diff --git a/.github/workflows/run-unit-tests.yaml b/.github/workflows/run-unit-tests.yaml index 8eb73b8..bc812bd 100644 --- a/.github/workflows/run-unit-tests.yaml +++ b/.github/workflows/run-unit-tests.yaml @@ -19,8 +19,11 @@ jobs: echo OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }} >> .env echo LAMOOM_API_URI=${{ secrets.LAMOOM_API_URI }} >> .env echo LAMOOM_API_TOKEN=${{ secrets.LAMOOM_API_TOKEN }} >> .env + echo LAMOOM_CUSTOM_PROVIDERS=${{ secrets.LAMOOM_CUSTOM_PROVIDERS }} >> .env echo NEBIUS_API_KEY=${{ secrets.NEBIUS_API_KEY }} >> .env echo CUSTOM_API_KEY=${{ secrets.CUSTOM_API_KEY }} >> .env + echo GOOGLE_API_KEY=${{ secrets.GOOGLE_API_KEY }} >> .env + echo SEARCH_ENGINE_ID=${{ secrets.SEARCH_ENGINE_ID }} >> .env cat .env - name: Install dependencies @@ -30,15 +33,21 @@ jobs: - name: Install Poetry run: pip install poetry + - name: Cache Poetry Dependencies + uses: actions/cache@v3 + with: + path: ~/.cache/pypoetry + key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }} + restore-keys: | + ${{ runner.os }}-poetry- + - name: Install Python uses: actions/setup-python@v3 with: python-version: 3.11 - cache: poetry - name: Install Python libraries - run: poetry install + run: poetry install --with dev - name: Run tests with pytest - run: | - poetry run make test + run: poetry run make test \ No newline at end of file diff --git a/.gitignore b/.gitignore index bdcbb4f..9e50489 100644 --- a/.gitignore +++ b/.gitignore @@ -10,4 +10,5 @@ dist .vscode .pytest_cache python -.env.test \ No newline at end of file +.env.test +*/logs/ \ No newline at end of file diff --git a/Makefile b/Makefile index 3eb2757..ea4d7c0 100644 --- a/Makefile +++ b/Makefile @@ -25,11 +25,7 @@ lint: poetry run isort --settings-path pyproject.toml --check-only . test: - poetry run pytest --cache-clear -vv tests \ - --cov=${PROJECT_FOLDER} \ - --cov-config=.coveragerc \ - --cov-fail-under=81 \ - --cov-report term-missing + poetry run pytest --cache-clear -vv tests .PHONY: format format: make-black isort-check flake8 make-mypy diff --git a/README.md b/README.md index 36fe248..a2f8fa3 100644 --- a/README.md +++ b/README.md @@ -124,6 +124,7 @@ Mix models easily, and districute the load across models. The system will automa - Gemini - OpenAI (w/ Azure OpenAI models) - Nebius with (Llama, DeepSeek, Mistral, Mixtral, dolphin, Qwen and others) +- OpenRouter woth open source models - Custom providers Model string format is the following for Claude, Gemini, OpenAI, Nebius: @@ -132,16 +133,17 @@ For Azure models format is the following: `"azure/{realm}/{model_name}"` ```python -response_llm = client.call(agent.id, context, model = "openai/gpt-4o") +response_llm = client.call(agent.id, context, model = "openai/o4-mini") response_llm = client.call(agent.id, context, model = "azure/useast/gpt-4o") ``` Custom model string format is the following: -`"custom/{model_name}"` -`provider_url` is required +`"custom/{provider_name}/{model_name}"` +where provider is provided in the env variable: +LAMOOM_CUSTOM_PROVIDERS={"provider_name": {"base_url": "https://","key":"key"}} ```python -response_llm = client.call(agent.id, context, model = "custom/gpt-4o", provider_url = "your_model_url") +response_llm = client.call(agent.id, context, model = "custom/provider_name/model_name") ``` ### Lamoom Keys @@ -169,14 +171,14 @@ prompt.add("You're {name}. Say Hello and ask what's their name.", role="system") # Call AI model with Lamoom context = {"name": "John Doe"} -response = client.call(prompt.id, context, "openai/gpt-4o") +response = client.call(prompt.id, context, "openai/o4-mini") print(response.content) ``` ### Creating Tests While Using Prompts ```python # Call with test_data to automatically generate tests -response = client.call(prompt.id, context, "openai/gpt-4o", test_data={ +response = client.call(prompt.id, context, "openai/o4-mini", test_data={ 'ideal_answer': "Hello, I'm John Doe. What's your name?", 'model_name': "gemini/gemini-1.5-flash" }) @@ -202,6 +204,14 @@ client.add_ideal_answer( ) ``` +### To Add Search Credentials: +- Add Search ENgine id from here: +https://programmablesearchengine.google.com/controlpanel/create + +- Get A google Search Key: +https://developers.google.com/custom-search/v1/introduction/?apix=true + + ### Monitoring and Management - **Test Dashboard**: Review created tests and scores at https://cloud.lamoom.com/tests - **Prompt Management**: Update prompts and rerun tests for published or saved versions @@ -219,4 +229,5 @@ We welcome contributions! Please see our Contribution Guidelines for more inform This project is licensed under the Apache2.0 License - see the [LICENSE](LICENSE.txt) file for details. ## Contact -For support or contributions, please contact us via GitHub Issues. \ No newline at end of file +For support or contributions, please contact us via GitHub Issues. + diff --git a/claude.config b/claude.config new file mode 100644 index 0000000..e69de29 diff --git a/docs/evaluate_prompts_quality/evaluate_prompt_quality.py b/docs/evaluate_prompts_quality/evaluate_prompt_quality.py index 2d1f925..fedb71c 100644 --- a/docs/evaluate_prompts_quality/evaluate_prompt_quality.py +++ b/docs/evaluate_prompts_quality/evaluate_prompt_quality.py @@ -9,20 +9,6 @@ lamoom = Lamoom() -gpt4_behaviour = behaviour.AIModelsBehaviour( - attempts=[ - AttemptToCall( - ai_model=AzureAIModel( - realm='useast', - deployment_id="gpt-4o", - max_tokens=C_128K, - support_functions=True, - ), - weight=100, - ), - ] -) - def main(): for prompt in get_all_prompts(): @@ -41,7 +27,7 @@ def main(): 'prompt_data': prompt_chats, 'prompt_id': prompt_id, } - result = lamoom.call(prompt_to_evaluate_prompt.id, context, gpt4_behaviour) + result = lamoom.call(prompt_to_evaluate_prompt.id, context, 'azure/useast/o4-mini') print(result.content) if __name__ == '__main__': diff --git a/lamoom/ai_models/ai_model.py b/lamoom/ai_models/ai_model.py index 4804657..e43b69a 100644 --- a/lamoom/ai_models/ai_model.py +++ b/lamoom/ai_models/ai_model.py @@ -1,26 +1,228 @@ +import json +import re import typing as t -from dataclasses import dataclass +from dataclasses import dataclass, field from enum import Enum - +import logging from _decimal import Decimal +from lamoom.prompt.base_prompt import BasePrompt +import tiktoken -from lamoom.responses import AIResponse +from lamoom import settings +from lamoom.ai_models.tools.base_tool import ToolCallResult, ToolDefinition, parse_tool_call_block +from lamoom.responses import AIResponse, StreamingResponse +from lamoom.exceptions import RetryableCustomError, StopStreamingError +from lamoom.utils import current_timestamp_ms +logger = logging.getLogger(__name__) class AI_MODELS_PROVIDER(Enum): OPENAI = "openai" AZURE = "azure" CLAUDE = "claude" GEMINI = "gemini" - NEBIUS = "nebius" CUSTOM = "custom" + def is_custom(self): + return self == AI_MODELS_PROVIDER.CUSTOM + + +encoding = tiktoken.get_encoding("cl100k_base") + +class TagParser: + """Parser for handling streaming content with ignore tags.""" + MAX_BUFFER_SIZE = 50 + + def __init__(self, ignore_tags: t.List[str] = None, writing_tags: t.List[str] = None): + self.ignore_tags = list(set(ignore_tags or [])) + self.writing_tags = list(set(writing_tags or [])) + self.reset() + + def is_custom_tags(self): + return bool(self.ignore_tags or self.writing_tags) + + def reset(self): + self.state = { + 'buffer': '', + 'ignored_tag': None, + 'writing_tag': None, + 'tags': [], + 'in_ignored_tag': False, + 'in_writing_tag': True, + } + if self.writing_tags: + self.state['in_writing_tag'] = False + if self.ignore_tags: + self.state['in_ignored_tag'] = False + + def parse_tags(self, chunk: str) -> t.List[str]: + matches = [] + ignored_length = len(self.ignore_tags) + for i, tag in enumerate(self.ignore_tags + self.writing_tags): + is_ignored = i < ignored_length + opening_tag = f'<{tag}(>| )' + closing_tag = f'' + for match in re.finditer(opening_tag, chunk): + matched_tag = { + 'tag': tag, 'type': 'opening', + 'position': match.start(), + 'is_ignored': is_ignored, + 'end_position': match.end() + } + matches.append(matched_tag) + logger.debug(f"[PARSE_TAGS] match {opening_tag}: {matched_tag}") + for match in re.finditer(closing_tag, chunk): + matched_tag = { + 'tag': tag, 'type': 'closing', + 'position': match.start(), + 'is_ignored': is_ignored, + 'end_position': match.end() + } + matches.append(matched_tag) + logger.debug(f"[PARSE_TAGS] closing match: {matched_tag}") + matches.sort(key=lambda x: x['position']) + matches.append({'tag': None, 'type': 'end', 'position': len(chunk), + 'is_ignored': False, 'end_position': len(chunk)}) + return matches + + def text_to_stream_chunk(self, chunk: str) -> str: + # Split only on newlines to handle them separately + for separator in ['<']: + if not separator in chunk: + continue + text_to_stream = [] + lines = chunk.split(separator) + for i, line in enumerate(lines): + if not line and i == 0: + continue + if i != 0: + line = separator + line + processed = self._text_to_stream_chunk(line) + if processed: + text_to_stream.append(processed) + return ''.join(text_to_stream) + + processed = self._text_to_stream_chunk(chunk) + return processed + + + def _text_to_stream_chunk(self, incoming_chunk: str) -> str: + logger = logging.getLogger("TagParser") + logger.debug(f"[INPUT] chunk: {incoming_chunk!r}: {self.state['buffer']}, tags: {self.state['tags']}") + + # Always process buffer first if it exists + chunk = self.state['buffer'] + incoming_chunk + self.state['buffer'] = '' + if '<' in chunk and not '>' in chunk and (chunk.rfind('<') - len(chunk)) < 5 and incoming_chunk: + # wait to get more data + self.state['buffer'] = chunk + logger.debug(f"[STREAM] waiting for more data: {chunk}") + return '' + if '<' not in chunk: + logger.debug(f"[STREAM] not '<' in chunk: {chunk}, in_ignored_tag: {self.state['in_ignored_tag']}, in_writing_tag: {self.state['in_writing_tag']}") + if not self.state['in_ignored_tag'] and self.state['in_writing_tag']: + return chunk + elif '>' not in chunk and incoming_chunk and len(chunk) < self.MAX_BUFFER_SIZE: + self.state['buffer'] = chunk + return '' + + if len(chunk) > self.MAX_BUFFER_SIZE: + # always output what wasn't added in FIFO buffer if it's not in ignored_tag; + chunk_to_process = chunk[:len(chunk) - self.MAX_BUFFER_SIZE] + if not self.state['in_ignored_tag'] and self.state['in_writing_tag']: + chunk = chunk[len(chunk) - self.MAX_BUFFER_SIZE:] + return chunk_to_process + + tag_matches = self.parse_tags(chunk) + last_match_index = 0 + # not_ignored, ignored + for match in tag_matches: + value = chunk[last_match_index:match['position']] + value_till_end = chunk[last_match_index:match['end_position']] + last_match_index = match['end_position'] + is_ignored = self.state['in_ignored_tag'] + in_writing_tag = self.state['in_writing_tag'] + + logger.debug(f"[STREAM] match {match['type']} {match['tag']}: {value}, ignored: {is_ignored}, writing: {in_writing_tag}") + if match['type'] == 'opening': + self.state['tags'].append(match['tag']) + if match['is_ignored']: + self.state['ignored_tag'] = match['tag'] + self.state['in_ignored_tag'] = True + else: + # TODO add writing tag closing like '>' which can be in inf characters + self.state['writing_tag'] = match['tag'] + self.state['in_writing_tag'] = True + + if not value: + continue + if not is_ignored and in_writing_tag: + logger.debug(f"[STREAM] Writing match: {match}: {value}: {self.state}") + self.state['buffer'] = chunk[match['end_position']:] + return value + logger.debug(f"[STREAM][NOT WRITING] {match}: {value}: {self.state}") + + if match['type'] == 'closing': + try: + index_of_closing_tag = len(self.state['tags']) - self.state['tags'][::-1].index(match['tag']) - 1 + except ValueError: + index_of_closing_tag = len(self.state['tags']) + self.state['buffer'] = chunk[match['end_position']:] + logger.debug(f"[STREAM] Closing match: {match}: {value} index_of_opening_tag: {index_of_closing_tag}, buffer {self.state['buffer']}") + # if it was not opened + if index_of_closing_tag == len(self.state['tags']): + logger.debug(f"[STREAM] Closing tag not opened: {match}: {value}") + if in_writing_tag and not is_ignored: + return value_till_end + return '' + pop_all_tags_starting_from_closed_tag = self.state['tags'][index_of_closing_tag:] + self.state['tags'] = self.state['tags'][:index_of_closing_tag] + logger.debug(f"[STREAM] self.state['tags']: {self.state['tags']}") + # what should we do with chunk? + partial_chunk = value + if match['is_ignored']: + list_tags = [tag for tag in self.state['tags'] if tag in self.ignore_tags] + self.state['in_ignored_tag'] = bool(list_tags) + self.state['ignored_tag'] = list_tags[-1] if list_tags else None + else: + list_tags = [tag for tag in self.state['tags'] if tag in self.writing_tags] + self.state['in_writing_tag'] = bool(list_tags) + self.state['writing_tag'] = list_tags[-1] if list_tags else None + logger.debug(f"[STREAM] in_ignored { self.state['in_ignored_tag']}, in_writing_tag: {self.state['in_writing_tag']}") + if not partial_chunk: + logger.debug(f"[STREAM] closing tag. partial_chunk: {partial_chunk}: {self.state}. Continueing") + continue + if not is_ignored and in_writing_tag: + return partial_chunk + + if match['type'] == 'end': + self.state['buffer'] = '' + if in_writing_tag and not is_ignored: + logger.debug(f"[STREAM] End match: {match}: {value}") + return value + self.state['buffer'] = chunk[last_match_index:] + return '' @dataclass(kw_only=True) class AIModel: + model: t.Optional[str] = '' tiktoken_encoding: t.Optional[str] = "cl100k_base" - provider: AI_MODELS_PROVIDER = None support_functions: bool = False + _provider_name: str = None + _tag_parser: TagParser = field(default=None, init=False) + + def _init_tag_parser(self, ignore_tags: t.List[str] = None, writing_tags: t.List[str] = None): + self._tag_parser = TagParser(ignore_tags=ignore_tags, writing_tags=writing_tags) + + def text_to_stream_chunk(self, chunk: str) -> str: + """Process incoming chunk of text and return the parsed result.""" + if not self._tag_parser: + return chunk + return self._tag_parser.text_to_stream_chunk(chunk) + + @property + def provider_name(self): + return self.provider.value if not self.provider.is_custom() else self._provider_name @property def name(self) -> str: @@ -32,8 +234,162 @@ def _decimal(self, value) -> Decimal: def get_params(self) -> t.Dict[str, t.Any]: return {} - def call(self, *args, **kwargs) -> AIResponse: - raise NotImplementedError - def get_metrics_data(self): return {} + + def call( + self, + current_messages: t.List[t.Dict[str, str]], + max_tokens: t.Optional[int], + tool_registry: t.Dict[str, ToolDefinition] = {}, + max_tool_iterations: int = 5, # Safety limit for sequential calls + stream_function: t.Callable = None, + check_connection: t.Callable = None, + stream_params: dict = {}, + client_secrets: dict = {}, + modelname='', + prompt: 'Prompt' = None, + context: str = '', + test_data: dict = {}, + client: t.Any = None, + ignore_tags: t.List[str] = None, + writing_tags: t.List[str] = None, + **kwargs, + ) -> AIResponse: + """Common call implementation that handles streaming and tool calls.""" + # self._reset_tag_parser() # Reset parser state for new call + model_client = self.get_client(client_secrets) + # Prepare streaming response + stream_response = StreamingResponse( + tool_registry=tool_registry, + messages=current_messages, + prompt=BasePrompt( + messages=current_messages, + functions=kwargs.get("tools"), + max_tokens=max_tokens, + temperature=kwargs.get("temperature"), + top_p=kwargs.get("top_p"), + ) + ) + modelname = modelname.replace('/', '_').replace('-', '_') + attempts = max_tool_iterations + self._init_tag_parser(ignore_tags=ignore_tags, writing_tags=writing_tags) + while attempts > 0: + try: + stream_response.update_to_another_attempt() + stream_response = self.streaming( + client=model_client, + stream_response=stream_response, + max_tokens=max_tokens, + stream_function=stream_function, + check_connection=check_connection, + stream_params=stream_params, + **kwargs + ) + logger.info(f'stream_response: {stream_response}') + if stream_response.is_detected_tool_call: + parsed_tool_call = parse_tool_call_block(stream_response.content) + + logger.info(f'parsed_tool_call {parsed_tool_call}') + if not parsed_tool_call or attempts <= 1: + stream_response.add_assistant_message() + self.save_call(stream_response, prompt, context, attempt=max_tool_iterations - attempts, client=client) + attempts -= 1 + continue + # Execute tool call + self.handle_tool_call(parsed_tool_call, tool_registry) + # Add messages to history + logger.info(f'executed parsed_tool_call {parsed_tool_call}') + stream_response.add_tool_result(parsed_tool_call) + self.save_call(stream_response, prompt, context, attempt=max_tool_iterations - attempts, client=client) + attempts -= 1 + continue + stream_response.add_assistant_message() + self.save_call(stream_response, prompt, context, test_data=test_data, client=client) + logger.info(f'Passing execution {modelname}, finished. {attempts}') + break + except RetryableCustomError as e: + logger.exception(f'RetryableCustomError {e}') + attempts -= 1 + continue + except StopStreamingError as e: + logger.exception(f'StopStreamingError {e}') + stream_response.add_assistant_message() + self.save_call(stream_response, prompt, context, attempt=max_tool_iterations - attempts, client=client) + logger.info(f'Failing execution {modelname} w/ StopStreamingError, finished. {attempts}') + raise e + return stream_response + + + def handle_tool_call(self, tool_call: ToolCallResult, tool_registry: t.Dict[str, ToolDefinition]) -> str: + """Handle a tool call by executing the corresponding function from the registry.""" + function = tool_call.tool_name + parameters = tool_call.parameters + + tool_function = tool_registry.get(function) + if not tool_function: + logger.warning(f"Tool '{function}' not found in registry") + return json.dumps({"error": f"Tool '{function}' is not available."}) + + try: + logger.info(f"Executing tool '{function}' with parameters: {parameters}") + result = tool_function.execution_function(**parameters) + logger.info(f"Tool '{function}' executed successfully") + tool_call.execution_result = result + return json.dumps({"result": result}) + except StopStreamingError as e: + logger.exception(f"Tool '{function}' execution stopped: {e}") + tool_call.execution_result = str(e) + raise e + except Exception as e: + result = f"Error executing tool '{function}', Please try second time." + logger.exception(result, exc_info=e) + tool_call.execution_result = result + return json.dumps({"error": f"{result}: {str(e)}"}) + + def streaming( + self, + client: t.Any, + stream_response: StreamingResponse, + max_tokens: int, + stream_function: t.Callable, + check_connection: t.Callable, + stream_params: dict, + **kwargs + ) -> StreamingResponse: + """Process streaming response. Must be implemented by subclasses.""" + raise NotImplementedError("Subclasses must implement streaming method") + + def get_client(self, client_secrets: dict = {}) -> t.Any: + """Get the client instance. Must be implemented by subclasses.""" + raise NotImplementedError("Subclasses must implement get_client method") + + + def calculate_budget_for_text(self, text: str) -> int: + if not text: + return 0 + return len(encoding.encode(text)) + + def save_call(self, stream_response: StreamingResponse, prompt: "Prompt", context: dict, attempt: int=0, test_data: dict = {}, client: t.Any = None): + + sample_budget = self.calculate_budget_for_text( + stream_response.get_message_str() + ) + stream_response.metrics.sample_tokens_used = sample_budget + stream_response.metrics.prompt_tokens_used = self.calculate_budget_for_text( + json.dumps(stream_response.messages) + ) + stream_response.metrics.ai_model_details = ( + self.get_metrics_data() + ) + stream_response.metrics.latency = current_timestamp_ms() - stream_response.started_tmst + + if settings.USE_API_SERVICE and client.api_token: + stream_response.id = f"{prompt.id}#{stream_response.started_tmst}" + (f"#{attempt}" if attempt else "") + client.worker.add_task( + client.api_token, + prompt.service_dump(), + context, + stream_response, + {**test_data, "call_model": self.model} + ) diff --git a/lamoom/ai_models/attempt_to_call.py b/lamoom/ai_models/attempt_to_call.py index 602bacb..650c5f4 100644 --- a/lamoom/ai_models/attempt_to_call.py +++ b/lamoom/ai_models/attempt_to_call.py @@ -14,6 +14,7 @@ class AttemptToCall: # ['function1', 'function2'] - if list of functions, only those functions will be called functions: t.List[str] = None attempt_number: int = 1 + api_key: str = None def __post_init__(self): self.id = ( diff --git a/lamoom/ai_models/behaviour.py b/lamoom/ai_models/behaviour.py index 8b340d8..986127d 100644 --- a/lamoom/ai_models/behaviour.py +++ b/lamoom/ai_models/behaviour.py @@ -1,11 +1,7 @@ import logging -import random -import typing as t -from copy import copy from dataclasses import dataclass from lamoom.ai_models.attempt_to_call import AttemptToCall -from lamoom.exceptions import BehaviourIsNotDefined logger = logging.getLogger(__name__) diff --git a/lamoom/ai_models/claude/claude_model.py b/lamoom/ai_models/claude/claude_model.py index 843d644..0669ac7 100644 --- a/lamoom/ai_models/claude/claude_model.py +++ b/lamoom/ai_models/claude/claude_model.py @@ -1,20 +1,14 @@ from lamoom.ai_models.ai_model import AI_MODELS_PROVIDER, AIModel import logging - -from lamoom.ai_models.constants import C_200K, C_4K -from lamoom.responses import AIResponse -from decimal import Decimal -from enum import Enum - import typing as t from dataclasses import dataclass -from lamoom.ai_models.claude.responses import ClaudeAIReponse from lamoom.ai_models.claude.constants import HAIKU, SONNET, OPUS -from lamoom.ai_models.utils import get_common_args +from lamoom.ai_models.constants import C_4K +from lamoom.responses import FINISH_REASON_ERROR, FINISH_REASON_FINISH, StreamingResponse +from lamoom.ai_models.tools.base_tool import TOOL_CALL_END_TAG, TOOL_CALL_START_TAG +from enum import Enum -from openai.types.chat import ChatCompletionMessage as Message -from lamoom.responses import Prompt from lamoom.exceptions import RetryableCustomError, ConnectionLostError import anthropic @@ -25,11 +19,10 @@ class FamilyModel(Enum): haiku = "Claude 3 Haiku" sonnet = "Claude 3 Sonnet" opus = "Claude 3 Opus" - + @dataclass(kw_only=True) class ClaudeAIModel(AIModel): - model: str max_tokens: int = C_4K api_key: str = None provider: AI_MODELS_PROVIDER = AI_MODELS_PROVIDER.CLAUDE @@ -47,18 +40,15 @@ def __post_init__(self): f"Unknown family for {self.model}. Please add it obviously. Setting as Claude 3 Opus" ) self.family = FamilyModel.opus.value - logger.debug(f"Initialized ClaudeAIModel: {self}") def get_client(self, client_secrets: dict) -> anthropic.Anthropic: return