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semantic-kernel/python/tests/end-to-end/memory.py
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Jordan Henkel 407fb36e59 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 Updates the out-of-date example in `README.md`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)
2023-04-13 16:12:36 -07:00

154 lines
4.3 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
import asyncio
from typing import Tuple
import semantic_kernel as sk
import semantic_kernel.ai.open_ai as sk_oai
def build_kernel() -> sk.Kernel:
# Setup kernel with OpenAI completion and embedding backends
api_key, org_id = sk.openai_settings_from_dot_env()
kernel = (
sk.kernel_builder()
.configure(
lambda c: c.add_text_backend(
"davinci-003",
sk_oai.OpenAITextCompletion("text-davinci-003", api_key, org_id),
)
)
.configure(
lambda c: c.add_embedding_backend(
"ada-002",
sk_oai.OpenAITextEmbedding("text-embedding-ada-002", api_key, org_id),
)
)
.with_memory_storage(sk.memory.VolatileMemoryStore())
.build()
)
kernel.import_skill(sk.core_skills.TextMemorySkill())
return kernel
async def populate_memory(kernel: sk.Kernel) -> None:
# Add some documents to the semantic memory
await kernel.memory.save_information_async(
"aboutMe", id="info1", text="My name is Andrea"
)
await kernel.memory.save_information_async(
"aboutMe", id="info2", text="I currently work as a tour guide"
)
await kernel.memory.save_information_async(
"aboutMe", id="info3", text="I've been living in Seattle since 2005"
)
await kernel.memory.save_information_async(
"aboutMe", id="info4", text="I visited France and Italy five times since 2015"
)
await kernel.memory.save_information_async(
"aboutMe", id="info5", text="My family is from New York"
)
async def search_memory_examples(kernel: sk.Kernel) -> None:
questions = [
"what's my name",
"where do I live?",
"where's my family from?",
"where have I traveled?",
"what do I do for work",
]
for question in questions:
print(f"Question: {question}")
result = await kernel.memory.search_async("aboutMe", question)
print(f"Answer: {result[0].text}\n")
async def setup_chat_with_memory(
kernel: sk.Kernel,
) -> Tuple[sk.SKFunctionBase, sk.SKContext]:
sk_prompt = """
ChatBot can have a conversation with you about any topic.
It can give explicit instructions or say 'I don't know' if
it does not have an answer.
Information about me, from previous conversations:
- {{$fact1}} {{recall $fact1}}
- {{$fact2}} {{recall $fact2}}
- {{$fact3}} {{recall $fact3}}
- {{$fact4}} {{recall $fact4}}
- {{$fact5}} {{recall $fact5}}
Chat:
{{$chat_history}}
User: {{$user_input}}
ChatBot: """.strip()
chat_func = kernel.create_semantic_function(
sk_prompt, max_tokens=200, temperature=0.8
)
context = kernel.create_new_context()
context["fact1"] = "what is my name?"
context["fact2"] = "where do I live?"
context["fact3"] = "where's my family from?"
context["fact4"] = "where have I traveled?"
context["fact5"] = "what do I do for work?"
context[sk.core_skills.TextMemorySkill.COLLECTION_PARAM] = "aboutMe"
context[sk.core_skills.TextMemorySkill.RELEVANCE_PARAM] = 0.8
context["chat_history"] = ""
return chat_func, context
async def chat(
kernel: sk.Kernel, chat_func: sk.SKFunctionBase, context: sk.SKContext
) -> bool:
try:
user_input = input("User:> ")
context["user_input"] = user_input
except KeyboardInterrupt:
print("\n\nExiting chat...")
return False
except EOFError:
print("\n\nExiting chat...")
return False
if user_input == "exit":
print("\n\nExiting chat...")
return False
answer = await kernel.run_on_vars_async(context.variables, chat_func)
context["chat_history"] += f"\nUser:> {user_input}\nChatBot:> {answer}\n"
print(f"ChatBot:> {answer}")
return True
async def main() -> None:
kernel = build_kernel()
print("Populating memory...")
await populate_memory(kernel)
print("Asking questions... (manually)")
await search_memory_examples(kernel)
print("Setting up a chat (with memory!)")
chat_func, context = await setup_chat_with_memory(kernel)
print("Begin chatting (type 'exit' to exit):\n")
chatting = True
while chatting:
chatting = await chat(kernel, chat_func, context)
if __name__ == "__main__":
asyncio.run(main())