text stringlengths 3 1.68M | id stringlengths 13 169 | metadata dict | __index_level_0__ int64 0 2.21k |
|---|---|---|---|
python_sources()
| llama_index/llama-index-integrations/readers/llama-index-readers-metal/llama_index/readers/metal/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-metal/llama_index/readers/metal/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,427 |
// 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/metacache/actions_test.go/0 | {
"file_path": "milvus/internal/datanode/metacache/actions_test.go",
"repo_id": "milvus",
"token_count": 1153
} | 1,805 |
.. _Ref-Service-Context:
Service Context
=================
The service context container is a utility container for LlamaIndex
index and query classes. The container contains the following
objects that are commonly used for configuring every index and
query, such as the LLM,
the PromptHelper (for configuring input si... | llama_index/docs/api_reference/service_context.rst/0 | {
"file_path": "llama_index/docs/api_reference/service_context.rst",
"repo_id": "llama_index",
"token_count": 211
} | 1,062 |
from langchain_community.vectorstores.pinecone import Pinecone
__all__ = ["Pinecone"]
| langchain/libs/langchain/langchain/vectorstores/pinecone.py/0 | {
"file_path": "langchain/libs/langchain/langchain/vectorstores/pinecone.py",
"repo_id": "langchain",
"token_count": 26
} | 584 |
# coding=utf-8
# Copyright 2018 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/convert_pytorch_checkpoint_to_tf2.py/0 | {
"file_path": "transformers/src/transformers/convert_pytorch_checkpoint_to_tf2.py",
"repo_id": "transformers",
"token_count": 7993
} | 618 |
"""Init file for langchain helpers."""
try:
import langchain # noqa
except ImportError:
raise ImportError(
"langchain not installed. "
"Please install langchain with `pip install llama_index[langchain]`."
)
| llama_index/llama-index-legacy/llama_index/legacy/langchain_helpers/__init__.py/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/langchain_helpers/__init__.py",
"repo_id": "llama_index",
"token_count": 86
} | 1,672 |
[project]
name = "chromadb"
dynamic = ["version"]
authors = [
{ name="Jeff Huber", email="jeff@trychroma.com" },
{ name="Anton Troynikov", email="anton@trychroma.com" }
]
description = "Chroma."
readme = "README.md"
requires-python = ">=3.8"
classifiers = [
"Programming Language :: Python :: 3",
"License :... | chroma/pyproject.toml/0 | {
"file_path": "chroma/pyproject.toml",
"repo_id": "chroma",
"token_count": 886
} | 55 |
# Copyright 2019 The TensorFlow Authors, The Hugging Face 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
#
# Unl... | transformers/src/transformers/optimization_tf.py/0 | {
"file_path": "transformers/src/transformers/optimization_tf.py",
"repo_id": "transformers",
"token_count": 6957
} | 698 |
import transformers
from tokenizers.implementations import SentencePieceUnigramTokenizer, BaseTokenizer
from tokenizers.processors import TemplateProcessing
from tokenizers.models import Unigram, BPE
from tokenizers import decoders
from tokenizers import Tokenizer, Regex
from tokenizers.normalizers import (
StripAc... | tokenizers/bindings/python/scripts/convert.py/0 | {
"file_path": "tokenizers/bindings/python/scripts/convert.py",
"repo_id": "tokenizers",
"token_count": 6438
} | 464 |
{
"openapi": "3.0.1",
"info": {
"title": "Shop",
"description": "Search for millions of products from the world's greatest brands.",
"version": "v1"
},
"servers": [
{
"url": "https://server.shop.app"
}
],
"paths": {
"/openai/search": {
"get": {
... | langchain/libs/community/tests/unit_tests/examples/test_specs/shop/apispec.json/0 | {
"file_path": "langchain/libs/community/tests/unit_tests/examples/test_specs/shop/apispec.json",
"repo_id": "langchain",
"token_count": 3242
} | 406 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | transformers/src/transformers/models/fastspeech2_conformer/configuration_fastspeech2_conformer.py/0 | {
"file_path": "transformers/src/transformers/models/fastspeech2_conformer/configuration_fastspeech2_conformer.py",
"repo_id": "transformers",
"token_count": 9839
} | 633 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from torch.nn import functional as F
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput
from diffusers import (
DiffusionPipeline,
ImagePipelineOu... | diffusers/examples/community/unclip_text_interpolation.py/0 | {
"file_path": "diffusers/examples/community/unclip_text_interpolation.py",
"repo_id": "diffusers",
"token_count": 11575
} | 217 |
use super::with_tracing::{linear, Embedding, Linear};
use candle::{Result, Tensor};
use candle_nn::{layer_norm, LayerNorm, VarBuilder};
#[derive(Debug, Clone)]
pub struct Config {
pub vocab_size: usize,
pub decoder_vocab_size: Option<usize>,
pub max_position_embeddings: usize,
pub encoder_layers: usize... | candle/candle-transformers/src/models/marian.rs/0 | {
"file_path": "candle/candle-transformers/src/models/marian.rs",
"repo_id": "candle",
"token_count": 8917
} | 71 |
<jupyter_start><jupyter_text>Préparer des données (TensorFlow) Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
import tensorflow as tf
import numpy as np
from transformers import AutoTokenizer, TFAutoModelForSequ... | notebooks/course/fr/chapter3/section2_tf.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter3/section2_tf.ipynb",
"repo_id": "notebooks",
"token_count": 1005
} | 268 |
"""Chain that interprets a prompt and executes bash operations."""
from __future__ import annotations
import logging
import warnings
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import ... | langchain/libs/experimental/langchain_experimental/llm_bash/base.py/0 | {
"file_path": "langchain/libs/experimental/langchain_experimental/llm_bash/base.py",
"repo_id": "langchain",
"token_count": 1878
} | 431 |
version: '3.5'
services:
etcd:
container_name: milvus-etcd
image: quay.io/coreos/etcd:v3.5.5
environment:
- ETCD_AUTO_COMPACTION_MODE=revision
- ETCD_AUTO_COMPACTION_RETENTION=1000
- ETCD_QUOTA_BACKEND_BYTES=4294967296
- ETCD_SNAPSHOT_COUNT=50000
volumes:
- ${DOCKER_VOLU... | milvus/deployments/docker/gpu/standalone/docker-compose.yml/0 | {
"file_path": "milvus/deployments/docker/gpu/standalone/docker-compose.yml",
"repo_id": "milvus",
"token_count": 844
} | 1,763 |
FROM ontotext/graphdb:10.5.1
RUN mkdir -p /opt/graphdb/dist/data/repositories/langchain
COPY config.ttl /opt/graphdb/dist/data/repositories/langchain/
COPY starwars-data.trig /
COPY graphdb_create.sh /run.sh
ENTRYPOINT bash /run.sh | langchain/libs/community/tests/integration_tests/graphs/docker-compose-ontotext-graphdb/Dockerfile/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/graphs/docker-compose-ontotext-graphdb/Dockerfile",
"repo_id": "langchain",
"token_count": 101
} | 358 |
/* eslint-disable react/jsx-props-no-spreading */
import React from "react";
import CodeBlock from "@theme-original/CodeBlock";
function Imports({ imports }) {
return (
<div
style={{
paddingTop: "1.3rem",
background: "var(--prism-background-color)",
color: "var(--prism-color)",
... | langchain/docs/src/theme/CodeBlock/index.js/0 | {
"file_path": "langchain/docs/src/theme/CodeBlock/index.js",
"repo_id": "langchain",
"token_count": 713
} | 201 |
import logging
from typing import Callable, List, Optional, cast
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.constants import DEFAULT_SIMILARITY_TOP_K
from llama_index.core.indices.keyword_table.utils import simple_ext... | llama_index/llama-index-integrations/retrievers/llama-index-retrievers-bm25/llama_index/retrievers/bm25/base.py/0 | {
"file_path": "llama_index/llama-index-integrations/retrievers/llama-index-retrievers-bm25/llama_index/retrievers/bm25/base.py",
"repo_id": "llama_index",
"token_count": 1550
} | 1,465 |
"""Simple reader that reads weather data from OpenWeatherMap API."""
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class WeatherReader(BaseReader):
"""Weather Reader.
Reads the forecast & current weather of any location using OpenWe... | llama_index/llama-index-integrations/readers/llama-index-readers-weather/llama_index/readers/weather/base.py/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-weather/llama_index/readers/weather/base.py",
"repo_id": "llama_index",
"token_count": 1358
} | 1,391 |
from langchain_community.document_loaders.git import GitLoader
__all__ = ["GitLoader"]
| langchain/libs/langchain/langchain/document_loaders/git.py/0 | {
"file_path": "langchain/libs/langchain/langchain/document_loaders/git.py",
"repo_id": "langchain",
"token_count": 27
} | 481 |
[tool.poetry]
name = "rag-pinecone-rerank"
version = "0.1.0"
description = ""
authors = [
"Lance Martin <lance@langchain.dev>",
]
readme = "README.md"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
langchain = "^0.1"
openai = "<2"
tiktoken = ">=0.5.1"
pinecone-client = ">=2.2.4"
cohere = ">=4.32"
[tool.poetry... | langchain/templates/rag-pinecone-rerank/pyproject.toml/0 | {
"file_path": "langchain/templates/rag-pinecone-rerank/pyproject.toml",
"repo_id": "langchain",
"token_count": 308
} | 663 |
use super::with_tracing::{layer_norm, linear, LayerNorm, Linear};
use candle::{DType, Device, Result, Tensor};
use candle_nn::{embedding, Embedding, Module, VarBuilder};
use serde::Deserialize;
pub const DTYPE: DType = DType::F32;
#[derive(Debug, Clone, Copy, PartialEq, Eq, Deserialize)]
#[serde(rename_all = "lowerca... | candle/candle-transformers/src/models/bert.rs/0 | {
"file_path": "candle/candle-transformers/src/models/bert.rs",
"repo_id": "candle",
"token_count": 7941
} | 69 |
"""Pandas toolkit."""
| langchain/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/__init__.py/0 | {
"file_path": "langchain/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/__init__.py",
"repo_id": "langchain",
"token_count": 8
} | 441 |
from typing import (
Dict,
Optional,
Sequence,
Type,
Union,
)
from langchain_core.output_parsers import (
BaseGenerationOutputParser,
BaseOutputParser,
)
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables impo... | langchain/libs/partners/google-vertexai/langchain_google_vertexai/chains.py/0 | {
"file_path": "langchain/libs/partners/google-vertexai/langchain_google_vertexai/chains.py",
"repo_id": "langchain",
"token_count": 1723
} | 686 |
"""Test Pipeline Cloud API wrapper."""
from langchain_community.llms.pipelineai import PipelineAI
def test_pipelineai_call() -> None:
"""Test valid call to Pipeline Cloud."""
