Managing a complex system like the one described, involving multiple factories, ...reconsumeralization/AutoGem#6

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# Repo & Issue Metadata
Repo: AutoGem: Agent Framework for Gemini Pro
Issue Title: Managing a complex system like the one described, involving multiple factories, agents, and an orchestrator, requires a structured approach to ensure smooth operation and maintainability!.

Issue Description: ### Details
 Here are some strategies and best practices to manage and enhance the system based on the provided llm_orchestrator/llm_orchestrator.py and the overall project structure:
1. Centralized Configuration Management
- Use environment variables or a configuration file to manage API keys and other sensitive information, rather than hardcoding them in the source files. This enhances security and flexibility.
- Implement a configuration management class or module that loads and provides access to these configurations throughout the application.
2. Logging and Monitoring
- Integrate comprehensive logging throughout the orchestrator and factory components to track operations, errors, and system states. This aids in debugging and monitoring the system's health.
- Consider using a monitoring tool or service to keep track of the system's performance and alert for any anomalies or failures.
3. Error Handling and Recovery
- Implement robust error handling within the orchestrator's orchestration logic and across factory components to manage exceptions gracefully.
- Design the system with recovery mechanisms in place, allowing it to resume operations after handling errors or failures.
4. Asynchronous Operations and Task Management
- Given the asynchronous nature of the orchestrator, ensure that all asynchronous operations are properly awaited and managed to prevent deadlocks or unhandled promises.
- Use task queues or a message broker to manage and distribute tasks among different components and agents efficiently.
5. Dependency Injection
- The orchestrator initializes all factories with a GeminiClient instance. Consider using a dependency injection framework to manage these dependencies more cleanly and flexibly.
6. Scalability and Performance Optimization
- Evaluate the performance of the system under load and identify bottlenecks. Optimize the code and infrastructure as necessary to handle the expected scale.
- Consider implementing caching strategies, especially for operations involving the MemoryFactory and EmbeddingFactory, to reduce redundant processing and improve response times.
7. Testing and Continuous Integration
- Develop comprehensive unit and integration tests for the orchestrator, factories, and agents to ensure reliability and facilitate continuous integration.
- Use a CI/CD pipeline to automate testing and deployment processes, ensuring that changes are tested and deployed smoothly.
8. Documentation and Knowledge Sharing
- Maintain up-to-date documentation for the system architecture, component interactions, and usage examples. This facilitates onboarding and collaboration among team members.
- Encourage knowledge sharing sessions among team members to discuss system design, challenges, and best practices.
9. Modular Design and Loose Coupling
- Ensure that the system's design remains modular, with loosely coupled components. This facilitates easier maintenance, testing, and future enhancements.
- Regularly review the system design to identify opportunities for refactoring and improvement.
10. Security Considerations
- Regularly audit the system for security vulnerabilities, especially concerning data handling and API interactions.
- Implement security best practices, such as using HTTPS for API calls, securing API keys, and validating inputs to prevent injection attacks.
import requests
import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

class GeminiClient:

    def __init__(self, api_key):
        """
        Initialize the GeminiClient with your API key.

        Args:
            api_key (str): Your Gemini API key.
        """
        self.api_key = api_key
        self.base_url = "https://generativelanguage.googleapis.com"
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }

    def clean_input_data(self, input_text):
        """
        Clean the input text by removing leading and trailing whitespace.

        Args:
            input_text (str): The input text to clean.

        Returns:
            str: The cleaned input text.
        """
        return input_text.strip()

    def generate_text_content(self, input_text):
        """
        Generate text content using the Gemini API.

        Args:
            input_text (str): The input text to generate content from.

        Returns:
            str: The generated text content.
        """
        url = f"{self.base_url}/v1beta/gemini-ultra:generateContent"
        data = {"contents": [{"parts": [{"text": input_text}]}]}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def generate_embedding(self, text, task_type="RETRIEVAL_DOCUMENT", title=None):
        """
        Generate an embedding for the given text.

        Args:
            text (str): The text to generate an embedding for.
            task_type (str, optional): The task type for which the embedding is being generated. Defaults to "RETRIEVAL_DOCUMENT".
            title (str, optional): The title of the document (if task_type is "RETRIEVAL_DOCUMENT").

        Returns:
            dict: The embedding in JSON format.
        """
        model_id = 'models/embedding-001'
        data = {"content": text, "task_type": task_type}
        if title and task_type == "RETRIEVAL_DOCUMENT":
            data["title"] = title
        url = f"{self.base_url}/v1beta/{model_id}:embedContent"
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def find_most_relevant_document(self, query_embedding, documents_df):
        """
        Find the most relevant document to the given query embedding.

        Args:
            query_embedding (dict): The query embedding in JSON format.
            documents_df (pandas.DataFrame): A DataFrame containing the documents and their embeddings.

        Returns:
            pandas.Series: The most relevant document.
        """
        dot_products = np.dot(np.stack(documents_df['Embeddings']), query_embedding)
        idx = np.argmax(dot_products)
        return documents_df.iloc[idx]

    def answer_query(self, query, documents_df):
        """
        Answer a query using the Gemini API.

        Args:
            query (str): The query to answer.
            documents_df (pandas.DataFrame): A DataFrame containing the documents and their embeddings.

        Returns:
            str: The answer to the query.
        """
        query_embedding = self.generate_embedding(query, task_type="RETRIEVAL_QUERY")['embedding']
        relevant_document = self.find_most_relevant_document(query_embedding, documents_df)
        prompt = f"Based on the following passage: {relevant_document['Text']}, answer the question: {query}"
        answer = self.generate_text_content(prompt)
        return answer

    def generate_multi_turn_conversation(self, conversation_parts):
        """
        Generate a multi-turn conversation using the Gemini API.

        Args:
            conversation_parts (list): A list of conversation parts, each of which is a dictionary containing the "speaker" and "text" fields.

        Returns:
            list: A list of conversation parts, each of which is a dictionary containing the "speaker" and "text" fields.
        """
        if not isinstance(conversation_parts, list):
            raise TypeError("conversation_parts must be a list")
        for part in conversation_parts:
            if not isinstance(part, dict):
                raise TypeError("Each conversation part must be a dictionary")
            if not set(["speaker", "text"]).issubset(part.keys()):
                raise ValueError("Each conversation part must have 'speaker' and 'text' fields")

        url = f"{self.base_url}/v1beta/chat-bison-001:generateContent"
        data = {"contents": conversation_parts}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def generate_embeddings(self, input_texts):
        """
        Generate embeddings for the given input texts.

        Args:
            input_texts (list): A list of input texts.

        Returns:
            dict: The embeddings in JSON format.
        """
        if not isinstance(input_texts, list):
            raise TypeError("input_texts must be a list")
        for text in input_texts:
            if not isinstance(text, str):
                raise TypeError("Each input text must be a string")

        url = f"{self.base_url}/v1beta/models/embedding-001:embedText"
        data = {"text": input_texts}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def process_embeddings(self, embeddings):
        """
        Process the embeddings to extract meaningful information.

        Args:
            embeddings (dict): The embeddings in JSON format.

        Returns:
            dict: The processed embeddings.
        """
        processed_embeddings = {}
        for embedding in embeddings:
            processed_embeddings[embedding['id']] = embedding['vector']
        return processed_embeddings

    def call_function(self, function_name, args, kwargs={}):
        """
        Call a function hosted on the Gemini platform.

        Args:
            function_name (str): The name of the function to call.
            args (dict): The arguments to pass to the function.
            kwargs (dict, optional): The keyword arguments to pass to the function.

        Returns:
            dict: The result of the function call.
        """
        url = f"{self.base_url}/v1beta/models/function:callFunction"
        data = {"function_name": function_name, "args": args, "kwargs": kwargs}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def _send_request(self, method, url, data=None):
        """
        Send a request to the Gemini API.

        Args:
            method (str): The HTTP method to use.
            url (str): The URL to send the request to.
            data (dict, optional): The data to send in the request body.

        Returns:
            requests.Response: The response from the API.
        """
        max_retries = 3
        for attempt in range(max_retries):
            try:
                if method.lower() == "post":
                    response = requests.post(url, headers=self.headers, json=data)
                elif method.lower() == "get":
                    response = requests.get(url, headers=self.headers)
                else:
                    raise ValueError("Unsupported HTTP method")

                if response.status_code == 200:
                    return response
                else:
                    print(f"Attempt {attempt + 1}: Error {response.status_code}, {response.text}")
            except requests.exceptions.RequestException as e:
                print(f"Request failed: {e}")
        return None

    def _parse_response(self, response):
        """
        Parse the response from the API.

        Args:
            response (requests.Response): The response from the API.

        Returns:
            dict: The parsed response.
        """
        if response and response.status_code == 200:
            return response.json()
        else:
            print(f"Error: {response.status_code}, {response.text}" if response else "No response")
            return None



**Summaries of links found in the content:**

https://generativelanguage.googleapis.com:

The page provides strategies and best practices for managing and enhancing a system based on a provided Python script and project structure. The recommendations include:

1. Centralized Configuration Management: Use environment variables or a configuration file to manage sensitive information.
2. Logging and Monitoring: Integrate comprehensive logging and consider using a monitoring tool to track system operations and errors.
3. Error Handling and Recovery: Implement robust error handling and recovery mechanisms.
4. Asynchronous Operations and Task Management: Properly await and manage asynchronous operations to prevent deadlocks.
5. Dependency Injection: Consider using a dependency injection framework to manage dependencies more cleanly.
6. Scalability and Performance Optimization: Evaluate system performance and optimize code and infrastructure as necessary.
7. Testing and Continuous Integration: Develop comprehensive tests and use a CI/CD pipeline for automated testing and deployment.
8. Documentation and Knowledge Sharing: Maintain up-to-date documentation and encourage knowledge sharing among team members.
9. Modular Design and Loose Coupling: Ensure a modular design with loosely coupled components for easier maintenance and future enhancements.
10. Security Considerations: Regularly audit the system for security vulnerabilities and implement best practices.

The page also includes a Python class called GeminiClient, which provides methods for interacting with the Gemini API. The class includes methods for cleaning input data, generating text content, generating embeddings, finding the most relevant document, answering queries, generating multi-turn conversations, processing embeddings, and calling functions hosted on the Gemini platform. The class also includes helper methods for sending requests and parsing responses from the API.

