From 1dd1854ecad59d11459e6497c1362da39e26da5c Mon Sep 17 00:00:00 2001 From: gcapuzzi <107040849+gcapuzzi@users.noreply.github.com> Date: Sun, 3 Mar 2024 11:42:13 +0100 Subject: [PATCH] Poc new release (#1) * deleted requirements file Signed-off-by: Gianluca Capuzzi * deleted main file Signed-off-by: Gianluca Capuzzi * deleted env.example file Signed-off-by: Gianluca Capuzzi * modified .gitignore file Signed-off-by: Gianluca Capuzzi * deleted image folder files Signed-off-by: Gianluca Capuzzi * deleted ingest folder files Signed-off-by: Gianluca Capuzzi * modified LICENSE and README file Signed-off-by: Gianluca Capuzzi * added NOTICE file Signed-off-by: Gianluca Capuzzi * added deps and src folders Signed-off-by: Gianluca Capuzzi * added images files Signed-off-by: Gianluca Capuzzi --------- Signed-off-by: Gianluca Capuzzi --- .gitignore | 2 - LICENSE.md | 26 +- NOTICE.md | 40 + README.md | 122 +- deps/bitsandbytes/LICENSE.txt | 21 + deps/bs4/LICENSE.txt | 26 + deps/langchain/LICENSE.txt | 21 + deps/sentence-transformers/LICENSE.txt | 201 + deps/torch/LICENSE.md | 27 + deps/transformers/LICENSE.txt | 203 + env.example | 1 - images/architecture_modern_qa.drawio.png | Bin 74115 -> 0 bytes images/developer_settings.png | Bin 0 -> 23120 bytes images/document_sources.png | Bin 0 -> 18635 bytes images/download_notebook_file.png | Bin 0 -> 3817 bytes images/fine_grained_token.png | Bin 0 -> 8514 bytes images/github_personal_token.png | Bin 0 -> 13957 bytes images/new_colab_notebook.png | Bin 0 -> 19931 bytes images/poc_schema_v2.png | Bin 0 -> 539341 bytes images/runtime_type.png | Bin 0 -> 5128 bytes images/ui_gradio_question.png | Bin 0 -> 94863 bytes images/xlm_r_architecture.drawio.png | Bin 13725 -> 0 bytes ingest/faq_es_format_v1.json | 9 - ingest/faq_es_format_v2.json | 16 - ingest/faq_es_format_v3_1.json | 205 - ingest/faq_es_format_v3_2.json | 310 - ingest/faq_squad_format.json | 358 - main.py | 73 - requirements.txt | 1 - src/hyperledger_aifaq_poc_v3.ipynb | 10545 +++++++++++++++++++++ 30 files changed, 11202 insertions(+), 1005 deletions(-) create mode 100644 NOTICE.md create mode 100644 deps/bitsandbytes/LICENSE.txt create mode 100644 deps/bs4/LICENSE.txt create mode 100644 deps/langchain/LICENSE.txt create mode 100644 deps/sentence-transformers/LICENSE.txt create mode 100644 deps/torch/LICENSE.md create mode 100644 deps/transformers/LICENSE.txt delete mode 100644 env.example delete mode 100644 images/architecture_modern_qa.drawio.png create mode 100644 images/developer_settings.png create mode 100644 images/document_sources.png create mode 100644 images/download_notebook_file.png create mode 100644 images/fine_grained_token.png create mode 100644 images/github_personal_token.png create mode 100644 images/new_colab_notebook.png create mode 100644 images/poc_schema_v2.png create mode 100644 images/runtime_type.png create mode 100644 images/ui_gradio_question.png delete mode 100644 images/xlm_r_architecture.drawio.png delete mode 100644 ingest/faq_es_format_v1.json delete mode 100644 ingest/faq_es_format_v2.json delete mode 100644 ingest/faq_es_format_v3_1.json delete mode 100644 ingest/faq_es_format_v3_2.json delete mode 100644 ingest/faq_squad_format.json delete mode 100644 main.py delete mode 100644 requirements.txt create mode 100644 src/hyperledger_aifaq_poc_v3.ipynb diff --git a/.gitignore b/.gitignore index 90d0f26..e69de29 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +0,0 @@ -.env -saved_models \ No newline at end of file diff --git a/LICENSE.md b/LICENSE.md index c61b663..8bca1b6 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -1,6 +1,6 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ +Apache License +Version 2.0, January 2004 +http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION @@ -127,6 +127,26 @@ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. + This product bundles torch, which is available under a + "3-clause BSD" license. For details, see deps/torch/ + + This product bundles bitsandbytes, which is available under a + "MIT" license. For details, see deps/bitsandbytes/ + + This product bundles langchain, which is available under a + "MIT" license. For details, see deps/langchain/ + + This product bundles bs4, which is available under a + "MIT" license. For details, see deps/bs4/ + + This product bundles transformers, which is available under a + "Apache 2.0" license. For details, see deps/transformers/ + + This product bundles sentence-transformers, which is available under a + "Apache 2.0" license. For details, see deps/sentence-transformers/ + + This product bundles transformers, peft, accelerate, safetensors, sentencepiece, chromadb, sentence-transformers, gradio, sentence-transformers/all-mpnet-base-v2 model, filipealmeida/Mistral-7B-Instruct-v0.1-sharded model, which are available under an "Apache 2.0" license. + 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of diff --git a/NOTICE.md b/NOTICE.md new file mode 100644 index 0000000..c2b62d1 --- /dev/null +++ b/NOTICE.md @@ -0,0 +1,40 @@ +The bitsandbytes dependecy has a NOTICE file containing the text below: +"The majority of bitsandbytes is licensed under MIT, however portions of the project are available under separate license terms: Pytorch is licensed under the BSD license. + +We thank Fabio Cannizzo for this work on FastBinarySearch which is included in this project." + +The sentence-transformers dependecy has a NOTICE file containing the text below: + +"Copyright 2019 +Ubiquitous Knowledge Processing (UKP) Lab +Technische Universität Darmstadt" + +The bs4 dependecy has a NOTICE file containing the text below: + +"Beautiful Soup is made available under the MIT license: + +Copyright (c) 2004-2015 Leonard Richardson + +Permission is hereby granted, free of charge, to any person obtaining +a copy of this software and associated documentation files (the +"Software"), to deal in the Software without restriction, including +without limitation the rights to use, copy, modify, merge, publish, +distribute, sublicense, and/or sell copies of the Software, and to +permit persons to whom the Software is furnished to do so, subject to +the following conditions: + +The above copyright notice and this permission notice shall be +included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS +BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +Beautiful Soup incorporates code from the html5lib library, which is +also made available under the MIT license. Copyright (c) 2006-2013 +James Graham and other contributors" diff --git a/README.md b/README.md index 1231be9..8fa3eb5 100644 --- a/README.md +++ b/README.md @@ -1,46 +1,114 @@ -# Hyperledger QA PoC +# Hyperledger QA PoC version 2 -This is a Proof-of-Concept application that allows you to ask questions to a python script chatbot, fine-tuned with Hyperledger Standard Documents. -I implemented this first version, as mentee, during the Hyperledger Mentorship Program 2023. +The scope of this Hyperledger Labs project is to support the users (users, developer, etc.) to their work, avoiding to wade through oceans of documents to find information they are looking for. We are implementing an open source conversational AI tool which replies to the questions related to specific context. This is a proof-of-concept software which allows to create a chatbot using Google Colab (or local notebook which requires GPU). Here the official Wiki page: [Hyperledger Labs aifaq](https://labs.hyperledger.org/labs/aifaq.html). Please, read also the [Antitrust Policy and the Code of Conduct](https://wiki.hyperledger.org/pages/viewpage.action?pageId=41587043). -## Use case +## Background -This NLP application allows people to access to the Hyperledger Standard Documentation. -The scope of the lab is to support the Hyperledger users (users, developer, etc.) to their work, avoiding to wade through oceans of documents to find information they are looking for. Large Language Models have yielded remarkable results, either pay and open source tools. Today we can implement a conversational AI tool which replies to questions related to specific context. +The system is an open source Jupyter Notebook (derived from here [medium.com](https://levelup.gitconnected.com/building-a-private-ai-chatbot-2c071f6715ad)) which implements an AI chatbot. The idea is to implement an open source framework/template, as example, for other communities. Last results in open LLMs allow to have good performance using common HW resources.