text stringlengths 3 1.68M | id stringlengths 13 169 | metadata dict | __index_level_0__ int64 0 2.21k |
|---|---|---|---|
__version__ = "0.27.0.dev0"
from typing import TYPE_CHECKING
from .utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_k_diffusion_available,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
... | diffusers/src/diffusers/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/__init__.py",
"repo_id": "diffusers",
"token_count": 13685
} | 208 |
import type { EmbeddingsInterface } from "./embeddings.js";
import type { DocumentInterface } from "./documents/document.js";
import {
BaseRetriever,
BaseRetrieverInterface,
type BaseRetrieverInput,
} from "./retrievers.js";
import { Serializable } from "./load/serializable.js";
import {
CallbackManagerForRetri... | langchainjs/langchain-core/src/vectorstores.ts/0 | {
"file_path": "langchainjs/langchain-core/src/vectorstores.ts",
"repo_id": "langchainjs",
"token_count": 3919
} | 837 |
<jupyter_start><jupyter_text>FalkorDBQAChain This notebook shows how to use LLMs to provide a natural language interface to FalkorDB database.FalkorDB is a low latency property graph database management system. You can simply run its docker locally:```bashdocker run -p 6379:6379 -it --rm falkordb/falkordb:edge```Once l... | langchain/docs/docs/use_cases/graph/graph_falkordb_qa.ipynb/0 | {
"file_path": "langchain/docs/docs/use_cases/graph/graph_falkordb_qa.ipynb",
"repo_id": "langchain",
"token_count": 1029
} | 202 |
python_tests()
| llama_index/llama-index-integrations/readers/llama-index-readers-memos/tests/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-memos/tests/BUILD",
"repo_id": "llama_index",
"token_count": 5
} | 1,426 |
<jupyter_start><jupyter_text>---sidebar_position: 0.5title: Why use LCEL---import { ColumnContainer, Column } from \"@theme/Columns\";<jupyter_code>:::tip
We recommend reading the LCEL [Get started](/docs/expression_language/get_started) section first.
:::
LCEL makes it easy to build complex chains from basic componen... | langchain/docs/docs/expression_language/why.ipynb/0 | {
"file_path": "langchain/docs/docs/expression_language/why.ipynb",
"repo_id": "langchain",
"token_count": 6548
} | 86 |
include timm/models/_pruned/*.txt
include timm/data/_info/*.txt
include timm/data/_info/*.json
| pytorch-image-models/MANIFEST.in/0 | {
"file_path": "pytorch-image-models/MANIFEST.in",
"repo_id": "pytorch-image-models",
"token_count": 34
} | 342 |
from llama_index.readers.s3.base import S3Reader
__all__ = ["S3Reader"]
| llama_index/llama-index-integrations/readers/llama-index-readers-s3/llama_index/readers/s3/__init__.py/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-s3/llama_index/readers/s3/__init__.py",
"repo_id": "llama_index",
"token_count": 29
} | 1,516 |
from typing import List
from unittest.mock import Mock
from langchain_core.embeddings import Embeddings
from langchain_astradb.vectorstores import AstraDBVectorStore
class SomeEmbeddings(Embeddings):
"""
Turn a sentence into an embedding vector in some way.
Not important how. It is deterministic is all ... | langchain/libs/partners/astradb/tests/unit_tests/test_vectorstores.py/0 | {
"file_path": "langchain/libs/partners/astradb/tests/unit_tests/test_vectorstores.py",
"repo_id": "langchain",
"token_count": 566
} | 623 |
# coding=utf-8
# Copyright 2021 Google Research 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-... | transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py/0 | {
"file_path": "transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py",
"repo_id": "transformers",
"token_count": 66863
} | 571 |
import * as uuid from "uuid";
import type { EmbeddingsInterface } from "@langchain/core/embeddings";
import { VectorStore } from "@langchain/core/vectorstores";
import { Document } from "@langchain/core/documents";
/**
* Type definition for the arguments required to initialize a
* TigrisVectorStore instance.
*/
ex... | langchainjs/libs/langchain-community/src/vectorstores/tigris.ts/0 | {
"file_path": "langchainjs/libs/langchain-community/src/vectorstores/tigris.ts",
"repo_id": "langchainjs",
"token_count": 2008
} | 987 |
# coding=utf-8
# Copyright Iz Beltagy, Matthew E. Peters, Arman Cohan and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.ap... | transformers/tests/models/led/test_modeling_tf_led.py/0 | {
"file_path": "transformers/tests/models/led/test_modeling_tf_led.py",
"repo_id": "transformers",
"token_count": 6455
} | 791 |
import traceback
import copy
import os
from utils.util_log import test_log as log
# enable_traceback = os.getenv('ENABLE_TRACEBACK', "True")
# log.info(f"enable_traceback:{enable_traceback}")
class Error:
def __init__(self, error):
self.code = getattr(error, 'code', -1)
self.message = getattr(err... | milvus/tests/python_client/utils/api_request.py/0 | {
"file_path": "milvus/tests/python_client/utils/api_request.py",
"repo_id": "milvus",
"token_count": 1125
} | 1,983 |
# 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/models/dpr/__init__.py/0 | {
"file_path": "transformers/src/transformers/models/dpr/__init__.py",
"repo_id": "transformers",
"token_count": 2002
} | 654 |
import streamlit as st
from pathlib import Path
from langchain.llms.openai import OpenAI
from langchain.agents import create_sql_agent
from langchain.sql_database import SQLDatabase
from langchain.agents.agent_types import AgentType
from langchain.callbacks import StreamlitCallbackHandler
from langchain.agents.agent_to... | streamlit-agent/streamlit_agent/chat_with_sql_db.py/0 | {
"file_path": "streamlit-agent/streamlit_agent/chat_with_sql_db.py",
"repo_id": "streamlit-agent",
"token_count": 1140
} | 2,062 |
"""
Github API client for the LlamaIndex library.