You have previously changed these files:
<changed_file file_path="src/config.py">
--- 
+++ 
@@ -0,0 +1,25 @@
+import json
+import os
+
+
+class Config:
+    def __init__(self):
+        self.configurations = {}
+        self.config_file_path = "config.json"
+        self.load_configurations()
+
+    def load_configurations(self):
+        if os.path.exists(self.config_file_path):
+            with open(self.config_file_path, 'r') as config_file:
+                self.configurations = json.load(config_file)
+        else:
+            print("Configuration file not found. Falling back to environment variables.")
+
+    def get_config(self, key):
+        return self.configurations.get(key, os.getenv(key))
+
+    def get_api_key(self):
+        api_key = self.get_config('API_KEY')
+        if not api_key:
+            raise ValueError("API key not found in configurations or environment variables.")
+        return api_key
</changed_file>
<changed_file file_path="src/logger.py">
--- 
+++ 
@@ -0,0 +1,22 @@
+import logging
+import logging.handlers
+
+
+def get_logger(name):
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.DEBUG)
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+
+    console_handler = logging.StreamHandler()
+    console_handler.setLevel(logging.DEBUG)
+    console_handler.setFormatter(formatter)
+
+    file_handler = logging.handlers.RotatingFileHandler('application.log', maxBytes=10485760, backupCount=5)
+    file_handler.setLevel(logging.DEBUG)
+    file_handler.setFormatter(formatter)
+
+    logger.addHandler(console_handler)
+    logger.addHandler(file_handler)
+    logger.propagate = False
+
+    return logger
</changed_file>

Relevant files in this PR:

<relevant_file file_path="src/config.py">
import json
import os


class Config:
    def __init__(self):
        self.configurations = {}
        self.config_file_path = "config.json"
        self.load_configurations()

    def load_configurations(self):
        if os.path.exists(self.config_file_path):
            with open(self.config_file_path, 'r') as config_file:
                self.configurations = json.load(config_file)
        else:
            print("Configuration file not found. Falling back to environment variables.")

    def get_config(self, key):
        return self.configurations.get(key, os.getenv(key))

    def get_api_key(self):
        api_key = self.get_config('API_KEY')
        if not api_key:
            raise ValueError("API key not found in configurations or environment variables.")
        return api_key

</relevant_file>

<section id="A">
import base64
import json

import requests
from google.cloud import gemini_pro_models, gemini_vision_pro_models
from src.utils import encode_image_to_base64


class GeminiClient:
    def __init__(self, api_key):
        self.api_key = api_key
        self.gemini_pro_client = gemini_pro_models.Ggemini_pro_models.GeminiProModelsServiceClient()
        self.gemini_vision_pro_client = gemini_vision_pro_models.GeminiVisionProModelsServiceClient()

</section id="A">
<section id="B">
    def predict_with_gemini_pro_models(self, model_name, image_path):
        image_bytes = encode_image_to_base64(image_path)
        request = gemini_pro_models.PredictRequest(
            name=model_name, instances=[{"b64": image_bytes}]
        )
        try:
            response = self.gemini_pro_client.predict(request=request)
            return self.parse_prediction_results(response.predictions)
        except Exception as e:
            print(f"Error during Gemini Pro Models prediction: {e}")
            return None

</section id="B">
<section id="C">
    def predict_with_gemini_vision_pro_models(self, model_name, image_path):
        image_bytes = encode_image_to_base64(image_path)
        request = gemini_vision_pro_models.PredictRequest(
            name=model_name, instances=[{"b64": image_bytes}]
        )
        try:
            response = self.gemini_vision_pro_client.predict(request=request)
            return self.parse_prediction_results(response.predictions)
        except Exception as e:
            print(f"Error during Gemini Vision Pro Models prediction: {e}")
            return None

</section id="C">
<section id="D">
    def parse_prediction_results(self, predictions):
        results = []
        for prediction in predictions:
            for label in prediction['labels']:
                results.append({
                    'category': label['category'],
                    'confidence': label['confidence'],
                    'bounding_box': label.get('bounding_box', None)
                })
        return results
</section id="D">

# Request
• Modify the `__init__` method of the `GeminiClient` class to load the API key using the new `Config` class from `src/config.py`.
• Import the `Config` class at the beginning of `src/gemini_client.py`.
• This change integrates centralized configuration management into `GeminiClient`.

SUCCESS
Here are the new code sections:

The following changes have been applied:
```diff
--- 
+++ 
@@ -1,14 +1,15 @@
 import base64
 import json
-
 import requests
+from src.config import Config
 from google.cloud import gemini_pro_models, gemini_vision_pro_models
 from src.utils import encode_image_to_base64
 
 
 class GeminiClient:
-    def __init__(self, api_key):
-        self.api_key = api_key
+    def __init__(self):
+        config = Config()
+        self.api_key = config.get_api_key()
         self.gemini_pro_client = gemini_pro_models.Ggemini_pro_models.GeminiProModelsServiceClient()
         self.gemini_vision_pro_client = gemini_vision_pro_models.GeminiVisionProModelsServiceClient()
 

```
Here are the new code sections:




<section id="A">
import base64
import json
import requests
from src.config import Config
from google.cloud import gemini_pro_models, gemini_vision_pro_models
from src.utils import encode_image_to_base64


class GeminiClient:
    def __init__(self):
        config = Config()
        self.api_key = config.get_api_key()
        self.gemini_pro_client = gemini_pro_models.Ggemini_pro_models.GeminiProModelsServiceClient()
        self.gemini_vision_pro_client = gemini_vision_pro_models.GeminiVisionProModelsServiceClient()

</section id="A">
<section id="B">
    def predict_with_gemini_pro_models(self, model_name, image_path):
        image_bytes = encode_image_to_base64(image_path)
        request = gemini_pro_models.PredictRequest(
            name=model_name, instances=[{"b64": image_bytes}]
        )
        try:
            response = self.gemini_pro_client.predict(request=request)
            return self.parse_prediction_results(response.predictions)
        except Exception as e:
            print(f"Error during Gemini Pro Models prediction: {e}")
            return None

</section id="B">
<section id="C">
    def predict_with_gemini_vision_pro_models(self, model_name, image_path):
        image_bytes = encode_image_to_base64(image_path)
        request = gemini_vision_pro_models.PredictRequest(
            name=model_name, instances=[{"b64": image_bytes}]
        )
        try:
            response = self.gemini_vision_pro_client.predict(request=request)
            return self.parse_prediction_results(response.predictions)
        except Exception as e:
            print(f"Error during Gemini Vision Pro Models prediction: {e}")
            return None

</section id="C">
<section id="D">
    def parse_prediction_results(self, predictions):
        results = []
        for prediction in predictions:
            for label in prediction['labels']:
                results.append({
                    'category': label['category'],
                    'confidence': label['confidence'],
                    'bounding_box': label.get('bounding_box', None)
                })
        return results
</section id="D">

You can continue to make changes to the code sections and call the `search_and_replace` function again.

# Repo & Issue Metadata
Repo: AutoGem: Agent Framework for Gemini Pro
Issue Title: Managing a complex system like the one described, involving multiple factories, agents, and an orchestrator, requires a structured approach to ensure smooth operation and maintainability!.

Issue Description: ### Details
 Here are some strategies and best practices to manage and enhance the system based on the provided llm_orchestrator/llm_orchestrator.py and the overall project structure:
1. Centralized Configuration Management
- Use environment variables or a configuration file to manage API keys and other sensitive information, rather than hardcoding them in the source files. This enhances security and flexibility.
- Implement a configuration management class or module that loads and provides access to these configurations throughout the application.
2. Logging and Monitoring
- Integrate comprehensive logging throughout the orchestrator and factory components to track operations, errors, and system states. This aids in debugging and monitoring the system's health.
- Consider using a monitoring tool or service to keep track of the system's performance and alert for any anomalies or failures.
3. Error Handling and Recovery
- Implement robust error handling within the orchestrator's orchestration logic and across factory components to manage exceptions gracefully.
- Design the system with recovery mechanisms in place, allowing it to resume operations after handling errors or failures.
4. Asynchronous Operations and Task Management
- Given the asynchronous nature of the orchestrator, ensure that all asynchronous operations are properly awaited and managed to prevent deadlocks or unhandled promises.
- Use task queues or a message broker to manage and distribute tasks among different components and agents efficiently.
5. Dependency Injection
- The orchestrator initializes all factories with a GeminiClient instance. Consider using a dependency injection framework to manage these dependencies more cleanly and flexibly.
6. Scalability and Performance Optimization
- Evaluate the performance of the system under load and identify bottlenecks. Optimize the code and infrastructure as necessary to handle the expected scale.
- Consider implementing caching strategies, especially for operations involving the MemoryFactory and EmbeddingFactory, to reduce redundant processing and improve response times.
7. Testing and Continuous Integration
- Develop comprehensive unit and integration tests for the orchestrator, factories, and agents to ensure reliability and facilitate continuous integration.
- Use a CI/CD pipeline to automate testing and deployment processes, ensuring that changes are tested and deployed smoothly.
8. Documentation and Knowledge Sharing
- Maintain up-to-date documentation for the system architecture, component interactions, and usage examples. This facilitates onboarding and collaboration among team members.
- Encourage knowledge sharing sessions among team members to discuss system design, challenges, and best practices.
9. Modular Design and Loose Coupling
- Ensure that the system's design remains modular, with loosely coupled components. This facilitates easier maintenance, testing, and future enhancements.
- Regularly review the system design to identify opportunities for refactoring and improvement.
10. Security Considerations
- Regularly audit the system for security vulnerabilities, especially concerning data handling and API interactions.
- Implement security best practices, such as using HTTPS for API calls, securing API keys, and validating inputs to prevent injection attacks.
import requests
import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

class GeminiClient:

    def __init__(self, api_key):
        """
        Initialize the GeminiClient with your API key.

        Args:
            api_key (str): Your Gemini API key.
        """
        self.api_key = api_key
        self.base_url = "https://generativelanguage.googleapis.com"
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }

    def clean_input_data(self, input_text):
        """
        Clean the input text by removing leading and trailing whitespace.

        Args:
            input_text (str): The input text to clean.

        Returns:
            str: The cleaned input text.
        """
        return input_text.strip()

    def generate_text_content(self, input_text):
        """
        Generate text content using the Gemini API.

        Args:
            input_text (str): The input text to generate content from.

        Returns:
            str: The generated text content.
        """
        url = f"{self.base_url}/v1beta/gemini-ultra:generateContent"
        data = {"contents": [{"parts": [{"text": input_text}]}]}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def generate_embedding(self, text, task_type="RETRIEVAL_DOCUMENT", title=None):
        """
        Generate an embedding for the given text.

        Args:
            text (str): The text to generate an embedding for.
            task_type (str, optional): The task type for which the embedding is being generated. Defaults to "RETRIEVAL_DOCUMENT".
            title (str, optional): The title of the document (if task_type is "RETRIEVAL_DOCUMENT").

        Returns:
            dict: The embedding in JSON format.
        """
        model_id = 'models/embedding-001'
        data = {"content": text, "task_type": task_type}
        if title and task_type == "RETRIEVAL_DOCUMENT":
            data["title"] = title
        url = f"{self.base_url}/v1beta/{model_id}:embedContent"
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def find_most_relevant_document(self, query_embedding, documents_df):
        """
        Find the most relevant document to the given query embedding.

