From c1070d45ea867fe80abf12b1096b224418c2d6c5 Mon Sep 17 00:00:00 2001 From: Harish Kotra Date: Wed, 8 Jan 2025 15:10:22 +0530 Subject: [PATCH] added authentication page to include Gaia API Keys information and documentation --- .../agent-frameworks-and-apps/index.html | 4 +- build/1.0.0/category/creator-guide/index.html | 4 +- .../category/domain-operator-guide/index.html | 4 +- .../index.html | 4 +- build/1.0.0/category/how-do-i-/index.html | 4 +- .../1.0.0/category/knowledge-bases/index.html | 4 +- .../category/node-operator-guide/index.html | 4 +- build/1.0.0/category/tutorial/index.html | 4 +- build/1.0.0/category/user-guide/index.html | 4 +- .../creator-guide/finetune/intro/index.html | 4 +- .../finetune/llamacpp/index.html | 4 +- .../knowledge/concepts/index.html | 4 +- .../creator-guide/knowledge/csv/index.html | 4 +- .../knowledge/firecrawl/index.html | 4 +- .../knowledge/markdown/index.html | 4 +- .../creator-guide/knowledge/pdf/index.html | 4 +- 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4 +- .../user-guide/apps/openwebui/index.html | 4 +- .../1.0.0/user-guide/apps/stockbot/index.html | 4 +- .../apps/translation-agent/index.html | 4 +- build/1.0.0/user-guide/apps/zed/index.html | 4 +- build/1.0.0/user-guide/mynode/index.html | 4 +- build/1.0.0/user-guide/nodes/index.html | 4 +- build/404.html | 4 +- .../agent-integrations/agent-zero/index.html | 4 +- .../anything_llm/index.html | 4 +- build/agent-integrations/codegpt/index.html | 4 +- build/agent-integrations/continue/index.html | 4 +- build/agent-integrations/cursor/index.html | 4 +- build/agent-integrations/dify/index.html | 4 +- .../flowiseai-tool-call/index.html | 4 +- build/agent-integrations/flowiseai/index.html | 4 +- .../agent-integrations/gpt-planner/index.html | 4 +- build/agent-integrations/index.html | 4 +- build/agent-integrations/intro/index.html | 4 +- .../agent-integrations/llamacoder/index.html | 4 +- .../llamaedgebook/index.html | 4 +- .../agent-integrations/llamaparse/index.html | 4 +- .../agent-integrations/llamatutor/index.html | 4 +- build/agent-integrations/lobechat/index.html | 4 +- build/agent-integrations/obsidian/index.html | 4 +- build/agent-integrations/openwebui/index.html | 4 +- build/agent-integrations/stockbot/index.html | 4 +- .../translation-agent/index.html | 4 +- build/agent-integrations/zed/index.html | 4 +- ...eated-bbfb92a8c7df41f38a6ac3f7d448e757.png | Bin 0 -> 132362 bytes ...i-key-5911b944e10616af0e31670920beff5c.png | Bin 0 -> 108101 bytes ...r-api-12fe9b183d793b1b6c67c2836b49c790.png | Bin 0 -> 1635762 bytes ...-keys-9796ca33d6aa6b8ce205bf388c3e07eb.png | Bin 0 -> 87402 bytes build/assets/js/22dd74f7.2a9f9a5e.js | 1 + build/assets/js/22dd74f7.5a0afbe7.js | 1 - build/assets/js/588bd741.6798061e.js | 1 - build/assets/js/588bd741.9c01509d.js | 1 + build/assets/js/8b5e2407.25614a42.js | 1 - build/assets/js/8b5e2407.457ebad6.js | 1 + build/assets/js/a77ba7fa.904a652e.js | 1 + build/assets/js/main.8cf9f2f7.js | 2 + ...CENSE.txt => main.8cf9f2f7.js.LICENSE.txt} | 0 build/assets/js/main.b7abaaf1.js | 2 - build/assets/js/runtime~main.9f603006.js | 1 - build/assets/js/runtime~main.a303bb81.js | 1 + .../advanced-deployment-options/index.html | 6 +- build/domain-guide/quick-start/index.html | 4 +- build/domain-operator-guide/index.html | 4 +- build/faqs/index.html | 4 +- .../aws/index.html | 6 +- .../cuda/index.html | 6 +- .../docker/index.html | 6 +- .../local/index.html | 6 +- .../multiple/index.html | 6 +- .../protect/index.html | 6 +- .../getting-started/api-reference/index.html | 6 +- .../getting-started/authentication/index.html | 53 ++++++++++++++++++ build/getting-started/cli-options/index.html | 6 +- build/getting-started/customize/index.html | 6 +- build/getting-started/index.html | 6 +- build/getting-started/install/index.html | 6 +- build/getting-started/mynode/index.html | 8 +-- build/getting-started/quick-start/index.html | 8 +-- build/getting-started/register/index.html | 6 +- .../system-requirements/index.html | 6 +- .../troubleshooting/index.html | 6 +- build/getting-started/uninstall/index.html | 6 +- .../getting-started/what-is-a-node/index.html | 6 +- build/guides-and-tutorials/index.html | 4 +- build/index.html | 4 +- build/intro/index.html | 4 +- build/knowledge-bases/how-to/csv/index.html | 4 +- .../how-to/firecrawl/index.html | 4 +- build/knowledge-bases/how-to/index.html | 4 +- .../how-to/markdown/index.html | 4 +- build/knowledge-bases/how-to/pdf/index.html | 4 +- build/knowledge-bases/how-to/text/index.html | 4 +- build/knowledge-bases/index.html | 4 +- build/knowledge-bases/intro/index.html | 4 +- build/knowledge-bases/web-tool/index.html | 4 +- build/litepaper/index.html | 4 +- build/markdown-page/index.html | 4 +- build/nodes/index.html | 4 +- build/search-index.json | 2 +- build/search/index.html | 4 +- build/sitemap.xml | 2 +- build/tutorial/coinbase/index.html | 4 +- build/tutorial/concepts/index.html | 4 +- build/tutorial/eliza/index.html | 4 +- build/tutorial/llamacpp/index.html | 4 +- .../prompt-engineering-tool/index.html | 4 +- build/tutorial/tool-call/index.html | 4 +- build/tutorial/translator-agent/index.html | 4 +- docs/getting-started/api-key-created.png | Bin 0 -> 132362 bytes docs/getting-started/authentication.md | 45 +++++++++++++++ docs/getting-started/create-api-key.png | Bin 0 -> 108101 bytes .../getting-started/settings-for-api-keys.png | Bin 0 -> 87402 bytes docs/getting-started/settings-for-api.png | Bin 0 -> 1635762 bytes 153 files changed, 385 insertions(+), 286 deletions(-) create mode 100644 build/assets/images/api-key-created-bbfb92a8c7df41f38a6ac3f7d448e757.png create mode 100644 build/assets/images/create-api-key-5911b944e10616af0e31670920beff5c.png create mode 100644 build/assets/images/settings-for-api-12fe9b183d793b1b6c67c2836b49c790.png create mode 100644 build/assets/images/settings-for-api-keys-9796ca33d6aa6b8ce205bf388c3e07eb.png create mode 100644 build/assets/js/22dd74f7.2a9f9a5e.js 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Exit ...","u":"/1.0.0/node-guide/troubleshooting","h":"#failed-to-start-the-node-with-an-error-message-port-8080-is-in-use-exit-","p":796},{"i":803,"t":"Load library failed: libgomp.so.1: cannot open shared object file: No such file or directory","u":"/1.0.0/node-guide/troubleshooting","h":"#load-library-failed-libgompso1-cannot-open-shared-object-file-no-such-file-or-directory","p":796},{"i":805,"t":"Failed to remove the default collection","u":"/1.0.0/node-guide/troubleshooting","h":"#failed-to-remove-the-default-collection","p":796},{"i":807,"t":"File I/O error","u":"/1.0.0/node-guide/troubleshooting","h":"#file-io-error","p":796},{"i":809,"t":"The \"Failed to open the file\" Error","u":"/1.0.0/node-guide/troubleshooting","h":"#the-failed-to-open-the-file-error","p":796},{"i":811,"t":"The \"Too many open files\" Error on macOS","u":"/1.0.0/node-guide/troubleshooting","h":"#the-too-many-open-files-error-on-macos","p":796},{"i":813,"t":"Permission denied when use the installer script 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could fine-tune an open-source LLM to Teach it to follow conversations. Teach it to respect and follow instructions. Make it refuse to answer certain questions. Give it a specific \"speaking\" style. Make it response in certain formats (e.g., JSON). Give it focus on a specific domain area. Teach it certain knowledge. To do that, you need to create a set of question and answer pairs to show the model the prompt and the expected response. Then, you can use a fine-tuning tool to perform the training and make the model respond the expected answer for each question.","s":"Fine-tune LLMs","u":"/1.0.0/creator-guide/finetune/intro","h":"","p":550},{"i":553,"t":"The LLM app requires both long-term and short-term memories. Long-term memory includes factual knowledge, historical facts, background stories etc. They are best added to the context as complete chapters instead of small chunks of text to maintain the internal consistency of the knowledge. RAG is an important technique to inject contextual knowledge into an LLM application. It improves accuracy and reduces the hallucination of LLMs. An effective RAG application combines real-time and user-specific short-term memory (chunks) with stable long-term memory (chapters) in the prompt context. Since the application's long-term memory is stable (even immutable), we package it in a vector database tightly coupled with the LLM. The client app assembles the short-term memory in the prompt and is supplemented with the long-term memory on the LLM server. We call the approach \"server-side RAG\". The long context length supported by modern LLMs are especially well-suited for long-term knowledge that are best represented by chapters of text. A Gaia node is an OpenAI compatible LLM service that is grounded by long-term knowledge on the server side. The client application can simply chat with it or provide realtime / short-term memory since the LLM is already aware of the domain or background. For example, if you ask ChatGPT the question What is Layer 2, the answer is that Layer 2 is a concept from the computer network. However, if you ask a blockchain person, they answer that Layer 2 is a way to scale the original Ethereum network. That's the difference between a generic LLM and knowledge-supplemented LLMs. We will cover the external knowledge preparation and how a knowledge-supplemented LLM completes a conversation. If you have learned how a RAG application works, go to Build a RAG application with Gaia to start building one. Create embeddings for your own knowledge as the long-term memory. Lifecycle of a user query on a knowledge-supplemented LLM. For this solution, we will use a chat model like Llama-3-8B for generating responses to the user. a text embedding model like nomic-embed-text for creating and retrieving embeddings. a Vector DB like Qdrant for storing embeddings.","s":"Gaia nodes with long-term knowledge","u":"/1.0.0/creator-guide/knowledge/concepts","h":"","p":552},{"i":555,"t":"The first step is to create embeddings for our knowledge base and store the embeddings in a vector DB. First of all, we split the long text into sections (i.e, chunks). All LLMs have a maximum context length. The model can't read the context if the text is too long. The most used rule for a Gaia node is to put the content in one chapter together. Remember, insert a blank line between two chunks. You can also use other algorithms to chunk your text. After chunking the document, we can convert these chunks into embeddings leveraging the embedding model. The embedding model is trained to create embeddings based on text and search for similar embeddings. We will use the latter function in the process of user query. Additionally, we will need a vector DB to store the embeddings so that we can retrieve these embeddings quickly at any time. On a Gaia node, we will get a database snapshot with the embeddings to use at last. Check out how to create your embeddings using Gaia web tool, from a plain text file, and from a markdown file.","s":"Workflow for creating knowledge embeddings","u":"/1.0.0/creator-guide/knowledge/concepts","h":"#workflow-for-creating-knowledge-embeddings","p":552},{"i":557,"t":"Next, let's learn the lifecycle of a user query on a knowledge-supplemented LLM. We will take a Gaia Node with Gaia knowledge as an example.","s":"Lifecycle of a user query on a knowledge-supplemented LLM","u":"/1.0.0/creator-guide/knowledge/concepts","h":"#lifecycle-of-a-user-query-on-a-knowledge-supplemented-llm","p":552},{"i":559,"t":"when you send a question in human language to the node, the embedding model will first convert your question to embedding.","s":"Ask a question","u":"/1.0.0/creator-guide/knowledge/concepts","h":"#ask-a-question","p":552},{"i":561,"t":"Then, the embedding model will search all the embeddings stored in the Qdrant vector DB and retrieve the embeddings that are similar to the question embeddings.","s":"Retrieve similar embeddings","u":"/1.0.0/creator-guide/knowledge/concepts","h":"#retrieve-similar-embeddings","p":552},{"i":563,"t":"The embedding node will return the retrieved embeddings to the chat model. The chat model will use the retrieved embeddings plus your input questions as context to answer your queries finally.","s":"Response to the user query","u":"/1.0.0/creator-guide/knowledge/concepts","h":"#response-to-the-user-query","p":552},{"i":565,"t":"In this section, we will discuss how to create a vector collection snapshot from a Web URL. First, we will parse the URL to a structured markdown file. Then, we will follow the steps from Knowledge base from a markdown file to create embedding for your URL.","s":"Knowledge base from a URL","u":"/1.0.0/creator-guide/knowledge/firecrawl","h":"","p":564},{"i":567,"t":"Firecrawl can crawl and convert any website into LLM-ready markdown or structured data. It also supports crawling a URL and all accessible subpages. To use Firecrawl, you need to sign up on Firecrawl and get an API key. First, install the dependencies. We are assuming that you already have Node.JS 20+ installed. git clone https://github.com/JYC0413/firecrawl-integration.git cd firecrawl-integration npm install Then, export the API key in the terminal. export FIRECRAWL_KEY=\"your_api_key_here\" next, we can use the following command line to run the service. node crawlWebToMd.js After the application is running successfully, you will see the prompt appear on the Terminal. You can type your URL in the terminal right now. Here we have two choices. Multiple pages: input your link with / at the end, the program will crawl and convert the page and its subpages to one single markdown file. This way will cost lots of API token usage. One single page: input your link without / at the end. the program will crawl and convert the current page to one single markdown file. The output markdown file will be located in this folder named output.md.","s":"Parse the URL content to a markdown file","u":"/1.0.0/creator-guide/knowledge/firecrawl","h":"#parse-the-url-content-to-a-markdown-file","p":564},{"i":569,"t":"Please follow the tutorial Knowledge base from a markdown file to convert your markdown file to a snapshot of embeddings that can be imported into a GaiaNet node.","s":"Create embeddings from the markdown files","u":"/1.0.0/creator-guide/knowledge/firecrawl","h":"#create-embeddings-from-the-markdown-files","p":564},{"i":571,"t":"In this section, we will discuss how to create a vector collection snapshot for optimal retrieval of long-form text documents. The approach is to create two columns of text in a CSV file. The first column is the long-form source text from the knowledge document, such as a book chapter or a markdown section. The long-form source text is difficult to search. The second column is a \"search-friendly\" summary of the source text. It could contain a list of questions that can be answered by the first column source text. We will create a vector snapshot where each vector is computed from the summary text (second column), but the retrieved source text for that vector is from the first column. The snapshot file can then be loaded by a Gaia node as its knowledge base. We have a simple Python script to build properly formatted CSV files from a set of articles or chapters. See how it works.","s":"Knowledge base from source / summary pairs","u":"/1.0.0/creator-guide/knowledge/csv","h":"","p":570},{"i":573,"t":"Install the WasmEdge Runtime, the cross-platform LLM runtime. curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install_v2.sh | bash -s Download an embedding model. curl -LO https://huggingface.co/gaianet/Nomic-embed-text-v1.5-Embedding-GGUF/resolve/main/nomic-embed-text-v1.5.f16.gguf The embedding model is a special kind of LLM that turns sentences into vectors. The vectors can then be stored in a vector database and searched later. When the sentences are from