From 311b49fc3505e7a352dc385972fa5d321e95d30f Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 7 Feb 2025 08:02:31 +0100 Subject: [PATCH 01/81] chore(docs): update RAG Chatbot API documentation --- .../rag-chatbot-api/10_overview_and_usage.md | 219 ++++++++++++++++++ docs/runtime_suite/rag-chatbot-api/20_apis.md | 204 ++++++++++++++++ .../rag-chatbot-api/_category_.json | 4 + .../rag-chatbot-api/changelog.md | 64 +++++ 4 files changed, 491 insertions(+) create mode 100644 docs/runtime_suite/rag-chatbot-api/10_overview_and_usage.md create mode 100644 docs/runtime_suite/rag-chatbot-api/20_apis.md create mode 100644 docs/runtime_suite/rag-chatbot-api/_category_.json create mode 100644 docs/runtime_suite/rag-chatbot-api/changelog.md diff --git a/docs/runtime_suite/rag-chatbot-api/10_overview_and_usage.md b/docs/runtime_suite/rag-chatbot-api/10_overview_and_usage.md new file mode 100644 index 0000000000..e2b4d9ca98 --- /dev/null +++ b/docs/runtime_suite/rag-chatbot-api/10_overview_and_usage.md @@ -0,0 +1,219 @@ +--- +id: overview_and_usage +title: AI RAG Template +sidebar_label: Overview And Usage +--- + + + +The _AI RAG Template_ is a template to build and run your own RAG application and build a Chatbot that is capable to perform a conversation with a user. + +The service is developed using the [LangChain](https://python.langchain.com/docs/get_started/introduction/) framework, which enables creating sequences of complex interactions using Large Language Models. The web server is implemented using the [FastAPI](https://fastapi.tiangolo.com/) framework. + +In order to work, it is required to have a MongoDB instance to be used as Vector Store and that supports [MongoDB Vector Search indexes](https://www.mongodb.com/docs/atlas/atlas-vector-search/tutorials/vector-search-quick-start/), which means an Atlas instance with version 6 or above. + +## Overview + +The following is the high-level architecture of the template. + +```mermaid +flowchart LR + fe[Frontend] + be[Backend] + vs[(Vector Store)] + llm[LLM API] + eg[Embeddings Generator API] + + fe --1. user question +\nchat history--> be + be --2. user question--> eg + eg --3. embedding-->be + be --4. similarity search-->vs + vs --5. similar docs-->be + be --6. user question +\nchat history +\nsimilar docs-->llm + llm --7. bot answer--> be + be --8. bot answer--> fe +``` + +### Embeddings + +Please mind that the template does not include embeddings or any logic to create them. It is intended that the Vector Store will include the embeddings (or these are generated separately). In any case, please ensure that the embedding model used the populate the Vector Store is the same embedding model used when running the service, otherwise the service will generate answers only based on its own knowledge, without being able to use the Vector Store, with the risk of hallucinations when chatting with the user. + +### API + +Read more at [the related page](./20_apis.md) + +## Environment Variables + +The following environment variables are required for the service to work: + +- **PORT**: the port used to expose the API (default: _3000_) +- **LOG_LEVEL**: the level of the logger (default: _INFO_) +- **CONFIGURATION_PATH**: the path that contains the [JSON configuration file](#configuration) +- **MONGODB_CLUSTER_URI**: the MongoDB connection string +- **LLM_API_KEY**: the API Key of the LLM (_NOTE_: currently, we support only the OpenAI models, thus the API Key is the same as the OpenAI API Key) +- **EMBEDDINGS_API_KEY**: the API Key of the embeddings model (_NOTE_: currently, we support only the OpenAI models, thus the API Key is the same as the OpenAI API Key) + +It is suggested to save the environment variables in a `.env` file. + +## Configuration + +The service requires several configuration parameters for execution. Below is an example of configuration: + +```json +{ + "llm": { + "type": "openai", + "name": "gpt-3.5-turbo", + "temperature": 0.7, + }, + "embeddings": { + "type": "openai", + "name": "text-embedding-3-small" + }, + "vectorStore": { + "dbName": "database-test", + "collectionName": "assistant-documents", + "indexName": "vector_index", + "relevanceScoreFn": "euclidean", + "embeddingKey": "embedding", + "textKey": "text", + "maxDocumentsToRetrieve": 4, + "minScoreDistance": 0.5 + }, + "chain": { + "aggregateMaxTokenNumber": 2000, + "rag": { + "promptsFilePath": { + "system": "/path/to/system-prompt.txt", + "user": "/path/to/user-prompt.txt" + } + } + } +} +``` + +Description of configuration parameters: + +| Param Name | Description | +|------------|-------------| +| LLM Type | Identifier of the provider to use for the LLM. Default: `openai`. See more in [Supported LLM providers](#supported-llm-providers) | +| LLM Name | Name of the chat model to use. [Must be supported by LangChain.](https://python.langchain.com/docs/integrations/chat/) | +| LLM Temperature | Temperature parameter for the LLM, intended as the grade of variability and randomness