diff --git a/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb b/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb
index 5f087813ae13ecb0933b76f72a3f477f7529de73..7cc2a373ff491fd7757be04b46dc479284571069 100644
--- a/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb
+++ b/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb
@@ -13,7 +13,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -21,217 +21,7 @@
"id": "eiJsOa29ej7G",
"outputId": "71cbfc5e-4073-4bb7-e7e0-8af67bc76fb7"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- ]
- }
- ],
+ "outputs": [],
"source": [
"!pip install llama-index\n",
"!pip install llama-index-embeddings-huggingface\n",
@@ -240,7 +30,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {
"id": "sG25Zlzsfg3K"
},
@@ -260,7 +50,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -268,25 +58,7 @@
"id": "CrQQkzV8dOJg",
"outputId": "5ef72923-d717-424b-9088-a1d35952967b"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "--2024-07-03 16:20:59-- https://openreview.net/pdf?id=VtmBAGCN7o\n",
- "Resolving openreview.net (openreview.net)... 35.184.86.251\n",
- "Connecting to openreview.net (openreview.net)|35.184.86.251|:443... connected.\n",
- "HTTP request sent, awaiting response... 200 OK\n",
- "Length: 16911937 (16M) [application/pdf]\n",
- "Saving to: ‘metagpt.pdf’\n",
- "\n",
- "metagpt.pdf 100%[===================>] 16.13M 6.46MB/s in 2.5s \n",
- "\n",
- "2024-07-03 16:21:02 (6.46 MB/s) - ‘metagpt.pdf’ saved [16911937/16911937]\n",
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"!wget \"https://openreview.net/pdf?id=VtmBAGCN7o\" -O metagpt.pdf"
]
@@ -379,7 +151,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {
"id": "6zi9eRc2dOMQ"
},
@@ -429,7 +201,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {
"id": "QkaALpnIQ01b"
},
@@ -448,7 +220,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -456,35 +228,7 @@
"id": "d9wVsIt586s8",
"outputId": "aed1974d-5724-4543-d010-ca9997f95973"
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
- "To disable this warning, you can either:\n",
- "\t- Avoid using `tokenizers` before the fork if possible\n",
- "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[1;3;38;5;200mThought: The current language of the user is: English. I need to use a tool to help me answer the question.\n",
- "Action: vector_tool_metagpt\n",
- "Action Input: {'query': 'agent roles in MetaGPT', 'page_numbers': None}\n",
- "\u001b[0m\u001b[1;3;34mObservation: In MetaGPT, there are five defined roles: Product Manager, Architect, Project Manager, Engineer, and QA Engineer. Each role has a specific profile, goal, and constraints, and is initialized with specific context and skills.\n",
- "\u001b[0m\u001b[1;3;38;5;200mThought: I have some information about the agent roles in MetaGPT, but I need more information about how they communicate with each other.\n",
- "Action: vector_tool_metagpt\n",
- "Action Input: {'query': 'communication between agent roles in MetaGPT'}\n",
- "\u001b[0m\u001b[1;3;34mObservation: In MetaGPT, agents communicate through structured outputs such as documents and diagrams, rather than dialogue. This communication is facilitated by a shared message pool, where agents publish their outputs and can access messages from other entities transparently. Additionally, a subscription mechanism allows agents to extract relevant information based on their role profiles, reducing information overload.\n",
- "\u001b[0m\u001b[1;3;38;5;200mThought: I have enough information to answer the question about the agent roles in MetaGPT and how they communicate with each other.\n",
- "Answer: In MetaGPT, there are five defined agent roles: Product Manager, Architect, Project Manager, Engineer, and QA Engineer, each with specific profiles, goals, and constraints. These agents communicate with each other through structured outputs such as documents and diagrams, rather than dialogue, using a shared message pool and subscription mechanism that allows them to extract relevant information based on their role profiles.\n",
- "\u001b[0mIn MetaGPT, there are five defined agent roles: Product Manager, Architect, Project Manager, Engineer, and QA Engineer, each with specific profiles, goals, and constraints. These agents communicate with each other through structured outputs such as documents and diagrams, rather than dialogue, using a shared message pool and subscription mechanism that allows them to extract relevant information based on their role profiles.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"response = agent.query(\n",
