diff --git a/.gitignore b/.gitignore index cb5b0650..c27bd775 100644 --- a/.gitignore +++ b/.gitignore @@ -174,4 +174,4 @@ cython_debug/ src/controlflow/_version.py all_code.md all_docs.md -llm_guides.md \ No newline at end of file +llm_guides.md diff --git a/docs/guides/configure-llms.mdx b/docs/guides/configure-llms.mdx index 8df6e55d..438db196 100644 --- a/docs/guides/configure-llms.mdx +++ b/docs/guides/configure-llms.mdx @@ -47,6 +47,7 @@ At this time, supported providers for automatic configuration include: | Anthropic | `anthropic` | (included) | | Google | `google` | `langchain_google_genai` | | Groq | `groq` | `langchain_groq` | +| Ollama | `ollama` | `langchain-ollama` | If the required dependencies are not installed, ControlFlow will be unable to load the model and will raise an error. diff --git a/examples/asyncpg-memory.py b/examples/asyncpg-memory.py new file mode 100644 index 00000000..59856546 --- /dev/null +++ b/examples/asyncpg-memory.py @@ -0,0 +1,55 @@ +import asyncio + +import controlflow as cf +from controlflow.memory.async_memory import AsyncMemory +from controlflow.memory.providers.postgres import AsyncPostgresMemory + +provider = AsyncPostgresMemory( + database_url="postgresql+psycopg://postgres:postgres@localhost:5432/database", + # embedding_dimension=1536, + # embedding_fn=OpenAIEmbeddings(), + table_name="vector_db_async", +) + +# Create a memory module for user preferences +user_preferences = AsyncMemory( + key="user_preferences", + instructions="Store and retrieve user preferences.", + provider=provider, +) + +# Create an agent with access to the memory +agent = cf.Agent(memories=[user_preferences]) + + +# Create a flow to ask for the user's favorite color +@cf.flow +async def remember_pet(): + return await cf.run_async( + "Ask the user for their favorite animal and store it in memory", + agents=[agent], + interactive=True, + ) + + +# Create a flow to recall the user's favorite color +@cf.flow +async def recall_pet(): + return await cf.run_async( + "What is the user's favorite animal?", + agents=[agent], + ) + + +async def main(): + print("First flow:") + await remember_pet() + + print("\nSecond flow:") + result = await recall_pet() + print(result) + return result + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/pg-memory.py b/examples/pg-memory.py index 6087fb06..2a7cc312 100644 --- a/examples/pg-memory.py +++ b/examples/pg-memory.py @@ -3,7 +3,7 @@ from controlflow.memory.providers.postgres import PostgresMemory provider = PostgresMemory( - database_url="postgresql://postgres:postgres@localhost:5432/your_database", + database_url="postgresql://postgres:postgres@localhost:5432/database", # embedding_dimension=1536, # embedding_fn=OpenAIEmbeddings(), table_name="vector_db", diff --git a/pyproject.toml b/pyproject.toml index e03f0c96..6e9d28ed 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,6 +51,7 @@ tests = [ "langchain_community", "langchain_google_genai", "langchain_groq", + "langchain-ollama', "pytest-asyncio>=0.18.2,!=0.22.0,<0.23.0", "pytest-env>=0.8,<2.0", "pytest-rerunfailures>=10,<14", diff --git a/src/controlflow/__init__.py b/src/controlflow/__init__.py index 8fd513b7..59cd90fe 100644 --- a/src/controlflow/__init__.py +++ b/src/controlflow/__init__.py @@ -13,6 +13,7 @@ # functions, utilites, and decorators from .memory import Memory +from .memory.async_memory import AsyncMemory from .instructions import instructions from .decorators import flow, task from .tools import tool diff --git a/src/controlflow/agents/agent.py b/src/controlflow/agents/agent.py index 6e2ab985..8b691b3c 100644 --- a/src/controlflow/agents/agent.py +++ b/src/controlflow/agents/agent.py @@ -32,6 +32,7 @@ from controlflow.llm.models import get_model as get_model_from_string from controlflow.llm.rules import LLMRules from controlflow.memory