\ +Below the application architecture: -## Architecture +![LLM chatbot schema](/images/poc_schema_v2.png) -The model is XML-R pre-trained ([HuggingFace deepset/xlm-roberta-large-squad2](https://huggingface.co/deepset/xlm-roberta-large-squad2)) with SQuAD Dataset. Below the architecture of the model:\ -![alt text](./images/xlm_r_architecture.drawio.png) +We use RAG (Retrieval Augmented Generation [arxiv.org](https://arxiv.org/abs/2312.10997)) for question answering use case. That technique aims to improve LLM answers by incorporating knowledge from external database (e.g. vector database). -## Pipeline +The image depicts two workflow: -In this PoC I use Haystack ([Haystack by Deepset](https://haystack.deepset.ai/)) to Build the QA pipeline. -Below an image of the architecture:\ -![alt text](./images/architecture_modern_qa.drawio.png) +1. The data ingestion workflow +2. The chat workflow -I use Elastic Search ([Elastic Search website](https://www.elastic.co/)) as Retriever component. +During the ingestion phase, the system loads context documents and creates a vector database. In our case, the document sources are: + +- An online software guide (readthedocs template) +- The GitHub issues and pull requests + +After the first phase, the system is ready to reply to user questions. + +Currently, we use the open source [HuggingFace Zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha). But, in the future we want to investigate other open source models. Moreover, the User Interface uses [Gradio](https://www.gradio.app/). + +## Open Source Version + +The software is under Apache 2.0 License (please check LICENSE and NOTICE files included). For the dependencies, it is [ASF 3rd Party License Policy](https://www.apache.org/legal/resolved.html) compliant: the **LICENSE** file contains "pointers" to the dependency's licenses and a list of Apache 2.0-licensed dependecies ([Assembling LICENSE and NOTICE files](https://infra.apache.org/licensing-howto.html#mod-notice)). ## Installation -For the installation istructions read the links below:\ -[Haystack installation](https://haystack.deepset.ai/integrations/elasticsearch-document-store) +Below the main steps to set up the system: + +1. Download the **hyperledger_aifaq_poc_v3.ipynb** notebook file from the **src** folder +2. Create a new Google Colab notebook +3. Load the downloaded notebook file +4. Set up the runtime GPU +5. Set the URL and GitHub repo document sources +6. Create a new GitHub personal token +7. Add the token, as new secret, to the Google Colab notebook + +The first step is straightforward: just click the **src** folder to open it, then click the **hyperledger_aifaq_poc_v3.ipynb** file and the click the button below: + +![download button](/images/download_notebook_file.png) + +Now, in Google Drive click on **New** button -> **Other** and **Google Colaboratory** + +![new Google Colab notebook](/images/new_colab_notebook.png) + +Inside the new notebook, select the **File** menu, then select **Load notebook** and then click on the "Browse" button and select the downloaded file (hyperledger_aifaq_poc_v3.ipynb). + +We need a GPU to execute the notebook. So, we can set it up from the **Runtime** menu, then change runtime: + +![set up the runtime](/images/runtime_type.png) + +If you have a free account you can use only the T4 GPU. + +The notebook takes the documents for RAG from two sources: + +1. An online website +2. A GitHub repository + +The image below shows how to set them up: + +![document sources](/images/document_sources.png) + +In our case, we get the **Hyperledger Iroha** readthedocs guide and its GitHub repository (getting issues and pull requests). +Into **url** string we specify the website, while in **repo** string we set the GitHub repository.\\ + +From your personal GitHub account, inside the profile settings, select the developer settings: + +![developer settings](/images/developer_settings.png) + +Then select the **fine-grained token** + +![fine-grained token](/images/fine_grained_token.png) + +and click on the generate button: now copy the token.\\ +Into the Google Colab notebook, select the **secret key** and add a new secret, like the image below: + +![github personal token](/images/github_personal_token.png) + +- The token must have the access to the notebook +- The name should be **GITHUB_PERSONAL_ACCESS_TOKEN** +- Past it inside the **Value** field + +## Usage + +Now, we can test the PoC by executing the notebook: in Google Colab notebook -> **Runtime menu**, select **Execute all**: + +- It will take 5-15 minutes (it depends on the GPU and the documents) +- When the execution finishes, it loads an UI which allows to ask questions and replies in around 30 seconds -[Elastic Search Windows installation](https://www.elastic.co/guide/en/elasticsearch/reference/current/zip-windows.html) +Below an example: -## Ingestion files +![UI Gradio example](/images/ui_gradio_question.png) -In ingest folder, you can find two kinds of files: +For any reason, please, contact us on Discord Channel: -1. es format (Elastic Search) which contains data for the unstructured documents -2. one squad format file ([Stanford Question Anwsering Dataset](https://huggingface.co/datasets/squad_v2)) for the fine-tuning process +- Server: Hyperledger +- Channel: #aifaq ## Current version notes -That is the first version of a PoC. Below a list of improvements that will be applied soon: +That is a proof-of-concept: a list of future improvement below: -1. Model: more sophisticated model (e.g. Zephyr 7B alpha) -2. Dataset: currently I implemented only 2 documents as example, but real systems work with hundreds of documents -3. Retriever: more sophisticated techniques use embeddings -4. QA type: I will use generative (RAG) instead of extractive QA -5. Hardware: now the system requires 10 minutes to ingest the files, GPU can help to save much time +1. We want to implement a prototype starting from that PoC: container architecture installed on a GPU Cloud Server +2. At the same time, we'd like to pass to the next step: the Hyperledger Incubation Stage +3. We will investigate other open source models +4. Evaluation of the system using standard metrics +5. We would like to improve the system, some ideas are: fine-tuning, Corrective RAG, Decomposed LoRA +6. Add "guardrails" which are a specific ways of controlling the output of a LLM, such as talking avoid specific topics, responding in a particular way to specific user requests, etc. diff --git a/deps/bitsandbytes/LICENSE.txt b/deps/bitsandbytes/LICENSE.txt new file mode 100644 index 0000000..b96dcb0 --- /dev/null +++ b/deps/bitsandbytes/LICENSE.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) Facebook, Inc. and its affiliates. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/env.example b/env.example deleted file mode 100644 index 8aff88d..0000000 --- a/env.example +++ /dev/null @@ -1 +0,0 @@ -PASSWORD=elastic_search_password diff --git 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policy that is specified in the chaincode definition.", - "meta": { - "name": "Hyperledger FAQ", - "id": "q1" - } - }, - { - "content": "Clients only need to connect to as many peers as are required by the endorsement policy for the chaincode.", - "meta": { - "name": "Hyperledger FAQ", - "id": "q2" - } - } -] diff --git a/ingest/faq_es_format_v3_1.json b/ingest/faq_es_format_v3_1.json deleted file mode 100644 index 1413353..0000000 --- a/ingest/faq_es_format_v3_1.json +++ /dev/null @@ -1,205 +0,0 @@ -[ - { - "content": "The number of peers required to endorse a transaction is driven by the endorsement policy that is specified in the chaincode definition.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "Clients only need to connect to as many peers as are required by the endorsement policy for the chaincode.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "There are various aspects to data privacy. First, you can segregate your network into channels, where each channel represents a subset of participants that are authorized to see the data for the chaincodes that are deployed to that channel.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "Second, you can use private-data to keep ledger data private from other organizations on the channel. A private data collection allows a defined subset of organizations on a channel the ability to endorse, commit, or query private data without having to create a separate channel. Other participants on the channel receive only a hash of the data. For more information refer to the Using Private Data in Fabric tutorial. Note that the key concepts topic also explains when to use private data instead of a channel.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "Third, as an alternative to Fabric hashing the data using private data, the client application can hash or encrypt the data before calling chaincode. If you hash the data then you will need to provide a means to share the source data. If you encrypt the data then you will need to provide a means to share the decryption keys.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "Fourth, you can restrict data access to certain roles in your organization, by building access control into the chaincode logic.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "Fifth, ledger data at rest can be encrypted via file system encryption on the peer, and data in-transit is encrypted via TLS.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "Orderers receive endorsed transactions that are submitted from application clients. The endorsed payload contains the chaincode execution results including the ReadSet and WriteSet information. The orderers only validate the submitter’s identity and order transactions, they do not open the endorsed transactions.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "If you do not want the data to go through the orderers at all, then utilize the private data feature of Fabric. Alternatively, you can hash or encrypt the data in the client application before calling chaincode. If you encrypt the data then you will need to provide a means to share the decryption keys.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "The transaction simulation results are returned to the client by the endorser in the proposal response. If there are multiple endorsers, the client can check that the responses are all the same, and submit the results and endorsements for ordering and commitment. Ultimately the committing peers will validate or invalidate the transaction, and the client becomes aware of the outcome via an event, that the SDK makes available to the application client.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "Within chaincode you can query based on keys. Keys can be queried by range, and composite keys can be modeled to enable equivalence queries against multiple parameters. For example a composite key of (owner,asset_id) can be used to query all assets owned by a certain entity. These key-based queries can be used for read-only queries against the ledger, as well as in transactions that update the ledger.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "If you model asset data as JSON in chaincode and use CouchDB as the state database, you can also perform complex rich queries against the chaincode data values, using the CouchDB JSON query language within chaincode. The application client can perform read-only queries, but these responses are not typically submitted as part of transactions to the ordering service.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "The chaincode API GetHistoryForKey() will return history of values for a key.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "The client can query multiple peers, compare their block heights, compare their query results, and favor the peers at the higher block heights.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "Yes. We call this feature Chaincode. It is our interpretation of the smart contract method/algorithm, with additional features.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "A chaincode is programmatic code deployed on the network, where it is executed and validated by chain validators together during the consensus process. Developers can use chaincodes to develop business contracts, asset definitions, and collectively-managed decentralized applications.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "There are generally two ways to develop business contracts: the first way is to code individual contracts into standalone instances of chaincode; the second way, and probably the more efficient way, is to use chaincode to create decentralized applications that manage the life cycle of one or multiple types of business contracts, and let end users instantiate instances of contracts within these applications.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "Users can use chaincode (for business rules) and membership service (for digital tokens) to design assets, as well as the logic that manages them.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "There are two popular approaches to defining assets in most blockchain solutions: the stateless UTXO model, where account balances are encoded into past transaction records; and the account model, where account balances are kept in state storage space on the ledger.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "Each approach carries its own benefits and drawbacks. This blockchain technology does not advocate either one over the other. Instead, one of our first requirements was to ensure that both approaches can be easily implemented.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "Chaincode can be written in any programming language and executed in containers. Currently, Go, Node.js and Java chaincode are supported.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "No. However, if you really need a native currency for your chain network, you can develop your own native currency with chaincode. One common attribute of native currency is that some amount will get transacted (the chaincode defining that currency will get called) every time a transaction is processed on its chain.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "The differences between any subsequent releases are provided together with the Releases.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "Please use StackOverflow.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "This is explicitly not supported.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "Although this is possible, it is a highly discouraged configuration. By default the /Channel/Orderer/BlockValidation policy allows any valid certificate of the ordering organizations to sign blocks. If an organization is acting both in an ordering and application role, then this policy should be updated to restrict block signers to the subset of certificates authorized for ordering.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "A consensus plugin needs to implement the Consenter and Chain interfaces defined in the consensus package. There is a plugin built against raft . You can study it to learn more for your own implementation. The ordering service code can be found under the orderer package.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "This falls under reconfiguring the network. Please consult the topic on configtxlator.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - }, - { - "content": "No date has been set. We are working towards a release during the 2.x cycle, i.e. it will come with a minor version upgrade in Fabric.", - "meta": { - "name": "Hyperledger FAQ", - "id": "hl1" - } - } -] diff --git a/ingest/faq_es_format_v3_2.json b/ingest/faq_es_format_v3_2.json deleted file mode 100644 index 5da3aea..0000000 --- a/ingest/faq_es_format_v3_2.json +++ /dev/null @@ -1,310 +0,0 @@ -[ - { - "content": "In general terms, a blockchain is an immutable transaction ledger, maintained within a distributed network of peer nodes. These nodes each maintain a copy of the ledger by applying transactions that have been validated by a consensus protocol, grouped into blocks that include a hash that bind each block to the preceding block.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "The first and most widely recognized application of blockchain is the Bitcoin cryptocurrency, though others have followed in its footsteps. Ethereum, an alternative cryptocurrency, took a different approach, integrating many of the same characteristics as Bitcoin but adding smart contracts to create a platform for distributed applications. Bitcoin and Ethereum fall into a class of blockchain that we would classify as public permissionless blockchain technology. Basically, these are public networks, open to anyone, where participants interact anonymously.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "As the popularity of Bitcoin, Ethereum and a few other derivative technologies grew, interest in applying the underlying technology of the blockchain, distributed ledger and distributed application platform to more innovative enterprise use cases also grew. However, many enterprise use cases require performance characteristics that the permissionless blockchain technologies are unable (presently) to deliver. In addition, in many use cases, the identity of the participants is a hard requirement, such as in the case of financial transactions where Know-Your-Customer (KYC) and Anti-Money Laundering (AML) regulations must be followed.