From 2fbb8d84303447f0a08a691f4cebb2a3d5bb8d4a Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>
Date: Thu, 4 Sep 2025 18:31:38 -0300
Subject: [PATCH] Implement async fetching of ingestion flow configuration

This commit adds functionality to retrieve and set ingestion-specific defaults from the Langflow API based on the flow configuration. It includes error handling for the API call and updates the settings with values for chunk size, chunk overlap, separator, and embedding model based on the flow data. This enhancement improves the flexibility and robustness of the ingestion process.
---
 src/api/settings.py | 70 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 70 insertions(+)

diff --git a/src/api/settings.py b/src/api/settings.py
index 43813e99..b6148464 100644
--- a/src/api/settings.py
+++ b/src/api/settings.py
@@ -29,6 +29,76 @@ async def get_settings(request, session_manager):
                 f"{LANGFLOW_PUBLIC_URL.rstrip('/')}/flow/{LANGFLOW_INGEST_FLOW_ID}"
             )
 
+        # Fetch ingestion flow configuration to get actual component defaults
+        if LANGFLOW_INGEST_FLOW_ID:
+            try:
+                from config.settings import generate_langflow_api_key
+                import httpx
+
+                api_key = await generate_langflow_api_key()
+                if api_key:
+                    async with httpx.AsyncClient(timeout=10.0) as client:
+                        response = await client.get(
+                            f"{LANGFLOW_URL}/api/v1/flows/{LANGFLOW_INGEST_FLOW_ID}",
+                            headers={"x-api-key": api_key},
+                        )
+                        if response.status_code == 200:
+                            flow_data = response.json()
+
+                            # Extract component defaults (ingestion-specific settings only)
+                            ingestion_defaults = {
+                                "chunkSize": 1000,
+                                "chunkOverlap": 200,
+                                "separator": "\\n",
+                                "embeddingModel": "text-embedding-3-small",
+                            }
+
+                            if flow_data.get("data", {}).get("nodes"):
+                                for node in flow_data["data"]["nodes"]:
+                                    node_template = (
+                                        node.get("data", {})
+                                        .get("node", {})
+                                        .get("template", {})
+                                    )
+
+                                    # Split Text component (SplitText-QIKhg)
+                                    if node.get("id") == "SplitText-QIKhg":
+                                        if node_template.get("chunk_size", {}).get(
+                                            "value"
+                                        ):
+                                            ingestion_defaults["chunkSize"] = (
+                                                node_template["chunk_size"]["value"]
+                                            )
+                                        if node_template.get("chunk_overlap", {}).get(
+                                            "value"
+                                        ):
+                                            ingestion_defaults["chunkOverlap"] = (
+                                                node_template["chunk_overlap"]["value"]
+                                            )
+                                        if node_template.get("separator", {}).get(
+                                            "value"
+                                        ):
+                                            ingestion_defaults["separator"] = (
+                                                node_template["separator"]["value"]
+                                            )
+
+                                    # OpenAI Embeddings component (OpenAIEmbeddings-joRJ6)
+                                    elif node.get("id") == "OpenAIEmbeddings-joRJ6":
+                                        if node_template.get("model", {}).get("value"):
+                                            ingestion_defaults["embeddingModel"] = (
+                                                node_template["model"]["value"]
+                                            )
+
+                                    # Note: OpenSearch component settings are not exposed for ingestion
+                                    # (search-related parameters like number_of_results, score_threshold
+                                    # are for retrieval, not ingestion)
+
+                            settings["ingestion_defaults"] = ingestion_defaults
+
+            except Exception as e:
+                print(f"[WARNING] Failed to fetch ingestion flow defaults: {e}")
+                # Continue without ingestion defaults
+
         return JSONResponse(settings)
 
     except Exception as e: