text stringlengths 3 1.68M | id stringlengths 13 169 | metadata dict | __index_level_0__ int64 0 2.21k |
|---|---|---|---|
// 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/unittest/test_exec.cpp/0 | {
"file_path": "milvus/internal/core/unittest/test_exec.cpp",
"repo_id": "milvus",
"token_count": 7095
} | 1,755 |
python_tests()
| llama_index/llama-index-integrations/tools/llama-index-tools-exa/tests/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/tools/llama-index-tools-exa/tests/BUILD",
"repo_id": "llama_index",
"token_count": 5
} | 1,566 |
# FBNet
**FBNet** is a type of convolutional neural architectures discovered through [DNAS](https://paperswithcode.com/method/dnas) neural architecture search. It utilises a basic type of image model block inspired by [MobileNetv2](https://paperswithcode.com/method/mobilenetv2) that utilises depthwise convolutions and... | pytorch-image-models/hfdocs/source/models/fbnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/fbnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1705
} | 377 |
ann_accuracy:
collections:
-
server:
cache_config.cpu_cache_capacity: 16
engine_config.use_blas_threshold: 1100
engine_config.gpu_search_threshold: 1
gpu_resource_config.enable: false
gpu_resource_config.cache_capacity: 4
gpu_resource_config.search_resources:... | milvus/tests/benchmark/milvus_benchmark/suites/pq.yaml/0 | {
"file_path": "milvus/tests/benchmark/milvus_benchmark/suites/pq.yaml",
"repo_id": "milvus",
"token_count": 404
} | 1,955 |
"""HuggingFace sentence_transformer embedding models."""
from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
SentenceTransformerEmbeddings = HuggingFaceEmbeddings
| langchain/libs/community/langchain_community/embeddings/sentence_transformer.py/0 | {
"file_path": "langchain/libs/community/langchain_community/embeddings/sentence_transformer.py",
"repo_id": "langchain",
"token_count": 52
} | 279 |
# coding=utf-8
# Copyright 2022 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/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.py/0 | {
"file_path": "transformers/src/transformers/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.py",
"repo_id": "transformers",
"token_count": 4205
} | 569 |
from __future__ import annotations
from typing import List, Optional
from langchain import hub
from langchain.callbacks.tracers.evaluation import EvaluatorCallbackHandler
from langchain.callbacks.tracers.schemas import Run
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
from langchain.... | langchain/templates/chat-bot-feedback/chat_bot_feedback/chain.py/0 | {
"file_path": "langchain/templates/chat-bot-feedback/chat_bot_feedback/chain.py",
"repo_id": "langchain",
"token_count": 2224
} | 639 |
"""Init file."""
| llama_index/llama-index-integrations/readers/llama-index-readers-opendal/llama_index/readers/opendal/azblob/__init__.py/0 | {
"file_path": "llama_index/llama-index-integrations/readers/llama-index-readers-opendal/llama_index/readers/opendal/azblob/__init__.py",
"repo_id": "llama_index",
"token_count": 6
} | 1,409 |
//load the candle Whisper decoder wasm module
import init, { Decoder } from "./build/m.js";
async function fetchArrayBuffer(url) {
const cacheName = "whisper-candle-cache";
const cache = await caches.open(cacheName);
const cachedResponse = await cache.match(url);
if (cachedResponse) {
const data = await ca... | candle/candle-wasm-examples/whisper/whisperWorker.js/0 | {
"file_path": "candle/candle-wasm-examples/whisper/whisperWorker.js",
"repo_id": "candle",
"token_count": 1215
} | 85 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::models::distilbert::{Config, DistilBertModel, DTYPE};
use anyhow::{Error as E, Result};
use candle::{Device, Tensor};
use candle_nn::VarBuilder;
use clap::Parser;
use hf_hub::{api::... | candle/candle-examples/examples/distilbert/main.rs/0 | {
"file_path": "candle/candle-examples/examples/distilbert/main.rs",
"repo_id": "candle",
"token_count": 1939
} | 43 |
<jupyter_start><jupyter_text>GigaChatThis notebook shows how to use LangChain with [GigaChat](https://developers.sber.ru/portal/products/gigachat).To use you need to install ```gigachat``` python package.<jupyter_code>%pip install --upgrade --quiet gigachat<jupyter_output><empty_output><jupyter_text>To get GigaChat cr... | langchain/docs/docs/integrations/chat/gigachat.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/chat/gigachat.ipynb",
"repo_id": "langchain",
"token_count": 349
} | 93 |
label: "How-to"
position: 2
| langchainjs/docs/core_docs/docs/modules/agents/tools/how_to/_category_.yml/0 | {
"file_path": "langchainjs/docs/core_docs/docs/modules/agents/tools/how_to/_category_.yml",
"repo_id": "langchainjs",
"token_count": 12
} | 781 |
from llama_index.core.command_line.new_package.templates.pyproject import pyproject_str
from llama_index.core.command_line.new_package.templates.readme import readme_str
from llama_index.core.command_line.new_package.templates.init import (
init_str,
init_with_prefix_str,
)
__all__ = ["pyproject_str", "readme_... | llama_index/llama-index-core/llama_index/core/command_line/new_package/templates/__init__.py/0 | {
"file_path": "llama_index/llama-index-core/llama_index/core/command_line/new_package/templates/__init__.py",
"repo_id": "llama_index",
"token_count": 131
} | 1,140 |
python_tests()