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "For enterprise use, we need to consider the following requirements: Participants must be identified/identifiable, Networks need to be permissioned, High transaction throughput performance, Low latency of transaction confirmation, Privacy and confidentiality of transactions and data pertaining to business transactions", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "While many early blockchain platforms are currently being adapted for enterprise use, Hyperledger Fabric has been designed for enterprise use from the outset. The following sections describe how Hyperledger Fabric (Fabric) differentiates itself from other blockchain platforms and describes some of the motivation for its architectural decisions.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Hyperledger Fabric is an open source enterprise-grade permissioned distributed ledger technology (DLT) platform, designed for use in enterprise contexts, that delivers some key differentiating capabilities over other popular distributed ledger or blockchain platforms.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "One key point of differentiation is that Hyperledger was established under the Linux Foundation, which itself has a long and very successful history of nurturing open source projects under open governance that grow strong sustaining communities and thriving ecosystems. Hyperledger is governed by a diverse technical steering committee, and the Hyperledger Fabric project by a diverse set of maintainers from multiple organizations. It has a development community that has grown to over 35 organizations and nearly 200 developers since its earliest commits.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Fabric has a highly modular and configurable architecture, enabling innovation, versatility and optimization for a broad range of industry use cases including banking, finance, insurance, healthcare, human resources, supply chain and even digital music delivery.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Fabric is the first distributed ledger platform to support smart contracts authored in general-purpose programming languages such as Java, Go and Node.js, rather than constrained domain-specific languages (DSL). This means that most enterprises already have the skill set needed to develop smart contracts, and no additional training to learn a new language or DSL is needed.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "The Fabric platform is also permissioned, meaning that, unlike with a public permissionless network, the participants are known to each other, rather than anonymous and therefore fully untrusted. This means that while the participants may not fully trust one another (they may, for example, be competitors in the same industry), a network can be operated under a governance model that is built off of what trust does exist between participants, such as a legal agreement or framework for handling disputes.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "One of the most important of the platform's differentiators is its support for pluggable consensus protocols that enable the platform to be more effectively customized to fit particular use cases and trust models. For instance, when deployed within a single enterprise, or operated by a trusted authority, fully byzantine fault tolerant consensus might be considered unnecessary and an excessive drag on performance and throughput. In situations such as that, a crash fault-tolerant (CFT) consensus protocol might be more than adequate whereas, in a multi-party, decentralized use case, a more traditional byzantine fault tolerant (BFT) consensus protocol might be required.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Fabric can leverage consensus protocols that do not require a native cryptocurrency to incent costly mining or to fuel smart contract execution. Avoidance of a cryptocurrency reduces some significant risk/attack vectors, and absence of cryptographic mining operations means that the platform can be deployed with roughly the same operational cost as any other distributed system.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "The combination of these differentiating design features makes Fabric one of the better performing platforms available today both in terms of transaction processing and transaction confirmation latency, and it enables privacy and confidentiality of transactions and the smart contracts (what Fabric calls “chaincode”) that implement them.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Hyperledger Fabric has been specifically architected to have a modular architecture. Whether it is pluggable consensus, pluggable identity management protocols such as LDAP or OpenID Connect, key management protocols or cryptographic libraries, the platform has been designed at its core to be configured to meet the diversity of enterprise use case requirements.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "At a high level, Fabric is comprised of the following modular components: A pluggable ordering service establishes consensus on the order of transactions and then broadcasts blocks to peers, A pluggable membership service provider is responsible for associating entities in the network with cryptographic identities, An optional peer-to-peer gossip service disseminates the blocks output by ordering service to other peers, Smart contracts (”chaincode”) run within a container environment (e.g. Docker) for isolation. They can be written in standard programming languages but do not have direct access to the ledger state. The ledger can be configured to support a variety of DBMSs. A pluggable endorsement and validation policy enforcement that can be independently configured per application.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "There is fair agreement in the industry that there is no “one blockchain to rule them all”. Hyperledger Fabric can be configured in multiple ways to satisfy the diverse solution requirements for multiple industry use cases.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "In a permissionless blockchain, virtually anyone can participate, and every participant is anonymous. In such a context, there can be no trust other than that the state of the blockchain, prior to a certain depth, is immutable. In order to mitigate this absence of trust, permissionless blockchains typically employ a “mined” native cryptocurrency or transaction fees to provide economic incentive to offset the extraordinary costs of participating in a form of byzantine fault tolerant consensus based on “proof of work” (PoW).", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Permissioned blockchains, on the other hand, operate a blockchain amongst a set of known, identified and often vetted participants operating under a governance model that yields a certain degree of trust. A permissioned blockchain provides a way to secure the interactions among a group of entities that have a common goal but which may not fully trust each other. By relying on the identities of the participants, a permissioned blockchain can use more traditional crash fault tolerant (CFT) or byzantine fault tolerant (BFT) consensus protocols that do not require costly mining.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Additionally, in such a permissioned context, the risk of a participant intentionally introducing malicious code through a smart contract is diminished. First, the participants are known to one another and all actions, whether submitting application transactions, modifying the configuration of the network or deploying a smart contract are recorded on the blockchain following an endorsement policy that was established for the network and relevant transaction type. Rather than being completely anonymous, the guilty party can be easily identified and the incident handled in accordance with the terms of the governance model.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "A smart contract, or what Fabric calls “chaincode”, functions as a trusted distributed application that gains its security/trust from the blockchain and the underlying consensus among the peers. It is the business logic of a blockchain application.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "There are three key points that apply to smart contracts, especially when applied to a platform: many smart contracts run concurrently in the network, they may be deployed dynamically (in many cases by anyone), and application code should be treated as untrusted, potentially even malicious.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Most existing smart-contract capable blockchain platforms follow an order-execute architecture in which the consensus protocol: validates and orders transactions then propagates them to all peer nodes, each peer then executes the transactions sequentially.