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Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Eduard van ValkenburgandAbby Harrison
|
85ee34b924 |
Python: Implement Function calling for Chat (#2356)
### 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. --> I built support for function calling into SK! Related to/fixes: - #2315 - #2175 - #1450 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> This implementation builds on top of all the existing pieces, but did require some major work, so feel free to comment on where that is or is not appropriate. - Added a `ChatMessage` class to capture the relevant pieces of function calling (name and content) - Added a `complete_chat_with_functions_async` into OpenAIChatCompletions class - Added a `function_call` field to ChatRequestSettings class - Added several helper functions and smaller changes - Added a sample with updated core_skill that uses function calling to demonstrate - Added a second sample that shows how to use function_calling with non-sk functions. ### 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 😄 --------- Co-authored-by: Abby Harrison <[email protected]> |
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Sun ZhigangandDmytro Struk
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9ef8138569 |
Python: Fix wrong output sample of SequentialPlanner (#3095)
### Motivation and Context To help learners understand `SequentialPlanner` more smooth. ### Description Just modifying a string which may be involved from action_planner.py ### 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 😄 Co-authored-by: Dmytro Struk <[email protected]> |
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Eduard van ValkenburgandAbby Harrison
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e1595f4b30 |
Python: fixes for openapi sample (#2655)
### 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. --> I tried the openapi sample, didn't work because the context variables check if the values are strings, otherwise breaks. Now using strings, and parsing those in the openapi runner. ### Description Parse the required values in the openapi runner and updated the sample to use a string of a dict. <!-- 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 😄 --------- Co-authored-by: Abby Harrison <[email protected]> |
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73afea986a |
Python: added settings function to load direct into constructor (#2069)
### 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. --> Noticed that another commonly edited environment setting for api_version was missing from the settings util. And want to pass the env to the constructor. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Added api_version to the settings util so that that can be controlled through .env as well. Added convenience function that outputs a dict that can be passed directly into Completion constructors. Updated sample to use that. ### 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 - [ ] I didn't break anyone 😄 --------- Co-authored-by: Abby Harrison <[email protected]> Co-authored-by: Lee Miller <[email protected]> |
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Abby HarrisonandLee Miller
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052fb2ea96 |
Python: Integration tests 'needs' path filter job (#2557)
### Motivation and Context PRs #2533 and #2555 updated the integration tests to trigger off of python changes only, but did not actually have the resulting jobs require the path filtering. This means that the tests never run. ### Description Add path-filter job as a requirement for subsequent jobs. ### 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 😄 --------- Co-authored-by: Lee Miller <[email protected]> |
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e78242c887 |
Python : Adding Sample for Azure Cognitive Search Memory (#2510)
### Motivation and Context This PR resolves https://github.com/microsoft/semantic-kernel/issues/2493 ### Description The ACS implementation is different from VoltalileMemoryStore and we don't have any samples for Azure Cognitive Search Memory Store. I want to add Python Sample for using AzureCognitiveSearchMemoryStore with OpenAI embeddings. ### 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 😄 --------- Co-authored-by: Abby Harrison <[email protected]> Co-authored-by: Lee Miller <[email protected]> Co-authored-by: Devis Lucato <[email protected]> |
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0a477b198c |
Python: Update examples and notebooks to stop using text-davinci-003 (#2359)
### Motivation and Context text-davinci-003 is being retired by Azure OpenAI and is no longer supported for new deployments Resolves - https://github.com/microsoft/semantic-kernel/issues/2309 <!-- 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 Update examples and notebooks to use gpt-35-turbo / gpt-3.5-turbo instead <!-- 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: --> - [ ] 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 😄 Signed-off-by: karthikmurali60 <[email protected]> Co-authored-by: Lee Miller <[email protected]> Co-authored-by: Gil LaHaye <[email protected]> |
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59cbbdb2af |
Python: Add Google PaLM connector with chat completion and text embedding (#2258)
### Motivation and Context Implementation of Google PaLM connector with chat completion, text embedding and three example files to demonstrate their functionality. Closes #2098 I also opened a PR for text completion #2076 <!-- 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 **What's new:** 1. Implemented Google Palm connector with chat completion and text embedding 2. Added 3 example files to `python/samples/kernel-syntax-examples` 3. The example files show PaLM chat in action with skills, memory, and just with user messages. The memory example uses PaLM embedding as well 5. Added integration tests to test chat with skills and embedding with kernel.memory functions 6. Added unit tests to verify successful class initialization and correct behavior from class functions **There are some important differences between google palm chat and open ai chat:** 1. PaLM has two functions for chatting, `chat` and `reply`. The chat function in google's genai library starts a new conversation, and reply continues the conversation. Reply is an attribute of the response object returned by chat. So an instance of the `GooglePalmChatCompletion` class needs a way to determine which function to use, which is why I introduced a private attribute to store the response object. See https://developers.generativeai.google/api/python/google/generativeai/types/ChatResponse 2. PaLM does not use system messages. Instead, the chat function takes a parameter called context. It serves the same purpose, to prime the assistant with certain behaviors and information. So when the user passes a system message to `complete_chat_async`, it is passed to `chat` as the context parameter. 3. Semantic memory works with the chat service as long as the user creates a chat prompt template. The prompt containing the memory needs to be added to the chat prompt template as a system message. See `python\samples\kernel-syntax-examples\google_palm_chat_with_memory.py` for more details. If the only purpose of `complete_async` in `GooglePalmChatCompletion` is to send memories + user messages to the chat service as a text prompt, then `complete_async` is not fulfilling its intended purpose. A possible solution would be to send the text prompt as a request to the text service within `complete_async`. <!-- 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 😄 Currently no warnings, there was 1 warning when first installing genai with `poetry add google.generativeai==v0.1.0rc2` from within poetry shell: "The locked version 0.1.0rc2 for google-generativeai is a yanked version. Reason for being yanked: Release is marked as supporting Py3.8, but in practice it requires 3.9". We would need to require later versions of python to fix it. --------- Co-authored-by: Abby Harrison <[email protected]> Co-authored-by: Abby Harrison <[email protected]> Co-authored-by: Abby Harrison <[email protected]> |
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b029e50b6d |
Python: Add Google PaLM connector with text completion and example file (#2076)
### 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. --> Implementation of Google PaLM connector with text completion and an example file to demonstrate its functionality. Closes #1979 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Implemented Google Palm connector with text completion 2. Added example file to ```python/samples/kernel-syntax-examples``` 3. Added integration tests with different inputs to kernel.run_async 4. Added unit tests to ensure successful initialization of the class and successful API calls 5. 3 optional arguments (top_k, safety_settings, client) for google.generativeai.generate_text were not included. See more information about the function and its arguments: https://developers.generativeai.google/api/python/google/generativeai/generate_text I also opened a PR for text embedding and chat completion #2258 ### 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 😄 Currently no warnings, there was 1 warning when first installing genai with `poetry add google.generativeai==v0.1.0rc2` from within poetry shell: "The locked version 0.1.0rc2 for google-generativeai is a yanked version. Reason for being yanked: Release is marked as supporting Py3.8, but in practice it requires 3.9". We would need to require later versions of python to fix it. --------- Co-authored-by: Abby Harrison <[email protected]> Co-authored-by: Abby Harrison <[email protected]> |
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Mark KarleandAbby Harrison
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2733a5574f |
Python: Import OpenAPI documents into the semantic kernel (#2297)
### 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. --> This allows us to import OpenAPI documents, including ChatGPT plugins, into the Semantic Kernel. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - The interface reads the operationIds of the openapi spec into a skill: ```python from semantic_kernel.connectors.openapi import register_openapi_skill skill = register_openapi_skill(kernel=kernel, skill_name="test", openapi_document="url/or/path/to/openapi.yamlorjson") skill['operationId'].invoke_async() ``` - Parse an OpenAPI document - For each operation in the document, create a function that will execute the operation - Add all those operations to a skill in the kernel - Modified `import_skill` to accept a dictionary of functions instead of just class so that we can import dynamically created functions - Created unit tests TESTING: I've been testing this with the following ChatGPT plugins: - [Semantic Kernel Starter's Python Flask plugin](https://github.com/microsoft/semantic-kernel-starters/tree/main/sk-python-flask) - [ChatGPT's example retrieval plugin](https://github.com/openai/chatgpt-retrieval-plugin/blob/main/docs/providers/azuresearch/setup.md) - This one was annoying to setup. I didn't get the plugin functioning, but I was able to send the right API requests - Also, their openapi file was invalid. The "servers" attribute is misindented - [Google ChatGPT plugin](https://github.com/Sogody/google-chatgpt-plugin) - [Chat TODO plugin](https://github.com/lencx/chat-todo-plugin) - This openapi file is also invalid. I checked with an online validator. I had to remove"required" from the referenced request objects' properties: https://github.com/lencx/chat-todo-plugin/blob/main/openapi.yaml#L85 Then I used this python file to test the examples: ```python import asyncio import logging import semantic_kernel as sk from semantic_kernel import ContextVariables, Kernel from semantic_kernel.connectors.ai.open_ai import AzureTextCompletion from semantic_kernel.connectors.openapi.sk_openapi import register_openapi_skill # Example usage chatgpt_retrieval_plugin = { "openapi": # location of the plugin's openapi.yaml file, "payload": { "queries": [ { "query": "string", "filter": { "document_id": "string", "source": "email", "source_id": "string", "author": "string", "start_date": "string", "end_date": "string", }, "top_k": 3, } ] }, "operation_id": "query_query_post", } sk_python_flask = { "openapi": # location of the plugin's openapi.yaml file, "path_params": {"skill_name": "FunSkill", "function_name": "Joke"}, "payload": {"input": "dinosaurs"}, "operation_id": "executeFunction", } google_chatgpt_plugin = { "openapi": # location of the plugin's openapi.yaml file, "query_params": {"q": "dinosaurs"}, "operation_id": "searchGet", } todo_plugin_add = { "openapi": # location of the plugin's openapi.yaml file, "path_params": {"username": "markkarle"}, "payload": {"todo": "finish this"}, "operation_id": "addTodo", } todo_plugin_get = { "openapi": # location of the plugin's openapi.yaml file, "path_params": {"username": "markkarle"}, "operation_id": "getTodos", } todo_plugin_delete = { "openapi": # location of the plugin's openapi.yaml file, "path_params": {"username": "markkarle"}, "payload": {"todo_idx": 0}, "operation_id": "deleteTodo", } plugin = todo_plugin_get # set this to the plugin you want to try logger = logging.getLogger(__name__) logger.addHandler(logging.StreamHandler()) logger.setLevel(logging.DEBUG) kernel = Kernel(log=logger) deployment, api_key, endpoint = sk.azure_openai_settings_from_dot_env() kernel.add_text_completion_service( "dv", AzureTextCompletion(deployment, endpoint, api_key) ) skill = register_openapi_skill( kernel=kernel, skill_name="test", openapi_document=plugin["openapi"] ) context_variables = ContextVariables(variables=plugin) result = asyncio.run( skill[plugin["operation_id"]].invoke_async(variables=context_variables) ) print(result) ``` ### 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 😄 --------- Co-authored-by: Abby Harrison <[email protected]> |
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0d37271499 |
Python: Action Planner implementation (#1948)
### 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. --> This PR is a first draft for implementation of ActionPlanner in Python. I try to follow the C# implementation as much as possible. Fixes: #1917 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - I have implemented ActionPlanner in the `python/semantic_kernel/planning/action_planner` directory. The implementation is in a file called `action_planner.py` and the prompt used by the planner (a replica of the prompt used in C#) is found in `skprompt.txt` in the same directory. - The functions `good_examples`, `edge_case_examples` and `list_of_functions` are used by the prompt to populate examples and list of available functions in the kernel. - After creating the plan, I parse the JSON generated by the planner by using regex matching to extract out pure JSON as implemented in #1496 - Based on whether or not the planner was able to identify a function, I create a Plan instance and return it. ### Here is some example code for using the planner ```python import logging import semantic_kernel as sk from semantic_kernel.planning import ActionPlanner from semantic_kernel.connectors.ai.open_ai import ( OpenAIChatCompletion, OpenAITextCompletion ) from semantic_kernel.core_skills import ( MathSkill, FileIOSkill, TimeSkill, TextSkill ) async def main(): kernel = sk.Kernel() api_key, org_id = sk.openai_settings_from_dot_env() kernel.add_text_completion_service("dv", OpenAITextCompletion("text-davinci-003", api_key, org_id)) kernel.import_skill(MathSkill(), "math") kernel.import_skill(FileIOSkill(), "fileIO") kernel.import_skill(TimeSkill(), "time") kernel.import_skill(TextSkill(), "text") planner = ActionPlanner(kernel) ask = "What is the sum of 110 and 990?" plan = await planner.create_plan_async(goal=ask) result = await plan.invoke_async() print(result.result) if __name__ == "__main__": import asyncio asyncio.run(main()) ``` ### Queries - In the C# implementation, when populating the function parameters in the plan object, the `plan.parameters` variable is updated with the function parameters. When I did the same in python, these parameters weren't being passed onto the function in `invoke_async` method of the plan instance. The parameters are only being passed when I update them in the `plan.state` variable. This code is in `line 121` of `action_planner.py`. What is the right way to do this? - Some skills / plugins require one of the inputs to be passed as the `input` variable and not in the context. How should this be handled in the planner?  **OR**  ### Issues that I faced - The `text-davinci-002` text-completion model regularly output incorrectly formatted JSON with commas missing. For example ```json {""plan"":{ ""rationale"": ""The list contains a function that allows for writing to files, so the plan is to use that function to write the desired content to the file 'output.txt'."", ""function"": ""fileIO.writeAsync"", ""parameters"": { ""input"": null, ""content"": ""A brief history of computer science"" ""path"": ""output.txt"" }}} ``` Pending Items - [x] Create unit tests - [x] Send the function parameters to the planner in list of available functions with/without input parameter. ### 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 😄 --------- Co-authored-by: Abby Harrison <[email protected]> Co-authored-by: Abby Harrison <[email protected]> Co-authored-by: Shawn Callegari <[email protected]> |
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a2da17d336 |
Python: google connector (#2071)
### 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. --> Implementation of Google search connector for web search skill. Fixes: #1862 Sample usage can be found in `python/samples/kernel-syntax-examples/google_search_skill.py` ### 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#dev-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [x] I didn't break anyone 😄 --------- Co-authored-by: Abby Harrison <[email protected]> Co-authored-by: Abby Harrison <[email protected]> |
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484b39d144 |
Python: Add sequential planner to python (#1916)
### 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. --> fixes #1915 ### 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: --> - [ ] The code builds clean without any errors or warnings - [ ] 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 - [ ] I didn't break anyone 😄 --------- Co-authored-by: Abby Harrison <[email protected]> Co-authored-by: Abby Harrison <[email protected]> Co-authored-by: Shawn Callegari <[email protected]> |
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eab7a8f63a |
Python: implemented web search engine skill with bing connector (#1813)