import Memory +from controlflow.memory.async_memory import AsyncMemory from controlflow.tools.tools import ( Tool, as_lc_tools, @@ -82,7 +83,7 @@ class Agent(ControlFlowModel, abc.ABC): default=False, description="If True, the agent is given tools for interacting with a human user.", ) - memories: list[Memory] = Field( + memories: list[Memory] | list[AsyncMemory] = Field( default=[], description="A list of memory modules for the agent to use.", ) diff --git a/src/controlflow/defaults.py b/src/controlflow/defaults.py index 39e52ad9..b5836e9a 100644 --- a/src/controlflow/defaults.py +++ b/src/controlflow/defaults.py @@ -6,6 +6,7 @@ import controlflow.utilities import controlflow.utilities.logging from controlflow.llm.models import BaseChatModel +from controlflow.memory.async_memory import AsyncMemoryProvider, get_memory_provider from controlflow.memory.memory import MemoryProvider, get_memory_provider from controlflow.utilities.general import ControlFlowModel @@ -39,7 +40,9 @@ class Defaults(ControlFlowModel): model: Optional[Any] history: History agent: Agent - memory_provider: Optional[Union[MemoryProvider, str]] + memory_provider: ( + Optional[Union[MemoryProvider, str]] | Optional[Union[AsyncMemoryProvider, str]] + ) # add more defaults here def __repr__(self) -> str: diff --git a/src/controlflow/llm/models.py b/src/controlflow/llm/models.py index 6938e197..11a66c8e 100644 --- a/src/controlflow/llm/models.py +++ b/src/controlflow/llm/models.py @@ -60,6 +60,14 @@ def get_model( "To use Groq as an LLM provider, please install the `langchain_groq` package." ) cls = ChatGroq + elif provider == "ollama": + try: + from langchain_ollama import ChatOllama + except ImportError: + raise ImportError( + "To use Ollama as an LLM provider, please install the `langchain-ollama` package." + ) + cls = ChatOllama else: raise ValueError( f"Could not load provider `{provider}` automatically. Please provide the LLM class manually." diff --git a/src/controlflow/memory/__init__.py b/src/controlflow/memory/__init__.py index db0261eb..49f6baf2 100644 --- a/src/controlflow/memory/__init__.py +++ b/src/controlflow/memory/__init__.py @@ -1 +1,2 @@ from .memory import Memory +from .async_memory import AsyncMemory diff --git a/src/controlflow/memory/async_memory.py b/src/controlflow/memory/async_memory.py new file mode 100644 index 00000000..a0ea3a20 --- /dev/null +++ b/src/controlflow/memory/async_memory.py @@ -0,0 +1,149 @@ +import abc +import re +from typing import Dict, List, Optional, Union + +from pydantic import Field, field_validator, model_validator + +import controlflow +from controlflow.tools.tools import Tool +from controlflow.utilities.general import ControlFlowModel, unwrap +from controlflow.utilities.logging import get_logger + +logger = get_logger("controlflow.memory") + + +def sanitize_memory_key(key: str) -> str: + # Remove any characters that are not alphanumeric or underscore + return re.sub(r"[^a-zA-Z0-9_]", "", key) + + +class AsyncMemoryProvider(ControlFlowModel, abc.ABC): + async def configure(self, memory_key: str) -> None: + """Configure the provider for a specific memory.""" + pass + + @abc.abstractmethod + async def add(self, memory_key: str, content: str) -> str: + """Create a new memory and return its ID.""" + pass + + @abc.abstractmethod + async def delete(self, memory_key: str, memory_id: str) -> None: + """Delete a memory by its ID.""" + pass + + @abc.abstractmethod + async def search(self, memory_key: str, query: str, n: int = 20) -> Dict[str, str]: + """Search for n memories using a string query.""" + pass + + +class AsyncMemory(ControlFlowModel): + """ + A memory module is a partitioned collection of memories that are stored in a + vector database, configured by a MemoryProvider. + """ + + key: str + instructions: str = Field( + description="Explain