diff --git a/pyproject.toml b/pyproject.toml index cf446e52..8f1a529b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = [ {name = "Plastic Labs", email = "hello@plasticlabs.ai"}, ] readme = "README.md" -requires-python = ">=3.9" +requires-python = ">=3.10" dependencies = [ "fastapi[standard]>=0.111.0", "python-dotenv>=1.0.0", @@ -22,9 +22,10 @@ dependencies = [ "anthropic>=0.36.0", "nanoid>=2.0.0", "alembic>=1.14.0", - "langfuse>=2.57.1", + "langfuse<3", "pyjwt>=2.10.0", "google-genai>=1.10.0", + "mirascope[anthropic,google,groq]>=1.24.2", ] [tool.uv] dev-dependencies = [ diff --git a/src/agent.py b/src/agent.py index bb53aa90..af5d8f3c 100644 --- a/src/agent.py +++ b/src/agent.py @@ -4,141 +4,75 @@ import logging import os from collections.abc import Iterable from typing import Any, Optional +from inspect import cleandoc as c import sentry_sdk -from anthropic import MessageStreamManager from dotenv import load_dotenv -from langfuse.decorators import langfuse_context, observe from sentry_sdk.ai.monitoring import ai_track from sqlalchemy import select from sqlalchemy.ext.asyncio import AsyncSession -from src import crud, models, schemas +from src import crud, models from src.dependencies import tracked_db from src.deriver.tom import get_tom_inference from src.deriver.tom.embeddings import CollectionEmbeddingStore from src.deriver.tom.long_term import get_user_representation_long_term from src.utils import history, parse_xml_content -from src.utils.model_client import ModelClient, ModelProvider + +from mirascope import llm, prompt_template +from mirascope.integrations.langfuse import with_langfuse # Configure logging logger = logging.getLogger(__name__) USER_REPRESENTATION_METAMESSAGE_TYPE = "honcho_user_representation" -DEF_DIALECTIC_PROVIDER = ModelProvider.ANTHROPIC -DEF_DIALECTIC_MODEL = "claude-3-7-sonnet-20250219" - -DEF_QUERY_GENERATION_PROVIDER = ModelProvider.GROQ -DEF_QUERY_GENERATION_MODEL = "llama-3.1-8b-instant" -QUERY_GENERATION_SYSTEM = """Given this query about a user, generate 3 focused search queries that would help retrieve relevant facts about the user. - Each query should focus on a specific aspect related to the original query, rephrased to maximize semantic search effectiveness. - For example, if the original query asks "what does the user like to eat?", generated queries might include "user's food preferences", "user's favorite cuisine", etc. - - Format your response as a JSON array of strings, with each string being a search query. - Respond only in valid JSON, without markdown formatting or quotes, and nothing else. - Example: - ["query about interests", "query about personality", "query about experiences"]""" load_dotenv() -class Dialectic: - def __init__(self, agent_input: str, user_representation: str, chat_history: str): - self.agent_input = agent_input - self.user_representation = user_representation - self.chat_history = chat_history - self.client = ModelClient( - provider=DEF_DIALECTIC_PROVIDER, model=DEF_DIALECTIC_MODEL - ) - self.system_prompt = """You are operating as a context service that helps maintain psychological understanding of users across applications. Alongside a query, you'll receive: 1) previously collected psychological context about the user that I've maintained, 2) a series of long-term facts about the user, and 3) their current conversation/interaction from the requesting application. Your goal is to analyze this information and provide theory-of-mind insights that help applications personalize their responses. Please respond in a brief, matter-of-fact, and appropriate manner to convey as much relevant information to the application based on its query and the user's most recent message. You are encouraged to provide any context from the provided resources that helps provide a more complete or nuanced understanding of the user, as long as it is somewhat relevant to the query. If the context provided doesn't help address the query, write absolutely NOTHING but "None".""" +@prompt_template() +def dialectic_prompt(query: str, user_representation: str, chat_history: str) -> str: + return c( + f""" + You are operating as a context service that helps maintain psychological understanding of users across applications. Alongside a query, you'll receive: - @ai_track("Dialectic Call") - @observe() - async def call(self): - with sentry_sdk.start_transaction( - op="dialectic-inference", name="Dialectic API Response" - ): - logger.debug( - f"Starting call() method with query length: {len(self.agent_input)}" - ) - call_start = asyncio.get_event_loop().time() + 1. previously collected psychological context about the user that I've maintained, + 2. a series of long-term facts about the user, and + 3. their current conversation/interaction from the requesting application. - prompt = f""" - {self.agent_input} - {self.user_representation} - {self.chat_history} - """ - logger.debug( - f"Prompt constructed with context length: {len(self.user_representation)} chars" - ) + Your goal is to analyze this information and provide theory-of-mind insights that help applications personalize their responses. Please respond in a brief, matter-of-fact, and appropriate manner to convey as much relevant information to the application based on its query and the user's most recent message. You are encouraged to provide any context from the provided resources that helps provide a more complete or nuanced understanding of the user, as long as it is somewhat relevant to the query. If the context provided doesn't help address the query, write absolutely NOTHING but "None". - # Create a properly formatted message - message: dict[str, Any] = {"role": "user", "content": prompt} - - # Generate the response - logger.debug("Calling model for generation") - model_start = asyncio.get_event_loop().time() - response = await self.client.generate( - messages=[message], system=self.system_prompt, max_tokens=1000 - ) - model_time = asyncio.get_event_loop().time() - model_start - logger.debug( - f"Model response received in {model_time:.2f}s: {len(response)} chars" - ) - - total_time = asyncio.get_event_loop().time() - call_start - logger.debug(f"call() completed in {total_time:.2f}s") - return [{"text": response}] - - @ai_track("Dialectic Call") - @observe() - async def stream(self): - with sentry_sdk.start_transaction( - op="dialectic-inference", name="Dialectic API Response" - ): - logger.debug( - f"Starting stream() method with query length: {len(self.agent_input)}" - ) - stream_start = asyncio.get_event_loop().time() - - prompt = f""" - {self.agent_input} - {self.user_representation} - {self.chat_history} - """ - logger.debug( - f"Prompt constructed with context length: {len(self.user_representation)} chars" - ) - - # Create a properly formatted message - message: dict[str, Any] = {"role": "user", "content": prompt} - - # Stream the response - logger.debug("Calling model for streaming") - model_start = asyncio.get_event_loop().time() - stream = await self.client.stream( - messages=[message], system=self.system_prompt, max_tokens=1000 - ) - - stream_setup_time = asyncio.get_event_loop().time() - model_start - logger.debug(f"Stream started in {stream_setup_time:.2f}s") - - total_time = asyncio.get_event_loop().time() - stream_start - logger.debug(f"stream() setup completed in {total_time:.2f}s") - return stream + {query} + {user_representation} + {chat_history} + """ + ) + + +@ai_track("Dialectic Call") +@with_langfuse() +@llm.call(provider="anthropic", model="claude-3-7-sonnet-20250219") +async def dialectic_call(query: str, user_representation: str, chat_history: str): + return dialectic_prompt(query, user_representation, chat_history) + + +@ai_track("Dialectic Stream") +@with_langfuse() +@llm.call(provider="anthropic", model="claude-3-7-sonnet-20250219", stream=True) +async def dialectic_stream(query: str, user_representation: str, chat_history: str): + return dialectic_prompt(query, user_representation, chat_history) -@observe() async def chat( app_id: str, user_id: str, session_id: str, queries: str | list[str], stream: bool = False, -) -> schemas.DialecticResponse | MessageStreamManager: +) -> llm.Stream | llm.CallResponse: """ - Chat with the Dialectic API using on-demand user representation generation. + Chat with the Dialectic API usingx on-demand user representation generation. This function: 1. Sets up resources needed (embedding store, latest message ID) @@ -223,40 +157,21 @@ async def chat( f"User representation generated: {len(user_representation)} characters" ) - # Create a Dialectic chain with the fresh user representation - chain = Dialectic( - agent_input=final_query, - user_representation=user_representation, - chat_history=chat_history, - ) - generation_time = asyncio.get_event_loop().time() - start_time logger.debug(f"User representation generation completed in {generation_time:.2f}s") - langfuse_context.update_current_trace( - session_id=session_id, - user_id=user_id, - release=os.getenv("SENTRY_RELEASE"), - metadata={"environment": os.getenv("SENTRY_ENVIRONMENT")}, - ) - - # Use streaming or non-streaming response based on the request - logger.debug(f"Calling Dialectic with streaming={stream}") - query_start_time = asyncio.get_event_loop().time() if stream: - response_stream = await chain.stream() - logger.debug( - f"Dialectic stream started after {asyncio.get_event_loop().time() - query_start_time:.2f}s" + logger.debug("Calling Dialectic with streaming") + response = await dialectic_stream( + final_query, user_representation, chat_history ) - return response_stream - - response = await chain.call() - query_time = asyncio.get_event_loop().time() - query_start_time - total_time = asyncio.get_event_loop().time() - start_time - logger.debug( - f"Dialectic response received in {query_time:.2f}s (total: {total_time:.2f}s)" - ) - return schemas.DialecticResponse(content=response[0]["text"]) + else: + logger.debug("Calling Dialectic with non-streaming") + query_start_time = asyncio.get_event_loop().time() + response = await dialectic_call(final_query, user_representation, chat_history) + query_time = asyncio.get_event_loop().time() - query_start_time + logger.debug(f"Dialectic response received in {query_time:.2f}s") + return response async def get_long_term_facts( @@ -344,67 +259,23 @@ async def run_tom_inference(chat_history: str, session_id: str) -> str: return prediction -async def generate_semantic_queries(query: str) -> list[str]: - """ - Generate multiple semantically relevant queries based on the original query using LLM. - This helps retrieve more diverse and relevant facts from the vector store. +@with_langfuse() +@llm.call(provider="groq", model="llama-3.1-8b-instant", response_model=list[str]) +async def generate_semantic_queries(query: str): + return c( + f""" + Given this query about a user, generate 3 focused search queries that would help retrieve relevant facts about the user. + Each query should focus on a specific aspect related to the original query, rephrased to maximize semantic search effectiveness. + For example, if the original query asks "what does the user like to eat?", generated queries might include "user's food preferences", "user's favorite cuisine", etc. + + Format your response as a list of strings, with each string being a search query. + Example: + ["some query about interests", "some query about personality", "some query about experiences"] - Args: - query: The original dialectic query - - Returns: - A list of semantically relevant queries - """ - logger.debug(f"Generating semantic queries from: {query}") - query_start = asyncio.get_event_loop().time() - - logger.debug("Calling LLM for query generation") - llm_start = asyncio.get_event_loop().time() - - # Create a new model client - client = ModelClient( - provider=DEF_QUERY_GENERATION_PROVIDER, model=DEF_QUERY_GENERATION_MODEL + {query} + """ ) - # Prepare the messages for Anthropic - messages: list[dict[str, Any]] = [{"role": "user", "content": query}] - - # Generate the response - try: - result = await client.generate( - messages=messages, - system=QUERY_GENERATION_SYSTEM, - max_tokens=1000, - use_caching=True, # Likely not caching because the system prompt is under 1000 tokens - ) - llm_time = asyncio.get_event_loop().time() - llm_start - logger.debug(f"LLM response received in {llm_time:.2f}s: {result[:100]}...") - - # Parse the JSON response to get a list of queries - try: - queries = json.loads(result) - if not isinstance(queries, list): - # Fallback if response is not a valid list - logger.debug("LLM response not a list, using as single query") - queries = [result] - except json.JSONDecodeError: - # Fallback if response is not valid JSON - logger.debug("Failed to parse JSON response, using raw response as query") - queries = [query] # Fall back to the original query - - # Ensure we always include the original query - if query not in queries: - logger.debug("Adding original query to results") - queries.append(query) - - total_time = asyncio.get_event_loop().time() - query_start - logger.debug(f"Generated {len(queries)} queries in {total_time:.2f}s") - - return queries - except Exception as e: - logger.error(f"Error during API call: {str(e)}") - raise - async def generate_user_representation( app_id: str, @@ -466,10 +337,6 @@ async def generate_user_representation( logger.debug(f"get_user_representation completed in {gen_time:.2f}s") # Extract the representation from the response - representation = parse_xml_content( - user_representation_response, "representation" - ) - logger.debug(f"Extracted representation: {len(representation)} characters") else: representation = f""" PREDICTION ABOUT THE USER'S CURRENT MENTAL STATE: diff --git a/src/deriver/consumer.py b/src/deriver/consumer.py index cc6525d0..ffb0913d 100644 --- a/src/deriver/consumer.py +++ b/src/deriver/consumer.py @@ -108,9 +108,10 @@ async def process_user_message( # Extract facts from chat history logger.debug("Extracting facts from chat history") extract_start = os.times()[4] - facts = await extract_facts_long_term(chat_history_str) + fact_extraction = await extract_facts_long_term(chat_history_str) + facts = fact_extraction.facts extract_time = os.times()[4] - extract_start - console.print(f"Extracted Facts: {facts}", style="bright_blue") + console.print(f"Extracted Facts: {fact_extraction.facts}", style="bright_blue") logger.debug(f"Extracted {len(facts)} facts in {extract_time:.2f}s") # Save the facts to the collection diff --git a/src/deriver/tom/long_term.py b/src/deriver/tom/long_term.py index a9a7f2c5..c5a356d0 100644 --- a/src/deriver/tom/long_term.py +++ b/src/deriver/tom/long_term.py @@ -1,213 +1,156 @@ -import json import logging -import time from typing import Optional +from pydantic import BaseModel -from langfuse.decorators import observe from sentry_sdk.ai.monitoring import ai_track -from src.utils import parse_xml_content -from src.utils.model_client import ModelClient, ModelProvider +from mirascope import llm +from mirascope.integrations.langfuse import with_langfuse +from inspect import cleandoc as c # Configure logging logger = logging.getLogger(__name__) -# Constants for fact extraction -FACT_EXTRACTION_PROVIDER = ModelProvider.GEMINI -FACT_EXTRACTION_MODEL = "gemini-2.0-flash-lite" -USER_REPRESENTATION_PROVIDER = ModelProvider.GROQ -USER_REPRESENTATION_MODEL = "llama-3.3-70b-versatile" +class PotentialSurprise(BaseModel): + content: str + reason: str + confidence_level: float -MAX_FACT_DISTANCE = 0.85 + +class UserRepresentation(BaseModel): + current_state: str + tentative_patterns: list[str] + knowledge_gaps: list[str] + expectation_violations: list[PotentialSurprise] + updates: list[str] @ai_track("User Representation") -@observe() +@with_langfuse() +@llm.call( + provider="groq", model="llama-3.3-70b-versatile", response_model=UserRepresentation +) async def get_user_representation_long_term( chat_history: str, session_id: str, user_representation: str = "None", tom_inference: str = "None", facts: Optional[list[str]] = None, -) -> str: - if facts is None: - facts = [] - facts_str = "\n".join([f"- {fact}" for fact in facts]) - logger.debug(f"Facts: {facts_str}") +): + return c( + f""" + You are a system for maintaining factual user representations based on conversation history and theory of mind analysis. - system_prompt = """You are a system for maintaining factual user representations based on conversation history and theory of mind analysis. + Your job is to update the existing user representation (if provided) with the new information from the conversation history and theory of mind analysis. -Your job is to update the existing user representation (if provided) with the new information from the conversation history and theory of mind analysis. + REQUIREMENTS: + 1. Distinguish between temporary states and persistent patterns + 2. Only incorporate verified information into core profile + 3. Track certainty levels for all information + 4. Maintain areas of uncertainty explicitly + 5. Update representation incrementally + 6. DO NOT generate persistent information - it will be injected separately. -REQUIREMENTS: -1. Distinguish between temporary states and persistent patterns -2. Only incorporate verified information into core profile -3. Track certainty levels for all information -4. Maintain areas of uncertainty explicitly -5. Update representation incrementally -6. DO NOT generate persistent information - it will