" \"Tell me about the agent roles in MetaGPT, and how they communicate with each other.\"\n",
@@ -494,7 +238,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -502,63 +246,14 @@
"id": "_vrBO-S1-HjF",
"outputId": "b8e8d35a-25f0-48d9-f992-3e5c3f573efe"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "page_label: 4\n",
- "file_name: metagpt.pdf\n",
- "file_path: metagpt.pdf\n",
- "file_type: application/pdf\n",
- "file_size: 16911937\n",
- "creation_date: 2024-07-03\n",
- "last_modified_date: 2024-07-03\n",
- "\n",
- "Preprint\n",
- "Figure 2: An example of the communication protocol (left) and iterative programming with exe-\n",
- "cutable feedback (right). Left: Agents use a shared message pool to publish structured messages.\n",
- "They can also subscribe to relevant messages based on their profiles. Right : After generating the\n",
- "initial code, the Engineer agent runs and checks for errors. If errors occur, the agent checks past\n",
- "messages stored in memory and compares them with the PRD, system design, and code files.\n",
- "3 M ETAGPT: A M ETA-PROGRAMMING FRAMEWORK\n",
- "MetaGPT is a meta-programming framework for LLM-based multi-agent systems. Sec. 3.1 pro-\n",
- "vides an explanation of role specialization, workflow and structured communication in this frame-\n",
- "work, and illustrates how to organize a multi-agent system within the context of SOPs. Sec. 3.2\n",
- "presents a communication protocol that enhances role communication efficiency. We also imple-\n",
- "ment structured communication interfaces and an effective publish-subscribe mechanism. These\n",
- "methods enable agents to obtain directional information from other roles and public information\n",
- "from the environment. Finally, we introduce executable feedback—a self-correction mechanism for\n",
- "further enhancing code generation quality during run-time in Sec. 3.3.\n",
- "3.1 A GENTS IN STANDARD OPERATING PROCEDURES\n",
- "Specialization of Roles Unambiguous role specialization enables the breakdown of complex work\n",
- "into smaller and more specific tasks. Solving complex tasks or problems often requires the collab-\n",
- "oration of agents with diverse skills and expertise, each contributing specialized outputs tailored to\n",
- "specific issues.\n",
- "In a software company, a Product Manager typically conducts business-oriented analysis and derives\n",
- "insights, while a software engineer is responsible for programming. We define five roles in our\n",
- "software company: Product Manager, Architect, Project Manager, Engineer, and QA Engineer, as\n",
- "shown in Figure 1. In MetaGPT, we specify the agent’s profile, which includes their name, profile,\n",
- "goal, and constraints for each role. We also initialize the specific context and skills for each role.\n",
- "For instance, a Product Manager can use web search tools, while an Engineer can execute code, as\n",
- "shown in Figure 2. All agents adhere to the React-style behavior as described in Yao et al. (2022).\n",
- "Every agent monitors the environment ( i.e., the message pool in MetaGPT) to spot important ob-\n",
- "servations ( e.g.,, messages from other agents). These messages can either directly trigger actions or\n",
- "assist in finishing the job.\n",
- "Workflow across Agents By defining the agents’ roles and operational skills, we can establish\n",
- "basic workflows. In our work, we follow SOP in software development, which enables all agents to\n",
- "work in a sequential manner.\n",
- "4\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print(response.source_nodes[0].get_content(metadata_mode=\"all\"))"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -566,25 +261,7 @@
"id": "HQ0dzdD1AZn7",
"outputId": "632142ae-891b-4dfc-cae8-5153b8b58bc6"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[1;3;38;5;200mThought: The current language of the user is: English. I need to use a tool to help me answer the question.\n",
- "Action: vector_tool_metagpt\n",
- "Action Input: {'query': 'evaluation datasets', 'page_numbers': None}\n",
- "\u001b[0m\u001b[1;3;34mObservation: The evaluation dataset mentioned is the SoftwareDev dataset, which includes 70 diverse software development tasks.\n",
- "\u001b[0m\u001b[1;3;38;5;200mThought: I have some information about the evaluation datasets, but I want to know more.\n",