llm = PipelineAI()
output = llm("Say foo:")
assert isinstance(output, str)
| langchain/libs/community/tests/integration_tests/llms/test_pipelineai.py/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/llms/test_pipelineai.py",
"repo_id": "langchain",
"token_count": 90
} | 373 |
[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 = ["GmailToolSpec", "GoogleCalendarToolSpec", "GoogleSearchToolSpec", "QUERY_URL_TMPL"]
conta... | llama_index/llama-index-integrations/tools/llama-index-tools-google/pyproject.toml/0 | {
"file_path": "llama_index/llama-index-integrations/tools/llama-index-tools-google/pyproject.toml",
"repo_id": "llama_index",
"token_count": 737
} | 1,568 |
"""Integration test for self ask with search."""
from langchain_community.llms.openai import OpenAI
from langchain_community.utilities.searchapi import SearchApiAPIWrapper
from langchain.agents.self_ask_with_search.base import SelfAskWithSearchChain
def test_self_ask_with_search() -> None:
"""Test functionality ... | langchain/libs/langchain/tests/integration_tests/chains/test_self_ask_with_search.py/0 | {
"file_path": "langchain/libs/langchain/tests/integration_tests/chains/test_self_ask_with_search.py",
"repo_id": "langchain",
"token_count": 249
} | 615 |
python_sources()
python_requirements(
name="reqs",
)
| llama_index/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/beautiful_soup_web/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/beautiful_soup_web/BUILD",
"repo_id": "llama_index",
"token_count": 24
} | 1,454 |
# flake8: noqa
from langchain_core.prompts import PromptTemplate
prompt_template = """Write a concise summary of the following:
"{text}"
CONCISE SUMMARY:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
| langchain/libs/langchain/langchain/chains/summarize/map_reduce_prompt.py/0 | {
"file_path": "langchain/libs/langchain/langchain/chains/summarize/map_reduce_prompt.py",
"repo_id": "langchain",
"token_count": 77
} | 476 |
import pytest
from importlib_resources import files
from typing import Generator, List, Callable
import chromadb.db.migrations as migrations
from chromadb.db.impl.sqlite import SqliteDB
from chromadb.config import System, Settings
from pytest import FixtureRequest
import copy
def sqlite() -> Generator[migrations.Migr... | chroma/chromadb/test/db/test_migrations.py/0 | {
"file_path": "chroma/chromadb/test/db/test_migrations.py",
"repo_id": "chroma",
"token_count": 2014
} | 21 |
import multiprocessing
import numbers
import random
import numpy
import threading
import pytest
import pandas as pd
import decimal
from decimal import Decimal, getcontext
from time import sleep
import heapq
from pymilvus import DataType
from base.client_base import TestcaseBase
from utils.util_log import test_log as l... | milvus/tests/python_client/milvus_client/test_milvus_client_collection.py/0 | {
"file_path": "milvus/tests/python_client/milvus_client/test_milvus_client_collection.py",
"repo_id": "milvus",
"token_count": 24531
} | 2,124 |
---
hide_table_of_contents: true
---
# MultiVector Retriever
It can often be beneficial to store multiple vectors per document.
LangChain has a base MultiVectorRetriever which makes querying this type of setup easier!
A lot of the complexity lies in how to create the multiple vectors per document.
This notebook cove... | langchainjs/docs/core_docs/docs/modules/data_connection/retrievers/multi-vector-retriever.mdx/0 | {
"file_path": "langchainjs/docs/core_docs/docs/modules/data_connection/retrievers/multi-vector-retriever.mdx",
"repo_id": "langchainjs",
"token_count": 640
} | 788 |
from neo4j_semantic_layer.agent import agent_executor
__all__ = ["agent_executor"]
| langchain/templates/neo4j-semantic-layer/neo4j_semantic_layer/__init__.py/0 | {
"file_path": "langchain/templates/neo4j-semantic-layer/neo4j_semantic_layer/__init__.py",
"repo_id": "langchain",
"token_count": 29
} | 677 |
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { Document } from "@langchain/core/documents";
const text = `Some other considerations include:
- Do you deploy your backend and frontend together, or separately?
- Do you deploy your backend co-located with your database, or separately?... | langchainjs/examples/src/indexes/recursive_text_splitter_custom_separators.ts/0 | {
"file_path": "langchainjs/examples/src/indexes/recursive_text_splitter_custom_separators.ts",
"repo_id": "langchainjs",
"token_count": 966
} | 844 |
"""**Docstores** are classes to store and load Documents.
The **Docstore** is a simplified version of the Document Loader.
**Class hierarchy:**
.. code-block::
Docstore --> <name> # Examples: InMemoryDocstore, Wikipedia
**Main helpers:**
.. code-block::
Document, AddableMixin
"""
from langchain_community... | langchain/libs/community/langchain_community/docstore/__init__.py/0 | {
"file_path": "langchain/libs/community/langchain_community/docstore/__init__.py",
"repo_id": "langchain",
"token_count": 162
} | 242 |
from typing import TYPE_CHECKING
from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501
TreeSitterSegmenter,
)
if TYPE_CHECKING:
from tree_sitter import Language
CHUNK_QUERY = """
[
(function_declaration) @function
(class_declaration) @cla... | langchain/libs/community/langchain_community/document_loaders/parsers/language/kotlin.py/0 | {
"file_path": "langchain/libs/community/langchain_community/document_loaders/parsers/language/kotlin.py",
"repo_id": "langchain",
"token_count": 281
} | 255 |
"""Test Vertex AI API wrapper.