This module contains the Github API client for the LlamaIndex library.
It is used by the Github readers to retrieve the data from Github.
"""
import os
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
from dataclasses_json import DataCla... | llama_index/llama-index-legacy/llama_index/legacy/readers/github_readers/github_api_client.py/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/readers/github_readers/github_api_client.py",
"repo_id": "llama_index",
"token_count": 5438
} | 1,704 |
from llama_index.llms.cohere.base import Cohere
__all__ = ["Cohere"]
| llama_index/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/__init__.py/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/__init__.py",
"repo_id": "llama_index",
"token_count": 27
} | 1,393 |
from langchain_community.document_loaders.base import BaseBlobParser, BaseLoader
__all__ = ["BaseLoader", "BaseBlobParser"]
| langchain/libs/langchain/langchain/document_loaders/base.py/0 | {
"file_path": "langchain/libs/langchain/langchain/document_loaders/base.py",
"repo_id": "langchain",
"token_count": 37
} | 479 |
// Licensed to the LF AI & Data foundation under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use th... | milvus/internal/querycoordv2/balance/rowcount_based_balancer_test.go/0 | {
"file_path": "milvus/internal/querycoordv2/balance/rowcount_based_balancer_test.go",
"repo_id": "milvus",
"token_count": 16675
} | 1,750 |
# 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/perceiver/test_modeling_perceiver.py/0 | {
"file_path": "transformers/tests/models/perceiver/test_modeling_perceiver.py",
"repo_id": "transformers",
"token_count": 20715
} | 727 |
{"[MASK]": 0, "[UNK]": 1, "[PAD]": 2, "DUMMY": 3, "DUMMY2": 4, "[MASK2]": 5} | transformers/tests/fixtures/test_entity_vocab.json/0 | {
"file_path": "transformers/tests/fixtures/test_entity_vocab.json",
"repo_id": "transformers",
"token_count": 45
} | 794 |
python_sources()
| llama_index/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/ipynb/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/ipynb/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,317 |
<jupyter_start><jupyter_text>Extracting Metadata for Better Document Indexing and UnderstandingIn many cases, especially with long documents, a chunk of text may lack the context necessary to disambiguate the chunk from other similar chunks of text. One method of addressing this is manually labelling each chunk in our ... | llama_index/docs/examples/metadata_extraction/MetadataExtractionSEC.ipynb/0 | {
"file_path": "llama_index/docs/examples/metadata_extraction/MetadataExtractionSEC.ipynb",
"repo_id": "llama_index",
"token_count": 3577
} | 1,067 |
from __future__ import annotations
import concurrent.futures
from typing import Any, Iterable, List, Optional
import numpy as np
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.retrie... | langchain/libs/community/langchain_community/retrievers/svm.py/0 | {
"file_path": "langchain/libs/community/langchain_community/retrievers/svm.py",
"repo_id": "langchain",
"token_count": 1826
} | 292 |
from __future__ import annotations
import asyncio
import json
from typing import Any, Dict, List, Optional
import aiohttp
import requests
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, root_validator
def is_endpoint_live(url: str, headers: Optional[dict], payload:... | langchain/libs/community/langchain_community/embeddings/nemo.py/0 | {
"file_path": "langchain/libs/community/langchain_community/embeddings/nemo.py",
"repo_id": "langchain",
"token_count": 2312
} | 258 |
from rag_fusion.chain import chain
if __name__ == "__main__":
original_query = "impact of climate change"
print(chain.invoke(original_query)) # noqa: T201
| langchain/templates/rag-fusion/main.py/0 | {
"file_path": "langchain/templates/rag-fusion/main.py",
"repo_id": "langchain",
"token_count": 57
} | 674 |
<jupyter_start><jupyter_text>AstraDB DataStax [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on Cassandra and made conveniently available through an easy-to-use JSON API. Overview The AstraDB Document Loader returns a list of Langchain Documents from an Ast... | langchain/docs/docs/integrations/document_loaders/astradb.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/document_loaders/astradb.ipynb",
"repo_id": "langchain",
"token_count": 608
} | 96 |
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | diffusers/examples/amused/train_amused.py/0 | {
"file_path": "diffusers/examples/amused/train_amused.py",
"repo_id": "diffusers",
"token_count": 17459
} | 200 |
"use node";
import { v } from "convex/values";
import { BufferMemory } from "langchain/memory";
import { ChatOpenAI } from "@langchain/openai";
import { ConversationChain } from "langchain/chains";
import { ConvexChatMessageHistory } from "@langchain/community/stores/message/convex";
import { action } from "./_generat... | langchainjs/examples/src/memory/convex/convex.ts/0 | {
"file_path": "langchainjs/examples/src/memory/convex/convex.ts",
"repo_id": "langchainjs",
"token_count": 546
} | 815 |
from llama_index.core.storage.kvstore.types import BaseKVStore
from llama_index.storage.kvstore.mongodb import MongoDBKVStore
def test_class():
names_of_base_classes = [b.__name__ for b in MongoDBKVStore.__mro__]
assert BaseKVStore.__name__ in names_of_base_classes
| llama_index/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-mongodb/tests/test_storage_kvstore_mongodb.py/0 | {
"file_path": "llama_index/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-mongodb/tests/test_storage_kvstore_mongodb.py",
"repo_id": "llama_index",
