[](./examples/Youtube-Search-QA-Bot/main.py) [](./examples/Youtube-Search-QA-Bot/index.js) [](#) [](#)||
| [Langchain: Code Docs QA bot](/examples/Code-Documentation-QA-Bot/) | [](./examples/Code-Documentation-QA-Bot/main.py) [](./examples/Code-Documentation-QA-Bot/index.js) [](#) [](#)||
-| [Chatbot with any website using Crawl4AI ](/examples/CrawlerQ&A_website/) | [](./examples/Code-Documentation-QA-Bot/main.py) [](#) [](#)|
+| [Chatbot with any website using Crawl4AI ](/examples/CrawlerQ&A_website/) | [](./examples/Code-Documentation-QA-Bot/main.py) [](#) [](#)|
| [Context-Aware Chatbot using Llama 2 & LanceDB](./tutorials/chatbot_using_Llama2_&_lanceDB) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb) [](#) [](#)| [](https://blog.lancedb.com/context-aware-chatbot-using-llama-2-lancedb-as-vector-database-4d771d95c755) |
||||
@@ -133,7 +133,6 @@ Develop an evaluation application. Input reference and candidate texts to measur
| Evaluation | Interactive Notebook & Scripts | Blog |
| --------- | -------------------------- | ----------- |
||||
-| [Evaluating Prompts with Prompttools](/examples/prompttools-eval-prompts/) | [](#) [](#) [](#)| |
| [Evaluating RAG with RAGAs](./examples/Evaluating_RAG_with_RAGAs/) | [](#) [](#)| |
||||
@@ -189,7 +188,7 @@ These are ready to use applications built using LanceDB serverless vector databa
| Project Name | Description | Screenshot |
|-----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
| [Writing assistant](https://github.com/lancedb/vectordb-recipes/tree/main/applications/node/lanchain_writing_assistant) | Writing assistant app using lanchain.js with LanceDB, allows you to get real time relevant suggestions and facts based on you written text to help you with your writing. |  |
-| [Sentance auto complete](https://github.com/lancedb/vectordb-recipes/tree/main/applications/node/sentance_auto_complete) | Sentance auto complete app using lanchain.js with LanceDB, allows you to get real time relevant auto complete suggestions and facts based on you written text to help you with your writing.You can also upload your data source in the form of a pdf file.You can switch between gpt models to get faster results. |  |
+| [Sentence auto complete](https://github.com/lancedb/vectordb-recipes/tree/main/applications/node/sentance_auto_complete) | Sentance auto complete app using lanchain.js with LanceDB, allows you to get real time relevant auto complete suggestions and facts based on you written text to help you with your writing.You can also upload your data source in the form of a pdf file.You can switch between gpt models to get faster results. |  |
||||
| Project Name | Description | Screenshot |
diff --git a/examples/AI-Trends-with-CrewAI/CrewAI_AI_Trends.ipynb b/examples/AI-Trends-with-CrewAI/CrewAI_AI_Trends.ipynb
index e0ef4104..dbd7d460 100644
--- a/examples/AI-Trends-with-CrewAI/CrewAI_AI_Trends.ipynb
+++ b/examples/AI-Trends-with-CrewAI/CrewAI_AI_Trends.ipynb
@@ -1,142 +1,168 @@
{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "provenance": []
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3"
- },
- "language_info": {
- "name": "python"
- }
- },
"cells": [
{
"cell_type": "markdown",
- "source": [
- "## AI Trends Searcher using CrewAI RAG"
- ],
"metadata": {
"id": "L6CYH07ysGHi"
- }
+ },
+ "source": [
+ "## AI Trends Searcher using CrewAI RAG"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {
+ "id": "sSLA0KQ8qrcX"
+ },
"source": [
"Using Crew AI to create sophisticated AI news search and writer agents. Using CrewAI RAG create AI Assistants to Run News Agency for AI trends with summarized reports.\n",
"\n",
"This innovative approach combines Retrieving and Generating information, revolutionising how we search for AI news articles, analyse them, and deliver in-depth reports.\n",
"\n",
""
- ],
- "metadata": {
- "id": "sSLA0KQ8qrcX"
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "Get Free News API Key at [link](https://newsapi.org/)"
- ],
"metadata": {
"id": "Q_wvRRAXxPSR"
- }
+ },
+ "source": [
+ "Get Free News API Key at [link](https://newsapi.org/)"
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Install required packages\n"
- ],
"metadata": {
"id": "HV1DyBd2RHrN"
- }
+ },
+ "source": [
+ "### Install required packages\n"
+ ]
},
{
"cell_type": "code",
- "source": [
- "!pip install crewai langchain-community langchain-openai langchain-google-genai requests duckduckgo-search lancedb -q"
- ],
+ "execution_count": 1,
"metadata": {
- "id": "xOj-BuKARJrH",
"colab": {
"base_uri": "https://localhost:8080/"
},
- "outputId": "ff6cfb82-0400-4c09-f797-c57adf95a0ed"
+ "id": "xOj-BuKARJrH",
+ "outputId": "a0a33fc4-65f6-4a83-e2f1-cf24825c3739"
},
- "execution_count": 5,
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
- "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/62.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.0/42.0 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m698.9/698.9 kB\u001b[0m \u001b[31m51.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m69.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.6/67.6 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.9/71.9 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.4/341.4 kB\u001b[0m \u001b[31m32.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m86.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m54.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25h Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
+ " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.5/48.5 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.4/215.4 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m49.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.7/50.7 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.3/41.3 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.4/27.4 MB\u001b[0m \u001b[31m59.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.5/30.5 MB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m131.8/131.8 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m628.3/628.3 kB\u001b[0m \u001b[31m45.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m85.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m463.0/463.0 kB\u001b[0m \u001b[31m31.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m70.1/70.1 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m55.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m409.5/409.5 kB\u001b[0m \u001b[31m32.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.4/6.4 MB\u001b[0m \u001b[31m96.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m389.9/389.9 kB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.8/55.8 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.2/59.2 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m90.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m81.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m756.0/756.0 kB\u001b[0m \u001b[31m47.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.6/14.6 MB\u001b[0m \u001b[31m85.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m278.6/278.6 kB\u001b[0m \u001b[31m23.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m147.8/147.8 kB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.4/211.4 kB\u001b[0m \u001b[31m18.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.5/49.5 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.2/93.2 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m83.2/83.2 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m298.0/298.0 kB\u001b[0m \u001b[31m25.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.1/71.1 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.2/73.2 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m481.7/481.7 kB\u001b[0m \u001b[31m34.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m84.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m442.6/442.6 kB\u001b[0m \u001b[31m34.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m168.2/168.2 kB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m80.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m267.2/267.2 kB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.9/78.9 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m80.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.9/5.9 MB\u001b[0m \u001b[31m18.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.5/57.5 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+ "tensorflow 2.17.1 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.29.0 which is incompatible.\n",
+ "tensorflow-metadata 1.13.1 requires protobuf<5,>=3.20.3, but you have protobuf 5.29.0 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0m"
]
}
+ ],
+ "source": [
+ "!pip install crewai langchain-community langchain-openai langchain-google-genai requests duckduckgo-search lancedb -q"
]
},
{
"cell_type": "markdown",
- "source": [
- "### Importing modules"
- ],
"metadata": {
"id": "HNnorUJjbVPH"
- }
+ },
+ "source": [
+ "### Importing modules"
+ ]
},
{
"cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "sk-GEhxCbUDU",
+ "outputId": "3ca7f240-718d-4c62-9b88-db69e0f148a3"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:langchain_community.utils.user_agent:USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
+ ]
+ }
+ ],
"source": [
"from crewai import Agent, Task, Crew, Process\n",
"from langchain_openai import ChatOpenAI\n",
@@ -150,85 +176,85 @@
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain_community.vectorstores import LanceDB\n",
"from langchain_community.tools import DuckDuckGoSearchRun"
- ],
- "metadata": {
- "id": "sk-GEhxCbUDU"
- },
- "execution_count": 6,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Set API keys as environment variable"
- ],
"metadata": {
"id": "nRv8-Ja1R8S6"
- }
+ },
+ "source": [
+ "### Set API keys as environment variable"
+ ]
},
{
"cell_type": "code",
- "source": [
- "os.environ[\"NEWSAPI_KEY\"] = \"*********\""
- ],
+ "execution_count": null,
"metadata": {
"id": "J9U8qs3aRLfh"
},
- "execution_count": 7,
- "outputs": []
+ "outputs": [],
+ "source": [
+ "os.environ[\"NEWSAPI_KEY\"] = \"....\""
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Using OpenAI embedding function and LLM"
- ],
"metadata": {
"id": "s6cLIDz9fzB5"
- }
+ },
+ "source": [
+ "### Using OpenAI embedding function and LLM"