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "The order-execute architecture can be found in virtually all existing blockchain systems, ranging from public/permissionless platforms such as Ethereum (with PoW-based consensus) to permissioned platforms such as Tendermint, Chain, and Quorum.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Smart contracts executing in a blockchain that operates with the order-execute architecture must be deterministic; otherwise, consensus might never be reached. To address the non-determinism issue, many platforms require that the smart contracts be written in a non-standard, or domain-specific language (such as Solidity) so that non-deterministic operations can be eliminated. This hinders wide-spread adoption because it requires developers writing smart contracts to learn a new language and may lead to programming errors.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Further, since all transactions are executed sequentially by all nodes, performance and scale is limited. The fact that the smart contract code executes on every node in the system demands that complex measures be taken to protect the overall system from potentially malicious contracts in order to ensure resiliency of the overall system.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Fabric introduces a new architecture for transactions that we call execute-order-validate. It addresses the resiliency, flexibility, scalability, performance and confidentiality challenges faced by the order-execute model by separating the transaction flow into three steps: execute a transaction and check its correctness, thereby endorsing it, order transactions via a (pluggable) consensus protocol, and validate transactions against an application-specific endorsement policy before committing them to the ledger", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "This design departs radically from the order-execute paradigm in that Fabric executes transactions before reaching final agreement on their order.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "In Fabric, an application-specific endorsement policy specifies which peer nodes, or how many of them, need to vouch for the correct execution of a given smart contract. Thus, each transaction need only be executed (endorsed) by the subset of the peer nodes necessary to satisfy the transaction’s endorsement policy. This allows for parallel execution increasing overall performance and scale of the system. This first phase also eliminates any non-determinism, as inconsistent results can be filtered out before ordering.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Because we have eliminated non-determinism, Fabric is the first blockchain technology that enables use of standard programming languages.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "As we have discussed, in a public, permissionless blockchain network that leverages PoW for its consensus model, transactions are executed on every node. This means that neither can there be confidentiality of the contracts themselves, nor of the transaction data that they process. Every transaction, and the code that implements it, is visible to every node in the network. In this case, we have traded confidentiality of contract and data for byzantine fault tolerant consensus delivered by PoW.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "This lack of confidentiality can be problematic for many business/enterprise use cases. For example, in a network of supply-chain partners, some consumers might be given preferred rates as a means of either solidifying a relationship, or promoting additional sales. If every participant can see every contract and transaction, it becomes impossible to maintain such business relationships in a completely transparent network — everyone will want the preferred rates!", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "As a second example, consider the securities industry, where a trader building a position (or disposing of one) would not want her competitors to know of this, or else they will seek to get in on the game, weakening the trader’s gambit.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "In order to address the lack of privacy and confidentiality for purposes of delivering on enterprise use case requirements, blockchain platforms have adopted a variety of approaches. All have their trade-offs.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Encrypting data is one approach to providing confidentiality; however, in a permissionless network leveraging PoW for its consensus, the encrypted data is sitting on every node. Given enough time and computational resource, the encryption could be broken. For many enterprise use cases, the risk that their information could become compromised is unacceptable.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Zero knowledge proofs (ZKP) are another area of research being explored to address this problem, the trade-off here being that, presently, computing a ZKP requires considerable time and computational resources. Hence, the trade-off in this case is performance for confidentiality.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "In a permissioned context that can leverage alternate forms of consensus, one might explore approaches that restrict the distribution of confidential information exclusively to authorized nodes.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Hyperledger Fabric, being a permissioned platform, enables confidentiality through its channel architecture and private data feature. In channels, participants on a Fabric network establish a sub-network where every member has visibility to a particular set of transactions. Thus, only those nodes that participate in a channel have access to the smart contract (chaincode) and data transacted, preserving the privacy and confidentiality of both. Private data allows collections between members on a channel, allowing much of the same protection as channels without the maintenance overhead of creating and maintaining a separate channel.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "The ordering of transactions is delegated to a modular component for consensus that is logically decoupled from the peers that execute transactions and maintain the ledger. Specifically, the ordering service. Since consensus is modular, its implementation can be tailored to the trust assumption of a particular deployment or solution. This modular architecture allows the platform to rely on well-established toolkits for CFT (crash fault-tolerant) or BFT (byzantine fault-tolerant) ordering.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Fabric currently offers a CFT ordering service implementation based on the etcd library of the Raft protocol. For information about currently available ordering services, check out our conceptual documentation about ordering.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Note also that these are not mutually exclusive. A Fabric network can have multiple ordering services supporting different applications or application requirements.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Performance of a blockchain platform can be affected by many variables such as transaction size, block size, network size, as well as available hardware resources such as CPU, Memory, Disk Space, Disk and network I/O.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Any serious evaluation of blockchain platforms should include Hyperledger Fabric in its short list.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Combined, the differentiating capabilities of Fabric make it a highly scalable system for permissioned blockchains supporting flexible trust assumptions that enable the platform to support a wide range of industry use cases ranging from government, to finance, to supply-chain logistics, to healthcare and so much more.