what this memory is for and how it should be used." + ) + provider: AsyncMemoryProvider = Field( + default_factory=lambda: controlflow.defaults.memory_provider, + validate_default=True, + ) + + def __hash__(self) -> int: + return id(self) + + @field_validator("provider", mode="before") + @classmethod + def validate_provider( + cls, v: Optional[Union[AsyncMemoryProvider, str]] + ) -> AsyncMemoryProvider: + if isinstance(v, str): + return get_memory_provider(v) + if v is None: + raise ValueError( + unwrap( + """ + Memory modules require a MemoryProvider to configure the + underlying vector database. No provider was passed as an + argument, and no default value has been configured. + + For more information on configuring a memory provider, see + the [Memory + documentation](https://controlflow.ai/patterns/memory), and + please review the [default provider + guide](https://controlflow.ai/guides/default-memory) for + information on configuring a default provider. + + Please note that if you are using ControlFlow for the first + time, this error is expected because ControlFlow does not include + vector dependencies by default. + """ + ) + ) + return v + + @field_validator("key") + @classmethod + def validate_key(cls, v: str) -> str: + sanitized = sanitize_memory_key(v) + if sanitized != v: + raise ValueError( + "Memory key must contain only alphanumeric characters and underscores" + ) + return sanitized + + async def _configure_provider(self): + await self.provider.configure(self.key) + return self + + async def add(self, content: str) -> str: + return await self.provider.add(self.key, content) + + async def delete(self, memory_id: str) -> None: + await self.provider.delete(self.key, memory_id) + + async def search(self, query: str, n: int = 20) -> Dict[str, str]: + return await self.provider.search(self.key, query, n) + + def get_tools(self) -> List[Tool]: + return [ + Tool.from_function( + self.add, + name=f"store_memory_{self.key}", + description=f'Create a new memory in Memory: "{self.key}".', + ), + Tool.from_function( + self.delete, + name=f"delete_memory_{self.key}", + description=f'Delete a memory by its ID from Memory: "{self.key}".', + ), + Tool.from_function( + self.search, + name=f"search_memories_{self.key}", + description=f'Search for memories relevant to a string query in Memory: "{self.key}". Returns a dictionary of memory IDs and their contents.', + ), + ] + + +def get_memory_provider(provider: str) -> AsyncMemoryProvider: + logger.debug(f"Loading memory provider: {provider}") + + # --- async postgres --- + + if provider.startswith("async-postgres"): + try: + import sqlalchemy + except ImportError: + raise ImportError( + """To use async Postgres as a memory provider, please install the `sqlalchemy, `psycopg-pool`, + `psycopg-binary`, and `psycopg` packages.""" + ) + + import controlflow.memory.providers.postgres as postgres_providers + + return postgres_providers.AsyncPostgresMemory() + raise ValueError(f'Memory provider "{provider}" could not be loaded from a string.') diff --git a/src/controlflow/memory/memory.py b/src/controlflow/memory/memory.py index 1e0dd840..18d28e4d 100644 --- a/src/controlflow/memory/memory.py +++ b/src/controlflow/memory/memory.py @@ -172,10 +172,12 @@ def get_memory_provider(provider: str) -> MemoryProvider: import sqlalchemy except ImportError: raise ImportError( - "To use Postgres as a memory provider, please install the `sqlalchemy` package." + """To use Postgres as a memory provider, please install the `sqlalchemy, `psycopg-pool`, + `psycopg-binary`, and `psycopg` `psycopg2-binary` packages.""" ) import controlflow.memory.providers.postgres as postgres_providers return postgres_providers.PostgresMemory() + raise ValueError(f'Memory provider "{provider}" could not be loaded from a string.') diff --git