- "Action: vector_tool_metagpt\n",
- "Action Input: {'query': 'evaluation datasets besides SoftwareDev'}\n",
- "\u001b[0m\u001b[1;3;34mObservation: HumanEval and MBPP.\n",
- "\u001b[0m\u001b[1;3;38;5;200mThought: I have a good understanding of the evaluation datasets used, including SoftwareDev, HumanEval, and MBPP.\n",
- "Answer: The evaluation datasets used in the MetaGPT paper include SoftwareDev, which consists of 70 diverse software development tasks, HumanEval, and MBPP.\n",
- "\u001b[0m"
- ]
- }
- ],
+ "outputs": [],
"source": [
"response = agent.query(\n",
" \"Tell me about the evaluation datasets used.\"\n",
@@ -593,7 +270,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -601,60 +278,14 @@
"id": "7gg99LMhA_vq",
"outputId": "44c5844f-8aec-4ad7-e38c-f62ac0298b35"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "page_label: 23\n",
- "file_name: metagpt.pdf\n",
- "file_path: metagpt.pdf\n",
- "file_type: application/pdf\n",
- "file_size: 16911937\n",
- "creation_date: 2024-07-03\n",
- "last_modified_date: 2024-07-03\n",
- "\n",
- "Preprint\n",
- "Figure 10: The “Drawing App” generated by MetaGPT.\n",
- "C E XPERIMENTS\n",
- "C.1 D ETAILS OF THE SOFTWARE DEVDATASET\n",
- "The SoftwareDev dataset includes 70 diverse software development tasks. Table 8 displays the\n",
- "names and detailed prompts of 11 tasks within the dataset. Note that the first seven tasks listed are\n",
- "used in the main experiments of this paper.\n",
- "C.2 A DDITIONAL RESULTS\n",
- "Quantitative results of MetaGPT As shown in Table 4, MetaGPT achieves an average score\n",
- "of 3.9, surpassing ChatDev’s score of 2.1 Zhao et al. (2023), which is based on the Chat chain.\n",
- "Compare the scores of general intelligent algorithms, including AutoGPT Torantulino et al. (2023),\n",
- "which all score 1.0, failing to generate executable code. We observe that the generated code is often\n",
- "short, lacks comprehensive logic, and tends to fail to handle cross-file dependencies correctly.\n",
- "While models such as AutoGPT (Torantulino et al., 2023), Langchain (Chase, 2022), and Agent-\n",
- "Verse (Chen et al., 2023) display robust general problem-solving capabilities, they lack an essential\n",
- "element for developing complex systems: systematically deconstructing requirements. Conversely,\n",
- "MetaGPT simplifies the process of transforming abstract requirements into detailed class and func-\n",
- "tion designs through a specialized division of labor and SOPs workflow. When compared to Chat-\n",
- "Dev (Zhao et al., 2023), MetaGPT’s structured messaging and feedback mechanisms not only reduce\n",
- "loss of communication information but also improve the execution of code.\n",
- "Quantitative results of MetaGPT w/o executable feedback Table 9 presents the performance of\n",
- "MetaGPT with GPT-4 32K on 11 tasks within the SoftwareDev dataset. It also shows the average\n",
- "performance across all 70 tasks (in the last line). Note that the version of MetaGPT used here is the\n",
- "basic version without the executable feedback mechanism.\n",
- "Quantitative results of MetaGPT with different LLMs To verify the performance of MetaGPT\n",
- "on different LLMs, we randomly selected 5 SoftwareDev tasks and conducted experiments using\n",
- "GPT-3.5 and Deepseek Coder 33B5as backends. As shown in Table 5, the results indicate that\n",
- "although MetaGPT can complete tasks with these LLMs, using GPT-4 as the backend yields superior\n",
- "performance.\n",
- "5https://deepseekcoder.github.io\n",
- "23\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print(response.source_nodes[0].get_content(metadata_mode=\"all\"))"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -662,21 +293,7 @@
"id": "9FplhNMmBO7o",
"outputId": "1eab921a-4670-48a1-c2a0-0c3f4142ed33"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[1;3;38;5;200mThought: The current language of the user is: English. I need to use a tool to help me answer the question.\n",
- "Action: vector_tool_metagpt\n",
- "Action Input: {'query': 'results over softwaredev dataset', 'page_numbers': None}\n",
- "\u001b[0m\u001b[1;3;34mObservation: The results of MetaGPT over the SoftwareDev dataset include an average score of 3.9, surpassing ChatDev's score of 2.1. Additionally, the results show that MetaGPT achieves a score of 3.75 in terms of executability, which is very close to 4 (flawless). It also takes less time (503 seconds) and requires fewer tokens to generate one line of code compared to ChatDev.\n",
