text stringlengths 3 1.68M | id stringlengths 13 169 | metadata dict | __index_level_0__ int64 0 2.21k |
|---|---|---|---|
// 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/querycoordv2/job/job.go/0 | {
"file_path": "milvus/internal/querycoordv2/job/job.go",
"repo_id": "milvus",
"token_count": 738
} | 1,860 |
from llama_index.packs.zephyr_query_engine.base import ZephyrQueryEnginePack
__all__ = ["ZephyrQueryEnginePack"]
| llama_index/llama-index-packs/llama-index-packs-zephyr-query-engine/llama_index/packs/zephyr_query_engine/__init__.py/0 | {
"file_path": "llama_index/llama-index-packs/llama-index-packs-zephyr-query-engine/llama_index/packs/zephyr_query_engine/__init__.py",
"repo_id": "llama_index",
"token_count": 39
} | 1,829 |
python_sources()
| llama_index/llama-index-integrations/embeddings/llama-index-embeddings-ollama/llama_index/embeddings/ollama/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/embeddings/llama-index-embeddings-ollama/llama_index/embeddings/ollama/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,271 |
[tool.poetry]
name = "rewrite-retrieve-read"
version = "0.0.1"
description = "Query transformation using the rewrite-retrieve-read to improve retrieval"
authors = []
readme = "README.md"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
langchain = "^0.1"
duckduckgo-search = "^3.9.3"
openai = "<2"
[tool.poetry.group... | langchain/templates/rewrite-retrieve-read/pyproject.toml/0 | {
"file_path": "langchain/templates/rewrite-retrieve-read/pyproject.toml",
"repo_id": "langchain",
"token_count": 272
} | 679 |
import { test, expect, jest } from "@jest/globals";
import { insecureHash } from "@langchain/core/utils/hash";
import { StoredGeneration } from "@langchain/core/messages";
import { UpstashRedisCache } from "../upstash_redis.js";
const sha1 = (str: string) => insecureHash(str);
test("UpstashRedisCache", async () => {... | langchainjs/libs/langchain-community/src/caches/tests/upstash_redis.test.ts/0 | {
"file_path": "langchainjs/libs/langchain-community/src/caches/tests/upstash_redis.test.ts",
"repo_id": "langchainjs",
"token_count": 288
} | 980 |
poetry_requirements(
name="poetry",
)
| llama_index/llama-index-integrations/embeddings/llama-index-embeddings-bedrock/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/embeddings/llama-index-embeddings-bedrock/BUILD",
"repo_id": "llama_index",
"token_count": 18
} | 1,234 |
# 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/schedulers/test_scheduler_flax.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_flax.py",
"repo_id": "diffusers",
"token_count": 18869
} | 281 |
// 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/proxy/data_coord_mock_test.go/0 | {
"file_path": "milvus/internal/proxy/data_coord_mock_test.go",
"repo_id": "milvus",
"token_count": 4787
} | 1,825 |
# Copyright 2023-present 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 law or... | peft/src/peft/tuners/prompt_tuning/config.py/0 | {
"file_path": "peft/src/peft/tuners/prompt_tuning/config.py",
"repo_id": "peft",
"token_count": 1090
} | 327 |
poetry_requirements(
name="poetry",
)
python_requirements(
name="reqs",
)
| llama_index/llama-index-integrations/readers/llama-index-readers-singlestore/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-singlestore/BUILD",
"repo_id": "llama_index",
"token_count": 36
} | 1,429 |
import type {
SupabaseFilterRPCCall,
SupabaseMetadata,
SupabaseVectorStore,
} from "@langchain/community/vectorstores/supabase";
import {
Comparator,
Comparators,
Comparison,
Operation,
Operator,
Operators,
StructuredQuery,
} from "../../chains/query_constructor/ir.js";
import { BaseTranslator } fro... | langchainjs/langchain/src/retrievers/self_query/supabase.ts/0 | {
"file_path": "langchainjs/langchain/src/retrievers/self_query/supabase.ts",
"repo_id": "langchainjs",
"token_count": 3862
} | 960 |
from typing import List
import pytest
from llama_index.legacy.indices.document_summary.base import DocumentSummaryIndex
from llama_index.legacy.response_synthesizers import get_response_synthesizer
from llama_index.legacy.schema import Document
from llama_index.legacy.service_context import ServiceContext
from tests.... | llama_index/llama-index-legacy/tests/indices/document_summary/conftest.py/0 | {
"file_path": "llama_index/llama-index-legacy/tests/indices/document_summary/conftest.py",
"repo_id": "llama_index",
"token_count": 470
} | 1,660 |
from llama_index.core.storage.docstore.keyval_docstore import KVDocumentStore
from llama_index.storage.docstore.mongodb import MongoDocumentStore
def test_class():
names_of_base_classes = [b.__name__ for b in MongoDocumentStore.__mro__]
assert KVDocumentStore.__name__ in names_of_base_classes
| llama_index/llama-index-integrations/storage/docstore/llama-index-storage-docstore-mongodb/tests/test_storage_docstore_mongodb.py/0 | {
"file_path": "llama_index/llama-index-integrations/storage/docstore/llama-index-storage-docstore-mongodb/tests/test_storage_docstore_mongodb.py",
"repo_id": "llama_index",
"token_count": 101
} | 1,490 |
import { NextRequest, NextResponse } from "next/server";
import { Message as VercelChatMessage, StreamingTextResponse } from "ai";
import { ChatOpenAI } from "@langchain/openai";
import { PromptTemplate } from "@langchain/core/prompts";
import { HttpResponseOutputParser } from "langchain/output_parsers";
export const... | langchain-nextjs-template/app/api/chat/route.ts/0 | {
"file_path": "langchain-nextjs-template/app/api/chat/route.ts",
"repo_id": "langchain-nextjs-template",
"token_count": 781
} | 65 |
# we define a fixture function below and it will be "used" by
# referencing its name from tests
import os
import pytest
from attr import dataclass
os.environ["AWS_DEFAULT_REGION"] = "us-east-1" # defaults region
@dataclass
class SageMakerTestEnvironment:
framework: str
role = "arn:aws:iam::558105141721:r... | transformers/tests/sagemaker/conftest.py/0 | {
"file_path": "transformers/tests/sagemaker/conftest.py",
"repo_id": "transformers",
"token_count": 1035
} | 788 |
- title: Get started
sections:
- local: index
title: 🤗 PEFT
- local: quicktour
title: Quicktour
- local: install
title: Installation
- title: Tutorial
sections:
- local: tutorial/peft_model_config
title: Configurations and models
- local: tutorial/peft_integrations
title: Integration... | peft/docs/source/_toctree.yml/0 | {
"file_path": "peft/docs/source/_toctree.yml",
"repo_id": "peft",
"token_count": 1041
} | 319 |
from langchain_community.chat_models.javelin_ai_gateway import (
ChatJavelinAIGateway,
ChatParams,
)
__all__ = ["ChatJavelinAIGateway", "ChatParams"]
| langchain/libs/langchain/langchain/chat_models/javelin_ai_gateway.py/0 | {
"file_path": "langchain/libs/langchain/langchain/chat_models/javelin_ai_gateway.py",
"repo_id": "langchain",
"token_count": 65
} | 471 |
# Copyright 2024 FABRIC 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/LICENSE-2.0
#
# Unless re... | diffusers/examples/community/pipeline_fabric.py/0 | {
"file_path": "diffusers/examples/community/pipeline_fabric.py",
"repo_id": "diffusers",
"token_count": 16483
} | 201 |
# Text Generation Inference Python gRPC Server
A Python gRPC server for Text Generation Inference
## Install
```shell
make install
```
## Run
```shell
make run-dev
``` | text-generation-inference/server/README.md/0 | {
"file_path": "text-generation-inference/server/README.md",
"repo_id": "text-generation-inference",
"token_count": 55
} | 375 |
"""Database Tool."""
from typing import Any, List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.core.utilities.sql_wrapper import SQLDatabase
from sqlalchemy import MetaData, t... | llama_index/llama-index-integrations/tools/llama-index-tools-database/llama_index/tools/database/base.py/0 | {
"file_path": "llama_index/llama-index-integrations/tools/llama-index-tools-database/llama_index/tools/database/base.py",
"repo_id": "llama_index",
"token_count": 2047
} | 1,619 |
<jupyter_start><jupyter_text>Custom MemoryAlthough there are a few predefined types of memory in LangChain, it is highly possible you will want to add your own type of memory that is optimal for your application. This notebook covers how to do that. For this notebook, we will add a custom memory type to `ConversationCh... | langchain/docs/docs/modules/memory/custom_memory.ipynb/0 | {
"file_path": "langchain/docs/docs/modules/memory/custom_memory.ipynb",
"repo_id": "langchain",
"token_count": 1661
} | 203 |
// 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/paramtable/runtime.go/0 | {
"file_path": "milvus/pkg/util/paramtable/runtime.go",
"repo_id": "milvus",
"token_count": 858
} | 2,065 |
syntax = "proto3";
package milvus.proto.internal;
option go_package = "github.com/milvus-io/milvus/internal/proto/internalpb";
import "common.proto";
import "schema.proto";
message GetTimeTickChannelRequest {
}
message GetStatisticsChannelRequest {
}
message GetDdChannelRequest {
}
message NodeInfo {
common.Addr... | milvus/internal/proto/internal.proto/0 | {
"file_path": "milvus/internal/proto/internal.proto",
"repo_id": "milvus",
"token_count": 2368
} | 1,737 |
"""
SGDP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/sgdp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import ... | pytorch-image-models/timm/optim/sgdp.py/0 | {
"file_path": "pytorch-image-models/timm/optim/sgdp.py",
"repo_id": "pytorch-image-models",
"token_count": 1186
} | 382 |
"""Test Baichuan LLM Endpoint."""
from langchain_core.outputs import LLMResult
from langchain_community.llms.baichuan import BaichuanLLM
def test_call() -> None:
"""Test valid call to baichuan."""
llm = BaichuanLLM()
output = llm("Who won the second world war?")