a/src/controlflow/memory/providers/postgres.py b/src/controlflow/memory/providers/postgres.py index 6887722a..4ba8bcc8 100644 --- a/src/controlflow/memory/providers/postgres.py +++ b/src/controlflow/memory/providers/postgres.py @@ -1,16 +1,25 @@ import uuid from typing import Callable, Dict, Optional +# async pg +import anyio import sqlalchemy from pgvector.sqlalchemy import Vector from pydantic import Field from sqlalchemy import Column, String, select, text from sqlalchemy.dialects.postgresql import ARRAY from sqlalchemy.exc import ProgrammingError +from sqlalchemy.ext.asyncio import ( + AsyncEngine, + AsyncSession, + async_sessionmaker, + create_async_engine, +) from sqlalchemy.orm import Session, declarative_base, sessionmaker from sqlalchemy_utils import create_database, database_exists import controlflow +from controlflow.memory.async_memory import AsyncMemoryProvider from controlflow.memory.memory import MemoryProvider try: @@ -72,6 +81,32 @@ class PostgresMemory(MemoryProvider): description="A function that turns a string into a vector.", ) + # Connection pool settings + pool_size: int = Field( + 5, + description="Number of connections to keep open in the pool.", + ) + + max_overflow: int = Field( + 10, + description="Number of connections to allow that can overflow the pool.", + ) + + pool_timeout: int = Field( + 30, + description="Number of seconds to wait before giving up on getting a connection.", + ) + + pool_recycle: int = Field( + 1800, + description="Number of seconds a connection can be idle before being recycled.", + ) + + pool_pre_ping: bool = Field( + True, + description="Check the connection health upon checkout.", + ) + # Internal: keep a cached Session maker _SessionLocal: Optional[sessionmaker] = None @@ -80,10 +115,17 @@ class PostgresMemory(MemoryProvider): def configure(self, memory_key: str) -> None: """ - Configure a SQLAlchemy session and ensure the table for this + Configure a SQLAlchemy session w/connection pooling and ensure the table for this memory partition is created if it does not already exist. """ - engine = sqlalchemy.create_engine(self.database_url) + engine = sqlalchemy.create_engine( + self.database_url, + pool_size=self.pool_size, + max_overflow=self.max_overflow, + pool_timeout=self.pool_timeout, + pool_recycle=self.pool_recycle, + pool_pre_ping=self.pool_pre_ping, + ) # 2) If DB doesn't exist, create it! if not database_exists(engine.url): @@ -195,3 +237,221 @@ def search(self, memory_key: str, query: str, n: int = 20) -> Dict[str, str]: ).all() return {row.id: row.text for row in results} + + +class AsyncPostgresMemory(AsyncMemoryProvider): + """ + An async MemoryProvider storing text + embeddings in PostgreSQL + using SQLAlchemy + pg_vector, but with full async support. + """ + + database_url: str = Field( + default="postgresql+asyncpg://user:password@localhost:5432/your_database", + description="Async Postgres URL with the asyncpg driver, e.g. " + "'postgresql+asyncpg://user:pass@host:5432/dbname'.", + ) + + table_name: str = Field( + "memory_{key}", + description=""" + Name of the table for this memory partition. "{key}" gets replaced by the memory key. + """, + ) + + embedding_dimension: int = Field( + default=1536, + description="Dimension of the embedding vectors. Must match your model output size.", + ) + + embedding_fn: Callable = Field( + default_factory=lambda: OpenAIEmbeddings(model="text-embedding-ada-002"), + description="Function that turns a string into a numeric vector.", + ) + + # -- Pool / Engine settings (SQLAlchemy will do the pooling) + pool_size: int = Field( + 5, description="Number of permanent connections in the async pool." + ) + max_overflow: int = Field( + 10, description="Number of 'overflow' connections if the pool is full." + ) + pool_timeout: int = Field( + 30, description="Seconds