Support o1 developer message format

cookbook/examples/streamlit/paperpal/app.py

@@ -68,7 +68,7 @@ def main() -> None:
             with st.container():
                 search_terms_container = st.empty()
                 search_generator_input = {"topic": report_topic, "num_terms": num_search_terms}
-                search_terms = search_term_generator.run(json.dumps(search_generator_input))
+                search_terms = search_term_generator.run(json.dumps(search_generator_input)).content
                 if search_terms:
                     search_terms_container.json(search_terms.model_dump())
             status.update(label="Search Terms Generated", state="complete", expanded=False)
@@ -88,7 +88,11 @@ def main() -> None:
                         exa_search_results = exa_search_agent.run(search_terms.model_dump_json(indent=4))
                         if isinstance(exa_search_results, str):
                             raise ValueError("Unexpected string response from exa_search_agent")
-                        if exa_search_results and len(exa_search_results.content.results) > 0:
+                        if (
+                            exa_search_results
+                            and exa_search_results.content
+                            and len(exa_search_results.content.results) > 0
+                        ):
                             exa_content = exa_search_results.model_dump_json(indent=4)
                             exa_container.json(exa_search_results.content.results)
                             status.update(label="Exa Search Complete", state="complete", expanded=False)
@@ -102,11 +106,11 @@ def main() -> None:
                 with st.container():
                     arxiv_container = st.empty()
                     arxiv_search_results = arxiv_search_agent.run(search_terms.model_dump_json(indent=4))
-                    if arxiv_search_results and arxiv_search_results.content.results:
+                    if arxiv_search_results and arxiv_search_results.content and arxiv_search_results.content.results:
                         arxiv_container.json([result.model_dump() for result in arxiv_search_results.content.results])
                 status.update(label="ArXiv Search Complete", state="complete", expanded=False)
 
-            if arxiv_search_results and arxiv_search_results.content.results:
+            if arxiv_search_results and arxiv_search_results.content and arxiv_search_results.content.results:
                 paper_summaries = []
                 for result in arxiv_search_results.content.results:
                     summary = {

cookbook/providers/openai/o1/o1_mini.py

@@ -0,0 +1,7 @@
+from phi.agent import Agent, RunResponse  # noqa
+from phi.model.openai import OpenAIChat
+
+agent = Agent(model=OpenAIChat(id="o1-mini"))
+
+# Print the response in the terminal
+agent.print_response("What is the closest galaxy to milky way?")

cookbook/providers/openai/o1/o1_mini_stream.py

@@ -0,0 +1,8 @@
+from typing import Iterator  # noqa
+from phi.agent import Agent, RunResponse  # noqa
+from phi.model.openai import OpenAIChat
+
+agent = Agent(model=OpenAIChat(id="o1-mini"))
+
+# Print the response in the terminal
+agent.print_response("What is the closest galaxy to milky way?", stream=True)

cookbook/providers/openai/o1/basic.py → cookbook/providers/openai/o1/o1_preview.py

@@ -3,9 +3,5 @@
 
 agent = Agent(model=OpenAIChat(id="o1-preview"))
 
-# Get the response in a variable
-# run: RunResponse = agent.run("What is the closest galaxy to milky way?")
-# print(run.content)
-
 # Print the response in the terminal
 agent.print_response("What is the closest galaxy to milky way?")

cookbook/providers/openai/o1/basic_stream.py → cookbook/providers/openai/o1/o1_preview_stream.py

@@ -4,10 +4,5 @@
 
 agent = Agent(model=OpenAIChat(id="o1-preview"))
 
-# Get the response in a variable
-# run_response: Iterator[RunResponse] = agent.run("What is the closest galaxy to milky way?", stream=True)
-# for chunk in run_response:
-#     print(chunk.content)
-
 # Print the response in the terminal
 agent.print_response("What is the closest galaxy to milky way?", stream=True)

cookbook/vectordb/mongodb.py

@@ -2,11 +2,11 @@
 
 from phi.agent import Agent
 from phi.knowledge.pdf import PDFUrlKnowledgeBase
-import os
-#os.environ["OPENAI_API_KEY"] = ""
+
+# os.environ["OPENAI_API_KEY"] = ""
 from phi.vectordb.mongodb import MongoDBVector
 
-# MongoDB Atlas connection string 
+# MongoDB Atlas connection string
 """
 Example connection strings:
 "mongodb+srv://<username>:<password>@cluster0.mongodb.net/?retryWrites=true&w=majority"
@@ -16,10 +16,12 @@
 
 knowledge_base = PDFUrlKnowledgeBase(
     urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
-    vector_db=MongoDBVector(collection_name="recipes", db_url=mdb_connection_string, wait_until_index_ready=60, wait_after_insert=300),
-) #adjust wait_after_insert and wait_until_index_ready to your needs
-knowledge_base.load(recreate=True)  
+    vector_db=MongoDBVector(
+        collection_name="recipes", db_url=mdb_connection_string, wait_until_index_ready=60, wait_after_insert=300
+    ),
+)  # adjust wait_after_insert and wait_until_index_ready to your needs
+knowledge_base.load(recreate=True)
 