of the generated response. Default: `0.7` (suggested value). | +| Embeddings Type | Identifier of the provider to use for the Embeddings. Default: `openai`. See more in [Supported Embeddings providers](#supported-embeddings-providers) | +| Embeddings Name | Name of the encoder to use. [Must be supported by LangChain.](https://python.langchain.com/docs/integrations/text_embedding/) | +| Vector Store DB Name | Name of the MongoDB database to use as a knowledge base. | +| Vector Store Collection Name | Name of the MongoDB collection to use for storing documents and document embeddings. | +| Vector Store Index Name | Name of the vector index to use for retrieving documents related to the user's query. The application will check at startup if a vector index with this name exists, it needs to be updated or needs to be created. | +| Vector Store Relevance Score Function | Name of the similarity function used for extracting similar documents using the created vector index. In case the existing vector index uses a different similarity function, the index will be updated using this as a similarity function. | +| Vector Store Embeddings Key | Name of the field used to save the semantic encoding of documents. In case the existing vector index uses a different key to store the embedding in the collection, the index will be updated using this as key. Please mind that any change of this value might require to recreate the embeddings. | +| Vector Store Text Key | Name of the field used to save the raw document (or chunk of document). | +| Vector Store Max. Documents To Retrieve | Maximum number of documents to retrieve from the Vector Store. | +| Vector Store Min. Score Distance | Minimum distance beyond which retrieved documents from the Vector Store are discarded. | +| Chain RAG System Prompts File Path | ath to the file containing system prompts for the RAG model. If omitted, the application will use a standard system prompt. | +| Chain RAG User Prompts File Path | Path to the file containing user prompts for the RAG model. If omitted, the application will use a standard system prompt. | + +### Supported LLM providers + +The property `type` inside the `llm` object of the configuration should be one of the supported providers for the LLM. +Currently, the supported LLM providers are: + +- OpenAI (`openai`), in which case the `llm` configuration could be the following: + ```json + { + "type": "openai", + "name": "gpt-3.5-turbo", + "temperature": 0.7, + } + ``` + with the properties explained above. + +- Azure OpenAI (`azure`), in which case the `llm` configuration could be the following: + ```json + { + "type": "azure", + "name": "gpt-3.5-turbo", + "deploymentName": "dep-gpt-3.5-turbo", + "url": "https://my-company.openai.azure.com/", + "apiVersion": "my-azure-api-version", + "temperature": 0.7 + } + ``` + + While, `type` is always `azure`, and `name` and `temperature` have been already explained, the other properties are: + | Name | Description | + |------|-------------| + | `deploymentName` | Name of the deployment to use. | + | `url` | URL of the Azure OpenAI service to call. | + | `apiVersion` | API version of the Azure OpenAI service. | + +### Supported Embeddings providers + +The property `type` inside the `embeddings` object of the configuration should be one of the supported providers for the Embeddings. +Currently, the supported Embeddings providers are: + +- OpenAI (`openai`), in which case the `embeddings` configuration could be the following: + ```json + { + "type": "openai", + "name": "text-embedding-3-small", + } + ``` + with the properties explained above. + + - Azure OpenAI (`azure`), in which case the `embeddings` configuration could be the following: + ```json + { + "type": "azure", + "name": "text-embedding-3-small", + "deploymentName": "dep-text-embedding-3-small", + "url": "https://my-company.openai.azure.com/", + "apiVersion": "my-azure-api-version" + } + ``` + While, `type` is always `azure`, and `name` have been already explained, the other properties are: + + | Name | Description | + |------|-------------| + | `deploymentName` | Name of the deployment to use. | + | `url` | URL of the Azure OpenAI service to call. | + | `apiVersion` | API version of the Azure OpenAI service. | + +### Create a Vector Index + +:::info +MongoDB Vector Search Index is updated automatically by the application at its startup, always updating the `path`, the `numDimensions` and the `similarity` fields according to the configuration. + +It also creates the index with the name `vectorStore.indexName` if it does not exist. + +This part is included only for information purposes. +::: + +This template requires a [MongoDB Vector Search Index](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-type/) to function correctly, and requires using MongoDB Atlas or a MongoDB on-premise cluster with version 6 or above. + +The Vector Search Index should have the following structure: + +```json +{ + "fields": [ + { + "type": "vector", + "path": "<>", + "numDimensions": 768, + "similarity": "<>" + } + ] +} +``` + +where: +- `embeddingsKey` is the name of the field used to store the semantic encoding of documents. +- `relevanceScoreFn` is the name of the similarity function used for extracting similar documents using the created vector index. In case the existing vector index uses a different similarity function, the index will be updated using this as a similarity function. +- the `numDimensions` value depends on the Embedding Model used (supported: `text-embedding-3-small`, `text-embedding-3-large` and its deployment versions - if using Azure OpenAI). + +:::warning +In the event that an error occurs during the creation or update of the Vector Index, the exception will be logged, but the application will still start. However, the functioning of the service is not guaranteed. +::: diff --git a/docs/runtime_suite/rag-chatbot-api/20_apis.md b/docs/runtime_suite/rag-chatbot-api/20_apis.md new file mode 100644 index 0000000000..4708e38668 --- /dev/null +++ b/docs/runtime_suite/rag-chatbot-api/20_apis.md @@ -0,0 +1,204 @@ +--- +id: apis +title: APIs +sidebar_label: APIs +--- + + + +The following documents includes a comprehensible list of the available APIs exposed by the service. + +When running the service, the application exposes a Swagger UI at the `/docs` endpoint. + +### Chat Endpoint (`/chat/completions`) + +The `/chat/completions` endpoint generates responses to user queries based on provided context and chat history. It leverages information from the configured Vector Store to formulate relevant responses, enhancing the conversational experience. + +***Example***: + +
+Request + +```curl +curl 'http://localhost:3000/chat/completions' \ + -H 'content-type: application/json' \ + --data-raw '{"chat_query":"Design a CRUD schema for an online store selling merchandise items","chat_history":[]}' +``` + +
+ +
+Response + +```json +{ + "message": "For an online store selling merchandise items, we can design a CRUD schema for a `Product` entity with the following properties: ...", + "references": [ + { + "content": "### Create CRUD to Read and Write Table Data \n...", + "url": "../../microfrontend-composer/tutorials/basics" + }, + { + "content": "### Create CRUD to Read and Write Table Data \n...", + "url": "../../microfrontend-composer/tutorials/basics" + }, + { + "content": "### Create a CRUD for persistency \n...", + "url": "../../console/tutorials/configure-marketplace-components/flow-manager" + }, + { + "content": "### Create a CRUD for persistency \n...", + "url": "../../console/tutorials/configure-marketplace-components/flow-manager" + } + ] +} +``` + +
+ +### Embedding Endpoints + +#### Generate from website (`/embeddings/generate`) + +The `/embeddings/generate` endpoint is a HTTP POST method that takes as input: + +- `url` (string, *required*), a web URL used as a starting point +- `filterPath` (string, not required), a more specific web URL that the one specified above + +- crawl the webpage +- check for links on the same domain (and, if included, that begins with the `filterPath`) of the webpage and store them in a list +- scrape the page for text +- generate the embeddings using the [configured embedding model](#configuration) +- start again from every link still in the list + +> **NOTE**: +> This method can be run only one at a time, as it uses a lock to prevent multiple requests from starting the process at the same time. +> +> No information are returned when the process ends, either as completed or stopped because of an error. + +***Eg***: + +
+Request + +```curl +curl 'http://localhost:3000/embedding/generate' \ + -H 'content-type: application/json' \ + --data-raw '{"url":"https://docs.mia-platform.eu/", "domain": "../../runtime_suite_templates" }' +``` + +
+ +
+Response in case the runner is idle + +```json +200 OK +{ + "statusOk": "true" +} +``` +
+ +
+Response in case the runner is running + +```json +409 Conflict +{ + "detail": "A process to generate embeddings is already in progress." +} +``` +
+ +#### Generate from file (`/embeddings/generateFromFile`) + +The `/embeddings/generateFromFile` endpoint is a HTTP POST method that takes as input: + +- `file` (binary, *required*), a file to be uploaded containing the text that will be transformed into embeddings. + +The file must be of format: + +- a text file (`.txt`) +- a markdown file (`.md`) +- a PDF file (`.pdf`) +- a zip file (formats available: `.zip`, `.tar`, `.gz`) containing files of the same formats as above (folders and other files will be skipped). + +For this file, of each file inside the archive, the text will be retrieved, chunked and the embeddings generated. + +> **NOTE**: +> This method can be run only one at a time, as it uses a lock to prevent multiple requests from starting the process at the same time. +> +> No information are returned when the process ends, either as completed or stopped because of an error. + +***Eg***: + +