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - }, - { - "content": "Hyperledger Fabric is the most active of the Hyperledger projects. The community building around the platform is growing steadily, and the innovation delivered with each successive release far out-paces any of the other enterprise blockchain platforms.", - "meta": { - "name": "Hyperledger Introduction", - "id": "hl1" - } - } -] diff --git a/ingest/faq_squad_format.json b/ingest/faq_squad_format.json deleted file mode 100644 index 6c97db1..0000000 --- a/ingest/faq_squad_format.json +++ /dev/null @@ -1,358 +0,0 @@ -{ - "data": [ - { - "paragraphs": [ - { - "context": "The number of peers required to endorse a transaction is driven by the endorsement policy that is specified in the chaincode definition.", - "qas": [ - { - "question": "How many peers in the network need to endorse a transaction?", - "id": "q1", - "answers": [ - { - "text": "The number of peers required to endorse a transaction is driven by the endorsement policy that is specified in the chaincode definition.", - "answer_start": 0 - } - ] - } - ] - }, - { - "context": "Clients only need to connect to as many peers as are required by the endorsement policy for the chaincode.", - "qas": [ - { - "question": "Does an application client need to connect to all peers?", - "id": "q2", - "answers": [ - { - "text": "Clients only need to connect to as many peers as are required by the endorsement policy for the chaincode.", - "answer_start": 0 - } - ] - } - ] - }, - { - "context": "There are various aspects to data privacy. First, you can segregate your network into channels, where each channel represents a subset of participants that are authorized to see the data for the chaincodes that are deployed to that channel.", - "qas": [ - { - "question": "How can I ensure my privacy?", - "id": "q3", - "answers": [ - { - "text": "you can segregate your network into channels, where each channel represents a subset of participants that are authorized to see the data for the chaincodes that are deployed to that channel.", - "answer_start": 50 - } - ] - } - ] - }, - { - "context": "Second, you can use private-data to keep ledger data private from other organizations on the channel. A private data collection allows a defined subset of organizations on a channel the ability to endorse, commit, or query private data without having to create a separate channel. Other participants on the channel receive only a hash of the data. For more information refer to the Using Private Data in Fabric tutorial. Note that the key concepts topic also explains when to use private data instead of a channel.", - "qas": [ - { - "question": "How can keep data private from other organizations?", - "id": "q4", - "answers": [ - { - "text": "you can use private-data to keep ledger data private from other organizations on the channel. A private data collection allows a defined subset of organizations on a channel the ability to endorse, commit, or query private data without having to create a separate channel.", - "answer_start": 8 - }, - { - "text": "A private data collection allows a defined subset of organizations on a channel the ability to endorse, commit, or query private data without having to create a separate channel. Other participants on the channel receive only a hash of the data.", - "answer_start": 102 - } - ] - } - ] - }, - { - "context": "Third, as an alternative to Fabric hashing the data using private data, the client application can hash or encrypt the data before calling chaincode. If you hash the data then you will need to provide a means to share the source data. If you encrypt the data then you will need to provide a means to share the decryption keys.", - "qas": [ - { - "question": "How to encrypt data?", - "id": "q5", - "answers": [ - { - "text": "the client application can hash or encrypt the data before calling chaincode.", - "answer_start": 72 - } - ] - } - ] - }, - { - "context": "Fourth, you can restrict data access to certain roles in your organization, by building access control into the chaincode logic.", - "qas": [ - { - "question": "How can I restrict data to access to certain roles?", - "id": "q6", - "answers": [ - { - "text": "by building access control into the chaincode logic.", - "answer_start": 76 - } - ] - } - ] - }, - { - "context": "Fifth, ledger data at rest can be encrypted via file system encryption on the peer, and data in-transit is encrypted via TLS.", - "qas": [ - { - "question": "Ledger data can be encrypted?", - "id": "q7", - "answers": [ - { - "text": "via file system encryption on the peer, and data in-transit is encrypted via TLS.", - "answer_start": 44 - } - ] - } - ] - }, - { - "context": "Orderers receive endorsed transactions that are submitted from application clients. The endorsed payload contains the chaincode execution results including the ReadSet and WriteSet information. The orderers only validate the submitter’s identity and order transactions, they do not open the endorsed transactions. If you do not want the data to go through the orderers at all, then utilize the private data feature of Fabric. Alternatively, you can hash or encrypt the data in the client application before calling chaincode. If you encrypt the data then you will need to provide a means to share the decryption keys.", - "qas": [ - { - "question": "Do the orderers see the transaction data?", - "id": "q8", - "answers": [ - { - "text": "via file system encryption on the peer, and data in-transit is encrypted via TLS.", - "answer_start": 0 - } - ] - }, - { - "question": "How can I avoid data to go through the orderers at all?", - "id": "q9", - "answers": [ - { - "text": "If you do not want the data to go through the orderers at all, then utilize the private data feature of Fabric", - "answer_start": 314 - }, - { - "text": "via file system encryption on the peer, and data in-transit is encrypted via TLS.", - "answer_start": 441 - } - ] - } - ] - } - ], - "title": "Hyperledger FAQ" - }, - { - "paragraphs": [ - { - "context": "In general terms, a blockchain is an immutable transaction ledger, maintained within a distributed network of peer nodes. These nodes each maintain a copy of the ledger by applying transactions that have been validated by a consensus protocol, grouped into blocks that include a hash that bind each block to the preceding block.", - "qas": [ - { - "question": "What is a blockchain?", - "id": "q1", - "answers": [ - { - "text": "a blockchain is an immutable transaction ledger, maintained within a distributed network of peer nodes", - "answer_start": 18 - } - ] - } - ] - }, - { - "context": "The first and most widely recognized application of blockchain is the Bitcoin cryptocurrency, though others have followed in its footsteps. Ethereum, an alternative cryptocurrency, took a different approach, integrating many of the same characteristics as Bitcoin but adding smart contracts to create a platform for distributed applications. Bitcoin and Ethereum fall into a class of blockchain that we would classify as public permissionless blockchain technology. Basically, these are public networks, open to anyone, where participants interact anonymously.", - "qas": [ - { - "question": "What is the first application of blockchain?", - "id": "q1", - "answers": [ - { - "text": "The first and most widely recognized application of blockchain is the Bitcoin cryptocurrency, though others have followed in its footsteps", - "answer_start": 0 - } - ] - } - ] - }, - { - "context": "As the popularity of Bitcoin, Ethereum and a few other derivative technologies grew, interest in applying the underlying technology of the blockchain, distributed ledger and distributed application platform to more innovative enterprise use cases also grew. However, many enterprise use cases require performance characteristics that the permissionless blockchain technologies are unable (presently) to deliver. In addition, in many use cases, the identity of the participants is a hard requirement, such as in the case of financial transactions where Know-Your-Customer (KYC) and Anti-Money Laundering (AML) regulations must be followed.", - "qas": [ - { - "question": "Why the interest in blockchain is growing?", - "id": "q1", - "answers": [ - { - "text": "As the popularity of Bitcoin, Ethereum and a few other derivative technologies grew, interest in applying the underlying technology of the blockchain, distributed ledger and distributed application platform to more innovative enterprise use cases", - "answer_start": 0 - } - ] - } - ] - }, - { - "context": "For enterprise use, we need to consider the following requirements: Participants must be identified/identifiable, Networks need to be permissioned, High transaction throughput performance, Low latency of transaction confirmation, Privacy and confidentiality of transactions and data pertaining to business transactions", - "qas": [ - { - "question": "What are the requirements for enterprise use?", - "id": "q1", - "answers": [ - { - "text": "the following requirements: Participants must be identified/identifiable, Networks need to be permissioned, High transaction throughput performance, Low latency of transaction confirmation, Privacy and confidentiality of transactions and data pertaining to business transactions", - "answer_start": 68 - } - ] - } - ] - }, - { - "context": "While many early blockchain platforms are currently being adapted for enterprise use, Hyperledger Fabric has been designed for enterprise use from the outset. The following sections describe how Hyperledger Fabric (Fabric) differentiates itself from other blockchain platforms and describes some of the motivation for its architectural