diff --git a/.github/workflows/run_notebooks.yml b/.github/workflows/run_notebooks.yml index 75de638dca2d2..33a0525f69577 100644 --- a/.github/workflows/run_notebooks.yml +++ b/.github/workflows/run_notebooks.yml @@ -40,6 +40,10 @@ jobs: pip install -e libs//partners/mistralai pip install jupyter langgraph click pypdf vcrpy + - name: Pre-download tiktoken files + run: | + python docs/scripts/download_tiktoken.py + - name: Prepare notebooks run: | @@ -47,7 +51,6 @@ jobs: - name: Run notebooks env: - TIKTOKEN_CACHE_DIR: '' # these won't actually be used because of the VCR cassettes # but need to set them to avoid triggering getpass() ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} diff --git a/docs/cassettes/qa_chat_history_0005810b-1b95-4666-a795-08d80e478b83.yaml b/docs/cassettes/qa_chat_history_0005810b-1b95-4666-a795-08d80e478b83.yaml index fc870b6f9876e..8de1aa2387c79 100644 --- a/docs/cassettes/qa_chat_history_0005810b-1b95-4666-a795-08d80e478b83.yaml +++ b/docs/cassettes/qa_chat_history_0005810b-1b95-4666-a795-08d80e478b83.yaml @@ -14,8 +14,8 @@ interactions: content-type: - application/json cookie: - - __cf_bm=cOXiJt3ZhX.U8bFoK4IRnAyuukfzOuljTSL8Djv6RAU-1727447395-1.0.1.1-nIkPz3.kq2Kro4YDVASMK53qwkXJSHFWAahgsq25k3AEaJ3ca1lMg4435NfPXMCNK.0.lEkyt_Bv41KgEOpqIg; - _cfuvid=tQiGoA94xaHAHCJPWqSaRbG4swZHYRF3d3SVkdW5i6k-1727447395171-0.0.1.1-604800000 + - __cf_bm=VfEiWL8lVjWDJ1eYYOi41T46H54QmaeZKam3keUKU8Y-1727449089-1.0.1.1-RKntjzRCcHu9Mqp_E4kPBhvAI4uXxVtq.F7.1g6AhkOTdaLeADzeqv_utJ.0mo8mN._ZdF5bpjA.oMZNgSmYMg; + _cfuvid=UwB1KnBNeptIb7UOULQlELR2aOjMHWHKYazoDYURRcw-1727449089799-0.0.1.1-604800000 host: - api.openai.com user-agent: @@ -41,120 +41,121 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA1SaWw+yPrvmz99P8eR/yqwICLS8Z+wEZFcEVJhMJoCKoohsWmhX1nef6DOZzYkJ - SETo3fv6XVf7n//68+efvmqv9fzPv//883pM8z//7XvuUs7lP//+89//9efPnz//+fv8/668dtX1 - cnm8m9/lvy8f78t1/efff/j/c+b/XvTvP/9Ao1IxiK4XNu3jTwaFt9wQ80aeI9WzhwalKbkR6/1w - 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Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n\nFig. - 11. Illustration of how HuggingGPT works. (Image source: Shen et al. 2023)\nThe - system comprises of 4 stages:\n(1) Task planning: LLM works as the brain and - parses the user requests into multiple tasks. There are four attributes associated - with each task: task type, ID, dependencies, and arguments. They use few-shot - examples to guide LLM to do task parsing and planning.\nInstruction:", "role": - "system"}, {"content": "What is Task Decomposition?", "role": "user"}, {"content": - "Task decomposition involves breaking down complex tasks into smaller and simpler - steps to make them more manageable for an agent or model. Techniques like Chain - of Thought (CoT) and Tree of Thoughts help in decomposing hard tasks into multiple - smaller steps, enhancing model performance on complex tasks. Task decomposition - can be achieved through simple prompting, task-specific instructions, or human - inputs.", "role": "assistant"}, {"content": "What are common ways of doing it?", - "role": "user"}], "model": "gpt-3.5-turbo", "n": 1, "stream": false, "temperature": - 0.0}' + 9. Comparison of MIPS algorithms, measured in recall@10. (Image source: Google + Blog, 2020)\nCheck more MIPS algorithms and performance comparison in ann-benchmarks.com.