+Request + +```curl +curl -X 'POST' \ + 'https://rag-app-test.console.gcp.mia-platform.eu/api/embeddings/generateFromFile' \ + -H 'accept: application/json' \ + -H 'Content-Type: multipart/form-data' \ + -F 'file=@my-archive.zip;type=application/zip' +``` + +
+ +
+Response in case the runner is idle + +```json +200 OK +{ + "statusOk": "true" +} +``` +
+ +
+Response in case the runner is running + +```json +409 Conflict +{ + "detail": "A process to generate embeddings is already in progress." +} +``` +
+ +#### Generation status (`/embeddings/status`) + +This request returns to the user information regarding the [embeddings generation runner](#generate-embedding-endpoint-embeddingsgenerate). Could be either `idle` (no process currently running) or `running` (a process of generating embeddings is actually happenning). + +***Eg***: + +
+Request + +```curl +curl 'http://localhost:3000/embedding/status' \ + -H 'content-type: application/json' \ +``` + +
+ +
+Response + +```json +200 OK +{ + "status": "idle" +} +``` +
+ +### Metrics Endpoint (`/-/metrics`) + +The `/-/metrics` endpoint exposes the metrics collected by Prometheus. diff --git a/docs/runtime_suite/rag-chatbot-api/_category_.json b/docs/runtime_suite/rag-chatbot-api/_category_.json new file mode 100644 index 0000000000..5f68cc8efc --- /dev/null +++ b/docs/runtime_suite/rag-chatbot-api/_category_.json @@ -0,0 +1,4 @@ +{ + "label": "RAG Chatbot API", + "position": 10 +} \ No newline at end of file diff --git a/docs/runtime_suite/rag-chatbot-api/changelog.md b/docs/runtime_suite/rag-chatbot-api/changelog.md new file mode 100644 index 0000000000..52984b4a8a --- /dev/null +++ b/docs/runtime_suite/rag-chatbot-api/changelog.md @@ -0,0 +1,64 @@ +--- +id: changelog +title: Changelog +sidebar_label: CHANGELOG +--- + + + +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## 0.5.2 - 2025-01-29 + +### Fixed + +- At service startup, if the Vector Search collection does not exist, it is automatically created +- Support file extension `mdx` for embedding generation +- File uploaded for embedding generation is validated either from the content-type or the file extension + +## 0.5.1 - 2024-12-20 + +## 0.5.0 - 2024-12-19 + +### Added + +- Created new pipeline flow for testing, linting, security (with `bandit` and `pip-audit`) and docker image publishing on tags. + +## 0.4.0 - 2024-12-18 + +- updated dependencies (FastAPI, Langchain, OpenAI) + +### Added + +- add endpoint `POST /embeddings/generateFromFile` for embedding generation +- add support for _Azure OpenAI_ provider for embedding generation and LLM usage + +## 0.3.1 - 2024-09-05 + +## 0.3.0 - 2024-09-05 + +### Added + +- Automatic creation/update of the Vector Index +- add endpoints `POST /embeddings/generate` and `GET /embeddings/status` for embedding generation + +## 0.2.0 - 2024-08-21 + +### Updated + +- updated dependencies (FastAPI, Langchain, OpenAI) +- the application is now using Python version 3.12.3 +- improved documentation + +## 0.1.1 - 2024-05-09 + +### Added + +- first template implementation From a8ab1009308c9b7b7d01b409428c3d948cc8b0b3 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 10 Feb 2025 08:02:28 +0100 Subject: [PATCH 02/81] chore(docs): update RAG Chatbot API documentation --- .../rag-chatbot-api/10_overview_and_usage.md | 31 +++++++++++++++++-- docs/runtime_suite/rag-chatbot-api/20_apis.md | 2 +- .../rag-chatbot-api/changelog.md | 12 +++++++ 3 files changed, 42 insertions(+), 3 deletions(-) diff --git a/docs/runtime_suite/rag-chatbot-api/10_overview_and_usage.md b/docs/runtime_suite/rag-chatbot-api/10_overview_and_usage.md index e2b4d9ca98..4495050f88 100644 --- a/docs/runtime_suite/rag-chatbot-api/10_overview_and_usage.md +++ b/docs/runtime_suite/rag-chatbot-api/10_overview_and_usage.md @@ -113,8 +113,9 @@ Description of configuration parameters: | Vector Store Text Key | Name of the field used to save the raw document (or chunk of document). | | Vector Store Max. Documents To Retrieve | Maximum number of documents to retrieve from the Vector Store. | | Vector Store Min. Score Distance | Minimum distance beyond which retrieved documents from the Vector Store are discarded. | -| Chain RAG System Prompts File Path | ath to the file containing system prompts for the RAG model. If omitted, the application will use a standard system prompt. | -| Chain RAG User Prompts File Path | Path to the file containing user prompts for the RAG model. If omitted, the application will use a standard system prompt. | +| Chain Aggregate Max Token Number | Maximum number of tokens extracted from the retrieved documents from the Vector Store to be included in the prompt (1 token is approximately 4 characters). Default is `2000`. | +| Chain RAG System Prompts File Path | Path to the file containing system prompts for the RAG model. If omitted, the application will use a standard system prompt. More details in the [dedicated paragraph](#configure-your-own-system-and-user-prompts). | +| Chain