"token_count": 103
} | 1,500 |
export * from "@langchain/community/agents/toolkits/base";
| langchainjs/langchain/src/agents/toolkits/base.ts/0 | {
"file_path": "langchainjs/langchain/src/agents/toolkits/base.ts",
"repo_id": "langchainjs",
"token_count": 18
} | 882 |
[tool.poetry]
name = "rag-multi-modal-mv-local"
version = "0.1.0"
description = "Multi-modal RAG using Chroma and multi-vector retriever"
authors = [
"Lance Martin <lance@langchain.dev>",
]
readme = "README.md"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
langchain = ">=0.0.353,<0.2"
openai = "<2"
tiktoken =... | langchain/templates/rag-multi-modal-mv-local/pyproject.toml/0 | {
"file_path": "langchain/templates/rag-multi-modal-mv-local/pyproject.toml",
"repo_id": "langchain",
"token_count": 380
} | 697 |
"""Test OpenAI Chat API wrapper."""
import json
from typing import Any
from unittest.mock import MagicMock, patch
import pytest
from langchain_core.messages import (
AIMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
)
from langchain_community.adapters.openai import convert_dict_to_message
from ... | langchain/libs/community/tests/unit_tests/chat_models/test_openai.py/0 | {
"file_path": "langchain/libs/community/tests/unit_tests/chat_models/test_openai.py",
"repo_id": "langchain",
"token_count": 1399
} | 384 |
# Keras Dreambooth event! 🤗
This document summarises all the relevant information required for the event 📋.
## Introduction
Dreambooth is a fine-tuning technique to teach new visual concepts to text-conditioned Diffusion models with just 3-5 images. With Dreambooth, you could generate funny and realistic images ... | diffusion-models-class/units/en/events/3.mdx/0 | {
"file_path": "diffusion-models-class/units/en/events/3.mdx",
"repo_id": "diffusion-models-class",
"token_count": 3063
} | 278 |
"""Wordpress reader."""
import json
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class WordpressReader(BaseReader):
"""Wordpress reader. Reads data from a Wordpress workspace.
Args:
wordpress_subdomain (str): Wordpress subd... | llama_index/llama-index-integrations/readers/llama-index-readers-wordpress/llama_index/readers/wordpress/base.py/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-wordpress/llama_index/readers/wordpress/base.py",
"repo_id": "llama_index",
"token_count": 1204
} | 1,439 |
poetry_requirements(
name="poetry",
)
python_requirements(
name="reqs",
)
| llama_index/llama-index-integrations/readers/llama-index-readers-jira/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-jira/BUILD",
"repo_id": "llama_index",
"token_count": 36
} | 1,464 |
## Textual Inversion fine-tuning example
[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.
The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion... | diffusers/examples/research_projects/onnxruntime/textual_inversion/README.md/0 | {
"file_path": "diffusers/examples/research_projects/onnxruntime/textual_inversion/README.md",
"repo_id": "diffusers",
"token_count": 1117
} | 220 |
/* eslint-disable no-process-env */
import { expect, test } from "@jest/globals";
import { HumanMessage } from "@langchain/core/messages";
import { ChatPromptValue } from "@langchain/core/prompt_values";
import {
PromptTemplate,
ChatPromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
SystemM... | langchainjs/libs/langchain-anthropic/src/tests/chat_models.int.test.ts/0 | {
"file_path": "langchainjs/libs/langchain-anthropic/src/tests/chat_models.int.test.ts",
"repo_id": "langchainjs",
"token_count": 3270
} | 941 |
<jupyter_start><jupyter_text>Huggingface Sagemaker-sdk - Distributed Training Demo Distributed Summarization with `transformers` scripts + `Trainer` and `samsum` dataset 1. [Tutorial](Tutorial) 2. [Set up a development environment and install sagemaker](Set-up-a-development-environment-and-install-sagemaker) 1. [In... | notebooks/sagemaker/08_distributed_summarization_bart_t5/sagemaker-notebook.ipynb/0 | {
"file_path": "notebooks/sagemaker/08_distributed_summarization_bart_t5/sagemaker-notebook.ipynb",
"repo_id": "notebooks",
"token_count": 5673
} | 314 |
// 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/pipeline_test.go/0 | {
"file_path": "milvus/internal/querynodev2/pipeline/pipeline_test.go",
"repo_id": "milvus",
"token_count": 2095
} | 1,908 |
# Unit Testing with Pytest
[](https://github.com/langchain-ai/langsmith-cookbook/tree/main/./testing-examples/pytest-ut/README.md)
This tutorial shows how to use LangSmith datasets to write unit tests directly in your pytest test suite. Th... | langsmith-cookbook/testing-examples/pytest-ut/README.md/0 | {
"file_path": "langsmith-cookbook/testing-examples/pytest-ut/README.md",
"repo_id": "langsmith-cookbook",
"token_count": 2507
} | 1,061 |
<jupyter_start><jupyter_text>Airbyte Hubspot >[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.This loader exposes the Hubspot connector as a ... | langchain/docs/docs/integrations/document_loaders/airbyte_hubspot.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/document_loaders/airbyte_hubspot.ipynb",
"repo_id": "langchain",
"token_count": 932
} | 105 |
<script lang="ts">
import { createEventDispatcher, onDestroy, onMount } from "svelte";
import { cubicOut } from "svelte/easing";
import { fade } from "svelte/transition";
import Portal from "./Portal.svelte";
import { browser } from "$app/environment";
export let width = "max-w-sm";
let backdropEl: HTMLDivElem... | chat-ui/src/lib/components/Modal.svelte/0 | {
"file_path": "chat-ui/src/lib/components/Modal.svelte",
"repo_id": "chat-ui",
"token_count": 647
} | 96 |
"""ReAct agent.
