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Python: cleanup/sync/fixes (for examples, docs, tests, notebooks) (#317) ### Motivation and Context The `python-preview` branch is under active development and many docs/tests/examples are getting a bit out of sync. There are also a few small cleanup chores/refactors to make _usage_ easier (e.g., to make examples cleaner). I've grouped these small fixes (plus a few bug fixes) into this "cleanup" PR. ### Description This PR does the following: 1. Updates the out-of-date example in `README.md` 2. Fixes all 5 of the notebooks we support right now (1,2,3,4, and 6; notebook 5 is planner, I've blanked out most of the code there so we don't confuse people, especially as the planner API is changing) 3. Fixes a bug w/ `stop_sequences` in `(Azure)OpenAITextCompletion` 4. Fixes a bug introduced in our upgrades to the embeddings cosine-sim check (we should add more thorough tests here) 5. Cleans up tests 6. Ensures end-to-end tests are all runnable 7. Fixes up the `kernel_extensions` to work more naturally (so that end users can use them directly on the `kernel` object and get good type-hinting)
2023-04-04 16:44:59 -05:00
OPENAI_API_KEY=""
Python: removed underscore in .env for open_ai (#7625) ### Description When running the getting-started Python notebooks, the user is instructed to create and populate their own .env file. The values for the OpenAI specifications included an underscore between 'open' and 'ai'. This led to an error when initializing the chat/text/embedding model service. I have removed the underscore from the base .env files, README, and in the evaluate_with_prompt_flow.py script when reading from the .env file.
2024-08-02 14:01:01 -07:00
OPENAI_CHAT_MODEL_ID=""
OPENAI_TEXT_MODEL_ID=""
OPENAI_EMBEDDING_MODEL_ID=""
Python: cleanup/sync/fixes (for examples, docs, tests, notebooks) (#317) ### Motivation and Context The `python-preview` branch is under active development and many docs/tests/examples are getting a bit out of sync. There are also a few small cleanup chores/refactors to make _usage_ easier (e.g., to make examples cleaner). I've grouped these small fixes (plus a few bug fixes) into this "cleanup" PR. ### Description This PR does the following: 1. Updates the out-of-date example in `README.md` 2. Fixes all 5 of the notebooks we support right now (1,2,3,4, and 6; notebook 5 is planner, I've blanked out most of the code there so we don't confuse people, especially as the planner API is changing) 3. Fixes a bug w/ `stop_sequences` in `(Azure)OpenAITextCompletion` 4. Fixes a bug introduced in our upgrades to the embeddings cosine-sim check (we should add more thorough tests here) 5. Cleans up tests 6. Ensures end-to-end tests are all runnable 7. Fixes up the `kernel_extensions` to work more naturally (so that end users can use them directly on the `kernel` object and get good type-hinting)
2023-04-04 16:44:59 -05:00
OPENAI_ORG_ID=""
Python: Fix broken concept samples. Run concept examples as tests. (#6660) ### Motivation and Context We had many broken concept examples. These samples are also not covered by tests so it's hard to know when things are out of sync. <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> ### Description Fix the Python concept samples, and also cover them with tests so we aren't checking in code that is going to break them. <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [X] The code builds clean without any errors or warnings - [X] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [X] All unit tests pass, and I have added new tests where possible - [X] I didn't break anyone :smile:
2024-06-11 11:28:12 -04:00
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=""
AZURE_OPENAI_TEXT_DEPLOYMENT_NAME=""
AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=""
Python: docs update + ruff config + fix tests
2023-04-13 14:13:19 -07:00
AZURE_OPENAI_ENDPOINT=""
AZURE_OPENAI_API_KEY=""
Python: switch to uv (#8320) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Switches from poetry to uv much faster and easier lock management easier maintenance of pyproject.toml fixes: #6584 fixes: #8326 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
2024-08-26 13:34:29 +02:00
AZURE_OPENAI_API_VERSION=""
Python: Fix AI Search Environment Variables (#9160) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Updating the AI Search environment variables to follow convention and match the code as it was throwing an error and unable to connect to the service as it was. It also loads in the environment properly using the dotenv package. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [X] The code builds clean without any errors or warnings - [X] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [X] All unit tests pass, and I have added new tests where possible - [X] I didn't break anyone :smile: --------- Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2024-10-11 09:38:33 -04:00
