feat(llm): add bam adapter verbose example

examples/llms/providers/bam_verbose.ts

@@ -0,0 +1,56 @@
+import "dotenv/config";
+import { BaseMessage } from "bee-agent-framework/llms/primitives/message";
+import { PromptTemplate } from "bee-agent-framework/template";
+import { z } from "zod";
+import { BAMLLM } from "bee-agent-framework/adapters/bam/llm";
+import { BAMChatLLM } from "bee-agent-framework/adapters/bam/chat";
+
+const template = new PromptTemplate({
+  schema: z.object({
+    messages: z.array(z.record(z.array(z.string()))),
+  }),
+  template: `{{#messages}}{{#system}}<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+{{system}}<|eot_id|>{{/system}}{{#user}}<|start_header_id|>user<|end_header_id|>
+
+{{user}}<|eot_id|>{{/user}}{{#assistant}}<|start_header_id|>assistant<|end_header_id|>
+
+{{assistant}}<|eot_id|>{{/assistant}}{{#ipython}}<|start_header_id|>ipython<|end_header_id|>
+
+{{ipython}}<|eot_id|>{{/ipython}}{{/messages}}<|start_header_id|>assistant<|end_header_id|>
+`,
+});
+
+const llm = new BAMLLM({
+  modelId: "meta-llama/llama-3-1-70b-instruct",
+  parameters: {
+    decoding_method: "greedy",
+    max_new_tokens: 50,
+  },
+});
+
+const chatLLM = new BAMChatLLM({
+  llm,
+  config: {
+    messagesToPrompt(messages: BaseMessage[]) {
+      return template.render({
+        messages: messages.map((message) => ({
+          system: message.role === "system" ? [message.text] : [],
+          user: message.role === "user" ? [message.text] : [],
+          assistant: message.role === "assistant" ? [message.text] : [],
+          ipython: message.role === "ipython" ? [message.text] : [],
+        })),
+      });
+    },
+  },
+});
+
+console.info("Meta", await chatLLM.meta());
+
+const response = await chatLLM.generate([
+  BaseMessage.of({
+    role: "user",
+    text: "Hello world!",
+  }),
+]);
+console.info(response.messages[0]);