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "6tNMJXRSVy7e"
+ },
+ "outputs": [],
"source": [
"# @title LLM Config\n",
"\n",
"LLM = \"OPENAI_GPT4\" # @param [\"OPENAI_GPT4\", \"GEMINI_PRO\"]\n",
- "API_KEY = \"Enter your API Key\" # @param {type:\"string\"}"
- ],
- "metadata": {
- "id": "6tNMJXRSVy7e"
- },
- "execution_count": 20,
- "outputs": []
+ "API_KEY = \"sk-proj-...\" # @param {type:\"string\"}"
+ ]
},
{
"cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "id": "ljQ1FLt2fx_F"
+ },
+ "outputs": [],
"source": [
"if LLM == \"OPENAI_GPT4\":\n",
" os.environ[\"OPENAI_API_KEY\"] = API_KEY\n",
" embedding_function = OpenAIEmbeddings()\n",
- " llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n",
+ " llm = ChatOpenAI(model=\"gpt-4o\")\n",
"elif LLM == \"GEMINI_PRO\":\n",
" os.environ[\"GOOGLE_API_KEY\"] = API_KEY\n",
" embeddding_function = LlamafileEmbeddings()\n",
" llm = ChatGoogleGenerativeAI(model=\"gemini-pro\")"
- ],
- "metadata": {
- "id": "ljQ1FLt2fx_F"
- },
- "execution_count": 9,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Set up the LanceDB vectorDB"
- ],
"metadata": {
"id": "251A9PjVT6s3"
- }
+ },
+ "source": [
+ "### Set up the LanceDB vectorDB"
+ ]
},
{
"cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "id": "-jAOVSSISABN"
+ },
+ "outputs": [],
"source": [
"import lancedb\n",
"\n",
@@ -246,33 +272,39 @@
"\n",
"# LanceDB as vector store\n",
"table = lanceDBConnection(dataset)"
- ],
- "metadata": {
- "id": "-jAOVSSISABN"
- },
- "execution_count": 10,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Save latest AI News in vectorDB"
- ],
"metadata": {
"id": "0AJygbxmb_gd"
- }
+ },
+ "source": [
+ "### Save latest AI News in vectorDB"
+ ]
},
{
"cell_type": "code",
+ "execution_count": 54,
+ "metadata": {
+ "id": "ZffJSsX-uKt3"
+ },
+ "outputs": [],
"source": [
- "# Save the news articles in a database\n",
- "class SearchNewsDB:\n",
- " @tool(\"News DB Tool\")\n",
- " def news(query: str):\n",
+ "from crewai_tools import BaseTool\n",
+ "\n",
+ "\n",
+ "class SearchNewsDB(BaseTool):\n",
+ " def __init__(self):\n",
+ " super().__init__(\n",
+ " name=\"News DB Tool\",\n",
+ " description=\"Fetch and process news articles from various sources\",\n",
+ " )\n",
+ "\n",
+ " def _run(self, query: str):\n",
" \"\"\"Fetch news articles and process their contents.\"\"\"\n",
" API_KEY = os.getenv(\"NEWSAPI_KEY\") # Fetch API key from environment variable\n",
- " base_url = f\"https://newsapi.org/v2/top-headlines?sources=techcrunch\"\n",
- "\n",
+ " base_url = \"https://newsapi.org/v2/top-headlines?sources=techcrunch\"\n",
" params = {\n",
" \"sortBy\": \"publishedAt\",\n",
" \"apiKey\": API_KEY,\n",
@@ -286,178 +318,220 @@
"\n",
" articles = response.json().get(\"articles\", [])\n",
" all_splits = []\n",
- " for article in articles:\n",
- " # Assuming WebBaseLoader can handle a list of URLs\n",
- " loader = WebBaseLoader(article[\"url\"])\n",
- " docs = loader.load()\n",
"\n",
- " text_splitter = RecursiveCharacterTextSplitter(\n",
- " chunk_size=1000, chunk_overlap=200\n",
- " )\n",
- " splits = text_splitter.split_documents(docs)\n",
- " all_splits.extend(splits) # Accumulate splits from all articles\n",
+ " for article in articles:\n",
+ " try:\n",
+ " # Assuming WebBaseLoader can handle a list of URLs\n",
+ " loader = WebBaseLoader(article[\"url\"])\n",
+ " docs = loader.load()\n",
+ " text_splitter = RecursiveCharacterTextSplitter(\n",
+ " chunk_size=1000, chunk_overlap=200\n",
+ " )\n",
+ " splits = text_splitter.split_documents(docs)\n",
+ " all_splits.extend(splits) # Accumulate splits from all articles\n",
+ " except Exception as e:\n",
+ " print(f\"Error processing article {article['url']}: {e}\")\n",
"\n",
" # Index the accumulated content splits if there are any\n",
" if all_splits:\n",
- " vectorstore = LanceDB.from_documents(\n",
- " all_splits, embedding=embedding_function, connection=table\n",
- " )\n",
- " retriever = vectorstore.similarity_search(query)\n",
- " return retriever\n",
+ " try:\n",
+ " vectorstore = LanceDB.from_documents(\n",
+ " all_splits, embedding=embedding_function\n",
+ " )\n",
+ " retriever = vectorstore.similarity_search(query)\n",
+ " return retriever\n",
+ " except Exception as e:\n",
+ " return f\"Error indexing documents: {e}\"\n",
" else:\n",
" return \"No content available for processing.\""
- ],
- "metadata": {
- "id": "NQE3rljxb4SI"
- },
- "execution_count": 11,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Building RAG to get news from vectorDB"
- ],
"metadata": {
"id": "tKrbDvCdcRoK"
- }
+ },
+ "source": [
+ "### Building RAG to get news from vectorDB"
+ ]
},
{
"cell_type": "code",
- "source": [
- "# Get the news articles from the database\n",
- "class GetNews:\n",
- " @tool(\"Get News Tool\")\n",
- " def news(query: str) -> str:\n",
- " \"\"\"Search LanceDB for relevant news information based on a query.\"\"\"\n",
- " vectorstore = LanceDB(embedding=embedding_function, connection=table)\n",
- " retriever = vectorstore.similarity_search(query)\n",
- " return retriever"
- ],
+ "execution_count": 63,
"metadata": {
- "id": "xGtyZZaQcPmr"
+ "id": "N05IrsK0vE5g"
},
- "execution_count": 12,
- "outputs": []
+ "outputs": [],
+ "source": [
+ "class GetNews(BaseTool):\n",
+ " def __init__(self):\n",
+ " super().__init__(\n",
+ " name=\"Get News Tool\",\n",
+ " description=\"Search LanceDB for relevant news information based on a query\",\n",
+ " )\n",
+ "\n",
+ " def _run(self, query: str) -> str:\n",
+ " \"\"\"Search LanceDB for relevant news information based on a query.\"\"\"\n",
+ " try:\n",
+ " vectorstore = LanceDB(embedding=embedding_function)\n",
+ " retriever = vectorstore.similarity_search(query)\n",
+ " return retriever\n",
+ " except Exception as e:\n",
+ " return f\"Error retrieving news: {e}\""
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Setup search tool for News articles on the web"
- ],
"metadata": {
"id": "BSumWCcRUUDl"
- }
+ },
+ "source": [
+ "### Setup search tool for News articles on the web"
+ ]
},
{
"cell_type": "code",
+ "execution_count": 56,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "DiaqNKjsT4jc",
+ "outputId": "e467b781-d176-4894-ae2c-68d3399d1e41"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: duckduckgo_search in /usr/local/lib/python3.10/dist-packages (6.3.7)\n",
+ "Requirement already satisfied: click>=8.1.7 in /usr/local/lib/python3.10/dist-packages (from duckduckgo_search) (8.1.7)\n",
+ "Requirement already satisfied: primp>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from duckduckgo_search) (0.8.1)\n"
+ ]
+ }
+ ],
"source": [
"# Make sure to Install duckduckgo-search for this example\n",
- "# !pip install -U duckduckgo-search\n",
+ "!pip install -U duckduckgo_search\n",
"\n",
- "search_tool = DuckDuckGoSearchRun()"
- ],
- "metadata": {
- "id": "DiaqNKjsT4jc"
- },
- "execution_count": 13,
- "outputs": []
+ "from duckduckgo_search import DDGS\n",
+ "\n",
+ "search_tool = DDGS()"
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Setting up Agents"
- ],
"metadata": {
"id": "-a9pqtdqUgtJ"
- }
+ },
+ "source": [
+ "### Setting up Agents"
+ ]
},
{
"cell_type": "code",
+ "execution_count": 64,
+ "metadata": {
+ "id": "SLDSHOuCUXna"
+ },
+ "outputs": [],
"source": [
"# Defining Search and Writer agents with roles and goals\n",
"news_search_agent = Agent(\n",
" role=\"AI News Searcher\",\n",
" goal=\"Generate key points for each news article from the latest news\",\n",
" backstory=\"\"\"You work at a leading tech think tank.\n",
- " Your expertise lies in identifying emerging trends in field of AI.\n",
- " You have a knack for dissecting complex data and presenting\n",
- " actionable insights.\"\"\",\n",
- " tools=[SearchNewsDB().news],\n",
+ " Your expertise lies in identifying emerging trends in the field of AI.\n",
+ " You have a knack for dissecting complex data and presenting\n",
+ " actionable insights.\"\"\",\n",
+ " tools=[SearchNewsDB()], # Note the instantiation here\n",
" allow_delegation=True,\n",
" verbose=True,\n",
- " llm=llm,\n",
")\n",
"\n",
+ "# Agent configuration\n",
"writer_agent = Agent(\n",
" role=\"Writer\",\n",
- " goal=\"Identify all the topics received. Use the Get News Tool to verify the each topic to search. Use the Search tool for detailed exploration of each topic. Summarise the retrieved information in depth for every topic.\",\n",
+ " goal=\"Identify all the topics received. Use the GetNews tool to verify each topic. Use the search tool for detailed exploration of each topic. Summarise the retrieved information in depth for every topic.\",\n",
" backstory=\"\"\"You are a renowned Content Strategist, known for\n",
- " your insightful and engaging articles.\n",
- " You transform complex concepts into compelling narratives.\"\"\",\n",
- " tools=[GetNews().news, search_tool],\n",
+ " your insightful and engaging articles.\n",
+ " You transform complex concepts into compelling narratives.\"\"\",\n",
+ " tools=[GetNews()], # Instantiate GetNews directly\n",
" allow_delegation=True,\n",
" verbose=True,\n",
- " llm=llm,\n",
")"
- ],
- "metadata": {
- "id": "SLDSHOuCUXna"
- },
- "execution_count": 14,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Tasks to perform"
- ],
"metadata": {
"id": "dmdxvELxV6J1"
- }
+ },
+ "source": [
+ "### Tasks to perform"
+ ]
},
{
"cell_type": "code",
+ "execution_count": 65,
+ "metadata": {
+ "id": "9ZG9GxqWV0md"
+ },
+ "outputs": [],
"source": [
+ "from crewai import Task\n",
+ "\n",
"# Creating search and writer tasks for agents\n",
"news_search_task = Task(\n",
" description=\"\"\"Conduct a comprehensive analysis of the latest advancements in AI in 2024.\n",
- " Identify key trends, breakthrough technologies, and potential industry impacts.\n",
- " Your final answer MUST be a full analysis report\"\"\",\n",
+ " Identify key trends, breakthrough technologies, and potential industry impacts.\n",
+ " Your final answer MUST be a full analysis report\"\"\",\n",
" expected_output=\"Create key points list for each news\",\n",
" agent=news_search_agent,\n",
- " tools=[SearchNewsDB().news],\n",
+ " tools=[SearchNewsDB()], # Pass the tool instance, not the _run method\n",
")\n",
"\n",
"writer_task = Task(\n",
- " description=\"\"\"Using the insights provided, summaries each post of them\n",
- " highlights the most significant AI advancements.\n",