anthropic.Anthropic(api_key=client_secrets.get("api_key")) - def uny_all_messages_with_same_role(self, messages: t.List[dict]) -> t.List[dict]: + def unify_messages_with_same_role(self, messages: t.List[dict]) -> t.List[dict]: result = [] last_role = None for message in messages: - if message.get("role") == "system": - message["role"] = "user" if last_role != message.get("role"): result.append(message) last_role = message.get("role") @@ -66,66 +56,84 @@ def uny_all_messages_with_same_role(self, messages: t.List[dict]) -> t.List[dict result[-1]["content"] += message.get("content") return result - - def call(self, messages: t.List[dict], max_tokens: int, client_secrets: dict = {}, **kwargs) -> AIResponse: - max_tokens = min(max_tokens, self.max_tokens) - - common_args = get_common_args(max_tokens) - kwargs = { - **common_args, - **self.get_params(), - **kwargs, - } - messages = self.uny_all_messages_with_same_role(messages) - - logger.debug( - f"Calling {messages} with max_tokens {max_tokens} and kwargs {kwargs}" - ) - client = self.get_client(client_secrets) - - stream_function = kwargs.get("stream_function") - check_connection = kwargs.get("check_connection") - stream_params = kwargs.get("stream_params") - + def streaming( + self, + client: anthropic.Anthropic, + stream_response: StreamingResponse, + max_tokens: int, + stream_function: t.Callable, + check_connection: t.Callable, + stream_params: dict, + **kwargs + ) -> StreamingResponse: + """Process streaming response from Claude.""" + tool_call_started = False content = "" - + try: - if kwargs.get("stream"): - with client.messages.stream( - model=self.model, max_tokens=max_tokens, messages=messages - ) as stream: - idx = 0 - for text in stream.text_stream: - if idx % 5 == 0: - if not check_connection(**stream_params): - raise ConnectionLostError("Connection was lost!") - - stream_function(text, **stream_params) - content += text - idx += 1 - else: - response = client.messages.create( - model=self.model, max_tokens=max_tokens, messages=messages - ) - content = response.content[0].text - return ClaudeAIReponse( - message=Message(content=content, role="assistant"), - content=content, - prompt=Prompt( - messages=kwargs.get("messages"), - functions=kwargs.get("tools"), - max_tokens=max_tokens, - temperature=kwargs.get("temperature"), - top_p=kwargs.get("top_p"), - ), - ) + unified_messages = self.unify_messages_with_same_role(stream_response.messages) + call_kwargs = { + "model": self.model, + "max_tokens": max_tokens, + "messages": unified_messages, + **kwargs + } + # Extract system prompt if present + system_prompt = [] + for i, msg in enumerate(unified_messages): + if msg.get('role') == "system": + system_prompt.append(unified_messages.pop(i- len(system_prompt)).get('content')) + + if system_prompt: + call_kwargs["system"] = '\n'.join(system_prompt) + + with client.messages.stream(**call_kwargs) as stream: + for text_chunk in stream.text_stream: + if check_connection and not check_connection(**stream_params): + raise ConnectionLostError("Connection was lost!") + + stream_response.set_streaming() + content += text_chunk + + # Only stream content if not in ignored tag + if stream_function or self._tag_parser.is_custom_tags(): + text_chunk = self.text_to_stream_chunk(text_chunk) + if text_chunk and not tool_call_started: + stream_response.streaming_content += text_chunk + if stream_function: + stream_function(text_chunk, **stream_params) + + # Check for tool call markers + if tool_call_started and TOOL_CALL_END_TAG in content: + stream_response.is_detected_tool_call = True + stream_response.content = content + logger.info(f'Found tool call request in {content}') + break + if TOOL_CALL_START_TAG in content: + if not tool_call_started: + tool_call_started = True + continue + + if stream_function or self._tag_parser.is_custom_tags(): + text_to_stream = self.text_to_stream_chunk('') + if text_to_stream: + if stream_function: + stream_function(text_to_stream, **stream_params) + stream_response.streaming_content += text_to_stream + + stream_response.content = content + stream_response.set_finish_reason(FINISH_REASON_FINISH) + return stream_response + except Exception as e: - logger.exception("[CLAUDEAI] failed to handle chat stream", exc_info=e) - raise RetryableCustomError(f"Claude AI call failed!") + stream_response.content = content + stream_response.set_finish_reason(FINISH_REASON_ERROR) + logger.exception("Exception during stream processing", exc_info=e) + raise RetryableCustomError(f"Claude AI stream processing failed: {e}") from e @property def name(self) -> str: - return self.model + return f"Claude {self.family}" def get_params(self) -> t.Dict[str, t.Any]: return { @@ -136,5 +144,6 @@ def get_params(self) -> t.Dict[str, t.Any]: def get_metrics_data(self) -> t.Dict[str, t.Any]: return { "model": self.model, + "family": self.family, "max_tokens": self.max_tokens, } diff --git a/lamoom/ai_models/openai/azure_models.py b/lamoom/ai_models/openai/azure_models.py index 736831f..f0b7f26 100644 --- a/lamoom/ai_models/openai/azure_models.py +++ b/lamoom/ai_models/openai/azure_models.py @@ -26,9 +26,9 @@ def __post_init__(self): self.family = FamilyModel.instruct_gpt.value elif self.deployment_id.startswith(("gpt3", "gpt-3")): self.family = FamilyModel.chat.value - elif self.deployment_id.startswith("gpt-4o-mini"): + elif self.deployment_id.startswith("o4-mini-mini"): self.family = FamilyModel.gpt4o_mini.value - elif self.deployment_id.startswith("gpt-4o"): + elif self.deployment_id.startswith("o4-mini"): self.family = FamilyModel.gpt4o.value elif self.deployment_id.startswith(("gpt4", "gpt-4", "gpt")): self.family = FamilyModel.gpt4.value diff --git a/lamoom/ai_models/openai/openai_models.py b/lamoom/ai_models/openai/openai_models.py index 6dce49c..3c60d50 100644 --- a/lamoom/ai_models/openai/openai_models.py +++ b/lamoom/ai_models/openai/openai_models.py @@ -1,21 +1,18 @@ import logging import typing as t from dataclasses import dataclass -from decimal import Decimal from enum import Enum from openai import OpenAI from lamoom.ai_models.ai_model import AI_MODELS_PROVIDER, AIModel from lamoom.ai_models.constants import C_128K, C_16K, C_32K, C_4K -from lamoom.ai_models.openai.responses import OpenAIResponse -from lamoom.ai_models.utils import get_common_args -from lamoom.exceptions import ConnectionLostError +from lamoom.ai_models.openai.responses import StreamingResponse +from lamoom.exceptions import ConnectionLostError, RetryableCustomError +from lamoom.ai_models.tools.base_tool import TOOL_CALL_END_TAG, TOOL_CALL_START_TAG -from openai.types.chat import ChatCompletionMessage as Message -from lamoom.responses import Prompt +from lamoom.responses import FINISH_REASON_ERROR -from .utils import raise_openai_exception M_DAVINCI = "davinci" @@ -25,25 +22,25 @@ class FamilyModel(Enum): chat = "GPT-3.5" gpt4 = "GPT-4" - gpt4o = "GPT-4o" - gpt4o_mini = "GPT-4o-mini" + gpt4o = "o4-mini" + gpt4o_mini = "o4-mini-mini" instruct_gpt = "InstructGPT" + BASE_URL_MAPPING = { - 'gemini': "https://generativelanguage.googleapis.com/v1beta/openai/", - 'nebius': 'https://api.studio.nebius.ai/v1/' + 'gemini': "https://generativelanguage.googleapis.com/v1beta/openai/" } @dataclass(kw_only=True) class OpenAIModel(AIModel): - model: t.Optional[str] max_tokens: int = C_16K support_functions: bool = False provider: AI_MODELS_PROVIDER = AI_MODELS_PROVIDER.OPENAI family: str = None max_sample_budget: int = C_4K base_url: str = None + api_key: str = None def __str__(self) -> str: return f"openai-{self.model}-{self.family}" @@ -53,9 +50,9 @@ def __post_init__(self): self.family = FamilyModel.instruct_gpt.value elif self.model.startswith("gpt-3"): self.family = FamilyModel.chat.value - elif self.model.startswith("gpt-4o-mini"): + elif self.model.startswith("o4-mini-mini"): self.family = FamilyModel.gpt4o_mini.value - elif self.model.startswith("gpt-4o"): + elif self.model.startswith("o4-mini"): self.family = FamilyModel.gpt4o.value elif self.model.startswith(("gpt4", "gpt-4", "gpt")): self.family = FamilyModel.gpt4.value @@ -75,139 +72,102 @@ def get_params(self) -> t.Dict[str, t.Any]: "model": self.model, } - def get_base_url(self) -> str | None: - return BASE_URL_MAPPING.get(self.provider.value, None) + + def is_provider_openai(self): + return self.provider == AI_MODELS_PROVIDER.OPENAI + def get_metrics_data(self): return { "model": self.model, "family": self.family, - "provider": self.provider.value, - "base_url": self.get_base_url() if self.base_url is None else self.base_url + "provider": self.provider.value if not self.provider.is_custom() else self.provider_name, + "base_url": self.base_url } - def call( - self, - messages, - max_tokens, - stream_function: t.Callable = None, - check_connection: t.Callable = None, - stream_params: dict = {}, - client_secrets: dict = {}, - **kwargs, - ) -> OpenAIResponse: - logger.debug( - f"Calling {messages} with max_tokens {max_tokens} and kwargs {kwargs}" - ) - if self.family in [ - FamilyModel.chat.value, - FamilyModel.gpt4.value, - FamilyModel.gpt4o.value, - FamilyModel.gpt4o_mini.value, - ]: - return self.call_chat_completion( - messages, - max_tokens, - stream_function=stream_function, - check_connection=check_connection, - stream_params=stream_params, - client_secrets=client_secrets, - **kwargs, - ) - raise NotImplementedError(f"Openai family {self.family} is not implemented") - def get_client(self, client_secrets: dict = {}): + base_url = client_secrets.get("base_url", None) return OpenAI( organization=client_secrets.get("organization", None), api_key=client_secrets["api_key"], - base_url=self.get_base_url() if self.base_url is None else self.base_url + base_url=client_secrets.get("base_url", None), ) - def call_chat_completion( + def streaming( self, - messages: t.List[t.Dict[str, str]], - max_tokens: t.Optional[int], - functions: t.List[t.Dict[str, str]] = [], - stream_function: t.Callable = None, - check_connection: t.Callable = None, - stream_params: dict = {}, - client_secrets: dict = {}, - **kwargs, - ) -> OpenAIResponse: - - kwargs = { - **{ - "messages": messages, - }, - **self.get_params(), - **kwargs, - } - if functions: - kwargs["tools"] = functions - try: - client = self.get_client(client_secrets) - result = client.chat.completions.create( - **kwargs, - ) + client: OpenAI, + stream_response: StreamingResponse, + max_tokens: int, + stream_function: t.Callable, + check_connection: t.Callable, + stream_params: dict, + **kwargs + ) -> StreamingResponse: + """Process streaming response from OpenAI.""" + tool_call_started = False + content = "" - if kwargs.get("stream"): - return OpenAIStreamResponse( - stream_function=stream_function, - check_connection=check_connection, - stream_params=stream_params, - original_result=result, - prompt=Prompt( - messages=kwargs.get("messages"), - functions=kwargs.get("tools"), - max_tokens=max_tokens, - temperature=kwargs.get("temperature"), - top_p=kwargs.get("top_p"), - ), - ).stream() - logger.debug(f"Result: {result.choices[0]}") - return OpenAIResponse( - finish_reason=result.choices[0].finish_reason, - message=result.choices[0].message, - content=result.choices[0].message.content, - original_result=result, - prompt=Prompt( - messages=kwargs.get("messages"), - functions=kwargs.get("tools"), - max_tokens=max_tokens, - temperature=kwargs.get("temperature"), - top_p=kwargs.get("top_p"), - ), - ) + try: + call_kwargs = { + "messages": stream_response.messages, + "stream": True, + **self.get_params(), + **kwargs + } + if max_tokens: + call_kwargs["max_completion_tokens"] = min(max_tokens, self.max_sample_budget) + logger.info(f"Calling OpenAI with params: {call_kwargs}") + completion = client.chat.completions.create(**call_kwargs) + for part in completion: + if not part.choices: + continue + + delta = part.choices[0].delta + if part.choices and 'finish_reason' in part.choices[0]: + logger.info(f'Finish reason: {part.choices[0].finish_reason}') + stream_response.set_finish_reason(part.choices[0].finish_reason) + + # Check for tool call markers + if TOOL_CALL_START_TAG in content and not tool_call_started: + tool_call_started = True + logger.info(f'tool_call_started: {tool_call_started}') + + if not delta or (not delta.content and getattr(delta, 'reasoning', None)): + continue + + if delta.content: + content += delta.content + if stream_function or self._tag_parser.is_custom_tags(): + text_to_stream = self.text_to_stream_chunk(delta.content) + if text_to_stream: + stream_response.streaming_content += text_to_stream + if stream_function: + stream_function(text_to_stream, **stream_params) + + if getattr(delta, 'reasoning', None) and delta.reasoning: + logger.debug(f'Adding reasoning {delta.reasoning}') + stream_response.reasoning += delta.reasoning + + if tool_call_started and TOOL_CALL_END_TAG in content: + logger.info(f'tool_call_ended: {content}') + stream_response.is_detected_tool_call = True + stream_response.content = content + break + + if check_connection and not check_connection(**stream_params): + raise ConnectionLostError("Connection was lost!") + + if stream_function: + text_to_stream = self.text_to_stream_chunk('') + if text_to_stream: + stream_response.streaming_content += text_to_stream + if stream_function: + stream_function(text_to_stream, **stream_params) + stream_response.content = content + return stream_response + except Exception as e: - logger.exception("[OPENAI] failed to handle chat stream", exc_info=e) - raise_openai_exception(e) - - -@dataclass(kw_only=True) -class OpenAIStreamResponse(OpenAIResponse): - stream_function: t.Callable - check_connection: t.Callable - stream_params: dict - - def process_message(self, text: str, idx: int): - if idx % 5 == 0: - if not self.check_connection(**self.stream_params): - raise ConnectionLostError("Connection was lost!") - if not text: - return - self.stream_function(text, **self.stream_params) - - def stream(self): - content = "" - for i, data in enumerate(self.original_result): - if not data.choices: - continue - choice = data.choices[0] - if choice.delta: - content += choice.delta.content or "" - self.process_message(choice.delta.content, i) - self.message = Message( - content=content, - role="assistant", - ) - return self + stream_response.content = content + stream_response.set_finish_reason(FINISH_REASON_ERROR) + logger.exception("Exception during stream processing", exc_info=e) + raise RetryableCustomError(f"OpenAI stream processing failed: {e}") from e \ No newline at end of file diff --git a/lamoom/ai_models/openai/responses.py b/lamoom/ai_models/openai/responses.py index fc66708..8d29bcf 100644 --- a/lamoom/ai_models/openai/responses.py +++ b/lamoom/ai_models/openai/responses.py @@ -1,21 +1,28 @@ import json import logging import typing as t -from dataclasses import dataclass +from dataclasses import dataclass, field from openai.types.chat import ChatCompletionMessage as Message from openai.types.chat import ChatCompletionMessageToolCall as ToolCall -from lamoom.responses import AIResponse +from lamoom.responses import FINISH_REASON_LENGTH, FINISH_REASON_TOOL_CALLS, AIResponse +from lamoom.ai_models.tools.base_tool import ToolDefinition -FINISH_REASON_LENGTH = "length" -FINISH_REASON_ERROR = "error" -FINISH_REASON_FINISH = "stop" -FINISH_REASON_TOOL_CALLS = "tool_calls" logger = logging.getLogger(__name__) +@dataclass(kw_only=True) +class StreamingResponse(AIResponse): + is_detected_tool_call: bool = False + tool_registry: t.Dict[str, ToolDefinition] = field(default_factory=dict) + messages: t.List[dict] = field(default_factory=list) + + def add_message(self, role: str, content: str): + self.messages.append({"role": role, "content": content}) + + @dataclass(kw_only=True) class OpenAIResponse(AIResponse): message: Message = None diff --git a/lamoom/ai_models/tools/base_tool.py b/lamoom/ai_models/tools/base_tool.py new file mode 100644 index 0000000..d311ffd --- /dev/null +++ b/lamoom/ai_models/tools/base_tool.py @@ -0,0 +1,212 @@ +import logging +import typing as t +from dataclasses import dataclass +import json +import re + +from lamoom.utils import resolve + + +logger = logging.getLogger(__name__) + +TOOL_CALL_NAME = 'function_call' +TOOL_CALL_RESULT_NAME = 'function_result' +# --- Constants for Prompting --- +TOOL_CALL_START_TAG = f"<{TOOL_CALL_NAME}>" +TOOL_CALL_END_TAG = f"" + + +def get_tool_system_prompt(tool_descriptions: str, context: t.Dict[str, str]): + return f"""You have next tools: +``` +{resolve(tool_descriptions, context)} +``` +# Tool calling procedure + +Before calling any function, please follow procedure. You're doing unnecessary calls of tools. Make a mindset of what you need to do. +## 1. Think out loud what you need to do +## 2. Provide 5 whys; +## 3. Call a tool, you can call it when in ; + + +If you need to use a function, use the next exactly format. That format will be parsed from your answer: + +``` +""" + TOOL_CALL_START_TAG + """ +{ +"function": "...", +"parameters": { + // parameters of the tool +} +} +""" + TOOL_CALL_END_TAG + """ +``` +""" + + +@dataclass +class ToolCallResult: + content: str + has_tool_call: bool + tool_name: t.Optional[str] = None + parameters: t.Optional[dict] = None + execution_result: str = None + + +@dataclass +class ToolParameter: + name: str + type: str + description: str + required: bool = True + + +@dataclass +class ToolDefinition: + name: str + description: str + parameters: t.List[ToolParameter] + execution_function: t.Callable + + +def format_tool_description(tool: ToolDefinition) -> str: + """Formats a single tool's description for the prompt.""" + param_desc = ",\n".join([f'"{p.name}": ... \\ {p.type} - ({p.description})' for p in tool.parameters]) + return f"//{tool.description}\n- {tool.name}({{{param_desc}}})" + + +def inject_tool_prompts( + messages: t.List[dict], + available_tools: t.List[ToolDefinition], + context: t.Dict[str, str] + ) -> t.List[dict]: + """Injects tool descriptions and usage instructions into the system prompt.""" + if not available_tools: + logger.debug("[inject_tool_prompts] No tools available. Returning original messages.") + return messages + + tool_descriptions = "\n".join([format_tool_description(tool) for tool in available_tools]) + tool_system_prompt = get_tool_system_prompt(tool_descriptions, context) + # Find system prompt or prepend to user prompt + modified_messages = list(messages) # Create a copy + found_system = False + for i, msg in enumerate(modified_messages): + if msg.get("role") == "system": + # Append to existing system prompt + modified_messages[i]["content"] = f"{msg.get('content', '')}\n\n{tool_system_prompt}" + logger.debug(f"[inject_tool_prompts] Injected tool system prompt:\n{modified_messages[i]['content']}") + found_system = True + break + + if not found_system: + # Prepend a new system message + modified_messages.insert(0, {"role": "system", "content": tool_system_prompt}) + + logger.debug(f"[inject_tool_prompts] Msg not found. Injected tool system prompt as a first msg :\n{tool_system_prompt}") + return modified_messages + + +def parse_tool_call_block(text_response: str) -> t.Optional[ToolCallResult]: + """ + Parses the block from the model's text response using regex. + Returns a ToolCallResult object if a valid tool call is found, None otherwise. + """ + logger.debug(f"Parsing tool call block: {text_response}") + if not text_response: + return None + if not TOOL_CALL_END_TAG in text_response: + return None + # Regex to find the block, allowing for whitespace variations + # DOTALL allows '.' to match newlines within the JSON block + json_content = text_response[text_response.find( + TOOL_CALL_START_TAG) + len(TOOL_CALL_START_TAG): text_response.rfind(TOOL_CALL_END_TAG)] + if '```' in json_content.split('\n'): + json_content = '\n'.join(json_content.split('\n')[1:-1]) + + logger.debug(f"Found potential tool call JSON block: {json_content}") + + try: + parsed_data = json.loads(json_content) + # Basic validation + if "function" in parsed_data and ( + "parameters" in parsed_data and isinstance(parsed_data["parameters"], dict) or "parameters" not in parsed_data + ): + logger.info(f"Successfully parsed tool call: {parsed_data['function']}") + return ToolCallResult( + content=text_response, + has_tool_call=True, + tool_name=parsed_data.get("function"), + parameters=parsed_data.get("parameters", {}), + execution_result="" + ) + else: + logger.warning(f"Parsed JSON block lacks required 'function' or 'parameters': {json_content}") + return None + except json.JSONDecodeError as e: + logger.error(f"Failed to decode JSON from tool call block: {json_content}", exc_info=e) + return ToolCallResult( + content=text_response, + has_tool_call=True, + tool_name=None, + parameters=None, + execution_result=str(e) + ) + + +def call_function(tool_name: str, parameters: dict, tool_registry={}) -> str: + """Handle a tool call by executing the corresponding function from the MCP registry. + + Args: + tool_name: Name of the tool to execute + parameters: Parameters for the tool + + Returns: + String representation of the tool result + """ + tool_function = tool_registry.get(tool_name) + if not tool_function: + logger.warning(f"Tool '{tool_name}' not found in MCP registry") + return json.dumps({"error": f"Tool '{tool_name}' is not available."