diff --git a/.gitignore b/.gitignore index b1c6cec..6846339 100644 Binary files a/.gitignore and b/.gitignore differ diff --git a/phi3_model/README.md b/phi3_model/README.md new file mode 100644 index 0000000..e69de29 diff --git a/phi3_model/data/source.txt b/phi3_model/data/source.txt new file mode 100644 index 0000000..d822c48 --- /dev/null +++ b/phi3_model/data/source.txt @@ -0,0 +1,35 @@ +Apollo 11 – Lunar Descent and Moonwalk + +As the descent began, Armstrong and Aldrin found themselves passing landmarks on the +surface two or three seconds early, and reported that they were “long”; they would land +miles west of their target point. Eagle was traveling too fast. The problem could have been +mascons—concentrations of high mass in a region or regions of the Moon’s crust that +contains a gravitational anomaly, potentially altering Eagle’s trajectory. + +Five minutes into the descent burn, and 6,000 feet (1,800 m) above the surface of the +Moon, the LM guidance computer (LGC) distracted the crew with the first of several +unexpected 1201 and 1202 program alarms. Inside Mission Control Center, computer +engineer Jack Garman told Guidance Officer Steve Bales it was safe to continue the +descent, and this was relayed to the crew. The program alarms indicated “executive +overflows”, meaning the guidance computer could not complete all its tasks in real-time and +had to postpone some of them. Margaret Hamilton, the Director of Apollo Flight Computer +Programming at the MIT Charles Stark Draper Laboratory later recalled: “To blame the +computer for the Apollo 11 problems is like blaming the person who spots a fire and calls +the fire department. Actually, the computer was programmed to do more than recognize +error conditions. A complete set of recovery programs was incorporated into the software. +The software’s action, in this case, was to eliminate lower priority tasks and re-establish +the more important ones. The computer, rather than almost forcing an abort, prevented an +abort. If the computer hadn’t recognized this problem and taken recovery action, I doubt if +Apollo 11 would have been the successful Moon landing it was.” + +When Armstrong again looked outside, he saw that the computer’s landing target was in a +boulder-strewn area just north and east of a 300-foot-diameter (91 m) crater, so he took +semi-automatic control. Throughout the descent, Aldrin called out navigation data to +Armstrong, who was busy piloting Eagle. Now 107 feet (33 m) above the surface, +Armstrong knew their propellant supply was dwindling and was determined to land at the +first possible landing site. + +Armstrong found a clear patch of ground and maneuvered the spacecraft towards it. They +were now 100 feet (30 m) from the surface, with only 90 seconds of propellant remaining. +Lunar dust kicked up by the LM’s engine began to impair his ability to determine the +spacecraft’s motion. diff --git a/phi3_model/emissions.csv b/phi3_model/emissions.csv new file mode 100644 index 0000000..78fc776 --- /dev/null +++ b/phi3_model/emissions.csv @@ -0,0 +1,2 @@ +timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,water_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue,wue +2025-11-13T06:09:56,codecarbon,c909bc4e-b083-4230-a78a-99ebf1ebeb1e,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,0.08208100000047125,0.013644424732649086,0.16623121955837222,42.5,0.0,10.0,0.01936782330256946,0.0,0.00455692013805555,0.023924743440625004,0.0,Egypt,EGY,,,,Windows-11-10.0.26100-SP0,3.13.5,3.0.8,12,12th Gen Intel(R) Core(TM) i5-1235U,,,,,7.692127227783203,machine,N,1.0,0.0 diff --git a/phi3_model/emissions_base_c909bc4e-b083-4230-a78a-99ebf1ebeb1e.csv b/phi3_model/emissions_base_c909bc4e-b083-4230-a78a-99ebf1ebeb1e.csv new file mode 100644 index 0000000..2afd9ca --- /dev/null +++ b/phi3_model/emissions_base_c909bc4e-b083-4230-a78a-99ebf1ebeb1e.csv @@ -0,0 +1,3 @@ +task_name,timestamp,project_name,run_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,water_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud +Recursive RAG