diff --git a/llama_hub/tools/exa/base.py b/llama_hub/tools/exa/base.py index 0998e7a8c8..a6bed001b0 100644 --- a/llama_hub/tools/exa/base.py +++ b/llama_hub/tools/exa/base.py @@ -19,12 +19,19 @@ class ExaToolSpec(BaseToolSpec): "current_date", ] - def __init__(self, api_key: str, verbose: bool = True) -> None: + def __init__( + self, + api_key: str, + verbose: bool = True, + max_characters: int = 2000, + ) -> None: """Initialize with parameters.""" from exa_py import Exa self.client = Exa(api_key=api_key, user_agent="llama-index") self._verbose = verbose + # max characters for the text field in the search_and_contents function + self._max_characters = max_characters def search( self, @@ -128,7 +135,7 @@ def search_and_retrieve_documents( start_published_date=start_published_date, end_published_date=end_published_date, use_autoprompt=True, - text={"max_characters": 500}, + text={"max_characters": self._max_characters}, ) if self._verbose: print(f"[Exa Tool] Autoprompt: {response.autoprompt_string}") diff --git a/llama_hub/tools/notebooks/exa.ipynb b/llama_hub/tools/notebooks/exa.ipynb index 1fa2a992a6..ddc6b777d4 100644 --- a/llama_hub/tools/notebooks/exa.ipynb +++ b/llama_hub/tools/notebooks/exa.ipynb @@ -14,6 +14,16 @@ "We will import the relevant agents and tools and pass them our keys here:" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a53dd54-e816-43c8-a3c0-069c397c10e0", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install exa_py" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -46,6 +56,7 @@ "\n", "exa_tool = ExaToolSpec(\n", " api_key=os.environ[\"EXA_API_KEY\"],\n", + " # max_characters=2000 # this is the default\n", ")\n", "\n", "exa_tool_list = exa_tool.to_tool_list()\n", @@ -67,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "e64da618-b4ab-42d7-903d-f4eeb624f43c", "metadata": {}, "outputs": [ @@ -75,18 +86,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "[Exa Tool] Autoprompt: Here is a great article about machine learning transformers:\n" + "[Exa Tool] Autoprompt: Here is a link to an article about machine learning transformers:\n" ] }, { "data": { "text/plain": [ - "[Document(id_='d9291e44-f359-466d-ae3b-20e2cdac90ea', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='The famous paper “Attention is all you need” in 2017 changed the way we were thinking about attention. With enough data, matrix multiplications, linear layers, and layer normalization we can perform state-of-the-art-machine-translation.Nonetheless, 2020 was definitely the year of transformers! From natural language now they are into computer vision tasks. How did we go from attention to self-attention? Why does the transformer work so damn well? What are the critical components for its success?R', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n", - " Document(id_='8e140d86-adab-4665-af6d-93d2f2ce7743', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n December 18, 2021\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n 7 minute read\\n \\n \\n \\n \\n \\n \\n \\n \\n \\nSummary\\nTransformers architectures are the hottest thing in supervised and unsupervised learning, achieving SOTA results on natural language processing, vision, audio and multimodal tasks. Their key capability is to capture which elements in a long sequence are worthy of attention, resulting in great summarisation and generative skills. Can we transfer any of these skills to reinforcement learning? The ans', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n", - " Document(id_='835eafc8-fd53-463b-86af-c4d088e4c51d', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='Hands-on TutorialsThis article aims to introduce/refresh the main ideas behind Transformers and to present the latest advancements on using these models for Computer Vision applications.After reading this article you will know…… why Transformers outperformed SOTA models in NLP tasks.… how the Transformer model works at a glance.… which are the main limitations of convolutional models.… how Transformers can overcome limitations in convolutional models.