Fix typos in the repo (agentscope-ai#530)

Author: co63oc

docs/tutorial/en/source/tutorial/rag.py

@@ -72,7 +72,7 @@
 
 flex_knowledge_config = {
     "knowledge_id": "agentscope_qa_flex",
-    "knowlege_type": "llamaindex_knowledge",
+    "knowledge_type": "llamaindex_knowledge",
     "emb_model_config_name": "qwen_emb_config",
     "chunk_size": 1024,
     "chunk_overlap": 40,

docs/tutorial/zh/source/tutorial/rag.py

@@ -67,7 +67,7 @@
 
 flex_knowledge_config = {
     "knowledge_id": "agentscope_qa_flex",
-    "knowlege_type": "llamaindex_knowledge",
+    "knowledge_type": "llamaindex_knowledge",
     "emb_model_config_name": "qwen_emb_config",
     "chunk_size": 1024,
     "chunk_overlap": 40,
@@ -119,8 +119,8 @@
 # %%
 # Create a Batch of Knowledge Instances
 # ----------------------------------------------
-# For some cases where different knowledge sources exists and require different preprocessing and/or post-proprocess, a good strategy is to create multiple knolwedge instances.
-# Thus, we introduce `KnowledgeBank` to better manage the knowledge instances. One can initialize a batch of knowledge with a file of mulltiple knodledge configurations.
+# For some cases where different knowledge sources exists and require different preprocessing and/or post-process, a good strategy is to create multiple knowledge instances.
+# Thus, we introduce `KnowledgeBank` to better manage the knowledge instances. One can initialize a batch of knowledge with a file of multiple knowledge configurations.
 #
 # .. code-block:: python
 #

examples/conversation_nl2sql/sql_utils.py

@@ -112,8 +112,8 @@ def __init__(self) -> None:
         self.template_info = "/* Given the following database schema: */\n{}"
         self.template_question = "/* Answer the following: {} */"
         self.template_agent_prompt = (
-            "You are a helpful agent that preform"
-            "SQL querys base on natural language instructions."
+            "You are a helpful agent that perform"
+            "SQL queries base on natural language instructions."
             "Please describe the database schema provided"
             "in a simple and understandable manner. "
         )

examples/conversation_with_RAG_agents/configs/knowledge_config.json

@@ -1,7 +1,7 @@
 [
   {
     "knowledge_id": "agentscope_code_rag",
-    "knowlege_type": "llamaindex_knowledge",
+    "knowledge_type": "llamaindex_knowledge",
     "emb_model_config_name": "qwen_emb_config",
     "chunk_size": 2048,
     "chunk_overlap": 40,
@@ -39,7 +39,7 @@
   },
   {
     "knowledge_id": "agentscope_api_rag",
-    "knowlege_type": "llamaindex_knowledge",
+    "knowledge_type": "llamaindex_knowledge",
     "emb_model_config_name": "qwen_emb_config",
     "chunk_size": 1024,
     "chunk_overlap": 40,

examples/conversation_with_RAG_agents/rag_example.py

@@ -91,7 +91,7 @@ def main() -> None:
         },
     )
 
-    # let knowledgebank to equip rag agent with a (set of) knowledge
+    # let knowledge_bank to equip rag agent with a (set of) knowledge
     # corresponding to its knowledge_id_list
     for agent in rag_agent_list:
         knowledge_bank.equip(agent, agent.knowledge_id_list)

examples/conversation_with_codeact_agent/codeact_agent_example_tools.ipynb

@@ -10,7 +10,7 @@
     "More details can be found in the project's related [github repo](https://github.com/xingyaoww/code-act). \n",
     "\n",
     "In the following CodeAct agent example, we demonstrate another method of empowering the agent with the capability to invoke tools, specifically by directly providing the agent with the corresponding code for the tools and then allowing the agent to utilize them independently.\n",
-    "Interm of tool usage, ReAct agent also enables the agent to use tools to solve problems, but in a different way. You can refer to the [ReAct agent](https://github.com/modelscope/agentscope/blob/main/examples/conversation_with_react_agent/main.ipynb) example for more details and compare the two.\n",
+    "In term of tool usage, ReAct agent also enables the agent to use tools to solve problems, but in a different way. You can refer to the [ReAct agent](https://github.com/modelscope/agentscope/blob/main/examples/conversation_with_react_agent/main.ipynb) example for more details and compare the two.\n",
     "\n",
     "## Prerequisites\n",
     "\n",