### 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.
-->
In this PR, I have tried my hand at an implementation of web search
engine skill in python semantic kernel using the Bing Web Search API.
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
In the semantic kernel directory, I have added a new directory called
web_skills (To replicate Skills.Web from C#) and added the web search
skill here. For now, I have implemented web search using the bing web
search API. If this approach is fine, then I can implement the same with
the google search API too. I have tried to stick with similar naming
conventions as used in the C# implementation with matching context
parameters and arguments.
I can also add some unit tests for the connectors and the search skill,
and add something like exponential backoff to avoid rate limit errors
while querying the search APIs.
Here is some sample code that checks the working of the search skill.
```python
import os
import semantic_kernel as sk
from semantic_kernel.web_skills.web_search_engine_skill import WebSearchEngineSkill
from semantic_kernel.web_skills.connectors import BingConnector
from semantic_kernel.connectors.ai.open_ai import OpenAITextCompletion
async def main():
kernel = sk.Kernel()
api_key, org_id = sk.openai_settings_from_dot_env()
kernel.add_text_completion_service(
"dv", OpenAITextCompletion("text-davinci-003", api_key, org_id)
)
connector = BingConnector(api_key=os.getenv("BING_API_KEY"))
web_skill = kernel.import_skill(WebSearchEngineSkill(connector), "WebSearch")
prompt = "Who is Leonardo DiCaprio's current girlfriend?"
search_async = web_skill["searchAsync"]
result = await search_async.invoke_async(prompt)
print(result)
"""
Output:
["Celebrity Celebrity News Everything You Need to Know About Leonardo DiCaprio and Camila Morrone's Relationship From the beginning of their romance to today, we track their relationship here. By..."]
"""
prompt = """
Answer the question using only the data that is provided in the data section. Do not use any prior knowledge to answer the question.
Data: {{WebSearch.SearchAsync "What is semantic kernel?"}}
Question: What is semantic kernel?
Answer:
"""
qna = kernel.create_semantic_function(prompt, temperature=0.2)
context = kernel.create_new_context()
context["count"] = "10"
context["offset"] = "0"
result = await qna.invoke_async(context=context)
print(result)
"""
Output:
Semantic Kernel is an open-source SDK that lets you easily combine AI services like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C# and Python. By doing so, you can create AI apps that combine the best of both worlds. Semantic Kernel is at the center of the copilot stack.
"""
if __name__ == "__main__":
import asyncio
asyncio.run(main())
```
### 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`
- [ ] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄
---------
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
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378a1a949c |
Python: Add Logit-Bias to TextCompletion abstractions (#1880)
### 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. Adds the Logit-Bias feature that is in the C# into Python. 2. It adds the token bias configuration to the text and chat completion settings. 3. It contributes to flexibility in prompt engineering by allowing the user to influence the likelihood of specified tokens appearing in the text and chat completion. 4. Closes #1854 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> We added a dictionary field called token_selection_biases to the classes CompleteRequestSettings and ChatRequestSettings to implement logit bias and included an example to demonstrate functionality. ### 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 😄 --------- Co-authored-by: sneha <[email protected]> Co-authored-by: Alisha Maddy <[email protected]> Co-authored-by: Alisha Maddy <[email protected]> Co-authored-by: Abby Harrison <[email protected]> Co-authored-by: Sneha <[email protected]> |
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Shawn Callegariandname
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7a3fad9dbb |
Python: move kernel-syntax-examples to python/samples folder (#1650)
Moving kernel-syntax-examples to the python/samples folder. Leaving README behind to point to the new folders (will delete in a few sprints) ### 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 😄 Co-authored-by: name <email> |