Feat/open ai compatible functioncall (#2783)

Co-authored-by: jyong <jyong@dify.ai>
2 years ago · e54c9cd401
parent f8951d7f57
commit e54c9cd401
8 changed files with 105 additions and 40 deletions
--- a/api/core/model_runtime/model_providers/cohere/llm/llm.py
+++ b/api/core/model_runtime/model_providers/cohere/llm/llm.py
@ -472,7 +472,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
        else:
            raise ValueError(f"Got unknown type {message}")

-        if message.name is not None:
+        if message.name:
            message_dict["user_name"] = message.name

        return message_dict
--- a/api/core/model_runtime/model_providers/mistralai/llm/mistral-large-latest.yaml
+++ b/api/core/model_runtime/model_providers/mistralai/llm/mistral-large-latest.yaml
@ -24,7 +24,7 @@ parameter_rules:
    min: 1
    max: 8000
  - name: safe_prompt
-    defulat: false
+    default: false
    type: boolean
    help:
      en_US: Whether to inject a safety prompt before all conversations.
--- a/api/core/model_runtime/model_providers/mistralai/llm/mistral-medium-latest.yaml
+++ b/api/core/model_runtime/model_providers/mistralai/llm/mistral-medium-latest.yaml
@ -24,7 +24,7 @@ parameter_rules:
    min: 1
    max: 8000
  - name: safe_prompt
-    defulat: false
+    default: false
    type: boolean
    help:
      en_US: Whether to inject a safety prompt before all conversations.
--- a/api/core/model_runtime/model_providers/mistralai/llm/mistral-small-latest.yaml
+++ b/api/core/model_runtime/model_providers/mistralai/llm/mistral-small-latest.yaml
@ -24,7 +24,7 @@ parameter_rules:
    min: 1
    max: 8000
  - name: safe_prompt
-    defulat: false
+    default: false
    type: boolean
    help:
      en_US: Whether to inject a safety prompt before all conversations.
--- a/api/core/model_runtime/model_providers/mistralai/llm/open-mistral-7b.yaml
+++ b/api/core/model_runtime/model_providers/mistralai/llm/open-mistral-7b.yaml
@ -24,7 +24,7 @@ parameter_rules:
    min: 1
    max: 2048
  - name: safe_prompt
-    defulat: false
+    default: false
    type: boolean
    help:
      en_US: Whether to inject a safety prompt before all conversations.
--- a/api/core/model_runtime/model_providers/mistralai/llm/open-mixtral-8x7b.yaml
+++ b/api/core/model_runtime/model_providers/mistralai/llm/open-mixtral-8x7b.yaml
@ -24,7 +24,7 @@ parameter_rules:
    min: 1
    max: 8000
  - name: safe_prompt
-    defulat: false
+    default: false
    type: boolean
    help:
      en_US: Whether to inject a safety prompt before all conversations.
--- a/api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py
+++ b/api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py
@ -25,6 +25,7 @@ from core.model_runtime.entities.model_entities import (
    AIModelEntity,
    DefaultParameterName,
    FetchFrom,
+    ModelFeature,
    ModelPropertyKey,
    ModelType,
    ParameterRule,
@ -166,11 +167,23 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
        """
            generate custom model entities from credentials
        """
+        support_function_call = False
+        features = []
+        function_calling_type = credentials.get('function_calling_type', 'no_call')
+        if function_calling_type == 'function_call':
+            features = [ModelFeature.TOOL_CALL]
+            support_function_call = True
+        endpoint_url = credentials["endpoint_url"]
+        # if not endpoint_url.endswith('/'):
+        #     endpoint_url += '/'
+        # if 'https://api.openai.com/v1/' == endpoint_url:
+        #     features = [ModelFeature.STREAM_TOOL_CALL]
        entity = AIModelEntity(
            model=model,
            label=I18nObject(en_US=model),
            model_type=ModelType.LLM,
            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            features=features if support_function_call else [],
            model_properties={
                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size', "4096")),
                ModelPropertyKey.MODE: credentials.get('mode'),
@ -194,14 +207,6 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                    max=1,
                    precision=2
                ),
-                ParameterRule(
-                    name="top_k",
-                    label=I18nObject(en_US="Top K"),
-                    type=ParameterType.INT,
-                    default=int(credentials.get('top_k', 1)),
-                    min=1,
-                    max=100
-                ),
                ParameterRule(
                    name=DefaultParameterName.FREQUENCY_PENALTY.value,
                    label=I18nObject(en_US="Frequency Penalty"),
@ -232,7 +237,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                output=Decimal(credentials.get('output_price', 0)),
                unit=Decimal(credentials.get('unit', 0)),
                currency=credentials.get('currency', "USD")
-            )
+            ),
        )

        if credentials['mode'] == 'chat':
@ -292,14 +297,22 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
            raise ValueError("Unsupported completion type for model configuration.")

