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examples/Leveraging_model_distillation_to_fine-tune_a_model.ipynb

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"\n",
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"We're looking at a classification problem where we'd like to guess the grape variety based on all other criterias available, including description, subregion and province that we'll include in the prompt. It gives a lot of information to the model, you're free to also remove some information that can help significantly the model such as the region in which it was produced to see if it does a good job at finding the grape.\n",
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"\n",
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"Let's filter the grape varieties that have less than 5 occurences in reviews.\n",
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"Let's filter the grape varieties that have less than 5 occurrences in reviews.\n",
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"\n",
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"Let's proceed with a subset of 500 random rows from this dataset."
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examples/Question_answering_using_embeddings.ipynb

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"id": "1af18d66-d47a-496d-ae5f-4c5d53caa434",
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"metadata": {},
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"source": [
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"In this case, the model has no knowledge of 2024 and is unable to answer the question. In a similar way, if you ask a question pertaining to a recent political event (that occured in Nov 2024 for example), GPT-4o-mini models will not be able to answer due to its knowledge cut-off date of Oct 2023. "
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"In this case, the model has no knowledge of 2024 and is unable to answer the question. In a similar way, if you ask a question pertaining to a recent political event (that occurred in Nov 2024 for example), GPT-4o-mini models will not be able to answer due to its knowledge cut-off date of Oct 2023. "
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{

examples/chatgpt/gpt_actions_library/gpt_action_googleads_adzviser.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"![GPT Search Term Analysis Part 1](../../../images/gptactions_googleads_search_term_analysis_1.png)",
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"![GPT Search Term Analysis Part 1](../../../images/gptactions_googleads_search_term_analysis_1.png)\n",
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"![GPT Search Term Analysis Part 2](../../../images/gptactions_googleads_search_term_analysis_2.png)"
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]
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" },\n",
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" \"date_ranges\": {\n",
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" \"type\": \"array\",\n",
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" \"description\": \"A list of date ranges requested from the user. They needs to be calculated seperately with Code Interpreter and Python every single time for accuracy. For example, if the user requests \\\"Google Ads search impression share in May and August\\\", then this array should be [[\\\"2024-05-01\\\", \\\"2024-05-31\\\"], [\\\"2024-08-01\\\", \\\"2024-08-31\\\"]].\",\n",
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" \"description\": \"A list of date ranges requested from the user. They needs to be calculated separately with Code Interpreter and Python every single time for accuracy. For example, if the user requests \\\"Google Ads search impression share in May and August\\\", then this array should be [[\\\"2024-05-01\\\", \\\"2024-05-31\\\"], [\\\"2024-08-01\\\", \\\"2024-08-31\\\"]].\",\n",
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" \"items\": {\n",
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" \"type\": \"array\",\n",
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" \"items\": {\n",

examples/evaluation/Evaluate_RAG_with_LlamaIndex.ipynb

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"source": [
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"We will use `gpt-3.5-turbo` for generating response for a given query and `gpt-4` for evaluation.\n",
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"\n",
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"Let's create service_context seperately for `gpt-3.5-turbo` and `gpt-4`."
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"Let's create service_context separately for `gpt-3.5-turbo` and `gpt-4`."
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]
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{

examples/object_oriented_agentic_approach/Secure_code_interpreter_tool_for_LLM_agents.ipynb

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"- Uses gpt-4o model.\n",
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"\n",
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"2.\t**Agent 2: Python Code Generator and Executor (with Dynamically Generated Tool Calling and Code Execution)**\n",
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"- Recieve the file content's context from Agent 1.\n",
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"- Receive the file content's context from Agent 1.\n",
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"- Instructions to generate a Python script to answer the user's question.\n",
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"- Has access to the code interpreter within the Docker container, which is used to execute Python code.\n",
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"- Has access only to the file system inside the Docker container (not the host).\n",

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