to wait for a connection before raising an error." + ) + pool_recycle: int = Field( + 1800, + description="Recycle connections after N seconds to avoid stale connections.", + ) + pool_pre_ping: bool = Field( + True, description="Check connection health before using from the pool." + ) + + # We'll store an async engine + session factory: + _engine: Optional[AsyncEngine] = None + _SessionLocal: Optional[async_sessionmaker[AsyncSession]] = None + + # Cache for dynamically generated table classes + _table_class_cache: Dict[str, Base] = {} + + _configured: bool = False + + async def configure(self, memory_key: str) -> None: + """ + 1) Create an async engine. + 2) Optionally create the DB if it doesn't exist (requires sync workaround). + 3) Install pgvector extension. + 4) Generate the memory table if missing. + 5) Initialize the async sessionmaker. + """ + if self._configured: + return + # 1) Create an async engine. Use the asyncpg dialect. + # The pool settings are configured in 'create_async_engine' with 'pool_size', etc. + else: + self._engine = create_async_engine( + self.database_url, + pool_size=self.pool_size, + max_overflow=self.max_overflow, + pool_timeout=self.pool_timeout, + pool_recycle=self.pool_recycle, + pool_pre_ping=self.pool_pre_ping, + ) + + exists = await anyio.to_thread.run_sync(database_exists, self.database_url) + if not exists: + await anyio.to_thread.run_sync(create_database, self.database_url) + + # 3) Run migrations / create extension in an async context: + async with self._engine.begin() as conn: + # Create the pgvector extension if not exists + await conn.execute(text("CREATE EXTENSION IF NOT EXISTS vector")) + # We'll create the table for the memory_key specifically + # (1) Build the dynamic table class + table_name = self.table_name.format(key=memory_key) + if table_name not in Base.metadata.tables: + memory_model = type( + f"SQLMemoryTable_{memory_key}", + (SQLMemoryTable,), + { + "__tablename__": table_name, + "vector": Column(Vector(dim=self.embedding_dimension)), + }, + ) + self._table_class_cache[table_name] = memory_model + + # (2) Actually create it (async): + def _sync_create(connection): + """Helper function to run table creation in sync context.""" + Base.metadata.create_all( + connection, tables=[memory_model.__table__] + ) + + try: + await conn.run_sync(_sync_create) + except ProgrammingError as e: + raise RuntimeError( + f"Failed to create table '{table_name}': {e}" + ) + + # 4) Now that the DB and table are ready, create a session factory + self._SessionLocal = async_sessionmaker( + self._engine, + expire_on_commit=False, + ) + + self._configured = True + + def _get_table(self, memory_key: str) -> Base: + """ + Return or create the dynamic model class for 'memory_{key}' table. + """ + table_name = self.table_name.format(key=memory_key) + if table_name in self._table_class_cache: + return self._table_class_cache[table_name] + + # If not found, define it at runtime (we won't auto-create it here though) + memory_model = type( + f"SQLMemoryTable_{memory_key}", + (SQLMemoryTable,), + { + "__tablename__": table_name, + "vector": Column(Vector(dim=self.embedding_dimension)), + }, + ) + self._table_class_cache[table_name] = memory_model + return memory_model + + async def add(self, memory_key: str, content: str) -> str: + """ + Insert a new record with an embedding vector. + Returns the inserted record's UUID. + """ + # lazy config + if not self._configured: + await self.configure(memory_key) + + if not self._SessionLocal: + raise RuntimeError("Call 'configure(...)' before using this provider.") + + memory_id = str(uuid.uuid4()) + model_cls = self._get_table(memory_key) + embedding = self.embedding_fn.embed_query(content) + + async with self._SessionLocal() as session: + record = model_cls( + id=memory_id, + text=content, + vector=embedding, + ) + session.add(record) + await session.commit() + + return memory_id + + async def delete(self, memory_key: str, memory_id: str) -> None: + """ + Delete a record by UUID. + """ + # lazy config + if not self._configured: + await self.configure(memory_key) + + if not self._SessionLocal: + raise RuntimeError("Not configured. Call 'configure(...)' first.") + + model_cls = self._get_table(memory_key) + + async with self._SessionLocal() as session: + await session.execute( + sqlalchemy.delete(model_cls).where(model_cls.id == memory_id) + ) + await session.commit() + + async def search(self, memory_key: str, query: str, n: int = 20) -> Dict[str, str]: + """ + Async nearest-neighbor search via pgvector <-> operator or .l2_distance(), + returning up to N results as {id: text}. + """ + + # lazy config + if not self._configured: + await self.configure(memory_key) + + if not self._SessionLocal: + raise RuntimeError("Not configured. Call 'configure(...)' first.") + + model_cls = self._get_table(memory_key) + embedding = self.embedding_fn.embed_query(query) + embedding_col = model_cls.vector + + async with self._SessionLocal() as session: + # Example using l2_distance: + results = await session.execute( + select(model_cls.id, model_cls.text) + .order_by(embedding_col.l2_distance(embedding)) + .limit(n) + ) + rows = results.all() + + # Convert list of Row objects -> dict + return {row.id: row.text for row in rows} diff --git a/src/controlflow/orchestration/orchestrator.py b/src/controlflow/orchestration/orchestrator.py index 2fbdccec..03a09dec 100644 --- a/src/controlflow/orchestration/orchestrator.py +++ b/src/controlflow/orchestration/orchestrator.py @@ -21,6 +21,7 @@ from controlflow.instructions import get_instructions from controlflow.llm.messages import BaseMessage from controlflow.memory import Memory +from controlflow.memory.async_memory import AsyncMemory from controlflow.orchestration.conditions import ( AllComplete, FnCondition, @@ -187,7 +188,7 @@ def get_tools(self) -> list[Tool]: tools = as_tools(tools) return tools - def get_memories(self) -> list[Memory]: + def get_memories(self) -> list[Memory] | list[AsyncMemory]: memories = set() memories.update(self.agent.memories) diff --git a/src/controlflow/orchestration/prompt_templates.py b/src/controlflow/orchestration/prompt_templates.py index 58de81eb..4a74df15 100644 --- a/src/controlflow/orchestration/prompt_templates.py +++ b/src/controlflow/orchestration/prompt_templates.py @@ -4,6 +4,7 @@ from controlflow.agents.agent import Agent from controlflow.flows import Flow +from controlflow.memory.async_memory import AsyncMemory from controlflow.memory.memory import Memory from controlflow.tasks.task import Task from controlflow.tools.tools import Tool @@ -97,7 +98,7 @@ def should_render(self) -> bool: class MemoryTemplate(Template): template_path: str = "memories.jinja" - memories: list[Memory] + memories: list[Memory] | list[AsyncMemory] def should_render(self) -> bool: return bool(self.memories) diff --git a/src/controlflow/tasks/task.py b/src/controlflow/tasks/task.py index 44a55070..b08a548d 100644 --- a/src/controlflow/tasks/task.py +++ b/src/controlflow/tasks/task.py @@ -37,6 +37,7 @@ import controlflow from controlflow.agents import Agent from controlflow.instructions import get_instructions +from controlflow.memory.async_memory import AsyncMemory from controlflow.memory.memory import Memory from controlflow.tools import Tool, tool from controlflow.tools.input import cli_input @@ -161,7 +162,7 @@ class Task(ControlFlowModel): description="Agents that are allowed to mark this task as complete. If None, all agents are allowed.", ) interactive: bool = False - memories: list[Memory] = Field( + memories: list[Memory] | list[AsyncMemory] = Field( default=[], description="A list of memory