        Args:
            query_embedding (dict): The query embedding in JSON format.
            documents_df (pandas.DataFrame): A DataFrame containing the documents and their embeddings.

        Returns:
            pandas.Series: The most relevant document.
        """
        dot_products = np.dot(np.stack(documents_df['Embeddings']), query_embedding)
        idx = np.argmax(dot_products)
        return documents_df.iloc[idx]

    def answer_query(self, query, documents_df):
        """
        Answer a query using the Gemini API.

        Args:
            query (str): The query to answer.
            documents_df (pandas.DataFrame): A DataFrame containing the documents and their embeddings.

        Returns:
            str: The answer to the query.
        """
        query_embedding = self.generate_embedding(query, task_type="RETRIEVAL_QUERY")['embedding']
        relevant_document = self.find_most_relevant_document(query_embedding, documents_df)
        prompt = f"Based on the following passage: {relevant_document['Text']}, answer the question: {query}"
        answer = self.generate_text_content(prompt)
        return answer

    def generate_multi_turn_conversation(self, conversation_parts):
        """
        Generate a multi-turn conversation using the Gemini API.

        Args:
            conversation_parts (list): A list of conversation parts, each of which is a dictionary containing the "speaker" and "text" fields.

        Returns:
            list: A list of conversation parts, each of which is a dictionary containing the "speaker" and "text" fields.
        """
        if not isinstance(conversation_parts, list):
            raise TypeError("conversation_parts must be a list")
        for part in conversation_parts:
            if not isinstance(part, dict):
                raise TypeError("Each conversation part must be a dictionary")
            if not set(["speaker", "text"]).issubset(part.keys()):
                raise ValueError("Each conversation part must have 'speaker' and 'text' fields")

        url = f"{self.base_url}/v1beta/chat-bison-001:generateContent"
        data = {"contents": conversation_parts}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def generate_embeddings(self, input_texts):
        """
        Generate embeddings for the given input texts.

        Args:
            input_texts (list): A list of input texts.

        Returns:
            dict: The embeddings in JSON format.
        """
        if not isinstance(input_texts, list):
            raise TypeError("input_texts must be a list")
        for text in input_texts:
            if not isinstance(text, str):
                raise TypeError("Each input text must be a string")

        url = f"{self.base_url}/v1beta/models/embedding-001:embedText"
        data = {"text": input_texts}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def process_embeddings(self, embeddings):
        """
        Process the embeddings to extract meaningful information.

        Args:
            embeddings (dict): The embeddings in JSON format.

        Returns:
            dict: The processed embeddings.
        """
        processed_embeddings = {}
        for embedding in embeddings:
            processed_embeddings[embedding['id']] = embedding['vector']
        return processed_embeddings

    def call_function(self, function_name, args, kwargs={}):
        """
        Call a function hosted on the Gemini platform.

        Args:
            function_name (str): The name of the function to call.
            args (dict): The arguments to pass to the function.
            kwargs (dict, optional): The keyword arguments to pass to the function.

        Returns:
            dict: The result of the function call.
        """
        url = f"{self.base_url}/v1beta/models/function:callFunction"
        data = {"function_name": function_name, "args": args, "kwargs": kwargs}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def _send_request(self, method, url, data=None):
        """
        Send a request to the Gemini API.

        Args:
            method (str): The HTTP method to use.
            url (str): The URL to send the request to.
            data (dict, optional): The data to send in the request body.

        Returns:
            requests.Response: The response from the API.
        """
        max_retries = 3
        for attempt in range(max_retries):
            try:
                if method.lower() == "post":
                    response = requests.post(url, headers=self.headers, json=data)
                elif method.lower() == "get":
                    response = requests.get(url, headers=self.headers)
                else:
                    raise ValueError("Unsupported HTTP method")

                if response.status_code == 200:
                    return response
                else:
                    print(f"Attempt {attempt + 1}: Error {response.status_code}, {response.text}")
            except requests.exceptions.RequestException as e:
                print(f"Request failed: {e}")
        return None

    def _parse_response(self, response):
        """
        Parse the response from the API.

        Args:
            response (requests.Response): The response from the API.

        Returns:
            dict: The parsed response.
        """
        if response and response.status_code == 200:
            return response.json()
        else:
            print(f"Error: {response.status_code}, {response.text}" if response else "No response")
            return None



**Summaries of links found in the content:**

https://generativelanguage.googleapis.com:

The page provides strategies and best practices for managing and enhancing a system based on a provided Python script and project structure. The recommendations include:

1. Centralized Configuration Management: Use environment variables or a configuration file to manage sensitive information.
2. Logging and Monitoring: Integrate comprehensive logging and consider using a monitoring tool to track system operations and errors.
3. Error Handling and Recovery: Implement robust error handling and recovery mechanisms.
4. Asynchronous Operations and Task Management: Properly await and manage asynchronous operations to prevent deadlocks.
5. Dependency Injection: Consider using a dependency injection framework to manage dependencies more cleanly.
6. Scalability and Performance Optimization: Evaluate system performance and optimize code and infrastructure as necessary.
7. Testing and Continuous Integration: Develop comprehensive tests and use a CI/CD pipeline for automated testing and deployment.
8. Documentation and Knowledge Sharing: Maintain up-to-date documentation and encourage knowledge sharing among team members.
9. Modular Design and Loose Coupling: Ensure a modular design with loosely coupled components for easier maintenance and future enhancements.
10. Security Considerations: Regularly audit the system for security vulnerabilities and implement best practices.

The page also includes a Python class called GeminiClient, which provides methods for interacting with the Gemini API. The class includes methods for cleaning input data, generating text content, generating embeddings, finding the most relevant document, answering queries, generating multi-turn conversations, processing embeddings, and calling functions hosted on the Gemini platform. The class also includes helper methods for sending requests and parsing responses from the API.

You have previously changed these files:
<changed_file file_path="src/config.py">
--- 
+++ 
@@ -0,0 +1,25 @@
+import json
+import os
+
+
+class Config:
+    def __init__(self):
+        self.configurations = {}
+        self.config_file_path = "config.json"
+        self.load_configurations()
+
+    def load_configurations(self):
+        if os.path.exists(self.config_file_path):
+            with open(self.config_file_path, 'r') as config_file:
+                self.configurations = json.load(config_file)
+        else:
+            print("Configuration file not found. Falling back to environment variables.")
+
+    def get_config(self, key):
+        return self.configurations.get(key, os.getenv(key))
+
+    def get_api_key(self):
+        api_key = self.get_config('API_KEY')
+        if not api_key:
+            raise ValueError("API key not found in configurations or environment variables.")
+        return api_key
</changed_file>
<changed_file file_path="src/logger.py">
--- 
+++ 
@@ -0,0 +1,22 @@
+import logging
+import logging.handlers
+
+
+def get_logger(name):
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.DEBUG)
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+
+    console_handler = logging.StreamHandler()
+    console_handler.setLevel(logging.DEBUG)
+    console_handler.setFormatter(formatter)
+
+    file_handler = logging.handlers.RotatingFileHandler('application.log', maxBytes=10485760, backupCount=5)
+    file_handler.setLevel(logging.DEBUG)
+    file_handler.setFormatter(formatter)
+
+    logger.addHandler(console_handler)
+    logger.addHandler(file_handler)
+    logger.propagate = False
+
+    return logger
</changed_file>
<changed_file file_path="src/gemini_client.py">
--- 
+++ 
@@ -1,14 +1,15 @@
 import base64
 import json
-
 import requests
+from src.config import Config
 from google.cloud import gemini_pro_models, gemini_vision_pro_models
 from src.utils import encode_image_to_base64
 
 
 class GeminiClient:
-    def __init__(self, api_key):
-        self.api_key = api_key
+    def __init__(self):
+        config = Config()
+        self.api_key = config.get_api_key()
         self.gemini_pro_client = gemini_pro_models.Ggemini_pro_models.GeminiProModelsServiceClient()
         self.gemini_vision_pro_client = gemini_vision_pro_models.GeminiVisionProModelsServiceClient()
 

</changed_file>

Relevant files in this PR:

<relevant_file file_path="src/logger.py">
import json
import os


class Config:
    def __init__(self):
        self.configurations = {}
        self.config_file_path = "config.json"
        self.load_configurations()

    def load_configurations(self):
        if os.path.exists(self.config_file_path):
            with open(self.config_file_path, 'r') as config_file:
                self.configurations = json.load(config_file)
        else:
            print("Configuration file not found. Falling back to environment variables.")

    def get_config(self, key):
        return self.configurations.get(key, os.getenv(key))

    def get_api_key(self):
        api_key = self.get_config('API_KEY')
        if not api_key:
            raise ValueError("API key not found in configurations or environment variables.")
        return api_key

</relevant_file>

<section id="A">
import base64
import json
import requests
from src.config import Config
from google.cloud import gemini_pro_models, gemini_vision_pro_models
from src.utils import encode_image_to_base64


class GeminiClient:
    def __init__(self):
        config = Config()
        self.api_key = config.get_api_key()
        self.gemini_pro_client = gemini_pro_models.Ggemini_pro_models.GeminiProModelsServiceClient()
        self.gemini_vision_pro_client = gemini_vision_pro_models.GeminiVisionProModelsServiceClient()

</section id="A">
<section id="B">
    def predict_with_gemini_pro_models(self, model_name, image_path):
        image_bytes = encode_image_to_base64(image_path)
        request = gemini_pro_models.PredictRequest(
            name=model_name, instances=[{"b64": image_bytes}]
        )
        try:
            response = self.gemini_pro_client.predict(request=request)
            return self.parse_prediction_results(response.predictions)
        except Exception as e:
            print(f"Error during Gemini Pro Models prediction: {e}")
            return None

</section id="B">
<section id="C">
    def predict_with_gemini_vision_pro_models(self, model_name, image_path):
        image_bytes = encode_image_to_base64(image_path)
        request = gemini_vision_pro_models.PredictRequest(
            name=model_name, instances=[{"b64": image_bytes}]
        )
        try:
            response = self.gemini_vision_pro_client.predict(request=request)
            return self.parse_prediction_results(response.predictions)
        except Exception as e:
            print(f"Error during Gemini Vision Pro Models prediction: {e}")
            return None

</section id="C">
<section id="D">
    def parse_prediction_results(self, predictions):
        results = []
        for prediction in predictions:
            for label in prediction['labels']:
                results.append({
                    'category': label['category'],
                    'confidence': label['confidence'],
                    'bounding_box': label.get('bounding_box', None)
                })
        return results
</section id="D">

# Request
• Integrate logging by importing `get_logger` from `src/logger.py` and initializing a logger at the beginning of the `GeminiClient` class.
• Replace `print` statements with appropriate logging methods (`logger.info`, `logger.error`, etc.) in the `predict_with_gemini_pro_models` and `predict_with_gemini_vision_pro_models` methods.
• This modification enhances error logging and monitoring capabilities.

SUCCESS
Here are the new code sections:

The following changes have been applied:
```diff
--- 
+++ 
@@ -4,6 +4,7 @@
 from src.config import Config
 from google.cloud import gemini_pro_models, gemini_vision_pro_models
 from src.utils import encode_image_to_base64
+from src.logger import get_logger
 
 
 class GeminiClient:
@@ -12,6 +13,7 @@
         self.api_key = config.get_api_key()
         self.gemini_pro_client = gemini_pro_models.Ggemini_pro_models.GeminiProModelsServiceClient()
         self.gemini_vision_pro_client = gemini_vision_pro_models.GeminiVisionProModelsServiceClient()
+        self.logger = get_logger('GeminiClient')
 
     def predict_with_gemini_pro_models(self, model_name, image_path):
         image_bytes = encode_image_to_base64(image_path)

```
Here are the new code sections:




<section id="A">
import base64
import json
import requests
from src.config import Config
from google.cloud import gemini_pro_models, gemini_vision_pro_models
from src.utils import encode_image_to_base64
from src.logger import get_logger


class GeminiClient:
</section id="A">
<section id="B">
    def __init__(self):
        config = Config()
        self.api_key = config.get_api_key()
        self.gemini_pro_client = gemini_pro_models.Ggemini_pro_models.GeminiProModelsServiceClient()
        self.gemini_vision_pro_client = gemini_vision_pro_models.GeminiVisionProModelsServiceClient()
        self.logger = get_logger('GeminiClient')

</section id="B">
<section id="C">
    def predict_with_gemini_pro_models(self, model_name, image_path):
        image_bytes = encode_image_to_base64(image_path)
        request = gemini_pro_models.PredictRequest(
            name=model_name, instances=[{"b64": image_bytes}]
        )
        try:
            response = self.gemini_pro_client.predict(request=request)
            return self.parse_prediction_results(response.predictions)
        except Exception as e:
            print(f"Error during Gemini Pro Models prediction: {e}")
            return None

</section id="C">
<section id="D">
    def predict_with_gemini_vision_pro_models(self, model_name, image_path):
        image_bytes = encode_image_to_base64(image_path)
        request = gemini_vision_pro_models.PredictRequest(
            name=model_name, instances=[{"b64": image_bytes}]
        )
        try:
            response = self.gemini_vision_pro_client.predict(request=request)
            return self.parse_prediction_results(response.predictions)
        except Exception as e:
            print(f"Error during Gemini Vision Pro Models prediction: {e}")
            return None

</section id="D">
<section id="E">
    def parse_prediction_results(self, predictions):
        results = []
        for prediction in predictions:
            for label in prediction['labels']:
                results.append({
                    'category': label['category'],
                    'confidence': label['confidence'],
                    'bounding_box': label.get('bounding_box', None)
                })
        return results
</section id="E">

You can continue to make changes to the code sections and call the `search_and_replace` function again.

ERROR
No changes were made due to the following error:

The old_code in the 0th replace_to_make does not appear to be present in section B. The old_code contains:
```
print(f"Error during Gemini Pro Models prediction: {e}")
```
But section B has code:
```
    def __init__(self):
        config = Config()
        self.api_key = config.get_api_key()
        self.gemini_pro_client = gemini_pro_models.Ggemini_pro_models.GeminiProModelsServiceClient()
        self.gemini_vision_pro_client = gemini_vision_pro_models.GeminiVisionProModelsServiceClient()
        self.logger = get_logger('GeminiClient')

```

Did you mean one of the following sections?
<section id="C">
    def predict_with_gemini_pro_models(self, model_name, image_path):
        image_bytes = encode_image_to_base64(image_path)
        request = gemini_pro_models.PredictRequest(
            name=model_name, instances=[{"b64": image_bytes}]
        )
        try:
            response = self.gemini_pro_client.predict(request=request)
            return self.parse_prediction_results(response.predictions)
        except Exception as e:
            print(f"Error during Gemini Pro Models prediction: {e}")
            return None

</section>
```

ERROR
No changes were made due to the following error:

The old_code in the 0th replace_to_make does not appear to be present in section B. The old_code contains:
```
        except Exception as e:
            print(f"Error during Gemini Pro Models prediction: {e}")
```
But section B has code:
```
    def __init__(self):
        config = Config()
        self.api_key = config.get_api_key()
        self.gemini_pro_client = gemini_pro_models.Ggemini_pro_models.GeminiProModelsServiceClient()
        self.gemini_vision_pro_client = gemini_vision_pro_models.GeminiVisionProModelsServiceClient()
        self.logger = get_logger('GeminiClient')

```

Did you mean one of the following sections?
<section id="C">
    def predict_with_gemini_pro_models(self, model_name, image_path):
        image_bytes = encode_image_to_base64(image_path)
        request = gemini_pro_models.PredictRequest(
            name=model_name, instances=[{"b64": image_bytes}]
        )
        try:
            response = self.gemini_pro_client.predict(request=request)
            return self.parse_prediction_results(response.predictions)
        except Exception as e:
            print(f"Error during Gemini Pro Models prediction: {e}")
            return None

</section>
```

SUCCESS
Here are the new code sections:

The following changes have been applied:
```diff
--- 
+++ 
@@ -24,7 +24,7 @@
             response = self.gemini_pro_client.predict(request=request)
             return self.parse_prediction_results(response.predictions)
         except Exception as e:
-            print(f"Error during Gemini Pro Models prediction: {e}")
+            self.logger.error(f"Error during Gemini Pro Models prediction: {e}")
             return None
 
     def predict_with_gemini_vision_pro_models(self, model_name, image_path):
@@ -36,7 +36,7 @@
             response = self.gemini_vision_pro_client.predict(request=request)
             return self.parse_prediction_results(response.predictions)
         except Exception as e:
-            print(f"Error during Gemini Vision Pro Models prediction: {e}")
+            self.logger.error(f"Error during Gemini Vision Pro Models prediction: {e}")
             return None
 
     def parse_prediction_results(self, predictions):

```
Here are the new code sections:




<section id="A">
import base64
import json
import requests
from src.config import Config
from google.cloud import gemini_pro_models, gemini_vision_pro_models
from src.utils import encode_image_to_base64
from src.logger import get_logger


class GeminiClient:
</section id="A">
<section id="B">
    def __init__(self):
        config = Config()
        self.api_key = config.get_api_key()
        self.gemini_pro_client = gemini_pro_models.Ggemini_pro_models.GeminiProModelsServiceClient()
        self.gemini_vision_pro_client = gemini_vision_pro_models.GeminiVisionProModelsServiceClient()
        self.logger = get_logger('GeminiClient')

</section id="B">
<section id="C">
    def predict_with_gemini_pro_models(self, model_name, image_path):
        image_bytes = encode_image_to_base64(image_path)
        request = gemini_pro_models.PredictRequest(
            name=model_name, instances=[{"b64": image_bytes}]
        )
        try:
            response = self.gemini_pro_client.predict(request=request)
            return self.parse_prediction_results(response.predictions)
        except Exception as e:
            self.logger.error(f"Error during Gemini Pro Models prediction: {e}")
            return None

</section id="C">
<section id="D">
    def predict_with_gemini_vision_pro_models(self, model_name, image_path):
        image_bytes = encode_image_to_base64(image_path)
        request = gemini_vision_pro_models.PredictRequest(
            name=model_name, instances=[{"b64": image_bytes}]
        )
        try:
            response = self.gemini_vision_pro_client.predict(request=request)
            return self.parse_prediction_results(response.predictions)
        except Exception as e:
            self.logger.error(f"Error during Gemini Vision Pro Models prediction: {e}")
            return None

</section id="D">
<section id="E">
    def parse_prediction_results(self, predictions):
        results = []
        for prediction in predictions:
            for label in prediction['labels']:
                results.append({
                    'category': label['category'],
                    'confidence': label['confidence'],
                    'bounding_box': label.get('bounding_box', None)
                })
        return results
</section id="E">

You can continue to make changes to the code sections and call the `search_and_replace` function again.

# Repo & Issue Metadata
Repo: AutoGem: Agent Framework for Gemini Pro
Issue Title: Managing a complex system like the one described, involving multiple factories, agents, and an orchestrator, requires a structured approach to ensure smooth operation and maintainability!.

Issue Description: ### Details
 Here are some strategies and best practices to manage and enhance the system based on the provided llm_orchestrator/llm_orchestrator.py and the overall project structure:
1. Centralized Configuration Management
- Use environment variables or a configuration file to manage API keys and other sensitive information, rather than hardcoding them in the source files. This enhances security and flexibility.
- Implement a configuration management class or module that loads and provides access to these configurations throughout the application.
2. Logging and Monitoring
- Integrate comprehensive logging throughout the orchestrator and factory components to track operations, errors, and system states. This aids in debugging and monitoring the system's health.
- Consider using a monitoring tool or service to keep track of the system's performance and alert for any anomalies or failures.
3. Error Handling and Recovery
- Implement robust error handling within the orchestrator's orchestration logic and across factory components to manage exceptions gracefully.
- Design the system with recovery mechanisms in place, allowing it to resume operations after handling errors or failures.
4. Asynchronous Operations and Task Management
- Given the asynchronous nature of the orchestrator, ensure that all asynchronous operations are properly awaited and managed to prevent deadlocks or unhandled promises.
- Use task queues or a message broker to manage and distribute tasks among different components and agents efficiently.
5. Dependency Injection
- The orchestrator initializes all factories with a GeminiClient instance. Consider using a dependency injection framework to manage these dependencies more cleanly and flexibly.
6. Scalability and Performance Optimization
- Evaluate the performance of the system under load and identify bottlenecks. Optimize the code and infrastructure as necessary to handle the expected scale.
- Consider implementing caching strategies, especially for operations involving the MemoryFactory and EmbeddingFactory, to reduce redundant processing and improve response times.
7. Testing and Continuous Integration
- Develop comprehensive unit and integration tests for the orchestrator, factories, and agents to ensure reliability and facilitate continuous integration.
- Use a CI/CD pipeline to automate testing and deployment processes, ensuring that changes are tested and deployed smoothly.
8. Documentation and Knowledge Sharing
- Maintain up-to-date documentation for the system architecture, component interactions, and usage examples. This facilitates onboarding and collaboration among team members.
- Encourage knowledge sharing sessions among team members to discuss system design, challenges, and best practices.
9. Modular Design and Loose Coupling
- Ensure that the system's design remains modular, with loosely coupled components. This facilitates easier maintenance, testing, and future enhancements.
- Regularly review the system design to identify opportunities for refactoring and improvement.
10. Security Considerations
- Regularly audit the system for security vulnerabilities, especially concerning data handling and API interactions.
- Implement security best practices, such as using HTTPS for API calls, securing API keys, and validating inputs to prevent injection attacks.
import requests
import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

class GeminiClient:

    def __init__(self, api_key):
        """
        Initialize the GeminiClient with your API key.

