wire llms, ui tweaks

mcp_servers/shared_llm.py

@@ -0,0 +1,108 @@
+"""Shared LLM client for MCP server narrative tools.
+
+Multi-provider support — selected via LLM_PROVIDER env var:
+
+  groq        (default) — Groq API, OpenAI-compatible. Fast, free tier.
+                           Needs GROQ_API_KEY.
+
+  anthropic   — Direct Anthropic SDK. Needs ANTHROPIC_API_KEY.
+
+  bedrock     — AWS Bedrock Converse API. Needs AWS credentials.
+
+  openai      — Any OpenAI-compatible endpoint.
+                Set OPENAI_API_KEY and optionally OPENAI_BASE_URL.
+
+Usage:
+    LLM_PROVIDER=groq GROQ_API_KEY=gsk_... python -m mcp_servers.shared
+"""
+
+import os
+from typing import Literal
+
+Provider = Literal["groq", "anthropic", "bedrock", "openai"]
+
+
+def _get_provider() -> Provider:
+    p = os.getenv("LLM_PROVIDER", "groq").lower()
+    if p in ("groq", "anthropic", "bedrock", "openai"):
+        return p
+    return "groq"
+
+
+async def generate(system_prompt: str, user_content: str, max_tokens: int = 1024) -> str:
+    """Call an LLM and return the text response."""
+    provider = _get_provider()
+
+    if provider == "anthropic":
+        return await _generate_anthropic(system_prompt, user_content, max_tokens)
+    elif provider == "bedrock":
+        return await _generate_bedrock(system_prompt, user_content, max_tokens)
+    else:
+        # groq, openai, or any OpenAI-compatible provider
+        return await _generate_openai_compat(system_prompt, user_content, max_tokens)
+
+
+async def _generate_openai_compat(
+    system_prompt: str, user_content: str, max_tokens: int
+) -> str:
+    """OpenAI-compatible API (Groq, OpenAI, local, etc)."""
+    import openai
+
+    provider = _get_provider()
+
+    if provider == "groq":
+        api_key = os.getenv("GROQ_API_KEY")
+        base_url = "https://api.groq.com/openai/v1"
+        model = os.getenv("GROQ_MODEL", "llama-3.3-70b-versatile")
+    else:
+        api_key = os.getenv("OPENAI_API_KEY")
+        base_url = os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
+        model = os.getenv("OPENAI_MODEL", "gpt-4o")
+
+    client = openai.AsyncOpenAI(api_key=api_key, base_url=base_url)
+    response = await client.chat.completions.create(
+        model=model,
+        max_tokens=max_tokens,
+        temperature=0.7,
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_content},
+        ],
+    )
+    return response.choices[0].message.content
+
+
+async def _generate_anthropic(
+    system_prompt: str, user_content: str, max_tokens: int
+) -> str:
+    import anthropic
+
+    client = anthropic.AsyncAnthropic()
+    response = await client.messages.create(
+        model=os.getenv("ANTHROPIC_MODEL", "claude-sonnet-4-20250514"),
+        max_tokens=max_tokens,
+        temperature=0.7,
+        system=system_prompt,
+        messages=[{"role": "user", "content": user_content}],
+    )
+    return response.content[0].text
+
+
+async def _generate_bedrock(
+    system_prompt: str, user_content: str, max_tokens: int
+) -> str:
+    import json
+
+    import boto3
+
+    bedrock = boto3.client(
+        "bedrock-runtime",
+        region_name=os.getenv("AWS_DEFAULT_REGION", "us-east-1"),
+    )
+    response = bedrock.converse(
+        modelId=os.getenv("BEDROCK_MODEL_ID", "anthropic.claude-sonnet-4-20250514-v1:0"),
+        system=[{"text": system_prompt}],
+        messages=[{"role": "user", "content": [{"text": user_content}]}],
+        inferenceConfig={"maxTokens": max_tokens, "temperature": 0.7},
+    )
+    return response["output"]["message"]["content"][0]["text"]