a body of text that represents a knowledge domain, that vector database becomes our RAG knowledge base.","s":"Prerequisites","u":"/1.0.0/creator-guide/knowledge/csv","h":"#prerequisites","p":570},{"i":575,"t":"By default, we use Qdrant as the vector database. You can start a Qdrant instance by starting a Gaia node with a knowledge snapshot. note Or, you can start a Qdrant server using Docker. The following command starts it in the background. mkdir qdrant_storage mkdir qdrant_snapshots nohup docker run -d -p 6333:6333 -p 6334:6334 \\ -v $(pwd)/qdrant_storage:/qdrant/storage:z \\ -v $(pwd)/qdrant_snapshots:/qdrant/snapshots:z \\ qdrant/qdrant","s":"Start a vector database","u":"/1.0.0/creator-guide/knowledge/csv","h":"#start-a-vector-database","p":570},{"i":577,"t":"Delete the default collection if it exists. curl -X DELETE 'http://localhost:6333/collections/default' Create a new collection called default. Notice that it is 768 dimensions. That is the output vector size of the embedding model nomic-embed-text-v1.5. If you are using a different embedding model, you should use a dimension that fits the model. curl -X PUT 'http://localhost:6333/collections/default' \\ -H 'Content-Type: application/json' \\ --data-raw '{ \"vectors\": { \"size\": 768, \"distance\": \"Cosine\", \"on_disk\": true } }' Download a program to create embeddings from the CSV file. curl -LO https://github.com/GaiaNet-AI/embedding-tools/raw/main/csv_embed/csv_embed.wasm You can check out the Rust source code here and modify it if you need to use a different CSV layout. Next, you can run the program by passing a collection name, vector dimension, and the CSV document. The --ctx_size option matches the embedding model's context window size, which in this case is 8192 tokens allowing it to process long sections of text. Make sure that Qdrant is running on your local machine. The model is preloaded under the name embedding. The wasm app then uses the embedding model to create the 768-dimension vectors from paris.csv and saves them into the default collection. curl -LO https://huggingface.co/datasets/gaianet/paris/raw/main/paris.csv wasmedge --dir .:. \\ --nn-preload embedding:GGML:AUTO:nomic-embed-text-v1.5.f16.gguf \\ csv_embed.wasm embedding default 768 paris.csv --ctx_size 8192","s":"Create the vector collection snapshot","u":"/1.0.0/creator-guide/knowledge/csv","h":"#create-the-vector-collection-snapshot","p":570},{"i":579,"t":"You can pass the following options to the program. Using -c or --ctx_size to specify the context size of the input. This defaults to 512. Using -m or --maximum_context_length to specify a context length in the CLI argument. That is to truncate and warn for each text segment that goes above the context length. Using -s or --start_vector_id to specify the start vector ID in the CLI argument. This will allow us to run this app multiple times on multiple documents on the same vector collection. Example: the above example but to append the London guide to the end of an existing collection starting from index 42. wasmedge --dir .:. \\ --nn-preload embedding:GGML:AUTO:nomic-embed-text-v1.5.f16.gguf \\ csv_embed.wasm embedding default 768 london.csv -c 8192 -l 1 -s 42","s":"Options","u":"/1.0.0/creator-guide/knowledge/csv","h":"#options","p":570},{"i":581,"t":"You can create a snapshot of the collection, which can be shared and loaded into a different Qdrant database. You can find the snapshot file in the qdrant_snapshots directory, or the ~/gaianet/qdrant/snapshots directory in the Gaia node. curl -X POST 'http://localhost:6333/collections/default/snapshots' We also recommend you to compress the snapshot file. tar czvf my.snapshot.tar.gz my.snapshot Finally, upload the my.snapshot.tar.gz file to Huggingface so that the Gaia node can download and use it.","s":"Create a vector snapshot","u":"/1.0.0/creator-guide/knowledge/csv","h":"#create-a-vector-snapshot","p":570},{"i":583,"t":"Start a new Gaia node Customize the Gaia node Have fun!","s":"Next steps","u":"/1.0.0/creator-guide/knowledge/csv","h":"#next-steps","p":570},{"i":585,"t":"In this section, we will discuss how to create a vector collection snapshot from a PDF file. First, we will parse the unstructured PDF file to a structured markdown file. Then, we will follow the steps from Knowledge base from a markdown file to create embedding for your PDF files.","s":"Knowledge base from a PDF file","u":"/1.0.0/creator-guide/knowledge/pdf","h":"","p":584},{"i":588,"t":"LlamaParse is a tool to parse files for optimal RAG. You will need a LlamaCloud key from https://cloud.llamaindex.ai. First, install the dependencies. we are assuming that you already have Node.JS 20+ installed. git clone https://github.com/alabulei1/llamaparse-integration.git cd llamaparse-integration npm install llamaindex npm install dotenv Then, edit the .env file to set up the PDF file path and LlamaCloud Key. In this case, you don't need to care about the LLM-related settings. After that, run the following command line to parse your pdf into a markdown file. npx tsx transMd.ts The output markdown file will be located in this folder named output.md by default. You can change the path in the .env file.","s":"Tool #1: LlamaParse","u":"/1.0.0/creator-guide/knowledge/pdf","h":"#tool-1-llamaparse","p":584},{"i":590,"t":"GPTPDF