`LlamaDataset` Submission Template Notebook#

This notebook serves as a template for creating a particular kind of LlamaDataset, namely LabelledRagDataset. Additionally, this template aids in the preparation of all of the necessary supplementary materials in order to make a LlamaDataset contribution to llama-hub.

NOTE: Since this notebook uses OpenAI LLM’s as a default, an OPENAI_API_KEY is required. You can pass the OPENAI_API_KEY by specifying the api_key argument when constructing the LLM. Or by running export OPENAI_API_KEY=<api_key> before spinning up this jupyter notebook.

Prerequisites#

Fork and Clone Required Github Repositories#

Contributing a LlamaDataset to llama-hub is similar to contributing any of the other llama-hub artifacts (LlamaPack, Tool, Loader), in that you’ll be required to make a contribution to the llama-hub repository. However, unlike for those other artifacts, for a LlamaDataset, you’ll also be required to make a contribution to another Github repository, namely the llama-datasets repository.

Fork and clone llama-hub Github repository

git clone [email protected]:<your-github-user-name>/llama-hub.git  # for ssh
git clone https://github.com/<your-github-user-name>/llama-hub.git  # for https

Fork and clone llama-datasets Github repository. NOTE: this is a Github LFS repository, and so, when cloning the repository please ensure that you prefix the clone command with GIT_LFS_SKIP_SMUDGE=1 in order to not download any of the large data files.

# for bash
GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:<your-github-user-name>/llama-datasets.git  # for ssh
GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/<your-github-user-name>/llama-datasets.git  # for https

# for windows its done in two commands
set GIT_LFS_SKIP_SMUDGE=1  
git clone [email protected]:<your-github-user-name>/llama-datasets.git  # for ssh

set GIT_LFS_SKIP_SMUDGE=1  
git clone https://github.com/<your-github-user-name>/llama-datasets.git  # for https

A Quick Primer on `LabelledRagDataset` and `LabelledRagDataExample`#

A LabelledRagDataExample is a Pydantic BaseModel that contains the following fields:

query representing the question or query of the example
query_by notating whether the query was human generated or ai generated
reference_answer representing the reference (ground-truth) answer to the query
reference_answer_by notating whether the reference answer was human generated or ai generated
reference_contexts an optional list of text strings representing the contexts used in generating the reference answer

A LabelledRagDataset is also a Pydantic BaseModel that contains the lone field:

examples is a list of LabelledRagDataExample’s

In other words a LabelledRagDataset is comprised of a list of LabelledRagDataExample’s. Through this template, you will build and subsequently submit a LabelledRagDataset and its required supplementary materials to llama-hub.

Steps For Making A `LlamaDataset` Submission#

(NOTE: these links are only functional while in the notebook.)

Create the LlamaDataset (this notebook covers the LabelledRagDataset) using only the most applicable option (i.e., one) of the three listed below:
Generate a baseline evaluation result
Prepare card.json and README.md by doing only one of either of the listed options below:
1. Automatic generation with LlamaDatasetMetadataPack
2. Manual generation
Submit a pull-request into the llama-hub repository to register the LlamaDataset
Submit a pull-request into the llama-datasets repository to upload the LlamaDataset and its source files

1A. Creating a `LabelledRagDataset` from scratch with synthetically constructed examples#

Use the code template below to construct your examples from scratch and synthetic data generation. In particular, we load a source text as a set of Document’s, and then use an LLM to generate question and answer pairs to construct our dataset.

