v2.5.5

cookbook/agents_101/.gitignore

@@ -0,0 +1 @@
+tmp

cookbook/agents_101/README.md

@@ -0,0 +1,60 @@
+# Agents 101
+
+> Note: Fork and clone this repository if needed
+
+### 1. Create and activate a virtual environment
+
+```shell
+python3 -m venv ~/.venvs/aienv
+source ~/.venvs/aienv/bin/activate
+```
+
+### 2. Export your `OPENAI_API_KEY`
+
+```shell
+export OPENAI_API_KEY=***
+```
+
+### 3. Install libraries
+
+```shell
+pip install -U openai duckduckgo-search duckdb yfinance lancedb tantivy pypdf sqlalchemy 'fastapi[standard]' phidata
+```
+
+### 4. Web Search Agent
+
+```shell
+python cookbook/agents_101/web_search.py
+```
+
+### 5. Web Reader Agent
+
+```shell
+python cookbook/agents_101/web_reader.py
+```
+
+### 5. Finance Agent
+
+```shell
+python cookbook/agents_101/finance_agent.py
+```
+
+### 6. RAG Agent
+
+```shell
+python cookbook/agents_101/rag_agent.py
+```
+
+### 7. Playground
+
+Authenticate with phidata.app
+
+```
+phi auth
+```
+
+Run the playground
+
+```shell
+python cookbook/agents_101/playground.py
+```

cookbook/agents_101/finance_agent.py

@@ -0,0 +1,16 @@
+"""Run `pip install yfinance` to install dependencies."""
+
+from phi.agent import Agent
+from phi.model.openai import OpenAIChat
+from phi.tools.yfinance import YFinanceTools
+
+finance_agent = Agent(
+    name="Finance Agent",
+    role="Get financial data",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
+    instructions=["Always use tables to display data"],
+    markdown=True,
+    show_tool_calls=True,
+)
+finance_agent.print_response("Share analyst recommendations for NVDA and provide a recommendation", stream=True)

cookbook/agents_101/playground.py

@@ -0,0 +1,41 @@
+from phi.agent import Agent
+from phi.model.openai import OpenAIChat
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.tools.yfinance import YFinanceTools
+from phi.storage.agent.sqlite import SqlAgentStorage
+from phi.playground import Playground, serve_playground_app
+
+web_agent = Agent(
+    name="Web Agent",
+    agent_id="web_agent",
+    role="Search the web for information",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[DuckDuckGo()],
+    instructions=["Always include sources"],
+    storage=SqlAgentStorage(table_name="web_agent_sessions", db_file="tmp/agents.db"),
+    markdown=True,
+)
+
+finance_agent = Agent(
+    name="Finance Agent",
+    agent_id="finance_agent",
+    role="Get financial data",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
+    instructions=["Always use tables to display data"],
+    storage=SqlAgentStorage(table_name="finance_agent_sessions", db_file="tmp/agents.db"),
+    markdown=True,
+)
+
+agent_team = Agent(
+    name="Agent Team",
+    agent_id="agent_team",
+    team=[web_agent, finance_agent],
+    storage=SqlAgentStorage(table_name="agent_team_sessions", db_file="tmp/agents.db"),
+    markdown=True,
+)
+
+app = Playground(agents=[finance_agent, web_agent, agent_team]).get_app()
+
+if __name__ == "__main__":
+    serve_playground_app("playground:app", reload=True)

cookbook/agents_101/rag_agent.py

@@ -0,0 +1,25 @@
+"""Run `pip install openai lancedb tantivy` to install dependencies."""
+
+from phi.agent import Agent
+from phi.model.openai import OpenAIChat
+from phi.knowledge.pdf import PDFUrlKnowledgeBase
+from phi.vectordb.lancedb import LanceDb, SearchType
+
+db_uri = "tmp/lancedb"
+knowledge_base = PDFUrlKnowledgeBase(
+    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    vector_db=LanceDb(table_name="recipes", uri=db_uri, search_type=SearchType.vector),
+)
+# Load the knowledge base: Comment out after first run
+knowledge_base.load(upsert=True)
+
+agent = Agent(
+    model=OpenAIChat(id="gpt-4o"),
+    knowledge=knowledge_base,
+    # Add a tool to read chat history.
+    read_chat_history=True,
+    show_tool_calls=True,
+    markdown=True,
+    # debug_mode=True,
+)
+agent.print_response("How do I make chicken and galangal in coconut milk soup", stream=True)

cookbook/agents_101/sqlite_storage.py

@@ -0,0 +1,13 @@
+"""Run `pip install duckduckgo-search sqlalchemy openai` to install dependencies."""
+
+from phi.agent import Agent
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.storage.agent.sqlite import SqlAgentStorage
+
+agent = Agent(
+    storage=SqlAgentStorage(table_name="agent_runs", db_file="tmp/data.db"),
+    tools=[DuckDuckGo()],
+    add_history_to_messages=True,
+)
+agent.print_response("How many people live in Canada?")
+agent.print_response("What is their national anthem called?")

cookbook/agents_101/web_search.py

@@ -0,0 +1,17 @@
+"""Run `pip install openai duckduckgo-search` to install dependencies."""
+
+from phi.agent import Agent
+from phi.model.openai import OpenAIChat
+from phi.tools.duckduckgo import DuckDuckGo
+
+web_agent = Agent(
+    name="Web Agent",
+    role="Search the web for information",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[DuckDuckGo()],
+    instructions=["Always include sources"],
+    markdown=True,
+    show_tool_calls=True,
+    add_datetime_to_instructions=True,
+)
+web_agent.print_response("Write a report on the US election", stream=True)

phi/agent/agent.py

@@ -2568,7 +2568,7 @@ def print_response(
                             _response_content += resp.content
                         if resp.extra_data is not None and resp.extra_data.reasoning_steps is not None:
                             reasoning_steps = resp.extra_data.reasoning_steps
-                    response_content = Markdown(_response_content) if self.markdown else _response_content
+                    response_content_stream = Markdown(_response_content) if self.markdown else _response_content
 
                     panels = [status]
 
@@ -2614,7 +2614,7 @@ def print_response(
                         render = True
                         # Create panel for response
                         response_panel = self.create_panel(
-                            content=response_content,
+                            content=response_content_stream,
                             title=f"Response ({response_timer.elapsed:.1f}s)",
                             border_style="blue",
                         )
@@ -2669,28 +2669,30 @@ def print_response(
                         )
                         panels.append(reasoning_panel)
 
-                response_content = ""
+                response_content_batch: Union[str, JSON, Markdown] = ""
                 if isinstance(run_response, RunResponse):
                     if isinstance(run_response.content, str):
-                        response_content = (
+                        response_content_batch = (
                             Markdown(run_response.content)
                             if self.markdown
                             else run_response.get_content_as_string(indent=4)
                         )
                     elif self.response_model is not None and isinstance(run_response.content, BaseModel):
                         try:
-                            response_content = JSON(run_response.content.model_dump_json(exclude_none=True), indent=2)
+                            response_content_batch = JSON(
+                                run_response.content.model_dump_json(exclude_none=True), indent=2
+                            )
                         except Exception as e:
                             logger.warning(f"Failed to convert response to JSON: {e}")
                     else:
                         try:
-                            response_content = JSON(json.dumps(run_response.content), indent=4)
+                            response_content_batch = JSON(json.dumps(run_response.content), indent=4)
                         except Exception as e:
                             logger.warning(f"Failed to convert response to JSON: {e}")
 
                 # Create panel for response
                 response_panel = self.create_panel(
-                    content=response_content,
+                    content=response_content_batch,
                     title=f"Response ({response_timer.elapsed:.1f}s)",
                     border_style="blue",
                 )
@@ -2739,7 +2741,7 @@ async def aprint_response(
                             _response_content += resp.content
                         if resp.extra_data is not None and resp.extra_data.reasoning_steps is not None:
                             reasoning_steps = resp.extra_data.reasoning_steps
-                    response_content = Markdown(_response_content) if self.markdown else _response_content
+                    response_content_stream = Markdown(_response_content) if self.markdown else _response_content
 
                     panels = [status]
 
@@ -2785,7 +2787,7 @@ async def aprint_response(
                         render = True
                         # Create panel for response
                         response_panel = self.create_panel(
-                            content=response_content,
+                            content=response_content_stream,
                             title=f"Response ({response_timer.elapsed:.1f}s)",
                             border_style="blue",
                         )
@@ -2840,28 +2842,30 @@ async def aprint_response(
                         )
                         panels.append(reasoning_panel)
 
-                response_content = ""
+                response_content_batch: Union[str, JSON, Markdown] = ""
                 if isinstance(run_response, RunResponse):
                     if isinstance(run_response.content, str):
-                        response_content = (
+                        response_content_batch = (
                             Markdown(run_response.content)
                             if self.markdown
                             else run_response.get_content_as_string(indent=4)
                         )
                     elif self.response_model is not None and isinstance(run_response.content, BaseModel):
                         try:
-                            response_content = JSON(run_response.content.model_dump_json(exclude_none=True), indent=2)
+                            response_content_batch = JSON(
+                                run_response.content.model_dump_json(exclude_none=True), indent=2
+                            )
                         except Exception as e:
                             logger.warning(f"Failed to convert response to JSON: {e}")
                     else:
                         try:
-                            response_content = JSON(json.dumps(run_response.content), indent=4)
+                            response_content_batch = JSON(json.dumps(run_response.content), indent=4)
                         except Exception as e:
                             logger.warning(f"Failed to convert response to JSON: {e}")
 
                 # Create panel for response
                 response_panel = self.create_panel(
-                    content=response_content,
+                    content=response_content_batch,
                     title=f"Response ({response_timer.elapsed:.1f}s)",
                     border_style="blue",
                 )

phi/tools/jina_tools.py

@@ -1,5 +1,7 @@
-from typing import Optional, Dict
 import httpx
+from os import getenv
+from typing import Optional, Dict
+
 from pydantic import BaseModel, HttpUrl, Field
 from phi.tools import Toolkit
 from phi.utils.log import logger
@@ -9,24 +11,39 @@ class JinaReaderToolsConfig(BaseModel):
     api_key: Optional[str] = Field(None, description="API key for Jina Reader")
     base_url: HttpUrl = Field("https://r.jina.ai/", description="Base URL for Jina Reader API")  # type: ignore
     search_url: HttpUrl = Field("https://s.jina.ai/", description="Search URL for Jina Reader API")  # type: ignore
-    max_content_length: int = Field(4000, description="Maximum content length in characters")
+    max_content_length: int = Field(10000, description="Maximum content length in characters")
+    timeout: Optional[int] = Field(None, description="Timeout for Jina Reader API requests")
 
 
 class JinaReaderTools(Toolkit):
-    def __init__(self, api_key: Optional[str] = None, max_content_length: int = 4000):
+    def __init__(
+        self,
+        api_key: Optional[str] = getenv("JINA_API_KEY"),
+        base_url: str = "https://r.jina.ai/",
+        search_url: str = "https://s.jina.ai/",
+        max_content_length: int = 10000,
+        timeout: Optional[int] = None,
+        read_url: bool = True,
+        search_query: bool = False,
+    ):
         super().__init__(name="jina_reader_tools")
-        config = JinaReaderToolsConfig(api_key=api_key, max_content_length=max_content_length)
-        self.api_key = config.api_key
-        self.base_url = config.base_url
-        self.search_url = config.search_url
-        self.max_content_length = config.max_content_length
 
-        self.register(self.read_url)
-        self.register(self.search_query)
+        self.config: JinaReaderToolsConfig = JinaReaderToolsConfig(
+            api_key=api_key,
+            base_url=base_url,
+            search_url=search_url,
+            max_content_length=max_content_length,
+            timeout=timeout,
+        )
+
+        if read_url:
+            self.register(self.read_url)
+        if search_query:
+            self.register(self.search_query)
 
     def read_url(self, url: str) -> str:
         """Reads a URL and returns the truncated content using Jina Reader API."""
-        full_url = f"{self.base_url}{url}"
+        full_url = f"{self.config.base_url}{url}"
         logger.info(f"Reading URL: {full_url}")
         try:
             response = httpx.get(full_url, headers=self._get_headers())
@@ -40,7 +57,7 @@ def read_url(self, url: str) -> str:
 
     def search_query(self, query: str) -> str:
         """Performs a web search using Jina Reader API and returns the truncated results."""
-        full_url = f"{self.search_url}{query}"
+        full_url = f"{self.config.search_url}{query}"
         logger.info(f"Performing search: {full_url}")
         try:
             response = httpx.get(full_url, headers=self._get_headers())
@@ -55,17 +72,19 @@ def search_query(self, query: str) -> str:
     def _get_headers(self) -> Dict[str, str]:
         headers = {
             "Accept": "application/json",
-            "X-With-Generated-Alt": "true",
             "X-With-Links-Summary": "true",
             "X-With-Images-Summary": "true",
         }
-        if self.api_key:
-            headers["Authorization"] = f"Bearer {self.api_key}"
+        if self.config.api_key:
+            headers["Authorization"] = f"Bearer {self.config.api_key}"
+        if self.config.timeout:
+            headers["X-Timeout"] = str(self.config.timeout)
+
         return headers
 
     def _truncate_content(self, content: str) -> str:
         """Truncate content to the maximum allowed length."""
-        if len(content) > self.max_content_length:
-            truncated = content[: self.max_content_length]
+        if len(content) > self.config.max_content_length:
+            truncated = content[: self.config.max_content_length]
             return truncated + "... (content truncated)"
         return content

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "phidata"
-version = "2.5.4"
+version = "2.5.5"
 description = "Build AI Agents with memory, knowledge and tools."
 requires-python = ">=3.7"
 readme = "README.md"