Spaces:
Sleeping
Sleeping
JAIGANESAN N
commited on
Commit
•
471ad41
1
Parent(s):
c2b7d18
upgrade model from GPT-4o-mini to Gemini-1.5-flash
Browse files
notebooks/04-RAG_with_VectorStore.ipynb
CHANGED
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/towardsai/ai-tutor-rag-system/blob/main/notebooks/04-RAG_with_VectorStore.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "QPJzr-I9XQ7l"
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},
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"outputs": [],
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"source": [
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"!pip install -q llama-index==0.10.57 llama-index-vector-stores-chroma
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"id": "riuXwpSPcvWC"
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"# Set the following API Keys in the Python environment. Will be used later.\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"<YOUR_API_KEY>\"\n",
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"os.environ[\"GOOGLE_API_KEY\"] = \"<YOUR_API_KEY>\""
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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"id": "-QTUkdfJjY4N",
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"outputId": "
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" % Total % Received % Xferd Average Speed Time Time Time Current\n",
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" Dload Upload Total Spent Left Speed\n",
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"100 169k 100 169k 0 0
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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"id": "7CYwRT6R0o0I",
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"outputId": "
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"171044\n"
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},
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "STACTMUR1z9N",
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"outputId": "
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"335\n"
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"id": "CtdsIUQ81_hT"
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"id": "mXi56KTXk2sp"
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"id": "jKXURvLtkuTS"
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"source": [
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"id": "mzS13x1ZlZ5X"
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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"outputId": "5066a06c-77ff-48a2-ee61-3abe2e9755e2"
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The
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"\n"
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},
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"id": "SMPAniL2e4NP"
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},
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"id": "2xas7HkuhJ8A"
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"\n",
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"# Add the documents to chroma DB and create Index / embeddings\n",
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"\n",
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"embeddings = OpenAIEmbeddings(model=\"text-embedding-
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"chroma_db = Chroma.from_documents(\n",
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" documents=documents,\n",
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" embedding=embeddings,\n",
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},
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"outputs": [],
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"source": [
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"from
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"\n",
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"# Initializing the LLM model\n",
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"llm = ChatOpenAI(temperature=0, model=\"gpt-4o-mini\", max_tokens=512)"
