diff --git a/.github/workflows/build-multiarch.yml b/.github/workflows/build-multiarch.yml index b8ee1a28..f9a83400 100644 --- a/.github/workflows/build-multiarch.yml +++ b/.github/workflows/build-multiarch.yml @@ -53,58 +53,63 @@ jobs: # backend - image: backend file: ./Dockerfile.backend - tag: phact/openrag-backend + tag: langflowai/openrag-backend platform: linux/amd64 arch: amd64 runs-on: ubuntu-latest-16-cores - image: backend file: ./Dockerfile.backend - tag: phact/openrag-backend + tag: langflowai/openrag-backend platform: linux/arm64 arch: arm64 - runs-on: [self-hosted, linux, ARM64, langflow-ai-arm64-2] + #runs-on: [self-hosted, linux, ARM64, langflow-ai-arm64-2] + runs-on: RagRunner # frontend - image: frontend file: ./Dockerfile.frontend - tag: phact/openrag-frontend + tag: langflowai/openrag-frontend platform: linux/amd64 arch: amd64 runs-on: ubuntu-latest-16-cores - image: frontend file: ./Dockerfile.frontend - tag: phact/openrag-frontend + tag: langflowai/openrag-frontend platform: linux/arm64 arch: arm64 - runs-on: [self-hosted, linux, ARM64, langflow-ai-arm64-2] + #runs-on: [self-hosted, linux, ARM64, langflow-ai-arm64-2] + runs-on: RagRunner # langflow - image: langflow file: ./Dockerfile.langflow - tag: phact/openrag-langflow + tag: langflowai/openrag-langflow platform: linux/amd64 arch: amd64 runs-on: ubuntu-latest-16-cores - image: langflow file: ./Dockerfile.langflow - tag: phact/openrag-langflow + tag: langflowai/openrag-langflow platform: linux/arm64 arch: arm64 - runs-on: self-hosted + #runs-on: self-hosted + runs-on: RagRunner # opensearch - image: opensearch file: ./Dockerfile - tag: phact/openrag-opensearch + tag: langflowai/openrag-opensearch platform: linux/amd64 arch: amd64 runs-on: ubuntu-latest-16-cores - image: opensearch file: ./Dockerfile - tag: phact/openrag-opensearch + tag: langflowai/openrag-opensearch platform: linux/arm64 arch: arm64 - runs-on: [self-hosted, linux, ARM64, langflow-ai-arm64-2] + #runs-on: [self-hosted, linux, ARM64, langflow-ai-arm64-2] + #runs-on: self-hosted + runs-on: RagRunner runs-on: ${{ matrix.runs-on }} @@ -165,40 +170,40 @@ jobs: VERSION=${{ steps.version.outputs.version }} # Create versioned tags - docker buildx imagetools create -t phact/openrag-backend:$VERSION \ - phact/openrag-backend:$VERSION-amd64 \ - phact/openrag-backend:$VERSION-arm64 + docker buildx imagetools create -t langflowai/openrag-backend:$VERSION \ + langflowai/openrag-backend:$VERSION-amd64 \ + langflowai/openrag-backend:$VERSION-arm64 - docker buildx imagetools create -t phact/openrag-frontend:$VERSION \ - phact/openrag-frontend:$VERSION-amd64 \ - phact/openrag-frontend:$VERSION-arm64 + docker buildx imagetools create -t langflowai/openrag-frontend:$VERSION \ + langflowai/openrag-frontend:$VERSION-amd64 \ + langflowai/openrag-frontend:$VERSION-arm64 - docker buildx imagetools create -t phact/openrag-langflow:$VERSION \ - phact/openrag-langflow:$VERSION-amd64 \ - phact/openrag-langflow:$VERSION-arm64 + docker buildx imagetools create -t langflowai/openrag-langflow:$VERSION \ + langflowai/openrag-langflow:$VERSION-amd64 \ + langflowai/openrag-langflow:$VERSION-arm64 - docker buildx imagetools create -t phact/openrag-opensearch:$VERSION \ - phact/openrag-opensearch:$VERSION-amd64 \ - phact/openrag-opensearch:$VERSION-arm64 + docker buildx imagetools create -t langflowai/openrag-opensearch:$VERSION \ + langflowai/openrag-opensearch:$VERSION-amd64 \ + langflowai/openrag-opensearch:$VERSION-arm64 # Only update latest tags if version is numeric if [[ "$VERSION" =~ ^[0-9.-]+$ ]]; then echo "Updating latest tags for production release: $VERSION" - docker buildx imagetools create -t phact/openrag-backend:latest \ - phact/openrag-backend:$VERSION-amd64 \ - phact/openrag-backend:$VERSION-arm64 + docker buildx imagetools create -t langflowai/openrag-backend:latest \ + langflowai/openrag-backend:$VERSION-amd64 \ + langflowai/openrag-backend:$VERSION-arm64 - docker buildx imagetools create -t phact/openrag-frontend:latest \ - phact/openrag-frontend:$VERSION-amd64 \ - phact/openrag-frontend:$VERSION-arm64 + docker buildx imagetools create -t langflowai/openrag-frontend:latest \ + langflowai/openrag-frontend:$VERSION-amd64 \ + langflowai/openrag-frontend:$VERSION-arm64 - docker buildx imagetools create -t phact/openrag-langflow:latest \ - phact/openrag-langflow:$VERSION-amd64 \ - phact/openrag-langflow:$VERSION-arm64 + docker buildx imagetools create -t langflowai/openrag-langflow:latest \ + langflowai/openrag-langflow:$VERSION-amd64 \ + langflowai/openrag-langflow:$VERSION-arm64 - docker buildx imagetools create -t phact/openrag-opensearch:latest \ - phact/openrag-opensearch:$VERSION-amd64 \ - phact/openrag-opensearch:$VERSION-arm64 + docker buildx imagetools create -t langflowai/openrag-opensearch:latest \ + langflowai/openrag-opensearch:$VERSION-amd64 \ + langflowai/openrag-opensearch:$VERSION-arm64 else echo "Skipping latest tags - version: $VERSION (not numeric)" fi diff --git a/.github/workflows/test-integration.yml b/.github/workflows/test-integration.yml index 1b4c6162..92c9a976 100644 --- a/.github/workflows/test-integration.yml +++ b/.github/workflows/test-integration.yml @@ -31,14 +31,22 @@ jobs: steps: - run: df -h - #- name: "node-cleanup" - #run: | - # sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc /opt/hostedtoolcache/CodeQL - # sudo docker image prune --all --force - # sudo docker builder prune -a + + - name: Cleanup Docker cache + run: | + docker system prune -af || true + docker builder prune -af || true + - run: df -h + - name: Checkout uses: actions/checkout@v4 + + - name: Verify workspace + run: | + echo "Current directory: $(pwd)" + echo "Workspace: ${GITHUB_WORKSPACE}" + ls -la - name: Set up UV uses: astral-sh/setup-uv@v3 diff --git a/Dockerfile.langflow b/Dockerfile.langflow index 5343585a..6119de84 100644 --- a/Dockerfile.langflow +++ b/Dockerfile.langflow @@ -1,4 +1,4 @@ -FROM langflowai/langflow-nightly:1.7.0.dev5 +FROM langflowai/langflow-nightly:1.7.0.dev19 EXPOSE 7860 diff --git a/Makefile b/Makefile index f47bba28..b5804f77 100644 --- a/Makefile +++ b/Makefile @@ -210,7 +210,7 @@ test-ci: echo "Pulling latest images..."; \ docker compose -f docker-compose-cpu.yml pull; \ echo "Building OpenSearch image override..."; \ - docker build --no-cache -t phact/openrag-opensearch:latest -f Dockerfile .; \ + docker build --no-cache -t langflowai/openrag-opensearch:latest -f Dockerfile .; \ echo "Starting infra (OpenSearch + Dashboards + Langflow) with CPU containers"; \ docker compose -f docker-compose-cpu.yml up -d opensearch dashboards langflow; \ echo "Starting docling-serve..."; \ @@ -288,10 +288,10 @@ test-ci-local: echo "Cleaning up old containers and volumes..."; \ docker compose -f docker-compose-cpu.yml down -v 2>/dev/null || true; \ echo "Building all images locally..."; \ - docker build -t phact/openrag-opensearch:latest -f Dockerfile .; \ - docker build -t phact/openrag-backend:latest -f Dockerfile.backend .; \ - docker build -t phact/openrag-frontend:latest -f Dockerfile.frontend .; \ - docker build -t phact/openrag-langflow:latest -f Dockerfile.langflow .; \ + docker build -t langflowai/openrag-opensearch:latest -f Dockerfile .; \ + docker build -t langflowai/openrag-backend:latest -f Dockerfile.backend .; \ + docker build -t langflowai/openrag-frontend:latest -f Dockerfile.frontend .; \ + docker build -t langflowai/openrag-langflow:latest -f Dockerfile.langflow .; \ echo "Starting infra (OpenSearch + Dashboards + Langflow) with CPU containers"; \ docker compose -f docker-compose-cpu.yml up -d opensearch dashboards langflow; \ echo "Starting docling-serve..."; \ diff --git a/README.md b/README.md index d7d1b614..4e861740 100644 --- a/README.md +++ b/README.md @@ -18,27 +18,29 @@ OpenRAG is a comprehensive Retrieval-Augmented Generation platform that enables
Quickstart   |   - TUI Interface   |   - Docker Deployment   |   + Python package   |   + Docker or Podman   |   Development   |   Troubleshooting
## Quickstart -To quickly run OpenRAG without creating or modifying any project files, use `uvx`: +To run OpenRAG without creating or modifying any project files, use `uvx`: ```bash uvx openrag ``` -This runs OpenRAG without installing it to your project or globally. -To run a specific version of OpenRAG, add the version to the command, such as: `uvx --from openrag==0.1.25 openrag`. + +This command runs OpenRAG without installing it to your project or globally. + +To run a specific version of OpenRAG, run `uvx --from openrag==VERSION openrag`. ## Install Python package -To first set up a project and then install the OpenRAG Python package, do the following: +To add the OpenRAG Python package to a Python project, use `uv`: -1. Create a new project with a virtual environment using `uv init`. +1. Create a new project with a virtual environment using `uv init`: ```bash uv init YOUR_PROJECT_NAME @@ -48,33 +50,33 @@ To first set up a project and then install the OpenRAG Python package, do the fo The `(venv)` prompt doesn't change, but `uv` commands will automatically use the project's virtual environment. For more information on virtual environments, see the [uv documentation](https://docs.astral.sh/uv/pip/environments). -2. Add OpenRAG to your project. +2. Add OpenRAG to your project: + ```bash uv add openrag ``` - To add a specific version of OpenRAG: - ```bash - uv add openrag==0.1.25 - ``` + To add a specific version of OpenRAG, run `uv add openrag==VERSION`. + +3. Start the OpenRAG terminal user interface (TUI): -3. Start the OpenRAG TUI. ```bash uv run openrag ``` 4. Continue with the [Quickstart](https://docs.openr.ag/quickstart). -For the full TUI installation guide, see [TUI](https://docs.openr.ag/install). +For all installation options, see the [OpenRAG installation guide](https://docs.openr.ag/install). ## Docker or Podman installation -For more information, see [Install OpenRAG containers](https://docs.openr.ag/docker). - -## Troubleshooting - -For common issues and fixes, see [Troubleshoot](https://docs.openr.ag/support/troubleshoot). +By default, OpenRAG automatically starts the required containers and helps you manage them. +To install OpenRAG with self-managed containers, see the [OpenRAG installation guide](https://docs.openr.ag/docker). ## Development -For developers wanting to contribute to OpenRAG or set up a development environment, see [CONTRIBUTING.md](CONTRIBUTING.md). \ No newline at end of file +For developers wanting to contribute to OpenRAG or set up a development environment, see [CONTRIBUTING.md](CONTRIBUTING.md). + +## Troubleshooting + +For common issues and fixes, see [Troubleshoot OpenRAG](https://docs.openr.ag/support/troubleshoot). \ No newline at end of file diff --git a/docker-compose-cpu.yml b/docker-compose-cpu.yml new file mode 100644 index 00000000..c5cd15f3 --- /dev/null +++ b/docker-compose-cpu.yml @@ -0,0 +1,141 @@ +services: + opensearch: + image: langflowai/openrag-opensearch:${OPENRAG_VERSION:-latest} + #build: + # context: . + # dockerfile: Dockerfile + container_name: os + depends_on: + - openrag-backend + environment: + - discovery.type=single-node + - OPENSEARCH_INITIAL_ADMIN_PASSWORD=${OPENSEARCH_PASSWORD} + # Run security setup in background after OpenSearch starts + command: > + bash -c " + # Ensure data directory has correct permissions + sudo chown -R opensearch:opensearch /usr/share/opensearch/data || true + + # Start OpenSearch in background + /usr/share/opensearch/opensearch-docker-entrypoint.sh opensearch & + + # Wait a bit for OpenSearch to start, then apply security config + sleep 10 && /usr/share/opensearch/setup-security.sh & + + # Wait for background processes + wait + " + ports: + - "9200:9200" + - "9600:9600" + volumes: + - ${OPENSEARCH_DATA_PATH:-./opensearch-data}:/usr/share/opensearch/data:Z + + dashboards: + image: opensearchproject/opensearch-dashboards:3.0.0 + container_name: osdash + depends_on: + - opensearch + environment: + OPENSEARCH_HOSTS: '["https://opensearch:9200"]' + OPENSEARCH_USERNAME: "admin" + OPENSEARCH_PASSWORD: ${OPENSEARCH_PASSWORD} + ports: + - "5601:5601" + + openrag-backend: + image: langflowai/openrag-backend:${OPENRAG_VERSION:-latest} + # build: + # context: . + # dockerfile: Dockerfile.backend + container_name: openrag-backend + depends_on: + - langflow + environment: + - OPENSEARCH_HOST=opensearch + - LANGFLOW_URL=http://langflow:7860 + - LANGFLOW_PUBLIC_URL=${LANGFLOW_PUBLIC_URL} + - LANGFLOW_AUTO_LOGIN=${LANGFLOW_AUTO_LOGIN} + - LANGFLOW_SECRET_KEY=${LANGFLOW_SECRET_KEY} + - LANGFLOW_SUPERUSER=${LANGFLOW_SUPERUSER} + - LANGFLOW_SUPERUSER_PASSWORD=${LANGFLOW_SUPERUSER_PASSWORD} + - LANGFLOW_CHAT_FLOW_ID=${LANGFLOW_CHAT_FLOW_ID} + - LANGFLOW_INGEST_FLOW_ID=${LANGFLOW_INGEST_FLOW_ID} + - LANGFLOW_URL_INGEST_FLOW_ID=${LANGFLOW_URL_INGEST_FLOW_ID} + - DISABLE_INGEST_WITH_LANGFLOW=${DISABLE_INGEST_WITH_LANGFLOW:-false} + - NUDGES_FLOW_ID=${NUDGES_FLOW_ID} + - OPENSEARCH_PORT=9200 + - OPENSEARCH_USERNAME=admin + - OPENSEARCH_PASSWORD=${OPENSEARCH_PASSWORD} + - OPENAI_API_KEY=${OPENAI_API_KEY} + - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY} + - WATSONX_API_KEY=${WATSONX_API_KEY} + - WATSONX_ENDPOINT=${WATSONX_ENDPOINT} + - WATSONX_PROJECT_ID=${WATSONX_PROJECT_ID} + - OLLAMA_ENDPOINT=${OLLAMA_ENDPOINT} + - GOOGLE_OAUTH_CLIENT_ID=${GOOGLE_OAUTH_CLIENT_ID} + - GOOGLE_OAUTH_CLIENT_SECRET=${GOOGLE_OAUTH_CLIENT_SECRET} + - MICROSOFT_GRAPH_OAUTH_CLIENT_ID=${MICROSOFT_GRAPH_OAUTH_CLIENT_ID} + - MICROSOFT_GRAPH_OAUTH_CLIENT_SECRET=${MICROSOFT_GRAPH_OAUTH_CLIENT_SECRET} + - WEBHOOK_BASE_URL=${WEBHOOK_BASE_URL} + - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID} + - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY} + volumes: + - ./documents:/app/documents:Z + - ./keys:/app/keys:Z + - ./flows:/app/flows:U,z + + openrag-frontend: + image: langflowai/openrag-frontend:${OPENRAG_VERSION:-latest} + # build: + # context: . + # dockerfile: Dockerfile.frontend + container_name: openrag-frontend + depends_on: + - openrag-backend + environment: + - OPENRAG_BACKEND_HOST=openrag-backend + ports: + - "3000:3000" + + langflow: + volumes: + - ./flows:/app/flows:U,z + image: langflowai/openrag-langflow:${LANGFLOW_VERSION:-latest} + # build: + # context: . + # dockerfile: Dockerfile.langflow + container_name: langflow + ports: + - "7860:7860" + environment: + - LANGFLOW_DEACTIVATE_TRACING=true + - OPENAI_API_KEY=${OPENAI_API_KEY} + - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY} + - WATSONX_API_KEY=${WATSONX_API_KEY} + - WATSONX_ENDPOINT=${WATSONX_ENDPOINT} + - WATSONX_PROJECT_ID=${WATSONX_PROJECT_ID} + - OLLAMA_BASE_URL=${OLLAMA_ENDPOINT} + - LANGFLOW_LOAD_FLOWS_PATH=/app/flows + - LANGFLOW_SECRET_KEY=${LANGFLOW_SECRET_KEY} + - JWT=None + - OWNER=None + - OWNER_NAME=None + - OWNER_EMAIL=None + - CONNECTOR_TYPE=system + - CONNECTOR_TYPE_URL=url + - OPENRAG-QUERY-FILTER="{}" + - OPENSEARCH_PASSWORD=${OPENSEARCH_PASSWORD} + - FILENAME=None + - MIMETYPE=None + - FILESIZE=0 + - LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT=JWT,OPENRAG-QUERY-FILTER,OPENSEARCH_PASSWORD,OWNER,OWNER_NAME,OWNER_EMAIL,CONNECTOR_TYPE,FILENAME,MIMETYPE,FILESIZE + - LANGFLOW_LOG_LEVEL=DEBUG + - LANGFLOW_AUTO_LOGIN=${LANGFLOW_AUTO_LOGIN} + - LANGFLOW_SUPERUSER=${LANGFLOW_SUPERUSER} + - LANGFLOW_SUPERUSER_PASSWORD=${LANGFLOW_SUPERUSER_PASSWORD} + - LANGFLOW_NEW_USER_IS_ACTIVE=${LANGFLOW_NEW_USER_IS_ACTIVE} + - LANGFLOW_ENABLE_SUPERUSER_CLI=${LANGFLOW_ENABLE_SUPERUSER_CLI} + # - DEFAULT_FOLDER_NAME=OpenRAG + - HIDE_GETTING_STARTED_PROGRESS=true + diff --git a/docker-compose.yml b/docker-compose.yml index 2627aa14..615640e9 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,6 +1,6 @@ services: opensearch: - image: phact/openrag-opensearch:${OPENRAG_VERSION:-latest} + image: langflowai/openrag-opensearch:${OPENRAG_VERSION:-latest} #build: #context: . #dockerfile: Dockerfile @@ -44,7 +44,7 @@ services: - "5601:5601" openrag-backend: - image: phact/openrag-backend:${OPENRAG_VERSION:-latest} + image: langflowai/openrag-backend:${OPENRAG_VERSION:-latest} # build: # context: . # dockerfile: Dockerfile.backend @@ -86,7 +86,7 @@ services: - ./flows:/app/flows:U,z openrag-frontend: - image: phact/openrag-frontend:${OPENRAG_VERSION:-latest} + image: langflowai/openrag-frontend:${OPENRAG_VERSION:-latest} # build: # context: . # dockerfile: Dockerfile.frontend @@ -101,7 +101,7 @@ services: langflow: volumes: - ./flows:/app/flows:U,z - image: phact/openrag-langflow:${LANGFLOW_VERSION:-latest} + image: langflowai/openrag-langflow:${LANGFLOW_VERSION:-latest} # build: # context: . # dockerfile: Dockerfile.langflow diff --git a/docs/docs/get-started/docker.mdx b/docs/docs/get-started/docker.mdx index b9bb312a..0771b08e 100644 --- a/docs/docs/get-started/docker.mdx +++ b/docs/docs/get-started/docker.mdx @@ -18,7 +18,7 @@ OpenRAG has two Docker Compose files. Both files deploy the same applications an - Install the following: - - [Python](https://www.python.org/downloads/release/python-3100/) version 3.10 to 3.13. + - [Python](https://www.python.org/downloads/release/python-3100/) version 3.13 or later. - [uv](https://docs.astral.sh/uv/getting-started/installation/). - [Podman](https://podman.io/docs/installation) (recommended) or [Docker](https://docs.docker.com/get-docker/). - [`podman-compose`](https://docs.podman.io/en/latest/markdown/podman-compose.1.html) or [Docker Compose](https://docs.docker.com/compose/install/). To use Docker Compose with Podman, you must alias Docker Compose commands to Podman commands. diff --git a/docs/docs/get-started/install.mdx b/docs/docs/get-started/install.mdx index 3c1c4bcc..921c94d6 100644 --- a/docs/docs/get-started/install.mdx +++ b/docs/docs/get-started/install.mdx @@ -22,7 +22,7 @@ If you prefer running Podman or Docker containers and manually editing `.env` fi ## Prerequisites -- All OpenRAG installations require [Python](https://www.python.org/downloads/release/python-3100/) version 3.10 to 3.13. +- All OpenRAG installations require [Python](https://www.python.org/downloads/release/python-3100/) version 3.13 or later. - If you aren't using the automatic installer script, install the following: diff --git a/docs/docs/get-started/quickstart.mdx b/docs/docs/get-started/quickstart.mdx index 80eb0902..2e333a14 100644 --- a/docs/docs/get-started/quickstart.mdx +++ b/docs/docs/get-started/quickstart.mdx @@ -18,7 +18,7 @@ This quickstart requires the following: This quickstart uses OpenAI for simplicity. For other providers, see the complete [installation guide](/install). -- [Python](https://www.python.org/downloads/release/python-3100/) version 3.10 to 3.13. +- [Python](https://www.python.org/downloads/release/python-3100/) version 3.13 or later. - Microsoft Windows only: To run OpenRAG on Windows, you must use the Windows Subsystem for Linux (WSL). diff --git a/flows/ingestion_flow.json b/flows/ingestion_flow.json index abf51e90..d517c1aa 100644 --- a/flows/ingestion_flow.json +++ b/flows/ingestion_flow.json @@ -319,6 +319,7 @@ }, { "animated": false, + "className": "", "data": { "sourceHandle": { "dataType": "EmbeddingModel", @@ -1231,7 +1232,7 @@ "x": 2218.9287723423276, "y": 1332.2598463956504 }, - "selected": true, + "selected": false, "type": "genericNode" }, { @@ -1255,7 +1256,7 @@ ], "frozen": false, "icon": "braces", - "last_updated": "2025-11-11T15:06:45.096Z", + "last_updated": "2025-11-24T18:01:42.358Z", "legacy": false, "lf_version": "1.6.3.dev0", "metadata": {}, @@ -1345,6 +1346,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -1375,6 +1377,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -1405,6 +1408,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -1435,6 +1439,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -2617,7 +2622,7 @@ ], "frozen": false, "icon": "table", - "last_updated": "2025-11-11T15:06:45.203Z", + "last_updated": "2025-11-24T18:01:42.468Z", "legacy": false, "lf_version": "1.6.3.dev0", "metadata": { @@ -3033,7 +3038,7 @@ ], "frozen": false, "icon": "table", - "last_updated": "2025-11-11T15:06:45.204Z", + "last_updated": "2025-11-24T18:01:42.469Z", "legacy": false, "lf_version": "1.6.3.dev0", "metadata": { @@ -3449,7 +3454,7 @@ ], "frozen": false, "icon": "table", - "last_updated": "2025-11-11T15:06:45.205Z", + "last_updated": "2025-11-24T18:01:42.469Z", "legacy": false, "lf_version": "1.6.3.dev0", "metadata": { @@ -3889,23 +3894,32 @@ "request_timeout", "max_retries", "show_progress_bar", - "model_kwargs" + "model_kwargs", + "truncate_input_tokens", + "input_text" ], "frozen": false, "icon": "binary", - "last_updated": "2025-11-11T15:06:53.974Z", "legacy": false, "metadata": { - "code_hash": "bb03f97be707", + "code_hash": "c5e0a4535a27", "dependencies": { "dependencies": [ + { + "name": "requests", + "version": "2.32.5" + }, + { + "name": "ibm_watsonx_ai", + "version": "1.4.2" + }, { "name": "langchain_openai", "version": "0.3.23" }, { "name": "lfx", - "version": null + "version": "0.2.0.dev19" }, { "name": "langchain_ollama", @@ -3920,9 +3934,9 @@ "version": "0.3.19" } ], - "total_dependencies": 5 + "total_dependencies": 7 }, - "module": "lfx.components.models.embedding_model.EmbeddingModelComponent" + "module": "custom_components.embedding_model" }, "minimized": false, "output_types": [], @@ -3967,6 +3981,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -3985,6 +4000,7 @@ "required": true, "show": true, "title_case": false, + "track_in_telemetry": false, "type": "str", "value": "OPENAI_API_KEY" }, @@ -4015,6 +4031,7 @@ "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "https://us-south.ml.cloud.ibm.com" }, @@ -4033,6 +4050,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": 1000 }, @@ -4052,7 +4070,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.model_utils import get_ollama_models, is_valid_ollama_url\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS, WATSONX_EMBEDDING_MODEL_NAMES\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageTextInput,\n SecretStrInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.utils.util import transform_localhost_url\n\n# Ollama API constants\nHTTP_STATUS_OK = 200\nJSON_MODELS_KEY = \"models\"\nJSON_NAME_KEY = \"name\"\nJSON_CAPABILITIES_KEY = \"capabilities\"\nDESIRED_CAPABILITY = \"embedding\"\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n display_name = \"Embedding Model\"\n description = \"Generate embeddings using a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n icon = \"binary\"\n name = \"EmbeddingModel\"\n category = \"models\"\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\", \"Ollama\", \"IBM watsonx.ai\"],\n value=\"OpenAI\",\n info=\"Select the embedding model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Ollama\"}, {\"icon\": \"WatsonxAI\"}],\n ),\n MessageTextInput(\n name=\"api_base\",\n display_name=\"API Base URL\",\n info=\"Base URL for the API. Leave empty for default.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"ollama_base_url\",\n display_name=\"Ollama API URL\",\n info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n value=DEFAULT_OLLAMA_URL,\n show=False,\n real_time_refresh=True,\n load_from_db=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n show=False,\n real_time_refresh=True,\n ),\n DropdownInput(\n name=\"model\",\n display_name=\"Model Name\",\n options=OPENAI_EMBEDDING_MODEL_NAMES,\n value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n info=\"Select the embedding model to use\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=True,\n show=True,\n real_time_refresh=True,\n ),\n # Watson-specific inputs\n MessageTextInput(\n name=\"project_id\",\n display_name=\"Project ID\",\n info=\"IBM watsonx.ai Project ID (required for IBM watsonx.ai)\",\n show=False,\n ),\n IntInput(\n name=\"dimensions\",\n display_name=\"Dimensions\",\n info=\"The number of dimensions the resulting output embeddings should have. \"\n \"Only supported by certain models.\",\n advanced=True,\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n DictInput(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\n ]\n\n def build_embeddings(self) -> Embeddings:\n provider = self.provider\n model = self.model\n api_key = self.api_key\n api_base = self.api_base\n base_url_ibm_watsonx = self.base_url_ibm_watsonx\n ollama_base_url = self.ollama_base_url\n dimensions = self.dimensions\n chunk_size = self.chunk_size\n request_timeout = self.request_timeout\n max_retries = self.max_retries\n show_progress_bar = self.show_progress_bar\n model_kwargs = self.model_kwargs or {}\n\n if provider == \"OpenAI\":\n if not api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n return OpenAIEmbeddings(\n model=model,\n dimensions=dimensions or None,\n base_url=api_base or None,\n api_key=api_key,\n chunk_size=chunk_size,\n max_retries=max_retries,\n timeout=request_timeout or None,\n show_progress_bar=show_progress_bar,\n model_kwargs=model_kwargs,\n )\n\n if provider == \"Ollama\":\n try:\n from langchain_ollama import OllamaEmbeddings\n except ImportError:\n try:\n from langchain_community.embeddings import OllamaEmbeddings\n except ImportError:\n msg = \"Please install langchain-ollama: pip install langchain-ollama\"\n raise ImportError(msg) from None\n\n transformed_base_url = transform_localhost_url(ollama_base_url)\n\n # Check if URL contains /v1 suffix (OpenAI-compatible mode)\n if transformed_base_url and transformed_base_url.rstrip(\"/\").endswith(\"/v1\"):\n # Strip /v1 suffix and log warning\n transformed_base_url = transformed_base_url.rstrip(\"/\").removesuffix(\"/v1\")\n logger.warning(\n \"Detected '/v1' suffix in base URL. The Ollama component uses the native Ollama API, \"\n \"not the OpenAI-compatible API. The '/v1' suffix has been automatically removed. \"\n \"If you want to use the OpenAI-compatible API, please use the OpenAI component instead. \"\n \"Learn more at https://docs.ollama.com/openai#openai-compatibility\"\n )\n\n return OllamaEmbeddings(\n model=model,\n base_url=transformed_base_url or \"http://localhost:11434\",\n **model_kwargs,\n )\n\n if provider == \"IBM watsonx.ai\":\n try:\n from langchain_ibm import WatsonxEmbeddings\n except ImportError:\n msg = \"Please install langchain-ibm: pip install langchain-ibm\"\n raise ImportError(msg) from None\n\n if not api_key:\n msg = \"IBM watsonx.ai API key is required when using IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n project_id = self.project_id\n\n if not project_id:\n msg = \"Project ID is required for IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n params = {\n \"model_id\": model,\n \"url\": base_url_ibm_watsonx or \"https://us-south.ml.cloud.ibm.com\",\n \"apikey\": api_key,\n }\n\n params[\"project_id\"] = project_id\n\n return WatsonxEmbeddings(**params)\n\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n async def update_build_config(\n self, build_config: dotdict, field_value: Any, field_name: str | None = None\n ) -> dotdict:\n if field_name == \"provider\":\n if field_value == \"OpenAI\":\n build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n build_config[\"api_base\"][\"advanced\"] = True\n build_config[\"api_base\"][\"show\"] = True\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n elif field_value == \"Ollama\":\n build_config[\"ollama_base_url\"][\"show\"] = True\n\n if await is_valid_ollama_url(url=self.ollama_base_url):\n try:\n models = await get_ollama_models(\n base_url_value=self.ollama_base_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n else:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n\n build_config[\"api_key\"][\"display_name\"] = \"API Key (Optional)\"\n build_config[\"api_key\"][\"required\"] = False\n build_config[\"api_key\"][\"show\"] = False\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n elif field_value == \"IBM watsonx.ai\":\n build_config[\"model\"][\"options\"] = WATSONX_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = WATSONX_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"IBM watsonx.ai API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = True\n build_config[\"project_id\"][\"show\"] = True\n\n elif field_name == \"ollama_base_url\":\n # # Refresh Ollama models when base URL changes\n # if hasattr(self, \"provider\") and self.provider == \"Ollama\":\n # Use field_value if provided, otherwise fall back to instance attribute\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n await logger.awarning(\"Failed to fetch Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n\n elif field_name == \"model\" and self.provider == \"Ollama\":\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n except ValueError:\n await logger.awarning(\"Failed to refresh Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n\n return build_config\n" + "value": "from typing import Any\n\nimport requests\nfrom ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.model_utils import get_ollama_models, is_valid_ollama_url\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.base.models.watsonx_constants import (\n IBM_WATSONX_URLS,\n WATSONX_EMBEDDING_MODEL_NAMES,\n)\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageTextInput,\n SecretStrInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.utils.util import transform_localhost_url\n\n# Ollama API constants\nHTTP_STATUS_OK = 200\nJSON_MODELS_KEY = \"models\"\nJSON_NAME_KEY = \"name\"\nJSON_CAPABILITIES_KEY = \"capabilities\"\nDESIRED_CAPABILITY = \"embedding\"\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n display_name = \"Embedding Model\"\n description = \"Generate embeddings using a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n icon = \"binary\"\n name = \"EmbeddingModel\"\n category = \"models\"\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\", \"Ollama\", \"IBM watsonx.ai\"],\n value=\"OpenAI\",\n info=\"Select the embedding model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Ollama\"}, {\"icon\": \"WatsonxAI\"}],\n ),\n MessageTextInput(\n name=\"api_base\",\n display_name=\"API Base URL\",\n info=\"Base URL for the API. Leave empty for default.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"ollama_base_url\",\n display_name=\"Ollama API URL\",\n info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n value=DEFAULT_OLLAMA_URL,\n show=False,\n real_time_refresh=True,\n load_from_db=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n show=False,\n real_time_refresh=True,\n ),\n DropdownInput(\n name=\"model\",\n display_name=\"Model Name\",\n options=OPENAI_EMBEDDING_MODEL_NAMES,\n value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n info=\"Select the embedding model to use\",\n real_time_refresh=True,\n refresh_button=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=True,\n show=True,\n real_time_refresh=True,\n ),\n # Watson-specific inputs\n MessageTextInput(\n name=\"project_id\",\n display_name=\"Project ID\",\n info=\"IBM watsonx.ai Project ID (required for IBM watsonx.ai)\",\n show=False,\n ),\n IntInput(\n name=\"dimensions\",\n display_name=\"Dimensions\",\n info=\"The number of dimensions the resulting output embeddings should have. \"\n \"Only supported by certain models.\",\n advanced=True,\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n DictInput(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\n IntInput(\n name=\"truncate_input_tokens\",\n display_name=\"Truncate Input Tokens\",\n advanced=True,\n value=200,\n show=False,\n ),\n BoolInput(\n name=\"input_text\",\n display_name=\"Include the original text in the output\",\n value=True,\n advanced=True,\n show=False,\n ),\n ]\n\n @staticmethod\n def fetch_ibm_models(base_url: str) -> list[str]:\n \"\"\"Fetch available models from the watsonx.ai API.\"\"\"\n try:\n endpoint = f\"{base_url}/ml/v1/foundation_model_specs\"\n params = {\n \"version\": \"2024-09-16\",\n \"filters\": \"function_embedding,!lifecycle_withdrawn:and\",\n }\n response = requests.get(endpoint, params=params, timeout=10)\n response.raise_for_status()\n data = response.json()\n models = [model[\"model_id\"] for model in data.get(\"resources\", [])]\n return sorted(models)\n except Exception: # noqa: BLE001\n logger.exception(\"Error fetching models\")\n return WATSONX_EMBEDDING_MODEL_NAMES\n\n def build_embeddings(self) -> Embeddings:\n provider = self.provider\n model = self.model\n api_key = self.api_key\n api_base = self.api_base\n base_url_ibm_watsonx = self.base_url_ibm_watsonx\n ollama_base_url = self.ollama_base_url\n dimensions = self.dimensions\n chunk_size = self.chunk_size\n request_timeout = self.request_timeout\n max_retries = self.max_retries\n show_progress_bar = self.show_progress_bar\n model_kwargs = self.model_kwargs or {}\n\n if provider == \"OpenAI\":\n if not api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n return OpenAIEmbeddings(\n model=model,\n dimensions=dimensions or None,\n base_url=api_base or None,\n api_key=api_key,\n chunk_size=chunk_size,\n max_retries=max_retries,\n timeout=request_timeout or None,\n show_progress_bar=show_progress_bar,\n model_kwargs=model_kwargs,\n )\n\n if provider == \"Ollama\":\n try:\n from langchain_ollama import OllamaEmbeddings\n except ImportError:\n try:\n from langchain_community.embeddings import OllamaEmbeddings\n except ImportError:\n msg = \"Please install langchain-ollama: pip install langchain-ollama\"\n raise ImportError(msg) from None\n\n transformed_base_url = transform_localhost_url(ollama_base_url)\n\n # Check if URL contains /v1 suffix (OpenAI-compatible mode)\n if transformed_base_url and transformed_base_url.rstrip(\"/\").endswith(\"/v1\"):\n # Strip /v1 suffix and log warning\n transformed_base_url = transformed_base_url.rstrip(\"/\").removesuffix(\"/v1\")\n logger.warning(\n \"Detected '/v1' suffix in base URL. The Ollama component uses the native Ollama API, \"\n \"not the OpenAI-compatible API. The '/v1' suffix has been automatically removed. \"\n \"If you want to use the OpenAI-compatible API, please use the OpenAI component instead. \"\n \"Learn more at https://docs.ollama.com/openai#openai-compatibility\"\n )\n\n return OllamaEmbeddings(\n model=model,\n base_url=transformed_base_url or \"http://localhost:11434\",\n **model_kwargs,\n )\n\n if provider == \"IBM watsonx.ai\":\n try:\n from langchain_ibm import WatsonxEmbeddings\n except ImportError:\n msg = \"Please install langchain-ibm: pip install langchain-ibm\"\n raise ImportError(msg) from None\n\n if not api_key:\n msg = \"IBM watsonx.ai API key is required when using IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n project_id = self.project_id\n\n if not project_id:\n msg = \"Project ID is required for IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n from ibm_watsonx_ai import APIClient, Credentials\n\n credentials = Credentials(\n api_key=self.api_key,\n url=base_url_ibm_watsonx or \"https://us-south.ml.cloud.ibm.com\",\n )\n\n api_client = APIClient(credentials)\n\n params = {\n EmbedTextParamsMetaNames.TRUNCATE_INPUT_TOKENS: self.truncate_input_tokens,\n EmbedTextParamsMetaNames.RETURN_OPTIONS: {\"input_text\": self.input_text},\n }\n\n return WatsonxEmbeddings(\n model_id=model,\n params=params,\n watsonx_client=api_client,\n project_id=project_id,\n )\n\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n async def update_build_config(\n self, build_config: dotdict, field_value: Any, field_name: str | None = None\n ) -> dotdict:\n if field_name == \"provider\":\n if field_value == \"OpenAI\":\n build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n build_config[\"api_base\"][\"advanced\"] = True\n build_config[\"api_base\"][\"show\"] = True\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n build_config[\"truncate_input_tokens\"][\"show\"] = False\n build_config[\"input_text\"][\"show\"] = False\n elif field_value == \"Ollama\":\n build_config[\"ollama_base_url\"][\"show\"] = True\n\n if await is_valid_ollama_url(url=self.ollama_base_url):\n try:\n models = await get_ollama_models(\n base_url_value=self.ollama_base_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n else:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n build_config[\"truncate_input_tokens\"][\"show\"] = False\n build_config[\"input_text\"][\"show\"] = False\n build_config[\"api_key\"][\"display_name\"] = \"API Key (Optional)\"\n build_config[\"api_key\"][\"required\"] = False\n build_config[\"api_key\"][\"show\"] = False\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n elif field_value == \"IBM watsonx.ai\":\n build_config[\"model\"][\"options\"] = self.fetch_ibm_models(base_url=self.base_url_ibm_watsonx)\n build_config[\"model\"][\"value\"] = self.fetch_ibm_models(base_url=self.base_url_ibm_watsonx)[0]\n build_config[\"api_key\"][\"display_name\"] = \"IBM watsonx.ai API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = True\n build_config[\"project_id\"][\"show\"] = True\n build_config[\"truncate_input_tokens\"][\"show\"] = True\n build_config[\"input_text\"][\"show\"] = True\n elif field_name == \"base_url_ibm_watsonx\":\n build_config[\"model\"][\"options\"] = self.fetch_ibm_models(base_url=field_value)\n build_config[\"model\"][\"value\"] = self.fetch_ibm_models(base_url=field_value)[0]\n elif field_name == \"ollama_base_url\":\n # # Refresh Ollama models when base URL changes\n # if hasattr(self, \"provider\") and self.provider == \"Ollama\":\n # Use field_value if provided, otherwise fall back to instance attribute\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n await logger.awarning(\"Failed to fetch Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n\n elif field_name == \"model\" and self.provider == \"Ollama\":\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n except ValueError:\n await logger.awarning(\"Failed to refresh Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n\n return build_config\n" }, "dimensions": { "_input_type": "IntInput", @@ -4069,9 +4087,29 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": "" }, + "input_text": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Include the original text in the output", + "dynamic": false, + "info": "", + "list": false, + "list_add_label": "Add More", + "name": "input_text", + "placeholder": "", + "required": false, + "show": false, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "bool", + "value": true + }, "max_retries": { "_input_type": "IntInput", "advanced": true, @@ -4087,6 +4125,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": 3 }, @@ -4107,12 +4146,15 @@ ], "options_metadata": [], "placeholder": "", + "real_time_refresh": true, + "refresh_button": true, "required": false, "show": true, "title_case": false, "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "text-embedding-3-small" }, @@ -4131,6 +4173,7 @@ "title_case": false, "tool_mode": false, "trace_as_input": true, + "track_in_telemetry": false, "type": "dict", "value": {} }, @@ -4155,6 +4198,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -4178,6 +4222,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -4215,6 +4260,7 @@ "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "OpenAI" }, @@ -4233,6 +4279,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "float", "value": "" }, @@ -4251,8 +4298,28 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "bool", "value": false + }, + "truncate_input_tokens": { + "_input_type": "IntInput", + "advanced": true, + "display_name": "Truncate Input Tokens", + "dynamic": false, + "info": "", + "list": false, + "list_add_label": "Add More", + "name": "truncate_input_tokens", + "placeholder": "", + "required": false, + "show": false, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "int", + "value": 200 } }, "tool_mode": false @@ -4275,16 +4342,16 @@ } ], "viewport": { - "x": -35.55006677196684, - "y": -678.0353419327157, - "zoom": 0.5646686365360081 + "x": 354.22064006192994, + "y": -436.1821097171422, + "zoom": 0.45965778621327413 } }, "description": "Load your data for chat context with Retrieval Augmented Generation.", "endpoint_name": null, "id": "5488df7c-b93f-4f87-a446-b67028bc0813", "is_component": false, - "last_tested_version": "1.7.0", + "last_tested_version": "1.7.0.dev19", "name": "OpenSearch Ingestion Flow", "tags": [ "openai", diff --git a/flows/openrag_agent.json b/flows/openrag_agent.json index 9640c6a8..af0ed600 100644 --- a/flows/openrag_agent.json +++ b/flows/openrag_agent.json @@ -173,6 +173,7 @@ }, { "animated": false, + "className": "", "data": { "sourceHandle": { "dataType": "CalculatorComponent", @@ -242,7 +243,7 @@ ], "frozen": false, "icon": "OpenSearch", - "last_updated": "2025-11-11T21:40:35.507Z", + "last_updated": "2025-11-24T18:02:41.464Z", "legacy": false, "lf_version": "1.6.0", "metadata": { @@ -713,6 +714,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "tools", "value": [ { @@ -992,7 +994,7 @@ "frozen": false, "icon": "Mcp", "key": "mcp_lf-starter_project", - "last_updated": "2025-11-11T21:40:35.508Z", + "last_updated": "2025-11-24T18:02:41.465Z", "legacy": false, "mcpServerName": "lf-starter_project", "metadata": { @@ -1141,6 +1143,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "tools", "value": [ { @@ -1821,23 +1824,32 @@ "request_timeout", "max_retries", "show_progress_bar", - "model_kwargs" + "model_kwargs", + "truncate_input_tokens", + "input_text" ], "frozen": false, "icon": "binary", - "last_updated": "2025-11-11T21:40:35.510Z", "legacy": false, "metadata": { - "code_hash": "bb03f97be707", + "code_hash": "c5e0a4535a27", "dependencies": { "dependencies": [ + { + "name": "requests", + "version": "2.32.5" + }, + { + "name": "ibm_watsonx_ai", + "version": "1.4.2" + }, { "name": "langchain_openai", "version": "0.3.23" }, { "name": "lfx", - "version": null + "version": "0.2.0.dev19" }, { "name": "langchain_ollama", @@ -1852,9 +1864,9 @@ "version": "0.3.19" } ], - "total_dependencies": 5 + "total_dependencies": 7 }, - "module": "lfx.components.models.embedding_model.EmbeddingModelComponent" + "module": "custom_components.embedding_model" }, "minimized": false, "output_types": [], @@ -1899,6 +1911,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -1917,6 +1930,7 @@ "required": true, "show": true, "title_case": false, + "track_in_telemetry": false, "type": "str", "value": "OPENAI_API_KEY" }, @@ -1947,6 +1961,7 @@ "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "https://us-south.ml.cloud.ibm.com" }, @@ -1965,6 +1980,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": 1000 }, @@ -1984,7 +2000,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.model_utils import get_ollama_models, is_valid_ollama_url\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS, WATSONX_EMBEDDING_MODEL_NAMES\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageTextInput,\n SecretStrInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.utils.util import transform_localhost_url\n\n# Ollama API constants\nHTTP_STATUS_OK = 200\nJSON_MODELS_KEY = \"models\"\nJSON_NAME_KEY = \"name\"\nJSON_CAPABILITIES_KEY = \"capabilities\"\nDESIRED_CAPABILITY = \"embedding\"\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n display_name = \"Embedding Model\"\n description = \"Generate embeddings using a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n icon = \"binary\"\n name = \"EmbeddingModel\"\n category = \"models\"\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\", \"Ollama\", \"IBM watsonx.ai\"],\n value=\"OpenAI\",\n info=\"Select the embedding model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Ollama\"}, {\"icon\": \"WatsonxAI\"}],\n ),\n MessageTextInput(\n name=\"api_base\",\n display_name=\"API Base URL\",\n info=\"Base URL for the API. Leave empty for default.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"ollama_base_url\",\n display_name=\"Ollama API URL\",\n info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n value=DEFAULT_OLLAMA_URL,\n show=False,\n real_time_refresh=True,\n load_from_db=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n show=False,\n real_time_refresh=True,\n ),\n DropdownInput(\n name=\"model\",\n display_name=\"Model Name\",\n options=OPENAI_EMBEDDING_MODEL_NAMES,\n value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n info=\"Select the embedding model to use\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=True,\n show=True,\n real_time_refresh=True,\n ),\n # Watson-specific inputs\n MessageTextInput(\n name=\"project_id\",\n display_name=\"Project ID\",\n info=\"IBM watsonx.ai Project ID (required for IBM watsonx.ai)\",\n show=False,\n ),\n IntInput(\n name=\"dimensions\",\n display_name=\"Dimensions\",\n info=\"The number of dimensions the resulting output embeddings should have. \"\n \"Only supported by certain models.\",\n advanced=True,\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n DictInput(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\n ]\n\n def build_embeddings(self) -> Embeddings:\n provider = self.provider\n model = self.model\n api_key = self.api_key\n api_base = self.api_base\n base_url_ibm_watsonx = self.base_url_ibm_watsonx\n ollama_base_url = self.ollama_base_url\n dimensions = self.dimensions\n chunk_size = self.chunk_size\n request_timeout = self.request_timeout\n max_retries = self.max_retries\n show_progress_bar = self.show_progress_bar\n model_kwargs = self.model_kwargs or {}\n\n if provider == \"OpenAI\":\n if not api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n return OpenAIEmbeddings(\n model=model,\n dimensions=dimensions or None,\n base_url=api_base or None,\n api_key=api_key,\n chunk_size=chunk_size,\n max_retries=max_retries,\n timeout=request_timeout or None,\n show_progress_bar=show_progress_bar,\n model_kwargs=model_kwargs,\n )\n\n if provider == \"Ollama\":\n try:\n from langchain_ollama import OllamaEmbeddings\n except ImportError:\n try:\n from langchain_community.embeddings import OllamaEmbeddings\n except ImportError:\n msg = \"Please install langchain-ollama: pip install langchain-ollama\"\n raise ImportError(msg) from None\n\n transformed_base_url = transform_localhost_url(ollama_base_url)\n\n # Check if URL contains /v1 suffix (OpenAI-compatible mode)\n if transformed_base_url and transformed_base_url.rstrip(\"/\").endswith(\"/v1\"):\n # Strip /v1 suffix and log warning\n transformed_base_url = transformed_base_url.rstrip(\"/\").removesuffix(\"/v1\")\n logger.warning(\n \"Detected '/v1' suffix in base URL. The Ollama component uses the native Ollama API, \"\n \"not the OpenAI-compatible API. The '/v1' suffix has been automatically removed. \"\n \"If you want to use the OpenAI-compatible API, please use the OpenAI component instead. \"\n \"Learn more at https://docs.ollama.com/openai#openai-compatibility\"\n )\n\n return OllamaEmbeddings(\n model=model,\n base_url=transformed_base_url or \"http://localhost:11434\",\n **model_kwargs,\n )\n\n if provider == \"IBM watsonx.ai\":\n try:\n from langchain_ibm import WatsonxEmbeddings\n except ImportError:\n msg = \"Please install langchain-ibm: pip install langchain-ibm\"\n raise ImportError(msg) from None\n\n if not api_key:\n msg = \"IBM watsonx.ai API key is required when using IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n project_id = self.project_id\n\n if not project_id:\n msg = \"Project ID is required for IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n params = {\n \"model_id\": model,\n \"url\": base_url_ibm_watsonx or \"https://us-south.ml.cloud.ibm.com\",\n \"apikey\": api_key,\n }\n\n params[\"project_id\"] = project_id\n\n return WatsonxEmbeddings(**params)\n\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n async def update_build_config(\n self, build_config: dotdict, field_value: Any, field_name: str | None = None\n ) -> dotdict:\n if field_name == \"provider\":\n if field_value == \"OpenAI\":\n build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n build_config[\"api_base\"][\"advanced\"] = True\n build_config[\"api_base\"][\"show\"] = True\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n elif field_value == \"Ollama\":\n build_config[\"ollama_base_url\"][\"show\"] = True\n\n if await is_valid_ollama_url(url=self.ollama_base_url):\n try:\n models = await get_ollama_models(\n base_url_value=self.ollama_base_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n else:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n\n build_config[\"api_key\"][\"display_name\"] = \"API Key (Optional)\"\n build_config[\"api_key\"][\"required\"] = False\n build_config[\"api_key\"][\"show\"] = False\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n elif field_value == \"IBM watsonx.ai\":\n build_config[\"model\"][\"options\"] = WATSONX_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = WATSONX_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"IBM watsonx.ai API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = True\n build_config[\"project_id\"][\"show\"] = True\n\n elif field_name == \"ollama_base_url\":\n # # Refresh Ollama models when base URL changes\n # if hasattr(self, \"provider\") and self.provider == \"Ollama\":\n # Use field_value if provided, otherwise fall back to instance attribute\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n await logger.awarning(\"Failed to fetch Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n\n elif field_name == \"model\" and self.provider == \"Ollama\":\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n except ValueError:\n await logger.awarning(\"Failed to refresh Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n\n return build_config\n" + "value": "from typing import Any\n\nimport requests\nfrom ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.model_utils import get_ollama_models, is_valid_ollama_url\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.base.models.watsonx_constants import (\n IBM_WATSONX_URLS,\n WATSONX_EMBEDDING_MODEL_NAMES,\n)\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageTextInput,\n SecretStrInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.utils.util import transform_localhost_url\n\n# Ollama API constants\nHTTP_STATUS_OK = 200\nJSON_MODELS_KEY = \"models\"\nJSON_NAME_KEY = \"name\"\nJSON_CAPABILITIES_KEY = \"capabilities\"\nDESIRED_CAPABILITY = \"embedding\"\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n display_name = \"Embedding Model\"\n description = \"Generate embeddings using a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n icon = \"binary\"\n name = \"EmbeddingModel\"\n category = \"models\"\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\", \"Ollama\", \"IBM watsonx.ai\"],\n value=\"OpenAI\",\n info=\"Select the embedding model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Ollama\"}, {\"icon\": \"WatsonxAI\"}],\n ),\n MessageTextInput(\n name=\"api_base\",\n display_name=\"API Base URL\",\n info=\"Base URL for the API. Leave empty for default.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"ollama_base_url\",\n display_name=\"Ollama API URL\",\n info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n value=DEFAULT_OLLAMA_URL,\n show=False,\n real_time_refresh=True,\n load_from_db=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n show=False,\n real_time_refresh=True,\n ),\n DropdownInput(\n name=\"model\",\n display_name=\"Model Name\",\n options=OPENAI_EMBEDDING_MODEL_NAMES,\n value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n info=\"Select the embedding model to use\",\n real_time_refresh=True,\n refresh_button=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=True,\n show=True,\n real_time_refresh=True,\n ),\n # Watson-specific inputs\n MessageTextInput(\n name=\"project_id\",\n display_name=\"Project ID\",\n info=\"IBM watsonx.ai Project ID (required for IBM watsonx.ai)\",\n show=False,\n ),\n IntInput(\n name=\"dimensions\",\n display_name=\"Dimensions\",\n info=\"The number of dimensions the resulting output embeddings should have. \"\n \"Only supported by certain models.\",\n advanced=True,\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n DictInput(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\n IntInput(\n name=\"truncate_input_tokens\",\n display_name=\"Truncate Input Tokens\",\n advanced=True,\n value=200,\n show=False,\n ),\n BoolInput(\n name=\"input_text\",\n display_name=\"Include the original text in the output\",\n value=True,\n advanced=True,\n show=False,\n ),\n ]\n\n @staticmethod\n def fetch_ibm_models(base_url: str) -> list[str]:\n \"\"\"Fetch available models from the watsonx.ai API.\"\"\"\n try:\n endpoint = f\"{base_url}/ml/v1/foundation_model_specs\"\n params = {\n \"version\": \"2024-09-16\",\n \"filters\": \"function_embedding,!lifecycle_withdrawn:and\",\n }\n response = requests.get(endpoint, params=params, timeout=10)\n response.raise_for_status()\n data = response.json()\n models = [model[\"model_id\"] for model in data.get(\"resources\", [])]\n return sorted(models)\n except Exception: # noqa: BLE001\n logger.exception(\"Error fetching models\")\n return WATSONX_EMBEDDING_MODEL_NAMES\n\n def build_embeddings(self) -> Embeddings:\n provider = self.provider\n model = self.model\n api_key = self.api_key\n api_base = self.api_base\n base_url_ibm_watsonx = self.base_url_ibm_watsonx\n ollama_base_url = self.ollama_base_url\n dimensions = self.dimensions\n chunk_size = self.chunk_size\n request_timeout = self.request_timeout\n max_retries = self.max_retries\n show_progress_bar = self.show_progress_bar\n model_kwargs = self.model_kwargs or {}\n\n if provider == \"OpenAI\":\n if not api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n return OpenAIEmbeddings(\n model=model,\n dimensions=dimensions or None,\n base_url=api_base or None,\n api_key=api_key,\n chunk_size=chunk_size,\n max_retries=max_retries,\n timeout=request_timeout or None,\n show_progress_bar=show_progress_bar,\n model_kwargs=model_kwargs,\n )\n\n if provider == \"Ollama\":\n try:\n from langchain_ollama import OllamaEmbeddings\n except ImportError:\n try:\n from langchain_community.embeddings import OllamaEmbeddings\n except ImportError:\n msg = \"Please install langchain-ollama: pip install langchain-ollama\"\n raise ImportError(msg) from None\n\n transformed_base_url = transform_localhost_url(ollama_base_url)\n\n # Check if URL contains /v1 suffix (OpenAI-compatible mode)\n if transformed_base_url and transformed_base_url.rstrip(\"/\").endswith(\"/v1\"):\n # Strip /v1 suffix and log warning\n transformed_base_url = transformed_base_url.rstrip(\"/\").removesuffix(\"/v1\")\n logger.warning(\n \"Detected '/v1' suffix in base URL. The Ollama component uses the native Ollama API, \"\n \"not the OpenAI-compatible API. The '/v1' suffix has been automatically removed. \"\n \"If you want to use the OpenAI-compatible API, please use the OpenAI component instead. \"\n \"Learn more at https://docs.ollama.com/openai#openai-compatibility\"\n )\n\n return OllamaEmbeddings(\n model=model,\n base_url=transformed_base_url or \"http://localhost:11434\",\n **model_kwargs,\n )\n\n if provider == \"IBM watsonx.ai\":\n try:\n from langchain_ibm import WatsonxEmbeddings\n except ImportError:\n msg = \"Please install langchain-ibm: pip install langchain-ibm\"\n raise ImportError(msg) from None\n\n if not api_key:\n msg = \"IBM watsonx.ai API key is required when using IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n project_id = self.project_id\n\n if not project_id:\n msg = \"Project ID is required for IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n from ibm_watsonx_ai import APIClient, Credentials\n\n credentials = Credentials(\n api_key=self.api_key,\n url=base_url_ibm_watsonx or \"https://us-south.ml.cloud.ibm.com\",\n )\n\n api_client = APIClient(credentials)\n\n params = {\n EmbedTextParamsMetaNames.TRUNCATE_INPUT_TOKENS: self.truncate_input_tokens,\n EmbedTextParamsMetaNames.RETURN_OPTIONS: {\"input_text\": self.input_text},\n }\n\n return WatsonxEmbeddings(\n model_id=model,\n params=params,\n watsonx_client=api_client,\n project_id=project_id,\n )\n\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n async def update_build_config(\n self, build_config: dotdict, field_value: Any, field_name: str | None = None\n ) -> dotdict:\n if field_name == \"provider\":\n if field_value == \"OpenAI\":\n build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n build_config[\"api_base\"][\"advanced\"] = True\n build_config[\"api_base\"][\"show\"] = True\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n build_config[\"truncate_input_tokens\"][\"show\"] = False\n build_config[\"input_text\"][\"show\"] = False\n elif field_value == \"Ollama\":\n build_config[\"ollama_base_url\"][\"show\"] = True\n\n if await is_valid_ollama_url(url=self.ollama_base_url):\n try:\n models = await get_ollama_models(\n base_url_value=self.ollama_base_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n else:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n build_config[\"truncate_input_tokens\"][\"show\"] = False\n build_config[\"input_text\"][\"show\"] = False\n build_config[\"api_key\"][\"display_name\"] = \"API Key (Optional)\"\n build_config[\"api_key\"][\"required\"] = False\n build_config[\"api_key\"][\"show\"] = False\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n elif field_value == \"IBM watsonx.ai\":\n build_config[\"model\"][\"options\"] = self.fetch_ibm_models(base_url=self.base_url_ibm_watsonx)\n build_config[\"model\"][\"value\"] = self.fetch_ibm_models(base_url=self.base_url_ibm_watsonx)[0]\n build_config[\"api_key\"][\"display_name\"] = \"IBM watsonx.ai API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = True\n build_config[\"project_id\"][\"show\"] = True\n build_config[\"truncate_input_tokens\"][\"show\"] = True\n build_config[\"input_text\"][\"show\"] = True\n elif field_name == \"base_url_ibm_watsonx\":\n build_config[\"model\"][\"options\"] = self.fetch_ibm_models(base_url=field_value)\n build_config[\"model\"][\"value\"] = self.fetch_ibm_models(base_url=field_value)[0]\n elif field_name == \"ollama_base_url\":\n # # Refresh Ollama models when base URL changes\n # if hasattr(self, \"provider\") and self.provider == \"Ollama\":\n # Use field_value if provided, otherwise fall back to instance attribute\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n await logger.awarning(\"Failed to fetch Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n\n elif field_name == \"model\" and self.provider == \"Ollama\":\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n except ValueError:\n await logger.awarning(\"Failed to refresh Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n\n return build_config\n" }, "dimensions": { "_input_type": "IntInput", @@ -2001,9 +2017,29 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": "" }, + "input_text": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Include the original text in the output", + "dynamic": false, + "info": "", + "list": false, + "list_add_label": "Add More", + "name": "input_text", + "placeholder": "", + "required": false, + "show": false, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "bool", + "value": true + }, "max_retries": { "_input_type": "IntInput", "advanced": true, @@ -2019,6 +2055,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": 3 }, @@ -2033,16 +2070,21 @@ "info": "Select the embedding model to use", "name": "model", "options": [ - "text-embedding-3-small" + "text-embedding-3-small", + "text-embedding-3-large", + "text-embedding-ada-002" ], "options_metadata": [], "placeholder": "", + "real_time_refresh": true, + "refresh_button": true, "required": false, "show": true, "title_case": false, "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "text-embedding-3-small" }, @@ -2061,6 +2103,7 @@ "title_case": false, "tool_mode": false, "trace_as_input": true, + "track_in_telemetry": false, "type": "dict", "value": {} }, @@ -2085,6 +2128,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -2108,6 +2152,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -2145,6 +2190,7 @@ "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "OpenAI" }, @@ -2163,6 +2209,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "float", "value": "" }, @@ -2181,8 +2228,28 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "bool", "value": false + }, + "truncate_input_tokens": { + "_input_type": "IntInput", + "advanced": true, + "display_name": "Truncate Input Tokens", + "dynamic": false, + "info": "", + "list": false, + "list_add_label": "Add More", + "name": "truncate_input_tokens", + "placeholder": "", + "required": false, + "show": false, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "int", + "value": 200 } }, "tool_mode": false @@ -2193,7 +2260,7 @@ "dragging": false, "id": "EmbeddingModel-oPi95", "measured": { - "height": 451, + "height": 369, "width": 320 }, "position": { @@ -2247,10 +2314,9 @@ ], "frozen": false, "icon": "bot", - "last_updated": "2025-11-11T21:40:35.510Z", "legacy": false, "metadata": { - "code_hash": "adf733969280", + "code_hash": "d64b11c24a1c", "dependencies": { "dependencies": [ { @@ -2263,12 +2329,12 @@ }, { "name": "lfx", - "version": null + "version": "0.2.0.dev19" } ], "total_dependencies": 3 }, - "module": "lfx.components.agents.agent.AgentComponent" + "module": "custom_components.agent" }, "minimized": false, "output_types": [], @@ -2308,6 +2374,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "bool", "value": true }, @@ -2333,6 +2400,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "A helpful assistant with access to the following tools:" }, @@ -2390,6 +2458,7 @@ "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "OpenAI" }, @@ -2408,6 +2477,7 @@ "required": false, "show": true, "title_case": false, + "track_in_telemetry": false, "type": "str", "value": "OPENAI_API_KEY" }, @@ -2427,6 +2497,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -2446,7 +2517,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import json\nimport re\n\nfrom langchain_core.tools import StructuredTool, Tool\nfrom pydantic import ValidationError\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.models.model_input_constants import (\n ALL_PROVIDER_FIELDS,\n MODEL_DYNAMIC_UPDATE_FIELDS,\n MODEL_PROVIDERS_DICT,\n MODEL_PROVIDERS_LIST,\n MODELS_METADATA,\n)\nfrom lfx.base.models.model_utils import get_model_name\nfrom lfx.components.helpers.current_date import CurrentDateComponent\nfrom lfx.components.helpers.memory import MemoryComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.custom.utils import update_component_build_config\nfrom lfx.helpers.base_model import build_model_from_schema\nfrom lfx.inputs.inputs import BoolInput, SecretStrInput, StrInput\nfrom lfx.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n # Filter out json_mode from OpenAI inputs since we handle structured output differently\n if \"OpenAI\" in MODEL_PROVIDERS_DICT:\n openai_inputs_filtered = [\n input_field\n for input_field in MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"]\n if not (hasattr(input_field, \"name\") and input_field.name == \"json_mode\")\n ]\n else:\n openai_inputs_filtered = []\n\n inputs = [\n DropdownInput(\n name=\"agent_llm\",\n display_name=\"Model Provider\",\n info=\"The provider of the language model that the agent will use to generate responses.\",\n options=[*MODEL_PROVIDERS_LIST],\n value=\"OpenAI\",\n real_time_refresh=True,\n refresh_button=False,\n input_types=[],\n options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST if key in MODELS_METADATA],\n external_options={\n \"fields\": {\n \"data\": {\n \"node\": {\n \"name\": \"connect_other_models\",\n \"display_name\": \"Connect other models\",\n \"icon\": \"CornerDownLeft\",\n }\n }\n },\n },\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"The API key to use for the model.\",\n required=True,\n ),\n StrInput(\n name=\"base_url\",\n display_name=\"Base URL\",\n info=\"The base URL of the API.\",\n required=True,\n show=False,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"Project ID\",\n info=\"The project ID of the model.\",\n required=True,\n show=False,\n ),\n IntInput(\n name=\"max_output_tokens\",\n display_name=\"Max Output Tokens\",\n info=\"The maximum number of tokens to generate.\",\n show=False,\n ),\n *openai_inputs_filtered,\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n ]\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n llm_model, display_name = await self.get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n self.model_name = get_model_name(llm_model, display_name=display_name)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n self.tools.append(current_date_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self.system_prompt,\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n def _preprocess_schema(self, schema):\n \"\"\"Preprocess schema to ensure correct data types for build_model_from_schema.\"\"\"\n processed_schema = []\n for field in schema:\n processed_field = {\n \"name\": str(field.get(\"name\", \"field\")),\n \"type\": str(field.get(\"type\", \"str\")),\n \"description\": str(field.get(\"description\", \"\")),\n \"multiple\": field.get(\"multiple\", False),\n }\n # Ensure multiple is handled correctly\n if isinstance(processed_field[\"multiple\"], str):\n processed_field[\"multiple\"] = processed_field[\"multiple\"].lower() in [\n \"true\",\n \"1\",\n \"t\",\n \"y\",\n \"yes\",\n ]\n processed_schema.append(processed_field)\n return processed_schema\n\n async def build_structured_output_base(self, content: str):\n \"\"\"Build structured output with optional BaseModel validation.\"\"\"\n json_pattern = r\"\\{.*\\}\"\n schema_error_msg = \"Try setting an output schema\"\n\n # Try to parse content as JSON first\n json_data = None\n try:\n json_data = json.loads(content)\n except json.JSONDecodeError:\n json_match = re.search(json_pattern, content, re.DOTALL)\n if json_match:\n try:\n json_data = json.loads(json_match.group())\n except json.JSONDecodeError:\n return {\"content\": content, \"error\": schema_error_msg}\n else:\n return {\"content\": content, \"error\": schema_error_msg}\n\n # If no output schema provided, return parsed JSON without validation\n if not hasattr(self, \"output_schema\") or not self.output_schema or len(self.output_schema) == 0:\n return json_data\n\n # Use BaseModel validation with schema\n try:\n processed_schema = self._preprocess_schema(self.output_schema)\n output_model = build_model_from_schema(processed_schema)\n\n # Validate against the schema\n if isinstance(json_data, list):\n # Multiple objects\n validated_objects = []\n for item in json_data:\n try:\n validated_obj = output_model.model_validate(item)\n validated_objects.append(validated_obj.model_dump())\n except ValidationError as e:\n await logger.aerror(f\"Validation error for item: {e}\")\n # Include invalid items with error info\n validated_objects.append({\"data\": item, \"validation_error\": str(e)})\n return validated_objects\n\n # Single object\n try:\n validated_obj = output_model.model_validate(json_data)\n return [validated_obj.model_dump()] # Return as list for consistency\n except ValidationError as e:\n await logger.aerror(f\"Validation error: {e}\")\n return [{\"data\": json_data, \"validation_error\": str(e)}]\n\n except (TypeError, ValueError) as e:\n await logger.aerror(f\"Error building structured output: {e}\")\n # Fallback to parsed JSON without validation\n return json_data\n\n async def json_response(self) -> Data:\n \"\"\"Convert agent response to structured JSON Data output with schema validation.\"\"\"\n # Always use structured chat agent for JSON response mode for better JSON formatting\n try:\n system_components = []\n\n # 1. Agent Instructions (system_prompt)\n agent_instructions = getattr(self, \"system_prompt\", \"\") or \"\"\n if agent_instructions:\n system_components.append(f\"{agent_instructions}\")\n\n # 2. Format Instructions\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n if format_instructions:\n system_components.append(f\"Format instructions: {format_instructions}\")\n\n # 3. Schema Information from BaseModel\n if hasattr(self, \"output_schema\") and self.output_schema and len(self.output_schema) > 0:\n try:\n processed_schema = self._preprocess_schema(self.output_schema)\n output_model = build_model_from_schema(processed_schema)\n schema_dict = output_model.model_json_schema()\n schema_info = (\n \"You are given some text that may include format instructions, \"\n \"explanations, or other content alongside a JSON schema.\\n\\n\"\n \"Your task:\\n\"\n \"- Extract only the JSON schema.\\n\"\n \"- Return it as valid JSON.\\n\"\n \"- Do not include format instructions, explanations, or extra text.\\n\\n\"\n \"Input:\\n\"\n f\"{json.dumps(schema_dict, indent=2)}\\n\\n\"\n \"Output (only JSON schema):\"\n )\n system_components.append(schema_info)\n except (ValidationError, ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"Could not build schema for prompt: {e}\", exc_info=True)\n\n # Combine all components\n combined_instructions = \"\\n\\n\".join(system_components) if system_components else \"\"\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=combined_instructions,\n )\n\n # Create and run structured chat agent\n try:\n structured_agent = self.create_agent_runnable()\n except (NotImplementedError, ValueError, TypeError) as e:\n await logger.aerror(f\"Error with structured chat agent: {e}\")\n raise\n try:\n result = await self.run_agent(structured_agent)\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n RuntimeError,\n ) as e:\n await logger.aerror(f\"Error with structured agent result: {e}\")\n raise\n # Extract content from structured agent result\n if hasattr(result, \"content\"):\n content = result.content\n elif hasattr(result, \"text\"):\n content = result.text\n else:\n content = str(result)\n\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as e:\n await logger.aerror(f\"Error with structured chat agent: {e}\")\n # Fallback to regular agent\n content_str = \"No content returned from agent\"\n return Data(data={\"content\": content_str, \"error\": str(e)})\n\n # Process with structured output validation\n try:\n structured_output = await self.build_structured_output_base(content)\n\n # Handle different output formats\n if isinstance(structured_output, list) and structured_output:\n if len(structured_output) == 1:\n return Data(data=structured_output[0])\n return Data(data={\"results\": structured_output})\n if isinstance(structured_output, dict):\n return Data(data=structured_output)\n return Data(data={\"content\": content})\n\n except (ValueError, TypeError) as e:\n await logger.aerror(f\"Error in structured output processing: {e}\")\n return Data(data={\"content\": content, \"error\": str(e)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n async def get_llm(self):\n if not isinstance(self.agent_llm, str):\n return self.agent_llm, None\n\n try:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if not provider_info:\n msg = f\"Invalid model provider: {self.agent_llm}\"\n raise ValueError(msg)\n\n component_class = provider_info.get(\"component_class\")\n display_name = component_class.display_name\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\", \"\")\n\n return self._build_llm_model(component_class, inputs, prefix), display_name\n\n except (AttributeError, ValueError, TypeError, RuntimeError) as e:\n await logger.aerror(f\"Error building {self.agent_llm} language model: {e!s}\")\n msg = f\"Failed to initialize language model: {e!s}\"\n raise ValueError(msg) from e\n\n def _build_llm_model(self, component, inputs, prefix=\"\"):\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n return component.set(**model_kwargs).build_model()\n\n def set_component_params(self, component):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\")\n # Filter out json_mode and only use attributes that exist on this component\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n\n return component.set(**model_kwargs)\n return component\n\n def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n \"\"\"Delete specified fields from build_config.\"\"\"\n for field in fields:\n if build_config is not None and field in build_config:\n build_config.pop(field, None)\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self, build_config: dotdict, field_value: str, field_name: str | None = None\n ) -> dotdict:\n # Iterate over all providers in the MODEL_PROVIDERS_DICT\n # Existing logic for updating build_config\n if field_name in (\"agent_llm\",):\n build_config[\"agent_llm\"][\"value\"] = field_value\n provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call the component class's update_build_config method\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n\n provider_configs: dict[str, tuple[dict, list[dict]]] = {\n provider: (\n MODEL_PROVIDERS_DICT[provider][\"fields\"],\n [\n MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n for other_provider in MODEL_PROVIDERS_DICT\n if other_provider != provider\n ],\n )\n for provider in MODEL_PROVIDERS_DICT\n }\n if field_value in provider_configs:\n fields_to_add, fields_to_delete = provider_configs[field_value]\n\n # Delete fields from other providers\n for fields in fields_to_delete:\n self.delete_fields(build_config, fields)\n\n # Add provider-specific fields\n if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n build_config.update(fields_to_add)\n else:\n build_config.update(fields_to_add)\n # Reset input types for agent_llm\n build_config[\"agent_llm\"][\"input_types\"] = []\n build_config[\"agent_llm\"][\"display_name\"] = \"Model Provider\"\n elif field_value == \"connect_other_models\":\n # Delete all provider fields\n self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n # # Update with custom component\n custom_component = DropdownInput(\n name=\"agent_llm\",\n display_name=\"Language Model\",\n info=\"The provider of the language model that the agent will use to generate responses.\",\n options=[*MODEL_PROVIDERS_LIST],\n real_time_refresh=True,\n refresh_button=False,\n input_types=[\"LanguageModel\"],\n placeholder=\"Awaiting model input.\",\n options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST if key in MODELS_METADATA],\n external_options={\n \"fields\": {\n \"data\": {\n \"node\": {\n \"name\": \"connect_other_models\",\n \"display_name\": \"Connect other models\",\n \"icon\": \"CornerDownLeft\",\n },\n }\n },\n },\n )\n build_config.update({\"agent_llm\": custom_component.to_dict()})\n # Update input types for all fields\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"agent_llm\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n if (\n isinstance(self.agent_llm, str)\n and self.agent_llm in MODEL_PROVIDERS_DICT\n and field_name in MODEL_DYNAMIC_UPDATE_FIELDS\n ):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n component_class = self.set_component_params(component_class)\n prefix = provider_info.get(\"prefix\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call each component class's update_build_config method\n # remove the prefix from the field_name\n if isinstance(field_name, str) and isinstance(prefix, str):\n field_name = field_name.replace(prefix, \"\")\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" + "value": "import json\nimport re\n\nfrom langchain_core.tools import StructuredTool, Tool\nfrom pydantic import ValidationError\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.models.model_input_constants import (\n ALL_PROVIDER_FIELDS,\n MODEL_DYNAMIC_UPDATE_FIELDS,\n MODEL_PROVIDERS_DICT,\n MODEL_PROVIDERS_LIST,\n MODELS_METADATA,\n)\nfrom lfx.base.models.model_utils import get_model_name\nfrom lfx.components.helpers import CurrentDateComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.components.models_and_agents.memory import MemoryComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.custom.utils import update_component_build_config\nfrom lfx.helpers.base_model import build_model_from_schema\nfrom lfx.inputs.inputs import BoolInput, SecretStrInput, StrInput\nfrom lfx.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n display_name: str = \"Agent\"\n description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n documentation: str = \"https://docs.langflow.org/agents\"\n icon = \"bot\"\n beta = False\n name = \"Agent\"\n\n memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n # Filter out json_mode from OpenAI inputs since we handle structured output differently\n if \"OpenAI\" in MODEL_PROVIDERS_DICT:\n openai_inputs_filtered = [\n input_field\n for input_field in MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"]\n if not (hasattr(input_field, \"name\") and input_field.name == \"json_mode\")\n ]\n else:\n openai_inputs_filtered = []\n\n inputs = [\n DropdownInput(\n name=\"agent_llm\",\n display_name=\"Model Provider\",\n info=\"The provider of the language model that the agent will use to generate responses.\",\n options=[*MODEL_PROVIDERS_LIST],\n value=\"OpenAI\",\n real_time_refresh=True,\n refresh_button=False,\n input_types=[],\n options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST if key in MODELS_METADATA],\n external_options={\n \"fields\": {\n \"data\": {\n \"node\": {\n \"name\": \"connect_other_models\",\n \"display_name\": \"Connect other models\",\n \"icon\": \"CornerDownLeft\",\n }\n }\n },\n },\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"The API key to use for the model.\",\n required=True,\n ),\n StrInput(\n name=\"base_url\",\n display_name=\"Base URL\",\n info=\"The base URL of the API.\",\n required=True,\n show=False,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"Project ID\",\n info=\"The project ID of the model.\",\n required=True,\n show=False,\n ),\n IntInput(\n name=\"max_output_tokens\",\n display_name=\"Max Output Tokens\",\n info=\"The maximum number of tokens to generate.\",\n show=False,\n ),\n *openai_inputs_filtered,\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n advanced=False,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent.get_base_inputs(),\n # removed memory inputs from agent component\n # *memory_inputs,\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n ]\n outputs = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n ]\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n llm_model, display_name = await self.get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n self.model_name = get_model_name(llm_model, display_name=display_name)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n await logger.adebug(f\"Retrieved {len(self.chat_history)} chat history messages\")\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n self.tools.append(current_date_tool)\n\n # Set shared callbacks for tracing the tools used by the agent\n self.set_tools_callbacks(self.tools, self._get_shared_callbacks())\n\n return llm_model, self.chat_history, self.tools\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=self.system_prompt,\n )\n agent = self.create_agent_runnable()\n result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n def _preprocess_schema(self, schema):\n \"\"\"Preprocess schema to ensure correct data types for build_model_from_schema.\"\"\"\n processed_schema = []\n for field in schema:\n processed_field = {\n \"name\": str(field.get(\"name\", \"field\")),\n \"type\": str(field.get(\"type\", \"str\")),\n \"description\": str(field.get(\"description\", \"\")),\n \"multiple\": field.get(\"multiple\", False),\n }\n # Ensure multiple is handled correctly\n if isinstance(processed_field[\"multiple\"], str):\n processed_field[\"multiple\"] = processed_field[\"multiple\"].lower() in [\n \"true\",\n \"1\",\n \"t\",\n \"y\",\n \"yes\",\n ]\n processed_schema.append(processed_field)\n return processed_schema\n\n async def build_structured_output_base(self, content: str):\n \"\"\"Build structured output with optional BaseModel validation.\"\"\"\n json_pattern = r\"\\{.*\\}\"\n schema_error_msg = \"Try setting an output schema\"\n\n # Try to parse content as JSON first\n json_data = None\n try:\n json_data = json.loads(content)\n except json.JSONDecodeError:\n json_match = re.search(json_pattern, content, re.DOTALL)\n if json_match:\n try:\n json_data = json.loads(json_match.group())\n except json.JSONDecodeError:\n return {\"content\": content, \"error\": schema_error_msg}\n else:\n return {\"content\": content, \"error\": schema_error_msg}\n\n # If no output schema provided, return parsed JSON without validation\n if not hasattr(self, \"output_schema\") or not self.output_schema or len(self.output_schema) == 0:\n return json_data\n\n # Use BaseModel validation with schema\n try:\n processed_schema = self._preprocess_schema(self.output_schema)\n output_model = build_model_from_schema(processed_schema)\n\n # Validate against the schema\n if isinstance(json_data, list):\n # Multiple objects\n validated_objects = []\n for item in json_data:\n try:\n validated_obj = output_model.model_validate(item)\n validated_objects.append(validated_obj.model_dump())\n except ValidationError as e:\n await logger.aerror(f\"Validation error for item: {e}\")\n # Include invalid items with error info\n validated_objects.append({\"data\": item, \"validation_error\": str(e)})\n return validated_objects\n\n # Single object\n try:\n validated_obj = output_model.model_validate(json_data)\n return [validated_obj.model_dump()] # Return as list for consistency\n except ValidationError as e:\n await logger.aerror(f\"Validation error: {e}\")\n return [{\"data\": json_data, \"validation_error\": str(e)}]\n\n except (TypeError, ValueError) as e:\n await logger.aerror(f\"Error building structured output: {e}\")\n # Fallback to parsed JSON without validation\n return json_data\n\n async def json_response(self) -> Data:\n \"\"\"Convert agent response to structured JSON Data output with schema validation.\"\"\"\n # Always use structured chat agent for JSON response mode for better JSON formatting\n try:\n system_components = []\n\n # 1. Agent Instructions (system_prompt)\n agent_instructions = getattr(self, \"system_prompt\", \"\") or \"\"\n if agent_instructions:\n system_components.append(f\"{agent_instructions}\")\n\n # 2. Format Instructions\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n if format_instructions:\n system_components.append(f\"Format instructions: {format_instructions}\")\n\n # 3. Schema Information from BaseModel\n if hasattr(self, \"output_schema\") and self.output_schema and len(self.output_schema) > 0:\n try:\n processed_schema = self._preprocess_schema(self.output_schema)\n output_model = build_model_from_schema(processed_schema)\n schema_dict = output_model.model_json_schema()\n schema_info = (\n \"You are given some text that may include format instructions, \"\n \"explanations, or other content alongside a JSON schema.\\n\\n\"\n \"Your task:\\n\"\n \"- Extract only the JSON schema.\\n\"\n \"- Return it as valid JSON.\\n\"\n \"- Do not include format instructions, explanations, or extra text.\\n\\n\"\n \"Input:\\n\"\n f\"{json.dumps(schema_dict, indent=2)}\\n\\n\"\n \"Output (only JSON schema):\"\n )\n system_components.append(schema_info)\n except (ValidationError, ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"Could not build schema for prompt: {e}\", exc_info=True)\n\n # Combine all components\n combined_instructions = \"\\n\\n\".join(system_components) if system_components else \"\"\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=combined_instructions,\n )\n\n # Create and run structured chat agent\n try:\n structured_agent = self.create_agent_runnable()\n except (NotImplementedError, ValueError, TypeError) as e:\n await logger.aerror(f\"Error with structured chat agent: {e}\")\n raise\n try:\n result = await self.run_agent(structured_agent)\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n RuntimeError,\n ) as e:\n await logger.aerror(f\"Error with structured agent result: {e}\")\n raise\n # Extract content from structured agent result\n if hasattr(result, \"content\"):\n content = result.content\n elif hasattr(result, \"text\"):\n content = result.text\n else:\n content = str(result)\n\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as e:\n await logger.aerror(f\"Error with structured chat agent: {e}\")\n # Fallback to regular agent\n content_str = \"No content returned from agent\"\n return Data(data={\"content\": content_str, \"error\": str(e)})\n\n # Process with structured output validation\n try:\n structured_output = await self.build_structured_output_base(content)\n\n # Handle different output formats\n if isinstance(structured_output, list) and structured_output:\n if len(structured_output) == 1:\n return Data(data=structured_output[0])\n return Data(data={\"results\": structured_output})\n if isinstance(structured_output, dict):\n return Data(data=structured_output)\n return Data(data={\"content\": content})\n\n except (ValueError, TypeError) as e:\n await logger.aerror(f\"Error in structured output processing: {e}\")\n return Data(data={\"content\": content, \"error\": str(e)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n context_id=self.context_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n async def get_llm(self):\n if not isinstance(self.agent_llm, str):\n return self.agent_llm, None\n\n try:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if not provider_info:\n msg = f\"Invalid model provider: {self.agent_llm}\"\n raise ValueError(msg)\n\n component_class = provider_info.get(\"component_class\")\n display_name = component_class.display_name\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\", \"\")\n\n return self._build_llm_model(component_class, inputs, prefix), display_name\n\n except (AttributeError, ValueError, TypeError, RuntimeError) as e:\n await logger.aerror(f\"Error building {self.agent_llm} language model: {e!s}\")\n msg = f\"Failed to initialize language model: {e!s}\"\n raise ValueError(msg) from e\n\n def _build_llm_model(self, component, inputs, prefix=\"\"):\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n return component.set(**model_kwargs).build_model()\n\n def set_component_params(self, component):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\")\n # Filter out json_mode and only use attributes that exist on this component\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n\n return component.set(**model_kwargs)\n return component\n\n def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n \"\"\"Delete specified fields from build_config.\"\"\"\n for field in fields:\n if build_config is not None and field in build_config:\n build_config.pop(field, None)\n\n def update_input_types(self, build_config: dotdict) -> dotdict:\n \"\"\"Update input types for all fields in build_config.\"\"\"\n for key, value in build_config.items():\n if isinstance(value, dict):\n if value.get(\"input_types\") is None:\n build_config[key][\"input_types\"] = []\n elif hasattr(value, \"input_types\") and value.input_types is None:\n value.input_types = []\n return build_config\n\n async def update_build_config(\n self, build_config: dotdict, field_value: str, field_name: str | None = None\n ) -> dotdict:\n # Iterate over all providers in the MODEL_PROVIDERS_DICT\n # Existing logic for updating build_config\n if field_name in (\"agent_llm\",):\n build_config[\"agent_llm\"][\"value\"] = field_value\n provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call the component class's update_build_config method\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n\n provider_configs: dict[str, tuple[dict, list[dict]]] = {\n provider: (\n MODEL_PROVIDERS_DICT[provider][\"fields\"],\n [\n MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n for other_provider in MODEL_PROVIDERS_DICT\n if other_provider != provider\n ],\n )\n for provider in MODEL_PROVIDERS_DICT\n }\n if field_value in provider_configs:\n fields_to_add, fields_to_delete = provider_configs[field_value]\n\n # Delete fields from other providers\n for fields in fields_to_delete:\n self.delete_fields(build_config, fields)\n\n # Add provider-specific fields\n if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n build_config.update(fields_to_add)\n else:\n build_config.update(fields_to_add)\n # Reset input types for agent_llm\n build_config[\"agent_llm\"][\"input_types\"] = []\n build_config[\"agent_llm\"][\"display_name\"] = \"Model Provider\"\n elif field_value == \"connect_other_models\":\n # Delete all provider fields\n self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n # # Update with custom component\n custom_component = DropdownInput(\n name=\"agent_llm\",\n display_name=\"Language Model\",\n info=\"The provider of the language model that the agent will use to generate responses.\",\n options=[*MODEL_PROVIDERS_LIST],\n real_time_refresh=True,\n refresh_button=False,\n input_types=[\"LanguageModel\"],\n placeholder=\"Awaiting model input.\",\n options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST if key in MODELS_METADATA],\n external_options={\n \"fields\": {\n \"data\": {\n \"node\": {\n \"name\": \"connect_other_models\",\n \"display_name\": \"Connect other models\",\n \"icon\": \"CornerDownLeft\",\n },\n }\n },\n },\n )\n build_config.update({\"agent_llm\": custom_component.to_dict()})\n # Update input types for all fields\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"agent_llm\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n if (\n isinstance(self.agent_llm, str)\n and self.agent_llm in MODEL_PROVIDERS_DICT\n and field_name in MODEL_DYNAMIC_UPDATE_FIELDS\n ):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n component_class = self.set_component_params(component_class)\n prefix = provider_info.get(\"prefix\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call each component class's update_build_config method\n # remove the prefix from the field_name\n if isinstance(field_name, str) and isinstance(prefix, str):\n field_name = field_name.replace(prefix, \"\")\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n # here we do not use the shared callbacks as we are exposing the agent as a tool\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n\n return tools\n" }, "context_id": { "_input_type": "MessageTextInput", @@ -2468,6 +2539,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -2493,6 +2565,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "You are an AI that extracts structured JSON objects from unstructured text. Use a predefined schema with expected types (str, int, float, bool, dict). Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. Fill missing or ambiguous values with defaults: null for missing values. Remove exact duplicates but keep variations that have different field values. Always return valid JSON in the expected format, never throw errors. If multiple objects can be extracted, return them all in the structured format." }, @@ -2511,6 +2584,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "bool", "value": true }, @@ -2534,6 +2608,7 @@ "tool_mode": true, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -2552,6 +2627,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": 15 }, @@ -2570,6 +2646,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": "" }, @@ -2588,6 +2665,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": 5 }, @@ -2612,6 +2690,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": "" }, @@ -2630,6 +2709,7 @@ "title_case": false, "tool_mode": false, "trace_as_input": true, + "track_in_telemetry": false, "type": "dict", "value": {} }, @@ -2642,9 +2722,29 @@ "dynamic": false, "external_options": {}, "info": "To see the model names, first choose a provider. Then, enter your API key and click the refresh button next to the model name.", + "load_from_db": false, "name": "model_name", "options": [ - "gpt-4o" + "gpt-4o-mini", + "gpt-4o", + "gpt-4.1", + "gpt-4.1-mini", + "gpt-4.1-nano", + "gpt-4-turbo", + "gpt-4-turbo-preview", + "gpt-4", + "gpt-3.5-turbo", + "gpt-5.1", + "gpt-5", + "gpt-5-mini", + "gpt-5-nano", + "gpt-5-chat-latest", + "o1", + "o3-mini", + "o3", + "o3-pro", + "o4-mini", + "o4-mini-high" ], "options_metadata": [], "placeholder": "", @@ -2655,6 +2755,7 @@ "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "gpt-4o" }, @@ -2673,6 +2774,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": 100 }, @@ -2692,6 +2794,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -2752,6 +2855,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": false, "trigger_icon": "Table", "trigger_text": "Open table", "type": "table", @@ -2773,6 +2877,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -2791,6 +2896,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": 1 }, @@ -2816,6 +2922,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "You are the OpenRAG Agent. You answer questions using retrieval, reasoning, and tool use.\nYou have access to several tools. Your job is to determine **which tool to use and when**.\n### Available Tools\n- OpenSearch Retrieval Tool:\n Use this to search the indexed knowledge base. Use when the user asks about product details, internal concepts, processes, architecture, documentation, roadmaps, or anything that may be stored in the index.\n- Conversation History:\n Use this to maintain continuity when the user is referring to previous turns. \n Do not treat history as a factual source.\n- Conversation File Context:\n Use this when the user asks about a document they uploaded or refers directly to its contents.\n- URL Ingestion Tool:\n Use this **only** when the user explicitly asks you to read, summarize, or analyze the content of a URL.\n Do not ingest URLs automatically.\n- Calculator / Expression Evaluation Tool:\n Use this when the user asks to compare numbers, compute estimates, calculate totals, analyze pricing, or answer any question requiring mathematics or quantitative reasoning.\n If the answer requires arithmetic, call the calculator tool rather than calculating internally.\n### Retrieval Decision Rules\nUse OpenSearch **whenever**:\n1. The question may be answered from internal or indexed data.\n2. The user references team names, product names, release plans, configurations, requirements, or official information.\n3. The user needs a factual, grounded answer.\nDo **not** use retrieval if:\n- The question is purely creative (e.g., storytelling, analogies) or personal preference.\n- The user simply wants text reformatted or rewritten from what is already present in the conversation.\nWhen uncertain → **Retrieve.** Retrieval is low risk and improves grounding.\n### URL Ingestion Rules\nOnly ingest URLs when the user explicitly says:\n- \"Read this link\"\n- \"Summarize this webpage\"\n- \"What does this site say?\"\n- \"Ingest this URL\"\nIf unclear → ask a clarifying question.\n### Calculator Usage Rules\nUse the calculator when:\n- Performing arithmetic\n- Estimating totals\n- Comparing values\n- Modeling cost, time, effort, scale, or projections\nDo not perform math internally. **Call the calculator tool instead.**\n### Answer Construction Rules\n1. When asked: \"What is OpenRAG\", answer the following:\n\"OpenRAG is an open-source package for building agentic RAG systems. It supports integration with a wide range of orchestration tools, vector databases, and LLM providers. OpenRAG connects and amplifies three popular, proven open-source projects into one powerful platform:\n**Langflow** – Langflow is a powerful tool to build and deploy AI agents and MCP servers. [Read more](https://www.langflow.org/)\n**OpenSearch** – OpenSearch is an open source, search and observability suite that brings order to unstructured data at scale. [Read more](https://opensearch.org/)\n**Docling** – Docling simplifies document processing with advanced PDF understanding, OCR support, and seamless AI integrations. Parse PDFs, DOCX, PPTX, images & more. [Read more](https://www.docling.ai/)\"\n2. Synthesize retrieved or ingested content in your own words.\n3. Support factual claims with citations in the format:\n (Source: )\n4. If no supporting evidence is found:\n Say: \"No relevant supporting sources were found for that request.\"\n5. Never invent facts or hallucinate details.