- " Your post should be informative yet accessible, catering to a tech-savvy audience.\n",
- " Make it sound cool, avoid complex words so it doesn't sound like AI.\n",
- " Your final answer MUST not be the more than 50 words.\"\"\",\n",
- " expected_output=\"Write a short summary under 50 words for each news Headline seperately\",\n",
+ " description=\"\"\"Using the insights provided, summarize each post and\n",
+ " highlight the most significant AI advancements.\n",
+ " Your post should be informative yet accessible, catering to a tech-savvy audience.\n",
+ " Make it sound cool, avoid complex words so it doesn't sound like AI.\n",
+ " Your final answer MUST not be more than 50 words.\"\"\",\n",
+ " expected_output=\"Write a short summary under 50 words for each news Headline separately\",\n",
" agent=writer_agent,\n",
" context=[news_search_task],\n",
- " tools=[GetNews().news, search_tool],\n",
+ " tools=[GetNews()], # Pass the tool instance, not the _run method\n",
")"
- ],
- "metadata": {
- "id": "9ZG9GxqWV0md"
- },
- "execution_count": 15,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Create a Crew"
- ],
"metadata": {
"id": "yvjya2FzWR5m"
- }
+ },
+ "source": [
+ "### Create a Crew"
+ ]
},
{
"cell_type": "code",
+ "execution_count": 66,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "yYykbGq2WRSP",
+ "outputId": "9a024678-75ce-4e6a-a420-a89693e34574"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:opentelemetry.trace:Overriding of current TracerProvider is not allowed\n"
+ ]
+ }
+ ],
"source": [
"# Instantiate Crew with Agents and their tasks\n",
"news_crew = Crew(\n",
@@ -466,140 +540,231 @@
" process=Process.sequential,\n",
" manager_llm=llm,\n",
")"
- ],
- "metadata": {
- "id": "yYykbGq2WRSP"
- },
- "execution_count": 16,
- "outputs": []
+ ]
},
{
"cell_type": "code",
- "source": [
- "news_crew"
- ],
+ "execution_count": 67,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xSBr8D6tWloT",
- "outputId": "2d2114b3-ecf5-47c4-84f8-95cb5fe3280a"
+ "outputId": "274e6f3d-32e1-46fb-aa95-9a6b8c38a642"
},
- "execution_count": 17,
"outputs": [
{
- "output_type": "execute_result",
"data": {
"text/plain": [
- "Crew(id=25414146-d590-489a-87db-cbcfe14ddda9, process=sequential, number_of_agents=2, number_of_tasks=2)"
+ "Crew(id=d9e467a2-39d5-4ab8-aa97-e2c0929c1a85, process=sequential, number_of_agents=2, number_of_tasks=2)"
]
},
+ "execution_count": 67,
"metadata": {},
- "execution_count": 17
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "news_crew"
]
},
{
"cell_type": "markdown",
- "source": [
- "### Kickoff the crew - let the magic happen"
- ],
"metadata": {
"id": "-tjmzH9cXOaB"
- }
+ },
+ "source": [
+ "### Kickoff the crew - let the magic happen"
+ ]
},
{
"cell_type": "code",
- "source": [
- "# Execute the crew to see RAG in action\n",
- "result = news_crew.kickoff()"
- ],
+ "execution_count": 68,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VU3LdZmIWqQH",
- "outputId": "3e462e46-a359-41bb-aedd-409b9a85e596"
+ "outputId": "a1364e3b-df46-485d-ca2e-ba9bc0754ad7"
},
- "execution_count": 18,
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
+ "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mAI News Searcher\u001b[00m\n",
+ "\u001b[95m## Task:\u001b[00m \u001b[92mConduct a comprehensive analysis of the latest advancements in AI in 2024.\n",
+ " Identify key trends, breakthrough technologies, and potential industry impacts.\n",
+ " Your final answer MUST be a full analysis report\u001b[00m\n",
+ "\u001b[91m \n",
+ "\n",
+ "I encountered an error while trying to use the tool. This was the error: 1 validation error for SearchNewsDBSchema\n",
+ "query\n",
+ " Input should be a valid string [type=string_type, input_value={'description': 'latest a...AI 2024', 'type': 'str'}, input_type=dict]\n",
+ " For further information visit https://errors.pydantic.dev/2.9/v/string_type.\n",
+ " Tool News DB Tool accepts these inputs: Tool Name: News DB Tool\n",
+ "Tool Arguments: {'query': {'description': None, 'type': 'str'}}\n",
+ "Tool Description: Fetch and process news articles from various sources\n",
+ "\u001b[00m\n",
"\n",
"\n",
- "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
- "\u001b[32;1m\u001b[1;3mI need to gather the latest information on advancements in AI from 2024 to identify key trends, breakthrough technologies, and potential industry impacts. To do this effectively, I will first use the News DB Tool to fetch recent news articles related to AI advancements in 2024. This will provide a foundation for a comprehensive analysis.\n",
+ "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mAI News Searcher\u001b[00m\n",
+ "\u001b[95m## Thought:\u001b[00m \u001b[92mI need to gather data on the latest advancements in AI for 2024. This will involve searching for relevant news articles to identify key trends, breakthrough technologies, and potential industry impacts.\u001b[00m\n",
+ "\u001b[95m## Using tool:\u001b[00m \u001b[92mNews DB Tool\u001b[00m\n",
+ "\u001b[95m## Tool Input:\u001b[00m \u001b[92m\n",
+ "\"{\\\"query\\\": {\\\"description\\\": \\\"latest advancements in AI 2024\\\", \\\"type\\\": \\\"str\\\"}}\"\u001b[00m\n",
+ "\u001b[95m## Tool Output:\u001b[00m \u001b[92m\n",
"\n",
- "Action: News DB Tool\n",
+ "I encountered an error while trying to use the tool. This was the error: 1 validation error for SearchNewsDBSchema\n",
+ "query\n",
+ " Input should be a valid string [type=string_type, input_value={'description': 'latest a...AI 2024', 'type': 'str'}, input_type=dict]\n",
+ " For further information visit https://errors.pydantic.dev/2.9/v/string_type.\n",
+ " Tool News DB Tool accepts these inputs: Tool Name: News DB Tool\n",
+ "Tool Arguments: {'query': {'description': None, 'type': 'str'}}\n",
+ "Tool Description: Fetch and process news articles from various sources.\n",
+ "Moving on then. I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. To Use the following format:\n",
"\n",
- "Action Input: {\"query\": \"AI advancements 2024\"}\n",
- "\u001b[0m\u001b[93m \n",
+ "Thought: you should always think about what to do\n",
+ "Action: the action to take, should be one of [News DB Tool]\n",
+ "Action Input: the input to the action, dictionary enclosed in curly braces\n",
+ "Observation: the result of the action\n",
+ "... (this Thought/Action/Action Input/Result can repeat N times)\n",
+ "Thought: I now can give a great answer\n",
+ "Final Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n",
"\n",
- "[Document(page_content='This is likely a big part of the reason it might view home robots as “the next big thing” (to quote Bloomberg quoting its sources). Apple has no doubt pumped a tremendous amount of resources into driving technologies. If those could be repurposed for a different project, maybe it won’t all be for naught.\\nWhile the reports note that Apple “hasn’t committed” to either the robotic smart screen or mobile robot that are said to exist somewhere inside the company’s skunkworks, it has already put Apple Home execs Matt Costello and Brian Lynch on the hardware side of things, while SVP of Machine Learning and AI Strategy John Giannandrea is said to be involved on the AI side of things.\\nImage Credits: Brian Heater', metadata={'vector': [0.004069294314831495, -0.020910432562232018, 0.008496551774442196, -0.0071997810155153275, 0.012758336029946804, 5.0971713790204376e-05, -0.012109951116144657, 0.025989454239606857, -0.0032014036551117897, -0.026070501655340195, 0.01812777854502201, 0.01337970606982708, -0.015101980417966843, -0.00028641248354688287, -0.006784409284591675, 0.01982979103922844, 0.02367958053946495, 0.02078886143863201, 0.0065885428339242935, -0.004569091834127903, -0.027772514149546623, 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The total potential value of all the task orders over the next 13 years is $4.6 billion.\\nThe three teams are also keeping specifications, like range or battery technology, close to the chest, though NASA specified that the rover would have to have an incredible 10-year life span and be capable of carrying two suited astronauts.\\nIntuitive Machines is leading a team that includes AVL, Boeing, Michelin, and Northrop Grumman; Lunar Outpost is leading the “Lunar Dawn” team that includes Lockheed Martin, General Motors, Goodyear and MDA Space. Astrolab is joined by Axiom Space and Odyssey Space Research.\\nNASA lunar terrain vehicle. 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TechCrunch\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nTechCrunch\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nplus-bold\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nTechCrunch\\n\\n\\nOpen Navigation\\n\\n\\n\\n\\n\\n\\nTechCrunch\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nI have a group chat with three AI friends, thanks to Nomi AI — they’re getting too smart\\n\\n\\n\\n\\n\\t\\t\\tAmanda Silberling\\t\\t\\n\\n\\t\\t\\t\\t\\t\\t2 days\", metadata={'vector': [-0.01198604516685009, -0.02511361986398697, 0.022259799763560295, -0.018196502700448036, 0.009370043873786926, 0.03639300540089607, -0.00867017824202776, 0.005598923657089472, -0.026635656133294106, -0.02495054341852665, 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+ " \u001b[00m\n",
+ "\u001b[91m \n",
+ "\n",
+ "I encountered an error while trying to use the tool. This was the error: 1 validation error for SearchNewsDBSchema\n",
+ "query\n",
+ " Input should be a valid string [type=string_type, input_value={'description': 'latest a...or 2024', 'type': 'str'}, input_type=dict]\n",
+ " For further information visit https://errors.pydantic.dev/2.9/v/string_type.\n",
+ " Tool News DB Tool accepts these inputs: Tool Name: News DB Tool\n",
+ "Tool Arguments: {'query': {'description': None, 'type': 'str'}}\n",
+ "Tool Description: Fetch and process news articles from various sources\n",
"\u001b[00m\n",
- "\u001b[32;1m\u001b[1;3mThought: \n",
- "Based on the observation of the documents retrieved by the News DB Tool, it's clear that each document contains information about different advancements or applications in AI. These documents range from details about NASA's new lunar rover contracts, to Apple's potential venture into home robots, Elon Musk's involvement in AI communication tools, and more. To conduct a comprehensive analysis, I need to synthesize key points from each article to identify the latest trends, breakthrough technologies, and their potential impact on various industries.\n",