\nComponent + Three: Tool Use#\nTool use is a remarkable and distinguishing characteristic + of human beings. We create, modify and utilize external objects to do things + that go beyond our physical and cognitive limits. Equipping LLMs with external + tools can significantly extend the model capabilities.", "role": "system"}, + {"content": "What is Task Decomposition?", "role": "user"}, {"content": "Task + decomposition involves breaking down complex tasks into smaller and simpler + steps to make them more manageable. Techniques like Chain of Thought and Tree + of Thoughts help agents decompose hard tasks into multiple manageable tasks + by instructing models to think step by step or explore multiple reasoning possibilities + at each step. Task decomposition can be done using simple prompting, task-specific + instructions, or human inputs.", "role": "assistant"}, {"content": "What are + common ways of doing it?", "role": "user"}], "model": "gpt-3.5-turbo", "n": + 1, "stream": false, "temperature": 0.0}' headers: accept: - application/json @@ -689,12 +691,12 @@ interactions: connection: - keep-alive content-length: - - '3176' + - '3283' content-type: - application/json cookie: - - __cf_bm=leU1rnrd5Mk7Zbv8nf22ZmPrT0VrL1sEyGEWl3U1wFQ-1727447397-1.0.1.1-_2BIynft9V5Snkjj0VTPA9WUaBVOo9jSC2N.S6EUVGXe1WFQlbf2GV8XCts3aJQlM6UOlclKs_C23Kw7GtjR5A; - _cfuvid=B7aDmti5xRKNceE.Zd020VfOM7Z5AVbpTiXyU9DlShM-1727447397769-0.0.1.1-604800000 + - __cf_bm=7LquqTwFy9BQq57W1c5I.wU5NwfZwUcplc3QqnnmI1c-1727449093-1.0.1.1-sNVx5oEwffvbSIbZ4_6FnFzbDNZSYM67VLAcsn9HioqTVF4LPuWefKMHYruYY9iOJpYmCth5MllBIf1UkNpqJw; + _cfuvid=lsTAvzmFn.L_wigWK6RJysxROIwzFjlrgXoZv_VsmSY-1727449093898-0.0.1.1-604800000 host: - api.openai.com user-agent: @@ -720,21 +722,21 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA3RSTW8aMRC98ytGPrUSIEhIULlFUdVLoh7aKk1LhWa9w66L17PyjENIlP9e2RBI - VfXiw3vz8fzmPQ8AjKvNAoxtUW3X+9HV9eWnztnt2dX19E7X1bcnqu4/tjq7/EzeDHMHV7/J6mvX - 2HLXe1LHYU/bSKiUp07nZ/PZbD6bTArRcU0+tzW9js7HFyNNseLRZHp2cehs2VkSs4CfAwCA5/Jm - jaGmR7OAMqcgHYlgQ2ZxLAIwkX1GDIo4UQxqhifSclAKRfZXlA3UlIWzuKwcLAaoCGoOBC6A5a7j - AFvcCUiyLaBAEhcauMHQJGwIbvNv4N3Nze172DptQVz2AfrIXa+51LsNwdJ8UeoF1hzh+/2PpQGO - gLLJBRmTVDWMXkAZ0LaOHggQpCfr1s6ComzGkAWPjpgLojHZrFuKcPTCWX0Sqof7tYcNyrA0d9Fp - Gaocd8BJvQu0NGX9NrqiFSHwA/kxXNV1cQS93w2hTR0GcKFPKq8eJXXePVFd+vVfK5WhioQbqHlb - nOw9PZZCAReUoeNI0GHAhrDyBJINGr+9VaR1EsxZCcn7A/5yPL7npo9cyYE/4msXnLSrSCgc8qFF - uTeFfRkA/CohS3/lxuzPtVLeUMgDL8+n+3nmlOsTO/9wIJUV/Rt8Ovlf16omReflTVTNXqELzWnC - 5Ciz/NPITpS61dqFhmIfXUluceNl8AcAAP//AwCnB/rHuAMAAA== + H4sIAAAAAAAAAwAAAP//dFJNjxoxDL3zK6ycWgkQ+8F+cKtQ90QPbZFaqVuhTMbDuGTibOwRoGr/ + e5XAAlXVy4zkl2e/Z7/fAwBDtZmBca1V10U/+jC/o4fmI31vPn8Ntn1i1ZS+LHbzl6fpwgwzg6tf + 6PSNNXbcRY9KHA6wS2gVc9er++v729vHyeNdATqu0WfaOuroZjwdaZ8qHk2urqdHZsvkUMwMfgwA + AH6Xb9YYatyZGUyGb5UORewazez0CMAk9rlirAiJ2qBmeAYdB8VQZC+tbKDGLJyFsnJwNkCFUHNA + 6IXCGhRdG+ilRwFPG4R5aykAN7BsuV+3Cu/mvHwPnGCZEC8AGcK2JdcCBdHUO4ViXEAZtKWwAVGM + UO0Pf06Au+g5IXS9V4oeIaEVDllFZBGqyJMSClgFtK4txDHMues4wNbuJY/Xf11RcL6v3xwJ5UNB + TNxFzYUtaQuLxadh4Y4koqOG3Ek4cTiafzbfEimCBVFOe+BePQUcPpusn4LjFDnZ0rXtO5tHx16L + 5yqh3UDN2wCHqOzKOAEKyiCd9R4T2FAfBaZiT8aXx0vY9GJzeELv/bH+ekqD53VMXMkRP9UbCiTt + 6rDOfHlRjqagrwOAnyV1/V9BMof1rJQ3GHLDu5ubQz9zDvoZvZ8eQWW1/qI+efgfa1WjWvJykV1z + Ovi5w+Qks/g0shfFbtVQWGOKiUqUyzZeB38AAAD//wMAvrnEI8kDAAA= headers: CF-Cache-Status: - DYNAMIC CF-RAY: - - 8c9c306a88048f69-BOS + - 8c9c59d2292f8f6a-BOS Connection: - keep-alive Content-Encoding: @@ -742,7 +744,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 27 Sep 2024 14:30:01 GMT + - Fri, 27 Sep 2024 14:58:17 