In order to run this test, you need to install VertexAI SDK
pip install google-cloud-aiplatform>=1.35.0
Your end-user credentials would be used to make the calls (make sure you've run
`gcloud auth login` first).
"""
import pytest
from langchain_community.embeddings import VertexAIEmbedd... | langchain/libs/community/tests/integration_tests/embeddings/test_vertexai.py/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/embeddings/test_vertexai.py",
"repo_id": "langchain",
"token_count": 804
} | 363 |
import type { Conversation } from "$lib/types/Conversation";
import type { TextGenerationStreamOutput } from "@huggingface/inference";
import { endpointTgi, endpointTgiParametersSchema } from "./tgi/endpointTgi";
import { z } from "zod";
import endpointAws, { endpointAwsParametersSchema } from "./aws/endpointAws";
impo... | chat-ui/src/lib/server/endpoints/endpoints.ts/0 | {
"file_path": "chat-ui/src/lib/server/endpoints/endpoints.ts",
"repo_id": "chat-ui",
"token_count": 495
} | 101 |
python_tests()
| llama_index/llama-index-integrations/llms/llama-index-llms-everlyai/tests/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-everlyai/tests/BUILD",
"repo_id": "llama_index",
"token_count": 5
} | 1,220 |
"""Unit tests for ReAct."""
from typing import Union
from langchain_community.llms.fake import FakeListLLM
from langchain_core.agents import AgentAction
from langchain_core.documents import Document
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.tools import Tool
from langchain.agents.r... | langchain/libs/langchain/tests/unit_tests/agents/test_react.py/0 | {
"file_path": "langchain/libs/langchain/tests/unit_tests/agents/test_react.py",
"repo_id": "langchain",
"token_count": 859
} | 601 |
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. 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.a... | diffusers/src/diffusers/pipelines/onnx_utils.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/onnx_utils.py",
"repo_id": "diffusers",
"token_count": 3622
} | 262 |
version: "3.8"
services:
neo4j:
image: neo4j:5.11.0
restart: on-failure:0
hostname: neo4j-test
container_name: neo4j-test
ports:
- 7474:7474
- 7687:7687
environment:
- NEO4J_AUTH=neo4j/pleaseletmein
| langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/neo4j.yml/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/neo4j.yml",
"repo_id": "langchain",
"token_count": 128
} | 371 |
from sql_ollama.chain import chain
__all__ = ["chain"]
| langchain/templates/sql-ollama/sql_ollama/__init__.py/0 | {
"file_path": "langchain/templates/sql-ollama/sql_ollama/__init__.py",
"repo_id": "langchain",
"token_count": 19
} | 704 |
include Cargo.toml
include pyproject.toml
include rust-toolchain
include ../../LICENSE
recursive-include src *
recursive-include tokenizers-lib *
recursive-exclude tokenizers-lib/target *
| tokenizers/bindings/python/MANIFEST.in/0 | {
"file_path": "tokenizers/bindings/python/MANIFEST.in",
"repo_id": "tokenizers",
"token_count": 57
} | 439 |
[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 = ["MangoppsGuidesReader"]
contains_example = false
import_path = "llama_index.readers.mangoa... | llama_index/llama-index-integrations/readers/llama-index-readers-mangoapps-guides/pyproject.toml/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-mangoapps-guides/pyproject.toml",
"repo_id": "llama_index",
"token_count": 695
} | 1,518 |
<jupyter_start><jupyter_text>Xorbits InferenceIn this demo notebook, we show how to use Xorbits Inference (Xinference for short) to deploy local LLMs in three steps.We will be using the Llama 2 chat model in GGML format in the example, but the code should be easily transfrerable to all LLM chat models supported by Xinf... | llama_index/docs/examples/llm/xinference_local_deployment.ipynb/0 | {
"file_path": "llama_index/docs/examples/llm/xinference_local_deployment.ipynb",
"repo_id": "llama_index",
"token_count": 2182
} | 1,197 |
import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar
from ..features import Features
T = TypeVar("T", bound="TaskTemplate")
@dataclass(frozen=True)
class TaskTemplate(abc.ABC):
# `task` is not a ClassVar since we want it to be part of the ... | datasets/src/datasets/tasks/base.py/0 | {
"file_path": "datasets/src/datasets/tasks/base.py",
"repo_id": "datasets",
"token_count": 417
} | 156 |
python_sources()
| llama_index/llama-index-legacy/llama_index/legacy/query_pipeline/BUILD/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/query_pipeline/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,514 |
from typing import Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.pydantic_v1 import Field
from langchain_core.tools import BaseTool
from langchain_community.utilities.pubmed import PubMedAPIWrapper
class PubmedQueryRun(BaseTool):
"""Tool that searches the PubMed API... | langchain/libs/community/langchain_community/tools/pubmed/tool.py/0 | {
"file_path": "langchain/libs/community/langchain_community/tools/pubmed/tool.py",
"repo_id": "langchain",
"token_count": 331
} | 293 |
# neo4j-semantic-layer
This template is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using OpenAI function calling.