Query,2025-11-13T06:09:34,codecarbon,c909bc4e-b083-4230-a78a-99ebf1ebeb1e,142.94884759999877,0.001188882070760797,8.316882338340505e-06,42.5,0.0,10.0,0.0016875667336111143,0.0,0.00039707216333332955,0.002084638896944442,0.0,Egypt,EGY,,,,Windows-11-10.0.26100-SP0,3.13.5,3.0.8,12,12th Gen Intel(R) Core(TM) i5-1235U,,,,,7.692127227783203,machine,N +Recursive RAG Query_f351b95b-3d59-40c2-9d44-ffb9a25be233,2025-11-13T06:09:56,codecarbon,c909bc4e-b083-4230-a78a-99ebf1ebeb1e,0.07393939999928989,6.526370375566776e-07,8.310068383589976e-06,42.5,0.0,10.0,9.271493055622304e-07,0.0,2.172136111108433e-07,1.144362916673941e-06,0.0,Egypt,EGY,,,,Windows-11-10.0.26100-SP0,3.13.5,3.0.8,12,12th Gen Intel(R) Core(TM) i5-1235U,,,,,7.692127227783203,machine,N diff --git a/phi3_model/main.ipynb b/phi3_model/main.ipynb new file mode 100644 index 0000000..257ebe2 --- /dev/null +++ b/phi3_model/main.ipynb @@ -0,0 +1,2623 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a35eae70", + "metadata": {}, + "source": [ + "# RAG + Recursive Editing with Phi-3-mini\n", + "\n", + "This notebook demonstrates a simple **Retrieval-Augmented Generation (RAG)** pipeline combined with **Recursive Editing** using Microsoft's small language model **Phi-3-mini (4K instruct)**." + ] + }, + { + "cell_type": "markdown", + "id": "f130cbc0", + "metadata": {}, + "source": [ + "## Step 1: Setup and Installation\n", + "\n", + "We install all required libraries for running the Phi-3-mini model and RAG pipeline:\n", + "\n", + "- **API connection** (`huggingface_hub`) \n", + "- **RAG and indexing** (`llama-index-core`, `llama-index-embeddings-huggingface`) \n", + "- **Text embeddings** (`sentence-transformers`) \n", + "- **PDF reading** (`pypdf`) \n", + "- **PyTorch backend** (`torch`) \n", + "- **Energy tracking** (`codecarbon`) \n", + "- **TensorFlow/Keras backend support** (`tf-keras`)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0842f5d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: huggingface_hub in c:\\users\\dell\\appdata\\local\\programs\\python\\python313\\lib\\site-packages (0.36.0)\n", + "Requirement already satisfied: filelock in c:\\users\\dell\\appdata\\roaming\\python\\python313\\site-packages (from huggingface_hub) (3.18.0)\n", + 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[tf-keras]\n", + " ------------------------------------- -- 18/19 [tf-keras]\n", + " ------------------------------------- -- 18/19 [tf-keras]\n", + " ------------------------------------- -- 18/19 [tf-keras]\n", + " ------------------------------------- -- 18/19 [tf-keras]\n", + " ---------------------------------------- 19/19 [tf-keras]\n", + "\n", + "Successfully installed absl-py-2.3.1 astunparse-1.6.3 flatbuffers-25.9.23 gast-0.6.0 google_pasta-0.2.0 h5py-3.15.1 keras-3.12.0 libclang-18.1.1 markdown-3.10 ml_dtypes-0.5.3 namex-0.1.0 opt_einsum-3.4.0 optree-0.17.0 tensorboard-2.20.0 tensorboard-data-server-0.7.2 tensorflow-2.20.0 tf-keras-2.20.1 werkzeug-3.1.3 wheel-0.45.1\n" + ] + } + ], + "source": [ + "!pip install huggingface_hub\n", + "!pip install llama-index-core\n", + "!pip install llama-index-embeddings-huggingface\n", + "!pip install sentence-transformers\n", + "!pip install pypdf\n", + "!pip install torch\n", + "!pip install codecarbon\n", + "!pip install tf-keras\n" + ] + }, + { + "cell_type": "markdown", + "id": "172dd072", + "metadata": {}, + "source": [ + "## Step 2: Import Libraries\n", + "We import all libraries and configure:\n", + "\n", + "- **Environment variables** \n", + "- **API key** \n", + "- **Path to Apollo dataset** \n", + "- **Country code** for emissions tracking\n", + "\n", + "Make sure to update `DATA_PATH` with the location of your Apollo documents.