assert isinstance(output, str)
def t... | langchain/libs/community/tests/integration_tests/llms/test_baichuan.py/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/llms/test_baichuan.py",
"repo_id": "langchain",
"token_count": 199
} | 335 |
import { OllamaFunctions } from "langchain/experimental/chat_models/ollama_functions";
import { HumanMessage } from "@langchain/core/messages";
const model = new OllamaFunctions({
temperature: 0.1,
model: "mistral",
}).bind({
functions: [
{
name: "get_current_weather",
description: "Get the curre... | langchainjs/examples/src/models/chat/ollama_functions/function_calling.ts/0 | {
"file_path": "langchainjs/examples/src/models/chat/ollama_functions/function_calling.ts",
"repo_id": "langchainjs",
"token_count": 477
} | 870 |
[tool.poetry]
name = "openai-functions-agent-gmail"
version = "0.1.0"
description = "Agent using OpenAI function calling to execute functions, including search"
authors = [
"Lance Martin <lance@langchain.dev>",
]
readme = "README.md"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
langchain = ">=0.0.349,<0.1.0"... | langchain/templates/openai-functions-agent-gmail/pyproject.toml/0 | {
"file_path": "langchain/templates/openai-functions-agent-gmail/pyproject.toml",
"repo_id": "langchain",
"token_count": 402
} | 661 |
import pandas as pd
import pytest
from langchain_core.documents import Document
from langchain_community.document_loaders import DataFrameLoader
@pytest.fixture
def sample_data_frame() -> pd.DataFrame:
data = {
"text": ["Hello", "World"],
"author": ["Alice", "Bob"],
"date": ["2022-01-01",... | langchain/libs/community/tests/integration_tests/document_loaders/test_dataframe.py/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/document_loaders/test_dataframe.py",
"repo_id": "langchain",
"token_count": 537
} | 340 |
# coding=utf-8
# Copyright 2020 The HuggingFace Team 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 clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | transformers/tests/generation/test_beam_constraints.py/0 | {
"file_path": "transformers/tests/generation/test_beam_constraints.py",
"repo_id": "transformers",
"token_count": 1723
} | 779 |
/* eslint-disable import/no-extraneous-dependencies */
import { async as glob } from "fast-glob";
import fs from "fs";
import path from "path";
interface CopyOption {
cwd?: string;
rename?: (basename: string) => string;
parents?: boolean;
}
const identity = (x: string) => x;
export const copy = async (
src: ... | langchainjs/libs/create-langchain-integration/helpers/copy.ts/0 | {
"file_path": "langchainjs/libs/create-langchain-integration/helpers/copy.ts",
"repo_id": "langchainjs",
"token_count": 477
} | 1,036 |
from langchain_community.tools.arxiv.tool import ArxivInput, ArxivQueryRun
__all__ = ["ArxivInput", "ArxivQueryRun"]
| langchain/libs/langchain/langchain/tools/arxiv/tool.py/0 | {
"file_path": "langchain/libs/langchain/langchain/tools/arxiv/tool.py",
"repo_id": "langchain",
"token_count": 44
} | 545 |
import * as readline from "readline";
import { JsonOutputToolsParser } from "langchain/output_parsers";
import { callToolList, model } from "./helpers.js";
// Use readline to ask the user for approval
function askQuestion(question: string): Promise<string> {
const rl = readline.createInterface({
input: process.s... | langchainjs/examples/src/use_cases/human_in_the_loop/accept-feedback.ts/0 | {
"file_path": "langchainjs/examples/src/use_cases/human_in_the_loop/accept-feedback.ts",
"repo_id": "langchainjs",
"token_count": 740
} | 825 |
import json
import os
from ghapi.all import GhApi
FOLDER_STRING = os.environ.get("FOLDER_STRING", "")
folder = f"benchmark/trl/{FOLDER_STRING}"
host_url = f"https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/benchmark/{FOLDER_STRING}"
# Create a GitHub API instance
github_contex... | trl/benchmark/post_github_comment.py/0 | {
"file_path": "trl/benchmark/post_github_comment.py",
"repo_id": "trl",
"token_count": 358
} | 770 |
from skeleton_of_thought.chain import chain
__all__ = ["chain"]
| langchain/templates/skeleton-of-thought/skeleton_of_thought/__init__.py/0 | {
"file_path": "langchain/templates/skeleton-of-thought/skeleton_of_thought/__init__.py",
"repo_id": "langchain",
"token_count": 20
} | 700 |
import json
import sys
def format_json_to_md(input_json_file, output_md_file):
with open(input_json_file, encoding="utf-8") as f:
results = json.load(f)
output_md = ["<details>", "<summary>Show updated benchmarks!</summary>", " "]
for benchmark_name in sorted(results):
benchmark_res = re... | datasets/benchmarks/format.py/0 | {
"file_path": "datasets/benchmarks/format.py",
"repo_id": "datasets",
"token_count": 746
} | 118 |
"""Simple reader that reads wikipedia."""
from typing import Any, List
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
class WikipediaReader(BasePydanticReader):
"""Wikipedia reader.
Reads a page.
"""
is_remote: bool = True
def __init_... | llama_index/llama-index-integrations/readers/llama-index-readers-wikipedia/llama_index/readers/wikipedia/base.py/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-wikipedia/llama_index/readers/wikipedia/base.py",
"repo_id": "llama_index",
"token_count": 502
} | 1,544 |
""" Image to Patch Embedding using Conv2d
A convolution based approach to patchifying a 2D image w/ embedding projection.
Based on code in:
* https://github.com/google-research/vision_transformer
* https://github.com/google-research/big_vision/tree/main/big_vision
Hacked together by / Copyright 2020 Ross Wightma... | pytorch-image-models/timm/layers/patch_embed.py/0 | {
"file_path": "pytorch-image-models/timm/layers/patch_embed.py",
"repo_id": "pytorch-image-models",
"token_count": 4705
} | 335 |
from typing import Any, Dict, Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
from langchain_community.chat_models import ChatOpenAI
from langchain_communi... | langchain/libs/community/langchain_community/tools/amadeus/closest_airport.py/0 | {
"file_path": "langchain/libs/community/langchain_community/tools/amadeus/closest_airport.py",
"repo_id": "langchain",
"token_count": 891
} | 292 |
# Quicktour
Let's have a quick look at the 🤗 Tokenizers library features. The
library provides an implementation of today's most used tokenizers that
is both easy to use and blazing fast.