Simple wrapper around AgentRunner + ReActAgentWorker.
For the legacy implementation see:
```python
from llama_index.legacy.agent.legacy.react.base import ReActAgent
```
"""
| llama_index/llama-index-legacy/llama_index/legacy/agent/react/agent.py/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/agent/react/agent.py",
"repo_id": "llama_index",
"token_count": 60
} | 1,549 |
"""Tool for the DuckDuckGo search API."""
import warnings
from typing import Any, 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.utilities.duckduckgo_search import... | langchain/libs/community/langchain_community/tools/ddg_search/tool.py/0 | {
"file_path": "langchain/libs/community/langchain_community/tools/ddg_search/tool.py",
"repo_id": "langchain",
"token_count": 1018
} | 281 |
"""Requests toolkit."""
from __future__ import annotations
from typing import Any, List
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import Tool
from langchain_community.agent_toolkits.base import BaseToolkit
from langchain_community.agent_toolkits.json.base import create_js... | langchain/libs/community/langchain_community/agent_toolkits/openapi/toolkit.py/0 | {
"file_path": "langchain/libs/community/langchain_community/agent_toolkits/openapi/toolkit.py",
"repo_id": "langchain",
"token_count": 1189
} | 231 |
""" A dataset reader that reads tarfile based datasets
This reader can extract image samples from:
* a single tar of image files
* a folder of multiple tarfiles containing imagefiles
* a tar of tars containing image files
Labels are based on the combined folder and/or tar name structure.
Hacked together by / Copyrig... | pytorch-image-models/timm/data/readers/reader_image_in_tar.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_image_in_tar.py",
"repo_id": "pytorch-image-models",
"token_count": 4050
} | 388 |
"""Tests for verifying that testing utility code works as expected."""
from itertools import cycle
from typing import Any, Dict, List, Optional, Union
from uuid import UUID
from langchain_core.callbacks.base import AsyncCallbackHandler
from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
from lan... | langserve/tests/unit_tests/utils/test_fake_chat_model.py/0 | {
"file_path": "langserve/tests/unit_tests/utils/test_fake_chat_model.py",
"repo_id": "langserve",
"token_count": 2690
} | 1,037 |
import argparse
import inspect
import logging
import math
import os
from pathlib import Path
import accelerate
import datasets
import torch
import torch.nn.functional as F
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration
from datasets import... | diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/train_unconditional.py/0 | {
"file_path": "diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/train_unconditional.py",
"repo_id": "diffusers",
"token_count": 12740
} | 223 |
from langchain.llms import OpenAI
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.callbacks import StreamlitCallbackHandler
import streamlit as st
llm = OpenAI(temperature=0, streaming=True)
tools = load_tools(["ddg-search"])
agent = initialize_agent(
tools, llm, agent=AgentType... | streamlit-agent/streamlit_agent/minimal_agent.py/0 | {
"file_path": "streamlit-agent/streamlit_agent/minimal_agent.py",
"repo_id": "streamlit-agent",
"token_count": 217
} | 2,003 |
from typing import Optional
from llama_index.legacy.core.base_selector import BaseSelector
from llama_index.legacy.selectors.llm_selectors import (
LLMMultiSelector,
LLMSingleSelector,
)
from llama_index.legacy.selectors.pydantic_selectors import (
PydanticMultiSelector,
PydanticSingleSelector,
)
from ... | llama_index/llama-index-legacy/llama_index/legacy/selectors/utils.py/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/selectors/utils.py",
"repo_id": "llama_index",
"token_count": 462
} | 1,616 |
"""Test Annoy functionality."""