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d49330db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Loaded HF API key and model: microsoft/phi-3-mini-4k-instruct\n", + " Data path: phi3_model/data\n" + ] + } + ], + "source": [ + "import time\n", + "import textwrap\n", + "from huggingface_hub import InferenceClient\n", + "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n", + "from llama_index.core.embeddings import resolve_embed_model\n", + "from codecarbon import OfflineEmissionsTracker\n", + "from dotenv import load_dotenv\n", + "import os\n", + "from llama_index.core.embeddings import resolve_embed_model\n", + "\n", + "\n", + "# Load API key and model from .env\n", + "load_dotenv()\n", + "HF_API_KEY = os.getenv(\"HF_TOKEN\")\n", + "HF_MODEL_ID = os.getenv(\"HF_MODEL\")\n", + "\n", + "# this points to your the Apollo dataset\n", + "DATA_PATH = r\"phi3_model/data\" \n", + "\n", + "YOUR_COUNTRY_ISO_CODE = \"EGY\"\n", + "\n", + "if not HF_API_KEY or not HF_MODEL_ID:\n", + " raise ValueError(\" HF_TOKEN or HF_MODEL not found. Check your .env file!\")\n", + "\n", + "print(f\" Loaded HF API key and model: {HF_MODEL_ID}\")\n", + "print(f\" Data path: {DATA_PATH}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "4802af26", + "metadata": {}, + "source": [ + "## Step 3 — Define Prompts\n", + "\n", + "We define three prompt templates for our recursive refinement loop:\n", + "\n", + "1. **Draft Prompt** — initial answer generation. \n", + "2. **Critic Prompt** — evaluates draft for accuracy and relevance. \n", + "3. **Refiner Prompt** — rewrites the draft based on critic feedback.\n", + "\n", + "This allows for **structured, iterative improvement** of answers.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c706d253", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Prompts defined!\n" + ] + } + ], + "source": [ + "DRAFT_SYSTEM_PROMPT = (\n", + " \"You are an expert assistant. Answer the user's question based *only* on the provided context. \"\n", + " \"Do not mention the context or 'the text'. If the answer is not in the context, say so.\"\n", + ")\n", + "DRAFT_USER_TEMPLATE = \"Context:\\n{context_str}\\n\\nQuestion:\\n{query_str}\"\n", + "\n", + "CRITIC_SYSTEM_PROMPT = (\n", + " \"You are a 'Critic' AI. Evaluate the 'Draft Answer' based *only* on the 'Source Context'. \"\n", + " \"Find flaws in Faithfulness (is it supported by context?) and Relevance (does it answer the question?). \"\n", + " \"Provide feedback as bullet points. If perfect, respond with 'The draft is perfect.'\"\n", + ")\n", + "CRITIC_USER_TEMPLATE = \"Source Context:\\n{context}\\n\\nOriginal Question:\\n{question}\\n\\nDraft Answer:\\n{draft}\"\n", + "\n", + "REFINER_SYSTEM_PROMPT = (\n", + " \"You are a 'Refiner' AI. Rewrite the 'Original Draft' to incorporate the 'Critic's Feedback'. \"\n", + " \"Do not add new info. Do not mention the context, draft, or feedback. Just provide the final, improved answer.\"\n", + ")\n", + "REFINER_USER_TEMPLATE = \"Original Draft:\\n{draft}\\n\\nCritic's Feedback:\\n{feedback}\"\n", + "\n", + "print(\" Prompts defined!\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "b44323f4", + "metadata": {}, + "source": [ + "## Step 4 — Initialize API and Index Data\n", + "\n", + "- Connect to the Hugging Face API. \n", + "- Create an **embedding model** for retrieval. \n", + "- Load Apollo documents and build a **vector index**. \n", + "- Prepare a **retriever** to fetch the most relevant document chunks.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a4628bc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Folder exists? True\n", + "Folder contents: ['source.txt']\n", + "✅ Retriever is ready.\n" + ] + } + ], + "source": [ + "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n", + "from llama_index.core.embeddings import resolve_embed_model\n", + "import os\n", + "\n", + "DATA_PATH = r\"C:\\Users\\DELL\\ELO2_GREEN_AI\\ELO2_GREEN_AI\\phi3_model\\data\"\n", + "\n", + "# check folder\n", + "print(\"Folder exists?\", os.path.exists(DATA_PATH))\n", + "print(\"Folder contents:\", os.listdir(DATA_PATH))\n", + "\n", + "# Embeddings for retrieval\n", + "embed_model = resolve_embed_model(\"local:BAAI/bge-small-en-v1.5\")\n", + "\n", + "# Load documents\n", + "documents = SimpleDirectoryReader(DATA_PATH).load_data()\n", + "\n", + "# Build vector index\n", + "index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)\n", + "retriever = index.as_retriever(similarity_top_k=3)\n", + "\n", + "print(\" Retriever is ready.