tokenization_ernie_m.py/0 | {
"file_path": "transformers/src/transformers/models/ernie_m/tokenization_ernie_m.py",
"repo_id": "transformers",
"token_count": 7974
} | 599 |
from typing import Any, Dict
def _resolve_schema_references(schema: Any, definitions: Dict[str, Any]) -> Any:
"""
Resolves the $ref keys in a JSON schema object using the provided definitions.
"""
if isinstance(schema, list):
for i, item in enumerate(schema):
schema[i] = _resolve_s... | langchain/libs/langchain/langchain/chains/openai_functions/utils.py/0 | {
"file_path": "langchain/libs/langchain/langchain/chains/openai_functions/utils.py",
"repo_id": "langchain",
"token_count": 522
} | 508 |
# coding=utf-8
# Copyright 2023 Amazon and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | transformers/src/transformers/models/informer/modeling_informer.py/0 | {
"file_path": "transformers/src/transformers/models/informer/modeling_informer.py",
"repo_id": "transformers",
"token_count": 43141
} | 648 |
from __future__ import annotations
from pathlib import Path
from typing import (
TYPE_CHECKING,
Any,
Iterator,
List,
Literal,
Optional,
Sequence,
Union,
)
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseBlobParser, BaseLoader
fro... | langchain/libs/community/langchain_community/document_loaders/generic.py/0 | {
"file_path": "langchain/libs/community/langchain_community/document_loaders/generic.py",
"repo_id": "langchain",
"token_count": 2701
} | 236 |
"""Test empty index."""
from llama_index.core.data_structs.data_structs import EmptyIndexStruct
from llama_index.core.indices.empty.base import EmptyIndex
from llama_index.core.service_context import ServiceContext
def test_empty(
mock_service_context: ServiceContext,
) -> None:
"""Test build list."""
em... | llama_index/llama-index-core/tests/indices/empty/test_base.py/0 | {
"file_path": "llama_index/llama-index-core/tests/indices/empty/test_base.py",
"repo_id": "llama_index",
"token_count": 185
} | 1,156 |
"""Agent worker that takes in a query pipeline."""
import uuid
from typing import (
Any,
List,
Optional,
cast,
)
from llama_index.legacy.agent.types import (
BaseAgentWorker,
Task,
TaskStep,
TaskStepOutput,
)
from llama_index.legacy.bridge.pydantic import BaseModel, Field
from llama_in... | llama_index/llama-index-legacy/llama_index/legacy/agent/custom/pipeline_worker.py/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/agent/custom/pipeline_worker.py",
"repo_id": "llama_index",
"token_count": 2782
} | 1,635 |
<jupyter_start><jupyter_text>File processingThis client will be uploading a PDF file to the langserve server which will read the PDF and extract content from the first page. Let's load the file in base64 encoding:<jupyter_code>import base64
with open("sample.pdf", "rb") as f:
data = f.read()
encoded_data = base64... | langserve/examples/file_processing/client.ipynb/0 | {
"file_path": "langserve/examples/file_processing/client.ipynb",
"repo_id": "langserve",
"token_count": 300
} | 1,082 |
import { BaseClient } from "@xata.io/client";
import type { EmbeddingsInterface } from "@langchain/core/embeddings";
import { VectorStore } from "@langchain/core/vectorstores";
import { Document } from "@langchain/core/documents";
/**
* Interface for the arguments required to create a XataClient. Includes
* the clie... | langchainjs/libs/langchain-community/src/vectorstores/xata.ts/0 | {
"file_path": "langchainjs/libs/langchain-community/src/vectorstores/xata.ts",
"repo_id": "langchainjs",
"token_count": 1642
} | 1,092 |
import logging
import warnings
from typing import Any, Dict, List, Mapping, Optional
from langchain_core.callbacks import (
CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
AIMessage,
BaseMessage,
ChatMessage,
Fun... | langchain/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py/0 | {
"file_path": "langchain/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py",
"repo_id": "langchain",
"token_count": 2928
} | 226 |
---
sidebar_position: 1
sidebar_label: Cheerio
hide_table_of_contents: true
---
# Webpages, with Cheerio
This example goes over how to load data from webpages using Cheerio. One document will be created for each webpage.