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]
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"
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"outputId": "93c9ad64-1cd1-4f52-c51e-6f3ec5d6542d"
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},
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"outputs": [
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"source": [
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"from langchain.chains import RetrievalQA\n",
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"\n",
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"query = \"How many parameters
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"retriever = chroma_db.as_retriever(search_kwargs={\"k\":
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"# Define a RetrievalQA chain that is responsible for retrieving related pieces of text,\n",
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"# and using a LLM to formulate the final answer.\n",
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"chain = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=retriever)\n",
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"\n",
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"response = chain(query)\n",
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"print(response[\"result\"])"
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]
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"metadata": {
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"colab": {
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"provenance": []
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"kernelspec": {
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"display_name": "Python 3",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.4"
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|
| 453 |
}
|
| 454 |
},
|
| 455 |
"nbformat": 4,
|
| 456 |
"nbformat_minor": 0
|
| 457 |
-
}
|
|
|
|
| 3 |
{
|
| 4 |
"cell_type": "markdown",
|
| 5 |
"metadata": {
|
| 6 |
+
"id": "view-in-github",
|
| 7 |
+
"colab_type": "text"
|
| 8 |
},
|
| 9 |
"source": [
|
| 10 |
+
"<a href=\"https://colab.research.google.com/github/towardsai/ai-tutor-rag-system/blob/main/notebooks/04-RAG_with_VectorStore.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
| 11 |
]
|
| 12 |
},
|
| 13 |
{
|
|
|
|
| 23 |
"cell_type": "code",
|
| 24 |
"execution_count": null,
|
| 25 |
"metadata": {
|
| 26 |
+
"collapsed": true,
|
| 27 |
"id": "QPJzr-I9XQ7l"
|
| 28 |
},
|
| 29 |
"outputs": [],
|
| 30 |
"source": [
|
| 31 |
+
"!pip install -q llama-index==0.10.57 llama-index-vector-stores-chroma llama-index-llms-gemini==0.1.11 langchain_google_genai google-generativeai==0.5.4 langchain==0.1.17 langchain-chroma langchain_openai==0.1.5 openai==1.37.0 chromadb"
|
| 32 |
]
|
| 33 |
},
|
| 34 |
{
|
| 35 |
"cell_type": "code",
|
| 36 |
+
"execution_count": null,
|
| 37 |
"metadata": {
|
| 38 |
"id": "riuXwpSPcvWC"
|
| 39 |
},
|
| 40 |
"outputs": [],
|
| 41 |
"source": [
|
| 42 |
"import os\n",
|
|
|
|
| 43 |
"# Set the following API Keys in the Python environment. Will be used later.\n",
|
| 44 |
"os.environ[\"OPENAI_API_KEY\"] = \"<YOUR_API_KEY>\"\n",
|
| 45 |
+
"os.environ[\"GOOGLE_API_KEY\"] = \"<YOUR_API_KEY>\"\n",
|
| 46 |
+
"\n",
|
| 47 |
+
"# from google.colab import userdata\n",
|
| 48 |
+
"# os.environ[\"OPENAI_API_KEY\"] = userdata.get('openai_api_key')\n",
|
| 49 |
+
"# os.environ[\"GOOGLE_API_KEY\"] = userdata.get('Google_api_key')"
|
| 50 |
]
|
| 51 |
},
|
| 52 |
{
|
|
|
|
| 78 |
},
|
| 79 |
{
|
| 80 |
"cell_type": "code",
|
| 81 |
+
"execution_count": null,
|
| 82 |
"metadata": {
|
| 83 |
"colab": {
|
| 84 |
"base_uri": "https://localhost:8080/"
|
| 85 |
},
|
| 86 |
"id": "-QTUkdfJjY4N",
|
| 87 |
+
"outputId": "34becd46-808a-42ee-e620-3e6b18f79e1d"
|
| 88 |
},
|
| 89 |
"outputs": [
|
| 90 |
{
|
|
|
|
| 91 |
"output_type": "stream",
|
| 92 |
+
"name": "stdout",
|
| 93 |
"text": [
|
| 94 |
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
|
| 95 |
" Dload Upload Total Spent Left Speed\n",
|
