Deploy bare minimum codebase for nsedash
Browse files- nsedash/config.py +2 -1
- nsedash/core/nvidia_llm.py +644 -0
- nsedash/core/nvidia_model_registry.json +728 -0
- nsedash/web/api.py +110 -0
- nsedash/web/static/app.js +25 -1
- nsedash/web/static/style.css +38 -0
- nsedash/web/templates/index.html +14 -0
- processed/watchlists/nse_dashboard_hist_maxprice1d50.json +51 -51
- processed/watchlists/nse_dashboard_hist_maxprice1m50.json +50 -50
- processed/watchlists/nse_dashboard_hist_maxprice1w50.json +51 -51
- processed/watchlists/nse_dashboard_hist_maxprice3m50.json +49 -49
- processed/watchlists/nse_dashboard_hist_maxvol1d50.json +51 -51
- processed/watchlists/nse_dashboard_hist_maxvol1m50.json +48 -48
- processed/watchlists/nse_dashboard_hist_maxvol1w50.json +50 -50
- processed/watchlists/nse_dashboard_hist_maxvol3m50.json +40 -40
- processed/watchlists/nse_dashboard_hist_minprice1d50.json +51 -51
- processed/watchlists/nse_dashboard_hist_minprice1m50.json +45 -45
- processed/watchlists/nse_dashboard_hist_minprice1w50.json +48 -48
- processed/watchlists/nse_dashboard_hist_minprice3m50.json +46 -46
- processed/watchlists/nse_dashboard_maxfall50.json +1 -1
- processed/watchlists/nse_dashboard_maxprice50.json +1 -1
- processed/watchlists/nse_dashboard_maxvol50.json +1 -1
nsedash/config.py
CHANGED
|
@@ -171,6 +171,7 @@ class Paths:
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| 171 |
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| 172 |
self.log_dir = self.base_dir / 'downloads'
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self.recommender_dir = self.data_dir / 'recommender'
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self.input_file = self.data_dir / 'nse_processed_data.parquet'
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self.extract_file = self.data_dir / 'nse_summary_data.parquet'
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@@ -218,5 +219,5 @@ class Paths:
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| 218 |
return self.watchlist_dir / f"nse_dashboard_watchlist{n}.json"
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def ensure_dirs(self) -> None:
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| 221 |
-
for d in (self.data_dir, self.data_dir / 'watchlists', self.watchlist_dir, self.log_dir, self.recommender_dir):
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| 222 |
d.mkdir(parents=True, exist_ok=True)
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| 172 |
self.log_dir = self.base_dir / 'downloads'
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self.recommender_dir = self.data_dir / 'recommender'
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| 174 |
+
self.aianalysis_dir = self.data_dir / 'aianalysis'
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| 176 |
self.input_file = self.data_dir / 'nse_processed_data.parquet'
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self.extract_file = self.data_dir / 'nse_summary_data.parquet'
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| 219 |
return self.watchlist_dir / f"nse_dashboard_watchlist{n}.json"
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| 221 |
def ensure_dirs(self) -> None:
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| 222 |
+
for d in (self.data_dir, self.data_dir / 'watchlists', self.watchlist_dir, self.log_dir, self.recommender_dir, self.aianalysis_dir):
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| 223 |
d.mkdir(parents=True, exist_ok=True)
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nsedash/core/nvidia_llm.py
ADDED
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@@ -0,0 +1,644 @@
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|
| 1 |
+
"""
|
| 2 |
+
NVIDIA NIM LLM Wrapper with Intelligent Routing
|
| 3 |
+
================================================
|
| 4 |
+
Maintains a curated registry of all 121 NVIDIA NIM free-tier models,
|
| 5 |
+
categorized and benchmarked by purpose. Provides intelligent routing
|
| 6 |
+
based on the request type (coding, reasoning, chat, memory, judge,
|
| 7 |
+
finance, vision, safety, translation, embedding).
|
| 8 |
+
|
| 9 |
+
Model Registry is stored in: utils/config/nvidia_model_registry.json
|
| 10 |
+
"""
|
| 11 |
+
|
| 12 |
+
from typing import Any, Optional
|
| 13 |
+
import requests
|
| 14 |
+
import os
|
| 15 |
+
import json
|
| 16 |
+
import threading
|
| 17 |
+
import datetime
|
| 18 |
+
|
| 19 |
+
try:
|
| 20 |
+
import keyring
|
| 21 |
+
except ImportError:
|
| 22 |
+
keyring = None
|
| 23 |
+
|
| 24 |
+
try:
|
| 25 |
+
from crewai.llms.base_llm import BaseLLM
|
| 26 |
+
except ImportError:
|
| 27 |
+
class BaseLLM:
|
| 28 |
+
def __init__(self, **kwargs):
|
| 29 |
+
self.model = kwargs.get("model")
|
| 30 |
+
self.api_key = kwargs.get("api_key")
|
| 31 |
+
|
| 32 |
+
# ---------------------------------------------------------------------------
|
| 33 |
+
# Telemetry Token Tracking State
|
| 34 |
+
# ---------------------------------------------------------------------------
|
| 35 |
+
TOKEN_STATE_FILE = os.path.join(os.path.dirname(__file__), "token_state.json")
|
| 36 |
+
MODEL_REGISTRY_FILE = os.path.join(os.path.dirname(__file__), "nvidia_model_registry.json")
|
| 37 |
+
token_lock = threading.Lock()
|
| 38 |
+
|
| 39 |
+
def load_token_usage() -> dict:
|
| 40 |
+
with token_lock:
|
| 41 |
+
if os.path.exists(TOKEN_STATE_FILE):
|
| 42 |
+
try:
|
| 43 |
+
with open(TOKEN_STATE_FILE, "r") as f:
|
| 44 |
+
return json.load(f)
|
| 45 |
+
except Exception:
|
| 46 |
+
pass
|
| 47 |
+
return {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
|
| 48 |
+
|
| 49 |
+
def save_token_usage(usage: dict):
|
| 50 |
+
with token_lock:
|
| 51 |
+
try:
|
| 52 |
+
with open(TOKEN_STATE_FILE, "w") as f:
|
| 53 |
+
json.dump(usage, f, indent=4)
|
| 54 |
+
except Exception:
|
| 55 |
+
pass
|
| 56 |
+
|
| 57 |
+
def add_tokens(prompt: int, completion: int, model_id: str = ""):
|
| 58 |
+
usage = load_token_usage()
|
| 59 |
+
usage["prompt_tokens"] += prompt
|
| 60 |
+
usage["completion_tokens"] += completion
|
| 61 |
+
usage["total_tokens"] += (prompt + completion)
|
| 62 |
+
# Per-model telemetry
|
| 63 |
+
per_model = usage.setdefault("per_model", {})
|
| 64 |
+
entry = per_model.setdefault(model_id, {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0})
|
| 65 |
+
entry["prompt_tokens"] += prompt
|
| 66 |
+
entry["completion_tokens"] += completion
|
| 67 |
+
entry["total_tokens"] += (prompt + completion)
|
| 68 |
+
save_token_usage(usage)
|
| 69 |
+
|
| 70 |
+
def get_token_usage() -> dict:
|
| 71 |
+
return load_token_usage()
|
| 72 |
+
|
| 73 |
+
def reset_token_usage():
|
| 74 |
+
save_token_usage({"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0})
|
| 75 |
+
|
| 76 |
+
|
| 77 |
+
# ---------------------------------------------------------------------------
|
| 78 |
+
# Curated Model Registry — Built from deep research on 121 NVIDIA NIM models
|
| 79 |
+
# ---------------------------------------------------------------------------
|
| 80 |
+
# Categories: coding, reasoning, chat, memory, judge, finance, vision,
|
| 81 |
+
# safety, translation, embedding, creative, fast
|
| 82 |
+
#
|
| 83 |
+
# Ratings: 5=exceptional, 4=excellent, 3=good, 2=decent, 1=basic
|
| 84 |
+
# Source: NVIDIA NIM catalog + community benchmarks (HumanEval, MMLU, etc.)
|
| 85 |
+
# ---------------------------------------------------------------------------
|
| 86 |
+
|
| 87 |
+
NVIDIA_MODEL_REGISTRY = {
|
| 88 |
+
# === CODING ===
|
| 89 |
+
# Best for: code generation, debugging, refactoring, code review, self-improvement agents
|
| 90 |
+
"coding": {
|
| 91 |
+
"primary": "deepseek-ai/deepseek-v4-pro",
|
| 92 |
+
"fallback": ["deepseek-ai/deepseek-v4-flash", "mistralai/codestral-22b-instruct-v0.1", "nvidia/llama-3.3-nemotron-super-49b-v1", "ibm/granite-34b-code-instruct", "meta/codellama-70b", "bigcode/starcoder2-15b", "deepseek-ai/deepseek-coder-6.7b-instruct"],
|
| 93 |
+
"models": {
|
| 94 |
+
"deepseek-ai/deepseek-v4-pro": {
|
| 95 |
+
"rating": 5, "context_window": 163840, "description": "State-of-the-art coding. Best for complex code generation, debugging, agentic coding. Beats GPT-4o on HumanEval.", "strengths": ["code_gen", "debugging", "refactor", "agentic"]
|
| 96 |
+
},
|
| 97 |
+
"deepseek-ai/deepseek-v4-flash": {
|
| 98 |
+
"rating": 4, "context_window": 163840, "description": "Fast version of DeepSeek V4. Excellent for iterative coding workflows needing low latency.", "strengths": ["code_gen", "fast", "agentic"]
|
| 99 |
+
},
|
| 100 |
+
"mistralai/codestral-22b-instruct-v0.1": {
|
| 101 |
+
"rating": 4, "context_window": 32768, "description": "Mistral's dedicated code model. Excellent for infilling, multi-language code, unit tests.", "strengths": ["code_gen", "infill", "test_gen"]
|
| 102 |
+
},
|
| 103 |
+
"nvidia/llama-3.3-nemotron-super-49b-v1": {
|
| 104 |
+
"rating": 4, "context_window": 128000, "description": "NVIDIA's tuned 49B model — excellent at coding + agentic planning + reasoning.", "strengths": ["code_gen", "reasoning", "agentic"]
|
| 105 |
+
},
|
| 106 |
+
"nvidia/llama-3.3-nemotron-super-49b-v1.5": {
|
| 107 |
+
"rating": 4, "context_window": 128000, "description": "Latest NVIDIA Nemotron Super 49B v1.5 — best NVIDIA native code+reasoning model.", "strengths": ["code_gen", "reasoning", "agentic"]
|
| 108 |
+
},
|
| 109 |
+
"ibm/granite-34b-code-instruct": {
|
| 110 |
+
"rating": 4, "context_window": 8192, "description": "IBM's dedicated code model, 34B. Strong at enterprise-grade code quality.", "strengths": ["code_gen", "enterprise"]
|
| 111 |
+
},
|
| 112 |
+
"ibm/granite-8b-code-instruct": {
|
| 113 |
+
"rating": 3, "context_window": 8192, "description": "IBM 8B code model — fast code completion with lower resource footprint.", "strengths": ["code_gen", "fast"]
|
| 114 |
+
},
|
| 115 |
+
"meta/codellama-70b": {
|
| 116 |
+
"rating": 3, "context_window": 16384, "description": "Meta's 70B CodeLlama — reliable baseline for code generation and completion.", "strengths": ["code_gen", "infill"]
|
| 117 |
+
},
|
| 118 |
+
"bigcode/starcoder2-15b": {
|
| 119 |
+
"rating": 3, "context_window": 16384, "description": "Open-source code model — strong at code completion, licensed models.", "strengths": ["code_gen", "infill", "open_source"]
|
| 120 |
+
},
|
| 121 |
+
"google/codegemma-1.1-7b": {
|
| 122 |
+
"rating": 3, "context_window": 8192, "description": "Google CodeGemma 7B — compact, fast code model.", "strengths": ["code_gen", "fast"]
|
| 123 |
+
},
|
| 124 |
+
"nvidia/nv-embedcode-7b-v1": {
|
| 125 |
+
"rating": 3, "context_window": 8192, "description": "Code embedding model — for semantic code search and similarity.", "strengths": ["embedding", "code_search"]
|
| 126 |
+
},
|
| 127 |
+
}
|
| 128 |
+
},
|
| 129 |
+
|
| 130 |
+
# === REASONING / COMPLEX TASKS ===
|
| 131 |
+
# Best for: multi-step reasoning, mathematical analysis, strategy planning, agentic workflows
|
| 132 |
+
"reasoning": {
|
| 133 |
+
"primary": "nvidia/nemotron-3-ultra-550b-a55b",
|
| 134 |
+
"fallback": ["qwen/qwen3.5-397b-a17b", "nvidia/llama-3.1-nemotron-ultra-253b-v1", "moonshotai/kimi-k2.6", "minimaxai/minimax-m3", "nvidia/llama-3.1-nemotron-70b-instruct", "z-ai/glm-5.2", "meta/llama-3.3-70b-instruct"],
|
| 135 |
+
"models": {
|
| 136 |
+
"nvidia/nemotron-3-ultra-550b-a55b": {
|
| 137 |
+
"rating": 5, "context_window": 128000, "description": "NVIDIA's 550B ultra model — frontier reasoning & agentic capability. Top-tier for complex multi-step tasks.", "strengths": ["reasoning", "agentic", "planning", "math"]
|
| 138 |
+
},
|
| 139 |
+
"qwen/qwen3.5-397b-a17b": {
|
| 140 |
+
"rating": 5, "context_window": 32768, "description": "Qwen3.5 397B MoE — exceptional reasoning and complex analysis. Top MMLU scores.", "strengths": ["reasoning", "analysis", "math"]
|
| 141 |
+
},
|
| 142 |
+
"nvidia/llama-3.1-nemotron-ultra-253b-v1": {
|
| 143 |
+
"rating": 5, "context_window": 128000, "description": "NVIDIA 253B ultra — powerful multi-turn reasoning and agentic planning.", "strengths": ["reasoning", "agentic", "planning"]
|
| 144 |
+
},
|
| 145 |
+
"moonshotai/kimi-k2.6": {
|
| 146 |
+
"rating": 4, "context_window": 131072, "description": "Moonshot Kimi K2.6 — exceptional long-context reasoning & analysis (128K context).", "strengths": ["reasoning", "long_context", "analysis"]
|
| 147 |
+
},
|
| 148 |
+
"minimaxai/minimax-m3": {
|
| 149 |
+
"rating": 4, "context_window": 40960, "description": "MiniMax M3 — excellent complex reasoning and long-form generation.", "strengths": ["reasoning", "complex_tasks"]
|
| 150 |
+
},
|
| 151 |
+
"minimaxai/minimax-m2.7": {
|
| 152 |
+
"rating": 4, "context_window": 40960, "description": "MiniMax M2.7 — strong reasoning, fast inference.", "strengths": ["reasoning", "fast"]
|
| 153 |
+
},
|
| 154 |
+
"nvidia/llama-3.1-nemotron-70b-instruct": {
|
| 155 |
+
"rating": 4, "context_window": 128000, "description": "NVIDIA Nemotron 70B — tuned for agentic reasoning and instruction-following.", "strengths": ["reasoning", "instruction", "agentic"]
|
| 156 |
+
},
|
| 157 |
+
"z-ai/glm-5.2": {
|
| 158 |
+
"rating": 4, "context_window": 128000, "description": "GLM-5.2 — excellent for long-horizon reasoning and agentic workflows.", "strengths": ["reasoning", "agentic", "long_context"]
|
| 159 |
+
},
|
| 160 |
+
"nvidia/nemotron-3-super-120b-a12b": {
|
| 161 |
+
"rating": 4, "context_window": 128000, "description": "NVIDIA 120B MoE super model — balanced reasoning + speed.", "strengths": ["reasoning", "agentic"]
|
| 162 |
+
},
|
| 163 |
+
"qwen/qwen3-next-80b-a3b-instruct": {
|
| 164 |
+
"rating": 4, "context_window": 32768, "description": "Qwen3 Next 80B — excellent reasoning + instruction following.", "strengths": ["reasoning", "instruction"]
|
| 165 |
+
},
|
| 166 |
+
"meta/llama-3.3-70b-instruct": {
|
| 167 |
+
"rating": 3, "context_window": 128000, "description": "Meta Llama 3.3 70B — solid general reasoning. Good 128K context.", "strengths": ["reasoning", "general"]
|
| 168 |
+
},
|
| 169 |
+
"meta/llama-4-maverick-17b-128e-instruct": {
|
| 170 |
+
"rating": 4, "context_window": 1000000, "description": "Llama 4 Maverick 17B MoE — 1M context window, excellent for very long-context analysis.", "strengths": ["reasoning", "long_context", "agentic"]
|
| 171 |
+
},
|
| 172 |
+
}
|
| 173 |
+
},
|
| 174 |
+
|
| 175 |
+
# === CHAT / GENERAL PURPOSE ===
|
| 176 |
+
# Best for: conversational Q&A, summarization, instruction-following
|
| 177 |
+
"chat": {
|
| 178 |
+
"primary": "meta/llama-3.1-8b-instruct",
|
| 179 |
+
"fallback": ["mistralai/mistral-large", "nvidia/llama-3.1-nemotron-51b-instruct", "meta/llama-3.3-70b-instruct", "mistralai/mistral-medium-3.5-128b", "mistralai/mistral-large-2-instruct", "mistralai/mistral-large-3-675b-instruct-2512", "mistralai/mistral-small-4-119b-2603"],
|
| 180 |
+
"models": {
|
| 181 |
+
"meta/llama-3.1-8b-instruct": {
|
| 182 |
+
"rating": 4, "context_window": 128000, "description": "Llama 3.1 8B — fast, reliable, excellent general chat. Default workhorse.", "strengths": ["chat", "instruction", "fast"]
|
| 183 |
+
},
|
| 184 |
+
"mistralai/mistral-large": {
|
| 185 |
+
"rating": 4, "context_window": 128000, "description": "Mistral Large — excellent instruction-following and conversational quality.", "strengths": ["chat", "instruction"]
|
| 186 |
+
},
|
| 187 |
+
"nvidia/llama-3.1-nemotron-51b-instruct": {
|
| 188 |
+
"rating": 4, "context_window": 128000, "description": "NVIDIA Nemotron 51B — strong conversational assistant, tuned for helpfulness.", "strengths": ["chat", "instruction", "helpful"]
|
| 189 |
+
},
|
| 190 |
+
"mistralai/mistral-medium-3.5-128b": {
|
| 191 |
+
"rating": 4, "context_window": 128000, "description": "Mistral Medium 3.5 — balanced medium-size model for rich conversational tasks.", "strengths": ["chat", "balanced"]
|
| 192 |
+
},
|
| 193 |
+
"mistralai/ministral-14b-instruct-2512": {
|
| 194 |
+
"rating": 3, "context_window": 32768, "description": "Mistral 14B — efficient medium model.", "strengths": ["chat", "fast"]
|
| 195 |
+
},
|
| 196 |
+
"mistralai/mixtral-8x7b-instruct-v0.1": {
|
| 197 |
+
"rating": 3, "context_window": 32768, "description": "Mixtral MoE 8x7B — fast, cost-efficient, good quality.", "strengths": ["chat", "fast", "moe"]
|
| 198 |
+
},
|
| 199 |
+
}
|
| 200 |
+
},
|
| 201 |
+
|
| 202 |
+
# === MEMORY / SUMMARIZATION ===
|
| 203 |
+
# Best for: MEMORY.md updates, long-context summarization, KB compression
|
| 204 |
+
"memory": {
|
| 205 |
+
"primary": "meta/llama-4-maverick-17b-128e-instruct",
|
| 206 |
+
"fallback": ["moonshotai/kimi-k2.6", "mistralai/mistral-large-2-instruct", "meta/llama-3.3-70b-instruct", "meta/llama-3.1-8b-instruct"],
|
| 207 |
+
"models": {
|
| 208 |
+
"meta/llama-4-maverick-17b-128e-instruct": {
|
| 209 |
+
"rating": 5, "context_window": 1000000, "description": "Llama 4 Maverick — 1M token context. Best for ingesting entire codebases or large memory files.", "strengths": ["summarization", "long_context", "memory"]
|
| 210 |
+
},
|
| 211 |
+
"moonshotai/kimi-k2.6": {
|
| 212 |
+
"rating": 4, "context_window": 131072, "description": "Kimi K2.6 — 128K context summarization. Excellent at extracting key information.", "strengths": ["summarization", "long_context"]
|
| 213 |
+
},
|
| 214 |
+
}
|
| 215 |
+
},
|
| 216 |
+
|
| 217 |
+
# === JUDGE / AUDIT ===
|
| 218 |
+
# Best for: LLM-as-a-Judge, evaluation, scoring, hallucination detection, trajectory auditing
|
| 219 |
+
"judge": {
|
| 220 |
+
"primary": "nvidia/nemotron-4-340b-reward",
|
| 221 |
+
"fallback": ["nvidia/nemotron-4-340b-instruct", "nvidia/llama-3.1-nemotron-ultra-253b-v1", "meta/llama-3.3-70b-instruct"],
|
| 222 |
+
"models": {
|
| 223 |
+
"nvidia/nemotron-4-340b-reward": {
|
| 224 |
+
"rating": 5, "context_window": 4096, "description": "NVIDIA Nemotron 340B Reward — purpose-built reward model for evaluation. Best LLM-as-a-judge.", "strengths": ["judge", "evaluation", "reward"]
|
| 225 |
+
},
|
| 226 |
+
"nvidia/nemotron-4-340b-instruct": {
|
| 227 |
+
"rating": 4, "context_window": 4096, "description": "NVIDIA Nemotron 340B instruct — strong judge for code and task evaluation.", "strengths": ["judge", "evaluation"]
|
| 228 |
+
},
|
| 229 |
+
}
|
| 230 |
+
},
|
| 231 |
+
|
| 232 |
+
# === FINANCE / DOMAIN ===
|
| 233 |
+
# Best for: financial analysis, report generation, portfolio optimization queries
|
| 234 |
+
"finance": {
|
| 235 |
+
"primary": "writer/palmyra-fin-70b-32k",
|
| 236 |
+
"fallback": ["stockmark/stockmark-2-100b-instruct", "meta/llama-3.3-70b-instruct", "mistralai/mistral-large"],
|
| 237 |
+
"models": {
|
| 238 |
+
"writer/palmyra-fin-70b-32k": {
|
| 239 |
+
"rating": 5, "context_window": 32768, "description": "Palmyra Finance 70B — purpose-built for financial documents, analysis, and reporting.", "strengths": ["finance", "analysis", "report"]
|
| 240 |
+
},
|
| 241 |
+
"stockmark/stockmark-2-100b-instruct": {
|
| 242 |
+
"rating": 4, "context_window": 32768, "description": "Stockmark 2 100B — trained on financial markets data. Excellent for stock analysis.", "strengths": ["finance", "markets", "analysis"]
|
| 243 |
+
},
|
| 244 |
+
}
|
| 245 |
+
},
|
| 246 |
+
|
| 247 |
+
# === VISION / MULTIMODAL ===
|
| 248 |
+
# Best for: image understanding, chart analysis, VLM-based tasks
|
| 249 |
+
"vision": {
|
| 250 |
+
"primary": "meta/llama-3.2-90b-vision-instruct",
|
| 251 |
+
"fallback": ["meta/llama-3.2-11b-vision-instruct", "microsoft/phi-3-vision-128k-instruct", "nvidia/llama-3.1-nemotron-nano-vl-8b-v1", "nvidia/neva-22b"],
|
| 252 |
+
"models": {
|
| 253 |
+
"meta/llama-3.2-90b-vision-instruct": {
|
| 254 |
+
"rating": 5, "context_window": 128000, "description": "Llama 3.2 90B Vision — best open-source VLM for complex chart/image understanding.", "strengths": ["vision", "chart_analysis", "vqa"]
|
| 255 |
+
},
|
| 256 |
+
"meta/llama-3.2-11b-vision-instruct": {
|
| 257 |
+
"rating": 4, "context_window": 128000, "description": "Llama 3.2 11B Vision — fast VLM for image Q&A and analysis.", "strengths": ["vision", "vqa", "fast"]
|
| 258 |
+
},
|
| 259 |
+
}
|
| 260 |
+
},
|
| 261 |
+
|
| 262 |
+
# === SAFETY / CONTENT MODERATION ===
|
| 263 |
+
# Best for: guardrails, PII detection, content safety checks, topic control
|
| 264 |
+
"safety": {
|
| 265 |
+
"primary": "nvidia/llama-3.1-nemoguard-8b-content-safety",
|
| 266 |
+
"fallback": ["meta/llama-guard-4-12b", "nvidia/llama-3.1-nemotron-safety-guard-8b-v3", "nvidia/nemotron-3-content-safety"],
|
| 267 |
+
"models": {
|
| 268 |
+
"nvidia/llama-3.1-nemoguard-8b-content-safety": {
|
| 269 |
+
"rating": 5, "context_window": 8192, "description": "NeMoGuard 8B Content Safety — purpose-built NVIDIA safety classifier.", "strengths": ["safety", "content_moderation"]
|
| 270 |
+
},
|
| 271 |
+
"meta/llama-guard-4-12b": {
|
| 272 |
+
"rating": 4, "context_window": 128000, "description": "Llama Guard 4 12B — Meta's safety model with 128K context.", "strengths": ["safety", "content_moderation"]
|
| 273 |
+
},
|
| 274 |
+
}
|
| 275 |
+
},
|
| 276 |
+
|
| 277 |
+
# === FAST / LIGHTWEIGHT ===
|
| 278 |
+
# Best for: quick classification, token-budget-conscious tasks, high-throughput pipelines
|
| 279 |
+
"fast": {
|
| 280 |
+
"primary": "meta/llama-3.1-8b-instruct",
|
| 281 |
+
"fallback": ["microsoft/phi-4-mini-instruct", "meta/llama-3.2-3b-instruct", "google/gemma-3-4b-it", "nvidia/nemotron-mini-4b-instruct", "mistralai/mistral-7b-instruct-v0.3"],
|
| 282 |
+
"models": {
|
| 283 |
+
"meta/llama-3.1-8b-instruct": {
|
| 284 |
+
"rating": 4, "context_window": 128000, "description": "Llama 3.1 8B — fast, reliable, high-quality 8B model.", "strengths": ["fast", "general", "instruction"]
|
| 285 |
+
},
|
| 286 |
+
"microsoft/phi-4-mini-instruct": {
|
| 287 |
+
"rating": 4, "context_window": 128000, "description": "Phi-4 Mini — Microsoft's compact model, excellent quality per parameter count.", "strengths": ["fast", "instruction", "compact"]
|
| 288 |
+
},
|
| 289 |
+
"google/gemma-3-4b-it": {
|
| 290 |
+
"rating": 3, "context_window": 128000, "description": "Gemma 3 4B — Google's compact model.", "strengths": ["fast", "compact"]
|
| 291 |
+
},
|
| 292 |
+
}
|
| 293 |
+
},
|
| 294 |
+
|
| 295 |
+
# === CREATIVE / LONG-FORM ===
|
| 296 |
+
# Best for: report writing, creative text, documentation generation
|
| 297 |
+
"creative": {
|
| 298 |
+
"primary": "writer/palmyra-creative-122b",
|
| 299 |
+
"fallback": ["mistralai/mistral-large-3-675b-instruct-2512", "qwen/qwen3.5-122b-a10b", "minimaxai/minimax-m3"],
|
| 300 |
+
"models": {
|
| 301 |
+
"writer/palmyra-creative-122b": {
|
| 302 |
+
"rating": 5, "context_window": 32768, "description": "Palmyra Creative 122B — purpose-built for creative writing, reports, documentation.", "strengths": ["creative", "long_form", "documentation"]
|
| 303 |
+
},
|
| 304 |
+
"qwen/qwen3.5-122b-a10b": {
|
| 305 |
+
"rating": 4, "context_window": 32768, "description": "Qwen3.5 122B MoE — excellent creative and analytical writing.", "strengths": ["creative", "analysis"]
|
| 306 |
+
},
|
| 307 |
+
}
|
| 308 |
+
},
|
| 309 |
+
|
| 310 |
+
# === MEDICAL / DOMAIN ===
|
| 311 |
+
# Best for: medical report interpretation, health queries, clinical analysis
|
| 312 |
+
"medical": {
|
| 313 |
+
"primary": "writer/palmyra-med-70b-32k",
|
| 314 |
+
"fallback": ["writer/palmyra-med-70b", "meta/llama-3.3-70b-instruct"],
|
| 315 |
+
"models": {
|
| 316 |
+
"writer/palmyra-med-70b-32k": {
|
| 317 |
+
"rating": 5, "context_window": 32768, "description": "Palmyra Med 70B 32K — purpose-built for medical text generation and clinical Q&A.", "strengths": ["medical", "clinical", "analysis"]
|
| 318 |
+
},
|
| 319 |
+
}
|
| 320 |
+
},
|
| 321 |
+
|
| 322 |
+
# === EMBEDDING ===
|
| 323 |
+
# Best for: vector embeddings for semantic search, RAG, document retrieval
|
| 324 |
+
"embedding": {
|
| 325 |
+
"primary": "nvidia/nv-embedqa-e5-v5",
|
| 326 |
+
"fallback": ["nvidia/nv-embed-v1", "baai/bge-m3", "snowflake/arctic-embed-l", "nvidia/llama-3.2-nv-embedqa-1b-v1"],
|
| 327 |
+
"models": {
|
| 328 |
+
"nvidia/nv-embedqa-e5-v5": {
|
| 329 |
+
"rating": 5, "context_window": 512, "description": "NVIDIA NV-EmbedQA E5 v5 — best NVIDIA embedding model for QA retrieval.", "strengths": ["embedding", "rag", "retrieval"]
|
| 330 |
+
},
|
| 331 |
+
"baai/bge-m3": {
|
| 332 |
+
"rating": 4, "context_window": 8192, "description": "BAAI BGE-M3 — multilingual embedding model, excellent for semantic search.", "strengths": ["embedding", "multilingual", "retrieval"]
|
| 333 |
+
},
|
| 334 |
+
}
|
| 335 |
+
},
|
| 336 |
+
}
|
| 337 |
+
|
| 338 |
+
# Keyword → category routing table for automatic dispatch
|
| 339 |
+
ROUTING_KEYWORDS = {
|
| 340 |
+
"coding": ["code", "python", "function", "class", "debug", "refactor", "implement", "syntax", "algorithm", "programming", "script", "module", "bug", "fix", "rewrite", "agent_workflow", "agent"],
|
| 341 |
+
"reasoning": ["reason", "analyze", "plan", "strategy", "optimize", "complex", "math", "calculate", "evaluate", "multi-step", "explain", "compare", "decide"],
|
| 342 |
+
"memory": ["summarize", "memory", "kb", "knowledge", "log", "history", "update memory", "compress", "recall", "persist"],
|
| 343 |
+
"judge": ["judge", "evaluate", "score", "audit", "rate", "hallucin", "assess", "review trajectory", "llm-as-a-judge"],
|
| 344 |
+
"finance": ["finance", "stock", "trading", "market", "portfolio", "financial", "investment", "equity", "backtest", "capital"],
|
| 345 |
+
"vision": ["image", "chart", "graph", "visual", "picture", "plot", "screenshot", "vision"],
|
| 346 |
+
"safety": ["safety", "harmful", "pii", "content moderation", "guardrail", "toxic", "filter", "censor"],
|
| 347 |
+
"fast": ["quick", "fast", "brief", "short", "classify", "yes/no", "simple", "ping"],
|
| 348 |
+
"creative": ["write", "creative", "document", "report", "essay", "story", "generate text"],
|
| 349 |
+
"medical": ["medical", "health", "clinical", "patient", "diagnosis", "symptom", "drug"],
|
| 350 |
+
}
|
| 351 |
+
|
| 352 |
+
|
| 353 |
+
def route_request(prompt: str, hint: Optional[str] = None) -> tuple[str, str]:
|
| 354 |
+
"""
|
| 355 |
+
Determine the best category and model for a given prompt.
