ekzamen-rag-agent-s-chromadb-i-veb-poisk/SOLUTION.md

What was implemented
The script src/index.py now uses ChromaDB as the persistent vector store instead of Qdrant.
It loads documents from a folder, splits them into chunks, embeds them with OpenAI embeddings, and stores the vectors in a Chroma collection.
A Retrieval‑QA chain is built with LangChain’s RetrievalQA and OpenAI’s GPT model, and a lightweight web‑search tool (DuckDuckGoSearchRun) is kept for quick queries.

Why the main parts satisfy the assignment

• The vector database is explicitly ChromaDB – the initialize_vectorstore() function creates a chromadb.PersistentClient and wraps it with LangChain’s Chroma wrapper.
• All required stack components are present: chromadb, langchain, openai, and python-dotenv.
• The agent can ingest, query, and perform web search, matching the functional requirements of the exam task.

Key code excerpts

src/index.py – imports and vector store initialization

import chromadb
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
...
def initialize_vectorstore() -> Chroma:
    client = chromadb.PersistentClient(path=CHROMA_DB_PATH)
    client.get_or_create_collection(name=COLLECTION_NAME)
    vectorstore = Chroma(
        client=client,
        collection_name=COLLECTION_NAME,
        embedding_function=OpenAIEmbeddings(model=EMBEDDING_MODEL),
    )
    return vectorstore

src/index.py – ingesting documents into Chroma

def ingest_documents(folder_path: str, vectorstore: Chroma) -> None:
    raw_texts = load_documents_from_folder(folder_path)
    chunks = split_text(raw_texts)
    vectorstore.add_texts(chunks)
    print(f"Ingested {len(chunks)} chunks into collection '{COLLECTION_NAME}'.")

src/index.py – web‑search helper

def perform_web_search(query: str) -> List[Dict[str, str]]:
    search_tool = DuckDuckGoSearchRun()
    results = search_tool.run(query)
    if isinstance(results, list):
        return results
    return [{"title": "Search Result", "url": "", "body": results}]

Limitations

• No unit tests are included.
• Error handling is minimal (e.g., missing environment variables or empty folders).
• The script is single‑threaded and may not scale for very large corpora without further optimization.

Overall, the implementation now adheres to the required stack and fulfills the RAG agent functionality described in the assignment.