"\n",
- "Final Answer:\n",
- "The latest advancements in AI as of 2024 showcase a dynamic range of applications and developments in various sectors. Here are the key points identified from the recent news articles:\n",
"\n",
- "1. **Apple's Home Robot Initiative**:\n",
- " - Apple is speculated to be entering the home robot market, leveraging its extensive experience in driving technologies.\n",
- " - Despite no official commitment, Apple has allocated significant resources, including top executives from its Home and AI divisions, hinting at a serious exploration of robotics.\n",
- " - The potential focus areas include robotic smart screens and mobile robots, indicating a broad vision for home automation and personal assistance technologies.\n",
+ "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mAI News Searcher\u001b[00m\n",
+ "\u001b[95m## Thought:\u001b[00m \u001b[92mThought: I need to conduct another search for the latest advancements in AI for 2024 to gather comprehensive insights. To do this, I will format my query correctly to comply with the tool's requirements.\u001b[00m\n",
+ "\u001b[95m## Using tool:\u001b[00m \u001b[92mNews DB Tool\u001b[00m\n",
+ "\u001b[95m## Tool Input:\u001b[00m \u001b[92m\n",
+ "\"{\\\"query\\\": {\\\"description\\\": \\\"latest advancements in AI for 2024\\\", \\\"type\\\": \\\"str\\\"}}\"\u001b[00m\n",
+ "\u001b[95m## Tool Output:\u001b[00m \u001b[92m\n",
+ "\n",
+ "I encountered an error while trying to use the tool. This was the error: 1 validation error for SearchNewsDBSchema\n",
+ "query\n",
+ " Input should be a valid string [type=string_type, input_value={'description': 'latest a...or 2024', 'type': 'str'}, input_type=dict]\n",
+ " For further information visit https://errors.pydantic.dev/2.9/v/string_type.\n",
+ " Tool News DB Tool accepts these inputs: Tool Name: News DB Tool\n",
+ "Tool Arguments: {'query': {'description': None, 'type': 'str'}}\n",
+ "Tool Description: Fetch and process news articles from various sources.\n",
+ "Moving on then. I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. To Use the following format:\n",
+ "\n",
+ "Thought: you should always think about what to do\n",
+ "Action: the action to take, should be one of [News DB Tool]\n",
+ "Action Input: the input to the action, dictionary enclosed in curly braces\n",
+ "Observation: the result of the action\n",
+ "... (this Thought/Action/Action Input/Result can repeat N times)\n",
+ "Thought: I now can give a great answer\n",
+ "Final Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n",
+ "\n",
+ " \u001b[00m\n",
"\n",
- "2. **Elon Musk's AI Communication Tool**:\n",
- " - Elon Musk has introduced a new AI communication tool through Nomi AI, enhancing the capabilities of group chats with AI friends.\n",
- " - This innovation underscores the increasing sophistication of AI in understanding and participating in human-like conversations, pushing the boundaries of interactive AI technologies.\n",
"\n",
- "3. **NASA's Lunar Rover Contracts**:\n",
- " - NASA has awarded contracts worth $30 million to Intuitive Machines as part of its initiative to develop lunar rovers, with a potential expenditure of $4.6 billion over 13 years.\n",
- " - The contracts involve key industry players such as AVL, Boeing, Michelin, and Northrop Grumman, indicating a collaborative approach towards pioneering space exploration technologies.\n",
- " - The lunar rovers are expected to have a 10-year lifespan and the capability to carry astronauts, highlighting significant advancements in durability and functionality for space exploration.\n",
+ "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mAI News Searcher\u001b[00m\n",
+ "\u001b[95m## Thought:\u001b[00m \u001b[92mThought: I need to reformulate my approach to query the news database correctly, ensuring that I utilize a valid string for the search. This will help me identify the latest advancements in AI for 2024.\u001b[00m\n",
+ "\u001b[95m## Using tool:\u001b[00m \u001b[92mNews DB Tool\u001b[00m\n",
+ "\u001b[95m## Tool Input:\u001b[00m \u001b[92m\n",
+ "\"{\\\"query\\\": \\\"latest advancements in AI 2024\\\"}\"\u001b[00m\n",
+ "\u001b[95m## Tool Output:\u001b[00m \u001b[92m\n",
+ "[Document(metadata={'description': 'Autonomous, AI-based players are coming to a gaming experience near you, and a new startup,\\xa0Altera, is joining the fray to build this new guard of AI', 'language': 'en-US', 'source': 'https://techcrunch.com/2024/05/08/bye-bye-bots-alteras-game-playing-ai-agents-get-backing-from-eric-schmidt/', 'title': 'Bye-bye bots: Altera’s game-playing AI agents get backing from Eric Schmidt | TechCrunch'}, page_content='At the helm of Altera is Robert Yang, a neuroscientist and former assistant professor at MIT. In December 2023, Yang and Altera’s other co-founders — Andrew Ahn, Nico Christie and Shuying Luo — stepped away from their applied research lab at MIT to focus on a new goal: developing AI agents (or “AI buddies,” as Yang calls them) with “social-emotional intelligence” that can interact with players and make their own decisions in-game.\\n“It has been my life goal as a neuroscientist to go all the way and build a digital human being — redefining what we thought AI was capable of,” Yang told TechCrunch. That is not to say that Yang is coming from a misanthropic point of view. “Our solidly pro-human framework means that we are building agents that will enhance humanity, not replace it,” he insists.'), Document(metadata={'description': 'Autonomous, AI-based players are coming to a gaming experience near you, and a new startup,\\xa0Altera, is joining the fray to build this new guard of AI', 'language': 'en-US', 'source': 'https://techcrunch.com/2024/05/08/bye-bye-bots-alteras-game-playing-ai-agents-get-backing-from-eric-schmidt/', 'title': 'Bye-bye bots: Altera’s game-playing AI agents get backing from Eric Schmidt | TechCrunch'}, page_content='Bye-bye bots: Altera’s game-playing AI agents get backing from Eric Schmidt \\n\\n\\n\\n\\n\\nLauren Forristal\\n\\n\\n\\n\\n\\n8:14 AM PDT · May 8, 2024 \\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n \\n\\n \\n\\n \\n\\n \\n\\n \\n\\n \\n\\n \\n\\n\\n\\n\\n\\n\\nAutonomous, AI-based players are coming to a gaming experience near you, and a new startup,\\xa0Altera, is joining the fray to build this new guard of AI agents.\\nThe company announced Wednesday that it raised $9 million in an oversubscribed seed round, co-led by First Spark Ventures (Eric Schmidt’s deep-tech fund) and Patron (the seed-stage fund co-founded by Riot Games alums).'), Document(metadata={'description': 'Google DeepMind has taken the wraps off a new version of AlphaFold, their transformative machine learning model that predicts the shape and behavior of', 'language': 'en-US', 'source': 'https://techcrunch.com/2024/05/08/google-deepmind-debuts-huge-alphafold-update-and-free-proteomics-as-a-service-web-app/', 'title': 'Google DeepMind debuts huge AlphaFold update and free proteomics-as-a-service web app | TechCrunch'}, page_content=\"Topics\\n\\nAI, AlphaFold, Biotech & Health, DeepMind, Google, google deepmind \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nMost Popular\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n AWS brings prompt routing and caching to its Bedrock LLM service\\n\\n\\n\\nFrederic Lardinois\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n Hugging Face CEO has concerns about Chinese open source AI models\\n\\n\\n\\nCharles Rollet\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n SpaceX mulls tender offer at $350B valuation\\n\\n\\n\\nAria Alamalhodaei\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n Why does the name ‘David Mayer’ crash ChatGPT? OpenAI says privacy tool went rogue\\n\\n\\n\\nDevin Coldewey\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n Amazon announces Nova, a new family of multimodal AI models\\n\\n\\n\\nKyle Wiggers\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n AWS announces Aurora DSQL, a new distributed SQL database that promises virtually unlimited scalability\\n\\n\\n\\nFrederic Lardinois\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n Judge confirms decision to sink Elon Musk’s $56B pay package despite Tesla shareholder vote\\n\\n\\n\\nSean O'Kane\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n \\n\\n \\n\\n \\n\\n \\n\\n \\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\t\\t\\tDevin Coldewey\\t\\n\\n\\n\\nWriter & Photographer\"), Document(metadata={'description': 'Autonomous, AI-based players are coming to a gaming experience near you, and a new startup,\\xa0Altera, is joining the fray to build this new guard of AI', 'language': 'en-US', 'source': 'https://techcrunch.com/2024/05/08/bye-bye-bots-alteras-game-playing-ai-agents-get-backing-from-eric-schmidt/', 'title': 'Bye-bye bots: Altera’s game-playing AI agents get backing from Eric Schmidt | TechCrunch'}, page_content='The funding follows Altera’s previous raising a pre-seed $2 million from a16z SPEEDRUN and others in January of this year. Now, Altera wants to use the new capital to hire more scientists, engineers and team members to help with product development and growth.\\nIf the first wave of AI for end users was about AI bots; and more recently, AI “copilots” use generative AI to help understand and respond to increasingly sophisticated queries, then AI agents are emerging as the next stage of development. The focus is on how AI can be used to create increasingly more human-like, nuanced entities that can respond to and interact with actual humans.\\nOne early use case for these agents has been gaming — specifically to use in games that support modifications (mods) like Minecraft.\\xa0Voyager\\xa0is one recent project, built on the Minedojo framework, that creates and develops Minecraft AI agents, and this, too, is where Altera is getting its start.')]\u001b[00m\n",
"\n",