The semantic layer equips the agent with a suite of robust tools, allowing it to interact with the graph databas based on the user's int... | langchain/templates/neo4j-semantic-layer/README.md/0 | {
"file_path": "langchain/templates/neo4j-semantic-layer/README.md",
"repo_id": "langchain",
"token_count": 1113
} | 651 |
from llama_index.packs.llama_guard_moderator.base import LlamaGuardModeratorPack
__all__ = ["LlamaGuardModeratorPack"]
| llama_index/llama-index-packs/llama-index-packs-llama-guard-moderator/llama_index/packs/llama_guard_moderator/__init__.py/0 | {
"file_path": "llama_index/llama-index-packs/llama-index-packs-llama-guard-moderator/llama_index/packs/llama_guard_moderator/__init__.py",
"repo_id": "llama_index",
"token_count": 43
} | 1,574 |
from llama_index.legacy.indices.managed.base import BaseManagedIndex
from llama_index.legacy.indices.managed.vectara.base import VectaraIndex
from llama_index.legacy.indices.managed.vectara.retriever import VectaraRetriever
from llama_index.legacy.indices.managed.zilliz.base import ZillizCloudPipelineIndex
from llama_i... | llama_index/llama-index-legacy/llama_index/legacy/indices/managed/__init__.py/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/indices/managed/__init__.py",
"repo_id": "llama_index",
"token_count": 216
} | 1,498 |
version: "3.8"
services:
meilisearch:
image: getmeili/meilisearch:latest
environment:
- MEILI_MASTER_KEY=${MEILI_MASTER_KEY:-masterKey}
- MEILI_NO_ANALYTICS=${MEILI_NO_ANALYTICS:-true}
- MEILI_ENV=${MEILI_ENV:-development}
ports:
- ${MEILI_PORT:-7700}:7700
restart: unless-stop... | langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/meilisearch.yaml/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/meilisearch.yaml",
"repo_id": "langchain",
"token_count": 227
} | 379 |
from llama_index.storage.kvstore.dynamodb.base import DynamoDBKVStore
__all__ = ["DynamoDBKVStore"]
| llama_index/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-dynamodb/llama_index/storage/kvstore/dynamodb/__init__.py/0 | {
"file_path": "llama_index/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-dynamodb/llama_index/storage/kvstore/dynamodb/__init__.py",
"repo_id": "llama_index",
"token_count": 40
} | 1,466 |
from langchain_community.chat_models.human import (
HumanInputChatModel,
)
__all__ = ["HumanInputChatModel"]
| langchain/libs/langchain/langchain/chat_models/human.py/0 | {
"file_path": "langchain/libs/langchain/langchain/chat_models/human.py",
"repo_id": "langchain",
"token_count": 37
} | 474 |
import re
import unittest
from typing import Tuple
import pytest
from langchain_experimental.tot.base import ToTChain
from langchain_experimental.tot.checker import ToTChecker
from langchain_experimental.tot.controller import ToTController
from langchain_experimental.tot.memory import ToTDFSMemory
from langchain_expe... | langchain/libs/experimental/tests/unit_tests/test_tot.py/0 | {
"file_path": "langchain/libs/experimental/tests/unit_tests/test_tot.py",
"repo_id": "langchain",
"token_count": 2725
} | 460 |
# 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/unispeech_sat/convert_unispeech_original_s3prl_checkpoint_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/unispeech_sat/convert_unispeech_original_s3prl_checkpoint_to_pytorch.py",
"repo_id": "transformers",
"token_count": 1692
} | 743 |
from __future__ import annotations
from typing import Any, List, Literal
from langchain_core.load.serializable import Serializable
from langchain_core.pydantic_v1 import Field
class Document(Serializable):
"""Class for storing a piece of text and associated metadata."""
page_content: str
"""String text... | langchain/libs/core/langchain_core/documents/base.py/0 | {
"file_path": "langchain/libs/core/langchain_core/documents/base.py",
"repo_id": "langchain",
"token_count": 355
} | 411 |
from langchain_core.exceptions import OutputParserException
from langchain.output_parsers import ResponseSchema, StructuredOutputParser
def test_parse() -> None:
response_schemas = [
ResponseSchema(name="name", description="desc"),
ResponseSchema(name="age", description="desc"),
]
parser ... | langchain/libs/langchain/tests/unit_tests/output_parsers/test_structured_parser.py/0 | {
"file_path": "langchain/libs/langchain/tests/unit_tests/output_parsers/test_structured_parser.py",
"repo_id": "langchain",
"token_count": 331
} | 601 |
# 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 applicabl... | transformers/examples/legacy/seq2seq/seq2seq_training_args.py/0 | {
"file_path": "transformers/examples/legacy/seq2seq/seq2seq_training_args.py",
"repo_id": "transformers",
"token_count": 888
} | 511 |
# 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 applicabl... | transformers/src/transformers/commands/serving.py/0 | {
"file_path": "transformers/src/transformers/commands/serving.py",
"repo_id": "transformers",
"token_count": 3477
} | 591 |
from langchain_core.messages import AIMessage, HumanMessage
from langchain_community.chat_models.baichuan import ChatBaichuan
# For testing, run:
# TEST_FILE=tests/integration_tests/chat_models/test_baichuan.py make test
def test_chat_baichuan_default() -> None:
chat = ChatBaichuan(streaming=True)
message =... | langchain/libs/community/tests/integration_tests/chat_models/test_baichuan.py/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/chat_models/test_baichuan.py",
"repo_id": "langchain",
"token_count": 795
} | 335 |
from typing import Any, Dict
from llama_index.core.agent import ReActAgent
from llama_index.core.llama_pack.base import BaseLlamaPack
class CogniswitchAgentPack(BaseLlamaPack):
def __init__(self, cogniswitch_tool_kwargs: Dict[str, Any]) -> None:
"""Init params."""
