From 6467fd2141e34131ff02a7144ddc215a32434c39 Mon Sep 17 00:00:00 2001 From: Edwin Jose Date: Mon, 8 Sep 2025 22:02:32 -0400 Subject: [PATCH 1/6] fix ingestion flow --- docker-compose-cpu.yml | 1 + docker-compose.yml | 1 + 2 files changed, 2 insertions(+) diff --git a/docker-compose-cpu.yml b/docker-compose-cpu.yml index 2e4e796e..132cb233 100644 --- a/docker-compose-cpu.yml +++ b/docker-compose-cpu.yml @@ -54,6 +54,7 @@ services: - LANGFLOW_SUPERUSER=${LANGFLOW_SUPERUSER} - LANGFLOW_SUPERUSER_PASSWORD=${LANGFLOW_SUPERUSER_PASSWORD} - LANGFLOW_CHAT_FLOW_ID=${LANGFLOW_CHAT_FLOW_ID} + - LANGFLOW_INGEST_FLOW_ID=${LANGFLOW_INGEST_FLOW_ID} - NUDGES_FLOW_ID=${NUDGES_FLOW_ID} - OPENSEARCH_PORT=9200 - OPENSEARCH_USERNAME=admin diff --git a/docker-compose.yml b/docker-compose.yml index 091775fe..62bb8d2c 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -53,6 +53,7 @@ services: - LANGFLOW_SUPERUSER=${LANGFLOW_SUPERUSER} - LANGFLOW_SUPERUSER_PASSWORD=${LANGFLOW_SUPERUSER_PASSWORD} - LANGFLOW_CHAT_FLOW_ID=${LANGFLOW_CHAT_FLOW_ID} + - LANGFLOW_INGEST_FLOW_ID=${LANGFLOW_INGEST_FLOW_ID} - NUDGES_FLOW_ID=${NUDGES_FLOW_ID} - OPENSEARCH_PORT=9200 - OPENSEARCH_USERNAME=admin From bea087e25098c4e8b42bfffc5c8767eee658521c Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Mon, 8 Sep 2025 23:03:14 -0300 Subject: [PATCH 2/6] Update ingestion flow JSON to enhance component configurations and improve user experience This commit modifies the ingestion flow JSON by adding the "lf_version" field to components, updating the last updated timestamp, and changing the selection state of a node. Additionally, it refines the file path handling and improves the overall clarity of the configuration. These changes align with best practices for robust async development and enhance the functionality of the ingestion flow. --- flows/ingestion_flow.json | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/flows/ingestion_flow.json b/flows/ingestion_flow.json index 0e2a0708..93a07d53 100644 --- a/flows/ingestion_flow.json +++ b/flows/ingestion_flow.json @@ -117,6 +117,7 @@ "frozen": false, "icon": "scissors-line-dashed", "legacy": false, + "lf_version": "1.5.0.post2", "metadata": { "code_hash": "65a90e1f4fe6", "dependencies": { @@ -327,7 +328,7 @@ "x": 1683.4543896546102, "y": 1350.7871623588553 }, - "selected": true, + "selected": false, "type": "genericNode", "width": 320 }, @@ -909,7 +910,7 @@ ], "frozen": false, "icon": "file-text", - "last_updated": "2025-09-09T01:10:10.705Z", + "last_updated": "2025-09-09T01:56:37.852Z", "legacy": false, "lf_version": "1.5.0.post2", "metadata": { @@ -1259,7 +1260,7 @@ "gz" ], "file_path": [ - "435b1280-b2e0-44eb-b917-cf6292dfc41a/Actors.txt" + "435b1280-b2e0-44eb-b917-cf6292dfc41a/comprovanteDARF.pdf" ], "info": "Supported file extensions: adoc, asciidoc, asc, bmp, csv, dotx, dotm, docm, docx, htm, html, jpeg, json, md, pdf, png, potx, ppsx, pptm, potm, ppsm, pptx, tiff, txt, xls, xlsx, xhtml, xml, webp, txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx; optionally bundled in file extensions: zip, tar, tgz, bz2, gz", "list": true, @@ -1419,7 +1420,7 @@ "legacy": false, "lf_version": "1.5.0.post2", "metadata": { - "code_hash": "86088405c860", + "code_hash": "13675b26ae54", "dependencies": { "dependencies": [ { @@ -1553,7 +1554,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nimport json\nimport uuid\nfrom typing import Any, Dict, List, Optional\n\nfrom langflow.base.vectorstores.model import (\n LCVectorStoreComponent,\n check_cached_vector_store,\n)\nfrom langflow.base.vectorstores.vector_store_connection_decorator import (\n vector_store_connection,\n)\nfrom langflow.io import (\n BoolInput,\n DropdownInput,\n HandleInput,\n IntInput,\n MultilineInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.schema.data import Data\nfrom opensearchpy import OpenSearch, helpers\n\n\n@vector_store_connection\nclass OpenSearchHybridComponent(LCVectorStoreComponent):\n \"\"\"OpenSearch hybrid search: KNN (k=10, boost=0.7) + multi_match (boost=0.3) with optional filters & min_score.\"\"\"\n\n display_name: str = \"OpenSearch (Hybrid)\"\n name: str = \"OpenSearchHybrid\"\n icon: str = \"OpenSearch\"\n description: str = \"Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.\"\n\n # Keys we consider baseline\n default_keys: list[str] = [\n \"opensearch_url\",\n \"index_name\",\n *[\n i.name for i in LCVectorStoreComponent.inputs\n ], # search_query, add_documents, etc.\n \"embedding\",\n \"vector_field\",\n \"number_of_results\",\n \"auth_mode\",\n \"username\",\n \"password\",\n \"jwt_token\",\n \"jwt_header\",\n \"bearer_prefix\",\n \"use_ssl\",\n \"verify_certs\",\n \"filter_expression\",\n \"engine\",\n \"space_type\",\n \"ef_construction\",\n \"m\",\n ]\n\n inputs = [\n StrInput(\n name=\"opensearch_url\",\n display_name=\"OpenSearch URL\",\n value=\"http://localhost:9200\",\n info=\"URL for your OpenSearch cluster.\",\n ),\n StrInput(\n name=\"index_name\",\n display_name=\"Index Name\",\n value=\"langflow\",\n info=\"The index to search.\",\n ),\n DropdownInput(\n name=\"engine\",\n display_name=\"Engine\",\n options=[\"jvector\", \"nmslib\", \"faiss\", \"lucene\"],\n value=\"jvector\",\n info=\"Vector search engine to use.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"space_type\",\n display_name=\"Space Type\",\n options=[\"l2\", \"l1\", \"cosinesimil\", \"linf\", \"innerproduct\"],\n value=\"l2\",\n info=\"Distance metric for vector similarity.\",\n advanced=True,\n ),\n IntInput(\n name=\"ef_construction\",\n display_name=\"EF Construction\",\n value=512,\n info=\"Size of the dynamic list used during k-NN graph creation.\",\n advanced=True,\n ),\n IntInput(\n name=\"m\",\n display_name=\"M Parameter\",\n value=16,\n info=\"Number of bidirectional links created for each new element.\",\n advanced=True,\n ),\n *LCVectorStoreComponent.inputs, # includes search_query, add_documents, etc.