Auto PR from main to live

README.md

@@ -1,7 +1,7 @@
 # GroqCall.ai - Lightning-Fast LLM Function Calls
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1q3is7qynCsx4s7FBznCfTMnokbKWIv1F?usp=sharing)
-[![Version](https://img.shields.io/badge/version-0.0.2-blue.svg)](https://github.com/unclecode/groqcall)
+[![Version](https://img.shields.io/badge/version-0.0.4-blue.svg)](https://github.com/unclecode/groqcall)
 [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 
 GroqCall is a proxy server that enables lightning-fast function calls for Groq's Language Processing Unit (LPU) and other AI providers. It simplifies the creation of AI assistants by offering a wide range of built-in functions hosted on the cloud.

app/main.py

@@ -58,13 +58,13 @@ async def index(request: Request):
 # Add an get endpoint simple return the evrsion of the app
 @app.get("/version")
 async def version():
-    return {"version": "0.0.3"}
+    return {"version": "0.0.4"}
 
 
 if __name__ == "__main__":
     import uvicorn
 
     # uvicorn.run("main:app", host=os.getenv("HOST"), port=int(os.getenv('PORT')), workers=1, reload=True)
     uvicorn.run(
-        "main:app", host=os.getenv("HOST"), port=int(os.getenv("PORT")), workers=1
+        "main:app", host=os.getenv("HOST"), port=int(os.getenv("PORT")), workers=1, reload=False
     )

app/utils.py

@@ -30,12 +30,13 @@ def create_logger(logger_name: str, log_path: str = ".logs/access.log", show_on_
     return logger
 
 
-def get_tool_call_response(completion, unresolved_tol_calls, resolved_responses):
+def get_tool_call_response(tool_calls_result, unresolved_tol_calls, resolved_responses):
+    last_completion = tool_calls_result["last_completion"]
     tool_response = {
-        "id": "chatcmpl-" + completion.id,
+        "id": "chatcmpl-" + last_completion.id if last_completion else None,
         "object": "chat.completion",
-        "created": completion.created,
-        "model": completion.model,
+        "created": last_completion.created if last_completion else None,
+        "model": last_completion.model if last_completion else None,
         "choices": [
             {
                 "index": 0,
@@ -49,14 +50,9 @@ def get_tool_call_response(completion, unresolved_tol_calls, resolved_responses)
             }
         ],
         "resolved": resolved_responses,
-        "usage": {
-            "prompt_tokens": completion.usage.prompt_tokens,
-            "completion_tokens": completion.usage.completion_tokens,
-            "total_tokens": completion.usage.total_tokens,
-        },
-        "system_fingerprint": completion.system_fingerprint,
+        "usage": tool_calls_result["usage"],
+        "system_fingerprint": last_completion.system_fingerprint if last_completion else None,
     }
-
     return tool_response
 
 def describe(prompt: str, image_url_or_base64 : str, **kwargs) -> str:

cookbook/ai_assistant_custome_tools.py

@@ -1,5 +1,5 @@
 
-import os
+import os, json
 from typing import Optional, List
 from phi.llm.openai.like import OpenAILike
 from phi.assistant import Assistant
@@ -10,6 +10,8 @@
 from phi.tools.email import EmailTools
 from phi.utils.log import logger
 from phi.tools.email import EmailTools
+from phi.knowledge.base import AssistantKnowledge
+from phi.knowledge.base import Document
 from resources import vector_db
 from rich.prompt import Prompt
 from dotenv import load_dotenv
@@ -18,6 +20,32 @@
 # To run this example, first make sure to follow the instructions below:
 # 1. Install the phidata: pip install phidata
 # 2. Run the following command to start a docker, with pgvector db running: phi start resources.py 
+# 3. Download the sample of JSON knowledge base from the same folder of this file: cinemax.json
+
+class CinemaSerachDB(Toolkit):
+    def __init__(
+        self,
+        knowledge_base  : Optional[AssistantKnowledge] = None,
+        num_documents: int = None
+    ):
+        super().__init__(name="get_available_slots")
+        self.knowledge_base = knowledge_base
+        self.num_documents = num_documents
+        self.register(self.get_available_slots)
+
+    def get_available_slots(self, movie_slot_query: str ) -> str:
+        """Use this function to search the Cinemax database of available movies, show time, and date.
+
+        :param query: The query to search the Cinemax database of available movies, show time, and date.
+        :return: A string containing the response to the query.
+        """
+        relevant_docs: List[Document] = self.knowledge_base.search(query=movie_slot_query, num_documents=self.num_documents)
+        if len(relevant_docs) == 0:
+            return None
+
+        return json.dumps([doc.to_dict() for doc in relevant_docs], indent=2)
+
+
 
 class CinemaTools(Toolkit):
     def __init__(
@@ -29,7 +57,6 @@ def __init__(
         self.register(self.book_cinema_ticket)
 
     def book_cinema_ticket(self, movie_name: str, date: Optional[str] = None, time: Optional[str] = None, user_email: Optional[str] = None) -> str:
-        # """Books a cinema ticket for the given movie, date, and time, and sends an email to the user.
         """Use this function ONLY for booking a ticket, when all info is available (movie name, date, time and suer email). Do NOT use this function when user asks for movie details and other things
 
         Args:
@@ -118,12 +145,22 @@ def cinemax_assistant(new: bool = False, user: str = "user"):
         run_id=run_id,
         user_id="test_user",
         llm=my_groq,
-        knowledge_base=kb,
+        # knowledge_base=kb, Since I created my own tool CinemaSerachDB
         storage=storage,
         use_tools=True,
-        add_chat_history_to_messages=True,
-        tools=[CinemaTools(EmailTools("YOUR_EMAIL_ADDRESS", "SENDER_NAME", "SENDER_EMAIL", os.environ['email_pass_key'] ))], show_tool_calls=True, markdown=True
+        tools=[
+            CinemaSerachDB(knowledge_base=kb, num_documents=5),
+            CinemaTools(EmailTools("YOUR_EMAIL_ADDRESS", "SENDER_NAME", "SENDER_EMAIL", os.environ['email_pass_key'] ))
+        ], show_tool_calls=True, markdown=True,
+        add_chat_history_to_messages=True, # Inject history in every prompt
+        add_datetime_to_instructions=True,
+        limit_tool_access=True,
+        extra_instructions="",
+        output_model= None, # Pydantic
+        prevent_prompt_injection=True,
+        add_references_to_prompt=False, # It searches in the kb and add it to every prompt
         # add_references_to_prompt=True,
+        add_to_system_prompt="\n\nRemember:As a Cinemax agent, your responsibilities include:  \n\n1. Assisting individuals in locating their preferred movies using the provided database information. \n2. Facilitating ticket booking through the tools at your disposal. \n3. If critical information is missing and the registration system returns an error, generate a polite, humanized version of the error message and request the necessary additional information from users. \n4. When interacting with users, never disclose that you are an AI model unable to perform specific functions or activate certain features. \n5. When users inquire about movie showtimes, available movies, or related details, utilize your tools to access data from the knowledge base, rather than relying on your own knowledge. However, you may provide plot information about specific movies in a concise and succinct manner, ensuring responses are not excessively lengthy. \n6.When engaging in a discussion with users about films and proposing movies based on their preferences and the available options, it would be beneficial to list the available dates and times tailored to the user's interests and conversation history. This approach would simplify the booking process for the user. If the user has already specified a date, it is essential to remember and adhere to it, avoiding the suggestion of alternative dates."
     )
     assistant.knowledge_base.load(recreate=False)