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Update README.rst #17

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volkfox Jul 11, 2024
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97 changes: 58 additions & 39 deletions README.rst
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
|PyPI| |Python Version| |Codecov| |Tests| |License|
|PyPI| |Python Version| |Codecov| |Tests|

.. |PyPI| image:: https://img.shields.io/pypi/v/datachain.svg
:target: https://pypi.org/project/datachain/
Expand Down Expand Up @@ -39,21 +39,24 @@ For example, let us consider a dataset from Karlsruhe Institute of Technology de
# this example requires a free Mistral API key, get yours at https://console.mistral.ai
# add the key to your shell environment: $ export MISTRAL_API_KEY= your key

# pip install mistralai
# this example requires a free Mistral API key, get yours at https://console.mistral.ai
# add the key to your shell environment: $ export MISTRAL_API_KEY= your key

import os
import pandas as pd
from datachain.lib.feature import Feature

from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage
from datachain.lib.dc import Column, DataChain

source = "gs://datachain-demo/chatbot-KiT/"
from datachain.lib.dc import DataChain, Column

PROMPT = "Was this bot dialog successful? Describe the 'result' as 'Yes' or 'No' in a short JSON"

model = "mistral-large-latest"
api_key = os.environ["MISTRAL_API_KEY"]

chain = (
DataChain.from_storage(source)
DataChain.from_storage("gs://datachain-demo/chatbot-KiT/")
.limit(5)
.settings(cache=True, parallel=5)
.map(
Expand All @@ -64,17 +67,21 @@ For example, let us consider a dataset from Karlsruhe Institute of Technology de
messages=[
ChatMessage(role="user", content=f"{PROMPT}: {file.get_value()}")
],
).choices[0].message.content,
)
.choices[0]
.message.content,
)
.save()
)

try:
print(chain.select("mistral_response").results())
print(chain.select("mistral_response").results())
except Exception as e:
print(f"do you have the right Mistral API key? {e}")
print(f"do you have the right Mistral API key? {e}")

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Let's not handle any exceptions in the tutorials

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There is a good reason for this piece of code:

most errors in this example are related to a wrong API key


.. code:: shell

->
[('{"result": "Yes"}',), ('{"result": "No"}',), ... , ('{"result": "Yes"}',)]

Now we have parallel-processed an LLM API-based query over cloud data and persisted the results.
Expand All @@ -90,8 +97,10 @@ Datachain internally represents datasets as tables, so analytical queries on the
success_rate = failed_dialogs.count() / chain.count()
print(f"Chatbot dialog success rate: {100*success_rate:.2f}%")

->
"40.00%" (results may vary)

.. code:: shell

"40.00%"

Note that DataChain represents file samples as pointers into their respective storage locations. This means a newly created dataset version does not duplicate files in storage, and storage remains the single source of truth for the original samples

Expand All @@ -104,29 +113,37 @@ For example, instead of collecting just a text response from Mistral API, we mig
.. code:: py

import os
from datachain.lib.feature import Feature
from datachain.lib.dc import Column, DataChain

from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage

source = "gs://datachain-demo/chatbot-KiT/"
PROMPT = "Was this dialog successful? Describe the 'result' as 'Yes' or 'No' in a short JSON"
from datachain.lib.dc import DataChain
from datachain.lib.feature import Feature


PROMPT = (
"Was this dialog successful? Describe the 'result' as 'Yes' or 'No' in a short JSON"
)

model = "mistral-large-latest"
api_key = os.environ["MISTRAL_API_KEY"]


## define the data model ###
class Usage(Feature):
prompt_tokens: int = 0
completion_tokens: int = 0


class MyChatMessage(Feature):
role: str = ""
content: str = ""


class CompletionResponseChoice(Feature):
message: MyChatMessage = MyChatMessage()


class MistralModel(Feature):
id: str = ""
choices: list[CompletionResponseChoice]
Expand All @@ -135,7 +152,7 @@ For example, instead of collecting just a text response from Mistral API, we mig