try:
from llama_inde... | llama_index/llama-index-packs/llama-index-packs-cogniswitch-agent/llama_index/packs/cogniswitch_agent/base.py/0 | {
"file_path": "llama_index/llama-index-packs/llama-index-packs-cogniswitch-agent/llama_index/packs/cogniswitch_agent/base.py",
"repo_id": "llama_index",
"token_count": 360
} | 1,695 |
""" Pytorch Inception-V4 implementation
Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
"""
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET... | pytorch-image-models/timm/models/inception_v4.py/0 | {
"file_path": "pytorch-image-models/timm/models/inception_v4.py",
"repo_id": "pytorch-image-models",
"token_count": 5528
} | 386 |
// 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/rootcoord/create_partition_task_test.go/0 | {
"file_path": "milvus/internal/rootcoord/create_partition_task_test.go",
"repo_id": "milvus",
"token_count": 2460
} | 2,055 |
# StripeDocs Loader
This loader asynchronously loads data from the [Stripe documentation](https://stripe.com/docs). It iterates through the Stripe sitemap to get all `/docs` references.
It is based on the [Async Website Loader](https://llamahub.ai/l/web-async_web).
## Usage
```python
from llama_index import VectorS... | llama_index/llama-index-integrations/readers/llama-index-readers-stripe-docs/README.md/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-stripe-docs/README.md",
"repo_id": "llama_index",
"token_count": 335
} | 1,424 |
<jupyter_start><jupyter_text>Fine-Tuning and GuidanceIn this notebook, we're going to cover two main approaches for adapting existing diffusion models:* With **fine-tuning**, we'll re-train existing models on new data to change the type of output they produce* With **guidance**, we'll take an existing model and steer t... | diffusion-models-class/unit2/01_finetuning_and_guidance.ipynb/0 | {
"file_path": "diffusion-models-class/unit2/01_finetuning_and_guidance.ipynb",
"repo_id": "diffusion-models-class",
"token_count": 11877
} | 292 |
"""Web base loader class."""
import asyncio
import logging
import warnings
from typing import Any, Dict, Iterator, List, Optional, Sequence, Union
import aiohttp
import requests
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
logger = logging.getLogger(_... | langchain/libs/community/langchain_community/document_loaders/web_base.py/0 | {
"file_path": "langchain/libs/community/langchain_community/document_loaders/web_base.py",
"repo_id": "langchain",
"token_count": 4893
} | 251 |
import { expect, test } from "@jest/globals";
import chroma from "./initClient";
import { DOCUMENTS, EMBEDDINGS, IDS, METADATAS } from "./data";
test("it should get a collection", async () => {
await chroma.reset();
const collection = await chroma.createCollection({ name: "test" });
await collection.add({ ids: I... | chroma/clients/js/test/get.collection.test.ts/0 | {
"file_path": "chroma/clients/js/test/get.collection.test.ts",
"repo_id": "chroma",
"token_count": 880
} | 34 |
from langchain_community.retrievers.chaindesk import ChaindeskRetriever
__all__ = ["ChaindeskRetriever"]
| langchain/libs/langchain/langchain/retrievers/chaindesk.py/0 | {
"file_path": "langchain/libs/langchain/langchain/retrievers/chaindesk.py",
"repo_id": "langchain",
"token_count": 35
} | 529 |
package msgstream
import "context"
type MockMqFactory struct {
Factory
NewMsgStreamFunc func(ctx context.Context) (MsgStream, error)
}
func NewMockMqFactory() *MockMqFactory {
return &MockMqFactory{}
}
func (m MockMqFactory) NewMsgStream(ctx context.Context) (MsgStream, error) {
return m.NewMsgStreamFunc(ctx)
}... | milvus/pkg/mq/msgstream/mock_mq_factory.go/0 | {
"file_path": "milvus/pkg/mq/msgstream/mock_mq_factory.go",
"repo_id": "milvus",
"token_count": 158
} | 2,093 |
from llama_index.question_gen.guidance.base import GuidanceQuestionGenerator
__all__ = ["GuidanceQuestionGenerator"]
| llama_index/llama-index-integrations/question_gen/llama-index-question-gen-guidance/llama_index/question_gen/guidance/__init__.py/0 | {
"file_path": "llama_index/llama-index-integrations/question_gen/llama-index-question-gen-guidance/llama_index/question_gen/guidance/__init__.py",
"repo_id": "llama_index",
"token_count": 36
} | 1,273 |
"""Utilities for running language models or Chains over datasets."""
