Update

cookbook/claude/README.md

@@ -20,35 +20,51 @@ export ANTHROPIC_API_KEY=xxx
 ### 3. Install libraries
 
 ```shell
-pip install -U anthropic phidata duckduckgo-search duckdb yfinance exa_py
+pip install -U anthropic duckduckgo-search duckdb yfinance exa_py phidata
 ```
 
-### 4. Web search function calling
+### 4. Run Assistant
+
+- stream on
 
 ```shell
-python cookbook/claude/web_search.py
+python cookbook/claude/assistant.py
 ```
 
-### 5. YFinance function calling
+- stream off
 
 ```shell
-python cookbook/claude/finance.py
+python cookbook/claude/assistant_stream_off.py
 ```
 
-### 6. Structured output
+### 5. Run Assistant with Tools
+
+- Web search
 
 ```shell
-python cookbook/claude/structured_output.py
+python cookbook/claude/web_search.py
+```
+
+- YFinance
+
+```shell
+python cookbook/claude/finance.py
 ```
 
-### 7. Data Analyst
+- Data Analyst
 
 ```shell
 python cookbook/claude/data_analyst.py
 ```
 
-### 8. Exa Search
+- Exa Search
 
 ```shell
 python cookbook/claude/exa_search.py
 ```
+
+### 6. Run Assistant with Structured output
+
+```shell
+python cookbook/claude/structured_output.py
+```

cookbook/claude/exa_search.py

@@ -1,6 +1,12 @@
 from phi.assistant import Assistant
-from phi.llm.anthropic import Claude
 from phi.tools.exa import ExaTools
+from phi.tools.website import WebsiteTools
+from phi.llm.anthropic import Claude
 
-assistant = Assistant(llm=Claude(), tools=[ExaTools()], show_tool_calls=True)
-assistant.cli_app(markdown=True)
+assistant = Assistant(llm=Claude(), tools=[ExaTools(), WebsiteTools()], show_tool_calls=True)
+assistant.print_response(
+    "Produce this table: research chromatic homotopy theory."
+    "Access each link in the result outputting the summary for that article, its link, and keywords; "
+    "After the table output make conceptual ascii art of the overarching themes and constructions",
+    markdown=True,
+)

cookbook/claude/finance.py

@@ -3,9 +3,13 @@
 from phi.llm.anthropic import Claude
 
 assistant = Assistant(
-    llm=Claude(model="claude-3-opus-20240229"),
+    name="Finance Assistant",
+    llm=Claude(model="claude-3-haiku-20240307"),
     tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, stock_fundamentals=True)],
     show_tool_calls=True,
+    description="You are an investment analyst that researches stock prices, analyst recommendations, and stock fundamentals.",
+    instructions=["Format your response using markdown and use tables to display data where possible."],
+    debug_mode=True,
 )
-assistant.print_response("Share the NVDA stock price and some analyst recommendations", markdown=True)
-assistant.print_response("Summarize fundamentals for TSLA", markdown=True)
+assistant.print_response("Share the NVDA stock price and analyst recommendations", markdown=True)
+# assistant.print_response("Summarize fundamentals for TSLA", markdown=True)

cookbook/cohere/README.md

@@ -1,4 +1,4 @@
-# Cohere function calling
+# CohereChat function calling
 
 Currently "command-r" model supports function calling
 
@@ -11,7 +11,7 @@ python3 -m venv ~/.venvs/aienv
 source ~/.venvs/aienv/bin/activate
 ```
 
-### 2. Export your Cohere API Key
+### 2. Export your CohereChat API Key
 
 ```shell
 export CO_API_KEY=xxx
@@ -20,7 +20,7 @@ export CO_API_KEY=xxx
 ### 3. Install libraries
 
 ```shell
-pip install -U cohere phidata duckduckgo-search yfinance exa_py
+pip install -U cohere duckduckgo-search yfinance exa_py phidata
 ```
 
 ### 4. Web search function calling

cookbook/cohere/assistant.py

@@ -1,7 +1,9 @@
 from phi.assistant import Assistant
-from phi.llm.cohere import Cohere
+from phi.llm.cohere import CohereChat
 
 assistant = Assistant(
-    llm=Cohere(model="command-r"), description="You help people with their health and fitness goals.", debug_mode=True
+    llm=CohereChat(model="command-r"),
+    description="You help people with their health and fitness goals.",
+    debug_mode=True,
 )
 assistant.print_response("Share a quick healthy breakfast recipe.", markdown=True)

cookbook/cohere/assistant_stream_off.py

@@ -1,7 +1,9 @@
 from phi.assistant import Assistant
-from phi.llm.cohere import Cohere
+from phi.llm.cohere import CohereChat
 
 assistant = Assistant(
-    llm=Cohere(model="command-r"), description="You help people with their health and fitness goals.", debug_mode=True
+    llm=CohereChat(model="command-r"),
+    description="You help people with their health and fitness goals.",
+    debug_mode=True,
 )
 assistant.print_response("Share a quick healthy breakfast recipe.", markdown=True, stream=False)

cookbook/cohere/exa_search.py

@@ -1,6 +1,6 @@
 from phi.assistant import Assistant
-from phi.llm.cohere import Cohere
+from phi.llm.cohere import CohereChat
 from phi.tools.exa import ExaTools
 
-assistant = Assistant(llm=Cohere(model="command-r"), tools=[ExaTools()], show_tool_calls=True)
+assistant = Assistant(llm=CohereChat(model="command-r"), tools=[ExaTools()], show_tool_calls=True)
 assistant.cli_app(markdown=True)

cookbook/cohere/finance.py

@@ -1,11 +1,11 @@
 from phi.assistant import Assistant
 from phi.tools.yfinance import YFinanceTools
-from phi.llm.cohere import Cohere
+from phi.llm.cohere import CohereChat
 
 assistant = Assistant(
-    llm=Cohere(model="command-r"),
+    llm=CohereChat(model="command-r"),
     tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, stock_fundamentals=True)],
     show_tool_calls=True,
 )
-assistant.print_response("Share the NVDA stock price and some analyst recommendations", markdown=True)
+assistant.print_response("Share the NVDA stock price and analyst recommendations", markdown=True)
 assistant.print_response("Summarize fundamentals for TSLA", markdown=True)

cookbook/cohere/structured_output.py

@@ -2,7 +2,7 @@
 from pydantic import BaseModel, Field
 from rich.pretty import pprint
 from phi.assistant import Assistant
-from phi.llm.cohere import Cohere
+from phi.llm.cohere import CohereChat
 
 
 class MovieScript(BaseModel):
@@ -17,7 +17,7 @@ class MovieScript(BaseModel):
 
 
 movie_assistant = Assistant(
-    llm=Cohere(model="command-r"),
+    llm=CohereChat(model="command-r"),
     description="You help people write movie scripts.",
     output_model=MovieScript,
     # debug_mode=True,

cookbook/cohere/web_search.py

@@ -1,6 +1,6 @@
 from phi.assistant import Assistant
 from phi.tools.duckduckgo import DuckDuckGo
-from phi.llm.cohere import Cohere
+from phi.llm.cohere import CohereChat
 
-assistant = Assistant(llm=Cohere(model="command-r"), tools=[DuckDuckGo()], show_tool_calls=True)
+assistant = Assistant(llm=CohereChat(model="command-r"), tools=[DuckDuckGo()], show_tool_calls=True)
 assistant.print_response("Share 1 story from france and 1 from germany?", markdown=True)

cookbook/groq/finance.py

@@ -6,6 +6,7 @@
     llm=Groq(model="mixtral-8x7b-32768"),
     tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, stock_fundamentals=True)],
     show_tool_calls=True,
+    # debug_mode=True,
 )
-assistant.print_response("Share the NVDA stock price and some analyst recommendations", markdown=True, stream=False)
-assistant.print_response("Summarize fundamentals for TSLA", markdown=True, stream=False)
+assistant.print_response("Share the NVDA stock price and analyst recommendations", markdown=True, stream=False)
+# assistant.print_response("Summarize fundamentals for TSLA", markdown=True, stream=False)

cookbook/groq/web_search.py

@@ -2,5 +2,10 @@
 from phi.tools.duckduckgo import DuckDuckGo
 from phi.llm.groq import Groq
 
-assistant = Assistant(llm=Groq(model="mixtral-8x7b-32768"), tools=[DuckDuckGo()], show_tool_calls=True, debug_mode=True)
+assistant = Assistant(
+    llm=Groq(model="mixtral-8x7b-32768"),
+    tools=[DuckDuckGo()],
+    show_tool_calls=True,
+    # debug_mode=True
+)
 assistant.print_response("Tell me about OpenAI Sora", markdown=True, stream=False)

cookbook/hermes2/finance.py

@@ -7,5 +7,5 @@
     tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, stock_fundamentals=True)],
     show_tool_calls=True,
 )
-assistant.print_response("Share the NVDA stock price and some analyst recommendations", markdown=True)
+assistant.print_response("Share the NVDA stock price and analyst recommendations", markdown=True)
 assistant.print_response("Summarize fundamentals for TSLA", markdown=True)

phi/assistant/assistant.py

@@ -575,11 +575,11 @@ def _run(
 
         # -*- Update run output
         self.output = run_output
+        logger.debug(f"*********** Run End: {self.run_id} ***********")
 
         # -*- Yield final response if not streaming
         if not stream:
             yield run_output
-        logger.debug(f"*********** Run End: {self.run_id} ***********")
 
     def run(
         self, message: Optional[Union[List, Dict, str]] = None, stream: bool = True, **kwargs: Any

phi/llm/base.py

@@ -46,6 +46,9 @@ class LLM(BaseModel):
     system_prompt: Optional[str] = None
     instructions: Optional[List[str]] = None
 
+    # State from the run
+    run_id: Optional[str] = None
+
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
     @property

phi/llm/cohere/__init__.py

@@ -1 +1 @@
-from phi.llm.cohere.cohere import Cohere
+from phi.llm.cohere.chat import CohereChat

phi/llm/cohere/cohere.py → phi/llm/cohere/chat.py

@@ -2,15 +2,12 @@
 from textwrap import dedent
 from typing import Optional, List, Dict, Any
 