be injected separately. Always include the tag in your response in order to inject the facts. -OUTPUT FORMAT: - -CURRENT STATE: -- Active Context: Current situation/activity -- Temporary Conditions: Immediate circumstances - -- Present Mood/Activity: What user is doing right now + OUTPUT FORMAT: + current_state: str + - Active Context: Current situation/activity + - Temporary Conditions: Immediate circumstances + - Present Mood/Activity: What user is doing right now + tentative_patterns: list[str] + - Possible Traits: Mark confidence (Low/Medium/High) + - Potential Interests: Need more evidence + - Speculative Elements: Clearly marked as unconfirmed + knowledge_gaps: list[str] + - List key missing information + - Note areas needing clarification + expectation_violations: list + content: str + reason: str + confidence_level: float + - Based on the above information, if the next message were to surprise you, what could it contain? + - Include 3-5 possible surprises + updates: list[str] + - New Information: Recent observations + - Changes: Modified interpretations + - Removals: Information no longer supported - + CONVERSATION: + {chat_history} -TENTATIVE PATTERNS: -- Possible Traits: Mark confidence (Low/Medium/High) -- Potential Interests: Need more evidence -- Speculative Elements: Clearly marked as unconfirmed + PREDICTION OF USER MENTAL STATE - MIGHT BE INCORRECT: + {tom_inference or "Doesn't exist"} -KNOWLEDGE GAPS: -- List key missing information -- Note areas needing clarification - -EXPECTATION VIOLATIONS: -- Based on the above information, if the next message were to surprise you, what could it contain? -- Format: "POTENTIAL SURPRISE: [possible content] [reason] [confidence level]" -- Include 3-5 possible surprises - -UPDATES: -- New Information: Recent observations -- Changes: Modified interpretations -- Removals: Information no longer supported - -""" - - # Build the context message - context_str = f"CONVERSATION:\n{chat_history}\n\n" - if tom_inference != "None": - context_str += f"PREDICTION OF USER MENTAL STATE - MIGHT BE INCORRECT:\n{tom_inference}\n\n" - if user_representation != "None": - context_str += f"EXISTING USER REPRESENTATION - INCOMPLETE, TO BE UPDATED:\n{user_representation}" - - messages = [ - { - "role": "user", - "content": f"Please analyze this information and provide an updated user representation. DO NOT generate persistent information - it will be injected separately:\n{context_str}", - } - ] - - # Create a new model client - client = ModelClient( - provider=USER_REPRESENTATION_PROVIDER, model=USER_REPRESENTATION_MODEL + EXISTING USER REPRESENTATION - INCOMPLETE, TO BE UPDATED: + {user_representation or "Doesn't exist"} + """ ) - # Generate the response with caching enabled - response = await client.generate( - messages=messages, - system=system_prompt, - max_tokens=1000, - temperature=0, - use_caching=True, # Enable caching for the system prompt - ) - # Inject the facts into the response - persistent_info = f"""PERSISTENT INFORMATION: -{facts_str}""" +class InformationPiece(BaseModel): + quote: str + category: str + explanation: str + semantic_retrieval: str - return response.replace("", persistent_info) + +class InformationExtraction(BaseModel): + pieces: list[InformationPiece] + challenge: str + + +class FactExtraction(BaseModel): + information_extraction: InformationExtraction + facts: list[str] @ai_track("Fact Extraction") -@observe() -async def extract_facts_long_term(chat_history: str) -> list[str]: - logger.debug("Starting fact extraction from chat history") - extract_start = time.time() +@with_langfuse() +@llm.call( + provider="google", model="gemini-2.0-flash-lite", response_model=FactExtraction +) +async def extract_facts_long_term(chat_history: str): + return c( + f""" + You are an AI assistant specialized in extracting and formatting relevant information about users from conversations. Your task is to analyze a given conversation and create a list of concise, factual statements about the user. These statements will be stored in a vector embedding database to enhance future interactions. - system_prompt = """ - You are an AI assistant specialized in extracting and formatting relevant information about users from conversations. Your task is to analyze a given conversation and create a list of concise, factual statements about the user. These statements will be stored in a vector embedding database to enhance future interactions. + Here is the conversation you need to analyze: -Here is the conversation you need to analyze: + + {chat_history} + - -{chat_history} - + Instructions: -Instructions: + 1. Carefully read through the conversation. Extract only new facts, from only the last message sent by the user - treat the rest of the conversation only as context. Ignore facts in the last message that are already stated in the conversation. -1. Carefully read through the conversation. Extract only new facts, from only the last message sent by the user - treat the rest of the conversation only as context. Ignore facts in the last message that are already stated in the conversation. + 2. Identify key new pieces of information from the last message sent by the user that would be valuable for future interactions. Look for: + - Personal details (name, age, occupation, location, etc.) + - Preferences (likes, dislikes, interests, hobbies) + - Experiences (travel, education, work history) + - Expressive style (writing style, tone, etc.) + - Relationships (family, friends, pets) + - Goals or aspirations + - Challenges or problems they're facing + - Opinions or beliefs -2. Identify key new pieces of information from the last message sent by the user that would be valuable for future interactions. Look for: - - Personal details (name, age, occupation, location, etc.) - - Preferences (likes, dislikes, interests, hobbies) - - Experiences (travel, education, work history) - - Expressive style (writing style, tone, etc.) - - Relationships (family, friends, pets) - - Goals or aspirations - - Challenges or problems they're facing - - Opinions or beliefs + 3. For each piece of information you identify: + a. Verify that it is factual and explicitly stated in the conversation, not inferred. + b. Formulate it as a concise statement that would aid in semantic retrieval. + c. Ensure it is not similar to information previously stated in the conversation. -3. For each piece of information you identify: - a. Verify that it is factual and explicitly stated in the conversation, not inferred. - b. Formulate it as a concise statement that would aid in semantic retrieval. - c. Ensure it is not similar to information previously stated in the conversation. + 4. Before providing your final output, wrap your analysis in information_extraction. In this analysis: + - List each piece of information you've identified. + - For each piece of information: + * Quote the relevant part of the conversation. + * Categorize the information (e.g., personal detail, preference, experience). + * Explain why you've included this information. + * Show how you've formulated the fact for optimal semantic retrieval. + - Discuss any challenges you encountered in extracting or formatting the information. -4. Before providing your final output, wrap your analysis in tags. In this analysis: - - List each piece of information you've identified. - - For each piece of information: - * Quote the relevant part of the conversation. - * Categorize the information (e.g., personal detail, preference, experience). - * Explain why you've included this information. - * Show how you've formulated the fact for optimal semantic retrieval. - - Discuss any challenges you encountered in extracting or formatting the information. - -5. After your analysis, provide your final output as a JSON array of strings. Each string should be a single fact about the user. Wrap the facts in tags. - -Example of the expected output format: - -[Analysis goes here] - - -{{ -"facts": -[ - "User is 28 years old", - "User's friend Mary works as a software engineer", - "Favorite food is sushi" -] -}} - - -Remember to focus on clear, concise statements that capture key information about the user. Each fact should be worded in a way that will aid its semantic retrieval from a vector embedding database. It's OK for this section to be quite long. - """ - message = system_prompt.format(chat_history=chat_history) - messages = [{"role": "user", "content": message}] - - logger.debug("Calling LLM for fact extraction") - llm_start = time.time() - - # Create a new model client - client = ModelClient(provider=FACT_EXTRACTION_PROVIDER, model=FACT_EXTRACTION_MODEL) - - # Generate the response with caching enabled - response = await client.generate( - messages=messages, - max_tokens=1000, - temperature=0.0, - use_caching=True, # Enable caching for the system prompt + 5. After your analysis, provide your final output as a list of strings. Each string should be a single fact about the user. + + Remember to focus on clear, concise statements that capture key information about the user. Each fact should be worded in a way that will aid its semantic retrieval from a vector embedding database. + """ ) - - llm_time = time.time() - llm_start - logger.debug(f"LLM response received in {llm_time:.2f}s") - - try: - logger.debug("Parsing JSON response") - facts_str = parse_xml_content(response, "facts") - response_data = json.loads(facts_str) - facts = response_data["facts"] - logger.debug(f"Extracted {len(facts)} facts") - if facts: - logger.debug(f"Sample facts: {facts[:3] if len(facts) > 3 else facts}") - except (json.JSONDecodeError, KeyError) as e: - logger.error(f"Error parsing response: {str(e)}") - facts = [] - - total_time = time.time() - extract_start - logger.debug(f"Total extraction completed in {total_time:.2f}s") - return facts diff --git a/src/deriver/tom/single_prompt.py b/src/deriver/tom/single_prompt.py index 3696eb5e..8bc6c6eb 100644 --- a/src/deriver/tom/single_prompt.py +++ b/src/deriver/tom/single_prompt.py @@ -1,52 +1,27 @@ import logging -from typing import Any, Optional +from typing import Optional +from inspect import cleandoc as c +from enum import Enum +from pydantic import BaseModel -import sentry_sdk -from langfuse.decorators import langfuse_context, observe -from sentry_sdk.ai.monitoring import ai_track +from mirascope import llm +from mirascope.integrations.langfuse import with_langfuse -from src.utils.model_client import ModelClient, ModelProvider logger = logging.getLogger(__name__) -DEF_PROVIDER = ModelProvider.GROQ -DEF_MODEL = "llama-3.3-70b-versatile" -TOM_SYSTEM_PROMPT = """You are a system for analyzing conversations to make evidence-based inferences about user mental states. +# Enums for strongly typed fields +class InfoType(str, Enum): + STYLE = "STYLE" + STATEMENT = "STATEMENT" -REQUIREMENTS: -1. Only make inferences that are directly supported by conversation evidence -2. For each inference, cite the specific message that supports it -3. Use uncertainty qualifiers (may, might, possibly) for speculative inferences -4. Do not make assumptions about demographics unless explicitly stated -5. Focus on current mental state and immediate context -6. Consider your own knowledge gaps and violations of expectations (what would surprise you) -7. Always wrap your prediction in tags. -OUTPUT FORMAT: - -CURRENT STATE: -- Immediate Context: User's current situation -- Active Goals: What user is trying to achieve -- Present Mood: Observable emotional state +class CertaintyLevel(str, Enum): + LIKELY = "LIKELY" + POTENTIAL = "POTENTIAL" + SPECULATIVE = "SPECULATIVE" -SUPPORTED OBSERVATIONS: -- List only behaviors/preferences with direct evidence -- Format: "OBSERVATION: [detail] (SOURCE: [exact message])" - -TENTATIVE INFERENCES: -- List possible but uncertain interpretations -- Format: "POSSIBLE: [interpretation] (BASIS: [supporting message])" - -KNOWLEDGE GAPS: -- List important unknown information -- Format: "UNKNOWN: [topic/question]" - -EXPECTATION VIOLATIONS: -- Based on the above information, if the next message were to surprise you, what could it contain? -- Format: "POTENTIAL SURPRISE: [possible content] [reason] [confidence level]" -- Include 3-5 possible surprises -""" USER_REPRESENTATION_SYSTEM_PROMPT = """You are a system for maintaining factual user representations based on conversation history and theory of mind analysis. @@ -101,98 +76,224 @@ UPDATES: """ -@ai_track("Tom Inference") -@observe() +class CurrentState(BaseModel): + immediate_context: str + active_goals: str + present_mood: str + + +class SupportedObservation(BaseModel): + detail: str + source: str + + +class TentativeInference(BaseModel): + interpretation: str + basis: str + + +class KnowledgeGap(BaseModel): + topic: str + + +class ExpectationViolation(BaseModel): + possible_surprise: str + reason: str + confidence_level: float + + +class TomInferenceOutput(BaseModel): + current_state: CurrentState + tentative_inferences: list[TentativeInference] + knowledge_gaps: list[KnowledgeGap] + expectation_violations: list[ExpectationViolation] + + +# User Representation Output Models +class SourcedInfo(BaseModel): + detail: str + source: str + + +class UserCurrentState(BaseModel): + active_context: SourcedInfo + temporary_conditions: SourcedInfo + present_mood_activity: SourcedInfo + + +class PersistentInfo(BaseModel): + detail: str + source: str + info_type: InfoType + + +class TentativePattern(BaseModel): + pattern: str + source: str + certainty_level: CertaintyLevel + + +class UserKnowledgeGap(BaseModel): + missing_info: str + + +class UserExpectationViolation(BaseModel): + potential_surprise: str + reason: str + confidence_level: float + + +class UpdateSection(BaseModel): + new_information: list[SourcedInfo] + changes: list[SourcedInfo] + removals: list[SourcedInfo] + + +class UserRepresentationOutput(BaseModel): + current_state: UserCurrentState + persistent_information: list[PersistentInfo] + tentative_patterns: list[TentativePattern] + knowledge_gaps: list[UserKnowledgeGap] + expectation_violations: list[UserExpectationViolation] + updates: UpdateSection + + +@with_langfuse() +@llm.call( + provider="groq", model="llama-3.3-70b-versatile", response_model=TomInferenceOutput +) +async def tom_inference( + chat_history: str, + user_representation: Optional[str] = None, +): + return c( + f""" + You are a system for analyzing conversations to make evidence-based inferences about user mental states. + + REQUIREMENTS: + 1. Only make inferences that are directly supported by conversation evidence + 2. For each inference, cite the specific message that supports it + 3. Use uncertainty qualifiers (may, might, possibly) for speculative inferences + 4. Do not make assumptions about demographics unless explicitly stated + 5. Focus on current mental state and immediate context + 6. Consider your own knowledge gaps and violations of expectations (what would surprise you) + 7. Always wrap your prediction in tags. + + OUTPUT FORMAT: + current_state: + - immediate_context: User's current situation + - active_goals: What user is trying to achieve + - present_mood: Observable emotional state + + tentative_inferences: list of objects with: + - interpretation: Possible but uncertain interpretation + - basis: Supporting message or evidence + + knowledge_gaps: list of objects with: + - topic: Important unknown information or question + + expectation_violations: list of objects with: + - possible_surprise: What content could surprise you in the next message + - reason: Why this would be surprising based on current information + - confidence_level: Float between 0.0 and 1.0 indicating confidence + - Include 3-5 possible surprises + + + {chat_history or "Not provided"} + + + + {user_representation or "Not provided"} + + """ + ) + + async def get_tom_inference_single_prompt( chat_history: str, - session_id: str, user_representation: Optional[str] = None, - **kwargs, -) -> str: - with sentry_sdk.start_transaction(op="tom-inference", name="ToM Inference"): - # Create a new model client - client = ModelClient(provider=DEF_PROVIDER, model=DEF_MODEL) - - # Prepare the messages - messages: list[dict[str, Any]] = [ - { - "role": "user", - "content": f"Please analyze this conversation and provide a prediction following the format above:\n{chat_history}", - } - ] - - # Add existing user representation if available - if user_representation: - messages.append( - { - "role": "user", - "content": f"Consider this existing user representation for context, but focus on current state:\n{user_representation}", - } - ) - - langfuse_context.update_current_observation(input=messages, model=DEF_MODEL) - - # Generate the response with caching enabled - try: - response = await client.generate( - messages=messages, - system=TOM_SYSTEM_PROMPT, - max_tokens=1000, - temperature=0, - use_caching=True, # Enable caching for the system prompt - ) - except Exception as e: - sentry_sdk.capture_exception(e) - logger.error(f"Error generating Tom inference: {e}") - raise e - - return response +): + inference = await tom_inference(chat_history, user_representation) + return inference.model_dump_json() -@ai_track("User Representation") -@observe() -async def get_user_representation_single_prompt( +@with_langfuse() +@llm.call( + provider="groq", + model="llama-3.3-70b-versatile", + response_model=UserRepresentationOutput, +) +async def user_representation_inference( chat_history: str, - session_id: str, user_representation: Optional[str] = None, tom_inference: Optional[str] = None, - **kwargs, -) -> str: - with sentry_sdk.start_transaction( - op="user-representation-inference", name="User Representation" - ): - # Create a new model client - client = ModelClient(provider=DEF_PROVIDER, model=DEF_MODEL) +): + return c( + f""" + You are a system for maintaining factual user representations based on conversation history and theory of mind analysis. - # Build the context message - context_str = f"CONVERSATION:\n{chat_history}\n\n" - if tom_inference: - context_str += f"PREDICTION OF USER MENTAL STATE - MIGHT BE INCORRECT:\n{tom_inference}\n\n" - if user_representation: - context_str += f"EXISTING USER REPRESENTATION - INCOMPLETE, TO BE UPDATED:\n{user_representation}" + Your job is to update the existing user representation (if provided) with the new information from the conversation history and theory of mind analysis. - # Prepare the messages - messages: list[dict[str, Any]] = [ - { - "role": "user", - "content": f"Please analyze this information and provide an updated user representation:\n{context_str}", - } - ] + Copy over information as-is from the existing user representation. Add new information as needed. Only remove content from this section if new information contradicts it. This is especially important for Persistent Information and Tentative Patterns. - langfuse_context.update_current_observation(input=messages, model=DEF_MODEL) + REQUIREMENTS: + 1. Distinguish between temporary states and persistent patterns + 2. Only incorporate verified information into core profile + 3. Track certainty levels for all information + 4. Maintain areas of uncertainty explicitly + 5. Update representation incrementally - # Generate the response with caching enabled - try: - response = await client.generate( - messages=messages, - system=USER_REPRESENTATION_SYSTEM_PROMPT, - max_tokens=1000, - temperature=0, - use_caching=True, # Enable caching for the system prompt - ) - except Exception as e: - sentry_sdk.capture_exception(e) - logger.error(f"Error generating user representation: {e}") - raise e + OUTPUT FORMAT: + current_state: + - active_context: object with "detail" (current situation/activity/location) and "source" (exact message) + - temporary_conditions: object with "detail" (immediate circumstances) and "source" (exact message) + - present_mood_activity: object with "detail" (what user is doing right now) and "source" (exact message) - return response + persistent_information: list of objects with: + - detail: The specific information or pattern + - source: Exact message that supports this + - info_type: Must be exactly "STYLE" for communication patterns or "STATEMENT" for explicit facts + + tentative_patterns: list of objects with: + - pattern: The observed pattern + - source: Supporting evidence from specific message + - certainty_level: Must be exactly "LIKELY" (almost certain), "POTENTIAL" (possible), or "SPECULATIVE" (uncertain) + + knowledge_gaps: list of objects with: + - missing_info: Key information that is missing or needs clarification + + expectation_violations: list of objects with: + - potential_surprise: What could surprise you in the next message + - reason: Why this would be surprising based on current information + - confidence_level: Float between 0.0 and 1.0 + - Include 3-5 possible surprises + + updates: + - new_information: List of objects with "detail" (recent observation) and "source" (supporting message) + - changes: List of objects with "detail" (modified interpretation) and "source" (supporting message) + - removals: List of objects with "detail" (information no longer supported) and "source" (contradicting message) + + + {chat_history or "Not provided"} + + + + {user_representation or "Not provided"} + + + + {tom_inference or "Not provided"} + + """ + ) + + +async def get_user_representation_single_prompt( + chat_history: str, + user_representation: Optional[str] = None, + tom_inference: Optional[str] = None, +): + representation = await user_representation_inference( + chat_history, user_representation, tom_inference + ) + return representation.model_dump_json() diff --git a/src/routers/sessions.py b/src/routers/sessions.py index b7108251..ba5481b7 100644 --- a/src/routers/sessions.py +++ b/src/routers/sessions.py @@ -1,7 +1,7 @@ import logging from typing import Optional -from anthropic import AsyncMessageStreamManager +from mirascope.llm import Stream from fastapi import APIRouter, Body, Depends, Path, Query from fastapi.exceptions import HTTPException from fastapi.responses import StreamingResponse @@ -94,11 +94,11 @@ async def get_sessions( is_active_param = False # Default to None, meaning no filter on is_active if options: - if hasattr(options, 'filter') and options.filter: + if hasattr(options, "filter") and options.filter: filter_param = options.filter - if filter_param == {}: # Explicitly check for empty dict + if filter_param == {}: # Explicitly check for empty dict filter_param = None - if hasattr(options, 'is_active'): # Check if is_active is present + if hasattr(options, "is_active"): # Check if is_active is present is_active_param = options.is_active return await paginate( @@ -217,15 +217,17 @@ async def chat( ..., description="Dialectic Endpoint Parameters" ), ): - """Chat with the Dialectic API""" if not options.stream: - return await agent.chat( + response = await agent.chat( app_id=app_id, user_id=user_id, session_id=session_id, queries=options.queries, ) + return schemas.DialecticResponse( + content=response.content, + ) else: async def parse_stream(): @@ -237,10 +239,9 @@ async def chat( queries=options.queries, stream=True, ) - if type(stream) is AsyncMessageStreamManager: - async with stream as stream_manager: - async for text in stream_manager.text_stream: - yield text + if type(stream) is Stream: + async for chunk, _ in stream: + yield chunk.content except Exception as e: logger.error(f"Error in stream: {str(e)}") raise HTTPException(status_code=500, detail=str(e)) from e diff --git a/src/utils/history.py b/src/utils/history.py index 6cec266f..2cb396b3 100644 --- a/src/utils/history.py +++ b/src/utils/history.py @@ -4,8 +4,11 @@ from typing import Optional, Union, cast from sqlalchemy import select from sqlalchemy.ext.asyncio import AsyncSession +from inspect import cleandoc as c + +from mirascope import llm +from mirascope.integrations.langfuse import with_langfuse -from src.utils.model_client import ModelClient, ModelProvider from .. import models @@ -37,11 +40,6 @@ class SummaryType(Enum): LONG = "honcho_chat_summary_long" -# Default model settings for summary generation -DEFAULT_PROVIDER = ModelProvider.GEMINI -DEFAULT_MODEL = "gemini-2.0-flash-lite" - - async def get_session_summaries( db: AsyncSession, session_id: str, @@ -123,99 +121,93 @@ async def get_messages_since_message( return list(result.scalars().all()) +@with_langfuse() +@llm.call( + provider="google", + model="gemini-2.0-flash-lite", + call_params={"max_tokens": 1000}, +) +async def create_short_summary( + messages: list[models.Message], + previous_summary: Optional[str] = None, +): + return c( + f""" + You are a system that summarizes parts of a conversation to create a concise and accurate summary. + Focus on capturing: + 1. Key facts and information shared + 2. User preferences, opinions, and questions + 3. Important context and requests + 4. Core topics discussed + 5. User's apparent emotional state + + It is very important that you clearly distinguish between the user's messages and the assistant's messages, and that only the user's literal words are attributed to them. + + Provide a concise, factual summary that captures the essence of the conversation. + Your summary should be detailed enough to serve as context for future messages, + but brief enough to be helpful. + + Return only the summary without any explanation or meta-commentary. + + + {messages} + + + + {previous_summary} + + """ + ) + + +@with_langfuse() +@llm.call( + provider="google", + model="gemini-2.0-flash-lite", + call_params={"max_tokens": 2000}, +) +async def create_long_summary( + messages: list[models.Message], + previous_summary: Optional[str] = None, +): + return c( + f""" + You are a system that creates comprehensive summaries of conversations. + Focus on capturing: + 1. Key facts and information shared + 2. User preferences, opinions, and questions + 3. Important context and requests + 4. Core topics discussed in detail + 5. User's apparent emotional state and personality traits + 6. Important themes and patterns across the conversation + + It is very important that you clearly distinguish between the user's messages