examples/conversation_with_swe-agent/main.ipynb

@@ -10,11 +10,11 @@
     "More details can be found in the project's [homepage](https://swe-agent.com/) and related [github repo](https://swe-agent.com/).\n",
     "\n",
     "In the example here, we partially implement the SWE-agent, and provide a simple example of how to use the implemented SWE-agent to fix a bug in a python file.\n",
-    "You should note that currently how to enable agents with stronger programming capabilities remains an open challenge, and the performance of the paritially implemented SWE-agent is not guaranteed.\n",
+    "You should note that currently how to enable agents with stronger programming capabilities remains an open challenge, and the performance of the partially implemented SWE-agent is not guaranteed.\n",
     "\n",
     "## Prerequisites\n",
     "\n",
-    "- Follow [READMD.md](https://github.com/modelscope/agentscope) to install AgentScope. We require the lastest version, so you should build from source by running `pip install -e .` instead of intalling from pypi. \n",
+    "- Follow [README.md](https://github.com/modelscope/agentscope) to install AgentScope. We require the latest version, so you should build from source by running `pip install -e .` instead of installing from pypi. \n",
     "- Prepare a model configuration. AgentScope supports both local deployed model services (CPU or GPU) and third-party services. More details and example model configurations please refer to our [tutorial](https://doc.agentscope.io/build_tutorial/model.html).\n",
     "- Understand the ServiceToolkit module and how to use it to pre-process the tool functions for LLMs. You can refer to the [ReAct agent example](../conversation_with_react_agent/main.ipynb) and you should also refer to the [tutorial](https://doc.agentscope.io/build_tutorial/tool.html) for service functions.\n",
     "\n",
@@ -55,7 +55,7 @@
     "\n",
     "The code of SWE-agent is in `swe_agent.py`, and the related prompts are in `swe_agent_prompts.py`.\n",
     "\n",
-    "If you are interested in the details, please refer to the code and the origianl SWE-agent repo [here](https://github.com/princeton-nlp/SWE-agent)."
+    "If you are interested in the details, please refer to the code and the original SWE-agent repo [here](https://github.com/princeton-nlp/SWE-agent)."
    ]
   },
   {
@@ -228,7 +228,7 @@
       "}\n",
       "assistant: \n",
       "====Observation====\n",
-      "Current task finished, exitting.\n"
+      "Current task finished, exiting.\n"
      ]
     }
    ],
@@ -261,7 +261,7 @@
     "Above we shown a example of how to use the SWE-agent to fix code errors.\n",
     "Although the design of the SWE-agent is primarily aimed at addressing GitHub issues, with modifications, it can also be utilized for more general programming tasks.\n",
     "\n",
-    "Currently, how to enable agent with general programming ablities remains a challenging open question, with the efficacy of agent programming potentially influenced by factors such as prompt construction, model capabilities, and the complexity of the task at hand. Here we just provide an interesting toy example. \n",
+    "Currently, how to enable agent with general programming abilities remains a challenging open question, with the efficacy of agent programming potentially influenced by factors such as prompt construction, model capabilities, and the complexity of the task at hand. Here we just provide an interesting toy example. \n",
     "\n",
     "We encourage users to experiment by altering the prompts within this example or by assigning different tasks to the agent, among other methods of exploration. Please feel free to experiment and explore on your own. The AgentScope team will continue to provide updates, enhancing the capabilities of the Programming Agents in the future!"
    ]

src/agentscope/rag/knowledge.py

@@ -154,7 +154,7 @@ def build_knowledge_instance(
         """
         raise NotImplementedError(
             f"{knowledge_id} of {cls.__name__} does not support "
-            "auto build knowledgebase",
+            "auto build knowledge base instance",
         )
 
     def post_processing(

src/agentscope/rag/llama_index_knowledge.py

@@ -826,7 +826,7 @@ def build_knowledge_instance(
                 """
                 {
                     "knowledge_id": "xxx_rag",
-                    "knowlege_type": "llamaindex_knowledge",
+                    "knowledge_type": "llamaindex_knowledge",
                     "emb_model_config_name": "qwen_emb_config",
                     ....
                 }

src/agentscope/service/multi_modality/dashscope_services.py

@@ -233,7 +233,7 @@ def dashscope_text_to_audio(
             The model to use. Full model list can be found in
             https://help.aliyun.com/zh/dashscope/model-list
         sample_rate (`int`, defaults to 48000):
-            Samplerate of the audio.
+            Sample rate of the audio.
 
     Returns:
         `ServiceResponse`: