ساخت چت‌ بات مبتنی بر RAG با استفاده از LangChain

 1===== Human Message =====
 2
 3What is Task Decomposition?
 4======= Ai Message ======
 5Tool Calls:
 6  blog_post_retriever (call_WKHdiejvg4In982Hr3EympuI)
 7 Call ID: call_WKHdiejvg4In982Hr3EympuI
 8  Args:
 9    query: Task Decomposition
10====== Tool Message =====
11Name: blog_post_retriever
12
13Fig. 1. Overview of a LLM-powered autonomous agent system.
14Component One: Planning#
15A complicated task usually involves many steps. An agent needs to know what they are and plan ahead.
16Task Decomposition#
17Chain 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 “think step by step” 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’s thinking process.
18
19Tree 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.
20Task 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.
21
22(3) Task execution: Expert models execute on the specific tasks and log results.
23Instruction:
24
25With 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.
26
27Fig. 11. Illustration of how HuggingGPT works. (Image source: Shen et al. 2023)
28The system comprises of 4 stages:
29(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.
30Instruction:
31======= Ai Message ======
32
33Task Decomposition is a process used in complex problem-solving where a larger task is broken down into smaller, more manageable sub-tasks. This approach enhances the ability of models, particularly large language models (LLMs), to handle intricate tasks by allowing them to think step by step.
34
35There are several methods for task decomposition:
36
371. **Chain of Thought (CoT)**: This technique encourages the model to articulate its reasoning process by thinking through the task in a sequential manner. It transforms a big task into smaller, manageable steps, which also provides insight into the model's thought process.
38
392. **Tree of Thoughts**: An extension of CoT, this method explores multiple reasoning possibilities at each step. It decomposes the problem into various thought steps and generates multiple thoughts for each step, creating a tree structure. The evaluation of each state can be done using breadth-first search (BFS) or depth-first search (DFS).
40
413. **Prompting Techniques**: Task decomposition can be achieved through simple prompts like "Steps for XYZ" or "What are the subgoals for achieving XYZ?" Additionally, task-specific instructions can guide the model, such as asking it to "Write a story outline" for creative tasks.
42
434. **Human Inputs**: In some cases, human guidance can be used to assist in breaking down tasks.
44
45Overall, task decomposition is a crucial component in planning and executing complex tasks, allowing for better organization and clarity in the problem-solving process.

 1===== Human Message =====
 2
 3What is Task Decomposition?
 4===== Ai Message ======
 5Tool Calls:
 6  blog_post_retriever (call_0rhrUJiHkoOQxwqCpKTkSkiu)
 7 Call ID: call_0rhrUJiHkoOQxwqCpKTkSkiu
 8  Args:
 9    query: Task Decomposition
10===== Tool Message =====
11Name: blog_post_retriever
12
13Fig. 1. Overview of a LLM-powered autonomous agent system.
14Component One: Planning#
15A complicated task usually involves many steps. An agent needs to know what they are and plan ahead.
16Task Decomposition#
17Chain 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 “think step by step” 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’s thinking process.
18
19Tree 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.
20Task 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.
21
22(3) Task execution: Expert models execute on the specific tasks and log results.
23Instruction:
24
25With 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.
26
27Fig. 11. Illustration of how HuggingGPT works. (Image source: Shen et al. 2023)
28The system comprises of 4 stages:
29(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.
30Instruction:
31===== Ai Message ======
32
33Task Decomposition is a technique used to break down complex tasks into smaller, more manageable steps. This approach is particularly useful in the context of autonomous agents and large language models (LLMs). Here are some key points about Task Decomposition:
34
351. **Chain of Thought (CoT)**: This is a prompting technique that encourages the model to "think step by step." By doing so, it can utilize more computational resources to decompose difficult tasks into simpler ones, making them easier to handle.
36
372. **Tree of Thoughts**: An extension of CoT, this method explores multiple reasoning possibilities at each step. It decomposes a problem into various thought steps and generates multiple thoughts for each step, creating a tree structure. This can be evaluated using search methods like breadth-first search (BFS) or depth-first search (DFS).
38
393. **Methods of Decomposition**: Task decomposition can be achieved through:
40   - Simple prompting (e.g., asking for steps to achieve a goal).
41   - Task-specific instructions (e.g., requesting a story outline for writing).
42   - Human inputs to guide the decomposition process.
43
444. **Execution**: After decomposition, expert models execute the specific tasks and log the results, allowing for a structured approach to complex problem-solving.
45
46Overall, Task Decomposition enhances the model's ability to tackle intricate tasks by breaking them down into simpler, actionable components.