}) + tool_execution_function = tool_function.execution_function + try: + logger.info(f"Executing MCP tool '{tool_name}' with parameters: {parameters}") + result = tool_execution_function(**parameters) + logger.info(f"MCP tool '{tool_name}' executed successfully: {result}") + return json.dumps({"result": result}) + except Exception as e: + logger.exception(f"Error executing MCP tool '{tool_name}'", exc_info=e) + return json.dumps({"error": f"Failed to execute tool '{tool_name}': {str(e)}"}) + + +def handle_tool_call(current_stream_part_content, tool_registry) -> ToolCallResult: + """Handle a tool call by executing the corresponding function from the MCP registry. + + Args: + current_stream_part_content: The current content of the stream + tool_registry: Registry of available tools + + Returns: + ToolCallResult object containing the result of the tool call + """ + parsed_tool_call = parse_tool_call_block(current_stream_part_content) + + if not parsed_tool_call or parsed_tool_call.error: + return parsed_tool_call + tool_name = parsed_tool_call.tool_name + parameters = parsed_tool_call.parameters + logger.info(f"Custom tool call block parsed: {tool_name}") + + # Execute the tool and get result + tool_result_str = call_function(tool_name, parameters, tool_registry=tool_registry) + + return ToolCallResult( + content=current_stream_part_content, + has_tool_call=True, + tool_name=tool_name, + parameters=parameters, + execution_result=tool_result_str, + ) + + +def format_tool_result_message(tool_result: ToolCallResult): + return f'\n<{TOOL_CALL_RESULT_NAME}="{tool_result.tool_name}">\n{json.dumps(tool_result.execution_result)}\n\n## Please, Analyze tool_call_result! Answer next using the provided response from the user' \ No newline at end of file diff --git a/lamoom/ai_models/tools/errors.py b/lamoom/ai_models/tools/errors.py new file mode 100644 index 0000000..12d5c4a --- /dev/null +++ b/lamoom/ai_models/tools/errors.py @@ -0,0 +1,2 @@ +class ToolCallError(Exception): + pass diff --git a/lamoom/ai_models/tools/web_tool.py b/lamoom/ai_models/tools/web_tool.py new file mode 100644 index 0000000..9ec2d21 --- /dev/null +++ b/lamoom/ai_models/tools/web_tool.py @@ -0,0 +1,115 @@ + +import os +import requests +from bs4 import BeautifulSoup +from dataclasses import dataclass +import typing as t +from dotenv import load_dotenv +import logging + +from lamoom.ai_models.tools.base_tool import ToolDefinition, ToolParameter +from lamoom.settings import LAMOOM_GOOGLE_SEARCH_RESULTS_COUNT + +load_dotenv() + +logger = logging.getLogger(__name__) + +API_KEY = os.getenv("GOOGLE_API_KEY") +SEARCH_ID = os.getenv("SEARCH_ENGINE_ID") + +@dataclass +class WebSearchResult: + url: str + title: str + snippet: str + content: str + +class WebCall: + @staticmethod + def scrape_webpage(url: str) -> str: + """ + Scrapes the content of a webpage and returns the text. + """ + if not url.startswith(("https://", "http://")): + url = "https://" + url + response = requests.get(url) + + if response.status_code == 200: + soup = BeautifulSoup(response.content, "html.parser") + text = soup.get_text() + clean_text = text.splitlines() + clean_text = [element.strip() + for element in clean_text if element.strip()] + clean_text = '\n'.join(clean_text) + return clean_text + else: + return "Failed to retrieve the website content." + + @staticmethod + def perform_web_search(query: str) -> t.List[WebSearchResult]: + """ + Performs a web search and returns a list of search results. + """ + url = "https://www.googleapis.com/customsearch/v1" + params = { + 'q': query, + 'key': API_KEY, + 'cx': SEARCH_ID, + 'num': LAMOOM_GOOGLE_SEARCH_RESULTS_COUNT + } + + response = requests.get(url, params=params) + results = response.json() + + search_results = [] + + if 'items' in results: + for result in results['items']: + content = WebCall.scrape_webpage(result['link']) + search_results.append(WebSearchResult( + url=result['link'], + title=result['title'], + snippet=result['snippet'], + content=content + )) + logger.debug(f"Retrieved search_results {search_results}") + return search_results + + @staticmethod + def format_search_results(results: t.List[WebSearchResult]) -> str: + """ + Formats search results into a readable string. + """ + formatted = "" + for i, result in enumerate(results, 1): + formatted += f"\n" + formatted += f"Title: {result.title}\n" + formatted += f"URL: {result.url}\n" + formatted += f"Snippet: {result.snippet}\n" + formatted += f"Content: {result.content[:500]}...\n" + formatted += f'' + return formatted + + @staticmethod + def execute(query: str) -> str: + return WebCall.format_search_results( + WebCall.perform_web_search(query) + ) + + +def perform_web_search(query: str) -> str: + """ + Performs a web search and returns formatted results. + """ + results = WebCall.execute(query) + return results + + +WEB_SEARCH_TOOL = ToolDefinition( + name="web_call", + description="Performs a web search using a search engine to find up-to-date information or details not present in the internal knowledge. Today is {current_datetime_strftime} {timezone}.", + parameters=[ + ToolParameter(name="query", type="string", description="The search query to use.", required=True) + ], + execution_function=perform_web_search +) diff --git a/lamoom/exceptions.py b/lamoom/exceptions.py index 8797504..75f8d42 100644 --- a/lamoom/exceptions.py +++ b/lamoom/exceptions.py @@ -6,6 +6,10 @@ class RetryableCustomError(LamoomError): pass +class StopStreamingError(LamoomError): + pass + + class LamoomPromptIsnotFoundError(LamoomError): pass diff --git a/lamoom/prompt/base_prompt.py b/lamoom/prompt/base_prompt.py index ae6d9cd..cd85d9f 100644 --- a/lamoom/prompt/base_prompt.py +++ b/lamoom/prompt/base_prompt.py @@ -3,6 +3,7 @@ from collections import defaultdict from dataclasses import dataclass, field +from lamoom.ai_models.tools.base_tool import ToolDefinition from lamoom.prompt.chat import ChatsEntity logger = logging.getLogger(__name__) @@ -16,8 +17,12 @@ class BasePrompt: chats: t.List[ChatsEntity] = field(default_factory=list) pipe: t.List[str] = field(default_factory=list) functions: t.List[dict] = None + messages: t.List[dict] = field(default_factory=list) + max_tokens: int = 0 top_p: float = 0.0 temperature: float = 0.0 + # Add tool registry + tool_registry: t.Dict[str, ToolDefinition] = field(default_factory=dict) def get_params(self): return { @@ -72,3 +77,14 @@ def add_function(self, function: dict): if not self.functions: self.functions = [] self.functions.append(function) + + def add_tool( + self, tool: ToolDefinition + ): + self.tool_registry[tool.name] = tool + + def add_tools( + self, tools: list[ToolDefinition] + ): + for tool in tools: + self.tool_registry[tool.name] = tool diff --git a/lamoom/prompt/lamoom.py b/lamoom/prompt/lamoom.py index 157806a..a3649c6 100644 --- a/lamoom/prompt/lamoom.py +++ b/lamoom/prompt/lamoom.py @@ -1,9 +1,9 @@ +from datetime import datetime import logging import typing as t -from dataclasses import dataclass -from decimal import Decimal +from dataclasses import dataclass, field import requests -import time +from lamoom.ai_models.tools.base_tool import inject_tool_prompts from lamoom.settings import LAMOOM_API_URI from lamoom import Secrets, settings from lamoom.ai_models.ai_model import AI_MODELS_PROVIDER @@ -12,23 +12,24 @@ from lamoom.ai_models.openai.azure_models import AzureAIModel from lamoom.ai_models.claude.claude_model import ClaudeAIModel from lamoom.ai_models.openai.openai_models import OpenAIModel -from lamoom.ai_models.constants import C_16K from lamoom.exceptions import ( LamoomPromptIsnotFoundError, - RetryableCustomError + RetryableCustomError, + StopStreamingError ) from lamoom.services.SaveWorker import SaveWorker from lamoom.prompt.prompt import Prompt -from lamoom.prompt.user_prompt import UserPrompt from lamoom.responses import AIResponse from lamoom.services.lamoom import LamoomService -from lamoom.utils import current_timestamp_ms import json logger = logging.getLogger(__name__) +BASE_URL_MAPPING = { + 'gemini': "https://generativelanguage.googleapis.com/v1beta/openai/" +} @dataclass class Lamoom: @@ -38,8 +39,7 @@ class Lamoom: claude_key: str = None gemini_key: str = None azure_keys: t.Dict[str, str] = None - nebius_key: str = None - custom_key: str = None + custom_keys: t.Dict[str, str] = field(default_factory=dict) secrets: Secrets = None clients = {} @@ -52,6 +52,12 @@ def __post_init__(self): self.azure_keys = self.secrets.azure_keys else: logger.debug(f"Azure keys not found in secrets") + if not self.custom_keys: + if self.secrets.custom_keys: + logger.debug(f"Using Custom keys from secrets") + self.custom_keys = self.secrets.custom_keys + else: + logger.debug(f"Custom keys not found in secrets") if not self.api_token and self.secrets.API_TOKEN: logger.debug(f"Using API token from secrets") self.api_token = self.secrets.API_TOKEN @@ -67,36 +73,39 @@ def __post_init__(self): if not self.claude_key and self.secrets.CLAUDE_API_KEY: logger.debug(f"Using Claude API key from secrets") self.claude_key = self.secrets.CLAUDE_API_KEY - if not self.nebius_key and self.secrets.NEBIUS_API_KEY: - logger.debug(f"Using Nebius API key from secrets") - self.nebius_key = self.secrets.NEBIUS_API_KEY - if not self.custom_key and self.secrets.CUSTOM_API_KEY: - logger.debug(f"Using Custom API key from secrets") - self.custom_key = self.secrets.CUSTOM_API_KEY self.service = LamoomService() if self.openai_key: - self.clients[AI_MODELS_PROVIDER.OPENAI] = { + self.clients[AI_MODELS_PROVIDER.OPENAI.value] = { "organization": self.openai_org, "api_key": self.openai_key, } if self.azure_keys: - if not self.clients.get(AI_MODELS_PROVIDER.AZURE): - self.clients[AI_MODELS_PROVIDER.AZURE] = {} + if not self.clients.get(AI_MODELS_PROVIDER.AZURE.value): + self.clients[AI_MODELS_PROVIDER.AZURE.value] = {} for realm, key_data in self.azure_keys.items(): - self.clients[AI_MODELS_PROVIDER.AZURE][realm] = { + self.clients[AI_MODELS_PROVIDER.AZURE.value][realm] = { "api_version": key_data.get("api_version", "2023-07-01-preview"), "azure_endpoint": key_data["url"], "api_key": key_data["key"], } logger.debug(f"Initialized Azure client for {realm} {key_data['url']}") if self.claude_key: - self.clients[AI_MODELS_PROVIDER.CLAUDE] = {"api_key": self.claude_key} + self.clients[AI_MODELS_PROVIDER.CLAUDE.value] = {"api_key": self.claude_key} if self.gemini_key: - self.clients[AI_MODELS_PROVIDER.GEMINI] = {"api_key": self.gemini_key} - if self.nebius_key: - self.clients[AI_MODELS_PROVIDER.NEBIUS] = {"api_key": self.nebius_key} - if self.custom_key: - self.clients[AI_MODELS_PROVIDER.CUSTOM] = {"api_key": self.custom_key} + self.clients[AI_MODELS_PROVIDER.GEMINI.value] = { + "api_key": self.gemini_key, + "base_url": BASE_URL_MAPPING.get(AI_MODELS_PROVIDER.GEMINI.value) + } + # Initialize custom providers from environment + for provider_name, provider_config in self.custom_keys.items(): + logger.info(f"Initializing custom provider {provider_name} {provider_config.get('base_url')}") + provider_key = f"custom_{provider_name}" + if provider_key not in self.clients: + self.clients[provider_key] = { + "api_key": provider_config.get("key"), + "base_url": provider_config.get("base_url") + } + logger.debug(f"Initialized custom provider {provider_key} {provider_config.get('base_url')}") self.worker = SaveWorker() def create_test( @@ -125,78 +134,93 @@ def create_test( else: logger.error(response) - def extract_provider_name(self, model: str, provider_url: str = None) -> dict: + def extract_provider_name(self, model: str) -> dict: parts = model.split("/") + + if "openai" in parts[0].lower() and len(parts) == 2: + return { + 'provider': parts[0].lower(), + 'model_name': parts[1] + } - if "azure" in parts[0].lower() and len(parts) == 3: + elif "azure" in parts[0].lower() and len(parts) == 3: model_provider, realm, model_name = parts return { 'provider': model_provider.lower(), 'model_name': model_name, 'realm': realm, - 'base_url': None } - elif "nebius" in parts[0].lower() and len(parts) == 3: - model_provider = parts[0] - model_name = f"{parts[1]}/{parts[2]}" + elif "claude" in parts[0].lower() and len(parts) == 2: return { - 'provider': model_provider.lower(), - 'model_name': model_name, - 'realm': None, - 'base_url': None - } - elif "custom" in parts[0].lower(): - if len(parts) == 3: - model_provider = parts[0] - model_name = f"{parts[1]}/{parts[2]}" - else: - model_provider, model_name = parts - return { - 'provider': model_provider.lower(), - 'model_name': model_name, - 'realm': None, - 'base_url': provider_url + 'provider': 'claude', + 'model_name': parts[1] } - else: - model_provider, model_name = parts + elif "gemini" in parts[0].lower() and len(parts) == 2: return { - 'provider': model_provider.lower(), - 'model_name': model_name, - 'realm': None, - 'base_url': None + 'provider': 'gemini', + 'model_name': parts[1] } - + elif "custom" in parts[0].lower() and len(parts) >= 3: + model_name = '/'.join(parts[2:]) + provider_name = parts[1].lower() + # Check if this is a registered custom provider + if