feat: Reworked chunking and retrieval logic to operate on entire stories instead of chunks.

.gitignore

@@ -13,5 +13,5 @@ wheels/
 mlruns/
 mlartifacts/
 
-# ChromaDB
+# Weaviate vector store
-chroma_db/
+weaviate/

classify.py

@@ -0,0 +1,21 @@
+from langchain_core.documents import Document
+from langchain_core.language_models import BaseChatModel
+
+
+def categorize(doc: Document, llm: BaseChatModel) -> set[str]:
+    # Create a prompt for category extraction
+    prompt = f"""
+    Extract up to 3 relevant categories from the following document.
+    Return only the category names as a list of JSON strings.
+
+    If you cannot find any relevant categories, return an empty list.
+
+    Title: {doc.metadata.get('title', 'No title')}
+    Content: {doc.page_content}...
+
+    Categories:"""
+
+    # Get response from LLM
+    result = llm.with_structured_output(method="json_mode").invoke(prompt)
+    categories = result.get("categories", [])
+    return set(categories)

compose.yml

@@ -0,0 +1,25 @@
+services:
+  weaviate:
+    command:
+      - --host
+      - 0.0.0.0
+      - --port
+      - "8080"
+      - --scheme
+      - http
+    image: cr.weaviate.io/semitechnologies/weaviate:1.31.4
+    ports:
+      - 8080:8080
+      - 50051:50051
+    volumes:
+      - ./weaviate:/var/lib/weaviate
+    restart: on-failure
+    environment:
+      QUERY_DEFAULTS_LIMIT: 25
+      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: "true"
+      PERSISTENCE_DATA_PATH: "/var/lib/weaviate"
+      ENABLE_API_BASED_MODULES: "true"
+      ENABLE_MODULES: "text2vec-openai,generative-openai"
+      CLUSTER_HOSTNAME: "node1"
+      OPENAI_APIKEY: ${OPENAI_API_KEY}
+      DISABLE_TELEMETRY: "true"

hn.py

@@ -1,3 +1,4 @@
+import asyncio
 from datetime import datetime
 
 import dateutil.parser
@@ -16,16 +17,16 @@ class Story(BaseModel):
 
 
 class HackerNewsClient:
-    def __init__(self, client: httpx.Client | None = None):
+    def __init__(self, client: httpx.AsyncClient | None = None):
         base_url = "https://hn.algolia.com/api/v1"
         if client:
             client.base_url = base_url
             self._client = client
         else:
-            self._client = httpx.Client(base_url=base_url)
+            self._client = httpx.AsyncClient(base_url=base_url)
 
-    def get_top_stories(self, limit: int = 10) -> list[Story]:
+    async def get_top_stories(self, limit: int = 10) -> list[Story]:
-        resp = self._client.get(
+        resp = await self._client.get(
             "search",
             params={"tags": "front_page", "hitsPerPage": limit, "page": 0},
         )
@@ -40,7 +41,24 @@ class HackerNewsClient:
             for hit in resp.json().get("hits", [])
         ]
 
-    def get_item(self, item_id):
+    async def get_item(self, item_id):
-        resp = self._client.get(f"items/{item_id}")
+        resp = await self._client.get(f"items/{item_id}")
         resp.raise_for_status()
         return resp.json()
+
+    async def close(self):
+        """Close the underlying HTTP client."""
+
+        if self._client and not self._client.is_closed:
+            await self._client.aclose()
+
+    def __del__(self):
+        """Ensure the HTTP client is closed when the object is deleted."""
+        try:
+            loop = asyncio.get_event_loop()
+            if loop.is_running():
+                loop.create_task(self.close())
+            else:
+                loop.run_until_complete(self.close())
+        except Exception:
+            pass

indexing.py

@@ -1,119 +1,242 @@
+import logging
 import os
-from typing import TypedDict
+from typing import Iterable, TypedDict
 
 import langchain
 import langchain.chat_models
-import langchain.hub
+import langchain.prompts
 import langchain.text_splitter
-import langchain_chroma
 import langchain_core
 import langchain_core.documents
 import langchain_openai
+import langchain_weaviate
 import langgraph.graph
-import mlflow
 
+import weaviate
 from hn import HackerNewsClient, Story
 from scrape import JinaScraper
 
 llm = langchain.chat_models.init_chat_model(
-    model="gpt-4.1-nano", model_provider="openai"
+    model="gpt-4o-mini", model_provider="openai"
 )
-embeddings = langchain_openai.OpenAIEmbeddings(model="text-embedding-3-small")
+embeddings = langchain_openai.OpenAIEmbeddings(model="text-embedding-3-large")
-vector_store = langchain_chroma.Chroma(
+
-    collection_name="hn_stories",
+weaviate_client = weaviate.connect_to_local()
-    embedding_function=embeddings,
+vector_store = langchain_weaviate.WeaviateVectorStore(
-    persist_directory="./chroma_db",
+    weaviate_client,
-    create_collection_if_not_exists=True,
+    index_name="hn_stories",
+    text_key="page_content",
+    embedding=embeddings,
 )
 