        # annotate tools with names, descriptions, etc.
+        function_calling_type = credentials.get('function_calling_type', 'no_call')
        formatted_tools = []
        if tools:
-            data["tool_choice"] = "auto"
+            if function_calling_type == 'function_call':
+                data['functions'] = [{
+                    "name": tool.name,
+                    "description": tool.description,
+                    "parameters": tool.parameters
+                } for tool in tools]
+            elif function_calling_type == 'tool_call':
+                data["tool_choice"] = "auto"

-            for tool in tools:
-                formatted_tools.append(helper.dump_model(PromptMessageFunction(function=tool)))
+                for tool in tools:
+                    formatted_tools.append(helper.dump_model(PromptMessageFunction(function=tool)))

-            data["tools"] = formatted_tools
+                data["tools"] = formatted_tools

        if stop:
            data["stop"] = stop
@ -367,7 +380,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):

        for chunk in response.iter_lines(decode_unicode=True, delimiter=delimiter):
            if chunk:
-                #ignore sse comments
+                # ignore sse comments
                if chunk.startswith(':'):
                    continue
                decoded_chunk = chunk.strip().lstrip('data: ').lstrip()
@ -452,10 +465,13 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):

        response_content = ''
        tool_calls = None
-
+        function_calling_type = credentials.get('function_calling_type', 'no_call')
        if completion_type is LLMMode.CHAT:
            response_content = output.get('message', {})['content']
-            tool_calls = output.get('message', {}).get('tool_calls')
+            if function_calling_type == 'tool_call':
+                tool_calls = output.get('message', {}).get('tool_calls')
+            elif function_calling_type == 'function_call':
+                tool_calls = output.get('message', {}).get('function_call')

        elif completion_type is LLMMode.COMPLETION:
            response_content = output['text']
@ -463,7 +479,10 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
        assistant_message = AssistantPromptMessage(content=response_content, tool_calls=[])

        if tool_calls:
-            assistant_message.tool_calls = self._extract_response_tool_calls(tool_calls)
+            if function_calling_type == 'tool_call':
+                assistant_message.tool_calls = self._extract_response_tool_calls(tool_calls)
+            elif function_calling_type == 'function_call':
+                assistant_message.tool_calls = [self._extract_response_function_call(tool_calls)]

        usage = response_json.get("usage")
        if usage:
@ -522,33 +541,34 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
            message = cast(AssistantPromptMessage, message)
            message_dict = {"role": "assistant", "content": message.content}
            if message.tool_calls:
-                message_dict["tool_calls"] = [helper.dump_model(PromptMessageFunction(function=tool_call)) for tool_call
-                                              in
-                                              message.tool_calls]
-                # function_call = message.tool_calls[0]
-                # message_dict["function_call"] = {
-                #     "name": function_call.function.name,
-                #     "arguments": function_call.function.arguments,
-                # }
+                # message_dict["tool_calls"] = [helper.dump_model(PromptMessageFunction(function=tool_call)) for tool_call
+                #                               in
+                #                               message.tool_calls]
+
+                function_call = message.tool_calls[0]
+                message_dict["function_call"] = {
+                    "name": function_call.function.name,
+                    "arguments": function_call.function.arguments,
+                }
        elif isinstance(message, SystemPromptMessage):
            message = cast(SystemPromptMessage, message)
            message_dict = {"role": "system", "content": message.content}
        elif isinstance(message, ToolPromptMessage):
            message = cast(ToolPromptMessage, message)
-            message_dict = {
-                "role": "tool",
-                "content": message.content,
-                "tool_call_id": message.tool_call_id
-            }
            # message_dict = {
-            #     "role": "function",
+            #     "role": "tool",
            #     "content": message.content,
-            #     "name": message.tool_call_id
+            #     "tool_call_id": message.tool_call_id
            # }
+            message_dict = {
+                "role": "function",
+                "content": message.content,
+                "name": message.tool_call_id
+            }
        else:
            raise ValueError(f"Got unknown type {message}")

-        if message.name is not None:
+        if message.name:
            message_dict["name"] = message.name

        return message_dict
@ -693,3 +713,26 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                tool_calls.append(tool_call)

        return tool_calls
+
+    def _extract_response_function_call(self, response_function_call) \
+            -> AssistantPromptMessage.ToolCall:
+        """
+        Extract function call from response
+
+        :param response_function_call: response function call
+        :return: tool call
+        """
+        tool_call = None
+        if response_function_call:
+            function = AssistantPromptMessage.ToolCall.ToolCallFunction(
+                name=response_function_call['name'],
+                arguments=response_function_call['arguments']
+            )
+
+            tool_call = AssistantPromptMessage.ToolCall(
+                id=response_function_call['name'],
+                type="function",
+                function=function
+            )
+
+        return tool_call
--- a/api/core/model_runtime/model_providers/openai_api_compatible/openai_api_compatible.yaml
+++ b/api/core/model_runtime/model_providers/openai_api_compatible/openai_api_compatible.yaml
@ -75,6 +75,28 @@ model_credential_schema:
          value: llm
      default: '4096'
      type: text-input
+    - variable: function_calling_type
+      show_on:
+        - variable: __model_type
+          value: llm
+      label:
+        en_US: Function calling
+      type: select
+      required: false
+      default: no_call
+      options:
+        - value: function_call
+          label:
+            en_US: Support
+            zh_Hans: 支持
+#        - value: tool_call
+#          label:
+#            en_US: Tool Call
+#            zh_Hans: Tool Call
+        - value: no_call
+          label:
+            en_US: Not Support
+            zh_Hans: 不支持
    - variable: stream_mode_delimiter
      label:
        zh_Hans: 流模式返回结果的分隔符