… how novel works use Transformers for Comput', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]" + "[Document(id_='a2eef546-811c-4bbf-bfe0-1c7fd7fe526d', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='b846333f5fffd9a45abfcb16388bb7192676eee95333ff623321c092c24d5bb2', text='\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n December 18, 2021\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n 7 minute read\\n \\n \\n \\n \\n \\n \\n \\n \\n \\nSummary\\nTransformers architectures are the hottest thing in supervised and unsupervised learning, achieving SOTA results on natural language processing, vision, audio and multimodal tasks. Their key capability is to capture which elements in a long sequence are worthy of attention, resulting in great summarisation and generative skills. Can we transfer any of these skills to reinforcement learning? The ans', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n", + " Document(id_='174bd84d-6913-43cd-b193-b807bfd1f6b8', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='cd1041b5e04c3b476f1737818579c80820cb35d0d7573d25c9c48abb5bee008f', text='

Machine Learning Theory

The Model Responsible for your Siri, Alexa and Google Home

Image by Author

Transformers have revolutionized the field', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n", + " Document(id_='87070de8-fc49-4f86-ab8f-6dadf6b4dcc5', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='1379dd71e7af4983def3384acebd57645fb2a0ae4b7fd8c9bba9127a10075d53', text='Hands-on TutorialsThis article aims to introduce/refresh the main ideas behind Transformers and to present the latest advancements on using these models for Computer Vision applications.After reading this article you will know…… why Transformers outperformed SOTA models in NLP tasks.… how the Transformer model works at a glance.… which are the main limitations of convolutional models.… how Transformers can overcome limitations in convolutional models.… how novel works use Transformers for Comput', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]" ] }, - "execution_count": 2, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -97,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "c55a0c7b-4c58-4725-8543-29bb1b7278ed", "metadata": {}, "outputs": [ @@ -115,7 +126,7 @@ " 'id': 'RKL4_dd9kKX_OThZCXo8Yg'}]" ] }, - "execution_count": 3, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -128,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "1fc8665d-ddb8-411f-b187-93a132d19e7a", "metadata": {}, "outputs": [ @@ -136,23 +147,61 @@ "name": "stdout", "output_type": "stream", "text": [ - "[Exa Tool] Autoprompt: Here is a great explanation for machine learning transformers:\n" + "[Exa Tool] Autoprompt: Here is a recent research paper about diffusion models:\n" ] }, { "data": { "text/plain": [ - "[Document(id_='3a0f92c4-acfe-4198-8727-ed8f9b6ba5a2', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='\\n \\n \\n \\n Background: Representation Learning for NLP\\n Enter the Transformer\\n Transformers vs. CNNs \\n Language\\n Vision\\n Multimodal Tasks\\n \\n \\n Breaking down the Transformer \\n Background \\n One-hot encoding \\n Overview\\n Idea\\n Example: Basic Dataset\\n Example: NLP\\n \\n \\n Dot product \\n Algebraic Definition\\n Geometric Definition\\n Properties of the dot product\\n \\n \\n Matrix multiplication as a series of dot products \\n Matrix multiplication as a table lookup\\n \\n \\n First order sequence model\\n Second order sequenc', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]" + "[Document(id_='556c9b58-67d1-470a-b03a-1596d1d6c7dd', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='Diffusion Models Beat GANs on Image Synthesis\\n\\nDate Published: 2021-06-01\\n\\nAuthors:\\nPrafulla Dhariwal, prafulla@openai.com\\nAlex Nichol\\n\\nAbstract\\n\\nWe show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128×128, 4.59 on ImageNet 256×256, and 7.72 on ImageNet 512×512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256×256 and 3.85 on ImageNet 512×512. We release our code at https://github.com/openai/guided-diffusion.\\n\\n1 Introduction\\n\\nOver the past few years, generative models have gained the ability to generate human-like natural language [6] , infinite high-quality synthetic images [5, 28, 51] and highly diverse human speech and music [64, 13] . These models can be used in a variety of ways, such as generating images from text prompts [72, 50] or learning useful feature representations [14, 7] . While these models are already capable of producing realistic images and sound, there is still much room for improvement beyond the current state-of-the-art, and better generative models could have wide-ranging impacts on graphic design, games, music production, and countless other fields.