 
 class State(TypedDict):
-    question: str
+    preferences: Iterable[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)
+    # Search for relevant documents
-    return {"context": retrieved_docs}
+    retrieved_docs = vector_store.similarity_search(
+        "Categories: " + ", ".join(state["preferences"]), k=10
+    )
+
+    # If you're using chunks, group them back into complete stories
+    story_groups = {}
+    for doc in retrieved_docs:
+        story_id = doc.metadata.get("story_id")
+        if story_id not in story_groups:
+            story_groups[story_id] = []
+        story_groups[story_id].append(doc)
+
+    # Reconstruct complete stories or use the best chunk per story
+    complete_stories = []
+    for story_id, chunks in story_groups.items():
+        if len(chunks) == 1:
+            complete_stories.append(chunks[0])
+        else:
+            # Combine chunks back into complete story
+            combined_content = "\n\n".join(
+                chunk.page_content
+                for chunk in sorted(
+                    chunks, key=lambda x: x.metadata.get("chunk_index", 0)
+                )
+            )
+            complete_story = langchain_core.documents.Document(
+                page_content=combined_content,
+                metadata=chunks[0].metadata,  # Use metadata from first chunk
+            )
+            complete_stories.append(complete_story)
+
+    return {"context": complete_stories[:5]}  # Limit to top 5 stories
 
 
 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})
+    prompt = langchain.prompts.PromptTemplate(
+        input_variables=["preferences", "context"],
+        template=(
+            "You are a helpful assistant that can provide updates on technology topics based on the topics a user has expressed interest in and additional context.\n\n"
+            "Please respond in Markdown format and group your answers based on the categories of the items in the context.\n"
+            "If applicable, add hyperlinks to the original source as part of the headline for each story.\n"
+            "Limit your summaries to approximately 100 words per item.\n\n"
+            "Preferences: {preferences}\n\n"
+            "Context:\n{context}\n\n"
+            "Answer:"
+        ),
+    )
+
+    messages = prompt.invoke(
+        {"preferences": state["preferences"], "context": docs_content}
+    )
     response = llm.invoke(messages)
     return {"answer": response.content}
 
 
-def run_query(question: str):
+def run_query(preferences: Iterable[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})
+    response = graph.invoke(State(preferences=preferences, context=[], answer=""))
     print(response["answer"])
 
 
+def get_existing_story_ids() -> set[str]:
+    """Get the IDs of stories that already exist in the vector store."""
+    try:
+        collection = vector_store._collection
+        existing_ids = set()
+        for doc in collection.iterator():
+            story_id = doc.properties.get("story_id")
+            if story_id:
+                existing_ids.add(story_id)
+        return existing_ids
+    except Exception:
+        logging.warning("Could not retrieve existing story IDs", exc_info=True)
+        return set()
+
+
 async def fetch_hn_top_stories(
     limit: int = 10,
 ) -> list[langchain_core.documents.Document]:
     hn = HackerNewsClient()
-    stories = hn.get_top_stories(limit=limit)
+    stories = await hn.get_top_stories(limit=limit)
+
+    # Get existing story IDs to avoid re-fetching
+    existing_ids = get_existing_story_ids()
+    logging.info(f"Existing story IDs: {len(existing_ids)} found in vector store")
+
+    new_stories = [story for story in stories if story.id not in existing_ids]
+
+    print(f"Found {len(stories)} top stories, {len(new_stories)} are new")
+
+    if not new_stories:
+        print("No new stories to fetch")
+        return []
 
     contents = {}
 
-    # Fetch content for each story asynchronously
+    # Fetch content for each new story asynchronously
     scraper = JinaScraper(os.getenv("JINA_API_KEY"))
 
     async def _fetch_content(story: Story) -> tuple[str, str]:
-        if not story.url:
+        try:
-            return story.id, story.title
+            if not story.url:
-        return story.id, await scraper.get_content(story.url)
+                return story.id, story.title
+            return story.id, await scraper.get_content(story.url)
+        except Exception as e:
+            logging.warning(f"Failed to fetch content for story {story.id}: {e}")
+            return story.id, story.title  # Fallback to title if content fetch fails
 
-    tasks = [_fetch_content(story) for story in stories]
+    tasks = [_fetch_content(story) for story in new_stories]
-    results = await asyncio.gather(*tasks)
+    results = await asyncio.gather(*tasks, return_exceptions=True)
-    contents = dict(results)
+
+    # Filter out exceptions and convert to dict
+    contents = {}
+    for result in results:
+        if isinstance(result, Exception):
+            logging.error(f"Task failed with exception: {result}")
+            continue
+        if isinstance(result, tuple) and len(result) == 2:
+            story_id, content = result
+            contents[story_id] = content
 
     documents = [
         langchain_core.documents.Document(
             page_content=contents[story.id],
             metadata={
-                "id": story.id,
+                "story_id": story.id,
                 "title": story.title,
                 "source": story.url,
                 "created_at": story.created_at.isoformat(),
             },
         )
-        for story in stories
+        for story in new_stories
     ]
     return documents
 
 
 async def main():
+    import mlflow
+
     mlflow.set_tracking_uri("http://localhost:5000")
     mlflow.set_experiment("langchain-rag-hn")
     mlflow.langchain.autolog()
 