GMT Server: - cloudflare Transfer-Encoding: @@ -754,7 +756,7 @@ interactions: openai-organization: - user-wzxwdcuddhvwm09z43ibeucf openai-processing-ms: - - '1087' + - '1002' openai-version: - '2020-10-01' strict-transport-security: @@ -766,13 +768,13 @@ interactions: x-ratelimit-remaining-requests: - '4999' x-ratelimit-remaining-tokens: - - '3999252' + - '3999225' x-ratelimit-reset-requests: - 12ms x-ratelimit-reset-tokens: - 11ms x-request-id: - - req_dee4024a0ddf89effff51f53af3a9516 + - req_a5236127b3dee52e343ae5c4969567a6 status: code: 200 message: OK diff --git a/docs/cassettes/qa_chat_history_0e89c75f-7ad7-4331-a2fe-57579eb8f840.yaml b/docs/cassettes/qa_chat_history_0e89c75f-7ad7-4331-a2fe-57579eb8f840.yaml index 2fa2d60b31c81..2f2c732dea2ba 100644 --- a/docs/cassettes/qa_chat_history_0e89c75f-7ad7-4331-a2fe-57579eb8f840.yaml +++ b/docs/cassettes/qa_chat_history_0e89c75f-7ad7-4331-a2fe-57579eb8f840.yaml @@ -6,10 +6,10 @@ interactions: just reformulate it if needed and otherwise return it as is.", "role": "system"}, {"content": "What is Task Decomposition?", "role": "user"}, {"content": "Task decomposition is a technique used to break down complex tasks into smaller and - simpler steps. This approach helps agents or models tackle difficult tasks by - dividing them into more manageable subtasks. Task decomposition can be achieved - through methods like Chain of Thought (CoT) or Tree of Thoughts, which guide - the model in thinking step by step or exploring multiple reasoning possibilities + simpler steps. This process helps agents or models handle intricate tasks more + effectively by dividing them into manageable subtasks. Task decomposition can + be achieved through methods like Chain of Thought (CoT) or Tree of Thoughts, + which guide the model to think step by step or explore multiple reasoning possibilities at each step.", "role": "assistant"}, {"content": "What are common ways of doing it?", "role": "user"}], "model": "gpt-3.5-turbo", "n": 1, "stream": false, "temperature": 0.0}' @@ -21,12 +21,12 @@ interactions: connection: - keep-alive content-length: - - '968' + - '974' content-type: - application/json cookie: - - __cf_bm=leU1rnrd5Mk7Zbv8nf22ZmPrT0VrL1sEyGEWl3U1wFQ-1727447397-1.0.1.1-_2BIynft9V5Snkjj0VTPA9WUaBVOo9jSC2N.S6EUVGXe1WFQlbf2GV8XCts3aJQlM6UOlclKs_C23Kw7GtjR5A; - _cfuvid=B7aDmti5xRKNceE.Zd020VfOM7Z5AVbpTiXyU9DlShM-1727447397769-0.0.1.1-604800000 + - __cf_bm=7LquqTwFy9BQq57W1c5I.wU5NwfZwUcplc3QqnnmI1c-1727449093-1.0.1.1-sNVx5oEwffvbSIbZ4_6FnFzbDNZSYM67VLAcsn9HioqTVF4LPuWefKMHYruYY9iOJpYmCth5MllBIf1UkNpqJw; + 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status: - code: 200 - message: OK -- request: - body: '{"messages": [{"content": "What is Task Decomposition?", "role": "user"}, - {"content": null, "role": "assistant", "tool_calls": [{"type": "function", "id": - "call_Vq2fx0tXDEW8f3uqmd32X3V2", "function": {"name": "blog_post_retriever", - "arguments": "{\"query\": \"Task Decomposition\"}"}}]}, {"content": "Fig. 1. - Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA - complicated task usually involves many steps. An agent needs to know what they - are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; Wei et al. - 2022) has become a standard prompting technique for enhancing model performance - on complex tasks. The model is instructed to \u201cthink step by step\u201d - to utilize more test-time computation to decompose hard tasks into smaller and - simpler steps. CoT transforms big tasks into multiple manageable tasks and shed - lights into an interpretation of the model\u2019s thinking process.