is an open-source tool using GPT-4o to parse PDF into markdown. You will need an OpenAI key here. First, install the gptpdf software. pip install gptpdf Then, enter the Python environment. python Next, use the following command to parse your pdf. from gptpdf import parse_pdf api_key = 'Your OpenAI API Key' content, image_paths = parse_pdf(Your_Pdf_Path, api_key=api_key) print(content) The output markdown files called output.md will be located in your root directory.","s":"Tool #2: GPTPDF","u":"/1.0.0/creator-guide/knowledge/pdf","h":"#tool-2-gptpdf","p":584},{"i":592,"t":"Please follow the tutorial Knowledge base from a markdown file to convert your markdown file to a snapshot of embeddings that can be imported into a GaiaNet node.","s":"Create embeddings from the markdown files","u":"/1.0.0/creator-guide/knowledge/pdf","h":"#create-embeddings-from-the-markdown-files","p":584},{"i":594,"t":"GaiaNet is a decentralized computing infrastructure that enables everyone to create, deploy, scale, and monetize their own AI agents that reflect their styles, values, knowledge, and expertise. It allows individuals and businesses to create AI agents. Each GaiaNet node provides: a web-based chatbot UI Chat with a GaiaNet node that is an expert on the Rust programming language. an OpenAI compatible API. See how to use a GaiaNet node as a drop-in OpenAI replacement in your favorite AI agent app. 100% of today's AI agents are applications in the OpenAI ecosystem. With our API approach, GaiaNet is an alternative to OpenAI. Each GaiaNet node has the ability to be customized with a fine-tuned model supplemented by domain knowledge which eliminates the generic responses many have come to expect. For example, a GaiaNet node for a financial analyst agent can write SQL code to query SEC 10K filings to respond to user questions. Similar GaiaNet nodes are organized into GaiaNet domains, to provide stable services by load balancing across the nodes. GaiaNet domains have public-facing URLs and promote agent services to their communities. When a user or an agent app sends an API request to the domain's API endpoint URL, the domain is responsible for directing the request to a node that is ready.","s":"Overview","u":"/1.0.0/intro","h":"","p":593},{"i":597,"t":"If you are an end user of AI agent applications, you can: Find a list of interesting GaiaNet nodes you can chat with on the web, or access via API. Use a GaiaNet node as the backend AI engine for your favorite AI agent apps.","s":"Users","u":"/1.0.0/intro","h":"#users","p":593},{"i":599,"t":"If you are interested in running GaiaNet nodes, you can Get started with a GaiaNet node. Customize the GaiaNet node with a finetuned model and custom knowledge base. Join the Gaia Protocol","s":"Node operators","u":"/1.0.0/intro","h":"#node-operators","p":593},{"i":601,"t":"If you are a Gaia Domain Name owner, you can Launch your domain.","s":"Domain operators","u":"/1.0.0/intro","h":"#domain-operators","p":593},{"i":603,"t":"If you are a creator or knowledge worker interested in creating your own AI agent service, you can: Create your own knowledge base. Finetune a model to \"speak\" like you.","s":"Creators","u":"/1.0.0/intro","h":"#creators","p":593},{"i":605,"t":"In this section, we will discuss how to create a vector collection snapshot from a plain text file. The snapshot file can then be loaded by a Gaia node as its knowledge base. The text file is segmented into multiple chunks by blank lines. See an example. Each chunk is turned into a vector, and when retrieved, added to the prompt context for the LLM.","s":"Knowledge base from a plain text file","u":"/1.0.0/creator-guide/knowledge/text","h":"","p":604},{"i":607,"t":"Install the WasmEdge Runtime, the cross-platform LLM runtime. curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install_v2.sh | bash -s Download an embedding model. curl -LO https://huggingface.co/gaianet/Nomic-embed-text-v1.5-Embedding-GGUF/resolve/main/nomic-embed-text-v1.5.f16.gguf The embedding model is a special kind of LLM that turns sentences into vectors. The vectors can then be stored in a vector database and searched later. When the sentences are from a body of text that represents a knowledge domain, that vector database becomes our RAG knowledge base.","s":"Prerequisites","u":"/1.0.0/creator-guide/knowledge/text","h":"#prerequisites","p":604},{"i":609,"t":"By default, we use Qdrant as the vector database. You can start a Qdrant instance by starting a Gaia node with a knowledge snapshot. note Or, you can start a Qdrant server using Docker. The following command starts it in the background. mkdir qdrant_storage mkdir qdrant_snapshots nohup docker run -d -p 6333:6333 -p 6334:6334 \\ -v $(pwd)/qdrant_storage:/qdrant/storage:z \\ -v $(pwd)/qdrant_snapshots:/qdrant/snapshots:z \\ qdrant/qdrant","s":"Start a vector database","u":"/1.0.0/creator-guide/knowledge/text","h":"#start-a-vector-database","p":604},{"i":611,"t":"Delete the default collection if it exists. curl -X DELETE 'http://localhost:6333/collections/default' Create a new collection called default. Notice that it is 768 dimensions. That is the output vector size of the embedding model nomic-embed-text-v1.5. If you are using a different embedding model, you should use a dimension that fits the model. curl -X