Initial commit: add .gitignore and README

fusionagi/adapters/native_adapter.py

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+"""
+Native adapter: implements LLMAdapter using FusionAGI's internal reasoning.
+
+No external API calls. Used for synthesis (e.g. Witness compose) when operating
+in fully native AGI mode.
+"""
+
+from typing import Any
+
+from fusionagi.adapters.base import LLMAdapter
+
+
+def _synthesize_from_messages(messages: list[dict[str, Any]]) -> str:
+    """
+    Synthesize narrative from message content using native logic only.
+    Extracts head summaries and agreed claims, produces coherent narrative.
+    """
+    if not messages:
+        return ""
+
+    content_parts: list[str] = []
+    for msg in messages:
+        content = msg.get("content", "")
+        if isinstance(content, str) and content.strip():
+            content_parts.append(content)
+
+    if not content_parts:
+        return ""
+
+    full_content = "\n".join(content_parts)
+
+    # Extract "User asked:" for context
+    user_prompt = ""
+    if "User asked:" in full_content:
+        idx = full_content.index("User asked:") + len("User asked:")
+        end = full_content.find("\n\n", idx)
+        user_prompt = full_content[idx:end if end > 0 else None].strip()
+
+    narrative_parts: list[str] = []
+
+    if user_prompt:
+        truncated = user_prompt[:120] + ("..." if len(user_prompt) > 120 else "")
+        narrative_parts.append(f"Regarding your question: {truncated}")
+
+    # Extract head summaries
+    if "Head summaries:" in full_content:
+        start = full_content.index("Head summaries:") + len("Head summaries:")
+        end = full_content.find("\n\nAgreed claims:", start)
+        if end < 0:
+            end = full_content.find("Agreed claims:", start)
+        if end < 0:
+            end = len(full_content)
+        summaries = full_content[start:end].strip()
+        for line in summaries.split("\n"):
+            line = line.strip()
+            if line.startswith("-") and ":" in line:
+                narrative_parts.append(line[1:].strip())
+
+    # Extract agreed claims as key points
+    if "Agreed claims:" in full_content:
+        start = full_content.index("Agreed claims:") + len("Agreed claims:")
+        rest = full_content[start:].strip()
+        claims_section = rest.split("\n\nDisputed:")[0].split("\n\n")[0]
+        claim_lines = [ln.strip()[1:].strip() for ln in claims_section.split("\n") if ln.strip().startswith("-")]
+        for c in claim_lines[:5]:
+            if " (confidence:" in c:
+                c = c.split(" (confidence:")[0].strip()
+            if c:
+                narrative_parts.append(c)
+
+    if not narrative_parts:
+        paragraphs = [p.strip() for p in full_content.split("\n\n") if len(p.strip()) > 20]
+        narrative_parts = paragraphs[:5] if paragraphs else [full_content[:500]]
+
+    return "\n\n".join(narrative_parts)
+
+
+class NativeAdapter(LLMAdapter):
+    """
+    Adapter that uses FusionAGI's native synthesis—no external LLM calls.
+
+    For complete(): synthesizes narrative from message content.
+    For complete_structured(): returns None (use NativeReasoningProvider for heads).
+    """
+
+    def complete(
+        self,
+        messages: list[dict[str, str]],
+        **kwargs: Any,
+    ) -> str:
+        """Synthesize response from message content using native logic."""
+        return _synthesize_from_messages(messages)
+
+    def complete_structured(
+        self,
+        messages: list[dict[str, str]],
+        schema: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Not supported; use NativeReasoningProvider for structured HeadOutput."""
+        return None