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "e147888b", + "metadata": {}, + "source": [ + "## Step 5 — Helper Function to Call Hugging Face API\n", + "\n", + "This function sends prompts to the API and returns the response. \n", + "\n", + "- Includes **retry logic** in case of temporary API failure. \n", + "- Ensures that **all API calls are consistent** for draft, critic, and refiner steps.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "d50cea31", + "metadata": {}, + "outputs": [], + "source": [ + "def call_hf_api(system_prompt, user_prompt):\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": user_prompt},\n", + " ]\n", + " retries = 3\n", + " for attempt in range(retries):\n", + " try:\n", + " response_full = client.chat_completion(\n", + " messages=messages,\n", + " model=HF_MODEL_ID,\n", + " max_tokens=1024,\n", + " temperature=0.1,\n", + " )\n", + " return response_full.choices[0].message.content.strip()\n", + " except Exception as e:\n", + " print(f\"API Error (Attempt {attempt + 1}/{retries}): {e}\")\n", + " if attempt < retries - 1:\n", + " time.sleep(10)\n", + " return \"Error: API failed.\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "dfe01137", + "metadata": {}, + "source": [ + "## Step 6 — Helper Function to Call Hugging Face API\n", + "\n", + "This function sends prompts to the API and returns the response. \n", + "\n", + "- Includes **retry logic** in case of temporary API failure. \n", + "- Ensures that **all API calls are consistent** for draft, critic, and refiner steps.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0bb881ce", + "metadata": {}, + "outputs": [], + "source": [ + "def call_hf_api(system_prompt, user_prompt):\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": user_prompt},\n", + " ]\n", + " retries = 3\n", + " for attempt in range(retries):\n", + " try:\n", + " response_full = client.chat_completion(\n", + " messages=messages,\n", + " model=HF_MODEL_ID,\n", + " max_tokens=1024,\n", + " temperature=0.1,\n", + " )\n", + " return response_full.choices[0].message.content.strip()\n", + " except Exception as e:\n", + " print(f\"API Error (Attempt {attempt + 1}/{retries}): {e}\")\n", + " if attempt < retries - 1:\n", + " time.sleep(10)\n", + " return \"Error: API failed.\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "6ef6c010", + "metadata": {}, + "source": [ + "## Step 7 — Recursive RAG Loop with CodeCarbon\n", + "\n", + "- Ask a question about Apollo. \n", + "- Retrieve relevant context using the retriever. \n", + "- Generate an initial **draft answer**. \n", + "- Run **refinement cycles**:\n", + " - Critic evaluates draft\n", + " - Refiner updates draft based on feedback\n", + "- Track **CPU energy and CO2 emissions** for each query. \n", + "- Print final refined answer with **time and emission metrics**.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "b9679e39", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[codecarbon INFO @ 05:41:59] offline tracker init\n", + "[codecarbon WARNING @ 05:41:59] Multiple instances of codecarbon are allowed to run at the same time.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Query Engine Ready (API-Powered, 3 cycles) ---\n", + "Type 'exit' to quit.\n", + "\n", + "API Error (Attempt 1/3): \n", + "API Error (Attempt 2/3): \n", + "API Error (Attempt 3/3): \n", + "\n", + "📝 Initial Draft:\n", + " Error: API failed.\n", + "\n", + "🔁 Refinement Cycle 1/3\n", + "API Error (Attempt 1/3): \n", + "API Error (Attempt 2/3): \n", + "API Error (Attempt 3/3): \n", + "\n", + "🧩 Critic Feedback:\n", + " Error: API failed.\n", + "API Error (Attempt 1/3): \n", + "API Error (Attempt 2/3): \n", + "API Error (Attempt 3/3): \n", + "\n", + "✏️ Refined Draft:\n", + " Error: API failed.\n", + "\n", + "🔁 Refinement Cycle 2/3\n", + "API Error (Attempt 1/3): \n", + "API Error (Attempt 2/3): \n", + "API Error (Attempt 3/3): \n", + "\n", + "🧩 Critic Feedback:\n", + " Error: API failed.\n", + "API Error (Attempt 1/3): \n", + "API Error (Attempt 2/3): \n", + "API Error (Attempt 3/3): \n", + "\n", + "✏️ Refined Draft:\n", + " Error: API failed.\n", + "\n", + "🔁 Refinement Cycle 3/3\n", + "API Error (Attempt 1/3): \n", + "API Error (Attempt 2/3): \n", + "API Error (Attempt 3/3): \n", + "\n", + "🧩 Critic Feedback:\n", + " Error: API failed.