Issue with /generate Command Producing Invalid Filenames #990
Labels
bug
Something isn't working
Comments
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A change would be needed here: Ensuring a file name is valid cross-platform is not trivial: https://stackoverflow.com/questions/295135/turn-a-string-into-a-valid-filename but at least removing colons seems like a good idea (as these can be also used for Jupyter drives). |
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I think removing colons, slashes, and backslashes would be a good start. Restricting filenames to just ASCII alphanumerics might be good, with some kind of fallback logic if the title has only non-ASCII characters (e.g., if it's in Chinese or Japanese). |
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I have the same problems with the "/generate" command.
All code content is generated in memory successfully, but fails to save to
the file system. For complete tutorials and long code documents, it
generates everything, but the save-to-file operation fails.
"I think removing colons, slashes, and backslashes would be a good
start."
I removed all colons, slashes, backslashes, commas, and all other
punctuation. None of that works for me. The longer the prompt the more
often it fails to save the generated content. I removed all punctuation
and shortened the prompts to trivial lengths. Certain words seem to
trigger the insertion of COLON characters in the filename, like "tutorial",
"lessons", "detailed explanations", and other longish prompts.
*MISSING DESIGN REQUIREMENT:* Give the user the option to EXPLICITLY
specify the output file name, and not let AI LLMs do that.
I tried hacking the code generate.py" from the chat_handlers folder for
JupyterLab-AI in the site-packages directory,
"
C:\ProgramData\Anaconda3\envs\JupyterLabAI_Tools\Lib\site-packages\jupyter_ai\chat_handlers\generate.py
"
I added this function to the beginning of the file:
import re
# Function to sanitize filenames
def sanitize_filename(filename):
# Replace invalid characters for Windows, MacOS, and Linux
return re.sub(r'[\\/:*?"<>|]', '_', filename)
and then added the following code (in blue) to the _generate_notebook()
function in generate.py.
async def _generate_notebook(self, prompt: str):
"""Generate a notebook and save to local disk"""
# create outline
outline = await generate_outline(prompt, llm=self.llm, verbose=True)
# Save the user input prompt, the description property is now LLM
generated.
outline["prompt"] = prompt
if self.llm.allows_concurrency:
# fill the outline concurrently
await afill_outline(outline, llm=self.llm, verbose=True)
else:
# fill outline
await fill_outline(outline, llm=self.llm, verbose=True)
# create and write the notebook to disk
notebook = create_notebook(outline)
final_path = os.path.join(self.output_dir,
sanitize_filename(outline["title"])[:250] + ".ipynb")
try:
# Truncate the sanitized title if it's too long
sanitized_title = sanitize_filename(outline["title"])[:250]
final_path = os.path.join(self.output_dir, sanitized_title + ".ipynb")
nbformat.write(notebook, final_path)
except Exception as e:
print(f"Failed to save the notebook to {final_path}: {str(e)}")
raise
* See the changed code below my signature in this message. *
The code change did not fix the problem. I restarted the Jupyter Server to
make sure the change was picked up from the back-end component for
JupyterLabAI.
But now I get a security error. I have not set up a full development
project. I simply added the code to convert non-valid characters in a
Windows, MacOS, or Linux filepath. I am on Windows so I am most concerned
about getting it working for Windows right now, but it needs to be done
right for all platforms, not just a hack for one.
[W 2024-09-10 13:16:50.411 ServerApp] 404 GET /api/ai/chats?token=[secret]
(5cc00c5dc7d04d669b976d2f569697c5@::1) 2.04ms referer=None
[W 2024-09-10 13:16:51.164 ServerApp] 404 GET
/api/ai/completion/inline?token=[secret] (5cc00c5dc7d04d669b976d2f569697c5@::1)
2.00ms referer=None
When I have time later to analyze this more fully, and fix it, I will
submit a pull request. If someone has time to test this with a full
development environment, I would appreciate it.
Thank you.
Best Regards,
Rich Lysakowski, Ph.D.
Data Scientist, AI & Analytics Engineer,
and Senior Business Systems Analyst
781-640-2048 mobile
On Tue, Sep 10, 2024 at 5:24 PM Jason Weill ***@***.***> wrote:
I think removing colons, slashes, and backslashes would be a good start.
Restricting filenames to just ASCII alphanumerics might be good, with some
kind of fallback logic if the title has only non-ASCII characters (e.g., if
it's in Chinese or Japanese).
—
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import asyncio
import os
import time
import traceback
from pathlib import Path
import re
import os
# Function to sanitize filenames
def sanitize_filename(filename):
# Replace invalid characters for Windows, MacOS, and Linux
return re.sub(r'[\\/:*?"<>|]', '_', filename)
from typing import Dict, List, Optional, Type
import nbformat
from jupyter_ai.chat_handlers import BaseChatHandler,
SlashCommandRoutingType
from jupyter_ai.models import HumanChatMessage
from jupyter_ai_magics.providers import BaseProvider
from langchain.chains import LLMChain
from langchain.llms import BaseLLM
from langchain.output_parsers import PydanticOutputParser
from langchain.pydantic_v1 import BaseModel
from langchain.schema.output_parser import BaseOutputParser
from langchain_core.prompts import PromptTemplate
class OutlineSection(BaseModel):
title: str
content: str
class Outline(BaseModel):
description: Optional[str] = None
sections: List[OutlineSection]
class NotebookOutlineChain(LLMChain):
"""Chain to generate a notebook outline, with section titles and
descriptions."""
@classmethod
def from_llm(
cls, llm: BaseLLM, parser: BaseOutputParser[Outline], verbose: bool
= False
) -> LLMChain:
task_creation_template = (
"You are an AI that creates a detailed content outline for a
Jupyter notebook on a given topic.\n"
"{format_instructions}\n"
"Here is a description of the notebook you will create an
outline for: {description}\n"
"Don't include an introduction or conclusion section in the
outline, focus only on description and sections that will need code.\n"
)
prompt = PromptTemplate(
template=task_creation_template,
input_variables=["description"],
partial_variables={"format_instructions":
parser.get_format_instructions()},
)
return cls(prompt=prompt, llm=llm, verbose=verbose)
async def generate_outline(description, llm=None, verbose=False):
"""Generate an outline of sections given a description of a notebook."""
parser = PydanticOutputParser(pydantic_object=Outline)
chain = NotebookOutlineChain.from_llm(llm=llm, parser=parser,
verbose=verbose)
outline = await chain.apredict(description=description)
outline = parser.parse(outline)
return outline.dict()
class CodeImproverChain(LLMChain):
"""Chain to improve source code."""
@classmethod
def from_llm(cls, llm: BaseLLM, verbose: bool = False) -> LLMChain:
task_creation_template = (
"Improve the following code and make sure it is valid. Make
sure to return the improved code only - don't give an explanation of the
improvements.\n"
"{code}"
)
prompt = PromptTemplate(
template=task_creation_template,
input_variables=[
"code",
],
)
return cls(prompt=prompt, llm=llm, verbose=verbose)
class NotebookSectionCodeChain(LLMChain):
"""Chain to generate source code for a notebook section."""
@classmethod
def from_llm(cls, llm: BaseLLM, verbose: bool = False) -> LLMChain:
task_creation_template = (
"You are an AI that writes code for a single section of a
Jupyter notebook.\n"
"Overall topic of the notebook: {description}\n"
"Title of the notebook section: {title}\n"
"Description of the notebok section: {content}\n"
"Given this information, write all the code for this section
and this section only."
" Your output should be valid code with inline comments.\n"
)
prompt = PromptTemplate(
template=task_creation_template,
input_variables=["description", "title", "content"],
)
return cls(prompt=prompt, llm=llm, verbose=verbose)
class NotebookSummaryChain(LLMChain):
"""Chain to generate a short summary of a notebook."""
@classmethod
def from_llm(cls, llm: BaseLLM, verbose: bool = False) -> LLMChain:
task_creation_template = (
"Create a markdown summary for a Jupyter notebook with the
following content."
" The summary should consist of a single paragraph.\n"
"Content:\n{content}"
)
prompt = PromptTemplate(
template=task_creation_template,
input_variables=[
"content",
],
)
return cls(prompt=prompt, llm=llm, verbose=verbose)
class NotebookTitleChain(LLMChain):
"""Chain to generate the title of a notebook."""
@classmethod
def from_llm(cls, llm: BaseLLM, verbose: bool = False) -> LLMChain:
task_creation_template = (
"Create a short, few word, descriptive title for a Jupyter
notebook with the following content.\n"
"Content:\n{content}\n"
"Don't return anything other than the title."
)
prompt = PromptTemplate(
template=task_creation_template,
input_variables=[
"content",
],
)
return cls(prompt=prompt, llm=llm, verbose=verbose)
async def improve_code(code, llm=None, verbose=False):
"""Improve source code using an LLM."""
chain = CodeImproverChain.from_llm(llm=llm, verbose=verbose)
improved_code = await chain.apredict(code=code)
improved_code = "\n".join(
[line for line in improved_code.split("\n") if not
line.startswith("```")]
)
return improved_code
async def generate_code(section, description, llm=None, verbose=False) ->
None:
"""
Function that accepts a section and adds code under the "code" key when
awaited.
"""
chain = NotebookSectionCodeChain.from_llm(llm=llm, verbose=verbose)
code = await chain.apredict(
description=description,
title=section["title"],
content=section["content"],
)
improved_code = await improve_code(code, llm=llm, verbose=verbose)
section["code"] = improved_code
async def generate_title(outline, llm=None, verbose: bool = False):
"""Generate a title of a notebook outline using an LLM."""
title_chain = NotebookTitleChain.from_llm(llm=llm, verbose=verbose)
title = await title_chain.apredict(content=outline)
title = title.strip()
title = title.strip("'\"")
outline["title"] = title
async def generate_summary(outline, llm=None, verbose: bool = False):
"""Generate a summary of a notebook using an LLM."""
summary_chain = NotebookSummaryChain.from_llm(llm=llm, verbose=verbose)
summary = await summary_chain.apredict(content=outline)
outline["summary"] = summary
async def fill_outline(outline, llm, verbose=False):
"""Generate title and content of a notebook sections using an LLM."""
shared_kwargs = {"outline": outline, "llm": llm, "verbose": verbose}
await generate_title(**shared_kwargs)
await generate_summary(**shared_kwargs)
for section in outline["sections"]:
await generate_code(section, outline["description"], llm=llm,
verbose=verbose)
async def afill_outline(outline, llm, verbose=False):
"""Concurrently generate title and content of notebook sections using
an LLM."""
shared_kwargs = {"outline": outline, "llm": llm, "verbose": verbose}
all_coros = []
all_coros.append(generate_title(**shared_kwargs))
all_coros.append(generate_summary(**shared_kwargs))
for section in outline["sections"]:
all_coros.append(
generate_code(section, outline["description"], llm=llm,
verbose=verbose)
)
await asyncio.gather(*all_coros)
def create_notebook(outline):
"""Create an nbformat Notebook object for a notebook outline."""
nbf = nbformat.v4
nb = nbf.new_notebook()
nb["cells"].append(nbf.new_markdown_cell("# " + outline["title"]))
nb["cells"].append(nbf.new_markdown_cell("## Introduction"))
disclaimer = f"This notebook was created by [Jupyter AI](
https:/jupyterlab/jupyter-ai) with the following prompt:\n\n>
{outline['prompt']}"
nb["cells"].append(nbf.new_markdown_cell(disclaimer))
nb["cells"].append(nbf.new_markdown_cell(outline["summary"]))
for section in outline["sections"][1:]:
nb["cells"].append(nbf.new_markdown_cell("## " + section["title"]))
for code_block in section["code"].split("\n\n"):
nb["cells"].append(nbf.new_code_cell(code_block))
return nb
class GenerateChatHandler(BaseChatHandler):
id = "generate"
name = "Generate Notebook"
help = "Generate a Jupyter notebook from a text prompt"
routing_type = SlashCommandRoutingType(slash_id="generate")
uses_llm = True
def __init__(self, log_dir: Optional[str], *args, **kwargs):
super().__init__(*args, **kwargs)
self.log_dir = Path(log_dir) if log_dir else None
self.llm = None
def create_llm_chain(
self, provider: Type[BaseProvider], provider_params: Dict[str, str]
):
unified_parameters = {
**provider_params,
**(self.get_model_parameters(provider, provider_params)),
}
llm = provider(**unified_parameters)
self.llm = llm
return llm
async def _generate_notebook(self, prompt: str):
"""Generate a notebook and save to local disk"""
# create outline
outline = await generate_outline(prompt, llm=self.llm, verbose=True)
# Save the user input prompt, the description property is now LLM
generated.
outline["prompt"] = prompt
if self.llm.allows_concurrency:
# fill the outline concurrently
await afill_outline(outline, llm=self.llm, verbose=True)
else:
# fill outline
await fill_outline(outline, llm=self.llm, verbose=True)
# create and write the notebook to disk
notebook = create_notebook(outline)
final_path = os.path.join(self.output_dir,
sanitize_filename(outline["title"])[:250] + ".ipynb")
try:
# Truncate the sanitized title if it's too long
sanitized_title = sanitize_filename(outline["title"])[:250]
final_path = os.path.join(self.output_dir, sanitized_title + ".ipynb")
nbformat.write(notebook, final_path)
except Exception as e:
print(f"Failed to save the notebook to {final_path}: {str(e)}")
raise
return final_path
async def process_message(self, message: HumanChatMessage):
self.get_llm_chain()
# first send a verification message to user
response = "👍 Great, I will get started on your notebook. It may
take a few minutes, but I will reply here when the notebook is ready. In
the meantime, you can continue to ask me other questions."
self.reply(response, message)
final_path = await self._generate_notebook(prompt=message.body)
response = f"""🎉 I have created your notebook and saved it to the
location {final_path}. I am still learning how to create notebooks, so
please review all code before running it."""
self.reply(response, message)
async def handle_exc(self, e: Exception, message: HumanChatMessage):
timestamp = time.strftime("%Y-%m-%d-%H.%M.%S")
default_log_dir = Path(self.output_dir) / "jupyter-ai-logs"
log_dir = self.log_dir or default_log_dir
log_dir.mkdir(parents=True, exist_ok=True)
log_path = log_dir / f"generate-{timestamp}.log"
with log_path.open("w") as log:
traceback.print_exc(file=log)
response = f"An error occurred while generating the notebook. The
error details have been saved to `./{log_path}`.\n\nTry running `/generate`
again, as some language models require multiple attempts before a notebook
is generated."
self.reply(response, message)
… Message ID: ***@***.***>
|
As a simple and deterministic fix, can we just add an option to the /generate command? I am thinking it could be something like one of these two suggestions below:
This is to let the user provide an explicit "notebook_filename" for the NB to override the AI-generated filename. I don't know how to implement this myself yet, because I don't understand the code for the slash commands implementation yet. |
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Description
I've been using the /generate command to create Jupyter notebooks from text prompts, but it seems to be generating filenames that contain colons (:). This is causing issues, especially on Windows systems where colons are not allowed in filenames. Could you please provide a fix or a workaround to prevent colons from being included in the generated filenames?
Reproduce
This issue is random.
There are 2 examples in this screenshot.. here only a portion of the file name (probably before the colon) is displayed.
Here are some examples of prompts and file names that are rendered:
prompt:
/generate how to format markdown cells in jupyter notebooks
result:
I have created your notebook and saved it to the location C:\Users\steri\Desktop\GitHub\JupyterLabAI\Mastering Markdown in Jupyter Notebooks: A Comprehensive Guide.ipynb
prompt:
/generate formatting markdown cells in jupyter notebooks
result:
I have created your notebook and saved it to the location C:\Users\steri\Desktop\GitHub\JupyterLabAI\Markdown Formatting in Jupyter Notebooks: A Comprehensive Guide.ipynb
prompt:
/generate formatting markdown cells in jupyter notebooks. name this notebook markdown.ipynb
result: SUCCESS! Third time was a charm!
I have created your notebook and saved it to the location C:\Users\steri\Desktop\GitHub\JupyterLabAI\Mastering Markdown in Jupyter Notebooks.ipynb
prompt:
/generate using Langchain Extraction via the Langchain REST API with with detailed code examples and detailed explanations
result:
I have created your notebook and saved it to the location C:\Users\steri\Desktop\GitHub\JupyterLabAI\Langchain Text Extraction: API Integration & Advanced Techniques.ipynb
prompt:
/generate Langchain REST API Langchain Extraction
result: SUCCESS!
I have created your notebook and saved it to the location C:\Users\steri\Desktop\GitHub\JupyterLabAI\Extracting Data from REST APIs with Langchain in Python.ipynb
I'll paste the details from the server below..
Expected behavior
I'm expecting files to be generated without a colon in the name. Every jupyter notebook file generated without a colon in the name is usable.
Context
Operating System and version: Microsoft Windows 11 Pro 10.0.22621 Build 22621
Browser and version: Chrome Version 128.0.6613.113
JupyterLab version: Version 4.2.5
Troubleshoot Output
Command Line Output
Prompt after formatting: Create a markdown summary for a Jupyter notebook with the following content. The summary should consist of a single paragraph. Content: {'description': 'A Jupyter notebook focused on generating text using Langchain Extraction via the Langchain REST API with detailed code examples and explanations.', 'sections': [{'title': 'Setting Up the Environment', 'content': 'In this section, we will install the necessary libraries and set up the environment for using Langchain Extraction via the Langchain REST API.\n\n```python\n# Install necessary libraries\n!pip install requests langchain\n\n# Import libraries\nimport requests\nfrom langchain import LangchainClient\n```'}, {'title': 'Introduction to Langchain Extraction', 'content': "This section provides a brief introduction to Langchain and its text extraction capabilities. We will also cover the basics of interacting with the Langchain REST API.\n\n```python\n# Initialize the Langchain client\nclient = LangchainClient(api_key='your_api_key')\n\n# Basic API endpoint information\napi_endpoint = 'https://api.langchain.com/extract'\n```"}, {'title': 'Authenticating with Langchain REST API', 'content': "Learn how to authenticate with the Langchain REST API using your API key. This is a crucial step before making any API calls.\n\n```python\n# Set up headers for authentication\nheaders = {\n 'Authorization': f'Bearer {client.api_key}',\n 'Content-Type': 'application/json'\n}\n\n# Verify authentication\nresponse = requests.get(api_endpoint, headers=headers)\nif response.status_code == 200:\n print('Authentication successful')\nelse:\n print('Authentication failed')\n```"}, {'title': 'Extracting Text from Documents', 'content': "In this section, we will provide a detailed example of extracting text from a document using the Langchain REST API. We will cover the necessary API call and how to handle the response.\n\n```python\n# Define the document URL or path\ndocument_url = 'https://example.com/sample.pdf'\n\n# API request payload\npayload = {\n 'url': document_url,\n 'extract_options': {\n 'format': 'text'\n }\n}\n\n# Make the API request\nresponse = requests.post(api_endpoint, headers=headers, json=payload)\n\n# Handle the response\nif response.status_code == 200:\n extracted_text = response.json().get('text', '')\n print('Extracted Text:', extracted_text)\nelse:\n print('Failed to extract text')\n```"}, {'title': 'Handling API Errors', 'content': "Learn how to gracefully handle errors when interacting with the Langchain REST API. This section includes examples of common error scenarios and how to manage them in your code.\n\n```python\n# Example function to handle API errors\ndef handle_api_errors(response):\n if response.status_code == 400:\n print('Bad Request: ', response.json().get('message', 'Unknown error'))\n elif response.status_code == 401:\n print('Unauthorized: Invalid API key')\n elif response.status_code == 403:\n print('Forbidden: Access denied')\n elif response.status_code == 404:\n print('Not Found: Invalid endpoint or resource')\n elif response.status_code == 500:\n print('Server Error: Try again later')\n else:\n print('Unexpected Error: ', response.status_code)\n\n# Example usage\nresponse = requests.post(api_endpoint, headers=headers, json=payload)\nhandle_api_errors(response)\n```"}, {'title': 'Advanced Extraction Techniques', 'content': "Explore advanced text extraction techniques available through the Langchain REST API. This includes extracting structured data and working with different document formats.