diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 00000000..c8c34c39 --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,102 @@ +name: Tests + +on: + pull_request: + branches: + - main + - develop + types: + - opened + - synchronize + - reopened + +jobs: + unit-tests: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + + # Set up Python environment + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: ">=3.9 <3.13" + + # Install Poetry + - name: Install Poetry + run: | + curl -sSL https://install.python-poetry.org | python3 - + + # Cache Poetry Dependencies + - name: Cache Poetry dependencies + uses: actions/cache@v3 + with: + path: ~/.cache/pypoetry + key: poetry-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }} + restore-keys: | + poetry-${{ runner.os }}- + + # Install lib and dev dependencies + - name: Install llmstudio-core + working-directory: ./libs/core + run: | + poetry install + UNIT_ENV=$(poetry env info --path) + echo $UNIT_ENV + echo "UNIT_ENV=$UNIT_ENV" >> $GITHUB_ENV + + - name: Run unit tests + run: | + echo ${{ env.UNIT_ENV }} + source ${{ env.UNIT_ENV }}/bin/activate + poetry run pytest libs/core + + integration-tests: + needs: unit-tests + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + + # Set up Python environment + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: ">=3.9 <3.13" + + # Install Poetry + - name: Install Poetry + run: | + curl -sSL https://install.python-poetry.org | python3 - + + # Inject Secrets as Environment Variables + - name: Set up environment variables + run: | + echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }}" >> $GITHUB_ENV + echo "GOOGLE_API_KEY=${{ secrets.GOOGLE_API_KEY }}" >> $GITHUB_ENV + echo "BEDROCK_ACCESS_KEY=${{ secrets.BEDROCK_ACCESS_KEY }}" >> $GITHUB_ENV + echo "BEDROCK_SECRET_KEY=${{ secrets.BEDROCK_SECRET_KEY }}" >> $GITHUB_ENV + echo "BEDROCK_REGION=${{ secrets.BEDROCK_REGION }}" >> $GITHUB_ENV + + # Cache Poetry Dependencies + - name: Cache Poetry dependencies (Integration Tests) + uses: actions/cache@v3 + with: + path: ~/.cache/pypoetry + key: poetry-integration-${{ runner.os }}-${{ hashFiles('libs/llmstudio/poetry.lock') }} + restore-keys: | + poetry-integration-${{ runner.os }}- + + # Install llmstudio + - name: Install llmstudio + working-directory: ./libs/llmstudio + run: | + poetry install + INTEGRATION_ENV=$(poetry env info --path) + echo $INTEGRATION_ENV + echo "INTEGRATION_ENV=$INTEGRATION_ENV" >> $GITHUB_ENV + + # Run Integration Tests + - name: Run Integration Tests + run: | + source ${{ env.INTEGRATION_ENV }}/bin/activate + poetry run pytest libs/llmstudio/tests/integration_tests \ No newline at end of file diff --git a/.github/workflows/upload-pypi-dev.yml b/.github/workflows/upload-pypi-dev.yml index b13bf403..85cf886c 100644 --- a/.github/workflows/upload-pypi-dev.yml +++ b/.github/workflows/upload-pypi-dev.yml @@ -1,4 +1,4 @@ -name: PyPI prerelease and build/push Docker image. +name: PyPI prerelease any module. on: workflow_dispatch: diff --git a/.gitignore b/.gitignore index 19015866..d80305ef 100644 --- a/.gitignore +++ b/.gitignore @@ -56,6 +56,7 @@ env3 .env* .env*.local .venv* +*venv* env*/ venv*/ ENV/ @@ -66,6 +67,7 @@ venv.bak/ config.yaml bun.lockb + # Jupyter Notebook .ipynb_checkpoints @@ -76,4 +78,4 @@ bun.lockb llmstudio/llm_engine/logs/execution_logs.jsonl *.db .prettierignore -db \ No newline at end of file +db diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 6ee1c7ad..a637756c 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -9,9 +9,30 @@ Thank you for expressing your interest in contributing to LLMstudio. To ensure t 3. Follow our repo guidelines - Ensure that you update any relevant docstrings and comments within your code - Run `pre-commit run --all-files` to lint your code +4. Sign your commits. Without signed commits, your changes will not be accepted for main. ## Branches - All development happens in per-feature branches prefixed by contributor's initials. For example `feat/feature_name`. - Approved PRs are merged to the `main` branch. + +## Alpha releases: +You need to have your changes in the `develop` branch in order to push a new alpha version of any library `(llmstudio, llmstudio-proxy, llmstudio-tracker)`. Therefore, first guarantee that you feature branch is reviewed and working before merging to develop. + +Process: +- Ensure the `feature/**` you worked is passing the tests and has the approvals necessary. +- Merge to `develop` +- Ensure the changes are in the develop branch +- Use GitHub Actions to initiate the pre-release process: [PyPI pre-release any module](https://github.com/TensorOpsAI/LLMstudio/actions/workflows/upload-pypi-dev.yml) +- Select the target library `(llmstudio, llmstudio-proxy, llmstudio-tracker)` and the target version for the final release (e.g., 1.1.0). Consult main branch and PyPI for current versions. +- Run the workflow. +- The workflow will automatically bump the version and create an alpha release of the library/module specified +- The workflow will automatically push changes back (bump version) to the develop branch + +Repeat the process in case your `development` branch contains changes in multiple libraries. + +## Final releases: +Once you're happy with the versions, create the Release notes on the PR between `develop` and `main` and merge to main branch when ready for full release. The workflow will automatically remove any `alpha` tag in your libraries and push the versions for every library/module that suffered changes. + + diff --git a/Makefile b/Makefile index 5a43a3e6..6c957607 100644 --- a/Makefile +++ b/Makefile @@ -1,2 +1,5 @@ format: pre-commit run --all-files + +unit-tests: + pytest libs/core/tests/unit_tests diff --git a/examples/_config.yaml b/examples/_config.yaml index ae82394b..12f84a16 100644 --- a/examples/_config.yaml +++ b/examples/_config.yaml @@ -115,16 +115,29 @@ providers: keys: - OPENAI_API_KEY models: + o1-preview: + mode: chat + max_completion_tokens: 128000 + input_token_cost: 0.000015 + output_token_cost: 0.000060 + o1-mini: + mode: chat + max_completion_tokens: 128000 + input_token_cost: 0.000003 + cached_token_cost: 0.0000015 + output_token_cost: 0.000012 gpt-4o-mini: mode: chat max_tokens: 128000 input_token_cost: 0.00000015 + cached_token_cost: 0.000000075 output_token_cost: 0.00000060 gpt-4o: mode: chat max_tokens: 128000 - input_token_cost: 0.000005 - output_token_cost: 0.000015 + input_token_cost: 0.0000025 + cached_token_cost: 0.00000125 + output_token_cost: 0.00001 gpt-4-turbo: mode: chat max_tokens: 128000 diff --git a/examples/core.py b/examples/core.py index 6cae128a..1eab6c81 100644 --- a/examples/core.py +++ b/examples/core.py @@ -4,22 +4,59 @@ from pprint import pprint import os +import asyncio from dotenv import load_dotenv load_dotenv() -def run_provider(provider, model, api_key, **kwargs): +def run_provider(provider, model, api_key=None, **kwargs): + print(f"\n\n###RUNNING for <{provider}>, <{model}> ###") llm = LLMCore(provider=provider, api_key=api_key, **kwargs) latencies = {} + + print("\nAsync Non-Stream") chat_request = build_chat_request(model, chat_input="Hello, my name is Jason Json", is_stream=False) + string = """ +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + + """ + #chat_request = build_chat_request(model, chat_input=string, is_stream=False) + - import asyncio response_async = asyncio.run(llm.achat(**chat_request)) pprint(response_async) latencies["async (ms)"]= response_async.metrics["latency_s"]*1000 - - # stream - print("\nasync stream") + + + print("\nAsync Stream") async def async_stream(): chat_request = build_chat_request(model, chat_input="Hello, my name is Tom Json", is_stream=True) @@ -36,14 +73,15 @@ async def async_stream(): asyncio.run(async_stream()) - print("# Now sync calls") + print("\nSync Non-Stream") chat_request = build_chat_request(model, chat_input="Hello, my name is Alice Json", is_stream=False) response_sync = llm.chat(**chat_request) pprint(response_sync) latencies["sync (ms)"]= response_sync.metrics["latency_s"]*1000 + - print("# Now sync calls streaming") + print("\nSync Stream") chat_request = build_chat_request(model, chat_input="Hello, my name is Mary Json", is_stream=True) response_sync_stream = llm.chat(**chat_request) @@ -59,7 +97,7 @@ async def async_stream(): return latencies def build_chat_request(model: str, chat_input: str, is_stream: bool, max_tokens: int=1000): - if model == "o1-preview" or model == "o1-mini": + if model.startswith(('o1', 'o3')): chat_request = { "chat_input": chat_input, "model": model, @@ -69,6 +107,16 @@ def build_chat_request(model: str, chat_input: str, is_stream: bool, max_tokens: "max_completion_tokens": max_tokens } } + elif 'amazon.nova' in model or 'anthropic.claude' in model: + chat_request = { + "chat_input": chat_input, + "model": model, + "is_stream": is_stream, + "retries": 0, + "parameters": { + "maxTokens": max_tokens + } + } else: chat_request = { "chat_input": chat_input, @@ -85,76 +133,34 @@ def build_chat_request(model: str, chat_input: str, is_stream: bool, max_tokens: return chat_request +def multiple_provider_runs(provider:str, model:str, num_runs:int, api_key:str, **kwargs): + for _ in range(num_runs): + latencies = run_provider(provider=provider, model=model, api_key=api_key, **kwargs) + pprint(latencies) + + + +# OpenAI +multiple_provider_runs(provider="openai", model="gpt-4o-mini", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) +multiple_provider_runs(provider="openai", model="o3-mini", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) +#multiple_provider_runs(provider="openai", model="o1-preview", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) + +# Azure +multiple_provider_runs(provider="azure", model="gpt-4o-mini", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) +#multiple_provider_runs(provider="azure", model="gpt-4o", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) +#multiple_provider_runs(provider="azure", model="o1-mini", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) +#multiple_provider_runs(provider="azure", model="o1-preview", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) -provider = "openai" -model = "gpt-4o-mini" -for _ in range(1): - latencies = run_provider(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) - pprint(latencies) +#multiple_provider_runs(provider="anthropic", model="claude-3-opus-20240229", num_runs=1, api_key=os.environ["ANTHROPIC_API_KEY"]) - -provider = "openai" -model = "o1-preview" -for _ in range(1): - latencies = run_provider(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) - pprint(latencies) - -provider = "openai" -model = "o1-mini" -for _ in range(1): - latencies = run_provider(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) - pprint(latencies) - -# provider = "anthropic" -# model = "claude-3-opus-20240229" -# for _ in range(1): -# latencies = run_provider(provider=provider, model=model, api_key=os.environ["ANTHROPIC_API_KEY"]) -# pprint(latencies) -# # we need credits - -provider = "azure" -model = "gpt-4o-mini" -for _ in range(1): - latencies = run_provider(provider=provider, model=model, - api_key=os.environ["AZURE_API_KEY"], - api_version=os.environ["AZURE_API_VERSION"], - api_endpoint=os.environ["AZURE_API_ENDPOINT"]) - pprint(latencies) - -provider = "azure" -model = "o1-preview" -for _ in range(1): - latencies = run_provider(provider=provider, model=model, - api_key=os.environ["AZURE_API_KEY"], - api_version=os.environ["AZURE_API_VERSION"], - api_endpoint=os.environ["AZURE_API_ENDPOINT"]) - pprint(latencies) - -provider = "azure" -model = "o1-mini" -for _ in range(1): - latencies = run_provider(provider=provider, model=model, - api_key=os.environ["AZURE_API_KEY"], - api_version=os.environ["AZURE_API_VERSION"], - api_endpoint=os.environ["AZURE_API_ENDPOINT"]) - pprint(latencies) - -# provider = "azure" -# model = "gpt-4o" -# for _ in range(1): -# latencies = run_provider(provider=provider, model=model, -# api_key=os.environ["AZURE_API_KEY_llama"], -# base_url=os.environ["AZURE_BASE_URL"] -# ) -# pprint(latencies) - - -# provider = "vertexai" -# model = "gemini-1.5-pro-latest" -# for _ in range(1): -# latencies = run_provider(provider=provider, model=model, -# api_key=os.environ["GOOGLE_API_KEY"], -# ) -# pprint(latencies) +#multiple_provider_runs(provider="azure", model="o1-preview", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) +#multiple_provider_runs(provider="azure", model="o1-mini", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) + + +multiple_provider_runs(provider="vertexai", model="gemini-1.5-flash", num_runs=1, api_key=os.environ["GOOGLE_API_KEY"]) + +# Bedrock +multiple_provider_runs(provider="bedrock", model="us.amazon.nova-lite-v1:0", num_runs=1, api_key=None, region=os.environ["BEDROCK_REGION"], secret_key=os.environ["BEDROCK_SECRET_KEY"], access_key=os.environ["BEDROCK_ACCESS_KEY"]) +#multiple_provider_runs(provider="bedrock", model="anthropic.claude-3-5-sonnet-20241022-v2:0", num_runs=1, api_key=None, region=os.environ["BEDROCK_REGION"], secret_key=os.environ["BEDROCK_SECRET_KEY"], access_key=os.environ["BEDROCK_ACCESS_KEY"]) diff --git a/examples/langchain_integration.py b/examples/langchain_integration.py index 4d408535..51940624 100644 --- a/examples/langchain_integration.py +++ b/examples/langchain_integration.py @@ -18,7 +18,12 @@ # %% from langchain.tools import tool -from langchain.agents import AgentType, initialize_agent +from langchain.agents import AgentType, initialize_agent, AgentExecutor + +from langchain.agents.openai_functions_agent.base import ( + create_openai_functions_agent, +) +from langchain import hub # # %% # print("\n", chat_llm.invoke('Hello')) @@ -192,9 +197,16 @@ def assistant(question: str)->str: tools = [power_disco_ball, start_music, dim_lights] print(tools) - #rebuild agent with new tools - agent_executor = initialize_agent( - tools, chat_llm, agent=AgentType.OPENAI_FUNCTIONS, verbose = True, debug = True + #rebuild agent with new tools - This is the old outdated way of using agents in langchain + #agent_executor = initialize_agent( + # tools, chat_llm, agent=AgentType.OPENAI_FUNCTIONS, verbose = True, debug = True + #) + prompt = hub.pull("hwchase17/openai-functions-agent") + + agent = create_openai_functions_agent(llm=chat_llm, tools=tools, prompt=prompt) + + agent_executor = AgentExecutor( + agent=agent, tools=tools, verbose=True, return_intermediate_steps=True ) response = agent_executor.invoke( diff --git a/libs/core/llmstudio_core/config.yaml b/libs/core/llmstudio_core/config.yaml index 5f0920b2..46813bd6 100644 --- a/libs/core/llmstudio_core/config.yaml +++ b/libs/core/llmstudio_core/config.yaml @@ -73,7 +73,7 @@ providers: step: 1 bedrock: id: bedrock - name: Bedrock + name: Bedrock ConverseAPI chat: true embed: true keys: @@ -126,6 +126,22 @@ providers: max_tokens: 100000 input_token_cost: 0.000008 output_token_cost: 0.000024 + us.amazon.nova-pro-v1:0: + mode: chat + max_tokens: 300000 + input_token_cost: 0.0000008 + output_token_cost: 0.0000016 + us.amazon.nova-lite-v1:0: + mode: chat + max_tokens: 300000 + input_token_cost: 0.00000006 + output_token_cost: 0.00000012 + us.amazon.nova-micro-v1:0: + mode: chat + max_tokens: 128000 + input_token_cost: 0.000000035 + output_token_cost: 0.00000007 + parameters: temperature: name: "Temperature" @@ -208,22 +224,32 @@ providers: mode: chat max_completion_tokens: 128000 input_token_cost: 0.000015 + cached_token_cost: 0.0000075 output_token_cost: 0.000060 o1-mini: mode: chat max_completion_tokens: 128000 - input_token_cost: 0.000003 - output_token_cost: 0.000012 + input_token_cost: 0.0000011 + cached_token_cost: 0.00000055 + output_token_cost: 0.0000044 + o3-mini: + mode: chat + max_completion_tokens: 200000 + input_token_cost: 0.0000011 + cached_token_cost: 0.00000055 + output_token_cost: 0.0000044 gpt-4o-mini: mode: chat max_tokens: 128000 input_token_cost: 0.00000015 + cached_token_cost: 0.000000075 output_token_cost: 0.00000060 gpt-4o: mode: chat max_tokens: 128000 - input_token_cost: 0.000005 - output_token_cost: 0.000015 + input_token_cost: 0.0000025 + cached_token_cost: 0.00000125 + output_token_cost: 0.00001 gpt-4-turbo: mode: chat max_tokens: 128000 @@ -294,11 +320,13 @@ providers: mode: chat max_completion_tokens: 128000 input_token_cost: 0.0000165 + cached_token_cost: 0.00000825 output_token_cost: 0.000066 o1-mini: mode: chat max_completion_tokens: 128000 input_token_cost: 0.0000033 + cached_token_cost: 0.00000165 output_token_cost: 0.0000132 gpt-4o-mini: mode: chat @@ -308,8 +336,9 @@ providers: gpt-4o: mode: chat max_tokens: 128000 - input_token_cost: 0.000005 - output_token_cost: 0.000015 + input_token_cost: 0.0000025 + cached_token_cost: 0.00000125 + output_token_cost: 0.00001 gpt-4-turbo: mode: chat max_tokens: 128000 diff --git a/libs/core/llmstudio_core/providers/__init__.py b/libs/core/llmstudio_core/providers/__init__.py index 6aa6c4e7..330fe48e 100644 --- a/libs/core/llmstudio_core/providers/__init__.py +++ b/libs/core/llmstudio_core/providers/__init__.py @@ -2,7 +2,7 @@ from typing import Optional from llmstudio_core.providers.azure import AzureProvider -from llmstudio_core.providers.bedrock.provider import BedrockProvider +from llmstudio_core.providers.bedrock_converse import BedrockConverseProvider # from llmstudio_core.providers.ollama import OllamaProvider #TODO: adapt it from llmstudio_core.providers.openai import OpenAIProvider diff --git a/libs/core/llmstudio_core/providers/azure.py b/libs/core/llmstudio_core/providers/azure.py index 2dbd7307..f558f9d6 100644 --- a/libs/core/llmstudio_core/providers/azure.py +++ b/libs/core/llmstudio_core/providers/azure.py @@ -62,7 +62,25 @@ async def agenerate_client(self, request: ChatRequest) -> Any: return self.generate_client(request=request) def generate_client(self, request: ChatRequest) -> Any: - """Generate an AzureOpenAI client""" + """ + Generates an AzureOpenAI client for processing a chat request. + + This method prepares and configures the arguments required to create a client + request to AzureOpenAI's chat completions API. It determines model-specific + configurations (e.g., whether tools or functions are enabled) and combines + these with the base arguments for the API call. + + Args: + request (ChatRequest): The chat request object containing the model, + parameters, and other necessary details. + + Returns: + Any: The result of the chat completions API call. + + Raises: + ProviderError: If there is an issue with the API connection or an error + returned from the API. + """ self.is_llama = "llama" in request.model.lower() self.is_openai = "gpt" in request.model.lower() @@ -72,7 +90,6 @@ def generate_client(self, request: ChatRequest) -> Any: try: messages = self.prepare_messages(request) - # Prepare the optional tool-related arguments tool_args = {} if not self.is_llama and self.has_tools and self.is_openai: tool_args = { @@ -80,7 +97,6 @@ def generate_client(self, request: ChatRequest) -> Any: "tool_choice": "auto" if request.parameters.get("tools") else None, } - # Prepare the optional function-related arguments function_args = {} if not self.is_llama and self.has_functions and self.is_openai: function_args = { @@ -90,14 +106,12 @@ def generate_client(self, request: ChatRequest) -> Any: else None, } - # Prepare the base arguments base_args = { "model": request.model, "messages": messages, "stream": True, } - # Combine all arguments combined_args = { **base_args, **tool_args, @@ -116,13 +130,13 @@ def prepare_messages(self, request: ChatRequest): if self.is_llama and (self.has_tools or self.has_functions): user_message = self.convert_to_openai_format(request.chat_input) content = "<|begin_of_text|>" - content = self.add_system_message( + content = self.build_llama_system_message( user_message, content, request.parameters.get("tools"), request.parameters.get("functions"), ) - content = self.add_conversation(user_message, content) + content = self.build_llama_conversation(user_message, content) return [{"role": "user", "content": content}] else: return ( @@ -139,6 +153,20 @@ async def aparse_response( yield chunk def parse_response(self, response: AsyncGenerator, **kwargs) -> Any: + """ + Processes a generator response and yields processed chunks. + + If `is_llama` is True and tools or functions are enabled, it processes the response + using `handle_tool_response`. Otherwise, it processes each chunk and yields only those + containing "choices". + + Args: + response (Generator): The response generator to process. + **kwargs: Additional arguments for tool handling. + + Yields: + Any: Processed response chunks. + """ if self.is_llama and (self.has_tools or self.has_functions): for chunk in self.handle_tool_response(response, **kwargs): if chunk: @@ -388,9 +416,25 @@ def convert_to_openai_format(self, message: Union[str, list]) -> list: return [{"role": "user", "content": message}] return message - def add_system_message( + def build_llama_system_message( self, openai_message: list, llama_message: str, tools: list, functions: list ) -> str: + """ + Builds a complete system message for Llama based on OpenAI's message, tools, and functions. + + If a system message is present in the OpenAI message, it is included in the result. + Otherwise, a default system message is used. Additional tool and function instructions + are appended if provided. + + Args: + openai_message (list): List of OpenAI messages. + llama_message (str): The message to prepend to the system message. + tools (list): List of tools to include in the system message. + functions (list): List of functions to include in the system message. + + Returns: + str: The formatted system message combined with Llama message. + """ system_message = "" system_message_found = False for message in openai_message: @@ -407,15 +451,31 @@ def add_system_message( """ if tools: - system_message = system_message + self.add_tool_instructions(tools) + system_message = system_message + self.build_tool_instructions(tools) if functions: - system_message = system_message + self.add_function_instructions(functions) + system_message = system_message + self.build_function_instructions( + functions + ) end_tag = "\n<|eot_id|>" return llama_message + system_message + end_tag - def add_tool_instructions(self, tools: list) -> str: + def build_tool_instructions(self, tools: list) -> str: + """ + Builds a detailed instructional prompt for tools available to the assistant. + + This function generates a message describing the available tools, focusing on tools + of type "function." It explains to the LLM how to use each tool and provides an example of the + correct response format for function calls. + + Args: + tools (list): A list of tool dictionaries, where each dictionary contains tool + details such as type, function name, description, and parameters. + + Returns: + str: A formatted string detailing the tool instructions and usage examples. + """ tool_prompt = """ You have access to the following tools: """ @@ -449,7 +509,21 @@ def add_tool_instructions(self, tools: list) -> str: return tool_prompt - def add_function_instructions(self, functions: list) -> str: + def build_function_instructions(self, functions: list) -> str: + """ + Builds a detailed instructional prompt for available functions. + + This method creates a message describing the functions accessible to the assistant. + It includes the function name, description, and required parameters, along with + specific guidelines for calling functions. + + Args: + functions (list): A list of function dictionaries, each containing details such as + name, description, and parameters. + + Returns: + str: A formatted string with instructions on using the provided functions. + """ function_prompt = """ You have access to the following functions: """ @@ -479,35 +553,60 @@ def add_function_instructions(self, functions: list) -> str: """ return function_prompt - def add_conversation(self, openai_message: list, llama_message: str) -> str: + def build_llama_conversation(self, openai_message: list, llama_message: str) -> str: + """ + Appends the OpenAI message to the Llama message while formatting OpenAI messages. + + This function iterates through a list of OpenAI messages and formats them for inclusion + in a Llama message. It handles user messages that might include nested content (lists of + messages) by safely evaluating the content. System messages are skipped. + + Args: + openai_message (list): A list of dictionaries representing the OpenAI messages. Each + dictionary should have "role" and "content" keys. + llama_message (str): The initial Llama message to which the conversation is appended. + + Returns: + str: The Llama message with the conversation appended. + """ conversation_parts = [] for message in openai_message: if message["role"] == "system": continue elif message["role"] == "user" and isinstance(message["content"], str): try: - # Attempt to safely evaluate the string to a Python object content_as_list = ast.literal_eval(message["content"]) if isinstance(content_as_list, list): - # If the content is a list, process each nested message for nested_message in content_as_list: conversation_parts.append( self.format_message(nested_message) ) else: - # If the content is not a list, append it directly conversation_parts.append(self.format_message(message)) except (ValueError, SyntaxError): - # If evaluation fails or content is not a list/dict string, append the message directly conversation_parts.append(self.format_message(message)) else: - # For all other messages, use the existing formatting logic conversation_parts.append(self.format_message(message)) return llama_message + "".join(conversation_parts) def format_message(self, message: dict) -> str: - """Format a single message for the conversation.""" + """ + Formats a single message dictionary into a structured string for a conversation. + + The formatting depends on the content of the message, such as tool calls, + function calls, or simple user/assistant messages. Each type of message + is formatted with specific headers and tags. + + Args: + message (dict): A dictionary containing message details. Expected keys + include "role", "content", and optionally "tool_calls", + "tool_call_id", or "function_call". + + Returns: + str: A formatted string representing the message. Returns an empty + string if the message cannot be formatted. + """ if "tool_calls" in message: for tool_call in message["tool_calls"]: function_name = tool_call["function"]["name"] diff --git a/libs/core/llmstudio_core/providers/bedrock/provider.py b/libs/core/llmstudio_core/providers/bedrock/provider.py deleted file mode 100644 index 7087dac2..00000000 --- a/libs/core/llmstudio_core/providers/bedrock/provider.py +++ /dev/null @@ -1,43 +0,0 @@ -from typing import Any, AsyncGenerator, Coroutine, Generator - -from llmstudio_core.providers.bedrock.anthropic import BedrockAnthropicProvider -from llmstudio_core.providers.provider import ChatRequest, ProviderCore, provider - - -@provider -class BedrockProvider(ProviderCore): - def __init__(self, config, **kwargs): - super().__init__(config, **kwargs) - self.kwargs = kwargs - self.selected_model = None - - def _get_provider(self, model): - if "anthropic." in model: - return BedrockAnthropicProvider(config=self.config, **self.kwargs) - - raise ValueError(f" provider is not yet supported.") - - @staticmethod - def _provider_config_name(): - return "bedrock" - - def validate_request(self, request: ChatRequest): - return ChatRequest(**request) - - async def agenerate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Any]: - self.selected_model = self._get_provider(request.model) - return await self.selected_model.agenerate_client(request) - - def generate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Generator]: - self.selected_model = self._get_provider(request.model) - return self.selected_model.generate_client(request=request) - - async def aparse_response( - self, response: Any, **kwargs - ) -> AsyncGenerator[Any, None]: - result = await self.selected_model.aparse_response(response=response, **kwargs) - for chunk in result: - yield chunk - - def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> Any: - return self.selected_model.parse_response(response=response, **kwargs) diff --git a/libs/core/llmstudio_core/providers/bedrock/anthropic.py b/libs/core/llmstudio_core/providers/bedrock_converse.py similarity index 92% rename from libs/core/llmstudio_core/providers/bedrock/anthropic.py rename to libs/core/llmstudio_core/providers/bedrock_converse.py index f2b3cfc1..dc756e0a 100644 --- a/libs/core/llmstudio_core/providers/bedrock/anthropic.py +++ b/libs/core/llmstudio_core/providers/bedrock_converse.py @@ -23,6 +23,7 @@ ChoiceDelta, ChoiceDeltaToolCall, ChoiceDeltaToolCallFunction, + CompletionUsage, ) from pydantic import ValidationError @@ -30,7 +31,7 @@ @provider -class BedrockAnthropicProvider(ProviderCore): +class BedrockConverseProvider(ProviderCore): def __init__(self, config, **kwargs): super().__init__(config, **kwargs) self._client = boto3.client( @@ -46,17 +47,17 @@ def __init__(self, config, **kwargs): @staticmethod def _provider_config_name(): - return "bedrock-antropic" + return "bedrock" def validate_request(self, request: ChatRequest): return ChatRequest(**request) async def agenerate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Any]: - """Generate an AWS Bedrock client""" + """Generate an AWS Bedrock Converse client""" return self.generate_client(request=request) def generate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Generator]: - """Generate an AWS Bedrock client""" + """Generate an AWS Bedrock Converse client""" try: messages, system_prompt = self._process_messages(request.chat_input) tools = self._process_tools(request.parameters) @@ -83,7 +84,9 @@ def generate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Generator async def aparse_response( self, response: Any, **kwargs ) -> AsyncGenerator[Any, None]: - return self.parse_response(response=response, **kwargs) + result = self.parse_response(response=response, **kwargs) + for chunk in result: + yield chunk def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> Any: tool_name = None @@ -222,6 +225,22 @@ def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> Any: ) yield final_chunk.model_dump() + elif chunk.get("metadata"): + usage = chunk["metadata"].get("usage") + final_stream_chunk = ChatCompletionChunk( + id=str(uuid.uuid4()), + choices=[], + created=int(time.time()), + model=kwargs.get("request").model, + object="chat.completion.chunk", + usage=CompletionUsage( + completion_tokens=usage["outputTokens"], + prompt_tokens=usage["inputTokens"], + total_tokens=usage["totalTokens"], + ), + ) + yield final_stream_chunk.model_dump() + @staticmethod def _process_messages( chat_input: Union[str, List[Dict[str, str]]] diff --git a/libs/core/llmstudio_core/providers/data_structures.py b/libs/core/llmstudio_core/providers/data_structures.py new file mode 100644 index 00000000..374ad6eb --- /dev/null +++ b/libs/core/llmstudio_core/providers/data_structures.py @@ -0,0 +1,148 @@ +from typing import Any, List, Optional + +from openai.types.chat import ChatCompletion, ChatCompletionChunk +from pydantic import BaseModel + + +class Metrics(BaseModel): + input_tokens: int + """Number of tokens in the input.""" + + output_tokens: int + """Number of tokens in the output.""" + + reasoning_tokens: int + """Number of reasoning tokens used by the model.""" + + total_tokens: int + """Total token count (input + output + reasoning).""" + + cached_tokens: int + """Number of cached tokens which will lower the price of input.""" + + cost_usd: float + """Total cost of the response in USD (input + output + reasoning - cached).""" + + latency_s: float + """Total time taken for the response, in seconds.""" + + time_to_first_token_s: Optional[float] = None + """Time to receive the first token, in seconds.""" + + inter_token_latency_s: Optional[float] = None + """Average time between tokens, in seconds. Defaults to None if not provided.""" + + tokens_per_second: Optional[float] = None + """Processing rate of tokens per second. Defaults to None if not provided.""" + + def __getitem__(self, key: str) -> Any: + """ + Allows subscriptable access to class fields. + + Parameters + ---------- + key : str + The name of the field to retrieve. + + Returns + ------- + Any + The value of the specified field. + + Raises + ------ + KeyError + If the key does not exist. + """ + try: + return getattr(self, key) + except AttributeError: + raise KeyError(f"'{key}' not found in MetricsStream.") + + def __iter__(self): + """ + Allows iteration over the class fields as key-value pairs. + """ + return iter(self.model_dump().items()) + + def __len__(self): + """ + Returns the number of fields in the class. + """ + return len(self.model_fields) + + def keys(self): + """ + Returns the keys of the fields. + """ + return self.model_fields.keys() + + def values(self): + """ + Returns the values of the fields. + """ + return self.model_dump().values() + + def items(self): + """ + Returns the key-value pairs of the fields. + """ + return self.model_dump().items() + + +class ChatCompletionLLMstudio(ChatCompletion): + chat_input: str + """The input prompt for the chat completion.""" + + chat_output: str + """The final response generated by the model.""" + + chat_output_stream: str + """Incremental chunks of the response for streaming.""" + + context: List[dict] + """The conversation history or context provided to the model.""" + + provider: str + """Identifier for the backend service provider.""" + + deployment: Optional[str] + """Information about the deployment configuration used.""" + + timestamp: float + """The timestamp when the response was generated.""" + + parameters: dict + """The parameters used in the request.""" + + metrics: Optional[Metrics] + """Performance and usage metrics calculated for the response.""" + + +class ChatCompletionChunkLLMstudio(ChatCompletionChunk): + chat_input: str + """The input prompt for the chat completion.""" + + chat_output: Optional[str] + """The final response generated by the model.""" + + chat_output_stream: Optional[str] + """Incremental chunks of the response for streaming.""" + + context: List[dict] + """The conversation history or context provided to the model.""" + + provider: str + """Identifier for the backend service provider.""" + + deployment: Optional[str] + """Information about the deployment configuration used.""" + + timestamp: float + """The timestamp when the chunk was generated.""" + + parameters: dict + """The parameters used in the request.""" + + metrics: Optional[Metrics] + """Performance and usage metrics calculated for the response chunk.""" diff --git a/libs/core/llmstudio_core/providers/openai.py b/libs/core/llmstudio_core/providers/openai.py index e75fcad4..d4c86536 100644 --- a/libs/core/llmstudio_core/providers/openai.py +++ b/libs/core/llmstudio_core/providers/openai.py @@ -39,6 +39,7 @@ def generate_client(self, request: ChatRequest) -> Generator: else request.chat_input ), stream=True, + stream_options={"include_usage": True}, **request.parameters, ) except openai._exceptions.APIError as e: diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index 07e03835..9646919f 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -15,6 +15,11 @@ import tiktoken from llmstudio_core.exceptions import ProviderError +from llmstudio_core.providers.data_structures import ( + ChatCompletionChunkLLMstudio, + ChatCompletionLLMstudio, + Metrics, +) from openai.types.chat import ( ChatCompletion, ChatCompletionChunk, @@ -137,6 +142,10 @@ async def aparse_response(self, response: AsyncGenerator, **kwargs) -> Any: def parse_response(self, response: AsyncGenerator, **kwargs) -> Any: raise NotImplementedError("ProviderCore needs a parse_response method.") + @abstractmethod + def get_usage(self, response: AsyncGenerator, **kwargs) -> Any: + raise NotImplementedError("ProviderCore needs a get_usage method.") + def validate_model(self, request: ChatRequest): if request.model not in self.config.models: raise ProviderError( @@ -152,8 +161,38 @@ async def achat( parameters: Optional[dict] = {}, **kwargs, ): - - """Makes a chat connection with the provider's API""" + """ + Asynchronously establishes a chat connection with the provider’s API, handling retries, + request validation, and streaming response options. + + Parameters + ---------- + chat_input : Any + The input data for the chat request, such as a string or dictionary, to be sent to the API. + model : str + The identifier of the model to be used for the chat request. + is_stream : Optional[bool], default=False + Flag to indicate if the response should be streamed. If True, returns an async generator + for streaming content; otherwise, returns the complete response. + retries : Optional[int], default=0 + Number of retry attempts on error. Retries will be attempted for specific HTTP errors like rate limits. + parameters : Optional[dict], default={} + Additional configuration parameters for the request, such as temperature or max tokens. + **kwargs + Additional keyword arguments to customize the request. + + Returns + ------- + Union[AsyncGenerator, Any] + - If `is_stream` is True, returns an async generator yielding response chunks. + - If `is_stream` is False, returns the first complete response chunk. + + Raises + ------ + ProviderError + - Raised if the request validation fails or if all retry attempts are exhausted. + - Also raised for unexpected exceptions during request handling. + """ try: request = self.validate_request( dict( @@ -174,7 +213,7 @@ async def achat( try: start_time = time.time() response = await self.agenerate_client(request) - response_handler = self.ahandle_response(request, response, start_time) + response_handler = self._ahandle_response(request, response, start_time) if request.is_stream: return response_handler @@ -198,8 +237,38 @@ def chat( parameters: Optional[dict] = {}, **kwargs, ): - - """Makes a chat connection with the provider's API""" + """ + Establishes a chat connection with the provider’s API, handling retries, request validation, + and streaming response options. + + Parameters + ---------- + chat_input : Any + The input data for the chat request, often a string or dictionary, to be sent to the API. + model : str + The model identifier for selecting the model used in the chat request. + is_stream : Optional[bool], default=False + Flag to indicate if the response should be streamed. If True, the function returns a generator + for streaming content. Otherwise, it returns the complete response. + retries : Optional[int], default=0 + Number of retry attempts on error. Retries will be attempted on specific HTTP errors like rate limits. + parameters : Optional[dict], default={} + Additional configuration parameters for the request, such as temperature or max tokens. + **kwargs + Additional keyword arguments that can be passed to customize the request. + + Returns + ------- + Union[Generator, Any] + - If `is_stream` is True, returns a generator that yields chunks of the response. + - If `is_stream` is False, returns the first complete response chunk. + + Raises + ------ + ProviderError + - Raised if the request validation fails or if the request fails after the specified number of retries. + - Also raised on other unexpected exceptions during request handling. + """ try: request = self.validate_request( dict( @@ -220,7 +289,7 @@ def chat( try: start_time = time.time() response = self.generate_client(request) - response_handler = self.handle_response(request, response, start_time) + response_handler = self._handle_response(request, response, start_time) if request.is_stream: return response_handler @@ -235,15 +304,38 @@ def chat( raise ProviderError(str(e)) raise ProviderError("Too many requests") - async def ahandle_response( + async def _ahandle_response( self, request: ChatRequest, response: AsyncGenerator, start_time: float ) -> AsyncGenerator[str, None]: - """Handles the response from an API""" + """ + Asynchronously handles the response from an API, processing response chunks for either + streaming or non-streaming responses. + + Buffers response chunks for non-streaming responses to output one single message. For streaming responses sends incremental chunks. + + Parameters + ---------- + request : ChatRequest + The chat request object, which includes input data, model name, and streaming options. + response : AsyncGenerator + The async generator yielding response chunks from the API. + start_time : float + The timestamp when the response handling started, used for latency calculations. + + Yields + ------ + Union[ChatCompletionChunk, ChatCompletion] + - If `request.is_stream` is True, yields `ChatCompletionChunk` objects with incremental + response chunks for streaming. + - If `request.is_stream` is False, yields a final `ChatCompletion` object after processing all chunks. + """ first_token_time = None previous_token_time = None token_times = [] token_count = 0 chunks = [] + is_next_usage = False + usage = {} async for chunk in self.aparse_response(response, request=request): token_count += 1 @@ -253,50 +345,57 @@ async def ahandle_response( token_times.append(current_time - previous_token_time) previous_token_time = current_time + if is_next_usage: + usage = self.get_usage(chunk) + break + chunks.append(chunk) + finish_reason = chunk.get("choices")[0].get("finish_reason") + if finish_reason: + is_next_usage = True + if request.is_stream: chunk = chunk[0] if isinstance(chunk, tuple) else chunk model = chunk.get("model") - if chunk.get("choices")[0].get("finish_reason") != "stop": - chat_output = chunk.get("choices")[0].get("delta").get("content") - chunk = { - **chunk, - "id": str(uuid.uuid4()), - "chat_input": ( - request.chat_input - if isinstance(request.chat_input, str) - else request.chat_input[-1]["content"] - ), - "chat_output": None, - "chat_output_stream": chat_output if chat_output else "", - "context": ( - [{"role": "user", "content": request.chat_input}] - if isinstance(request.chat_input, str) - else request.chat_input - ), - "provider": self.config.id, - "model": ( - request.model - if model and model.startswith(request.model) - else (model or request.model) - ), - "deployment": ( - model - if model and model.startswith(request.model) - else (request.model if model != request.model else None) - ), - "timestamp": time.time(), - "parameters": request.parameters, - "metrics": None, - } - yield ChatCompletionChunk(**chunk) + chat_output = chunk.get("choices")[0].get("delta").get("content") + chunk = { + **chunk, + "id": str(uuid.uuid4()), + "chat_input": ( + request.chat_input + if isinstance(request.chat_input, str) + else request.chat_input[-1]["content"] + ), + "chat_output": None, + "chat_output_stream": chat_output if chat_output else "", + "context": ( + [{"role": "user", "content": request.chat_input}] + if isinstance(request.chat_input, str) + else request.chat_input + ), + "provider": self.config.id, + "model": ( + request.model + if model and model.startswith(request.model) + else (model or request.model) + ), + "deployment": ( + model + if model and model.startswith(request.model) + else (request.model if model != request.model else None) + ), + "timestamp": time.time(), + "parameters": request.parameters, + "metrics": None, + } + yield ChatCompletionChunkLLMstudio(**chunk) chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] model = next(chunk["model"] for chunk in chunks if chunk.get("model")) - response, output_string = self.join_chunks(chunks, request) + response, output_string = self._join_chunks(chunks) - metrics = self.calculate_metrics( + metrics = self._calculate_metrics( request.chat_input, response, request.model, @@ -305,6 +404,8 @@ async def ahandle_response( first_token_time, token_times, token_count, + is_stream=request.is_stream, + usage=usage, ) response = { @@ -339,19 +440,43 @@ async def ahandle_response( } if request.is_stream: - yield ChatCompletionChunk(**response) + yield ChatCompletionChunkLLMstudio(**response) else: - yield ChatCompletion(**response) + yield ChatCompletionLLMstudio(**response) - def handle_response( + def _handle_response( self, request: ChatRequest, response: Generator, start_time: float ) -> Generator: - """Handles the response from an API""" + """ + Processes API response chunks to build a structured, complete response, yielding + each chunk if streaming is enabled. + + If streaming, each chunk is yielded as soon as it’s processed. Otherwise, all chunks + are combined and yielded as a single response at the end. + + Parameters + ---------- + request : ChatRequest + The original request details, including model, input, and streaming preference. + response : Generator + A generator yielding partial response chunks from the API. + start_time : float + The start time for measuring response timing. + + Yields + ------ + Union[ChatCompletionChunk, ChatCompletion] + If streaming (`is_stream=True`), yields each `ChatCompletionChunk` as it’s processed. + Otherwise, yields a single `ChatCompletion` with the full response data. + + """ first_token_time = None previous_token_time = None token_times = [] token_count = 0 chunks = [] + is_next_usage = False + usage = {} for chunk in self.parse_response(response, request=request): token_count += 1 @@ -361,50 +486,58 @@ def handle_response( token_times.append(current_time - previous_token_time) previous_token_time = current_time + if is_next_usage: + usage = self.get_usage(chunk) + break + chunks.append(chunk) + finish_reason = chunk.get("choices")[0].get("finish_reason") + if finish_reason: + is_next_usage = True + if request.is_stream: chunk = chunk[0] if isinstance(chunk, tuple) else chunk model = chunk.get("model") - if chunk.get("choices")[0].get("finish_reason") != "stop": - chat_output = chunk.get("choices")[0].get("delta").get("content") - chunk = { - **chunk, - "id": str(uuid.uuid4()), - "chat_input": ( - request.chat_input - if isinstance(request.chat_input, str) - else request.chat_input[-1]["content"] - ), - "chat_output": None, - "chat_output_stream": chat_output if chat_output else "", - "context": ( - [{"role": "user", "content": request.chat_input}] - if isinstance(request.chat_input, str) - else request.chat_input - ), - "provider": self.config.id, - "model": ( - request.model - if model and model.startswith(request.model) - else (model or request.model) - ), - "deployment": ( - model - if model and model.startswith(request.model) - else (request.model if model != request.model else None) - ), - "timestamp": time.time(), - "parameters": request.parameters, - "metrics": None, - } - yield ChatCompletionChunk(**chunk) + + chat_output = chunk.get("choices")[0].get("delta").get("content") + chunk = { + **chunk, + "id": str(uuid.uuid4()), + "chat_input": ( + request.chat_input + if isinstance(request.chat_input, str) + else request.chat_input[-1]["content"] + ), + "chat_output": None, + "chat_output_stream": chat_output if chat_output else "", + "context": ( + [{"role": "user", "content": request.chat_input}] + if isinstance(request.chat_input, str) + else request.chat_input + ), + "provider": self.config.id, + "model": ( + request.model + if model and model.startswith(request.model) + else (model or request.model) + ), + "deployment": ( + model + if model and model.startswith(request.model) + else (request.model if model != request.model else None) + ), + "timestamp": time.time(), + "parameters": request.parameters, + "metrics": None, + } + yield ChatCompletionChunkLLMstudio(**chunk) chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] model = next(chunk["model"] for chunk in chunks if chunk.get("model")) - response, output_string = self.join_chunks(chunks, request) + response, output_string = self._join_chunks(chunks) - metrics = self.calculate_metrics( + metrics = self._calculate_metrics( request.chat_input, response, request.model, @@ -413,6 +546,8 @@ def handle_response( first_token_time, token_times, token_count, + is_stream=request.is_stream, + usage=usage, ) response = { @@ -447,11 +582,33 @@ def handle_response( } if request.is_stream: - yield ChatCompletionChunk(**response) + yield ChatCompletionChunkLLMstudio(**response) else: - yield ChatCompletion(**response) - - def join_chunks(self, chunks, request): + yield ChatCompletionLLMstudio(**response) + + def _join_chunks(self, chunks): + """ + Combine multiple response chunks from the model into a single, structured response. + Handles tool calls, function calls, and standard text completion based on the + purpose indicated by the final chunk. + + Parameters + ---------- + chunks : List[Dict] + A list of partial responses (chunks) from the model. + + Returns + ------- + Tuple[ChatCompletion, str] + - `ChatCompletion`: The structured response based on the type of completion + (tool calls, function call, or text). + - `str`: The concatenated content or arguments, depending on the completion type. + + Raises + ------ + Exception + If there is an issue constructing the response, an exception is raised. + """ finish_reason = chunks[-1].get("choices")[0].get("finish_reason") if finish_reason == "tool_calls": @@ -601,7 +758,7 @@ def join_chunks(self, chunks, request): stop_content, ) - def calculate_metrics( + def _calculate_metrics( self, input: Any, output: Any, @@ -611,41 +768,174 @@ def calculate_metrics( first_token_time: float, token_times: Tuple[float, ...], token_count: int, - ) -> Dict[str, Any]: - """Calculates metrics based on token times and output""" + is_stream: bool, + usage: Dict = {}, + ) -> Metrics: + """ + Calculates performance and cost metrics for a model response based on timing + information, token counts, and model-specific costs. + + Parameters + ---------- + input : Any + The input provided to the model, used to determine input token count. + output : Any + The output generated by the model, used to determine output token count. + model : str + The model identifier, used to retrieve model-specific configuration and costs. + start_time : float + The timestamp marking the start of the model response. + end_time : float + The timestamp marking the end of the model response. + first_token_time : float + The timestamp when the first token was received, used for latency calculations. + token_times : Tuple[float, ...] + A tuple of time intervals between received tokens, used for inter-token latency. + token_count : int + The total number of tokens processed in the response. + + Returns + ------- + Metrics + """ + model_config = self.config.models[model] - input_tokens = len(self.tokenizer.encode(self.input_to_string(input))) - output_tokens = len(self.tokenizer.encode(self.output_to_string(output))) - input_cost = self.calculate_cost(input_tokens, model_config.input_token_cost) - output_cost = self.calculate_cost(output_tokens, model_config.output_token_cost) + # Token counts + cached_tokens = 0 + reasoning_tokens = 0 + input_tokens = len(self.tokenizer.encode(self._input_to_string(input))) + output_tokens = len(self.tokenizer.encode(self._output_to_string(output))) + total_tokens = input_tokens + output_tokens + + if usage: + input_tokens = usage.get("prompt_tokens", input_tokens) + output_tokens = usage.get("completion_tokens", output_tokens) + total_tokens = usage.get("total_tokens", total_tokens) + + # Cost calculations + input_cost = self._calculate_cost(input_tokens, model_config.input_token_cost) + output_cost = self._calculate_cost( + output_tokens, model_config.output_token_cost + ) + total_cost_usd = input_cost + output_cost + + if usage: + if getattr(model_config, "cached_token_cost", None): + cached_tokens = usage["prompt_tokens_details"]["cached_tokens"] + cached_savings = self._calculate_cost( + cached_tokens, model_config.cached_token_cost + ) + total_cost_usd -= cached_savings + + completion_tokens_details = usage.get("completion_tokens_details") + if completion_tokens_details: + reasoning_tokens = completion_tokens_details.get( + "reasoning_tokens", None + ) + + if reasoning_tokens: + total_tokens += reasoning_tokens + reasoning_cost = self._calculate_cost( + reasoning_tokens, model_config.output_token_cost + ) # billed as output tokens + print(f"Reasoning Cost: {reasoning_cost}") + total_cost_usd += reasoning_cost + # Latency calculations total_time = end_time - start_time - return { - "input_tokens": input_tokens, - "output_tokens": output_tokens, - "total_tokens": input_tokens + output_tokens, - "cost_usd": input_cost + output_cost, - "latency_s": total_time, - "time_to_first_token_s": first_token_time - start_time, - "inter_token_latency_s": sum(token_times) / len(token_times), - "tokens_per_second": token_count / total_time, - } - def calculate_cost( + if is_stream: + time_to_first_token = first_token_time - start_time + inter_token_latency = ( + sum(token_times) / len(token_times) if token_times else 0.0 + ) + tokens_per_second = token_count / total_time if total_time > 0 else 0.0 + + return Metrics( + input_tokens=input_tokens, + output_tokens=output_tokens, + reasoning_tokens=reasoning_tokens, + total_tokens=total_tokens, + cached_tokens=cached_tokens, + cost_usd=total_cost_usd, + latency_s=total_time, + time_to_first_token_s=time_to_first_token, + inter_token_latency_s=inter_token_latency, + tokens_per_second=tokens_per_second, + ) + else: + return Metrics( + input_tokens=input_tokens, + output_tokens=output_tokens, + reasoning_tokens=reasoning_tokens, + total_tokens=total_tokens, + cached_tokens=cached_tokens, + cost_usd=total_cost_usd, + latency_s=total_time, + ) + + def _calculate_cost( self, token_count: int, token_cost: Union[float, List[Dict[str, Any]]] ) -> float: + """ + Calculates the cost for a given number of tokens based on a fixed cost per token + or a variable rate structure. + + If `token_cost` is a fixed float, the total cost is `token_count * token_cost`. + If `token_cost` is a list, it checks each range and calculates cost based on the applicable range's rate. + + Parameters + ---------- + token_count : int + The total number of tokens for which the cost is being calculated. + token_cost : Union[float, List[Dict[str, Any]]] + Either a fixed cost per token (as a float) or a list of dictionaries defining + variable cost ranges. Each dictionary in the list represents a range with + 'range' (a tuple of minimum and maximum token counts) and 'cost' (cost per token) keys. + + Returns + ------- + float + The calculated cost based on the token count and cost structure. + """ if isinstance(token_cost, list): for cost_range in token_cost: if token_count >= cost_range.range[0] and ( - token_count <= cost_range.range[1] or cost_range.range[1] is None + cost_range.range[1] is None or token_count <= cost_range.range[1] ): return cost_range.cost * token_count else: return token_cost * token_count return 0 - def input_to_string(self, input): + def get_usage(self, chunk) -> Dict: + """ + Gets Usage Object from chunk - usually the last one. + Returns an empty dictionary if usage does not exist or is None. + """ + if not chunk or "usage" not in chunk or chunk["usage"] is None: + return {} + return dict(chunk["usage"]) + + def _input_to_string(self, input): + """ + Converts an input, which can be a string or a structured list of messages, into a single concatenated string. + + Parameters + ---------- + input : Any + The input data to be converted. This can be: + - A simple string, which is returned as-is. + - A list of message dictionaries, where each dictionary may contain `content`, `role`, + and nested items like `text` or `image_url`. + + Returns + ------- + str + A concatenated string representing the text content of all messages, + including text and URLs from image content if present. + """ if isinstance(input, str): return input else: @@ -666,7 +956,24 @@ def input_to_string(self, input): result.append(url) return "".join(result) - def output_to_string(self, output): + def _output_to_string(self, output): + """ + Extracts and returns the content or arguments from the output based on + the `finish_reason` of the first choice in `output`. + + Parameters + ---------- + output : Any + The model output object, expected to have a `choices` attribute that should contain a `finish_reason` indicating the type of output + ("stop", "tool_calls", or "function_call") and corresponding content or arguments. + + Returns + ------- + str + - If `finish_reason` is "stop": Returns the message content. + - If `finish_reason` is "tool_calls": Returns the arguments for the first tool call. + - If `finish_reason` is "function_call": Returns the arguments for the function call. + """ if output.choices[0].finish_reason == "stop": return output.choices[0].message.content elif output.choices[0].finish_reason == "tool_calls": diff --git a/libs/core/llmstudio_core/utils.py b/libs/core/llmstudio_core/utils.py index bf8a2a2b..70c090f4 100644 --- a/libs/core/llmstudio_core/utils.py +++ b/libs/core/llmstudio_core/utils.py @@ -33,6 +33,7 @@ class ModelConfig(BaseModel): max_tokens: Optional[int] = Field(default=None, alias="max_completion_tokens") max_completion_tokens: Optional[int] = None input_token_cost: Union[float, List["CostRange"]] + cached_token_cost: Optional[Union[float, List["CostRange"]]] = None output_token_cost: Union[float, List["CostRange"]] diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock index 94272367..bea130b0 100644 --- a/libs/core/poetry.lock +++ b/libs/core/poetry.lock @@ -13,24 +13,24 @@ files = [ [[package]] name = "anyio" -version = "4.6.2.post1" +version = "4.8.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.9" files = [ - {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"}, - {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"}, + {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"}, + {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"}, ] [package.dependencies] exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} idna = ">=2.8" sniffio = ">=1.1" -typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} +typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""} [package.extras] -doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] -test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"] +doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"] trio = ["trio (>=0.26.1)"] [[package]] @@ -46,21 +46,18 @@ files = [ [[package]] name = "asttokens" -version = "2.4.1" +version = "3.0.0" description = "Annotate AST trees with source code positions" optional = false -python-versions = "*" +python-versions = ">=3.8" files = [ - {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"}, - {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"}, + {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"}, + {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"}, ] -[package.dependencies] -six = ">=1.12.0" - [package.extras] -astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"] -test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"] +astroid = ["astroid (>=2,<4)"] +test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"] [[package]] name = "black" @@ -110,32 +107,32 @@ uvloop = ["uvloop (>=0.15.2)"] [[package]] name = "boto3" -version = "1.35.66" +version = "1.36.9" description = "The AWS SDK for Python" optional = false python-versions = ">=3.8" files = [ - {file = "boto3-1.35.66-py3-none-any.whl", hash = "sha256:09a610f8cf4d3c22d4ca69c1f89079e3a1c82805ce94fa0eb4ecdd4d2ba6c4bc"}, - {file = "boto3-1.35.66.tar.gz", hash = "sha256:c392b9168b65e9c23483eaccb5b68d1f960232d7f967a1e00a045ba065ce050d"}, + {file = "boto3-1.36.9-py3-none-any.whl", hash = "sha256:440d0b70990efb732f63b40fa16c663c86fee80347eb4bf3bcc08b593e8ac77f"}, + {file = "boto3-1.36.9.tar.gz", hash = "sha256:035ed3868ff3b9afe05a49d0bde35582315bc438e60b5e76727a00b107567bfb"}, ] [package.dependencies] -botocore = ">=1.35.66,<1.36.0" +botocore = ">=1.36.9,<1.37.0" jmespath = ">=0.7.1,<2.0.0" -s3transfer = ">=0.10.0,<0.11.0" +s3transfer = ">=0.11.0,<0.12.0" [package.extras] crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.35.66" +version = "1.36.9" description = "Low-level, data-driven core of boto 3." optional = false python-versions = ">=3.8" files = [ - {file = "botocore-1.35.66-py3-none-any.whl", hash = "sha256:d0683e9c18bb6852f768da268086c3749d925332a664db0dd1459cfa7e96e475"}, - 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"virtualenv-20.29.1.tar.gz", hash = "sha256:b8b8970138d32fb606192cb97f6cd4bb644fa486be9308fb9b63f81091b5dc35"}, ] [package.dependencies] diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml index de5a22c2..883ce800 100644 --- a/libs/core/pyproject.toml +++ b/libs/core/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "llmstudio-core" -version = "1.0.2" +version = "1.0.3a1" description = "LLMStudio core capabilities for routing llm calls for any vendor. No proxy server required. For that use llmstudio[proxy]" authors = ["Cláudio Lemos "] license = "MIT" diff --git a/libs/core/tests/unit_tests/conftest.py b/libs/core/tests/unit_tests/conftest.py index 23f070e3..0a5feb41 100644 --- a/libs/core/tests/unit_tests/conftest.py +++ b/libs/core/tests/unit_tests/conftest.py @@ -1,6 +1,7 @@ from unittest.mock import MagicMock import pytest +from llmstudio_core.providers.azure import AzureProvider from llmstudio_core.providers.provider import ProviderCore @@ -11,15 +12,7 @@ async def aparse_response(self, response, **kwargs): def parse_response(self, response, **kwargs): return response - def chat(self, chat_input, model, **kwargs): - # Mock the response to match expected structure - return MagicMock(choices=[MagicMock(finish_reason="stop")]) - - async def achat(self, chat_input, model, **kwargs): - # Mock the response to match expected structure - return MagicMock(choices=[MagicMock(finish_reason="stop")]) - - def output_to_string(self, output): + def _output_to_string(self, output): # Handle string inputs if isinstance(output, str): return output @@ -27,6 +20,24 @@ def output_to_string(self, output): return output.choices[0].message.content return "" + def validate_request(self, request): + # For testing, simply return the request + return request + + async def agenerate_client(self, request): + # For testing, return an async generator + async def async_gen(): + yield {} + + return async_gen() + + def generate_client(self, request): + # For testing, return a generator + def gen(): + yield {} + + return gen() + @staticmethod def _provider_config_name(): return "mock_provider" @@ -42,3 +53,34 @@ def mock_provider(): tokenizer = MagicMock() tokenizer.encode = lambda x: x.split() # Simple tokenizer mock return MockProvider(config=config, tokenizer=tokenizer) + + +class MockAzureProvider(AzureProvider): + async def aparse_response(self, response, **kwargs): + return response + + async def agenerate_client(self, request): + # For testing, return an async generator + async def async_gen(): + yield {} + + return async_gen() + + @staticmethod + def _provider_config_name(): + return "mock_azure_provider" + + +@pytest.fixture +def mock_azure_provider(): + config = MagicMock() + config.id = "mock_azure_provider" + api_key = "key" + api_endpoint = "http://azureopenai.com" + api_version = "2025-01-01-preview" + return MockAzureProvider( + config=config, + api_endpoint=api_endpoint, + api_key=api_key, + api_version=api_version, + ) diff --git a/libs/core/tests/unit_tests/test_azure.py b/libs/core/tests/unit_tests/test_azure.py new file mode 100644 index 00000000..28e5f684 --- /dev/null +++ b/libs/core/tests/unit_tests/test_azure.py @@ -0,0 +1,232 @@ +from unittest.mock import MagicMock + + +class TestParseResponse: + def test_tool_response_handling(self, mock_azure_provider): + + mock_azure_provider.is_llama = True + mock_azure_provider.has_tools = True + mock_azure_provider.has_functions = False + + mock_azure_provider.handle_tool_response = MagicMock( + return_value=iter(["chunk1", None, "chunk2", None, "chunk3"]) + ) + + response = iter(["irrelevant"]) + + results = list(mock_azure_provider.parse_response(response)) + + assert results == ["chunk1", "chunk2", "chunk3"] + mock_azure_provider.handle_tool_response.assert_called_once_with(response) + + def test_direct_response_handling_with_choices(self, mock_azure_provider): + mock_azure_provider.is_llama = False + + chunk1 = MagicMock() + chunk1.model_dump.return_value = {"choices": ["choice1", "choice2"]} + chunk2 = MagicMock() + chunk2.model_dump.return_value = {"choices": ["choice2"]} + response = iter([chunk1, chunk2]) + + results = list(mock_azure_provider.parse_response(response)) + + assert results == [ + {"choices": ["choice1", "choice2"]}, + {"choices": ["choice2"]}, + ] + chunk1.model_dump.assert_called_once() + chunk2.model_dump.assert_called_once() + + def test_direct_response_handling_without_choices(self, mock_azure_provider): + mock_azure_provider.is_llama = False + + chunk1 = MagicMock() + chunk1.model_dump.return_value = {"key": "value"} + chunk2 = MagicMock() + chunk2.model_dump.return_value = {"another_key": "another_value"} + response = iter([chunk1, chunk2]) + + results = list(mock_azure_provider.parse_response(response)) + + assert results == [] + chunk1.model_dump.assert_called_once() + chunk2.model_dump.assert_called_once() + + +class TestFormatMessage: + def test_format_message_tool_calls(self, mock_azure_provider): + message = { + "tool_calls": [ + { + "function": { + "name": "example_tool", + "arguments": '{"arg1": "value1"}', + } + } + ] + } + result = mock_azure_provider.format_message(message) + expected = """ + <|start_header_id|>assistant<|end_header_id|> + {"arg1": "value1"} + <|eom_id|> + """ + assert result.strip() == expected.strip() + + def test_format_message_tool_call_id(self, mock_azure_provider): + message = {"tool_call_id": "123", "content": "This is the tool response."} + result = mock_azure_provider.format_message(message) + expected = """ + <|start_header_id|>ipython<|end_header_id|> + This is the tool response. + <|eot_id|> + """ + assert result.strip() == expected.strip() + + def test_format_message_function_call(self, mock_azure_provider): + message = { + "function_call": { + "name": "example_function", + "arguments": '{"arg1": "value1"}', + } + } + result = mock_azure_provider.format_message(message) + expected = """ + <|start_header_id|>assistant<|end_header_id|> + {"arg1": "value1"} + <|eom_id|> + """ + assert result.strip() == expected.strip() + + def test_format_message_user_message(self, mock_azure_provider): + message = {"role": "user", "content": "This is a user message."} + result = mock_azure_provider.format_message(message) + expected = """ + <|start_header_id|>user<|end_header_id|> + This is a user message. + <|eot_id|> + """ + assert result.strip() == expected.strip() + + def test_format_message_assistant_message(self, mock_azure_provider): + message = {"role": "assistant", "content": "This is an assistant message."} + result = mock_azure_provider.format_message(message) + expected = """ + <|start_header_id|>assistant<|end_header_id|> + This is an assistant message. + <|eot_id|> + """ + assert result.strip() == expected.strip() + + def test_format_message_function_response(self, mock_azure_provider): + message = {"role": "function", "content": "This is the function response."} + result = mock_azure_provider.format_message(message) + expected = """ + <|start_header_id|>ipython<|end_header_id|> + This is the function response. + <|eot_id|> + """ + assert result.strip() == expected.strip() + + def test_format_message_empty_message(self, mock_azure_provider): + message = {"role": "user", "content": None} + result = mock_azure_provider.format_message(message) + expected = "" + assert result == expected + + +class TestGenerateClient: + def test_generate_client_with_tools_and_functions(self, mock_azure_provider): + mock_azure_provider.prepare_messages = MagicMock( + return_value="prepared_messages" + ) + mock_azure_provider._client.chat.completions.create = MagicMock( + return_value="mock_response" + ) + + request = MagicMock() + request.model = "gpt-4" + request.parameters = { + "tools": ["tool1", "tool2"], + "functions": ["function1", "function2"], + "other_param": "value", + } + + result = mock_azure_provider.generate_client(request) + + expected_args = { + "model": "gpt-4", + "messages": "prepared_messages", + "stream": True, + "tools": ["tool1", "tool2"], + "tool_choice": "auto", + "functions": ["function1", "function2"], + "function_call": "auto", + "other_param": "value", + } + + assert result == "mock_response" + mock_azure_provider.prepare_messages.assert_called_once_with(request) + mock_azure_provider._client.chat.completions.create.assert_called_once_with( + **expected_args + ) + + def test_generate_client_without_tools_or_functions(self, mock_azure_provider): + mock_azure_provider.prepare_messages = MagicMock( + return_value="prepared_messages" + ) + mock_azure_provider._client.chat.completions.create = MagicMock( + return_value="mock_response" + ) + + request = MagicMock() + request.model = "gpt-4" + request.parameters = {"other_param": "value"} + + result = mock_azure_provider.generate_client(request) + + expected_args = { + "model": "gpt-4", + "messages": "prepared_messages", + "stream": True, + "other_param": "value", + } + + assert result == "mock_response" + mock_azure_provider.prepare_messages.assert_called_once_with(request) + mock_azure_provider._client.chat.completions.create.assert_called_once_with( + **expected_args + ) + + def test_generate_client_with_llama_model(self, mock_azure_provider): + mock_azure_provider.prepare_messages = MagicMock( + return_value="prepared_messages" + ) + mock_azure_provider._client.chat.completions.create = MagicMock( + return_value="mock_response" + ) + + request = MagicMock() + request.model = "llama-2" + request.parameters = { + "tools": ["tool1"], + "functions": ["function1"], + "other_param": "value", + } + + result = mock_azure_provider.generate_client(request) + + expected_args = { + "model": "llama-2", + "messages": "prepared_messages", + "stream": True, + "tools": ["tool1"], + "functions": ["function1"], + "other_param": "value", + } + + assert result == "mock_response" + mock_azure_provider.prepare_messages.assert_called_once_with(request) + mock_azure_provider._client.chat.completions.create.assert_called_once_with( + **expected_args + ) diff --git a/libs/core/tests/unit_tests/test_azure_build.py b/libs/core/tests/unit_tests/test_azure_build.py new file mode 100644 index 00000000..17d73f99 --- /dev/null +++ b/libs/core/tests/unit_tests/test_azure_build.py @@ -0,0 +1,279 @@ +from unittest.mock import MagicMock, patch + + +class TestBuildLlamaSystemMessage: + def test_build_llama_system_message_with_existing_sm(self, mock_azure_provider): + mock_azure_provider.build_tool_instructions = MagicMock( + return_value="Tool Instructions" + ) + mock_azure_provider.build_function_instructions = MagicMock( + return_value="\nFunction Instructions" + ) + + openai_message = [ + {"role": "user", "content": "Hello"}, + {"role": "system", "content": "Custom system message"}, + ] + llama_message = "Initial message" + tools = ["Tool1", "Tool2"] + functions = ["Function1"] + + result = mock_azure_provider.build_llama_system_message( + openai_message, llama_message, tools, functions + ) + + expected = ( + "Initial message\n" + " <|start_header_id|>system<|end_header_id|>\n" + " Custom system message\n" + " Tool Instructions\nFunction Instructions\n<|eot_id|>" # identation here exists because in Python when adding a newline to a triple quote string it keeps identation + ) + assert result == expected + mock_azure_provider.build_tool_instructions.assert_called_once_with(tools) + mock_azure_provider.build_function_instructions.assert_called_once_with( + functions + ) + + def test_build_llama_system_message_with_default_sm(self, mock_azure_provider): + mock_azure_provider.build_tool_instructions = MagicMock( + return_value="Tool Instructions" + ) + mock_azure_provider.build_function_instructions = MagicMock( + return_value="\nFunction Instructions" + ) + + openai_message = [{"role": "user", "content": "Hello"}] + llama_message = "Initial message" + tools = ["Tool1"] + functions = [] + + result = mock_azure_provider.build_llama_system_message( + openai_message, llama_message, tools, functions + ) + + expected = ( + "Initial message\n" + " <|start_header_id|>system<|end_header_id|>\n" + " You are a helpful AI assistant.\n" + " Tool Instructions\n<|eot_id|>" + ) + assert result == expected + mock_azure_provider.build_tool_instructions.assert_called_once_with(tools) + mock_azure_provider.build_function_instructions.assert_not_called() + + def test_build_llama_system_message_without_tools_or_functions( + self, mock_azure_provider + ): + mock_azure_provider.build_tool_instructions = MagicMock() + mock_azure_provider.build_function_instructions = MagicMock() + + openai_message = [{"role": "system", "content": "Minimal system message"}] + llama_message = "Initial message" + tools = [] + functions = [] + + result = mock_azure_provider.build_llama_system_message( + openai_message, llama_message, tools, functions + ) + + expected = ( + "Initial message\n" + " <|start_header_id|>system<|end_header_id|>\n" + " Minimal system message\n \n<|eot_id|>" + ) + assert result == expected + mock_azure_provider.build_tool_instructions.assert_not_called() + mock_azure_provider.build_function_instructions.assert_not_called() + + +class TestBuildInstructions: + def test_build_tool_instructions(self, mock_azure_provider): + tools = [ + { + "type": "function", + "function": { + "name": "python_repl_ast", + "description": "execute Python code", + "parameters": {"query": "string"}, + }, + }, + { + "type": "function", + "function": { + "name": "data_lookup", + "description": "retrieve data from a database", + "parameters": {"database": "string", "query": "string"}, + }, + }, + ] + + result = mock_azure_provider.build_tool_instructions(tools) + + expected = """ + You have access to the following tools: + Use the function 'python_repl_ast' to 'execute Python code': +Parameters format: +{ + "query": "string" +} + +Use the function 'data_lookup' to 'retrieve data from a database': +Parameters format: +{ + "database": "string", + "query": "string" +} + + +If you choose to use a function to produce this response, ONLY reply in the following format with no prefix or suffix: +§{"type": "function", "name": "FUNCTION_NAME", "parameters": {"PARAMETER_NAME": PARAMETER_VALUE}} +IMPORTANT: IT IS VITAL THAT YOU NEVER ADD A PREFIX OR A SUFFIX TO THE FUNCTION CALL. + +Here is an example of the output I desiere when performing function call: +§{"type": "function", "name": "python_repl_ast", "parameters": {"query": "print(df.shape)"}} +NOTE: There is no prefix before the symbol '§' and nothing comes after the call is done. + + Reminder: + - Function calls MUST follow the specified format. + - Only call one function at a time. + - Required parameters MUST be specified. + - Put the entire function call reply on one line. + - If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls. + - If you have already called a tool and got the response for the users question please reply with the response. + """ + assert result.strip() == expected.strip() + + def test_build_function_instructions(self, mock_azure_provider): + functions = [ + { + "name": "python_repl_ast", + "description": "execute Python code", + "parameters": {"query": "string"}, + }, + { + "name": "data_lookup", + "description": "retrieve data from a database", + "parameters": {"database": "string", "query": "string"}, + }, + ] + + result = mock_azure_provider.build_function_instructions(functions) + + expected = """ +You have access to the following functions: +Use the function 'python_repl_ast' to: 'execute Python code' +{ + "query": "string" +} + +Use the function 