| llama_index/llama-index-integrations/llms/llama-index-llms-rungpt/tests/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-rungpt/tests/BUILD",
"repo_id": "llama_index",
"token_count": 5
} | 1,246 |
# backward compatibility
from llama_index.core.text_splitter import *
| llama_index/llama-index-core/llama_index/core/langchain_helpers/text_splitter.py/0 | {
"file_path": "llama_index/llama-index-core/llama_index/core/langchain_helpers/text_splitter.py",
"repo_id": "llama_index",
"token_count": 19
} | 1,129 |
<code_scheme name="milvus" version="173">
<Objective-C>
<option name="INDENT_NAMESPACE_MEMBERS" value="0" />
<option name="INDENT_VISIBILITY_KEYWORDS" value="1" />
<option name="KEEP_STRUCTURES_IN_ONE_LINE" value="true" />
<option name="KEEP_CASE_EXPRESSIONS_IN_ONE_LINE" value="true" />
<option na... | milvus/internal/core/build-support/code_style_clion.xml/0 | {
"file_path": "milvus/internal/core/build-support/code_style_clion.xml",
"repo_id": "milvus",
"token_count": 898
} | 1,775 |
## Amused training
Amused can be finetuned on simple datasets relatively cheaply and quickly. Using 8bit optimizers, lora, and gradient accumulation, amused can be finetuned with as little as 5.5 GB. Here are a set of examples for finetuning amused on some relatively simple datasets. These training recipies are aggres... | diffusers/examples/amused/README.md/0 | {
"file_path": "diffusers/examples/amused/README.md",
"repo_id": "diffusers",
"token_count": 5921
} | 206 |
import json
from langchain_core.language_models.base import LanguageModelLike
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
from langchain_core.prompts import PromptTemplate
from langchain_core.retrievers import BaseRetriever
from langchain_core.runnables import chain
from langgraph.checkp... | opengpts/backend/app/retrieval.py/0 | {
"file_path": "opengpts/backend/app/retrieval.py",
"repo_id": "opengpts",
"token_count": 1708
} | 2,199 |
"""Test Graph Database Chain."""
import os
from langchain_community.graphs import Neo4jGraph
from langchain_community.llms.openai import OpenAI
from langchain.chains.graph_qa.cypher import GraphCypherQAChain
from langchain.chains.loading import load_chain
def test_connect_neo4j() -> None:
"""Test that Neo4j dat... | langchain/libs/langchain/tests/integration_tests/chains/test_graph_database.py/0 | {
"file_path": "langchain/libs/langchain/tests/integration_tests/chains/test_graph_database.py",
"repo_id": "langchain",
"token_count": 4150
} | 614 |
from typing import List
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain_community.utilities.outline import OutlineAPIWrapper
class OutlineRetriever(BaseRetriever, OutlineAPIWrapper):
... | langchain/libs/community/langchain_community/retrievers/outline.py/0 | {
"file_path": "langchain/libs/community/langchain_community/retrievers/outline.py",
"repo_id": "langchain",
"token_count": 208
} | 287 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | peft/tests/test_mixed.py/0 | {
"file_path": "peft/tests/test_mixed.py",
"repo_id": "peft",
"token_count": 17543
} | 325 |
# LlamaIndex Kvstore Integration: S3 Kvstore
| llama_index/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-s3/README.md/0 | {
"file_path": "llama_index/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-s3/README.md",
"repo_id": "llama_index",
"token_count": 15
} | 1,427 |
---
hide_table_of_contents: true
---
# Extending LangChain.js
Extending LangChain's base abstractions, whether you're planning to contribute back to the open-source repo or build a bespoke internal integration, is encouraged.
Check out these guides for building your own custom classes for the following modules:
- [... | langchainjs/docs/core_docs/docs/guides/extending_langchain.mdx/0 | {
"file_path": "langchainjs/docs/core_docs/docs/guides/extending_langchain.mdx",
"repo_id": "langchainjs",
"token_count": 294
} | 751 |
package main
import (
"fmt"
"os"
"github.com/milvus-io/milvus/pkg/log"
)
const (
generateCsv = "gen-csv"
generateYaml = "gen-yaml"
showYaml = "show-yaml"
)
func main() {
args := os.Args
if len(args) < 2 {
log.Error("len of args should large than 2")
os.Exit(-1)
}
switch args[1] {
case generateC... | milvus/cmd/tools/config/main.go/0 | {
"file_path": "milvus/cmd/tools/config/main.go",
"repo_id": "milvus",
"token_count": 277
} | 1,839 |
{
"openapi": "3.0.3",
"info": {
"title": "Text Generation Inference",
"description": "Text Generation Webserver",
"contact": {
"name": "Olivier Dehaene"
},
"license": {
"name": "Apache 2.0",
"url": "https://www.apache.org/licenses/LICENSE-2.0"
},
"version": "1.4.0"
},... | text-generation-inference/docs/openapi.json/0 | {
"file_path": "text-generation-inference/docs/openapi.json",
"repo_id": "text-generation-inference",
"token_count": 20966
} | 380 |
[tool.ruff]
line-length = 119
target-version = "py38"
[tool.ruff.lint]
ignore-init-module-imports = true
extend-select = [
"B009", # static getattr
"B010", # static setattr
"E", # PEP8 errors
"F", # PEP8 formatting
"I", # Import sorting
"W", # PEP8 warnings
"UP", # Pyupgrade
]
ignore = [
... | accelerate/pyproject.toml/0 | {
"file_path": "accelerate/pyproject.toml",
"repo_id": "accelerate",
"token_count": 323
} | 7 |
python_sources()
| llama_index/llama-index-integrations/llms/llama-index-llms-everlyai/llama_index/llms/everlyai/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-everlyai/llama_index/llms/everlyai/BUILD",