\n\n```python\n# Advanced extraction options\nextract_options = {\n 'format': 'structured',\n 'elements': ['tables', 'figures', 'references']\n}\n\n# Update payload with advanced options\npayload['extract_options'] = extract_options\n\n# Make the advanced extraction request\nresponse = requests.post(api_endpoint, headers=headers, json=payload)\n\n# Handle the response\nif response.status_code == 200:\n extracted_data = response.json()\n print('Extracted Structured Data:', extracted_data)\nelse:\n handle_api_errors(response)\n```"}, {'title': 'Integrating with Other Tools', 'content': "Demonstrate how to integrate the extracted text and data with other tools or workflows. This section includes examples of exporting the data to different formats and using it in further processing.\n\n```python\n# Example: Exporting extracted text to a file\nwith open('extracted_text.txt', 'w') as file:\n file.write(extracted_text)\n\n# Example: Using extracted data in further processing\nimport pandas as pd\n\n# Convert structured data to DataFrame\nif 'tables' in extracted_data:\n tables = extracted_data['tables']\n for table in tables:\n df = pd.DataFrame(table['data'], columns=table['headers'])\n print(df)\n```"}], 'prompt': '/generate using Langchain Extraction via the Langchain REST API with with detailed code examples and detailed explanations'}Prompt after formatting:
You are an AI that writes code for a single section of a Jupyter notebook.
Overall topic of the notebook: A Jupyter notebook focused on generating text using Langchain Extraction via the Langchain REST API with detailed code examples and explanations.
Title of the notebook section: Integrating with Other Tools
Description of the notebok section: Demonstrate how to integrate the extracted text and data with other tools or workflows. This section includes examples of exporting the data to different formats and using it in further processing.
Given this information, write all the code for this section and this section only. Your output should be valid code with inline comments.
Prompt after formatting:
You are an AI that writes code for a single section of a Jupyter notebook.
Overall topic of the notebook: A Jupyter notebook focused on generating text using Langchain Extraction via the Langchain REST API with detailed code examples and explanations.
Title of the notebook section: Authenticating with Langchain REST API
Description of the notebok section: Learn how to authenticate with the Langchain REST API using your API key. This is a crucial step before making any API calls.
Given this information, write all the code for this section and this section only. Your output should be valid code with inline comments.
Prompt after formatting:
You are an AI that writes code for a single section of a Jupyter notebook.
Overall topic of the notebook: A Jupyter notebook focused on generating text using Langchain Extraction via the Langchain REST API with detailed code examples and explanations.
Title of the notebook section: Handling API Errors
Description of the notebok section: Learn how to gracefully handle errors when interacting with the Langchain REST API. This section includes examples of common error scenarios and how to manage them in your code.
Given this information, write all the code for this section and this section only. Your output should be valid code with inline comments.
Prompt after formatting:
You are an AI that writes code for a single section of a Jupyter notebook.
Overall topic of the notebook: A Jupyter notebook focused on generating text using Langchain Extraction via the Langchain REST API with detailed code examples and explanations.
Title of the notebook section: Extracting Text from Documents
Description of the notebok section: In this section, we will provide a detailed example of extracting text from a document using the Langchain REST API. We will cover the necessary API call and how to handle the response.
Given this information, write all the code for this section and this section only. Your output should be valid code with inline comments.
Prompt after formatting:
You are an AI that writes code for a single section of a Jupyter notebook.
Overall topic of the notebook: A Jupyter notebook focused on generating text using Langchain Extraction via the Langchain REST API with detailed code examples and explanations.
Title of the notebook section: Advanced Extraction Techniques
Description of the notebok section: Explore advanced text extraction techniques available through the Langchain REST API. This includes extracting structured data and working with different document formats.