## Build a tokenizer from scratch
To illustrate how fast the 🤗 Tokenizers library is, let's train a new
tokenizer on [wikitext-... | tokenizers/docs/source-doc-builder/quicktour.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/quicktour.mdx",
"repo_id": "tokenizers",
"token_count": 7936
} | 423 |
# PyTorch Image Models
- [What's New](#whats-new)
- [Introduction](#introduction)
- [Models](#models)
- [Features](#features)
- [Results](#results)
- [Getting Started (Documentation)](#getting-started-documentation)
- [Train, Validation, Inference Scripts](#train-validation-inference-scripts)
- [Awesome PyTorch Resourc... | pytorch-image-models/README.md/0 | {
"file_path": "pytorch-image-models/README.md",
"repo_id": "pytorch-image-models",
"token_count": 19602
} | 332 |
# Würstchen text-to-image fine-tuning
## Running locally with PyTorch
Before running the scripts, make sure to install the library's training dependencies:
**Important**
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the i... | diffusers/examples/wuerstchen/text_to_image/README.md/0 | {
"file_path": "diffusers/examples/wuerstchen/text_to_image/README.md",
"repo_id": "diffusers",
"token_count": 1206
} | 223 |
---
sidebar_label: OpenAI
---
import CodeBlock from "@theme/CodeBlock";
# ChatOpenAI
You can use OpenAI's chat models as follows:
import OpenAI from "@examples/models/chat/integration_openai.ts";
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></... | langchainjs/docs/core_docs/docs/integrations/chat/openai.mdx/0 | {
"file_path": "langchainjs/docs/core_docs/docs/integrations/chat/openai.mdx",
"repo_id": "langchainjs",
"token_count": 861
} | 754 |
package allocator
import (
"sync"
"testing"
"github.com/stretchr/testify/assert"
)
func TestAllocatorFromList(t *testing.T) {
t.Run("de asc", func(t *testing.T) {
s := []int64{100000, 10000, 1000}
alloc := NewAllocatorFromList(s, true, true)
n := 100
wg := &sync.WaitGroup{}
for i := 0; i < n; i++ {
... | milvus/cmd/tools/migration/allocator/allocator_from_list_test.go/0 | {
"file_path": "milvus/cmd/tools/migration/allocator/allocator_from_list_test.go",
"repo_id": "milvus",
"token_count": 280
} | 1,629 |
from llama_index.llms.anyscale.base import Anyscale
__all__ = ["Anyscale"]
| llama_index/llama-index-integrations/llms/llama-index-llms-anyscale/llama_index/llms/anyscale/__init__.py/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-anyscale/llama_index/llms/anyscale/__init__.py",
"repo_id": "llama_index",
"token_count": 30
} | 1,276 |
import multiprocessing
from typing import Any, Dict, Generator, Optional, Tuple
import pytest
from chromadb import CloudClient
from chromadb.api import ServerAPI
from chromadb.auth.token import TokenTransportHeader
from chromadb.config import DEFAULT_DATABASE, DEFAULT_TENANT, Settings, System
from chromadb.errors impor... | chroma/chromadb/test/client/test_cloud_client.py/0 | {
"file_path": "chroma/chromadb/test/client/test_cloud_client.py",
"repo_id": "chroma",
"token_count": 1408
} | 25 |
// 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/log/Log.cpp/0 | {
"file_path": "milvus/internal/core/src/log/Log.cpp",
"repo_id": "milvus",
"token_count": 1365
} | 1,875 |
"""Helper functions for Titanic GPT-3 experiments."""
# form prompt, run GPT
import re
from typing import List, Optional, Tuple
import pandas as pd
from llama_index.indices.utils import extract_numbers_given_response
from llama_index.llms import OpenAI
from llama_index.prompts import BasePromptTemplate, PromptTemplat... | llama_index/experimental/classifier/utils.py/0 | {
"file_path": "llama_index/experimental/classifier/utils.py",
"repo_id": "llama_index",
"token_count": 2116
} | 1,154 |
# coding=utf-8
# Copyright 2021 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/speech_encoder_decoder/convert_mbart_wav2vec2_seq2seq_original_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/speech_encoder_decoder/convert_mbart_wav2vec2_seq2seq_original_to_pytorch.py",
"repo_id": "transformers",
"token_count": 6577
} | 743 |
pub const LANGUAGES: [(&str, &str); 99] = [
("en", "english"),
("zh", "chinese"),
("de", "german"),
("es", "spanish"),
("ru", "russian"),
("ko", "korean"),
("fr", "french"),
("ja", "japanese"),
("pt", "portuguese"),
("tr", "turkish"),
("pl", "polish"),
("ca", "catalan"),
... | candle/candle-wasm-examples/whisper/src/languages.rs/0 | {
"file_path": "candle/candle-wasm-examples/whisper/src/languages.rs",
"repo_id": "candle",
"token_count": 1175
} | 79 |
# Non-core Model Serving
TGI supports various LLM architectures (see full list [here](../supported_models)). If you wish to serve a model that is not one of the supported models, TGI will fallback to the `transformers` implementation of that model. This means you will be unable to use some of the features introduced b... | text-generation-inference/docs/source/basic_tutorials/non_core_models.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/non_core_models.md",
"repo_id": "text-generation-inference",
"token_count": 475
} | 401 |
#!python
from meta_gen import *
import re
def assemble(template, **kwargs):