\\n\\nGANs [19] currently hold the state-of-the-art on most image generation tasks [5, 68, 28] as measured by sample quality metrics such as FID [23] , Inception Score [54] and Precision [32] . However, some of these metrics do not fully capture diversity, and it has', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exa_tool.search_and_retrieve_documents(\n", - " \"This is the best explanation for machine learning transformers:\", num_results=1\n", + " \"This is a summary of recent research around diffusion models:\", num_results=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d9325841-9f9a-4b9e-a602-fe542be8f364", + "metadata": {}, + "source": [ + "While `search_and_retrieve_documents` returns raw text from the source document, `search_and_retrieve_highlights` returns relevant curated snippets." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a64b37f6-ec12-45e8-9291-fa2fe51de311", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Exa Tool] Autoprompt: Here is a research paper about diffusion models that you might find useful:\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(id_='695ad95b-edd8-4083-ad16-512203d43f9a', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='On a high level, diffusion models sample from a distribution by reversing a gradual noising process. In particular, sampling starts with noise x T and produces gradually less-noisy samples x T -1 , x T -2 , ... until reaching a final sample x 0 . Each timestep t corresponds to a certain noise level, and x t can be thought of as a mixture of a signal x 0 with some noise where the signal to noise ratio is determined by the timestep t. For the remainder of this paper, we assume that the noise is drawn from a diagonal Gaussian distribution, which works well for natural images and simplifies various derivations. A diffusion model learns to produce a slightly more \"denoised\" x t-1 from x t .', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exa_tool.search_and_retrieve_highlights(\n", + " \"This is a summary of recent research around diffusion models:\", num_results=1\n", ")" ] }, @@ -170,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "id": "9d88c2ee-184a-4371-995b-a086b34db24f", "metadata": {}, "outputs": [], @@ -184,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "id": "f69a53fd-55c4-4e18-8fbe-6a29d5f3cef0", "metadata": {}, "outputs": [ @@ -197,24 +246,24 @@ "Calling function: search with args: {\n", " \"query\": \"best restaurants in Toronto\"\n", "}\n", - "[Exa Tool] Autoprompt: Here is a great restaurant in Toronto to try:\n", - "Got output: [{'title': 'PATOIS • TORONTO', 'url': 'https://www.patoistoronto.com/', 'id': '5EC2l7fbaPoEydNVNwjc-A'}, {'title': 'Portuguese inspired seafood from around the world | Adega Restaurante', 'url': 'https://adegarestaurante.ca/', 'id': 'oQiAWWgzrU-ryPNmgj3UuA'}, {'title': 'Location', 'url': 'https://osteriagiulia.ca/', 'id': 'mpjelsyCOpNipFFI5AoZTQ'}, {'title': 'Enigma Yorkville | Modern European Restaurant in Toronto, ON', 'url': 'https://www.enigmayorkville.com/', 'id': 'jBOC2QfhTfuPjt0YdibEVA'}, {'title': 'Select A Restaurant', 'url': 'https://www.torontopho.com/', 'id': 'Hk6LQnLIZsCH8SYrNFoO2Q'}, {'title': 'Welcome to \"Woodlot Toronto\" restaurant! - Woodlot Toronto', 'url': 'https://woodlottoronto.com/', 'id': 'VUKoFW1gttmNySwHYhlgJw'}, {'title': 'Discover Opus Restaurant', 'url': 'https://www.opusrestaurant.com/#home', 'id': 'mCJBZ9lAxM2jouP0vcdbxA'}, {'title': 'Home', 'url': 'https://www.avelorestaurant.com/', 'id': 'NDfST6oMKpJ0I-VYUf_WHA'}, {'title': 'Home | Quetzal Toronto', 'url': 'https://www.quetzaltoronto.com/', 'id': 'vp3Uxb7Apiz4tE6QBz33lA'}, {'title': 'Oretta | Toronto, ON', 'url': 'https://www.oretta.to/', 'id': 'pSN1mTBx5hQ3R-aeXKrOug'}]\n", + "[Exa Tool] Autoprompt: Here's a great restaurant to try in Toronto:\n", + "Got output: [{'title': 'PATOIS • TORONTO', 'url': 'https://www.patoistoronto.com/', 'id': '5EC2l7fbaPoEydNVNwjc-A'}, {'title': 