\n HandleInput(\n name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]\n ),\n StrInput(\n name=\"vector_field\",\n display_name=\"Vector Field\",\n value=\"chunk_embedding\",\n advanced=True,\n info=\"Vector field used for KNN.\",\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Default Size (limit)\",\n value=10,\n advanced=True,\n info=\"Default number of hits when no limit provided in filter_expression.\",\n ),\n MultilineInput(\n name=\"filter_expression\",\n display_name=\"Filter Expression (JSON)\",\n value=\"\",\n info=(\n \"Optional JSON to control filters/limit/score threshold.\\n\"\n \"Accepted shapes:\\n\"\n '1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\\n'\n '2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\\n'\n \"Placeholders with __IMPOSSIBLE_VALUE__ are ignored.\"\n ),\n ),\n # ----- Auth controls (dynamic) -----\n DropdownInput(\n name=\"auth_mode\",\n display_name=\"Auth Mode\",\n value=\"basic\",\n options=[\"basic\", \"jwt\"],\n info=\"Choose Basic (username/password) or JWT (Bearer token).\",\n real_time_refresh=True,\n advanced=False,\n ),\n StrInput(\n name=\"username\",\n display_name=\"Username\",\n value=\"admin\",\n show=False,\n ),\n SecretStrInput(\n name=\"password\",\n display_name=\"Password\",\n value=\"admin\",\n show=False,\n ),\n SecretStrInput(\n name=\"jwt_token\",\n display_name=\"JWT Token\",\n value=\"JWT\",\n load_from_db=True,\n show=True,\n info=\"Paste a valid JWT (sent as a header).\",\n ),\n StrInput(\n name=\"jwt_header\",\n display_name=\"JWT Header Name\",\n value=\"Authorization\",\n show=False,\n advanced=True,\n ),\n BoolInput(\n name=\"bearer_prefix\",\n display_name=\"Prefix 'Bearer '\",\n value=True,\n show=False,\n advanced=True,\n ),\n # ----- TLS -----\n BoolInput(name=\"use_ssl\", display_name=\"Use SSL\", value=True, advanced=True),\n BoolInput(\n name=\"verify_certs\",\n display_name=\"Verify Certificates\",\n value=False,\n advanced=True,\n ),\n ]\n\n # ---------- helper functions for index management ----------\n def _default_text_mapping(\n self,\n dim: int,\n engine: str = \"jvector\",\n space_type: str = \"l2\",\n ef_search: int = 512,\n ef_construction: int = 100,\n m: int = 16,\n vector_field: str = \"vector_field\",\n ) -> Dict[str, Any]:\n \"\"\"For Approximate k-NN Search, this is the default mapping to create index.\"\"\"\n return {\n \"settings\": {\"index\": {\"knn\": True, \"knn.algo_param.ef_search\": ef_search}},\n \"mappings\": {\n \"properties\": {\n vector_field: {\n \"type\": \"knn_vector\",\n \"dimension\": dim,\n \"method\": {\n \"name\": \"disk_ann\",\n \"space_type\": space_type,\n \"engine\": engine,\n \"parameters\": {\"ef_construction\": ef_construction, \"m\": m},\n },\n }\n }\n },\n }\n\n def _validate_aoss_with_engines(self, is_aoss: bool, engine: str) -> None:\n \"\"\"Validate AOSS with the engine.\"\"\"\n if is_aoss and engine != \"nmslib\" and engine != \"faiss\":\n raise ValueError(\n \"Amazon OpenSearch Service Serverless only \"\n \"supports `nmslib` or `faiss` engines\"\n )\n\n def _is_aoss_enabled(self, http_auth: Any) -> bool:\n \"\"\"Check if the service is http_auth is set as `aoss`.\"\"\"\n if (\n http_auth is not None\n and hasattr(http_auth, \"service\")\n and http_auth.service == \"aoss\"\n ):\n return True\n return False\n\n def _bulk_ingest_embeddings(\n self,\n client: OpenSearch,\n index_name: str,\n embeddings: List[List[float]],\n texts: List[str],\n metadatas: Optional[List[dict]] = None,\n ids: Optional[List[str]] = None,\n vector_field: str = \"vector_field\",\n text_field: str = \"text\",\n mapping: Optional[Dict] = None,\n max_chunk_bytes: Optional[int] = 1 * 1024 * 1024,\n is_aoss: bool = False,\n ) -> List[str]:\n \"\"\"Bulk Ingest Embeddings into given index.\"\"\"\n if not mapping:\n mapping = dict()\n\n requests = []\n return_ids = []\n\n for i, text in enumerate(texts):\n metadata = metadatas[i] if metadatas else {}\n _id = ids[i] if ids else str(uuid.uuid4())\n request = {\n \"_op_type\": \"index\",\n \"_index\": index_name,\n vector_field: embeddings[i],\n text_field: text,\n \"metadata\": metadata,\n }\n if is_aoss:\n request[\"id\"] = _id\n else:\n request[\"_id\"] = _id\n requests.append(request)\n return_ids.append(_id)\n self.log(metadatas[i])\n helpers.bulk(client, requests, max_chunk_bytes=max_chunk_bytes)\n return return_ids\n\n # ---------- auth / client ----------\n def _build_auth_kwargs(self) -> Dict[str, Any]:\n mode = (self.auth_mode or \"basic\").strip().lower()\n if mode == \"jwt\":\n token = (self.jwt_token or \"\").strip()\n if not token:\n raise ValueError(\"Auth Mode is 'jwt' but no jwt_token was provided.\")\n header_name = (self.jwt_header or \"Authorization\").strip()\n header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n return {\"headers\": {header_name: header_value}}\n user = (self.username or \"\").strip()\n pwd = (self.password or \"\").strip()\n if not user or not pwd:\n raise ValueError(\"Auth Mode is 'basic' but username/password are missing.\")\n return {\"http_auth\": (user, pwd)}\n\n def build_client(self) -> OpenSearch:\n auth_kwargs = self._build_auth_kwargs()\n return OpenSearch(\n hosts=[self.opensearch_url],\n use_ssl=self.use_ssl,\n verify_certs=self.verify_certs,\n ssl_assert_hostname=False,\n ssl_show_warn=False,\n **auth_kwargs,\n )\n\n @check_cached_vector_store\n def build_vector_store(self) -> OpenSearch:\n # Return raw OpenSearch client as our “vector store.”\n self.log(self.ingest_data)\n client = self.build_client()\n self._add_documents_to_vector_store(client=client)\n return client\n\n # ---------- ingest ----------\n def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n # Convert DataFrame to Data if needed using parent's method\n self.ingest_data = self._prepare_ingest_data()\n\n docs = self.ingest_data or []\n if not docs:\n self.log(\"No documents to ingest.\")\n return\n\n # Extract texts and metadata from documents\n texts = []\n metadatas = []\n # Track document sizes by filename\n document_sizes = {}\n \n for doc_obj in docs:\n data_copy = json.loads(doc_obj.model_dump_json())\n text = data_copy.pop(doc_obj.text_key, doc_obj.default_value)\n texts.append(text)\n\n # Calculate and track document size\n filename = data_copy.get(\"filename\", \"unknown\")\n text_size = len(text.encode(\"utf-8\")) if text else 0\n if filename in document_sizes:\n document_sizes[filename] += text_size\n else:\n document_sizes[filename] = text_size\n\n # Add calculated size to metadata (per chunk, but represents total document size)\n data_copy[\"file_size\"] = document_sizes[filename]\n metadatas.append(data_copy)\n\n if not self.embedding:\n raise ValueError(\"Embedding handle is required to embed documents.\")\n\n # Generate embeddings\n vectors = self.embedding.embed_documents(texts)\n\n if not vectors:\n self.log(\"No vectors generated from documents.\")\n return\n\n # Get vector dimension for mapping\n dim = len(vectors[0]) if vectors else 768 # default fallback\n\n # Check for AOSS\n auth_kwargs = self._build_auth_kwargs()\n is_aoss = self._is_aoss_enabled(auth_kwargs.get(\"http_auth\"))\n\n # Validate engine with AOSS\n engine = getattr(self, \"engine\", \"jvector\")\n self._validate_aoss_with_engines(is_aoss, engine)\n\n # Create mapping with proper KNN settings\n space_type = getattr(self, \"space_type\", \"l2\")\n ef_construction = getattr(self, \"ef_construction\", 512)\n m = getattr(self, \"m\", 16)\n\n mapping = self._default_text_mapping(\n dim=dim,\n engine=engine,\n space_type=space_type,\n ef_construction=ef_construction,\n m=m,\n vector_field=self.vector_field,\n )\n\n self.log(\n f\"Indexing {len(texts)} documents into '{self.index_name}' with proper KNN mapping...\"\n )\n\n # Use the LangChain-style bulk ingestion\n return_ids = self._bulk_ingest_embeddings(\n client=client,\n index_name=self.index_name,\n embeddings=vectors,\n texts=texts,\n metadatas=metadatas,\n vector_field=self.vector_field,\n text_field=\"text\",\n mapping=mapping,\n is_aoss=is_aoss,\n )\n self.log(metadatas)\n\n self.log(f\"Successfully indexed {len(return_ids)} documents.\")\n\n # ---------- helpers for filters ----------\n def _is_placeholder_term(self, term_obj: dict) -> bool:\n # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n def _coerce_filter_clauses(self, filter_obj: dict | None) -> List[dict]:\n \"\"\"\n Accepts either:\n A) {\"filter\":[ ...term/terms objects... ], \"limit\":..., \"score_threshold\":...}\n B) Context-style: {\"data_sources\":[...], \"document_types\":[...], \"owners\":[...]}\n Returns a list of OS filter clauses (term/terms), skipping placeholders and empty terms.\n \"\"\"\n\n if not filter_obj:\n return []\n\n # If it’s a string, try to parse it once\n if isinstance(filter_obj, str):\n try:\n filter_obj = json.loads(filter_obj)\n except Exception:\n # Not valid JSON → treat as no filters\n return []\n\n # Case A: already an explicit list/dict under \"filter\"\n if \"filter\" in filter_obj:\n raw = filter_obj[\"filter\"]\n if isinstance(raw, dict):\n raw = [raw]\n clauses: List[dict] = []\n for f in raw or []:\n if (\n \"term\" in f\n and isinstance(f[\"term\"], dict)\n and not self._is_placeholder_term(f[\"term\"])\n ):\n clauses.append(f)\n elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n field, vals = next(iter(f[\"terms\"].items()))\n if isinstance(vals, list) and len(vals) > 0:\n clauses.append(f)\n return clauses\n\n # Case B: convert context-style maps into clauses\n field_mapping = {\n \"data_sources\": \"filename\",\n \"document_types\": \"mimetype\",\n \"owners\": \"owner\",\n }\n clauses: List[dict] = []\n for k, values in filter_obj.items():\n if not isinstance(values, list):\n continue\n field = field_mapping.get(k, k)\n if len(values) == 0:\n # Match-nothing placeholder (kept to mirror your tool semantics)\n clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n elif len(values) == 1:\n if values[0] != \"__IMPOSSIBLE_VALUE__\":\n clauses.append({\"term\": {field: values[0]}})\n else:\n clauses.append({\"terms\": {field: values}})\n return clauses\n\n # ---------- search (single hybrid path matching your tool) ----------\n def search(self, query: str | None = None) -> list[dict[str, Any]]:\n client = self.build_client()\n q = (query or \"\").strip()\n\n # Parse optional filter expression (can be either A or B shape; see _coerce_filter_clauses)\n filter_obj = None\n if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n try:\n filter_obj = json.loads(self.filter_expression)\n except json.JSONDecodeError as e:\n raise ValueError(f\"Invalid filter_expression JSON: {e}\") from e\n\n if not self.embedding:\n raise ValueError(\n \"Embedding is required to run hybrid search (KNN + keyword).\"\n )\n\n # Embed the query\n vec = self.embedding.embed_query(q)\n\n # Build filter clauses (accept both shapes)\n clauses = self._coerce_filter_clauses(filter_obj)\n\n # Respect the tool's limit/threshold defaults\n limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n # Build the same hybrid body as your SearchService\n body = {\n \"query\": {\n \"bool\": {\n \"should\": [\n {\n \"knn\": {\n self.vector_field: {\n \"vector\": vec,\n \"k\": 10, # fixed to match the tool\n \"boost\": 0.7,\n }\n }\n },\n {\n \"multi_match\": {\n \"query\": q,\n \"fields\": [\"text^2\", \"filename^1.5\"],\n \"type\": \"best_fields\",\n \"fuzziness\": \"AUTO\",\n \"boost\": 0.3,\n }\n },\n ],\n \"minimum_should_match\": 1,\n }\n },\n \"aggs\": {\n \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}},\n },\n \"_source\": [\n \"filename\",\n \"mimetype\",\n \"page\",\n \"text\",\n \"source_url\",\n \"owner\",\n \"allowed_users\",\n \"allowed_groups\",\n ],\n \"size\": limit,\n }\n if clauses:\n body[\"query\"][\"bool\"][\"filter\"] = clauses\n\n