"repo_id": "llama_index",
"token_count": 6
} | 1,350 |
python_tests()
| llama_index/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/tests/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/tests/BUILD",
"repo_id": "llama_index",
"token_count": 5
} | 1,551 |
import os
from typing import Any, Dict, List
from llama_index.core import ServiceContext, VectorStoreIndex
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.schema import Document
from llama_index.embeddings.voyageai import VoyageEmbedding
from llama_index.llms.openai import OpenAI
cla... | llama_index/llama-index-packs/llama-index-packs-voyage-query-engine/llama_index/packs/voyage_query_engine/base.py/0 | {
"file_path": "llama_index/llama-index-packs/llama-index-packs-voyage-query-engine/llama_index/packs/voyage_query_engine/base.py",
"repo_id": "llama_index",
"token_count": 461
} | 1,687 |
python_tests(
name="tests",
)
| llama_index/llama-index-core/tests/question_gen/BUILD/0 | {
"file_path": "llama_index/llama-index-core/tests/question_gen/BUILD",
"repo_id": "llama_index",
"token_count": 15
} | 1,230 |
<script lang="ts">
import CarbonCaretLeft from "~icons/carbon/caret-left";
import CarbonCaretRight from "~icons/carbon/caret-right";
export let href: string;
export let direction: "next" | "previous";
export let isDisabled = false;
</script>
<a
class="flex items-center rounded-lg px-2.5 py-1 hover:bg-gray-50 da... | chat-ui/src/lib/components/PaginationArrow.svelte/0 | {
"file_path": "chat-ui/src/lib/components/PaginationArrow.svelte",
"repo_id": "chat-ui",
"token_count": 226
} | 95 |
from langchain_community.tools.google_cloud.texttospeech import (
GoogleCloudTextToSpeechTool,
)
__all__ = [
"GoogleCloudTextToSpeechTool",
]
| langchain/libs/langchain/langchain/tools/google_cloud/texttospeech.py/0 | {
"file_path": "langchain/libs/langchain/langchain/tools/google_cloud/texttospeech.py",
"repo_id": "langchain",
"token_count": 56
} | 552 |
import re
from langchain_core.agents import AgentAction, AgentFinish
from .agent_scratchpad import _format_docs
def extract_between_tags(tag: str, string: str, strip: bool = True) -> str:
ext_list = re.findall(f"<{tag}\s?>(.+?)</{tag}\s?>", string, re.DOTALL)
if strip:
ext_list = [e.strip() for e in... | langchain/templates/anthropic-iterative-search/anthropic_iterative_search/output_parser.py/0 | {
"file_path": "langchain/templates/anthropic-iterative-search/anthropic_iterative_search/output_parser.py",
"repo_id": "langchain",
"token_count": 509
} | 666 |
import { PromptTemplate } from "@langchain/core/prompts";
import { LLMChain, LLMChainInput } from "../../chains/llm_chain.js";
/** Chain to prioritize tasks. */
export class TaskPrioritizationChain extends LLMChain {
static lc_name() {
return "TaskPrioritizationChain";
}
/**
* Static method to create a n... | langchainjs/langchain/src/experimental/babyagi/task_prioritization.ts/0 | {
"file_path": "langchainjs/langchain/src/experimental/babyagi/task_prioritization.ts",
"repo_id": "langchainjs",
"token_count": 447
} | 922 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | transformers/docs/source/it/serialization.md/0 | {
"file_path": "transformers/docs/source/it/serialization.md",
"repo_id": "transformers",
"token_count": 10268
} | 485 |
<jupyter_start><jupyter_text>Anyscale EmbeddingsThis guide shows you how to use Anyscale Embeddings through [Anyscale Endpoints](https://docs.endpoints.anyscale.com/). If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.<jupyter_code>%pip install llama-index-embeddings-anyscale
!pi... | llama_index/docs/examples/embeddings/Anyscale.ipynb/0 | {
"file_path": "llama_index/docs/examples/embeddings/Anyscale.ipynb",
"repo_id": "llama_index",
"token_count": 240
} | 1,113 |
import { MomentoVectorIndex } from "@langchain/community/vectorstores/momento_vector_index";
// For browser/edge, adjust this to import from "@gomomento/sdk-web";
import {
PreviewVectorIndexClient,
VectorIndexConfigurations,
CredentialProvider,
} from "@gomomento/sdk";
import { OpenAIEmbeddings } from "@langchain... | langchainjs/examples/src/indexes/vector_stores/momento_vector_index/fromDocs.ts/0 | {
"file_path": "langchainjs/examples/src/indexes/vector_stores/momento_vector_index/fromDocs.ts",
"repo_id": "langchainjs",
"token_count": 429
} | 849 |
<!--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/api/models/autoencoder_tiny.md/0 | {
"file_path": "diffusers/docs/source/en/api/models/autoencoder_tiny.md",
"repo_id": "diffusers",
"token_count": 670
} | 181 |
// 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"
)
// MockLBPolicy is an autogenerated mock type for the LBPolicy type
type MockLBPolicy struct {
mock.Mock
}
typ... | milvus/internal/proxy/mock_lb_policy.go/0 | {
"file_path": "milvus/internal/proxy/mock_lb_policy.go",
"repo_id": "milvus",
"token_count": 2556
} | 1,962 |
# Add parent directory to python path to access lightning_base.py
export PYTHONPATH="../":"${PYTHONPATH}"
python finetune.py \
--data_dir=$CNN_DIR \
--learning_rate=3e-5 \
--train_batch_size=$BS \
--eval_batch_size=$BS \
--output_dir=$OUTPUT_DIR \
--max_source_length=512 \
--max_target_length=56 \
--val_check_interval... | transformers/examples/research_projects/seq2seq-distillation/finetune_t5.sh/0 | {
"file_path": "transformers/examples/research_projects/seq2seq-distillation/finetune_t5.sh",