\n\nTree - of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities - at each step. It first decomposes the problem into multiple thought steps and - generates multiple thoughts per step, creating a tree structure. The search - process can be BFS (breadth-first search) or DFS (depth-first search) with each - state evaluated by a classifier (via a prompt) or majority vote.\nTask decomposition - can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\n1.\", \"What - are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; - e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\n\n(3) - Task execution: Expert models execute on the specific tasks and log results.\nInstruction:\n\nWith - the input and the inference results, the AI assistant needs to describe the - process and results. The previous stages can be formed as - User Input: {{ User - Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, - Task Execution: {{ Predictions }}. You must first answer the user''s request - in a straightforward manner. Then describe the task process and show your analysis - and model inference results to the user in the first person. If inference results - contain a file path, must tell the user the complete file path.\n\nFig. 11. - Illustration of how HuggingGPT works. (Image source: Shen et al. 2023)\nThe - system comprises of 4 stages:\n(1) Task planning: LLM works as the brain and - parses the user requests into multiple tasks. There are four attributes associated - with each task: task type, ID, dependencies, and arguments. 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I''m bob", "role": "user"}, {"content": "Hello Bob! How can I assist you today?", "role": "assistant"}, {"content": "What is Task Decomposition?", "role": "user"}, {"content": null, "role": "assistant", - "tool_calls": [{"type": "function", "id": "call_GjyqJJFU7Z0LxLYvK1HoWKH8", "function": + "tool_calls": [{"type": "function", "id": "call_OeFi7aNlRQvi1Ij5dvQv5eM5", "function": {"name": "blog_post_retriever", "arguments": "{\"query\": \"Task Decomposition\"}"}}]}, {"content": "Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs @@ -34,19 +34,26 @@ interactions: parses the user requests into multiple tasks. There are four attributes associated with each task: task type, ID, dependencies, and arguments. They use few-shot examples to guide LLM to do task parsing and planning.\nInstruction:", "role": - "tool", "tool_call_id": "call_GjyqJJFU7Z0LxLYvK1HoWKH8"}, {"content": "Task + "tool", "tool_call_id": "call_OeFi7aNlRQvi1Ij5dvQv5eM5"}, {"content": "Task decomposition is a technique used to break down complex tasks into smaller and - simpler steps. This approach helps autonomous agents or models to handle intricate + simpler steps. This approach helps autonomous agents or models handle intricate tasks by dividing them into more manageable subtasks. By decomposing tasks, - agents can effectively plan and execute each step to achieve the overall goal. - \n\nIf you would like more detailed information or examples related to task - decomposition, feel free to ask!", "role": "assistant"}, {"content": "What according - to the blog post are common ways of doing it? redo the search", "role": "user"}], - "model": "gpt-3.5-turbo", "n": 1, "stream": false, "temperature": 