Spaces:
Running
Running
File size: 8,786 Bytes
9db289b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
from __future__ import annotations
import json
from typing import Any, AsyncGenerator, Dict, List, Optional
from vanna.core.llm import (
LlmService,
LlmRequest,
LlmResponse,
LlmStreamChunk,
)
from vanna.core.tool import ToolCall, ToolSchema
from huggingface_hub import InferenceClient
class VannaHuggingFaceLlmService(LlmService):
def __init__(
self,
model: Optional[str] = None,
api_key: Optional[str] = None,
provider: Optional[str] = None,
**extra_client_kwargs: Any,
) -> None:
"""Initialise le client Hugging Face InferenceClient."""
client_kwargs = extra_client_kwargs.copy()
if model:
client_kwargs["model"] = model
if api_key:
client_kwargs["api_key"] = api_key
if provider:
client_kwargs["provider"] = provider
self.model = model
self._client = InferenceClient(**client_kwargs)
async def send_request(self, request: LlmRequest) -> LlmResponse:
"""Send a non-streaming request to OpenAI and return the response."""
payload = self._build_payload(request)
# Call the API synchronously; this function is async but we can block here.
resp = self._client.chat.completions.create(**payload, stream=False)
if not resp.choices:
return LlmResponse(content=None, tool_calls=None, finish_reason=None)
choice = resp.choices[0]
content: Optional[str] = getattr(choice.message, "content", None)
tool_calls = self._extract_tool_calls_from_message(choice.message)
usage: Dict[str, int] = {}
if getattr(resp, "usage", None):
usage = {
k: int(v)
for k, v in {
"prompt_tokens": getattr(resp.usage, "prompt_tokens", 0),
"completion_tokens": getattr(resp.usage, "completion_tokens", 0),
"total_tokens": getattr(resp.usage, "total_tokens", 0),
}.items()
}
return LlmResponse(
content=content,
tool_calls=tool_calls or None,
finish_reason=getattr(choice, "finish_reason", None),
usage=usage or None,
)
async def stream_request(
self, request: LlmRequest
) -> AsyncGenerator[LlmStreamChunk, None]:
"""Stream a request to OpenAI.
Emits `LlmStreamChunk` for textual deltas as they arrive. Tool-calls are
accumulated and emitted in a final chunk when the stream ends.
"""
payload = self._build_payload(request)
# Synchronous streaming iterator; iterate within async context.
stream = self._client.chat.completions.create(**payload, stream=True)
# Builders for streamed tool-calls (index -> partial)
tc_builders: Dict[int, Dict[str, Optional[str]]] = {}
last_finish: Optional[str] = None
for event in stream:
if not getattr(event, "choices", None):
continue
choice = event.choices[0]
delta = getattr(choice, "delta", None)
if delta is None:
# Some SDK versions use `event.choices[0].message` on the final packet
last_finish = getattr(choice, "finish_reason", last_finish)
continue
# Text content
content_piece: Optional[str] = getattr(delta, "content", None)
if content_piece:
yield LlmStreamChunk(content=content_piece)
# Tool calls (streamed)
streamed_tool_calls = getattr(delta, "tool_calls", None)
if streamed_tool_calls:
for tc in streamed_tool_calls:
idx = getattr(tc, "index", 0) or 0
b = tc_builders.setdefault(
idx, {"id": None, "name": None, "arguments": ""}
)
if getattr(tc, "id", None):
b["id"] = tc.id
fn = getattr(tc, "function", None)
if fn is not None:
if getattr(fn, "name", None):
b["name"] = fn.name
if getattr(fn, "arguments", None):
b["arguments"] = (b["arguments"] or "") + fn.arguments
last_finish = getattr(choice, "finish_reason", last_finish)
# Emit final tool-calls chunk if any
final_tool_calls: List[ToolCall] = []
for b in tc_builders.values():
if not b.get("name"):
continue
args_raw = b.get("arguments") or "{}"
try:
loaded = json.loads(args_raw)
if isinstance(loaded, dict):
args_dict: Dict[str, Any] = loaded
else:
args_dict = {"args": loaded}
except Exception:
args_dict = {"_raw": args_raw}
final_tool_calls.append(
ToolCall(
id=b.get("id") or "tool_call",
name=b["name"] or "tool",
arguments=args_dict,
)
)
if final_tool_calls:
yield LlmStreamChunk(tool_calls=final_tool_calls, finish_reason=last_finish)
else:
# Still emit a terminal chunk to signal completion
yield LlmStreamChunk(finish_reason=last_finish or "stop")
async def validate_tools(self, tools: List[ToolSchema]) -> List[str]:
"""Validate tool schemas. Returns a list of error messages."""
errors: List[str] = []
# Basic checks; OpenAI will enforce further validation server-side.
for t in tools:
if not t.name or len(t.name) > 64:
errors.append(f"Invalid tool name: {t.name!r}")
return errors
# Internal helpers
def _build_payload(self, request: LlmRequest) -> Dict[str, Any]:
messages: List[Dict[str, Any]] = []
# Add system prompt as first message if provided
if request.system_prompt:
messages.append({"role": "system", "content": request.system_prompt})
for m in request.messages:
msg: Dict[str, Any] = {"role": m.role, "content": m.content}
if m.role == "tool" and m.tool_call_id:
msg["tool_call_id"] = m.tool_call_id
elif m.role == "assistant" and m.tool_calls:
# Convert tool calls to OpenAI format
tool_calls_payload = []
for tc in m.tool_calls:
tool_calls_payload.append({
"id": tc.id,
"type": "function",
"function": {
"name": tc.name,
"arguments": json.dumps(tc.arguments)
}
})
msg["tool_calls"] = tool_calls_payload
messages.append(msg)
tools_payload: Optional[List[Dict[str, Any]]] = None
if request.tools:
tools_payload = [
{
"type": "function",
"function": {
"name": t.name,
"description": t.description,
"parameters": t.parameters,
},
}
for t in request.tools
]
payload: Dict[str, Any] = {
"model": self.model,
"messages": messages,
}
if request.max_tokens is not None:
payload["max_tokens"] = request.max_tokens
if tools_payload:
payload["tools"] = tools_payload
payload["tool_choice"] = "auto"
return payload
def _extract_tool_calls_from_message(self, message: Any) -> List[ToolCall]:
tool_calls: List[ToolCall] = []
raw_tool_calls = getattr(message, "tool_calls", None) or []
for tc in raw_tool_calls:
fn = getattr(tc, "function", None)
if not fn:
continue
args_raw = getattr(fn, "arguments", "{}")
try:
loaded = json.loads(args_raw)
if isinstance(loaded, dict):
args_dict: Dict[str, Any] = loaded
else:
args_dict = {"args": loaded}
except Exception:
args_dict = {"_raw": args_raw}
tool_calls.append(
ToolCall(
id=getattr(tc, "id", "tool_call"),
name=getattr(fn, "name", "tool"),
arguments=args_dict,
)
)
return tool_calls |