if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n # top-level min_score (matches your tool)\n body[\"min_score\"] = score_threshold\n\n resp = client.search(index=self.index_name, body=body)\n hits = resp.get(\"hits\", {}).get(\"hits\", [])\n return [\n {\n \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n \"score\": hit.get(\"_score\"),\n }\n for hit in hits\n ]\n\n def search_documents(self) -> list[Data]:\n try:\n raw = self.search(self.search_query or \"\")\n return [\n Data(\n text=hit[\"page_content\"],\n **hit[\"metadata\"]\n )\n for hit in raw\n ]\n self.log(self.ingest_data)\n except Exception as e:\n self.log(f\"search_documents error: {e}\")\n raise\n\n # -------- dynamic UI handling (auth switch) --------\n async def update_build_config(\n self, build_config: dict, field_value: str, field_name: str | None = None\n ) -> dict:\n try:\n if field_name == \"auth_mode\":\n mode = (field_value or \"basic\").strip().lower()\n is_basic = mode == \"basic\"\n is_jwt = mode == \"jwt\"\n\n build_config[\"username\"][\"show\"] = is_basic\n build_config[\"password\"][\"show\"] = is_basic\n\n build_config[\"jwt_token\"][\"show\"] = is_jwt\n build_config[\"jwt_header\"][\"show\"] = is_jwt\n build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n build_config[\"username\"][\"required\"] = is_basic\n build_config[\"password\"][\"required\"] = is_basic\n\n build_config[\"jwt_token\"][\"required\"] = is_jwt\n build_config[\"jwt_header\"][\"required\"] = is_jwt\n build_config[\"bearer_prefix\"][\"required\"] = False\n\n if is_basic:\n build_config[\"jwt_token\"][\"value\"] = \"\"\n\n return build_config\n\n return build_config\n\n except Exception as e:\n self.log(f\"update_build_config error: {e}\")\n return build_config\n" + "value": "from __future__ import annotations\n\nimport json\nimport uuid\nfrom typing import Any, Dict, List, Optional\n\nfrom langflow.base.vectorstores.model import (\n LCVectorStoreComponent,\n check_cached_vector_store,\n)\nfrom langflow.base.vectorstores.vector_store_connection_decorator import (\n vector_store_connection,\n)\nfrom langflow.io import (\n BoolInput,\n DropdownInput,\n HandleInput,\n IntInput,\n MultilineInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.logging import logger\nfrom langflow.schema.data import Data\nfrom opensearchpy import OpenSearch, helpers\n\n\n@vector_store_connection\nclass OpenSearchHybridComponent(LCVectorStoreComponent):\n \"\"\"OpenSearch hybrid search: KNN (k=10, boost=0.7) + multi_match (boost=0.3) with optional filters & min_score.\"\"\"\n\n display_name: str = \"OpenSearch (Hybrid)\"\n name: str = \"OpenSearchHybrid\"\n icon: str = \"OpenSearch\"\n description: str = \"Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.\"\n\n # Keys we consider baseline\n default_keys: list[str] = [\n \"opensearch_url\",\n \"index_name\",\n *[\n i.name for i in LCVectorStoreComponent.inputs\n ], # search_query, add_documents, etc.\n \"embedding\",\n \"vector_field\",\n \"number_of_results\",\n \"auth_mode\",\n \"username\",\n \"password\",\n \"jwt_token\",\n \"jwt_header\",\n \"bearer_prefix\",\n \"use_ssl\",\n \"verify_certs\",\n \"filter_expression\",\n \"engine\",\n \"space_type\",\n \"ef_construction\",\n \"m\",\n ]\n\n inputs = [\n StrInput(\n name=\"opensearch_url\",\n display_name=\"OpenSearch URL\",\n value=\"http://localhost:9200\",\n info=\"URL for your OpenSearch cluster.\",\n ),\n StrInput(\n name=\"index_name\",\n display_name=\"Index Name\",\n value=\"langflow\",\n info=\"The index to search.\",\n ),\n DropdownInput(\n name=\"engine\",\n display_name=\"Engine\",\n options=[\"jvector\", \"nmslib\", \"faiss\", \"lucene\"],\n value=\"jvector\",\n info=\"Vector search engine to use.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"space_type\",\n display_name=\"Space Type\",\n options=[\"l2\", \"l1\", \"cosinesimil\", \"linf\", \"innerproduct\"],\n value=\"l2\",\n info=\"Distance metric for vector similarity.\",\n advanced=True,\n ),\n IntInput(\n name=\"ef_construction\",\n display_name=\"EF Construction\",\n value=512,\n info=\"Size of the dynamic list used during k-NN graph creation.\",\n advanced=True,\n ),\n IntInput(\n name=\"m\",\n display_name=\"M Parameter\",\n value=16,\n info=\"Number of bidirectional links created for each new element.\",\n advanced=True,\n ),\n *LCVectorStoreComponent.inputs, # includes search_query, add_documents, etc.\n HandleInput(\n name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]\n ),\n StrInput(\n name=\"vector_field\",\n display_name=\"Vector Field\",\n value=\"chunk_embedding\",\n advanced=True,\n info=\"Vector field used for KNN.\",\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Default Size (limit)\",\n value=10,\n advanced=True,\n info=\"Default number of hits when no limit provided in filter_expression.\",\n ),\n MultilineInput(\n name=\"filter_expression\",\n display_name=\"Filter Expression (JSON)\",\n value=\"\",\n info=(\n \"Optional JSON to control filters/limit/score threshold.\\n\"\n \"Accepted shapes:\\n\"\n '1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\\n'\n '2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\\n'\n \"Placeholders with __IMPOSSIBLE_VALUE__ are ignored.\"\n ),\n ),\n # ----- Auth controls (dynamic) -----\n DropdownInput(\n name=\"auth_mode\",\n display_name=\"Auth Mode\",\n value=\"basic\",\n options=[\"basic\", \"jwt\"],\n info=\"Choose Basic (username/password) or JWT (Bearer token).\",\n real_time_refresh=True,\n advanced=False,\n ),\n StrInput(\n name=\"username\",\n display_name=\"Username\",\n value=\"admin\",\n show=False,\n ),\n SecretStrInput(\n name=\"password\",\n display_name=\"Password\",\n value=\"admin\",\n show=False,\n ),\n SecretStrInput(\n name=\"jwt_token\",\n display_name=\"JWT Token\",\n value=\"JWT\",\n load_from_db=True,\n show=True,\n info=\"Paste a valid JWT (sent as a header).