|
| 1 | +from typing import Dict, List, Any, Optional |
| 2 | +import json |
| 3 | +import os |
| 4 | + |
| 5 | +from langchain_core.language_models import BaseChatModel |
| 6 | +from langchain_core.tools import BaseTool, StructuredTool |
| 7 | +from langgraph.prebuilt import create_react_agent |
| 8 | +from langchain.docstore.document import Document |
| 9 | +from langchain_huggingface import HuggingFaceEmbeddings |
| 10 | +from langchain_milvus import Milvus |
| 11 | + |
| 12 | +from evaluator.components.data_provider import QuerySpecification |
| 13 | +from evaluator.config.schema import ModelConfig |
| 14 | +from evaluator.utils.module_extractor import register_algorithm |
| 15 | +from evaluator.interfaces.algorithm import Algorithm, AlgoResponse |
| 16 | +from evaluator.utils.utils import log_verbose |
| 17 | + |
| 18 | + |
| 19 | +# Constants |
| 20 | +DEFAULT_EMBEDDING_MODEL = "all-MiniLM-L6-v2" |
| 21 | +DEFAULT_SEARCH_K = 8 # LLM dynamic search default |
| 22 | +DEFAULT_MAX_RESULT_CHARS = 4000 |
| 23 | +DEFAULT_DROP_OLD_COLLECTION = True |
| 24 | +DEFAULT_COLLECTION_NAME = "tool_fetcher_tools_collection" |
| 25 | +MAX_EMBEDDING_TEXT_LENGTH = 2048 # Typical embedding model context limit |
| 26 | +MIN_K = 1 |
| 27 | +MAX_K = 50 |
| 28 | +# Substring search scoring weights |
| 29 | +NAME_MATCH_WEIGHT = 2 |
| 30 | +DESC_MATCH_WEIGHT = 1 |
| 31 | + |
| 32 | + |
| 33 | +@register_algorithm("tool_fetcher") |
| 34 | +class ToolFetcherAlgorithm(Algorithm): |
| 35 | + """ |
| 36 | + Single-tool orchestration algorithm. |
| 37 | +
|
| 38 | + Exposes one tool (tool_hub) that the model can call to search and fetch |
| 39 | + other tools dynamically based on a natural-language request. The fetched |
| 40 | + tools are then made available to the agent on subsequent invocations of |
| 41 | + tool_hub during the same query. |
| 42 | + """ |
| 43 | + |
| 44 | + def __init__(self, settings: Dict, model_config: List[ModelConfig], label: str = None): |
| 45 | + super().__init__(settings, model_config, label) |
| 46 | + self._all_tools = None |
| 47 | + self._tool_map = None # Cache for name->tool lookup |
| 48 | + self._active_tools = None |
| 49 | + self._vector_store = None |
| 50 | + self._embeddings = None |
| 51 | + |
| 52 | + def set_up(self, model: BaseChatModel, tools: List[BaseTool]) -> None: |
| 53 | + super().set_up(model, tools) |
| 54 | + self._all_tools = tools |
| 55 | + self._tool_map = {t.name: t for t in tools} # Build lookup cache once |
| 56 | + self._active_tools = [] |
| 57 | + self._build_vector_index(tools) |
| 58 | + |
| 59 | + def _build_vector_index(self, tools: List[BaseTool]) -> None: |
| 60 | + """Build Milvus vector index for tool retrieval. Falls back to None on failure.""" |
| 61 | + try: |
| 62 | + embedding_model_id = self._settings.get("embedding_model_id", DEFAULT_EMBEDDING_MODEL) |
| 63 | + log_verbose(f"Initializing embeddings with model: {embedding_model_id}") |
| 64 | + self._embeddings = HuggingFaceEmbeddings(model_name=embedding_model_id) |
| 65 | + |
| 66 | + milvus_uri = os.getenv("MILVUS_URL") or "http://localhost:19530" |
| 67 | + collection = self._settings.get("collection_name", DEFAULT_COLLECTION_NAME) |
| 68 | + drop_old = bool(self._settings.get("drop_old_collection", DEFAULT_DROP_OLD_COLLECTION)) |
| 69 | + |
| 70 | + docs = [ |
| 71 | + Document( |
| 72 | + page_content=f"name: {t.name or ''} | desc: {getattr(t, 'description', '') or ''}"[:MAX_EMBEDDING_TEXT_LENGTH], |
| 73 | + metadata={"name": t.name or ""} |
| 74 | + ) |
| 75 | + for t in tools |
| 76 | + ] |
| 77 | + |
| 78 | + log_verbose(f"Building Milvus collection: {collection} (drop_old={drop_old})") |
| 79 | + self._vector_store = Milvus.from_documents( |
| 80 | + documents=docs, |
| 81 | + embedding=self._embeddings, |
| 82 | + collection_name=collection, |
| 83 | + connection_args={"uri": milvus_uri}, |
| 84 | + drop_old=drop_old, |
| 85 | + index_params={"index_type": "FLAT", "metric_type": "COSINE"}, |