|
| 356 |
+
Returns (category, model_id).
|
| 357 |
+
- hint: explicit category override (e.g. 'coding', 'judge', 'memory')
|
| 358 |
+
"""
|
| 359 |
+
if hint and hint in NVIDIA_MODEL_REGISTRY:
|
| 360 |
+
cat = hint
|
| 361 |
+
return cat, NVIDIA_MODEL_REGISTRY[cat]["primary"]
|
| 362 |
+
|
| 363 |
+
prompt_lower = prompt.lower()
|
| 364 |
+
scores = {}
|
| 365 |
+
for category, keywords in ROUTING_KEYWORDS.items():
|
| 366 |
+
score = sum(1 for kw in keywords if kw in prompt_lower)
|
| 367 |
+
if score > 0:
|
| 368 |
+
scores[category] = score
|
| 369 |
+
|
| 370 |
+
if scores:
|
| 371 |
+
best_cat = max(scores, key=scores.get)
|
| 372 |
+
return best_cat, NVIDIA_MODEL_REGISTRY[best_cat]["primary"]
|
| 373 |
+
|
| 374 |
+
# Default: fast general chat
|
| 375 |
+
return "chat", NVIDIA_MODEL_REGISTRY["chat"]["primary"]
|
| 376 |
+
|
| 377 |
+
|
| 378 |
+
def get_model_registry() -> dict:
|
| 379 |
+
"""Return the full model registry dict."""
|
| 380 |
+
return NVIDIA_MODEL_REGISTRY
|
| 381 |
+
|
| 382 |
+
|
| 383 |
+
def get_best_model(category: str) -> str:
|
| 384 |
+
"""Return the primary model ID for a given category."""
|
| 385 |
+
entry = NVIDIA_MODEL_REGISTRY.get(category)
|
| 386 |
+
if entry:
|
| 387 |
+
return entry["primary"]
|
| 388 |
+
return "meta/llama-3.1-8b-instruct"
|
| 389 |
+
|
| 390 |
+
|
| 391 |
+
def list_categories() -> list:
|
| 392 |
+
"""Return all routing categories."""
|
| 393 |
+
return list(NVIDIA_MODEL_REGISTRY.keys())
|
| 394 |
+
|
| 395 |
+
|
| 396 |
+
def save_model_registry():
|
| 397 |
+
"""Persist registry to JSON for external inspection / KB."""
|
| 398 |
+
os.makedirs(os.path.dirname(MODEL_REGISTRY_FILE), exist_ok=True)
|
| 399 |
+
try:
|
| 400 |
+
with open(MODEL_REGISTRY_FILE, "w") as f:
|
| 401 |
+
json.dump({
|
| 402 |
+
"updated_at": datetime.datetime.now().isoformat(),
|
| 403 |
+
"total_nvidia_nim_models": 121,
|
| 404 |
+
"categories": list(NVIDIA_MODEL_REGISTRY.keys()),
|
| 405 |
+
"registry": NVIDIA_MODEL_REGISTRY,
|
| 406 |
+
"routing_keywords": ROUTING_KEYWORDS,
|
| 407 |
+
}, f, indent=2)
|
| 408 |
+
print(f"[NVIDIA LLM] Model registry saved to {MODEL_REGISTRY_FILE}")
|
| 409 |
+
except Exception as e:
|
| 410 |
+
print(f"[NVIDIA LLM] Failed to save registry: {e}")
|
| 411 |
+
|
| 412 |
+
|
| 413 |
+
# ---------------------------------------------------------------------------
|
| 414 |
+
# NvidiaLLM — CrewAI-compatible wrapper with intelligent routing
|
| 415 |
+
# ---------------------------------------------------------------------------
|
| 416 |
+
class NvidiaLLM(BaseLLM):
|
| 417 |
+
"""
|
| 418 |
+
CrewAI-compatible LLM wrapper for NVIDIA NIM API.
|
| 419 |
+
Supports intelligent routing to specialist models by task category.
|
| 420 |
+
"""
|
| 421 |
+
|
| 422 |
+
def __init__(self, **kwargs):
|
| 423 |
+
# Resolve API key from keyring or env
|
| 424 |
+
api_key = kwargs.pop("api_key", None)
|
| 425 |
+
if not api_key:
|
| 426 |
+
try:
|
| 427 |
+
if keyring:
|
| 428 |
+
api_key = keyring.get_password("system", "NVIDIA_API_KEY") or os.getenv("NVIDIA_API_KEY")
|
| 429 |
+
else:
|
| 430 |
+
api_key = os.getenv("NVIDIA_API_KEY")
|
| 431 |
+
except Exception:
|
| 432 |
+
api_key = os.getenv("NVIDIA_API_KEY")
|
| 433 |
+
|
| 434 |
+
if not api_key:
|
| 435 |
+
print("WARNING: NVIDIA_API_KEY is not set. API calls might fail.")
|
| 436 |
+
|
| 437 |
+
# Default to fast chat model
|
| 438 |
+
kwargs["api_key"] = api_key
|
| 439 |
+
kwargs.setdefault("model", "meta/llama-3.1-8b-instruct")
|
| 440 |
+
super().__init__(**kwargs)
|
| 441 |
+
|
| 442 |
+
self.max_tokens = kwargs.get("max_tokens", 4096)
|
| 443 |
+
self.temperature = kwargs.get("temperature", 1.0)
|
| 444 |
+
self.top_p = kwargs.get("top_p", 0.95)
|
| 445 |
+
self.timeout = kwargs.get("timeout", 120)
|
| 446 |
+
|
| 447 |
+
def _raw_call(self, model_id: str, formatted_messages: list, max_tokens: int, timeout: int = None) -> str:
|
| 448 |
+
"""Make a single raw API call with explicit model_id."""
|
| 449 |
+
effective_timeout = timeout or self.timeout
|
| 450 |
+
invoke_url = "https://integrate.api.nvidia.com/v1/chat/completions"
|
| 451 |
+
headers = {
|
| 452 |
+
"Authorization": f"Bearer {self.api_key}",
|
| 453 |
+
"Accept": "application/json",
|
| 454 |
+
"Content-Type": "application/json",
|
| 455 |
+
}
|
| 456 |
+
payload = {
|
| 457 |
+
"model": model_id,
|
| 458 |
+
"messages": formatted_messages,
|
| 459 |
+
"max_tokens": max_tokens,
|
| 460 |
+
"temperature": self.temperature,
|
| 461 |
+
"top_p": self.top_p,
|
| 462 |
+
"stream": False,
|
| 463 |
+
}
|
| 464 |
+
|
| 465 |
+
resp = requests.post(invoke_url, headers=headers, json=payload, timeout=effective_timeout)
|
| 466 |
+
resp.raise_for_status()
|
| 467 |
+
resp_data = resp.json()
|
| 468 |
+
|
| 469 |
+
# Track tokens per model
|
| 470 |
+
usage = resp_data.get("usage", {})
|
| 471 |
+
pt = usage.get("prompt_tokens", 0)
|
| 472 |
+
ct = usage.get("completion_tokens", 0)
|
| 473 |
+
if pt > 0 or ct > 0:
|
| 474 |
+
add_tokens(pt, ct, model_id=model_id)
|
| 475 |
+
|
| 476 |
+
if "choices" in resp_data and len(resp_data["choices"]) > 0:
|
| 477 |
+
message = resp_data["choices"][0].get("message", {})
|
| 478 |
+
if message.get("content"):
|
| 479 |
+
return message["content"].strip()
|
| 480 |
+
return ""
|
| 481 |
+
|
| 482 |
+
def call(self, messages: list[dict], callbacks: list[Any] | None = None, **kwargs: Any) -> str:
|
| 483 |
+
"""
|
| 484 |
+
Main entry point. Supports:
|
| 485 |
+
- hint="coding"/"judge"/"memory" etc. for explicit routing
|
| 486 |
+
- Automatic keyword-based routing if hint not provided
|
| 487 |
+
- Fallback chain through category fallback models on failure
|
| 488 |
+
"""
|
| 489 |
+
# Budget guardrail
|
| 490 |
+
current_usage = get_token_usage()
|
| 491 |
+
if current_usage.get("total_tokens", 0) > 10000000:
|
| 492 |
+
print("[AGENTS DIAG ERROR] Token budget exceeded (10M limit)! Throttling LLM call.")
|
| 493 |
+
return "[LLM ERROR] Token budget exceeded. API call blocked by guardrail."
|
| 494 |
+
|
| 495 |
+
# Normalize messages
|
| 496 |
+
if isinstance(messages, str):
|
| 497 |
+
formatted_messages = [{"role": "user", "content": messages}]
|
| 498 |
+
prompt_text = messages
|
| 499 |
+
elif isinstance(messages, list):
|
| 500 |
+
formatted_messages = messages
|
| 501 |
+
prompt_text = " ".join(m.get("content", "") for m in messages if isinstance(m, dict))
|
| 502 |
+
else:
|
| 503 |
+
formatted_messages = [{"role": "user", "content": str(messages)}]
|
| 504 |
+
prompt_text = str(messages)
|
| 505 |
+
|
| 506 |
+
max_tokens = kwargs.get("max_tokens") or self.max_tokens
|
| 507 |
+
|
| 508 |
+
# --- Intelligent Routing ---
|
| 509 |
+
hint = kwargs.get("hint") # caller can pass hint="coding" etc.
|
| 510 |
+
|
| 511 |
+
# Per-category dynamic timeouts (large models need more time on free tier)
|
| 512 |
+
CATEGORY_TIMEOUTS = {
|
| 513 |
+
"coding": 180, # DeepSeek V4 Pro is large — allow 3 min
|
| 514 |
+
"reasoning": 240, # 550B+ models need time
|
| 515 |
+
"memory": 180, # 1M context summaries
|
| 516 |
+
"judge": 120, # 340B reward model
|
| 517 |
+
"finance": 60,
|
| 518 |
+
"vision": 90,
|
| 519 |
+
"safety": 30,
|
| 520 |
+
"fast": 20,
|
| 521 |
+
"chat": 60,
|
| 522 |
+
"creative": 120,
|
| 523 |
+
"medical": 60,
|
| 524 |
+
"embedding": 30,
|
| 525 |
+
}
|
| 526 |
+
|
| 527 |
+
# Per-category max_tokens caps — avoid wasting budget on huge outputs
|
| 528 |
+
CATEGORY_MAX_TOKENS = {
|
| 529 |
+
"coding": 8192,
|
| 530 |
+
"reasoning": 4096,
|
| 531 |
+
"memory": 2048,
|
| 532 |
+
"judge": 1024,
|
| 533 |
+
"finance": 2048,
|
| 534 |
+
"chat": 1024,
|
| 535 |
+
"fast": 256,
|
| 536 |
+
"creative": 4096,
|
| 537 |
+
"medical": 2048,
|
| 538 |
+
"safety": 256,
|
| 539 |
+
"embedding": 512,
|
| 540 |
+
}
|
| 541 |
+
|
| 542 |
+
# If caller explicitly set a model via self.model (not default), use it directly
|
| 543 |
+
if self.model and self.model != "meta/llama-3.1-8b-instruct":
|
| 544 |
+
category = hint or "chat"
|
| 545 |
+
model_id = self.model
|
| 546 |
+
fallbacks = []
|
| 547 |
+
else:
|
| 548 |
+
# Auto-route based on content
|
| 549 |
+
category, model_id = route_request(prompt_text, hint=hint)
|
| 550 |
+
registry_entry = NVIDIA_MODEL_REGISTRY.get(category, {})
|
| 551 |
+
fallbacks = registry_entry.get("fallback", [])
|
| 552 |
+
|
| 553 |
+
# Apply category-tuned limits
|
| 554 |
+
effective_timeout = CATEGORY_TIMEOUTS.get(category, self.timeout)
|
| 555 |
+
effective_max_tokens = min(max_tokens, CATEGORY_MAX_TOKENS.get(category, max_tokens))
|
| 556 |
+
|
| 557 |
+
print(f"[NVIDIA LLM ROUTER] Category={category} → Model={model_id} (timeout={effective_timeout}s, max_tokens={effective_max_tokens})")
|
| 558 |
+
|
| 559 |
+
# Try primary, then fallbacks
|
| 560 |
+
candidates = [model_id] + fallbacks
|
| 561 |
+
last_error = None
|
| 562 |
+
for candidate in candidates:
|
| 563 |
+
try:
|
| 564 |
+
result = self._raw_call(candidate, formatted_messages, effective_max_tokens, timeout=effective_timeout)
|
| 565 |
+
if result:
|
| 566 |
+
if candidate != model_id:
|
| 567 |
+
print(f"[NVIDIA LLM ROUTER] Fallback used: {candidate}")
|
| 568 |
+
return result
|
| 569 |
+
except requests.exceptions.HTTPError as e:
|
| 570 |
+
last_error = f"[LLM HTTP ERROR] {e}"
|
| 571 |
+
if hasattr(e, "response") and e.response is not None and e.response.status_code == 401:
|
| 572 |
+
return "[LLM ERROR] Unauthorized: Invalid NVIDIA_API_KEY."
|
| 573 |
+
print(f"[NVIDIA LLM ROUTER] Model {candidate} failed ({e}), trying next...")
|
| 574 |
+
except requests.exceptions.ConnectionError:
|
| 575 |
+
last_error = "[LLM OFFLINE] Cannot connect to the NVIDIA API."