'Location', 'url': 'https://osteriagiulia.ca/', 'id': 'mpjelsyCOpNipFFI5AoZTQ'}, {'title': 'Enigma Yorkville | Modern European Restaurant in Toronto, ON', 'url': 'https://www.enigmayorkville.com/', 'id': 'jBOC2QfhTfuPjt0YdibEVA'}, {'title': 'GOA | by Hemant Bhagwani', 'url': 'https://www.goatoronto.ca/', 'id': 'e8sCvTX5NVVbwzoOr0o6aw'}, {'title': 'Portuguese inspired seafood from around the world | Adega Restaurante', 'url': 'https://adegarestaurante.ca/', 'id': 'oQiAWWgzrU-ryPNmgj3UuA'}, {'title': 'Home', 'url': 'https://www.avelorestaurant.com/', 'id': 'NDfST6oMKpJ0I-VYUf_WHA'}, {'title': 'PAI Northern Thai Kitchen', 'url': 'https://www.paitoronto.com/', 'id': 'aP7DB4WaZWCfqoGNeqJ4Kw'}, {'title': 'Oretta | Toronto, ON', 'url': 'https://www.oretta.to/', 'id': 'pSN1mTBx5hQ3R-aeXKrOug'}, {'title': 'Welcome to \"Woodlot Toronto\" restaurant! - Woodlot Toronto', 'url': 'https://woodlottoronto.com/', 'id': 'VUKoFW1gttmNySwHYhlgJw'}, {'title': 'Discover Opus Restaurant', 'url': 'https://www.opusrestaurant.com/#home', 'id': 'mCJBZ9lAxM2jouP0vcdbxA'}]\n", "========================\n", "\n", "Here are some of the best restaurants in Toronto:\n", "\n", "1. [PATOIS • TORONTO](https://www.patoistoronto.com/)\n", - "2. [Adega Restaurante](https://adegarestaurante.ca/)\n", - "3. [Osteria Giulia](https://osteriagiulia.ca/)\n", - "4. [Enigma Yorkville](https://www.enigmayorkville.com/)\n", - "5. [Toronto Pho](https://www.torontopho.com/)\n", - "6. [Woodlot Toronto](https://woodlottoronto.com/)\n", - "7. [Opus Restaurant](https://www.opusrestaurant.com/#home)\n", - "8. [Alo Restaurant](https://www.alorestaurant.com/)\n", - "9. [Quetzal Toronto](https://www.quetzaltoronto.com/)\n", - "10. [Oretta](https://www.oretta.to/)\n", + "2. [Osteria Giulia](https://osteriagiulia.ca/)\n", + "3. [Enigma Yorkville | Modern European Restaurant](https://www.enigmayorkville.com/)\n", + "4. [GOA | by Hemant Bhagwani](https://www.goatoronto.ca/)\n", + "5. [Adega Restaurante](https://adegarestaurante.ca/)\n", + "6. [Alo Restaurant](https://www.alorestaurant.com/)\n", + "7. [PAI Northern Thai Kitchen](https://www.paitoronto.com/)\n", + "8. [Oretta](https://www.oretta.to/)\n", + "9. [Woodlot Toronto](https://woodlottoronto.com/)\n", + "10. [Opus Restaurant](https://www.opusrestaurant.com/#home)\n", "\n", - "Please note that these are just a few recommendations and there are many more amazing restaurants in Toronto to explore.\n" + "Please note that these are just a few recommendations and there are many more amazing restaurants in Toronto.\n" ] } ], @@ -224,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "id": "f44035e9-27ab-47b7-abc5-cf2fe5d1482f", "metadata": {}, "outputs": [ @@ -233,15 +282,38 @@ "output_type": "stream", "text": [ "Added user message to memory: tell me more about Osteria Giulia\n", - "Osteria Giulia is a restaurant located in Toronto, Ontario. It offers a unique dining experience with a focus on Italian cuisine. The restaurant is known for its warm and inviting atmosphere, making it a popular choice for both casual dining and special occasions.\n", + "=== Calling Function ===\n", + "Calling function: retrieve_documents with args: {\n", + " \"ids\": [\"mpjelsyCOpNipFFI5AoZTQ\"]\n", + "}\n", + "Got output: Error: 'results'\n", + "========================\n", + "\n", + "=== Calling Function ===\n", + "Calling function: search with args: {\n", + " \"query\": \"Osteria Giulia Toronto\"\n", + "}\n", + "[Exa Tool] Autoprompt: Here's a great Italian restaurant in Toronto called Osteria Giulia:\n", + "Got output: [{'title': 'Giulietta', 'url': 'https://giu.ca/', 'id': 'GWjEUcgDP26STPskrgMM5g'}, {'title': 'Location', 'url': 'https://osteriagiulia.ca/', 'id': 'mpjelsyCOpNipFFI5AoZTQ'}, {'title': 'Gusto 501', 'url': 'https://www.restaurantji.com/on/toronto/gusto-501-/', 'id': 'Oa5NvNXMnweapgE1tO8WYQ'}, {'title': 'Menu - Gia Restaurant', 'url': 'https://giarestaurant.ca/', 'id': '35CymOCvS59-Lmdilq5XLw'}, {'title': 'Osteria Ilaria', 'url': 'https://www.osteriailaria.com/', 'id': 'SN6NKvpXluVgWCv5GPI88Q'}, {'title': 'Osteria Fortunato', 'url': 'https://osteriafortunato.ca/', 'id': 'MC6pGvtQMnb6IueV5navRg'}, {'title': 'King West | Oretta | Toronto, ON', 'url': 'https://www.oretta.to/kingwest/home', 'id': 'qddpWk4WMhFHzwQNjX_M9w'}, {'title': 'Oretta | Toronto, ON', 'url': 'https://www.oretta.to/', 'id': 'pSN1mTBx5hQ3R-aeXKrOug'}, {'title': 'Giulietta - Pizzeria Napoletana', 'url': 'https://www.giuliettapizzeria.ca/', 'id': '4Zj0Mk2X_FZ3BdQvPw79RQ'}, {'title': None, 'url': 'https://osteriapazzia.blogspot.com/', 'id': 'VhpAyEf3ieGxM0CsCdw6WA'}]\n", + "========================\n", + "\n", + "Osteria Giulia is an Italian restaurant located in Toronto. Unfortunately, I couldn't find specific information about Osteria Giulia in the available search results. However, I found some related information that might be of interest:\n", "\n", - "The menu at Osteria Giulia features a variety of traditional Italian dishes, prepared with fresh and high-quality ingredients. From homemade pasta and risotto to wood-fired pizzas and seafood, there is something to satisfy every palate. The restaurant also offers a selection of fine wines to complement the flavors of the dishes.\n", + "1. [Giulietta](https://giu.ca/): Giulietta is another Italian restaurant in Toronto that you might want to check out.\n", "\n", - "One of the highlights of Osteria Giulia is its commitment to using locally sourced ingredients. The restaurant takes pride in supporting local farmers and suppliers, ensuring that each dish is made with the freshest and most sustainable ingredients available.\n", + "2. [Gusto 501](https://www.restaurantji.com/on/toronto/gusto-501-/): Gusto 501 is a restaurant located in Toronto that offers Italian cuisine.\n", "\n", - "In addition to its delicious food, Osteria Giulia provides excellent service, with a knowledgeable and friendly staff that is dedicated to creating a memorable dining experience for guests. The restaurant's cozy and intimate setting adds to the overall ambiance, making it a great choice for a romantic dinner or a gathering with friends and family.\n", + "3. [Gia Restaurant](https://giarestaurant.ca/): Gia Restaurant is another Italian restaurant in Toronto that you might want to explore.\n", "\n", - "Whether you're looking for a classic Italian dish or a modern twist on traditional flavors, Osteria Giulia offers a culinary experience that is sure to delight. It is a must-visit destination for food lovers in Toronto who appreciate authentic Italian cuisine in a welcoming and charming setting.\n" + "4. [Osteria Ilaria](https://www.osteriailaria.com/): Osteria Ilaria is an Italian restaurant located in Vancouver, British Columbia.\n", + "\n", + "5. [Osteria Fortunato](https://osteriafortunato.ca/): Osteria Fortunato is an Italian restaurant located in Toronto.\n", + "\n", + "6. [Oretta](https://www.oretta.to/kingwest/home): Oretta is an Italian restaurant located in Toronto.\n", + "\n", + "7. [Giulietta - Pizzeria Napoletana](https://www.giuliettapizzeria.ca/): Giulietta is a pizzeria in Toronto that specializes in Neapolitan-style pizza.\n", + "\n", + "Please note that the information provided is based on the search results and may not directly pertain to Osteria Giulia. It is recommended to visit the official website or contact the restaurant directly for more accurate and up-to-date information.\n" ] } ], @@ -258,12 +330,114 @@ "\n", "The above example shows the core uses of the Exa tool. We can easily retrieve a clean list of links related to a query, and then we can fetch the content of the article as a cleaned up html extract. Alternatively, the search_and_retrieve_documents tool directly returns the documents from our search result.\n", "\n", - "We can see that the content of the articles is somewhat long compared to current LLM context windows, and so to allow retrieval and summary of many documents we will set up and use another tool from LlamaIndex that allows us to load text into a VectorStore, and query it for retrieval. This is where the `search_and_retrieve_documents` tool become particularly useful. The Agent can make a single query to retrieve a large number of documents, using a very small number of tokens, and then make queries to retrieve specific information from the documents." + "We can see that the content of the articles is somewhat long and may overflow current LLM context windows. \n", + "\n", + "1. Use `search_and_retrieve_highlights`: This is an endpoint offered by Exa that directly retrieves relevant highlight snippets from the web, instead of full web articles. As a result you don't need to worry about indexing/chunking offline yourself!