RAG User Prompts File Path | Path to the file containing user prompts for the RAG model. If omitted, the application will use a standard system prompt. More details in the [dedicated paragraph](#configure-your-own-system-and-user-prompts). | ### Supported LLM providers @@ -182,6 +183,32 @@ Currently, the supported Embeddings providers are: | `url` | URL of the Azure OpenAI service to call. | | `apiVersion` | API version of the Azure OpenAI service. | +### Configure your own system and user prompts + +The application sends to the LLM a prompt that is composed of a _system prompt_ and a _user prompt_: + +- the _system prompt_ is a message that provides instructions to the LLM on how to respond to the user's input. +- the _user prompt_ is a message that contains the user's input. + +A default version of these prompts are included in the application, but you can also use your own prompts to instruct the LLM to behave in a more specific way, such as behaving as a generic assistant in any field or as an expert in a specific field related to the embedding documents you are using. + +Both the system and user prompts are optional, but if you want to use your own prompts, you need to create a text file with the content of the prompt and specify the path to the file in the configuration at `chain.rag.systemPromptsFilePath` and `chain.rag.userPromptsFilePath` respectively. + +Moreover, the _system prompt_ must include the following placeholders: + +- `{chat_history}`: placeholder that will be replaced by the chat history, which is a list of messages exchanged between the user and the chatbot until then (received via the `chat_history` property from the body of the [`/chat/completions` endpoint](#chat-endpoint-chatcompletions)) +- `{output_text}`: placeholder that will be replaced by the text extracted from the embedding documents + +> **Note** +> +> The application already includes some context text to explain what the chat history is and what the output text is, so you don't need to add your explanation to the system prompt. + +Also, the _user prompt_ must include the following placeholder: + +- `{query}`: placeholder that will be replaced by the user's input (received via the `chat_query` property from the body of the [`/chat/completions` endpoint](#chat-endpoint-chatcompletions)) + +Generally speaking, it is suggested to have a _system prompt_ tailored to the needs of your application, to specify what type of information the chatbot should provide and the tone and style of the responses. The _user prompt_ can be omitted unless you need to specify particular instructions or constraints specific to each question. + ### Create a Vector Index :::info diff --git a/docs/runtime_suite/rag-chatbot-api/20_apis.md b/docs/runtime_suite/rag-chatbot-api/20_apis.md index 4708e38668..e53c23803f 100644 --- a/docs/runtime_suite/rag-chatbot-api/20_apis.md +++ b/docs/runtime_suite/rag-chatbot-api/20_apis.md @@ -124,7 +124,7 @@ The `/embeddings/generateFromFile` endpoint is a HTTP POST method that takes as The file must be of format: - a text file (`.txt`) -- a markdown file (`.md`) +- a markdown file (`.md`, `.mdx`) - a PDF file (`.pdf`) - a zip file (formats available: `.zip`, `.tar`, `.gz`) containing files of the same formats as above (folders and other files will be skipped). diff --git a/docs/runtime_suite/rag-chatbot-api/changelog.md b/docs/runtime_suite/rag-chatbot-api/changelog.md index 52984b4a8a..e6e8731cb9 100644 --- a/docs/runtime_suite/rag-chatbot-api/changelog.md +++ b/docs/runtime_suite/rag-chatbot-api/changelog.md @@ -15,8 +15,20 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## 0.5.3 - 2025-02-07 + +### Fixed + +- Version `0.5.2` included an error with the `mdx` files for embedding generation from `generateFromFile` API. This has been fixed. +- Fixed several typos related to the `aggregateMaxTokenNumber` configurable parameter.s + +### Changed + +- Updated documentation related to the Aggregate Max Token Number and custom prompts (both system and user prompts) + ## 0.5.2 - 2025-01-29 + ### Fixed - At service startup, if the Vector Search collection does not exist, it is automatically created From 836c74d5b083a6f25ddb353a85777f6c56d3d9fc Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 11 Feb 2025 08:02:25 +0100 Subject: [PATCH 03/81] chore(docs): update RAG Chatbot API documentation From c51f856cf9c5e8459414d2d73c2101d636fcbd18 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 12 Feb 2025 08:02:24 +0100 Subject: [PATCH 04/81] chore(docs): update RAG Chatbot API documentation From e5b03dab3ae9a38ca7285f414a0ed2b1f15e3739 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 13 Feb 2025 08:02:22 +0100 Subject: [PATCH 05/81] chore(docs): update RAG Chatbot API documentation From 72b5b7872e51db20e8d54c601f4d192137308239 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 14 Feb 2025 08:02:10 +0100 Subject: [PATCH 06/81] chore(docs): update RAG Chatbot API documentation From 