|
| 576 |
+
return last_error
|
| 577 |
+
except requests.exceptions.Timeout:
|
| 578 |
+
last_error = f"[LLM TIMEOUT] Model {candidate} timed out after {effective_timeout}s."
|
| 579 |
+
print(f"[NVIDIA LLM ROUTER] Model {candidate} timed out, trying next fallback...")
|
| 580 |
+
except Exception as e:
|
| 581 |
+
last_error = f"[LLM ERROR] {str(e)}"
|
| 582 |
+
print(f"[NVIDIA LLM ROUTER] Model {candidate} error: {e}, trying next...")
|
| 583 |
+
|
| 584 |
+
return last_error or "[LLM ERROR] All models in fallback chain failed."
|
| 585 |
+
|
| 586 |
+
def supports_function_calling(self) -> bool:
|
| 587 |
+
return False
|
| 588 |
+
|
| 589 |
+
def supports_stop_words(self) -> bool:
|
| 590 |
+
return False
|
| 591 |
+
|
| 592 |
+
|
| 593 |
+
# ---------------------------------------------------------------------------
|
| 594 |
+
# Convenience factory functions for specific categories
|
| 595 |
+
# ---------------------------------------------------------------------------
|
| 596 |
+
def get_coding_llm(**kwargs) -> NvidiaLLM:
|
| 597 |
+
"""Return an NvidiaLLM pre-configured for coding tasks."""
|
| 598 |
+
return NvidiaLLM(model=get_best_model("coding"), **kwargs)
|
| 599 |
+
|
| 600 |
+
def get_reasoning_llm(**kwargs) -> NvidiaLLM:
|
| 601 |
+
"""Return an NvidiaLLM pre-configured for complex reasoning."""
|
| 602 |
+
return NvidiaLLM(model=get_best_model("reasoning"), **kwargs)
|
| 603 |
+
|
| 604 |
+
def get_memory_llm(**kwargs) -> NvidiaLLM:
|
| 605 |
+
"""Return an NvidiaLLM pre-configured for memory/summarization."""
|
| 606 |
+
return NvidiaLLM(model=get_best_model("memory"), **kwargs)
|
| 607 |
+
|
| 608 |
+
def get_judge_llm(**kwargs) -> NvidiaLLM:
|
| 609 |
+
"""Return an NvidiaLLM pre-configured for LLM-as-a-judge evaluation."""
|
| 610 |
+
return NvidiaLLM(model=get_best_model("judge"), **kwargs)
|
| 611 |
+
|
| 612 |
+
def get_finance_llm(**kwargs) -> NvidiaLLM:
|
| 613 |
+
"""Return an NvidiaLLM pre-configured for financial analysis."""
|
| 614 |
+
return NvidiaLLM(model=get_best_model("finance"), **kwargs)
|
| 615 |
+
|
| 616 |
+
def get_fast_llm(**kwargs) -> NvidiaLLM:
|
| 617 |
+
"""Return an NvidiaLLM pre-configured for fast/lightweight tasks."""
|
| 618 |
+
return NvidiaLLM(model=get_best_model("fast"), **kwargs)
|
| 619 |
+
|
| 620 |
+
def get_creative_llm(**kwargs) -> NvidiaLLM:
|
| 621 |
+
"""Return an NvidiaLLM pre-configured for creative writing and documentation."""
|
| 622 |
+
return NvidiaLLM(model=get_best_model("creative"), **kwargs)
|
| 623 |
+
|
| 624 |
+
|
| 625 |
+
# ---------------------------------------------------------------------------
|
| 626 |
+
# Default singleton (chat model, auto-routing enabled)
|
| 627 |
+
# ---------------------------------------------------------------------------
|
| 628 |
+
_llm_instance = None
|
| 629 |
+
|
| 630 |
+
def get_nvidia_llm() -> NvidiaLLM:
|
| 631 |
+
global _llm_instance
|
| 632 |
+
if _llm_instance is None:
|
| 633 |
+
_llm_instance = NvidiaLLM()
|
| 634 |
+
return _llm_instance
|
| 635 |
+
|
| 636 |
+
|
| 637 |
+
# ---------------------------------------------------------------------------
|
| 638 |
+
# Eager initialization on main thread + save registry to JSON
|
| 639 |
+
# ---------------------------------------------------------------------------
|
| 640 |
+
try:
|
| 641 |
+
get_nvidia_llm()
|
| 642 |
+
save_model_registry()
|
| 643 |
+
except Exception as e:
|
| 644 |
+
print(f"[NVIDIA LLM] Initialization warning: {e}")
|
nsedash/core/nvidia_model_registry.json
ADDED
|
@@ -0,0 +1,728 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"updated_at": "2026-07-09T23:55:28.280873",
|
| 3 |
+
"total_nvidia_nim_models": 121,
|
| 4 |
+
"categories": [
|
| 5 |
+
"coding",
|
| 6 |
+
"reasoning",
|
| 7 |
+
"chat",
|
| 8 |
+
"memory",
|
| 9 |
+
"judge",
|
| 10 |
+
"finance",
|
| 11 |
+
"vision",
|
| 12 |
+
"safety",
|
| 13 |
+
"fast",
|
| 14 |
+
"creative",
|
| 15 |
+
"medical",
|
| 16 |
+
"embedding"
|
| 17 |
+
],
|
| 18 |
+
"registry": {
|
| 19 |
+
"coding": {
|
| 20 |
+
"primary": "deepseek-ai/deepseek-v4-pro",
|
| 21 |
+
"fallback": [
|
| 22 |
+
"deepseek-ai/deepseek-v4-flash",
|
| 23 |
+
"mistralai/codestral-22b-instruct-v0.1",
|
| 24 |
+
"nvidia/llama-3.3-nemotron-super-49b-v1",
|
| 25 |
+
"ibm/granite-34b-code-instruct",
|
| 26 |
+
"meta/codellama-70b",
|
| 27 |
+
"bigcode/starcoder2-15b",
|
| 28 |
+
"deepseek-ai/deepseek-coder-6.7b-instruct"
|
| 29 |
+
],
|
| 30 |
+
"models": {
|
| 31 |
+
"deepseek-ai/deepseek-v4-pro": {
|
| 32 |
+
"rating": 5,
|
| 33 |
+
"context_window": 163840,
|
| 34 |
+
"description": "State-of-the-art coding. Best for complex code generation, debugging, agentic coding. Beats GPT-4o on HumanEval.",
|
| 35 |
+
"strengths": [
|
| 36 |
+
"code_gen",
|
| 37 |
+
"debugging",
|
| 38 |
+
"refactor",
|
| 39 |
+
"agentic"
|
| 40 |
+
]
|
| 41 |
+
},
|
| 42 |
+
"deepseek-ai/deepseek-v4-flash": {
|
| 43 |
+
"rating": 4,
|
| 44 |
+
"context_window": 163840,
|
| 45 |
+
"description": "Fast version of DeepSeek V4. Excellent for iterative coding workflows needing low latency.",
|
| 46 |
+
"strengths": [
|
| 47 |
+
"code_gen",
|
| 48 |
+
"fast",
|
| 49 |
+
"agentic"
|
| 50 |
+
]
|
| 51 |
+
},
|
| 52 |
+
"mistralai/codestral-22b-instruct-v0.1": {
|
| 53 |
+
"rating": 4,
|
| 54 |
+
"context_window": 32768,
|
| 55 |
+
"description": "Mistral's dedicated code model. Excellent for infilling, multi-language code, unit tests.",
|
| 56 |
+
"strengths": [
|
| 57 |
+
"code_gen",
|
| 58 |
+
"infill",
|
| 59 |
+
"test_gen"
|
| 60 |
+
]
|
| 61 |
+
},
|
| 62 |
+
"nvidia/llama-3.3-nemotron-super-49b-v1": {
|
| 63 |
+
"rating": 4,
|
| 64 |
+
"context_window": 128000,
|
| 65 |
+
"description": "NVIDIA's tuned 49B model \u2014 excellent at coding + agentic planning + reasoning.",
|
| 66 |
+
"strengths": [
|
| 67 |
+
"code_gen",
|
| 68 |
+
"reasoning",
|
| 69 |
+
"agentic"
|
| 70 |
+
]
|
| 71 |
+
},
|
| 72 |
+
"nvidia/llama-3.3-nemotron-super-49b-v1.5": {
|
| 73 |
+
"rating": 4,
|
| 74 |
+
"context_window": 128000,
|
| 75 |
+
"description": "Latest NVIDIA Nemotron Super 49B v1.5 \u2014 best NVIDIA native code+reasoning model.",
|
| 76 |
+
"strengths": [
|
| 77 |
+
"code_gen",
|
| 78 |
+
"reasoning",
|
| 79 |
+
"agentic"
|
| 80 |
+
]
|
| 81 |
+
},
|
| 82 |
+
"ibm/granite-34b-code-instruct": {
|
| 83 |
+
"rating": 4,
|
| 84 |
+
"context_window": 8192,
|
| 85 |
+
"description": "IBM's dedicated code model, 34B. Strong at enterprise-grade code quality.",
|
| 86 |
+
"strengths": [
|
| 87 |
+
"code_gen",
|
| 88 |
+
"enterprise"
|
| 89 |
+
]
|
| 90 |
+
},
|
| 91 |
+
"ibm/granite-8b-code-instruct": {
|
| 92 |
+
"rating": 3,
|
| 93 |
+
"context_window": 8192,
|
| 94 |
+
"description": "IBM 8B code model \u2014 fast code completion with lower resource footprint.",
|
| 95 |
+
"strengths": [
|
| 96 |
+
"code_gen",
|
| 97 |
+
"fast"
|
| 98 |
+
]
|
| 99 |
+
},
|
| 100 |
+
"meta/codellama-70b": {
|
| 101 |
+
"rating": 3,
|
| 102 |
+
"context_window": 16384,
|
| 103 |
+
"description": "Meta's 70B CodeLlama \u2014 reliable baseline for code generation and completion.",
|
| 104 |
+
"strengths": [
|
| 105 |
+
"code_gen",
|
| 106 |
+
"infill"
|
| 107 |
+
]
|
| 108 |
+
},
|
| 109 |
+
"bigcode/starcoder2-15b": {
|
| 110 |
+
"rating": 3,
|
| 111 |
+
"context_window": 16384,
|
| 112 |
+
"description": "Open-source code model \u2014 strong at code completion, licensed models.",
|
| 113 |
+
"strengths": [
|
| 114 |
+
"code_gen",
|
| 115 |
+
"infill",
|
| 116 |
+
"open_source"
|
| 117 |
+
]
|
| 118 |
+
},
|
| 119 |
+
"google/codegemma-1.1-7b": {
|
| 120 |
+
"rating": 3,
|
| 121 |
+
"context_window": 8192,
|
| 122 |
+
"description": "Google CodeGemma 7B \u2014 compact, fast code model.",
|
| 123 |
+
"strengths": [
|
| 124 |
+
"code_gen",
|
| 125 |
+
"fast"
|
| 126 |
+
]
|
| 127 |
+
},
|
| 128 |
+
"nvidia/nv-embedcode-7b-v1": {
|
| 129 |
+
"rating": 3,
|
| 130 |
+
"context_window": 8192,
|
| 131 |
+
"description": "Code embedding model \u2014 for semantic code search and similarity.",
|
| 132 |
+
"strengths": [
|
| 133 |
+
"embedding",
|
| 134 |
+
"code_search"
|
| 135 |
+
]
|
| 136 |
+
}
|
| 137 |
+
}
|
| 138 |
+
},
|
| 139 |
+
"reasoning": {
|
| 140 |
+
"primary": "nvidia/nemotron-3-ultra-550b-a55b",
|
| 141 |
+
"fallback": [
|
| 142 |
+
"qwen/qwen3.5-397b-a17b",
|
| 143 |
+
"nvidia/llama-3.1-nemotron-ultra-253b-v1",
|
| 144 |
+
"moonshotai/kimi-k2.6",
|
| 145 |
+
"minimaxai/minimax-m3",
|
| 146 |
+
"nvidia/llama-3.1-nemotron-70b-instruct",
|
| 147 |
+
"z-ai/glm-5.2",
|
| 148 |
+
"meta/llama-3.3-70b-instruct"
|
| 149 |
+
],
|
| 150 |
+
"models": {
|
| 151 |
+
"nvidia/nemotron-3-ultra-550b-a55b": {
|
| 152 |
+
"rating": 5,
|
| 153 |
+
"context_window": 128000,
|
| 154 |
+
"description": "NVIDIA's 550B ultra model \u2014 frontier reasoning & agentic capability. Top-tier for complex multi-step tasks.",
|
| 155 |
+
"strengths": [
|
| 156 |
+
"reasoning",
|
| 157 |
+
"agentic",
|
| 158 |
+
"planning",
|
| 159 |
+
"math"
|
| 160 |
+
]
|
| 161 |
+
},
|
| 162 |
+
"qwen/qwen3.5-397b-a17b": {
|
| 163 |
+
"rating": 5,
|
| 164 |
+
"context_window": 32768,
|
| 165 |
+
"description": "Qwen3.5 397B MoE \u2014 exceptional reasoning and complex analysis. Top MMLU scores.",
|
| 166 |
+
"strengths": [
|
| 167 |
+
"reasoning",
|
| 168 |
+
"analysis",
|
| 169 |
+
"math"
|
| 170 |
+
]
|
| 171 |
+
},
|
| 172 |
+
"nvidia/llama-3.1-nemotron-ultra-253b-v1": {
|
| 173 |
+
"rating": 5,
|
| 174 |
+
"context_window": 128000,
|
| 175 |
+
"description": "NVIDIA 253B ultra \u2014 powerful multi-turn reasoning and agentic planning.",
|
| 176 |
+
"strengths": [
|
| 177 |
+
"reasoning",
|
| 178 |
+
"agentic",
|
| 179 |
+
"planning"
|
| 180 |
+
]
|
| 181 |
+
},
|
| 182 |
+
"moonshotai/kimi-k2.6": {
|
| 183 |
+
"rating": 4,
|
| 184 |
+
"context_window": 131072,
|
| 185 |
+
"description": "Moonshot Kimi K2.6 \u2014 exceptional long-context reasoning & analysis (128K context).",
|
| 186 |
+
"strengths": [
|
| 187 |
+
"reasoning",
|
| 188 |
+
"long_context",
|
| 189 |
+
"analysis"
|
| 190 |
+
]
|
| 191 |
+
},
|
| 192 |
+
"minimaxai/minimax-m3": {
|
| 193 |
+
"rating": 4,
|
| 194 |
+
"context_window": 40960,
|
| 195 |
+
"description": "MiniMax M3 \u2014 excellent complex reasoning and long-form generation.",
|
| 196 |
+
"strengths": [
|
| 197 |
+
"reasoning",
|
| 198 |
+
"complex_tasks"
|
| 199 |
+
]
|
| 200 |
+
},
|
| 201 |
+
"minimaxai/minimax-m2.7": {
|
| 202 |
+
"rating": 4,
|
| 203 |
+
"context_window": 40960,
|
| 204 |
+
"description": "MiniMax M2.7 \u2014 strong reasoning, fast inference.",
|
| 205 |
+
"strengths": [
|
| 206 |
+
"reasoning",
|
| 207 |
+
"fast"
|
| 208 |
+
]
|
| 209 |
+
},
|
| 210 |
+
"nvidia/llama-3.1-nemotron-70b-instruct": {
|
| 211 |
+
"rating": 4,
|
| 212 |
+
"context_window": 128000,
|
| 213 |
+
"description": "NVIDIA Nemotron 70B \u2014 tuned for agentic reasoning and instruction-following.",
|
| 214 |
+
"strengths": [
|
| 215 |
+
"reasoning",
|
| 216 |
+
"instruction",
|
| 217 |
+
"agentic"
|
| 218 |
+
]
|
| 219 |
+
},
|
| 220 |
+
"z-ai/glm-5.2": {
|
| 221 |
+
"rating": 4,
|
| 222 |
+
"context_window": 128000,
|
| 223 |
+
"description": "GLM-5.2 \u2014 excellent for long-horizon reasoning and agentic workflows.",
|
| 224 |
+
"strengths": [
|
| 225 |
+
"reasoning",
|
| 226 |
+
"agentic",
|
| 227 |
+
"long_context"
|
| 228 |
+
]
|
| 229 |
+
},
|
| 230 |
+
"nvidia/nemotron-3-super-120b-a12b": {
|
| 231 |
+
"rating": 4,
|
| 232 |
+
"context_window": 128000,
|
| 233 |
+
"description": "NVIDIA 120B MoE super model \u2014 balanced reasoning + speed.",
|
| 234 |
+
"strengths": [
|
| 235 |
+
"reasoning",
|
| 236 |
+
"agentic"
|
| 237 |
+
]
|
| 238 |
+
},
|
| 239 |
+
"qwen/qwen3-next-80b-a3b-instruct": {
|
| 240 |
+
"rating": 4,
|
| 241 |
+
"context_window": 32768,
|
| 242 |
+
"description": "Qwen3 Next 80B \u2014 excellent reasoning + instruction following.",
|
| 243 |
+
"strengths": [
|
| 244 |
+
"reasoning",
|
| 245 |
+
"instruction"
|
| 246 |
+
]
|
| 247 |
+
},
|
| 248 |
+
"meta/llama-3.3-70b-instruct": {
|
| 249 |
+
"rating": 3,
|
| 250 |
+
"context_window": 128000,
|
| 251 |
+
"description": "Meta Llama 3.3 70B \u2014 solid general reasoning. Good 128K context.",
|
| 252 |
+
"strengths": [
|
| 253 |
+
"reasoning",
|
| 254 |
+
"general"
|
| 255 |
+
]
|
| 256 |
+
},
|
| 257 |
+
"meta/llama-4-maverick-17b-128e-instruct": {
|
| 258 |
+
"rating": 4,
|
| 259 |
+
"context_window": 1000000,
|
| 260 |
+
"description": "Llama 4 Maverick 17B MoE \u2014 1M context window, excellent for very long-context analysis.",
|
| 261 |
+
"strengths": [
|
| 262 |
+
"reasoning",
|
| 263 |
+
"long_context",
|
| 264 |
+
"agentic"
|
| 265 |
+
]
|
| 266 |
+
}
|
| 267 |
+
}
|
| 268 |
+
},
|
| 269 |
+
"chat": {
|
| 270 |
+
"primary": "meta/llama-3.1-8b-instruct",
|
| 271 |
+
"fallback": [
|
| 272 |
+
"mistralai/mistral-large",
|
| 273 |
+
"nvidia/llama-3.1-nemotron-51b-instruct",
|
| 274 |
+
"meta/llama-3.3-70b-instruct",
|
| 275 |
+
"mistralai/mistral-medium-3.5-128b",
|
| 276 |
+
"mistralai/mistral-large-2-instruct",
|
| 277 |
+
"mistralai/mistral-large-3-675b-instruct-2512",
|
| 278 |
+
"mistralai/mistral-small-4-119b-2603"
|
| 279 |
+
],
|
| 280 |
+
"models": {
|
| 281 |
+
"meta/llama-3.1-8b-instruct": {
|
| 282 |
+
"rating": 4,
|
| 283 |
+
"context_window": 128000,
|
| 284 |
+
"description": "Llama 3.1 8B \u2014 fast, reliable, excellent general chat. Default workhorse.",
|
| 285 |
+
"strengths": [
|
| 286 |
+
"chat",
|
| 287 |
+
"instruction",
|
| 288 |
+
"fast"
|
| 289 |
+
]
|
| 290 |
+
},
|
| 291 |
+
"mistralai/mistral-large": {
|
| 292 |
+
"rating": 4,
|
| 293 |
+
"context_window": 128000,
|
| 294 |
+
"description": "Mistral Large \u2014 excellent instruction-following and conversational quality.",
|
| 295 |
+
"strengths": [
|
| 296 |
+
"chat",
|
| 297 |
+
"instruction"
|
| 298 |
+
]
|
| 299 |
+
},
|
| 300 |
+
"nvidia/llama-3.1-nemotron-51b-instruct": {
|
| 301 |
+
"rating": 4,
|
| 302 |
+
"context_window": 128000,
|
| 303 |
+
"description": "NVIDIA Nemotron 51B \u2014 strong conversational assistant, tuned for helpfulness.",
|
| 304 |
+
"strengths": [
|
| 305 |
+
"chat",
|
| 306 |
+
"instruction",
|
| 307 |
+
"helpful"
|
| 308 |
+
]
|
| 309 |
+
},
|
| 310 |
+
"mistralai/mistral-medium-3.5-128b": {
|
| 311 |
+
"rating": 4,
|
| 312 |
+
"context_window": 128000,
|
| 313 |
+
"description": "Mistral Medium 3.5 \u2014 balanced medium-size model for rich conversational tasks.",
|
| 314 |
+
"strengths": [
|
| 315 |
+
"chat",
|
| 316 |
+
"balanced"
|
| 317 |
+
]
|
| 318 |
+
},
|
| 319 |
+
"mistralai/ministral-14b-instruct-2512": {
|
| 320 |
+
"rating": 3,
|
| 321 |
+
"context_window": 32768,
|
| 322 |
+
"description": "Mistral 14B \u2014 efficient medium model.",
|
| 323 |
+
"strengths": [
|
| 324 |
+
"chat",
|
| 325 |
+
"fast"
|
| 326 |
+
]
|
| 327 |
+
},
|
| 328 |
+
"mistralai/mixtral-8x7b-instruct-v0.1": {
|
| 329 |
+
"rating": 3,
|
| 330 |
+
"context_window": 32768,
|
| 331 |
+
"description": "Mixtral MoE 8x7B \u2014 fast, cost-efficient, good quality.",
|
| 332 |
+
"strengths": [
|
| 333 |
+
"chat",
|
| 334 |
+
"fast",
|
| 335 |
+
"moe"
|
| 336 |
+
]
|
| 337 |
+
}
|
| 338 |
+
}
|
| 339 |
+
},
|
| 340 |
+
"memory": {
|
| 341 |
+
"primary": "meta/llama-4-maverick-17b-128e-instruct",
|
| 342 |
+
"fallback": [
|
| 343 |
+
"moonshotai/kimi-k2.6",
|
| 344 |
+
"mistralai/mistral-large-2-instruct",
|
| 345 |
+
"meta/llama-3.3-70b-instruct",
|
| 346 |
+
"meta/llama-3.1-8b-instruct"
|
| 347 |
+
],
|
| 348 |
+
"models": {
|
| 349 |
+
"meta/llama-4-maverick-17b-128e-instruct": {
|
| 350 |
+
"rating": 5,
|
| 351 |
+
"context_window": 1000000,
|
| 352 |
+
"description": "Llama 4 Maverick \u2014 1M token context. Best for ingesting entire codebases or large memory files.",
|
| 353 |
+
"strengths": [
|
| 354 |
+
"summarization",
|
| 355 |
+
"long_context",
|
| 356 |
+
"memory"
|
| 357 |
+
]
|
| 358 |
+
},
|
| 359 |
+
"moonshotai/kimi-k2.6": {
|
| 360 |
+
"rating": 4,
|
| 361 |
+
"context_window": 131072,
|
| 362 |
+
"description": "Kimi K2.6 \u2014 128K context summarization. Excellent at extracting key information.",
|
| 363 |
+
"strengths": [
|
| 364 |
+
"summarization",
|
| 365 |
+
"long_context"
|
| 366 |
+
]
|
| 367 |
+
}
|
| 368 |
+
}
|
| 369 |
+
},
|
| 370 |
+
"judge": {
|
| 371 |
+
"primary": "nvidia/nemotron-4-340b-reward",
|
| 372 |
+
"fallback": [
|
| 373 |
+
"nvidia/nemotron-4-340b-instruct",
|
| 374 |
+
"nvidia/llama-3.1-nemotron-ultra-253b-v1",
|
| 375 |
+
"meta/llama-3.3-70b-instruct"
|
| 376 |
+
],
|
| 377 |
+
"models": {
|
| 378 |
+
"nvidia/nemotron-4-340b-reward": {
|
| 379 |
+
"rating": 5,
|
| 380 |
+
"context_window": 4096,
|
| 381 |
+
"description": "NVIDIA Nemotron 340B Reward \u2014 purpose-built reward model for evaluation. Best LLM-as-a-judge.",
|
| 382 |
+
"strengths": [
|
| 383 |
+
"judge",
|
| 384 |
+
"evaluation",
|
| 385 |
+
"reward"
|
| 386 |
+
]
|
| 387 |
+
},
|
| 388 |
+
"nvidia/nemotron-4-340b-instruct": {
|
| 389 |
+
"rating": 4,
|
| 390 |
+
"context_window": 4096,
|
| 391 |
+
"description": "NVIDIA Nemotron 340B instruct \u2014 strong judge for code and task evaluation.",
|
| 392 |
+
"strengths": [
|
| 393 |
+
"judge",
|
| 394 |
+
"evaluation"
|
| 395 |
+
]
|
| 396 |
+
}
|
| 397 |
+
}
|
| 398 |
+
},
|
| 399 |
+
"finance": {
|
| 400 |
+
"primary": "writer/palmyra-fin-70b-32k",
|
| 401 |
+
"fallback": [
|
| 402 |
+
"stockmark/stockmark-2-100b-instruct",
|
| 403 |
+
"meta/llama-3.3-70b-instruct",
|
| 404 |
+
"mistralai/mistral-large"
|
| 405 |
+
],
|
| 406 |
+
"models": {
|
| 407 |
+
"writer/palmyra-fin-70b-32k": {
|
| 408 |
+
"rating": 5,
|
| 409 |
+
"context_window": 32768,
|
| 410 |
+
"description": "Palmyra Finance 70B \u2014 purpose-built for financial documents, analysis, and reporting.",
|
| 411 |
+
"strengths": [
|
| 412 |
+
"finance",
|
| 413 |
+
"analysis",
|
| 414 |
+
"report"
|
| 415 |
+
]
|
| 416 |
+
},
|
| 417 |
+
"stockmark/stockmark-2-100b-instruct": {
|
| 418 |
+
"rating": 4,
|
| 419 |
+
"context_window": 32768,
|
| 420 |
+
"description": "Stockmark 2 100B \u2014 trained on financial markets data. Excellent for stock analysis.",
|
| 421 |
+
"strengths": [
|
| 422 |
+
"finance",
|
| 423 |
+
"markets",
|
| 424 |
+
"analysis"
|
| 425 |
+
]
|
| 426 |
+
}
|
| 427 |
+
}
|
| 428 |
+
},
|
| 429 |
+
"vision": {
|
| 430 |
+
"primary": "meta/llama-3.2-90b-vision-instruct",
|
| 431 |
+
"fallback": [
|
| 432 |
+
"meta/llama-3.2-11b-vision-instruct",
|
| 433 |
+
"microsoft/phi-3-vision-128k-instruct",
|
| 434 |
+
"nvidia/llama-3.1-nemotron-nano-vl-8b-v1",
|
| 435 |
+
"nvidia/neva-22b"
|
| 436 |
+
],
|
| 437 |
+
"models": {
|
| 438 |
+
"meta/llama-3.2-90b-vision-instruct": {
|
| 439 |
+
"rating": 5,
|
| 440 |
+
"context_window": 128000,
|
| 441 |
+
"description": "Llama 3.2 90B Vision \u2014 best open-source VLM for complex chart/image understanding.",
|
| 442 |
+
"strengths": [
|
| 443 |
+
"vision",
|
| 444 |
+
"chart_analysis",
|
| 445 |
+
"vqa"
|
| 446 |
+
]
|
| 447 |
+
},
|
| 448 |
+
"meta/llama-3.2-11b-vision-instruct": {
|
| 449 |
+
"rating": 4,
|
| 450 |
+
"context_window": 128000,
|
| 451 |
+
"description": "Llama 3.2 11B Vision \u2014 fast VLM for image Q&A and analysis.",
|
| 452 |
+
"strengths": [
|
| 453 |
+
"vision",
|
| 454 |
+
"vqa",
|
| 455 |
+
"fast"
|
| 456 |
+
]
|
| 457 |
+
}
|
| 458 |
+
}
|
| 459 |
+
},
|
| 460 |
+
"safety": {
|
| 461 |
+
"primary": "nvidia/llama-3.1-nemoguard-8b-content-safety",
|
| 462 |
+
"fallback": [
|
| 463 |
+
"meta/llama-guard-4-12b",
|
| 464 |
+
"nvidia/llama-3.1-nemotron-safety-guard-8b-v3",
|
| 465 |
+
"nvidia/nemotron-3-content-safety"
|
| 466 |
+
],
|
| 467 |
+
"models": {
|
| 468 |
+
"nvidia/llama-3.1-nemoguard-8b-content-safety": {
|
| 469 |
+
"rating": 5,
|
| 470 |
+
"context_window": 8192,
|
| 471 |
+
"description": "NeMoGuard 8B Content Safety \u2014 purpose-built NVIDIA safety classifier.",
|
| 472 |
+
"strengths": [
|
| 473 |
+
"safety",
|
| 474 |
+
"content_moderation"
|
| 475 |
+
]
|
| 476 |
+
},
|
| 477 |
+
"meta/llama-guard-4-12b": {
|
| 478 |
+
"rating": 4,
|
| 479 |
+
"context_window": 128000,
|
| 480 |
+
"description": "Llama Guard 4 12B \u2014 Meta's safety model with 128K context.",
|
| 481 |
+
"strengths": [
|
| 482 |
+
"safety",
|
| 483 |
+
"content_moderation"
|
| 484 |
+
]
|
| 485 |
+
}
|
| 486 |
+
}
|
| 487 |
+
},
|
| 488 |
+
"fast": {
|
| 489 |
+
"primary": "meta/llama-3.1-8b-instruct",
|
| 490 |
+
"fallback": [
|
| 491 |
+
"microsoft/phi-4-mini-instruct",
|
| 492 |
+
"meta/llama-3.2-3b-instruct",
|
| 493 |
+
"google/gemma-3-4b-it",
|
| 494 |
+
"nvidia/nemotron-mini-4b-instruct",
|
| 495 |
+
"mistralai/mistral-7b-instruct-v0.3"
|
| 496 |
+
],
|
| 497 |
+
"models": {
|
| 498 |
+
"meta/llama-3.1-8b-instruct": {
|
| 499 |
+
"rating": 4,
|
| 500 |
+
"context_window": 128000,
|
| 501 |
+
"description": "Llama 3.1 8B \u2014 fast, reliable, high-quality 8B model.",
|
| 502 |
+
"strengths": [
|
| 503 |
+
"fast",
|
| 504 |
+
"general",
|
| 505 |
+
"instruction"
|
| 506 |
+
]
|
| 507 |
+
},
|
| 508 |
+
"microsoft/phi-4-mini-instruct": {
|
| 509 |
+
"rating": 4,
|
| 510 |
+
"context_window": 128000,
|
| 511 |
+
"description": "Phi-4 Mini \u2014 Microsoft's compact model, excellent quality per parameter count.",
|
| 512 |
+
"strengths": [
|
| 513 |
+
"fast",
|
| 514 |
+
"instruction",
|
| 515 |
+
"compact"
|
| 516 |
+
]
|
| 517 |
+
},
|
| 518 |
+
"google/gemma-3-4b-it": {
|
| 519 |
+
"rating": 3,
|
| 520 |
+
"context_window": 128000,
|
| 521 |
+
"description": "Gemma 3 4B \u2014 Google's compact model.",
|
| 522 |
+
"strengths": [
|
| 523 |
+
"fast",
|
| 524 |
+
"compact"
|
| 525 |
+
]
|
| 526 |
+
}
|
| 527 |
+
}
|
| 528 |
+
},
|
| 529 |
+
"creative": {
|
| 530 |
+
"primary": "writer/palmyra-creative-122b",
|
| 531 |
+
"fallback": [
|
| 532 |
+
"mistralai/mistral-large-3-675b-instruct-2512",
|
| 533 |
+
"qwen/qwen3.5-122b-a10b",
|
| 534 |
+
"minimaxai/minimax-m3"
|
| 535 |
+
],
|
| 536 |
+
"models": {
|
| 537 |
+
"writer/palmyra-creative-122b": {
|
| 538 |
+
"rating": 5,
|
| 539 |
+
"context_window": 32768,
|
| 540 |
+
"description": "Palmyra Creative 122B \u2014 purpose-built for creative writing, reports, documentation.",
|
| 541 |
+
"strengths": [
|
| 542 |
+
"creative",
|
| 543 |
+
"long_form",
|
| 544 |
+
"documentation"
|
| 545 |
+
]
|
| 546 |
+
},
|
| 547 |
+
"qwen/qwen3.5-122b-a10b": {
|
| 548 |
+
"rating": 4,
|
| 549 |
+
"context_window": 32768,
|
| 550 |
+
"description": "Qwen3.5 122B MoE \u2014 excellent creative and analytical writing.",
|
| 551 |
+
"strengths": [
|
| 552 |
+
"creative",
|
| 553 |
+
"analysis"
|
| 554 |
+
]
|
| 555 |
+
}
|
| 556 |
+
}
|
| 557 |
+
},
|
| 558 |
+
"medical": {
|
| 559 |
+
"primary": "writer/palmyra-med-70b-32k",
|
| 560 |
+
"fallback": [
|
| 561 |
+
"writer/palmyra-med-70b",
|
| 562 |
+
"meta/llama-3.3-70b-instruct"
|
| 563 |
+
],
|
| 564 |
+
"models": {
|
| 565 |
+
"writer/palmyra-med-70b-32k": {
|
| 566 |
+
"rating": 5,
|
| 567 |
+
"context_window": 32768,
|
| 568 |
+
"description": "Palmyra Med 70B 32K \u2014 purpose-built for medical text generation and clinical Q&A.",
|
| 569 |
+
"strengths": [
|
| 570 |
+
"medical",
|
| 571 |
+
"clinical",
|
| 572 |
+
"analysis"
|
| 573 |
+
]
|
| 574 |
+
}
|
| 575 |
+
}
|
| 576 |
+
},
|
| 577 |
+
"embedding": {
|
| 578 |
+
"primary": "nvidia/nv-embedqa-e5-v5",
|
| 579 |
+
"fallback": [
|
| 580 |
+
"nvidia/nv-embed-v1",
|
| 581 |
+
"baai/bge-m3",
|
| 582 |
+
"snowflake/arctic-embed-l",
|
| 583 |
+
"nvidia/llama-3.2-nv-embedqa-1b-v1"
|
| 584 |
+
],
|
| 585 |
+
"models": {
|
| 586 |
+
"nvidia/nv-embedqa-e5-v5": {
|
| 587 |
+
"rating": 5,
|
| 588 |
+
"context_window": 512,
|
| 589 |
+
"description": "NVIDIA NV-EmbedQA E5 v5 \u2014 best NVIDIA embedding model for QA retrieval.",
|
| 590 |
+
"strengths": [
|
| 591 |
+
"embedding",
|
| 592 |
+
"rag",
|
| 593 |
+
"retrieval"
|
| 594 |
+
]
|
| 595 |
+
},
|
| 596 |
+
"baai/bge-m3": {
|
| 597 |
+
"rating": 4,
|
| 598 |
+
"context_window": 8192,
|
| 599 |
+
"description": "BAAI BGE-M3 \u2014 multilingual embedding model, excellent for semantic search.",
|
| 600 |
+
"strengths": [
|
| 601 |
+
"embedding",
|
| 602 |
+
"multilingual",
|
| 603 |
+
"retrieval"
|
| 604 |
+
]
|
| 605 |
+
}
|
| 606 |
+
}
|
| 607 |
+
}
|
| 608 |
+
},
|
| 609 |
+
"routing_keywords": {
|
| 610 |
+
"coding": [
|
| 611 |
+
"code",
|
| 612 |
+
"python",
|
| 613 |
+
"function",
|
| 614 |
+
"class",
|
| 615 |
+
"debug",
|
| 616 |
+
"refactor",
|
| 617 |
+
"implement",
|
| 618 |
+
"syntax",
|
| 619 |
+
"algorithm",
|
| 620 |
+
"programming",
|
| 621 |
+
"script",
|
| 622 |
+
"module",
|
| 623 |
+
"bug",
|
| 624 |
+
"fix",
|
| 625 |
+
"rewrite",
|
| 626 |
+
"agent_workflow",
|
| 627 |
+
"agent"
|
| 628 |
+
],
|
| 629 |
+
"reasoning": [
|
| 630 |
+
"reason",
|
| 631 |
+
"analyze",
|
| 632 |
+
"plan",
|
| 633 |
+
"strategy",
|
| 634 |
+
"optimize",
|
| 635 |
+
"complex",
|
| 636 |
+
"math",
|
| 637 |
+
"calculate",
|
| 638 |
+
"evaluate",
|
| 639 |
+
"multi-step",
|
| 640 |
+
"explain",
|
| 641 |
+
"compare",
|
| 642 |
+
"decide"
|
| 643 |
+
],
|
| 644 |
+
"memory": [
|
| 645 |
+
"summarize",
|
| 646 |
+
"memory",
|
| 647 |
+
"kb",
|
| 648 |
+
"knowledge",
|
| 649 |
+
"log",
|
| 650 |
+
"history",
|
| 651 |
+
"update memory",
|
| 652 |
+
"compress",
|
| 653 |
+
"recall",
|
| 654 |
+
"persist"
|
| 655 |
+
],
|
| 656 |
+
"judge": [
|
| 657 |
+
"judge",
|
| 658 |
+
"evaluate",
|
| 659 |
+
"score",
|
| 660 |
+
"audit",
|
| 661 |
+
"rate",
|
| 662 |
+
"hallucin",
|
| 663 |
+
"assess",
|
| 664 |
+
"review trajectory",
|
| 665 |
+
"llm-as-a-judge"
|
| 666 |
+
],
|
| 667 |
+
"finance": [
|
| 668 |
+
"finance",
|
| 669 |
+
"stock",
|
| 670 |
+
"trading",
|
| 671 |
+
"market",
|
| 672 |
+
"portfolio",
|
| 673 |
+
"financial",
|
| 674 |
+
"investment",
|
| 675 |
+
"equity",
|
| 676 |
+
"backtest",
|
| 677 |
+
"capital"
|
| 678 |
+
],
|
| 679 |
+
"vision": [
|
| 680 |
+
"image",
|
| 681 |
+
"chart",
|
| 682 |
+
"graph",
|
| 683 |
+
"visual",
|
| 684 |
+
"picture",
|
| 685 |
+
"plot",
|
| 686 |
+
"screenshot",
|
| 687 |
+
"vision"
|
| 688 |
+
],
|
| 689 |
+
"safety": [
|
| 690 |
+
"safety",
|
| 691 |
+
"harmful",
|
| 692 |
+
"pii",
|
| 693 |
+
"content moderation",
|
| 694 |
+
"guardrail",
|
| 695 |
+
"toxic",
|
| 696 |
+
"filter",
|
| 697 |
+
"censor"
|
| 698 |
+
],
|
| 699 |
+
"fast": [
|
| 700 |
+
"quick",
|
| 701 |
+
"fast",
|
| 702 |
+
"brief",
|
| 703 |
+
"short",
|
| 704 |
+
"classify",
|
| 705 |
+
"yes/no",
|
| 706 |
+
"simple",
|
| 707 |
+
"ping"
|
| 708 |
+
],
|
| 709 |
+
"creative": [
|
| 710 |
+
"write",
|
| 711 |
+
"creative",
|
| 712 |
+
"document",
|
| 713 |
+
"report",
|
| 714 |
+
"essay",
|
| 715 |
+
"story",
|
| 716 |
+
"generate text"
|
| 717 |
+
],
|
| 718 |
+
"medical": [
|
| 719 |
+
"medical",
|
| 720 |
+
"health",
|
| 721 |
+
"clinical",
|
| 722 |
+
"patient",
|
| 723 |
+
"diagnosis",
|
| 724 |
+
"symptom",
|
| 725 |
+
"drug"
|
| 726 |
+
]
|
| 727 |
+
}
|
| 728 |
+
}
|
nsedash/web/api.py
CHANGED
|
@@ -1023,3 +1023,113 @@ def admin_action():
|
|
| 1023 |
return jsonify({"ok": False, "message": "Unknown action."}), 400
|
| 1024 |
|
| 1025 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1023 |
return jsonify({"ok": False, "message": "Unknown action."}), 400
|
| 1024 |
|
| 1025 |
|
| 1026 |
+
@api.get("/stocks/<symbol>/ai-analysis")
|
| 1027 |
+
def get_ai_analysis(symbol: str):
|
| 1028 |
+
"""
|
| 1029 |
+
Get or generate AI-driven stock analysis in markdown format.
|
| 1030 |
+
Refreshes if more than 24 hours old. Saves to processed/aianalysis.
|
| 1031 |
+
"""
|
| 1032 |
+
token = get_session_token()
|
| 1033 |
+
base_dir = current_app.extensions["nsedash"]["base_dir"]
|
| 1034 |
+
from ..config import Paths
|
| 1035 |
+
paths = Paths(base_dir=base_dir, session_token=token)
|
| 1036 |
+
|
| 1037 |
+
aianalysis_dir = paths.aianalysis_dir
|
| 1038 |
+
file_path = aianalysis_dir / f"{symbol}.md"
|
| 1039 |
+
|
| 1040 |
+
# 24-hour cache freshness check
|
| 1041 |
+
import time
|
| 1042 |
+
refresh = True
|
| 1043 |
+
if file_path.exists():
|
| 1044 |
+
try:
|
| 1045 |
+
mtime = file_path.stat().st_mtime
|
| 1046 |
+
if time.time() - mtime < 24 * 3600:
|
| 1047 |
+
refresh = False
|
| 1048 |
+
except Exception:
|
| 1049 |
+
pass
|
| 1050 |
+
|
| 1051 |
+
if not refresh:
|
| 1052 |
+
try:
|
| 1053 |
+
analysis = file_path.read_text(encoding='utf-8')
|
| 1054 |
+
return jsonify({"ok": True, "symbol": symbol, "analysis": analysis, "cached": True})
|
| 1055 |
+
except Exception as exc:
|
| 1056 |
+
logging.error("Failed to read cached AI analysis for %s: %s", symbol, exc)
|
| 1057 |
+
|
| 1058 |
+
# Gather stock data
|
| 1059 |
+
svc_inst = _svc()
|
| 1060 |
+
cell = svc_inst._find_cell(symbol)
|
| 1061 |
+
from ..core.dashboard_service import Cell
|
| 1062 |
+
if not cell:
|
| 1063 |
+
# Stock is not on the board; create a temp Cell
|
| 1064 |
+
cell = Cell(symbol)
|
| 1065 |
+
|
| 1066 |
+
# Get live metrics if they are missing
|
| 1067 |
+
if cell.current_cmp == 0.0:
|
| 1068 |
+
from ..core.live_fetch import LiveFetcher
|
| 1069 |
+
fetcher = LiveFetcher(prev_volume_lookup=lambda s: 0.0)
|
| 1070 |
+
live_data = fetcher.fetch_symbol_gf(symbol) or fetcher.fetch_symbol_yf(symbol)
|
| 1071 |
+
if live_data:
|
| 1072 |
+
cell.current_cmp = live_data.get("CLOSE_PRICE", 0.0)
|
| 1073 |
+
cell.prev_close = live_data.get("PREV_CL_PR", 0.0)
|
| 1074 |
+
cell.live = live_data
|
| 1075 |
+
|
| 1076 |
+
payload = svc_inst._cell_payload(cell, include_chart=False)
|
| 1077 |
+
|
| 1078 |
+
# Build prompt
|
| 1079 |
+
prompt = f"""
|
| 1080 |
+
You are a senior equity research analyst. Generate a comprehensive, professional stock analysis report in Markdown format for the following NSE stock using the provided real-time and historical market data:
|
| 1081 |
+
|
| 1082 |
+
**Stock Details:**
|
| 1083 |
+
- Symbol: {payload.get('symbol')}
|
| 1084 |
+
- Company Name: {payload.get('security', 'N/A')}
|
| 1085 |
+
- Current Market Price (CMP): INR {payload.get('current_cmp', 0.0):,.2f}
|
| 1086 |
+
- Previous Close: INR {payload.get('prev_close', 0.0):,.2f}
|
| 1087 |
+
- 52-Week High: INR {payload.get('hi_52wk', 0.0):,.2f} (Diff: {payload.get('hi_diff', 0.0):.2f}%)
|
| 1088 |
+
- 52-Week Low: INR {payload.get('lo_52wk', 0.0):,.2f} (Diff: {payload.get('lo_diff', 0.0):.2f}%)
|
| 1089 |
+
|
| 1090 |
+
**Technical Analysis & Price Performance:**
|
| 1091 |
+
- 1-Day Return: {payload.get('changes', {}).get('1D', 0.0):+.2f}%
|
| 1092 |
+
- 1-Week Return: {payload.get('changes', {}).get('1W', 0.0):+.2f}%
|
| 1093 |
+
- 1-Month Return: {payload.get('changes', {}).get('1M', 0.0):+.2f}%
|
| 1094 |
+
- 3-Month Return: {payload.get('changes', {}).get('3M', 0.0):+.2f}%
|
| 1095 |
+
- Volume Trend (last 5 sessions): {", ".join(f"{v:+.1f}%" for v in payload.get('volume_changes', []))}
|
| 1096 |
+
|
| 1097 |
+
**LSTM Model Prediction:**
|
| 1098 |
+
- Action: {payload.get('recommendation', {}).get('action', 'N/A') if payload.get('recommendation') else 'N/A'}
|
| 1099 |
+
- Confidence: {payload.get('recommendation', {}).get('confidence', 0.0) if payload.get('recommendation') else 0.0:.2f}
|
| 1100 |
+
|
| 1101 |
+
**Moneycontrol Scorecard:**
|
| 1102 |
+
- Financial Score: {payload.get('mc', {}).get('score', 'N/A')} / 100
|
| 1103 |
+
- Market Cap: {payload.get('mc', {}).get('mcap')} Cr
|
| 1104 |
+
- Face Value: INR {payload.get('mc', {}).get('fv')}
|
| 1105 |
+
|
| 1106 |
+
Please structure your report with:
|
| 1107 |
+
1. **Executive Summary**: Overview of the stock's current valuation, relative performance, and short-term vs medium-term trends.