\n6. Be concise, direct, and confident. \n7. Do not reveal internal chain-of-thought." }, @@ -2844,6 +2951,7 @@ "slider_input": false, "title_case": false, "tool_mode": false, + "track_in_telemetry": false, "type": "slider", "value": 0.1 }, @@ -2862,6 +2970,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": 700 }, @@ -2882,6 +2991,7 @@ "show": true, "title_case": false, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "other", "value": "" }, @@ -2900,6 +3010,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "bool", "value": true } @@ -2919,7 +3030,7 @@ "x": 1629.578423203229, "y": 451.946400444934 }, - "selected": true, + "selected": false, "type": "genericNode" }, { @@ -2941,7 +3052,7 @@ ], "frozen": false, "icon": "calculator", - "last_updated": "2025-11-11T21:40:50.133Z", + "last_updated": "2025-11-24T18:02:41.468Z", "legacy": false, "metadata": { "code_hash": "5fcfa26be77d", @@ -3037,6 +3148,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "tools", "value": [ { @@ -3074,24 +3186,24 @@ "width": 320 }, "position": { - "x": 720.5458956206228, - "y": 1220.8583072055585 + "x": 740.6385299138288, + "y": 1287.3185590984713 }, "selected": false, "type": "genericNode" } ], "viewport": { - "x": -37.553133726465376, - "y": 28.26764925979927, - "zoom": 0.6144649479685779 + "x": 91.72060325190853, + "y": -32.331644762411656, + "zoom": 0.6206869895512208 } }, "description": "OpenRAG OpenSearch Agent", "endpoint_name": null, "id": "1098eea1-6649-4e1d-aed1-b77249fb8dd0", "is_component": false, - "last_tested_version": "1.7.0", + "last_tested_version": "1.7.0.dev19", "name": "OpenRAG OpenSearch Agent", "tags": [ "assistants", diff --git a/flows/openrag_nudges.json b/flows/openrag_nudges.json index 5bbfef8d..702353a3 100644 --- a/flows/openrag_nudges.json +++ b/flows/openrag_nudges.json @@ -174,6 +174,7 @@ }, { "animated": false, + "className": "", "data": { "sourceHandle": { "dataType": "TextInput", @@ -1784,23 +1785,32 @@ "request_timeout", "max_retries", "show_progress_bar", - "model_kwargs" + "model_kwargs", + "truncate_input_tokens", + "input_text" ], "frozen": false, "icon": "binary", - "last_updated": "2025-11-11T21:49:56.409Z", "legacy": false, "metadata": { - "code_hash": "bb03f97be707", + "code_hash": "c5e0a4535a27", "dependencies": { "dependencies": [ + { + "name": "requests", + "version": "2.32.5" + }, + { + "name": "ibm_watsonx_ai", + "version": "1.4.2" + }, { "name": "langchain_openai", "version": "0.3.23" }, { "name": "lfx", - "version": null + "version": "0.2.0.dev19" }, { "name": "langchain_ollama", @@ -1815,9 +1825,9 @@ "version": "0.3.19" } ], - "total_dependencies": 5 + "total_dependencies": 7 }, - "module": "lfx.components.models.embedding_model.EmbeddingModelComponent" + "module": "custom_components.embedding_model" }, "minimized": false, "output_types": [], @@ -1844,7 +1854,7 @@ "_type": "Component", "api_base": { "_input_type": "MessageTextInput", - "advanced": false, + "advanced": true, "display_name": "API Base URL", "dynamic": false, "info": "Base URL for the API. Leave empty for default.", @@ -1862,6 +1872,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -1880,6 +1891,7 @@ "required": true, "show": true, "title_case": false, + "track_in_telemetry": false, "type": "str", "value": "OPENAI_API_KEY" }, @@ -1910,6 +1922,7 @@ "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "https://us-south.ml.cloud.ibm.com" }, @@ -1928,6 +1941,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": 1000 }, @@ -1947,7 +1961,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.model_utils import get_ollama_models, is_valid_ollama_url\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS, WATSONX_EMBEDDING_MODEL_NAMES\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageTextInput,\n SecretStrInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.utils.util import transform_localhost_url\n\n# Ollama API constants\nHTTP_STATUS_OK = 200\nJSON_MODELS_KEY = \"models\"\nJSON_NAME_KEY = \"name\"\nJSON_CAPABILITIES_KEY = \"capabilities\"\nDESIRED_CAPABILITY = \"embedding\"\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n display_name = \"Embedding Model\"\n description = \"Generate embeddings using a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n icon = \"binary\"\n name = \"EmbeddingModel\"\n category = \"models\"\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\", \"Ollama\", \"IBM watsonx.ai\"],\n value=\"OpenAI\",\n info=\"Select the embedding model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Ollama\"}, {\"icon\": \"WatsonxAI\"}],\n ),\n MessageTextInput(\n name=\"api_base\",\n display_name=\"API Base URL\",\n info=\"Base URL for the API. Leave empty for default.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"ollama_base_url\",\n display_name=\"Ollama API URL\",\n info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n value=DEFAULT_OLLAMA_URL,\n show=False,\n real_time_refresh=True,\n load_from_db=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n show=False,\n real_time_refresh=True,\n ),\n DropdownInput(\n name=\"model\",\n display_name=\"Model Name\",\n options=OPENAI_EMBEDDING_MODEL_NAMES,\n value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n info=\"Select the embedding model to use\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=True,\n show=True,\n real_time_refresh=True,\n ),\n # Watson-specific inputs\n MessageTextInput(\n name=\"project_id\",\n display_name=\"Project ID\",\n info=\"IBM watsonx.ai Project ID (required for IBM watsonx.ai)\",\n show=False,\n ),\n IntInput(\n name=\"dimensions\",\n display_name=\"Dimensions\",\n info=\"The number of dimensions the resulting output embeddings should have. \"\n \"Only supported by certain models.\",\n advanced=True,\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n DictInput(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\n ]\n\n def build_embeddings(self) -> Embeddings:\n provider = self.provider\n model = self.model\n api_key = self.api_key\n api_base = self.api_base\n base_url_ibm_watsonx = self.base_url_ibm_watsonx\n ollama_base_url = self.ollama_base_url\n dimensions = self.dimensions\n chunk_size = self.chunk_size\n request_timeout = self.request_timeout\n max_retries = self.max_retries\n show_progress_bar = self.show_progress_bar\n model_kwargs = self.model_kwargs or {}\n\n if provider == \"OpenAI\":\n if not api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n return OpenAIEmbeddings(\n model=model,\n dimensions=dimensions or None,\n base_url=api_base or None,\n api_key=api_key,\n chunk_size=chunk_size,\n max_retries=max_retries,\n timeout=request_timeout or None,\n show_progress_bar=show_progress_bar,\n model_kwargs=model_kwargs,\n )\n\n if provider == \"Ollama\":\n try:\n from langchain_ollama import OllamaEmbeddings\n except ImportError:\n try:\n from langchain_community.embeddings import OllamaEmbeddings\n except ImportError:\n msg = \"Please install langchain-ollama: pip install langchain-ollama\"\n raise ImportError(msg) from None\n\n transformed_base_url = transform_localhost_url(ollama_base_url)\n\n # Check if URL contains /v1 suffix (OpenAI-compatible mode)\n if transformed_base_url and transformed_base_url.rstrip(\"/\").endswith(\"/v1\"):\n # Strip /v1 suffix and log warning\n transformed_base_url = transformed_base_url.rstrip(\"/\").removesuffix(\"/v1\")\n logger.warning(\n \"Detected '/v1' suffix in base URL. The Ollama component uses the native Ollama API, \"\n \"not the OpenAI-compatible API. The '/v1' suffix has been automatically removed. \"\n \"If you want to use the OpenAI-compatible API, please use the OpenAI component instead. \"\n \"Learn more at https://docs.ollama.com/openai#openai-compatibility\"\n )\n\n return OllamaEmbeddings(\n model=model,\n base_url=transformed_base_url or \"http://localhost:11434\",\n **model_kwargs,\n )\n\n if provider == \"IBM watsonx.ai\":\n try:\n from langchain_ibm import WatsonxEmbeddings\n except ImportError:\n msg = \"Please install langchain-ibm: pip install langchain-ibm\"\n raise ImportError(msg) from None\n\n if not api_key:\n msg = \"IBM watsonx.ai API key is required when using IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n project_id = self.project_id\n\n if not project_id:\n msg = \"Project ID is required for IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n params = {\n \"model_id\": model,\n \"url\": base_url_ibm_watsonx or \"https://us-south.ml.cloud.ibm.com\",\n \"apikey\": api_key,\n }\n\n params[\"project_id\"] = project_id\n\n return WatsonxEmbeddings(**params)\n\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n async def update_build_config(\n self, build_config: dotdict, field_value: Any, field_name: str | None = None\n ) -> dotdict:\n if field_name == \"provider\":\n if field_value == \"OpenAI\":\n build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n build_config[\"api_base\"][\"advanced\"] = True\n build_config[\"api_base\"][\"show\"] = True\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n elif field_value == \"Ollama\":\n build_config[\"ollama_base_url\"][\"show\"] = True\n\n if await is_valid_ollama_url(url=self.ollama_base_url):\n try:\n models = await get_ollama_models(\n base_url_value=self.ollama_base_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n else:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n\n build_config[\"api_key\"][\"display_name\"] = \"API Key (Optional)\"\n build_config[\"api_key\"][\"required\"] = False\n build_config[\"api_key\"][\"show\"] = False\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n elif field_value == \"IBM watsonx.ai\":\n build_config[\"model\"][\"options\"] = WATSONX_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = WATSONX_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"IBM watsonx.ai API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = True\n build_config[\"project_id\"][\"show\"] = True\n\n elif field_name == \"ollama_base_url\":\n # # Refresh Ollama models when base URL changes\n # if hasattr(self, \"provider\") and self.provider == \"Ollama\":\n # Use field_value if provided, otherwise fall back to instance attribute\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n await logger.awarning(\"Failed to fetch Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n\n elif field_name == \"model\" and self.provider == \"Ollama\":\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n except ValueError:\n await logger.awarning(\"Failed to refresh Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n\n return build_config\n" + "value": "from typing import Any\n\nimport requests\nfrom ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.model_utils import get_ollama_models, is_valid_ollama_url\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.base.models.watsonx_constants import (\n IBM_WATSONX_URLS,\n WATSONX_EMBEDDING_MODEL_NAMES,\n)\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageTextInput,\n SecretStrInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.utils.util import transform_localhost_url\n\n# Ollama API constants\nHTTP_STATUS_OK = 200\nJSON_MODELS_KEY = \"models\"\nJSON_NAME_KEY = \"name\"\nJSON_CAPABILITIES_KEY = \"capabilities\"\nDESIRED_CAPABILITY = \"embedding\"\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n display_name = \"Embedding Model\"\n description = \"Generate embeddings using a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n icon = \"binary\"\n name = \"EmbeddingModel\"\n category = \"models\"\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\", \"Ollama\", \"IBM watsonx.ai\"],\n value=\"OpenAI\",\n info=\"Select the embedding model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Ollama\"}, {\"icon\": \"WatsonxAI\"}],\n ),\n MessageTextInput(\n name=\"api_base\",\n display_name=\"API Base URL\",\n info=\"Base URL for the API. Leave empty for default.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"ollama_base_url\",\n display_name=\"Ollama API URL\",\n info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n value=DEFAULT_OLLAMA_URL,\n show=False,\n real_time_refresh=True,\n load_from_db=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n show=False,\n real_time_refresh=True,\n ),\n DropdownInput(\n name=\"model\",\n display_name=\"Model Name\",\n options=OPENAI_EMBEDDING_MODEL_NAMES,\n value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n info=\"Select the embedding model to use\",\n real_time_refresh=True,\n refresh_button=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=True,\n show=True,\n real_time_refresh=True,\n ),\n # Watson-specific inputs\n MessageTextInput(\n name=\"project_id\",\n display_name=\"Project ID\",\n info=\"IBM watsonx.ai Project ID (required for IBM watsonx.ai)\",\n show=False,\n ),\n IntInput(\n name=\"dimensions\",\n display_name=\"Dimensions\",\n info=\"The number of dimensions the resulting output embeddings should have. \"\n \"Only supported by certain models.\",\n advanced=True,\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n DictInput(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\n IntInput(\n name=\"truncate_input_tokens\",\n display_name=\"Truncate Input Tokens\",\n advanced=True,\n value=200,\n show=False,\n ),\n BoolInput(\n name=\"input_text\",\n display_name=\"Include the original text in the output\",\n value=True,\n advanced=True,\n show=False,\n ),\n ]\n\n @staticmethod\n def fetch_ibm_models(base_url: str) -> list[str]:\n \"\"\"Fetch available models from the watsonx.ai API.\"\"\"\n try:\n endpoint = f\"{base_url}/ml/v1/foundation_model_specs\"\n params = {\n \"version\": \"2024-09-16\",\n \"filters\": \"function_embedding,!lifecycle_withdrawn:and\",\n }\n response = requests.get(endpoint, params=params, timeout=10)\n response.raise_for_status()\n data = response.json()\n models = [model[\"model_id\"] for model in data.get(\"resources\", [])]\n return sorted(models)\n except Exception: # noqa: BLE001\n logger.exception(\"Error fetching models\")\n return WATSONX_EMBEDDING_MODEL_NAMES\n\n def build_embeddings(self) -> Embeddings:\n provider = self.provider\n model = self.model\n api_key = self.api_key\n api_base = self.api_base\n base_url_ibm_watsonx = self.base_url_ibm_watsonx\n ollama_base_url = self.ollama_base_url\n dimensions = self.dimensions\n chunk_size = self.chunk_size\n request_timeout = self.request_timeout\n max_retries = self.max_retries\n show_progress_bar = self.show_progress_bar\n model_kwargs = self.model_kwargs or {}\n\n if provider == \"OpenAI\":\n if not api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n return OpenAIEmbeddings(\n model=model,\n dimensions=dimensions or None,\n base_url=api_base or None,\n api_key=api_key,\n chunk_size=chunk_size,\n max_retries=max_retries,\n timeout=request_timeout or None,\n show_progress_bar=show_progress_bar,\n model_kwargs=model_kwargs,\n )\n\n if provider == \"Ollama\":\n try:\n from langchain_ollama import OllamaEmbeddings\n except ImportError:\n try:\n from langchain_community.embeddings import OllamaEmbeddings\n except ImportError:\n msg = \"Please install langchain-ollama: pip install langchain-ollama\"\n raise ImportError(msg) from None\n\n transformed_base_url = transform_localhost_url(ollama_base_url)\n\n # Check if URL contains /v1 suffix (OpenAI-compatible mode)\n if transformed_base_url and transformed_base_url.rstrip(\"/\").endswith(\"/v1\"):\n # Strip /v1 suffix and log warning\n transformed_base_url = transformed_base_url.rstrip(\"/\").removesuffix(\"/v1\")\n logger.warning(\n \"Detected '/v1' suffix in base URL. The Ollama component uses the native Ollama API, \"\n \"not the OpenAI-compatible API. The '/v1' suffix has been automatically removed. \"\n \"If you want to use the OpenAI-compatible API, please use the OpenAI component instead. \"\n \"Learn more at https://docs.ollama.com/openai#openai-compatibility\"\n )\n\n return OllamaEmbeddings(\n model=model,\n base_url=transformed_base_url or \"http://localhost:11434\",\n **model_kwargs,\n )\n\n if provider == \"IBM watsonx.ai\":\n try:\n from langchain_ibm import WatsonxEmbeddings\n except ImportError:\n msg = \"Please install langchain-ibm: pip install langchain-ibm\"\n raise ImportError(msg) from None\n\n if not api_key:\n msg = \"IBM watsonx.ai API key is required when using IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n project_id = self.project_id\n\n if not project_id:\n msg = \"Project ID is required for IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n from ibm_watsonx_ai import APIClient, Credentials\n\n credentials = Credentials(\n api_key=self.api_key,\n url=base_url_ibm_watsonx or \"https://us-south.ml.cloud.ibm.com\",\n )\n\n api_client = APIClient(credentials)\n\n params = {\n EmbedTextParamsMetaNames.TRUNCATE_INPUT_TOKENS: self.truncate_input_tokens,\n EmbedTextParamsMetaNames.RETURN_OPTIONS: {\"input_text\": self.input_text},\n }\n\n return WatsonxEmbeddings(\n model_id=model,\n params=params,\n watsonx_client=api_client,\n project_id=project_id,\n )\n\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n async def update_build_config(\n self, build_config: dotdict, field_value: Any, field_name: str | None = None\n ) -> dotdict:\n if field_name == \"provider\":\n if field_value == \"OpenAI\":\n build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n build_config[\"api_base\"][\"advanced\"] = True\n build_config[\"api_base\"][\"show\"] = True\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n build_config[\"truncate_input_tokens\"][\"show\"] = False\n build_config[\"input_text\"][\"show\"] = False\n elif field_value == \"Ollama\":\n build_config[\"ollama_base_url\"][\"show\"] = True\n\n if await is_valid_ollama_url(url=self.ollama_base_url):\n try:\n models = await get_ollama_models(\n base_url_value=self.ollama_base_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n else:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n build_config[\"truncate_input_tokens\"][\"show\"] = False\n build_config[\"input_text\"][\"show\"] = False\n build_config[\"api_key\"][\"display_name\"] = \"API Key (Optional)\"\n build_config[\"api_key\"][\"required\"] = False\n build_config[\"api_key\"][\"show\"] = False\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n elif field_value == \"IBM watsonx.ai\":\n build_config[\"model\"][\"options\"] = self.fetch_ibm_models(base_url=self.base_url_ibm_watsonx)\n build_config[\"model\"][\"value\"] = self.fetch_ibm_models(base_url=self.base_url_ibm_watsonx)[0]\n build_config[\"api_key\"][\"display_name\"] = \"IBM watsonx.ai API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = True\n build_config[\"project_id\"][\"show\"] = True\n build_config[\"truncate_input_tokens\"][\"show\"] = True\n build_config[\"input_text\"][\"show\"] = True\n elif field_name == \"base_url_ibm_watsonx\":\n build_config[\"model\"][\"options\"] = self.fetch_ibm_models(base_url=field_value)\n build_config[\"model\"][\"value\"] = self.fetch_ibm_models(base_url=field_value)[0]\n elif field_name == \"ollama_base_url\":\n # # Refresh Ollama models when base URL changes\n # if hasattr(self, \"provider\") and self.provider == \"Ollama\":\n # Use field_value if provided, otherwise fall back to instance attribute\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n await logger.awarning(\"Failed to fetch Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n\n elif field_name == \"model\" and self.provider == \"Ollama\":\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n except ValueError:\n await logger.awarning(\"Failed to refresh Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n\n return build_config\n" }, "dimensions": { "_input_type": "IntInput", @@ -1964,9 +1978,29 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": "" }, + "input_text": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Include the original text in the output", + "dynamic": false, + "info": "", + "list": false, + "list_add_label": "Add More", + "name": "input_text", + "placeholder": "", + "required": false, + "show": false, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "bool", + "value": true + }, "max_retries": { "_input_type": "IntInput", "advanced": true, @@ -1982,6 +2016,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": 3 }, @@ -1996,16 +2031,21 @@ "info": "Select the embedding model to use", "name": "model", "options": [ - "text-embedding-3-small" + "text-embedding-3-small", + "text-embedding-3-large", + "text-embedding-ada-002" ], "options_metadata": [], "placeholder": "", + "real_time_refresh": true, + "refresh_button": true, "required": false, "show": true, "title_case": false, "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "text-embedding-3-small" }, @@ -2024,6 +2064,7 @@ "title_case": false, "tool_mode": false, "trace_as_input": true, + "track_in_telemetry": false, "type": "dict", "value": {} }, @@ -2048,6 +2089,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -2071,6 +2113,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -2108,6 +2151,7 @@ "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "OpenAI" }, @@ -2126,6 +2170,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "float", "value": "" }, @@ -2144,8 +2189,28 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "bool", "value": false + }, + "truncate_input_tokens": { + "_input_type": "IntInput", + "advanced": true, + "display_name": "Truncate Input Tokens", + "dynamic": false, + "info": "", + "list": false, + "list_add_label": "Add More", + "name": "truncate_input_tokens", + "placeholder": "", + "required": false, + "show": false, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "int", + "value": 200 } }, "tool_mode": false @@ -2156,7 +2221,7 @@ "dragging": false, "id": "EmbeddingModel-26o1e", "measured": { - "height": 451, + "height": 369, "width": 320 }, "position": { @@ -2195,7 +2260,7 @@ ], "frozen": false, "icon": "brain-circuit", - "last_updated": "2025-11-11T21:51:53.901Z", + "last_updated": "2025-11-24T18:03:45.208Z", "legacy": false, "metadata": { "code_hash": "694ffc4b17b8", @@ -2589,7 +2654,7 @@ "x": 2151.7120459180214, "y": 419.8653051925172 }, - "selected": true, + "selected": false, "type": "genericNode" }, { @@ -2707,16 +2772,16 @@ } ], "viewport": { - "x": -98.41517694131858, - "y": 122.09140842027057, - "zoom": 0.5043780525896302 + "x": -206.89994136968403, + "y": 74.41941219497068, + "zoom": 0.6126010241500153 } }, "description": "OpenRAG OpenSearch Nudges generator, based on the OpenSearch documents and the chat history.", "endpoint_name": null, "id": "ebc01d31-1976-46ce-a385-b0240327226c", "is_component": false, - "last_tested_version": "1.7.0", + "last_tested_version": "1.7.0.dev19", "name": "OpenRAG OpenSearch Nudges", "tags": [ "assistants", diff --git a/flows/openrag_url_mcp.json b/flows/openrag_url_mcp.json index 278ee2c8..0333faf1 100644 --- a/flows/openrag_url_mcp.json +++ b/flows/openrag_url_mcp.json @@ -231,6 +231,8 @@ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-0XHyoœ,œinputTypesœ:[œDataœ,œDataFrameœ,œMessageœ],œtypeœ:œotherœ}" }, { + "animated": false, + "className": "", "data": { "sourceHandle": { "dataType": "EmbeddingModel", @@ -250,6 +252,7 @@ } }, "id": "xy-edge__EmbeddingModel-XjV5v{œdataTypeœ:œEmbeddingModelœ,œidœ:œEmbeddingModel-XjV5vœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}-OpenSearchHybrid-Ve6bS{œfieldNameœ:œembeddingœ,œidœ:œOpenSearchHybrid-Ve6bSœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}", + "selected": false, "source": "EmbeddingModel-XjV5v", "sourceHandle": "{œdataTypeœ:œEmbeddingModelœ,œidœ:œEmbeddingModel-XjV5vœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}", "target": "OpenSearchHybrid-Ve6bS", @@ -1553,7 +1556,7 @@ ], "frozen": false, "icon": "table", - "last_updated": "2025-11-11T15:07:55.352Z", + "last_updated": "2025-11-24T17:58:32.464Z", "legacy": false, "lf_version": "1.6.0", "metadata": { @@ -1969,7 +1972,7 @@ ], "frozen": false, "icon": "table", - "last_updated": "2025-11-11T15:07:55.353Z", + "last_updated": "2025-11-24T17:58:32.465Z", "legacy": false, "lf_version": "1.6.0", "metadata": { @@ -2385,7 +2388,7 @@ ], "frozen": false, "icon": "table", - "last_updated": "2025-11-11T15:07:55.353Z", + "last_updated": "2025-11-24T17:58:32.465Z", "legacy": false, "metadata": { "code_hash": "b4d6b19b6eef", @@ -3355,23 +3358,32 @@ "request_timeout", "max_retries", "show_progress_bar", - "model_kwargs" + "model_kwargs", + "truncate_input_tokens", + "input_text" ], "frozen": false, "icon": "binary", - "last_updated": "2025-11-11T15:08:05.989Z", "legacy": false, "metadata": { - "code_hash": "bb03f97be707", + "code_hash": "c5e0a4535a27", "dependencies": { "dependencies": [ + { + "name": "requests", + "version": "2.32.5" + }, + { + "name": "ibm_watsonx_ai", + "version": "1.4.2" + }, { "name": "langchain_openai", "version": "0.3.23" }, { "name": "lfx", - "version": null + "version": "0.2.0.dev19" }, { "name": "langchain_ollama", @@ -3386,9 +3398,9 @@ "version": "0.3.19" } ], - "total_dependencies": 5 + "total_dependencies": 7 }, - "module": "lfx.components.models.embedding_model.EmbeddingModelComponent" + "module": "custom_components.embedding_model" }, "minimized": false, "output_types": [], @@ -3433,6 +3445,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -3451,6 +3464,7 @@ "required": true, "show": true, "title_case": false, + "track_in_telemetry": false, "type": "str", "value": "OPENAI_API_KEY" }, @@ -3481,6 +3495,7 @@ "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "https://us-south.ml.cloud.ibm.com" }, @@ -3499,6 +3514,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": 1000 }, @@ -3518,7 +3534,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.model_utils import get_ollama_models, is_valid_ollama_url\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS, WATSONX_EMBEDDING_MODEL_NAMES\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageTextInput,\n SecretStrInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.utils.util import transform_localhost_url\n\n# Ollama API constants\nHTTP_STATUS_OK = 200\nJSON_MODELS_KEY = \"models\"\nJSON_NAME_KEY = \"name\"\nJSON_CAPABILITIES_KEY = \"capabilities\"\nDESIRED_CAPABILITY = \"embedding\"\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n display_name = \"Embedding Model\"\n description = \"Generate embeddings using a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n icon = \"binary\"\n name = \"EmbeddingModel\"\n category = \"models\"\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\", \"Ollama\", \"IBM watsonx.ai\"],\n value=\"OpenAI\",\n info=\"Select the embedding model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Ollama\"}, {\"icon\": \"WatsonxAI\"}],\n ),\n MessageTextInput(\n name=\"api_base\",\n display_name=\"API Base URL\",\n info=\"Base URL for the API. Leave empty for default.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"ollama_base_url\",\n display_name=\"Ollama API URL\",\n info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n value=DEFAULT_OLLAMA_URL,\n show=False,\n real_time_refresh=True,\n load_from_db=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n show=False,\n real_time_refresh=True,\n ),\n DropdownInput(\n name=\"model\",\n display_name=\"Model Name\",\n options=OPENAI_EMBEDDING_MODEL_NAMES,\n value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n info=\"Select the embedding model to use\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=True,\n show=True,\n real_time_refresh=True,\n ),\n # Watson-specific inputs\n MessageTextInput(\n name=\"project_id\",\n display_name=\"Project ID\",\n info=\"IBM watsonx.ai Project ID (required for IBM watsonx.ai)\",\n show=False,\n ),\n IntInput(\n name=\"dimensions\",\n display_name=\"Dimensions\",\n info=\"The number of dimensions the resulting output embeddings should have. \"\n \"Only supported by certain models.\",\n advanced=True,\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n DictInput(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\n ]\n\n def build_embeddings(self) -> Embeddings:\n provider = self.provider\n model = self.model\n api_key = self.api_key\n api_base = self.api_base\n base_url_ibm_watsonx = self.base_url_ibm_watsonx\n ollama_base_url = self.ollama_base_url\n dimensions = self.dimensions\n chunk_size = self.chunk_size\n request_timeout = self.request_timeout\n max_retries = self.max_retries\n show_progress_bar = self.show_progress_bar\n model_kwargs = self.model_kwargs or {}\n\n if provider == \"OpenAI\":\n if not api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n return OpenAIEmbeddings(\n model=model,\n dimensions=dimensions or None,\n base_url=api_base or None,\n api_key=api_key,\n chunk_size=chunk_size,\n max_retries=max_retries,\n timeout=request_timeout or None,\n show_progress_bar=show_progress_bar,\n model_kwargs=model_kwargs,\n )\n\n if provider == \"Ollama\":\n try:\n from langchain_ollama import OllamaEmbeddings\n except ImportError:\n try:\n from langchain_community.embeddings import OllamaEmbeddings\n except ImportError:\n msg = \"Please install langchain-ollama: pip install langchain-ollama\"\n raise ImportError(msg) from None\n\n transformed_base_url = transform_localhost_url(ollama_base_url)\n\n # Check if URL contains /v1 suffix (OpenAI-compatible mode)\n if transformed_base_url and transformed_base_url.rstrip(\"/\").endswith(\"/v1\"):\n # Strip /v1 suffix and log warning\n transformed_base_url = transformed_base_url.rstrip(\"/\").removesuffix(\"/v1\")\n logger.warning(\n \"Detected '/v1' suffix in base URL. The Ollama component uses the native Ollama API, \"\n \"not the OpenAI-compatible API. The '/v1' suffix has been automatically removed. \"\n \"If you want to use the OpenAI-compatible API, please use the OpenAI component instead. \"\n \"Learn more at https://docs.ollama.com/openai#openai-compatibility\"\n )\n\n return OllamaEmbeddings(\n model=model,\n base_url=transformed_base_url or \"http://localhost:11434\",\n **model_kwargs,\n )\n\n if provider == \"IBM watsonx.ai\":\n try:\n from langchain_ibm import WatsonxEmbeddings\n except ImportError:\n msg = \"Please install langchain-ibm: pip install langchain-ibm\"\n raise ImportError(msg) from None\n\n if not api_key:\n msg = \"IBM watsonx.ai API key is required when using IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n project_id = self.project_id\n\n if not project_id:\n msg = \"Project ID is required for IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n params = {\n \"model_id\": model,\n \"url\": base_url_ibm_watsonx or \"https://us-south.ml.cloud.ibm.com\",\n \"apikey\": api_key,\n }\n\n params[\"project_id\"] = project_id\n\n return WatsonxEmbeddings(**params)\n\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n async def update_build_config(\n self, build_config: dotdict, field_value: Any, field_name: str | None = None\n ) -> dotdict:\n if field_name == \"provider\":\n if field_value == \"OpenAI\":\n build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n build_config[\"api_base\"][\"advanced\"] = True\n build_config[\"api_base\"][\"show\"] = True\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n elif field_value == \"Ollama\":\n build_config[\"ollama_base_url\"][\"show\"] = True\n\n if await is_valid_ollama_url(url=self.ollama_base_url):\n try:\n models = await get_ollama_models(\n base_url_value=self.ollama_base_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n else:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n\n build_config[\"api_key\"][\"display_name\"] = \"API Key (Optional)\"\n build_config[\"api_key\"][\"required\"] = False\n build_config[\"api_key\"][\"show\"] = False\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n elif field_value == \"IBM watsonx.ai\":\n build_config[\"model\"][\"options\"] = WATSONX_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = WATSONX_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"IBM watsonx.ai API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = True\n build_config[\"project_id\"][\"show\"] = True\n\n elif field_name == \"ollama_base_url\":\n # # Refresh Ollama models when base URL changes\n # if hasattr(self, \"provider\") and self.provider == \"Ollama\":\n # Use field_value if provided, otherwise fall back to instance attribute\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n await logger.awarning(\"Failed to fetch Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n\n elif field_name == \"model\" and self.provider == \"Ollama\":\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n except ValueError:\n await logger.awarning(\"Failed to refresh Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n\n return build_config\n" + "value": "from typing import Any\n\nimport requests\nfrom ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.model_utils import get_ollama_models, is_valid_ollama_url\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.base.models.watsonx_constants import (\n IBM_WATSONX_URLS,\n WATSONX_EMBEDDING_MODEL_NAMES,\n)\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageTextInput,\n SecretStrInput,\n)\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.utils.util import transform_localhost_url\n\n# Ollama API constants\nHTTP_STATUS_OK = 200\nJSON_MODELS_KEY = \"models\"\nJSON_NAME_KEY = \"name\"\nJSON_CAPABILITIES_KEY = \"capabilities\"\nDESIRED_CAPABILITY = \"embedding\"\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n display_name = \"Embedding Model\"\n description = \"Generate embeddings using a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n icon = \"binary\"\n name = \"EmbeddingModel\"\n category = \"models\"\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\", \"Ollama\", \"IBM watsonx.ai\"],\n value=\"OpenAI\",\n info=\"Select the embedding model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Ollama\"}, {\"icon\": \"WatsonxAI\"}],\n ),\n MessageTextInput(\n name=\"api_base\",\n display_name=\"API Base URL\",\n info=\"Base URL for the API. Leave empty for default.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"ollama_base_url\",\n display_name=\"Ollama API URL\",\n info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n value=DEFAULT_OLLAMA_URL,\n show=False,\n real_time_refresh=True,\n load_from_db=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n show=False,\n real_time_refresh=True,\n ),\n DropdownInput(\n name=\"model\",\n display_name=\"Model Name\",\n options=OPENAI_EMBEDDING_MODEL_NAMES,\n value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n info=\"Select the embedding model to use\",\n real_time_refresh=True,\n refresh_button=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=True,\n show=True,\n real_time_refresh=True,\n ),\n # Watson-specific inputs\n MessageTextInput(\n name=\"project_id\",\n display_name=\"Project ID\",\n info=\"IBM watsonx.ai Project ID (required for IBM watsonx.ai)\",\n show=False,\n ),\n IntInput(\n name=\"dimensions\",\n display_name=\"Dimensions\",\n info=\"The number of dimensions the resulting output embeddings should have. \"\n \"Only supported by certain models.\",\n advanced=True,\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n DictInput(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\n IntInput(\n name=\"truncate_input_tokens\",\n display_name=\"Truncate Input Tokens\",\n advanced=True,\n value=200,\n show=False,\n ),\n BoolInput(\n name=\"input_text\",\n display_name=\"Include the original text in the output\",\n value=True,\n advanced=True,\n show=False,\n ),\n ]\n\n @staticmethod\n def fetch_ibm_models(base_url: str) -> list[str]:\n \"\"\"Fetch available models from the watsonx.ai API.\"\"\"\n try:\n endpoint = f\"{base_url}/ml/v1/foundation_model_specs\"\n params = {\n \"version\": \"2024-09-16\",\n \"filters\": \"function_embedding,!lifecycle_withdrawn:and\",\n }\n response = requests.get(endpoint, params=params, timeout=10)\n response.raise_for_status()\n data = response.json()\n models = [model[\"model_id\"] for model in data.get(\"resources\", [])]\n return sorted(models)\n except Exception: # noqa: BLE001\n logger.exception(\"Error fetching models\")\n return WATSONX_EMBEDDING_MODEL_NAMES\n\n def build_embeddings(self) -> Embeddings:\n provider = self.provider\n model = self.model\n api_key = self.api_key\n api_base = self.api_base\n base_url_ibm_watsonx = self.base_url_ibm_watsonx\n ollama_base_url = self.ollama_base_url\n dimensions = self.dimensions\n chunk_size = self.chunk_size\n request_timeout = self.request_timeout\n max_retries = self.max_retries\n show_progress_bar = self.show_progress_bar\n model_kwargs = self.model_kwargs or {}\n\n if provider == \"OpenAI\":\n if not api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n return OpenAIEmbeddings(\n model=model,\n dimensions=dimensions or None,\n base_url=api_base or None,\n api_key=api_key,\n chunk_size=chunk_size,\n max_retries=max_retries,\n timeout=request_timeout or None,\n show_progress_bar=show_progress_bar,\n model_kwargs=model_kwargs,\n )\n\n if provider == \"Ollama\":\n try:\n from langchain_ollama import OllamaEmbeddings\n except ImportError:\n try:\n from langchain_community.embeddings import OllamaEmbeddings\n except ImportError:\n msg = \"Please install langchain-ollama: pip install langchain-ollama\"\n raise ImportError(msg) from None\n\n transformed_base_url = transform_localhost_url(ollama_base_url)\n\n # Check if URL contains /v1 suffix (OpenAI-compatible mode)\n if transformed_base_url and transformed_base_url.rstrip(\"/\").endswith(\"/v1\"):\n # Strip /v1 suffix and log warning\n transformed_base_url = transformed_base_url.rstrip(\"/\").removesuffix(\"/v1\")\n logger.warning(\n \"Detected '/v1' suffix in base URL. The Ollama component uses the native Ollama API, \"\n \"not the OpenAI-compatible API. The '/v1' suffix has been automatically removed. \"\n \"If you want to use the OpenAI-compatible API, please use the OpenAI component instead. \"\n \"Learn more at https://docs.ollama.com/openai#openai-compatibility\"\n )\n\n return OllamaEmbeddings(\n model=model,\n base_url=transformed_base_url or \"http://localhost:11434\",\n **model_kwargs,\n )\n\n if provider == \"IBM watsonx.ai\":\n try:\n from langchain_ibm import WatsonxEmbeddings\n except ImportError:\n msg = \"Please install langchain-ibm: pip install langchain-ibm\"\n raise ImportError(msg) from None\n\n if not api_key:\n msg = \"IBM watsonx.ai API key is required when using IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n project_id = self.project_id\n\n if not project_id:\n msg = \"Project ID is required for IBM watsonx.ai provider\"\n raise ValueError(msg)\n\n from ibm_watsonx_ai import APIClient, Credentials\n\n credentials = Credentials(\n api_key=self.api_key,\n url=base_url_ibm_watsonx or \"https://us-south.ml.cloud.ibm.com\",\n )\n\n api_client = APIClient(credentials)\n\n params = {\n EmbedTextParamsMetaNames.TRUNCATE_INPUT_TOKENS: self.truncate_input_tokens,\n EmbedTextParamsMetaNames.RETURN_OPTIONS: {\"input_text\": self.input_text},\n }\n\n return WatsonxEmbeddings(\n model_id=model,\n params=params,\n watsonx_client=api_client,\n project_id=project_id,\n )\n\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n async def update_build_config(\n self, build_config: dotdict, field_value: Any, field_name: str | None = None\n ) -> dotdict:\n if field_name == \"provider\":\n if field_value == \"OpenAI\":\n build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n build_config[\"api_base\"][\"advanced\"] = True\n build_config[\"api_base\"][\"show\"] = True\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n build_config[\"truncate_input_tokens\"][\"show\"] = False\n build_config[\"input_text\"][\"show\"] = False\n elif field_value == \"Ollama\":\n build_config[\"ollama_base_url\"][\"show\"] = True\n\n if await is_valid_ollama_url(url=self.ollama_base_url):\n try:\n models = await get_ollama_models(\n base_url_value=self.ollama_base_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n else:\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n build_config[\"truncate_input_tokens\"][\"show\"] = False\n build_config[\"input_text\"][\"show\"] = False\n build_config[\"api_key\"][\"display_name\"] = \"API Key (Optional)\"\n build_config[\"api_key\"][\"required\"] = False\n build_config[\"api_key\"][\"show\"] = False\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"project_id\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = False\n\n elif field_value == \"IBM watsonx.ai\":\n build_config[\"model\"][\"options\"] = self.fetch_ibm_models(base_url=self.base_url_ibm_watsonx)\n build_config[\"model\"][\"value\"] = self.fetch_ibm_models(base_url=self.base_url_ibm_watsonx)[0]\n build_config[\"api_key\"][\"display_name\"] = \"IBM watsonx.ai API Key\"\n build_config[\"api_key\"][\"required\"] = True\n build_config[\"api_key\"][\"show\"] = True\n build_config[\"api_base\"][\"show\"] = False\n build_config[\"ollama_base_url\"][\"show\"] = False\n build_config[\"base_url_ibm_watsonx\"][\"show\"] = True\n build_config[\"project_id\"][\"show\"] = True\n build_config[\"truncate_input_tokens\"][\"show\"] = True\n build_config[\"input_text\"][\"show\"] = True\n elif field_name == \"base_url_ibm_watsonx\":\n build_config[\"model\"][\"options\"] = self.fetch_ibm_models(base_url=field_value)\n build_config[\"model\"][\"value\"] = self.fetch_ibm_models(base_url=field_value)[0]\n elif field_name == \"ollama_base_url\":\n # # Refresh Ollama models when base URL changes\n # if hasattr(self, \"provider\") and self.provider == \"Ollama\":\n # Use field_value if provided, otherwise fall back to instance attribute\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n build_config[\"model\"][\"value\"] = models[0] if models else \"\"\n except ValueError:\n await logger.awarning(\"Failed to fetch Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n build_config[\"model\"][\"value\"] = \"\"\n\n elif field_name == \"model\" and self.provider == \"Ollama\":\n ollama_url = self.ollama_base_url\n if await is_valid_ollama_url(url=ollama_url):\n try:\n models = await get_ollama_models(\n base_url_value=ollama_url,\n desired_capability=DESIRED_CAPABILITY,\n json_models_key=JSON_MODELS_KEY,\n json_name_key=JSON_NAME_KEY,\n json_capabilities_key=JSON_CAPABILITIES_KEY,\n )\n build_config[\"model\"][\"options\"] = models\n except ValueError:\n await logger.awarning(\"Failed to refresh Ollama embedding models.