| 86 | + search_params={"metric_type": "COSINE"}, |
| 87 | + ) |
| 88 | + except Exception as e: |
| 89 | + log_verbose(f"Vector store initialization failed: {e}. Falling back to substring search.") |
| 90 | + self._vector_store = None |
| 91 | + |
| 92 | + def _clamp_k(self, k: Optional[int], default: int) -> int: |
| 93 | + """Clamp k value to valid range.""" |
| 94 | + try: |
| 95 | + value = int(k) if k is not None else default |
| 96 | + except (ValueError, TypeError): |
| 97 | + value = default |
| 98 | + return max(MIN_K, min(value, MAX_K)) |
| 99 | + |
| 100 | + def _search_tools(self, query: str, limit: int) -> List[BaseTool]: |
| 101 | + """Search tools using vector similarity or substring matching.""" |
| 102 | + if not self._all_tools: |
| 103 | + return [] |
| 104 | + |
| 105 | + # Try vector search first |
| 106 | + if self._vector_store is not None: |
| 107 | + try: |
| 108 | + results = self._vector_store.similarity_search_with_score(query or "", k=limit) |
| 109 | + ordered = [ |
| 110 | + self._tool_map[doc.metadata["name"]] |
| 111 | + for doc, _score in results |
| 112 | + if doc.metadata.get("name") in self._tool_map |
| 113 | + ] |
| 114 | + if ordered: |
| 115 | + return ordered |
| 116 | + except Exception as e: |
| 117 | + log_verbose(f"Vector search failed: {e}. Falling back to substring search.") |
| 118 | + |
| 119 | + # Fallback: substring search |
| 120 | + q = (query or "").strip().lower() |
| 121 | + ranked = [] |
| 122 | + for tool in self._all_tools: |
| 123 | + name = (tool.name or "").lower() |
| 124 | + desc = (getattr(tool, "description", "") or "").lower() |
| 125 | + score = (NAME_MATCH_WEIGHT if q in name else 0) + (DESC_MATCH_WEIGHT if q in desc else 0) |
| 126 | + if score > 0: |
| 127 | + ranked.append((score, tool)) |
| 128 | + |
| 129 | + ranked.sort(key=lambda x: x[0], reverse=True) |
| 130 | + return [t for _, t in ranked[:limit]] or self._all_tools[:limit] |
| 131 | + |
| 132 | + def _handle_search(self, query: str, k: Optional[int]) -> str: |
| 133 | + """Handle tool search action.""" |
| 134 | + default_k = self._settings.get("default_search_k", DEFAULT_SEARCH_K) |
| 135 | + limit = self._clamp_k(k, default_k) |
| 136 | + matches = self._search_tools(query, limit) |
| 137 | + |
| 138 | + existing_names = {t.name for t in self._active_tools} |
| 139 | + newly_added = [] |
| 140 | + for t in matches: |
| 141 | + if t.name not in existing_names: |
| 142 | + self._active_tools.append(t) |
| 143 | + newly_added.append(t.name) |
| 144 | + |
| 145 | + return json.dumps({ |
| 146 | + "mode": "search", |
| 147 | + "fetched": newly_added, |
| 148 | + "active": [t.name for t in self._active_tools], |
| 149 | + }) |
| 150 | + |
| 151 | + def _handle_call(self, tool_name: str, tool_input: str) -> str: |
| 152 | + """Handle tool invocation action.""" |
| 153 | + tool = self._tool_map.get(tool_name) |
| 154 | + |
| 155 | + if tool is None: |
| 156 | + return json.dumps({"mode": "call", "error": f"tool '{tool_name}' not found"}) |
| 157 | + |
| 158 | + # Parse input as JSON if possible |
| 159 | + try: |
| 160 | + parsed = json.loads(tool_input) if tool_input else tool_input |
| 161 | + except json.JSONDecodeError: |
| 162 | + parsed = tool_input |
| 163 | + |
| 164 | + # Add to active tools |
| 165 | + if tool.name not in {t.name for t in self._active_tools}: |
| 166 | + self._active_tools.append(tool) |
| 167 | + |
| 168 | + # Log tool usage |
| 169 | + self._log_tool_usage(tool.name) |
| 170 | + |
| 171 | + # Invoke tool |
| 172 | + try: |
| 173 | + result = tool.invoke(parsed) |
| 174 | + result_str = json.dumps(result) if isinstance(result, (dict, list)) else str(result) |
| 175 | + except Exception as e: |
| 176 | + log_verbose(f"Tool invocation failed for {tool.name}: {e}") |
| 177 | + return json.dumps({"mode": "call", "tool": tool.name, "error": str(e)}) |
| 178 | + |