pattern = re.compile("@@@@(.*?)\n((.|\n)*?)\n####", re.MULTILINE)
temp_info = pattern.findall(template)
# print(temp_info)
mapping = dict()
rep_map = dict()
# drop repetive field from mapping
for k, v in kwargs.ite... | milvus/tools/core_gen/assemble.py/0 | {
"file_path": "milvus/tools/core_gen/assemble.py",
"repo_id": "milvus",
"token_count": 522
} | 2,134 |
python_sources()
| llama_index/llama-index-integrations/readers/llama-index-readers-astra-db/llama_index/readers/astra_db/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-astra-db/llama_index/readers/astra_db/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,464 |
import type {
OpenAIClientOptions,
AzureExtensionsOptions,
ChatRequestMessage,
} from "@azure/openai";
import type { BaseLanguageModelCallOptions } from "@langchain/core/language_models/base";
import type { TiktokenModel } from "js-tiktoken/lite";
import type { EmbeddingsParams } from "@langchain/core/embeddings"... | langchainjs/libs/langchain-azure-openai/src/types.ts/0 | {
"file_path": "langchainjs/libs/langchain-azure-openai/src/types.ts",
"repo_id": "langchainjs",
"token_count": 1993
} | 931 |
<jupyter_start><jupyter_text>Tool error handlingUsing a model to invoke a tool has some obvious potential failure modes. Firstly, the model needs to return a output that can be parsed at all. Secondly, the model needs to return tool arguments that are valid.We can build error handling into our chains to mitigate these ... | langchain/docs/docs/use_cases/tool_use/tool_error_handling.ipynb/0 | {
"file_path": "langchain/docs/docs/use_cases/tool_use/tool_error_handling.ipynb",
"repo_id": "langchain",
"token_count": 2323
} | 218 |
import { ChatMistralAI } from "@langchain/mistralai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";
const model = new ChatMistralAI({
apiKey: process.env.MISTRAL_API_KEY,
modelName: "mistral-small",
});
const prompt = ChatPromptTe... | langchainjs/examples/src/models/chat/chat_stream_mistralai.ts/0 | {
"file_path": "langchainjs/examples/src/models/chat/chat_stream_mistralai.ts",
"repo_id": "langchainjs",
"token_count": 325
} | 827 |
from __future__ import annotations
import logging
import os
import uuid
import warnings
from typing import TYPE_CHECKING, Any, Callable, Iterable, List, Optional, Tuple, Union
import numpy as np
from langchain_core._api.deprecation import deprecated
from langchain_core.documents import Document
from langchain_core.em... | langchain/libs/community/langchain_community/vectorstores/pinecone.py/0 | {
"file_path": "langchain/libs/community/langchain_community/vectorstores/pinecone.py",
"repo_id": "langchain",
"token_count": 7871
} | 334 |
FROM debian:buster
# arg that specifies the image name (for debugging)
ARG IMAGE_ARG
# arg that specifies the go version to install
ARG GO_VERSION
# add envs:
# - so we can debug with the image name:tag
# - adding gsutil etc. to path (where we will install them)
# - disabling prompts when installing gsutil etc.
# - ... | milvus/build/docker/krte/Dockerfile/0 | {
"file_path": "milvus/build/docker/krte/Dockerfile",
"repo_id": "milvus",
"token_count": 1823
} | 1,704 |
- sections:
- local: index
title: 🤗 Transformers
- local: quicktour
title: త్వరిత పర్యటన
title: ప్రారంభించడానికి
| transformers/docs/source/te/_toctree.yml/0 | {
"file_path": "transformers/docs/source/te/_toctree.yml",
"repo_id": "transformers",
"token_count": 125
} | 512 |
<!--Copyright 2022 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... | transformers/docs/source/en/model_doc/plbart.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/plbart.md",
"repo_id": "transformers",
"token_count": 1586
} | 514 |
<jupyter_start><jupyter_text>News URLThis covers how to load HTML news articles from a list of URLs into a document format that we can use downstream.<jupyter_code>from langchain_community.document_loaders import NewsURLLoader
urls = [
"https://www.bbc.com/news/world-us-canada-66388172",
"https://www.bbc.com/ne... | langchain/docs/docs/integrations/document_loaders/news.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/document_loaders/news.ipynb",
"repo_id": "langchain",
"token_count": 568
} | 103 |
# (Tensorflow) EfficientNet CondConv
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method unifo... | pytorch-image-models/hfdocs/source/models/tf-efficientnet-condconv.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/tf-efficientnet-condconv.mdx",
"repo_id": "pytorch-image-models",
"token_count": 3303
} | 382 |
<jupyter_start><jupyter_text>OpenAI ToolsThese output parsers extract tool calls from OpenAI's function calling API responses. This means they are only usable with models that support function calling, and specifically the latest `tools` and `tool_choice` parameters. We recommend familiarizing yourself with [function c... | langchain/docs/docs/modules/model_io/output_parsers/types/openai_tools.ipynb/0 | {
"file_path": "langchain/docs/docs/modules/model_io/output_parsers/types/openai_tools.ipynb",
"repo_id": "langchain",
"token_count": 1265
} | 197 |