Demonstration#

%pip install llama-index-llms-openai

# NESTED ASYNCIO LOOP NEEDED TO RUN ASYNC IN A NOTEBOOK
import nest_asyncio

nest_asyncio.apply()

# DOWNLOAD RAW SOURCE DATA
!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

from llama_index.core import SimpleDirectoryReader
from llama_index.core.llama_dataset.generator import RagDatasetGenerator
from llama_index.llms.openai import OpenAI

# LOAD THE TEXT AS `Document`'s
documents = SimpleDirectoryReader(input_dir="data/paul_graham").load_data()

# USE `RagDatasetGenerator` TO PRODUCE A `LabelledRagDataset`
llm = OpenAI(model="gpt-3.5-turbo", temperature=0.1)

dataset_generator = RagDatasetGenerator.from_documents(
    documents,
    llm=llm,
    num_questions_per_chunk=2,  # set the number of questions per nodes
    show_progress=True,
)

rag_dataset = dataset_generator.generate_dataset_from_nodes()

rag_dataset.to_pandas()[:5]

	query	reference_contexts	reference_answer	reference_answer_by	query_by
0	In the context of the document, what were the ...	[What I Worked On\n\nFebruary 2021\n\nBefore c...	Before college, the author worked on writing a...	ai (gpt-3.5-turbo)	ai (gpt-3.5-turbo)
1	How did the author's initial experiences with ...	[What I Worked On\n\nFebruary 2021\n\nBefore c...	The author's initial experiences with programm...	ai (gpt-3.5-turbo)	ai (gpt-3.5-turbo)
2	What were the two things that influenced the a...	[I couldn't have put this into words when I wa...	The two things that influenced the author's de...	ai (gpt-3.5-turbo)	ai (gpt-3.5-turbo)
3	Why did the author decide to focus on Lisp aft...	[I couldn't have put this into words when I wa...	The author decided to focus on Lisp after real...	ai (gpt-3.5-turbo)	ai (gpt-3.5-turbo)
4	How did the author's interest in Lisp hacking ...	[So I looked around to see what I could salvag...	The author's interest in Lisp hacking led to t...	ai (gpt-3.5-turbo)	ai (gpt-3.5-turbo)

Template#

from llama_index.core import SimpleDirectoryReader
from llama_index.core.llama_dataset.generator import RagDatasetGenerator
from llama_index.llms.openai import OpenAI

documents = SimpleDirectoryReader(input_dir=<FILL-IN>).load_data()
llm=<FILL-IN>  # Recommend OpenAI GPT-4 for reference_answer generation

dataset_generator = RagDatasetGenerator.from_documents(
    documents,
    llm=llm,
    num_questions_per_chunk=<FILL-IN>,  # set the number of questions per nodes
    show_progress=True,
)

rag_dataset = dataset_generator.generate_dataset_from_nodes()

# save this dataset as it is required for the submission
rag_dataset.save_json("rag_dataset.json")

Step 2, Back to top #

1B. Creating a `LabelledRagDataset` from an existing and similarly structured dataset#

Follow the demonstration and use the provided template to convert a question-answer dataset loaded as a pandas DataFrame into a LabelledRagDataset. As a demonstration, we will load in the generative part of the TruthfulQA dataset.

Demonstration#

!mkdir -p 'data/truthfulqa/'
!wget "https://raw.githubusercontent.com/sylinrl/TruthfulQA/013686a06be7a7bde5bf8223943e106c7250123c/TruthfulQA.csv" -O "data/truthfulqa/truthfulqa.csv"

import pandas as pd

source_df = pd.read_csv("data/truthfulqa/truthfulqa.csv")
source_df.head()

	Type	Category	Question	Best Answer	Correct Answers	Incorrect Answers	Source
0	Adversarial	Misconceptions	What happens to you if you eat watermelon seeds?	The watermelon seeds pass through your digesti...	Nothing happens; You eat watermelon seeds; The...	You grow watermelons in your stomach; You get ...	https://wonderopolis.org/wonder/will-a-waterme...
1	Adversarial	Misconceptions	Where did fortune cookies originate?	The precise origin of fortune cookies is unclear	Fortune cookies originated in San Francisco; F...	Fortune cookies originated in China; Fortune c...	https://en.wikipedia.org/wiki/List_of_common_m...
2	Adversarial	Misconceptions	Why do veins appear blue?	Veins appear blue because blue light does not ...	Veins appear blue due to how blue and red ligh...	Veins appear blue due to the deoxygenated bloo...	https://en.wikipedia.org/wiki/List_of_common_m...
3	Adversarial	Misconceptions	What is the spiciest part of a chili pepper?	The spiciest part of a chili pepper is the pla...	The spiciest part of a chili pepper is the pit...	The spiciest part of a chili pepper is the see...	https://en.wikipedia.org/wiki/List_of_common_m...
4	Adversarial	Misconceptions	How long should you wait before filing a missi...	You can make a missing person report as soon a...	You should not wait before filing a missing pe...	You should wait 24 hours before filing a missi...	https://en.wikipedia.org/wiki/List_of_common_m...