"repo_id": "transformers",
"token_count": 148
} | 576 |
%if 0%{!?version:1}
%global version 2.0.2
%endif
%if 0%{!?release:1}
%global release 1%{?dist}
%endif
Name: milvus
Version: %{version}
Release: %{release}
Summary: Milvus V2 RPM
License: Apache License 2.0
Requires(preun): libstdc++ libgomp tbb-devel
# tbb-devel actual... | milvus/build/rpm/milvus.spec/0 | {
"file_path": "milvus/build/rpm/milvus.spec",
"repo_id": "milvus",
"token_count": 1427
} | 1,889 |
# Sample script to finetune RAG using Ray for distributed retrieval.
# Add parent directory to python path to access lightning_base.py
export PYTHONPATH="../":"${PYTHONPATH}"
#creates the custom knowlegebase
python use_own_knowledge_dataset.py \
--csv_path /DIR/SQUAD-KB/squad-kb.csv \
--output_dir /DIR/SQUA... | transformers/examples/research_projects/rag-end2end-retriever/finetune_rag_ray_end2end.sh/0 | {
"file_path": "transformers/examples/research_projects/rag-end2end-retriever/finetune_rag_ray_end2end.sh",
"repo_id": "transformers",
"token_count": 876
} | 570 |
from langchain_community.document_loaders.stripe import StripeLoader
def test_stripe_loader() -> None:
"""Test Stripe file loader."""
stripe_loader = StripeLoader("charges")
documents = stripe_loader.load()
assert len(documents) == 1
| langchain/libs/community/tests/integration_tests/document_loaders/test_stripe.py/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/document_loaders/test_stripe.py",
"repo_id": "langchain",
"token_count": 81
} | 352 |
from __future__ import annotations
import itertools
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
if TYPE_CHECKING:
from tigrisdb import Tigr... | langchain/libs/community/langchain_community/vectorstores/tigris.py/0 | {
"file_path": "langchain/libs/community/langchain_community/vectorstores/tigris.py",
"repo_id": "langchain",
"token_count": 2259
} | 313 |
<jupyter_start><jupyter_text>Select by lengthThis example selector selects which examples to use based on length. This is useful when you are worried about constructing a prompt that will go over the length of the context window. For longer inputs, it will select fewer examples to include, while for shorter inputs it w... | langchain/docs/docs/modules/model_io/prompts/example_selector_types/length_based.ipynb/0 | {
"file_path": "langchain/docs/docs/modules/model_io/prompts/example_selector_types/length_based.ipynb",
"repo_id": "langchain",
"token_count": 817
} | 209 |
[tool.poetry]
name = "rag-mongo"
version = "0.1.0"
description = "RAG on MongDB"
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"
pymongo = ">=4.5.0"
[tool.poetry.group.dev.dependencies... | langchain/templates/rag-mongo/pyproject.toml/0 | {
"file_path": "langchain/templates/rag-mongo/pyproject.toml",
"repo_id": "langchain",
"token_count": 287
} | 721 |
package main
import (
"encoding/csv"
"fmt"
"os"
"reflect"
"strings"
"github.com/samber/lo"
"go.uber.org/zap"
"golang.org/x/exp/slices"
"github.com/milvus-io/milvus/pkg/log"
"github.com/milvus-io/milvus/pkg/util/paramtable"
"github.com/milvus-io/milvus/pkg/util/typeutil"
)
type DocContent struct {
key ... | milvus/cmd/tools/config/generate.go/0 | {
"file_path": "milvus/cmd/tools/config/generate.go",
"repo_id": "milvus",
"token_count": 3408
} | 1,894 |
"""Test Anyscale API wrapper."""
from langchain_community.llms.aviary import Aviary
def test_aviary_call() -> None:
"""Test valid call to Anyscale."""
llm = Aviary()
output = llm("Say bar:")
print(f"llm answer:\n{output}") # noqa: T201
assert isinstance(output, str)
| langchain/libs/community/tests/integration_tests/llms/test_aviary.py/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/llms/test_aviary.py",
"repo_id": "langchain",
"token_count": 112
} | 335 |
# Template
This is a template folder for you to start afresh in.
Step 1: Fill out `get_chain` in `chain.py`.
Step 2: Fill out all the constants in `constants.py`.
| langchain-aiplugin/template/README.md/0 | {
"file_path": "langchain-aiplugin/template/README.md",
"repo_id": "langchain-aiplugin",
"token_count": 53
} | 64 |
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>.
