deploy: 8771ba9

en/_modules/agentscope/service/service_toolkit.html

@@ -504,17 +504,9 @@ <h1>Source code for agentscope.service.service_toolkit</h1><div class="highlight
 
         execute_results = []
         for i, cmd in enumerate(cmds):
-            func_name = cmd[&quot;name&quot;]
             service_func = self.service_funcs[cmd[&quot;name&quot;]]
             kwargs = cmd.get(&quot;arguments&quot;, {})
 
-            print(f&quot;&gt;&gt;&gt; Executing function {func_name} with arguments:&quot;)
-            for key, value in kwargs.items():
-                value = (
-                    value if len(str(value)) &lt; 50 else str(value)[:50] + &quot;...&quot;
-                )
-                print(f&quot;&gt;&gt;&gt; \t{key}: {value}&quot;)
-
             # Execute the function
             try:
                 func_res = service_func.processed_func(**kwargs)
@@ -524,8 +516,6 @@ <h1>Source code for agentscope.service.service_toolkit</h1><div class="highlight
                     content=str(e),
                 )
 
-            print(&quot;&gt;&gt;&gt; END &quot;)
-
             status = (
                 &quot;SUCCESS&quot;
                 if func_res.status == ServiceExecStatus.SUCCESS

en/_modules/index.html

@@ -143,6 +143,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="agentscope/parsers/code_block_parser.html">agentscope.parsers.code_block_parser</a></li>
 <li><a href="agentscope/parsers/json_object_parser.html">agentscope.parsers.json_object_parser</a></li>
 <li><a href="agentscope/parsers/parser_base.html">agentscope.parsers.parser_base</a></li>
+<li><a href="agentscope/parsers/regex_tagged_content_parser.html">agentscope.parsers.regex_tagged_content_parser</a></li>
 <li><a href="agentscope/parsers/tagged_content_parser.html">agentscope.parsers.tagged_content_parser</a></li>
 <li><a href="agentscope/pipelines/functional.html">agentscope.pipelines.functional</a></li>
 <li><a href="agentscope/pipelines/pipeline.html">agentscope.pipelines.pipeline</a></li>

en/_sources/agentscope.parsers.regex_tagged_content_parser.rst.txt

@@ -0,0 +1,7 @@
+agentscope.parsers.regex\_tagged\_content\_parser module
+========================================================
+
+.. automodule:: agentscope.parsers.regex_tagged_content_parser
+   :members:
+   :undoc-members:
+   :show-inheritance:

en/_sources/agentscope.parsers.rst.txt

@@ -10,6 +10,7 @@ Submodules
    agentscope.parsers.code_block_parser
    agentscope.parsers.json_object_parser
    agentscope.parsers.parser_base
+   agentscope.parsers.regex_tagged_content_parser
    agentscope.parsers.tagged_content_parser
 
 Module contents

en/_sources/tutorial/203-parser.md.txt

@@ -65,13 +65,15 @@ You should generate python code in a fenced code block as follows
 
 AgentScope provides multiple built-in parsers, and developers can choose according to their needs.
 
-| Target Format | Parser Class | Description                                                                                                                                                                  |
-| --- | --- |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| String | `MarkdownCodeBlockParser` | Requires LLM to generate specified text within a Markdown code block marked by ```. The result is a string.                                                                  |
-| Dictionary | `MarkdownJsonDictParser` | Requires LLM to produce a specified dictionary within the code block marked by \```json and \```. The result is a Python dictionary.                                         |
-|  | `MultiTaggedContentParser` | Requires LLM to generate specified content within multiple tags. Contents from different tags will be parsed into a single Python dictionary with different key-value pairs. |
+| Target Format             | Parser Class               | Description                                                                                                                                                                  |
+|---------------------------|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| String                    | `MarkdownCodeBlockParser`  | Requires LLM to generate specified text within a Markdown code block marked by ```. The result is a string.                                                                  |
+| Dictionary                | `MarkdownJsonDictParser`   | Requires LLM to produce a specified dictionary within the code block marked by \```json and \```. The result is a Python dictionary.                                         |
+|                           | `MultiTaggedContentParser` | Requires LLM to generate specified content within multiple tags. Contents from different tags will be parsed into a single Python dictionary with different key-value pairs. |
+|                           | `RegexTaggedContentParser` | For uncertain tag names and quantities, allows users to modify regular expressions, and the return result is a dictionary.                                                   |
 | JSON / Python Object Type | `MarkdownJsonObjectParser` | Requires LLM to produce specified content within the code block marked by \```json and \```. The result will be converted into a Python object via json.loads.               |
 
+
 > **NOTE**: Compared to `MarkdownJsonDictParser`, `MultiTaggedContentParser` is more suitable for weak LLMs and when the required format is too complex.
 > For example, when LLM is required to generate Python code, if the code is returned directly within a dictionary, LLM needs to be aware of escaping characters (\t, \n, ...), and the differences between double and single quotes when calling `json.loads`
 >
@@ -263,12 +265,34 @@ In AgentScope, we achieve post-processing by calling the `to_content`, `to_memor
 >   None
 >   ```
 
+#### Parsers
 
-Next we will introduce two parsers for dictionary type.
+For dictionary type return values, AgentScope provides multiple parsers for developers to choose from according to their needs.
 
