feat: add document pipeline, ReAct agent, and knowledge core services

Document Pipeline (Team A):
- LibrarianService: document storage with filesystem backend, metadata
  persistence, child document hierarchy, collection management
- ChunkingService: recursive character text splitter with configurable
  chunk size/overlap, FlowProcessor pattern
- KnowledgeExtractService: combined relationship + definition extraction
  using prompt service and LLM, emits RDF triples and entity contexts
- KnowledgeCoreService: knowledge core CRUD with streaming export and
  flow-based loading

ReAct Agent (Team B):
- StreamingReActParser: state machine for parsing LLM output into
  Thought/Action/ActionInput/FinalAnswer sections
- Three MVP tools: KnowledgeQuery (GraphRAG), DocumentQuery (DocRAG),
  TriplesQuery with RequestResponse clients
- AgentService FlowProcessor with ReAct loop, tool execution, and
  streaming chunk responses (thought/observation/answer)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

ts/packages/flow/src/agent/react/prompt.ts

@@ -0,0 +1,50 @@
+/**
+ * Build the ReAct system prompt for the agent.
+ *
+ * Formats available tools into the prompt template so the LLM knows what tools
+ * it can use and what format to follow.
+ */
+
+import type { AgentTool } from "./types.js";
+
+export function buildReActPrompt(
+  tools: AgentTool[],
+  question: string,
+): { system: string; prompt: string } {
+  const toolDescriptions = tools
+    .map((t) => {
+      const argDesc = t.args
+        .map((a) => `  - ${a.name} (${a.type}): ${a.description}`)
+        .join("\n");
+      return `${t.name}: ${t.description}\n  Arguments:\n${argDesc}`;
+    })
+    .join("\n\n");
+
+  const toolNames = tools.map((t) => t.name).join(", ");
+
+  const system = `You are a helpful AI assistant that answers questions using available tools.
+
+You have access to the following tools:
+
+${toolDescriptions}
+
+Use this exact format for your response:
+
+Thought: [your reasoning about what to do]
+Action: [tool name, one of: ${toolNames}]
+Action Input: {"argument_name": "value"}
+Observation: [tool result will be inserted here]
+... (repeat Thought/Action/Action Input/Observation as needed)
+Thought: I now have enough information to answer.
+Final Answer: [your comprehensive answer]
+
+Important:
+- Always start with a Thought.
+- Action must be one of: ${toolNames}
+- Action Input must be valid JSON.
+- After receiving an Observation, continue with another Thought.
+- When you have enough information, provide a Final Answer.
+- Do NOT make up observations. Wait for the tool result.`;
+
+  return { system, prompt: question };
+}