\n", + "API Error (Attempt 1/3): \n", + "API Error (Attempt 2/3): \n", + "API Error (Attempt 3/3): \n", + "\n", + "✏️ Refined Draft:\n", + " Error: API failed.\n", + "\n", + "✅ Final Answer:\n", + " Error: API failed.\n", + "\n", + "--- Query Metrics ---\n", + "Time: 142.94s\n", + "Emissions: 1.188882 gCO2eq\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\codecarbon\\output_methods\\file.py:94: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " df = pd.concat([df, new_df], ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "🌍 Total Emissions Summary:\n", + "Total Local CO2: 13.644425 gCO2eq\n", + "Report saved as 'emissions.csv'\n" + ] + }, + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for *: 'NoneType' and 'float'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[23]\u001b[39m\u001b[32m, line 17\u001b[39m\n\u001b[32m 14\u001b[39m tracker.start_task(\u001b[33m\"\u001b[39m\u001b[33mRecursive RAG Query\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 15\u001b[39m start_time = time.time()\n\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m retrieved_nodes = \u001b[43mretriever\u001b[49m\u001b[43m.\u001b[49m\u001b[43mretrieve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 18\u001b[39m context_str = \u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m---\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m.join([node.get_content() \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m retrieved_nodes])\n\u001b[32m 20\u001b[39m user_prompt = DRAFT_USER_TEMPLATE.format(\n\u001b[32m 21\u001b[39m context_str=context_str, query_str=query\n\u001b[32m 22\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\llama_index_instrumentation\\dispatcher.py:335\u001b[39m, in \u001b[36mDispatcher.span..wrapper\u001b[39m\u001b[34m(func, instance, args, kwargs)\u001b[39m\n\u001b[32m 332\u001b[39m _logger.debug(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mFailed to reset active_span_id: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 334\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m335\u001b[39m result = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 336\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, asyncio.Future):\n\u001b[32m 337\u001b[39m \u001b[38;5;66;03m# If the result is a Future, wrap it\u001b[39;00m\n\u001b[32m 338\u001b[39m new_future = asyncio.ensure_future(result)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\llama_index\\core\\base\\base_retriever.py:210\u001b[39m, in \u001b[36mBaseRetriever.retrieve\u001b[39m\u001b[34m(self, str_or_query_bundle)\u001b[39m\n\u001b[32m 205\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.callback_manager.as_trace(\u001b[33m\"\u001b[39m\u001b[33mquery\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m 206\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.callback_manager.event(\n\u001b[32m 207\u001b[39m CBEventType.RETRIEVE,\n\u001b[32m 208\u001b[39m payload={EventPayload.QUERY_STR: query_bundle.query_str},\n\u001b[32m 209\u001b[39m ) \u001b[38;5;28;01mas\u001b[39;00m retrieve_event:\n\u001b[32m--> \u001b[39m\u001b[32m210\u001b[39m nodes = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_retrieve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery_bundle\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 211\u001b[39m nodes = \u001b[38;5;28mself\u001b[39m._handle_recursive_retrieval(query_bundle, nodes)\n\u001b[32m 212\u001b[39m retrieve_event.on_end(\n\u001b[32m 213\u001b[39m payload={EventPayload.NODES: nodes},\n\u001b[32m 214\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\llama_index_instrumentation\\dispatcher.py:335\u001b[39m, in \u001b[36mDispatcher.span..wrapper\u001b[39m\u001b[34m(func, instance, args, kwargs)\u001b[39m\n\u001b[32m 332\u001b[39m _logger.debug(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mFailed to reset active_span_id: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 334\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m335\u001b[39m result = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 336\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, asyncio.Future):\n\u001b[32m 337\u001b[39m \u001b[38;5;66;03m# If the result is a