Rename retreiver to tool

docs/agents.md

@@ -8,10 +8,10 @@ but multiple agents can also interact to embody more complex workflows.
 The [`Agent`][pydantic_ai.Agent] class has full API documentation, but conceptually you can think of an agent as a container for:
 
 * A [system prompt](#system-prompts) — a set of instructions for the LLM written by the developer
-* One or more [retrievers](#retrievers) — functions that the LLM may call to get information while generating a response
+* One or more [retrieval tool](#tools) — functions that the LLM may call to get information while generating a response
 * An optional structured [result type](results.md) — the structured datatype the LLM must return at the end of a run
-* A [dependency](dependencies.md) type constraint — system prompt functions, retrievers and result validators may all use dependencies when they're run
-* Agents may optionally also have a default [model](api/models/base.md) associated with them; the model to use can also be specified when running the agent
+* A [dependency](dependencies.md) type constraint — system prompt functions, tools and result validators may all use dependencies when they're run
+* Agents may optionally also have a default [LLM model](api/models/base.md) associated with them; the model to use can also be specified when running the agent
 
 In typing terms, agents are generic in their dependency and result types, e.g., an agent which required dependencies of type `#!python Foobar` and returned results of type `#!python list[str]` would have type `#!python Agent[Foobar, list[str]]`.
 
@@ -31,7 +31,7 @@ roulette_agent = Agent(  # (1)!
 )
 