9fed0c8a4663c160ab03b5668b2b09f1a5ec7b83 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 17 Feb 2025 08:02:25 +0100 Subject: [PATCH 07/81] chore(docs): update RAG Chatbot API documentation From d732896f371f5f2f2a8af3ff9f027246058d8ee6 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 18 Feb 2025 08:02:17 +0100 Subject: [PATCH 08/81] chore(docs): update RAG Chatbot API documentation From b4bb9a2436bf6e03c2ebab23f40b432ce3c87263 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 19 Feb 2025 08:02:10 +0100 Subject: [PATCH 09/81] chore(docs): update RAG Chatbot API documentation From ee0b7b2f912d820fa24477abdc03f59c3d21e0a4 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 20 Feb 2025 08:02:18 +0100 Subject: [PATCH 10/81] chore(docs): update RAG Chatbot API documentation From 3ea56b1dbc8db4dd593781d4c7c378ccc8486638 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 20 Feb 2025 10:44:22 +0100 Subject: [PATCH 11/81] chore(docs): update RAG Chatbot API documentation From f6c98967491eb853242dec4ed8acc718291dc843 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 20 Feb 2025 10:48:08 +0100 Subject: [PATCH 12/81] chore(docs): update RAG Chatbot API documentation From 236a15380831bb655de407834008e82915625704 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 20 Feb 2025 11:29:51 +0100 Subject: [PATCH 13/81] chore(docs): update RAG Chatbot API documentation From 0cc0afddff814299a47ea097274038135935eb81 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 20 Feb 2025 12:23:25 +0100 Subject: [PATCH 14/81] chore(docs): update RAG Chatbot API documentation From 9909181d156e705764e9dcbe10f65f496e11e5bd Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 21 Feb 2025 08:01:59 +0100 Subject: [PATCH 15/81] chore(docs): update RAG Chatbot API documentation From 1e28cda520cd62c30c0587ef8c540391efc35c33 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 24 Feb 2025 08:02:06 +0100 Subject: [PATCH 16/81] chore(docs): update RAG Chatbot API documentation From 4b04114ff1a6f37b5fa6141ba47cdb7df2ebebf0 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 25 Feb 2025 08:02:00 +0100 Subject: [PATCH 17/81] chore(docs): update RAG Chatbot API documentation From 5010d54cf8221cc15618111f363096644bd61a52 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 26 Feb 2025 08:02:18 +0100 Subject: [PATCH 18/81] chore(docs): update RAG Chatbot API documentation From bec0a3b976fc342d7800a4fb2976029c64292f6c Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 27 Feb 2025 08:02:13 +0100 Subject: [PATCH 19/81] chore(docs): update RAG Chatbot API documentation From 70f49148dac83b038c8442fe5e104597e204da16 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 28 Feb 2025 08:02:15 +0100 Subject: [PATCH 20/81] chore(docs): update RAG Chatbot API documentation From 821361386d9917a5ac67b14040ba9a07acc7f7b9 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 3 Mar 2025 08:02:27 +0100 Subject: [PATCH 21/81] chore(docs): update RAG Chatbot API documentation From 2b1554ad74ded668ddb919fd66659e693b8e1856 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 4 Mar 2025 08:02:09 +0100 Subject: [PATCH 22/81] chore(docs): update RAG Chatbot API documentation From 4c46d0c3076cceb236a12aeab5f18ec62af6403b Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 5 Mar 2025 08:02:00 +0100 Subject: [PATCH 23/81] chore(docs): update RAG Chatbot API documentation From 17c4f4390339c573606a25483d5f00135a5b76bc Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 5 Mar 2025 10:39:55 +0100 Subject: [PATCH 24/81] chore(docs): update RAG Chatbot API documentation From 661decacff9be1847d20c9d671487e601ec447b5 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 6 Mar 2025 08:02:06 +0100 Subject: [PATCH 25/81] chore(docs): update RAG Chatbot API documentation From bf4a95dde6e421e21a2b2973db184122a39ae654 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 7 Mar 2025 08:02:22 +0100 Subject: [PATCH 26/81] chore(docs): update RAG Chatbot API documentation From ea18ef1e57288cd884afb9bdc0926adb68d4bb5e Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 10 Mar 2025 08:02:01 +0100 Subject: [PATCH 27/81] chore(docs): update RAG Chatbot API documentation From b731320b6b4c3083276a847e44416e0aa91aa93a Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 11 Mar 2025 08:02:21 +0100 Subject: [PATCH 28/81] chore(docs): update RAG Chatbot API documentation From fcbfbec67da6e316e9a922b9dd36157f6c07018a Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 12 Mar 2025 08:02:20 +0100 Subject: [PATCH 29/81] chore(docs): update RAG Chatbot API documentation From d1c89faee25e4968533ca7e19e237cbf2d898b5f Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 13 Mar 2025 08:02:20 +0100 Subject: [PATCH 30/81] chore(docs): update RAG Chatbot API documentation From 8b4ec3d0088b7cc2874d098d4e0c20cb8653e31b Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 14 Mar 2025 08:00:56 +0100 Subject: [PATCH 31/81] chore(docs): update RAG Chatbot API documentation From 