two version of R2R are here HEAD master

1 files changed, 150 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/r2r/mcp.py b/.venv/lib/python3.12/site-packages/r2r/mcp.py
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+++ b/.venv/lib/python3.12/site-packages/r2r/mcp.py
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+# Add to your local machine with `mcp install r2r/mcp.py -v R2R_API_URL=http://localhost:7272` or so.
+from r2r import R2RClient
+
+
+def id_to_shorthand(id: str) -> str:
+    return str(id)[:7]
+
+
+def format_search_results_for_llm(
+    results,
+) -> str:
+    """
+    Instead of resetting 'source_counter' to 1, we:
+     - For each chunk / graph / web / doc in `results`,
+     - Find the aggregator index from the collector,
+     - Print 'Source [X]:' with that aggregator index.
+    """
+    lines = []
+
+    # We'll build a quick helper to locate aggregator indices for each object:
+    # Or you can rely on the fact that we've added them to the collector
+    # in the same order. But let's do a "lookup aggregator index" approach:
+
+    # 1) Chunk search
+    if results.chunk_search_results:
+        lines.append("Vector Search Results:")
+        for c in results.chunk_search_results:
+            lines.append(f"Source ID [{id_to_shorthand(c.id)}]:")
+            lines.append(c.text or "")  # or c.text[:200] to truncate
+
+    # 2) Graph search
+    if results.graph_search_results:
+        lines.append("Graph Search Results:")
+        for g in results.graph_search_results:
+            lines.append(f"Source ID [{id_to_shorthand(g.id)}]:")
+            if hasattr(g.content, "summary"):
+                lines.append(f"Community Name: {g.content.name}")
+                lines.append(f"ID: {g.content.id}")
+                lines.append(f"Summary: {g.content.summary}")
+                # etc. ...
+            elif hasattr(g.content, "name") and hasattr(
+                g.content, "description"
+            ):
+                lines.append(f"Entity Name: {g.content.name}")
+                lines.append(f"Description: {g.content.description}")
+            elif (
+                hasattr(g.content, "subject")
+                and hasattr(g.content, "predicate")
+                and hasattr(g.content, "object")
+            ):
+                lines.append(
+                    f"Relationship: {g.content.subject}-{g.content.predicate}-{g.content.object}"
+                )
+            # Add metadata if needed
+
+    # 3) Web search
+    if results.web_search_results:
+        lines.append("Web Search Results:")
+        for w in results.web_search_results:
+            lines.append(f"Source ID [{id_to_shorthand(w.id)}]:")
+            lines.append(f"Title: {w.title}")
+            lines.append(f"Link: {w.link}")
+            lines.append(f"Snippet: {w.snippet}")
+
+    # 4) Local context docs
+    if results.document_search_results:
+        lines.append("Local Context Documents:")
+        for doc_result in results.document_search_results:
+            doc_title = doc_result.title or "Untitled Document"
+            doc_id = doc_result.id
+            summary = doc_result.summary
+
+            lines.append(f"Full Document ID: {doc_id}")
+            lines.append(f"Shortened Document ID: {id_to_shorthand(doc_id)}")
+            lines.append(f"Document Title: {doc_title}")
+            if summary:
+                lines.append(f"Summary: {summary}")
+
+            if doc_result.chunks:
+                # Then each chunk inside:
+                for chunk in doc_result.chunks:
+                    lines.append(
+                        f"\nChunk ID {id_to_shorthand(chunk['id'])}:\n{chunk['text']}"
+                    )
+
+    result = "\n".join(lines)
+    return result
+
+
+# Create a FastMCP server
+
+try:
+    from mcp.server.fastmcp import FastMCP
+
+    mcp = FastMCP("R2R Retrieval System")
+except Exception as e:
+    raise ImportError(
+        "MCP is not installed. Please run `pip install mcp`"
+    ) from e
+
+# Pass lifespan to server
+mcp = FastMCP("R2R Retrieval System")
+
+
+# RAG query tool
+@mcp.tool()
+async def search(query: str) -> str:
+    """
+    Performs a
+
+    Args:
+        query: The question to answer using the knowledge base
+
+    Returns:
+        A response generated based on relevant context from the knowledge base
+    """
+    client = R2RClient()
+
+    # Call the RAG endpoint
+    search_response = client.retrieval.search(
+        query=query,
+    )
+    return format_search_results_for_llm(search_response.results)
+
+
+# RAG query tool
+@mcp.tool()
+async def rag(query: str) -> str:
+    """
+    Perform a Retrieval-Augmented Generation query
+
+    Args:
+        query: The question to answer using the knowledge base
+
+    Returns:
+        A response generated based on relevant context from the knowledge base
+    """
+    client = R2RClient()
+
+    # Call the RAG endpoint
+    rag_response = client.retrieval.rag(
+        query=query,
+    )
+
+    return rag_response.results.generated_answer  # type: ignore
+
+
+# Run the server if executed directly
+if __name__ == "__main__":
+    mcp.run()