\",\n ),\n StrInput(\n name=\"jwt_header\",\n display_name=\"JWT Header Name\",\n value=\"Authorization\",\n show=False,\n advanced=True,\n ),\n BoolInput(\n name=\"bearer_prefix\",\n display_name=\"Prefix 'Bearer '\",\n value=True,\n show=False,\n advanced=True,\n ),\n # ----- TLS -----\n BoolInput(name=\"use_ssl\", display_name=\"Use SSL\", value=True, advanced=True),\n BoolInput(\n name=\"verify_certs\",\n display_name=\"Verify Certificates\",\n value=False,\n advanced=True,\n ),\n ]\n\n # ---------- helper functions for index management ----------\n def _default_text_mapping(\n self,\n dim: int,\n engine: str = \"jvector\",\n space_type: str = \"l2\",\n ef_search: int = 512,\n ef_construction: int = 100,\n m: int = 16,\n vector_field: str = \"vector_field\",\n ) -> Dict[str, Any]:\n \"\"\"For Approximate k-NN Search, this is the default mapping to create index.\"\"\"\n return {\n \"settings\": {\"index\": {\"knn\": True, \"knn.algo_param.ef_search\": ef_search}},\n \"mappings\": {\n \"properties\": {\n vector_field: {\n \"type\": \"knn_vector\",\n \"dimension\": dim,\n \"method\": {\n \"name\": \"disk_ann\",\n \"space_type\": space_type,\n \"engine\": engine,\n \"parameters\": {\"ef_construction\": ef_construction, \"m\": m},\n },\n }\n }\n },\n }\n\n def _validate_aoss_with_engines(self, is_aoss: bool, engine: str) -> None:\n \"\"\"Validate AOSS with the engine.\"\"\"\n if is_aoss and engine != \"nmslib\" and engine != \"faiss\":\n raise ValueError(\n \"Amazon OpenSearch Service Serverless only \"\n \"supports `nmslib` or `faiss` engines\"\n )\n\n def _is_aoss_enabled(self, http_auth: Any) -> bool:\n \"\"\"Check if the service is http_auth is set as `aoss`.\"\"\"\n if (\n http_auth is not None\n and hasattr(http_auth, \"service\")\n and http_auth.service == \"aoss\"\n ):\n return True\n return False\n\n def _bulk_ingest_embeddings(\n self,\n client: OpenSearch,\n index_name: str,\n embeddings: List[List[float]],\n texts: List[str],\n metadatas: Optional[List[dict]] = None,\n ids: Optional[List[str]] = None,\n vector_field: str = \"vector_field\",\n text_field: str = \"text\",\n mapping: Optional[Dict] = None,\n max_chunk_bytes: Optional[int] = 1 * 1024 * 1024,\n is_aoss: bool = False,\n ) -> List[str]:\n \"\"\"Bulk Ingest Embeddings into given index.\"\"\"\n if not mapping:\n mapping = dict()\n\n requests = []\n return_ids = []\n\n for i, text in enumerate(texts):\n metadata = metadatas[i] if metadatas else {}\n _id = ids[i] if ids else str(uuid.uuid4())\n request = {\n \"_op_type\": \"index\",\n \"_index\": index_name,\n vector_field: embeddings[i],\n text_field: text,\n **metadata,\n }\n if is_aoss:\n request[\"id\"] = _id\n else:\n request[\"_id\"] = _id\n requests.append(request)\n return_ids.append(_id)\n self.log(metadatas[i])\n helpers.bulk(client, requests, max_chunk_bytes=max_chunk_bytes)\n return return_ids\n\n # ---------- auth / client ----------\n def _build_auth_kwargs(self) -> Dict[str, Any]:\n mode = (self.auth_mode or \"basic\").strip().lower()\n if mode == \"jwt\":\n token = (self.jwt_token or \"\").strip()\n if not token:\n raise ValueError(\"Auth Mode is 'jwt' but no jwt_token was provided.\")\n header_name = (self.jwt_header or \"Authorization\").strip()\n header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n return {\"headers\": {header_name: header_value}}\n user = (self.username or \"\").strip()\n pwd = (self.password or \"\").strip()\n if not user or not pwd:\n raise ValueError(\"Auth Mode is 'basic' but username/password are missing.\")\n return {\"http_auth\": (user, pwd)}\n\n def build_client(self) -> OpenSearch:\n auth_kwargs = self._build_auth_kwargs()\n return OpenSearch(\n hosts=[self.opensearch_url],\n use_ssl=self.use_ssl,\n verify_certs=self.verify_certs,\n ssl_assert_hostname=False,\n ssl_show_warn=False,\n **auth_kwargs,\n )\n\n @check_cached_vector_store\n def build_vector_store(self) -> OpenSearch:\n # Return raw OpenSearch client as our “vector store.”\n self.log(self.ingest_data)\n client = self.build_client()\n self._add_documents_to_vector_store(client=client)\n return client\n\n # ---------- ingest ----------\n def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n # Convert DataFrame to Data if needed using parent's method\n self.ingest_data = self._prepare_ingest_data()\n\n docs = self.ingest_data or []\n if not docs:\n self.log(\"No documents to ingest.\")\n return\n\n # Extract texts and metadata from documents\n texts = []\n metadatas = []\n # Track document sizes by filename\n document_sizes = {}\n \n for doc_obj in docs:\n data_copy = json.loads(doc_obj.model_dump_json())\n text = data_copy.pop(doc_obj.text_key, doc_obj.default_value)\n texts.append(text)\n\n # Calculate and track document size\n filename = data_copy.get(\"filename\", \"unknown\")\n text_size = len(text.encode(\"utf-8\")) if text else 0\n if filename in document_sizes:\n document_sizes[filename] += text_size\n else:\n document_sizes[filename] = text_size\n\n # Add calculated size to metadata (per chunk, but represents total document size)\n data_copy[\"file_size\"] = document_sizes[filename]\n metadatas.append(data_copy)\n self.log(metadatas)\n if not self.embedding:\n raise ValueError(\"Embedding handle is required to embed documents.\")\n\n # Generate embeddings\n vectors = self.embedding.embed_documents(texts)\n\n if not vectors:\n self.log(\"No vectors generated from documents.\")\n return\n\n # Get vector dimension for mapping\n dim = len(vectors[0]) if vectors else 768 # default fallback\n\n # Check for AOSS\n auth_kwargs = self._build_auth_kwargs()\n is_aoss = self._is_aoss_enabled(auth_kwargs.get(\"http_auth\"))\n\n # Validate engine with AOSS\n engine = getattr(self, \"engine\", \"jvector\")\n self._validate_aoss_with_engines(is_aoss, engine)\n\n # Create mapping with proper KNN settings\n space_type = getattr(self, \"space_type\", \"l2\")\n ef_construction = getattr(self, \"ef_construction\", 512)\n m = getattr(self, \"m\", 16)\n\n mapping = self._default_text_mapping(\n dim=dim,\n engine=engine,\n space_type=space_type,\n ef_construction=ef_construction,\n m=m,\n vector_field=self.vector_field,\n )\n\n self.log(\n f\"Indexing {len(texts)} documents into '{self.index_name}' with proper KNN mapping...