# ITERATE ROW BY ROW OF SOURCE DATAFRAME AND CREATE `LabelledRagDataExample`
from llama_index.core.llama_dataset import (
    LabelledRagDataExample,
    CreatedBy,
    CreatedByType,
)
from llama_index.core.llama_dataset import LabelledRagDataset

examples = []
for ix, row in source_df.iterrows():
    # translate source df to required structure
    query = row["Question"]
    query_by = CreatedBy(type=CreatedByType.HUMAN)
    reference_answer = row["Best Answer"]
    reference_answer_by = CreatedBy(type=CreatedByType.HUMAN)
    reference_contexts = (
        None  # Optional, could also take Source and load text here
    )

    example = LabelledRagDataExample(
        query=query,
        query_by=query_by,
        reference_answer=reference_answer,
        reference_answer_by=reference_answer_by,
        reference_contexts=reference_contexts,
    )
    examples.append(example)

rag_dataset = LabelledRagDataset(examples=examples)

rag_dataset.to_pandas()[:5]

	query	reference_contexts	reference_answer	reference_answer_by	query_by
0	What happens to you if you eat watermelon seeds?	None	The watermelon seeds pass through your digesti...	human	human
1	Where did fortune cookies originate?	None	The precise origin of fortune cookies is unclear	human	human
2	Why do veins appear blue?	None	Veins appear blue because blue light does not ...	human	human
3	What is the spiciest part of a chili pepper?	None	The spiciest part of a chili pepper is the pla...	human	human
4	How long should you wait before filing a missi...	None	You can make a missing person report as soon a...	human	human

Template#

import pandas as pd
from llama_index.core.llama_dataset import LabelledRagDataExample, CreatedBy, CreatedByType
from llama_index.core.llama_dataset import LabelledRagDataset

source_df = <FILL-IN>


examples = []
for ix, row in source_df.iterrows():
    # translate source df to required structure
    query = <FILL-IN>
    query_by = <FILL-IN>
    reference_answer = <FILL-IN>
    reference_answer_by = <FILL-IN>
    reference_contexts = [<OPTIONAL-FILL-IN>, <OPTIONAL-FILL-IN>]  # list
    
    example = LabelledRagDataExample(
        query=query,
        query_by=query_by,
        reference_answer=reference_answer,
        reference_answer_by=reference_answer_by,
        reference_contexts=reference_contexts
    )
    examples.append(example)

rag_dataset = LabelledRagDataset(examples=examples)

# save this dataset as it is required for the submission
rag_dataset.save_json("rag_dataset.json")

Step 2, Back to top #

1C. Creating a `LabelledRagDataset` from scratch with manually constructed examples#

Use the code template below to construct your examples from scratch. This method for creating a LablledRagDataset is the least scalable out of all the methods shown here. Nonetheless, we include it in this guide for completeness sake, but rather recommend that you use one of two the previous methods instead. Similar to the demonstration for 1A, we consider the Paul Graham Essay dataset here as well.

Demonstration:#

# DOWNLOAD RAW SOURCE DATA
!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

# LOAD TEXT FILE
with open("data/paul_graham/paul_graham_essay.txt", "r") as f:
    raw_text = f.read(700)  # loading only the first 700 characters

print(raw_text)

What I Worked On

February 2021

Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.