0.0, "tools": - [{"type": "function", "function": {"name": "blog_post_retriever", "description": - "Searches and returns excerpts from the Autonomous Agents blog post.", "parameters": - {"properties": {"query": {"description": "query to look up in retriever", "type": - "string"}}, "required": ["query"], "type": "object"}}}]}' + agents can effectively plan and execute each step, leading to improved performance + on complex tasks. \n\nOne common method for task decomposition is the Chain + of Thought (CoT) technique, which prompts models to think step by step and decompose + difficult tasks into smaller components. Another approach, the Tree of Thoughts, + extends CoT by exploring multiple reasoning possibilities at each step, creating + a tree structure of task decomposition.\n\nTask decomposition can be achieved + through various methods, such as using language models with simple prompting, + task-specific instructions, or human inputs. This process enables agents to + efficiently plan and execute tasks by breaking them down into manageable steps.", + "role": "assistant"}, {"content": "What according to the blog post are common + ways of doing it? redo the search", "role": "user"}], "model": "gpt-3.5-turbo", + "n": 1, "stream": false, "temperature": 0.0, "tools": [{"type": "function", + "function": {"name": "blog_post_retriever", "description": "Searches and returns + excerpts from the Autonomous Agents blog post.", "parameters": {"properties": + {"query": {"description": "query to look up in retriever", "type": "string"}}, + "required": ["query"], "type": "object"}}}]}' headers: accept: - application/json @@ -55,12 +62,12 @@ interactions: connection: - keep-alive content-length: - - '3774' + - '4301' content-type: - application/json cookie: - - __cf_bm=S9CaKJjlRKMgapR4feoPhcVCXu5HNw9ouMhsxuXq.sU-1727447408-1.0.1.1-B03gfQ5b8oM_0t6ZzWgUnOaIkcduwHoYgZP7H25Nf0pCKbUSHSBUbDPq2zhHWxP7OPqNXr8lGZCszwLJOlg8YQ; - 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Transfer-Encoding: @@ -321,7 +327,7 @@ interactions: openai-organization: - user-wzxwdcuddhvwm09z43ibeucf openai-processing-ms: - - '18' + - '20' openai-version: - '2020-10-01' strict-transport-security: @@ -339,7 +345,7 @@ interactions: x-ratelimit-reset-tokens: - 0s x-request-id: - - req_eb27466451517ad89f45550e25adc4ab + - req_b9ae9101ceefbd944f226e274aed26c8 status: code: 200 message: OK @@ -347,7 +353,7 @@ interactions: body: '{"messages": [{"content": "Hi! I''m bob", "role": "user"}, {"content": "Hello Bob! How can I assist you today?", "role": "assistant"}, {"content": "What is Task Decomposition?", "role": "user"}, {"content": null, "role": "assistant", - "tool_calls": [{"type": "function", "id": "call_GjyqJJFU7Z0LxLYvK1HoWKH8", "function": + "tool_calls": [{"type": "function", "id": "call_OeFi7aNlRQvi1Ij5dvQv5eM5", "function": {"name": "blog_post_retriever", "arguments": "{\"query\": \"Task Decomposition\"}"}}]}, {"content": "Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs @@ -378,33 +384,40 @@ interactions: parses the user requests into multiple tasks. There are four attributes associated with each task: task type, ID, dependencies, and arguments. They use few-shot examples to guide LLM to do task parsing and planning.