\"\n )\n\n # Use the LangChain-style bulk ingestion\n return_ids = self._bulk_ingest_embeddings(\n client=client,\n index_name=self.index_name,\n embeddings=vectors,\n texts=texts,\n metadatas=metadatas,\n vector_field=self.vector_field,\n text_field=\"text\",\n mapping=mapping,\n is_aoss=is_aoss,\n )\n self.log(metadatas)\n\n self.log(f\"Successfully indexed {len(return_ids)} documents.\")\n\n # ---------- helpers for filters ----------\n def _is_placeholder_term(self, term_obj: dict) -> bool:\n # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n def _coerce_filter_clauses(self, filter_obj: dict | None) -> List[dict]:\n \"\"\"\n Accepts either:\n A) {\"filter\":[ ...term/terms objects... ], \"limit\":..., \"score_threshold\":...}\n B) Context-style: {\"data_sources\":[...], \"document_types\":[...], \"owners\":[...]}\n Returns a list of OS filter clauses (term/terms), skipping placeholders and empty terms.\n \"\"\"\n\n if not filter_obj:\n return []\n\n # If it’s a string, try to parse it once\n if isinstance(filter_obj, str):\n try:\n filter_obj = json.loads(filter_obj)\n except Exception:\n # Not valid JSON → treat as no filters\n return []\n\n # Case A: already an explicit list/dict under \"filter\"\n if \"filter\" in filter_obj:\n raw = filter_obj[\"filter\"]\n if isinstance(raw, dict):\n raw = [raw]\n clauses: List[dict] = []\n for f in raw or []:\n if (\n \"term\" in f\n and isinstance(f[\"term\"], dict)\n and not self._is_placeholder_term(f[\"term\"])\n ):\n clauses.append(f)\n elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n field, vals = next(iter(f[\"terms\"].items()))\n if isinstance(vals, list) and len(vals) > 0:\n clauses.append(f)\n return clauses\n\n # Case B: convert context-style maps into clauses\n field_mapping = {\n \"data_sources\": \"filename\",\n \"document_types\": \"mimetype\",\n \"owners\": \"owner\",\n }\n clauses: List[dict] = []\n for k, values in filter_obj.items():\n if not isinstance(values, list):\n continue\n field = field_mapping.get(k, k)\n if len(values) == 0:\n # Match-nothing placeholder (kept to mirror your tool semantics)\n clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n elif len(values) == 1:\n if values[0] != \"__IMPOSSIBLE_VALUE__\":\n clauses.append({\"term\": {field: values[0]}})\n else:\n clauses.append({\"terms\": {field: values}})\n return clauses\n\n # ---------- search (single hybrid path matching your tool) ----------\n def search(self, query: str | None = None) -> list[dict[str, Any]]:\n logger.info(self.ingest_data)\n client = self.build_client()\n q = (query or \"\").strip()\n\n # Parse optional filter expression (can be either A or B shape; see _coerce_filter_clauses)\n filter_obj = None\n if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n try:\n filter_obj = json.loads(self.filter_expression)\n except json.JSONDecodeError as e:\n raise ValueError(f\"Invalid filter_expression JSON: {e}\") from e\n\n if not self.embedding:\n raise ValueError(\n \"Embedding is required to run hybrid search (KNN + keyword).\"\n )\n\n # Embed the query\n vec = self.embedding.embed_query(q)\n\n # Build filter clauses (accept both shapes)\n clauses = self._coerce_filter_clauses(filter_obj)\n\n # Respect the tool's limit/threshold defaults\n limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n # Build the same hybrid body as your SearchService\n body = {\n \"query\": {\n \"bool\": {\n \"should\": [\n {\n \"knn\": {\n self.vector_field: {\n \"vector\": vec,\n \"k\": 10, # fixed to match the tool\n \"boost\": 0.7,\n }\n }\n },\n {\n \"multi_match\": {\n \"query\": q,\n \"fields\": [\"text^2\", \"filename^1.5\"],\n \"type\": \"best_fields\",\n \"fuzziness\": \"AUTO\",\n \"boost\": 0.3,\n }\n },\n ],\n \"minimum_should_match\": 1,\n }\n },\n \"aggs\": {\n \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}},\n },\n \"_source\": [\n \"filename\",\n \"mimetype\",\n \"page\",\n \"text\",\n \"source_url\",\n \"owner\",\n \"allowed_users\",\n \"allowed_groups\",\n ],\n \"size\": limit,\n }\n if clauses:\n body[\"query\"][\"bool\"][\"filter\"] = clauses\n\n if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n # top-level min_score (matches your tool)\n body[\"min_score\"] = score_threshold\n\n resp = client.search(index=self.index_name, body=body)\n hits = resp.get(\"hits\", {}).get(\"hits\", [])\n return [\n {\n \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n \"score\": hit.get(\"_score\"),\n }\n for hit in hits\n ]\n\n def search_documents(self) -> list[Data]:\n try:\n raw = self.search(self.search_query or \"\")\n return [\n Data(\n text=hit[\"page_content\"],\n **hit[\"metadata\"]\n )\n for hit in raw\n ]\n self.log(self.ingest_data)\n except Exception as e:\n self.log(f\"search_documents error: {e}\")\n raise\n\n # -------- dynamic UI handling (auth switch) --------\n async def update_build_config(\n self, build_config: dict, field_value: str, field_name: str | None = None\n ) -> dict:\n try:\n if field_name == \"auth_mode\":\n mode = (field_value or \"basic\").strip().lower()\n is_basic = mode == \"basic\"\n is_jwt = mode == \"jwt\"\n\n build_config[\"username\"][\"show\"] = is_basic\n build_config[\"password\"][\"show\"] = is_basic\n\n build_config[\"jwt_token\"][\"show\"] = is_jwt\n build_config[\"jwt_header\"][\"show\"] = is_jwt\n build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n build_config[\"username\"][\"required\"] = is_basic\n build_config[\"password\"][\"required\"] = is_basic\n\n build_config[\"jwt_token\"][\"required\"] = is_jwt\n