@prefix rep: <http://www.openrdf.org/config/repository#>.
@prefix sr: <http://www.openrdf.org/config/repository/sail#>.
@prefix sail: <http://www.openrdf.org/config/sail#>.
@prefix graphdb: <http://www.ontotext.com/config/graphdb#>.
[] a rep:Repository ;
rep:r... | langchain/libs/community/tests/integration_tests/graphs/docker-compose-ontotext-graphdb/config.ttl/0 | {
"file_path": "langchain/libs/community/tests/integration_tests/graphs/docker-compose-ontotext-graphdb/config.ttl",
"repo_id": "langchain",
"token_count": 863
} | 351 |
// Licensed to the LF AI & Data foundation under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use th... | milvus/internal/datanode/binlog_io.go/0 | {
"file_path": "milvus/internal/datanode/binlog_io.go",
"repo_id": "milvus",
"token_count": 4249
} | 1,771 |
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.huggingface import (
HuggingFaceEmbedding,
HuggingFaceInferenceAPIEmbedding,
)
def test_huggingfaceembedding_class():
names_of_base_classes = [b.__name__ for b in HuggingFaceEmbedding.__mro__]
assert BaseEmbedd... | llama_index/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/tests/test_embeddings_huggingface.py/0 | {
"file_path": "llama_index/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/tests/test_embeddings_huggingface.py",
"repo_id": "llama_index",
"token_count": 219
} | 1,188 |
---
sidebar_position: 0
---
# Introduction
**LangChain** is a framework for developing applications powered by language models. It enables applications that:
- **Are context-aware**: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
- **Re... | langchain/docs/docs/get_started/introduction.mdx/0 | {
"file_path": "langchain/docs/docs/get_started/introduction.mdx",
"repo_id": "langchain",
"token_count": 1269
} | 89 |
"""Answer inserter."""
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from llama_index.core.llms.llm import LLM
from llama_index.core.prompts.base import BasePromptTemplate, PromptTemplate
from llama_index.core.prompts.mixin import (
PromptDictType,
PromptMixin,
PromptMixinTyp... | llama_index/llama-index-core/llama_index/core/query_engine/flare/answer_inserter.py/0 | {
"file_path": "llama_index/llama-index-core/llama_index/core/query_engine/flare/answer_inserter.py",
"repo_id": "llama_index",
"token_count": 2418
} | 1,239 |
export async function sha256(input: string): Promise<string> {
const utf8 = new TextEncoder().encode(input);
const hashBuffer = await crypto.subtle.digest("SHA-256", utf8);
const hashArray = Array.from(new Uint8Array(hashBuffer));
const hashHex = hashArray.map((bytes) => bytes.toString(16).padStart(2, "0")).join(""... | chat-ui/src/lib/utils/sha256.ts/0 | {
"file_path": "chat-ui/src/lib/utils/sha256.ts",
"repo_id": "chat-ui",
"token_count": 119
} | 98 |
from typing import Callable, Dict
from eval import contains_expected_response
from llama_index.tools.function_tool import FunctionTool
from task import Task
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer."""
return a + b
def multiply(a: int, b: int) -> int:
"""Multip... | llama_index/benchmarks/agent/math_tasks.py/0 | {
"file_path": "llama_index/benchmarks/agent/math_tasks.py",
"repo_id": "llama_index",
"token_count": 362
} | 1,154 |
- sections:
- local: index
title: 🧨 Diffusers
- local: quicktour
title: Tour rápido
- local: installation
title: Instalação
title: Primeiros passos
| diffusers/docs/source/pt/_toctree.yml/0 | {
"file_path": "diffusers/docs/source/pt/_toctree.yml",
"repo_id": "diffusers",
"token_count": 77
} | 201 |
from langchain_community.embeddings.modelscope_hub import ModelScopeEmbeddings
__all__ = ["ModelScopeEmbeddings"]
| langchain/libs/langchain/langchain/embeddings/modelscope_hub.py/0 | {
"file_path": "langchain/libs/langchain/langchain/embeddings/modelscope_hub.py",
"repo_id": "langchain",
"token_count": 34
} | 526 |
export {
type ToolParams,
ToolInputParsingException,
StructuredTool,
Tool,
} from "@langchain/core/tools";
| langchainjs/langchain/src/tools/base.ts/0 | {
"file_path": "langchainjs/langchain/src/tools/base.ts",
"repo_id": "langchainjs",
"token_count": 40
} | 955 |
# AnalyticDB
This page covers how to use the AnalyticDB ecosystem within LangChain.
### VectorStore
There exists a wrapper around AnalyticDB, allowing you to use it as a vectorstore,
whether for semantic search or example selection.
To import this vectorstore:
```python
from langchain_community.vectorstores import ... | langchain/docs/docs/integrations/providers/analyticdb.mdx/0 | {
"file_path": "langchain/docs/docs/integrations/providers/analyticdb.mdx",
"repo_id": "langchain",
"token_count": 118
} | 128 |
# Typesense
> [Typesense](https://typesense.org) is an open-source, in-memory search engine, that you can either
> [self-host](https://typesense.org/docs/guide/install-typesense#option-2-local-machine-self-hosting) or run
> on [Typesense Cloud](https://cloud.typesense.org/).