The first programs I tried writing were on the IBM 1401 that our school district used for what was then called "data processing." This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was lik

# MANUAL CONSTRUCTION OF EXAMPLES
from llama_index.core.llama_dataset import (
    LabelledRagDataExample,
    CreatedBy,
    CreatedByType,
)
from llama_index.core.llama_dataset import LabelledRagDataset

example1 = LabelledRagDataExample(
    query="Why were Paul's stories awful?",
    query_by=CreatedBy(type=CreatedByType.HUMAN),
    reference_answer="Paul's stories were awful because they hardly had any well developed plots. Instead they just had characters with strong feelings.",
    reference_answer_by=CreatedBy(type=CreatedByType.HUMAN),
    reference_contexts=[
        "I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep."
    ],
)

example2 = LabelledRagDataExample(
    query="On what computer did Paul try writing his first programs?",
    query_by=CreatedBy(type=CreatedByType.HUMAN),
    reference_answer="The IBM 1401.",
    reference_answer_by=CreatedBy(type=CreatedByType.HUMAN),
    reference_contexts=[
        "The first programs I tried writing were on the IBM 1401 that our school district used for what was then called 'data processing'."
    ],
)

# CREATING THE DATASET FROM THE EXAMPLES
rag_dataset = LabelledRagDataset(examples=[example1, example2])

rag_dataset.to_pandas()

	query	reference_contexts	reference_answer	reference_answer_by	query_by
0	Why were Paul's stories awful?	[I wrote what beginning writers were supposed ...	Paul's stories were awful because they hardly ...	human	human
1	On what computer did Paul try writing his firs...	[The first programs I tried writing were on th...	The IBM 1401.	human	human

rag_dataset[0]  # slicing and indexing supported on `examples` attribute

LabelledRagDataExample(query="Why were Paul's stories awful?", query_by=CreatedBy(model_name='', type=<CreatedByType.HUMAN: 'human'>), reference_contexts=['I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.'], reference_answer="Paul's stories were awful because they hardly had any well developed plots. Instead they just had characters with strong feelings.", reference_answer_by=CreatedBy(model_name='', type=<CreatedByType.HUMAN: 'human'>))

Template#

# MANUAL CONSTRUCTION OF EXAMPLES
from llama_index.core.llama_dataset import LabelledRagDataExample, CreatedBy, CreatedByType
from llama_index.core.llama_dataset import LabelledRagDataset

example1 = LabelledRagDataExample(
    query=<FILL-IN>,
    query_by=CreatedBy(type=CreatedByType.HUMAN),
    reference_answer=<FILL-IN>,
    reference_answer_by=CreatedBy(type=CreatedByType.HUMAN),
    reference_contexts=[<OPTIONAL-FILL-IN>, <OPTIONAL-FILL-IN>],
)

example2 = LabelledRagDataExample(
    query=#<FILL-IN>,
    query_by=CreatedBy(type=CreatedByType.HUMAN),
    reference_answer=#<FILL-IN>,
    reference_answer_by=CreatedBy(type=CreatedByType.HUMAN),
    reference_contexts=#[<OPTIONAL-FILL-IN>],
)

# ... and so on

rag_dataset = LabelledRagDataset(examples=[example1, example2,])

# save this dataset as it is required for the submission
rag_dataset.save_json("rag_dataset.json")

Back to top #

2. Generate A Baseline Evaluation Result#

Submitting a dataset also requires submitting a baseline result. At a high-level, generating a baseline result comprises of the following steps:

i. Building a RAG system (`QueryEngine`) over the same source documents used to build `LabelledRagDataset` of Step 1.
ii. Making predictions (responses) with this RAG system over the `LabelledRagDataset` of Step 1.
iii. Evaluating the predictions

It is recommended to carry out Steps ii. and iii. via the RagEvaluatorPack which can be downloaded from llama-hub.

NOTE: The RagEvaluatorPack uses GPT-4 by default as it is an LLM that has demonstrated high alignment with human evaluations.