Cheerio is a fast and lightweight library that allows you to parse and traverse HTML documents u... | langchainjs/docs/core_docs/docs/integrations/document_loaders/web_loaders/web_cheerio.mdx/0 | {
"file_path": "langchainjs/docs/core_docs/docs/integrations/document_loaders/web_loaders/web_cheerio.mdx",
"repo_id": "langchainjs",
"token_count": 419
} | 782 |
# Transformations
A transformation is something that takes a list of nodes as an input, and returns a list of nodes. Each component that implements the `Transformation` base class has both a synchronous `__call__()` definition and an async `acall()` definition.
Currently, the following components are `Transformation`... | llama_index/docs/module_guides/loading/ingestion_pipeline/transformations.md/0 | {
"file_path": "llama_index/docs/module_guides/loading/ingestion_pipeline/transformations.md",
"repo_id": "llama_index",
"token_count": 941
} | 1,157 |
use tokenizers::models::bpe::BPE;
use tokenizers::pre_tokenizers::whitespace::Whitespace;
use tokenizers::{DecoderWrapper, NormalizerWrapper, PostProcessorWrapper, PreTokenizerWrapper};
use tokenizers::{Model, Tokenizer, TokenizerBuilder};
#[test]
fn bpe_values_after_training() {
let mut tokenizer = TokenizerBuild... | tokenizers/tokenizers/tests/training.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/training.rs",
"repo_id": "tokenizers",
"token_count": 851
} | 443 |
python_sources()
| llama_index/llama-index-core/llama_index/core/data_structs/BUILD/0 | {
"file_path": "llama_index/llama-index-core/llama_index/core/data_structs/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,113 |
python_sources()
| llama_index/llama-index-integrations/readers/llama-index-readers-psychic/llama_index/readers/psychic/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-psychic/llama_index/readers/psychic/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,362 |
import { ChatOpenAI } from "@langchain/openai";
import { initializeAgentExecutorWithOptions } from "langchain/agents";
import { RequestsGetTool, RequestsPostTool } from "langchain/tools";
import { AIPluginTool } from "@langchain/community/tools/aiplugin";
export const run = async () => {
const tools = [
new Requ... | langchainjs/examples/src/agents/aiplugin-tool.ts/0 | {
"file_path": "langchainjs/examples/src/agents/aiplugin-tool.ts",
"repo_id": "langchainjs",
"token_count": 256
} | 837 |
""" Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'Exploring Plain Vision Transformer Backbones for Object Detection'
- https://arxiv.org/abs/2203.16527
'Segment Anything Model (SAM)'
- https://github.com/facebookresearch/segment-anything/
"""
import logging... | pytorch-image-models/timm/models/vision_transformer_sam.py/0 | {
"file_path": "pytorch-image-models/timm/models/vision_transformer_sam.py",
"repo_id": "pytorch-image-models",
"token_count": 12451
} | 379 |
from __future__ import annotations
import logging
import os
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Iterable,
List,
Optional,
Tuple,
TypeVar,
)
import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from ... | langchain/libs/partners/pinecone/langchain_pinecone/vectorstores.py/0 | {
"file_path": "langchain/libs/partners/pinecone/langchain_pinecone/vectorstores.py",
"repo_id": "langchain",
"token_count": 7973
} | 639 |
# Research projects
This folder contains various research projects using 🧨 Diffusers.
They are not really maintained by the core maintainers of this library and often require a specific version of Diffusers that is indicated in the requirements file of each folder.
Updating them to the most recent version of the libr... | diffusers/examples/research_projects/README.md/0 | {
"file_path": "diffusers/examples/research_projects/README.md",
"repo_id": "diffusers",
"token_count": 143
} | 195 |
from typing import Callable, List
from llama_index.bridge.pydantic import BaseModel
from llama_index.tools.types import BaseTool
class Task(BaseModel):
message: str
expected_response: str
tools: List[BaseTool]
eval_fn: Callable[[str, str], bool]
class Config:
arbitrary_types_allowed = Tr... | llama_index/benchmarks/agent/task.py/0 | {
"file_path": "llama_index/benchmarks/agent/task.py",
"repo_id": "llama_index",
"token_count": 111
} | 1,085 |
<jupyter_start><jupyter_text>ArceeThis notebook demonstrates how to use the `Arcee` class for generating text using Arcee's Domain Adapted Language Models (DALMs). SetupBefore using Arcee, make sure the Arcee API key is set as `ARCEE_API_KEY` environment variable. You can also pass the api key as a named parameter.<ju... | langchain/docs/docs/integrations/llms/arcee.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/llms/arcee.ipynb",
"repo_id": "langchain",
"token_count": 823
} | 116 |
"""
**LLM** classes provide
access to the large language model (**LLM**) APIs and services.