import tempfile
import pytest
from langchain_core.documents import Document
from langchain_community.docstore.in_memory import InMemoryDocstore
from langchain_community.vectorstores.annoy import Annoy
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def... | langchain/libs/community/tests/integration_tests/vectorstores/test_annoy.py/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/vectorstores/test_annoy.py",
"repo_id": "langchain",
"token_count": 1867
} | 355 |
// 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/storage/stats.go/0 | {
"file_path": "milvus/internal/storage/stats.go",
"repo_id": "milvus",
"token_count": 3077
} | 1,931 |
// eslint-disable-next-line import/no-extraneous-dependencies
import {
DocumentByInfo,
FieldPaths,
FilterExpression,
FunctionReference,
GenericActionCtx,
GenericDataModel,
GenericTableInfo,
NamedTableInfo,
NamedVectorIndex,
TableNamesInDataModel,
VectorFilterBuilder,
VectorIndexNames,
makeFunc... | langchainjs/libs/langchain-community/src/vectorstores/convex.ts/0 | {
"file_path": "langchainjs/libs/langchain-community/src/vectorstores/convex.ts",
"repo_id": "langchainjs",
"token_count": 4109
} | 992 |
// noinspection DuplicatedCode
import fs from "fs";
import { fileURLToPath } from "node:url";
import * as path from "path";
import { describe, test } from "@jest/globals";
import { HumanMessage } from "@langchain/core/messages";
import { ChatGooglePaLM } from "@langchain/community/chat_models/googlepalm";
import { Go... | langchainjs/langchain/src/experimental/hubs/makersuite/tests/googlemakersuitehub.int.test.ts/0 | {
"file_path": "langchainjs/langchain/src/experimental/hubs/makersuite/tests/googlemakersuitehub.int.test.ts",
"repo_id": "langchainjs",
"token_count": 1260
} | 883 |
use candle::{Device, Tensor};
use candle_transformers::generation::LogitsProcessor;
pub use candle_transformers::models::quantized_t5::{
Config, T5EncoderModel, T5ForConditionalGeneration, VarBuilder,
};
use candle_wasm_example_t5::console_log;
use tokenizers::Tokenizer;
use wasm_bindgen::prelude::*;
const DEVICE:... | candle/candle-wasm-examples/t5/src/bin/m-quantized.rs/0 | {
"file_path": "candle/candle-wasm-examples/t5/src/bin/m-quantized.rs",
"repo_id": "candle",
"token_count": 3555
} | 91 |
// Code generated from Plan.g4 by ANTLR 4.9. DO NOT EDIT.
package planparserv2 // Plan
import (
"fmt"
"reflect"
"strconv"
"github.com/antlr/antlr4/runtime/Go/antlr"
)
// Suppress unused import errors
var _ = fmt.Printf
var _ = reflect.Copy
var _ = strconv.Itoa
var parserATN = []uint16{
3, 24715, 42794, 33075, ... | milvus/internal/parser/planparserv2/generated/plan_parser.go/0 | {
"file_path": "milvus/internal/parser/planparserv2/generated/plan_parser.go",
"repo_id": "milvus",
"token_count": 27838
} | 1,871 |
# Databerry
This page covers how to use the [Databerry](https://databerry.ai) within LangChain.
## What is Databerry?
Databerry is an [open source](https://github.com/gmpetrov/databerry) document retrieval platform that helps to connect your personal data with Large Language Models.
![Databerry](/img/DataberryDashb... | langchainjs/docs/core_docs/docs/ecosystem/integrations/databerry.mdx/0 | {
"file_path": "langchainjs/docs/core_docs/docs/ecosystem/integrations/databerry.mdx",
"repo_id": "langchainjs",
"token_count": 376
} | 745 |
<jupyter_start><jupyter_text>Metal>[Metal](https://github.com/getmetal/metal-python) is a managed service for ML Embeddings.This notebook shows how to use [Metal's](https://docs.getmetal.io/introduction) retriever.First, you will need to sign up for Metal and get an API key. You can do so [here](https://docs.getmetal.i... | langchain/docs/docs/integrations/retrievers/metal.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/retrievers/metal.ipynb",
"repo_id": "langchain",
"token_count": 352
} | 158 |
# 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/altclip/test_modeling_altclip.py/0 | {
"file_path": "transformers/tests/models/altclip/test_modeling_altclip.py",
"repo_id": "transformers",
"token_count": 9801
} | 702 |
import pytest
from langchain_community.embeddings.ernie import ErnieEmbeddings
def test_embedding_documents_1() -> None:
documents = ["foo bar"]
embedding = ErnieEmbeddings()
output = embedding.embed_documents(documents)
assert len(output) == 1
assert len(output[0]) == 384
def test_embedding_do... | langchain/libs/community/tests/integration_tests/embeddings/test_ernie.py/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/embeddings/test_ernie.py",
"repo_id": "langchain",
"token_count": 424
} | 361 |
<jupyter_start><jupyter_text>Query Pipeline with Async/Parallel ExecutionHere we showcase our query pipeline with async + parallel execution.We do this by setting up a RAG pipeline that does the following:1. Send query to multiple RAG query engines.2. Combine results.In the process we'll also show some nice abstraction... | llama_index/docs/examples/pipeline/query_pipeline_async.ipynb/0 | {
"file_path": "llama_index/docs/examples/pipeline/query_pipeline_async.ipynb",
"repo_id": "llama_index",
"token_count": 1463
} | 1,100 |
import { OpenAIEmbeddings } from "@langchain/openai";