build_config[\"jwt_header\"][\"required\"] = is_jwt\n build_config[\"bearer_prefix\"][\"required\"] = False\n\n if is_basic:\n build_config[\"jwt_token\"][\"value\"] = \"\"\n\n return build_config\n\n return build_config\n\n except Exception as e:\n self.log(f\"update_build_config error: {e}\")\n return build_config\n" }, "ef_construction": { "_input_type": "IntInput", @@ -1719,7 +1720,7 @@ "show": true, "title_case": false, "type": "str", - "value": "eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJodHRwOi8vb3BlbnJhZy1iYWNrZW5kOjgwMDAiLCJzdWIiOiIxMDMwNzA3MzY1NDU0NjQyNDYxMTMiLCJhdWQiOlsib3BlbnNlYXJjaCIsIm9wZW5yYWciXSwiZXhwIjoxNzU3OTg1NDkyLCJpYXQiOjE3NTczODA2OTIsImF1dGhfdGltZSI6MTc1NzM5MTQ5MiwidXNlcl9pZCI6IjEwMzA3MDczNjU0NTQ2NDI0NjExMyIsImVtYWlsIjoiZ2FicmllbEBsYW5nZmxvdy5vcmciLCJuYW1lIjoiR2FicmllbCBBbG1laWRhIiwicHJlZmVycmVkX3VzZXJuYW1lIjoiZ2FicmllbEBsYW5nZmxvdy5vcmciLCJlbWFpbF92ZXJpZmllZCI6dHJ1ZSwicm9sZXMiOlsib3BlbnJhZ191c2VyIl19.ArlITY6ISZTc-7_WTSnmMjA93BDM1J20mTbrebIyxWKwIsRDq5OblirZi9pgiGaersPC-zANzPHhUyv076QWt03l1Awt1IKf600vqd0eeG4YCfh4ANdtfxWsu7Y_aFyOU57Rc-VM8_XuSRB3l570IolwkjMlBuyRULvtP8oNt7haRp3DV9iW8zEW0vWxGLji33Axh0MTzqeANtZ1-lxw0oGaQ6XDg4liEhuydCeX89ykI27lI8ori358RgHT7PlD9t9R04UgZytt35ildLtd2M_6OWxtMy6o9OP-l-QQpflqMvE4y01kphOCPmo8MUbck554l4CShEuDkyCwbDMWew" + "value": "" }, "m": { "_input_type": "IntInput", @@ -1953,13 +1954,13 @@ "x": 2218.9287723423276, "y": 1332.2598463956504 }, - "selected": false, + "selected": true, "type": "genericNode" } ], "viewport": { - "x": -813.2209660078397, - "y": -898.7176624994117, + "x": -761.2209660078397, + "y": -883.7176624994117, "zoom": 0.7898282762479812 } }, From 54f03b468a21baa2e5f9ecac12aedb898d030975 Mon Sep 17 00:00:00 2001 From: Gabriel Luiz Freitas Almeida Date: Mon, 8 Sep 2025 23:20:38 -0300 Subject: [PATCH 3/6] Enhance ingestion flow JSON with updated component configurations and improved file handling This commit updates the ingestion flow JSON by modifying the last updated timestamp, changing the code hash, and adding the "anyio" dependency to the metadata. Additionally, it refines the File component's description and input options for advanced document processing, ensuring better clarity and functionality. These changes align with best practices for robust async development and enhance the overall user experience. --- flows/ingestion_flow.json | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/flows/ingestion_flow.json b/flows/ingestion_flow.json index 93a07d53..8a5b9256 100644 --- a/flows/ingestion_flow.json +++ b/flows/ingestion_flow.json @@ -910,19 +910,23 @@ ], "frozen": false, "icon": "file-text", - "last_updated": "2025-09-09T01:56:37.852Z", + "last_updated": "2025-09-09T02:18:48.064Z", "legacy": false, "lf_version": "1.5.0.post2", "metadata": { - "code_hash": "4223fbeb87a0", + "code_hash": "086578fbbd54", "dependencies": { "dependencies": [ { "name": "langflow", "version": "1.5.0.post2" + }, + { + "name": "anyio", + "version": "4.10.0" } ], - "total_dependencies": 1 + "total_dependencies": 2 }, "module": "custom_components.file" }, @@ -1002,7 +1006,7 @@ "show": true, "title_case": false, "type": "code", - "value": "\"\"\"Enhanced file component with clearer structure and Docling isolation.\n\nNotes:\n-----\n- Functionality is preserved with minimal behavioral changes.\n- ALL Docling parsing/export runs in a separate OS process to prevent memory\n growth and native library state from impacting the main Langflow process.\n- Standard text/structured parsing continues to use existing BaseFileComponent\n utilities (and optional threading via `parallel_load_data`).\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport subprocess\nimport sys\nimport textwrap\nfrom copy import deepcopy\nfrom typing import TYPE_CHECKING, Any\n\nfrom langflow.base.data.base_file import BaseFileComponent\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parallel_load_data, parse_text_file_to_data\nfrom langflow.io import (\n BoolInput,\n DropdownInput,\n FileInput,\n IntInput,\n MessageTextInput,\n Output,\n StrInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.message import Message\nfrom pathlib import Path\nfrom langflow.services.storage.utils import build_content_type_from_extension\nif TYPE_CHECKING:\n from langflow.schema import DataFrame\n\n\nclass FileComponent(BaseFileComponent):\n \"\"\"File component with optional Docling processing (isolated in a subprocess).\"\"\"\n\n display_name = \"File\"\n description = \"Loads content from files with optional advanced document processing and export using Docling.\"\n documentation: str = \"https://docs.langflow.org/components-data#file\"\n icon = \"file-text\"\n name = \"File\"\n\n # Docling-supported/compatible extensions; TEXT_FILE_TYPES are supported by the base loader.\n VALID_EXTENSIONS = [\n \"adoc\",\n \"asciidoc\",\n \"asc\",\n \"bmp\",\n \"csv\",\n \"dotx\",\n \"dotm\",\n \"docm\",\n \"docx\",\n \"htm\",\n \"html\",\n \"jpeg\",\n \"json\",\n \"md\",\n \"pdf\",\n \"png\",\n \"potx\",\n \"ppsx\",\n \"pptm\",\n \"potm\",\n \"ppsm\",\n \"pptx\",\n \"tiff\",\n \"txt\",\n \"xls\",\n \"xlsx\",\n \"xhtml\",\n \"xml\",\n \"webp\",\n *TEXT_FILE_TYPES,\n ]\n\n # Fixed export settings used when markdown export is requested.\n EXPORT_FORMAT = \"Markdown\"\n IMAGE_MODE = \"placeholder\"\n\n # ---- Inputs / Outputs (kept as close to original as possible) -------------------\n _base_inputs = deepcopy(BaseFileComponent._base_inputs)\n for input_item in _base_inputs:\n if isinstance(input_item, FileInput) and input_item.name == \"path\":\n input_item.real_time_refresh = True\n break\n\n inputs = [\n *_base_inputs,\n BoolInput(\n name=\"advanced_mode\",\n display_name=\"Advanced Parser\",\n value=False,\n real_time_refresh=True,\n info=(\n \"Enable advanced document processing and export with Docling for PDFs, images, and office documents. \"\n \"Available only for single file processing.