Update a2a example

examples/a2a-server/README.md

@@ -41,9 +41,9 @@ uv run haiku-rag-a2a serve --host 0.0.0.0 --port 8080
 ```
 
 By default, the server uses the same database location as `haiku-rag`:
-- Linux: `~/.local/share/haiku.rag`
-- macOS: `~/Library/Application Support/haiku.rag`
-- Windows: `C:/Users/<USER>/AppData/Roaming/haiku.rag`
+- Linux: `~/.local/share/haiku.rag/haiku.rag.lancedb`
+- macOS: `~/Library/Application Support/haiku.rag/haiku.rag.lancedb`
+- Windows: `C:/Users/<USER>/AppData/Roaming/haiku.rag/haiku.rag.lancedb`
 
 ### Interactive Client
 

examples/a2a-server/a2a.md

@@ -129,7 +129,7 @@ All operations create artifacts for traceability:
 
 - **search_results**: Created for each `search_documents` tool call
 
-  - Contains query and array of SearchResult objects (content, score, document_title, document_uri)
+  - Contains query and formatted search results string
 
 - **document**: Created for each `get_full_document` tool call
 

examples/a2a-server/haiku_rag_a2a/a2a/__init__.py

@@ -9,7 +9,7 @@ from haiku.rag.config import AppConfig, Config
 from haiku.rag.utils import get_model
 
 from .context import load_message_history, save_message_history
-from .models import A2AConfig, AgentDependencies, SearchResult
+from .models import A2AConfig, AgentDependencies
 from .prompts import A2A_SYSTEM_PROMPT
 from .skills import extract_question_from_task, get_agent_skills
 from .storage import LRUMemoryStorage
@@ -78,24 +78,16 @@ def create_a2a_app(
         ctx: RunContext[AgentDependencies],
         query: str,
         limit: int = 3,
-    ) -> list[SearchResult]:
+    ) -> str:
         """Search the knowledge base for relevant documents.
 
         Returns chunks of text with their relevance scores and document URIs.
         Use get_full_document if you need to see the complete document content.
         """
         search_results = await ctx.deps.client.search(query, limit=limit)
-        expanded_results = await ctx.deps.client.expand_context(search_results)
-
-        return [
-            SearchResult(
-                content=result.content,
-                score=result.score,
-                document_title=result.document_title,
-                document_uri=(result.document_uri or ""),
-            )
-            for result in expanded_results
-        ]
+        results = await ctx.deps.client.expand_context(search_results)
+        parts = [r.format_for_agent() for r in results]
+        return "\n\n".join(parts) if parts else "No results found."
 
     @agent.tool
     async def get_full_document(

examples/a2a-server/haiku_rag_a2a/a2a/models.py

@@ -11,17 +11,6 @@ class A2AConfig(BaseModel):
     )
 
 
-class SearchResult(BaseModel):
-    """Search result with both title and URI for A2A agent."""
-
-    content: str = Field(description="The document text content")
-    score: float = Field(description="Relevance score (higher is more relevant)")
-    document_title: str | None = Field(
-        description="Human-readable document title", default=None
-    )
-    document_uri: str = Field(description="Document URI/path for get_full_document")
-
-
 class AgentDependencies(BaseModel):
     """Dependencies for the A2A conversational agent."""
 

examples/a2a-server/haiku_rag_a2a/a2a/prompts.py

@@ -3,9 +3,29 @@ A2A_SYSTEM_PROMPT = """You are Haiku.rag, an AI assistant that helps users find
 IMPORTANT: You are NOT any person mentioned in the documents. You retrieve and present information about them.
 
 Tools available:
-- search_documents: Query for relevant text chunks (returns SearchResult objects with content, score, document_title, document_uri)
+- search_documents: Query for relevant text chunks
 - get_full_document: Get complete document content by document_uri
 
+The search tool returns results like:
+[chunk_abc123] (score: 0.85)
+Source: "Document Title" > Section > Subsection
+Type: paragraph
+Content:
+The actual text content here...
+
+[chunk_def456] (score: 0.72)
+Source: "Another Document"
+Type: table
+Content:
+| Column 1 | Column 2 |
+...
+
+Each result includes:
+- chunk_id in brackets and relevance score
+- Source: document title and section hierarchy (when available)
+- Type: content type like paragraph, table, code, list_item (when available)
+- Content: the actual text
+
 Your behavior depends on the operation:
 
 ## For direct search requests:

examples/a2a-server/haiku_rag_a2a/cli.py

@@ -50,10 +50,10 @@ def serve(
         config = AppConfig.model_validate(yaml_data)
 
     if db is None:
-        db = get_default_data_dir()
+        db = get_default_data_dir() / "haiku.rag.lancedb"
 
     if not db.exists():
-        typer.echo(f"Error: Database directory {db} does not exist")
+        typer.echo(f"Error: Database {db} does not exist")
         raise typer.Exit(1)
 
     logger.info(f"Starting A2A server on {host}:{port}")

examples/a2a-server/pyproject.toml

@@ -9,9 +9,9 @@ requires-python = ">=3.12"
 keywords = ["RAG", "a2a", "agent", "conversational-ai"]
 
 dependencies = [
-    "haiku.rag>=0.15.0",
-    "fasta2a>=0.1.0",
-    "pydantic-ai-slim[a2a]>=1.17.0",
+    "haiku.rag>=0.23.1",
+    "fasta2a>=0.6.0",
+    "pydantic-ai-slim[a2a]>=1.39.0",
     "rich>=14.2.0",
     "httpx>=0.28.1",
 ]