\")\n build_config[\"model\"][\"options\"] = []\n\n return build_config\n" }, "dimensions": { "_input_type": "IntInput", @@ -3535,9 +3551,29 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": "" }, + "input_text": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Include the original text in the output", + "dynamic": false, + "info": "", + "list": false, + "list_add_label": "Add More", + "name": "input_text", + "placeholder": "", + "required": false, + "show": false, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "bool", + "value": true + }, "max_retries": { "_input_type": "IntInput", "advanced": true, @@ -3553,6 +3589,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "int", "value": 3 }, @@ -3573,12 +3610,15 @@ ], "options_metadata": [], "placeholder": "", + "real_time_refresh": true, + "refresh_button": true, "required": false, "show": true, "title_case": false, "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "text-embedding-3-small" }, @@ -3597,6 +3637,7 @@ "title_case": false, "tool_mode": false, "trace_as_input": true, + "track_in_telemetry": false, "type": "dict", "value": {} }, @@ -3621,6 +3662,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -3644,6 +3686,7 @@ "tool_mode": false, "trace_as_input": true, "trace_as_metadata": true, + "track_in_telemetry": false, "type": "str", "value": "" }, @@ -3681,6 +3724,7 @@ "toggle": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "str", "value": "OpenAI" }, @@ -3699,6 +3743,7 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "float", "value": "" }, @@ -3717,8 +3762,28 @@ "title_case": false, "tool_mode": false, "trace_as_metadata": true, + "track_in_telemetry": true, "type": "bool", "value": false + }, + "truncate_input_tokens": { + "_input_type": "IntInput", + "advanced": true, + "display_name": "Truncate Input Tokens", + "dynamic": false, + "info": "", + "list": false, + "list_add_label": "Add More", + "name": "truncate_input_tokens", + "placeholder": "", + "required": false, + "show": false, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "int", + "value": 200 } }, "tool_mode": false @@ -3736,22 +3801,22 @@ "x": 2066.3681917820168, "y": 2053.0594731518368 }, - "selected": false, + "selected": true, "type": "genericNode" } ], "viewport": { - "x": -376.03081395180425, - "y": -548.1133759270245, - "zoom": 0.5197673958703216 + "x": -425.92312760235086, + "y": -699.5243463797829, + "zoom": 0.5890166507565666 } }, "description": "This flow is to ingest the URL to open search.", "endpoint_name": null, "id": "72c3d17c-2dac-4a73-b48a-6518473d7830", "is_component": false, - "last_tested_version": "1.7.0", "mcp_enabled": true, + "last_tested_version": "1.7.0.dev19", "name": "OpenSearch URL Ingestion Flow", "tags": [ "openai", diff --git a/frontend/app/knowledge/page.tsx b/frontend/app/knowledge/page.tsx index 1b9e6f85..9a526159 100644 --- a/frontend/app/knowledge/page.tsx +++ b/frontend/app/knowledge/page.tsx @@ -31,6 +31,11 @@ import { DialogTrigger, } from "@/components/ui/dialog"; import { StatusBadge } from "@/components/ui/status-badge"; +import { + Tooltip, + TooltipContent, + TooltipTrigger, +} from "@/components/ui/tooltip"; import { DeleteConfirmationDialog, formatFilesToDelete, @@ -156,9 +161,16 @@ function SearchPage() { }} > {getSourceIcon(data?.connector_type)} - - {value} - + + + + {value} + + + + {value} + + ); diff --git a/pyproject.toml b/pyproject.toml index 09ce2e9b..faa5820b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "openrag" -version = "0.1.38" +version = "0.1.40" description = "Add your description here" readme = "README.md" requires-python = ">=3.13" diff --git a/src/tui/managers/container_manager.py b/src/tui/managers/container_manager.py index 53fc385e..2da92322 100644 --- a/src/tui/managers/container_manager.py +++ b/src/tui/managers/container_manager.py @@ -43,6 +43,7 @@ class ServiceInfo: image: Optional[str] = None image_digest: Optional[str] = None created: Optional[str] = None + error_message: Optional[str] = None def __post_init__(self): if self.ports is None: @@ -135,6 +136,96 @@ class ContainerManager: return self.platform_detector.get_compose_installation_instructions() return self.platform_detector.get_installation_instructions() + def _extract_ports_from_compose(self) -> Dict[str, List[int]]: + """Extract port mappings from compose files. + + Returns: + Dict mapping service name to list of host ports + """ + service_ports: Dict[str, List[int]] = {} + + compose_files = [self.compose_file] + if hasattr(self, 'cpu_compose_file') and self.cpu_compose_file and self.cpu_compose_file.exists(): + compose_files.append(self.cpu_compose_file) + + for compose_file in compose_files: + if not compose_file.exists(): + continue + + try: + import re + content = compose_file.read_text() + current_service = None + in_ports_section = False + + for line in content.splitlines(): + # Detect service names + service_match = re.match(r'^ (\w[\w-]*):$', line) + if service_match: + current_service = service_match.group(1) + in_ports_section = False + if current_service not in service_ports: + service_ports[current_service] = [] + continue + + # Detect ports section + if current_service and re.match(r'^ ports:$', line): + in_ports_section = True + continue + + # Exit ports section on new top-level key + if in_ports_section and re.match(r'^ \w+:', line): + in_ports_section = False + + # Extract port mappings + if in_ports_section and current_service: + # Match patterns like: - "3000:3000", - "9200:9200", - 7860:7860 + port_match = re.search(r'["\']?(\d+):\d+["\']?', line) + if port_match: + host_port = int(port_match.group(1)) + if host_port not in service_ports[current_service]: + service_ports[current_service].append(host_port) + + except Exception as e: + logger.debug(f"Error parsing {compose_file} for ports: {e}") + continue + + return service_ports + + async def check_ports_available(self) -> tuple[bool, List[tuple[str, int, str]]]: + """Check if required ports are available. + + Returns: + Tuple of (all_available, conflicts) where conflicts is a list of + (service_name, port, error_message) tuples + """ + import socket + + service_ports = self._extract_ports_from_compose() + conflicts: List[tuple[str, int, str]] = [] + + for service_name, ports in service_ports.items(): + for port in ports: + try: + # Try to bind to the port to check if it's available + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + sock.settimeout(0.5) + result = sock.connect_ex(('127.0.0.1', port)) + sock.close() + + if result == 0: + # Port is in use + conflicts.append(( + service_name, + port, + f"Port {port} is already in use" + )) + except Exception as e: + logger.debug(f"Error checking port {port}: {e}") + continue + + return (len(conflicts) == 0, conflicts) + async def _run_compose_command( self, args: List[str], cpu_mode: Optional[bool] = None ) -> tuple[bool, str, str]: @@ -696,6 +787,17 @@ class ContainerManager: yield False, f"ERROR: Base compose file not found at {self.compose_file.absolute()}", False return + # Check for port conflicts before starting + yield False, "Checking port availability...", False + ports_available, conflicts = await self.check_ports_available() + if not ports_available: + yield False, "ERROR: Port conflicts detected:", False + for service_name, port, error_msg in conflicts: + yield False, f" - {service_name}: {error_msg}", False + yield False, "Please stop the conflicting services and try again.", False + yield False, "Services not started due to port conflicts.", False + return + yield False, "Starting OpenRAG services...", False missing_images: List[str] = [] @@ -718,13 +820,37 @@ class ContainerManager: yield False, "Creating and starting containers...", False up_success = {"value": True} + error_messages = [] + async for message, replace_last in self._stream_compose_command(["up", "-d"], up_success, cpu_mode): + # Detect error patterns in the output + import re + lower_msg = message.lower() + + # Check for common error patterns + if any(pattern in lower_msg for pattern in [ + "port.*already.*allocated", + "address already in use", + "bind.*address already in use", + "port is already allocated" + ]): + error_messages.append("Port conflict detected") + up_success["value"] = False + elif "error" in lower_msg or "failed" in lower_msg: + # Generic error detection + if message not in error_messages: + error_messages.append(message) + yield False, message, replace_last if up_success["value"]: yield True, "Services started successfully", False else: yield False, "Failed to start services. See output above for details.", False + if error_messages: + yield False, "\nDetected errors:", False + for err in error_messages[:5]: # Limit to first 5 errors + yield False, f" - {err}", False async def stop_services(self) -> AsyncIterator[tuple[bool, str]]: """Stop all services and yield progress updates.""" diff --git a/src/tui/managers/docling_manager.py b/src/tui/managers/docling_manager.py index 109cb7c1..7f7c2a78 100644 --- a/src/tui/managers/docling_manager.py +++ b/src/tui/managers/docling_manager.py @@ -143,6 +143,29 @@ class DoclingManager: self._external_process = False return False + def check_port_available(self) -> tuple[bool, Optional[str]]: + """Check if the native service port is available. + + Returns: + Tuple of (available, error_message) + """ + import socket + + try: + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + sock.settimeout(0.5) + result = sock.connect_ex(('127.0.0.1', self._port)) + sock.close() + + if result == 0: + # Port is in use + return False, f"Port {self._port} is already in use" + return True, None + except Exception as e: + logger.debug(f"Error checking port {self._port}: {e}") + # If we can't check, assume it's available + return True, None + def get_status(self) -> Dict[str, Any]: """Get current status of docling serve.""" # Check for starting state first diff --git a/src/tui/screens/monitor.py b/src/tui/screens/monitor.py index 7d7269ab..cc6ea18c 100644 --- a/src/tui/screens/monitor.py +++ b/src/tui/screens/monitor.py @@ -319,17 +319,46 @@ class MonitorScreen(Screen): """Start services with progress updates.""" self.operation_in_progress = True try: + # Check for port conflicts before attempting to start + ports_available, conflicts = await self.container_manager.check_ports_available() + if not ports_available: + # Show error notification instead of modal + conflict_msgs = [] + for service_name, port, error_msg in conflicts[:3]: # Show first 3 + conflict_msgs.append(f"{service_name} (port {port})") + + conflict_str = ", ".join(conflict_msgs) + if len(conflicts) > 3: + conflict_str += f" and {len(conflicts) - 3} more" + + self.notify( + f"Cannot start services: Port conflicts detected for {conflict_str}. " + f"Please stop the conflicting services first.", + severity="error", + timeout=10 + ) + # Refresh to show current state + await self._refresh_services() + return + # Show command output in modal dialog command_generator = self.container_manager.start_services(cpu_mode) modal = CommandOutputModal( "Starting Services", command_generator, - on_complete=None, # We'll refresh in on_screen_resume instead + on_complete=self._on_start_complete, # Refresh after completion ) self.app.push_screen(modal) + except Exception as e: + self.notify(f"Error starting services: {str(e)}", severity="error") + await self._refresh_services() finally: self.operation_in_progress = False + async def _on_start_complete(self) -> None: + """Callback after service start completes.""" + await self._refresh_services() + async def _stop_services(self) -> None: """Stop services with progress updates.""" self.operation_in_progress = True @@ -394,6 +423,19 @@ class MonitorScreen(Screen): """Start docling serve.""" self.operation_in_progress = True try: + # Check for port conflicts before attempting to start + port_available, error_msg = self.docling_manager.check_port_available() + if not port_available: + self.notify( + f"Cannot start docling serve: {error_msg}. " + f"Please stop the conflicting service first.", + severity="error", + timeout=10 + ) + # Refresh to show current state + await self._refresh_services() + return + # Start the service (this sets _starting = True internally at the start) # Create task and let it begin executing (which sets the flag) start_task = asyncio.create_task(self.docling_manager.start()) diff --git a/src/tui/screens/welcome.py b/src/tui/screens/welcome.py index 64ad888a..673fae5b 100644 --- a/src/tui/screens/welcome.py +++ b/src/tui/screens/welcome.py @@ -385,6 +385,34 @@ class WelcomeScreen(Screen): async def _start_all_services(self) -> None: """Start all services: containers first, then native services.""" + # Check for port conflicts before attempting to start anything + conflicts = [] + + # Check container ports + if self.container_manager.is_available(): + ports_available, port_conflicts = await self.container_manager.check_ports_available() + if not ports_available: + for service_name, port, error_msg in port_conflicts[:3]: # Show first 3 + conflicts.append(f"{service_name} (port {port})") + if len(port_conflicts) > 3: + conflicts.append(f"and {len(port_conflicts) - 3} more") + + # Check native service port + port_available, error_msg = self.docling_manager.check_port_available() + if not port_available: + conflicts.append(f"docling (port {self.docling_manager._port})") + + # If there are any conflicts, show error and return + if conflicts: + conflict_str = ", ".join(conflicts) + self.notify( + f"Cannot start services: Port conflicts detected for {conflict_str}. " + f"Please stop the conflicting services first.", + severity="error", + timeout=10 + ) + return + # Step 1: Start container services first (to create the network) if self.container_manager.is_available(): command_generator = self.container_manager.start_services() @@ -410,6 +438,20 @@ class WelcomeScreen(Screen): async def _start_native_services_after_containers(self) -> None: """Start native services after containers have been started.""" if not self.docling_manager.is_running(): + # Check for port conflicts before attempting to start + port_available, error_msg = self.docling_manager.check_port_available() + if not port_available: + self.notify( + f"Cannot start native services: {error_msg}. " + f"Please stop the conflicting service first.", + severity="error", + timeout=10 + ) + # Update state and return + self.docling_running = False + await self._refresh_welcome_content() + return + self.notify("Starting native services...", severity="information") success, message = await self.docling_manager.start() if success: diff --git a/src/tui/widgets/command_modal.py b/src/tui/widgets/command_modal.py index a5013031..a75a46ee 100644 --- a/src/tui/widgets/command_modal.py +++ b/src/tui/widgets/command_modal.py @@ -23,6 +23,7 @@ class CommandOutputModal(ModalScreen): ("p", "pause_waves", "Pause"), ("f", "speed_up", "Faster"), ("s", "speed_down", "Slower"), + ("escape", "close_modal", "Close"), ] DEFAULT_CSS = """ @@ -188,6 +189,8 @@ class CommandOutputModal(ModalScreen): self._output_lines: list[str] = [] self._layer_line_map: dict[str, int] = {} # Maps layer ID to line index self._status_task: Optional[asyncio.Task] = None + self._error_detected = False + self._command_complete = False def compose(self) -> ComposeResult: """Create the modal dialog layout.""" @@ -254,6 +257,12 @@ class CommandOutputModal(ModalScreen): for w in waves.wavelets: w.speed = max(0.1, w.speed * 0.8) + def action_close_modal(self) -> None: + """Close the modal (only if error detected or command complete).""" + close_btn = self.query_one("#close-btn", Button) + if not close_btn.disabled: + self.dismiss() + async def _run_command(self) -> None: """Run the command and update the output in real-time.""" output = self.query_one("#command-output", TextArea) @@ -273,8 +282,25 @@ class CommandOutputModal(ModalScreen): # Move cursor to end to trigger scroll output.move_cursor((len(self._output_lines), 0)) + # Detect error patterns in messages + import re + lower_msg = message.lower() if message else "" + if not self._error_detected and any(pattern in lower_msg for pattern in [ + "error:", + "failed", + "port.*already.*allocated", + "address already in use", + "not found", + "permission denied" + ]): + self._error_detected = True + # Enable close button when error detected + close_btn = self.query_one("#close-btn", Button) + close_btn.disabled = False + # If command is complete, update UI if is_complete: + self._command_complete = True self._update_output("Command completed successfully", False) output.text = "\n".join(self._output_lines) output.move_cursor((len(self._output_lines), 0)) diff --git a/uv.lock b/uv.lock index e99e9dc8..788cb852 100644 --- a/uv.lock +++ b/uv.lock @@ -2352,7 +2352,7 @@ wheels = [ [[package]] name = "openrag" -version = "0.1.37" +version = "0.1.40" source = { editable = "." } dependencies = [ { name = "agentd" },