and the assistant's messages, and that only the user's literal words are attributed to them. + + Provide a thorough and detailed summary that captures the essence of the conversation. + Your summary should serve as a comprehensive record of the important information in this conversation. + + Return only the summary without any explanation or meta-commentary. + + + {messages} + + + + {previous_summary} + + """ + ) + + async def create_summary( messages: list[models.Message], previous_summary: Optional[str] = None, summary_type: SummaryType = SummaryType.SHORT, -) -> str: - """ - Generate a summary of the provided messages using an LLM. - - Args: - messages: List of messages to summarize - previous_summary: Optional previous summary to provide context - summary_type: Type of summary to create ("short" or "long") - - Returns: - A summary of the conversation - """ - # Combine messages into a conversation format - conversation = "\n".join( - [ - f"{'human' if msg.is_user else 'assistant'}: {msg.content}" - for msg in messages - ] - ) - - # Adjust system prompt based on summary type - if summary_type == SummaryType.LONG: - system_prompt = """You are a system that creates comprehensive summaries of conversations. -Focus on capturing: -1. Key facts and information shared -2. User preferences, opinions, and questions -3. Important context and requests -4. Core topics discussed in detail -5. User's apparent emotional state and personality traits -6. Important themes and patterns across the conversation - -It is very important that you clearly distinguish between the user's messages and the assistant's messages, and that only the user's literal words are attributed to them. - -Provide a thorough and detailed summary that captures the essence of the conversation. -Your summary should serve as a comprehensive record of the important information in this conversation. - -Return only the summary without any explanation or meta-commentary.""" - else: # short summary - system_prompt = """You are a system that summarizes parts of a conversation to create a concise and accurate summary. -Focus on capturing: -1. Key facts and information shared -2. User preferences, opinions, and questions -3. Important context and requests -4. Core topics discussed -5. User's apparent emotional state - -It is very important that you clearly distinguish between the user's messages and the assistant's messages, and that only the user's literal words are attributed to them. - -Provide a concise, factual summary that captures the essence of the conversation. -Your summary should be detailed enough to serve as context for future messages, -but brief enough to be helpful. - -Return only the summary without any explanation or meta-commentary.""" - - # Include previous summary if available - if previous_summary: - user_prompt = f"""Here is a previous summary of the conversation: -{previous_summary} -Now please summarize these additional messages, incorporating the context from the previous summary. - -Your summary should summarize the entire conversation in a self-contained way, such that someone could read it and understand the entire conversation. -{conversation} -Provide a {"comprehensive" if summary_type == SummaryType.LONG else "concise"} summary that captures both the previous context and the new information.""" - else: - user_prompt = f"""Please summarize the following conversation segment: -{conversation} -Provide a {"comprehensive" if summary_type == SummaryType.LONG else "concise"} summary that captures the key points and context.""" - - # Create a model client - client = ModelClient(provider=DEFAULT_PROVIDER, model=DEFAULT_MODEL) - - # Generate the summary - llm_messages = [{"role": "user", "content": user_prompt}] - - try: - summary = await client.generate( - messages=llm_messages, - system=system_prompt, - max_tokens=1000 - if summary_type == SummaryType.SHORT - else 2000, # Allow longer responses for long summaries - temperature=0.0, - use_caching=True, - ) - return summary - except Exception as e: - logger.error(f"Error generating summary: {str(e)}") - # Fallback to a basic summary in case of error - return f"Conversation with {len(messages)} messages about {messages[-1].content[:30]}..." +): + if summary_type == SummaryType.SHORT: + return await create_short_summary(messages, previous_summary) + elif summary_type == SummaryType.LONG: + return await create_long_summary(messages, previous_summary) async def save_summary_metamessage( diff --git a/src/utils/model_client.py b/src/utils/model_client.py deleted file mode 100644 index 8c98a800..00000000 --- a/src/utils/model_client.py +++ /dev/null @@ -1,541 +0,0 @@ -""" -Utility functions for interacting with various language model APIs. -""" - -import os -from enum import Enum -from typing import Any, Optional, Protocol - -import sentry_sdk -from anthropic import AsyncAnthropic -from dotenv import load_dotenv -from google import genai -from google.genai import types as genai_types -from langfuse.decorators import langfuse_context, observe - -# from openai import AsyncOpenAI -from langfuse.openai import AsyncOpenAI - -# Load environment variables -load_dotenv() - - -# Supported model providers -class ModelProvider(str, Enum): - ANTHROPIC = "anthropic" - OPENAI = "openai" - OPENROUTER = "openrouter" - CEREBRAS = "cerebras" - GROQ = "groq" - GEMINI = "gemini" - # Add other providers as needed - - -# Default models for each provider -DEFAULT_MODELS = { - ModelProvider.ANTHROPIC: "claude-3-7-sonnet-20250219", - ModelProvider.OPENAI: "gpt-4o", - ModelProvider.OPENROUTER: "meta-llama/Llama-3.3-70B-Instruct", - ModelProvider.CEREBRAS: "llama-3.3-70b", - ModelProvider.GROQ: "llama-3.3-70b-versatile", - ModelProvider.GEMINI: "gemini-2.0-flash-lite", -} - -OPENAI_COMPATIBLE_PROVIDERS = [ - ModelProvider.OPENAI, - ModelProvider.OPENROUTER, - ModelProvider.CEREBRAS, - ModelProvider.GROQ, -] - -DEFAULT_TEMPERATURE = 0.0 -DEFAULT_MAX_TOKENS = 1000 - - -class Message(Protocol): - """Protocol for a message that works with any provider.""" - - role: str - content: str - - -class ModelClient: - """A client for interacting with various language model APIs.""" - - def __init__( - self, - provider: ModelProvider = ModelProvider.ANTHROPIC, - model: Optional[str] = None, - api_key: Optional[str] = None, - base_url: Optional[str] = None, - ): - """ - Initialize the model client. - - Args: - provider: The model provider to use - model: The specific model to use, or None to use the default model for the provider - api_key: The API key to use, or None to read from environment variables - base_url: Custom base URL for the API endpoints (used for OpenRouter) - """ - self.provider = provider - self.model = model or DEFAULT_MODELS[provider] - self.base_url = base_url - self.openai_client = None - self.gemini_client = None - - # Setup provider-specific clients - if provider == ModelProvider.ANTHROPIC: - self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY") - if not self.api_key: - raise ValueError("Anthropic API key is required") - self.client = AsyncAnthropic(api_key=self.api_key) - elif provider in OPENAI_COMPATIBLE_PROVIDERS: - self.api_key = api_key or os.getenv("OPENAI_COMPATIBLE_API_KEY") - self.base_url = base_url or os.getenv("OPENAI_COMPATIBLE_BASE_URL") - if not self.api_key: - raise ValueError("OpenAI-compatible API key is required") - self.openai_client = AsyncOpenAI( - api_key=self.api_key, base_url=self.base_url - ) - elif provider == ModelProvider.GEMINI: - self.api_key = api_key or os.getenv("GEMINI_API_KEY") - if not self.api_key: - raise ValueError("Gemini API key is required") - self.gemini_client = genai.Client(api_key=self.api_key) - else: - raise ValueError(f"Unsupported provider: {provider}") - - def create_message(self, role: str, content: str) -> dict[str, str]: - """ - Create a message that works with the current provider. - - Args: - role: The role of the message (e.g., "user", "assistant") - content: The message content - - Returns: - A message compatible with the current provider - """ - # For now, just return a dictionary that works with both Anthropic and OpenAI - return {"role": role, "content": content} - - @observe() - async def generate( - self, - messages: list[dict[str, Any]], - system: Optional[str] = None, - max_tokens: int = DEFAULT_MAX_TOKENS, - temperature: float = DEFAULT_TEMPERATURE, - extra_headers: Optional[dict[str, str]] = None, - use_caching: bool = False, - ) -> str: - """ - Generate a response using the configured model. - - Args: - messages: The conversation history - system: Optional system prompt - max_tokens: Maximum number of tokens to generate - temperature: Temperature for generation - extra_headers: Optional