|
| 1108 |
+
2. **Technical Outlook**: Interpretation of the recent price actions, 52-week boundaries, and volume momentum.
|
| 1109 |
+
3. **Model & Scorecard Assessment**: Critique of the LSTM recommendation and Moneycontrol financial score.
|
| 1110 |
+
4. **Bull vs Bear Thesis**: 3 key arguments for the upside, and 3 key risks/downsides.
|
| 1111 |
+
5. **Investment Recommendation**: Concluding action recommendation (Buy/Hold/Sell/Accumulate) and support thesis. Include a standard disclaimer stating this is for educational/informational purposes and not financial advice.
|
| 1112 |
+
"""
|
| 1113 |
+
try:
|
| 1114 |
+
from ..core.nvidia_llm import NvidiaLLM
|
| 1115 |
+
llm = NvidiaLLM()
|
| 1116 |
+
analysis = llm.call(prompt, hint="finance")
|
| 1117 |
+
if not analysis or "LLM ERROR" in analysis or "LLM OFFLINE" in analysis:
|
| 1118 |
+
return jsonify({"ok": False, "message": "Failed to generate analysis from NVIDIA LLM."}), 500
|
| 1119 |
+
|
| 1120 |
+
# Write to cache
|
| 1121 |
+
file_path.write_text(analysis, encoding='utf-8')
|
| 1122 |
+
|
| 1123 |
+
# Sync with HF dataset if enabled
|
| 1124 |
+
extensions = current_app.extensions["nsedash"]
|
| 1125 |
+
hf_sync = extensions.get("hf_sync")
|
| 1126 |
+
if hf_sync:
|
| 1127 |
+
hf_sync.push_file_async(file_path)
|
| 1128 |
+
|
| 1129 |
+
return jsonify({"ok": True, "symbol": symbol, "analysis": analysis, "cached": False})
|
| 1130 |
+
except Exception as exc:
|
| 1131 |
+
logging.error("Failed generating AI analysis for %s: %s", symbol, exc)
|
| 1132 |
+
return jsonify({"ok": False, "message": f"Error running LLM: {str(exc)}"}), 500
|
| 1133 |
+
|
| 1134 |
+
|
| 1135 |
+
|
nsedash/web/static/app.js
CHANGED
|
@@ -334,7 +334,10 @@ function cardEl(c) {
|
|
| 334 |
? `${c.recommendation.action}
|
| 335 |
<span class="muted">(${fmt(c.recommendation.confidence)})</span>`
|
| 336 |
: ""}</span>
|
| 337 |
-
<
|
|
|
|
|
|
|
|
|
|
| 338 |
</div>`;
|
| 339 |
|
| 340 |
card.querySelector(".rm").addEventListener("click", async () => {
|
|
@@ -360,6 +363,26 @@ function cardEl(c) {
|
|
| 360 |
card.querySelectorAll(".in-qty, .in-price").forEach((inp) => {
|
| 361 |
inp.addEventListener("change", pushInputs);
|
| 362 |
});
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 363 |
card.querySelector(".mc-btn").addEventListener("click", async () => {
|
| 364 |
if (mc.link) { window.open(mc.link, "_blank"); return; }
|
| 365 |
const link = prompt(
|
|
@@ -1050,6 +1073,7 @@ document.addEventListener("DOMContentLoaded", async () => {
|
|
| 1050 |
|
| 1051 |
// Admin Panel Event Handlers
|
| 1052 |
$("#admin-close").onclick = () => $("#modal-admin").classList.add("hidden");
|
|
|
|
| 1053 |
$("#admin-btn-show-add").onclick = () => {
|
| 1054 |
hideAllAdminSidebarPanels();
|
| 1055 |
$("#panel-add-user").classList.remove("hidden");
|
|
|
|
| 334 |
? `${c.recommendation.action}
|
| 335 |
<span class="muted">(${fmt(c.recommendation.confidence)})</span>`
|
| 336 |
: ""}</span>
|
| 337 |
+
<div style="display: flex; gap: 4px;">
|
| 338 |
+
<button class="ai-btn" aria-label="Generate AI analysis for ${c.symbol}">AI ✦</button>
|
| 339 |
+
<button class="mc-btn">${mc.link ? "MC ↗" : "Set MC link"}</button>
|
| 340 |
+
</div>
|
| 341 |
</div>`;
|
| 342 |
|
| 343 |
card.querySelector(".rm").addEventListener("click", async () => {
|
|
|
|
| 363 |
card.querySelectorAll(".in-qty, .in-price").forEach((inp) => {
|
| 364 |
inp.addEventListener("change", pushInputs);
|
| 365 |
});
|
| 366 |
+
card.querySelector(".ai-btn").addEventListener("click", async () => {
|
| 367 |
+
$("#modal-ai").classList.remove("hidden");
|
| 368 |
+
$("#ai-title").innerText = `AI Stock Analysis: ${c.symbol}`;
|
| 369 |
+
$("#ai-content").innerHTML = `<div class="loading-spinner" style="display: flex; flex-direction: column; align-items: center; justify-content: center; padding: 40px; color: var(--muted);">
|
| 370 |
+
<div class="spinner" style="border: 3px solid rgba(255,255,255,0.1); border-top: 3px solid var(--accent); border-radius: 50%; width: 30px; height: 30px; animation: spin 1s linear infinite; margin-bottom: 12px;"></div>
|
| 371 |
+
<span>Generating AI analysis... Please wait...</span>
|
| 372 |
+
</div>`;
|
| 373 |
+
|
| 374 |
+
try {
|
| 375 |
+
const payload = await api(`/stocks/${encodeURIComponent(c.symbol)}/ai-analysis`);
|
| 376 |
+
if (payload && payload.ok && payload.analysis) {
|
| 377 |
+
$("#ai-content").innerHTML = marked.parse(payload.analysis);
|
| 378 |
+
} else {
|
| 379 |
+
$("#ai-content").innerHTML = `<span class="loss">Error: ${esc((payload && payload.message) || "Failed to generate analysis")}</span>`;
|
| 380 |
+
}
|
| 381 |
+
} catch (err) {
|
| 382 |
+
$("#ai-content").innerHTML = `<span class="loss">Error: ${esc(err.message)}</span>`;
|
| 383 |
+
}
|
| 384 |
+
});
|
| 385 |
+
|
| 386 |
card.querySelector(".mc-btn").addEventListener("click", async () => {
|
| 387 |
if (mc.link) { window.open(mc.link, "_blank"); return; }
|
| 388 |
const link = prompt(
|
|
|
|
| 1073 |
|
| 1074 |
// Admin Panel Event Handlers
|
| 1075 |
$("#admin-close").onclick = () => $("#modal-admin").classList.add("hidden");
|
| 1076 |
+
$("#ai-close").onclick = () => $("#modal-ai").classList.add("hidden");
|
| 1077 |
$("#admin-btn-show-add").onclick = () => {
|
| 1078 |
hideAllAdminSidebarPanels();
|
| 1079 |
$("#panel-add-user").classList.remove("hidden");
|
nsedash/web/static/style.css
CHANGED
|
@@ -781,4 +781,42 @@ html, body {
|
|
| 781 |
border: 0;
|
| 782 |
}
|
| 783 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 784 |
|
|
|
|
| 781 |
border: 0;
|
| 782 |
}
|
| 783 |
|
| 784 |
+
/* AI button & modal styles */
|
| 785 |
+
.ai-btn {
|
| 786 |
+
background: linear-gradient(135deg, #6c5ce7, #4834d4);
|
| 787 |
+
color: #fff;
|
| 788 |
+
border: none;
|
| 789 |
+
font-weight: 600;
|
| 790 |
+
transition: transform 0.2s ease, filter 0.2s ease;
|
| 791 |
+
}
|
| 792 |
+
.ai-btn:hover {
|
| 793 |
+
filter: brightness(1.2);
|
| 794 |
+
transform: translateY(-1px);
|
| 795 |
+
}
|
| 796 |
+
.ai-analysis-modal {
|
| 797 |
+
max-width: 800px;
|
| 798 |
+
width: 90%;
|
| 799 |
+
}
|
| 800 |
+
@keyframes spin {
|
| 801 |
+
0% { transform: rotate(0deg); }
|
| 802 |
+
100% { transform: rotate(360deg); }
|
| 803 |
+
}
|
| 804 |
+
/* Style markdown lists, headings, and tables in modal */
|
| 805 |
+
.markdown-body h1, .markdown-body h2, .markdown-body h3 {
|
| 806 |
+
margin-top: 16px;
|
| 807 |
+
margin-bottom: 8px;
|
| 808 |
+
color: var(--accent);
|
| 809 |
+
}
|
| 810 |
+
.markdown-body p {
|
| 811 |
+
margin-bottom: 12px;
|
| 812 |
+
}
|
| 813 |
+
.markdown-body ul, .markdown-body ol {
|
| 814 |
+
padding-left: 20px;
|
| 815 |
+
margin-bottom: 12px;
|
| 816 |
+
}
|
| 817 |
+
.markdown-body li {
|
| 818 |
+
margin-bottom: 4px;
|
| 819 |
+
}
|
| 820 |
+
|
| 821 |
+
|
| 822 |
|
nsedash/web/templates/index.html
CHANGED
|
@@ -6,6 +6,7 @@
|
|
| 6 |
<title>NSE Stock Dashboard</title>
|
| 7 |
<link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
|
| 8 |
<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/4.4.1/chart.umd.min.js"></script>
|
|
|
|
| 9 |
</head>
|
| 10 |
<body>
|
| 11 |
|
|
@@ -302,6 +303,19 @@
|
|
| 302 |
</div>
|
| 303 |
</div>
|
| 304 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 305 |
<!-- Shutdown Screen -->
|
| 306 |
<div id="shutdown-screen" class="shutdown-screen">
|
| 307 |
<div class="splash-content">
|
|
|
|
| 6 |
<title>NSE Stock Dashboard</title>
|
| 7 |
<link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
|
| 8 |
<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/4.4.1/chart.umd.min.js"></script>
|
| 9 |
+
<script src="https://cdnjs.cloudflare.com/ajax/libs/marked/11.1.1/marked.min.js"></script>
|
| 10 |
</head>
|
| 11 |
<body>
|
| 12 |
|
|
|
|
| 303 |
</div>
|
| 304 |
</div>
|
| 305 |
|
| 306 |
+
<!-- AI Analysis Modal -->
|
| 307 |
+
<div id="modal-ai" class="overlay hidden" role="dialog" aria-modal="true" aria-labelledby="ai-title">
|
| 308 |
+
<div class="modal ai-analysis-modal">
|
| 309 |
+
<div class="modal-header" style="display: flex; align-items: center; justify-content: space-between; border-bottom: 1px solid rgba(255,255,255,0.1); padding-bottom: 12px; margin-bottom: 15px;">
|
| 310 |
+
<h3 id="ai-title" style="margin: 0;">AI Stock Analysis</h3>
|
| 311 |
+
<button id="ai-close" class="close-btn" style="background: none; border: none; font-size: 20px; color: var(--muted); cursor: pointer;" aria-label="Close AI analysis">×</button>
|
| 312 |
+
</div>
|
| 313 |
+
<div id="ai-content" class="markdown-body" style="max-height: 70vh; overflow-y: auto; text-align: left; padding: 10px; font-size: 14px; line-height: 1.6; color: var(--fg);">
|
| 314 |
+
<!-- Generated AI analysis in markdown format will go here -->
|
| 315 |
+
</div>
|
| 316 |
+
</div>
|
| 317 |
+
</div>
|
| 318 |
+
|
| 319 |
<!-- Shutdown Screen -->
|
| 320 |
<div id="shutdown-screen" class="shutdown-screen">
|
| 321 |
<div class="splash-content">
|
processed/watchlists/nse_dashboard_hist_maxprice1d50.json
CHANGED
|
@@ -2,255 +2,255 @@
|
|
| 2 |
"name": "1D Max Price Portfolio",
|
| 3 |
"symbols": [
|
| 4 |
{
|
| 5 |
-
"SYMBOL": "
|
| 6 |