modules for the task to use.", ) diff --git a/tests/llm/test_models.py b/tests/llm/test_models.py index b46bb0d0..fe2e3c27 100644 --- a/tests/llm/test_models.py +++ b/tests/llm/test_models.py @@ -2,6 +2,7 @@ from langchain_anthropic import ChatAnthropic from langchain_google_genai import ChatGoogleGenerativeAI from langchain_groq import ChatGroq +from langchain_ollama import ChatOllama from langchain_openai import AzureChatOpenAI, ChatOpenAI from controlflow.llm.models import get_model @@ -45,6 +46,10 @@ def test_get_groq_model(monkeypatch): assert isinstance(model, ChatGroq) assert model.model_name == "mixtral-8x7b-32768" +def test_get_ollama_model(monkeypatch): + model = get_model("ollama/qwen2.5") + assert isinstance(model, ChatOllama) + assert model.model == "qwen2.5" def test_get_model_with_invalid_format(): with pytest.raises(ValueError, match="The model `gpt-4o` is not valid."): diff --git a/uv.lock b/uv.lock index d118f666..23fd7d71 100644 --- a/uv.lock +++ b/uv.lock @@ -755,18 +755,28 @@ wheels = [ [[package]] name = "controlflow" -version = "0.11.1.dev1+g60674fa.d20241016" +version = "0.11.4.dev43+g191789e.d20250109" source = { editable = "." } dependencies = [ { name = "ipython" }, { name = "jinja2" }, + { name = "lancedb" }, { name = "langchain-anthropic" }, { name = "langchain-core" }, { name = "langchain-openai" }, { name = "markdownify" }, { name = "openai" }, + { name = "pgvector" }, { name = "prefect" }, + { name = "psycopg" }, + { name = "psycopg-binary" }, + { name = "psycopg-pool" }, + { name = "psycopg2-binary" }, + { name = "pydantic-async-validation" }, { name = "pydantic-settings" }, + { name = "python-dotenv" }, + { name = "sqlalchemy" }, + { name = "sqlalchemy-utils" }, { name = "textual" }, { name = "tiktoken" }, { name = "typer" }, @@ -815,9 +825,10 @@ requires-dist = [ { name = "chromadb", marker = "extra == 'tests'" }, { name = "controlflow", extras = ["tests"], marker = "extra == 'dev'" }, { name = "duckduckgo-search", marker = "extra == 'tests'" }, - { name = "ipython" }, + { name = "ipython", specifier = ">=8.18.1" }, { name = "ipython", marker = "extra == 'dev'" }, { name = "jinja2", specifier = ">=3.1.4" }, + { name = "lancedb", specifier = ">=0.17.0" }, { name = "langchain-anthropic", specifier = ">=0.2" }, { name = "langchain-community", marker = "extra == 'tests'" }, { name = "langchain-core", specifier = ">=0.3" }, @@ -826,10 +837,16 @@ requires-dist = [ { name = "langchain-openai", specifier = ">=0.2" }, { name = "markdownify", specifier = ">=0.12.1" }, { name = "mypy", marker = "extra == 'dev'" }, - { name = "openai", specifier = "<1.47" }, + { name = "openai", specifier = ">=1.55.3" }, { name = "pdbpp", marker = "extra == 'dev'" }, + { name = "pgvector", specifier = ">=0.3.6" }, { name = "pre-commit", marker = "extra == 'dev'" }, { name = "prefect", specifier = ">=3.0" }, + { name = "psycopg", specifier = ">=3.2.3" }, + { name = "psycopg-binary", specifier = ">=3.2.3" }, + { name = "psycopg-pool", specifier = ">=3.2.4" }, + { name = "psycopg2-binary", specifier = ">=2.9.10" }, + { name = "pydantic-async-validation", specifier = ">=0.3.0" }, { name = "pydantic-settings", specifier = ">=2.2.1" }, { name = "pytest", marker = "extra == 'tests'", specifier = ">=7.0" }, { name = "pytest-asyncio", marker = "extra == 'tests'", specifier = ">=0.18.2,!=0.22.0,<0.23.0" }, @@ -838,7 +855,10 @@ requires-dist = [ { name = "pytest-sugar", marker = "extra == 'tests'", specifier = ">=0.9,<2.0" }, { name = "pytest-timeout", marker = "extra == 'tests'" }, { name = "pytest-xdist", marker = "extra == 'tests'" }, + { name = "python-dotenv", specifier = ">=1.0.1" }, { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.3.4" }, + { name = "sqlalchemy", specifier = ">=2.0.36" }, + { 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