Given this information, write all the code for this section and this section only. Your output should be valid code with inline comments.
Prompt after formatting:
You are an AI that writes code for a single section of a Jupyter notebook.
Overall topic of the notebook: A Jupyter notebook focused on generating text using Langchain Extraction via the Langchain REST API with detailed code examples and explanations.
Title of the notebook section: Introduction to Langchain Extraction
Description of the notebok section: This section provides a brief introduction to Langchain and its text extraction capabilities. We will also cover the basics of interacting with the Langchain REST API.
Given this information, write all the code for this section and this section only. Your output should be valid code with inline comments.
This code snippet demonstrates how to authenticate with the Langchain REST API using your API key. Replace the placeholder values with your actual API key and the correct API endpoint to verify the authentication. The code sends a GET request to the specified endpoint and checks the status code to confirm whether the authentication was successful.
In this code, we have defined a
LangchainClientclass that helps to interact with the Langchain REST API. Theextractmethod sends a POST request to the API endpoint with the given text and returns the extracted information. We also provide an example demonstrating how to use the client to extract information from a sample text.This code defines a function
handle_api_errorsthat takes the response object from an API request and prints appropriate error messages based on the HTTP status code. The example usage demonstrates how to make a POST request to the Langchain REST API, handle potential errors, and print the generated text if the request is successful.This section provides functions and examples for exporting the extracted text and data to files and converting structured data tables into pandas DataFrames for further processing. The functions are modular and reusable, facilitating easy integration into broader workflows.
As an example, for the schema {"properties": {"foo": {"title": "Foo", "description": "a list of strings", "type": "array", "items": {"type": "string"}}}, "required": ["foo"]}
the object {"foo": ["bar", "baz"]} is a well-formatted instance of the schema. The object {"properties": {"foo": ["bar", "baz"]}} is not well-formatted.
Here is the output schema:
Here is a description of the notebook you will create an outline for: /generate Langchain REST API Langchain Extraction
Don't include an introduction or conclusion section in the outline, focus only on description and sections that will need code.
Prompt after formatting:
Create a short, few word, descriptive title for a Jupyter notebook with the following content.
Content:
{'description': 'This Jupyter notebook covers how to use Langchain to extract data from a REST API. It includes sections on setting up the environment, making API requests, processing the API response, and extracting relevant information using Langchain.', 'sections': [{'title': 'Environment Setup', 'content': 'In this section, we will install and import the necessary libraries for making REST API requests and using Langchain.'}, {'title': 'Making REST API Requests', 'content': "This section covers how to construct and send a REST API request using Python. We will use the 'requests' library to interact with the API."}, {'title': 'Processing API Response', 'content': 'Here, we will process the JSON response from the API. This includes parsing the JSON data and handling potential errors.'}, {'title': 'Introduction to Langchain', 'content': 'This section provides an overview of Langchain, its features, and how it can be used for data extraction from text.'}, {'title': 'Using Langchain for Data Extraction', 'content': 'In this section, we will demonstrate how to use Langchain to extract relevant information from the API response. This includes setting up Langchain and writing extraction rules.'}, {'title': 'Example Use Case', 'content': 'Here, we will provide a real-world example of using Langchain to extract specific data points from an API response. This will include a step-by-step walkthrough.'}], 'prompt': '/generate Langchain REST API Langchain Extraction'}
Don't return anything other than the title.
Prompt after formatting:
You are an AI that writes code for a single section of a Jupyter notebook.
Overall topic of the notebook: This Jupyter notebook covers how to use Langchain to extract data from a REST API. It includes sections on setting up the environment, making API requests, processing the API response, and extracting relevant information using Langchain.
Title of the notebook section: Example Use Case
Description of the notebok section: Here, we will provide a real-world example of using Langchain to extract specific data points from an API response. This will include a step-by-step walkthrough.
Given this information, write all the code for this section and this section only. Your output should be valid code with inline comments.
Prompt after formatting:
You are an AI that writes code for a single section of a Jupyter notebook.