# coding=utf-8
# Copyright 2022 Meta Platforms 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/LICEN... | transformers/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py/0 | {
"file_path": "transformers/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py",
"repo_id": "transformers",
"token_count": 32320
} | 597 |
from __future__ import annotations
import asyncio
import functools
import logging
import uuid
from abc import ABC, abstractmethod
from concurrent.futures import ThreadPoolExecutor
from contextlib import asynccontextmanager, contextmanager
from contextvars import copy_context
from typing import (
TYPE_CHECKING,
... | langchain/libs/core/langchain_core/callbacks/manager.py/0 | {
"file_path": "langchain/libs/core/langchain_core/callbacks/manager.py",
"repo_id": "langchain",
"token_count": 31740
} | 398 |
<jupyter_start><jupyter_text>Google Generative AI EmbeddingsConnect to Google's generative AI embeddings service using the `GoogleGenerativeAIEmbeddings` class, found in the [langchain-google-genai](https://pypi.org/project/langchain-google-genai/) package. Installation<jupyter_code>%pip install --upgrade --quiet lan... | langchain/docs/docs/integrations/text_embedding/google_generative_ai.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/text_embedding/google_generative_ai.ipynb",
"repo_id": "langchain",
"token_count": 647
} | 172 |
import type { BaseLanguageModelInterface } from "@langchain/core/language_models/base";
import { AgentStep } from "@langchain/core/agents";
import { ChainValues } from "@langchain/core/utils/types";
import {
BaseCallbackConfig,
Callbacks,
} from "@langchain/core/callbacks/manager";
import { BaseChain, LLMChain, LLM... | langchainjs/langchain/src/evaluation/base.ts/0 | {
"file_path": "langchainjs/langchain/src/evaluation/base.ts",
"repo_id": "langchainjs",
"token_count": 3417
} | 918 |
import { z } from "zod";
import type { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatOpenAI } from "@langchain/openai";
import { AgentExecutor, createOpenAIFunctionsAgent } from "langchain/agents";
import { pull } from "langchain/hub";
import { DynamicStructuredTool } from "@langchain/core/tools";
... | langchainjs/examples/src/agents/handle_parsing_error.ts/0 | {
"file_path": "langchainjs/examples/src/agents/handle_parsing_error.ts",
"repo_id": "langchainjs",
"token_count": 1014
} | 756 |
# 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/tests/models/sam/test_modeling_tf_sam.py/0 | {
"file_path": "transformers/tests/models/sam/test_modeling_tf_sam.py",
"repo_id": "transformers",
"token_count": 11717
} | 748 |
<jupyter_start><jupyter_text>Structured output parserThis output parser can be used when you want to return multiple fields. While the Pydantic/JSON parser is more powerful, this is useful for less powerful models.<jupyter_code>from langchain.output_parsers import ResponseSchema, StructuredOutputParser
from langchain.p... | langchain/docs/docs/modules/model_io/output_parsers/types/structured.ipynb/0 | {
"file_path": "langchain/docs/docs/modules/model_io/output_parsers/types/structured.ipynb",
"repo_id": "langchain",
"token_count": 453
} | 206 |
import json
from typing import Iterator, List, Mapping, Optional, Sequence, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class HuggingFaceDatasetLoader(BaseLoader):
"""Load from `Hugging Face Hub` datasets."""
def __init__(
sel... | langchain/libs/community/langchain_community/document_loaders/hugging_face_dataset.py/0 | {
"file_path": "langchain/libs/community/langchain_community/document_loaders/hugging_face_dataset.py",
"repo_id": "langchain",
"token_count": 1475
} | 252 |
<jupyter_start><jupyter_text>Reasoning without ObservationIn [ReWOO](https://arxiv.org/abs/2305.18323), Xu, et. al, propose an agent that combines a multi-step planner and variable substitution for effective tool use. It was designed to improve on the ReACT-style agent architecture in the following ways:1. Reduce token... | langgraph/examples/rewoo/rewoo.ipynb/0 | {
"file_path": "langgraph/examples/rewoo/rewoo.ipynb",
"repo_id": "langgraph",
"token_count": 2919
} | 1,031 |
<!--Copyright 2021 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... | transformers/docs/source/en/model_doc/luke.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/luke.md",
"repo_id": "transformers",
"token_count": 2521
} | 465 |
"""Internal representation of a structured query language."""
from __future__ import annotations
from abc import ABC, abstractmethod
from enum import Enum
from typing import Any, List, Optional, Sequence, Union
from langchain_core.pydantic_v1 import BaseModel
class Visitor(ABC):
"""Defines interface for IR tran... | langchain/libs/langchain/langchain/chains/query_constructor/ir.py/0 | {
"file_path": "langchain/libs/langchain/langchain/chains/query_constructor/ir.py",
"repo_id": "langchain",
"token_count": 1270
} | 497 |
# coding=utf-8
# Copyright 2023 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/convnextv2/convert_convnextv2_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/convnextv2/convert_convnextv2_to_pytorch.py",
"repo_id": "transformers",
"token_count": 5402
} | 584 |
"""Relevancy evaluation."""
