text stringlengths 3 1.68M | id stringlengths 13 169 | metadata dict | __index_level_0__ int64 0 2.21k |
|---|---|---|---|
import { Memory, Message, NotFoundError, ZepClient } from "@getzep/zep-js";
import {
InputValues,
OutputValues,
MemoryVariables,
getInputValue,
getOutputValue,
} from "@langchain/core/memory";
import {
getBufferString,
AIMessage,
BaseMessage,
ChatMessage,
HumanMessage,
SystemMessage,
} from "@lang... | langchainjs/libs/langchain-community/src/memory/zep.ts/0 | {
"file_path": "langchainjs/libs/langchain-community/src/memory/zep.ts",
"repo_id": "langchainjs",
"token_count": 2448
} | 1,006 |
# Metric Card for COMET
## Metric description
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments.
## How to use
COMET takes 3 lists of strings as inp... | datasets/metrics/comet/README.md/0 | {
"file_path": "datasets/metrics/comet/README.md",
"repo_id": "datasets",
"token_count": 2148
} | 118 |
# order by contributions
reviewers:
- fishpenguin
- xige-16
- scsven
- yhmo
- czs007
approvers:
- maintainers
| milvus/pkg/mq/msgstream/OWNERS/0 | {
"file_path": "milvus/pkg/mq/msgstream/OWNERS",
"repo_id": "milvus",
"token_count": 52
} | 1,896 |
<jupyter_start><jupyter_text>DeepLake Reader If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.<jupyter_code>%pip install llama-index-readers-deeplake
!pip install llama-index
import getpass
import os
import random
import textwrap
from llama_index.core import VectorStoreIndex
fr... | llama_index/docs/examples/data_connectors/DeepLakeReader.ipynb/0 | {
"file_path": "llama_index/docs/examples/data_connectors/DeepLakeReader.ipynb",
"repo_id": "llama_index",
"token_count": 424
} | 1,093 |
// Code generated by mockery v2.32.4. DO NOT EDIT.
package mocks
import (
context "context"
commonpb "github.com/milvus-io/milvus-proto/go-api/v2/commonpb"
clientv3 "go.etcd.io/etcd/client/v3"
datapb "github.com/milvus-io/milvus/internal/proto/datapb"
internalpb "github.com/milvus-io/milvus/internal/proto/int... | milvus/internal/mocks/mock_datanode.go/0 | {
"file_path": "milvus/internal/mocks/mock_datanode.go",
"repo_id": "milvus",
"token_count": 20797
} | 2,000 |
// 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/distributed/datanode/client/client.go/0 | {
"file_path": "milvus/internal/distributed/datanode/client/client.go",
"repo_id": "milvus",
"token_count": 4016
} | 1,713 |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2022 University of Cambridge, Tencent AI Lab, DeepMind and The University of Hong Kong Authors 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 th... | transformers/examples/pytorch/text-generation/run_generation_contrastive_search.py/0 | {
"file_path": "transformers/examples/pytorch/text-generation/run_generation_contrastive_search.py",
"repo_id": "transformers",
"token_count": 1865
} | 553 |
// 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/index/InvertedIndexTantivy.cpp/0 | {
"file_path": "milvus/internal/core/src/index/InvertedIndexTantivy.cpp",
"repo_id": "milvus",
"token_count": 7248
} | 1,738 |
poetry_requirements(
name="poetry",
)
| llama_index/llama-index-integrations/llms/llama-index-llms-anthropic/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-anthropic/BUILD",
"repo_id": "llama_index",
"token_count": 18
} | 1,247 |
"""Simple Reader for Memos."""
from typing import Dict, List
from urllib.parse import urljoin
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class MemosReader(BaseReader):
"""Memos reader.
Reads content from an Memos.
"""
def __init__(self, host:... | llama_index/llama-index-integrations/readers/llama-index-readers-memos/llama_index/readers/memos/base.py/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-memos/llama_index/readers/memos/base.py",
"repo_id": "llama_index",
"token_count": 660
} | 1,335 |
# 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/vilt/test_modeling_vilt.py/0 | {
"file_path": "transformers/tests/models/vilt/test_modeling_vilt.py",
"repo_id": "transformers",
"token_count": 11966
} | 819 |
// 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/querynodev2/pipeline/manager.go/0 | {
"file_path": "milvus/internal/querynodev2/pipeline/manager.go",
"repo_id": "milvus",
"token_count": 1810
} | 1,840 |
use async_trait::async_trait;
use uuid::Uuid;
use crate::chroma_proto;
use crate::config::{Configurable, WorkerConfig};
use crate::types::{CollectionConversionError, SegmentConversionError};
use crate::{
chroma_proto::sys_db_client,
errors::{ChromaError, ErrorCodes},
types::{Collection, Segment, SegmentSco... | chroma/rust/worker/src/sysdb/sysdb.rs/0 | {
"file_path": "chroma/rust/worker/src/sysdb/sysdb.rs",
"repo_id": "chroma",
"token_count": 4139
} | 58 |
package kafka
import (
"sync"
"time"
"github.com/cockroachdb/errors"
"github.com/confluentinc/confluent-kafka-go/kafka"
"go.uber.org/zap"
"github.com/milvus-io/milvus/pkg/log"
"github.com/milvus-io/milvus/pkg/mq/msgstream/mqwrapper"
"github.com/milvus-io/milvus/pkg/util/merr"
"github.com/milvus-io/milvus/pk... | milvus/pkg/mq/msgstream/mqwrapper/kafka/kafka_consumer.go/0 | {
"file_path": "milvus/pkg/mq/msgstream/mqwrapper/kafka/kafka_consumer.go",
"repo_id": "milvus",
"token_count": 3153
} | 1,889 |
"""Faithfulness evaluation."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence, Union
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.multi_modal_llms.base import MultiModalLLM
from llama_index.core.prompts import BasePromptTempla... | llama_index/llama-index-core/llama_index/core/evaluation/multi_modal/faithfulness.py/0 | {
"file_path": "llama_index/llama-index-core/llama_index/core/evaluation/multi_modal/faithfulness.py",
"repo_id": "llama_index",
"token_count": 3596
} | 1,171 |
"""Test PandasDataframeParser"""
import pandas as pd
from langchain.output_parsers.pandas_dataframe import PandasDataFrameOutputParser
from langchain.schema import OutputParserException
df = pd.DataFrame(
{"chicken": [1, 2, 3, 4], "veggies": [5, 4, 3, 2], "steak": [9, 8, 7, 6]}
)
parser = PandasDataFrameOutputPa... | langchain/libs/langchain/tests/unit_tests/output_parsers/test_pandas_dataframe_parser.py/0 | {
"file_path": "langchain/libs/langchain/tests/unit_tests/output_parsers/test_pandas_dataframe_parser.py",
"repo_id": "langchain",
"token_count": 1320
} | 627 |
"""Init params."""