**Class hierarchy:**
.. code-block::
BaseLanguageModel --> BaseLLM --> LLM --> <name> # Examples: AI21, HuggingFaceHub, OpenAI
**Main helpers:**
.. code-block::
LLMResult, PromptValue,
CallbackManagerForLLMRun... | langchain/libs/langchain/langchain/llms/__init__.py/0 | {
"file_path": "langchain/libs/langchain/langchain/llms/__init__.py",
"repo_id": "langchain",
"token_count": 6970
} | 507 |
# ESE-VoVNet
**VoVNet** is a convolutional neural network that seeks to make [DenseNet](https://paperswithcode.com/method/densenet) more efficient by concatenating all features only once in the last feature map, which makes input size constant and enables enlarging new output channel.
Read about [one-shot aggregatio... | pytorch-image-models/docs/models/.templates/models/ese-vovnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/ese-vovnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1127
} | 343 |
# Query Transformations
LlamaIndex allows you to perform _query transformations_ over your index structures.
Query transformations are modules that will convert a query into another query. They can be **single-step**, as in the transformation is run once before the query is executed against an index.
They can also be... | llama_index/docs/optimizing/advanced_retrieval/query_transformations.md/0 | {
"file_path": "llama_index/docs/optimizing/advanced_retrieval/query_transformations.md",
"repo_id": "llama_index",
"token_count": 1017
} | 1,184 |
// Copyright (C) 2019-2020 Zilliz. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable l... | milvus/internal/core/src/segcore/Utils.cpp/0 | {
"file_path": "milvus/internal/core/src/segcore/Utils.cpp",
"repo_id": "milvus",
"token_count": 15968
} | 1,658 |
import { OpenAIEmbeddings } from "@langchain/openai";
import {
AstraDBVectorStore,
AstraLibArgs,
} from "@langchain/community/vectorstores/astradb";
const astraConfig: AstraLibArgs = {
token: process.env.ASTRA_DB_APPLICATION_TOKEN as string,
endpoint: process.env.ASTRA_DB_ENDPOINT as string,
collection: proc... | langchainjs/examples/src/indexes/vector_stores/astra.ts/0 | {
"file_path": "langchainjs/examples/src/indexes/vector_stores/astra.ts",
"repo_id": "langchainjs",
"token_count": 342
} | 782 |
---
sidebar_position: 2
sidebar_class_name: hidden
---
# Documents
These are the core chains for working with Documents. They are useful for summarizing documents, answering questions over documents, extracting information from documents, and more.
These chains are all loaded in a similar way:
import IntegrationIns... | langchainjs/docs/core_docs/docs/modules/chains/document/index.mdx/0 | {
"file_path": "langchainjs/docs/core_docs/docs/modules/chains/document/index.mdx",
"repo_id": "langchainjs",
"token_count": 469
} | 727 |
from csv_agent.agent import agent_executor
if __name__ == "__main__":
question = "who was in cabin c28?"
print(agent_executor.invoke({"input": question})) # noqa: T201
| langchain/templates/csv-agent/main.py/0 | {
"file_path": "langchain/templates/csv-agent/main.py",
"repo_id": "langchain",
"token_count": 65
} | 651 |
from llama_index.packs.corrective_rag.base import CorrectiveRAGPack
__all__ = ["CorrectiveRAGPack"]
| llama_index/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/__init__.py/0 | {
"file_path": "llama_index/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/__init__.py",
"repo_id": "llama_index",
"token_count": 32
} | 1,837 |
from typing import Dict
from ..utils import add_end_docstrings, is_vision_available
from .base import GenericTensor, Pipeline, build_pipeline_init_args
if is_vision_available():
from ..image_utils import load_image
@add_end_docstrings(
build_pipeline_init_args(has_image_processor=True),
"""
ima... | transformers/src/transformers/pipelines/image_feature_extraction.py/0 | {
"file_path": "transformers/src/transformers/pipelines/image_feature_extraction.py",
"repo_id": "transformers",
"token_count": 1476
} | 762 |
"""Test embedding model integration."""
from __module_name__.embeddings import __ModuleName__Embeddings
def test_initialization() -> None:
"""Test embedding model initialization."""