- "\u001b[0m\u001b[1;3;38;5;200mThought: I have the necessary information to answer the question without using any more tools. I'll use the user's language to answer\n",
- "Answer: According to the MetaGPT paper, the model achieves an average score of 3.9 on the SoftwareDev dataset, outperforming ChatDev's score of 2.1. It also demonstrates strong executability with a score of 3.75, and is more efficient in terms of time and tokens required to generate code.\n",
- "\u001b[0m"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# use agent.chat instead of agent.query to pass conversational history automatically to answer follow up questions\n",
"response = agent.chat(\"Tell me the results over one of the above datasets.\")"
@@ -684,7 +301,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -692,30 +309,7 @@
"id": "38YcfpmdB2_L",
"outputId": "cb71adc1-876f-4c41-def6-b2c638ec66f1"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[1;3;38;5;200mThought: The current language of the user is: English. I need to use a tool to help me answer the question.\n",
- "Action: vector_tool_metagpt\n",
- "Action Input: {'query': 'evaluation results on SoftwareDev dataset', 'page_numbers': None}\n",
- "\u001b[0m\u001b[1;3;34mObservation: The evaluation results on the SoftwareDev dataset show that MetaGPT achieves an average score of 3.9, surpassing ChatDev's score of 2.1. Additionally, MetaGPT outperforms ChatDev on nearly all metrics, including executability, code statistics, and cost of human revision.\n",
- "\u001b[0m\u001b[1;3;34mObservation: Error: Could not parse output. Please follow the thought-action-input format. Try again.\n",
- "\u001b[0m\u001b[1;3;38;5;200mThought: I need to get more information about the evaluation results on the SoftwareDev dataset.\n",
- "Action: vector_tool_metagpt\n",
- "Action Input: {'query': 'evaluation results on SoftwareDev dataset in detail'}\n",
- "\u001b[0m\u001b[1;3;34mObservation: The evaluation results on the SoftwareDev dataset are presented in Table 9, which shows the performance of MetaGPT with GPT-4 32K on 11 tasks within the SoftwareDev dataset. The average performance across all 70 tasks is also provided in the last line of the table.\n",
- "\u001b[0m\u001b[1;3;38;5;200mThought: I need to get more information about the specific tasks in the SoftwareDev dataset.\n",
- "Action: vector_tool_metagpt\n",
- "Action Input: {'query': 'tasks in the SoftwareDev dataset'}\n",
- "\u001b[0m\u001b[1;3;34mObservation: The SoftwareDev dataset includes 70 diverse software development tasks.\n",
- "\u001b[0m\u001b[1;3;38;5;200mThought: I have enough information to answer the question.\n",
- "Answer: The SoftwareDev dataset includes 70 diverse software development tasks, and the evaluation results on this dataset are presented in Table 9, which shows the performance of MetaGPT with GPT-4 32K on 11 tasks within the SoftwareDev dataset. The average performance across all 70 tasks is also provided in the last line of the table.\n",
- "\u001b[0m"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# use agent.chat instead of agent.query to pass conversational history automatically to answer follow up questions\n",
"response = agent.chat(\"Tell me more.\")"
@@ -747,7 +341,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {
"id": "rCBVOv5MFnhY"
},
@@ -761,7 +355,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -769,26 +363,14 @@
"id": "ovokdaUpFoUx",
"outputId": "f9cb5cc4-a2cf-4800-8bf2-b12f595cef4a"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[1;3;38;5;200mThought: The current language of the user is: English. I need to use a tool to help me answer the question.\n",
- "Action: vector_tool_metagpt\n",
- "Action Input: {'query': 'agent roles in MetaGPT'}\n",
- "\u001b[0m\u001b[1;3;34mObservation: In MetaGPT, there are five defined agent roles: Product Manager, Architect, Project Manager, Engineer, and QA Engineer. Each role has a specific profile, goal, and constraints, and is initialized with specific context and skills.\n",
- "\u001b[0m"
- ]
- }
- ],
+ "outputs": [],
"source": [
"step_output = agent.run_step(task.task_id)"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -796,16 +378,7 @@
"id": "iNQCyED2FqeP",
"outputId": "bcd1a12c-38f3-49ea-f1d6-821696be644c"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Num completed for task 08e7253c-842a-457e-8eda-9d2822b8540d: 1\n",
