From 9d0b423845666c72900a7c8940ea22792600ebc1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=90=D1=80=D1=82=D0=B5=D0=BC=20=D0=92=D0=BB=D0=B0=D0=B4?=
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 =?UTF-8?q?=D0=B1=D0=B0=D0=B9=D0=BA=D0=B8=D0=BD?= <ababaykin2016@litsey2.ru>
Date: Thu, 28 May 2026 12:08:19 +0000
Subject: [PATCH] feat: solution for 6a02e23da6fe2e4ac16acf65

---
 .../6a02e23da6fe2e4ac16acf65/solution.py      | 90 +++++++++++++++++++
 1 file changed, 90 insertions(+)
 create mode 100644 solutions/6a02e23da6fe2e4ac16acf65/solution.py

diff --git a/solutions/6a02e23da6fe2e4ac16acf65/solution.py b/solutions/6a02e23da6fe2e4ac16acf65/solution.py
new file mode 100644
index 0000000..d9f09cc
--- /dev/null
+++ b/solutions/6a02e23da6fe2e4ac16acf65/solution.py
@@ -0,0 +1,90 @@
+from pathlib import Path
+
+# LLM and embeddings via Ollama
+from langchain_ollama import ChatOllama, OllamaEmbeddings
+from langchain.tools import tool
+from langchain.agents import create_agent
+from langchain_core.documents import Document
+from langchain_qdrant import QdrantVectorStore
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams
+from langchain_core.messages import HumanMessage
+
+# ---------- Qdrant setup ----------
+client = QdrantClient(":memory:")
+client.create_collection(
+    collection_name="knowledge",
+    vectors_config=VectorParams(size=1024, distance=Distance.COSINE),
+)
+embeddings = OllamaEmbeddings(model="nomic-embed-text")
+vector_store = QdrantVectorStore(
+    client=client,
+    collection_name="knowledge",
+    embedding=embeddings,
+)
+
+# ---------- Tools ----------
+@tool
+def search_knowledge_base(query: str, max_results: int = 5) -> str:
+    """Search the knowledge base for relevant documents."""
+    docs_with_score = vector_store.similarity_search_with_score(query, k=max_results)
+    if not docs_with_score:
+        return "No results found."
+    return "\n".join(
+        f"{i+1}. {doc.page_content[:200]}..."
+        for i, (doc, _) in enumerate(docs_with_score)
+    )
+
+@tool
+def add_to_knowledge_base(content: str, title: str = "") -> str:
+    """Add a new document to the knowledge base."""
+    doc = Document(page_content=content, metadata={"title": title})
+    vector_store.add_documents([doc])
+    return f"Document '{title}' added."
+
+# ---------- Agent ----------
+llm = ChatOllama(model="llama3")
+agent = create_agent(
+    model=llm,
+    tools=[search_knowledge_base, add_to_knowledge_base],
+    system_prompt="You are a helpful assistant that can search and store knowledge.",
+)
+
+# ---------- CLI ----------
+def main():
+    print("RAG Agent CLI. Commands: /add <title> | <content>, /search <query>, /quit")
+    while True:
+        try:
+            inp = input("> ").strip()
+        except EOFError:
+            break
+        if not inp:
+            continue
+        if inp.lower() in ("quit", "/quit"):
+            print("Bye!")
+            break
+
+        # Add document
+        if inp.startswith("/add "):
+            _, rest = inp.split(maxsplit=1)
+            try:
+                title, content = rest.split("|", 1)
+            except ValueError:
+                print("Usage: /add <title> | <content>")
+                continue
+            res = agent.invoke({"messages": [HumanMessage(content=f"Add document {title}")]})
+            print(res.messages[-1].content)
+
+        # Search documents
+        elif inp.startswith("/search "):
+            query = inp[len("/search "):]
+            res = agent.invoke({"messages": [HumanMessage(content=f"Search for {query}")]})
+            print(res.messages[-1].content)
+
+        # General chat
+        else:
+            res = agent.invoke({"messages": [HumanMessage(content=inp)]})
+            print(res.messages[-1].content)
+
+if __name__ == "__main__":
+    main()
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