| llama_index/llama-index-core/tests/indices/struct_store/__init__.py/0 | {
"file_path": "llama_index/llama-index-core/tests/indices/struct_store/__init__.py",
"repo_id": "llama_index",
"token_count": 6
} | 1,189 |
import { logVersion010MigrationWarning } from "../util/entrypoint_deprecation.js";
/* #__PURE__ */ logVersion010MigrationWarning({
oldEntrypointName: "load/serializable",
newEntrypointName: "load/serializable",
newPackageName: "@langchain/core",
});
export * from "@langchain/core/load/serializable";
| langchainjs/langchain/src/load/serializable.ts/0 | {
"file_path": "langchainjs/langchain/src/load/serializable.ts",
"repo_id": "langchainjs",
"token_count": 96
} | 891 |
import { test } from "@jest/globals";
import { OpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "../../../vectorstores/memory.js";
import { TimeWeightedVectorStoreRetriever } from "../../../retrievers/time_weighted.js";
import { GenerativeAgentMemory, GenerativeAgent } from "../in... | langchainjs/langchain/src/experimental/generative_agents/tests/generative_agent.int.test.ts/0 | {
"file_path": "langchainjs/langchain/src/experimental/generative_agents/tests/generative_agent.int.test.ts",
"repo_id": "langchainjs",
"token_count": 7673
} | 937 |
# 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/mgp_str/tokenization_mgp_str.py/0 | {
"file_path": "transformers/src/transformers/models/mgp_str/tokenization_mgp_str.py",
"repo_id": "transformers",
"token_count": 1683
} | 632 |
from typing import Any, Dict, Optional, Sequence, Type, cast
from llama_index.legacy.bridge.pydantic import BaseModel
from llama_index.legacy.multi_modal_llms import MultiModalLLM, OpenAIMultiModal
from llama_index.legacy.output_parsers.pydantic import PydanticOutputParser
from llama_index.legacy.prompts.base import B... | llama_index/llama-index-legacy/llama_index/legacy/program/multi_modal_llm_program.py/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/program/multi_modal_llm_program.py",
"repo_id": "llama_index",
"token_count": 1726
} | 1,750 |
"""Test Titan Takeoff wrapper."""
import responses
from langchain_community.llms.titan_takeoff import TitanTakeoff
@responses.activate
def test_titan_takeoff_call() -> None:
"""Test valid call to Titan Takeoff."""
url = "http://localhost:8000/generate"
responses.add(responses.POST, url, json={"message"... | langchain/libs/community/tests/integration_tests/llms/test_titan_takeoff.py/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/llms/test_titan_takeoff.py",
"repo_id": "langchain",
"token_count": 168
} | 362 |
# coding=utf-8
# Copyright 2023 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 requir... | transformers/tests/models/mpt/test_modeling_mpt.py/0 | {
"file_path": "transformers/tests/models/mpt/test_modeling_mpt.py",
"repo_id": "transformers",
"token_count": 9258
} | 739 |
import json
from io import BytesIO
from typing import Any, Generator
import pytest
from botocore.response import StreamingBody
from botocore.stub import Stubber
from llama_index.core.base.llms.types import ChatMessage
from llama_index.llms.bedrock import Bedrock
from pytest import MonkeyPatch
class MockEventStream:
... | llama_index/llama-index-integrations/llms/llama-index-llms-bedrock/tests/test_bedrock.py/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-bedrock/tests/test_bedrock.py",
"repo_id": "llama_index",
"token_count": 3427
} | 1,391 |
from unittest.mock import MagicMock, patch
import pytest
from langchain_community.document_loaders import ArcGISLoader
@pytest.fixture
def arcgis_mocks(mock_feature_layer, mock_gis): # type: ignore
sys_modules = {
"arcgis": MagicMock(),
"arcgis.features.FeatureLayer": mock_feature_layer,
... | langchain/libs/community/tests/unit_tests/document_loaders/test_arcgis_loader.py/0 | {
"file_path": "langchain/libs/community/tests/unit_tests/document_loaders/test_arcgis_loader.py",
"repo_id": "langchain",
"token_count": 1530
} | 368 |
"""A tracer that collects all nested runs in a list."""