        Args:
            api_key (str): Your Gemini API key.
        """
        self.api_key = api_key
        self.base_url = "https://generativelanguage.googleapis.com"
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }

    def clean_input_data(self, input_text):
        """
        Clean the input text by removing leading and trailing whitespace.

        Args:
            input_text (str): The input text to clean.

        Returns:
            str: The cleaned input text.
        """
        return input_text.strip()

    def generate_text_content(self, input_text):
        """
        Generate text content using the Gemini API.

        Args:
            input_text (str): The input text to generate content from.

        Returns:
            str: The generated text content.
        """
        url = f"{self.base_url}/v1beta/gemini-ultra:generateContent"
        data = {"contents": [{"parts": [{"text": input_text}]}]}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def generate_embedding(self, text, task_type="RETRIEVAL_DOCUMENT", title=None):
        """
        Generate an embedding for the given text.

        Args:
            text (str): The text to generate an embedding for.
            task_type (str, optional): The task type for which the embedding is being generated. Defaults to "RETRIEVAL_DOCUMENT".
            title (str, optional): The title of the document (if task_type is "RETRIEVAL_DOCUMENT").

        Returns:
            dict: The embedding in JSON format.
        """
        model_id = 'models/embedding-001'
        data = {"content": text, "task_type": task_type}
        if title and task_type == "RETRIEVAL_DOCUMENT":
            data["title"] = title
        url = f"{self.base_url}/v1beta/{model_id}:embedContent"
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def find_most_relevant_document(self, query_embedding, documents_df):
        """
        Find the most relevant document to the given query embedding.

        Args:
            query_embedding (dict): The query embedding in JSON format.
            documents_df (pandas.DataFrame): A DataFrame containing the documents and their embeddings.

        Returns:
            pandas.Series: The most relevant document.
        """
        dot_products = np.dot(np.stack(documents_df['Embeddings']), query_embedding)
        idx = np.argmax(dot_products)
        return documents_df.iloc[idx]

    def answer_query(self, query, documents_df):
        """
        Answer a query using the Gemini API.

        Args:
            query (str): The query to answer.
            documents_df (pandas.DataFrame): A DataFrame containing the documents and their embeddings.

        Returns:
            str: The answer to the query.
        """
        query_embedding = self.generate_embedding(query, task_type="RETRIEVAL_QUERY")['embedding']
        relevant_document = self.find_most_relevant_document(query_embedding, documents_df)
        prompt = f"Based on the following passage: {relevant_document['Text']}, answer the question: {query}"
        answer = self.generate_text_content(prompt)
        return answer

    def generate_multi_turn_conversation(self, conversation_parts):
        """
        Generate a multi-turn conversation using the Gemini API.

        Args:
            conversation_parts (list): A list of conversation parts, each of which is a dictionary containing the "speaker" and "text" fields.

        Returns:
            list: A list of conversation parts, each of which is a dictionary containing the "speaker" and "text" fields.
        """
        if not isinstance(conversation_parts, list):
            raise TypeError("conversation_parts must be a list")
        for part in conversation_parts:
            if not isinstance(part, dict):
                raise TypeError("Each conversation part must be a dictionary")
            if not set(["speaker", "text"]).issubset(part.keys()):
                raise ValueError("Each conversation part must have 'speaker' and 'text' fields")

        url = f"{self.base_url}/v1beta/chat-bison-001:generateContent"
        data = {"contents": conversation_parts}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def generate_embeddings(self, input_texts):
        """
        Generate embeddings for the given input texts.

        Args:
            input_texts (list): A list of input texts.

        Returns:
            dict: The embeddings in JSON format.
        """
        if not isinstance(input_texts, list):
            raise TypeError("input_texts must be a list")
        for text in input_texts:
            if not isinstance(text, str):
                raise TypeError("Each input text must be a string")

        url = f"{self.base_url}/v1beta/models/embedding-001:embedText"
        data = {"text": input_texts}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def process_embeddings(self, embeddings):
        """
        Process the embeddings to extract meaningful information.

        Args:
            embeddings (dict): The embeddings in JSON format.

        Returns:
            dict: The processed embeddings.
        """
        processed_embeddings = {}
        for embedding in embeddings:
            processed_embeddings[embedding['id']] = embedding['vector']
        return processed_embeddings

    def call_function(self, function_name, args, kwargs={}):
        """
        Call a function hosted on the Gemini platform.

        Args:
            function_name (str): The name of the function to call.
            args (dict): The arguments to pass to the function.
            kwargs (dict, optional): The keyword arguments to pass to the function.

        Returns:
            dict: The result of the function call.
        """
        url = f"{self.base_url}/v1beta/models/function:callFunction"
        data = {"function_name": function_name, "args": args, "kwargs": kwargs}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def _send_request(self, method, url, data=None):
        """
        Send a request to the Gemini API.

        Args:
            method (str): The HTTP method to use.
            url (str): The URL to send the request to.
            data (dict, optional): The data to send in the request body.

        Returns:
            requests.Response: The response from the API.
        """
        max_retries = 3
        for attempt in range(max_retries):
            try:
                if method.lower() == "post":
                    response = requests.post(url, headers=self.headers, json=data)
                elif method.lower() == "get":
                    response = requests.get(url, headers=self.headers)
                else:
                    raise ValueError("Unsupported HTTP method")

                if response.status_code == 200:
                    return response
                else:
                    print(f"Attempt {attempt + 1}: Error {response.status_code}, {response.text}")
            except requests.exceptions.RequestException as e:
                print(f"Request failed: {e}")
        return None

    def _parse_response(self, response):
        """
        Parse the response from the API.

        Args:
            response (requests.Response): The response from the API.

        Returns:
            dict: The parsed response.
        """
        if response and response.status_code == 200:
            return response.json()
        else:
            print(f"Error: {response.status_code}, {response.text}" if response else "No response")
            return None



**Summaries of links found in the content:**

https://generativelanguage.googleapis.com:

The page provides strategies and best practices for managing and enhancing a system based on a provided Python script and project structure. The recommendations include:

1. Centralized Configuration Management: Use environment variables or a configuration file to manage sensitive information.
2. Logging and Monitoring: Integrate comprehensive logging and consider using a monitoring tool to track system operations and errors.
3. Error Handling and Recovery: Implement robust error handling and recovery mechanisms.
4. Asynchronous Operations and Task Management: Properly await and manage asynchronous operations to prevent deadlocks.
5. Dependency Injection: Consider using a dependency injection framework to manage dependencies more cleanly.
6. Scalability and Performance Optimization: Evaluate system performance and optimize code and infrastructure as necessary.
7. Testing and Continuous Integration: Develop comprehensive tests and use a CI/CD pipeline for automated testing and deployment.
8. Documentation and Knowledge Sharing: Maintain up-to-date documentation and encourage knowledge sharing among team members.
9. Modular Design and Loose Coupling: Ensure a modular design with loosely coupled components for easier maintenance and future enhancements.
10. Security Considerations: Regularly audit the system for security vulnerabilities and implement best practices.

The page also includes a Python class called GeminiClient, which provides methods for interacting with the Gemini API. The class includes methods for cleaning input data, generating text content, generating embeddings, finding the most relevant document, answering queries, generating multi-turn conversations, processing embeddings, and calling functions hosted on the Gemini platform. The class also includes helper methods for sending requests and parsing responses from the API.

You have previously changed these files:
<changed_file file_path="src/config.py">
--- 
+++ 
@@ -0,0 +1,25 @@
+import json
+import os
+
+
+class Config:
+    def __init__(self):
+        self.configurations = {}
+        self.config_file_path = "config.json"
+        self.load_configurations()
+
+    def load_configurations(self):
+        if os.path.exists(self.config_file_path):
+            with open(self.config_file_path, 'r') as config_file:
+                self.configurations = json.load(config_file)
+        else:
+            print("Configuration file not found. Falling back to environment variables.")
+
+    def get_config(self, key):
+        return self.configurations.get(key, os.getenv(key))
+
+    def get_api_key(self):
+        api_key = self.get_config('API_KEY')
+        if not api_key:
+            raise ValueError("API key not found in configurations or environment variables.")
+        return api_key
</changed_file>
<changed_file file_path="src/logger.py">
--- 
+++ 
@@ -0,0 +1,22 @@
+import logging
+import logging.handlers
+
+
+def get_logger(name):
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.DEBUG)
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+
+    console_handler = logging.StreamHandler()
+    console_handler.setLevel(logging.DEBUG)
+    console_handler.setFormatter(formatter)
+
+    file_handler = logging.handlers.RotatingFileHandler('application.log', maxBytes=10485760, backupCount=5)
+    file_handler.setLevel(logging.DEBUG)
+    file_handler.setFormatter(formatter)
+
+    logger.addHandler(console_handler)
+    logger.addHandler(file_handler)
+    logger.propagate = False
+
+    return logger
</changed_file>
<changed_file file_path="src/gemini_client.py">
--- 
+++ 
@@ -1,16 +1,19 @@
 import base64
 import json
-
 import requests
+from src.config import Config
 from google.cloud import gemini_pro_models, gemini_vision_pro_models
 from src.utils import encode_image_to_base64
+from src.logger import get_logger
 
 
 class GeminiClient:
-    def __init__(self, api_key):
-        self.api_key = api_key
+    def __init__(self):
+        config = Config()
+        self.api_key = config.get_api_key()
         self.gemini_pro_client = gemini_pro_models.Ggemini_pro_models.GeminiProModelsServiceClient()
         self.gemini_vision_pro_client = gemini_vision_pro_models.GeminiVisionProModelsServiceClient()
+        self.logger = get_logger('GeminiClient')
 
     def predict_with_gemini_pro_models(self, model_name, image_path):
         image_bytes = encode_image_to_base64(image_path)
@@ -21,7 +24,7 @@
             response = self.gemini_pro_client.predict(request=request)
             return self.parse_prediction_results(response.predictions)
         except Exception as e:
-            print(f"Error during Gemini Pro Models prediction: {e}")
+            self.logger.error(f"Error during Gemini Pro Models prediction: {e}")
             return None
 
     def predict_with_gemini_vision_pro_models(self, model_name, image_path):
@@ -33,7 +36,7 @@
             response = self.gemini_vision_pro_client.predict(request=request)
             return self.parse_prediction_results(response.predictions)
         except Exception as e:
-            print(f"Error during Gemini Vision Pro Models prediction: {e}")
+            self.logger.error(f"Error during Gemini Vision Pro Models prediction: {e}")
             return None
 
     def parse_prediction_results(self, predictions):