| 96 |
+
"100 169k 100 169k 0 0 609k 0 --:--:-- --:--:-- --:--:-- 612k\n"
|
| 97 |
]
|
| 98 |
}
|
| 99 |
],
|
|
|
|
| 112 |
},
|
| 113 |
{
|
| 114 |
"cell_type": "code",
|
| 115 |
+
"execution_count": null,
|
| 116 |
"metadata": {
|
| 117 |
"colab": {
|
| 118 |
"base_uri": "https://localhost:8080/"
|
| 119 |
},
|
| 120 |
"id": "7CYwRT6R0o0I",
|
| 121 |
+
"outputId": "394603bd-6d33-40aa-8e06-6ef802879234"
|
| 122 |
},
|
| 123 |
"outputs": [
|
| 124 |
{
|
|
|
|
| 125 |
"output_type": "stream",
|
| 126 |
+
"name": "stdout",
|
| 127 |
"text": [
|
| 128 |
"171044\n"
|
| 129 |
]
|
|
|
|
| 158 |
},
|
| 159 |
{
|
| 160 |
"cell_type": "code",
|
| 161 |
+
"execution_count": null,
|
| 162 |
"metadata": {
|
| 163 |
"colab": {
|
| 164 |
"base_uri": "https://localhost:8080/"
|
| 165 |
},
|
| 166 |
"id": "STACTMUR1z9N",
|
| 167 |
+
"outputId": "d5360ce2-2c1e-459b-a3b3-e9899fe762b5"
|
| 168 |
},
|
| 169 |
"outputs": [
|
| 170 |
{
|
|
|
|
| 171 |
"output_type": "stream",
|
| 172 |
+
"name": "stdout",
|
| 173 |
"text": [
|
| 174 |
"335\n"
|
| 175 |
]
|
|
|
|
| 197 |
},
|
| 198 |
{
|
| 199 |
"cell_type": "code",
|
| 200 |
+
"execution_count": null,
|
| 201 |
"metadata": {
|
| 202 |
"id": "CtdsIUQ81_hT"
|
| 203 |
},
|
|
|
|
| 220 |
},
|
| 221 |
{
|
| 222 |
"cell_type": "code",
|
| 223 |
+
"execution_count": null,
|
| 224 |
"metadata": {
|
| 225 |
"id": "mXi56KTXk2sp"
|
| 226 |
},
|
|
|
|
| 236 |
},
|
| 237 |
{
|
| 238 |
"cell_type": "code",
|
| 239 |
+
"execution_count": null,
|
| 240 |
"metadata": {
|
| 241 |
"id": "jKXURvLtkuTS"
|
| 242 |
},
|
|
|
|
| 252 |
},
|
| 253 |
{
|
| 254 |
"cell_type": "code",
|
| 255 |
+
"execution_count": null,
|
| 256 |
"metadata": {
|
| 257 |
+
"colab": {
|
| 258 |
+
"base_uri": "https://localhost:8080/",
|
| 259 |
+
"height": 81,
|
| 260 |
+
"referenced_widgets": [
|
| 261 |
+
"1a0185d42be8489c87874049e5d78424",
|
| 262 |
+
"e91d7972a14b4444808d649d5db15b7a",
|
| 263 |
+
"bd3eab2dfee94512b88d88fbd4cb3682",
|
| 264 |
+
"3ba2fa0f0c8449c3a22ff3593d2b6629",
|
| 265 |
+
"eb7ce1338f6a4c9a8fb9a92e8de78821",
|
| 266 |
+
"d9daeb567dcc4387a7ba406a559aa422",
|
| 267 |
+
"2a99f51fa5c24ce09f629d9d5322879d",
|
| 268 |
+
"edfd151ed26646d8a6d293a3cf1ecce6",
|
| 269 |
+
"032b979544624bd4a9f7eb91fa9dd2e8",
|
| 270 |
+
"28d35749a3fa4c6f80f626221f95cde1",
|
| 271 |
+
"3d78bf2951e04c21bd89c1d944412e5a",
|
| 272 |
+
"a0991085ef794c8cbf509370b67df911",
|
| 273 |
+
"780fa2ff5938403cb2c109c491368dce",
|
| 274 |
+
"f833fd603aa543f59a7e93fe980935cc",
|
| 275 |
+
"48e949bbaf7c4079bbe8fcf620066b8e",
|
| 276 |
+
"78fd41df7f604f0290dc034c9f01a18c",
|
| 277 |
+
"d2df0b3067574d46b951890b996a5751",
|
| 278 |
+
"b51937856bf64803a85ea7eb30f73cfe",
|
| 279 |
+
"74fe3cd416994417a2138d8a32beeed8",
|
| 280 |
+
"80626b461cce49e7bcb6e3e931c5d81c",
|
| 281 |
+
"23f0bdb422384eaf94a69d20502fdbe4",
|
| 282 |
+
"dfa74d96a1174e7ead6554b5c984f526"
|
| 283 |
+
]
|
| 284 |
+
},
|
| 285 |
+
"id": "WsD52wtrlESi",
|
| 286 |
+
"outputId": "2d522a85-cdea-477b-a693-e26b404c8ed9"
|
| 287 |
},
|
| 288 |
"outputs": [
|
| 289 |
{
|
| 290 |
+
"output_type": "display_data",
|
| 291 |
+
"data": {
|
| 292 |
+
"text/plain": [
|
| 293 |
+
"Parsing nodes: 0%| | 0/335 [00:00<?, ?it/s]"
|
| 294 |
+
],
|
| 295 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 296 |
+
"version_major": 2,
|
| 297 |
+
"version_minor": 0,
|
| 298 |
+
"model_id": "1a0185d42be8489c87874049e5d78424"
|
| 299 |
+
}
|
| 300 |
+
},
|
| 301 |
+
"metadata": {}
|
| 302 |
+
},
|
| 303 |
+
{
|
| 304 |
+
"output_type": "display_data",
|
| 305 |
+
"data": {
|
| 306 |
+
"text/plain": [
|
| 307 |
+
"Generating embeddings: 0%| | 0/335 [00:00<?, ?it/s]"
|
| 308 |
+
],
|
| 309 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 310 |
+
"version_major": 2,
|
| 311 |
+
"version_minor": 0,
|
| 312 |
+
"model_id": "a0991085ef794c8cbf509370b67df911"
|
| 313 |
+
}
|
| 314 |
+
},
|
| 315 |
+
"metadata": {}
|
| 316 |
}
|
| 317 |
],
|
| 318 |
"source": [
|
|
|
|
| 340 |
},
|
| 341 |
{
|
| 342 |
"cell_type": "code",
|
| 343 |
+
"execution_count": null,
|
| 344 |
"metadata": {
|
| 345 |
"id": "mzS13x1ZlZ5X"
|
| 346 |
},
|
|