\nInstruction:", "role": - "tool", "tool_call_id": "call_GjyqJJFU7Z0LxLYvK1HoWKH8"}, {"content": "Task + "tool", "tool_call_id": "call_OeFi7aNlRQvi1Ij5dvQv5eM5"}, {"content": "Task decomposition is a technique used to break down complex tasks into smaller and - simpler steps. This approach helps autonomous agents or models to handle intricate + simpler steps. This approach helps autonomous agents or models handle intricate tasks by dividing them into more manageable subtasks. By decomposing tasks, - agents can effectively plan and execute each step to achieve the overall goal. - \n\nIf you would like more detailed information or examples related to task - decomposition, feel free to ask!", "role": "assistant"}, {"content": "What according - to the blog post are common ways of doing it? redo the search", "role": "user"}, - {"content": null, "role": "assistant", "tool_calls": [{"type": "function", "id": - "call_tXKcwVFAhcA8QP40CFhGrZA2", "function": {"name": "blog_post_retriever", - "arguments": "{\"query\": \"Common ways of task decomposition\"}"}}]}, {"content": - "Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: - Planning#\nA complicated task usually involves many steps. An agent needs to - know what they are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; - Wei et al. 2022) has become a standard prompting technique for enhancing model - performance on complex tasks. The model is instructed to \u201cthink step by - step\u201d to utilize more test-time computation to decompose hard tasks into - smaller and simpler steps. CoT transforms big tasks into multiple manageable - tasks and shed lights into an interpretation of the model\u2019s thinking process.\n\nTree - of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities - at each step. It first decomposes the problem into multiple thought steps and - generates multiple thoughts per step, creating a tree structure. The search - process can be BFS (breadth-first search) or DFS (depth-first search) with each - state evaluated by a classifier (via a prompt) or majority vote.\nTask decomposition - can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\n1.\", \"What - are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; - e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\n\nResources:\n1. + agents can effectively plan and execute each step, leading to improved performance + on complex tasks. \n\nOne common method for task decomposition is the Chain + of Thought (CoT) technique, which prompts models to think step by step and decompose + difficult tasks into smaller components. Another approach, the Tree of Thoughts, + extends CoT by exploring multiple reasoning possibilities at each step, creating + a tree structure of task decomposition.\n\nTask decomposition can be achieved + through various methods, such as using language models with simple prompting, + task-specific instructions, or human inputs. This process enables agents to + efficiently plan and execute tasks by breaking them down into manageable steps.", + "role": "assistant"}, {"content": "What according to the blog post are common + ways of doing it? redo the search", "role": "user"}, {"content": null, "role": + "assistant", "tool_calls": [{"type": "function", "id": "call_17abRRo13m34gtFnPhB6kxrr", + "function": {"name": "blog_post_retriever", "arguments": "{\"query\": \"common + ways of task decomposition\"}"}}]}, {"content": "Tree of Thoughts (Yao et al. + 2023) extends CoT by exploring multiple reasoning possibilities at each step. + It first decomposes the problem into multiple thought steps and generates multiple + thoughts per step, creating a tree structure. The search process can be BFS + (breadth-first search) or DFS (depth-first search) with each state evaluated + by a classifier (via a prompt) or majority vote.