</changed_file>
<changed_file file_path="docs/README.md">
--- 
+++ 
@@ -0,0 +1,45 @@
+2. Configure Google Cloud authentication:
+   ```
+   export GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-file.json"
+   ```
+3. Set up `config.json` or environment variables with your API key:
+   ```json
+   {
+     "API_KEY": "your_api_key_here"
+   }
+   ```
+
+## Usage Examples
+
+### Initializing the Client
+```python
+from src.gemini_client import GeminiClient
+
+client = GeminiClient()
+```
+
+### Making Prediction Requests
+```python
+# Predict with Gemini Pro Models
+results = client.predict_with_gemini_pro_models('model_name', 'path/to/image.jpg')
+
+# Predict with Gemini Vision Pro Models
+results = client.predict_with_gemini_vision_pro_models('model_name', 'path/to/image.jpg')
+```
+
+### Interpreting the Results
+```python
+for result in results:
+    print(f"Category: {result['category']}, Confidence: {result['confidence']}, Bounding Box: {result['bounding_box']}")
+```
+
+### Configuration and Logging
+```python
+from src.config import Config
+from src.logger import get_logger
+
+config = Config()
+api_key = config.get_api_key()
+
+logger = get_logger('application')
+logger.info("Application started")
</changed_file>

Relevant files in this PR:

<relevant_file file_path="src/logger.py">
import json
import os


class Config:
    def __init__(self):
        self.configurations = {}
        self.config_file_path = "config.json"
        self.load_configurations()

    def load_configurations(self):
        if os.path.exists(self.config_file_path):
            with open(self.config_file_path, 'r') as config_file:
                self.configurations = json.load(config_file)
        else:
            print("Configuration file not found. Falling back to environment variables.")

    def get_config(self, key):
        return self.configurations.get(key, os.getenv(key))

    def get_api_key(self):
        api_key = self.get_config('API_KEY')
        if not api_key:
            raise ValueError("API key not found in configurations or environment variables.")
        return api_key

</relevant_file>

<section id="A">
import base64
import os

def encode_image_to_base64(image_path):
    if not os.path.exists(image_path):
        raise FileNotFoundError(f"The specified image file does not exist: {image_path}")
    with open(image_path, 'rb') as image_file:
        encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
    return encoded_string

def validate_image_file(image_path):
    if not os.path.isfile(image_path):
        raise ValueError(f"Invalid image file path: {image_path}")
    if not image_path.lower().endswith(('.png', '.jpg', '.jpeg')):
</section id="A">

# Request
• Integrate logging into the `encode_image_to_base64` and `validate_image_file` functions by importing `get_logger` from `src/logger.py` and using it to log errors or important information.
• This modification ensures that utility functions also benefit from comprehensive logging.

ERROR
No changes were made due to the following error:

No changes have been applied. This is because when the following changes are applied:

```diff
--- 
+++ 
@@ -1,8 +1,11 @@
 import base64
 import os
+from src.logger import get_logger
 
 def encode_image_to_base64(image_path):
     if not os.path.exists(image_path):
+                logger = get_logger(__name__)
+        logger.error(f"The specified image file does not exist: {image_path}")
         raise FileNotFoundError(f"The specified image file does not exist: {image_path}")
     with open(image_path, 'rb') as image_file:
         encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
@@ -10,6 +13,8 @@
 
 def validate_image_file(image_path):
     if not os.path.isfile(image_path):
-        raise ValueError(f"Invalid image file path: {image_path}")
+            logger = get_logger(__name__)
+    logger.error(f"Invalid image file path: {image_path}")
+    raise ValueError(f"Invalid image file path: {image_path}")
     if not image_path.lower().endswith(('.png', '.jpg', '.jpeg')):
         raise ValueError("Unsupported image format. Please use PNG or JPEG.")
```

It yields invalid code with the following error message:
```
Python syntax error: unindent does not match any outer indentation level at line 8
```

Please retry the search_and_replace with different changes that yield valid code.

ERROR
No changes were made due to the following error:

No changes have been applied. This is because when the following changes are applied:

```diff
--- 
+++ 
@@ -1,8 +1,11 @@
 import base64
 import os
+from src.logger import get_logger
 
 def encode_image_to_base64(image_path):
     if not os.path.exists(image_path):
+        logger = get_logger(__name__)
+        logger.error(f"The specified image file does not exist: {image_path}")
         raise FileNotFoundError(f"The specified image file does not exist: {image_path}")
     with open(image_path, 'rb') as image_file:
         encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
@@ -10,6 +13,8 @@
 
 def validate_image_file(image_path):
     if not os.path.isfile(image_path):
-        raise ValueError(f"Invalid image file path: {image_path}")
+    logger = get_logger(__name__)
+    logger.error(f"Invalid image file path: {image_path}")
+    raise ValueError(f"Invalid image file path: {image_path}")
     if not image_path.lower().endswith(('.png', '.jpg', '.jpeg')):
         raise ValueError("Unsupported image format. Please use PNG or JPEG.")
```

It yields invalid code with the following error message:
```
Python syntax error: expected an indented block after 'if' statement on line 15 at line 16
```

Please retry the search_and_replace with different changes that yield valid code.

ERROR
No changes were made due to the following error:

No changes have been applied. This is because when the following changes are applied:

```diff
--- 
+++ 
@@ -1,8 +1,12 @@
 import base64
 import os
+from src.logger import get_logger
+
+logger = get_logger(__name__)
 
 def encode_image_to_base64(image_path):
     if not os.path.exists(image_path):
+        logger.error(f"The specified image file does not exist: {image_path}")
         raise FileNotFoundError(f"The specified image file does not exist: {image_path}")
     with open(image_path, 'rb') as image_file:
         encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
@@ -10,6 +14,7 @@
 
 def validate_image_file(image_path):
     if not os.path.isfile(image_path):
-        raise ValueError(f"Invalid image file path: {image_path}")
+    logger.error(f"Invalid image file path: {image_path}")
+    raise ValueError(f"Invalid image file path: {image_path}")
     if not image_path.lower().endswith(('.png', '.jpg', '.jpeg')):
         raise ValueError("Unsupported image format. Please use PNG or JPEG.")
```

It yields invalid code with the following error message:
```
Python syntax error: expected an indented block after 'if' statement on line 16 at line 17
```

Please retry the search_and_replace with different changes that yield valid code.

ERROR
No changes were made due to the following error:

No changes have been applied. This is because when the following changes are applied:

```diff
--- 
+++ 
@@ -1,8 +1,11 @@
 import base64
 import os
+from src.logger import get_logger
 
 def encode_image_to_base64(image_path):
     if not os.path.exists(image_path):
+        logger = get_logger(__name__)
+        logger.error(f"The specified image file does not exist: {image_path}")
         raise FileNotFoundError(f"The specified image file does not exist: {image_path}")
     with open(image_path, 'rb') as image_file:
         encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
@@ -10,6 +13,8 @@
 
 def validate_image_file(image_path):
     if not os.path.isfile(image_path):
+    logger = get_logger(__name__)
+    logger.error(f"Invalid image file path: {image_path}")
         raise ValueError(f"Invalid image file path: {image_path}")
     if not image_path.lower().endswith(('.png', '.jpg', '.jpeg')):
         raise ValueError("Unsupported image format. Please use PNG or JPEG.")
```

It yields invalid code with the following error message:
```
Python syntax error: expected an indented block after 'if' statement on line 15 at line 16
```

Please retry the search_and_replace with different changes that yield valid code.

SUCCESS
Here are the new code sections:

The following changes have been applied:
```diff
--- 
+++ 
@@ -1,15 +1,22 @@
 import base64
 import os
+from src.logger import get_logger
 
 def encode_image_to_base64(image_path):
     if not os.path.exists(image_path):
+        raise FileNotFoundError(f"The specified image file does not exist: {image_path}")
+        logger = get_logger('utils')
+    if not os.path.exists(image_path):
+        logger.error(f"The specified image file does not exist: {image_path}")
         raise FileNotFoundError(f"The specified image file does not exist: {image_path}")
     with open(image_path, 'rb') as image_file:
         encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
     return encoded_string
 
 def validate_image_file(image_path):
+    logger = get_logger('utils')
     if not os.path.isfile(image_path):
+        logger.error(f"Invalid image file path: {image_path}")
         raise ValueError(f"Invalid image file path: {image_path}")
     if not image_path.lower().endswith(('.png', '.jpg', '.jpeg')):
         raise ValueError("Unsupported image format. Please use PNG or JPEG.")
```
Here are the new code sections:




<section id="A">
import base64
import os
from src.logger import get_logger

def encode_image_to_base64(image_path):
    if not os.path.exists(image_path):
        raise FileNotFoundError(f"The specified image file does not exist: {image_path}")
        logger = get_logger('utils')
    if not os.path.exists(image_path):
        logger.error(f"The specified image file does not exist: {image_path}")
        raise FileNotFoundError(f"The specified image file does not exist: {image_path}")
    with open(image_path, 'rb') as image_file:
        encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
    return encoded_string

def validate_image_file(image_path):
    logger = get_logger('utils')
    if not os.path.isfile(image_path):
        logger.error(f"Invalid image file path: {image_path}")
        raise ValueError(f"Invalid image file path: {image_path}")
    if not image_path.lower().endswith(('.png', '.jpg', '.jpeg')):
        raise ValueError("Unsupported image format. Please use PNG or JPEG.")
</section id="A">

You can continue to make changes to the code sections and call the `search_and_replace` function again.

# Repo & Issue Metadata
Repo: AutoGem: Agent Framework for Gemini Pro
Issue Title: Managing a complex system like the one described, involving multiple factories, agents, and an orchestrator, requires a structured approach to ensure smooth operation and maintainability!.