Future, wrap it\u001b[39;00m\n\u001b[32m 338\u001b[39m new_future = asyncio.ensure_future(result)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\llama_index\\core\\indices\\vector_store\\retrievers\\retriever.py:104\u001b[39m, in \u001b[36mVectorIndexRetriever._retrieve\u001b[39m\u001b[34m(self, query_bundle)\u001b[39m\n\u001b[32m 98\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m query_bundle.embedding \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(query_bundle.embedding_strs) > \u001b[32m0\u001b[39m:\n\u001b[32m 99\u001b[39m query_bundle.embedding = (\n\u001b[32m 100\u001b[39m \u001b[38;5;28mself\u001b[39m._embed_model.get_agg_embedding_from_queries(\n\u001b[32m 101\u001b[39m query_bundle.embedding_strs\n\u001b[32m 102\u001b[39m )\n\u001b[32m 103\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m104\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_nodes_with_embeddings\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery_bundle\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\llama_index\\core\\indices\\vector_store\\retrievers\\retriever.py:220\u001b[39m, in \u001b[36mVectorIndexRetriever._get_nodes_with_embeddings\u001b[39m\u001b[34m(self, query_bundle_with_embeddings)\u001b[39m\n\u001b[32m 216\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_nodes_with_embeddings\u001b[39m(\n\u001b[32m 217\u001b[39m \u001b[38;5;28mself\u001b[39m, query_bundle_with_embeddings: QueryBundle\n\u001b[32m 218\u001b[39m ) -> List[NodeWithScore]:\n\u001b[32m 219\u001b[39m query = \u001b[38;5;28mself\u001b[39m._build_vector_store_query(query_bundle_with_embeddings)\n\u001b[32m--> \u001b[39m\u001b[32m220\u001b[39m query_result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_vector_store\u001b[49m\u001b[43m.\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 222\u001b[39m nodes_to_fetch = \u001b[38;5;28mself\u001b[39m._determine_nodes_to_fetch(query_result)\n\u001b[32m 223\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m nodes_to_fetch:\n\u001b[32m 224\u001b[39m \u001b[38;5;66;03m# Fetch any missing nodes from the docstore and insert them into the query result\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\llama_index\\core\\vector_stores\\simple.py:303\u001b[39m, in \u001b[36mSimpleVectorStore.query\u001b[39m\u001b[34m(self, query, **kwargs)\u001b[39m\n\u001b[32m 295\u001b[39m top_similarities, top_ids = get_top_k_mmr_embeddings(\n\u001b[32m 296\u001b[39m query_embedding,\n\u001b[32m 297\u001b[39m embeddings,\n\u001b[32m (...)\u001b[39m\u001b[32m 300\u001b[39m mmr_threshold=mmr_threshold,\n\u001b[32m 301\u001b[39m )\n\u001b[32m 302\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m query.mode == VectorStoreQueryMode.DEFAULT:\n\u001b[32m--> \u001b[39m\u001b[32m303\u001b[39m top_similarities, top_ids = \u001b[43mget_top_k_embeddings\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 304\u001b[39m \u001b[43m \u001b[49m\u001b[43mquery_embedding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 305\u001b[39m \u001b[43m \u001b[49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 306\u001b[39m \u001b[43m \u001b[49m\u001b[43msimilarity_top_k\u001b[49m\u001b[43m=\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m.\u001b[49m\u001b[43msimilarity_top_k\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 307\u001b[39m \u001b[43m \u001b[49m\u001b[43membedding_ids\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnode_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 308\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 309\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 310\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid query mode: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquery.mode\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\llama_index\\core\\indices\\query\\embedding_utils.py:31\u001b[39m, in \u001b[36mget_top_k_embeddings\u001b[39m\u001b[34m(query_embedding, embeddings, similarity_fn, similarity_top_k, embedding_ids, similarity_cutoff)\u001b[39m\n\u001b[32m 29\u001b[39m similarity_heap: List[Tuple[\u001b[38;5;28mfloat\u001b[39m, Any]] = []\n\u001b[32m 30\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, emb \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(embeddings_np):\n\u001b[32m---> \u001b[39m\u001b[32m31\u001b[39m similarity = \u001b[43msimilarity_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery_embedding_np\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43memb\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[32m 32\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m similarity_cutoff \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m similarity > similarity_cutoff:\n\u001b[32m 33\u001b[39m heapq.heappush(similarity_heap, (similarity, embedding_ids[i]))\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\llama_index\\core\\base\\embeddings\\base.py:64\u001b[39m, in \u001b[36msimilarity\u001b[39m\u001b[34m(embedding1, embedding2, mode)\u001b[39m\n\u001b[32m 62\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m np.dot(embedding1, embedding2)\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m64\u001b[39m product = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdot\u001b[49m\u001b[43m(\u001b[49m\u001b[43membedding1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membedding2\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 65\u001b[39m norm = np.linalg.norm(embedding1) * np.linalg.norm(embedding2)\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m product / norm\n", + "\u001b[31mTypeError\u001b[39m: unsupported operand type(s) for *: 'NoneType' and 'float'" + ] + } + ], + "source": [ + "REFINEMENT_CYCLES = 3\n", + "tracker = OfflineEmissionsTracker(country_iso_code=YOUR_COUNTRY_ISO_CODE)\n", + "tracker.start()\n", + "\n", + "print(f\"\\n--- Query Engine Ready (API-Powered, {REFINEMENT_CYCLES} cycles) ---\")\n", + "print(\"Type 'exit' to quit.\\n\")\n", + "\n", + "try:\n", + " while True:\n", + " query = input(\"Ask a question about Apollo: \")\n", + " if query.lower() == \"exit\":\n", + " break\n", + "\n", + " tracker.start_task(\"Recursive RAG Query\")\n", + " start_time = time.time()\n", + "\n", + " retrieved_nodes = retriever.retrieve(query)\n", + " context_str = \"\\n---\\n\".join([node.get_content() for node in retrieved_nodes])\n", + "\n", + " user_prompt = DRAFT_USER_TEMPLATE.format(\n", + " context_str=context_str, query_str=query\n", + " )\n", + " draft_text = call_hf_api(DRAFT_SYSTEM_PROMPT, user_prompt)\n", + " print(\"\\n📝 Initial Draft:\\n\", textwrap.fill(draft_text, width=80))\n", + "\n", + " current_draft = draft_text\n", + "\n", + " for i in range(REFINEMENT_CYCLES):\n", + " print(f\"\\n🔁 Refinement Cycle {i + 1}/{REFINEMENT_CYCLES}\")\n", + "\n", + " critic_prompt = CRITIC_USER_TEMPLATE.format(\n", + " context=context_str, question=query, draft=current_draft\n", + " )\n", + " feedback = call_hf_api(CRITIC_SYSTEM_PROMPT, critic_prompt)\n", + " print(\"\\n🧩 Critic Feedback:\\n\", feedback)\n", + "\n", + " if \"The draft is perfect\" in feedback:\n", + " print(\"✅ Draft approved early!\")\n", + " break\n", + "\n", + " refiner_prompt = REFINER_USER_TEMPLATE.format(\n", + " draft=current_draft, feedback=feedback\n", + " )\n", + " current_draft = call_hf_api(REFINER_SYSTEM_PROMPT, refiner_prompt)\n", + " print(\"\\n✏️ Refined Draft:\\n\", textwrap.fill(current_draft, width=80))\n", + "\n", + " final_answer = current_draft\n", + " end_time = time.time()\n", + " emissions_data = tracker.stop_task()\n", + "\n", + " print(\"\\n✅ Final Answer:\\n\", textwrap.fill(final_answer, width=80))\n", + " print(f\"\\n--- Query Metrics ---\\nTime: {end_time - start_time:.2f}s\")\n", + " print(f\"Emissions: {emissions_data.emissions * 1000:.6f} gCO2eq\")\n", + "\n", + "finally:\n", + " total_emissions = tracker.stop()\n", + " print(\"\\n🌍 Total Emissions Summary:\")\n", + " print(f\"Total Local CO2: {total_emissions * 1000:.6f} gCO2eq\")\n", + " print(\"Report saved as 'emissions.csv'\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/phi3_model/requirements.txt b/phi3_model/requirements.txt new file mode 100644 index 0000000..340ddd4 --- /dev/null +++ b/phi3_model/requirements.txt @@ -0,0 +1,8 @@ +huggingface-hub +sentence-transformers +transformers +torch +faiss-cpu +python-dotenv +accelerate +numpy