from typing import Any, List, Optional, Union
from uuid import UUID
from langchain_core.tracers.base import BaseTracer
from langchain_core.tracers.schemas import Run
class RunCollectorCallbackHandler(BaseTracer):
"""
Tracer that collects all nested run... | langchain/libs/core/langchain_core/tracers/run_collector.py/0 | {
"file_path": "langchain/libs/core/langchain_core/tracers/run_collector.py",
"repo_id": "langchain",
"token_count": 613
} | 400 |
import { test, expect } from "@jest/globals";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { ClientSecretCredential, TokenCredential } from "@azure/identity";
import { AzureOpenAIEmbeddings } from "../embeddings.js";
test("Test OpenAIEmbeddings.embedQuery", async () => {
const embedding... | langchainjs/libs/langchain-azure-openai/src/tests/embeddings.int.test.ts/0 | {
"file_path": "langchainjs/libs/langchain-azure-openai/src/tests/embeddings.int.test.ts",
"repo_id": "langchainjs",
"token_count": 762
} | 974 |
# Metric Card for MAUVE
## Metric description
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure. It summarizes both Type I and Type II errors measured softly using [Kullback–Leibler (KL) divergences](https://en.wikip... | datasets/metrics/mauve/README.md/0 | {
"file_path": "datasets/metrics/mauve/README.md",
"repo_id": "datasets",
"token_count": 1650
} | 141 |
package utils
import (
"fmt"
"math"
"testing"
)
func mockHasher(member string, key string) uint64 {
members := []string{"a", "b", "c"}
for i, m := range members {
if m == member {
return uint64(i)
}
}
return 0
}
func TestRendezvousHash(t *testing.T) {
members := []string{"a", "b", "c"}
key := "key"
... | chroma/go/coordinator/internal/utils/rendezvous_hash_test.go/0 | {
"file_path": "chroma/go/coordinator/internal/utils/rendezvous_hash_test.go",
"repo_id": "chroma",
"token_count": 519
} | 54 |
<jupyter_start><jupyter_text>Segment Anything Model: automatic mask generation using `transformers` 🤗 libraryThis notebook demonstrates how to use the Segment Anything Model (SAM) to automatically generate segementation masks on any image. The model was released by Meta AI in the paper [Segment Anything Model](https:/... | notebooks/examples/automatic_mask_generation.ipynb/0 | {
"file_path": "notebooks/examples/automatic_mask_generation.ipynb",
"repo_id": "notebooks",
"token_count": 1453
} | 279 |
# Bedrock JCVD 🕺🥋
## Overview
LangChain template that uses [Anthropic's Claude on Amazon Bedrock](https://aws.amazon.com/bedrock/claude/) to behave like JCVD.
> I am the Fred Astaire of Chatbots! 🕺
'![Animated GIF of Jean-Claude Van Damme dancing.](https://media.tenor.com/CVp9l7g3axwAAAAj/jean-claude-van-damme-j... | langchain/templates/bedrock-jcvd/README.md/0 | {
"file_path": "langchain/templates/bedrock-jcvd/README.md",
"repo_id": "langchain",
"token_count": 1135
} | 683 |
"""Tool for the Wolfram Alpha API."""
from typing import Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper
class WolframAlphaQueryRun(BaseTool):
"""Tool that queries us... | langchain/libs/community/langchain_community/tools/wolfram_alpha/tool.py/0 | {
"file_path": "langchain/libs/community/langchain_community/tools/wolfram_alpha/tool.py",
"repo_id": "langchain",
"token_count": 310
} | 315 |
python_sources()
| llama_index/llama-index-core/llama_index/core/indices/vector_store/retrievers/BUILD/0 | {
"file_path": "llama_index/llama-index-core/llama_index/core/indices/vector_store/retrievers/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,133 |
<jupyter_start><jupyter_text>Gradient Base Model If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.<jupyter_code>%pip install llama-index-embeddings-langchain
%pip install llama-index-llms-gradient
!pip install llama-index
%pip install llama-index --quiet
%pip install gradientai ... | llama_index/docs/examples/llm/gradient_base_model.ipynb/0 | {
"file_path": "llama_index/docs/examples/llm/gradient_base_model.ipynb",
"repo_id": "llama_index",
"token_count": 761
} | 1,061 |
<jupyter_start><jupyter_text>Cohere Rerank If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.<jupyter_code>%pip install llama-index-postprocessor-cohere-rerank
!pip install llama-index
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
pprint_resp... | llama_index/docs/examples/node_postprocessor/CohereRerank.ipynb/0 | {
"file_path": "llama_index/docs/examples/node_postprocessor/CohereRerank.ipynb",
"repo_id": "llama_index",
"token_count": 1212
} | 1,117 |
import importlib
from typing import Type, TypeVar, cast
C = TypeVar("C")
def get_class(fqn: str, type: Type[C]) -> Type[C]:
"""Given a fully qualifed class name, import the module and return the class"""
module_name, class_name = fqn.rsplit(".", 1)
module = importlib.import_module(module_name)
cls = ... | chroma/chromadb/utils/__init__.py/0 | {
"file_path": "chroma/chromadb/utils/__init__.py",
"repo_id": "chroma",
"token_count": 139
} | 26 |
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.tools.notion import NotionToolSpec
def test_class():
names_of_base_classes = [b.__name__ for b in NotionToolSpec.__mro__]
assert BaseToolSpec.__name__ in names_of_base_classes
