feat: solution for 'Повторный экзамен: FAQ-бот — ChromaDB + один MCP-tool'

src/retriever.py

@@ -0,0 +1,42 @@
+"""
+Retrieval and answer generation logic using LangChain.
+"""
+
+import os
+from typing import Any
+
+from chromadb import Client
+from chromadb.config import Settings
+
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.llms.openai import OpenAIChat
+from langchain.chains import RetrievalQA
+from langchain.vectorstores import Chroma
+
+def get_answer(question: str, client: Client, collection_name: str, k: int = 3) -> str:
+    """
+    Retrieve relevant FAQ chunks and generate an answer using OpenAIChat.
+    """
+    # Set up embeddings and LLM
+    embedding = OpenAIEmbeddings()
+    llm = OpenAIChat(temperature=0)
+
+    # Load vector store
+    vectorstore = Chroma(
+        client=client,
+        collection_name=collection_name,
+        embedding_function=embedding
+    )
+
+    # Build RetrievalQA chain
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=vectorstore.as_retriever(search_kwargs={"k": k}),
+        return_source_documents=True
+    )
+
+    # Run chain
+    result = qa_chain({"question": question})
+    answer = result.get("answer", "")
+    return answer.strip()