 
-@roulette_agent.retriever
+@roulette_agent.tool
 async def roulette_wheel(ctx: CallContext[int], square: int) -> str:  # (2)!
     """check if the square is a winner"""
     return 'winner' if square == ctx.deps else 'loser'
@@ -49,7 +49,7 @@ print(result.data)
 ```
 
 1. Create an agent, which expects an integer dependency and returns a boolean result. This agent will have type `#!python Agent[int, bool]`.
-2. Define a retriever that checks if the square is a winner. Here [`CallContext`][pydantic_ai.dependencies.CallContext] is parameterized with the dependency type `int`; if you got the dependency type wrong you'd get a typing error.
+2. Define a tool that checks if the square is a winner. Here [`CallContext`][pydantic_ai.dependencies.CallContext] is parameterized with the dependency type `int`; if you got the dependency type wrong you'd get a typing error.
 3. In reality, you might want to use a random number here e.g. `random.randint(0, 36)`.
 4. `result.data` will be a boolean indicating if the square is a winner. Pydantic performs the result validation, it'll be typed as a `bool` since its type is derived from the `result_type` generic parameter of the agent.
 
@@ -166,23 +166,23 @@ print(result.data)
 
 _(This example is complete, it can be run "as is")_
 
-## Retrievers
+## Function Tools
 
-Retrievers provide a mechanism for models to request extra information to help them generate a response.
+Function tools provide a mechanism for models to retrieve extra information to help them generate a response.
 
 They're useful when it is impractical or impossible to put all the context an agent might need into the system prompt, or when you want to make agents' behavior more deterministic or reliable by deferring some of the logic required to generate a response to another (not necessarily AI-powered) tool.
 
-!!! info "Retrievers vs. RAG"
-    Retrievers are basically the "R" of RAG (Retrieval-Augmented Generation) — they augment what the model can do by letting it request extra information.
+!!! info "Function tools vs. RAG"
+    Function tools are basically the "R" of RAG (Retrieval-Augmented Generation) — they augment what the model can do by letting it request extra information.
 
-    The main semantic difference between PydanticAI Retrievers and RAG is RAG is synonymous with vector search, while PydanticAI retrievers are more general-purpose. (Note: we may add support for vector search functionality in the future, particularly an API for generating embeddings. See [#58](https://github.com/pydantic/pydantic-ai/issues/58))
+    The main semantic difference between PydanticAI Tools and RAG is RAG is synonymous with vector search, while PydanticAI tools are more general-purpose. (Note: we may add support for vector search functionality in the future, particularly an API for generating embeddings. See [#58](https://github.com/pydantic/pydantic-ai/issues/58))
 
-There are two different decorator functions to register retrievers:
+There are two different decorator functions to register tools:
 
-1. [`@agent.retriever_plain`][pydantic_ai.Agent.retriever_plain] — for retrievers that don't need access to the agent [context][pydantic_ai.dependencies.CallContext]
-2. [`@agent.retriever`][pydantic_ai.Agent.retriever] — for retrievers that do need access to the agent [context][pydantic_ai.dependencies.CallContext]
+1. [`@agent.tool`][pydantic_ai.Agent.tool] — for tools that need access to the agent [context][pydantic_ai.dependencies.CallContext]
+2. [`@agent.tool_plain`][pydantic_ai.Agent.tool_plain] — for tools that do not need access to the agent [context][pydantic_ai.dependencies.CallContext]
 
-`@agent.retriever` is the default since in the majority of cases retrievers will need access to the agent context.
+`@agent.tool` is the default since in the majority of cases tools will need access to the agent context.
 
 Here's an example using both:
 
@@ -202,13 +202,13 @@ agent = Agent(
 )
 
 
-@agent.retriever_plain  # (3)!
+@agent.tool_plain  # (3)!
 def roll_die() -> str:
     """Roll a six-sided die and return the result."""
     return str(random.randint(1, 6))
 
 
-@agent.retriever  # (4)!
+@agent.tool  # (4)!
 def get_player_name(ctx: CallContext[str]) -> str:
     """Get the player's name."""
     return ctx.deps
@@ -221,8 +221,8 @@ print(dice_result.data)
 
 1. This is a pretty simple task, so we can use the fast and cheap Gemini flash model.
 2. We pass the user's name as the dependency, to keep things simple we use just the name as a string as the dependency.
-3. This retriever doesn't need any context, it just returns a random number. You could probably use a dynamic system prompt in this case.
-4. This retriever needs the player's name, so it uses `CallContext` to access dependencies which are just the player's name in this case.
+3. This tool doesn't need any context, it just returns a random number. You could probably use a dynamic system prompt in this case.
+4. This tool needs the player's name, so it uses `CallContext` to access dependencies which are just the player's name in this case.
 5. Run the agent, passing the player's name as the dependency.
 
 _(This example is complete, it can be run "as is")_
@@ -297,19 +297,19 @@ sequenceDiagram
     Note over Agent: Send prompts
     Agent ->> LLM: System: "You're a dice game..."<br>User: "My guess is 4"
     activate LLM
-    Note over LLM: LLM decides to use<br>a retriever
+    Note over LLM: LLM decides to use<br>a tool
 
-    LLM ->> Agent: Call retriever<br>roll_die()
+    LLM ->> Agent: Call tool<br>roll_die()
     deactivate LLM
     activate Agent
     Note over Agent: Rolls a six-sided die
 
     Agent -->> LLM: ToolReturn<br>"4"
     deactivate Agent
     activate LLM
-    Note over LLM: LLM decides to use<br>another retriever
+    Note over LLM: LLM decides to use<br>another tool
 
-    LLM ->> Agent: Call retriever<br>get_player_name()
+    LLM ->> Agent: Call tool<br>get_player_name()
     deactivate LLM
     activate Agent
     Note over Agent: Retrieves player name
@@ -323,27 +323,29 @@ sequenceDiagram
     Note over Agent: Game session complete
 ```
 
-### Retrievers, tools, and schema
+### Function Tools vs. Structured Results
 
-Under the hood, retrievers use the model's "tools" or "functions" API to let the model know what retrievers are available to call. Tools or functions are also used to define the schema(s) for structured responses, thus a model might have access to many tools, some of which call retrievers while others end the run and return a result.
+As the name suggests, function tools use the model's "tools" or "functions" API to let the model know what is available to call. Tools or functions are also used to define the schema(s) for structured responses, thus a model might have access to many tools, some of which call function tools while others end the run and return a result.
+
+### Function tools and schema
 
 Function parameters are extracted from the function signature, and all parameters except `CallContext` are used to build the schema for that tool call.
 
-Even better, PydanticAI extracts the docstring from retriever functions and (thanks to [griffe](https://mkdocstrings.github.io/griffe/)) extracts parameter descriptions from the docstring and adds them to the schema.
+Even better, PydanticAI extracts the docstring from functions and (thanks to [griffe](https://mkdocstrings.github.io/griffe/)) extracts parameter descriptions from the docstring and adds them to the schema.
 
 [Griffe supports](https://mkdocstrings.github.io/griffe/reference/docstrings/#docstrings) extracting parameter descriptions from `google`, `numpy` and `sphinx` style docstrings, and PydanticAI will infer the format to use based on the docstring. We plan to add support in the future to explicitly set the style to use, and warn/error if not all parameters are documented; see [#59](https://github.com/pydantic/pydantic-ai/issues/59).
 
-To demonstrate a retriever's schema, here we use [`FunctionModel`][pydantic_ai.models.function.FunctionModel] to print the schema a model would receive:
+To demonstrate a tool's schema, here we use [`FunctionModel`][pydantic_ai.models.function.FunctionModel] to print the schema a model would receive:
 
-```py title="retriever_schema.py"
+```py title="tool_schema.py"
 from pydantic_ai import Agent
 from pydantic_ai.messages import Message, ModelAnyResponse, ModelTextResponse
 from pydantic_ai.models.function import AgentInfo, FunctionModel
 
 agent = Agent()
 
 
-@agent.retriever_plain
+@agent.tool_plain
 def foobar(a: int, b: str, c: dict[str, list[float]]) -> str:
     """Get me foobar.
 
@@ -356,10 +358,10 @@ def foobar(a: int, b: str, c: dict[str, list[float]]) -> str:
 
 
 def print_schema(messages: list[Message], info: AgentInfo) -> ModelAnyResponse:
-    retriever = info.retrievers['foobar']
-    print(retriever.description)
+    tool = info.function_tools['foobar']
+    print(tool.description)
     #> Get me foobar.
-    print(retriever.json_schema)
+    print(tool.json_schema)
     """
     {
         'description': 'Get me foobar.',
@@ -386,22 +388,22 @@ agent.run_sync('hello', model=FunctionModel(print_schema))
 
 _(This example is complete, it can be run "as is")_
 
-The return type of retriever can be any valid JSON object ([`JsonData`][pydantic_ai.dependencies.JsonData]) as some models (e.g. Gemini) support semi-structured return values, some expect text (OpenAI) but seem to be just as good at extracting meaning from the data. If a Python object is returned and the model expects a string, the value will be serialized to JSON.
+The return type of tool can be any valid JSON object ([`JsonData`][pydantic_ai.dependencies.JsonData]) as some models (e.g. Gemini) support semi-structured return values, some expect text (OpenAI) but seem to be just as good at extracting meaning from the data. If a Python object is returned and the model expects a string, the value will be serialized to JSON.
 
-If a retriever has a single parameter that can be represented as an object in JSON schema (e.g. dataclass, TypedDict, pydantic model), the schema for the retriever is simplified to be just that object. (TODO example)
+If a tool has a single parameter that can be represented as an object in JSON schema (e.g. dataclass, TypedDict, pydantic model), the schema for the tool is simplified to be just that object. (TODO example)
 
 ## Reflection and self-correction
 
-Validation errors from both retriever parameter validation and [structured result validation](results.md#structured-result-validation) can be passed back to the model with a request to retry.
+Validation errors from both tool parameter validation and [structured result validation](results.md#structured-result-validation) can be passed back to the model with a request to retry.
 