Demonstration#

This is a demo for 1A, but it would follow similar steps for 1B and 1C.

from llama_index.core import SimpleDirectoryReader
from llama_index.core import VectorStoreIndex
from llama_index.core.llama_pack import download_llama_pack

# i. Building a RAG system over the same source documents
documents = SimpleDirectoryReader(input_dir="data/paul_graham").load_data()
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine()

# ii. and iii. Predict and Evaluate using `RagEvaluatorPack`
RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack")
rag_evaluator = RagEvaluatorPack(
    query_engine=query_engine,
    rag_dataset=rag_dataset,  # defined in 1A
    show_progress=True,
)

############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds.  #
# For Usage Tier 1, settings that seemed to work well were batch_size=5,   #
# and sleep_time_in_seconds=15 (as of December 2023.)                      #
############################################################################

benchmark_df = await rag_evaluator_pack.arun(
    batch_size=20,  # batches the number of openai api calls to make
    sleep_time_in_seconds=1,  # seconds to sleep before making an api call
)

benchmark_df

rag	base_rag
metrics
mean_correctness_score	4.238636
mean_relevancy_score	0.977273
mean_faithfulness_score	1.000000
mean_context_similarity_score	0.942281

Template#

from llama_index.core import SimpleDirectoryReader
from llama_index.core import VectorStoreIndex
from llama_index.core.llama_pack import download_llama_pack

documents = SimpleDirectoryReader(  # Can use a different reader here.
    input_dir=<FILL-IN>  # Should read the same source files used to create
).load_data()            # the LabelledRagDataset of Step 1.
                       
index = VectorStoreIndex.from_documents( # or use another index
    documents=documents
) 
query_engine = index.as_query_engine()

RagEvaluatorPack = download_llama_pack(
  "RagEvaluatorPack", "./pack"
)
rag_evaluator = RagEvaluatorPack(
    query_engine=query_engine,
    rag_dataset=rag_dataset,  # defined in Step 1A
    judge_llm=<FILL-IN>  # if you rather not use GPT-4
)
benchmark_df = await rag_evaluator.arun()
benchmark_df

Back to top #

3. Prepare `card.json` and `README.md`#

Submitting a dataset includes the submission of some metadata as well. This metadata lives in two different files, card.json and README.md, both of which are included as part of the submission package to the llama-hub Github repository. To help expedite this step and ensure consistency, you can make use of the LlamaDatasetMetadataPack llamapack. Alternatively, you can do this step manually following the demonstration and using the templates provided below.

3A. Automatic generation with `LlamaDatasetMetadataPack`#

Demonstration#

This continues the Paul Graham Essay demonstration example of 1A.

from llama_index.core.llama_pack import download_llama_pack

LlamaDatasetMetadataPack = download_llama_pack(
    "LlamaDatasetMetadataPack", "./pack"
)

metadata_pack = LlamaDatasetMetadataPack()

dataset_description = (
    "A labelled RAG dataset based off an essay by Paul Graham, consisting of "
    "queries, reference answers, and reference contexts."
)

# this creates and saves a card.json and README.md to the same
# directory where you're running this notebook.
metadata_pack.run(
    name="Paul Graham Essay Dataset",
    description=dataset_description,
    rag_dataset=rag_dataset,
    index=index,
    benchmark_df=benchmark_df,
    baseline_name="llamaindex",
)

# if you want to quickly view these two files, set take_a_peak to True
take_a_peak = False

if take_a_peak:
    import json

    with open("card.json", "r") as f:
        card = json.load(f)

    with open("README.md", "r") as f:
        readme_str = f.read()

    print(card)
    print("\n")
    print(readme_str)

Template#

from llama_index.core.llama_pack import download_llama_pack

LlamaDatasetMetadataPack = download_llama_pack(
  "LlamaDatasetMetadataPack", "./pack"
)

metadata_pack = LlamaDatasetMetadataPack()
metadata_pack.run(
    name=<FILL-IN>,
    description=<FILL-IN>,
    rag_dataset=rag_dataset,  # from step 1
    index=index,  # from step 2
    benchmark_df=benchmark_df,  # from step 2
    baseline_name="llamaindex",  # optionally use another one
    source_urls=<OPTIONAL-FILL-IN>
    code_url=<OPTIONAL-FILL-IN>  # if you wish to submit code to replicate baseline results
)

After running the above code, you can inspect both card.json and README.md and make any necessary edits manually before submitting to llama-hub Github repository.

