feat: solution for 6a02e23da6fe2e4ac16acf65

solutions/6a02e23da6fe2e4ac16acf65/solution.py

@@ -1,61 +1,62 @@
-from pathlib import Path
+from langchain_openai import ChatOpenAI
+from pydantic import SecretStr
+from langchain.tools import tool
+from langchain_qdrant import QdrantVectorStore
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams
+from langchain_ollama import OllamaEmbeddings
+from langchain_core.documents import Document
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.agents import create_agent
+import os
 
-# ---------- LLM & Embeddings ----------
+# ---------- LLM ----------
-from langchain_ollama import Ollama, OllamaEmbeddings
+llm = ChatOpenAI(
-
+    model="openai/gpt-oss-20b",
-llm = Ollama(
+    base_url='https://platform.brojs.ru/jrnl-bh/api/inference/v1',
-    model="openai/gpt-oss-20b",  # e.g. "llama3"
+    api_key=SecretStr("jrnl_30283ab953615cbb6846ff9940a1eedce0b76d7b2f59a2394f29e74643e6a90d"),
-    base_url="http://localhost:11434",
     temperature=0.7,
 )
 
+# ---------- Embeddings ----------
 embeddings = OllamaEmbeddings(model="nomic-embed-text")
 
-# ---------- Qdrant Vector Store ----------
+# ---------- Qdrant ----------
-from qdrant_client import QdrantClient
+client = QdrantClient(":memory:")
-from qdrant_client.http.models import Distance, VectorParams
+collection_name = "knowledge_base"
-from langchain_qdrant import QdrantVectorStore
 
-client = QdrantClient(":memory:")  # in‑memory for demo; replace with path or URL as needed
+try:
-# Determine vector size from the embedding model
+    client.get_collection(collection_name)
-sample_vector = embeddings.embed_query("test")[0]
+except Exception:
-vector_size = len(sample_vector)
+    # Use a typical embedding size for nomic-embed-text (768)
-
+    client.create_collection(
-client.create_collection(
+        collection_name=collection_name,
-    collection_name="knowledge_base",
+        vectors_config=VectorParams(size=768, distance=Distance.COSINE),
-    vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE),
+    )
-)
 
 vector_store = QdrantVectorStore(
     client=client,
-    collection_name="knowledge_base",
+    collection_name=collection_name,
     embedding=embeddings,
 )
 
-# ---------- Text Splitter ----------
+# ---------- Text splitter ----------
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-
 splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
 
 # ---------- Tools ----------
-from langchain.tools import tool
+@tool
-from langchain_core.documents import Document
-
-@tool("search_knowledge_base")
 def search_knowledge_base(query: str, max_results: int = 5) -> str:
     """Search the knowledge base for relevant documents."""
-    docs = vector_store.similarity_search_with_score(query, k=max_results)
+    docs_with_score = vector_store.similarity_search_with_score(query, k=max_results)
-    if not docs:
+    if not docs_with_score:
         return "No results found."
-    response_lines = []
+    return "\n".join(
-    for i, (doc, score) in enumerate(docs, start=1):
+        f"{i+1}. {doc.page_content[:200]}..."
-        title = doc.metadata.get("title", "Untitled")
+        for i, (doc, _) in enumerate(docs_with_score)
-        snippet = doc.page_content[:200] + ("..." if len(doc.page_content) > 200 else "")
+    )
-        response_lines.append(f"{i}. [{score:.2f}] {title}\n{snippet}")
-    return "\n\n".join(response_lines)
 
-@tool("add_to_knowledge_base")
+@tool
-def add_to_knowledge_base(content: str, title: str = "Untitled") -> str:
+def add_to_knowledge_base(content: str, title: str = "") -> str:
     """Add a new document to the knowledge base."""
     chunks = splitter.split_text(content)
     docs = [Document(page_content=c, metadata={"title": title}) for c in chunks]
@@ -63,64 +64,59 @@ def add_to_knowledge_base(content: str, title: str = "Untitled") -> str:
     return f"Added {len(chunks)} chunks under title '{title}'."
 