 # Create and use the agent
 agent = Agent(knowledge_base=knowledge_base, show_tool_calls=True)
-agent.print_response("How to make Thai curry?", markdown=True)
+agent.print_response("How to make Thai curry?", markdown=True)

cookbook/examples/workflows/content_creator_workflow/scheduler.py → cookbook/workflows/content_creator_workflow/scheduler.py

@@ -4,7 +4,7 @@
 from pydantic import BaseModel
 from dotenv import load_dotenv
 
-from cookbook.examples.workflows.content_creator_workflow.config import TYPEFULLY_API_URL, HEADERS, PostType
+from cookbook.workflows.content_creator_workflow.config import TYPEFULLY_API_URL, HEADERS, PostType
 from phi.utils.log import logger
 
 load_dotenv()

cookbook/examples/workflows/content_creator_workflow/workflow.py → cookbook/workflows/content_creator_workflow/workflow.py

@@ -8,9 +8,9 @@
 from phi.model.openai import OpenAIChat
 from phi.tools.firecrawl import FirecrawlTools
 from phi.utils.log import logger
-from cookbook.examples.workflows.content_creator_workflow.scheduler import schedule
-from cookbook.examples.workflows.content_creator_workflow.prompts import agents_config, tasks_config
-from cookbook.examples.workflows.content_creator_workflow.config import PostType
+from cookbook.workflows.content_creator_workflow.scheduler import schedule
+from cookbook.workflows.content_creator_workflow.prompts import agents_config, tasks_config
+from cookbook.workflows.content_creator_workflow.config import PostType
 
 # Load environment variables
 load_dotenv()
@@ -34,7 +34,7 @@ class Tweet(BaseModel):
 
     content: str
     is_hook: bool = Field(default=False, description="Marks if this tweet is the 'hook' (first tweet)")
-    media_urls: Optional[List[str]] = Field(default_factory=list, description="Associated media URLs, if any")
+    media_urls: Optional[List[str]] = Field(default_factory=list, description="Associated media URLs, if any")  # type: ignore
 
 
 class Thread(BaseModel):

phi/model/ollama/chat.py

@@ -723,6 +723,6 @@ async def aresponse_stream(self, messages: List[Message]) -> Any:
                 yield post_tool_call_response
         logger.debug("---------- Ollama Async Response End ----------")
 
-    def model_copy(self, *, update: Optional[dict[str, Any]] = None, deep: bool = False) -> "Ollama":
+    def model_copy(self, *, update: Optional[Mapping[str, Any]] = None, deep: bool = False) -> "Ollama":
         new_model = Ollama(**self.model_dump(exclude={"client"}), client=self.client)
         return new_model

phi/model/openai/chat.py

@@ -308,6 +308,9 @@ def format_message(self, message: Message) -> Dict[str, Any]:
         Returns:
             Dict[str, Any]: The formatted message.
         """
+        if message.role == "system":
+            message.role = "developer"
+
         if message.role == "user":
             if message.images is not None:
                 message = self.add_images_to_message(message=message, images=message.images)
@@ -599,7 +602,7 @@ def response(self, messages: List[Message]) -> ModelResponse:
         # -*- Parse transcript if available
         if response_audio:
             if response_audio.transcript and not response_message.content:
-                response_message.content = response_message.audio.transcript
+                response_message.content = response_audio.transcript
 
         # -*- Parse structured outputs
         try:
@@ -677,7 +680,7 @@ async def aresponse(self, messages: List[Message]) -> ModelResponse:
         # -*- Parse transcript if available
         if response_audio:
             if response_audio.transcript and not response_message.content:
-                response_message.content = response_message.audio.transcript
+                response_message.content = response_audio.transcript
 
         # -*- Parse structured outputs
         try:

phi/vectordb/mongodb/mongodb.py

@@ -21,6 +21,7 @@
 except ImportError:
     raise ImportError("`pymongo` not installed. Please install using `pip install pymongo`")
 
+
 class MongoDBVector(VectorDb):
     """
     MongoDB Vector Database implementation with elegant handling of Atlas Search index creation.
@@ -34,8 +35,8 @@ def __init__(
         embedder: Embedder = OpenAIEmbedder(),
         distance_metric: str = Distance.cosine,
         overwrite: bool = False,
-        wait_until_index_ready: float = None,
-        wait_after_insert: float = None,
+        wait_until_index_ready: Optional[float] = None,
+        wait_after_insert: Optional[float] = None,
         **kwargs,
     ):
         """
@@ -71,9 +72,9 @@ def _get_client(self) -> MongoClient:
         """Create or retrieve the MongoDB client."""
         try:
             logger.debug("Creating MongoDB Client")
-            client = MongoClient(self.connection_string, **self.kwargs)
+            client: MongoClient = MongoClient(self.connection_string, **self.kwargs)
             # Trigger a connection to verify the client
-            client.admin.command('ping')
+            client.admin.command("ping")
             logger.info("Connected to MongoDB successfully.")
             return client
         except errors.ConnectionFailure as e:
@@ -85,7 +86,7 @@ def _get_client(self) -> MongoClient:
 
     def _get_or_create_collection(self) -> Collection:
         """Get or create the MongoDB collection, handling Atlas Search index creation."""
-        
+
         self._collection = self._db[self.collection_name]
 
         if not self.collection_exists():
@@ -120,7 +121,7 @@ def _create_search_index(self, overwrite: bool = True) -> None:
                             "type": "vector",
                             "numDimensions": 1536,
                             "path": "embedding",
-                            "similarity": self.distance_metric, #cosine
+                            "similarity": self.distance_metric,  # cosine
                         },
                     ]
                 },
@@ -157,7 +158,7 @@ def _wait_for_index_ready(self) -> None:
                     break
             except Exception as e:
                 logger.error(f"Error checking index status: {e}")
-            if time.time() - start_time > self.wait_until_index_ready:
+            if time.time() - start_time > self.wait_until_index_ready:  # type: ignore
                 raise TimeoutError("Timeout waiting for search index to become ready.")
             time.sleep(1)
 
@@ -171,7 +172,7 @@ def create(self) -> None:
 
     def doc_exists(self, document: Document) -> bool:
         """Check if a document exists in the MongoDB collection based on its content."""
-        doc_id = md5(document.content.encode('utf-8')).hexdigest()
+        doc_id = md5(document.content.encode("utf-8")).hexdigest()
         try:
             exists = self._collection.find_one({"_id": doc_id}) is not None
             logger.debug(f"Document {'exists' if exists else 'does not exist'}: {doc_id}")
@@ -219,7 +220,7 @@ def insert(self, documents: List[Document], filters: Optional[Dict[str, Any]] =
                 # lets wait for 5 minutes.... just in case
                 # feel free to 'optimize'... :)
                 if self.wait_after_insert and self.wait_after_insert > 0:
-                    time.sleep(self.wait_after_insert) 
+                    time.sleep(self.wait_after_insert)
             except errors.BulkWriteError as e:
                 logger.warning(f"Bulk write error while inserting documents: {e.details}")
             except Exception as e:
@@ -245,9 +246,7 @@ def upsert_available(self) -> bool:
         """Indicate that upsert functionality is available."""
         return True
 
-    def search(
-        self, query: str, limit: int = 5, filters: Optional[Dict[str, Any]] = None
-    ) -> List[Document]:
+    def search(self, query: str, limit: int = 5, filters: Optional[Dict[str, Any]] = None) -> List[Document]:
         """Search the MongoDB collection for documents relevant to the query."""
         query_embedding = self.embedder.get_embedding(query)
         if query_embedding is None:
@@ -268,7 +267,7 @@ def search(
                 {"$set": {"score": {"$meta": "vectorSearchScore"}}},
             ]
             pipeline.append({"$project": {"embedding": 0}})
-            agg = list(self._collection.aggregate(pipeline))
+            agg = list(self._collection.aggregate(pipeline))  # type: ignore
             docs = []
             for doc in agg:
                 docs.append(
@@ -366,7 +365,7 @@ def prepare_doc(self, document: Document) -> Dict[str, Any]:
             raise ValueError(f"Failed to generate embedding for document: {document.id}")
 
         cleaned_content = document.content.replace("\x00", "\ufffd")
-        doc_id = md5(cleaned_content.encode('utf-8')).hexdigest()
+        doc_id = md5(cleaned_content.encode("utf-8")).hexdigest()
         doc_data = {
             "_id": doc_id,
             "name": document.name,
@@ -385,4 +384,4 @@ def get_count(self) -> int:
             return count
         except Exception as e:
             logger.error(f"Error getting document count: {e}")
-            return 0
+            return 0

pyproject.toml

@@ -145,6 +145,7 @@ module = [
   "psycopg2.*",
   "pyarrow.*",
   "pycountry.*",
+  "pymongo.*",
   "pypdf.*",
   "pytz.*",
   "qdrant_client.*",