Add readme

README.md

@@ -0,0 +1,26 @@
+# LangChain RAG Demo
+
+This repository demonstrates how to use LangChain for a Retrieval-Augmented Generation (RAG) application.
+The code retrieves Hacker News front page stories, categorizes them, stores them in a vector store, and performs retrieval based on user preferences.
+
+## Getting Started
+
+1. Set the following environment variables:
+
+   - `OPENAI_API_KEY`: Your OpenAI API key for chat and embedding models.
+   - `JINA_AI_KEY`: Your [Jina AI Reader](https://jina.ai/reader/) key for text extraction.
+
+2. Start local Weaviate vector store instance:
+
+   ```bash
+   docker compose up -d
+   ```
+
+3. Run the RAG application:
+
+   ```bash
+   uv run python indexing.py
+   ```
+
+Adjust the constants in `indexing.py` to change the number of stories to fetch and the categories to use.
+You can optionally enable MLflow tracing by setting `ENABLE_MLFLOW_TRACING=True` there (make sure to run `mlflow server` first).

indexing.py

@@ -16,6 +16,11 @@ import weaviate
 from hn import HackerNewsClient, Story
 from scrape import JinaScraper
 
+NUM_STORIES = 20
+USER_PREFERENCES = []
+ENABLE_MLFLOW_TRACING = False  # Set to True if you want to use MLflow for tracing
+
+
 llm = langchain.chat_models.init_chat_model(
     model="gpt-4o-mini", model_provider="openai"
 )
@@ -180,14 +185,15 @@ async def fetch_hn_top_stories(
 
 
 async def main():
-    import mlflow
+    if ENABLE_MLFLOW_TRACING:
+        import mlflow
 
-    mlflow.set_tracking_uri("http://localhost:5000")
+        mlflow.set_tracking_uri("http://localhost:5000")
-    mlflow.set_experiment("langchain-rag-hn")
+        mlflow.set_experiment("langchain-rag-hn")
-    mlflow.langchain.autolog()
+        mlflow.langchain.autolog()
 
     # 1. Load only new stories
-    new_stories = await fetch_hn_top_stories(limit=20)
+    new_stories = await fetch_hn_top_stories(limit=NUM_STORIES)
 
     if new_stories:
         print(f"Processing {len(new_stories)} new stories")
@@ -204,7 +210,7 @@ async def main():
         documents_to_store = []
         for story in new_stories:
             # If article is short enough, store as-is
-            if len(story.page_content) <= 3000:  # Adjust threshold as needed
+            if len(story.page_content) <= 3000:
                 documents_to_store.append(story)
             else:
                 # For very long articles, chunk but keep story metadata
@@ -227,8 +233,7 @@ async def main():
         print("No new stories to process")
 
     # 4. Query
-    preferences = ["Software Engineering", "Machine Learning", "Games"]
+    run_query(USER_PREFERENCES)
-    run_query(preferences)
 
 
 if __name__ == "__main__":