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| from dotenv import load_dotenv | |
| import os | |
| import chromadb | |
| from chromadb.config import Settings | |
| from openai import OpenAI | |
| from sentence_transformers import SentenceTransformer | |
| from pypdf import PdfReader | |
| import requests | |
| from pydantic import BaseModel | |
| import gradio as gr | |
| import json | |
| load_dotenv(override=True) | |
| def handle_tool_call(tool_calls): | |
| results = [] | |
| for tool_call in tool_calls: | |
| tool_name = tool_call.function.name | |
| arguments = json.loads(tool_call.function.arguments) | |
| print(f"Tool called: {tool_name}", flush=True) | |
| tool = globals().get(tool_name) | |
| result = tool(**arguments) if tool else {} | |
| results.append({"role": "tool","content": json.dumps(result),"tool_call_id": tool_call.id}) | |
| return results | |
| class Evaluation(BaseModel): | |
| is_acceptable: bool | |
| feedback: str | |
| model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') | |
| def push(text): | |
| requests.post( | |
| "https://api.pushover.net/1/messages.json", | |
| data={ | |
| "token": os.getenv("PUSHOVER_TOKEN"), | |
| "user": os.getenv("PUSHOVER_USER"), | |
| "message": text, | |
| } | |
| ) | |
| def record_user_details(email, name="Name not provided", notes="not provided"): | |
| push(f"Recording {name} with email {email} and notes {notes}") | |
| return {"recorded": "ok"} | |
| def record_unknown_question(question): | |
| push(f"Recording {question}") | |
| return {"recorded": "ok"} | |
| record_user_details_json = { | |
| "name": "record_user_details", | |
| "description": "Use this tool to record that a user is interested in being in touch and provided an email address", | |
| "parameters": { | |
| "type": "object", | |
| "properties": { | |
| "email": { | |
| "type": "string", | |
| "description": "The email address of this user" | |
| }, | |
| "name": { | |
| "type": "string", | |
| "description": "The user's name, if they provided it" | |
| } | |
| , | |
| "notes": { | |
| "type": "string", | |
| "description": "Any additional information about the conversation that's worth recording to give context" | |
| } | |
| }, | |
| "required": ["email"], | |
| "additionalProperties": False | |
| } | |
| } | |
| record_unknown_question_json = { | |
| "name": "record_unknown_question", | |
| "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer", | |
| "parameters": { | |
| "type": "object", | |
| "properties": { | |
| "question": { | |
| "type": "string", | |
| "description": "The question that couldn't be answered" | |
| }, | |
| }, | |
| "required": ["question"], | |
| "additionalProperties": False | |
| } | |
| } | |
| tools = [{"type": "function", "function": record_user_details_json}, | |
| {"type": "function", "function": record_unknown_question_json}] | |
| class Me: | |
| def __init__(self, name, cv_path): | |
| self.name = name | |
| self.chroma_client = chromadb.HttpClient(host=os.environ["CHROMA_DB_CLIEN"], port=8000, settings=Settings( | |
| chroma_client_auth_provider="chromadb.auth.token_authn.TokenAuthClientProvider", | |
| chroma_client_auth_credentials=os.environ["CHROMA_TOKEN"] | |
| )) | |
| self.collection = self.chroma_client.get_collection(name="all-my-projects") | |
| self.model = model | |
| self.openai = OpenAI() | |
| self.cv = "" | |
| reader = PdfReader(cv_path) | |
| for page in reader.pages: | |
| text = page.extract_text() | |
| if text: | |
| self.cv += text | |
| self.system_prompt = f"You are acting as {self.name}. You are answering questions on {self.name}'s website, \ | |
| particularly questions related to {name}'s career, background, skills and experience. \ | |
| Your responsibility is to represent {name} for interactions on the website as faithfully as possible. \ | |
| You are given a summary of {name}'s background and LinkedIn profile which you can use to answer questions. \ | |
| Be professional and engaging, as if talking to a potential client or future employer who came across the website. \ | |
| " | |
| self.system_prompt += f"\n\## CV:\n{self.cv}\n\n" | |
| self.system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}." | |
| self.gemini = OpenAI( | |
| api_key=os.getenv("GOOGLE_API_KEY"), | |
