More Updates to Read the Docs (#856)

docs/source/building_applications/rag.md

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+## Memory & RAG
+
+Memory enables your applications to reference and recall information from previous interactions or external documents. Llama Stack's memory system is built around the concept of Memory Banks:
+
+1. **Vector Memory Banks**: For semantic search and retrieval
+2. **Key-Value Memory Banks**: For structured data storage
+3. **Keyword Memory Banks**: For basic text search
+4. **Graph Memory Banks**: For relationship-based retrieval
+
+Here's how to set up a vector memory bank for RAG:
+
+```python
+# Register a memory bank
+bank_id = "my_documents"
+response = client.memory_banks.register(
+    memory_bank_id=bank_id,
+    params={
+        "memory_bank_type": "vector",
+        "embedding_model": "all-MiniLM-L6-v2",
+        "chunk_size_in_tokens": 512
+    }
+)
+
+# Insert documents
+documents = [
+    {
+        "document_id": "doc1",
+        "content": "Your document text here",
+        "mime_type": "text/plain"
+    }
+]
+client.memory.insert(bank_id, documents)
+
+# Query documents
+results = client.memory.query(
+    bank_id=bank_id,
+    query="What do you know about...",
+)
+```
+
+
+### Building RAG-Enhanced Agents
+
+One of the most powerful patterns is combining agents with RAG capabilities. Here's a complete example:
+
+```python
+from llama_stack_client.types import Attachment
+
+# Create attachments from documents
+attachments = [
+    Attachment(
+        content="https://raw.githubusercontent.com/example/doc.rst",
+        mime_type="text/plain"
+    )
+]
+
+# Configure agent with memory
+agent_config = AgentConfig(
+    model="Llama3.2-3B-Instruct",
+    instructions="You are a helpful assistant",
+    tools=[{
+        "type": "memory",
+        "memory_bank_configs": [],
+        "query_generator_config": {"type": "default", "sep": " "},
+        "max_tokens_in_context": 4096,
+        "max_chunks": 10
+    }],
+    enable_session_persistence=True
+)
+
+agent = Agent(client, agent_config)
+session_id = agent.create_session("rag_session")
+
+# Initial document ingestion
+response = agent.create_turn(
+    messages=[{
+        "role": "user",
+        "content": "I am providing some documents for reference."
+    }],
+    attachments=attachments,
+    session_id=session_id
+)
+
+# Query with RAG
+response = agent.create_turn(
+    messages=[{
+        "role": "user",
+        "content": "What are the key topics in the documents?"
+    }],
+    session_id=session_id
+)
+```