headers to add to the request - use_caching: Whether to use provider-side caching for the response - - Returns: - The generated text - """ - with sentry_sdk.start_transaction( - op="llm-api", name=f"{self.provider} API Call" - ): - # Log to langfuse - langfuse_context.update_current_observation( - input=messages, model=self.model - ) - - if self.provider == ModelProvider.ANTHROPIC: - return await self._generate_anthropic( - messages, - system, - max_tokens, - temperature, - extra_headers, - use_caching, - ) - elif self.provider in OPENAI_COMPATIBLE_PROVIDERS: - return await self._generate_openai( - messages, system, max_tokens, temperature - ) - elif self.provider == ModelProvider.GEMINI: - return await self._generate_gemini( - messages, system, max_tokens, temperature - ) - else: - raise ValueError(f"Unsupported provider: {self.provider}") - - @observe(as_type="generation") - async def _generate_anthropic( - self, - messages: list[dict[str, Any]], - system: Optional[str] = None, - max_tokens: int = DEFAULT_MAX_TOKENS, - temperature: float = DEFAULT_TEMPERATURE, - extra_headers: Optional[dict[str, str]] = None, - use_caching: bool = False, - ) -> str: - """Generate a response using the Anthropic API.""" - if not self.client: - raise ValueError("Anthropic client not initialized.") - - params = { - "model": self.model, - "messages": messages, - "max_tokens": max_tokens, - "temperature": temperature, - } - - # Handle system prompt with caching if enabled - if system: - if use_caching: - params["system"] = [ - { - "type": "text", - "text": system, - "cache_control": {"type": "ephemeral"}, - } - ] - else: - params["system"] = system - - langfuse_context.update_current_observation(input=messages, model=self.model) - - response = await self.client.messages.create(**params) - - # Extract the text from the response - if response.content and len(response.content) > 0: - content_block = response.content[0] - # Check content_block by checking for attribute 'type' instead of using isinstance - if ( - content_block - and hasattr(content_block, "type") - and content_block.type == "text" - ): - return content_block.text - return str(content_block) - return "" - - @observe(as_type="generation") - async def _generate_openai( - self, - messages: list[dict[str, str]], - system: Optional[str] = None, - max_tokens: int = DEFAULT_MAX_TOKENS, - temperature: float = DEFAULT_TEMPERATURE, - ) -> str: - """Generate text using OpenAI or OpenRouter API.""" - if not self.openai_client: - raise ValueError("OpenAI client not initialized") - - # Prepare messages - formatted_messages = [] - - # Add system message if provided - if system: - formatted_messages.append({"role": "system", "content": system}) - - # Add the rest of the messages - formatted_messages.extend(messages) - - # Make the API call with the OpenAI client - response = await self.openai_client.chat.completions.create( - model=self.model, - messages=formatted_messages, - max_tokens=max_tokens, - temperature=temperature, - ) - - # Extract the generated text - choice = response.choices[0] - if choice and choice.message and choice.message.content: - return choice.message.content - return "" - - @observe() - async def stream( - self, - messages: list[dict[str, Any]], - system: Optional[str] = None, - max_tokens: int = DEFAULT_MAX_TOKENS, - temperature: float = DEFAULT_TEMPERATURE, - extra_headers: Optional[dict[str, str]] = None, - use_caching: bool = False, - ) -> Any: - """ - Stream a response using the configured model. - - Args: - messages: The conversation history - system: Optional system prompt - max_tokens: Maximum number of tokens to generate - temperature: Temperature for generation - extra_headers: Optional headers to add to the request - use_caching: Whether to use provider-side caching for the response - - Returns: - A streaming response from the provider - """ - with sentry_sdk.start_transaction( - op="llm-api-stream", name=f"{self.provider} API Stream" - ): - # Log to langfuse - langfuse_context.update_current_observation( - input=messages, model=self.model - ) - - if self.provider == ModelProvider.ANTHROPIC: - return await self._stream_anthropic( - messages, - system, - max_tokens, - temperature, - extra_headers, - use_caching, - ) - elif self.provider in OPENAI_COMPATIBLE_PROVIDERS: - return await self._stream_openai( - messages, system, max_tokens, temperature - ) - elif self.provider == ModelProvider.GEMINI: - return await self._stream_gemini( - messages, system, max_tokens, temperature - ) - else: - raise ValueError(f"Unsupported provider: {self.provider}") - - @observe(as_type="generation") - async def _stream_anthropic( - self, - messages: list[dict[str, Any]], - system: Optional[str] = None, - max_tokens: int = DEFAULT_MAX_TOKENS, - temperature: float = DEFAULT_TEMPERATURE, - extra_headers: Optional[dict[str, str]] = None, - use_caching: bool = False, - ) -> Any: - """Stream text using Anthropic API.""" - if not self.client: - raise ValueError("Anthropic client not initialized.") - - params = { - "model": self.model, - "messages": messages, - "max_tokens": max_tokens, - "temperature": temperature, - } - - # Handle system prompt with caching if enabled - if system: - if use_caching: - params["system"] = [ - { - "type": "text", - "text": system, - "cache_control": {"type": "ephemeral"}, - } - ] - else: - params["system"] = system - - langfuse_context.update_current_observation(input=messages, model=self.model) - - # Return the stream directly without awaiting it - return self.client.messages.stream(**params) - - @observe(as_type="generation") - async def _stream_openai( - self, - messages: list[dict[str, str]], - system: Optional[str] = None, - max_tokens: int = DEFAULT_MAX_TOKENS, - temperature: float = DEFAULT_TEMPERATURE, - ) -> Any: - """Stream text using OpenAI or OpenRouter API.""" - if not self.openai_client: - raise ValueError("OpenAI client not initialized") - - # Prepare messages - formatted_messages = [] - - # Add system message if provided - if system: - formatted_messages.append({"role": "system", "content": system}) - - # Add the rest of the messages - formatted_messages.extend(messages) - - # Make the API call with the OpenAI client - stream = await self.openai_client.chat.completions.create( - model=self.model, - messages=formatted_messages, - max_tokens=max_tokens, - temperature=temperature, - stream=True, - ) - - return stream - - @observe(as_type="generation") - async def _generate_gemini( - self, - messages: list[dict[str, str]], - system: Optional[str] = None, - max_tokens: int = DEFAULT_MAX_TOKENS, - temperature: float = DEFAULT_TEMPERATURE, - ) -> str: - """Generate text using Gemini API.""" - if not self.gemini_client: - raise ValueError("Gemini client not initialized") - - # Format messages for Gemini - gemini_messages = [] - - # Convert messages to Gemini format - for message in messages: - role = message["role"] - # Map roles to what Gemini expects - if role == "user": - gemini_role = "user" - elif role == "assistant": - gemini_role = "model" - else: - # Skip system messages as they're handled through config - continue - - gemini_messages.append( - genai_types.Content( - role=gemini_role, - parts=[genai_types.Part.from_text(text=message["content"])], - ) - ) - - # Set generation config - generate_content_config = genai_types.GenerateContentConfig( - temperature=temperature, - max_output_tokens=max_tokens, - response_mime_type="text/plain", - ) - - # Add system instruction if provided - if system: - generate_content_config.system_instruction = system - - # Make the API call - if not gemini_messages: - # If we have no messages but have a system prompt, create a default user message - if system: - default_content = genai_types.Content( - role="user", - parts=[ - genai_types.Part.from_text( - text="Please respond based on the system instructions." - ) - ], - ) - # model = self.gemini_client.get_model(self.model) - response = await self.gemini_client.aio.models.generate_content( - model=self.model, - contents=default_content, - config=generate_content_config, - ) - else: - raise ValueError("No messages provided for Gemini generation") - else: - # Normal case with messages - # model = get_model(self.model) - response = await self.gemini_client.aio.models.generate_content( - model=self.model, - contents=gemini_messages - if len(gemini_messages) > 1 - else gemini_messages[0], - config=generate_content_config, - ) - - # Extract text from response - if response and response.text: - return response.text - return "" - - @observe(as_type="generation") - async def _stream_gemini( - self, - messages: list[dict[str, str]], - system: Optional[str] = None, - max_tokens: int = DEFAULT_MAX_TOKENS, - temperature: float = DEFAULT_TEMPERATURE, - ) -> Any: - """Stream text using Gemini API.""" - if not self.gemini_client: - raise ValueError("Gemini client not initialized") - - # Format messages for Gemini - gemini_messages = [] - - # Convert messages to Gemini format - for message in messages: - role = message["role"] - # Map roles to what Gemini expects - if role == "user": - gemini_role = "user" - elif role == "assistant": - gemini_role = "model" - else: - # Skip system messages as they're handled through config - continue - - gemini_messages.append( - genai_types.Content( - role=gemini_role, - parts=[genai_types.Part.from_text(text=message["content"])], - ) - ) - - # Set generation config - generate_content_config = genai_types.GenerateContentConfig( - temperature=temperature, - max_output_tokens=max_tokens, - response_mime_type="text/plain", - ) - - # Add system instruction if provided - if system: - generate_content_config.system_instruction = system - - # Make the streaming API call - if not gemini_messages: - # If we have no messages but have a system prompt, create a default user message - if system: - default_content = genai_types.Content( - role="user", - parts=[ - genai_types.Part.from_text( - text="Please respond based on the system instructions." - ) - ], - ) - stream = await self.gemini_client.aio.models.generate_content_stream( - model=self.model, - contents=default_content, - config=generate_content_config, - ) - else: - raise ValueError("No messages provided for Gemini streaming") - else: - # Normal case with messages - stream = await self.gemini_client.aio.models.generate_content_stream( - model=self.model, - contents=gemini_messages - if len(gemini_messages) > 1 - else gemini_messages[0], - config=generate_content_config, - ) - - return stream diff --git a/tests/utils/test_model_client.py b/tests/utils/test_model_client.py deleted file mode 100644 index 2b51fc50..00000000 --- a/tests/utils/test_model_client.py +++ /dev/null @@ -1,191 +0,0 @@ -import os -from unittest.mock import AsyncMock, MagicMock, patch - -import pytest - -from src.utils.model_client import ( - DEFAULT_MAX_TOKENS, - DEFAULT_MODELS, - DEFAULT_TEMPERATURE, - ModelClient, - ModelProvider, -) - - -@pytest.fixture -def mock_env(monkeypatch): - monkeypatch.setenv("ANTHROPIC_API_KEY", "mock-anthropic-api-key") - monkeypatch.setenv("OPENAI_COMPATIBLE_API_KEY", "mock-openai-api-key") - monkeypatch.setenv("OPENAI_API_KEY", "mock-openai-api-key") - monkeypatch.setenv("GROQ_API_KEY", "mock-groq-api-key") - monkeypatch.setenv("CEREBRAS_API_KEY", "mock-cerebras-api-key") - monkeypatch.setenv("OPENROUTER_API_KEY", "mock-openrouter-api-key") - - -# Test fixtures -@pytest.fixture -def mock_anthropic_response(): - mock_response = MagicMock() - mock_content = MagicMock() - mock_content.type = "text" - mock_content.text = "Test response" - mock_response.content = [mock_content] - return mock_response - - -@pytest.fixture -def mock_openai_response(): - mock_response = MagicMock() - mock_message = MagicMock() - mock_message.content = "Test response" - mock_choice = MagicMock() - mock_choice.message = mock_message - mock_response.choices = [mock_choice] - return mock_response - - -@pytest.fixture -def mock_anthropic_client(): - with patch("src.utils.model_client.AsyncAnthropic") as mock: - client = MagicMock() - client.messages.create = AsyncMock() - client.messages.stream = AsyncMock() - mock.return_value = client - yield client - - -@pytest.fixture -def mock_openai_client(): - with patch("src.utils.model_client.AsyncOpenAI") as mock: - client = MagicMock() - client.chat.completions.create = AsyncMock() - mock.return_value = client - yield client - - -# Initialization Tests -def test_default_initialization(mock_env): - """Test default initialization with Anthropic provider.""" - client = ModelClient() - assert client.provider == ModelProvider.ANTHROPIC - assert client.model == DEFAULT_MODELS[ModelProvider.ANTHROPIC] - assert client.base_url is None - - -def test_custom_model_initialization(): - """Test initialization with custom model name.""" - custom_model = "custom-model" - client = ModelClient(model=custom_model) - assert client.model == custom_model - - -def test_custom_api_key_initialization(): - """Test initialization with custom API key.""" - custom_key = "test-api-key" - client = ModelClient(api_key=custom_key) - assert client.api_key == custom_key - - -def test_custom_base_url_initialization(): - """Test initialization with custom base URL.""" - custom_url = "https://custom-api.example.com" - client = ModelClient(base_url=custom_url) - assert client.base_url == custom_url - - -def test_unsupported_provider_initialization(): - """Test initialization with unsupported provider.""" - with pytest.raises(ValueError, match="is not a valid ModelProvider"): - ModelClient(provider=ModelProvider("unsupported")) - - -def test_missing_api_key_initialization(): - """Test initialization without required API key.""" - with patch.dict(os.environ, {}, clear=True): - with pytest.raises(ValueError, match="API key is required"): - ModelClient() - - -# Message Creation Tests -def test_create_message(): - """Test message creation with different roles.""" - client = ModelClient() - message = client.create_message("user", "Hello") - assert message == {"role": "user", "content": "Hello"} - - -# Generation Tests -@pytest.mark.asyncio -async def test_generate_anthropic(mock_anthropic_client, mock_anthropic_response): - """Test generation with Anthropic provider.""" - mock_anthropic_client.messages.create.return_value = mock_anthropic_response - - client = ModelClient(provider=ModelProvider.ANTHROPIC) - messages = [{"role": "user", "content": "Hello"}] - response = await client.generate(messages) - assert response == "Test response" - - # Verify the API call - mock_anthropic_client.messages.create.assert_called_once() - call_args = mock_anthropic_client.messages.create.call_args[1] - assert call_args["model"] == DEFAULT_MODELS[ModelProvider.ANTHROPIC] - assert call_args["messages"] == messages - assert call_args["max_tokens"] == DEFAULT_MAX_TOKENS - assert call_args["temperature"] == DEFAULT_TEMPERATURE - - -@pytest.mark.asyncio -async def test_generate_openai(mock_openai_client, mock_openai_response, mock_env): - """Test generation with OpenAI provider.""" - mock_openai_client.chat.completions.create.return_value = mock_openai_response - - client = ModelClient(provider=ModelProvider.OPENAI) - messages = [{"role": "user", "content": "Hello"}] - response = await client.generate(messages) - assert response == "Test response" - - # Verify the API call - mock_openai_client.chat.completions.create.assert_called_once() - call_args = mock_openai_client.chat.completions.create.call_args[1] - assert call_args["model"] == DEFAULT_MODELS[ModelProvider.OPENAI] - assert call_args["messages"] == messages - assert call_args["max_tokens"] == DEFAULT_MAX_TOKENS - assert call_args["temperature"] == DEFAULT_TEMPERATURE - - -@pytest.mark.asyncio -async def test_generate_with_system_prompt( - mock_anthropic_client, mock_anthropic_response -): - """Test generation with system prompt.""" - mock_anthropic_client.messages.create.return_value = mock_anthropic_response - - client = ModelClient(provider=ModelProvider.ANTHROPIC) - messages = [{"role": "user", "content": "Hello"}] - system = "You are a helpful assistant" - response = await client.generate(messages, system=system) - assert response == "Test response" - - # Verify the API call - mock_anthropic_client.messages.create.assert_called_once() - call_args = mock_anthropic_client.messages.create.call_args[1] - assert call_args["system"] == system - - -@pytest.mark.asyncio -async def test_generate_with_caching(mock_anthropic_client, mock_anthropic_response): - """Test generation with caching enabled.""" - mock_anthropic_client.messages.create.return_value = mock_anthropic_response - - client = ModelClient(provider=ModelProvider.ANTHROPIC) - messages = [{"role": "user", "content": "Hello"}] - system = "You are a helpful assistant" - response = await client.generate(messages, system=system, use_caching=True) - assert response == "Test response" - - # Verify the API call - mock_anthropic_client.messages.create.assert_called_once() - call_args = mock_anthropic_client.messages.create.call_args[1] - assert call_args["system"] == [ - {"type": "text", "text": system, "cache_control": {"type": "ephemeral"}} - ] diff --git a/uv.lock b/uv.lock index 632b21ad..11c52e1e 100644 --- a/uv.lock +++ b/uv.lock @@ -1,23 +1,23 @@ version = 1 -revision = 1 -requires-python = ">=3.9" +requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.10'", - 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