__ModuleName__Embeddings()
| langchain/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_embeddings.py/0 | {
"file_path": "langchain/libs/cli/langchain_cli/integration_template/tests/unit_tests/test_embeddings.py",
"repo_id": "langchain",
"token_count": 64
} | 200 |
import type { BaseLanguageModelInterface } from "@langchain/core/language_models/base";
import type { ToolInterface } from "@langchain/core/tools";
import {
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
renderTemplate,
} from "@langchain/core/prompts";
impor... | langchainjs/langchain/src/agents/chat_convo/index.ts/0 | {
"file_path": "langchainjs/langchain/src/agents/chat_convo/index.ts",
"repo_id": "langchainjs",
"token_count": 2316
} | 867 |
"""Util that can interact with Zapier NLA.
Full docs here: https://nla.zapier.com/start/
Note: this wrapper currently only implemented the `api_key` auth method for testing
and server-side production use cases (using the developer's connected accounts on
Zapier.com)
For use-cases where LangChain + Zapier NLA is powe... | langchain/libs/community/langchain_community/utilities/zapier.py/0 | {
"file_path": "langchain/libs/community/langchain_community/utilities/zapier.py",
"repo_id": "langchain",
"token_count": 4805
} | 327 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | diffusers/tests/fixtures/custom_pipeline/pipeline.py/0 | {
"file_path": "diffusers/tests/fixtures/custom_pipeline/pipeline.py",
"repo_id": "diffusers",
"token_count": 1738
} | 278 |
---
hide_table_of_contents: true
---
# HyDE Retriever
This example shows how to use the HyDE Retriever, which implements Hypothetical Document Embeddings (HyDE) as described in [this paper](https://arxiv.org/abs/2212.10496).
At a high level, HyDE is an embedding technique that takes queries, generates a hypothetical... | langchainjs/docs/core_docs/docs/integrations/retrievers/hyde.mdx/0 | {
"file_path": "langchainjs/docs/core_docs/docs/integrations/retrievers/hyde.mdx",
"repo_id": "langchainjs",
"token_count": 304
} | 740 |
[build-system]
build-backend = "poetry.core.masonry.api"
requires = ["poetry-core"]
[tool.codespell]
check-filenames = true
check-hidden = true
skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb"
[tool.llamahub]
classes = ["BagelReader"]
contains_example = false
import_path = "llama_index.readers.bagel"
[tool.m... | llama_index/llama-index-integrations/readers/llama-index-readers-bagel/pyproject.toml/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-bagel/pyproject.toml",
"repo_id": "llama_index",
"token_count": 672
} | 1,341 |
import unittest
from langchain_community.document_loaders.fauna import FaunaLoader
try:
import fauna # noqa: F401
fauna_installed = True
except ImportError:
fauna_installed = False
@unittest.skipIf(not fauna_installed, "fauna not installed")
class TestFaunaLoader(unittest.TestCase):
def setUp(self... | langchain/libs/community/tests/integration_tests/document_loaders/test_fauna.py/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/document_loaders/test_fauna.py",
"repo_id": "langchain",
"token_count": 609
} | 348 |
<jupyter_start><jupyter_text>TensorFlow Hub>[TensorFlow Hub](https://www.tensorflow.org/hub) is a repository of trained machine learning models ready for fine-tuning and deployable anywhere. Reuse trained models like `BERT` and `Faster R-CNN` with just a few lines of code.>>Let's load the TensorflowHub Embedding class.... | langchain/docs/docs/integrations/text_embedding/tensorflowhub.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/text_embedding/tensorflowhub.ipynb",
"repo_id": "langchain",
"token_count": 190
} | 166 |
python_sources()
| llama_index/llama-index-legacy/llama_index/legacy/response/BUILD/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/response/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,598 |
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | transformers/src/transformers/models/wav2vec2_bert/processing_wav2vec2_bert.py/0 | {
"file_path": "transformers/src/transformers/models/wav2vec2_bert/processing_wav2vec2_bert.py",
"repo_id": "transformers",
"token_count": 2889
} | 689 |
from typing import Any, Optional, Sequence
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType
from tonic_validate.metrics.answer_consistency_metric import (
AnswerConsistencyMetric,
)
from tonic_validate.services.... | llama_index/llama-index-integrations/evaluation/llama-index-evaluation-tonic-validate/llama_index/evaluation/tonic_validate/answer_consistency.py/0 | {
"file_path": "llama_index/llama-index-integrations/evaluation/llama-index-evaluation-tonic-validate/llama_index/evaluation/tonic_validate/answer_consistency.py",
"repo_id": "llama_index",
"token_count": 790
} | 1,376 |
"""**Docstores** are classes to store and load Documents.