import { CacheBackedEmbeddings } from "langchain/embeddings/cache_backed";
import { InMemoryStore } from "langchain/storage/in_memory";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { FaissStore } from "@langchain/community/vectors... | langchainjs/examples/src/embeddings/cache_backed_in_memory.ts/0 | {
"file_path": "langchainjs/examples/src/embeddings/cache_backed_in_memory.ts",
"repo_id": "langchainjs",
"token_count": 740
} | 833 |
python_sources()
| llama_index/llama-index-integrations/tools/llama-index-tools-shopify/llama_index/tools/shopify/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/tools/llama-index-tools-shopify/llama_index/tools/shopify/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,498 |
poetry_requirements(
name="poetry",
)
| llama_index/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/BUILD",
"repo_id": "llama_index",
"token_count": 18
} | 1,352 |
import { test } from "@jest/globals";
import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import { MemoryVectorStore } from "../../../vectorstores/memory.js";
import { createConversationalRetrievalAgent } from "../conversational_retrieval/openai_functions.js";
import { createRetrieverTool } from "../conve... | langchainjs/langchain/src/agents/toolkits/tests/conversational_retrieval.int.test.ts/0 | {
"file_path": "langchainjs/langchain/src/agents/toolkits/tests/conversational_retrieval.int.test.ts",
"repo_id": "langchainjs",
"token_count": 551
} | 899 |
<!--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 agreed... | transformers/docs/source/ja/tasks/image_to_image.md/0 | {
"file_path": "transformers/docs/source/ja/tasks/image_to_image.md",
"repo_id": "transformers",
"token_count": 2420
} | 489 |
// Code generated by mockery v2.32.4. DO NOT EDIT.
package proxy
import (
context "context"
internalpb "github.com/milvus-io/milvus/internal/proto/internalpb"
mock "github.com/stretchr/testify/mock"
)
// MockLBBalancer is an autogenerated mock type for the LBBalancer type
type MockLBBalancer struct {
mock.Mock
... | milvus/internal/proxy/mock_lb_balancer.go/0 | {
"file_path": "milvus/internal/proxy/mock_lb_balancer.go",
"repo_id": "milvus",
"token_count": 2742
} | 2,018 |
# 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/git/configuration_git.py/0 | {
"file_path": "transformers/src/transformers/models/git/configuration_git.py",
"repo_id": "transformers",
"token_count": 4236
} | 638 |
def test_import() -> None:
from langchain.chains import OntotextGraphDBQAChain # noqa: F401
| langchain/libs/langchain/tests/unit_tests/chains/test_ontotext_graphdb_qa.py/0 | {
"file_path": "langchain/libs/langchain/tests/unit_tests/chains/test_ontotext_graphdb_qa.py",
"repo_id": "langchain",
"token_count": 34
} | 652 |
# Trainer
At TRL we support PPO (Proximal Policy Optimisation) with an implementation that largely follows the structure introduced in the paper "Fine-Tuning Language Models from Human Preferences" by D. Ziegler et al. [[paper](https://arxiv.org/pdf/1909.08593.pdf), [code](https://github.com/openai/lm-human-preferenc... | trl/docs/source/trainer.mdx/0 | {
"file_path": "trl/docs/source/trainer.mdx",
"repo_id": "trl",
"token_count": 322
} | 785 |
# coding=utf-8
# Copyright 2020 Optuna, Hugging Face
#
# 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 o... | transformers/src/transformers/utils/logging.py/0 | {
"file_path": "transformers/src/transformers/utils/logging.py",
"repo_id": "transformers",
"token_count": 4263
} | 758 |
import { initializeAgentExecutorWithOptions } from "langchain/agents";
import { ChatOpenAI } from "@langchain/openai";
import { Calculator } from "langchain/tools/calculator";
import { SerpAPI } from "@langchain/community/tools/serpapi";
const tools = [new Calculator(), new SerpAPI()];
const chat = new ChatOpenAI({ mo... | langchainjs/examples/src/agents/openai_custom_prompt.ts/0 | {
"file_path": "langchainjs/examples/src/agents/openai_custom_prompt.ts",
"repo_id": "langchainjs",
"token_count": 290
} | 839 |
<jupyter_start><jupyter_text>Faiss (Async)>[Facebook AI Similarity Search (Faiss)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vecto... | langchain/docs/docs/integrations/vectorstores/faiss_async.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/vectorstores/faiss_async.ipynb",
"repo_id": "langchain",
"token_count": 2387
} | 191 |
<jupyter_start><jupyter_text>Google Vertex AI PaLM >[Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) is a service on Google Cloud exposing the embedding models. Note: This integration is separate from the Google PaLM integration.By default, Google Cloud [does not use](https://c... | langchain/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb",
"repo_id": "langchain",
"token_count": 453
} | 169 |
python_sources()
| llama_index/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,553 |
import { ChatLlamaCpp } from "@langchain/community/chat_models/llama_cpp";
import { SystemMessage, HumanMessage } from "@langchain/core/messages";
const llamaPath = "/Replace/with/path/to/your/model/gguf-llama2-q4_0.bin";
const llamaCpp = new ChatLlamaCpp({ modelPath: llamaPath, temperature: 0.7 });