\n", + "\n", + "2. Wrap `search_and_retrieve_documents` with `LoadAndSearchToolSpec`: We set up and use a \"wrapper\" tool from LlamaIndex that allows us to load text from any tool into a VectorStore, and query it for retrieval. This is where the `search_and_retrieve_documents` tool become particularly useful. The Agent can make a single query to retrieve a large number of documents, using a very small number of tokens, and then make queries to retrieve specific information from the documents." + ] + }, + { + "cell_type": "markdown", + "id": "5b8c51fc-8a22-408e-94c9-14248bad61c1", + "metadata": {}, + "source": [ + "### 1. Using `search_and_retrieve_highlights`\n", + "\n", + "The easiest is to just use `search_and_retrieve_highlights` from Exa. This is essentially a \"web RAG\" endpoint - they handle chunking/embedding under the hood." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "197d241b-cd53-4038-a824-d493c69166b6", + "metadata": {}, + "outputs": [], + "source": [ + "tools = exa_tool.to_tool_list(\n", + " spec_functions=[\n", + " \"search_and_retrieve_highlights\", \n", + " \"current_date\"\n", + " ]\n", + ")" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, + "id": "1c412501-4fa3-4bb5-a324-075e809737d9", + "metadata": {}, + "outputs": [], + "source": [ + "agent = OpenAIAgent.from_tools(\n", + " tools,\n", + " verbose=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "242c779e-9dbc-4aec-8838-c152bf8f304b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Added user message to memory: Tell me more about the recent news on semiconductors\n", + "=== Calling Function ===\n", + "Calling function: current_date with args: {}\n", + "Got output: 2024-01-25\n", + "========================\n", + "\n", + "=== Calling Function ===\n", + "Calling function: search_and_retrieve_highlights with args: {\n", + " \"query\": \"semiconductors\",\n", + " \"num_results\": 5,\n", + " \"start_published_date\": \"2024-01-01\",\n", + " \"end_published_date\": \"2024-01-25\"\n", + "}\n", + "[Exa Tool] Autoprompt: \"Here is an informative resource about semiconductors:\n", + "Got output: [Document(id_='e72b143e-df3a-408e-b790-24a3931b18da', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='

Semiconductors are crucial for multiple applications in electronics, communications, automotive, agriculture, healthcare, finance, and energy. After a slight turmoil phase during 2023, it is expected that the chip industry will exhibit a strong recovery and thrive this year and beyond.

According to the latest forecast by the World Semiconductor Trade Statistics (WSTS), the global semiconductor market is anticipated to reach a valuation of $588 billion in 2024, indicating a growth of 13.1%. This growth will be primarily fuelled by the Memory sector, which is on track to surge to nearly $130 billion this year, up an impressive 40% from the prior year.

The U.S.-China chips rivalry has pushed the economy to take some serious steps to advance in-house chip manufacturing.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), Document(id_='55de1b8b-d50a-4226-8f12-d03523681154', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text=\" VC investment in the semiconductor vertical has been on a downward trajectory. Despite major policy shifts and renewed public interest, it declined in 2023 to a total of $10.3 billion, compared with 2022's $12.8 billion and 2021’s peak of $16.1 billion. With China eating up a record amount of VC dollars, what’s in store for the vertical is up in the air, but our latest Emerging Tech Research dives into the key trends and opportunities around semiconductors. By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts.\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), Document(id_='9cb57f41-bb3b-4f5e-bbc3-2c53955da825', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='A band gap is a material that can be turned on and off when an electric field is applied to it, which is how all transistors and silicon electronics work. The major question in graphene electronics research was how to switch it on and off so it can work like silicon.