- "In MetaGPT, there are five defined agent roles: Product Manager, Architect, Project Manager, Engineer, and QA Engineer. Each role has a specific profile, goal, and constraints, and is initialized with specific context and skills.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"completed_steps = agent.get_completed_steps(task.task_id)\n",
"print(f\"Num completed for task {task.task_id}: {len(completed_steps)}\")\n",
@@ -814,7 +387,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -822,25 +395,7 @@
"id": "DwnKrC63FuKw",
"outputId": "71151f4a-f540-4f3f-98a9-62d858e5dcb9"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Num upcoming steps for task 08e7253c-842a-457e-8eda-9d2822b8540d: 1\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "TaskStep(task_id='08e7253c-842a-457e-8eda-9d2822b8540d', step_id='76b6833d-6083-4d3e-808e-55ec9e667fc3', input=None, step_state={'is_first': False}, next_steps={}, prev_steps={}, is_ready=True)"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"upcoming_steps = agent.get_upcoming_steps(task.task_id)\n",
"print(f\"Num upcoming steps for task {task.task_id}: {len(upcoming_steps)}\")\n",
@@ -849,7 +404,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -857,20 +412,7 @@
"id": "JXlVID-NFwL6",
"outputId": "25e723c5-f4e3-4645-b45f-30046a1d95df"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Added user message to memory: What about how agents share information?\n",
- "\u001b[1;3;38;5;200mThought: I need to use a tool to help me answer the question.\n",
- "Action: vector_tool_metagpt\n",
- "Action Input: {'query': 'how agents share information in MetaGPT'}\n",
- "\u001b[0m\u001b[1;3;34mObservation: In MetaGPT, agents share information through a global message pool, where they publish their structured messages and can access messages from other entities transparently. This allows any agent to directly retrieve required information from the shared pool, eliminating the need to inquire about other agents and await their responses. Additionally, a subscription mechanism is used to filter out irrelevant information, where agents can select information to follow based on their role profiles, ensuring they only receive task-related information and avoid distractions.\n",
- "\u001b[0m"
- ]
- }
- ],
+ "outputs": [],
"source": [
"step_output = agent.run_step(\n",
" task.task_id, input=\"What about how agents share information?\"\n",
@@ -879,7 +421,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -887,17 +429,7 @@
"id": "m8cM1jA7GZHX",
"outputId": "f5e75a19-0fb6-4334-c1cd-0be31f6bc82b"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[1;3;38;5;200mThought: I can answer without using any more tools. I'll use the user's language to answer\n",
- "Answer: In MetaGPT, agents share information through a global message pool, where they publish their structured messages and can access messages from other entities transparently. This allows any agent to directly retrieve required information from the shared pool, eliminating the need to inquire about other agents and await their responses. Additionally, a subscription mechanism is used to filter out irrelevant information, where agents can select information to follow based on their role profiles, ensuring they only receive task-related information and avoid distractions.\n",
- "\u001b[0mTrue\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"step_output = agent.run_step(task.task_id)\n",
"print(step_output.is_last)"
@@ -905,7 +437,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {
"id": "DH3b_t9mGeP8"
},
@@ -916,7 +448,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -924,15 +456,7 @@
"id": "zgaq4yqZGhRm",
"outputId": "c1434fcc-003b-4e26-ff47-58f477127a52"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "In MetaGPT, agents share information through a global message pool, where they publish their structured messages and can access messages from other entities transparently. This allows any agent to directly retrieve required information from the shared pool, eliminating the need to inquire about other agents and await their responses. Additionally, a subscription mechanism is used to filter out irrelevant information, where agents can select information to follow based on their role profiles, ensuring they only receive task-related information and avoid distractions.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print(str(response))"
]