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454 | class Ollama(FunctionCallingLLM):
"""Ollama LLM.
Visit https://ollama.com/ to download and install Ollama.
Run `ollama serve` to start a server.
Run `ollama pull <name>` to download a model to run.
Examples:
`pip install llama-index-llms-ollama`
```python
from llama_index.llms.ollama import Ollama
llm = Ollama(model="llama2", request_timeout=60.0)
response = llm.complete("What is the capital of France?")
print(response)
```
"""
base_url: str = Field(
default="http://localhost:11434",
description="Base url the model is hosted under.",
)
model: str = Field(description="The Ollama model to use.")
temperature: float = Field(
default=0.75,
description="The temperature to use for sampling.",
ge=0.0,
le=1.0,
)
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description="The maximum number of context tokens for the model.",
gt=0,
)
request_timeout: float = Field(
default=DEFAULT_REQUEST_TIMEOUT,
description="The timeout for making http request to Ollama API server",
)
prompt_key: str = Field(
default="prompt", description="The key to use for the prompt in API calls."
)
json_mode: bool = Field(
default=False,
description="Whether to use JSON mode for the Ollama API.",
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict,
description="Additional model parameters for the Ollama API.",
)
is_function_calling_model: bool = Field(
default=True,
description="Whether the model is a function calling model.",
)
keep_alive: Optional[Union[float, str]] = Field(
default="5m",
description="controls how long the model will stay loaded into memory following the request(default: 5m)",
)
_client: Optional[Client] = PrivateAttr()
_async_client: Optional[AsyncClient] = PrivateAttr()
def __init__(
self,
model: str,
base_url: str = "http://localhost:11434",
temperature: float = 0.75,
context_window: int = DEFAULT_CONTEXT_WINDOW,
request_timeout: float = DEFAULT_REQUEST_TIMEOUT,
prompt_key: str = "prompt",
json_mode: bool = False,
additional_kwargs: Dict[str, Any] = {},
client: Optional[Client] = None,
async_client: Optional[AsyncClient] = None,
is_function_calling_model: bool = True,
keep_alive: Optional[Union[float, str]] = None,
**kwargs: Any,
) -> None:
super().__init__(
model=model,
base_url=base_url,
temperature=temperature,
context_window=context_window,
request_timeout=request_timeout,
prompt_key=prompt_key,
json_mode=json_mode,
additional_kwargs=additional_kwargs,
is_function_calling_model=is_function_calling_model,
keep_alive=keep_alive,
**kwargs,
)
self._client = client
self._async_client = async_client
@classmethod
def class_name(cls) -> str:
return "Ollama_llm"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=DEFAULT_NUM_OUTPUTS,
model_name=self.model,
is_chat_model=True, # Ollama supports chat API for all models
# TODO: Detect if selected model is a function calling model?
is_function_calling_model=self.is_function_calling_model,
)
@property
def client(self) -> Client:
if self._client is None:
self._client = Client(host=self.base_url, timeout=self.request_timeout)
return self._client
@property
def async_client(self) -> AsyncClient:
if self._async_client is None:
self._async_client = AsyncClient(
host=self.base_url, timeout=self.request_timeout
)
return self._async_client
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"temperature": self.temperature,
"num_ctx": self.context_window,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
def _convert_to_ollama_messages(self, messages: Sequence[ChatMessage]) -> Dict:
return [
{
"role": message.role.value,
"content": message.content or "",
**(
{"tool_calls": message.additional_kwargs["tool_calls"]}
if "tool_calls" in message.additional_kwargs
else {}
),
}
for message in messages
]
def _get_response_token_counts(self, raw_response: dict) -> dict:
"""Get the token usage reported by the response."""
try:
prompt_tokens = raw_response["prompt_eval_count"]
completion_tokens = raw_response["eval_count"]
total_tokens = prompt_tokens + completion_tokens
except KeyError:
return {}
return {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": total_tokens,
}
def _prepare_chat_with_tools(
self,
tools: List["BaseTool"],
user_msg: Optional[Union[str, ChatMessage]] = None,
chat_history: Optional[List[ChatMessage]] = None,
verbose: bool = False,
allow_parallel_tool_calls: bool = False,
**kwargs: Any,
) -> Dict[str, Any]:
tool_specs = [
tool.metadata.to_openai_tool(skip_length_check=True) for tool in tools
]
if isinstance(user_msg, str):
user_msg = ChatMessage(role=MessageRole.USER, content=user_msg)
messages = chat_history or []
if user_msg:
messages.append(user_msg)
return {
"messages": messages,
"tools": tool_specs or None,
}
def _validate_chat_with_tools_response(
self,
response: ChatResponse,
tools: List["BaseTool"],
allow_parallel_tool_calls: bool = False,
**kwargs: Any,
) -> ChatResponse:
"""Validate the response from chat_with_tools."""