-You can also raise [`ModelRetry`][pydantic_ai.exceptions.ModelRetry] from within a [retriever](#retrievers) or [result validator function](results.md#result-validators-functions) to tell the model it should retry generating a response.
+You can also raise [`ModelRetry`][pydantic_ai.exceptions.ModelRetry] from within a [tool](#tools) or [result validator function](results.md#result-validators-functions) to tell the model it should retry generating a response.
 
-- The default retry count is **1** but can be altered for the [entire agent][pydantic_ai.Agent.__init__], a [specific retriever][pydantic_ai.Agent.retriever], or a [result validator][pydantic_ai.Agent.__init__].
-- You can access the current retry count from within a retriever or result validator via [`ctx.retry`][pydantic_ai.dependencies.CallContext].
+- The default retry count is **1** but can be altered for the [entire agent][pydantic_ai.Agent.__init__], a [specific tool][pydantic_ai.Agent.tool], or a [result validator][pydantic_ai.Agent.__init__].
+- You can access the current retry count from within a tool or result validator via [`ctx.retry`][pydantic_ai.dependencies.CallContext].
 
 Here's an example:
 
-```py title="retriever_retry.py"
+```py title="tool_retry.py"
 from fake_database import DatabaseConn
 from pydantic import BaseModel
 
@@ -420,7 +422,7 @@ agent = Agent(
 )
 
 
-@agent.retriever(retries=2)
+@agent.tool(retries=2)
 def get_user_by_name(ctx: CallContext[DatabaseConn], name: str) -> int:
     """Get a user's ID from their full name."""
     print(name)
@@ -455,7 +457,7 @@ from pydantic_ai import Agent, ModelRetry, UnexpectedModelBehavior
 agent = Agent('openai:gpt-4o')
 
 
-@agent.retriever_plain
+@agent.tool_plain
 def calc_volume(size: int) -> int:  # (1)!
     if size == 42:
         return size**3
@@ -467,7 +469,7 @@ try:
     result = agent.run_sync('Please get me the volume of a box with size 6.')
 except UnexpectedModelBehavior as e:
     print('An error occurred:', e)
-    #> An error occurred: Retriever exceeded max retries count of 1
+    #> An error occurred: Tool exceeded max retries count of 1
     print('cause:', repr(e.__cause__))
     #> cause: ModelRetry('Please try again.')
     print('messages:', agent.last_run_messages)
@@ -513,6 +515,6 @@ except UnexpectedModelBehavior as e:
 else:
     print(result.data)
 ```
-1. Define a retriever that will raise `ModelRetry` repeatedly in this case.
+1. Define a tool that will raise `ModelRetry` repeatedly in this case.
 
 _(This example is complete, it can be run "as is")_

docs/api/agent.md

@@ -12,6 +12,6 @@
         - override_model
         - last_run_messages
         - system_prompt
-        - retriever
-        - retriever_plain
+        - tool
+        - tool_plain
         - result_validator

docs/dependencies.md

@@ -1,13 +1,12 @@
 # Dependencies
 
-PydanticAI uses a dependency injection system to provide data and services to your agent's [system prompts](agents.md#system-prompts), [retrievers](agents.md#retrievers) and [result validators](results.md#result-validators-functions).
+PydanticAI uses a dependency injection system to provide data and services to your agent's [system prompts](agents.md#system-prompts), [tools](agents.md#tools) and [result validators](results.md#result-validators-functions).
 
 Matching PydanticAI's design philosophy, our dependency system tries to use existing best practice in Python development rather than inventing esoteric "magic", this should make dependencies type-safe, understandable easier to test and ultimately easier to deploy in production.
 
 ## Defining Dependencies
 
-Dependencies can be any python type. While in simple cases you might be able to pass a single object
-as a dependency (e.g. an HTTP connection), [dataclasses][] are generally a convenient container when your dependencies included multiple objects.
+Dependencies can be any python type. While in simple cases you might be able to pass a single object as a dependency (e.g. an HTTP connection), [dataclasses][] are generally a convenient container when your dependencies included multiple objects.
 
 Here's an example of defining an agent that requires dependencies.
 
@@ -102,7 +101,7 @@ _(This example is complete, it can be run "as is")_
 
 ### Asynchronous vs. Synchronous dependencies
 
-System prompt functions, retriever functions and result validator are all run in the async context of an agent run.
+[System prompt functions](agents.md#system-prompts), [function tools](agents.md#function-tools) and [result validators](results.md#result-validators-functions) are all run in the async context of an agent run.
 
 If these functions are not coroutines (e.g. `async def`) they are called with
 [`run_in_executor`][asyncio.loop.run_in_executor] in a thread pool, it's therefore marginally preferable
@@ -159,7 +158,7 @@ _(This example is complete, it can be run "as is")_
 
 ## Full Example
 
-As well as system prompts, dependencies can be used in [retrievers](agents.md#retrievers) and [result validators](results.md#result-validators-functions).
+As well as system prompts, dependencies can be used in [tools](agents.md#tools) and [result validators](results.md#result-validators-functions).
 