from __future__ import annotations
import dataclasses
import functools
import inspect
import logging
import uuid
from datetime import datetime, timezone
from enum import Enum
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
... | langchain/libs/langchain/langchain/smith/evaluation/runner_utils.py/0 | {
"file_path": "langchain/libs/langchain/langchain/smith/evaluation/runner_utils.py",
"repo_id": "langchain",
"token_count": 23346
} | 551 |
.PHONY: quality style test docs
check_dirs := src tests examples docs scripts docker
# Check that source code meets quality standards
# this target runs checks on all files
quality:
ruff $(check_dirs)
ruff format --check $(check_dirs)
doc-builder style src/peft tests docs/source --max_len 119 --check_only
# Form... | peft/Makefile/0 | {
"file_path": "peft/Makefile",
"repo_id": "peft",
"token_count": 909
} | 332 |
from langchain_community.callbacks.tracers.comet import (
CometTracer,
import_comet_llm_api,
)
__all__ = ["import_comet_llm_api", "CometTracer"]
| langchain/libs/langchain/langchain/callbacks/tracers/comet.py/0 | {
"file_path": "langchain/libs/langchain/langchain/callbacks/tracers/comet.py",
"repo_id": "langchain",
"token_count": 64
} | 459 |
package kvfactory
import (
"fmt"
"sync"
clientv3 "go.etcd.io/etcd/client/v3"
"github.com/milvus-io/milvus/pkg/util/etcd"
"github.com/milvus-io/milvus/pkg/util/paramtable"
)
var clientCreator = &etcdClientCreator{}
var getEtcdAndPathFunction = getEtcdAndPath
type etcdClientCreator struct {
mu sync.Mute... | milvus/internal/util/dependency/kv/kv_client_handler.go/0 | {
"file_path": "milvus/internal/util/dependency/kv/kv_client_handler.go",
"repo_id": "milvus",
"token_count": 844
} | 1,895 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | transformers/src/transformers/tools/agent_types.py/0 | {
"file_path": "transformers/src/transformers/tools/agent_types.py",
"repo_id": "transformers",
"token_count": 3899
} | 731 |
<jupyter_start><jupyter_text>Metal Vector Store Creating a Metal Vector Store 1. Register an account for [Metal](https://app.getmetal.io/)2. Generate an API key in [Metal's Settings](https://app.getmetal.io/settings/organization). Save the `api_key` + `client_id`3. Generate an Index in [Metal's Dashboard](https://app.... | llama_index/docs/examples/vector_stores/MetalIndexDemo.ipynb/0 | {
"file_path": "llama_index/docs/examples/vector_stores/MetalIndexDemo.ipynb",
"repo_id": "llama_index",
"token_count": 619
} | 1,090 |
from __future__ import annotations
import asyncio
import json
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
if TYPE_CHECKING:
import pandas as pd
from telet... | langchain/libs/community/langchain_community/document_loaders/telegram.py/0 | {
"file_path": "langchain/libs/community/langchain_community/document_loaders/telegram.py",
"repo_id": "langchain",
"token_count": 4179
} | 249 |
# coding=utf-8
# Copyright 2021 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/rembert/test_modeling_tf_rembert.py/0 | {
"file_path": "transformers/tests/models/rembert/test_modeling_tf_rembert.py",
"repo_id": "transformers",
"token_count": 12938
} | 784 |
"""Callback Handler that prints to std out."""
import threading
from typing import Any, Dict, List
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
MODEL_COST_PER_1K_TOKENS = {
# GPT-4 input
"gpt-4": 0.03,
"gpt-4-0314": 0.03,
"gpt-4-0613": 0.03,
... | langchain/libs/community/langchain_community/callbacks/openai_info.py/0 | {
"file_path": "langchain/libs/community/langchain_community/callbacks/openai_info.py",
"repo_id": "langchain",
"token_count": 3978
} | 231 |
<jupyter_start><jupyter_text>Gradient Model Adapter<jupyter_code>%pip install llama-index-embeddings-langchain
%pip install llama-index-llms-gradient
%pip install llama-index --quiet
%pip install gradientai --quiet
import os
os.environ["GRADIENT_ACCESS_TOKEN"] = "{GRADIENT_ACCESS_TOKEN}"
os.environ["GRADIENT_WORKSPACE... | llama_index/docs/examples/llm/gradient_model_adapter.ipynb/0 | {
"file_path": "llama_index/docs/examples/llm/gradient_model_adapter.ipynb",
"repo_id": "llama_index",
"token_count": 725
} | 1,193 |
from langchain import storage
from tests.unit_tests import assert_all_importable
EXPECTED_ALL = [
"EncoderBackedStore",
"InMemoryStore",
"InMemoryByteStore",
"LocalFileStore",
"RedisStore",
"create_lc_store",
"create_kv_docstore",
"UpstashRedisByteStore",
"UpstashRedisStore",
]
de... | langchain/libs/langchain/tests/unit_tests/storage/test_imports.py/0 | {
"file_path": "langchain/libs/langchain/tests/unit_tests/storage/test_imports.py",
"repo_id": "langchain",
"token_count": 176
} | 654 |
#!/bin/bash
# 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... | milvus/build/build_image.sh/0 | {
"file_path": "milvus/build/build_image.sh",
"repo_id": "milvus",
"token_count": 646
} | 1,700 |
python_tests()
| llama_index/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/tests/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/tests/BUILD",
"repo_id": "llama_index",
"token_count": 5
} | 1,482 |
from langchain_community.graphs.hugegraph import HugeGraph
__all__ = ["HugeGraph"]
| langchain/libs/langchain/langchain/graphs/hugegraph.py/0 | {
"file_path": "langchain/libs/langchain/langchain/graphs/hugegraph.py",
"repo_id": "langchain",
"token_count": 26
} | 532 |
import { HandlebarsPromptTemplate } from "../handlebars.js";
describe.each([
["{{foo}}", { foo: "bar" }, "bar"],
["pre{{foo}}post", { foo: "bar" }, "prebarpost"],
["{{{foo}}}", { foo: "bar" }, "bar"],
["text", {}, "text"],
["}}", {}, "}}"],
["{{first}}_{{second}}", { first: "foo", second: "bar" }, "foo_bar... | langchainjs/langchain/src/experimental/prompts/tests/handlebars.test.ts/0 | {
"file_path": "langchainjs/langchain/src/experimental/prompts/tests/handlebars.test.ts",
"repo_id": "langchainjs",
"token_count": 319
} | 926 |
import { load as coreLoad } from "@langchain/core/load";
import { optionalImportEntrypoints } from "./import_constants.js";
import * as importMap from "./import_map.js";
import { OptionalImportMap } from "./import_type.js";
/**
* Load a LangChain module from a serialized text representation.
