add vector_store.py

vector_store.py

@@ -0,0 +1,42 @@
+"""
+Vector store initialization using Qdrant in-memory.
+
+The vector store is used by the search tool to perform semantic similarity search.
+"""
+import os
+from pathlib import Path
+from langchain_qdrant import QdrantVectorStore
+from qdrant_client import QdrantClient
+from qdrant_client.models import Distance, VectorParams
+from langchain_openai import OpenAIEmbeddings
+
+# Embedding model compatible with OpenRouter API (used by BroJS LLM)
+embeddings = OpenAIEmbeddings(
+    model="text-embedding-3-small",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+)
+
+# In-memory Qdrant client – no external server required
+client = QdrantClient(":memory:")
+client.create_collection(
+    "knowledge",
+    vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
+)
+vector_store = QdrantVectorStore(client=client, collection_name="knowledge", embedding=embeddings)
+
+# Helper to add documents – used in examples
+from langchain_core.documents import Document
+
+def add_documents(docs: list[Document]):
+    """Add a list of :class:`~langchain_core.documents.Document` objects to the store."""
+    vector_store.add_documents(docs)
+
+# Example documents – can be extended by users
+if __name__ == "__main__":
+    docs = [
+        Document(page_content="LangChain is a framework for building applications powered by language models.", metadata={"title": "LangChain Overview"}),
+        Document(page_content="Qdrant is an open-source vector database that stores embeddings and performs similarity search efficiently.", metadata={"title": "Qdrant Documentation"}),
+    ]
+    add_documents(docs)
+    print("Added example documents to Qdrant in-memory store")