3ad93cd679728796b8bafb2993b78d98f80f94cd Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 17 Mar 2025 08:00:46 +0100 Subject: [PATCH 32/81] chore(docs): update RAG Chatbot API documentation From e5c5a5e29560d22c0b6f83eed1fe27309e8a31f0 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 18 Mar 2025 08:01:00 +0100 Subject: [PATCH 33/81] chore(docs): update RAG Chatbot API documentation From c8d66ac3731e76985b48759956c65e92343afa23 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 18 Mar 2025 11:09:36 +0100 Subject: [PATCH 34/81] chore(docs): update RAG Chatbot API documentation From fae327521b9b49a823d2dae24c1c37beaffe3b2b Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 19 Mar 2025 08:01:45 +0100 Subject: [PATCH 35/81] chore(docs): update RAG Chatbot API documentation From fb9f83b7448584665bb49f41ef4a3d5e277dd729 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 20 Mar 2025 08:01:47 +0100 Subject: [PATCH 36/81] chore(docs): update RAG Chatbot API documentation From e8dcd6592694cce27b79e29b1f28ff6434053c31 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 21 Mar 2025 08:01:41 +0100 Subject: [PATCH 37/81] chore(docs): update RAG Chatbot API documentation From 414a3bf606191d4fa306c7e59500a468300b6ccd Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 24 Mar 2025 08:01:46 +0100 Subject: [PATCH 38/81] chore(docs): update RAG Chatbot API documentation From e4fcf11357b59dd7e17f13ac2ba06c294ec0e144 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 25 Mar 2025 08:01:40 +0100 Subject: [PATCH 39/81] chore(docs): update RAG Chatbot API documentation From 4974e3798fbd0624df300b4cfeeb5012ecd285fa Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 26 Mar 2025 08:01:54 +0100 Subject: [PATCH 40/81] chore(docs): update RAG Chatbot API documentation From 8af03e1f0f667e5314876d88db49bd80187b0917 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 27 Mar 2025 08:01:49 +0100 Subject: [PATCH 41/81] chore(docs): update RAG Chatbot API documentation From 0c2f2961e670c921ee49664aad42e96eda1f0b34 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 28 Mar 2025 08:01:38 +0100 Subject: [PATCH 42/81] chore(docs): update RAG Chatbot API documentation From 93e3bfd91947bbeb8016c969c6abe72bd843a6f3 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 28 Mar 2025 15:50:58 +0100 Subject: [PATCH 43/81] chore(docs): update RAG Chatbot API documentation From 77aa0a1755ac3d92e92c3c99e1d844e71dc7dafb Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 28 Mar 2025 16:51:24 +0100 Subject: [PATCH 44/81] chore(docs): update RAG Chatbot API documentation From 30561b188854349221b6c718ee211855faa1f96e Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 31 Mar 2025 09:01:37 +0200 Subject: [PATCH 45/81] chore(docs): update RAG Chatbot API documentation From 4c374b98cfc76a222879cbcfbb997a5fcd41556b Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 1 Apr 2025 09:01:35 +0200 Subject: [PATCH 46/81] chore(docs): update RAG Chatbot API documentation From 93d71192556b735c366da98061c8af6a4b92247d Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 2 Apr 2025 09:01:35 +0200 Subject: [PATCH 47/81] chore(docs): update RAG Chatbot API documentation From b0daa0c41973bd97aeacc596e60dff6afbb22921 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 3 Apr 2025 09:01:40 +0200 Subject: [PATCH 48/81] chore(docs): update RAG Chatbot API documentation From 455855c2f37cc7a2bb275ef920241b82ff2c5f00 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 4 Apr 2025 09:01:33 +0200 Subject: [PATCH 49/81] chore(docs): update RAG Chatbot API documentation From 495f166c7865172f3da1a8616c07c9c634dba8ed Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 4 Apr 2025 12:10:37 +0200 Subject: [PATCH 50/81] chore(docs): update RAG Chatbot API documentation From 5d4edc751291463e250dd2b2752bb8d65b94a615 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 7 Apr 2025 09:01:38 +0200 Subject: [PATCH 51/81] chore(docs): update RAG Chatbot API documentation From 4ecea105a57636a392a5ed1bd4c48a5356479b78 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 8 Apr 2025 09:01:42 +0200 Subject: [PATCH 52/81] chore(docs): update RAG Chatbot API documentation From 1fdef36ff049ef64ea8d31d2610aea0d2be67548 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 9 Apr 2025 09:02:08 +0200 Subject: [PATCH 53/81] chore(docs): update RAG Chatbot API documentation From 51b205e5e516084e2ae7e17716da4254e1497d18 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 10 Apr 2025 09:01:37 +0200 Subject: [PATCH 54/81] chore(docs): update RAG Chatbot API documentation From 97b93ecf33ccc0fd7f021ec10f51911a3dce1a27 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 11 Apr 2025 09:01:40 +0200 Subject: [PATCH 55/81] chore(docs): update RAG Chatbot API documentation From 3517dcb1251911d1d7dcf4e60bd462f3cd01b637 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 14 Apr 2025 09:01:32 +0200 Subject: [PATCH 56/81] chore(docs): update RAG Chatbot API documentation From 