The **Docstore** is a simplified version of the Document Loader.
**Class hierarchy:**
.. code-block::
Docstore --> <name> # Examples: InMemoryDocstore, Wikipedia
**Main helpers:**
.. code-block::
Document, AddableMixin
"""
import warnings
from typ... | langchain/libs/langchain/langchain/docstore/__init__.py/0 | {
"file_path": "langchain/libs/langchain/langchain/docstore/__init__.py",
"repo_id": "langchain",
"token_count": 414
} | 473 |
import { IEmbeddingFunction } from "./IEmbeddingFunction";
// Dynamically import module
let TransformersApi: Promise<any>;
export class DefaultEmbeddingFunction implements IEmbeddingFunction {
private pipelinePromise?: Promise<any> | null;
private transformersApi: any;
private model: string;
private revision:... | chroma/clients/js/src/embeddings/DefaultEmbeddingFunction.ts/0 | {
"file_path": "chroma/clients/js/src/embeddings/DefaultEmbeddingFunction.ts",
"repo_id": "chroma",
"token_count": 1263
} | 32 |
import { LunaryHandler } from "@langchain/community/callbacks/handlers/lunary";
import { initializeAgentExecutorWithOptions } from "langchain/agents";
import { ChatOpenAI } from "@langchain/openai";
import { Calculator } from "langchain/tools/calculator";
const tools = [new Calculator()];
const chat = new ChatOpenAI(... | langchainjs/examples/src/callbacks/lunary_langchain_agent.ts/0 | {
"file_path": "langchainjs/examples/src/callbacks/lunary_langchain_agent.ts",
"repo_id": "langchainjs",
"token_count": 227
} | 788 |
<jupyter_start><jupyter_text>XML parserThis output parser allows users to obtain results from LLM in the popular XML format. Keep in mind that large language models are leaky abstractions! You'll have to use an LLM with sufficient capacity to generate well-formed XML. In the following example we use Claude model (https... | langchain/docs/docs/modules/model_io/output_parsers/types/xml.ipynb/0 | {
"file_path": "langchain/docs/docs/modules/model_io/output_parsers/types/xml.ipynb",
"repo_id": "langchain",
"token_count": 869
} | 194 |
# coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | transformers/tests/models/vilt/test_image_processing_vilt.py/0 | {
"file_path": "transformers/tests/models/vilt/test_image_processing_vilt.py",
"repo_id": "transformers",
"token_count": 2455
} | 831 |
import { useEffect, useState } from "react";
import { simplifySchema } from "../utils/simplifySchema";
import { getDefaults } from "../utils/defaults";
export interface SchemaField {
type: string;
title: string;
description: string;
enum?: string[];
items?: SchemaField;
allOf?: SchemaField[];
}
export int... | opengpts/frontend/src/hooks/useSchemas.ts/0 | {
"file_path": "opengpts/frontend/src/hooks/useSchemas.ts",
"repo_id": "opengpts",
"token_count": 460
} | 1,991 |
use super::{
ConversionError, Operation, OperationConversionError, ScalarEncoding,
ScalarEncodingConversionError, SeqId, UpdateMetadata, UpdateMetadataValueConversionError,
};
use crate::{
chroma_proto,
errors::{ChromaError, ErrorCodes},
};
use thiserror::Error;
use uuid::Uuid;
#[derive(Debug)]
pub(cra... | chroma/rust/worker/src/types/embedding_record.rs/0 | {
"file_path": "chroma/rust/worker/src/types/embedding_record.rs",
"repo_id": "chroma",
"token_count": 4095
} | 61 |
from langchain_community.docstore.in_memory import InMemoryDocstore
__all__ = ["InMemoryDocstore"]
| langchain/libs/langchain/langchain/docstore/in_memory.py/0 | {
"file_path": "langchain/libs/langchain/langchain/docstore/in_memory.py",