from __future__ import annotations
import asyncio
from typing import Any, Optional, Sequence, Union
from llama_index.core import ServiceContext
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.indices import SummaryIndex
from llama_index.co... | llama_index/llama-index-core/llama_index/core/evaluation/relevancy.py/0 | {
"file_path": "llama_index/llama-index-core/llama_index/core/evaluation/relevancy.py",
"repo_id": "llama_index",
"token_count": 2090
} | 1,210 |
package coordinator
import (
"context"
"github.com/chroma/chroma-coordinator/internal/common"
"github.com/chroma/chroma-coordinator/internal/metastore"
"github.com/chroma/chroma-coordinator/internal/model"
"github.com/chroma/chroma-coordinator/internal/notification"
"github.com/chroma/chroma-coordinator/interna... | chroma/go/coordinator/internal/metastore/coordinator/memory_catalog.go/0 | {
"file_path": "chroma/go/coordinator/internal/metastore/coordinator/memory_catalog.go",
"repo_id": "chroma",
"token_count": 4908
} | 48 |
use crate::infer::InferError;
use crate::infer::InferStreamResponse;
use crate::validation::ValidGenerateRequest;
use nohash_hasher::{BuildNoHashHasher, IntMap};
use std::cmp::min;
use std::collections::VecDeque;
use text_generation_client::{Batch, Request};
use tokio::sync::{mpsc, oneshot};
use tokio::time::Instant;
u... | text-generation-inference/router/src/queue.rs/0 | {
"file_path": "text-generation-inference/router/src/queue.rs",
"repo_id": "text-generation-inference",
"token_count": 9950
} | 431 |
# Model arguments
model_name_or_path: alignment-handbook/zephyr-7b-sft-full
torch_dtype: null
# Data training arguments
dataset_mixer:
HuggingFaceH4/ultrafeedback_binarized: 1.0
dataset_splits:
- train_prefs
- test_prefs
preprocessing_num_workers: 12
# Training arguments with sensible defaults
bf16: true
beta: 0.01... | alignment-handbook/recipes/pref_align_scan/dpo/config_zephyr.yaml/0 | {
"file_path": "alignment-handbook/recipes/pref_align_scan/dpo/config_zephyr.yaml",
"repo_id": "alignment-handbook",
"token_count": 359
} | 19 |
# Overview
These examples show how to run [Diffuser](https://arxiv.org/abs/2205.09991) in Diffusers.
There are two ways to use the script, `run_diffuser_locomotion.py`.
The key option is a change of the variable `n_guide_steps`.
When `n_guide_steps=0`, the trajectories are sampled from the diffusion model, but not ... | diffusers/examples/reinforcement_learning/README.md/0 | {
"file_path": "diffusers/examples/reinforcement_learning/README.md",
"repo_id": "diffusers",
"token_count": 352
} | 216 |
<jupyter_start><jupyter_text>Airtable<jupyter_code>%pip install --upgrade --quiet pyairtable
from langchain_community.document_loaders import AirtableLoader<jupyter_output><empty_output><jupyter_text>* Get your API key [here](https://support.airtable.com/docs/creating-and-using-api-keys-and-access-tokens).* Get ID of ... | langchain/docs/docs/integrations/document_loaders/airtable.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/document_loaders/airtable.ipynb",
"repo_id": "langchain",
"token_count": 344
} | 96 |
"""OpenAPI spec agent."""
| langchain/libs/community/langchain_community/agent_toolkits/openapi/__init__.py/0 | {
"file_path": "langchain/libs/community/langchain_community/agent_toolkits/openapi/__init__.py",
"repo_id": "langchain",
"token_count": 8
} | 226 |
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional
from llama_index.legacy.readers.base import BaseReader
from llama_index.legacy.schema import Document
if TYPE_CHECKING:
from bs4 import Tag
class HTMLTagReader(BaseReader):
"""
Read HTML files and extract text from a specifi... | llama_index/llama-index-legacy/llama_index/legacy/readers/file/html_reader.py/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/readers/file/html_reader.py",
"repo_id": "llama_index",
"token_count": 1054
} | 1,594 |
import copy
import itertools
from typing import List, Optional, Tuple
import torch
import torch.nn.functional as F
from transformers import BartConfig
from transformers.generation import GenerationMixin
def _convert_past_list_to_tuple(past_key_values):
"""
In Bart model, the type of past_key_values is tuple... | transformers/examples/research_projects/onnx/summarization/bart_onnx/generation_onnx.py/0 | {
"file_path": "transformers/examples/research_projects/onnx/summarization/bart_onnx/generation_onnx.py",
"repo_id": "transformers",
"token_count": 15163
} | 549 |
"""GitHub Toolkit."""
from typing import Dict, List
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_community.agent_toolkits.base import BaseToolkit
from langchain_community.tools import BaseTool
from langchain_community.tools.github.prompt import (
COMMENT_ON_ISSUE_PROMPT,
CREATE_BRANC... | langchain/libs/community/langchain_community/agent_toolkits/github/toolkit.py/0 | {
"file_path": "langchain/libs/community/langchain_community/agent_toolkits/github/toolkit.py",
"repo_id": "langchain",
"token_count": 4910
} | 210 |
import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path
import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from... | transformers/examples/research_projects/seq2seq-distillation/_test_seq2seq_examples.py/0 | {
"file_path": "transformers/examples/research_projects/seq2seq-distillation/_test_seq2seq_examples.py",
"repo_id": "transformers",
"token_count": 7908
} | 615 |
from llama_index.core.extractors.interface import BaseExtractor
from llama_index.core.extractors.metadata_extractors import (
KeywordExtractor,
PydanticProgramExtractor,
QuestionsAnsweredExtractor,
SummaryExtractor,
TitleExtractor,
)
__all__ = [
"SummaryExtractor",
"QuestionsAnsweredExtract... | llama_index/llama-index-core/llama_index/core/extractors/__init__.py/0 | {
"file_path": "llama_index/llama-index-core/llama_index/core/extractors/__init__.py",
"repo_id": "llama_index",
"token_count": 157
} | 1,174 |
/* eslint-disable no-process-env */
/* eslint-disable @typescript-eslint/no-non-null-assertion */
import { test, expect } from "@jest/globals";
import { MotorheadMemory } from "../motorhead_memory.js";
test("Test managed motörhead memory", async () => {
const memory = new MotorheadMemory({
sessionId: new Date().... | langchainjs/libs/langchain-community/src/memory/tests/motorhead_memory.int.test.ts/0 | {
"file_path": "langchainjs/libs/langchain-community/src/memory/tests/motorhead_memory.int.test.ts",
"repo_id": "langchainjs",
"token_count": 284
} | 950 |
# langchain-cli
This package implements the official CLI for LangChain. Right now, it is most useful
for getting started with LangChain Templates!
