Spaces:
Running
Running
File size: 9,979 Bytes
4ef118d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 | """
Research plan generation services using Agno ReasoningTools.
This module replaces the original prompt-based plan generation with an
agent-based approach using ReasoningTools for transparent, structured planning.
"""
from __future__ import annotations
import json
from collections.abc import AsyncGenerator
from types import SimpleNamespace
from typing import Any
from agno.agent import Agent
from agno.tools.reasoning import ReasoningTools
from ..prompts import ACADEMIC_PLANNER_PROMPT, GENERAL_PLANNER_PROMPT
from .agent_registry import _apply_model_settings, _build_model
async def generate_research_plan(
*,
provider: str,
user_message: str,
api_key: str,
base_url: str | None = None,
model: str | None = None,
temperature: float | None = None,
top_p: float | None = None,
top_k: float | None = None,
frequency_penalty: float | None = None,
presence_penalty: float | None = None,
thinking: Any = None,
) -> str:
"""
Generate a research plan using Agent with ReasoningTools.
This replaces the original prompt-based approach with an agent that uses
think() and analyze() tools for transparent, structured planning.
"""
# Build model using the same approach as agent_registry
plan_model = _build_model(provider, api_key, base_url, model)
# Apply model settings (temperature, top_p, etc.)
request = SimpleNamespace(
provider=provider,
temperature=temperature,
top_p=top_p,
top_k=top_k,
frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty,
thinking=thinking,
)
_apply_model_settings(plan_model, request)
# Create planner agent with ReasoningTools
planner = Agent(
model=plan_model,
tools=[ReasoningTools(
add_instructions=True,
enable_think=True,
enable_analyze=True
)],
instructions=GENERAL_PLANNER_PROMPT
)
# Run the planner
response = await planner.arun(user_message)
# Extract content and format as JSON string (for backward compatibility)
if hasattr(response, 'content'):
plan_text = response.content
else:
plan_text = str(response)
plan_text = plan_text.strip()
# Remove markdown code blocks if present
if plan_text.startswith("```"):
parts = plan_text.split("```")
if len(parts) >= 2:
plan_text = parts[1]
if plan_text.startswith("json"):
plan_text = plan_text[4:]
plan_text = plan_text.rstrip("`").strip()
# Validate it's valid JSON
try:
plan = json.loads(plan_text)
return json.dumps(plan, ensure_ascii=True, indent=2)
except json.JSONDecodeError:
return plan_text
async def generate_academic_research_plan(
*,
provider: str,
user_message: str,
api_key: str,
base_url: str | None = None,
model: str | None = None,
temperature: float | None = None,
top_p: float | None = None,
top_k: float | None = None,
frequency_penalty: float | None = None,
presence_penalty: float | None = None,
thinking: Any = None,
) -> str:
"""
Generate an academic research plan using Agent with ReasoningTools.
"""
# Build model using the same approach as agent_registry
plan_model = _build_model(provider, api_key, base_url, model)
# Apply model settings
request = SimpleNamespace(
provider=provider,
temperature=temperature,
top_p=top_p,
top_k=top_k,
frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty,
thinking=thinking,
)
_apply_model_settings(plan_model, request)
# Create academic planner agent with ReasoningTools
planner = Agent(
model=plan_model,
tools=[ReasoningTools(
add_instructions=True,
enable_think=True,
enable_analyze=True
)],
instructions=ACADEMIC_PLANNER_PROMPT
)
# Run the planner with the user message
response = await planner.arun(user_message)
# Extract content and format as JSON string
if hasattr(response, 'content'):
plan_text = response.content
else:
plan_text = str(response)
plan_text = plan_text.strip()
# Remove markdown code blocks if present
if plan_text.startswith("```"):
parts = plan_text.split("```")
if len(parts) >= 2:
plan_text = parts[1]
if plan_text.startswith("json"):
plan_text = plan_text[4:]
plan_text = plan_text.rstrip("`").strip()
# Validate and format
try:
plan = json.loads(plan_text)
return json.dumps(plan, ensure_ascii=True, indent=2)
except json.JSONDecodeError:
return plan_text
async def stream_generate_research_plan(
*,
provider: str,
user_message: str,
api_key: str,
base_url: str | None = None,
model: str | None = None,
temperature: float | None = None,
top_p: float | None = None,
top_k: float | None = None,
frequency_penalty: float | None = None,
presence_penalty: float | None = None,
thinking: Any = None,
) -> AsyncGenerator[dict[str, Any], None]:
"""
Stream research plan generation using Agent with ReasoningTools.