'data_lookup' to: 'retrieve data from a database' +{ + "database": "string", + "query": "string" +} + + +If you choose to use a function to produce this response, ONLY reply in the following format with no prefix or suffix: +§{"type": "function", "name": "FUNCTION_NAME", "parameters": {"PARAMETER_NAME": PARAMETER_VALUE}} + +Here is an example of the output I desiere when performing function call: +§{"type": "function", "name": "python_repl_ast", "parameters": {"query": "print(df.shape)"}} + +Reminder: +- Function calls MUST follow the specified format. +- Only call one function at a time. +- NEVER call more than one function at a time. +- Required parameters MUST be specified. +- Put the entire function call reply on one line. +- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls. +- If you have already called a function and got the response for the user's question, please reply with the response. +""" + + assert result.strip() == expected.strip() + + +class TestBuildLlamaConversation: + def test_build_llama_conversation_with_nested_messages(self, mock_azure_provider): + mock_azure_provider.format_message = MagicMock( + side_effect=lambda msg: f"[formatted:{msg['content']}]" + ) + + openai_message = [ + { + "role": "user", + "content": "[{'content': 'nested message 1'}, {'content': 'nested message 2'}]", + }, + {"role": "assistant", "content": "assistant reply"}, + ] + llama_message = "Initial message: " + + result = mock_azure_provider.build_llama_conversation( + openai_message, llama_message + ) + + expected = "Initial message: [formatted:nested message 1][formatted:nested message 2][formatted:assistant reply]" + + assert result == expected + mock_azure_provider.format_message.assert_any_call( + {"content": "nested message 1"} + ) + mock_azure_provider.format_message.assert_any_call( + {"content": "nested message 2"} + ) + mock_azure_provider.format_message.assert_any_call( + {"role": "assistant", "content": "assistant reply"} + ) + + def test_build_llama_conversation_with_invalid_nested_content( + self, mock_azure_provider + ): + mock_azure_provider.format_message = MagicMock( + side_effect=lambda msg: f"[formatted:{msg['content']}]" + ) + + openai_message = [ + {"role": "user", "content": "[invalid json/dict]"}, + {"role": "assistant", "content": "assistant reply"}, + ] + llama_message = "Initial message: " + + with patch("ast.literal_eval", side_effect=ValueError) as mock_literal_eval: + result = mock_azure_provider.build_llama_conversation( + openai_message, llama_message + ) + + expected = "Initial message: [formatted:[invalid json/dict]][formatted:assistant reply]" + + assert result == expected + mock_azure_provider.format_message.assert_any_call( + {"role": "user", "content": "[invalid json/dict]"} + ) + mock_azure_provider.format_message.assert_any_call( + {"role": "assistant", "content": "assistant reply"} + ) + + mock_literal_eval.assert_called_once_with("[invalid json/dict]") + + def test_build_llama_conversation_skipping_system_messages( + self, mock_azure_provider + ): + mock_azure_provider.format_message = MagicMock( + side_effect=lambda msg: f"[formatted:{msg['content']}]" + ) + + openai_message = [ + {"role": "system", "content": "system message"}, + {"role": "user", "content": "user message"}, + ] + llama_message = "Initial message: " + + result = mock_azure_provider.build_llama_conversation( + openai_message, llama_message + ) + + expected = "Initial message: [formatted:user message]" + + assert result == expected + mock_azure_provider.format_message.assert_any_call( + {"role": "user", "content": "user message"} + ) diff --git a/libs/core/tests/unit_tests/test_provider.py b/libs/core/tests/unit_tests/test_provider.py index 118367a3..4baa5b3a 100644 --- a/libs/core/tests/unit_tests/test_provider.py +++ b/libs/core/tests/unit_tests/test_provider.py @@ -1,30 +1,39 @@ -from unittest.mock import AsyncMock, MagicMock +from unittest.mock import MagicMock import pytest -from llmstudio_core.providers.provider import ChatRequest, ProviderError +from llmstudio_core.providers.provider import ChatRequest, ProviderError, time -request = ChatRequest(chat_input="Hello", model="test_model") +request = ChatRequest(chat_input="Hello World", model="test_model") -def test_chat(mock_provider): - mock_provider.generate_client = MagicMock(return_value=MagicMock()) - mock_provider.handle_response = MagicMock(return_value=iter(["response"])) +def test_chat_response_non_stream(mock_provider): + mock_provider.validate_request = MagicMock() + mock_provider.validate_model = MagicMock() + mock_provider.generate_client = MagicMock(return_value="mock_response") + mock_provider._handle_response = MagicMock(return_value="final_response") - print(request.model_dump()) - response = mock_provider.chat(request.chat_input, request.model) + response = mock_provider.chat(chat_input="Hello", model="test_model") - assert response is not None + assert response == "final_response" + mock_provider.validate_request.assert_called_once() + mock_provider.validate_model.assert_called_once() -@pytest.mark.asyncio -async def test_achat(mock_provider): - mock_provider.agenerate_client = AsyncMock(return_value=AsyncMock()) - mock_provider.ahandle_response = AsyncMock(return_value=AsyncMock()) - - print(request.model_dump()) - response = await mock_provider.achat(request.chat_input, request.model) +def test_chat_streaming_response(mock_provider): + mock_provider.validate_request = MagicMock() + mock_provider.validate_model = MagicMock() + mock_provider.generate_client = MagicMock(return_value="mock_response_stream") + mock_provider._handle_response = MagicMock( + return_value=iter(["streamed_response_1", "streamed_response_2"]) + ) - assert response is not None + response_stream = mock_provider.chat( + chat_input="Hello", model="test_model", is_stream=True + ) + assert next(response_stream) == "streamed_response_1" + assert next(response_stream) == "streamed_response_2" + mock_provider.validate_request.assert_called_once() + mock_provider.validate_model.assert_called_once() def test_validate_model(mock_provider): @@ -36,22 +45,214 @@ def test_validate_model(mock_provider): mock_provider.validate_model(request_invalid) -def test_calculate_metrics(mock_provider): - metrics = mock_provider.calculate_metrics( - input="Hello", - output="World", - model="test_model", - start_time=0, - end_time=1, - first_token_time=0.5, - token_times=(0.1, 0.2), - token_count=2, - ) +def test_join_chunks_finish_reason_stop(mock_provider): + current_time = int(time.time()) + chunks = [ + { + "id": "test_id", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": {"content": "Hello, "}, + "finish_reason": None, + "index": 0, + } + ], + }, + { + "id": "test_id", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": {"content": "world!"}, + "finish_reason": "stop", + "index": 0, + } + ], + }, + ] + response, output_string = mock_provider._join_chunks(chunks) + + assert output_string == "Hello, world!" + assert response.choices[0].message.content == "Hello, world!" + + +def test_join_chunks_finish_reason_function_call(mock_provider): + current_time = int(time.time()) + chunks = [ + { + "id": "test_id", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": { + "function_call": {"name": "my_function", "arguments": "arg1"} + }, + "finish_reason": None, + "index": 0, + } + ], + }, + { + "id": "test_id", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": {"function_call": {"arguments": "arg2"}}, + "finish_reason": "function_call", + "index": 0, + } + ], + }, + { + "id": "test_id", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": {"function_call": {"arguments": "}"}}, + "finish_reason": "function_call", + "index": 0, + } + ], + }, + ] + response, output_string = mock_provider._join_chunks(chunks) + + assert output_string == "arg1arg2" + assert response.choices[0].message.function_call.arguments == "arg1arg2" + assert response.choices[0].message.function_call.name == "my_function" + + +def test_join_chunks_tool_calls(mock_provider): + current_time = int(time.time()) + + chunks = [ + { + "id": "test_id_1", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": { + "tool_calls": [ + { + "id": "tool_1", + "index": 0, + "function": { + "name": "search_tool", + "arguments": '{"query": "weather', + }, + "type": "function", + } + ] + }, + "finish_reason": None, + "index": 0, + } + ], + }, + { + "id": "test_id_2", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": { + "tool_calls": [ + { + "id": "tool_1", + "index": 0, + "function": { + "name": "search_tool", + "arguments": ' details"}', + }, + } + ] + }, + "finish_reason": "tool_calls", + "index": 0, + } + ], + }, + ] + + response, output_string = mock_provider._join_chunks(chunks) + + assert output_string == "['search_tool', '{\"query\": \"weather details\"}']" + + assert response.object == "chat.completion" + assert response.choices[0].finish_reason == "tool_calls" + tool_call = response.choices[0].message.tool_calls[0] + + assert tool_call.id == "tool_1" + assert tool_call.function.name == "search_tool" + assert tool_call.function.arguments == '{"query": "weather details"}' + assert tool_call.type == "function" + + +def test_input_to_string_with_string(mock_provider): + input_data = "Hello, world!" + assert mock_provider._input_to_string(input_data) == "Hello, world!" + + +def test_input_to_string_with_list_of_text_messages(mock_provider): + input_data = [ + {"content": "Hello"}, + {"content": " world!"}, + ] + assert mock_provider._input_to_string(input_data) == "Hello world!" + + +def test_input_to_string_with_list_of_text_and_url(mock_provider): + input_data = [ + {"role": "user", "content": [{"type": "text", "text": "Hello "}]}, + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": "http://example.com/image.jpg"}, + } + ], + }, + {"role": "user", "content": [{"type": "text", "text": " world!"}]}, + ] + expected_output = "Hello http://example.com/image.jpg world!" + assert mock_provider._input_to_string(input_data) == expected_output + + +def test_input_to_string_with_mixed_roles_and_missing_content(mock_provider): + input_data = [ + {"role": "assistant", "content": "Admin text;"}, + {"role": "user", "content": [{"type": "text", "text": "User text"}]}, + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": "http://example.com/another.jpg"}, + } + ], + }, + ] + expected_output = "Admin text;User texthttp://example.com/another.jpg" + assert mock_provider._input_to_string(input_data) == expected_output + + +def test_input_to_string_with_missing_content_key(mock_provider): + input_data = [ + {"role": "user"}, + {"role": "user", "content": [{"type": "text", "text": "Hello again"}]}, + ] + expected_output = "Hello again" + assert mock_provider._input_to_string(input_data) == expected_output + - assert metrics["input_tokens"] == pytest.approx(1) - assert metrics["output_tokens"] == pytest.approx(1) - assert metrics["cost_usd"] == pytest.approx(0.03) - assert metrics["latency_s"] == pytest.approx(1) - assert metrics["time_to_first_token_s"] == pytest.approx(0.5) - assert metrics["inter_token_latency_s"] == pytest.approx(0.15) - assert metrics["tokens_per_second"] == pytest.approx(2) +def test_input_to_string_with_empty_list(mock_provider): + input_data = [] + assert mock_provider._input_to_string(input_data) == "" diff --git a/libs/core/tests/unit_tests/test_provider_costs_and_metrics.py b/libs/core/tests/unit_tests/test_provider_costs_and_metrics.py new file mode 100644 index 00000000..de01f3ff --- /dev/null +++ b/libs/core/tests/unit_tests/test_provider_costs_and_metrics.py @@ -0,0 +1,130 @@ +from unittest.mock import MagicMock + + +def test_calculate_metrics(mock_provider): + + metrics = mock_provider._calculate_metrics( + input="Hello", + output="Hello World", + model="test_model", + start_time=0.0, + end_time=1.0, + first_token_time=0.5, + token_times=(0.1,), + token_count=2, + is_stream=True, + ) + + assert metrics["input_tokens"] == 1 + assert metrics["output_tokens"] == 2 + assert metrics["total_tokens"] == 3 + assert metrics["cost_usd"] == 0.01 * 1 + 0.02 * 2 # input_cost + output_cost + assert metrics["latency_s"] == 1.0 # end_time - start_time + assert ( + metrics["time_to_first_token_s"] == 0.5 - 0.0 + ) # first_token_time - start_time + assert metrics["inter_token_latency_s"] == 0.1 # Average of token_times + assert metrics["tokens_per_second"] == 2 / 1.0 # token_count / total_time + + +def test_calculate_metrics_single_token(mock_provider): + + metrics = mock_provider._calculate_metrics( + input="Hello", + output="World", + model="test_model", + start_time=0.0, + end_time=1.0, + first_token_time=0.5, + token_times=(), + token_count=1, + is_stream=True, + ) + + assert metrics["input_tokens"] == 1 + assert metrics["output_tokens"] == 1 + assert metrics["total_tokens"] == 2 + assert metrics["cost_usd"] == 0.01 * 1 + 0.02 * 1 + assert metrics["latency_s"] == 1.0 + assert metrics["time_to_first_token_s"] == 0.5 - 0.0 + assert metrics["inter_token_latency_s"] == 0 + assert metrics["tokens_per_second"] == 1 / 1.0 + + +def test_calculate_cost_fixed_cost(mock_provider): + fixed_cost = 0.02 + token_count = 100 + expected_cost = token_count * fixed_cost + assert mock_provider._calculate_cost(token_count, fixed_cost) == expected_cost + + +def test_calculate_cost_variable_cost(mock_provider): + cost_range_1 = MagicMock() + cost_range_1.range = (0, 50) + cost_range_1.cost = 0.01 + + cost_range_2 = MagicMock() + cost_range_2.range = (51, 100) + cost_range_2.cost = 0.02 + + variable_cost = [cost_range_1, cost_range_2] + token_count = 75 + expected_cost = token_count * 0.02 + assert mock_provider._calculate_cost(token_count, variable_cost) == expected_cost + + +def test_calculate_cost_variable_cost_higher_range(mock_provider): + cost_range_1 = MagicMock() + cost_range_1.range = (0, 50) + cost_range_1.cost = 0.01 + + cost_range_2 = MagicMock() + cost_range_2.range = (51, 100) + cost_range_2.cost = 0.02 + + cost_range_3 = MagicMock() + cost_range_3.range = (101, None) + cost_range_3.cost = 0.03 + + variable_cost = [cost_range_1, cost_range_2, cost_range_3] + token_count = 150 + expected_cost = token_count * 0.03 + assert mock_provider._calculate_cost(token_count, variable_cost) == expected_cost + + +def test_calculate_cost_variable_cost_no_matching_range(mock_provider): + cost_range_1 = MagicMock() + cost_range_1.range = (0, 50) + cost_range_1.cost = 0.01 + + cost_range_2 = MagicMock() + cost_range_2.range = (51, 100) + cost_range_2.cost = 0.02 + + cost_range_3 = MagicMock() + cost_range_3.range = (101, 150) + cost_range_3.cost = 0.03 + + variable_cost = [cost_range_1, cost_range_2, cost_range_3] + token_count = 200 + expected_cost = 0 + assert mock_provider._calculate_cost(token_count, variable_cost) == expected_cost + + +def test_calculate_cost_variable_cost_no_matching_range_inferior(mock_provider): + cost_range_1 = MagicMock() + cost_range_1.range = (10, 50) + cost_range_1.cost = 0.01 + + cost_range_2 = MagicMock() + cost_range_2.range = (51, 100) + cost_range_2.cost = 0.02 + + cost_range_3 = MagicMock() + cost_range_3.range = (101, 150) + cost_range_3.cost = 0.03 + + variable_cost = [cost_range_1, cost_range_2, cost_range_3] + token_count = 5 + expected_cost = 0 + assert mock_provider._calculate_cost(token_count, variable_cost) == expected_cost diff --git a/libs/core/tests/unit_tests/test_provider_handle_response.py b/libs/core/tests/unit_tests/test_provider_handle_response.py new file mode 100644 index 00000000..b9531be2 --- /dev/null +++ b/libs/core/tests/unit_tests/test_provider_handle_response.py @@ -0,0 +1,287 @@ +from unittest.mock import MagicMock + +import pytest +from llmstudio_core.providers.provider import ( + ChatCompletion, + ChatCompletionChunk, + ChatCompletionChunkLLMstudio, + ChatCompletionLLMstudio, + time, +) + + +@pytest.mark.asyncio +async def test_ahandle_response_non_streaming(mock_provider): + request = MagicMock( + is_stream=False, chat_input="Hello", model="test_model", parameters={} + ) + response_chunk = { + "choices": [ + { + "delta": {"content": "Non-streamed response"}, + "finish_reason": "stop", + "index": 0, + } + ], + "model": "test_model", + } + start_time = time.time() + + async def mock_aparse_response(*args, **kwargs): + yield response_chunk + + mock_provider.aparse_response = mock_aparse_response + mock_provider._join_chunks = MagicMock( + return_value=( + ChatCompletion( + id="id", + choices=[], + created=0, + model="test_model", + object="chat.completion", + ), + "Non-streamed response", + ) + ) + mock_provider._calculate_metrics = MagicMock(return_value=None) + + response = [] + async for chunk in mock_provider._ahandle_response( + request, mock_aparse_response(), start_time + ): + response.append(chunk) + + assert isinstance(response[0], ChatCompletionLLMstudio) + assert response[0].choices == [] + assert response[0].chat_output == "Non-streamed response" + + +@pytest.mark.asyncio +async def test_ahandle_response_streaming_length(mock_provider): + request = MagicMock( + is_stream=True, chat_input="Hello", model="test_model", parameters={} + ) + response_chunk = { + "choices": [ + { + "delta": {"content": "Streamed response"}, + "finish_reason": "length", + "index": 0, + } + ], + "model": "test_model", + "object": "chat.completion.chunk", + "created": 0, + } + start_time = time.time() + + async def mock_aparse_response(*args, **kwargs): + yield response_chunk + + mock_provider.aparse_response = mock_aparse_response + mock_provider._join_chunks = MagicMock( + return_value=( + ChatCompletion( + id="id", + choices=[], + created=0, + model="test_model", + object="chat.completion", + ), + "Streamed response", + ) + ) + mock_provider._calculate_metrics = MagicMock(return_value=None) + + response = [] + async for chunk in mock_provider._ahandle_response( + request, mock_aparse_response(), start_time + ): + response.append(chunk) + + assert isinstance(response[0], ChatCompletionChunkLLMstudio) + assert response[0].chat_output_stream == "Streamed response" + + +@pytest.mark.asyncio +async def test_ahandle_response_streaming_stop(mock_provider): + request = MagicMock( + is_stream=True, chat_input="Hello", model="test_model", parameters={} + ) + response_chunk = { + "choices": [ + { + "delta": {"content": "Streamed response"}, + "finish_reason": "stop", + "index": 0, + } + ], + "model": "test_model", + "object": "chat.completion.chunk", + "created": 0, + } + start_time = time.time() + + async def mock_aparse_response(*args, **kwargs): + yield response_chunk + + mock_provider.aparse_response = mock_aparse_response + mock_provider._join_chunks = MagicMock( + return_value=( + ChatCompletion( + id="id", + choices=[], + created=0, + model="test_model", + object="chat.completion", + ), + "Streamed response", + ) + ) + mock_provider._calculate_metrics = MagicMock(return_value=None) + + response = [] + async for chunk in mock_provider._ahandle_response( + request, mock_aparse_response(), start_time + ): + response.append(chunk) + + assert isinstance(response[0], ChatCompletionChunkLLMstudio) + assert response[0].chat_output_stream == "Streamed response" + + +def test_handle_response_non_streaming(mock_provider): + request = MagicMock( + is_stream=False, chat_input="Hello", model="test_model", parameters={} + ) + response_chunk = { + "choices": [ + { + "delta": {"content": "Non-streamed response"}, + "finish_reason": "stop", + "index": 0, + } + ], + "model": "test_model", + } + start_time = time.time() + + def mock_parse_response(*args, **kwargs): + yield response_chunk + + mock_provider.aparse_response = mock_parse_response + mock_provider._join_chunks = MagicMock( + return_value=( + ChatCompletion( + id="id", + choices=[], + created=0, + model="test_model", + object="chat.completion", + ), + "Non-streamed response", + ) + ) + mock_provider._calculate_metrics = MagicMock(return_value=None) + + response = [] + for chunk in mock_provider._handle_response( + request, mock_parse_response(), start_time + ): + response.append(chunk) + + assert isinstance(response[0], ChatCompletionLLMstudio) + assert response[0].choices == [] + assert response[0].chat_output == "Non-streamed response" + + +def test_handle_response_streaming_length(mock_provider): + request = MagicMock( + is_stream=True, chat_input="Hello", model="test_model", parameters={} + ) + response_chunk = { + "choices": [ + { + "delta": {"content": "Streamed response"}, + "finish_reason": "length", + "index": 0, + } + ], + "model": "test_model", + "object": "chat.completion.chunk", + "created": 0, + } + start_time = time.time() + + def mock_parse_response(*args, **kwargs): + yield response_chunk + + mock_provider.aparse_response = mock_parse_response + mock_provider._join_chunks = MagicMock( + return_value=( + ChatCompletion( + id="id", + choices=[], + created=0, + model="test_model", + object="chat.completion", + ), + "Streamed response", + ) + ) + mock_provider._calculate_metrics = MagicMock(return_value=None) + + response = [] + for chunk in mock_provider._handle_response( + request, mock_parse_response(), start_time + ): + response.append(chunk) + + assert isinstance(response[0], ChatCompletionChunkLLMstudio) + assert response[0].chat_output_stream == "Streamed response" + + +def test_handle_response_streaming_stop(mock_provider): + request = MagicMock( + is_stream=True, chat_input="Hello", model="test_model", parameters={} + ) + response_chunk = { + "choices": [ + { + "delta": {"content": "Streamed response"}, + "finish_reason": "stop", + "index": 0, + } + ], + "model": "test_model", + "object": "chat.completion.chunk", + "created": 0, + } + start_time = time.time() + + def mock_parse_response(*args, **kwargs): + yield response_chunk + + mock_provider.parse_response = mock_parse_response + mock_provider._join_chunks = MagicMock( + return_value=( + ChatCompletion( + id="id", + choices=[], + created=0, + model="test_model", + object="chat.completion", + metrics=None, + ), + "Streamed response", + ) + ) + mock_provider._calculate_metrics = MagicMock(return_value=None) + + response = [] + for chunk in mock_provider._handle_response( + request, mock_parse_response(), start_time + ): + response.append(chunk) + assert isinstance(response[0], ChatCompletionChunk) + assert response[0].chat_output_stream == "Streamed response" diff --git a/libs/llmstudio/llmstudio/langchain.py b/libs/llmstudio/llmstudio/langchain.py index 87180304..da1820a0 100644 --- a/libs/llmstudio/llmstudio/langchain.py +++ b/libs/llmstudio/llmstudio/langchain.py @@ -46,7 +46,7 @@ def _create_message_dicts( def _create_chat_result(self, response: Any) -> ChatResult: generations = [] if not isinstance(response, dict): - response = response.dict() + response = response.model_dump() for res in response["choices"]: message = convert_dict_to_message(res["message"]) generation_info = dict(finish_reason=res.get("finish_reason")) diff --git a/libs/llmstudio/poetry.lock b/libs/llmstudio/poetry.lock index 133edf8e..79aeb685 100644 --- a/libs/llmstudio/poetry.lock +++ b/libs/llmstudio/poetry.lock @@ -1,99 +1,104 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# 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"^1.0.1", optional = true } [tool.poetry.extras] diff --git a/libs/llmstudio/tests/integration_tests/test_cache_and_reasoning_costs.py b/libs/llmstudio/tests/integration_tests/test_cache_and_reasoning_costs.py new file mode 100644 index 00000000..50f2777b --- /dev/null +++ b/libs/llmstudio/tests/integration_tests/test_cache_and_reasoning_costs.py @@ -0,0 +1,192 @@ +import asyncio +import os +from pprint import pprint + +import pytest +from dotenv import load_dotenv +from llmstudio_core.providers import LLMCore + +load_dotenv() + +# input prompt has to be >1024 tokens to auto cache on OpenAI +input_prompt = """ +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + +After reading this answer just in one line, saying hello how are you in latin. +""" + + +def run_provider(provider, model, api_key, **kwargs): + print(f"\n\n###RUNNING for <{provider}>, <{model}> ###") + llm = LLMCore(provider=provider, api_key=api_key, **kwargs) + + metrics = {} + + print("\nAsync Non-Stream") + + chat_request = build_chat_request(model, chat_input=input_prompt, is_stream=False) + + response_async = asyncio.run(llm.achat(**chat_request)) + pprint(response_async) + metrics["async non-stream"] = response_async.metrics + + print("\nAsync Stream") + + async def async_stream(): + chat_request = build_chat_request( + model, chat_input=input_prompt, is_stream=True + ) + + response_async = await llm.achat(**chat_request) + async for p in response_async: + if not p.metrics: + print("that: ", p.chat_output_stream) + if p.metrics: + pprint(p) + metrics["async stream"] = p.metrics + + asyncio.run(async_stream()) + + print("\nSync Non-Stream") + chat_request = build_chat_request(model, chat_input=input_prompt, is_stream=False) + + response_sync = llm.chat(**chat_request) + pprint(response_sync) + metrics["sync non-stream"] = response_sync.metrics + + print("\nSync Stream") + chat_request = build_chat_request(model, chat_input=input_prompt, is_stream=True) + + response_sync_stream = llm.chat(**chat_request) + for p in response_sync_stream: + if p.metrics: + pprint(p) + metrics["sync stream"] = p.metrics + + print(f"\n\n###REPORT for <{provider}>, <{model}> ###") + return metrics + + +def build_chat_request( + model: str, chat_input: str, is_stream: bool, max_tokens: int = 1000 +): + if model.startswith(("o1", "o3")): + chat_request = { + "chat_input": chat_input, + "model": model, + "is_stream": is_stream, + "retries": 0, + "parameters": {"max_completion_tokens": max_tokens}, + } + else: + chat_request = { + "chat_input": chat_input, + "model": model, + "is_stream": is_stream, + "retries": 0, + "parameters": { + "temperature": 0, + "max_tokens": max_tokens, + "response_format": {"type": "json_object"}, + "functions": None, + }, + } + return chat_request + + +# Fixture for provider-model pairs +@pytest.fixture( + scope="module", params=[("openai", "gpt-4o-mini"), ("openai", "o3-mini")] +) +def provider_model(request): + return request.param + + +# Fixture for metrics, computes them once per provider-model pair +@pytest.fixture(scope="module") +def metrics(provider_model): + provider, model = provider_model + print(f"Running provider {provider} with model {model}") + return run_provider( + provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"] + ) + + +# Test cache metrics (runs for all models) +def test_metrics_cache(provider_model, metrics): + provider, model = provider_model + at_least_one_cached = False + for current_metrics in metrics.values(): + print(current_metrics) + assert current_metrics["input_tokens"] > current_metrics["cached_tokens"] + if current_metrics["cached_tokens"] > 0: + at_least_one_cached = True + assert at_least_one_cached == True + print(f"All Cache Tests Passed for {provider} - {model}") + + +# Test reasoning metrics (only runs for o1 and o3 family) +def test_metrics_reasoning(provider_model, metrics): + provider, model = provider_model + + if not model.startswith(("o1", "o3")): + pytest.skip(f"Reasoning metrics test not applicable for model {model}") + + for current_metrics in metrics.values(): + assert current_metrics["reasoning_tokens"] > 0 + assert current_metrics["reasoning_tokens"] < current_metrics["total_tokens"] + assert ( + current_metrics["input_tokens"] + + current_metrics["output_tokens"] + + current_metrics["reasoning_tokens"] + == current_metrics["total_tokens"] + ) + print(f"All Reasoning Tests Passed for {provider} - {model}") + + +def usage_when_max_tokens_reached(): + """ + Usefull to test handling of Usage in other finish_reason scenarios + """ + provider, model = ("openai", "o3-mini") + api_key = os.environ["OPENAI_API_KEY"] + + llm = LLMCore(provider=provider, api_key=api_key) + chat_request = build_chat_request( + model, chat_input=input_prompt, is_stream=False, max_tokens=7 + ) + response = asyncio.run(llm.achat(**chat_request)) + + assert response.metrics["async non-stream"]["reasoning_tokens"] > 0 + assert response.metrics["sync non-stream"]["reasoning_tokens"] > 0 + assert response.metrics["async stream"]["reasoning_tokens"] > 0 + assert response.metrics["sync stream"]["reasoning_tokens"] > 0 + print(f"All Max Tokens Usage Reached Tests Passed for {provider} - {model}") diff --git a/libs/llmstudio/tests/integration_tests/test_langchain_agents.py b/libs/llmstudio/tests/integration_tests/test_langchain_agents.py new file mode 100644 index 00000000..da3562b5 --- /dev/null +++ b/libs/llmstudio/tests/integration_tests/test_langchain_agents.py @@ -0,0 +1,93 @@ +import pytest +from langchain import hub +from langchain.agents import AgentExecutor, create_openai_tools_agent +from langchain.agents.openai_functions_agent.base import create_openai_functions_agent +from langchain.tools import tool +from langchain_core.prompts import ChatPromptTemplate +from llmstudio.langchain import ChatLLMstudio +from llmstudio.providers import LLM + + +@tool +def power_disco_ball(power: bool) -> bool: + """Powers the spinning disco ball.""" + print(f"Disco ball is {'spinning!' if power else 'stopped.'