\nTask decomposition can be + done (1) by LLM with simple prompting like \"Steps for XYZ.\\n1.\", \"What are + the subgoals for achieving XYZ?\", (2) by using task-specific instructions; + e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\n\nFig. + 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA + complicated task usually involves many steps. An agent needs to know what they + are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; Wei et al. + 2022) has become a standard prompting technique for enhancing model performance + on complex tasks. The model is instructed to \u201cthink step by step\u201d + to utilize more test-time computation to decompose hard tasks into smaller and + simpler steps. CoT transforms big tasks into multiple manageable tasks and shed + lights into an interpretation of the model\u2019s thinking process.\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your @@ -420,7 +433,7 @@ interactions: in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.", "role": "tool", - "tool_call_id": "call_tXKcwVFAhcA8QP40CFhGrZA2"}], "model": "gpt-3.5-turbo", + "tool_call_id": "call_17abRRo13m34gtFnPhB6kxrr"}], "model": "gpt-3.5-turbo", "n": 1, "stream": false, "temperature": 0.0, "tools": [{"type": "function", "function": {"name": "blog_post_retriever", "description": "Searches and returns excerpts from the Autonomous Agents blog post.", "parameters": {"properties": @@ -434,12 +447,12 @@ interactions: connection: - keep-alive content-length: - - '6582' + - '7109' content-type: - application/json cookie: - - __cf_bm=S9CaKJjlRKMgapR4feoPhcVCXu5HNw9ouMhsxuXq.sU-1727447408-1.0.1.1-B03gfQ5b8oM_0t6ZzWgUnOaIkcduwHoYgZP7H25Nf0pCKbUSHSBUbDPq2zhHWxP7OPqNXr8lGZCszwLJOlg8YQ; - _cfuvid=ty_zC0bzJUD.U5UPPZYqWFP.KB3qzsUYJFkIrsa_930-1727447408230-0.0.1.1-604800000 + - 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8c9c30c42cfb4d10-BOS + - 8c9c5a2cdf8d8fb4-BOS Connection: - keep-alive Content-Encoding: @@ -265,7 +264,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 27 Sep 2024 14:30:14 GMT + - Fri, 27 Sep 2024 14:58:30 GMT Server: - cloudflare Transfer-Encoding: @@ -281,7 +280,7 @@ interactions: openai-organization: - user-wzxwdcuddhvwm09z43ibeucf openai-processing-ms: - - '30' + - '23' openai-version: - '2020-10-01' strict-transport-security: @@ -299,7 +298,7 @@ interactions: x-ratelimit-reset-tokens: - 0s x-request-id: - - req_11a6c6dcfed85bcf8aa44ab71772b479 + - req_39f1923ae8a6a6d1e48c4e663877dec1 status: code: 200 message: OK @@ -307,7 +306,7 @@ interactions: body: '{"messages": [{"content": "Hi! I''m bob", "role": "user"}, {"content": "Hello Bob! How can I assist you today?", "role": "assistant"}, {"content": "What is Task Decomposition?", "role": "user"}, {"content": null, "role": "assistant", - "tool_calls": [{"type": "function", "id": "call_GjyqJJFU7Z0LxLYvK1HoWKH8", "function": + "tool_calls": [{"type": "function", "id": "call_OeFi7aNlRQvi1Ij5dvQv5eM5", "function": {"name": "blog_post_retriever", "arguments": "{\"query\": \"Task Decomposition\"}"}}]}, {"content": "Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs @@ -338,7 +337,7 @@ interactions: parses the user requests into multiple tasks. There are four attributes associated with each task: task type, ID, dependencies, and arguments. 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