const stream = a... | langchainjs/examples/src/models/chat/integration_llama_cpp_stream_multi.ts/0 | {
"file_path": "langchainjs/examples/src/models/chat/integration_llama_cpp_stream_multi.ts",
"repo_id": "langchainjs",
"token_count": 270
} | 801 |
# Text Environments
Text environments provide a learning ground for language agents. It allows a language model to use tools to accomplish a task such as using a Python interpreter to answer math questions or using a search index for trivia questions. Having access to tools allows language models to solve tasks that w... | trl/docs/source/text_environments.md/0 | {
"file_path": "trl/docs/source/text_environments.md",
"repo_id": "trl",
"token_count": 2826
} | 779 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/en/pipeline_tutorial.md/0 | {
"file_path": "transformers/docs/source/en/pipeline_tutorial.md",
"repo_id": "transformers",
"token_count": 4495
} | 463 |
[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 = ["Bedrock", "completion_response_to_chat_response", "completion_with_retry"]
contains_examp... | llama_index/llama-index-integrations/llms/llama-index-llms-bedrock/pyproject.toml/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-bedrock/pyproject.toml",
"repo_id": "llama_index",
"token_count": 685
} | 1,277 |
import warnings
from typing import Any, Dict, List, Set
from langchain_core.memory import BaseMemory
from langchain_core.pydantic_v1 import validator
from langchain.memory.chat_memory import BaseChatMemory
class CombinedMemory(BaseMemory):
"""Combining multiple memories' data together."""
memories: List[Ba... | langchain/libs/langchain/langchain/memory/combined.py/0 | {
"file_path": "langchain/libs/langchain/langchain/memory/combined.py",
"repo_id": "langchain",
"token_count": 1253
} | 571 |
# coding=utf-8
# Copyright 2023 The Intel AIA Team Authors, and 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/lice... | transformers/src/transformers/models/tvp/configuration_tvp.py/0 | {
"file_path": "transformers/src/transformers/models/tvp/configuration_tvp.py",
"repo_id": "transformers",
"token_count": 3880
} | 703 |
from rag_multi_modal_local.chain import chain
__all__ = ["chain"]
| langchain/templates/rag-multi-modal-local/rag_multi_modal_local/__init__.py/0 | {
"file_path": "langchain/templates/rag-multi-modal-local/rag_multi_modal_local/__init__.py",
"repo_id": "langchain",
"token_count": 23
} | 739 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/ja/model_doc/dinat.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/dinat.md",
"repo_id": "transformers",
"token_count": 2666
} | 544 |
import argparse
import logging
import os
import random
import sys
import numpy as np
import torch
from datasets import load_from_disk, load_metric
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
from transformers.trainer_utils import get_last_checkpoint
if __name... | notebooks/sagemaker/05_spot_instances/scripts/train.py/0 | {
"file_path": "notebooks/sagemaker/05_spot_instances/scripts/train.py",
"repo_id": "notebooks",
"token_count": 1799
} | 299 |
version: "3.8"
services:
pgvector:
image: ankane/pgvector:latest
environment:
POSTGRES_DB: ${PGVECTOR_DB:-postgres}
POSTGRES_USER: ${PGVECTOR_USER:-postgres}
POSTGRES_PASSWORD: ${PGVECTOR_PASSWORD:-postgres}
ports:
- ${PGVECTOR_PORT:-5432}:5432
restart: unless-stopped
heal... | langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/pgvector.yml/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/pgvector.yml",
"repo_id": "langchain",
"token_count": 208
} | 350 |
# Monte Carlo vs Temporal Difference Learning [[mc-vs-td]]
The last thing we need to discuss before diving into Q-Learning is the two learning strategies.
Remember that an RL agent **learns by interacting with its environment.** The idea is that **given the experience and the received reward, the agent will update it... | deep-rl-class/units/en/unit2/mc-vs-td.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit2/mc-vs-td.mdx",
"repo_id": "deep-rl-class",
"token_count": 2316
} | 157 |
<jupyter_start><jupyter_text>Using local modelsThe popularity of projects like [PrivateGPT](https://github.com/imartinez/privateGPT), [llama.cpp](https://github.com/ggerganov/llama.cpp), and [Ollama](https://github.com/ollama/ollama) underscore the importance of running LLMs locally.LangChain has [integrations](/docs/i... | langchainjs/docs/core_docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb/0 | {
"file_path": "langchainjs/docs/core_docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb",
"repo_id": "langchainjs",
"token_count": 2050
} | 824 |
from langchain_core.language_models import (
BaseLanguageModel,
LanguageModelInput,
LanguageModelOutput,
get_tokenizer,
)
from langchain_core.language_models.base import _get_token_ids_default_method
__all__ = [
"get_tokenizer",
"BaseLanguageModel",
"_get_token_ids_default_method",
"Lan... | langchain/libs/langchain/langchain/schema/language_model.py/0 | {
"file_path": "langchain/libs/langchain/langchain/schema/language_model.py",
"repo_id": "langchain",
"token_count": 131
} | 566 |
/// This example contains some simple benchmarks so that it's easy to run them in perf etc.