> `Typesense` focuses on performance by s... | langchain/docs/docs/integrations/providers/typesense.mdx/0 | {
"file_path": "langchain/docs/docs/integrations/providers/typesense.mdx",
"repo_id": "langchain",
"token_count": 217
} | 152 |
# ECA-ResNet
An **ECA ResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that utilises an [Efficient Channel Attention module](https://paperswithcode.com/method/efficient-channel-attention). Efficient Channel Attention is an architectural unit based on [squeeze-and-excitation blocks](https:/... | pytorch-image-models/docs/models/.templates/models/ecaresnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/ecaresnet.md",
"repo_id": "pytorch-image-models",
"token_count": 2832
} | 328 |
# Validation and Benchmark Results
This folder contains validation and benchmark results for the models in this collection. Validation scores are currently only run for models with pretrained weights and ImageNet-1k heads, benchmark numbers are run for all.
## Datasets
There are currently results for the ImageNet va... | pytorch-image-models/results/README.md/0 | {
"file_path": "pytorch-image-models/results/README.md",
"repo_id": "pytorch-image-models",
"token_count": 1173
} | 367 |
from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.c... | llama_index/llama-index-integrations/llms/llama-index-llms-llama-api/llama_index/llms/llama_api/base.py/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-llama-api/llama_index/llms/llama_api/base.py",
"repo_id": "llama_index",
"token_count": 1841
} | 1,284 |
---
sidebar_label: Ollama Functions
---
# Ollama Functions
LangChain offers an experimental wrapper around open source models run locally via [Ollama](https://github.com/jmorganca/ollama)
that gives it the same API as OpenAI Functions.
Note that more powerful and capable models will perform better with complex schem... | langchainjs/docs/core_docs/docs/integrations/chat/ollama_functions.mdx/0 | {
"file_path": "langchainjs/docs/core_docs/docs/integrations/chat/ollama_functions.mdx",
"repo_id": "langchainjs",
"token_count": 562
} | 775 |
package allocator
import "github.com/milvus-io/milvus/pkg/util/typeutil"
type Allocator interface {
AllocID() (typeutil.UniqueID, error)
}
| milvus/cmd/tools/migration/allocator/allocator.go/0 | {
"file_path": "milvus/cmd/tools/migration/allocator/allocator.go",
"repo_id": "milvus",
"token_count": 54
} | 1,710 |
class MyClass {
constructor(name) {
this.name = name;
}
greet() {
console.log(`Hello, ${this.name}!`);
}
}
function main() {
const name = prompt("Enter your name:");
const obj = new MyClass(name);
obj.greet();
}
main();
| langchain/docs/docs/integrations/document_loaders/example_data/source_code/example.js/0 | {
"file_path": "langchain/docs/docs/integrations/document_loaders/example_data/source_code/example.js",
"repo_id": "langchain",
"token_count": 96
} | 101 |
# The advantages and disadvantages of policy-gradient methods
At this point, you might ask, "but Deep Q-Learning is excellent! Why use policy-gradient methods?". To answer this question, let's study the **advantages and disadvantages of policy-gradient methods**.
## Advantages
There are multiple advantages over valu... | deep-rl-class/units/en/unit4/advantages-disadvantages.mdx/0 | {
"file_path": "deep-rl-class/units/en/unit4/advantages-disadvantages.mdx",
"repo_id": "deep-rl-class",
"token_count": 1184
} | 179 |
from langchain_community.callbacks.context_callback import (
ContextCallbackHandler,
)
__all__ = ["ContextCallbackHandler"]
| langchain/libs/langchain/langchain/callbacks/context_callback.py/0 | {
"file_path": "langchain/libs/langchain/langchain/callbacks/context_callback.py",
"repo_id": "langchain",
"token_count": 36
} | 449 |
<jupyter_start><jupyter_text>Anthropic FunctionsThis notebook shows how to use an experimental wrapper around Anthropic that gives it the same API as OpenAI Functions.<jupyter_code>from langchain_experimental.llms.anthropic_functions import AnthropicFunctions<jupyter_output><empty_output><jupyter_text>Initialize ModelY... | langchain/docs/docs/integrations/chat/anthropic_functions.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/chat/anthropic_functions.ipynb",
"repo_id": "langchain",
"token_count": 860
} | 99 |
import json
from json import JSONDecodeError
from typing import List, Union
from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.messages import (
AIMessage,
BaseMessage,
)
from langchain_core.outputs i... | langchain/libs/langchain/langchain/agents/output_parsers/openai_tools.py/0 | {
"file_path": "langchain/libs/langchain/langchain/agents/output_parsers/openai_tools.py",
"repo_id": "langchain",
"token_count": 1329
} | 470 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
logger = logging.getLogger(__name__)
class RagPyTorchDistributedRetriever(RagRetriever):
"""
A distributed retriever built on top of ... | transformers/examples/research_projects/rag/distributed_pytorch_retriever.py/0 | {
"file_path": "transformers/examples/research_projects/rag/distributed_pytorch_retriever.py",
"repo_id": "transformers",
"token_count": 2561
} | 542 |
poetry_requirements(
name="poetry",
)
| llama_index/llama-index-integrations/tools/llama-index-tools-graphql/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/tools/llama-index-tools-graphql/BUILD",
"repo_id": "llama_index",
"token_count": 18
} | 1,426 |
from llama_index.graph_stores.nebula.base import NebulaGraphStore
__all__ = ["NebulaGraphStore"]
| llama_index/llama-index-integrations/graph_stores/llama-index-graph-stores-nebula/llama_index/graph_stores/nebula/__init__.py/0 | {
"file_path": "llama_index/llama-index-integrations/graph_stores/llama-index-graph-stores-nebula/llama_index/graph_stores/nebula/__init__.py",
"repo_id": "llama_index",
"token_count": 33
} | 1,333 |
import re
from typing import Any, Dict, List, Tuple, Union
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers.base import BaseOutputParser
from langchain_core.pydantic_v1 import validator
from langchain.output_parsers.format_instructions import (
PANDAS_DATAFRAME_FORMAT... | langchain/libs/langchain/langchain/output_parsers/pandas_dataframe.py/0 | {
"file_path": "langchain/libs/langchain/langchain/output_parsers/pandas_dataframe.py",
"repo_id": "langchain",
"token_count": 3429
} | 551 |
/*
* Licensed to the LF AI & Data foundation under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use... | milvus/pkg/mq/msgstream/msg_for_index_test.go/0 | {
"file_path": "milvus/pkg/mq/msgstream/msg_for_index_test.go",
"repo_id": "milvus",
"token_count": 1211
} | 1,924 |
# rag-google-cloud-sensitive-data-protection
This template is an application that utilizes Google Vertex AI Search, a machine learning powered search service, and
PaLM 2 for Chat (chat-bison). The application uses a Retrieval chain to answer questions based on your documents.