 ```py title="full_example.py" hl_lines="27-35 38-48"
 from dataclasses import dataclass
@@ -188,7 +187,7 @@ async def get_system_prompt(ctx: CallContext[MyDeps]) -> str:
     return f'Prompt: {response.text}'
 
 
-@agent.retriever  # (1)!
+@agent.tool  # (1)!
 async def get_joke_material(ctx: CallContext[MyDeps], subject: str) -> str:
     response = await ctx.deps.http_client.get(
         'https://example.com#jokes',
@@ -220,7 +219,7 @@ async def main():
         #> Did you hear about the toothpaste scandal? They called it Colgate.
 ```
 
-1. To pass `CallContext` and to a retriever, us the [`retriever`][pydantic_ai.Agent.retriever] decorator.
+1. To pass `CallContext` to a tool, use the [`tool`][pydantic_ai.Agent.tool] decorator.
 2. `CallContext` may optionally be passed to a [`result_validator`][pydantic_ai.Agent.result_validator] function as the first argument.
 
 _(This example is complete, it can be run "as is")_
@@ -324,7 +323,7 @@ joke_agent = Agent(
 factory_agent = Agent('gemini-1.5-pro', result_type=list[str])
 
 
-@joke_agent.retriever
+@joke_agent.tool
 async def joke_factory(ctx: CallContext[MyDeps], count: int) -> str:
     r = await ctx.deps.factory_agent.run(f'Please generate {count} jokes.')
     return '\n'.join(r.data)

docs/examples/bank-support.md

@@ -4,7 +4,7 @@ Demonstrates:
 
 * [dynamic system prompt](../agents.md#system-prompts)
 * [structured `result_type`](../results.md#structured-result-validation)
-* [retrievers](../agents.md#retrievers)
+* [tools](../agents.md#tools)
 
 ## Running the Example
 

docs/examples/rag.md

@@ -4,12 +4,12 @@ RAG search example. This demo allows you to ask question of the [logfire](https:
 
 Demonstrates:
 
-* [retrievers](../agents.md#retrievers)
+* [tools](../agents.md#tools)
 * [agent dependencies](../dependencies.md)
 * RAG search
 
 This is done by creating a database containing each section of the markdown documentation, then registering
-the search tool as a retriever with the PydanticAI agent.
+the search tool with the PydanticAI agent.
 
 Logic for extracting sections from markdown files and a JSON file with that data is available in
 [this gist](https://gist.github.com/samuelcolvin/4b5bb9bb163b1122ff17e29e48c10992).

docs/examples/weather-agent.md

@@ -2,7 +2,7 @@ Example of PydanticAI with multiple tools which the LLM needs to call in turn to
 
 Demonstrates:
 
-* [retrievers](../agents.md#retrievers)
+* [tools](../agents.md#tools)
 * [agent dependencies](../dependencies.md)
 
 In this case the idea is a "weather" agent — the user can ask for the weather in multiple locations,