mirror of
https://github.com/shawnkim1997/All-in-one-Financial-Analysis.git
synced 2026-08-22 07:08:04 +00:00
shawnkim1997andClaude Opus 4.6
b2acda81ee
Complete migration from Streamlit to Next.js 14 App Router + FastAPI backend. Frontend (Next.js 14): - 10 pages: Overview, Research, Valuation, Technical, Markets, Earnings, News, Portfolio, Filings, Settings - Terminal Noir dark theme with custom Tailwind config - TradingView Lightweight Charts for candlestick/volume - Valuation: DCF, Sensitivity Matrix, Monte Carlo, Tornado, Reverse DCF - Financial Statements table with YoY growth badges and margin rows - SEC EDGAR inline filing viewer with section tabs - News split-view with iframe article embedding - Technical Analysis with RSI, MACD, Bollinger, Fibonacci, Moving Averages - Earnings beat/miss visualization - AI Copilot chat panel with Gemini integration Backend (FastAPI): - 13 routers: market_data, financials, valuation, technical, earnings, insider, edgar, news, portfolio, analysis, chat, estimates, fx - Services: DCF engine, Monte Carlo simulation, sensitivity analysis, risk metrics, SEC parser, technical indicators - yfinance + yahooquery data sources with fallback pattern - SQLite caching layer Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
325 lines
11 KiB
Python
325 lines
11 KiB
Python
"""Unified Multi-LLM router supporting Gemini, Claude, and OpenAI."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import asyncio
|
|
import logging
|
|
from enum import Enum
|
|
from typing import AsyncGenerator, Optional
|
|
|
|
from pydantic import BaseModel, Field
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Models
|
|
# ---------------------------------------------------------------------------
|
|
|
|
DEFAULT_MODELS: dict[str, str] = {
|
|
"gemini": "gemini-2.0-flash",
|
|
"claude": "claude-sonnet-4-20250514",
|
|
"openai": "gpt-4o-mini",
|
|
}
|
|
|
|
|
|
class LLMProvider(str, Enum):
|
|
"""Supported LLM providers."""
|
|
|
|
GEMINI = "gemini"
|
|
CLAUDE = "claude"
|
|
OPENAI = "openai"
|
|
|
|
|
|
class LLMConfig(BaseModel):
|
|
"""Configuration for a single LLM request."""
|
|
|
|
provider: LLMProvider
|
|
model: str = ""
|
|
api_key: str = ""
|
|
temperature: float = Field(default=0.3, ge=0.0, le=2.0)
|
|
max_tokens: int = Field(default=4096, ge=1, le=128_000)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Router
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class LLMRouter:
|
|
"""Routes requests to the appropriate LLM provider.
|
|
|
|
Register API keys via ``configure()``, then call ``generate()`` or
|
|
``stream()`` with an optional ``LLMConfig``. When no config is given the
|
|
router auto-selects a provider based on prompt length:
|
|
|
|
* < 5 000 chars -> Gemini (fast)
|
|
* > 10 000 chars -> Claude (long-context)
|
|
* fallback -> OpenAI
|
|
"""
|
|
|
|
def __init__(self) -> None:
|
|
self._providers: dict[LLMProvider, str] = {}
|
|
|
|
# -- configuration ------------------------------------------------------
|
|
|
|
def configure(self, provider: LLMProvider, api_key: str) -> None:
|
|
"""Register an API key for *provider*."""
|
|
self._providers[provider] = api_key
|
|
logger.info("LLM provider configured: %s", provider.value)
|
|
|
|
def get_available_providers(self) -> list[LLMProvider]:
|
|
"""Return the list of providers that have an API key configured."""
|
|
return list(self._providers.keys())
|
|
|
|
# -- public interface ---------------------------------------------------
|
|
|
|
def _resolve_config(
|
|
self, prompt: str, config: Optional[LLMConfig]
|
|
) -> LLMConfig:
|
|
"""Return a fully-resolved ``LLMConfig``.
|
|
|
|
If *config* is ``None`` the provider is auto-selected based on prompt
|
|
length and available keys.
|
|
"""
|
|
if config is not None:
|
|
resolved = config.model_copy()
|
|
if not resolved.api_key:
|
|
resolved.api_key = self._providers.get(resolved.provider, "")
|
|
if not resolved.model:
|
|
resolved.model = DEFAULT_MODELS.get(resolved.provider.value, "")
|
|
return resolved
|
|
|
|
provider = self._auto_select_provider(prompt)
|
|
return LLMConfig(
|
|
provider=provider,
|
|
model=DEFAULT_MODELS[provider.value],
|
|
api_key=self._providers.get(provider, ""),
|
|
)
|
|
|
|
def _auto_select_provider(self, prompt: str) -> LLMProvider:
|
|
"""Pick the best available provider for *prompt*."""