-
 from phi.llm.base import LLM
 from phi.llm.message import Message
 from phi.tools.function import FunctionCall
 from phi.utils.log import logger
 from phi.utils.timer import Timer
-from phi.utils.tools import (
-    get_function_call_for_tool_call,
-)
+from phi.utils.tools import get_function_call_for_tool_call
 
 try:
     from cohere import Client as CohereClient
@@ -27,21 +24,23 @@
     raise
 
 
-class Cohere(LLM):
+class CohereChat(LLM):
     name: str = "cohere"
     model: str = "command-r"
-    # # -*- Request parameters
+    # -*- Request parameters
     temperature: Optional[float] = None
     max_tokens: Optional[int] = None
     top_k: Optional[int] = None
     top_p: Optional[float] = None
     frequency_penalty: Optional[float] = None
     presence_penalty: Optional[float] = None
     request_params: Optional[Dict[str, Any]] = None
+    # Use cohere conversation_id to create a persistent conversation
+    use_conversation_id: bool = True
     # -*- Client parameters
     api_key: Optional[str] = None
     client_params: Optional[Dict[str, Any]] = None
-    # -*- Provide the client manually
+    # -*- Provide the Cohere client manually
     cohere_client: Optional[CohereClient] = None
 
     @property
@@ -57,6 +56,8 @@ def client(self) -> CohereClient:
     @property
     def api_kwargs(self) -> Dict[str, Any]:
         _request_params: Dict[str, Any] = {}
+        if self.use_conversation_id and self.run_id is not None:
+            _request_params["conversation_id"] = self.run_id
         if self.temperature:
             _request_params["temperature"] = self.temperature
         if self.max_tokens:
@@ -96,7 +97,26 @@ def get_tools(self) -> Optional[List[Dict[str, Any]]]:
 
     def invoke(self, messages: List[Message], tool_results: Optional[List[ChatRequestToolResultsItem]] = None) -> Chat:
         api_kwargs: Dict[str, Any] = self.api_kwargs
-        api_kwargs["chat_history"] = []
+        chat_message = None
+
+        if not self.use_conversation_id or self.run_id is None:
+            logger.debug("Providing chat_history to cohere.")
+            chat_history = []
+            for m in messages:
+                if m.role == "system":
+                    api_kwargs["preamble"] = m.content
+                elif m.role == "user":
+                    if last_user_message is not None:
+                        # Append the previously tracked user message to chat_history before updating it
+                        api_kwargs["chat_history"].append({"role": "USER", "message": last_user_message})
+                    # Update the last user message
+                    last_user_message = m.content
+                else:
+                    api_kwargs["chat_history"].append({"role": "CHATBOT", "message": m.content or ""})
+
+            api_kwargs["chat_history"] = chat_history
+
+
         user_message: List = []
         # Track the last user message to prevent adding it to chat_history
         last_user_message = None

phi/llm/openai/chat.py

@@ -57,7 +57,7 @@ class OpenAIChat(LLM):
     default_headers: Optional[Any] = None
     default_query: Optional[Any] = None
     client_params: Optional[Dict[str, Any]] = None
-    # -*- Provide the OpenAIClient manually
+    # -*- Provide the OpenAI client manually
     openai_client: Optional[OpenAIClient] = None
 
     @property

phi/task/llm/llm_task.py

@@ -171,19 +171,21 @@ def set_default_llm(self) -> None:
 
             self.llm = OpenAIChat()
 
-    def add_response_format_to_llm(self) -> None:
-        if self.output_model is not None and self.llm is not None:
-            self.llm.response_format = {"type": "json_object"}
-
-    def add_tools_to_llm(self) -> None:
+    def update_llm(self) -> None:
         if self.llm is None:
             logger.error(f"Task LLM is None: {self.__class__.__name__}")
             return
 
+        # Set response_format if it is not set on the llm
+        if self.output_model is not None and self.llm.response_format is None:
+            self.llm.response_format = {"type": "json_object"}
+
+        # Add tools to the LLM
         if self.tools is not None:
             for tool in self.tools:
                 self.llm.add_tool(tool)
 
+        # Add default tools to the LLM
         if self.use_tools:
             if self.read_chat_history_tool and self.memory is not None:
                 self.llm.add_tool(self.get_chat_history)
@@ -207,11 +209,13 @@ def add_tools_to_llm(self) -> None:
         if self.tool_call_limit is not None and self.tool_call_limit < self.llm.function_call_limit:
             self.llm.function_call_limit = self.tool_call_limit
 
+        if self.run_id is not None:
+            self.llm.run_id = self.run_id
+
     def prepare_task(self) -> None:
         self.set_task_id()
         self.set_default_llm()
-        self.add_response_format_to_llm()
-        self.add_tools_to_llm()
+        self.update_llm()
 
     def get_json_output_prompt(self) -> str:
         json_output_prompt = "\nProvide your output as a JSON containing the following fields:"