- "These advancements illustrate the rapid development and diverse application of AI and related technologies across different sectors, from home automation and social interaction to space exploration. The involvement of leading corporations and ambitious projects underscores the significant impact and potential of AI in shaping future technologies and industries.\u001b[0m\n",
"\n",
- "\u001b[1m> Finished chain.\u001b[0m\n",
+ "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mAI News Searcher\u001b[00m\n",
+ "\u001b[95m## Final Answer:\u001b[00m \u001b[92m\n",
+ "- **Key Points from the Articles:**\n",
+ " 1. Altera's pursuit of emotionally intelligent AI agents to enhance gaming experiences, backed by $9 million funding.\n",
+ " 2. Google's AlphaFold update is making significant advancements in protein structure prediction technology available to researchers.\n",
+ " 3. The trend of creating AI with social-emotional intelligence in gaming environments suggests a new frontier in user interaction.\n",
+ " 4. The democratization of powerful AI tools, notably in biotechnology, indicates a shift toward greater accessibility for research applications.\n",
+ " 5. Both trends point towards deeper integration of AI in everyday industries, promising advancements in engagement and productivity.\u001b[00m\n",
"\n",
"\n",
- "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
- "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3mThought: I now can give a great answer\n",
- "Final Answer: \n",
- "1. **Apple's Home Robot Initiative**: Apple might soon introduce robots to our homes, focusing on smart screens and mobility for better home automation.\n",
- "2. **Elon Musk's AI Communication Tool**: Elon Musk's Nomi AI is making group chats cooler by adding AI that chats just like humans.\n",
- "3. **NASA's Lunar Rover Contracts**: NASA's investing $4.6 billion in lunar rovers with a 10-year life span, hinting at long-term plans for space exploration.\u001b[0m\n",
+ "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mWriter\u001b[00m\n",
+ "\u001b[95m## Task:\u001b[00m \u001b[92mUsing the insights provided, summarize each post and\n",
+ " highlight the most significant AI advancements.\n",
+ " Your post should be informative yet accessible, catering to a tech-savvy audience.\n",
+ " Make it sound cool, avoid complex words so it doesn't sound like AI.\n",
+ " Your final answer MUST not be more than 50 words.\u001b[00m\n",
"\n",
- "\u001b[1m> Finished chain.\u001b[0m\n"
+ "\n",
+ "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mWriter\u001b[00m\n",
+ "\u001b[95m## Thought:\u001b[00m \u001b[92mI need to gather detailed information on the key points from the articles related to AI advancements. I'll use the Get News Tool to explore these topics further.\u001b[00m\n",
+ "\u001b[95m## Using tool:\u001b[00m \u001b[92mGet News Tool\u001b[00m\n",
+ "\u001b[95m## Tool Input:\u001b[00m \u001b[92m\n",
+ "\"{\\\"query\\\": \\\"Altera emotionally intelligent AI agents gaming experiences funding\\\"}\"\u001b[00m\n",
+ "\u001b[95m## Tool Output:\u001b[00m \u001b[92m\n",
+ "[Document(metadata={'description': 'Autonomous, AI-based players are coming to a gaming experience near you, and a new startup,\\xa0Altera, is joining the fray to build this new guard of AI', 'language': 'en-US', 'source': 'https://techcrunch.com/2024/05/08/bye-bye-bots-alteras-game-playing-ai-agents-get-backing-from-eric-schmidt/', 'title': 'Bye-bye bots: Altera’s game-playing AI agents get backing from Eric Schmidt | TechCrunch'}, page_content='The funding follows Altera’s previous raising a pre-seed $2 million from a16z SPEEDRUN and others in January of this year. Now, Altera wants to use the new capital to hire more scientists, engineers and team members to help with product development and growth.\\nIf the first wave of AI for end users was about AI bots; and more recently, AI “copilots” use generative AI to help understand and respond to increasingly sophisticated queries, then AI agents are emerging as the next stage of development. The focus is on how AI can be used to create increasingly more human-like, nuanced entities that can respond to and interact with actual humans.\\nOne early use case for these agents has been gaming — specifically to use in games that support modifications (mods) like Minecraft.\\xa0Voyager\\xa0is one recent project, built on the Minedojo framework, that creates and develops Minecraft AI agents, and this, too, is where Altera is getting its start.'), Document(metadata={'description': 'Autonomous, AI-based players are coming to a gaming experience near you, and a new startup,\\xa0Altera, is joining the fray to build this new guard of AI', 'language': 'en-US', 'source': 'https://techcrunch.com/2024/05/08/bye-bye-bots-alteras-game-playing-ai-agents-get-backing-from-eric-schmidt/', 'title': 'Bye-bye bots: Altera’s game-playing AI agents get backing from Eric Schmidt | TechCrunch'}, page_content='Bye-bye bots: Altera’s game-playing AI agents get backing from Eric Schmidt \\n\\n\\n\\n\\n\\nLauren Forristal\\n\\n\\n\\n\\n\\n8:14 AM PDT · May 8, 2024 \\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n \\n\\n \\n\\n \\n\\n \\n\\n \\n\\n \\n\\n \\n\\n\\n\\n\\n\\n\\nAutonomous, AI-based players are coming to a gaming experience near you, and a new startup,\\xa0Altera, is joining the fray to build this new guard of AI agents.\\nThe company announced Wednesday that it raised $9 million in an oversubscribed seed round, co-led by First Spark Ventures (Eric Schmidt’s deep-tech fund) and Patron (the seed-stage fund co-founded by Riot Games alums).'), Document(metadata={'description': 'Autonomous, AI-based players are coming to a gaming experience near you, and a new startup,\\xa0Altera, is joining the fray to build this new guard of AI', 'language': 'en-US', 'source': 'https://techcrunch.com/2024/05/08/bye-bye-bots-alteras-game-playing-ai-agents-get-backing-from-eric-schmidt/', 'title': 'Bye-bye bots: Altera’s game-playing AI agents get backing from Eric Schmidt | TechCrunch'}, page_content='“We see more potential in building agents within the gaming industry,” he said. “This approach allows us to iterate faster, collect data more effectively, and deliver a product where there are eager users and where emergent behavior is a feature, not a bug.”\\n(And yes, in keeping with its consumer focus, you should not be surprised that, for now, the company is not talking about monetization at all.)\\nAltera founders.Image Credits:Altera\\nSimilar to the Voyager GPT-4-powered Minecraft bot, Altera’s autonomous agents are capable of playing Minecraft as if they were humans, performing tasks like building, crafting, farming, trading, mining, attacking, equipping items, chatting and moving around.\\nAltera’s agents are designed to be companions for gamers, not assistants who do what you tell them to. Unlike NPCs (non-player characters), they have the freedom to make their own decisions, which could either make the game more entertaining or frustrating, depending on your playing style.'), Document(metadata={'description': 'Autonomous, AI-based players are coming to a gaming experience near you, and a new startup,\\xa0Altera, is joining the fray to build this new guard of AI', 'language': 'en-US', 'source': 'https://techcrunch.com/2024/05/08/bye-bye-bots-alteras-game-playing-ai-agents-get-backing-from-eric-schmidt/', 'title': 'Bye-bye bots: Altera’s game-playing AI agents get backing from Eric Schmidt | TechCrunch'}, page_content='At the helm of Altera is Robert Yang, a neuroscientist and former assistant professor at MIT. In December 2023, Yang and Altera’s other co-founders — Andrew Ahn, Nico Christie and Shuying Luo — stepped away from their applied research lab at MIT to focus on a new goal: developing AI agents (or “AI buddies,” as Yang calls them) with “social-emotional intelligence” that can interact with players and make their own decisions in-game.\\n“It has been my life goal as a neuroscientist to go all the way and build a digital human being — redefining what we thought AI was capable of,” Yang told TechCrunch. That is not to say that Yang is coming from a misanthropic point of view. “Our solidly pro-human framework means that we are building agents that will enhance humanity, not replace it,” he insists.')]\u001b[00m\n",
+ "\n",
+ "\n",
+ "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mWriter\u001b[00m\n",
+ "\u001b[95m## Final Answer:\u001b[00m \u001b[92m\n",
+ "1. Altera raised $9 million to develop emotionally intelligent AI agents for gaming, aiming to create more human-like interactions in games, particularly Minecraft, enhancing player experiences. \n",
+ "2. Google’s AlphaFold is advancing protein structure predictions, enabling researchers to better understand biological processes for breakthroughs in biotechnology and medicine. \n",
+ "3. The emergence of socially aware AI in gaming opens up new ways for players to interact, enhancing emotional connections and experiences. \n",
+ "4. The democratization of AI in biotech signals a shift towards making advanced tools widely accessible for researchers, driving innovation across the field. \n",
+ "5. These trends indicate that AI is deeply integrating into various industries, enhancing engagement and productivity in everyday life.\u001b[00m\n",
+ "\n",
+ "\n"
]
}
+ ],
+ "source": [
+ "# Execute the crew to see RAG in action\n",
+ "result = news_crew.kickoff()"
]
},
{
"cell_type": "code",
- "source": [
- "print(result)"
- ],
+ "execution_count": 69,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HB91fa7BXULb",
- "outputId": "2b3f3bf6-1a90-4d2c-bc7f-9158b7734628"
+ "outputId": "b88007db-b003-43d2-b71b-159ce0257e32"
},
- "execution_count": 19,
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
- "1. **Apple's Home Robot Initiative**: Apple might soon introduce robots to our homes, focusing on smart screens and mobility for better home automation.\n",
- "2. **Elon Musk's AI Communication Tool**: Elon Musk's Nomi AI is making group chats cooler by adding AI that chats just like humans.\n",
- "3. **NASA's Lunar Rover Contracts**: NASA's investing $4.6 billion in lunar rovers with a 10-year life span, hinting at long-term plans for space exploration.\n"
+ "1. Altera raised $9 million to develop emotionally intelligent AI agents for gaming, aiming to create more human-like interactions in games, particularly Minecraft, enhancing player experiences. \n",