decisions.", - "qas": [ - { - "question": "Is Hyperledger fabric useful for enterprise?", - "id": "q1", - "answers": [ - { - "text": "Hyperledger Fabric has been designed for enterprise use from the outset", - "answer_start": 86 - } - ] - } - ] - }, - { - "context": "Hyperledger Fabric is an open source enterprise-grade permissioned distributed ledger technology (DLT) platform, designed for use in enterprise contexts, that delivers some key differentiating capabilities over other popular distributed ledger or blockchain platforms.", - "qas": [ - { - "question": "What is hyperledger fabric?", - "id": "q1", - "answers": [ - { - "text": "Hyperledger Fabric is an open source enterprise-grade permissioned distributed ledger technology (DLT) platform, designed for use in enterprise contexts, that delivers some key differentiating capabilities over other popular distributed ledger or blockchain platforms.", - "answer_start": 0 - } - ] - } - ] - }, - { - "context": "One key point of differentiation is that Hyperledger was established under the Linux Foundation, which itself has a long and very successful history of nurturing open source projects under open governance that grow strong sustaining communities and thriving ecosystems. Hyperledger is governed by a diverse technical steering committee, and the Hyperledger Fabric project by a diverse set of maintainers from multiple organizations. It has a development community that has grown to over 35 organizations and nearly 200 developers since its earliest commits.", - "qas": [ - { - "question": "What is the main difference from Hyperledger fabric and other blockchians?", - "id": "q1", - "answers": [ - { - "text": "Hyperledger was established under the Linux Foundation", - "answer_start": 41 - } - ] - }, - { - "question": "What is the governance of Hyperledger fabric?", - "id": "q1", - "answers": [ - { - "text": "open governance that grow strong sustaining communities and thriving ecosystems", - "answer_start": 189 - } - ] - } - ] - }, - { - "context": "Fabric has a highly modular and configurable architecture, enabling innovation, versatility and optimization for a broad range of industry use cases including banking, finance, insurance, healthcare, human resources, supply chain and even digital music delivery.", - "qas": [ - { - "question": "Which are some general uses cases application of Fabric?", - "id": "q1", - "answers": [ - { - "text": "use cases including banking, finance, insurance, healthcare, human resources, supply chain and even digital music delivery", - "answer_start": 139 - } - ] - } - ] - }, - { - "context": "Fabric is the first distributed ledger platform to support smart contracts authored in general-purpose programming languages such as Java, Go and Node.js, rather than constrained domain-specific languages (DSL). This means that most enterprises already have the skill set needed to develop smart contracts, and no additional training to learn a new language or DSL is needed.", - "qas": [ - { - "question": "Does fabric support general-purpose programming languages smart contracts?", - "id": "q1", - "answers": [ - { - "text": "Fabric is the first distributed ledger platform to support smart contracts authored in general-purpose programming languages such as Java, Go and Node.js", - "answer_start": 0 - } - ] - } - ] - }, - { - "context": "The Fabric platform is also permissioned, meaning that, unlike with a public permissionless network, the participants are known to each other, rather than anonymous and therefore fully untrusted. This means that while the participants may not fully trust one another (they may, for example, be competitors in the same industry), a network can be operated under a governance model that is built off of what trust does exist between participants, such as a legal agreement or framework for handling disputes.", - "qas": [ - { - "question": "What does permissioned mean?", - "id": "q1", - "answers": [ - { - "text": "permissioned, meaning that, unlike with a public permissionless network, the participants are known to each other, rather than anonymous and therefore fully untrusted", - "answer_start": 28 - } - ] - } - ] - }, - { - "context": "One of the most important of the platform's differentiators is its support for pluggable consensus protocols that enable the platform to be more effectively customized to fit particular use cases and trust models. For instance, when deployed within a single enterprise, or operated by a trusted authority, fully byzantine fault tolerant consensus might be considered unnecessary and an excessive drag on performance and throughput. In situations such as that, a crash fault-tolerant (CFT) consensus protocol might be more than adequate whereas, in a multi-party, decentralized use case, a more traditional byzantine fault tolerant (BFT) consensus protocol might be required.", - "qas": [ - { - "question": "Does fabric can be an effective customized platform?", - "id": "q1", - "answers": [ - { - "text": "the platform's differentiators is its support for pluggable consensus protocols that enable the platform to be more effectively customized to fit particular use cases and trust models", - "answer_start": 29 - } - ] - } - ] - }, - { - "context": "Fabric can leverage consensus protocols that do not require a native cryptocurrency to incent costly mining or to fuel smart contract execution. Avoidance of a cryptocurrency reduces some significant risk/attack vectors, and absence of cryptographic mining operations means that the platform can be deployed with roughly the same operational cost as any other distributed system.", - "qas": [ - { - "question": "Fabric require a native cryptocurrency?", - "id": "q1", - "answers": [ - { - "text": "do not require a native cryptocurrency to incent costly mining or to fuel smart contract execution", - "answer_start": 45 - } - ] - } - ] - }, - { - "context": "The combination of these differentiating design features makes Fabric one of the better performing platforms available today both in terms of transaction processing and transaction confirmation latency, and it enables privacy and confidentiality of transactions and the smart contracts (what Fabric calls “chaincode”) that implement them.", - "qas": [ - { - "question": "What are performance of Hyperledger fabric in terms of transaction processing?", - "id": "q1", - "answers": [ - { - "text": "design features makes Fabric one of the better performing platforms available today both in terms of transaction processing and transaction confirmation latency", - "answer_start": 41 - } - ] - } - ] - } - ], - "title": "Hyperledger Introduction" - } - ], - "version": "2.0" -} diff --git a/main.py b/main.py deleted file mode 100644 index c8795ad..0000000 --- a/main.py +++ /dev/null @@ -1,73 +0,0 @@ -import json -import glob as glob -from dotenv import load_dotenv -import os - -# Load environment variables from a .env file (containing Elastich Search config and credentials) -load_dotenv() - -PASSWORD=os.environ.get("PASSWORD") - -BASEPATH = './ingest' -# file in Elastic Search format -dataset_path = BASEPATH + "faq_es_format_v3" - -# create Elastic Search Document Store connecting to local service -from haystack.document_stores import ElasticsearchDocumentStore -document_store = ElasticsearchDocumentStore(host="localhost", port=9200, username="elastic", password=PASSWORD, scheme="https", verify_certs=False, return_embedding=True) - -# read all file in Elastic Search format -es_files = glob.glob(dataset_path + "*") -# loop files -for file in es_files: - # create a dictionary using file in Elastic Search format - es_dict = {} - with open(file, "r", encoding="utf8") as f: - es_dict = json.load(f) - # write data in Document Store - document_store.write_documents(es_dict, index="document") - - -# create the Retriever of documents -from haystack.nodes.retriever import BM25Retriever -bm25_retriever = BM25Retriever(document_store=document_store) - -# create the Reader of documents -from haystack.nodes import FARMReader -#alternative models: deepset/roberta-base-squad2-distilled or deepset/xlm-roberta-large-squad2 or the tiny distilled model: deepset/tinyroberta-squad2 -model_ckpt = "deepset/xlm-roberta-large-squad2" -max_seq_length, doc_stride = 384, 128 -reader = FARMReader(model_name_or_path=model_ckpt, progress_bar=False, max_seq_len=max_seq_length, doc_stride=doc_stride, return_no_answer=False) - -# create the pipeline -from haystack.pipelines import ExtractiveQAPipeline -pipe = ExtractiveQAPipeline(reader=reader, retriever=bm25_retriever) - -# file in SQuAD format -dataset_path_squad = "faq_squad_format.json" -# fine-tuning of pre-trained model -reader.train(data_dir=BASEPATH, use_gpu=False, n_epochs=1, batch_size=16, train_filename=dataset_path_squad) - -# loop while user does not write "exit" -while True: - # read the questio from user prompt - query = input("Ask me something about Hyperledger documentation? \n\n") - - # stop the loop if user write "exit" - if query == "exit": - break - - item_id = "hl1" - # number of output answers - n_answers = 1 - # answers prediction - preds = pipe.run(query=query, params={"Retriever": {"top_k": 3}, "Reader": {"top_k": n_answers}}) - - # print the question - print(f"Question: {preds['query']} \n") - - # print the answers and the snippet of the text - for idx in range(n_answers): - print(f"Answer {idx+1}: {preds['answers'][idx].answer}") - print(f"Text snippet: ...