}") + return True + + +@tool +def start_music(energetic: bool, loud: bool, bpm: int) -> str: + """Play some music matching the specified parameters.""" + print(f"Starting music! {energetic=} {loud=}, {bpm=}") + return "Never gonna give you up." + + +@tool +def dim_lights(brightness: float) -> bool: + """Dim the lights.""" + print(f"Lights are now set to {brightness:.0%}") + return True + + +def assistant_functions(chat_llm: ChatLLMstudio, question: str) -> str: + tools = [power_disco_ball, start_music, dim_lights] + + prompt = hub.pull("hwchase17/openai-functions-agent") + + agent = create_openai_functions_agent(llm=chat_llm, tools=tools, prompt=prompt) + + agent_executor = AgentExecutor( + agent=agent, tools=tools, verbose=True, return_intermediate_steps=True + ) + + response = agent_executor.invoke({"input": question}) + + return response + + +@pytest.mark.parametrize( + "provider, model", + [ + ("openai", "gpt-4o-mini"), + ], +) +def test_function_calling(provider, model): + llm = LLM(provider=provider) + chat_llm = ChatLLMstudio(llm=llm, model=model, parameters={"temperature": 0}) + response = assistant_functions(chat_llm, "Turn this into a party!") + assert len(response["intermediate_steps"]) > 0 + + +def assistant_tools(chat_llm: ChatLLMstudio, question: str) -> str: + tools = [power_disco_ball, start_music, dim_lights] + + prompt = ChatPromptTemplate.from_messages( + [ + ("system", "You are a helpful assistant"), + ("human", "{input}"), + ("placeholder", "{agent_scratchpad}"), + ] + ) + + agent = create_openai_tools_agent(chat_llm, tools, prompt) + + agent_executor = AgentExecutor( + agent=agent, tools=tools, verbose=True, return_intermediate_steps=True + ) + + response = agent_executor.invoke({"input": question}) + + return response + + +@pytest.mark.parametrize( + "provider, model", + [ + ("openai", "gpt-4o-mini"), + ], +) +def test_tool_calling(provider, model): + llm = LLM(provider=provider) + chat_llm = ChatLLMstudio(llm=llm, model=model, parameters={"temperature": 0}) + response = assistant_tools(chat_llm, "Turn this into a party!") + assert len(response["intermediate_steps"]) > 0 diff --git a/libs/llmstudio/tests/unit_tests/__init__.py b/libs/llmstudio/tests/unit_tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/libs/llmstudio/tests/unit_tests/conftest.py b/libs/llmstudio/tests/unit_tests/conftest.py deleted file mode 100644 index e69de29b..00000000 diff --git a/libs/proxy/llmstudio_proxy/server.py b/libs/proxy/llmstudio_proxy/server.py index da9f3291..13ee7a5a 100644 --- a/libs/proxy/llmstudio_proxy/server.py +++ b/libs/proxy/llmstudio_proxy/server.py @@ -1,6 +1,6 @@ import json from threading import Event, Thread -from typing import Any, Dict, List, Optional, Union +from typing import Dict, Optional import requests import uvicorn @@ -9,6 +9,7 @@ from fastapi.responses import StreamingResponse from llmstudio_core.providers import _load_providers_config from llmstudio_core.providers.provider import provider_registry +from llmstudio_core.utils import ProviderConfig from llmstudio_proxy.config import ENGINE_HOST, ENGINE_PORT from llmstudio_proxy.utils import get_current_version from pydantic import BaseModel @@ -22,28 +23,6 @@ _proxy_server_started = False -class CostRange(BaseModel): - range: List[Optional[int]] - cost: float - - -class ModelConfig(BaseModel): - mode: str - max_tokens: int - input_token_cost: Union[float, List[CostRange]] - output_token_cost: Union[float, List[CostRange]] - - -class ProviderConfig(BaseModel): - id: str - name: str - chat: bool - embed: bool - keys: Optional[List[str]] = None - models: Optional[Dict[str, ModelConfig]] = None - parameters: Optional[Dict[str, Any]] = None - - class ProxyConfig(BaseModel): providers: Dict[str, ProviderConfig] diff --git a/libs/proxy/pyproject.toml b/libs/proxy/pyproject.toml index e6dfb2d6..d6a6080a 100644 --- a/libs/proxy/pyproject.toml +++ b/libs/proxy/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "llmstudio-proxy" -version = "1.0.4" +version = "1.0.5a1" description = "LLMstudio Proxy is the module of LLMstudio that allows calling any LLM as a Service Provider in proxy server. By leveraging LLMstudio Proxy, you can have a centralized point for connecting to any provider running independently from your application." authors = ["Claudio Lemos "] readme = "README.md" diff --git a/libs/core/llmstudio_core/providers/bedrock/__init__.py b/libs/tracker/llmstudio_tracker/prompt_manager/__init__.py similarity index 100% rename from libs/core/llmstudio_core/providers/bedrock/__init__.py rename to libs/tracker/llmstudio_tracker/prompt_manager/__init__.py diff --git a/libs/tracker/llmstudio_tracker/prompt_manager/crud.py b/libs/tracker/llmstudio_tracker/prompt_manager/crud.py new file mode 100644 index 00000000..7cff7362 --- /dev/null +++ b/libs/tracker/llmstudio_tracker/prompt_manager/crud.py @@ -0,0 +1,97 @@ +from llmstudio_tracker.prompt_manager import models, schemas +from sqlalchemy.orm import Session + + +def get_prompt_by_name_model_provider( + db: Session, name: str, model: str, provider: str +): + return ( + db.query(models.PromptDefault) + .filter( + models.PromptDefault.name == name, + models.PromptDefault.model == model, + models.PromptDefault.provider == provider, + models.PromptDefault.is_active == True, + ) + .order_by(models.PromptDefault.version.desc()) + .first() + ) + + +def get_prompt_by_id(db: Session, prompt_id: str): + return ( + db.query(models.PromptDefault) + .filter(models.PromptDefault.prompt_id == prompt_id) + .first() + ) + + +def get_prompt( + db: Session, + prompt_id: str = None, + name: str = None, + model: str = None, + provider: str = None, +): + prompt = ( + get_prompt_by_id(db, prompt_id) + if prompt_id + else get_prompt_by_name_model_provider(db, name, model, provider) + ) + + if not prompt: + return schemas.PromptDefault() + + return prompt + + +def add_prompt(db: Session, prompt: schemas.PromptDefault): + + prompt_created = models.PromptDefault.create_with_incremental_version( + db, + config=prompt.config, + prompt=prompt.prompt, + is_active=prompt.is_active, + name=prompt.name, + label=prompt.label, + model=prompt.model, + provider=prompt.provider, + ) + db.add(prompt_created) + db.commit() + db.refresh(prompt_created) + return prompt_created + + +def update_prompt(db: Session, prompt: schemas.PromptDefault): + if prompt.prompt_id: + existing_prompt = get_prompt_by_id(db, prompt.prompt_id) + else: + existing_prompt = get_prompt_by_name_model_provider( + db, prompt.name, prompt.model, prompt.provider + ) + + existing_prompt.config = prompt.config + existing_prompt.prompt = prompt.prompt + existing_prompt.is_active = prompt.is_active + existing_prompt.name = prompt.name + existing_prompt.model = prompt.model + existing_prompt.provider = prompt.provider + existing_prompt.version = prompt.version + existing_prompt.label = prompt.label + + db.commit() + db.refresh(existing_prompt) + return existing_prompt + + +def delete_prompt(db: Session, prompt: schemas.PromptDefault): + if prompt.prompt_id: + existing_prompt = get_prompt_by_id(db, prompt.prompt_id) + else: + existing_prompt = get_prompt_by_name_model_provider( + db, prompt.name, prompt.model, prompt.provider + ) + + db.delete(existing_prompt) + db.commit() diff --git a/libs/tracker/llmstudio_tracker/prompt_manager/endpoints.py b/libs/tracker/llmstudio_tracker/prompt_manager/endpoints.py new file mode 100644 index 00000000..bcffc07b --- /dev/null +++ b/libs/tracker/llmstudio_tracker/prompt_manager/endpoints.py @@ -0,0 +1,56 @@ +from fastapi import APIRouter, Depends +from llmstudio_tracker.database import engine, get_db +from llmstudio_tracker.prompt_manager import crud, models, schemas +from sqlalchemy.orm import Session + +models.Base.metadata.create_all(bind=engine) + + +class PromptsRoutes: + def __init__(self, router: APIRouter): + self.router = router + self.define_routes() + + def define_routes(self): + self.router.post( + "/add/prompt", + response_model=schemas.PromptDefault, + )(self.add_prompt) + + self.router.get("/get/prompt", response_model=schemas.PromptDefault)( + self.get_prompt + ) + + self.router.patch("/update/prompt", response_model=schemas.PromptDefault)( + self.update_prompt + ) + + self.router.delete("/delete/prompt")(self.delete_prompt) + + async def add_prompt( + self, prompt: schemas.PromptDefault, db: Session = Depends(get_db) + ): + return crud.add_prompt(db=db, prompt=prompt) + + async def update_prompt( + self, prompt: schemas.PromptDefault, db: Session = Depends(get_db) + ): + return crud.update_prompt(db, prompt) + + async def get_prompt( + self, + prompt_info: schemas.PromptInfo, + db: Session = Depends(get_db), + ): + return crud.get_prompt( + db, + prompt_id=prompt_info.prompt_id, + name=prompt_info.name, + model=prompt_info.model, + provider=prompt_info.provider, + ) + + async def delete_prompt( + self, prompt: schemas.PromptDefault, db: Session = Depends(get_db) + ): + return crud.delete_prompt(db, prompt) diff --git a/libs/tracker/llmstudio_tracker/prompt_manager/manager.py b/libs/tracker/llmstudio_tracker/prompt_manager/manager.py new file mode 100644 index 00000000..7f47ec09 --- /dev/null +++ b/libs/tracker/llmstudio_tracker/prompt_manager/manager.py @@ -0,0 +1,61 @@ +import json + +import requests +from llmstudio_tracker.prompt_manager.schemas import PromptDefault +from llmstudio_tracker.tracker import TrackingConfig + + +class PromptManager: + def __init__(self, tracking_config: TrackingConfig): + self.tracking_url = tracking_config.url + self._session = requests.Session() + + def add_prompt(self, prompt: PromptDefault): + req = self._session.post( + f"{self.tracking_url}/api/tracking/add/prompt", + headers={"accept": "application/json", "Content-Type": "application/json"}, + data=prompt.model_dump_json(), + timeout=100, + ) + return req + + def delete_prompt(self, prompt: PromptDefault): + req = self._session.delete( + f"{self.tracking_url}/api/tracking/delete/prompt", + headers={"accept": "application/json", "Content-Type": "application/json"}, + data=prompt.model_dump_json(), + timeout=100, + ) + return req + + def update_prompt(self, prompt: PromptDefault): + req = self._session.patch( + f"{self.tracking_url}/api/tracking/update/prompt", + headers={"accept": "application/json", "Content-Type": "application/json"}, + data=prompt.model_dump_json(), + timeout=100, + ) + return req + + def get_prompt( + self, + prompt_id: str = None, + name: str = None, + model: str = None, + provider: str = None, + ): + + data = { + "prompt_id": prompt_id, + "name": name, + "model": model, + "provider": provider, + } + + req = self._session.get( + f"{self.tracking_url}/api/tracking/get/prompt", + headers={"accept": "application/json", "Content-Type": "application/json"}, + timeout=100, + data=json.dumps(data), + ) + return req diff --git a/libs/tracker/llmstudio_tracker/prompt_manager/models.py b/libs/tracker/llmstudio_tracker/prompt_manager/models.py new file mode 100644 index 00000000..2d96e35b --- /dev/null +++ b/libs/tracker/llmstudio_tracker/prompt_manager/models.py @@ -0,0 +1,102 @@ +import uuid +from datetime import datetime, timezone + +from llmstudio_tracker.config import DB_TYPE +from llmstudio_tracker.database import Base +from llmstudio_tracker.db_utils import JSONEncodedDict +from sqlalchemy import ( + JSON, + Boolean, + Column, + DateTime, + Integer, + String, + UniqueConstraint, + event, + func, +) +from sqlalchemy.orm import Session + + +class PromptDefault(Base): + __tablename__ = "prompts" + + if DB_TYPE == "bigquery": + prompt_id = Column( + String, + primary_key=True, + default=lambda: str(uuid.uuid4()), + ) + config = Column(JSONEncodedDict, nullable=True) + else: + prompt_id = Column( + String, primary_key=True, default=lambda: str(uuid.uuid4()) + ) # Generate UUID as a string + config = Column(JSON, nullable=True) + + prompt = Column(String) + is_active = Column(Boolean, default=False) + name = Column(String, nullable=False) + model = Column(String, nullable=False) + provider = Column(String, nullable=False) + version = Column(Integer, nullable=False) + label = Column(String) + updated_at = Column( + DateTime(timezone=True), + onupdate=lambda: datetime.now(timezone.utc), + default=lambda: datetime.now(timezone.utc), + ) + created_at = Column( + DateTime(timezone=True), default=lambda: datetime.now(timezone.utc) + ) + + __table_args__ = ( + UniqueConstraint( + "name", "provider", "model", "version", name="uq_prompt_version" + ), + ) + + @staticmethod + def get_next_version(session, name, model, provider): + """ + Get the next version number for a combination of name, model, and provider. + """ + max_version = ( + session.query(func.max(PromptDefault.version)) + .filter_by(name=name, model=model, provider=provider) + .scalar() + ) + return (max_version or 0) + 1 + + @classmethod + def create_with_incremental_version(cls, session, **kwargs): + """ + Create a new PromptDefault entry with an incremental version. + """ + name = kwargs.get("name") + model = kwargs.get("model") + provider = kwargs.get("provider") + if not all([name, model, provider]): + raise ValueError("name, model, and provider must be provided") + + kwargs["version"] = cls.get_next_version(session, name, model, provider) + + instance = cls(**kwargs) + session.add(instance) + return instance + + @event.listens_for(Session, "before_flush") + def ensure_single_active_prompt(session, flush_context, instances): + """ + Ensures only one PromptDefault entry per (name, model, provider) can have is_active=True. + If a new entry is set as is_active=True, deactivate others in the same group. + """ + for instance in session.new.union(session.dirty): + if isinstance(instance, PromptDefault) and instance.is_active: + session.query(PromptDefault).filter( + PromptDefault.name == instance.name, + PromptDefault.model == instance.model, + PromptDefault.provider == instance.provider, + PromptDefault.is_active == True, + PromptDefault.prompt_id != instance.prompt_id, + ).update({"is_active": False}, synchronize_session="fetch") diff --git a/libs/tracker/llmstudio_tracker/prompt_manager/schemas.py b/libs/tracker/llmstudio_tracker/prompt_manager/schemas.py new file mode 100644 index 00000000..7ddbf992 --- /dev/null +++ b/libs/tracker/llmstudio_tracker/prompt_manager/schemas.py @@ -0,0 +1,25 @@ +from datetime import datetime +from typing import Dict, Optional + +from pydantic import BaseModel + + +class PromptInfo(BaseModel): + prompt_id: Optional[str] = None + name: Optional[str] = None + model: Optional[str] = None + provider: Optional[str] = None + + +class PromptDefault(BaseModel): + prompt_id: Optional[str] = None + config: Optional[Dict] = {} + prompt: Optional[str] = None + is_active: Optional[bool] = None + name: Optional[str] = None + version: Optional[int] = None + label: Optional[str] = "production" + model: Optional[str] = None + provider: Optional[str] = None + updated_at: Optional[datetime] = None + created_at: Optional[datetime] = None diff --git a/libs/tracker/llmstudio_tracker/server.py b/libs/tracker/llmstudio_tracker/server.py index ed22c94c..8caff406 100644 --- a/libs/tracker/llmstudio_tracker/server.py +++ b/libs/tracker/llmstudio_tracker/server.py @@ -6,6 +6,7 @@ from fastapi.middleware.cors import CORSMiddleware from llmstudio_tracker.config import TRACKING_HOST, TRACKING_PORT from llmstudio_tracker.logs.endpoints import LogsRoutes +from llmstudio_tracker.prompt_manager.endpoints import PromptsRoutes from llmstudio_tracker.session.endpoints import SessionsRoutes from llmstudio_tracker.utils import get_current_version @@ -42,6 +43,7 @@ def health_check(): tracking_router = APIRouter(prefix=TRACKING_BASE_ENDPOINT) LogsRoutes(tracking_router) SessionsRoutes(tracking_router) + PromptsRoutes(tracking_router) app.include_router(tracking_router) diff --git a/libs/tracker/pyproject.toml b/libs/tracker/pyproject.toml index ada25d21..8fdf0a43 100644 --- a/libs/tracker/pyproject.toml +++ b/libs/tracker/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "llmstudio-tracker" -version = "1.0.5" +version = "1.1.0a2" description = "LLMstudio Tracker is the module of LLMstudio that allows monitoring and logging your LLM calls. By leveraging LLMstudio Tracker, users can gain insights on model performance and streamline development workflows with actionable analytics." authors = ["Claudio Lemos "] readme = "README.md"