| llama_index/llama-index-integrations/tools/llama-index-tools-notion/tests/test_tools_notion.py/0 | {
"file_path": "llama_index/llama-index-integrations/tools/llama-index-tools-notion/tests/test_tools_notion.py",
"repo_id": "llama_index",
"token_count": 94
} | 1,492 |
from llama_index.vector_stores.bagel.base import BagelVectorStore
__all__ = ["BagelVectorStore"]
| llama_index/llama-index-integrations/vector_stores/llama-index-vector-stores-bagel/llama_index/vector_stores/bagel/__init__.py/0 | {
"file_path": "llama_index/llama-index-integrations/vector_stores/llama-index-vector-stores-bagel/llama_index/vector_stores/bagel/__init__.py",
"repo_id": "llama_index",
"token_count": 33
} | 1,594 |
# 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/src/transformers/models/swin2sr/image_processing_swin2sr.py/0 | {
"file_path": "transformers/src/transformers/models/swin2sr/image_processing_swin2sr.py",
"repo_id": "transformers",
"token_count": 3791
} | 683 |
<jupyter_start><jupyter_text>Recency FilteringShowcase capabilities of recency-weighted node postprocessor<jupyter_code>import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import (
FixedRecencyPostprocessor,
... | llama_index/docs/examples/node_postprocessor/RecencyPostprocessorDemo.ipynb/0 | {
"file_path": "llama_index/docs/examples/node_postprocessor/RecencyPostprocessorDemo.ipynb",
"repo_id": "llama_index",
"token_count": 1644
} | 1,203 |
# coding=utf-8
# Copyright 2021 The I-BERT Authors (Sehoon Kim, Amir Gholami, Zhewei Yao,
# Michael Mahoney, Kurt Keutzer - UC Berkeley) and The HuggingFace Inc. team.
# Copyright (c) 20121, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use t... | transformers/src/transformers/models/ibert/modeling_ibert.py/0 | {
"file_path": "transformers/src/transformers/models/ibert/modeling_ibert.py",
"repo_id": "transformers",
"token_count": 24475
} | 651 |
python_sources()
| llama_index/llama-index-core/llama_index/core/tools/BUILD/0 | {
"file_path": "llama_index/llama-index-core/llama_index/core/tools/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,143 |
from langchain_community.document_loaders.stripe import StripeLoader
__all__ = ["StripeLoader"]
| langchain/libs/langchain/langchain/document_loaders/stripe.py/0 | {
"file_path": "langchain/libs/langchain/langchain/document_loaders/stripe.py",
"repo_id": "langchain",
"token_count": 30
} | 516 |
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.s3_file import S3FileLoader
if TYPE_CHECKING:
import botocore
clas... | langchain/libs/community/langchain_community/document_loaders/s3_directory.py/0 | {
"file_path": "langchain/libs/community/langchain_community/document_loaders/s3_directory.py",
"repo_id": "langchain",
"token_count": 2438
} | 249 |
# RAG Fusion Pipeline Llama Pack
This LlamaPack creates the RAG Fusion Query Pipeline, which runs multiple retrievers in parallel (with varying chunk sizes), and aggregates the results in the end with reciprocal rank fusion.
You can run it out of the box, but we also encourage you to inspect the code to take a look a... | llama_index/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/README.md/0 | {
"file_path": "llama_index/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/README.md",
"repo_id": "llama_index",
"token_count": 632
} | 1,584 |
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Union,
)
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
if TYPE_C... | langchain/libs/community/langchain_community/vectorstores/baiducloud_vector_search.py/0 | {
"file_path": "langchain/libs/community/langchain_community/vectorstores/baiducloud_vector_search.py",
"repo_id": "langchain",
"token_count": 7950
} | 323 |
package syncmgr
import (
"context"
"fmt"
"strconv"
"github.com/cockroachdb/errors"
"go.uber.org/zap"
"github.com/milvus-io/milvus-proto/go-api/v2/msgpb"
"github.com/milvus-io/milvus-proto/go-api/v2/schemapb"
"github.com/milvus-io/milvus/internal/allocator"
"github.com/milvus-io/milvus/internal/datanode/meta... | milvus/internal/datanode/syncmgr/sync_manager.go/0 | {
"file_path": "milvus/internal/datanode/syncmgr/sync_manager.go",
"repo_id": "milvus",
"token_count": 2043
} | 1,792 |
<!---
Copyright 2023 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 ... | transformers/docs/source/ja/troubleshooting.md/0 | {
"file_path": "transformers/docs/source/ja/troubleshooting.md",
"repo_id": "transformers",
"token_count": 4420
} | 550 |
<jupyter_start><jupyter_text>Refine with Structured Answer FilteringWhen using our Refine response synthesizer for response synthesis, it's crucial to filter out non-answers. An issue often encountered is the propagation of a single unhelpful response like "I don't have the answer", which can persist throughout the syn... | llama_index/docs/examples/response_synthesizers/structured_refine.ipynb/0 | {
"file_path": "llama_index/docs/examples/response_synthesizers/structured_refine.ipynb",
"repo_id": "llama_index",
"token_count": 1370
} | 1,210 |
python_tests()
| llama_index/llama-index-integrations/readers/llama-index-readers-astra-db/tests/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-astra-db/tests/BUILD",
"repo_id": "llama_index",