"repo_id": "langchain",
"token_count": 30
} | 500 |
<jupyter_start><jupyter_text>Office365>[Microsoft 365](https://www.office.com/) is a product family of productivity software, collaboration and cloud-based services owned by `Microsoft`.>>Note: `Office 365` was rebranded as `Microsoft 365`.This notebook walks through connecting LangChain to `Office365` email and calend... | langchain/docs/docs/integrations/toolkits/office365.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/toolkits/office365.ipynb",
"repo_id": "langchain",
"token_count": 749
} | 170 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | transformers/tests/models/donut/test_modeling_donut_swin.py/0 | {
"file_path": "transformers/tests/models/donut/test_modeling_donut_swin.py",
"repo_id": "transformers",
"token_count": 6322
} | 730 |
- sections:
- local: index
title: 🤗 Tokenizers
- local: quicktour
title: Quicktour
- local: installation
title: Installation
- local: pipeline
title: The tokenization pipeline
- local: components
title: Components
- local: training_from_memory
title: Training from memory
title: G... | tokenizers/docs/source-doc-builder/_toctree.yml/0 | {
"file_path": "tokenizers/docs/source-doc-builder/_toctree.yml",
"repo_id": "tokenizers",
"token_count": 338
} | 410 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/using-diffusers/weighted_prompts.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/weighted_prompts.md",
"repo_id": "diffusers",
"token_count": 4105
} | 188 |
#!/bin/bash
# Define an array containing the base configs we wish to fine tune
configs=("zephyr" "openhermes")
# Define an array of loss types
loss_types=("sigmoid" "kto_pair" "ipo")
# Define an array of beta values
betas=("0.01" "0.1" "0.2" "0.3" "0.4" "0.5" "0.6" "0.7" "0.8" "0.9")
# Outer loop for loss types
for co... | alignment-handbook/recipes/pref_align_scan/launch_scan.sh/0 | {
"file_path": "alignment-handbook/recipes/pref_align_scan/launch_scan.sh",
"repo_id": "alignment-handbook",
"token_count": 430
} | 22 |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffusers/tests/pipelines/pixart_alpha/test_pixart.py/0 | {
"file_path": "diffusers/tests/pipelines/pixart_alpha/test_pixart.py",
"repo_id": "diffusers",
"token_count": 7102
} | 292 |
## 7. Query Coordinator
#### 7.1 Overview
<img src="./figs/query_coord.png" width=500>
#### 7.2 Query Coordinator Interface
```go
type QueryCoord interface {
Component
TimeTickProvider
// ShowCollections notifies RootCoord to list all collection names and other info in database at specified timestamp
ShowColl... | milvus/docs/developer_guides/chap07_query_coordinator.md/0 | {
"file_path": "milvus/docs/developer_guides/chap07_query_coordinator.md",
"repo_id": "milvus",
"token_count": 4316
} | 1,717 |
# Copyright 2024 Microsoft and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py",
"repo_id": "diffusers",
"token_count": 6462
} | 236 |
"""Test Titan Takeoff wrapper."""
import responses
from langchain_community.llms.titan_takeoff_pro import TitanTakeoffPro
@responses.activate
def test_titan_takeoff_pro_call() -> None:
"""Test valid call to Titan Takeoff."""
url = "http://localhost:3000/generate"
responses.add(responses.POST, url, json... | langchain/libs/community/tests/integration_tests/llms/test_titan_takeoff_pro.py/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/llms/test_titan_takeoff_pro.py",
"repo_id": "langchain",
"token_count": 174
} | 342 |
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