hello-world like langchain starter kit

README.md

@@ -24,7 +24,7 @@
 
 DataJourney demonstrates how organizations can effectively manage and utilize data by harnessing the power of open-source technologies. It's designed to help navigate the complex landscape of data tools, offering a structured approach to building **scalable**, and **reproducible** data workflows.
 
 Built on open-source principles, the framework guides users through essential steps—from **identifying** goals and **selecting tools** to **testing** and **customising** workflows. With its flexible, modular design, DataJourney can be tailored to individual needs, making it an invaluable toolkit for data professionals.
 
 ### 🧱 Design Philosophy (LEGO)
 Built with additive, subtractive capabilities glued with open source.
@@ -32,7 +32,7 @@
 
 - PO (Base): Static home(s) to keep it together `(GitHub)`
 - P1 (Tooling): Tooling, strings `(Powered by open source)`
 - P2 (Maintenance + Monitoring): Env, automations `(Pixi + GHA)`
 - P3 (Abstraction): Layer(s), CLI/task manager for users to interact with `(Pixi)`
 
 
@@ -46,15 +46,15 @@
 ✅ `GitHub actions` configured\
 ✅ `Vale.sh` configured at PR level\
 ✅ `Pre-commit hooks` configured for code linting/formatting\
+✨ [LangChain](https://python.langchain.com/) Basics & workflows\
 ✅ Environment management via [pixi](https://prefix.dev/)\
 ✅ Reading data from online sources using [intake](https://github.com/intake/intake)\
 ✅ Sample pipeline built using [Dagster](https://github.com/dagster-io/dagster)\
 ✅ Building Dashboard using [holoviews](https://holoviews.org/gallery/index.html) + [panel](https://panel.holoviz.org/reference/index.html)\
 ✅ Exploratory data analysis (EDA) using [mito](https://www.trymito.io/)\
 ✅ Web UI build on [Flask](https://flask.palletsprojects.com/en/3.0.x/) \
 ✅ Web UI re-done and expanded with [FastHTML](https://docs.fastht.ml/)\
 ✅ Leverage AI models to analyse data [GitHub AI models Beta](https://docs.github.com/en/github-models/prototyping-with-ai-models)
-✨ LangChain integration
 
 ### ☕️ Quickly getting started with DataJourney
 
@@ -62,8 +62,8 @@
 - Generate & add `GITHUB_TOKEN`, instructions [here](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens#creating-a-personal-access-token-classic)
   - Added requirement to run the LLM workflows
 - Switch directory `cd DataJourney`
 - Download pixi : [prefix.dev](https://prefix.dev/)
 - Activate env: `pixi shell`
 - Install DJ framework locally `pixi run DJ_package`
 - List all the tasks: `pixi task list`
 - Execute a task from the list: `pixi run <TASK>`
@@ -72,18 +72,18 @@
 ### 🏃🏽‍♀️ Active `tasks` under DJ
 
 - GIT_TOKEN_CHECK
 - DJ_package
 - DJ_pre_commit
 - DJ_dagster
 - DJ_fasthtml_app
 - DJ_flask_app
 - DJ_mito_app
 - DJ_panel_app
 - DJ_llm_analysis
 
 
 ### 🔌 About pre-commit-hooks and activating
 Just like the name suggests, pre-commit-hooks are designed to format the code based on PEP standards before committing. [More details](https://pre-commit.com/)
 
 ```shell
 pixi run DJ_pre_commit
@@ -95,12 +95,12 @@
 pixi run DJ_llm_analysis
 ```
 
 ### 🪼 Execute pre-configured Dagster pipeline
 
 ```shell
 pixi run DJ_dagster
 ```
 ![Dagit UI output](assets/pipeline/dagster_ui.png)
 
 ### 🐙 Panel app
 ```shell
@@ -108,11 +108,11 @@
 ```
 
 *NOTE:*
 The dashboard generated is exported into HTML format and saved as [stock_price_twilio_dashboard](analytics_framework%2Fdashboard%2Fstock_price_twilio_dashboard.html)
 
 ![Panel app output](assets/dashboard/panel_app_stock.png)
 
 ### 🐵 Mito
 
 To explore further visit [trymito.io](https://docs.trymito.io/)
 ```shell
@@ -122,8 +122,8 @@
 [//]: # (![mito output]&#40;assets/pipeline/mito_graph.png "Graph generated via mitosheet"&#41; ![mito output operation]&#40;assets/pipeline/mito_operations.png "Operations performed via mitosheet"&#41;)
 
 <div style="display: flex; justify-content: space-between;">
     <img src="assets/pipeline/mito_graph.png" alt="mito_output" width="400"/>
     <img src="assets/pipeline/mito_operations.png" alt="mito_output" width="400"/>
 </div>
 
 ### 🦋 Display all data sources present via web UI

analytics_framework/langchain/hello_world_lc.py

@@ -0,0 +1,39 @@
+import os
+from langchain.prompts import PromptTemplate
+from langchain.llms import OpenAI
+
+# NOTE: Using GitHub models here (add your preferred token attribute)
+
+token = os.environ["GITHUB_TOKEN"]
+endpoint = "https://models.inference.ai.azure.com"
+model_name = "gpt-4o-mini"
+
+llm = OpenAI(
+    base_url=endpoint,
+    api_key=token,
+    model=model_name,
+    temperature=0.7
+)
+
+# Step 2: Define a Prompt Template
+prompt = PromptTemplate(
+    input_variables=["question"],
+    template="You are a helpful assistant. Answer the following question in a clear and concise way: {question}"
+)
+
+
+# Step 3: Create a Function to Get AI Responses
+def get_answer(question):
+    # Format the prompt with the input question
+    formatted_prompt = prompt.format(question=question)
+
+    # Use the LLM to generate a response
+    response = llm(formatted_prompt)
+    return response
+
+
+# Example Usage
+if __name__ == "__main__":
+    question = "What is LangChain and why is it useful?"
+    answer = get_answer(question)
+    print("AI Speaks:", answer)

pixi.toml

@@ -55,5 +55,6 @@ DJ_panel_app = { cmd = "python stock_price_twilio_app.py", depends-on = "DJ_pack
 DJ_flask_app = { cmd = "python app.py", depends-on = "DJ_package", cwd = "analytics_framework/intake/web_ui_flask" }
 DJ_fasthtml_app = { cmd = "python app.py", depends-on = "DJ_package", cwd = "analytics_framework/intake/web_ui_fasthtml" }
 DJ_mito_app = { cmd = "jupyter notebook mito_exp.ipynb", depends-on = "DJ_package", cwd = "usage_guide"}
-DJ_llm_analysis_gpt_4o = {cmd = "python analyse_my_data__gpt_4o_mini.py", cwd= "analytics_framework/ai_modeling"}
-DJ_advance_llm_analysis = {cmd = "python advance_analysis_coral_bleeching.py", cwd= "analytics_framework/ai_modeling"}
+DJ_llm_analysis_gpt_4o = {cmd = "python analyse_my_data__gpt_4o_mini.py", cwd = "analytics_framework/ai_modeling"}
+DJ_advance_llm_analysis = {cmd = "python advance_analysis_coral_bleeching.py", cwd = "analytics_framework/ai_modeling"}
+DJ_hello_world_langchain = {cmd = "python hello_world_lc.py", cwd = "analytics_framework/langchain"}