\"\n ),\n show=False,\n ),\n DropdownInput(\n name=\"pipeline\",\n display_name=\"Pipeline\",\n info=\"Docling pipeline to use\",\n options=[\"standard\", \"vlm\"],\n value=\"standard\",\n advanced=True,\n ),\n DropdownInput(\n name=\"ocr_engine\",\n display_name=\"OCR Engine\",\n info=\"OCR engine to use. Only available when pipeline is set to 'standard'.\",\n options=[\"\", \"easyocr\"],\n value=\"\",\n show=False,\n advanced=True,\n ),\n StrInput(\n name=\"md_image_placeholder\",\n display_name=\"Image placeholder\",\n info=\"Specify the image placeholder for markdown exports.\",\n value=\"\",\n advanced=True,\n show=False,\n ),\n StrInput(\n name=\"md_page_break_placeholder\",\n display_name=\"Page break placeholder\",\n info=\"Add this placeholder between pages in the markdown output.\",\n value=\"\",\n advanced=True,\n show=False,\n ),\n MessageTextInput(\n name=\"doc_key\",\n display_name=\"Doc Key\",\n info=\"The key to use for the DoclingDocument column.\",\n value=\"doc\",\n advanced=True,\n show=False,\n ),\n # Deprecated input retained for backward-compatibility.\n BoolInput(\n name=\"use_multithreading\",\n display_name=\"[Deprecated] Use Multithreading\",\n advanced=True,\n value=True,\n info=\"Set 'Processing Concurrency' greater than 1 to enable multithreading.\",\n ),\n IntInput(\n name=\"concurrency_multithreading\",\n display_name=\"Processing Concurrency\",\n advanced=True,\n info=\"When multiple files are being processed, the number of files to process concurrently.\",\n value=1,\n ),\n BoolInput(\n name=\"markdown\",\n display_name=\"Markdown Export\",\n info=\"Export processed documents to Markdown format. Only available when advanced mode is enabled.\",\n value=False,\n show=False,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\"),\n ]\n\n # ------------------------------ UI helpers --------------------------------------\n\n def _path_value(self, template: dict) -> list[str]:\n \"\"\"Return the list of currently selected file paths from the template.\"\"\"\n return template.get(\"path\", {}).get(\"file_path\", [])\n\n def update_build_config(\n self,\n build_config: dict[str, Any],\n field_value: Any,\n field_name: str | None = None,\n ) -> dict[str, Any]:\n \"\"\"Show/hide Advanced Parser and related fields based on selection context.\"\"\"\n if field_name == \"path\":\n paths = self._path_value(build_config)\n file_path = paths[0] if paths else \"\"\n file_count = len(field_value) if field_value else 0\n\n # Advanced mode only for single (non-tabular) file\n allow_advanced = file_count == 1 and not file_path.endswith((\".csv\", \".xlsx\", \".parquet\"))\n build_config[\"advanced_mode\"][\"show\"] = allow_advanced\n if not allow_advanced:\n build_config[\"advanced_mode\"][\"value\"] = False\n for f in (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\"):\n if f in build_config:\n build_config[f][\"show\"] = False\n\n elif field_name == \"advanced_mode\":\n for f in (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\"):\n if f in build_config:\n build_config[f][\"show\"] = bool(field_value)\n\n return build_config\n\n def update_outputs(self, frontend_node: dict[str, Any], field_name: str, field_value: Any) -> dict[str, Any]: # noqa: ARG002\n \"\"\"Dynamically show outputs based on file count/type and advanced mode.\"\"\"\n if field_name not in [\"path\", \"advanced_mode\"]:\n return frontend_node\n\n template = frontend_node.get(\"template\", {})\n paths = self._path_value(template)\n if not paths:\n return frontend_node\n\n frontend_node[\"outputs\"] = []\n if len(paths) == 1:\n file_path = paths[0] if field_name == \"path\" else frontend_node[\"template\"][\"path\"][\"file_path\"][0]\n if file_path.endswith((\".csv\", \".xlsx\", \".parquet\")):\n frontend_node[\"outputs\"].append(\n Output(display_name=\"Structured Content\", name=\"dataframe\", method=\"load_files_structured\"),\n )\n elif file_path.endswith(\".json\"):\n frontend_node[\"outputs\"].append(\n Output(display_name=\"Structured Content\", name=\"json\", method=\"load_files_json\"),\n )\n\n advanced_mode = frontend_node.get(\"template\", {}).get(\"advanced_mode\", {}).get(\"value\", False)\n if advanced_mode:\n frontend_node[\"outputs\"].append(\n Output(display_name=\"Structured Output\", name=\"advanced\", method=\"load_files_advanced\"),\n )\n frontend_node[\"outputs\"].append(\n Output(display_name=\"Markdown\", name=\"markdown\", method=\"load_files_markdown\"),\n )\n frontend_node[\"outputs\"].append(\n Output(display_name=\"File Path\", name=\"path\", method=\"load_files_path\"),\n )\n else:\n frontend_node[\"outputs\"].append(\n Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\"),\n )\n frontend_node[\"outputs\"].append(\n Output(display_name=\"File Path\", name=\"path\", method=\"load_files_path\"),\n )\n else:\n # Multiple files => DataFrame output; advanced parser disabled\n frontend_node[\"outputs\"].append(Output(display_name=\"Files\", name=\"dataframe\", method=\"load_files\"))\n\n return frontend_node\n\n # ------------------------------ Core processing ----------------------------------\n\n def _is_docling_compatible(self, file_path: str) -> bool:\n \"\"\"Lightweight extension gate for Docling-compatible types.\"\"\"\n docling_exts = (\n \".adoc\",\n \".asciidoc\",\n \".asc\",\n \".bmp\",\n \".csv\",\n \".dotx\",\n \".dotm\",\n \".docm\",\n \".docx\",\n \".htm\",\n \".html\",\n \".jpeg\",\n \".json\",\n \".md\",\n \".pdf\",\n \".png\",\n \".potx\",\n \".ppsx\",\n \".pptm\",\n \".potm\",\n \".ppsm\",\n \".pptx\",\n \".tiff\",\n \".txt\",\n \".xls\",\n \".xlsx\",\n \".xhtml\",\n \".xml\",\n \".webp\",\n )\n return file_path.lower().endswith(docling_exts)\n\n def _process_docling_in_subprocess(self, file_path: str) -> Data | None:\n \"\"\"Run Docling in a separate OS process and map the result to a Data object.\n\n We avoid multiprocessing pickling by launching `python -c \"