[CLI Docs](https://github.com/langchain-ai/langchain/blob/master/libs/cli/DOCS.md)
[LangServe Templates Quickstart](https://github.com/langchain-ai/langchain/blob/master/... | langchain/libs/cli/README.md/0 | {
"file_path": "langchain/libs/cli/README.md",
"repo_id": "langchain",
"token_count": 111
} | 197 |
import logging
import os
from typing import Generator
import pytest
from llama_index.legacy.schema import TextNode
from llama_index.legacy.vector_stores import SingleStoreVectorStore
from llama_index.legacy.vector_stores.types import (
ExactMatchFilter,
MetadataFilters,
VectorStoreQuery,
)
logger = loggin... | llama_index/llama-index-legacy/tests/vector_stores/test_singlestoredb.py/0 | {
"file_path": "llama_index/llama-index-legacy/tests/vector_stores/test_singlestoredb.py",
"repo_id": "llama_index",
"token_count": 1104
} | 1,637 |
python_sources()
| llama_index/llama-index-integrations/embeddings/llama-index-embeddings-jinaai/llama_index/embeddings/jinaai/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/embeddings/llama-index-embeddings-jinaai/llama_index/embeddings/jinaai/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,367 |
// 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/datanode/binlog_io_test.go/0 | {
"file_path": "milvus/internal/datanode/binlog_io_test.go",
"repo_id": "milvus",
"token_count": 5056
} | 1,698 |
python_sources()
| llama_index/llama-index-legacy/llama_index/legacy/tools/tool_spec/load_and_search/BUILD/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/tools/tool_spec/load_and_search/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,780 |
<jupyter_start><jupyter_text>Tokenizers (PyTorch) Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install transformers[sentencepiece]
tokenized_text = "Jim Henson était marionnettiste".split()
print(tokenized_text)
from transformers import CamembertTokenizer
tokenizer = Camem... | notebooks/course/fr/chapter2/section4_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter2/section4_pt.ipynb",
"repo_id": "notebooks",
"token_count": 314
} | 288 |
// Code generated by mockery v2.32.4. DO NOT EDIT.
package eventlog
import mock "github.com/stretchr/testify/mock"
// MockLogger is an autogenerated mock type for the Logger type
type MockLogger struct {
mock.Mock
}
type MockLogger_Expecter struct {
mock *mock.Mock
}
func (_m *MockLogger) EXPECT() *MockLogger_Ex... | milvus/pkg/eventlog/mock_logger.go/0 | {
"file_path": "milvus/pkg/eventlog/mock_logger.go",
"repo_id": "milvus",
"token_count": 1484
} | 2,088 |
from langchain_community.vectorstores.hologres import (
Hologres,
)
__all__ = ["Hologres"]
| langchain/libs/langchain/langchain/vectorstores/hologres.py/0 | {
"file_path": "langchain/libs/langchain/langchain/vectorstores/hologres.py",
"repo_id": "langchain",
"token_count": 36
} | 582 |
package typeutil
type CacheOpType int32
const (
CacheAddUserToRole CacheOpType = iota + 1
CacheRemoveUserFromRole
CacheGrantPrivilege
CacheRevokePrivilege
CacheDeleteUser
CacheDropRole
CacheRefresh
)
type CacheOp struct {
OpType CacheOpType
OpKey string
}
| milvus/pkg/util/typeutil/cache.go/0 | {
"file_path": "milvus/pkg/util/typeutil/cache.go",
"repo_id": "milvus",
"token_count": 90
} | 2,122 |
"""Test Baidu Qianfan LLM Endpoint."""
from typing import Generator
from langchain_core.outputs import LLMResult
from langchain_community.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint
def test_call() -> None:
"""Test valid call to qianfan."""
llm = QianfanLLMEndpoint()
output = llm("write a joke... | langchain/libs/community/tests/integration_tests/llms/test_qianfan_endpoint.py/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/llms/test_qianfan_endpoint.py",
"repo_id": "langchain",
"token_count": 473
} | 344 |
poetry_requirements(
name="poetry",
)
| llama_index/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/BUILD",
"repo_id": "llama_index",
"token_count": 18
} | 1,310 |
from typing import Any, List
from llama_index.legacy.ingestion import IngestionCache
from llama_index.legacy.ingestion.pipeline import get_transformation_hash
from llama_index.legacy.schema import BaseNode, TextNode, TransformComponent
class DummyTransform(TransformComponent):
def __call__(self, nodes: List[Base... | llama_index/llama-index-legacy/tests/ingestion/test_cache.py/0 | {
"file_path": "llama_index/llama-index-legacy/tests/ingestion/test_cache.py",
"repo_id": "llama_index",
"token_count": 483
} | 1,638 |
python_sources()
| llama_index/llama-index-packs/llama-index-packs-rag-cli-local/llama_index/packs/rag_cli_local/BUILD/0 | {
"file_path": "llama_index/llama-index-packs/llama-index-packs-rag-cli-local/llama_index/packs/rag_cli_local/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,717 |
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