Issue Description: ### Details
 Here are some strategies and best practices to manage and enhance the system based on the provided llm_orchestrator/llm_orchestrator.py and the overall project structure:
1. Centralized Configuration Management
- Use environment variables or a configuration file to manage API keys and other sensitive information, rather than hardcoding them in the source files. This enhances security and flexibility.
- Implement a configuration management class or module that loads and provides access to these configurations throughout the application.
2. Logging and Monitoring
- Integrate comprehensive logging throughout the orchestrator and factory components to track operations, errors, and system states. This aids in debugging and monitoring the system's health.
- Consider using a monitoring tool or service to keep track of the system's performance and alert for any anomalies or failures.
3. Error Handling and Recovery
- Implement robust error handling within the orchestrator's orchestration logic and across factory components to manage exceptions gracefully.
- Design the system with recovery mechanisms in place, allowing it to resume operations after handling errors or failures.
4. Asynchronous Operations and Task Management
- Given the asynchronous nature of the orchestrator, ensure that all asynchronous operations are properly awaited and managed to prevent deadlocks or unhandled promises.
- Use task queues or a message broker to manage and distribute tasks among different components and agents efficiently.
5. Dependency Injection
- The orchestrator initializes all factories with a GeminiClient instance. Consider using a dependency injection framework to manage these dependencies more cleanly and flexibly.
6. Scalability and Performance Optimization
- Evaluate the performance of the system under load and identify bottlenecks. Optimize the code and infrastructure as necessary to handle the expected scale.
- Consider implementing caching strategies, especially for operations involving the MemoryFactory and EmbeddingFactory, to reduce redundant processing and improve response times.
7. Testing and Continuous Integration
- Develop comprehensive unit and integration tests for the orchestrator, factories, and agents to ensure reliability and facilitate continuous integration.
- Use a CI/CD pipeline to automate testing and deployment processes, ensuring that changes are tested and deployed smoothly.
8. Documentation and Knowledge Sharing
- Maintain up-to-date documentation for the system architecture, component interactions, and usage examples. This facilitates onboarding and collaboration among team members.
- Encourage knowledge sharing sessions among team members to discuss system design, challenges, and best practices.
9. Modular Design and Loose Coupling
- Ensure that the system's design remains modular, with loosely coupled components. This facilitates easier maintenance, testing, and future enhancements.
- Regularly review the system design to identify opportunities for refactoring and improvement.
10. Security Considerations
- Regularly audit the system for security vulnerabilities, especially concerning data handling and API interactions.
- Implement security best practices, such as using HTTPS for API calls, securing API keys, and validating inputs to prevent injection attacks.
import requests
import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

class GeminiClient:

    def __init__(self, api_key):
        """
        Initialize the GeminiClient with your API key.

        Args:
            api_key (str): Your Gemini API key.
        """
        self.api_key = api_key
        self.base_url = "https://generativelanguage.googleapis.com"
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }

    def clean_input_data(self, input_text):
        """
        Clean the input text by removing leading and trailing whitespace.

        Args:
            input_text (str): The input text to clean.

        Returns:
            str: The cleaned input text.
        """
        return input_text.strip()

    def generate_text_content(self, input_text):
        """
        Generate text content using the Gemini API.

        Args:
            input_text (str): The input text to generate content from.

        Returns:
            str: The generated text content.
        """
        url = f"{self.base_url}/v1beta/gemini-ultra:generateContent"
        data = {"contents": [{"parts": [{"text": input_text}]}]}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def generate_embedding(self, text, task_type="RETRIEVAL_DOCUMENT", title=None):
        """
        Generate an embedding for the given text.

        Args:
            text (str): The text to generate an embedding for.
            task_type (str, optional): The task type for which the embedding is being generated. Defaults to "RETRIEVAL_DOCUMENT".
            title (str, optional): The title of the document (if task_type is "RETRIEVAL_DOCUMENT").

        Returns:
            dict: The embedding in JSON format.
        """
        model_id = 'models/embedding-001'
        data = {"content": text, "task_type": task_type}
        if title and task_type == "RETRIEVAL_DOCUMENT":
            data["title"] = title
        url = f"{self.base_url}/v1beta/{model_id}:embedContent"
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def find_most_relevant_document(self, query_embedding, documents_df):
        """
        Find the most relevant document to the given query embedding.

        Args:
            query_embedding (dict): The query embedding in JSON format.
            documents_df (pandas.DataFrame): A DataFrame containing the documents and their embeddings.

        Returns:
            pandas.Series: The most relevant document.
        """
        dot_products = np.dot(np.stack(documents_df['Embeddings']), query_embedding)
        idx = np.argmax(dot_products)
        return documents_df.iloc[idx]

    def answer_query(self, query, documents_df):
        """
        Answer a query using the Gemini API.

        Args:
            query (str): The query to answer.
            documents_df (pandas.DataFrame): A DataFrame containing the documents and their embeddings.

        Returns:
            str: The answer to the query.
        """
        query_embedding = self.generate_embedding(query, task_type="RETRIEVAL_QUERY")['embedding']
        relevant_document = self.find_most_relevant_document(query_embedding, documents_df)
        prompt = f"Based on the following passage: {relevant_document['Text']}, answer the question: {query}"
        answer = self.generate_text_content(prompt)
        return answer

    def generate_multi_turn_conversation(self, conversation_parts):
        """
        Generate a multi-turn conversation using the Gemini API.

        Args:
            conversation_parts (list): A list of conversation parts, each of which is a dictionary containing the "speaker" and "text" fields.

        Returns:
            list: A list of conversation parts, each of which is a dictionary containing the "speaker" and "text" fields.
        """
        if not isinstance(conversation_parts, list):
            raise TypeError("conversation_parts must be a list")
        for part in conversation_parts:
            if not isinstance(part, dict):
                raise TypeError("Each conversation part must be a dictionary")
            if not set(["speaker", "text"]).issubset(part.keys()):
                raise ValueError("Each conversation part must have 'speaker' and 'text' fields")

        url = f"{self.base_url}/v1beta/chat-bison-001:generateContent"
        data = {"contents": conversation_parts}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def generate_embeddings(self, input_texts):
        """
        Generate embeddings for the given input texts.

        Args:
            input_texts (list): A list of input texts.

        Returns:
            dict: The embeddings in JSON format.
        """
        if not isinstance(input_texts, list):
            raise TypeError("input_texts must be a list")
        for text in input_texts:
            if not isinstance(text, str):
                raise TypeError("Each input text must be a string")

        url = f"{self.base_url}/v1beta/models/embedding-001:embedText"
        data = {"text": input_texts}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def process_embeddings(self, embeddings):
        """
        Process the embeddings to extract meaningful information.

        Args:
            embeddings (dict): The embeddings in JSON format.

        Returns:
            dict: The processed embeddings.
        """
        processed_embeddings = {}
        for embedding in embeddings:
            processed_embeddings[embedding['id']] = embedding['vector']
        return processed_embeddings

    def call_function(self, function_name, args, kwargs={}):
        """
        Call a function hosted on the Gemini platform.

        Args:
            function_name (str): The name of the function to call.
            args (dict): The arguments to pass to the function.
            kwargs (dict, optional): The keyword arguments to pass to the function.

        Returns:
            dict: The result of the function call.
        """
        url = f"{self.base_url}/v1beta/models/function:callFunction"
        data = {"function_name": function_name, "args": args, "kwargs": kwargs}
        response = self._send_request("post", url, data=data)
        return self._parse_response(response)

    def _send_request(self, method, url, data=None):
        """
        Send a request to the Gemini API.

        Args:
            method (str): The HTTP method to use.
            url (str): The URL to send the request to.
            data (dict, optional): The data to send in the request body.

        Returns:
            requests.Response: The response from the API.
        """
        max_retries = 3
        for attempt in range(max_retries):
            try:
                if method.lower() == "post":
                    response = requests.post(url, headers=self.headers, json=data)
                elif method.lower() == "get":
                    response = requests.get(url, headers=self.headers)
                else:
                    raise ValueError("Unsupported HTTP method")

                if response.status_code == 200:
                    return response
                else:
                    print(f"Attempt {attempt + 1}: Error {response.status_code}, {response.text}")
            except requests.exceptions.RequestException as e:
                print(f"Request failed: {e}")
        return None

    def _parse_response(self, response):
        """
        Parse the response from the API.

        Args:
            response (requests.Response): The response from the API.

        Returns:
            dict: The parsed response.
        """
        if response and response.status_code == 200:
            return response.json()
        else:
            print(f"Error: {response.status_code}, {response.text}" if response else "No response")
            return None



**Summaries of links found in the content:**

https://generativelanguage.googleapis.com:

The page provides strategies and best practices for managing and enhancing a system based on a provided Python script and project structure. The recommendations include:

1. Centralized Configuration Management: Use environment variables or a configuration file to manage sensitive information.
2. Logging and Monitoring: Integrate comprehensive logging and consider using a monitoring tool to track system operations and errors.
3. Error Handling and Recovery: Implement robust error handling and recovery mechanisms.
4. Asynchronous Operations and Task Management: Properly await and manage asynchronous operations to prevent deadlocks.
5. Dependency Injection: Consider using a dependency injection framework to manage dependencies more cleanly.
6. Scalability and Performance Optimization: Evaluate system performance and optimize code and infrastructure as necessary.
7. Testing and Continuous Integration: Develop comprehensive tests and use a CI/CD pipeline for automated testing and deployment.
8. Documentation and Knowledge Sharing: Maintain up-to-date documentation and encourage knowledge sharing among team members.
9. Modular Design and Loose Coupling: Ensure a modular design with loosely coupled components for easier maintenance and future enhancements.
10. Security Considerations: Regularly audit the system for security vulnerabilities and implement best practices.

The page also includes a Python class called GeminiClient, which provides methods for interacting with the Gemini API. The class includes methods for cleaning input data, generating text content, generating embeddings, finding the most relevant document, answering queries, generating multi-turn conversations, processing embeddings, and calling functions hosted on the Gemini platform. The class also includes helper methods for sending requests and parsing responses from the API.

