Use Chroma vector store

.gitignore

@@ -11,4 +11,7 @@ wheels/
 
 # MLflow
 mlruns/
-mlartifacts/
+mlartifacts/
+
+# ChromaDB
+chroma_db/

indexing.py

@@ -5,17 +5,56 @@ import langchain
 import langchain.chat_models
 import langchain.hub
 import langchain.text_splitter
+import langchain_chroma
 import langchain_core
 import langchain_core.documents
-import langchain_core.vectorstores
 import langchain_openai
-import langgraph
 import langgraph.graph
 import mlflow
 
 from hn import HackerNewsClient, Story
 from scrape import JinaScraper
 
+llm = langchain.chat_models.init_chat_model(
+    model="gpt-4.1-nano", model_provider="openai"
+)
+embeddings = langchain_openai.OpenAIEmbeddings(model="text-embedding-3-small")
+vector_store = langchain_chroma.Chroma(
+    collection_name="hn_stories",
+    embedding_function=embeddings,
+    persist_directory="./chroma_db",
+    create_collection_if_not_exists=True,
+)
+
+
+class State(TypedDict):
+    question: str
+    context: list[langchain_core.documents.Document]
+    answer: str
+
+
+# Define application steps
+def retrieve(state: State):
+    retrieved_docs = vector_store.similarity_search(state["question"], k=10)
+    return {"context": retrieved_docs}
+
+
+def generate(state: State):
+    docs_content = "\n\n".join(doc.page_content for doc in state["context"])
+    prompt = langchain.hub.pull("rlm/rag-prompt")
+    messages = prompt.invoke({"question": state["question"], "context": docs_content})
+    response = llm.invoke(messages)
+    return {"answer": response.content}
+
+
+def run_query(question: str):
+    graph_builder = langgraph.graph.StateGraph(State).add_sequence([retrieve, generate])
+    graph_builder.add_edge(langgraph.graph.START, "retrieve")
+    graph = graph_builder.compile()
+
+    response = graph.invoke({"question": question})
+    print(response["answer"])
+
 
 async def fetch_hn_top_stories(
     limit: int = 10,
@@ -57,14 +96,8 @@ async def main():
     mlflow.set_experiment("langchain-rag-hn")
     mlflow.langchain.autolog()
 
-    llm = langchain.chat_models.init_chat_model(
-        model="gpt-4o-mini", model_provider="openai"
-    )
-    embeddings = langchain_openai.OpenAIEmbeddings(model="text-embedding-3-small")
-    vector_store = langchain_core.vectorstores.InMemoryVectorStore(embeddings)
-
     # 1. Load
-    stories = await fetch_hn_top_stories(limit=20)
+    stories = await fetch_hn_top_stories(limit=3)
 
     # 2. Split
     splitter = langchain.text_splitter.RecursiveCharacterTextSplitter(
@@ -76,36 +109,8 @@ async def main():
     _ = vector_store.add_documents(all_splits)
 
     # 4. Query
-    prompt = langchain.hub.pull("rlm/rag-prompt")
-
-    # Define state for application
-    class State(TypedDict):
-        question: str
-        context: list[langchain_core.documents.Document]
-        answer: str
-
-    # Define application steps
-    def retrieve(state: State):
-        retrieved_docs = vector_store.similarity_search(state["question"], k=10)
-        return {"context": retrieved_docs}
-
-    def generate(state: State):
-        docs_content = "\n\n".join(doc.page_content for doc in state["context"])
-        messages = prompt.invoke(
-            {"question": state["question"], "context": docs_content}
-        )
-        response = llm.invoke(messages)
-        return {"answer": response.content}
-
-    # Compile application and test
-    graph_builder = langgraph.graph.StateGraph(State).add_sequence([retrieve, generate])
-    graph_builder.add_edge(langgraph.graph.START, "retrieve")
-    graph = graph_builder.compile()
-
-    response = graph.invoke(
-        {"question": "Are there any news stories related to AI and Machine Learning?"}
-    )
-    print(response["answer"])
+    question = "What are the top stories related to AI and Machine Learning right now?"
+    run_query(question)
 
 
 if __name__ == "__main__":

pyproject.toml

@@ -8,6 +8,7 @@ dependencies = [
     "hackernews>=2.0.0",
     "html2text>=2025.4.15",
     "httpx>=0.28.1",
+    "langchain-chroma>=0.2.4",
     "langchain[openai]>=0.3.26",
     "langgraph>=0.5.0",
     "mlflow>=3.1.1",

scrape.py

@@ -44,4 +44,7 @@ class JinaScraper(TextScraper):
     @override
     async def get_content(self, url: str) -> str:
         print(f"Fetching content from: {url}")
-        return await self._fetch_text(f"https://r.jina.ai/{url}")
+        try:
+            return await self._fetch_text(f"https://r.jina.ai/{url}")
+        except httpx.HTTPStatusError:
+            return ""