provider_name in self.custom_keys: + return { + 'provider': f"custom_{provider_name}", + 'model_name': model_name, + 'realm': None, + } + raise Exception(f"Unknown model: {model}") + + def get_default_context(self): + return { + 'current_datetime_strftime': datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + 'timezone': datetime.now().astimezone().tzname() + } + + def get_context(self, context: dict): + return { + **self.get_default_context(), + **context + } + def init_attempt(self, model_info: dict) -> AttemptToCall: provider = model_info['provider'] model_name = model_info['model_name'] - if provider in [AI_MODELS_PROVIDER.OPENAI.value, - AI_MODELS_PROVIDER.GEMINI.value, - AI_MODELS_PROVIDER.NEBIUS.value]: + if provider == AI_MODELS_PROVIDER.CLAUDE.value: return AttemptToCall( - ai_model=OpenAIModel( - provider=AI_MODELS_PROVIDER(provider), + ai_model=ClaudeAIModel( model=model_name, ), weight=100, - ) - elif provider == AI_MODELS_PROVIDER.CLAUDE.value: + ) + elif provider == AI_MODELS_PROVIDER.OPENAI.value: return AttemptToCall( - ai_model=ClaudeAIModel( + ai_model=OpenAIModel( + model=model_name + ), + weight=100, + ) + elif provider == AI_MODELS_PROVIDER.GEMINI.value: + return AttemptToCall( + ai_model=OpenAIModel( model=model_name, + provider=AI_MODELS_PROVIDER.GEMINI, ), weight=100, ) - elif provider == AI_MODELS_PROVIDER.CUSTOM.value: + elif provider.startswith('custom_'): + # Handle custom provider format return AttemptToCall( ai_model=OpenAIModel( model=model_name, provider=AI_MODELS_PROVIDER.CUSTOM, - base_url=model_info['base_url'] + _provider_name=model_info['provider'] ), weight=100, ) - else: + elif provider == AI_MODELS_PROVIDER.AZURE.value: return AttemptToCall( ai_model=AzureAIModel( realm=model_info['realm'], @@ -205,14 +229,14 @@ def init_attempt(self, model_info: dict) -> AttemptToCall: weight=100, ) - def init_behavior(self, model: str, provider_url: str = None) -> AIModelsBehaviour: - main_model_info = self.extract_provider_name(model, provider_url) + def init_behavior(self, model: str) -> AIModelsBehaviour: + main_model_info = self.extract_provider_name(model) main_attempt = self.init_attempt(main_model_info) fallback_attempts = [] for model in settings.FALLBACK_MODELS: - model_info = self.extract_provider_name(model, provider_url) + model_info = self.extract_provider_name(model) fallback_attempts.append(self.init_attempt(model_info)) return AIModelsBehaviour( @@ -225,24 +249,22 @@ def call( prompt_id: str, context: t.Dict[str, str], model: str, - provider_url: str = None, params: t.Dict[str, t.Any] = {}, version: str = None, count_of_retries: int = 5, test_data: dict = {}, stream_function: t.Callable = None, check_connection: t.Callable = None, - stream_params: dict = {}, + stream_params: dict = {} ) -> AIResponse: """ Call flow prompt with context and behaviour """ logger.debug(f"Calling {prompt_id}") - start_time = current_timestamp_ms() prompt = self.get_prompt(prompt_id, version) - behaviour = self.init_behavior(model, provider_url) + behaviour = self.init_behavior(model) logger.info(behaviour) @@ -251,60 +273,45 @@ def call( while prompt_attempts.initialize_attempt(): current_attempt = prompt_attempts.current_attempt user_prompt = prompt.create_prompt(current_attempt) - calling_messages = user_prompt.resolve(context) - + calling_context = self.get_context(context) + # Inject tool prompts into first message + calling_messages = user_prompt.resolve(calling_context, prompt.tool_registry) + messages = calling_messages.get_messages() + messages = inject_tool_prompts(messages, list(prompt.tool_registry.values()), calling_context) + logger.info(f'self.clients: {self.clients}, [current_attempt.ai_model.provider_name]: {current_attempt.ai_model.provider_name}') for _ in range(0, count_of_retries): try: result = current_attempt.ai_model.call( - calling_messages.get_messages(), + messages, calling_messages.max_sample_budget, + tool_registry=prompt.tool_registry, stream_function=stream_function, check_connection=check_connection, stream_params=stream_params, - client_secrets=self.clients[current_attempt.ai_model.provider], + client_secrets=self.clients[current_attempt.ai_model.provider_name], + modelname=model, + prompt=prompt, + context=context, + test_data=test_data, + client=self, **params, ) - - sample_budget = self.calculate_budget_for_text( - user_prompt, result.get_message_str() - ) - - try: - result.metrics.price_of_call = self.get_price( - current_attempt, - sample_budget, - calling_messages.prompt_budget, - ) - except Exception as e: - logger.exception(f"Error while getting price: {e}") - result.metrics.price_of_call = 0 - result.metrics.sample_tokens_used = sample_budget - result.metrics.prompt_tokens_used = calling_messages.prompt_budget - result.metrics.ai_model_details = ( - current_attempt.ai_model.get_metrics_data() - ) - result.metrics.latency = current_timestamp_ms() - start_time - - if settings.USE_API_SERVICE and self.api_token: - timestamp = int(time.time() * 1000) - result.id = f"{prompt_id}#{timestamp}" - - self.worker.add_task( - self.api_token, - prompt.service_dump(), - context, - result, - {**test_data, "call_model": model} - ) return result except RetryableCustomError as e: - logger.error( + logger.exception( f"Attempt failed: {prompt_attempts.current_attempt} with retryable error: {e}" ) + break + except StopStreamingError as e: + logger.exception( + f"Attempt Stopped: {prompt_attempts.current_attempt} with non-retryable error: {e}" + ) + raise e except Exception as e: - logger.error( + logger.exception( f"Attempt failed: {prompt_attempts.current_attempt} with non-retryable error: {e}" ) + raise e logger.exception( "Prompt call failed, no attempts worked" @@ -362,28 +369,3 @@ def add_ideal_answer( ) return response - - def calculate_budget_for_text(self, user_prompt: UserPrompt, text: str) -> int: - if not text: - return 0 - return len(user_prompt.encoding.encode(text)) - - def get_price( - self, attempt: AttemptToCall, sample_budget: int, prompt_budget: int - ) -> Decimal: - data = { - "provider": attempt.ai_model.provider.value, - "model": attempt.ai_model.name, - "output_tokens": sample_budget, - "input_tokens": prompt_budget, - } - - response = requests.post( - f"{LAMOOM_API_URI}/lib/pricing", - data=json.dumps(data), - ) - - if response.status_code != 200: - return 0 - - return response.json()["price"] \ No newline at end of file diff --git a/lamoom/prompt/prompt.py b/lamoom/prompt/prompt.py index 0ce3854..5d58936 100644 --- a/lamoom/prompt/prompt.py +++ b/lamoom/prompt/prompt.py @@ -1,6 +1,6 @@ +from dataclasses import dataclass, field import logging from copy import deepcopy -from dataclasses import dataclass from lamoom import settings from lamoom.ai_models.attempt_to_call import AttemptToCall diff --git a/lamoom/prompt/user_prompt.py b/lamoom/prompt/user_prompt.py index 5763d9c..4931d69 100644 --- a/lamoom/prompt/user_prompt.py +++ b/lamoom/prompt/user_prompt.py @@ -3,6 +3,7 @@ from collections import defaultdict from dataclasses import dataclass, field +from lamoom.ai_models.tools.base_tool import ToolDefinition, inject_tool_prompts import tiktoken from lamoom import settings @@ -63,7 +64,7 @@ class UserPrompt(BasePrompt): def __post_init__(self): self.encoding = tiktoken.get_encoding(self.tiktoken_encoding) - def resolve(self, context: t.Dict) -> CallingMessages: + def resolve(self, context: t.Dict, tool_registry: t.Dict[str, ToolDefinition]) -> CallingMessages: pipe = {} prompt_budget = 0 ordered_pipe = dict((value, i) for i, value in enumerate(self.pipe)) diff --git a/lamoom/responses.py b/lamoom/responses.py index 5abcb79..95d74b4 100644 --- a/lamoom/responses.py +++ b/lamoom/responses.py @@ -2,10 +2,19 @@ import json import logging from dataclasses import dataclass, field +import time import typing as t +from lamoom.ai_models.tools.base_tool import TOOL_CALL_NAME, TOOL_CALL_RESULT_NAME, ToolCallResult, ToolDefinition, format_tool_result_message +from lamoom.settings import SHOULD_INCLUDE_REASONING +from lamoom.utils import current_timestamp_ms logger = logging.getLogger(__name__) +FINISH_REASON_LENGTH = "length" +FINISH_REASON_ERROR = "error" +FINISH_REASON_FINISH = "stop" +FINISH_REASON_TOOL_CALLS = "tool_calls" + @dataclass class Prompt: @@ -18,10 +27,10 @@ class Prompt: @dataclass class Metrics: - price_of_call: Decimal = None - sample_tokens_used: int = None - prompt_tokens_used: int = None - ai_model_details: dict = None + price_of_call: Decimal = 0 + sample_tokens_used: int = 0 + prompt_tokens_used: int = 0 + ai_model_details: dict = 0 latency: int = None @@ -30,6 +39,7 @@ class AIResponse: _response: str = "" original_result: object = None content: str = "" + reasoning = '' finish_reason: str = "" prompt: Prompt = field(default_factory=Prompt) metrics: Metrics = field(default_factory=Metrics) @@ -37,7 +47,96 @@ class AIResponse: @property def response(self) -> str: - return self._response + return self._response or self.content or '{}' + + def get_message_str(self) -> str: + return self.response + + @property + def parsed_json(self) -> t.Optional[dict]: + parsed_json_response = get_json_from_response(self) + return parsed_json_response.parsed_content if parsed_json_response else None + + +@dataclass(kw_only=True) +class StreamingResponse(AIResponse): + is_detected_tool_call: bool = False + last_detected_tool_call: t.Optional[dict] = None + detected_tool_calls: list[t.Optional[dict]] = field(default_factory=list) + tool_registry: t.Dict[str, ToolDefinition] = field(default_factory=dict) + messages: t.List[dict] = field(default_factory=list) + started_tmst: int = field(default_factory=current_timestamp_ms) + first_stream_tmst: int = None + finished_tmst: int = None + streaming_content: str = "" + + def update_to_another_attempt(self): + self.is_detected_tool_call = False + self.content = '' + self.reasoning = '' + + def set_streaming(self): + if not self.started_tmst: + self.started_tmst = current_timestamp_ms() + + def set_finish_reason(self, reason: str): + self.finished_tmst = current_timestamp_ms() + self.finish_reason = reason + + def add_assistant_message(self): + if SHOULD_INCLUDE_REASONING: + self.add_message("assistant", self.reasoning + '\n' + self.content) + else: + self.add_message("assistant", self.content) + + def add_message(self, role: str, content: str): + self.messages.append({"role": role, "content": content}) + + def add_tool_result(self, tool_result: ToolCallResult): + self.add_assistant_message() + tool_result_message = format_tool_result_message(tool_result) + self.content += tool_result_message + self.add_message("user", tool_result_message) + + @property + def response(self) -> str: + return self.content + + @property + def tool_calls(self) -> t.List[t.Optional[dict]]: + return self.detected_tool_calls + + def get_function_name(self, tool_call: t.Optional[dict]) -> t.Optional[str]: + if tool_call.type != "function": + logger.error(f"function.type is not function: {tool_call.type}") + return None + return tool_call.function.name + + def get_function_args(self, tool_call: t.Optional[dict]) -> t.Dict[str, t.Any]: + if not self.is_function() or not tool_call.function: + return {} + arguments = tool_call.function.arguments + try: + return json.loads(arguments) + except json.JSONDecodeError as e: + logger.debug("Failed to parse function arguments", exc_info=e) + return {} + + def is_reached_limit(self) -> bool: + return self.finish_reason == FINISH_REASON_LENGTH + + def to_dict(self) -> t.Dict[str, str]: + return { + "finish_reason": self.finish_reason, + "message": json.dumps(self.messages, indent=2) + } def get_message_str(self) -> str: - return json.loads(self.response) + return json.dumps(self.messages, indent=2) + + def __str__(self) -> str: + result = ( + f"finish_reason: {self.finish_reason}\n" + f"message: {self.get_message_str()}\n" + ) + return result \ No newline at end of file diff --git a/lamoom/settings.py b/lamoom/settings.py index 20baa99..fd38334 100644 --- a/lamoom/settings.py +++ b/lamoom/settings.py @@ -28,14 +28,19 @@ USE_API_SERVICE = parse_bool(os.environ.get("LAMOOM_USE_API_SERVICE", True)) LAMOOM_API_URI = os.environ.get("LAMOOM_API_URI") or os.environ.get("FLOW_PROMPT_API_URI") or "https://api.lamoom.com" +LAMOOM_GOOGLE_SEARCH_RESULTS_COUNT = os.environ.get("LAMOOM_GOOGLE_SEARCH_RESULTS_COUNT", 3) CACHE_PROMPT_FOR_EACH_SECONDS = int( os.environ.get("LAMOOM_CACHE_PROMPT_FOR_EACH_SECONDS", 5 * 60) ) # 5 minutes by default RECEIVE_PROMPT_FROM_SERVER = parse_bool( os.environ.get("LAMOOM_RECEIVE_PROMPT_FROM_SERVER", True) ) +SHOULD_INCLUDE_REASONING = parse_bool(os.environ.get("SHOULD_INCLUDE_REASONING", True)) PIPE_PROMPTS = {} FALLBACK_MODELS = [] +LAMOOM_CUSTOM_PROVIDERS = json.loads( + os.getenv("custom_keys", os.getenv("LAMOOM_CUSTOM_PROVIDERS", "{}")) +) @dataclass @@ -45,6 +50,7 @@ class Secrets: CLAUDE_API_KEY: str = field(default_factory=lambda: os.getenv("CLAUDE_API_KEY")) GEMINI_API_KEY: str = field(default_factory=lambda: os.getenv("GEMINI_API_KEY")) NEBIUS_API_KEY: str = field(default_factory=lambda: os.getenv("NEBIUS_API_KEY")) + OPENROUTER_KEY: str = field(default_factory=lambda: os.getenv("OPENROUTER_KEY")) CUSTOM_API_KEY: str = field(default_factory=lambda: os.getenv("CUSTOM_API_KEY")) OPENAI_ORG: str = field(default_factory=lambda: os.getenv("OPENAI_ORG")) azure_keys: dict = field( @@ -52,3 +58,8 @@ class Secrets: os.getenv("azure_keys", os.getenv("AZURE_OPENAI_KEYS", os.getenv("AZURE_KEYS", "{}"))) ) ) + custom_keys: dict = field( + default_factory=lambda: json.loads( + os.getenv("custom_keys", os.getenv("LAMOOM_CUSTOM_PROVIDERS", "{}")) + ) + ) diff --git a/poetry.lock b/poetry.lock index 8bf1da0..b6e31f3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.0 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand. 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"sha256:fdaabebf6d03b5ba83ae0a02cfe96f48a716f4fae556461d180825866f75b728"}, + {file = "virtualenv-20.31.2-py3-none-any.whl", hash = "sha256:36efd0d9650ee985f0cad72065001e66d49a6f24eb44d98980f630686243cf11"}, + {file = "virtualenv-20.31.2.tar.gz", hash = "sha256:e10c0a9d02835e592521be48b332b6caee6887f332c111aa79a09b9e79efc2af"}, ] [package.dependencies] @@ -3022,7 +3062,7 @@ platformdirs = ">=3.9.1,<5" [package.extras] docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] -test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8) ; platform_python_implementation == \"PyPy\" or platform_python_implementation == \"CPython\" and sys_platform == \"win32\" and python_version >= \"3.13\"", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10) ; platform_python_implementation == \"CPython\""] +test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8) ; platform_python_implementation == \"PyPy\" or platform_python_implementation == \"GraalVM\" or platform_python_implementation == \"CPython\" and sys_platform == \"win32\" and python_version >= \"3.13\"", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10) ; platform_python_implementation == \"CPython\""] [[package]] name = "wcwidth" @@ -3146,4 +3186,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.1" python-versions = "^3.9" -content-hash = "0d6587f748072058643c5e9a3cae6db6e58e0e364027262e7c45fdde632c22d6" +content-hash = "ab93dc205dbc53207844558db3ba7d65afdc7d3e9682a82e4b25d31b1c1f8a6d" diff --git a/poetry.toml b/poetry.toml new file mode 100644 index 0000000..ab1033b --- /dev/null +++ b/poetry.toml @@ -0,0 +1,2 @@ +[virtualenvs] +in-project = true diff --git a/pyproject.toml b/pyproject.toml index 738fb9f..4b19659 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "lamoom" -version = "0.1.37" +version = "0.1.42" description = "" authors = ["Lamoom Engineering Team "] readme = "README.md" @@ -12,12 +12,16 @@ pyyaml = "^6.0.1" openai = "^1.65.1" anthropic = "^0.31.2" httpx = "^0.27.2" +beautifulsoup4 = "^4.13.4" -[tool.poetry.dev-dependencies] + +[tool.poetry.group.dev.dependencies] +black = "^24.4.2" +ipykernel = "^6.29.5" poetry = "^1.7.1" isort = "^5.13.2" flake8 = "^7.0.0" -pytest = "^7.1.2" +pytest = "^8.3.5" pytest-cov = "^3.0.0" pre-commit = "^3.6.0" autopep8 = "^2.0.4" @@ -25,10 +29,6 @@ python-dotenv = "^1.0.1" twine = "^5.0.0" -[tool.poetry.group.dev.dependencies] -black = "^24.4.2" -ipykernel = "^6.29.5" - [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" diff --git a/tests/conftest.py b/tests/conftest.py index 6d0ac7a..9737490 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -25,71 +25,6 @@ def lamoom(): azure_keys={"us-east-1": {"url": "https://us-east-1.api.azure.openai.org", "key": "123"}} ) -@pytest.fixture -def openai_gpt_4_behaviour(): - return behaviour.AIModelsBehaviour( - attempts=[ - AttemptToCall( - ai_model=OpenAIModel( - model="gpt-4-1106-preview", - max_tokens=C_128K, - support_functions=True, - ), - weight=100, - ), - ] - ) - - -@pytest.fixture -def azure_gpt_4_behaviour(): - return behaviour.AIModelsBehaviour( - attempts=[ - AttemptToCall( - ai_model=AzureAIModel( - realm='useast', - deployment_id="gpt-4o", - max_tokens=C_128K, - support_functions=True, - ), - weight=100, - ), - ] - ) - - -@pytest.fixture -def gpt_4_behaviour(): - return behaviour.AIModelsBehaviour( - attempts=[ - AttemptToCall( - ai_model=AzureAIModel( - realm='useast', - deployment_id='gpt-4o', - max_tokens=C_128K, - ), - weight=1, - ), - AttemptToCall( - ai_model=OpenAIModel( - model="gpt-4-1106-preview", - max_tokens=C_128K, - support_functions=True, - ), - weight=100, - ), - ], - fallback_attempt=AttemptToCall( - ai_model=AzureAIModel( - realm="useast", - deployment_id="gpt-4o", - max_tokens=C_32K, - support_functions=True, - ), - weight=1, - ), - ) - @pytest.fixture def hello_world_prompt(): prompt = Prompt(id='hello-world') diff --git a/tests/logs/.gitgnore b/tests/logs/.gitgnore new file mode 100644 index 0000000..2211df6 --- /dev/null +++ b/tests/logs/.gitgnore @@ -0,0 +1 @@ +*.txt diff --git a/tests/prompts/test_model.py b/tests/prompts/test_model.py index b22efa6..5fbc6e2 100644 --- a/tests/prompts/test_model.py +++ b/tests/prompts/test_model.py @@ -26,10 +26,10 @@ def test_model(client): prompt = Prompt(id=prompt_id) prompt.add("{text}", role='user') - result = client.call(prompt.id, context, "custom/deepseek-ai/DeepSeek-R1", provider_url="https://api.studio.nebius.ai/v1/") + result = client.call(prompt.id, context, "custom/nebius/deepseek-ai/DeepSeek-R1") assert result.content - result = client.call(prompt.id, context, "openai/gpt-4o") + result = client.call(prompt.id, context, "openai/o4-mini") assert result.content result = client.call(prompt.id, context, "azure/useast/gpt-4o") diff --git a/tests/prompts/test_pricing.py b/tests/prompts/test_pricing.py index 989ebc9..1378ad1 100644 --- a/tests/prompts/test_pricing.py +++ b/tests/prompts/test_pricing.py @@ -36,11 +36,10 @@ def test_openai_pricing(lamoom_client: Lamoom): prompt = Prompt(id=prompt_id) prompt.add("{text}", role='user') - result_4o = lamoom_client.call(prompt.id, context, "openai/gpt-4o", test_data={'ideal_answer': "There are eight", 'behavior_name': "gemini"}, stream_function=stream_function, check_connection=stream_check_connection, params={"stream": True}, stream_params={"validate": True, "end": "", "flush": True}) - result_4o_mini = lamoom_client.call(prompt.id, context, "openai/gpt-4o-mini", test_data={'ideal_answer': "There are eight", 'behavior_name': "gemini"}, stream_function=stream_function, check_connection=stream_check_connection, params={"stream": True}, stream_params={"validate": True, "end": "", "flush": True}) + result_4o_mini = lamoom_client.call(prompt.id, context, "openai/o4-mini-mini", test_data={'ideal_answer': "There are eight", 'behavior_name': "gemini"}, stream_function=stream_function, check_connection=stream_check_connection, params={"stream": True}, stream_params={"validate": True, "end": "", "flush": True}) - assert result_4o.metrics.price_of_call > 0 - assert result_4o_mini.metrics.price_of_call > 0 + assert result_4o_mini.metrics.prompt_tokens_used >= 0 + assert result_4o_mini.metrics.prompt_tokens_used >= 0 def test_claude_pricing(lamoom_client: Lamoom): @@ -51,12 +50,12 @@ def test_claude_pricing(lamoom_client: Lamoom): } # initial version of the prompt - prompt_id = f'test-{time.time()}' + prompt_id = f'test' lamoom_client.service.clear_cache() prompt = Prompt(id=prompt_id) prompt.add("{text}", role='user') result_haiku = lamoom_client.call(prompt.id, context, "claude/claude-3-5-haiku-latest", test_data={'ideal_answer': "There are eight", 'behavior_name': "gemini"}, stream_function=stream_function, check_connection=stream_check_connection, params={"stream": True}, stream_params={"validate": True, "end": "", "flush": True}) - result_sonnet = lamoom_client.call(prompt.id, context, "claude/claude-3-5-sonnet-latest", test_data={'ideal_answer': "There are eight", 'behavior_name': "gemini"}, stream_function=stream_function, check_connection=stream_check_connection, params={"stream": True}, stream_params={"validate": True, "end": "", "flush": True}) - - assert result_sonnet.metrics.price_of_call > result_haiku.metrics.price_of_call \ No newline at end of file + + assert result_haiku.metrics.prompt_tokens_used >= 0 + assert result_haiku.metrics.sample_tokens_used >= 0 diff --git a/tests/prompts/test_prompt.py b/tests/prompts/test_prompt.py index dce1ce4..02ee6ba 100644 --- a/tests/prompts/test_prompt.py +++ b/tests/prompts/test_prompt.py @@ -15,7 +15,7 @@ def azure_ai_attempt(): return AttemptToCall( ai_model=AzureAIModel( realm='useast', - deployment_id="gpt-4o", + deployment_id="o4-mini", max_tokens=C_128K, support_functions=True, ), @@ -54,7 +54,7 @@ def test_prompt_initialize(azure_ai_attempt: AttemptToCall): user_prompt.add("Hello, how can I help you today?") context = {} - initialized_pipe = user_prompt.resolve(context) + initialized_pipe = user_prompt.resolve(context, {}) messages = initialized_pipe.messages assert len(messages) == 1 assert messages[0].content == "Hello, how can I help you today?" @@ -69,7 +69,7 @@ def test_prompt_initialize_not_enough_budget(azure_ai_attempt: AttemptToCall): user_prompt.min_sample_tokens = 1299 # Not enough tokens for the message user_prompt.model_max_tokens = 1300 # Not enough tokens for the message with pytest.raises(NotEnoughBudgetError): - user_prompt.resolve(context) + user_prompt.resolve(context, {}) def test_prompt_show_pipe(): @@ -89,7 +89,7 @@ def test_prompt_left_budget(azure_ai_attempt: AttemptToCall): user_prompt = pipe.create_prompt(azure_ai_attempt) user_prompt.model_max_tokens = 2030 user_prompt.reserved_tokens_budget_for_sampling = 2030 - initialized_pipe = user_prompt.resolve({}) + initialized_pipe = user_prompt.resolve({}, {}) assert ( initialized_pipe.left_budget == user_prompt.model_max_tokens @@ -104,7 +104,7 @@ def test_prompt_prompt_price(azure_ai_attempt: AttemptToCall): user_prompt = pipe.create_prompt(azure_ai_attempt) user_prompt.model_max_tokens = 4030 user_prompt.add("Hello " + 'world ' * 1000) - pipe = user_prompt.resolve({}) + pipe = user_prompt.resolve({}, {}) assert len(pipe.get_messages()) == 1 @@ -119,7 +119,7 @@ def test_prompt_calculate_budget_for_values(azure_ai_attempt: AttemptToCall): "no priority. didn't fit. Hello {name}" + ("hello" * 1000), priority=2 ) user_prompt = pipe.create_prompt(azure_ai_attempt) - prompt = user_prompt.resolve({"name": "World"}) + prompt = user_prompt.resolve({"name": "World"}, {}) messages = prompt.get_messages() assert len(messages) == 2 assert messages[0]["content"] == "Priority. Hello World" diff --git a/tests/prompts/test_stream.py b/tests/prompts/test_stream.py index 8cfab56..642cc23 100644 --- a/tests/prompts/test_stream.py +++ b/tests/prompts/test_stream.py @@ -2,6 +2,7 @@ import logging import time +from lamoom.responses import StreamingResponse from pytest import fixture from lamoom import Lamoom, Prompt, OpenAIResponse logger = logging.getLogger(__name__) @@ -36,7 +37,7 @@ def test_stream(client: Lamoom): prompt.add("{text}") prompt.add("It's a system message, Hello {name}", role="assistant") - result: OpenAIResponse = client.call(prompt.id, context, "azure/useast/gpt-4o", stream_function=stream_function, check_connection=stream_check_connection, params={"stream": True}, stream_params={"validate": True, "end": "", "flush": True}) + result: StreamingResponse = client.call(prompt.id, context, "azure/useast/gpt-4o", stream_function=stream_function, check_connection=stream_check_connection, params={"stream": True}, stream_params={"validate": True, "end": "", "flush": True}) client.call(prompt.id, context, "claude/claude-3-haiku-20240307", stream_function=stream_function, check_connection=stream_check_connection, params={"stream": True}, stream_params={"validate": True, "end": "", "flush": True}) client.call(prompt.id, context, "gemini/gemini-1.5-flash", stream_function=stream_function, check_connection=stream_check_connection, params={"stream": True}, stream_params={"validate": True, "end": "", "flush": True}) diff --git a/tests/prompts/test_web_call.py b/tests/prompts/test_web_call.py new file mode 100644 index 0000000..6e60978 --- /dev/null +++ b/tests/prompts/test_web_call.py @@ -0,0 +1,61 @@ + +import json +import logging +from lamoom.ai_models.tools.web_tool import WEB_SEARCH_TOOL +from pytest import fixture +from lamoom import Lamoom, Prompt +logger = logging.getLogger(__name__) + + +@fixture +def client(): + import dotenv + dotenv.load_dotenv(dotenv.find_dotenv()) + lamoom = Lamoom() + return lamoom + +def stream_function(text, **kwargs): + print(text) + +def stream_check_connection(validate, **kwargs): + return validate + + +def test_web_call(client): + + context = { + 'text': f"What's the latest news in the San Francisco and AI?" + } + + # initial version of the prompt + prompt_id = 'test-web-search' + prompt = Prompt(id=prompt_id) + prompt.add("", role='system') + prompt.add("{text}", role='user') + + prompt.add_tool(WEB_SEARCH_TOOL) + print(f'prompt.tool_registry: {prompt.tool_registry}') + result = client.call(prompt.id, context, "openai/o4-mini") + with open('tests/logs/test_web_call_openai_o4.txt', 'w', encoding="utf-8") as f: + f.write(json.dumps(result.messages, indent=4 )) + assert result.content + + result = client.call(prompt.id, context, "claude/claude-3-7-sonnet-latest") + with open('tests/logs/test_web_call_claude_3_7.txt', 'w', encoding="utf-8") as f: + f.write(json.dumps(result.messages, indent=4)) + assert result.content + + result = client.call(prompt.id, context, "azure/useast/gpt-4o") + with open('tests/logs/test_web_call_azure_gpt_4o.txt', 'w', encoding="utf-8") as f: + f.write(json.dumps(result.messages, indent=4)) + assert result.content + + result = client.call(prompt.id, context, "custom/nvidia/deepseek-ai/deepseek-r1", stream_function=stream_function, check_connection=stream_check_connection, params={"stream": True}, stream_params={"validate": True, "end": "", "flush": True}) + assert result.content + with open('tests/logs/test_web_call_nvidia_deepseek_r1.txt', 'w', encoding="utf-8") as f: + f.write(json.dumps(result.messages, indent=4)) + + result = client.call(prompt.id, context, "custom/nebius/deepseek-ai/DeepSeek-R1") + assert result.content + with open('tests/logs/test_web_call_nebius_deepseek_r1.txt', 'w', encoding="utf-8") as f: + f.write(json.dumps(result.messages, indent=4)) diff --git a/tests/test_ai_model_tag_parser.py b/tests/test_ai_model_tag_parser.py new file mode 100644 index 0000000..caec92d --- /dev/null +++ b/tests/test_ai_model_tag_parser.py @@ -0,0 +1,198 @@ +import pytest +from unittest.mock import Mock, patch +from lamoom.ai_models.ai_model import AIModel, TagParser +from lamoom.responses import StreamingResponse +from lamoom.ai_models.tools.base_tool import TOOL_CALL_START_TAG, TOOL_CALL_END_TAG + + +class TestTagParser: + @pytest.fixture + def parser(self): + return TagParser(ignore_tags=['think', 'reason']) + + def test_basic_text(self, parser): + # Test text without tags + assert parser.text_to_stream_chunk("Hello world") == "Hello world" + assert parser.text_to_stream_chunk("") == "" + + def test_ignored_tags(self, parser): + parser.reset() + # Test ignored tags + assert parser.text_to_stream_chunk("Hello world" ) == "Hello " + assert parser.text_to_stream_chunk("") == "" + assert parser.text_to_stream_chunk("Hello worldworld there") == "" + assert parser.text_to_stream_chunk("") == "" # Close ignored tag + assert parser.text_to_stream_chunk(" there") == " there" + + def test_non_ignored_tags(self, parser): + parser.reset() + # Test non-ignored tags + assert parser.text_to_stream_chunk("Hello world") == "Hello world" + assert parser.text_to_stream_chunk("Hello world") == "Hello " + print(parser.state) + assert parser.text_to_stream_chunk("") == '' + + def test_partial_tags(self, parser): + parser.reset() + # Test partial tag handling + assert parser.text_to_stream_chunk("") == "" # Complete ignored tag + assert parser.text_to_stream_chunk("content") == "" # Content in ignored tag + assert parser.text_to_stream_chunk("") == "" # Close ignored tag + assert parser.text_to_stream_chunk("after") == "after" # Content after ignored tag + + # Test partial non-ignored tag + assert parser.text_to_stream_chunk("hey ") == "hey " # Output partial non-ignored tag + assert parser.text_to_stream_chunk("<") == "" # Output partial non-ignored tag + assert parser.text_to_stream_chunk("o") == "" # Output partial non-ignored tag + assert parser.text_to_stream_chunk("ther>") == "" # Complete non-ignored tag + + def test_writing_tags(self, parser): + parser = TagParser(writing_tags=['write']) + # Test partial tag handling + assert parser.text_to_stream_chunk("Simple") == "" + assert parser.text_to_stream_chunk("") == "" # Complete ignored tag + assert parser.text_to_stream_chunk("now") == "now" # Complete ignored tag + assert parser.text_to_stream_chunk(" ") == " " # Content in ignored tag + assert parser.text_to_stream_chunk("<") == "" # Close ignored tag + assert parser.text_to_stream_chunk("/") == "" + assert parser.text_to_stream_chunk("write>") == "" # Close ignored tag + assert parser.text_to_stream_chunk("after") == "" # Content after ignored tag + + # Test partial non-ignored tag + assert parser.text_to_stream_chunk("hey ") == "" # Output partial non-ignored tag + assert parser.text_to_stream_chunk("<") == "" # Output partial non-ignored tag + assert parser.text_to_stream_chunk("o") == "" # Output partial non-ignored tag + assert parser.text_to_stream_chunk("ther>") == "" # Complete non-ignored tag + + +class TestAIModelTagStreaming: + @pytest.fixture + def model(self): + model = AIModel() + model._init_tag_parser(ignore_tags=['think', 'reason'], writing_tags=[]) + return model + + @pytest.fixture + def mock_stream_function(self): + return Mock() + + @pytest.fixture + def stream_response(self): + return StreamingResponse(messages=[], tool_registry={}) + + def test_basic_streaming(self, model, mock_stream_function, stream_response): + # Test basic streaming with ignored tags + chunks = [ + "Hello ", + "", + "ignored", + "", + " world", + "" + ] + + for chunk in chunks: + stream_chunk = model.text_to_stream_chunk(chunk) + if not stream_chunk: + continue + mock_stream_function(stream_chunk) + + # Only non-ignored content should be streamed + assert mock_stream_function.call_count == 2 + mock_stream_function.assert_any_call("Hello ") + mock_stream_function.assert_any_call(" world") + + def test_tool_calls_with_tags(self, model, mock_stream_function, stream_response): + # Test tool calls with ignored tags + content = f"Before {TOOL_CALL_START_TAG}ignored{TOOL_CALL_END_TAG} After" + + # Simulate streaming + for chunk in content: + stream_chunk = model.text_to_stream_chunk(chunk) + if not stream_chunk: + continue + mock_stream_function(stream_chunk) + + # Tool call content should be accumulated but not streamed if in ignored tag + assert mock_stream_function.call_count > 0 + assert "Before" in "".join(call[0][0] for call in mock_stream_function.call_args_list) + assert "After" in "".join(call[0][0] for call in mock_stream_function.call_args_list) + + def test_nested_streaming(self, model, mock_stream_function, stream_response): + # Test nested tag streaming + chunks = [ + "Start ", + "", + "outer ", + "", + "inner", + "", + " more", + "", + " End", + "" + ] + + for chunk in chunks: + stream_chunk = model.text_to_stream_chunk(chunk) + if not stream_chunk: + continue + mock_stream_function(stream_chunk) + + # Only content outside ignored tags should be streamed + assert mock_stream_function.call_count == 2 + mock_stream_function.assert_any_call("Start ") + mock_stream_function.assert_any_call(" End") + + def test_partial_tag_streaming(self, model, mock_stream_function, stream_response): + # Test streaming with partial tags + chunks = [ + "Start ", + "", + "ignored", + "", + " End", + "" + ] + + for chunk in chunks: + stream_chunk = model.text_to_stream_chunk(chunk) + if not stream_chunk: + continue + mock_stream_function(stream_chunk) + + print(mock_stream_function.call_args_list) + # Partial tags should be handled correctly + assert mock_stream_function.call_count == 2 + mock_stream_function.assert_any_call("Start ") + mock_stream_function.assert_any_call(" End") + + def test_newline_streaming(self, model, mock_stream_function, stream_response): + # Test streaming with newlines + chunks = [ + "Line 1<<", + "ignored", + "Line 2<<", + "" + ] + + for chunk in chunks: + stream_chunk = model.text_to_stream_chunk(chunk) + if not stream_chunk: + continue + mock_stream_function(stream_chunk) + + print(mock_stream_function.call_args_list) + # Newlines should break tags and be streamed + assert mock_stream_function.call_count == 4 + assert "Line 1<<" in "".join(call[0][0] for call in mock_stream_function.call_args_list) + assert "Line 2<<" in "".join(call[0][0] for call in mock_stream_function.call_args_list) diff --git a/tests/test_integrational.py b/tests/test_integrational.py index 90b8404..a45fe36 100644 --- a/tests/test_integrational.py +++ b/tests/test_integrational.py @@ -54,4 +54,4 @@ def test_loading_prompt_from_service(client: Lamoom): result = client.call(prompt.id, context, "azure/useast/gpt-4o") # should call the prompt with music - assert result.prompt.messages[-1] == {'role': 'user', 'content': 'music1\nmusic2'} + assert result.messages[-2] == {'role': 'user', 'content': 'music1\nmusic2'}