AZURE_AI_SEARCH_ENDPOINT=""
AZURE_AI_SEARCH_SERVICE=""
AZURE_AI_SEARCH_API_KEY=""
AZURE_AI_SEARCH_INDEX_NAME=""
Python: Adding MongoDB Atlas Vector Search Connector (#2818) ### Motivation and Context Resolves: #2591 <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> * Added in the `MongoDBAtlasMemoryStore`. A memory store abstraction that facilitates connections between a MongoDB Atlas cluster to conduct [Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search) on the Microsoft Semantic Kernel. * Leverages our async python driver [motor](https://motor.readthedocs.io/en/stable/) to keep with the heuristic of asynchronous code ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [x] I didn't break anyone :smile: ### Callout * When writing my test, I noticed there's a habit of other tests conducting a `try` on imports. Grepping through those implementations, I see some of them [encapsulate their respective libraries in function calls rather than doing direct imports in the module.](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/memory/chroma/chroma_memory_store.py#L55-L63) Is this the general code pattern we should follow? Or is it fine to have our imports at the top of the module fine? --------- Co-authored-by: Steven Silvester <steven.silvester@ieee.org> Co-authored-by: Abby Harrison <54643756+awharrison-28@users.noreply.github.com> Co-authored-by: Abby Harrison <abby.harrison@microsoft.com>
2023-10-02 17:25:28 -04:00
MONGODB_ATLAS_CONNECTION_STRING=""
Creating Pinecone memory store for Python (#769) ### Motivation and Context Adding a Pinecone Memory store using the Pinecone vector database client for SK python Add Pinecone vector db memory to SK Python Memory storage for SK and vectors ### Description - Using the existing pinecone python module/client - Reviewing with console exceptions responses that come back from pinecone, will update if any were omitted - Currently investigating and would like feedback if I should respond to throttling errors in a different manner - Currently testing each API response, slight delay may occur due to throttling of my starter account in Pinecone. ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [ ] The code builds clean without any errors or warnings - [X] The PR follows SK Contribution Guidelines (https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) - [ ] The code follows the .NET coding conventions (https://learn.microsoft.com/dotnet/csharp/fundamentals/coding-style/coding-conventions) verified with `dotnet format` - [ ] All unit tests pass, and I have added new tests where possible - [X] I didn't break anyone :smile: --------- Co-authored-by: Lee Miller <lemiller@microsoft.com> Co-authored-by: Casey Schadewitz <casey.schadewitz@IntelliTect.com> Co-authored-by: cschadewitz <schadewitzcasey@gmail.com> Co-authored-by: Abby Harrison <54643756+awharrison-28@users.noreply.github.com> Co-authored-by: Devis Lucato <dluc@users.noreply.github.com> Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com> Co-authored-by: Shawn Callegari <36091529+shawncal@users.noreply.github.com>
2023-06-29 16:09:58 -04:00
PINECONE_API_KEY=""
PINECONE_ENVIRONMENT=""
Python: postgres memory store (#1354) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? Brings parity to python implementation 2. What problem does it solve? With the pgvector extension, Postgres is a viable option for the storing of embeddings for memory in Semantic Kernel. The C# implementation is already capable of this, but the feature has yet to be added to python. 3. What scenario does it contribute to? Additional options for memory storage 4. If it fixes an open issue, please link to the issue here. --> ### Description Implemention of the MemoryStore to use a Postgresql (with pgvector extension) to resolve #1270 Taking a different approach from the .net implementation: Collections as postgres schemas as opposed to collection being another column in the table, willing to change to match but would love to have a discussion comparing the options. ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows SK Contribution Guidelines (https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) - [x] The code follows the .NET coding conventions (https://learn.microsoft.com/dotnet/csharp/fundamentals/coding-style/coding-conventions) verified with `dotnet format` - [x] All unit tests pass, and I have added new tests where possible - [x] I didn't break anyone :smile: --------- Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com> Co-authored-by: Shawn Callegari <36091529+shawncal@users.noreply.github.com> Co-authored-by: Abby Harrison <abby.harrison@microsoft.com> Co-authored-by: Abby Harrison <54643756+awharrison-28@users.noreply.github.com>