+ "2. Google’s AlphaFold is advancing protein structure predictions, enabling researchers to better understand biological processes for breakthroughs in biotechnology and medicine. \n",
+ "3. The emergence of socially aware AI in gaming opens up new ways for players to interact, enhancing emotional connections and experiences. \n",
+ "4. The democratization of AI in biotech signals a shift towards making advanced tools widely accessible for researchers, driving innovation across the field. \n",
+ "5. These trends indicate that AI is deeply integrating into various industries, enhancing engagement and productivity in everyday life.\n"
]
}
+ ],
+ "source": [
+ "print(result)"
]
}
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/Code-Documentation-QA-Bot/main.ipynb b/examples/Code-Documentation-QA-Bot/main.ipynb
index fd2cdef4..f1470c37 100644
--- a/examples/Code-Documentation-QA-Bot/main.ipynb
+++ b/examples/Code-Documentation-QA-Bot/main.ipynb
@@ -3,7 +3,9 @@
{
"cell_type": "markdown",
"id": "13cb272e",
- "metadata": {},
+ "metadata": {
+ "id": "13cb272e"
+ },
"source": [
"# Code documentation Q&A bot example with LangChain\n",
"\n",
@@ -16,27 +18,140 @@
{
"cell_type": "markdown",
"id": "1991331f-4316-417a-b693-e2f27cbe9ea7",
- "metadata": {},
+ "metadata": {
+ "id": "1991331f-4316-417a-b693-e2f27cbe9ea7"
+ },
"source": [
"### Installing dependencies"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"id": "e8a49c31",
- "metadata": {},
- "outputs": [],
+ "metadata": {
+ "id": "e8a49c31",
+ "outputId": "a9aca639-0c3d-4164-bf9c-7bdae7bc92f1",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: langchain in /usr/local/lib/python3.10/dist-packages (0.3.9)\n",
+ "Requirement already satisfied: langchain-openai in /usr/local/lib/python3.10/dist-packages (0.2.10)\n",
+ "Collecting langchain-community\n",
+ " Downloading langchain_community-0.3.8-py3-none-any.whl.metadata (2.9 kB)\n",
+ "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (6.0.2)\n",
+ "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.0.36)\n",
+ "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (3.11.2)\n",
+ "Requirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (4.0.3)\n",
+ "Requirement already satisfied: langchain-core<0.4.0,>=0.3.21 in /usr/local/lib/python3.10/dist-packages (from langchain) (0.3.21)\n",
+ "Requirement already satisfied: langchain-text-splitters<0.4.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (0.3.2)\n",
+ "Requirement already satisfied: langsmith<0.2.0,>=0.1.17 in /usr/local/lib/python3.10/dist-packages (from langchain) (0.1.143)\n",
+ "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from langchain) (1.26.4)\n",
+ "Requirement already satisfied: pydantic<3.0.0,>=2.7.4 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.9.2)\n",
+ "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.32.3)\n",
+ "Requirement already satisfied: tenacity!=8.4.0,<10,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (9.0.0)\n",
+ "Requirement already satisfied: openai<2.0.0,>=1.54.0 in /usr/local/lib/python3.10/dist-packages (from langchain-openai) (1.54.4)\n",
+ "Requirement already satisfied: tiktoken<1,>=0.7 in /usr/local/lib/python3.10/dist-packages (from langchain-openai) (0.8.0)\n",
+ "Collecting SQLAlchemy<3,>=1.4 (from langchain)\n",
+ " Downloading SQLAlchemy-2.0.35-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.6 kB)\n",
+ "Requirement already satisfied: dataclasses-json<0.7,>=0.5.7 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (0.6.7)\n",
+ "Collecting httpx-sse<0.5.0,>=0.4.0 (from langchain-community)\n",
+ " Downloading httpx_sse-0.4.0-py3-none-any.whl.metadata (9.0 kB)\n",
+ "Collecting pydantic-settings<3.0.0,>=2.4.0 (from langchain-community)\n",
+ " Downloading pydantic_settings-2.6.1-py3-none-any.whl.metadata (3.5 kB)\n",
+ "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (2.4.3)\n",
+ "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\n",
+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (24.2.0)\n",
+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.5.0)\n",
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (6.1.0)\n",
+ "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (0.2.0)\n",
+ "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.17.2)\n",
+ "Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /usr/local/lib/python3.10/dist-packages (from dataclasses-json<0.7,>=0.5.7->langchain-community) (3.23.1)\n",
+ "Requirement already satisfied: typing-inspect<1,>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from dataclasses-json<0.7,>=0.5.7->langchain-community) (0.9.0)\n",
+ "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4.0,>=0.3.21->langchain) (1.33)\n",
+ "Requirement already satisfied: packaging<25,>=23.2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4.0,>=0.3.21->langchain) (24.2)\n",
+ "Requirement already satisfied: typing-extensions>=4.7 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4.0,>=0.3.21->langchain) (4.12.2)\n",
+ "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.17->langchain) (0.27.2)\n",
+ "Requirement already satisfied: orjson<4.0.0,>=3.9.14 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.17->langchain) (3.10.11)\n",
+ "Requirement already satisfied: requests-toolbelt<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.17->langchain) (1.0.0)\n",
+ "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.54.0->langchain-openai) (3.7.1)\n",
+ "Requirement already satisfied: distro<2,>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.54.0->langchain-openai) (1.9.0)\n",
+ "Requirement already satisfied: jiter<1,>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.54.0->langchain-openai) (0.7.1)\n",
+ "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.54.0->langchain-openai) (1.3.1)\n",
+ "Requirement already satisfied: tqdm>4 in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.54.0->langchain-openai) (4.66.6)\n",
+ "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3.0.0,>=2.7.4->langchain) (0.7.0)\n",
+ "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic<3.0.0,>=2.7.4->langchain) (2.23.4)\n",
+ "Collecting python-dotenv>=0.21.0 (from pydantic-settings<3.0.0,>=2.4.0->langchain-community)\n",
+ " Downloading python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (3.4.0)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (3.10)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (2.2.3)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (2024.8.30)\n",
+ "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from SQLAlchemy<3,>=1.4->langchain) (3.1.1)\n",
+ "Requirement already satisfied: regex>=2022.1.18 in /usr/local/lib/python3.10/dist-packages (from tiktoken<1,>=0.7->langchain-openai) (2024.9.11)\n",
+ "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai<2.0.0,>=1.54.0->langchain-openai) (1.2.2)\n",
+ "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.17->langchain) (1.0.7)\n",
+ "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.17->langchain) (0.14.0)\n",
+ "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.10/dist-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.4.0,>=0.3.21->langchain) (3.0.0)\n",
+ "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain-community) (1.0.0)\n",
+ "Downloading langchain_community-0.3.8-py3-none-any.whl (2.4 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m24.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading httpx_sse-0.4.0-py3-none-any.whl (7.8 kB)\n",
+ "Downloading pydantic_settings-2.6.1-py3-none-any.whl (28 kB)\n",
+ "Downloading SQLAlchemy-2.0.35-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m68.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n",
+ "Installing collected packages: SQLAlchemy, python-dotenv, httpx-sse, pydantic-settings, langchain-community\n",
+ " Attempting uninstall: SQLAlchemy\n",
+ " Found existing installation: SQLAlchemy 2.0.36\n",
+ " Uninstalling SQLAlchemy-2.0.36:\n",
+ " Successfully uninstalled SQLAlchemy-2.0.36\n",
+ "Successfully installed SQLAlchemy-2.0.35 httpx-sse-0.4.0 langchain-community-0.3.8 pydantic-settings-2.6.1 python-dotenv-1.0.1\n"
+ ]
+ }
+ ],
"source": [
- "! pip install -U langchain langchain-openai"
+ "! pip install -U langchain langchain-openai langchain-community"
]
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 2,
"id": "66638d6c",
- "metadata": {},
- "outputs": [],
+ "metadata": {
+ "id": "66638d6c",
+ "outputId": "558a51fe-3b59-460d-cc1e-c3d436173000",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/981.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m317.4/981.5 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m972.8/981.5 kB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m54.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m88.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.9/62.9 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.5/49.5 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m298.0/298.0 kB\u001b[0m \u001b[31m30.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.6/114.6 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
+ ]
+ }
+ ],
"source": [
"! pip install -qq tiktoken unstructured pandas lancedb"
]
@@ -44,16 +159,20 @@
{
"cell_type": "markdown",
"id": "d1cdcac3",
- "metadata": {},
+ "metadata": {
+ "id": "d1cdcac3"
+ },
"source": [
"First, let's get some setup out of the way. As we're using the OpenAI API, ensure that you've set your key (and organization if needed):"
]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 3,
"id": "58ee1868",
- "metadata": {},
+ "metadata": {
+ "id": "58ee1868"
+ },
"outputs": [],
"source": [
"import openai\n",
@@ -61,7 +180,7 @@
"\n",
"\n",
"# Configuring the environment variable OPENAI_API_KEY\n",
- "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-....\"\n",
"\n",
"if \"OPENAI_API_KEY\" not in os.environ:\n",
" os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n",
@@ -73,7 +192,9 @@
{
"cell_type": "markdown",
"id": "34f524d3",
- "metadata": {},
+ "metadata": {
+ "id": "34f524d3"
+ },
"source": [
"\n",
"We're going to use the power of LangChain to help us create our Q&A bot. It comes with several APIs that can make our development much easier as well as a LanceDB integration for vectorstore."
