feat: add document pipeline — PDF decoder, Ollama LLM, storage services

Add end-to-end document processing pipeline:
- PDF decoder service (pdfjs-dist) extracts text per page from librarian docs
- Ollama native LLM service for local model inference
- FalkorDB triples store FlowProcessor consumer
- Qdrant graph embeddings store FlowProcessor consumer
- Fix spec name collisions in chunker/extractor (input→chunk-input, etc.)
- Gateway /load endpoint to trigger document processing
- Align flow manager blueprint and seed config with full pipeline topics
- Add runner scripts and test coverage for document load

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

ts/packages/flow/src/model/text-completion/ollama.ts

@@ -0,0 +1,117 @@
+/**
+ * Ollama text completion service.
+ *
+ * Connects to a local Ollama instance for text generation.
+ *
+ * Python reference: trustgraph-flow/trustgraph/model/text_completion/ollama/llm.py
+ */
+
+import { Ollama } from "ollama";
+import { LlmService, type ProcessorConfig, type LlmResult, type LlmChunk } from "@trustgraph/base";
+
+export class OllamaProcessor extends LlmService {
+  private client: Ollama;
+  private readonly defaultModel: string;
+
+  constructor(config: ProcessorConfig & {
+    model?: string;
+    ollamaUrl?: string;
+  }) {
+    super(config);
+
+    this.defaultModel =
+      config.model ??
+      process.env.OLLAMA_MODEL ??
+      "qwen2.5:0.5b";
+
+    const host =
+      config.ollamaUrl ??
+      process.env.OLLAMA_URL ??
+      "http://localhost:11434";
+
+    this.client = new Ollama({ host });
+
+    console.log(
+      `[Ollama] LLM service initialized (host=${host}, model=${this.defaultModel})`,
+    );
+  }
+
+  async generateContent(
+    system: string,
+    prompt: string,
+    model?: string,
+    _temperature?: number,
+  ): Promise<LlmResult> {
+    const modelName = model ?? this.defaultModel;
+    const fullPrompt = system + "\n\n" + prompt;
+
+    const resp = await this.client.generate({
+      model: modelName,
+      prompt: fullPrompt,
+      stream: false,
+    });
+
+    return {
+      text: resp.response,
+      inToken: resp.prompt_eval_count ?? 0,
+      outToken: resp.eval_count ?? 0,
+      model: modelName,
+    };
+  }
+
+  override supportsStreaming(): boolean {
+    return true;
+  }
+
+  async *generateContentStream(
+    system: string,
+    prompt: string,
+    model?: string,
+    _temperature?: number,
+  ): AsyncGenerator<LlmChunk> {
+    const modelName = model ?? this.defaultModel;
+    const fullPrompt = system + "\n\n" + prompt;
+
+    const stream = await this.client.generate({
+      model: modelName,
+      prompt: fullPrompt,
+      stream: true,
+    });
+
+    let totalInputTokens = 0;
+    let totalOutputTokens = 0;
+
+    for await (const chunk of stream) {
+      // Token counts accumulate across chunks; keep the latest values
+      if (chunk.prompt_eval_count !== undefined) {
+        totalInputTokens = chunk.prompt_eval_count;
+      }
+      if (chunk.eval_count !== undefined) {
+        totalOutputTokens = chunk.eval_count;
+      }
+
+      if (chunk.response) {
+        yield {
+          text: chunk.response,
+          inToken: null,
+          outToken: null,
+          model: modelName,
+          isFinal: false,
+        };
+      }
+    }
+
+    // Final chunk with accumulated token counts
+    yield {
+      text: "",
+      inToken: totalInputTokens,
+      outToken: totalOutputTokens,
+      model: modelName,
+      isFinal: true,
+    };
+  }
+}
+
+export async function run(): Promise<void> {
+  await OllamaProcessor.launch("text-completion");
+}