add tools

tools.py

@@ -0,0 +1,29 @@
+from langchain.tools import tool
+from qdrant_client import QdrantClient
+from langchain.embeddings.ollama import OllamaEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+
+# Initialize Qdrant client and collection
+qdrant_url = os.getenv("QDRANT_URL", "http://localhost:6333")
+client = QdrantClient(url=qdrant_url)
+collection_name = "knowledge_base"
+if not client.has_collection(collection_name):
+    client.create_collection(name=collection_name, vectors_config={"size": 384, "distance": "Cosine"})
+
+embeddings = OllamaEmbeddings(model="nomic-embed-text")
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+
+@tool("search_knowledge_base", description="Semantic search in knowledge base")
+def search_knowledge_base(query: str, max_results: int = 5):
+    vector = embeddings.embed_query(query)
+    results = client.search(collection_name=collection_name, query_vector=vector, limit=max_results)
+    return [hit.payload["text"] for hit in results]
+
+@tool("add_to_knowledge_base", description="Add document to knowledge base")
+def add_to_knowledge_base(content: str, title: str):
+    docs = text_splitter.split_text(content)
+    vectors = embeddings.embed_documents(docs)
+    ids = [title + f"_{i}" for i in range(len(docs))]
+    client.upsert(collection_name=collection_name, points=[{"id": id_, "vector": vec, "payload": {"text": doc}} for id_, vec, doc in zip(ids, vectors, docs)])
+    return f"Added {len(docs)} chunks to knowledge base"