-    # 1. Load
+    # 1. Load only new stories
-    stories = await fetch_hn_top_stories(limit=3)
+    new_stories = await fetch_hn_top_stories(limit=20)
 
-    # 2. Split
+    if new_stories:
-    splitter = langchain.text_splitter.RecursiveCharacterTextSplitter(
+        print(f"Processing {len(new_stories)} new stories")
-        chunk_size=1000, chunk_overlap=200
-    )
-    all_splits = splitter.split_documents(stories)
 
-    # 3. Store
+        # Categorize stories (optional)
-    _ = vector_store.add_documents(all_splits)
+        from classify import categorize
+
+        for story in new_stories:
+            categories = categorize(story, llm)
+            story.metadata["categories"] = list(categories)
+            print(f"Story ID {story.metadata["story_id"]} categorized as: {categories}")
+
+        # 2. Split
+        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
+                documents_to_store.append(story)
+            else:
+                # For very long articles, chunk but keep story metadata
+                splitter = langchain.text_splitter.RecursiveCharacterTextSplitter(
+                    chunk_size=2000,
+                    chunk_overlap=200,
+                    add_start_index=True,
+                )
+                chunks = splitter.split_documents([story])
+                # Add chunk info to metadata
+                for i, chunk in enumerate(chunks):
+                    chunk.metadata["chunk_index"] = i
+                    chunk.metadata["total_chunks"] = len(chunks)
+                documents_to_store.extend(chunks)
+
+        # 3. Store
+        _ = vector_store.add_documents(documents_to_store)
+        print(f"Added {len(documents_to_store)} documents to vector store")
+    else:
+        print("No new stories to process")
 
     # 4. Query
-    question = "What are the top stories related to AI and Machine Learning right now?"
+    preferences = ["Software Engineering", "Machine Learning", "Games"]
-    run_query(question)
+    run_query(preferences)
 
 
 if __name__ == "__main__":
     import asyncio
 
-    asyncio.run(main())
+    logging.basicConfig(level=logging.INFO)
+
+    try:
+        asyncio.run(main())
+    finally:
+        weaviate_client.close()

pyproject.toml

@@ -8,7 +8,7 @@ dependencies = [
     "hackernews>=2.0.0",
     "html2text>=2025.4.15",
     "httpx>=0.28.1",
-    "langchain-chroma>=0.2.4",
+    "langchain-weaviate>=0.0.5",
     "langchain[openai]>=0.3.26",
     "langgraph>=0.5.0",
     "mlflow>=3.1.1",

scrape.py

@@ -1,3 +1,5 @@
+import asyncio
+import logging
 from abc import ABC, abstractmethod
 from typing import override
 
@@ -6,17 +8,41 @@ import httpx
 
 class TextScraper(ABC):
     def __init__(self):
-        self._client = httpx.AsyncClient()
+        self._client = httpx.AsyncClient(timeout=httpx.Timeout(5.0))
 
     async def _fetch_text(self, url: str) -> str:
         """Fetch the raw HTML content from the URL."""
-        response = await self._client.get(url)
+        response = None
-        response.raise_for_status()
+        try:
-        return response.text
+            response = await self._client.get(url)
+            response.raise_for_status()
+            return response.text
+        except Exception:
+            logging.warning(f"Failed to fetch text from {url}", exc_info=True)
+            raise
+        finally:
+            if response:
+                await response.aclose()
 
     @abstractmethod
     async def get_content(self, url: str) -> str: ...
 
+    async def close(self):
+        """Close the underlying HTTP client."""
+        if self._client and not self._client.is_closed:
+            await self._client.aclose()
+
+    def __del__(self):
+        """Ensure the HTTP client is closed when the object is deleted."""
+        try:
+            loop = asyncio.get_event_loop()
+            if loop.is_running():
+                loop.create_task(self.close())
+            else:
+                loop.run_until_complete(self.close())
+        except Exception:
+            pass
+
 
 class Html2textScraper(TextScraper):
     @override
@@ -39,12 +65,13 @@ class JinaScraper(TextScraper):
     def __init__(self, api_key: str | None = None):
         super().__init__()
         if api_key:
-            self._client.headers.update({"Authorization": "Bearer {api_key}"})
+            self._client.headers.update({"Authorization": f"Bearer {api_key}"})
 
     @override
     async def get_content(self, url: str) -> str:
         print(f"Fetching content from: {url}")
         try:
             return await self._fetch_text(f"https://r.jina.ai/{url}")
-        except httpx.HTTPStatusError:
+        except Exception:
+            logging.warning(f"Failed to fetch content from {url}", exc_info=True)
             return ""