But to make a functional transistor, a semiconducting material must be greatly manipulated, which can damage its properties. To prove that their platform could function as a viable semiconductor, the team needed to measure its electronic properties without damaging it.

They put atoms on the graphene that \"donate\" electrons to the system — a technique called doping, used to see whether the material was a good conductor. It worked without damaging the material or its properties.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), Document(id_='c7f49648-2dcf-4e6d-85c0-8e44e8f7ec3f', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text=\"These companies aren't talked about as much as Nvidia, AMD, or Intel, but they play an increasingly important role in the industry. *Stock prices used were from the trading day of Jan. 22, 2024. The video was published on Jan. 23, 2024.\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), Document(id_='aba45f22-8cbb-4309-8dc3-bf6f1b88bfc4', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='\">photolithography machines

Different types of semiconductors perform different roles. The key types are logic chips, which interpret and perform instructions; memory chips, which store information; and analog chips, which convert real-world data into digital data. Central processing and graphics processing units are the most recognizable logic chips due to their inclusion in the laptops and desktops that normal people interact with at home. However, field-programmable gate arrays and application-specific integrated circuits are key sources of revenue for chip manufacturers.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]\n", + "========================\n", + "\n", + "Response: Here are some recent news highlights on semiconductors:\n", + "\n", + "1. The global semiconductor market is expected to reach a valuation of $588 billion in 2024, indicating a growth of 13.1%. This growth will be primarily fueled by the Memory sector, which is projected to surge to nearly $130 billion this year, up 40% from the previous year. [Source: World Semiconductor Trade Statistics (WSTS)](https://www.wsts.org/76/Recent-News-Release)\n", + "\n", + "2. VC investment in the semiconductor vertical has been on a downward trajectory. In 2023, it declined to a total of $10.3 billion, compared to $12.8 billion in 2022 and a peak of $16.1 billion in 2021. The future trends and opportunities in the semiconductor industry are explored in the latest Emerging Tech Research report. [Source: Emerging Tech Research]\n", + "\n", + "3. Researchers have made progress in graphene electronics research by finding a way to switch it on and off like silicon. Graphene is a promising material for semiconductors, but manipulating it without damaging its properties has been a challenge. By using a technique called doping, the researchers were able to measure the electronic properties of graphene without damaging it. [Source: Research Study]\n", + "\n", + "4. While companies like Nvidia, AMD, and Intel are well-known in the semiconductor industry, there are other companies playing an increasingly important role. These companies may not receive as much attention, but they contribute significantly to the industry. [Source: Market Analysis]\n", + "\n", + "5. Different types of semiconductors perform different roles. Logic chips interpret and perform instructions, memory chips store information, and analog chips convert real-world data into digital data. Central processing units (CPUs) and graphics processing units (GPUs) are examples of logic chips, while field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs) are key sources of revenue for chip manufacturers. [Source: Semiconductor Overview]\n", + "\n", + "Please note that these are just highlights, and for more detailed information, you can refer to the respective sources provided.\n" + ] + } + ], + "source": [ + "response = agent.chat(\n", + " \"Tell me more about the recent news on semiconductors\"\n", + ")\n", + "print(f\"Response: {str(response)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "96c801b9-7f61-470b-9d05-c00622d5fbd7", + "metadata": {}, + "source": [ + "### 2. Using `LoadAndSearchToolSpec`\n", + "\n", + "Here we wrap the `search_and_retrieve_documents` functionality with the `load_and_search_tool_spec`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, "id": "a017cc61-1696-4a03-8d09-a628f9049cfd", "metadata": {}, "outputs": [], @@ -286,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "id": "e4f81bd3-a5b9-452c-93f4-91d16c4c0df1", "metadata": {}, "outputs": [ @@ -294,9 +468,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[Exa Tool] Autoprompt: \"Check out this article on the best explanation for machine learning transformers:\n", - "A transformer is a type of architecture used in Natural Language Processing (NLP) tasks. It is a breakthrough model that overcame the limitations of previous seq-to-seq models like RNNs in capturing long-term dependencies in text. The transformer architecture has become the foundation for various revolutionary models such as BERT, GPT, and T5, which have been widely used in NLP tasks.