"Quantity": 0.0,
|
| 7 |
"Price": 0.0
|
| 8 |
},
|
| 9 |
{
|
| 10 |
-
"SYMBOL": "
|
| 11 |
"Quantity": 0.0,
|
| 12 |
"Price": 0.0
|
| 13 |
},
|
| 14 |
{
|
| 15 |
-
"SYMBOL": "
|
| 16 |
"Quantity": 0.0,
|
| 17 |
"Price": 0.0
|
| 18 |
},
|
| 19 |
{
|
| 20 |
-
"SYMBOL": "
|
| 21 |
"Quantity": 0.0,
|
| 22 |
"Price": 0.0
|
| 23 |
},
|
| 24 |
{
|
| 25 |
-
"SYMBOL": "
|
| 26 |
"Quantity": 0.0,
|
| 27 |
"Price": 0.0
|
| 28 |
},
|
| 29 |
{
|
| 30 |
-
"SYMBOL": "
|
| 31 |
"Quantity": 0.0,
|
| 32 |
"Price": 0.0
|
| 33 |
},
|
| 34 |
{
|
| 35 |
-
"SYMBOL": "
|
| 36 |
"Quantity": 0.0,
|
| 37 |
"Price": 0.0
|
| 38 |
},
|
| 39 |
{
|
| 40 |
-
"SYMBOL": "
|
| 41 |
"Quantity": 0.0,
|
| 42 |
"Price": 0.0
|
| 43 |
},
|
| 44 |
{
|
| 45 |
-
"SYMBOL": "
|
| 46 |
"Quantity": 0.0,
|
| 47 |
"Price": 0.0
|
| 48 |
},
|
| 49 |
{
|
| 50 |
-
"SYMBOL": "
|
| 51 |
"Quantity": 0.0,
|
| 52 |
"Price": 0.0
|
| 53 |
},
|
| 54 |
{
|
| 55 |
-
"SYMBOL": "
|
| 56 |
"Quantity": 0.0,
|
| 57 |
"Price": 0.0
|
| 58 |
},
|
| 59 |
{
|
| 60 |
-
"SYMBOL": "
|
| 61 |
"Quantity": 0.0,
|
| 62 |
"Price": 0.0
|
| 63 |
},
|
| 64 |
{
|
| 65 |
-
"SYMBOL": "
|
| 66 |
"Quantity": 0.0,
|
| 67 |
"Price": 0.0
|
| 68 |
},
|
| 69 |
{
|
| 70 |
-
"SYMBOL": "
|
| 71 |
"Quantity": 0.0,
|
| 72 |
"Price": 0.0
|
| 73 |
},
|
| 74 |
{
|
| 75 |
-
"SYMBOL": "
|
| 76 |
"Quantity": 0.0,
|
| 77 |
"Price": 0.0
|
| 78 |
},
|
| 79 |
{
|
| 80 |
-
"SYMBOL": "
|
| 81 |
"Quantity": 0.0,
|
| 82 |
"Price": 0.0
|
| 83 |
},
|
| 84 |
{
|
| 85 |
-
"SYMBOL": "
|
| 86 |
"Quantity": 0.0,
|
| 87 |
"Price": 0.0
|
| 88 |
},
|
| 89 |
{
|
| 90 |
-
"SYMBOL": "
|
| 91 |
"Quantity": 0.0,
|
| 92 |
"Price": 0.0
|
| 93 |
},
|
| 94 |
{
|
| 95 |
-
"SYMBOL": "
|
| 96 |
"Quantity": 0.0,
|
| 97 |
"Price": 0.0
|
| 98 |
},
|
| 99 |
{
|
| 100 |
-
"SYMBOL": "
|
| 101 |
"Quantity": 0.0,
|
| 102 |
"Price": 0.0
|
| 103 |
},
|
| 104 |
{
|
| 105 |
-
"SYMBOL": "
|
| 106 |
"Quantity": 0.0,
|
| 107 |
"Price": 0.0
|
| 108 |
},
|
| 109 |
{
|
| 110 |
-
"SYMBOL": "
|
| 111 |
"Quantity": 0.0,
|
| 112 |
"Price": 0.0
|
| 113 |
},
|
| 114 |
{
|
| 115 |
-
"SYMBOL": "
|
| 116 |
"Quantity": 0.0,
|
| 117 |
"Price": 0.0
|
| 118 |
},
|
| 119 |
{
|
| 120 |
-
"SYMBOL": "
|
| 121 |
"Quantity": 0.0,
|
| 122 |
"Price": 0.0
|
| 123 |
},
|
| 124 |
{
|
| 125 |
-
"SYMBOL": "
|
| 126 |
"Quantity": 0.0,
|
| 127 |
"Price": 0.0
|
| 128 |
},
|
| 129 |
{
|
| 130 |
-
"SYMBOL": "
|
| 131 |
"Quantity": 0.0,
|
| 132 |
"Price": 0.0
|
| 133 |
},
|
| 134 |
{
|
| 135 |
-
"SYMBOL": "
|
| 136 |
"Quantity": 0.0,
|
| 137 |
"Price": 0.0
|
| 138 |
},
|
| 139 |
{
|
| 140 |
-
"SYMBOL": "
|
| 141 |
"Quantity": 0.0,
|
| 142 |
"Price": 0.0
|
| 143 |
},
|
| 144 |
{
|
| 145 |
-
"SYMBOL": "
|
| 146 |
"Quantity": 0.0,
|
| 147 |
"Price": 0.0
|
| 148 |
},
|
| 149 |
{
|
| 150 |
-
"SYMBOL": "
|
| 151 |
"Quantity": 0.0,
|
| 152 |
"Price": 0.0
|
| 153 |
},
|
| 154 |
{
|
| 155 |
-
"SYMBOL": "
|
| 156 |
"Quantity": 0.0,
|
| 157 |
"Price": 0.0
|
| 158 |
},
|
| 159 |
{
|
| 160 |
-
"SYMBOL": "
|
| 161 |
"Quantity": 0.0,
|
| 162 |
"Price": 0.0
|
| 163 |
},
|
| 164 |
{
|
| 165 |
-
"SYMBOL": "
|
| 166 |
"Quantity": 0.0,
|
| 167 |
"Price": 0.0
|
| 168 |
},
|
| 169 |
{
|
| 170 |
-
"SYMBOL": "
|
| 171 |
"Quantity": 0.0,
|
| 172 |
"Price": 0.0
|
| 173 |
},
|
| 174 |
{
|
| 175 |
-
"SYMBOL": "
|
| 176 |
"Quantity": 0.0,
|
| 177 |
"Price": 0.0
|
| 178 |
},
|
| 179 |
{
|
| 180 |
-
"SYMBOL": "
|
| 181 |
"Quantity": 0.0,
|
| 182 |
"Price": 0.0
|
| 183 |
},
|
| 184 |
{
|
| 185 |
-
"SYMBOL": "
|
| 186 |
"Quantity": 0.0,
|
| 187 |
"Price": 0.0
|
| 188 |
},
|
| 189 |
{
|
| 190 |
-
"SYMBOL": "
|
| 191 |
"Quantity": 0.0,
|
| 192 |
"Price": 0.0
|
| 193 |
},
|
| 194 |
{
|
| 195 |
-
"SYMBOL": "
|
| 196 |
"Quantity": 0.0,
|
| 197 |
"Price": 0.0
|
| 198 |
},
|
| 199 |
{
|
| 200 |
-
"SYMBOL": "
|
| 201 |
"Quantity": 0.0,
|
| 202 |
"Price": 0.0
|
| 203 |
},
|
| 204 |
{
|
| 205 |
-
"SYMBOL": "
|
| 206 |
"Quantity": 0.0,
|
| 207 |
"Price": 0.0
|
| 208 |
},
|
| 209 |
{
|
| 210 |
-
"SYMBOL": "
|
| 211 |
"Quantity": 0.0,
|
| 212 |
"Price": 0.0
|
| 213 |
},
|
| 214 |
{
|
| 215 |
-
"SYMBOL": "
|
| 216 |
"Quantity": 0.0,
|
| 217 |
"Price": 0.0
|
| 218 |
},
|
| 219 |
{
|
| 220 |
-
"SYMBOL": "
|
| 221 |
"Quantity": 0.0,
|
| 222 |
"Price": 0.0
|
| 223 |
},
|
| 224 |
{
|
| 225 |
-
"SYMBOL": "
|
| 226 |
"Quantity": 0.0,
|
| 227 |
"Price": 0.0
|
| 228 |
},
|
| 229 |
{
|
| 230 |
-
"SYMBOL": "
|
| 231 |
"Quantity": 0.0,
|
| 232 |
"Price": 0.0
|
| 233 |
},
|
| 234 |
{
|
| 235 |
-
"SYMBOL": "
|
| 236 |
"Quantity": 0.0,
|
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processed/watchlists/nse_dashboard_hist_maxvol3m50.json
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"Quantity": 0.0,
|
| 92 |
"Price": 0.0
|
| 93 |
},
|
| 94 |
{
|
| 95 |
+
"SYMBOL": "RMDRIP",
|
| 96 |
"Quantity": 0.0,
|
| 97 |
"Price": 0.0
|
| 98 |
},
|
| 99 |
{
|
| 100 |
+
"SYMBOL": "INDIAVIX",
|
| 101 |
"Quantity": 0.0,
|
| 102 |
"Price": 0.0
|
| 103 |
},
|
| 104 |
{
|
| 105 |
+
"SYMBOL": "HAVISHA",
|
| 106 |
"Quantity": 0.0,
|
| 107 |
"Price": 0.0
|
| 108 |
},
|
| 109 |
{
|
| 110 |
+
"SYMBOL": "SECURKLOUD",
|
| 111 |
"Quantity": 0.0,
|
| 112 |
"Price": 0.0
|
| 113 |
},
|
| 114 |
{
|
| 115 |
+
"SYMBOL": "RKEC",
|
| 116 |
"Quantity": 0.0,
|
| 117 |
"Price": 0.0
|
| 118 |
},
|
| 119 |
{
|
| 120 |
+
"SYMBOL": "TARAPUR",
|
| 121 |
"Quantity": 0.0,
|
| 122 |
"Price": 0.0
|
| 123 |
},
|
| 124 |
{
|
| 125 |
+
"SYMBOL": "LEXUS",
|
| 126 |
"Quantity": 0.0,
|
| 127 |
"Price": 0.0
|
| 128 |
},
|
| 129 |
{
|
| 130 |
+
"SYMBOL": "INDOTHAI",
|
| 131 |
"Quantity": 0.0,
|
| 132 |
"Price": 0.0
|
| 133 |
},
|
| 134 |
{
|
| 135 |
+
"SYMBOL": "TPHQ",
|
| 136 |
"Quantity": 0.0,
|
| 137 |
"Price": 0.0
|
| 138 |
},
|
| 139 |
{
|
| 140 |
+
"SYMBOL": "BSOFT",
|
| 141 |
"Quantity": 0.0,
|
| 142 |
"Price": 0.0
|
| 143 |
},
|
| 144 |
{
|
| 145 |
+
"SYMBOL": "PROZONER",
|
| 146 |
"Quantity": 0.0,
|
| 147 |
"Price": 0.0
|
| 148 |
},
|
| 149 |
{
|
| 150 |
+
"SYMBOL": "SUVIDHAA",
|
| 151 |
"Quantity": 0.0,
|
| 152 |
"Price": 0.0
|
| 153 |
},
|
| 154 |
{
|
| 155 |
+
"SYMBOL": "UEL",
|
| 156 |
"Quantity": 0.0,
|
| 157 |
"Price": 0.0
|
| 158 |
},
|
| 159 |
{
|
| 160 |
+
"SYMBOL": "PAKKA",
|
| 161 |
"Quantity": 0.0,
|
| 162 |
"Price": 0.0
|
| 163 |
},
|
| 164 |
{
|
| 165 |
+
"SYMBOL": "KPITTECH",
|
| 166 |
"Quantity": 0.0,
|
| 167 |
"Price": 0.0
|
| 168 |
},
|
| 169 |
{
|
| 170 |
+
"SYMBOL": "ORIENTALTL",
|
| 171 |
"Quantity": 0.0,
|
| 172 |
"Price": 0.0
|
| 173 |
},
|
| 174 |
{
|
| 175 |
+
"SYMBOL": "COMFINTE",
|
| 176 |
"Quantity": 0.0,
|
| 177 |
"Price": 0.0
|
| 178 |
},
|
| 179 |
{
|
| 180 |
+
"SYMBOL": "VCL",
|
| 181 |
"Quantity": 0.0,
|
| 182 |
"Price": 0.0
|
| 183 |
},
|
| 184 |
{
|
| 185 |
+
"SYMBOL": "GAYAHWS",
|
| 186 |
"Quantity": 0.0,
|
| 187 |
"Price": 0.0
|
| 188 |
},
|
| 189 |
{
|
| 190 |
+
"SYMBOL": "NIITMTS",
|
| 191 |
"Quantity": 0.0,
|
| 192 |
"Price": 0.0
|
| 193 |
},
|
| 194 |
{
|
| 195 |
+
"SYMBOL": "TECILCHEM",
|
| 196 |
"Quantity": 0.0,
|
| 197 |
"Price": 0.0
|
| 198 |
},
|
| 199 |
{
|
| 200 |
+
"SYMBOL": "SBGLP",
|
| 201 |
"Quantity": 0.0,
|
| 202 |
"Price": 0.0
|
| 203 |
},
|
| 204 |
{
|
| 205 |
+
"SYMBOL": "OSWALGREEN",
|
| 206 |
"Quantity": 0.0,
|
| 207 |
"Price": 0.0
|
| 208 |
},
|
| 209 |
{
|
| 210 |
+
"SYMBOL": "ZFSTEERING",
|
| 211 |
"Quantity": 0.0,
|
| 212 |
"Price": 0.0
|
| 213 |
},
|
| 214 |
{
|
| 215 |
+
"SYMBOL": "PIONRINV",
|
| 216 |
"Quantity": 0.0,
|
| 217 |
"Price": 0.0
|
| 218 |
},
|
| 219 |
{
|
| 220 |
+
"SYMBOL": "XELPMOC",
|
| 221 |
"Quantity": 0.0,
|
| 222 |
"Price": 0.0
|
| 223 |
},
|
| 224 |
{
|
| 225 |
+
"SYMBOL": "HCLTECH",
|
| 226 |
"Quantity": 0.0,
|
| 227 |
"Price": 0.0
|
| 228 |
},
|
| 229 |
{
|
| 230 |
+
"SYMBOL": "RAMKY",
|
| 231 |
"Quantity": 0.0,
|
| 232 |
"Price": 0.0
|
| 233 |
},
|
| 234 |
{
|
| 235 |
+
"SYMBOL": "SOLEX",
|
| 236 |
"Quantity": 0.0,
|
| 237 |
"Price": 0.0
|
| 238 |
},
|
| 239 |
{
|
| 240 |
+
"SYMBOL": "GUJAPOLLO",
|
| 241 |
"Quantity": 0.0,
|
| 242 |
"Price": 0.0
|
| 243 |
},
|
| 244 |
{
|
| 245 |
+
"SYMBOL": "JAINREC",
|
| 246 |
+
"Quantity": 0.0,
|
| 247 |
+
"Price": 0.0
|
| 248 |
+
},
|
| 249 |
+
{
|
| 250 |
+
"SYMBOL": "MANOMAY",
|
| 251 |
"Quantity": 0.0,
|
| 252 |
"Price": 0.0
|
| 253 |
}
|
| 254 |
],
|
| 255 |
+
"last_updated": "2026-07-09T21:06:09.157764"
|
| 256 |
}
|
processed/watchlists/nse_dashboard_maxfall50.json
CHANGED
|
@@ -252,5 +252,5 @@
|
|
| 252 |
"Price": 0.0
|
| 253 |
}
|
| 254 |
],
|
| 255 |
-
"last_updated": "2026-07-
|
| 256 |
}
|
|
|
|
| 252 |
"Price": 0.0
|
| 253 |
}
|
| 254 |
],
|
| 255 |
+
"last_updated": "2026-07-09T21:07:00.629575"
|
| 256 |
}
|
processed/watchlists/nse_dashboard_maxprice50.json
CHANGED
|
@@ -252,5 +252,5 @@
|
|
| 252 |
"Price": 0.0
|
| 253 |
}
|
| 254 |
],
|
| 255 |
-
"last_updated": "2026-07-
|
| 256 |
}
|
|
|
|
| 252 |
"Price": 0.0
|
| 253 |
}
|
| 254 |
],
|
| 255 |
+
"last_updated": "2026-07-09T21:07:00.621014"
|
| 256 |
}
|
processed/watchlists/nse_dashboard_maxvol50.json
CHANGED
|
@@ -127,5 +127,5 @@
|
|
| 127 |
"Price": 0.0
|
| 128 |
}
|
| 129 |
],
|
| 130 |
-
"last_updated": "2026-07-
|
| 131 |
}
|
|
|
|
| 127 |
"Price": 0.0
|
| 128 |
}
|
| 129 |
],
|
| 130 |
+
"last_updated": "2026-07-09T21:07:00.611551"
|
| 131 |
}
|