## Populate model instances ###
chain = (
DataChain.from_storage(source)
DataChain.from_storage("gs://datachain-demo/chatbot-KiT/")
.limit(5)
.settings(cache=True, parallel=5)
.map(
Expand All @@ -147,7 +164,8 @@ For example, instead of collecting just a text response from Mistral API, we mig
messages=[
ChatMessage(role="user", content=f"{PROMPT}: {file.get_value()}")
],
).dict()
)
.dict()
),
output=MistralModel,
)
Expand All @@ -158,19 +176,18 @@ After the chain execution, we can collect the objects:

.. code:: py

responses = chain.collect_one("mistral_response")
for object in responses:
print(type(object))
->
<class '__main__.MistralModel'>
<class '__main__.MistralModel'>
<class '__main__.MistralModel'>
<class '__main__.MistralModel'>
<class '__main__.MistralModel'>
for obj in responses:
assert isinstance(obj, MistralModel)
print(obj.dict())

.. code:: shell

{'choices': [{'message': {'role': 'assistant', 'content': '{"result": "Yes"}'}}], 'usage': {'prompt_tokens': 610, 'completion_tokens': 6}}
{'choices': [{'message': {'role': 'assistant', 'content': '{"result": "No"}'}}], 'usage': {'prompt_tokens': 3983, 'completion_tokens': 6}}
{'choices': [{'message': {'role': 'assistant', 'content': '{"result": "Yes"}'}}], 'usage': {'prompt_tokens': 706, 'completion_tokens': 6}}
{'choices': [{'message': {'role': 'assistant', 'content': '{"result": "No"}'}}], 'usage': {'prompt_tokens': 1250, 'completion_tokens': 6}}
{'choices': [{'message': {'role': 'assistant', 'content': '{"result": "Yes"}'}}], 'usage': {'prompt_tokens': 1217, 'completion_tokens': 6}}

print(responses[0].usage.prompt_tokens)
->
610

Dataset persistence
--------------------
Expand Down Expand Up @@ -215,9 +232,7 @@ Here is an example of reading a CSV file where schema is heuristically derived f
.. code:: py

from datachain.lib.dc import DataChain

uri="gs://datachain-demo/chatbot-csv/"
csv_dataset = DataChain.from_csv(uri)
csv_dataset = DataChain.from_csv("gs://datachain-demo/chatbot-csv/")

print(csv_dataset.to_pandas())

Expand Down Expand Up @@ -264,17 +279,21 @@ To deal with this layout, we can take the following steps:

from datachain.lib.dc import DataChain

image_uri="gs://datachain-demo/coco2017/images/val/"
coco_json="gs://datachain-demo/coco2017/annotations_captions"

images = DataChain.from_storage(image_uri)
meta = DataChain.from_json(coco_json, jmespath = "images")
images = DataChain.from_storage("gs://datachain-demo/coco2017/images/val/")
meta = DataChain.from_json("gs://datachain-demo/coco2017/annotations_captions", jmespath = "images")

images_with_meta = images.merge(meta, on="file.name", right_on="images.file_name")


print(images_with_meta.limit(1).results())

.. code:: shell


Processed: 5000 rows [00:00, 15481.66 rows/s]
Processed: 1 rows [00:00, 1291.75 rows/s]
Processed: 1 rows [00:00, 4.70 rows/s]
Generated: 5000 rows [00:00, 27128.67 rows/s]
[(1, 2336066478558845549, '', 0, 'coco2017/images/val', '000000000139.jpg', 'CNvXoemj8IYDEAE=', '1719096046021595', 1, datetime.datetime(2024, 6, 22, 22, 40, 46, 70000, tzinfo=datetime.timezone.utc), 161811, '', '', None, 'gs://datachain-demo', 'gs://datachain-demo', 'coco2017/images/val', '000000000139.jpg', 161811, '1719096046021595', 'CNvXoemj8IYDEAE=', 1, datetime.datetime(1970, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), None, '', 4146, 6967063844996569113, 2, '000000000139.jpg', 'http://images.cocodataset.org/val2017/000000000139.jpg', 426, 640, '2013-11-21 01:34:01', 'http://farm9.staticflickr.com/8035/8024364858_9c41dc1666_z.jpg', 139)]

Passing data to training
------------------------
Expand Down
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