\n", - "Vaswani et al. wrote the first paper on transformers.\n" + "[Exa Tool] Autoprompt: Here is a great article on the best explanation for machine learning transformers:\n", + "Transformers are semi-supervised machine learning models that are primarily used with text data. They have replaced recurrent neural networks in natural language processing tasks. Transformers are designed to work on sequence data and can take an input sequence and generate an output sequence one element at a time. They consist of an encoder that operates on the input sequence and a decoder that operates on the target output sequence during training. Transformers have revolutionized natural language processing in recent years and have been used for tasks such as machine translation.\n", + "The first paper on transformers was written by the authors of the paper \"Attention is all you need\".\n" ] } ], @@ -318,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "id": "3a893f26-dbb6-4b72-9795-702eaf749564", "metadata": {}, "outputs": [], @@ -332,7 +506,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "id": "5835d058-da9c-4d42-9d2a-941c73b88a51", "metadata": {}, "outputs": [ @@ -344,10 +518,10 @@ "=== Calling Function ===\n", "Calling function: search_and_retrieve_documents with args: {\n", " \"query\": \"news on superconductors\",\n", - " \"start_published_date\": \"2022-09-01\",\n", - " \"end_published_date\": \"2022-09-30\"\n", + " \"start_published_date\": \"2022-10-01\",\n", + " \"end_published_date\": \"2022-10-31\"\n", "}\n", - "[Exa Tool] Autoprompt: Here is a recent article about superconductors:\n", + "[Exa Tool] Autoprompt: Here is a recent news article on superconductors:\n", "Got output: Content loaded! You can now search the information using read_search_and_retrieve_documents\n", "========================\n", "\n", @@ -355,10 +529,10 @@ "Calling function: read_search_and_retrieve_documents with args: {\n", " \"query\": \"news on superconductors\"\n", "}\n", - "Got output: The journal Nature has retracted a ground-breaking paper claiming to show the first room-temperature superconductor. The retraction was made due to concerns around the data analysis and allegations of manipulated results. Superconductors are materials that exhibit no electrical resistance and have various applications, such as NMR machines, quantum computing, and particle accelerators. Currently, all superconducting materials require very cold temperatures or extremely high pressures.\n", + "Got output: There have been recent developments in the field of superconductors. A paper describing a material that exhibited superconductivity at a temperature of 15° Celsius was retracted by the journal Nature. However, a new experiment performed shortly after the retraction supports the claim of achieving superconductivity at this temperature. Superconductors are materials that can carry electricity with no resistance, making them highly efficient for transmitting energy. Currently, superconducting materials need to be cooled to very low temperatures, but the potential for room-temperature superconductivity has been a long-standing goal.\n", "========================\n", "\n", - "In the last month, there have been developments in the field of superconductors. One notable news is the retraction of a ground-breaking paper published in the journal Nature. The paper claimed to show the discovery of the first room-temperature superconductor. However, due to concerns about data analysis and allegations of manipulated results, the paper has been retracted. Superconductors are materials that exhibit no electrical resistance and have various applications in fields such as NMR machines, quantum computing, and particle accelerators. Currently, all superconducting materials require very cold temperatures or extremely high pressures.\n" + "In the last month, there have been some significant developments in the field of superconductors. One notable event was the retraction of a paper published in the journal Nature that described a material exhibiting superconductivity at a temperature of 15°C. However, shortly after the retraction, a new experiment was conducted that supported the claim of achieving superconductivity at this temperature. Superconductors are materials that can conduct electricity without any resistance, making them highly efficient for energy transmission. Currently, superconducting materials require extremely low temperatures, but the pursuit of room-temperature superconductivity has been a longstanding goal in the field.\n" ] } ], @@ -384,9 +558,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "llama_hub_jeffzwang", "language": "python", - "name": "python3" + "name": "llama_hub_jeffzwang" }, "language_info": { "codemirror_mode": {