PUT 'http://localhost:6333/collections/default' \\ -H 'Content-Type: application/json' \\ --data-raw '{ \"vectors\": { \"size\": 768, \"distance\": \"Cosine\", \"on_disk\": true } }' Download a program to chunk a document and create embeddings. curl -LO https://github.com/GaiaNet-AI/embedding-tools/raw/main/paragraph_embed/paragraph_embed.wasm It chunks the document based on empty lines. So, you MUST prepare your source document this way -- segment the document into sections of around 200 words with empty lines. You can check out the Rust source code here and modify it if you need to use a different chunking strategy. The paragraph_embed.wasm program would NOT break up code listings even if there are empty lines with in the listing. Next, you can run the program by passing a collection name, vector dimension, and the source document. Make sure that Qdrant is running on your local machine. The model is preloaded under the name embedding. The wasm app then uses the embedding model to create the 768-dimension vectors from paris_chunks.txt and saves them into the default collection. curl -LO https://huggingface.co/datasets/gaianet/paris/raw/main/paris_chunks.txt wasmedge --dir .:. \\ --nn-preload embedding:GGML:AUTO:nomic-embed-text-v1.5.f16.gguf \\ paragraph_embed.wasm embedding default 768 paris_chunks.txt -c 8192","s":"Create the vector collection snapshot","u":"/1.0.0/creator-guide/knowledge/text","h":"#create-the-vector-collection-snapshot","p":604},{"i":613,"t":"You can pass the following options to the program. Using -m or --maximum_context_length to specify a context length in the CLI argument. That is to truncate and warn for each text segment that goes above the context length. Using -s or --start_vector_id to specify the start vector ID in the CLI argument. This will allow us to run this app multiple times on multiple documents on the same vector collection. Using -c or --ctx_size to specify the context size of the input. This defaults to 512. Example: the above example but to append the London guide to the end of an existing collection starting from index 42. wasmedge --dir .:. \\ --nn-preload embedding:GGML:AUTO:nomic-embed-text-v1.5.f16.gguf \\ paragraph_embed.wasm embedding default 768 london.txt -c 8192 -s 42","s":"Options","u":"/1.0.0/creator-guide/knowledge/text","h":"#options","p":604},{"i":615,"t":"You can create a snapshot of the collection, which can be shared and loaded into a different Qdrant database. You can find the snapshot file in the qdrant_snapshots directory, or the ~/gaianet/qdrant/snapshots directory in the Gaia node. curl -X POST 'http://localhost:6333/collections/default/snapshots' We also recommend you to compress the snapshot file. tar czvf my.snapshot.tar.gz my.snapshot Finally, upload the my.snapshot.tar.gz file to Huggingface so that the Gaia node can download and use it.","s":"Create a vector snapshot","u":"/1.0.0/creator-guide/knowledge/text","h":"#create-a-vector-snapshot","p":604},{"i":617,"t":"Start a new Gaia node Customize the Gaia node Have fun!","s":"Next steps","u":"/1.0.0/creator-guide/knowledge/text","h":"#next-steps","p":604},{"i":619,"t":"In this section, we will discuss how to create a vector collection snapshot from a markdown file. The snapshot file can then be loaded by a Gaia node as its knowledge base. The markdown file is segmented into multiple sections by headings. See an example. Each section is turned into a vector, and when retrieved, added to the prompt context for the LLM.","s":"Knowledge base from a markdown file","u":"/1.0.0/creator-guide/knowledge/markdown","h":"","p":618},{"i":621,"t":"Install the WasmEdge Runtime, the cross-platform LLM runtime. curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install_v2.sh | bash -s Download an embedding model. curl -LO https://huggingface.co/gaianet/Nomic-embed-text-v1.5-Embedding-GGUF/resolve/main/nomic-embed-text-v1.5.f16.gguf The embedding model is a special kind of LLM that turns sentences into vectors. The vectors can then be stored in a vector database and searched later. When the sentences are from a body of text that represents a knowledge domain, that vector database becomes our RAG knowledge base.","s":"Prerequisites","u":"/1.0.0/creator-guide/knowledge/markdown","h":"#prerequisites","p":618},{"i":623,"t":"By default, we use Qdrant as the vector database. You can start a Qdrant instance by starting a Gaia node with a knowledge snapshot. note Or, you can start a Qdrant server using Docker. The following command starts it in the background. mkdir qdrant_storage mkdir qdrant_snapshots nohup docker run -d -p 6333:6333 -p 6334:6334 \\ -v $(pwd)/qdrant_storage:/qdrant/storage:z \\ -v $(pwd)/qdrant_snapshots:/qdrant/snapshots:z \\ qdrant/qdrant","s":"Start a vector database","u":"/1.0.0/creator-guide/knowledge/markdown","h":"#start-a-vector-database","p":618},{"i":625,"t":"Delete the default collection if it exists. curl -X DELETE 'http://localhost:6333/collections/default' Create a new collection called default. Notice that it is 768 dimensions. That is the output vector size of the embedding model nomic-embed-text-v1.5. If you are using a different embedding model, you should use a dimension that fits the model. curl -X PUT 'http://localhost:6333/collections/default' \\ -H 'Content-Type: application/json' \\ --data-raw '{ \"vectors\": { \"size\": 768, \"distance\": \"Cosine\", \"on_disk\": true } }' Download a program to segment the markdown document and create embeddings. curl -LO https://github.com/GaiaNet-AI/embedding-tools/raw/main/markdown_embed/markdown_embed.wasm It chunks the document based on markdown sections. You can check out the Rust source code here and modify it if you need to use a different chunking strategy. Next, you can run the program by passing a collection name, vector dimension, and the source document. You can pass in the desired markdown heading level for chunking using the --heading_level option. The --ctx_size option matches the embedding model's context window size, which in this case is 8192 tokens allowing it to process long sections of text. Make sure that Qdrant is running on your local machine. The model is preloaded under the name embedding. The wasm app then uses the embedding model to create the 768-dimension vectors from paris.md and saves them into the default collection. curl -LO https://huggingface.co/datasets/gaianet/paris/raw/main/paris.md wasmedge --dir .:. \\ --nn-preload embedding:GGML:AUTO:nomic-embed-text-v1.5.f16.gguf \\ markdown_embed.wasm embedding default 768 paris.md --heading_level 1 --ctx_size 8192","s":"Create the vector collection snapshot","u":"/1.0.0/creator-guide/knowledge/markdown","h":"#create-the-vector-collection-snapshot","p":618},{"i":627,"t":"You can pass the following options to the program. Using -c or --ctx_size to specify the context size of the input. This defaults to 512. Using -l or --heading_level to specify the markdown heading level for each vector. This defaults to 1. Using -m or --maximum_context_length to specify a context length in the CLI argument. That is to truncate and warn for each text segment that goes above the context length. Using -s or --start_vector_id to specify the start vector ID in the CLI argument. This will allow us to run this app multiple times on multiple documents on the same vector collection. Example: the above example but to append the London guide to the end of an existing collection starting from index 42. wasmedge --dir .:. \\ --nn-preload embedding:GGML:AUTO:nomic-embed-text-v1.5.f16.gguf \\ markdown_embed.wasm embedding default 768 london.md -c 8192 -l 1 -s 42","s":"Options","u":"/1.0.0/creator-guide/knowledge/markdown","h":"#options","p":618},{"i":629,"t":"You can create a snapshot of the collection, which can be shared and loaded into a different Qdrant database. You can find the snapshot file in the qdrant_snapshots directory, or the ~/gaianet/qdrant/snapshots directory in the Gaia node. curl -X POST 'http://localhost:6333/collections/default/snapshots' We also recommend you to compress the snapshot file. tar czvf my.snapshot.tar.gz my.snapshot Finally, upload the my.snapshot.tar.gz file to Huggingface so that the Gaia node can download and use it.","s":"Create a vector snapshot","u":"/1.0.0/creator-guide/knowledge/markdown","h":"#create-a-vector-snapshot","p":618},{"i":631,"t":"Start a new Gaia node Customize the Gaia node Have fun!","s":"Next steps","u":"/1.0.0/creator-guide/knowledge/markdown","h":"#next-steps","p":618},{"i":633,"t":"This guide provides all the information you need to quickly set up and run a Gaia Domain. Note: Ensure that you are the owner of a Gaia Domain Name before proceeding. You can verify your Gaia Domain Name in the \"Assets\" section of your profile. Gaia simplifies the process for domain operators to launch and host a Gaia Domain service in just a few clicks.","s":"Quick Start with Launching Gaia Domain","u":"/1.0.0/domain-guide/quick-start","h":"","p":632},{"i":635,"t":"Access the Create Gaia Domain Page Click LAUNCH DOMAIN in the \"Domain\" or \"Assets\" section under your profile. This will take you to the Create Gaia Domain page. Fill in Domain Details Enter the general information for your domain, including: Domain profile Domain Name Description System Prompt Choose a Gaia Domain Name Select a Gaia domain name from your assets. Select a Supplier Currently, Gaia Cloud is the only supplier. Pick a Gaia Domain Tier Choose a tier to enhance your domain's rewards, which is necessary. Configure Server and Management Options Confirm the server configuration for running your domain. Set management preferences, such as whether nodes can join automatically and the specific LLM to use. After completing these six steps, your Gaia Domain will be successfully launched and other nodes can join your domain.","s":"Steps to Launch Your Gaia Domain","u":"/1.0.0/domain-guide/quick-start","h":"#steps-to-launch-your-gaia-domain","p":632},{"i":637,"t":"GaiaNet has developed a tool for making vector collection snapshot files, so everyone can easily create their own knowledge base. Access it here: https://tools.gaianet.xyz/","s":"Build a knowledge base using Gaia web tool","u":"/1.0.0/creator-guide/knowledge/web-tool","h":"","p":636},{"i":639,"t":"First, copy unformatted text into a txt file. Then follow the two rules to chunk your content, ie putting similar content together. Each title and related content are a chunk. There is no blank lines in one chunk. Use a blank line to recognize different chunks. After that, save it as a txt file. For example, below is your source. After formatted, it will look like the following. What is a blockchain? A blockchain is a distributed, cryptographically-secure database structure that allows network participants to establish a trusted and immutable record of transactional data without the need for intermediaries. A blockchain can execute a variety of functions beyond transaction settlement, such as smart contracts. Smart contracts are digital agreements that are embedded in code and can have limitless formats and conditions. Blockchains have proven themselves as superior solutions for securely coordinating data, but they are capable of much more, including tokenization, incentive design, attack-resistance, and reducing counterparty risk. The very first blockchain was the Bitcoin blockchain, which was itself a culmination of over a century of advancements in cryptography and database technology. What is blockchain software? Blockchain software is like any other software. The first of its kind was Bitcoin, which was released as open source software, making it available to anyone to use or change. There are a wide variety of efforts across the blockchain ecosystem to improve upon Bitcoin's original software. Ethereum has its own open source blockchain software. Some blockchain software is proprietary and not available to the public.","s":"Segment your text file","u":"/1.0.0/creator-guide/knowledge/web-tool","h":"#segment-your-text-file","p":636},{"i":641,"t":"Visit this URL: https://tools.gaianet.xyz/, upload the above prepared txt file. Edit your dbname . Note: Do not include spaces or special characters in the dbname. Choose Embedding model, we suggest use nomic-embed-text-v1.5.f16. Click the \"Make RAG\" button and wait. When finished, the chatbot will display GaiaNet Node config info. It is a JSON format as follows. { \"embedding\": \"https://huggingface.co/gaianet/Nomic-embed-text-v1.5-Embedding-GGUF/resolve/main/nomic-embed-text-v1.5.f16.gguf\", \"embedding_ctx_size\": 768, \"snapshot\": \"https://huggingface.co/datasets/max-id/gaianet-qdrant-snapshot/resolve/main/test/test.snapshot\" }","s":"Generate the snapshot file","u":"/1.0.0/creator-guide/knowledge/web-tool","h":"#generate-the-snapshot-file","p":636},{"i":643,"t":"Run the following gaianet config \\ --snapshot https://huggingface.co/datasets/max-id/gaianet-qdrant-snapshot/resolve/main/test/test.snapshot \\ --embedding-url https://huggingface.co/gaianet/Nomic-embed-text-v1.5-Embedding-GGUF/resolve/main/nomic-embed-text-v1.5.f16.gguf \\ --embedding-ctx-size 768 and then gaianet init gaianet start Have fun!","s":"Update the node config","u":"/1.0.0/creator-guide/knowledge/web-tool","h":"#update-the-node-config","p":636},{"i":645,"t":"After installing the GaiaNet software, you can use the gaianet CLI to manage the node. The following are the CLI options.","s":"GaiaNet CLI options","u":"/1.0.0/node-guide/cli-options","h":"","p":644},{"i":647,"t":"You can use gaianet --help to check all the available CLI options. gaianet --help ## Output Usage: gaianet {config|init|run|stop|OPTIONS} Subcommands: config Update the configuration. init Initialize the GaiaNet node. run|start Start the GaiaNet node. stop Stop the GaiaNet node. Options: --help Show this help message","s":"help","u":"/1.0.0/node-guide/cli-options","h":"#help","p":644},{"i":649,"t":"You can use gaianet --version to check your GaiaNet version. gaianet --version","s":"version","u":"/1.0.0/node-guide/cli-options","h":"#version","p":644},{"i":651,"t":"The gaianet init command initializes the node according to the $HOME/gaianet/config.json file. You can use some of our pre-set configurations. gaianet init will init the default node. It's an RAG application with Gaianet knowledge. gaianet init --config mua will init a node with the MUA project knowledge. gaianet init --base will init a node in an alternative directory. You can also use gaianet init url_your_config_json to init your customized settings for the node. You can customize your node using the Gaianet node link. If you're familiar with the Gaianet config.json, you can create your own manually. See an example here. gaianet init --config https://raw.githubusercontent.com/GaiaNet-AI/node-configs/main/pure-llama-3-8b/config.json","s":"init","u":"/1.0.0/node-guide/cli-options","h":"#init","p":644},{"i":653,"t":"The gaianet start is to start running the node. Use gaianet start to start the node according to the $HOME/gaianet/config.json file. Use gaianet start --base $HOME/gaianet-2.alt to start the node according to the $HOME/gaianet-2/config.json file. Use gaianet start --local-only to start the node for local use according to the $HOME/gaianet/config.json file.","s":"start","u":"/1.0.0/node-guide/cli-options","h":"#start","p":644},{"i":655,"t":"The gaianet stop is to stop the running node. Use gaianet stop to stop running the node. Use gaianet stop --force to force stop the GaiaNet node. Use gaianet stop --base $HOME/gaianet-2.alt to stop the node according to the $HOME/gaianet-2/config.json file.","s":"stop","u":"/1.0.0/node-guide/cli-options","h":"#stop","p":644},{"i":657,"t":"The gaianet config can update the key fields defined in the config.json file. gaianet config --help will list all the available arguments gaianet config --chat-url will change the download link of the chat model. gaianet config --prompt-template