2023-07-11 14:52:08 -07:00
POSTGRES_CONNECTION_STRING=""
Python: Update .env with weaviate (#2148) Included weaviate env variables --------- Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com> Co-authored-by: Abby Harrison <54643756+awharrison-28@users.noreply.github.com> Co-authored-by: Abby Harrison <abby.harrison@microsoft.com> Co-authored-by: Lee Miller <lemiller@microsoft.com>
2023-08-25 19:06:36 -04:00
WEAVIATE_URL=""
WEAVIATE_API_KEY=""
GOOGLE_SEARCH_ENGINE_ID=""
Python: Added Brave search capability in SK(python) based on PR #9632 (#11531) ### Motivation and Context Added BRAVE SEARCH CAPABILITIES in Semantic Kernel Python based on PR #11308 . ### Description - Introduce BraveConnector & BraveSearch for Brave Search API integration, - BraveSettings and response models were added, respectively. - Add unit Tests ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [x] I didn't break anyone :smile: --------- Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2025-04-21 15:46:31 +05:30
BRAVE_API_KEY=""
Python: Redis memory connector (#2132) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> 1. .NET supports Redis memory while Python does not 2. Match up Python features w/ .NET 3. Users can utilize a Redis backend for memories 4. Closes #1981 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Implemented a memory connector for Redis and made unit tests for it. Also added .env configuration for a connection string. ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [X] The code builds clean without any errors or warnings - [X] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#dev-scripts) raises no violations - [X] All unit tests pass, and I have added new tests where possible - [X] I didn't break anyone :smile: --------- Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com> Co-authored-by: Abby Harrison <54643756+awharrison-28@users.noreply.github.com> Co-authored-by: Abby Harrison <abby.harrison@microsoft.com>
2023-08-31 13:38:16 -07:00
REDIS_CONNECTION_STRING=""
Python: switch to uv (#8320) ### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Switches from poetry to uv much faster and easier lock management easier maintenance of pyproject.toml fixes: #6584 fixes: #8326 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
2024-08-26 13:34:29 +02:00
AZCOSMOS_API=""
AZCOSMOS_CONNSTR=""
AZCOSMOS_DATABASE_NAME=""
AZCOSMOS_CONTAINER_NAME=""
Python: (Astra DB) Include caller identity as user-agent when issuing HTTP request to Astra DB's Data API (#5921) ### Motivation and Context This PR enriches the HTTP requests to the Astra DB Data API (occurring in the AstraClient) with a User-Agent header specifying the identity of the caller (specifically, "semantic_kernel"). While not mandatory, this is good practice with Astra DB and helps collecting aggregate usage data on the API side. ### Description Using the `importlib.metadata` library the package version is extracted (in a fail-safe way) and used, if present, to enrich the caller information (variable `ASTRA_CALLER_IDENTITY`, later set as the user-agent header for all HTTP requests to the Data API). ### Contribution Checklist - [X] The code builds clean without any errors or warnings - [X] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [X] All unit tests pass, and I have added new tests where possible - [X] I didn't break anyone :smile: --------- Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
2024-04-22 19:40:39 +02:00
ASTRADB_APP_TOKEN=""
Python: Add astra memory (#4374) ### Motivation and Context We are going to add [Astra](https://docs.datastax.com/en/home/docs/index.html) as a memory store. DataStax Astra DB is a serverless vector database that’s perfect for managing mission-critical AI workloads. It’s built on Apache Cassandra®, making Astra DB a highly scalable, reliable database technology. It’s a powerful all-in-one data storage solution, ideal for Generative AI projects. ### Description - Add astra into python/semantic_kernel/connectors/memory/astradb - Add test into python/tests/integration/connectors/memory/test_astradb.py ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [ ] The code builds clean without any errors or warnings - [ ] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [ ] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile: --------- Co-authored-by: jsbilgi <jsbilgi@yahoo.com> Co-authored-by: Obioma Anomnachi <onanomnachi@gmail.com>