"token_count": 5
} | 1,293 |
<script lang="ts">
import { applyAction, enhance } from "$app/forms";
import { invalidateAll } from "$app/navigation";
import Modal from "$lib/components/Modal.svelte";
import { createEventDispatcher } from "svelte";
const dispatch = createEventDispatcher<{ close: void }>();
let reason = "";
</script>
<Modal o... | chat-ui/src/routes/settings/assistants/[assistantId]/ReportModal.svelte/0 | {
"file_path": "chat-ui/src/routes/settings/assistants/[assistantId]/ReportModal.svelte",
"repo_id": "chat-ui",
"token_count": 593
} | 113 |
import { logVersion010MigrationWarning } from "../util/entrypoint_deprecation.js";
/* #__PURE__ */ logVersion010MigrationWarning({
oldEntrypointName: "vectorstores/base",
newEntrypointName: "vectorstores",
newPackageName: "@langchain/core",
});
export * from "@langchain/core/vectorstores";
| langchainjs/langchain/src/vectorstores/base.ts/0 | {
"file_path": "langchainjs/langchain/src/vectorstores/base.ts",
"repo_id": "langchainjs",
"token_count": 91
} | 980 |
<jupyter_start><jupyter_text>Run TemplateIn `server.py`, set -```add_routes(app, chain_private, path="/rag_chroma_private")```<jupyter_code>from langserve.client import RemoteRunnable
rag_app = RemoteRunnable("http://0.0.0.0:8001/rag_chroma_private/")
rag_app.invoke("How does agent memory work?")<jupyter_output>Based ... | langchain/templates/rag-chroma-private/rag_chroma_private.ipynb/0 | {
"file_path": "langchain/templates/rag-chroma-private/rag_chroma_private.ipynb",
"repo_id": "langchain",
"token_count": 302
} | 683 |
<!--Copyright 2020 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/cpm.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/cpm.md",
"repo_id": "transformers",
"token_count": 735
} | 472 |
# LlamaIndex Readers Integration: Myscale
| llama_index/llama-index-integrations/readers/llama-index-readers-myscale/README.md/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-myscale/README.md",
"repo_id": "llama_index",
"token_count": 11
} | 1,433 |
"""Test functionality of Python REPL."""
import sys
import pytest
from langchain_experimental.tools.python.tool import PythonAstREPLTool, PythonREPLTool
from langchain_experimental.utilities.python import PythonREPL
_SAMPLE_CODE = """
```
def multiply():
print(5*6) # noqa: T201
multiply()
```
"""
_AST_SAMPLE_... | langchain/libs/experimental/tests/unit_tests/python/test_python_1.py/0 | {
"file_path": "langchain/libs/experimental/tests/unit_tests/python/test_python_1.py",
"repo_id": "langchain",
"token_count": 1057
} | 473 |
import uuid
from typing import Any, Callable, Optional, cast
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.schema import AIMessage, HumanMessage
from langchain_core.prompt_values import ChatPromptValue, StringPromptValue
from langchain_experimental.comprehend_moderation.pii import ... | langchain/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation.py/0 | {
"file_path": "langchain/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation.py",
"repo_id": "langchain",
"token_count": 3495
} | 439 |
python_sources()
python_tests(
name="tests",
)
| llama_index/llama-index-core/tests/embeddings/BUILD/0 | {
"file_path": "llama_index/llama-index-core/tests/embeddings/BUILD",
"repo_id": "llama_index",
"token_count": 22
} | 1,216 |
"""Gradient Finetuning."""
import json
from typing import Any, Optional, overload
from llama_index.finetuning.types import BaseLLMFinetuneEngine
from llama_index.llms.gradient import GradientModelAdapterLLM
class GradientFinetuneEngine(BaseLLMFinetuneEngine):
@overload
def __init__(
self,
*,... | llama_index/llama-index-finetuning/llama_index/finetuning/gradient/base.py/0 | {
"file_path": "llama_index/llama-index-finetuning/llama_index/finetuning/gradient/base.py",
"repo_id": "llama_index",
"token_count": 2493
} | 1,283 |
import init, { Model } from "./build/m.js";
async function fetchArrayBuffer(url) {
const cacheName = "llama2c-candle-cache";
const cache = await caches.open(cacheName);
const cachedResponse = await cache.match(url);
if (cachedResponse) {
const data = await cachedResponse.arrayBuffer();
return new Uint8... | candle/candle-wasm-examples/llama2-c/llama2cWorker.js/0 | {
"file_path": "candle/candle-wasm-examples/llama2-c/llama2cWorker.js",
"repo_id": "candle",
"token_count": 1223
} | 82 |
<jupyter_start><jupyter_text>SQL (SQLAlchemy)>[Structured Query Language (SQL)](https://en.wikipedia.org/wiki/SQL) is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management sys... | langchain/docs/docs/integrations/memory/sql_chat_message_history.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/memory/sql_chat_message_history.ipynb",
"repo_id": "langchain",
"token_count": 931
} | 137 |
# sql-research-assistant
This package does research over a SQL database
## Usage
This package relies on multiple models, which have the following dependencies:
- OpenAI: set the `OPENAI_API_KEY` environment variables
- Ollama: [install and run Ollama](https://python.langchain.com/docs/integrations/chat/ollama)
- ll... | langchain/templates/sql-research-assistant/README.md/0 | {
"file_path": "langchain/templates/sql-research-assistant/README.md",