You have previously changed these files:
<changed_file file_path="src/config.py">
--- 
+++ 
@@ -0,0 +1,25 @@
+import json
+import os
+
+
+class Config:
+    def __init__(self):
+        self.configurations = {}
+        self.config_file_path = "config.json"
+        self.load_configurations()
+
+    def load_configurations(self):
+        if os.path.exists(self.config_file_path):
+            with open(self.config_file_path, 'r') as config_file:
+                self.configurations = json.load(config_file)
+        else:
+            print("Configuration file not found. Falling back to environment variables.")
+
+    def get_config(self, key):
+        return self.configurations.get(key, os.getenv(key))
+
+    def get_api_key(self):
+        api_key = self.get_config('API_KEY')
+        if not api_key:
+            raise ValueError("API key not found in configurations or environment variables.")
+        return api_key
</changed_file>
<changed_file file_path="src/logger.py">
--- 
+++ 
@@ -0,0 +1,22 @@
+import logging
+import logging.handlers
+
+
+def get_logger(name):
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.DEBUG)
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+
+    console_handler = logging.StreamHandler()
+    console_handler.setLevel(logging.DEBUG)
+    console_handler.setFormatter(formatter)
+
+    file_handler = logging.handlers.RotatingFileHandler('application.log', maxBytes=10485760, backupCount=5)
+    file_handler.setLevel(logging.DEBUG)
+    file_handler.setFormatter(formatter)
+
+    logger.addHandler(console_handler)
+    logger.addHandler(file_handler)
+    logger.propagate = False
+
+    return logger
</changed_file>
<changed_file file_path="src/gemini_client.py">
--- 
+++ 
@@ -1,16 +1,19 @@
 import base64
 import json
-
 import requests
+from src.config import Config
 from google.cloud import gemini_pro_models, gemini_vision_pro_models
 from src.utils import encode_image_to_base64
+from src.logger import get_logger
 
 
 class GeminiClient:
-    def __init__(self, api_key):
-        self.api_key = api_key
+    def __init__(self):
+        config = Config()
+        self.api_key = config.get_api_key()
         self.gemini_pro_client = gemini_pro_models.Ggemini_pro_models.GeminiProModelsServiceClient()
         self.gemini_vision_pro_client = gemini_vision_pro_models.GeminiVisionProModelsServiceClient()
+        self.logger = get_logger('GeminiClient')
 
     def predict_with_gemini_pro_models(self, model_name, image_path):
         image_bytes = encode_image_to_base64(image_path)
@@ -21,7 +24,7 @@
             response = self.gemini_pro_client.predict(request=request)
             return self.parse_prediction_results(response.predictions)
         except Exception as e:
-            print(f"Error during Gemini Pro Models prediction: {e}")
+            self.logger.error(f"Error during Gemini Pro Models prediction: {e}")
             return None
 
     def predict_with_gemini_vision_pro_models(self, model_name, image_path):
@@ -33,7 +36,7 @@
             response = self.gemini_vision_pro_client.predict(request=request)
             return self.parse_prediction_results(response.predictions)
         except Exception as e:
-            print(f"Error during Gemini Vision Pro Models prediction: {e}")
+            self.logger.error(f"Error during Gemini Vision Pro Models prediction: {e}")
             return None
 
     def parse_prediction_results(self, predictions):

</changed_file>
<changed_file file_path="docs/README.md">
--- 
+++ 
@@ -0,0 +1,45 @@
+2. Configure Google Cloud authentication:
+   ```
+   export GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-file.json"
+   ```
+3. Set up `config.json` or environment variables with your API key:
+   ```json
+   {
+     "API_KEY": "your_api_key_here"
+   }
+   ```
+
+## Usage Examples
+
+### Initializing the Client
+```python
+from src.gemini_client import GeminiClient
+
+client = GeminiClient()
+```
+
+### Making Prediction Requests
+```python
+# Predict with Gemini Pro Models
+results = client.predict_with_gemini_pro_models('model_name', 'path/to/image.jpg')
+
+# Predict with Gemini Vision Pro Models
+results = client.predict_with_gemini_vision_pro_models('model_name', 'path/to/image.jpg')
+```
+
+### Interpreting the Results
+```python
+for result in results:
+    print(f"Category: {result['category']}, Confidence: {result['confidence']}, Bounding Box: {result['bounding_box']}")
+```
+
+### Configuration and Logging
+```python
+from src.config import Config
+from src.logger import get_logger
+
+config = Config()
+api_key = config.get_api_key()
+
+logger = get_logger('application')
+logger.info("Application started")
</changed_file>
<changed_file file_path="src/utils.py">
--- 
+++ 
@@ -1,15 +1,22 @@
 import base64
 import os
+from src.logger import get_logger
 
 def encode_image_to_base64(image_path):
     if not os.path.exists(image_path):
+        raise FileNotFoundError(f"The specified image file does not exist: {image_path}")
+        logger = get_logger('utils')
+    if not os.path.exists(image_path):
+        logger.error(f"The specified image file does not exist: {image_path}")
         raise FileNotFoundError(f"The specified image file does not exist: {image_path}")
     with open(image_path, 'rb') as image_file:
         encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
     return encoded_string
 
 def validate_image_file(image_path):
+    logger = get_logger('utils')
     if not os.path.isfile(image_path):
+        logger.error(f"Invalid image file path: {image_path}")
         raise ValueError(f"Invalid image file path: {image_path}")
     if not image_path.lower().endswith(('.png', '.jpg', '.jpeg')):
         raise ValueError("Unsupported image format. Please use PNG or JPEG.")
</changed_file>

Relevant files in this PR:

<relevant_file file_path="docs/README.md">
import json
import os


class Config:
    def __init__(self):
        self.configurations = {}
        self.config_file_path = "config.json"
        self.load_configurations()

    def load_configurations(self):
        if os.path.exists(self.config_file_path):
            with open(self.config_file_path, 'r') as config_file:
                self.configurations = json.load(config_file)
        else:
            print("Configuration file not found. Falling back to environment variables.")

    def get_config(self, key):
        return self.configurations.get(key, os.getenv(key))

    def get_api_key(self):
        api_key = self.get_config('API_KEY')
        if not api_key:
            raise ValueError("API key not found in configurations or environment variables.")
        return api_key

</relevant_file>

<section id="A">
# AutoGem
Agent Framework for Gemini Pro

## Running Tests

To run the tests for AutoGem, navigate to the root directory of the project and execute the following command:

For Python:
```
python -m unittest discover tests
```

### Using the `GeminiClient` Class

The `GeminiClient` class provides an interface to the Google Gemini Pro Models API and Google Gemini Vision Pro Models API. Here's how you can use it:
</section id="A">
<section id="B">

#### Setup

Before using the `GeminiClient`, ensure you have installed the necessary dependencies:

```shell
pip install google-cloud-gemini-pro-models google-cloud-gemini-vision-pro-models
```

You must also configure your Google Cloud authentication by setting the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to the path of your service account key file:

```shell
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-file.json"
```

</section id="B">
<section id="C">
#### Initializing the Client

To initialize the `GeminiClient`, provide your Google Cloud API key:

```python
from src.gemini_client import GeminiClient

client = GeminiClient(api_key='YOUR_API_KEY')
```

#### Making Prediction Requests

To make prediction requests, use the `predict_with_gemini_pro_models` or `predict_with_gemini_vision_pro_models` methods. Provide the model name and the path to the image you wish to classify:

```python
</section id="C">
<section id="D">
# Predict with Gemini Pro Models
results = client.predict_with_gemini_pro_models('model_name', 'path/to/image.jpg')

# Predict with Gemini Vision Pro Models
results = client.predict_with_gemini_vision_pro_models('model_name', 'path/to/image.jpg')
```

#### Interpreting the Results

The methods return a list of dictionaries, each representing a prediction result. Here's how to interpret these results:

```python
for result in results:
    print(f"Category: {result['category']}, Confidence: {result['confidence']}, Bounding Box: {result['bounding_box']}")
```
</section id="D">
<section id="E">

### Known Issues and Limitations

- The current implementation does not support streaming predictions.
- Only prediction requests with single instances (images) are supported; batch predictions are not yet implemented.
- The API may impose limits on the number of requests per minute or other usage restrictions.

</section id="E">

# Request
• Update the `README.md` file to include a reference to the new `docs/README.md` for detailed documentation on system architecture, setup, and usage.
• This change ensures that users are directed to comprehensive documentation.

SUCCESS
Here are the lines containing the keywords:

The keyword was found in the following sections:

<section id='E'> (1 matches)
### Known Issues and Limitations
^

- The current implementation does not support streaming predictions.
- Only prediction requests with single instances (images) are supported; batch predictions are not yet implemented.
- The API may impose limits on the number of requests per minute or other usage restrictions.
</section>

SUCCESS
Here are the lines containing the keywords:

The keyword was found in the following sections:

<section id='A'> (1 matches)
# AutoGem
Agent Framework for Gemini Pro

## Running Tests
^

To run the tests for AutoGem, navigate to the root directory of the project and execute the following command:

For Python:
```
python -m unittest discover tests
```

### Using the `GeminiClient` Class

The `GeminiClient` class provides an interface to the Google Gemini Pro Models API and Google Gemini Vision Pro Models API. Here's how you can use it:
</section>

SUCCESS
Here are the new code sections:

The following changes have been applied:
```diff
--- 
+++ 
@@ -9,6 +9,8 @@
 ```
 python -m unittest discover tests
 ```
+
+For detailed documentation on system architecture, setup, and usage, refer to our [documentation](docs/README.md).
 
 ### Using the `GeminiClient` Class
 
@@ -61,6 +63,8 @@
 
 ### Known Issues and Limitations
 
+For detailed documentation on system architecture, setup, and usage, refer to our [documentation](docs/README.md).
+
 - The current implementation does not support streaming predictions.
 - Only prediction requests with single instances (images) are supported; batch predictions are not yet implemented.
 - The API may impose limits on the number of requests per minute or other usage restrictions.
```
Here are the new code sections:




<section id="A">
# AutoGem
Agent Framework for Gemini Pro

## Running Tests

To run the tests for AutoGem, navigate to the root directory of the project and execute the following command:

For Python:
```
python -m unittest discover tests
```

For detailed documentation on system architecture, setup, and usage, refer to our [documentation](docs/README.md).

### Using the `GeminiClient` Class
</section id="A">
<section id="B">

The `GeminiClient` class provides an interface to the Google Gemini Pro Models API and Google Gemini Vision Pro Models API. Here's how you can use it:

#### Setup

Before using the `GeminiClient`, ensure you have installed the necessary dependencies:

```shell
pip install google-cloud-gemini-pro-models google-cloud-gemini-vision-pro-models
```

You must also configure your Google Cloud authentication by setting the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to the path of your service account key file:

```shell
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-file.json"
</section id="B">
<section id="C">
```

#### Initializing the Client

To initialize the `GeminiClient`, provide your Google Cloud API key:

```python
from src.gemini_client import GeminiClient

client = GeminiClient(api_key='YOUR_API_KEY')
```

#### Making Prediction Requests

To make prediction requests, use the `predict_with_gemini_pro_models` or `predict_with_gemini_vision_pro_models` methods. Provide the model name and the path to the image you wish to classify:
</section id="C">
<section id="D">

```python
# Predict with Gemini Pro Models
results = client.predict_with_gemini_pro_models('model_name', 'path/to/image.jpg')

# Predict with Gemini Vision Pro Models
results = client.predict_with_gemini_vision_pro_models('model_name', 'path/to/image.jpg')
```

#### Interpreting the Results

The methods return a list of dictionaries, each representing a prediction result. Here's how to interpret these results:

```python
for result in results:
</section id="D">
<section id="E">
    print(f"Category: {result['category']}, Confidence: {result['confidence']}, Bounding Box: {result['bounding_box']}")
```

### Known Issues and Limitations

For detailed documentation on system architecture, setup, and usage, refer to our [documentation](docs/README.md).

- The current implementation does not support streaming predictions.
- Only prediction requests with single instances (images) are supported; batch predictions are not yet implemented.
- The API may impose limits on the number of requests per minute or other usage restrictions.

</section id="E">

You can continue to make changes to the code sections and call the `search_and_replace` function again.