-#### MarkdownJsonDictParser
+##### RegexTaggedContentParser
 
-##### Initialization & Format Instruction Template
+###### Initialization
+
+`RegexTaggedContentParser` is designed for scenarios where 1) the tag name is uncertain, and 2) the number of tags is uncertain.
+In this case, the parser cannot provide a general response format instruction, so developers need to provide the corresponding response format instruction (`format_instruction`) when initializing.
+Of course, the developers can handle the prompt engineering by themselves optionally.
+
+```python
+from agentscope.parsers import RegexTaggedContentParser
+
+parser = RegexTaggedContentParser(
+    format_instruction="""Respond with specific tags as outlined below
+<thought>what you thought</thought>
+<speak>what you speak</speak>
+""",
+    try_parse_json=True,                    # Try to parse the content of the tag as JSON object
+    required_keys=["thought", "speak"]      # Required keys in the returned dictionary
+)
+```
+
+##### MarkdownJsonDictParser
+
+###### Initialization & Format Instruction Template
 
 - `MarkdownJsonDictParser` requires LLM to generate dictionary within a code block fenced by \```json and \``` tags.
 
@@ -303,7 +327,7 @@ This parameter can be a string or a dictionary. For dictionary, it will be autom
   ```
   ````
 
-##### Validation
+###### Validation
 
 The `content_hint` parameter in `MarkdownJsonDictParser` also supports type validation based on Pydantic. When initializing, you can set `content_hint` to a Pydantic model class, and AgentScope will modify the `instruction_format` attribute based on this class. Besides, Pydantic will be used to validate the dictionary returned by LLM during parsing.
 
@@ -346,11 +370,11 @@ parser.parser("""
 """)
 ````
 
-#### MultiTaggedContentParser
+##### MultiTaggedContentParser
 
 `MultiTaggedContentParser` asks LLM to generate specific content within multiple tag pairs. The content from different tag pairs will be parsed into a single Python dictionary. Its usage is similar to `MarkdownJsonDictParser`, but the initialization method is different, and it is more suitable for weak LLMs or complex return content.
 
-##### Initialization & Format Instruction Template
+###### Initialization & Format Instruction Template
 
 Within `MultiTaggedContentParser`, each tag pair will be specified by as `TaggedContent` object, which contains
 - Tag name (`name`), the key value in the returned dictionary
@@ -393,7 +417,7 @@ Respond with specific tags as outlined below, and the content between [FINISH_DI
 [FINISH_DISCUSSION]true/false, whether the discussion is finished[/FINISH_DISCUSSION]
 ```
 
-##### Parse Function
+###### Parse Function
 
 - `MultiTaggedContentParser`'s parsing result is a dictionary, whose keys are the value of `name` in the `TaggedContent` objects.
 The following is an example of parsing the LLM response in the werewolf game:

en/agentscope.agents.html

@@ -744,7 +744,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Link to this headi
 their own needs.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="agentscope.agents.ReActAgent.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_config_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">service_toolkit</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="agentscope.service.service_toolkit.html#agentscope.service.service_toolkit.ServiceToolkit" title="agentscope.service.service_toolkit.ServiceToolkit"><span class="pre">ServiceToolkit</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sys_prompt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">&quot;You're</span> <span class="pre">a</span> <span class="pre">helpful</span> <span class="pre">assistant.</span> <span class="pre">Your</span> <span class="pre">name</span> <span class="pre">is</span> <span class="pre">{name}.&quot;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iters</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="_modules/agentscope/agents/react_agent.html#ReActAgent.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#agentscope.agents.ReActAgent.__init__" title="Link to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_config_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">service_toolkit</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="agentscope.service.service_toolkit.html#agentscope.service.service_toolkit.ServiceToolkit" title="agentscope.service.service_toolkit.ServiceToolkit"><span class="pre">ServiceToolkit</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sys_prompt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">&quot;You're</span> <span class="pre">a</span> <span class="pre">helpful</span> <span class="pre">assistant.</span> <span class="pre">Your</span> <span class="pre">name</span> <span class="pre">is</span> <span class="pre">{name}.&quot;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iters</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="_modules/agentscope/agents/react_agent.html#ReActAgent.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#agentscope.agents.ReActAgent.__init__" title="Link to this definition"></a></dt>
 <dd><p>Initialize the ReAct agent with the given name, model config name
 and tools.</p>
 <dl class="field-list simple">