"repo_id": "langchain",
"token_count": 656
} | 693 |
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
const text = `\\begin{document}
\\title{🦜️🔗 LangChain}
⚡ Building applications with LLMs through composability ⚡
\\section{Quick Install}
\\begin{verbatim}
Hopefully this code block isn't split
yarn add langchain
\\end{verbatim}
As an open ... | langchainjs/examples/src/indexes/latex_text_splitter.ts/0 | {
"file_path": "langchainjs/examples/src/indexes/latex_text_splitter.ts",
"repo_id": "langchainjs",
"token_count": 531
} | 809 |
import pytest
from llama_index.legacy.llm_predictor.vellum.utils import convert_to_kebab_case
@pytest.mark.parametrize(
("input_string", "expected"),
[
("HelloWorld", "helloworld"),
(
"LlamaIndex Demo: query_keyword_extract",
"llamaindex-demo-query-keyword-extract",
... | llama_index/llama-index-legacy/tests/llm_predictor/vellum/test_utils.py/0 | {
"file_path": "llama_index/llama-index-legacy/tests/llm_predictor/vellum/test_utils.py",
"repo_id": "llama_index",
"token_count": 213
} | 1,744 |
from rag_pinecone_rerank.chain import chain
__all__ = ["chain"]
| langchain/templates/rag-pinecone-rerank/rag_pinecone_rerank/__init__.py/0 | {
"file_path": "langchain/templates/rag-pinecone-rerank/rag_pinecone_rerank/__init__.py",
"repo_id": "langchain",
"token_count": 22
} | 742 |
python_sources()
| llama_index/llama-index-core/llama_index/core/indices/multi_modal/BUILD/0 | {
"file_path": "llama_index/llama-index-core/llama_index/core/indices/multi_modal/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,179 |
---
title: Developer Guide
---
import Contributing from "../../../CONTRIBUTING.md";
<Contributing />
| langchainjs/docs/core_docs/docs/contributing.mdx/0 | {
"file_path": "langchainjs/docs/core_docs/docs/contributing.mdx",
"repo_id": "langchainjs",
"token_count": 33
} | 766 |
[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 = ["SageMakerLLM"]
contains_example = false
import_path = "llama_index.llms.sagemaker_endpoin... | llama_index/llama-index-integrations/llms/llama-index-llms-sagemaker-endpoint/pyproject.toml/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-sagemaker-endpoint/pyproject.toml",
"repo_id": "llama_index",
"token_count": 666
} | 1,333 |
# Multi-Document AutoRetrieval (with Weaviate) Pack
This LlamaPack implements structured hierarchical retrieval over multiple documents, using multiple @weaviate_io collections.
## CLI Usage
You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:
```... | llama_index/llama-index-packs/llama-index-packs-multidoc-autoretrieval/README.md/0 | {
"file_path": "llama_index/llama-index-packs/llama-index-packs-multidoc-autoretrieval/README.md",
"repo_id": "llama_index",
"token_count": 851
} | 1,576 |
from langchain.prompts import PromptTemplate
template = """You are a teacher grading a quiz.
You are given a question, the student's answer, and the true answer, and are asked to score the student answer as either Correct or Incorrect.
Example Format:
QUESTION: question here
STUDENT ANSWER: student's answer here
TRU... | auto-evaluator/streamlit/prompts.py/0 | {
"file_path": "auto-evaluator/streamlit/prompts.py",
"repo_id": "auto-evaluator",
"token_count": 1762
} | 4 |
from typing import Any, Iterator, List, Mapping, Optional
import requests
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.outputs import GenerationChunk
from requests.exceptions import ConnectionError
from langchain_community.llms.u... | langchain/libs/community/langchain_community/llms/titan_takeoff.py/0 | {
"file_path": "langchain/libs/community/langchain_community/llms/titan_takeoff.py",
"repo_id": "langchain",
"token_count": 2266
} | 274 |
"""Google GenerativeAI Attributed Question and Answering (AQA) service.
The GenAI Semantic AQA API is a managed end to end service that allows
developers to create responses grounded on specified passages based on
a user query. For more information visit:
https://developers.generativeai.google/guide
"""
import loggin... | llama_index/llama-index-integrations/response_synthesizers/llama-index-response-synthesizers-google/llama_index/response_synthesizers/google/base.py/0 | {
"file_path": "llama_index/llama-index-integrations/response_synthesizers/llama-index-response-synthesizers-google/llama_index/response_synthesizers/google/base.py",
"repo_id": "llama_index",
"token_count": 3668
} | 1,548 |
import { logVersion010MigrationWarning } from "../util/entrypoint_deprecation.js";
/* #__PURE__ */ logVersion010MigrationWarning({
oldEntrypointName: "vectorstores/redis",
newEntrypointName: "",
newPackageName: "@langchain/redis",
});
export * from "@langchain/community/vectorstores/redis";
| langchainjs/langchain/src/vectorstores/redis.ts/0 | {
"file_path": "langchainjs/langchain/src/vectorstores/redis.ts",
"repo_id": "langchainjs",
"token_count": 94
} | 967 |
// 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/distributed/indexnode/client/client_test.go/0 | {
"file_path": "milvus/internal/distributed/indexnode/client/client_test.go",
"repo_id": "milvus",
"token_count": 1814
} | 1,814 |
// Auto-generated by `scripts/create-entrypoints.js`. Do not edit manually.
export * as agents from "../agents/index.js";
export * as agents__toolkits from "../agents/toolkits/index.js";
export * as agents__format_scratchpad from "../agents/format_scratchpad/openai_functions.js";
export * as agents__format_scratchpad_... | langchainjs/langchain/src/load/import_map.ts/0 | {
"file_path": "langchainjs/langchain/src/load/import_map.ts",
"repo_id": "langchainjs",
"token_count": 2736
} | 932 |
from langchain_community.utilities.google_scholar import GoogleScholarAPIWrapper
__all__ = ["GoogleScholarAPIWrapper"]
| langchain/libs/langchain/langchain/utilities/google_scholar.py/0 | {
"file_path": "langchain/libs/langchain/langchain/utilities/google_scholar.py",
"repo_id": "langchain",
"token_count": 35
} | 604 |
import { test } from "@jest/globals";
import { PromptTemplate } from "@langchain/core/prompts";
import { OpenAI } from "@langchain/openai";
import { ConstitutionalChain } from "../constitutional_ai/constitutional_chain.js";
import { ConstitutionalPrinciple } from "../constitutional_ai/constitutional_principle.js";
impo... | langchainjs/langchain/src/chains/tests/constitutional_chain.int.test.ts/0 | {
"file_path": "langchainjs/langchain/src/chains/tests/constitutional_chain.int.test.ts",
"repo_id": "langchainjs",
"token_count": 337
} | 961 |
import warnings
from typing import Any, Callable, Dict, Optional, Sequence, Tuple
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
MessageRole,
)
from llama_index.core.bridge.pydantic import Fi... | llama_index/llama-index-integrations/llms/llama-index-llms-xinference/llama_index/llms/xinference/base.py/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-xinference/llama_index/llms/xinference/base.py",
"repo_id": "llama_index",
"token_count": 4209
} | 1,425 |
use candle_metal_kernels::{call_unary_contiguous, call_unary_strided, unary, Kernels};
use half::{bf16, f16};
use metal::objc::rc::autoreleasepool;
use metal::{Device, MTLResourceOptions};
use rand;
use std::any::type_name;
use std::time::Instant;
fn main() {
let device = Device::system_default().unwrap();
let... | candle/candle-metal-kernels/tmp/unary.rs/0 | {
"file_path": "candle/candle-metal-kernels/tmp/unary.rs",
"repo_id": "candle",
"token_count": 3489
} | 61 |
use crate::tokenizer::pattern::Pattern;
use crate::{Offsets, Result};
use onig::Regex;
use std::error::Error;
#[derive(Debug)]
pub struct SysRegex {
regex: Regex,
}
impl SysRegex {
pub fn find_iter<'r, 't>(&'r self, inside: &'t str) -> onig::FindMatches<'r, 't> {
self.regex.find_iter(inside)
}
... | tokenizers/tokenizers/src/utils/onig.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/onig.rs",
"repo_id": "tokenizers",
"token_count": 571
} | 471 |
from rag_timescale_hybrid_search_time import chain
__all__ = ["chain"]
| langchain/templates/rag-timescale-hybrid-search-time/rag_timescale_hybrid_search_time/__init__.py/0 | {
"file_path": "langchain/templates/rag-timescale-hybrid-search-time/rag_timescale_hybrid_search_time/__init__.py",
"repo_id": "langchain",
"token_count": 24
} | 670 |
from langchain_community.tools.amadeus.base import AmadeusBaseTool
__all__ = ["AmadeusBaseTool"]
| langchain/libs/langchain/langchain/tools/amadeus/base.py/0 | {
"file_path": "langchain/libs/langchain/langchain/tools/amadeus/base.py",
"repo_id": "langchain",
"token_count": 33
} | 572 |
"""Configuration for run evaluators."""
from typing import Any, Dict, List, Optional, Union
from langchain_core.embeddings import Embeddings
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from l... | langchain/libs/langchain/langchain/smith/evaluation/config.py/0 | {
"file_path": "langchain/libs/langchain/langchain/smith/evaluation/config.py",
"repo_id": "langchain",
"token_count": 4776
} | 569 |
import pytest
from text_generation import __version__
from huggingface_hub.utils import build_hf_headers
@pytest.fixture
def flan_t5_xxl():
return "google/flan-t5-xxl"
@pytest.fixture
def fake_model():
return "fake/model"
@pytest.fixture
def unsupported_model():
return "gpt2"
@pytest.fixture
def ba... | text-generation-inference/clients/python/tests/conftest.py/0 | {
"file_path": "text-generation-inference/clients/python/tests/conftest.py",
"repo_id": "text-generation-inference",
"token_count": 390
} | 399 |
# Supervised Fine-tuning Trainer
Supervised fine-tuning (or SFT for short) is a crucial step in RLHF. In TRL we provide an easy-to-use API to create your SFT models and train them with few lines of code on your dataset.
Check out a complete flexible example at [`examples/scripts/sft.py`](https://github.com/huggingfac... | trl/docs/source/sft_trainer.mdx/0 | {
"file_path": "trl/docs/source/sft_trainer.mdx",
"repo_id": "trl",
"token_count": 8670
} | 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... | milvus/internal/proxy/trace_log_interceptor.go/0 | {
"file_path": "milvus/internal/proxy/trace_log_interceptor.go",
"repo_id": "milvus",
"token_count": 1393
} | 1,753 |
# coding=utf-8
# Copyright 2022, UCLA NLP, The Facebook AI Research Team Authors and 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/l... | transformers/src/transformers/models/plbart/tokenization_plbart.py/0 | {
"file_path": "transformers/src/transformers/models/plbart/tokenization_plbart.py",
"repo_id": "transformers",
"token_count": 9661
} | 728 |
# 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/tests/test_stablediffusion.py/0 | {
"file_path": "peft/tests/test_stablediffusion.py",
"repo_id": "peft",
"token_count": 4243
} | 341 |
<jupyter_start><jupyter_text>Semi-structured RAGMany documents contain a mixture of content types, including text and tables. Semi-structured data can be challenging for conventional RAG for at least two reasons: * Text splitting may break up tables, corrupting the data in retrieval* Embedding tables may pose challenge... | langchain/cookbook/Semi_Structured_RAG.ipynb/0 | {
"file_path": "langchain/cookbook/Semi_Structured_RAG.ipynb",
"repo_id": "langchain",
"token_count": 2478
} | 74 |
import shutil
from typing import Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.tools import BaseTool
from langchain_community.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixi... | langchain/libs/community/langchain_community/tools/file_management/move.py/0 | {
"file_path": "langchain/libs/community/langchain_community/tools/file_management/move.py",
"repo_id": "langchain",
"token_count": 768
} | 286 |
## For JS backend:
# LANGCHAIN_TRACING_V2=true
# LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
# LANGCHAIN_API_KEY="YOUR_LANGSMITH_KEY"
# LANGCHAIN_PROJECT="YOUR_PROJECT_NAME"
# NEXT_PUBLIC_API_BASE_URL="http://localhost:3000/api"
# OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
# TAVILY_API_KEY="YOUR_TAVILY_KEY" | weblangchain/nextjs/.env.example/0 | {
"file_path": "weblangchain/nextjs/.env.example",
"repo_id": "weblangchain",
"token_count": 145
} | 1,995 |
from llama_index.legacy.node_parser.text.semantic_splitter import (
SemanticSplitterNodeParser,
)
from llama_index.legacy.schema import Document
from tests.playground.test_base import MockEmbedding
def test_grouped_semantically() -> None:
document = Document(
text="They're taking the Hobbits to Isenga... | llama_index/llama-index-legacy/tests/node_parser/test_semantic_splitter.py/0 | {
"file_path": "llama_index/llama-index-legacy/tests/node_parser/test_semantic_splitter.py",
"repo_id": "llama_index",
"token_count": 669
} | 1,750 |
<jupyter_start><jupyter_text>**How to benchmark models with Transformers**With ever-larger language models, it is no longer enough to just compare models on their performance on a specific task. One should always be aware of the computational cost that is attached to a specific model. For a given computation environmen... | notebooks/examples/benchmark.ipynb/0 | {
"file_path": "notebooks/examples/benchmark.ipynb",
"repo_id": "notebooks",
"token_count": 12105
} | 291 |
import os
import sys
import time
import openai
from openai import OpenAI
from validate_json import validate_json
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
def launch_training(data_path: str) -> None:
validate_json(data_path)
# TODO: figure out how to specify file name in the new API
# file_n... | llama_index/experimental/openai_fine_tuning/launch_training.py/0 | {
"file_path": "llama_index/experimental/openai_fine_tuning/launch_training.py",
"repo_id": "llama_index",
"token_count": 456
} | 1,191 |
python_tests()
| llama_index/llama-index-integrations/llms/llama-index-llms-perplexity/tests/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-perplexity/tests/BUILD",
"repo_id": "llama_index",
"token_count": 5
} | 1,299 |
import { AdminClient } from "../src/AdminClient";
const PORT = process.env.PORT || "8000";
const URL = "http://localhost:" + PORT;
const adminClient = new AdminClient({ path: URL });
export default adminClient;
| chroma/clients/js/test/initAdminClient.ts/0 | {
"file_path": "chroma/clients/js/test/initAdminClient.ts",
"repo_id": "chroma",
"token_count": 62
} | 35 |
import { OpenAI } from "@langchain/openai";
import { UpstashRedisCache } from "@langchain/community/caches/upstash_redis";
// See https://docs.upstash.com/redis/howto/connectwithupstashredis#quick-start for connection options
const cache = new UpstashRedisCache({
config: {
url: "UPSTASH_REDIS_REST_URL",
toke... | langchainjs/examples/src/cache/upstash_redis.ts/0 | {
"file_path": "langchainjs/examples/src/cache/upstash_redis.ts",
"repo_id": "langchainjs",
"token_count": 147
} | 815 |
""" AutoAugment, RandAugment, AugMix, and 3-Augment for PyTorch
This code implements the searched ImageNet policies with various tweaks and improvements and
does not include any of the search code.
AA and RA Implementation adapted from:
https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/au... | pytorch-image-models/timm/data/auto_augment.py/0 | {
"file_path": "pytorch-image-models/timm/data/auto_augment.py",
"repo_id": "pytorch-image-models",
"token_count": 15929
} | 347 |
from sql_pgvector.chain import chain
__all__ = ["chain"]
| langchain/templates/sql-pgvector/sql_pgvector/__init__.py/0 | {
"file_path": "langchain/templates/sql-pgvector/sql_pgvector/__init__.py",
"repo_id": "langchain",
"token_count": 19
} | 682 |
"""Init file."""
| llama_index/llama-index-legacy/llama_index/legacy/token_counter/__init__.py/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/token_counter/__init__.py",
"repo_id": "llama_index",
"token_count": 6
} | 1,647 |
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