|
|
length = len(prompt)
|
|
|
|
if length < 5_000 and LLMProvider.GEMINI in self._providers:
|
|
return LLMProvider.GEMINI
|
|
if length > 10_000 and LLMProvider.CLAUDE in self._providers:
|
|
return LLMProvider.CLAUDE
|
|
if LLMProvider.OPENAI in self._providers:
|
|
return LLMProvider.OPENAI
|
|
|
|
# Fallback: use whatever is available
|
|
for p in (LLMProvider.GEMINI, LLMProvider.CLAUDE, LLMProvider.OPENAI):
|
|
if p in self._providers:
|
|
return p
|
|
|
|
raise RuntimeError("No LLM provider configured. Call configure() first.")
|
|
|
|
async def generate(
|
|
self,
|
|
prompt: str,
|
|
config: Optional[LLMConfig] = None,
|
|
system_prompt: str = "",
|
|
) -> str:
|
|
"""Generate a complete response from the best available LLM."""
|
|
cfg = self._resolve_config(prompt, config)
|
|
dispatch = {
|
|
LLMProvider.GEMINI: self._gemini_generate,
|
|
LLMProvider.CLAUDE: self._claude_generate,
|
|
LLMProvider.OPENAI: self._openai_generate,
|
|
}
|
|
handler = dispatch[cfg.provider]
|
|
return await handler(
|
|
prompt, system_prompt, cfg.model, cfg.api_key,
|
|
cfg.temperature, cfg.max_tokens,
|
|
)
|
|
|
|
async def stream(
|
|
self,
|
|
prompt: str,
|
|
config: Optional[LLMConfig] = None,
|
|
system_prompt: str = "",
|
|
) -> AsyncGenerator[str, None]:
|
|
"""Stream response chunks from the LLM."""
|
|
cfg = self._resolve_config(prompt, config)
|
|
dispatch = {
|
|
LLMProvider.GEMINI: self._gemini_stream,
|
|
LLMProvider.CLAUDE: self._claude_stream,
|
|
LLMProvider.OPENAI: self._openai_stream,
|
|
}
|
|
handler = dispatch[cfg.provider]
|
|
async for chunk in handler(
|
|
prompt, system_prompt, cfg.model, cfg.api_key,
|
|
cfg.temperature, cfg.max_tokens,
|
|
):
|
|
yield chunk
|
|
|
|
# -- Gemini -------------------------------------------------------------
|
|
|
|
async def _gemini_generate(
|
|
self, prompt: str, system: str, model: str,
|
|
api_key: str, temperature: float, max_tokens: int,
|
|
) -> str:
|
|
"""Call Google Gemini API (non-streaming)."""
|
|
try:
|
|
import google.generativeai as genai # lazy import
|
|
except ImportError as exc:
|
|
raise RuntimeError(
|
|
"google-generativeai is not installed. "
|
|
"Run: pip install google-generativeai"
|
|
) from exc
|
|
|
|
genai.configure(api_key=api_key)
|
|
gen_model = genai.GenerativeModel(
|
|
model_name=model,
|
|
system_instruction=system or None,
|
|
generation_config=genai.GenerationConfig(
|
|
temperature=temperature,
|
|
max_output_tokens=max_tokens,
|
|
),
|
|
)
|
|
response = await asyncio.to_thread(
|
|
gen_model.generate_content, prompt,
|
|
)
|
|
return response.text
|
|
|
|
async def _gemini_stream(
|
|
self, prompt: str, system: str, model: str,
|
|
api_key: str, temperature: float, max_tokens: int,
|
|
) -> AsyncGenerator[str, None]:
|
|
"""Call Google Gemini API (streaming)."""