| 179 | + max_chars = self._settings.get("max_result_chars", DEFAULT_MAX_RESULT_CHARS) |
| 180 | + return json.dumps({ |
| 181 | + "mode": "call", |
| 182 | + "tool": tool.name, |
| 183 | + "result": result_str[:max_chars], |
| 184 | + }) |
| 185 | + |
| 186 | + def _log_tool_usage(self, tool_name: str) -> None: |
| 187 | + """Log tool usage to file if TOOL_LOG_PATH is set.""" |
| 188 | + try: |
| 189 | + log_path = os.getenv("TOOL_LOG_PATH") |
| 190 | + if log_path: |
| 191 | + with open(log_path, "a") as f: |
| 192 | + f.write(f"[TOOL] {tool_name}\n") |
| 193 | + except Exception as e: |
| 194 | + log_verbose(f"Tool logging failed: {e}") |
| 195 | + |
| 196 | + def _make_tool_hub(self) -> BaseTool: |
| 197 | + """ |
| 198 | + Create the single tool_hub tool for searching and calling other tools. |
| 199 | +
|
| 200 | + The returned closure captures self for accessing instance state (_all_tools, |
| 201 | + _active_tools, _settings, etc.) and delegates to _handle_search/_handle_call. |
| 202 | + """ |
| 203 | + def run(action: str = "", query: str = "", k: Optional[int] = None, |
| 204 | + tool_name: str = "", tool_input: str = "") -> str: |
| 205 | + act = (action or "").strip().lower() |
| 206 | + |
| 207 | + if act in ("search", "find", "fetch") or (not act and query): |
| 208 | + return self._handle_search(query, k) |
| 209 | + |
| 210 | + if act == "call" or tool_name: |
| 211 | + return self._handle_call(tool_name, tool_input) |
| 212 | + |
| 213 | + return json.dumps({"error": "invalid action; use 'search' or 'call'"}) |
| 214 | + |
| 215 | + return StructuredTool.from_function( |
| 216 | + name="tool_hub", |
| 217 | + description=( |
| 218 | + "IMPORTANT: This is the ONLY tool you can call directly. All other tools must be accessed through tool_hub.\n\n" |
| 219 | + "To complete any task:\n" |
| 220 | + "1. FIRST search for relevant tools: action='search', query='description of what you need', k=8\n" |
| 221 | + "2. THEN call the found tools: action='call', tool_name='exact_tool_name', tool_input='{\"param\": \"value\"}'\n\n" |
| 222 | + "The search will return a list of available tools. You must then call each tool using action='call'." |
| 223 | + ), |
| 224 | + func=run, |
| 225 | + ) |
| 226 | + |
| 227 | + async def process_query(self, query_spec: QuerySpecification) -> AlgoResponse: |
| 228 | + """Process query using the tool hub pattern.""" |
| 229 | + if self._all_tools is None: |
| 230 | + raise RuntimeError("process_query called before set_up") |
| 231 | + |
| 232 | + # Reset active tools |
| 233 | + self._active_tools = [] |
| 234 | + |
| 235 | + # Create agent with tool_hub |
| 236 | + hub = self._make_tool_hub() |
| 237 | + agent = create_react_agent(self._model, [hub]) |
| 238 | + |
| 239 | + # No additional guidance - rely solely on tool_hub's built-in description |
| 240 | + # to isolate retrieval quality from prompt engineering effects |
| 241 | + response = await self._invoke_agent_on_query(agent, query_spec.query) |
| 242 | + |
| 243 | + # Return tools that the agent actually retrieved during execution |
| 244 | + retrieved = [t.name for t in self._active_tools] |
| 245 | + return response, retrieved |
| 246 | + |
| 247 | + def tear_down(self) -> None: |
| 248 | + """Clean up resources.""" |
| 249 | + self._all_tools = None |
| 250 | + self._tool_map = None |
| 251 | + self._active_tools = None |
| 252 | + self._vector_store = None |
| 253 | + self._embeddings = None |
| 254 | + |
| 255 | + def get_default_settings(self) -> Dict[str, Any]: |
| 256 | + """Return default configuration settings.""" |
| 257 | + return { |
| 258 | + "embedding_model_id": DEFAULT_EMBEDDING_MODEL, |
| 259 | + "default_search_k": DEFAULT_SEARCH_K, |
| 260 | + "drop_old_collection": DEFAULT_DROP_OLD_COLLECTION, |
| 261 | + "collection_name": DEFAULT_COLLECTION_NAME, |
| 262 | + "max_result_chars": DEFAULT_MAX_RESULT_CHARS, |
| 263 | + } |
| 264 | + |
| 265 | + |
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