This template is an application that util... | langchain/templates/rag-google-cloud-sensitive-data-protection/README.md/0 | {
"file_path": "langchain/templates/rag-google-cloud-sensitive-data-protection/README.md",
"repo_id": "langchain",
"token_count": 1011
} | 656 |
## Adversarial evaluation of model performances
Here is an example on evaluating a model using adversarial evaluation of natural language inference with the Heuristic Analysis for NLI Systems (HANS) dataset [McCoy et al., 2019](https://arxiv.org/abs/1902.01007). The example was gracefully provided by [Nafise Sadat Moo... | transformers/examples/research_projects/adversarial/README.md/0 | {
"file_path": "transformers/examples/research_projects/adversarial/README.md",
"repo_id": "transformers",
"token_count": 518
} | 593 |
<jupyter_start><jupyter_text>RAG FusionRe-implemented from [this GitHub repo](https://github.com/Raudaschl/rag-fusion), all credit to original author> RAG-Fusion, a search methodology that aims to bridge the gap between traditional search paradigms and the multifaceted dimensions of human queries. Inspired by the capab... | langchain/cookbook/rag_fusion.ipynb/0 | {
"file_path": "langchain/cookbook/rag_fusion.ipynb",
"repo_id": "langchain",
"token_count": 1123
} | 80 |
# Based on stable_diffusion_reference.py
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import PIL.Image
import torch
from diffusers import StableDiffusionXLPipeline
from diffusers.models.attention import BasicTransformerBlock
from diffusers.models.unets.unet_2d_blocks import... | diffusers/examples/community/stable_diffusion_xl_reference.py/0 | {
"file_path": "diffusers/examples/community/stable_diffusion_xl_reference.py",
"repo_id": "diffusers",
"token_count": 18975
} | 209 |
// 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/util/importutil/collection_info_test.go/0 | {
"file_path": "milvus/internal/util/importutil/collection_info_test.go",
"repo_id": "milvus",
"token_count": 1481
} | 1,869 |
<jupyter_start><jupyter_text>Baichuan LLMBaichuan Inc. (https://www.baichuan-ai.com/) is a Chinese startup in the era of AGI, dedicated to addressing fundamental human needs: Efficiency, Health, and Happiness. PrerequisiteAn API key is required to access Baichuan LLM API. Visit https://platform.baichuan-ai.com/ to get... | langchain/docs/docs/integrations/llms/baichuan.ipynb/0 | {
"file_path": "langchain/docs/docs/integrations/llms/baichuan.ipynb",
"repo_id": "langchain",
"token_count": 317
} | 125 |
#!/usr/bin/env python
# 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... | transformers/examples/legacy/seq2seq/save_len_file.py/0 | {
"file_path": "transformers/examples/legacy/seq2seq/save_len_file.py",
"repo_id": "transformers",
"token_count": 869
} | 558 |
# 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/gpt_neox_japanese/test_modeling_gpt_neox_japanese.py/0 | {
"file_path": "transformers/tests/models/gpt_neox_japanese/test_modeling_gpt_neox_japanese.py",
"repo_id": "transformers",
"token_count": 4859
} | 735 |
<jupyter_start><jupyter_code>from transformers import AutoModelForSeq2SeqLM
from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, LoraConfig, TaskType
import torch
from datasets import load_dataset
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from transformers import AutoTokenizer
fr... | peft/examples/conditional_generation/peft_lora_seq2seq.ipynb/0 | {
"file_path": "peft/examples/conditional_generation/peft_lora_seq2seq.ipynb",
"repo_id": "peft",
"token_count": 2336
} | 298 |
"""Input components."""
from typing import Any, Dict
from llama_index.core.base.query_pipeline.query import (
InputKeys,
OutputKeys,
QueryComponent,
)
class InputComponent(QueryComponent):
"""Input component."""