This is the streaming version of generate_research_plan that yields
events as the agent plans using think() and analyze() tools.
"""
# Build model using the same approach as agent_registry
plan_model = _build_model(provider, api_key, base_url, model)
# Apply model settings (temperature, top_p, etc.)
request = SimpleNamespace(
provider=provider,
temperature=temperature,
top_p=top_p,
top_k=top_k,
frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty,
thinking=thinking,
)
_apply_model_settings(plan_model, request)
# Create planner agent with ReasoningTools
planner = Agent(
model=plan_model,
tools=[ReasoningTools(
add_instructions=True,
enable_think=True,
enable_analyze=True
)],
instructions=GENERAL_PLANNER_PROMPT
)
# Stream the planner execution
full_content = ""
async for chunk in planner.arun(user_message, stream=True):
chunk_text = ""
if hasattr(chunk, "content"):
chunk_text = chunk.content or ""
elif isinstance(chunk, str):
chunk_text = chunk
else:
chunk_text = str(chunk)
if chunk_text:
full_content += chunk_text
yield {"type": "text", "content": chunk_text}
# Clean and finalize the plan
plan_text = full_content.strip()
# Remove markdown code blocks if present
if plan_text.startswith("```"):
parts = plan_text.split("```")
if len(parts) >= 2:
plan_text = parts[1]
if plan_text.startswith("json"):
plan_text = plan_text[4:]
plan_text = plan_text.rstrip("`").strip()
# Validate and format
try:
plan = json.loads(plan_text)
final_plan = json.dumps(plan, ensure_ascii=True, indent=2)
yield {"type": "done", "content": final_plan}
except json.JSONDecodeError:
yield {"type": "done", "content": plan_text}
async def stream_generate_academic_research_plan(
*,
provider: str,
user_message: str,
api_key: str,
base_url: str | None = None,
model: str | None = None,
temperature: float | None = None,
top_p: float | None = None,
top_k: float | None = None,
frequency_penalty: float | None = None,
presence_penalty: float | None = None,
thinking: Any = None,
) -> AsyncGenerator[dict[str, Any], None]:
"""
Stream academic research plan generation using Agent with ReasoningTools.
This is the streaming version of generate_academic_research_plan that yields
events as the agent plans using think() and analyze() tools.
"""
# Build model using the same approach as agent_registry
plan_model = _build_model(provider, api_key, base_url, model)
# Apply model settings
request = SimpleNamespace(
provider=provider,
temperature=temperature,
top_p=top_p,
top_k=top_k,
frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty,
thinking=thinking,
)
_apply_model_settings(plan_model, request)
# Create academic planner agent with ReasoningTools
planner = Agent(
model=plan_model,
tools=[ReasoningTools(
add_instructions=True,
enable_think=True,
enable_analyze=True
)],
instructions=ACADEMIC_PLANNER_PROMPT
)
# Stream the planner execution
full_content = ""
async for chunk in planner.arun(user_message, stream=True):
chunk_text = ""
if hasattr(chunk, "content"):
chunk_text = chunk.content or ""
elif isinstance(chunk, str):
chunk_text = chunk
else:
chunk_text = str(chunk)
if chunk_text:
full_content += chunk_text
yield {"type": "text", "content": chunk_text}
# Clean and finalize the plan
plan_text = full_content.strip()
# Remove markdown code blocks if present
if plan_text.startswith("```"):
parts = plan_text.split("```")
if len(parts) >= 2:
plan_text = parts[1]
if plan_text.startswith("json"):
plan_text = plan_text[4:]
plan_text = plan_text.rstrip("`").strip()
# Validate and format
try:
plan = json.loads(plan_text)
final_plan = json.dumps(plan, ensure_ascii=True, indent=2)
yield {"type": "done", "content": final_plan}
except json.JSONDecodeError:
yield {"type": "done", "content": plan_text}
|