@@ -82,16 +203,20 @@
{
"cell_type": "markdown",
"id": "5e11ed32-b742-4012-a37d-af54ea2a2715",
- "metadata": {},
+ "metadata": {
+ "id": "5e11ed32-b742-4012-a37d-af54ea2a2715"
+ },
"source": [
"### Importing all libraries"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"id": "b55d22f1",
- "metadata": {},
+ "metadata": {
+ "id": "b55d22f1"
+ },
"outputs": [],
"source": [
"import lancedb\n",
@@ -101,7 +226,7 @@
"import zipfile\n",
"from pathlib import Path\n",
"\n",
- "from langchain.document_loaders import UnstructuredHTMLLoader\n",
+ "from langchain_community.document_loaders import UnstructuredHTMLLoader\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain.vectorstores import LanceDB\n",
"from langchain_openai import OpenAI, OpenAIEmbeddings\n",
@@ -111,25 +236,31 @@
{
"cell_type": "markdown",
"id": "56cc6d50",
- "metadata": {},
+ "metadata": {
+ "id": "56cc6d50"
+ },
"source": [
- "To make this easier, we've downloaded Pandas documentation and stored the raw HTML files for you to download. We'll download them and then use LangChain's HTML document readers to parse them and store them in LanceDB as a vector store, along with relevant metadata. \n",
+ "To make this easier, we've downloaded Pandas documentation and stored the raw HTML files for you to download. We'll download them and then use LangChain's HTML document readers to parse them and store them in LanceDB as a vector store, along with relevant metadata.\n",
"By default we'll use numpy docs as it is much smaller than pandas docs, but you can replace this with your own docs as well."
]
},
{
"cell_type": "markdown",
"id": "6f5c79b5-7a43-403d-b456-9276cfe175b5",
- "metadata": {},
+ "metadata": {
+ "id": "6f5c79b5-7a43-403d-b456-9276cfe175b5"
+ },
"source": [
"### Get the data"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 7,
"id": "7da77e75",
- "metadata": {},
+ "metadata": {
+ "id": "7da77e75"
+ },
"outputs": [],
"source": [
"# pandas_docs = requests.get(\"https://eto-public.s3.us-west-2.amazonaws.com/datasets/pandas_docs/pandas.documentation.zip\")\n",
@@ -145,16 +276,20 @@
{
"cell_type": "markdown",
"id": "ae42496c",
- "metadata": {},
+ "metadata": {
+ "id": "ae42496c"
+ },
"source": [
"We'll create a simple **helper function** that can help to extract metadata, so we can use this downstream when we're wanting to query with filters. In this case, we want to keep the lineage of the uri or path for each document that we process:"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 8,
"id": "d171d062",
- "metadata": {},
+ "metadata": {
+ "id": "d171d062"
+ },
"outputs": [],
"source": [
"# Pre-processing and loading the documentation\n",
@@ -184,7 +319,9 @@
{
"cell_type": "markdown",
"id": "2abacdc6-6f40-4d96-99bb-0e4ef18ffdfa",
- "metadata": {},
+ "metadata": {
+ "id": "2abacdc6-6f40-4d96-99bb-0e4ef18ffdfa"
+ },
"source": [
"# Pre-processing and loading the documentation\n",
"\n",
@@ -194,7 +331,9 @@
{
"cell_type": "markdown",
"id": "5609932d-21fd-4b39-a3e9-12b709358751",
- "metadata": {},
+ "metadata": {
+ "id": "5609932d-21fd-4b39-a3e9-12b709358751"
+ },
"source": [
"If there is some issue with nltk package, kindly try using\n",
"```\n",
@@ -206,10 +345,14558 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "source": [
+ "import nltk\n",
+ "\n",
+ "nltk.download(\"punkt\")"
+ ],
+ "metadata": {
+ "id": "CMIplDGNSCIq",
+ "outputId": "c1971940-748b-44b2-9a89-ce9bcc6f8604",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "id": "CMIplDGNSCIq",
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+ "[nltk_data] Unzipping tokenizers/punkt.zip.\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 11
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
"id": "f5f683a7-123b-4e9e-a60b-115bc1340a66",
- "metadata": {},
- "outputs": [],
+ "metadata": {
+ "id": "f5f683a7-123b-4e9e-a60b-115bc1340a66",
+ "outputId": "d4952de1-932f-4512-9631-e520e30edd6a",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r0it [00:00, ?it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/index.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r1it [00:03, 3.51s/it]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/benchmarking.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r2it [00:04, 1.75s/it]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/release.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r3it [00:04, 1.25s/it]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/genindex.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "6it [00:05, 2.05it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/license.html\n",
+ "numpy_docs/glossary.html\n",
+ "numpy_docs/search.html\n",
+ "numpy_docs/getting_started.html\n",
+ "numpy_docs/release/1.18.2-notes.html\n"
+ ]
+ },
+ {
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+ "name": "stderr",
+ "text": [
+ "10it [00:05, 3.73it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
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+ "text": [
+ "numpy_docs/release/1.19.1-notes.html\n",
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+ ]
+ },
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+ "text": [
+ "\r11it [00:06, 3.71it/s]"
+ ]
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+ "numpy_docs/release/1.16.2-notes.html\n"
+ ]
+ },
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+ "text": [
+ "\r12it [00:06, 3.08it/s]"
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+ },
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+ "output_type": "stream",
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+ "numpy_docs/release/1.17.0-notes.html\n"
+ ]
+ },
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+ "\r13it [00:07, 2.91it/s]"
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+ },
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+ "numpy_docs/release/1.21.3-notes.html\n",
+ "numpy_docs/release/1.21.5-notes.html\n"
+ ]
+ },
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+ "16it [00:07, 3.53it/s]"
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+ },
+ {
+ "output_type": "stream",
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+ "numpy_docs/release/1.3.0-notes.html\n",
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+ ]
+ },
+ {
+ "output_type": "stream",
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+ ]
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+ {
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+ "name": "stderr",
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+ "numpy_docs/reference/generated/numpy.polynomial.hermite.poly2herm.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "2685it [08:16, 14.69it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/reference/generated/numpy.ma.apply_over_axes.html\n",
+ "numpy_docs/reference/generated/numpy.polynomial.laguerre.Laguerre.identity.html\n",
+ "numpy_docs/reference/generated/numpy.char.chararray.copy.html\n",
+ "numpy_docs/reference/c-api/index.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r2687it [08:16, 9.44it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/reference/c-api/deprecations.html\n",
+ "numpy_docs/reference/c-api/types-and-structures.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r2689it [08:17, 6.97it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/reference/c-api/coremath.html\n",
+ "numpy_docs/reference/c-api/generalized-ufuncs.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r2691it [08:17, 6.66it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/reference/c-api/config.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r2692it [08:17, 5.22it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/reference/c-api/dtype.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r2693it [08:18, 3.05it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/reference/c-api/iterator.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r2694it [08:19, 2.84it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/reference/c-api/ufunc.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r2695it [08:19, 3.12it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/reference/c-api/data_memory.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\r2696it [08:20, 2.42it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/reference/c-api/array.html\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "2699it [08:21, 5.38it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "numpy_docs/reference/distutils/misc_util.html\n",
+ "numpy_docs/_static/webpack-macros.html\n"
+ ]
+ }
+ ],
"source": [
"from tqdm import tqdm\n",
"\n",
@@ -218,7 +14905,8 @@
"for p in tqdm(Path(\"numpy_docs\").rglob(\"*.html\")):\n",
" if p.is_dir():\n",
" continue\n",
- " loader = UnstructuredHTMLLoader(p)\n",
+ " print(p)\n",
+ " loader = UnstructuredHTMLLoader(str(p))\n",
" raw_document = loader.load()\n",
" # docs.append(raw_document)\n",
" title = get_document_title(raw_document)\n",
@@ -239,10 +14927,27 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "c019d728-bb65-494a-b4a9-73a62bf8e155",
- "metadata": {},
- "outputs": [],
+ "metadata": {
+ "id": "c019d728-bb65-494a-b4a9-73a62bf8e155",
+ "outputId": "a63ba879-bac3-4017-818f-f1ad83e11f41",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "2699"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 15
+ }
+ ],
"source": [
"len(docs)"
]
@@ -250,7 +14955,9 @@
{
"cell_type": "markdown",
"id": "c3852dd3",
- "metadata": {},
+ "metadata": {
+ "id": "c3852dd3"
+ },
"source": [
"# Generating emebeddings from our docs\n",
"\n",
@@ -259,9 +14966,11 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 16,
"id": "82230563",
- "metadata": {},
+ "metadata": {
+ "id": "82230563"
+ },
"outputs": [],
"source": [
"text_splitter = RecursiveCharacterTextSplitter(\n",
@@ -275,7 +14984,9 @@
{
"cell_type": "markdown",
"id": "43e68215",
- "metadata": {},
+ "metadata": {
+ "id": "43e68215"
+ },
"source": [
"# Storing\n",
"\n",
@@ -285,16 +14996,20 @@
{
"cell_type": "markdown",
"id": "7ed0c792",
- "metadata": {},
+ "metadata": {
+ "id": "7ed0c792"
+ },
"source": [
"### For LanceDB cloud use the below cell :"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"id": "74780a58",
- "metadata": {},
+ "metadata": {
+ "id": "74780a58"
+ },
"outputs": [],
"source": [
"vectorstore = LanceDB(\n",
@@ -312,16 +15027,20 @@
{
"cell_type": "markdown",
"id": "9d6cf585",
- "metadata": {},
+ "metadata": {
