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

src/tools.py

@@ -0,0 +1,85 @@
+import json
+import os
+from pathlib import Path
+from typing import List, Dict, Any
+
+import httpx
+from langchain_community.document_loaders import TextLoader
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_core.documents import Document
+from langchain_core.tools import tool
+
+# Path to the data directory
+DATA_DIR = Path(__file__).parent.parent / "data"
+CHROMA_DIR = Path(__file__).parent.parent / "chroma_faq"
+
+def load_faq_to_chroma() -> Chroma:
+    """
+    Load all .md files from the data directory, chunk them, embed with Ollama,
+    and persist into a Chroma vector store.
+    """
+    # Check if the Chroma collection already exists
+    if CHROMA_DIR.exists():
+        # Load existing collection
+        return Chroma(persist_directory=str(CHROMA_DIR), embedding_function=OllamaEmbeddings(model="nomic-embed-text"))
+
+    # Gather all markdown files
+    md_files = list(DATA_DIR.glob("*.md"))
+    documents: List[Document] = []
+
+    for md_file in md_files:
+        loader = TextLoader(str(md_file), encoding="utf-8")
+        docs = loader.load()
+        documents.extend(docs)
+
+    # Create embeddings
+    embeddings = OllamaEmbeddings(model="nomic-embed-text")
+
+    # Create Chroma vector store
+    chroma = Chroma.from_documents(
+        documents=documents,
+        embedding=embeddings,
+        persist_directory=str(CHROMA_DIR),
+    )
+    return chroma
+
+@tool
+def search_course_docs(query: str, k: int = 3) -> List[Dict[str, Any]]:
+    """
+    Search the local FAQ Chroma vector store for relevant documents.
+
+    Returns a list of dictionaries containing the content and metadata.
+    """
+    chroma = load_faq_to_chroma()
+    results = chroma.similarity_search(query, k=k)
+    output = []
+    for doc in results:
+        output.append(
+            {
+                "content": doc.page_content,
+                "metadata": doc.metadata,
+            }
+        )
+    return output
+
+@tool
+def fetch_course_meta(query: str) -> Dict[str, Any]:
+    """
+    Simulate an MCP-style HTTP tool that returns course metadata
+    matching the query. The metadata is read from a local JSON file.
+    """
+    meta_path = DATA_DIR / "course_meta.json"
+    with open(meta_path, "r", encoding="utf-8") as f:
+        data = json.load(f)
+
+    # Simple keyword matching in schedule and instructor fields
+    results = {}
+    if "schedule" in query.lower():
+        results["schedule"] = data.get("schedule", [])
+    if "instructor" in query.lower() or "professor" in query.lower():
+        results["instructor"] = data.get("instructor", {})
+    if not results:
+        # Default to returning the whole metadata if no keyword matched
+        results = data
+    return results