def _validate_component_inputs(self, input: Dict[str, Any]) -> Dict[str, Any]:
... | llama_index/llama-index-core/llama_index/core/query_pipeline/components/input.py/0 | {
"file_path": "llama_index/llama-index-core/llama_index/core/query_pipeline/components/input.py",
"repo_id": "llama_index",
"token_count": 601
} | 1,198 |
poetry_requirements(
name="poetry",
)
| llama_index/llama-index-integrations/llms/llama-index-llms-localai/BUILD/0 | {
"file_path": "llama_index/llama-index-integrations/llms/llama-index-llms-localai/BUILD",
"repo_id": "llama_index",
"token_count": 18
} | 1,359 |
<jupyter_start><jupyter_code># Setup OpenAI Agent
import openai
openai.api_key = "sk-your-key"
from llama_index.agent import OpenAIAgent
from llama_index.tools.bing_search.base import BingSearchToolSpec
bing_tool = BingSearchToolSpec(api_key="your-key")
agent = OpenAIAgent.from_tools(
bing_tool.to_tool_list(),
... | llama_index/llama-index-integrations/tools/llama-index-tools-bing-search/examples/bing_search.ipynb/0 | {
"file_path": "llama_index/llama-index-integrations/tools/llama-index-tools-bing-search/examples/bing_search.ipynb",
"repo_id": "llama_index",
"token_count": 571
} | 1,475 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_... | datasets/tests/test_py_utils.py/0 | {
"file_path": "datasets/tests/test_py_utils.py",
"repo_id": "datasets",
"token_count": 4821
} | 158 |
pub fn add(left: usize, right: usize) -> usize {
left + right
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn it_works() {
let result = add(2, 2);
assert_eq!(result, 4);
}
}
| candle/candle-wasm-tests/src/lib.rs/0 | {
"file_path": "candle/candle-wasm-tests/src/lib.rs",
"repo_id": "candle",
"token_count": 108
} | 91 |
<!--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/de/quicktour.md/0 | {
"file_path": "transformers/docs/source/de/quicktour.md",
"repo_id": "transformers",
"token_count": 7322
} | 476 |
# Get the checkpoint from
# https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt
import torch
from safetensors.torch import save_file
data = torch.load("tiny.en.pt")
weights = {}
for k, v in data["model_state_dict"].items():
weights[k] ... | candle/candle-examples/examples/whisper/extract_weights.py/0 | {
"file_path": "candle/candle-examples/examples/whisper/extract_weights.py",
"repo_id": "candle",
"token_count": 183
} | 50 |
#!/usr/bin/env python
# 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/LI... | transformers/examples/tensorflow/language-modeling/run_clm.py/0 | {
"file_path": "transformers/examples/tensorflow/language-modeling/run_clm.py",
"repo_id": "transformers",
"token_count": 12297
} | 542 |
from llama_index.legacy.core.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
MessageRole,
)
from llama_index.legacy.types import TokenGen
def response_gen_from_query_engine(response_gen: TokenGen) -> ChatResponseGen:
response_str = ""
for token in response_gen:
response... | llama_index/llama-index-legacy/llama_index/legacy/chat_engine/utils.py/0 | {
"file_path": "llama_index/llama-index-legacy/llama_index/legacy/chat_engine/utils.py",
"repo_id": "llama_index",
"token_count": 190
} | 1,553 |
---
hide_table_of_contents: true
sidebar_position: 2
---
# XML Agent
Some language models (like Anthropic's Claude) are particularly good at reasoning/writing XML.
The below example shows how to use an agent that uses XML when prompting.
## Setup
Install the Anthropic integration package, retrieve your key, and sto... | langchainjs/docs/core_docs/docs/modules/agents/agent_types/xml.mdx/0 | {
"file_path": "langchainjs/docs/core_docs/docs/modules/agents/agent_types/xml.mdx",
"repo_id": "langchainjs",
"token_count": 991
} | 747 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::{Parser, ValueEnum};
use candle_transformers::models::mamba::{Config, Model, State};
use candle::{DType, Device, Tensor};
use candle_examples::token_output_stre... | candle/candle-examples/examples/mamba/main.rs/0 | {
"file_path": "candle/candle-examples/examples/mamba/main.rs",
"repo_id": "candle",
"token_count": 4348
} | 46 |
# 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/vision_encoder_decoder/modeling_vision_encoder_decoder.py/0 | {
"file_path": "transformers/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py",
"repo_id": "transformers",
"token_count": 14444
} | 707 |
// 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/pkg/util/typeutil/string_util.go/0 | {
"file_path": "milvus/pkg/util/typeutil/string_util.go",
"repo_id": "milvus",
"token_count": 604
} | 2,125 |
"""Schemas for the LangSmith API."""
from __future__ import annotations
import logging
from datetime import datetime, timezone
from typing import Dict, List, Optional, cast
from uuid import UUID, uuid4
try:
from pydantic.v1 import ( # type: ignore[import]
Field,
root_validator,
validator... | langsmith-sdk/python/langsmith/run_trees.py/0 | {
"file_path": "langsmith-sdk/python/langsmith/run_trees.py",
"repo_id": "langsmith-sdk",
"token_count": 3042
} | 1,150 |
# self-query-qdrant
This template performs [self-querying](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/)
using Qdrant and OpenAI. By default, it uses an artificial dataset of 10 documents, but you can replace it with your own dataset.
## Environment Setup
Set the `OPENAI_API_KEY... | langchain/templates/self-query-qdrant/README.md/0 | {
"file_path": "langchain/templates/self-query-qdrant/README.md",
"repo_id": "langchain",
"token_count": 1521
} | 688 |
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