if not allow_parallel_tool_calls:
force_single_tool_call(response)
return response
def get_tool_calls_from_response(
self,
response: "ChatResponse",
error_on_no_tool_call: bool = True,
) -> List[ToolSelection]:
"""Predict and call the tool."""
tool_calls = response.message.additional_kwargs.get("tool_calls", [])
if len(tool_calls) < 1:
if error_on_no_tool_call:
raise ValueError(
f"Expected at least one tool call, but got {len(tool_calls)} tool calls."
)
else:
return []
tool_selections = []
for tool_call in tool_calls:
argument_dict = tool_call["function"]["arguments"]
tool_selections.append(
ToolSelection(
# tool ids not provided by Ollama
tool_id=tool_call["function"]["name"],
tool_name=tool_call["function"]["name"],
tool_kwargs=argument_dict,
)
)
return tool_selections
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
ollama_messages = self._convert_to_ollama_messages(messages)
tools = kwargs.pop("tools", None)
response = self.client.chat(
model=self.model,
messages=ollama_messages,
stream=False,
format="json" if self.json_mode else "",
tools=tools,
options=self._model_kwargs,
keep_alive=self.keep_alive,
)
tool_calls = response["message"].get("tool_calls", [])
token_counts = self._get_response_token_counts(response)
if token_counts:
response["usage"] = token_counts
return ChatResponse(
message=ChatMessage(
content=response["message"]["content"],
role=response["message"]["role"],
additional_kwargs={"tool_calls": tool_calls},
),
raw=response,
)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
ollama_messages = self._convert_to_ollama_messages(messages)
tools = kwargs.pop("tools", None)
def gen() -> ChatResponseGen:
response = self.client.chat(
model=self.model,
messages=ollama_messages,
stream=True,
format="json" if self.json_mode else "",
tools=tools,
options=self._model_kwargs,
keep_alive=self.keep_alive,
)
response_txt = ""
for r in response:
if r["message"]["content"] is None:
continue
response_txt += r["message"]["content"]
tool_calls = r["message"].get("tool_calls", [])
token_counts = self._get_response_token_counts(r)
if token_counts:
r["usage"] = token_counts
yield ChatResponse(
message=ChatMessage(
content=response_txt,
role=r["message"]["role"],
additional_kwargs={"tool_calls": tool_calls},
),
delta=r["message"]["content"],
raw=r,
)
return gen()
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
ollama_messages = self._convert_to_ollama_messages(messages)
tools = kwargs.pop("tools", None)
async def gen() -> ChatResponseAsyncGen:
response = await self.async_client.chat(
model=self.model,
messages=ollama_messages,
stream=True,
format="json" if self.json_mode else "",
tools=tools,
options=self._model_kwargs,
keep_alive=self.keep_alive,
)
response_txt = ""
async for r in response:
if r["message"]["content"] is None:
continue
response_txt += r["message"]["content"]
tool_calls = r["message"].get("tool_calls", [])
token_counts = self._get_response_token_counts(r)
if token_counts:
r["usage"] = token_counts
yield ChatResponse(
message=ChatMessage(
content=response_txt,
role=r["message"]["role"],
additional_kwargs={"tool_calls": tool_calls},
),
delta=r["message"]["content"],
raw=r,
)
return gen()
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
ollama_messages = self._convert_to_ollama_messages(messages)
tools = kwargs.pop("tools", None)
response = await self.async_client.chat(
model=self.model,
messages=ollama_messages,
stream=False,
format="json" if self.json_mode else "",
tools=tools,
options=self._model_kwargs,
keep_alive=self.keep_alive,
)
tool_calls = response["message"].get("tool_calls", [])
token_counts = self._get_response_token_counts(response)
if token_counts:
response["usage"] = token_counts
return ChatResponse(
message=ChatMessage(
content=response["message"]["content"],
role=response["message"]["role"],
additional_kwargs={"tool_calls": tool_calls},
),
raw=response,
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
return chat_to_completion_decorator(self.chat)(prompt, **kwargs)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
return await achat_to_completion_decorator(self.achat)(prompt, **kwargs)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
return stream_chat_to_completion_decorator(self.stream_chat)(prompt, **kwargs)
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
return await astream_chat_to_completion_decorator(self.astream_chat)(
prompt, **kwargs
)
|