 # ---------- Agent ----------
-from langchain.agents import create_agent
+system_prompt = """
-from langchain_core.messages import HumanMessage
+You are an assistant that can search and add information to a knowledge base.
+Use the tools `search_knowledge_base` and `add_to_knowledge_base` as needed.
+"""
 
 agent = create_agent(
     model=llm,
     tools=[search_knowledge_base, add_to_knowledge_base],
-    system_message="You are a helpful assistant that can search and update the knowledge base.",
+    system_prompt=system_prompt,
 )
 
-# ---------- Document Loader ----------
-def load_documents_from_dir(directory: str):
-    """Load all .txt files from directory into vector store."""
-    for file_path in Path(directory).glob("*.txt"):
-        text = file_path.read_text(encoding="utf-8")
-        add_to_knowledge_base(text, title=file_path.stem)
-
 # ---------- CLI ----------
+def load_directory(path: str):
+    """Load all text files from a directory into the knowledge base."""
+    for root, _, files in os.walk(path):
+        for file in files:
+            if file.lower().endswith(".txt"):
+                with open(os.path.join(root, file), encoding="utf-8") as f:
+                    content = f.read()
+                add_to_knowledge_base(content=content, title=file)
+
 def main():
-    print(
+    print("Welcome to the RAG agent. Commands: /add <file>, /search <query>, /load <dir>, /quit")
-        "Welcome to the RAG Agent. Commands:\n"
-        "/add <title> <file>\n"
-        "/search <query>\n"
-        "/quit\n"
-    )
     while True:
-        user_input = input("> ").strip()
+        try:
-        if not user_input:
+            inp = input("> ").strip()
-            continue
+        except EOFError:
-        if user_input.lower() in ("/quit", "exit"):
             break
-
+        if not inp:
-        if user_input.startswith("/add"):
+            continue
+        if inp.lower() in ("/quit", "exit"):
+            print("Goodbye!")
+            break
+        if inp.startswith("/add "):
+            _, file_path = inp.split(maxsplit=1)
             try:
-                _, title, file_path = user_input.split(maxsplit=2)
+                with open(file_path, encoding="utf-8") as f:
-                content = Path(file_path).read_text(encoding="utf-8")
+                    content = f.read()
-                print(add_to_knowledge_base(content, title))
+                print(add_to_knowledge_base(content=content, title=os.path.basename(file_path)))
             except Exception as e:
-                print(f"Error adding document: {e}")
+                print(f"Error adding file: {e}")
-
+        elif inp.startswith("/search "):
-        elif user_input.startswith("/search"):
+            _, query = inp.split(maxsplit=1)
-            query = user_input[len("/search") :].strip()
+            print(search_knowledge_base(query=query))
-            if not query:
+        elif inp.startswith("/load "):
-                print("Please provide a search query.")
+            _, dir_path = inp.split(maxsplit=1)
-                continue
+            load_directory(dir_path)
-            result = agent.invoke({"messages": [HumanMessage(content=query)]})
+            print(f"Loaded documents from {dir_path}")
-            for msg in result["messages"]:
-                if hasattr(msg, "content"):
-                    print(msg.content)
-
         else:
-            # Treat as normal chat message
+            # Regular conversation
-            result = agent.invoke({"messages": [HumanMessage(content=user_input)]})
+            response = agent.invoke({"messages": [{"role": "human", "content": inp}]})
-            for msg in result["messages"]:
+            msg = response["messages"][-1]
-                if hasattr(msg, "content"):
+            print(msg.content)
-                    print(msg.content)
 
 if __name__ == "__main__":
-    # Optional: load initial docs from a folder named 'docs'
-    if Path("docs").exists():
-        load_documents_from_dir("docs")
     main()