7f751845a2645a6d1f6159affa08125c85a676d1 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 15 Apr 2025 09:01:36 +0200 Subject: [PATCH 57/81] chore(docs): update RAG Chatbot API documentation From d6ac99a76868dcab5413f3ae43d7797760070cb7 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 16 Apr 2025 09:01:36 +0200 Subject: [PATCH 58/81] chore(docs): update RAG Chatbot API documentation From 5faf75c49a291eb1a13ebb6d79558e2fa61776f0 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 17 Apr 2025 09:01:37 +0200 Subject: [PATCH 59/81] chore(docs): update RAG Chatbot API documentation From cfb6f6dfe4285a006f5236723d9dbfe1a343932f Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 18 Apr 2025 09:01:34 +0200 Subject: [PATCH 60/81] chore(docs): update RAG Chatbot API documentation From 2dfbddd86a20a10eaf6dbee0112bdf05044863e8 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 21 Apr 2025 09:01:37 +0200 Subject: [PATCH 61/81] chore(docs): update RAG Chatbot API documentation From b758c4fbe7fb79c5ded936d3f118c2bc28406e50 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 22 Apr 2025 09:01:37 +0200 Subject: [PATCH 62/81] chore(docs): update RAG Chatbot API documentation From 8f52a07435194da97f894b5abbd550380fd07717 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 22 Apr 2025 13:02:42 +0200 Subject: [PATCH 63/81] chore(docs): update RAG Chatbot API documentation From 454bee830988f6aa5bcd64d70d6535d440cef49f Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 23 Apr 2025 09:01:37 +0200 Subject: [PATCH 64/81] chore(docs): update RAG Chatbot API documentation From ffc7baf1de35b805ba1bed89735477d848f5af20 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 24 Apr 2025 09:01:48 +0200 Subject: [PATCH 65/81] chore(docs): update RAG Chatbot API documentation From 3a198f40cc00baef9300e7057b364ddce34d98d6 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 25 Apr 2025 09:01:37 +0200 Subject: [PATCH 66/81] chore(docs): update RAG Chatbot API documentation From 51edaa3d73cbb09fd644dd13fb4a6dc4f463e854 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 28 Apr 2025 09:02:24 +0200 Subject: [PATCH 67/81] chore(docs): update RAG Chatbot API documentation From 44cb9e654e004735bb94f4c45f917217863208ca Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 29 Apr 2025 09:02:30 +0200 Subject: [PATCH 68/81] chore(docs): update RAG Chatbot API documentation From a454b7ec0b8dc227e284ccf53eaf897207b394fe Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 30 Apr 2025 09:01:41 +0200 Subject: [PATCH 69/81] chore(docs): update RAG Chatbot API documentation From f93af37e70e6c31f042fa8058cbbf83973394d3e Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 1 May 2025 09:01:37 +0200 Subject: [PATCH 70/81] chore(docs): update RAG Chatbot API documentation From 0a146f2e95919bdfb1955a39ce547504e730fa3e Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 2 May 2025 09:01:40 +0200 Subject: [PATCH 71/81] chore(docs): update RAG Chatbot API documentation From 6d1420e025e887e6cbfa75bd0a535fdf5353efd0 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 5 May 2025 09:01:34 +0200 Subject: [PATCH 72/81] chore(docs): update RAG Chatbot API documentation From 44480e1291e57ae9c39463d2c06d6bba4a52e903 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 6 May 2025 09:01:53 +0200 Subject: [PATCH 73/81] chore(docs): update RAG Chatbot API documentation From 789a25af0a598ef96aa14a5f7f8faa61942cb7d2 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 7 May 2025 09:01:41 +0200 Subject: [PATCH 74/81] chore(docs): update RAG Chatbot API documentation From c3b078b4274fcd8a86b41b81495aba3337b0bb91 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 8 May 2025 09:02:39 +0200 Subject: [PATCH 75/81] chore(docs): update RAG Chatbot API documentation From 8d2d3e7d2091fb9a910e89492a1ad2103bfee586 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 9 May 2025 09:01:42 +0200 Subject: [PATCH 76/81] chore(docs): update RAG Chatbot API documentation From 0117d57254b9f655831b1ce9d1f86d8f969e3e88 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Mon, 12 May 2025 09:01:36 +0200 Subject: [PATCH 77/81] chore(docs): update RAG Chatbot API documentation From 1b895aefa092a01a23862af114b7c95b2e5e6552 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Tue, 13 May 2025 09:01:43 +0200 Subject: [PATCH 78/81] chore(docs): update RAG Chatbot API documentation From a40469264621bdeee9c1dd6cf0ce949fe77a6ce8 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Wed, 14 May 2025 09:02:02 +0200 Subject: [PATCH 79/81] chore(docs): update RAG Chatbot API documentation From 5be0dfb0ffb9471f508b31293b121b50007b88e4 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Thu, 15 May 2025 09:01:40 +0200 Subject: [PATCH 80/81] chore(docs): update RAG Chatbot API documentation From 9fbd4884b79a39056ad840662c8422d1dd63bc66 Mon Sep 17 00:00:00 2001 From: Bot-targa Date: Fri, 16 May 2025 09:01:42 +0200 Subject: [PATCH 81/81] chore(docs): update RAG Chatbot API documentation