+ "id": "9d6cf585"
+ },
"source": [
"### For LanceDB local use the below cell :"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 18,
"id": "71e15e3b",
- "metadata": {},
+ "metadata": {
+ "id": "71e15e3b"
+ },
"outputs": [],
"source": [
"vectorstore = LanceDB.from_documents(documents=documents, embedding=embeddings)"
@@ -330,16 +15049,20 @@
{
"cell_type": "markdown",
"id": "3cb1dc5d",
- "metadata": {},
+ "metadata": {
+ "id": "3cb1dc5d"
+ },
"source": [
"Now let's create our RetrievalQA chain using the LanceDB vector store:"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 19,
"id": "6a5891ad",
- "metadata": {},
+ "metadata": {
+ "id": "6a5891ad"
+ },
"outputs": [],
"source": [
"qa = RetrievalQA.from_chain_type(\n",
@@ -350,7 +15073,9 @@
{
"cell_type": "markdown",
"id": "28d93b85",
- "metadata": {},
+ "metadata": {
+ "id": "28d93b85"
+ },
"source": [
"And thats it! We're all setup. The next step is to run some queries, let's try a few:"
]
@@ -358,27 +15083,35 @@
{
"cell_type": "markdown",
"id": "82a5c946-5af3-45c1-862a-207ff4a34bb4",
- "metadata": {},
+ "metadata": {
+ "id": "82a5c946-5af3-45c1-862a-207ff4a34bb4"
+ },
"source": [
"### Querying"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 20,
"id": "70d88316",
- "metadata": {},
+ "metadata": {
+ "id": "70d88316",
+ "outputId": "da5c4c94-819f-489b-a546-87f5f2deb0b7",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
"outputs": [
{
+ "output_type": "execute_result",
"data": {
"text/plain": [
"{'query': 'tell me about the numpy library?',\n",
- " 'result': ' The NumPy library is an open-source Python library that provides efficient data structures and mathematical functions for working with multidimensional arrays and matrices. It is widely used in the fields of science and engineering and is a fundamental component of the scientific Python ecosystem. It can be installed using a scientific Python distribution or through the use of pip or conda. '}"
+ " 'result': ' The NumPy library is a Python library that provides multidimensional array and matrix data structures, as well as various routines for efficient operations on these structures. It is commonly used for scientific computing and has a vast library of mathematical functions. It also has a fixed array size and is used extensively in other popular data science packages such as Pandas and scikit-learn.'}"
]
},
- "execution_count": 10,
"metadata": {},
- "output_type": "execute_result"
+ "execution_count": 20
}
],
"source": [
@@ -388,20 +15121,26 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 21,
"id": "85a0397c",
- "metadata": {},
+ "metadata": {
+ "id": "85a0397c",
+ "outputId": "0ed3ddb5-9e46-4c6c-e830-252d6933c9f0",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
"outputs": [
{
+ "output_type": "execute_result",
"data": {
"text/plain": [
"{'query': \"What's the current version of numpy?\",\n",
- " 'result': ' The current version of NumPy is 1.21.6, according to the context provided.'}"
+ " 'result': ' The current version of NumPy is 1.26.'}"
]
},
- "execution_count": 12,
"metadata": {},
- "output_type": "execute_result"
+ "execution_count": 21
}
],
"source": [
@@ -411,20 +15150,26 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 22,
"id": "923f86c6",
- "metadata": {},
+ "metadata": {
+ "id": "923f86c6",
+ "outputId": "1e0fee97-df4e-4e0b-80f9-8715e489f873",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
"outputs": [
{
+ "output_type": "execute_result",
"data": {
"text/plain": [
"{'query': 'What kind of linear algebra related operations can be done in numpy?',\n",
- " 'result': ' Numpy provides a variety of linear algebra related operations, including decompositions, matrix eigenvalues, norms and other numbers, solving equations and inverting matrices, and linear algebra on several matrices at once.'}"
+ " 'result': \"\\n\\nSome possible operations that can be performed using numpy's linear algebra functions include matrix multiplication, finding eigenvalues and eigenvectors, calculating determinants and inverses, solving systems of linear equations, and performing decompositions such as LU, QR, and SVD.\"}"
]
},
- "execution_count": 13,
"metadata": {},
- "output_type": "execute_result"
+ "execution_count": 22
}
],
"source": [
@@ -435,7 +15180,9 @@
{
"cell_type": "markdown",
"id": "f8958d1b-0ad6-44d6-bca0-d81771c564a1",
- "metadata": {},
+ "metadata": {
+ "id": "f8958d1b-0ad6-44d6-bca0-d81771c564a1"
+ },
"source": [
"Thanks !"
]
@@ -463,8 +15210,11 @@
"interpreter": {
"hash": "511a7c77cb034b09af5465c01316a0f4bb20176d139e60e6d7915f9a637a5037"
}
+ },
+ "colab": {
+ "provenance": []
}
},
"nbformat": 4,
"nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/examples/Evaluating_RAG_with_RAGAs/Evaluating_RAG_with_RAGAs.ipynb b/examples/Evaluating_RAG_with_RAGAs/Evaluating_RAG_with_RAGAs.ipynb
index f69e55d3..21e98e90 100644
--- a/examples/Evaluating_RAG_with_RAGAs/Evaluating_RAG_with_RAGAs.ipynb
+++ b/examples/Evaluating_RAG_with_RAGAs/Evaluating_RAG_with_RAGAs.ipynb
@@ -1,569 +1,224 @@
{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "provenance": []
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AwhxwHTf4VZp"
+ },
+ "source": [
+ "## Evaluating RAG with RAGAs using GPT-4o\n",
+ "\n",
+ "Ragas is a **framework for evaluating Retrieval Augmented Generation (RAG) pipelines**.\n",
+ "\n",
+ "Ragas provides you with the tools/metrics based on the latest research for evaluating LLM-generated text to give you insights about your RAG pipeline. Ragas can be integrated with your CI/CD to provide continuous checks to ensure performance.\n",
+ "\n",
+ "GPT4-o is used as an LLM to generate responses out of semantically close context chunks.\n",
+ "\n",
+ ""
+ ]
},
- "kernelspec": {
- "name": "python3",
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+ "id": "1mr4fXemsYci",
+ "outputId": "d45c8226-3f4a-4bd6-cb4b-62e7e83d4023"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m37.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+ "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0m"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install langchain openai lancedb ragas -q"
+ ]
},
- "language_info": {
- "name": "python"
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "z8hT0Jn74ZmT"
+ },
+ "source": [
+ "### Setup `OPENAI_API_KEY` as an environment variable"
+ ]
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+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "YHgQd_1rI04R"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-....\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mM0tf_vo6GbI"
+ },
+ "source": [
+ "### Load .txt file and convert them into chunks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
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- "value": " 27/27 [00:23<00:00, 4.90s/it]"
- }
+ "id": "IkLbg-_1I3Rt",
+ "outputId": "38f3beb7-b4bd-423e-b77f-f2c79f16990e"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:langchain_text_splitters.base:Created a chunk of size 215, which is longer than the specified 200\n",
+ "WARNING:langchain_text_splitters.base:Created a chunk of size 232, which is longer than the specified 200\n",
+ "WARNING:langchain_text_splitters.base:Created a chunk of size 242, which is longer than the specified 200\n",
+ "WARNING:langchain_text_splitters.base:Created a chunk of size 219, which is longer than the specified 200\n",
+ "WARNING:langchain_text_splitters.base:Created a chunk of size 304, which is longer than the specified 200\n",
+ "WARNING:langchain_text_splitters.base:Created a chunk of size 205, which is longer than the specified 200\n",
+ "WARNING:langchain_text_splitters.base:Created a chunk of size 332, which is longer than the specified 200\n",
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+ "WARNING:langchain_text_splitters.base:Created a chunk of size 203, which is longer than the specified 200\n",
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+ "WARNING:langchain_text_splitters.base:Created a chunk of size 201, which is longer than the specified 200\n",
+ "WARNING:langchain_text_splitters.base:Created a chunk of size 250, which is longer than the specified 200\n",
+ "WARNING:langchain_text_splitters.base:Created a chunk of size 325, which is longer than the specified 200\n",
+ "WARNING:langchain_text_splitters.base:Created a chunk of size 242, which is longer than the specified 200\n"
+ ]
+ }
+ ],
+ "source": [
+ "import requests\n",
+ "from langchain.document_loaders import TextLoader\n",
+ "from langchain.text_splitter import CharacterTextSplitter\n",
+ "\n",
+ "url = \"https://raw.githubusercontent.com/hwchase17/chroma-langchain/master/state_of_the_union.txt\"\n",
+ "res = requests.get(url)\n",
+ "with open(\"state_of_the_union.txt\", \"w\") as f:\n",
+ " f.write(res.text)\n",
+ "\n",
+ "# Load the data\n",
+ "loader = TextLoader(\"./state_of_the_union.txt\")\n",
+ "documents = loader.load()\n",
+ "\n",
+ "# Chunk the data\n",
+ "text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=10)\n",
+ "chunks = text_splitter.split_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pgetSLZXEJ2Q"
+ },
+ "source": [
+ "### Setup Retriever\n",
+ "\n",
+ "Retriever utilizes **LanceDB** for scalable vector search and advanced retrieval in RAG, delivering blazing fast performance for searching large sets of embeddings."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "2PYhU_vvJC0P"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain.embeddings import OpenAIEmbeddings\n",
+ "from langchain.vectorstores import LanceDB\n",
+ "import lancedb\n",
+ "\n",
+ "openai_embed = OpenAIEmbeddings()\n",
+ "\n",
+ "# Setup lancedb\n",
+ "db = lancedb.connect(\"/tmp/lancedb\")\n",
+ "table = db.create_table(\n",
+ " \"raga_eval\",\n",
+ " data=[{\"vector\": openai_embed.embed_query(\"Hello World\"), \"text\": \"Hello World\"}],\n",
+ " mode=\"overwrite\",\n",
+ ")\n",
+ "\n",
+ "# Populate vector database\n",
+ "vectorstore = LanceDB.from_documents(\n",
+ " connection=db, documents=chunks, embedding=openai_embed\n",
+ ")\n",
+ "\n",
+ "# Define vectorstore as retriever to enable semantic search\n",
+ "retriever = vectorstore.as_retriever()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9CFnNEfuExj7"
+ },
+ "source": [
+ "### Setup RAG Pipeline with Prompt template"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
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