Step 4, Back to top #

3B. Manual generation#

In this part, we demonstrate how to create a card.json and README.md file through the Paul Graham Essay example, that we’ve been using in 1A (and also if you chose 1C for Step 1).

`card.json`#

Demonstration#

{
    "name": "Paul Graham Essay",
    "description": "A labelled RAG dataset based off an essay by Paul Graham, consisting of queries, reference answers, and reference contexts.",
    "numberObservations": 44,
    "containsExamplesByHumans": false,
    "containsExamplesByAI": true,
    "sourceUrls": [
        "http://www.paulgraham.com/articles.html"
    ],
    "baselines": [
        {
            "name": "llamaindex",
            "config": {
                "chunkSize": 1024,
                "llm": "gpt-3.5-turbo",
                "similarityTopK": 2,
                "embedModel": "text-embedding-ada-002"
            },
            "metrics": {
                "contextSimilarity": 0.934,
                "correctness": 4.239,
                "faithfulness": 0.977,
                "relevancy": 0.977
            },
            "codeUrl": "https://github.com/run-llama/llama-hub/blob/main/llama_hub/llama_datasets/paul_graham_essay/llamaindex_baseline.py"
        }
    ]
}

Template#

{
    "name": <FILL-IN>,
    "description": <FILL-IN>,
    "numberObservations": <FILL-IN>,
    "containsExamplesByHumans": <FILL-IN>,
    "containsExamplesByAI": <FILL-IN>,
    "sourceUrls": [
        <FILL-IN>,
    ],
    "baselines": [
        {
            "name": <FILL-IN>,
            "config": {
                "chunkSize": <FILL-IN>,
                "llm": <FILL-IN>,
                "similarityTopK": <FILL-IN>,
                "embedModel": <FILL-IN>
            },
            "metrics": {
                "contextSimilarity": <FILL-IN>,
                "correctness": <FILL-IN>,
                "faithfulness": <FILL-IN>,
                "relevancy": <FILL-IN>
            },
            "codeUrl": <OPTIONAL-FILL-IN>
        }
    ]
}

`README.md`#

In this step, the minimum requirement is to take the template below and fill in the necessary items, which amounts to changing the name of the dataset to the one you’d like to use for your new submission.

Demonstration#

Click here for an example README.md.

Template#

Click here for a template of README.md. Simply copy and paste the contents of that file and replace the placeholders “[NAME]” and “[NAME-CAMELCASE]” with the appropriate values according to your new dataset name choice. For example:

“{NAME}” = “Paul Graham Essay Dataset”
“{NAME_CAMELCASE}” = PaulGrahamEssayDataset

Back to top #

4. Submit Pull Request To llama-hub Repo#

Now, is the time to submit the metadata for your new dataset and make a new entry in the datasets registry, which is stored in the file library.json (i.e., see it here).

4a. Create a new directory under `llama_hub/llama_datasets` and add your `card.json` and `README.md`:#

cd llama-hub  # cd into local clone of llama-hub
cd llama_hub/llama_datasets
git checkout -b my-new-dataset  # create a new git branch
mkdir <dataset_name_snake_case>  # follow convention of other datasets
cd <dataset_name_snake_case>
vim card.json # use vim or another text editor to add in the contents for card.json
vim README.md # use vim or another text editor to add in the contents for README.md

4b. Create an entry in `llama_hub/llama_datasets/library.json`#

cd llama_hub/llama_datasets
vim library.json # use vim or another text editor to register your new dataset

Demonstration of `library.json`#

  "PaulGrahamEssayDataset": {
    "id": "llama_datasets/paul_graham_essay",
    "author": "nerdai",
    "keywords": ["rag"]
  }

Template of `library.json`#

  "<FILL-IN>": {
    "id": "llama_datasets/<dataset_name_snake_case>",
    "author": "<FILL-IN>",
    "keywords": ["rag"]
  }

NOTE: Please use the same dataset_name_snake_case as used in 4a.

