Merge pull request #973 from superagent-ai/feat/native-function-calling

Native Function Calling

fern/apis/prod/openapi/openapi.yaml

@@ -2,7 +2,7 @@ openapi: 3.0.2
 info:
   title: Superagent
   description: 🥷 Run AI-agents with an API
-  version: 0.2.29
+  version: 0.2.32
 servers:
   - url: https://api.beta.superagent.sh
 paths:
@@ -195,8 +195,8 @@ paths:
                 $ref: '#/components/schemas/HTTPValidationError'
       security:
         - HTTPBearer: []
-      x-fern-sdk-group-name: agent
       x-fern-sdk-method-name: invoke
+      x-fern-sdk-group-name: agent
   /api/v1/agents/{agent_id}/llms:
     post:
       tags:
@@ -1507,6 +1507,33 @@ paths:
                 $ref: '#/components/schemas/HTTPValidationError'
       security:
         - HTTPBearer: []
+    delete:
+      tags:
+        - Vector Database
+      summary: Delete
+      description: Delete a Vector Database
+      operationId: delete_api_v1_vector_dbs__vector_db_id__delete
+      parameters:
+        - required: true
+          schema:
+            title: Vector Db Id
+            type: string
+          name: vector_db_id
+          in: path
+      responses:
+        '200':
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
+        '422':
+          description: Validation Error
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/HTTPValidationError'
+      security:
+        - HTTPBearer: []
     patch:
       tags:
         - Vector Database
@@ -1783,6 +1810,8 @@ components:
         - TOGETHER_AI
         - ANTHROPIC
         - BEDROCK
+        - GROQ
+        - MISTRAL
       type: string
       description: An enumeration.
     OpenAiAssistantParameters:

libs/superagent/app/agents/base.py

@@ -6,7 +6,7 @@
 
 from app.models.request import LLMParams as LLMParamsRequest
 from app.utils.callbacks import CustomAsyncIteratorCallbackHandler
-from prisma.enums import AgentType
+from prisma.enums import AgentType, LLMProvider
 from prisma.models import LLM, Agent
 
 
@@ -21,9 +21,21 @@ class LLMParams(BaseModel):
 class LLMData(BaseModel):
     llm: LLM
     params: LLMParams
+    model: str
 
 
 class AgentBase(ABC):
+    _input: str
+    _messages: list = []
+    prompt: Any
+    tools: Any
+    session_id: str
+    enable_streaming: bool
+    output_schema: str
+    callbacks: List[CustomAsyncIteratorCallbackHandler]
+    agent_data: Agent
+    llm_data: LLMData
+
     def __init__(
         self,
         session_id: str,
@@ -40,10 +52,6 @@ def __init__(
         self.llm_data = llm_data
         self.agent_data = agent_data
 
-    _input: str
-    prompt: Any
-    tools: Any
-
     @property
     def input(self):
         return self._input
@@ -52,6 +60,14 @@ def input(self):
     def input(self, value: str):
         self._input = value
 
+    @property
+    def messages(self):
+        return self._messages
+
+    @messages.setter
+    def messages(self, value: list):
+        self._messages = value
+
     @property
     @abstractmethod
     def prompt(self) -> Any:
@@ -95,7 +111,31 @@ def llm_data(self):
             **(params),
         }
 
-        return LLMData(llm=llm, params=LLMParams.parse_obj(options))
+        params = LLMParams(
+            temperature=options.get("temperature"),
+            max_tokens=options.get("max_tokens"),
+            aws_access_key_id=(
+                options.get("aws_access_key_id")
+                if llm.provider == LLMProvider.BEDROCK
+                else None
+            ),
+            aws_secret_access_key=(
+                options.get("aws_secret_access_key")
+                if llm.provider == LLMProvider.BEDROCK
+                else None
+            ),
+            aws_region_name=(
+                options.get("aws_region_name")
+                if llm.provider == LLMProvider.BEDROCK
+                else None
+            ),
+        )
+
+        return LLMData(
+            llm=llm,
+            params=LLMParams.parse_obj(options),
+            model=self.agent_data.llmModel or self.agent_data.metadata.get("model"),
+        )
 
     async def get_agent(self):
         if self.agent_data.type == AgentType.OPENAI_ASSISTANT:

libs/superagent/app/agents/langchain.py

@@ -46,7 +46,7 @@ def _get_llm(self):
 
         if llm_data.llm.provider == LLMProvider.OPENAI:
             return ChatOpenAI(
-                model=LLM_MAPPING[self.agent_data.llmModel],
+                model=LLM_MAPPING[self.llm_data.model],
                 openai_api_key=llm_data.llm.apiKey,
                 streaming=self.enable_streaming,
                 callbacks=self.callbacks,