| base_url="https://generativelanguage.googleapis.com/v1beta/openai/") | |
| self.evaluator_system_prompt = f"You are an evaluator that decides whether a response to a question is acceptable. \ | |
| You are provided with a conversation between a User and an Agent. Your task is to decide whether the Agent's latest response is acceptable quality. \ | |
| The Agent is playing the role of {name} and is representing {self.name} on their website. \ | |
| The Agent has been instructed to be professional and engaging, as if talking to a potential client or future employer who came across the website. \ | |
| The Agent has been provided with context on {self.name} in the form of their summary and LinkedIn details. Here's the information:" | |
| self.evaluator_system_prompt += f"## CV:\n{self.cv}\n\n" | |
| self.evaluator_system_prompt += f"With this context, please evaluate the latest response, replying with whether the response is acceptable and your feedback." | |
| def evaluator_user_prompt(self, reply, message, history): | |
| user_prompt = f"Here's the conversation between the User and the Agent: \n\n{history}\n\n" | |
| user_prompt += f"Here's the latest message from the User: \n\n{message}\n\n" | |
| user_prompt += f"Here's the latest response from the Agent: \n\n{reply}\n\n" | |
| user_prompt += f"Please evaluate the response, replying with whether it is acceptable and your feedback." | |
| return user_prompt | |
| def evaluate(self, reply, message, history) -> Evaluation: | |
| messages = [{"role": "system", "content": self.evaluator_system_prompt}] + [{"role": "user", "content": self.evaluator_user_prompt(reply, message, history)}] | |
| response = self.gemini.beta.chat.completions.parse(model="gemini-2.0-flash", messages=messages, response_format=Evaluation) | |
| return response.choices[0].message.parsed | |
| def embed(self, text): | |
| return self.model.encode(text) | |
| def find_similars(self, question): | |
| results = self.collection.query(query_embeddings=self.embed(question).astype(float).tolist(), n_results=5,include=['documents',"distances"]) | |
| documents = results['documents'][0][:] | |
| distances=results['distances'][0][:] | |
| filtered_documents = [ | |
| doc for doc, dist in zip(documents, distances) if dist < 1.7 | |
| ] | |
| return filtered_documents | |
| def rerun(self, reply, message, history, feedback): | |
| updated_system_prompt = self.system_prompt + f"\n\n## Previous answer rejected\nYou just tried to reply, but the quality control rejected your reply\n" | |
| updated_system_prompt += f"## Your attempted answer:\n{reply}\n\n" | |
| updated_system_prompt += f"## Reason for rejection:\n{feedback}\n\n" | |
| messages = [{"role": "system", "content": updated_system_prompt}] + history + [{"role": "user", "content": message}] | |
| response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=messages) | |
| return response.choices[0].message.content | |
| def make_context(self,similars): | |
| if len(similars)==0: | |
| return "" | |
| message = f"To provide some context, here are some projects done by {self.name} that might be related to the question that you need to answer.\n\n" | |
| for similar in similars: | |
| message += f"Potentially related projects:\n{similar}\n\n" | |
| return message | |
| def chat(self,message,history): | |
| similars=self.find_similars(message) | |
| message+=self.make_context(similars) | |
| messages = [{"role": "system", "content": self.system_prompt}]+history + [{"role": "user", "content": message}] | |
| done = False | |
| while not done: | |
| response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=messages, tools=tools) | |
| if response.choices[0].finish_reason=="tool_calls": | |
| message = response.choices[0].message | |
| tool_calls = message.tool_calls | |
| results = handle_tool_call(tool_calls) | |
| messages.append(message) | |
| messages.extend(results) | |
| else: | |
| done = True | |
| reply=response.choices[0].message.content | |
| evaluation = self.evaluate(reply, message, history) | |
| if evaluation.is_acceptable: | |
| return reply | |
| else: | |
| return self.rerun(reply, message, history, evaluation.feedback) | |
| if __name__ == "__main__": | |
| me = Me(name="Djallel BRAHMIA", cv_path="documents/CV/CV.pdf") | |
| gr.ChatInterface(me.chat, type="messages").launch() | |