Interactive Q&A with Vector Search

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*Italic text* for subtle emphasis
`Code snippets` for technical terms

- Bullet points for lists
- Organized information

## Headings for structure

> Block quotes for citations

[Links to sources](https://example.com)

const messagesContainerRef = useRef<HTMLDivElement>(null);

useEffect(() => {
  if (messagesContainerRef.current) {
    messagesContainerRef.current.scrollTop =
      messagesContainerRef.current.scrollHeight;
  }
}, [messages, isLoading]);

class QdrantSourceStore:
    def __init__(
        self,
        qdrant_url: str = "localhost",
        collection_name: str = "sources",
        embedding_model: str = "text-embedding-3-small",
        chunk_size: int = 512,
        chunk_overlap: int = 50,
    ):
        self.qdrant_client = AsyncQdrantClient(
            url=qdrant_url,
            api_key=qdrant_api_key,
            prefer_grpc=True,
        )
        self.openai_client = AsyncOpenAI(api_key=openai_api_key)

def _chunk_text(self, text: str) -> List[str]:
    """Split text into chunks based on chunk size and overlap."""
    tokens = text.split()
    chunk_starts = range(0, len(tokens), self.chunk_size - self.chunk_overlap)
    
    chunks = [
        " ".join(tokens[i:i + self.chunk_size])
        for i in chunk_starts
        if i + self.chunk_size <= len(tokens)
    ]
    
    # Handle remaining tokens
    if tokens[chunk_starts[-1]:]:
        chunks.append(" ".join(tokens[chunk_starts[-1]:]))
    
    return chunks

async def search(
    self,
    query: str,
    notebook_id: Optional[str] = None,
    limit: int = 5,
) -> List[Dict[str, Any]]:
    """Search for sources based on query and notebook ID."""
    
    # Get query embedding
    query_embedding = await self._get_embedding(query)
    
    # Set up filter for notebook_id
    filter_param = None
    if notebook_id:
        filter_param = rest.Filter(
            must=[
                rest.FieldCondition(
                    key="notebook_id",
                    match=rest.MatchValue(value=notebook_id),
                )
            ]
        )
    
    # Search in Qdrant
    search_result = await self.qdrant_client.search(
        collection_name=self.collection_name,
        query_vector=query_embedding,
        limit=limit,
        query_filter=filter_param,
    )
    
    # Format and return results
    return [
        {
            "id": scored_point.id,
            "score": scored_point.score,
            "content_chunk": scored_point.payload.get("content_chunk"),
            "notebook_id": scored_point.payload.get("notebook_id"),
            "url": scored_point.payload.get("url"),
            "page_title": scored_point.payload.get("page_title"),
        }
        for scored_point in search_result
    ]

decipher_agent = Agent(
    role="Decipher",
    goal="Analyze and decode the user's questions to provide precise answers based on the relevant sources",
    backstory="""You're Decipher, an analytical assistant specialized in 
                 breaking down complex queries and providing clear, accurate 
                 responses drawn from available source materials. Created by 
                 Amit Wani. You have expertise in "{topic}".""",
    verbose=True,
    llm=llm,
)

answer_question_task = Task(
    description="""Answer the user's question based on the relevant sources 
                   and previous chat context (if any). Do not include any other 
                   text than the answer to the question.
                   
                   Chat History: {chat_history}
                   User Question: {question}
                   Relevant Sources: {relevant_sources}""",
    expected_output="""A markdown-formatted response with answer. Add a 
                       **Sources:** section at the end with source citations.""",
    agent=decipher_agent,
)

# Rewrite question based on chat history for better vector search
search_query = messages[-1].content
if chat_history:
    search_query = llm.call(f"""
    Given the following chat history and current question, rewrite the 
    question to include relevant context that would help with searching 
    a vector database. Focus on key concepts and terminology.
    
    Chat History:

Current Question:

Rewrite the question in a way that captures the full context. 
Only output the rewritten question, nothing else.
""")

relevant_sources = await get_relevant_sources(notebook_id, search_query)

**Sources:**
- [Climate Research Paper](https://nature.com/climate-2024)
- [IPCC Report](https://ipcc.ch/report-2024)
- Provided Text