{preds['answers'][idx].context}...") - print("\n\n") diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 3e338bf..0000000 --- a/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -python-dotenv \ No newline at end of file diff --git a/src/hyperledger_aifaq_poc_v3.ipynb b/src/hyperledger_aifaq_poc_v3.ipynb new file mode 100644 index 0000000..72521ca --- /dev/null +++ b/src/hyperledger_aifaq_poc_v3.ipynb @@ -0,0 +1,10545 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "LB2Q9pgs3y2k", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0e0dccb8-aeaa-49e4-e810-686c03cfcc62" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.38.1)\n", + "Collecting peft\n", + " Downloading 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h11, deprecated, colorama, chroma-hnswlib, bcrypt, backoff, asgiref, aiofiles, watchfiles, uvicorn, typing-inspect, starlette, posthog, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, jsonpatch, httpcore, coloredlogs, bs4, bitsandbytes, opentelemetry-sdk, opentelemetry-instrumentation, onnxruntime, langsmith, kubernetes, httpx, fastapi, dataclasses-json, accelerate, opentelemetry-instrumentation-asgi, opentelemetry-exporter-otlp-proto-grpc, langchain-core, gradio-client, sentence-transformers, peft, opentelemetry-instrumentation-fastapi, langchain-community, gradio, langchain, chromadb\n", + " Attempting uninstall: importlib-metadata\n", + " Found existing installation: importlib-metadata 7.0.1\n", + " Uninstalling importlib-metadata-7.0.1:\n", + " Successfully uninstalled importlib-metadata-7.0.1\n", + "Successfully installed accelerate-0.27.2 aiofiles-23.2.1 asgiref-3.7.2 backoff-2.2.1 bcrypt-4.1.2 bitsandbytes-0.42.0 bs4-0.0.2 chroma-hnswlib-0.7.3 chromadb-0.4.24 colorama-0.4.6 coloredlogs-15.0.1 dataclasses-json-0.6.4 deprecated-1.2.14 fastapi-0.110.0 ffmpy-0.3.2 gradio-4.19.2 gradio-client-0.10.1 h11-0.14.0 httpcore-1.0.4 httptools-0.6.1 httpx-0.27.0 humanfriendly-10.0 importlib-metadata-6.11.0 jsonpatch-1.33 jsonpointer-2.4 kubernetes-29.0.0 langchain-0.1.9 langchain-community-0.0.24 langchain-core-0.1.28 langsmith-0.1.10 marshmallow-3.21.0 mmh3-4.1.0 monotonic-1.6 mypy-extensions-1.0.0 onnxruntime-1.17.1 opentelemetry-api-1.23.0 opentelemetry-exporter-otlp-proto-common-1.23.0 opentelemetry-exporter-otlp-proto-grpc-1.23.0 opentelemetry-instrumentation-0.44b0 opentelemetry-instrumentation-asgi-0.44b0 opentelemetry-instrumentation-fastapi-0.44b0 opentelemetry-proto-1.23.0 opentelemetry-sdk-1.23.0 opentelemetry-semantic-conventions-0.44b0 opentelemetry-util-http-0.44b0 orjson-3.9.15 overrides-7.7.0 peft-0.9.0 posthog-3.4.2 pulsar-client-3.4.0 pydub-0.25.1 pypika-0.48.9 python-dotenv-1.0.1 python-multipart-0.0.9 ruff-0.3.0 semantic-version-2.10.0 sentence-transformers-2.5.1 shellingham-1.5.4 starlette-0.36.3 tomlkit-0.12.0 typing-inspect-0.9.0 uvicorn-0.27.1 uvloop-0.19.0 watchfiles-0.21.0 websockets-11.0.3\n" + ] + } + ], + "source": [ + "#install required packages\n", + "!pip install transformers peft accelerate bitsandbytes safetensors sentencepiece chromadb langchain sentence-transformers gradio bs4" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "As3Q9fo_Aska" + }, + "outputs": [], + "source": [ + "# fixing unicode error in google colab\n", + "import locale\n", + "locale.getpreferredencoding = lambda: \"UTF-8\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "b08wbHdz3y2m" + }, + "outputs": [], + "source": [ + "# import dependencies\n", + "import torch\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline\n", + "\n", + "import os\n", + "import gradio as gr\n", + "from google.colab import drive\n", + "\n", + "import chromadb\n", + "from langchain.llms import HuggingFacePipeline\n", + "from langchain.document_loaders import TextLoader\n", + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain.embeddings import HuggingFaceEmbeddings\n", + "from langchain.vectorstores import Chroma\n", + "from langchain import HuggingFacePipeline\n", + "from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader\n", + "from langchain_community.document_loaders import GitHubIssuesLoader ###\n", + "from langchain_community.document_loaders.merge import MergedDataLoader ###\n", + "from langchain_community.vectorstores.utils import filter_complex_metadata\n", + "from langchain.chains import ConversationalRetrievalChain\n", + "from langchain.memory import ConversationBufferMemory" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "kKkVcgAv8frU" + }, + "outputs": [], + "source": [ + "# specify model huggingface mode name\n", + "model_name = \"anakin87/zephyr-7b-alpha-sharded\"\n", + "\n", + "# function for loading 4-bit quantized model\n", + "def load_quantized_model(model_name: str):\n", + " \"\"\"\n", + " :param model_name: Name or path of the model to be loaded.\n", + " :return: Loaded quantized model.\n", + " \"\"\"\n", + " bnb_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_use_double_quant=True,\n", + " bnb_4bit_quant_type=\"nf4\",\n", + " bnb_4bit_compute_dtype=torch.bfloat16\n", + " )\n", + "\n", + " model = AutoModelForCausalLM.from_pretrained(\n", + " model_name,\n", + " torch_dtype=torch.bfloat16,\n", + " quantization_config=bnb_config\n", + " )\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "2C0DVleS8iwj" + }, + "outputs": [], + "source": [ + "# function for initializing tokenizer\n", + "def initialize_tokenizer(model_name: str):\n", + " \"\"\"\n", + " Initialize the tokenizer with the specified model_name.\n", + "\n", + " :param model_name: Name or path of the model for tokenizer initialization.\n", + " :return: Initialized tokenizer.\n", + " \"\"\"\n", + " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + " tokenizer.bos_token_id = 1 # Set beginning of sentence token id\n", + " return tokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 670, + "referenced_widgets": [ + "28236dfb592846bba5443bf6bfb41650", + "ce7b138a42ae49e7b5b3a5376e11d7cb", + "9509b527110e4ac09fc5054224fd7181", + "7639109c9064473692b2ab90d8aab217", + "959af09170494ef99bbb608889f1a318", + "a8373191f6524453b952665ca62393bd", + "5cedc31e76754902bda8037bf96d5a3d", + "b4375e29d33b4f05bcbeb39d21d52ad4", + "8d40850dee874028aa98703129407f4d", + "9d1367108831487898e89dfdb72ffcaa", + "9d7bf39faa4144dba1e9ab22b2f5218e", + "77ee1e776d2f49adb08b2a7602221edf", + 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datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/638 [00:00 tuple:\n", + " try:\n", + "\n", + " memory = ConversationBufferMemory(\n", + " memory_key='chat_history',\n", + " return_messages=False\n", + " )\n", + " qa_chain = ConversationalRetrievalChain.from_llm(\n", + " llm=llm,\n", + " retriever=retriever,\n", + " memory=memory,\n", + " get_chat_history=lambda h: h,\n", + " )\n", + "\n", + " result = qa_chain({'question': query, 'chat_history': chat_history})\n", + " chat_history.append((query, result['answer']))\n", + " return '', chat_history\n", + "\n", + "\n", + " except Exception as e:\n", + " chat_history.append((query, e))\n", + " return '', chat_history" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "dETfjAAAjuaU", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 626 + }, + "outputId": "abacc373-cdad-4421-bd43-25a61765d7b5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n", + "\n", + "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n", + "Running on public URL: https://b23e5830f7f2d5e9c4.gradio.live\n", + "\n", + "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "

" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "# build gradio ui\n", + "with gr.Blocks() as demo:\n", + "\n", + " chatbot = gr.Chatbot(label='Chat with your data (Zephyr 7B Alpha)')\n", + " msg = gr.Textbox()\n", + " clear = gr.ClearButton([msg, chatbot])\n", + "\n", + " msg.submit(create_conversation, [msg, chatbot], [msg, chatbot])\n", + "\n", + "demo.launch()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "V100", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "28236dfb592846bba5443bf6bfb41650": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": 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