|
|
| 358 |
},
|
| 359 |
{
|
| 360 |
"cell_type": "code",
|
| 361 |
+
"execution_count": null,
|
| 362 |
"metadata": {
|
| 363 |
+
"id": "AYsQ4uLN_Oxg",
|
| 364 |
"colab": {
|
| 365 |
+
"base_uri": "https://localhost:8080/",
|
| 366 |
+
"height": 52
|
| 367 |
},
|
| 368 |
+
"outputId": "f56876cc-f7bc-4e63-bb7b-9515e9b404cc"
|
|
|
|
| 369 |
},
|
| 370 |
"outputs": [
|
| 371 |
{
|
|
|
|
| 372 |
"output_type": "stream",
|
| 373 |
+
"name": "stdout",
|
| 374 |
"text": [
|
| 375 |
+
"The LLaMA 2 model has four different sizes: 7 billion, 13 billion, 34 billion, and 70 billion parameters. \n",
|
| 376 |
"\n"
|
| 377 |
]
|
| 378 |
}
|
|
|
|
| 393 |
},
|
| 394 |
{
|
| 395 |
"cell_type": "code",
|
| 396 |
+
"execution_count": null,
|
| 397 |
"metadata": {
|
| 398 |
"id": "SMPAniL2e4NP"
|
| 399 |
},
|
|
|
|
| 416 |
},
|
| 417 |
{
|
| 418 |
"cell_type": "code",
|
| 419 |
+
"execution_count": null,
|
| 420 |
"metadata": {
|
| 421 |
"id": "2xas7HkuhJ8A"
|
| 422 |
},
|
|
|
|
| 427 |
"\n",
|
| 428 |
"# Add the documents to chroma DB and create Index / embeddings\n",
|
| 429 |
"\n",
|
| 430 |
+
"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
|
| 431 |
"chroma_db = Chroma.from_documents(\n",
|
| 432 |
" documents=documents,\n",
|
| 433 |
" embedding=embeddings,\n",
|
|
|
|
| 453 |
},
|
| 454 |
"outputs": [],
|
| 455 |
"source": [
|
| 456 |
+
"from langchain_google_genai import ChatGoogleGenerativeAI\n",
|
| 457 |
"\n",
|
| 458 |
"# Initializing the LLM model\n",
|
| 459 |
+
"#llm = ChatOpenAI(temperature=0, model=\"gpt-4o-mini\", max_tokens=512)\n",
|
| 460 |
+
"\n",
|
| 461 |
+
"llm = ChatGoogleGenerativeAI(\n",
|
| 462 |
+
" model=\"gemini-1.5-flash\",\n",
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| 463 |
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" temperature=0,\n",
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| 464 |
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" max_tokens=512,\n",
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| 465 |
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")"
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| 466 |
]
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| 467 |
},
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{
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"metadata": {
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"id": "AxBqPNtthPaa",
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"base_uri": "https://localhost:8080/"
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},
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{
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"text": [
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| 483 |
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"The LLaMA 2 model comes in four different sizes: 7 billion, 13 billion, 34 billion, and 70 billion parameters. \n",
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| 484 |
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"\n"
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| 485 |
+
]
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| 486 |
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}
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],
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| 488 |
"source": [
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| 489 |
"from langchain.chains import RetrievalQA\n",
|
| 490 |
"\n",
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| 491 |
+
"query = \"How many parameters LLaMA 2 model has?\"\n",
|
| 492 |
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"retriever = chroma_db.as_retriever(search_kwargs={\"k\": 4})\n",
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| 494 |
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| 495 |
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| 496 |
"\n",
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+
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| 498 |
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]
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