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::quantized::GgmlType;
use candle::{CpuStorage, Device, Layout, Module, Result, Shape, Tensor, D};
use c... | candle/candle-nn/examples/cpu_benchmarks.rs/0 | {
"file_path": "candle/candle-nn/examples/cpu_benchmarks.rs",
"repo_id": "candle",
"token_count": 5283
} | 56 |
from langchain_community.document_loaders.twitter import (
TwitterTweetLoader,
)
__all__ = ["TwitterTweetLoader"]
| langchain/libs/langchain/langchain/document_loaders/twitter.py/0 | {
"file_path": "langchain/libs/langchain/langchain/document_loaders/twitter.py",
"repo_id": "langchain",
"token_count": 36
} | 502 |
"""Unittests for langchain.agents.chat package."""
from typing import Tuple
from langchain_core.agents import AgentAction
from langchain.agents.chat.output_parser import ChatOutputParser
output_parser = ChatOutputParser()
def get_action_and_input(text: str) -> Tuple[str, str]:
output = output_parser.parse(text... | langchain/libs/langchain/tests/unit_tests/agents/test_chat.py/0 | {
"file_path": "langchain/libs/langchain/tests/unit_tests/agents/test_chat.py",
"repo_id": "langchain",
"token_count": 441
} | 600 |
poetry_requirements(
name="poetry",
)
| llama_index/llama-index-integrations/postprocessor/llama-index-postprocessor-longllmlingua/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/postprocessor/llama-index-postprocessor-longllmlingua/BUILD",
"repo_id": "llama_index",
"token_count": 18
} | 1,263 |
ann_accuracy:
collections:
-
server:
cache_config.cpu_cache_capacity: 16GB
engine_config.use_blas_threshold: 1100
engine_config.gpu_search_threshold: 1
gpu_resource_config.enable: false
gpu_resource_config.cache_capacity: 4GB
gpu_resource_config.search_resourc... | milvus/tests/benchmark/milvus_benchmark/suites/ann_debug.yaml/0 | {
"file_path": "milvus/tests/benchmark/milvus_benchmark/suites/ann_debug.yaml",
"repo_id": "milvus",
"token_count": 383
} | 1,879 |
<jupyter_start><jupyter_text>Run TemplateIn `server.py`, set -```add_routes(app, chain, path="/sql_ollama")```This template includes an example DB of 2023 NBA rosters.We can ask questions related to NBA players.<jupyter_code>from langserve.client import RemoteRunnable
sql_app = RemoteRunnable("http://0.0.0.0:8001/sql... | langchain/templates/sql-ollama/sql-ollama.ipynb/0 | {
"file_path": "langchain/templates/sql-ollama/sql-ollama.ipynb",
"repo_id": "langchain",
"token_count": 147
} | 692 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | diffusers/docs/source/en/using-diffusers/svd.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/svd.md",
"repo_id": "diffusers",
"token_count": 1761
} | 197 |
package model
import pb "github.com/milvus-io/milvus/internal/proto/etcdpb"
type Alias struct {
Name string
CollectionID int64
CreatedTime uint64
State pb.AliasState
DbID int64
}
func (a *Alias) Available() bool {
return a.State == pb.AliasState_AliasCreated
}
func (a *Alias) Clone() *... | milvus/internal/metastore/model/alias.go/0 | {
"file_path": "milvus/internal/metastore/model/alias.go",
"repo_id": "milvus",
"token_count": 491
} | 1,861 |
[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 = ["DatabaseToolSpec"]
contains_example = false
import_path = "llama_index.tools.database"
[... | llama_index/llama-index-integrations/tools/llama-index-tools-database/pyproject.toml/0 | {
"file_path": "llama_index/llama-index-integrations/tools/llama-index-tools-database/pyproject.toml",
"repo_id": "llama_index",
"token_count": 658
} | 1,510 |
// 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/query/PlanNode.h/0 | {
"file_path": "milvus/internal/core/src/query/PlanNode.h",
"repo_id": "milvus",
"token_count": 649
} | 1,930 |
from llama_index.legacy.agent.react.base import ReActAgent
from llama_index.legacy.agent.react.formatter import ReActChatFormatter
from llama_index.legacy.agent.react.step import ReActAgentWorker
__all__ = ["ReActChatFormatter", "ReActAgentWorker", "ReActAgent"]
| llama_index/llama-index-legacy/llama_index/legacy/agent/react/__init__.py/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/agent/react/__init__.py",
"repo_id": "llama_index",
"token_count": 87
} | 1,577 |
---
sidebar_class_name: hidden
---
# Plan and execute
:::info Compatibility
This agent currently only supports Chat Models.
:::
Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babya... | langchainjs/docs/core_docs/docs/modules/agents/agent_types/plan_and_execute.mdx/0 | {
"file_path": "langchainjs/docs/core_docs/docs/modules/agents/agent_types/plan_and_execute.mdx",
"repo_id": "langchainjs",
"token_count": 395
} | 753 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.