2024-01-30 16:51:04 -05:00
ASTRADB_ID=""
ASTRADB_REGION=""
Python: Add ACA Python Sessions (Code Interpreter) Core Plugin, samples, and tests (#6158) ### Motivation and Context Adding a new core plugin to Semantic Kernel Python that leverages the Azure Container Apps Python Sessions Container. This container allows one, with the proper resource, to run Python in a safe, managed environment. <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> ### Description This PR introduces: - The Python Sessions (code interpreter) plugin to execute code, upload a file to the container, list files, and download files. - It includes a README.md with the steps to set up the ACA resource. - New samples to show use as a plugin and auto function calling - Unit tests <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [X] The code builds clean without any errors or warnings - [X] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [X] All unit tests pass, and I have added new tests where possible - [X] I didn't break anyone :smile:
2024-05-08 17:01:51 -04:00
ASTRADB_KEYSPACE=""
Python: Introduce Pydantic settings (#6193) ### Motivation and Context SK Python is tightly coupled to the use of a `.env` file to read all secrets, keys, endpoints, and more. This doesn't scale well for users who wish to be able to use environment variables with their SK Applications. By introducing Pydantic Settings, it is possible to use both environment variables as well as have a fall-back to a `.env` file (via a `env_file_path` parameter), if desired. By introducing Pydantic Settings, we are removing the requirement to have to create Text/Embedding/Chat completion objects with an `api_key` or other previously required information (in the case of AzureChatCompletion that means an `endpoint`, an `api_key`, a `deployment_name`, and an `api_version`). When the AI connector is created, the Pydantic settings are loaded either via env vars or the fall-back `.env` file, and that means the user can create a chat completion object like: ```python chat_completion = OpenAIChatCompletion(service_id="test") ``` or, to optionally override the `ai_model_id` env var: ```python chat_completion = OpenAIChatCompletion(service_id="test", ai_model_id="gpt-4-1106") ``` Note: we have left the ability to specific an `api_key`/`org_id` for `OpenAIChatCompletion` or a `deployment_name`, `endpoint`, `base_url`, and `api_version` for `AzureChatCompletion` as before, but if your settings are configured to use env vars/.env file then there is no need to pass this information. <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> ### Description The PR introduces the use of Pydantic settings and removes the use of the python-dotenv library. - Closes #1779 - Updates notebooks, samples, code and tests to remove the explicit config of api_key or other previous .env files values. - Adds new unit test config using monkeypatch to simulate env variables for testing - All unit and integration tests passing <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [X] The code builds clean without any errors or warnings - [X] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [X] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone :smile:
2024-05-16 07:44:40 -04:00
ACA_POOL_MANAGEMENT_ENDPOINT=""
BOOKING_SAMPLE_CLIENT_ID=""
BOOKING_SAMPLE_TENANT_ID=""
BOOKING_SAMPLE_CLIENT_SECRET=""
BOOKING_SAMPLE_BUSINESS_ID=""
Python: Adding Crew.AI as a plugin. (#10474) ### Motivation and Context Adding Crew.AI as a plugin to the Semantic Kernel will allow users to leverage the capabilities of Crew.AI in app. This plugin works with Crews that have been deployed to the Crew.AI Enterprise service. ### Description Adding Crew.AI as a plugin to the Semantic Kernel will allow users to leverage the capabilities of Crew.AI in app. This plugin works with Crews that have been deployed to the Crew.AI Enterprise service. The plugin functionality has been manually verified with the concept samples. Other tests will come in the next update. ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [x] I didn't break anyone :smile: --------- Co-authored-by: Ben Thomas <bentho@microsoft.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
2025-02-12 20:19:14 -08:00
BOOKING_SAMPLE_SERVICE_ID=""
CREW_AI_ENDPOINT=""
CREW_AI_TOKEN=""
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