* NOTE: This functionali... | langchainjs/langchain/src/load/index.ts/0 | {
"file_path": "langchainjs/langchain/src/load/index.ts",
"repo_id": "langchainjs",
"token_count": 302
} | 931 |
import { GithubRepoLoader } from "langchain/document_loaders/web/github";
export const run = async () => {
const loader = new GithubRepoLoader(
"https://github.com/langchain-ai/langchainjs",
{
branch: "main",
recursive: false,
unknown: "warn",
maxConcurrency: 3, // Defaults to 2
}... | langchainjs/examples/src/document_loaders/github_stream.ts/0 | {
"file_path": "langchainjs/examples/src/document_loaders/github_stream.ts",
"repo_id": "langchainjs",
"token_count": 169
} | 795 |
kind: NetworkChaos
apiVersion: chaos-mesh.org/v1alpha1
metadata:
name: test-pulsar-network-latency
namespace: chaos-testing
spec:
selector:
namespaces:
- chaos-testing
labelSelectors:
release: milvus-chaos
app: pulsar
mode: all
action: delay
delay:
latency: 200ms
correlatio... | milvus/tests/python_client/chaos/chaos_objects/network_latency/chaos_pulsar_network_latency.yaml/0 | {
"file_path": "milvus/tests/python_client/chaos/chaos_objects/network_latency/chaos_pulsar_network_latency.yaml",
"repo_id": "milvus",
"token_count": 218
} | 1,989 |
from langchain_community.document_loaders.iugu import IuguLoader
__all__ = ["IuguLoader"]
| langchain/libs/langchain/langchain/document_loaders/iugu.py/0 | {
"file_path": "langchain/libs/langchain/langchain/document_loaders/iugu.py",
"repo_id": "langchain",
"token_count": 29
} | 483 |
# coding=utf-8
# Copyright 2021 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/convbert/modeling_convbert.py/0 | {
"file_path": "transformers/src/transformers/models/convbert/modeling_convbert.py",
"repo_id": "transformers",
"token_count": 25449
} | 637 |
import { NIBittensorLLM } from "langchain/experimental/llms/bittensor";
const model = new NIBittensorLLM();
const res = await model.call(`What is Bittensor?`);
console.log({ res });
/*
{
res: "\nBittensor is opensource protocol..."
}
*/
| langchainjs/examples/src/models/llm/ni_bittensor.ts/0 | {
"file_path": "langchainjs/examples/src/models/llm/ni_bittensor.ts",
"repo_id": "langchainjs",
"token_count": 92
} | 904 |
#[macro_use]
extern crate criterion;
use criterion::Criterion;
use std::collections::HashMap;
use std::fs::read_to_string;
use std::time::{Duration, Instant};
use tokenizers::models::unigram::Unigram;
use tokenizers::models::unigram::UnigramTrainer;
pub fn bench_train(c: &mut Criterion) {
let trainer = UnigramTra... | tokenizers/tokenizers/benches/unigram_benchmark.rs/0 | {
"file_path": "tokenizers/tokenizers/benches/unigram_benchmark.rs",
"repo_id": "tokenizers",
"token_count": 1174
} | 453 |
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from langchain_core.language_models import BaseLanguageModel
from langchain_core.pydantic_v1 import Field
from langchain_core.tools import BaseTool
from langchain_community.agent_toolkits.base import BaseToolkit
from langchain_commun... | langchain/libs/community/langchain_community/agent_toolkits/nla/toolkit.py/0 | {
"file_path": "langchain/libs/community/langchain_community/agent_toolkits/nla/toolkit.py",
"repo_id": "langchain",
"token_count": 1902
} | 207 |
label: 'Adapters'
| langchain/docs/docs/integrations/adapters/_category_.yml/0 | {
"file_path": "langchain/docs/docs/integrations/adapters/_category_.yml",
"repo_id": "langchain",
"token_count": 7
} | 92 |
import { JsonForms } from "@jsonforms/react";
import { JsonFormsCore, JsonSchema } from "@jsonforms/core";
import { renderers, cells } from "../renderers";
export type ConfigValue = Pick<JsonFormsCore, "data" | "errors"> & {
defaults: boolean;
};
export function SectionConfigure(props: {
config: JsonSchema | unde... | langserve/langserve/playground/src/sections/SectionConfigure.tsx/0 | {
"file_path": "langserve/langserve/playground/src/sections/SectionConfigure.tsx",
"repo_id": "langserve",
"token_count": 742
} | 1,046 |
/******************************************************************************
* Copyright (c) 2023, Tri Dao.
******************************************************************************/
#pragma once
namespace flash {
/////////////////////////////////////////////////////////////////////////////////////////////... | candle/candle-flash-attn/kernels/block_info.h/0 | {
"file_path": "candle/candle-flash-attn/kernels/block_info.h",
"repo_id": "candle",
"token_count": 851
} | 53 |
# ChatGPT Plugin Integrations
**NOTE**: This is a work-in-progress, stay tuned for more exciting updates on this front!
## ChatGPT Retrieval Plugin Integrations
The [OpenAI ChatGPT Retrieval Plugin](https://github.com/openai/chatgpt-retrieval-plugin)
offers a centralized API specification for any document storage sy... | llama_index/docs/community/integrations/chatgpt_plugins.md/0 | {
"file_path": "llama_index/docs/community/integrations/chatgpt_plugins.md",
"repo_id": "llama_index",
"token_count": 1493
} | 1,099 |
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