|
|
try:
|
|
import google.generativeai as genai
|
|
except ImportError as exc:
|
|
raise RuntimeError(
|
|
"google-generativeai is not installed. "
|
|
"Run: pip install google-generativeai"
|
|
) from exc
|
|
|
|
genai.configure(api_key=api_key)
|
|
gen_model = genai.GenerativeModel(
|
|
model_name=model,
|
|
system_instruction=system or None,
|
|
generation_config=genai.GenerationConfig(
|
|
temperature=temperature,
|
|
max_output_tokens=max_tokens,
|
|
),
|
|
)
|
|
response = await asyncio.to_thread(
|
|
gen_model.generate_content, prompt, stream=True,
|
|
)
|
|
for chunk in response:
|
|
if chunk.text:
|
|
yield chunk.text
|
|
|
|
# -- Claude -------------------------------------------------------------
|
|
|
|
async def _claude_generate(
|
|
self, prompt: str, system: str, model: str,
|
|
api_key: str, temperature: float, max_tokens: int,
|
|
) -> str:
|
|
"""Call Anthropic Claude API (non-streaming)."""
|
|
try:
|
|
import anthropic # lazy import
|
|
except ImportError as exc:
|
|
raise RuntimeError(
|
|
"anthropic is not installed. Run: pip install anthropic"
|
|
) from exc
|
|
|
|
client = anthropic.AsyncAnthropic(api_key=api_key)
|
|
message = await client.messages.create(
|
|
model=model,
|
|
max_tokens=max_tokens,
|
|
temperature=temperature,
|
|
system=system or "You are a helpful financial analyst.",
|
|
messages=[{"role": "user", "content": prompt}],
|
|
)
|
|
return message.content[0].text
|
|
|
|
async def _claude_stream(
|
|
self, prompt: str, system: str, model: str,
|
|
api_key: str, temperature: float, max_tokens: int,
|
|
) -> AsyncGenerator[str, None]:
|
|
"""Call Anthropic Claude API (streaming)."""
|
|
try:
|
|
import anthropic
|
|
except ImportError as exc:
|
|
raise RuntimeError(
|
|
"anthropic is not installed. Run: pip install anthropic"
|
|
) from exc
|
|
|
|
client = anthropic.AsyncAnthropic(api_key=api_key)
|
|
async with client.messages.stream(
|
|
model=model,
|
|
max_tokens=max_tokens,
|
|
temperature=temperature,
|
|
system=system or "You are a helpful financial analyst.",
|
|
messages=[{"role": "user", "content": prompt}],
|
|
) as stream:
|
|
async for text in stream.text_stream:
|
|
yield text
|
|
|
|
# -- OpenAI -------------------------------------------------------------
|
|
|
|
async def _openai_generate(
|
|
self, prompt: str, system: str, model: str,
|
|
api_key: str, temperature: float, max_tokens: int,
|
|
) -> str:
|
|
"""Call OpenAI API (non-streaming)."""
|
|
try:
|
|
import openai # lazy import
|
|
except ImportError as exc:
|
|
raise RuntimeError(
|
|
"openai is not installed. Run: pip install openai"
|
|
) from exc
|
|
|
|
client = openai.AsyncOpenAI(api_key=api_key)
|
|
messages: list[dict[str, str]] = []
|
|
if system:
|
|
messages.append({"role": "system", "content": system})
|
|
messages.append({"role": "user", "content": prompt})
|
|
|
|
response = await client.chat.completions.create(
|
|
model=model,
|
|
messages=messages, # type: ignore[arg-type]
|
|
temperature=temperature,
|
|
max_tokens=max_tokens,
|
|
)
|
|
choice = response.choices[0]
|
|
return choice.message.content or ""
|
|
|
|
async def _openai_stream(
|
|
self, prompt: str, system: str, model: str,
|
|
api_key: str, temperature: float, max_tokens: int,
|
|
) -> AsyncGenerator[str, None]:
|
|
"""Call OpenAI API (streaming)."""
|
|
try:
|
|
import openai
|
|
except ImportError as exc:
|
|
raise RuntimeError(
|
|
"openai is not installed. Run: pip install openai"
|
|
) from exc
|
|
|
|
client = openai.AsyncOpenAI(api_key=api_key)
|
|
messages: list[dict[str, str]] = []
|
|
if system:
|
|
messages.append({"role": "system", "content": system})
|
|
messages.append({"role": "user", "content": prompt})
|
|
|
|
stream = await client.chat.completions.create(
|
|
model=model,
|
|
messages=messages, # type: ignore[arg-type]
|
|
temperature=temperature,
|
|
max_tokens=max_tokens,
|
|
stream=True,
|
|
)
|
|
async for chunk in stream:
|
|
delta = chunk.choices[0].delta
|
|
if delta.content:
|
|
yield delta.content
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Singleton
|
|
# ---------------------------------------------------------------------------
|
|
llm_router = LLMRouter()
|