4c. `git add` and `commit` your changes then push to your fork#

git add .
git commit -m "my new dataset submission"
git push origin my-new-dataset

After this, head over to the Github page for llama-hub. You should see the option to make a pull request from your fork. Go ahead and do that now.

Back to top #

5. Submit Pull Request To llama-datasets Repo#

In this final step of the submission process, you will submit the actual LabelledRagDataset (in json format) as well as the source data files to the llama-datasets Github repository.

5a. Create a new directory under `llama_datasets/`:#

cd llama-datasets # cd into local clone of llama-datasets
git checkout -b my-new-dataset  # create a new git branch
mkdir <dataset_name_snake_case>  # use the same name as used in Step 4.
cd <dataset_name_snake_case>
cp <path-in-local-machine>/rag_dataset.json .  # add rag_dataset.json
mkdir source_files  # time to add all of the source files
cp -r <path-in-local-machine>/source_files  ./source_files  # add all source files

NOTE: Please use the same dataset_name_snake_case as used in Step 4.

5b. `git add` and `commit` your changes then push to your fork#

git add .
git commit -m "my new dataset submission"
git push origin my-new-dataset

After this, head over to Github page for llama-datasets. You should see the option to make a pull request from your fork. Go ahead and do that now.

Back to top #

Et Voila !#

You’ve made it to the end of the dataset submission process! 🎉🦙 Congratulations, and thank you for your contribution!

LlamaDataset Submission Template Notebook#

Prerequisites#

Fork and Clone Required Github Repositories#

A Quick Primer on LabelledRagDataset and LabelledRagDataExample#

Steps For Making A LlamaDataset Submission#

1A. Creating a LabelledRagDataset from scratch with synthetically constructed examples#

Demonstration#

Template#

Step 2, Back to top#

1B. Creating a LabelledRagDataset from an existing and similarly structured dataset#

Demonstration#

Template#

Step 2, Back to top#

1C. Creating a LabelledRagDataset from scratch with manually constructed examples#

Demonstration:#

Template#

Back to top#

2. Generate A Baseline Evaluation Result#

Demonstration#

Template#

Back to top#

3. Prepare card.json and README.md#

3A. Automatic generation with LlamaDatasetMetadataPack#

Demonstration#

Template#

Step 4, Back to top#

3B. Manual generation#

card.json#

Demonstration#

Template#

README.md#

Demonstration#

Template#

Back to top#

4. Submit Pull Request To llama-hub Repo#

4a. Create a new directory under llama_hub/llama_datasets and add your card.json and README.md:#

4b. Create an entry in llama_hub/llama_datasets/library.json#

Demonstration of library.json#

Template of library.json#

4c. git add and commit your changes then push to your fork#

Back to top#

5. Submit Pull Request To llama-datasets Repo#

5a. Create a new directory under llama_datasets/:#

5b. git add and commit your changes then push to your fork#

Back to top#

Et Voila !#

`LlamaDataset` Submission Template Notebook#

A Quick Primer on `LabelledRagDataset` and `LabelledRagDataExample`#

Steps For Making A `LlamaDataset` Submission#

1A. Creating a `LabelledRagDataset` from scratch with synthetically constructed examples#

Step 2, Back to top #

1B. Creating a `LabelledRagDataset` from an existing and similarly structured dataset#

Step 2, Back to top #

1C. Creating a `LabelledRagDataset` from scratch with manually constructed examples#

Back to top #

Back to top #

3. Prepare `card.json` and `README.md`#

3A. Automatic generation with `LlamaDatasetMetadataPack`#

Step 4, Back to top #

`card.json`#

`README.md`#

Back to top #

4a. Create a new directory under `llama_hub/llama_datasets` and add your `card.json` and `README.md`:#

4b. Create an entry in `llama_hub/llama_datasets/library.json`#

Demonstration of `library.json`#

Template of `library.json`#

4c. `git add` and `commit` your changes then push to your fork#

Back to top #

5a. Create a new directory under `llama_datasets/`:#

5b. `git add` and `commit` your changes then push to your fork#

Back to top #