Overall topic of the notebook: This Jupyter notebook covers how to use Langchain to extract data from a REST API. It includes sections on setting up the environment, making API requests, processing the API response, and extracting relevant information using Langchain.
Title of the notebook section: Processing API Response
Description of the notebok section: Here, we will process the JSON response from the API. This includes parsing the JSON data and handling potential errors.
Given this information, write all the code for this section and this section only. Your output should be valid code with inline comments.
Prompt after formatting:
You are an AI that writes code for a single section of a Jupyter notebook.
Overall topic of the notebook: This Jupyter notebook covers how to use Langchain to extract data from a REST API. It includes sections on setting up the environment, making API requests, processing the API response, and extracting relevant information using Langchain.
Title of the notebook section: Using Langchain for Data Extraction
Description of the notebok section: In this section, we will demonstrate how to use Langchain to extract relevant information from the API response. This includes setting up Langchain and writing extraction rules.
Given this information, write all the code for this section and this section only. Your output should be valid code with inline comments.
Prompt after formatting:
You are an AI that writes code for a single section of a Jupyter notebook.
Overall topic of the notebook: This Jupyter notebook covers how to use Langchain to extract data from a REST API. It includes sections on setting up the environment, making API requests, processing the API response, and extracting relevant information using Langchain.
Title of the notebook section: Environment Setup
Description of the notebok section: In this section, we will install and import the necessary libraries for making REST API requests and using Langchain.
Given this information, write all the code for this section and this section only. Your output should be valid code with inline comments.
Prompt after formatting:
Create a markdown summary for a Jupyter notebook with the following content. The summary should consist of a single paragraph.
Content:
{'description': 'This Jupyter notebook covers how to use Langchain to extract data from a REST API. It includes sections on setting up the environment, making API requests, processing the API response, and extracting relevant information using Langchain.', 'sections': [{'title': 'Environment Setup', 'content': 'In this section, we will install and import the necessary libraries for making REST API requests and using Langchain.'}, {'title': 'Making REST API Requests', 'content': "This section covers how to construct and send a REST API request using Python. We will use the 'requests' library to interact with the API."}, {'title': 'Processing API Response', 'content': 'Here, we will process the JSON response from the API. This includes parsing the JSON data and handling potential errors.'}, {'title': 'Introduction to Langchain', 'content': 'This section provides an overview of Langchain, its features, and how it can be used for data extraction from text.'}, {'title': 'Using Langchain for Data Extraction', 'content': 'In this section, we will demonstrate how to use Langchain to extract relevant information from the API response. This includes setting up Langchain and writing extraction rules.'}, {'title': 'Example Use Case', 'content': 'Here, we will provide a real-world example of using Langchain to extract specific data points from an API response. This will include a step-by-step walkthrough.'}], 'prompt': '/generate Langchain REST API Langchain Extraction'}
Prompt after formatting:
You are an AI that writes code for a single section of a Jupyter notebook.
Overall topic of the notebook: This Jupyter notebook covers how to use Langchain to extract data from a REST API. It includes sections on setting up the environment, making API requests, processing the API response, and extracting relevant information using Langchain.
Title of the notebook section: Introduction to Langchain
Description of the notebok section: This section provides an overview of Langchain, its features, and how it can be used for data extraction from text.
Given this information, write all the code for this section and this section only. Your output should be valid code with inline comments.
This code provides a step-by-step walkthrough of a real-world example using Langchain to extract specific data points from an API response. It starts by setting up the API endpoint and making a request, then processes the response and uses Langchain to extract the desired information.
This code snippet demonstrates how to set up Langchain to extract specific data fields from an API response. It installs necessary packages, defines extraction rules, initializes the Langchain with these rules, and processes a sample API response to extract and display relevant information.
In this section, we have defined a function
process_api_responseto handle the JSON response from the API. The function includes error handling for JSON decoding issues and missing keys in the response. The relevant information is extracted and printed out.This code provides an overview of Langchain, its features, and a simple example of how to use its
TextExtractorandTextParserto extract and parse data from text. This serves as an introductory section to get users familiarized with the basics of Langchain before moving on to more advanced topics in the subsequent sections.Browser Output
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