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This server provides tools to list sources and workflows.\n\n## Setup\n\n1. Install dependencies:\n- `uv add \"mcp[cli]\"`\n- `uv pip install --upgrade unstructured-client python-dotenv`\n\nor use `uv sync`.\n\n2. Set your Unstructured API key as an environment variable.\n - Create a `.env` file in the root directory, and add a line with your key: `UNSTRUCTURED_API_KEY=\"YOUR_KEY\"`\n\n\nTo test in local, any working key that pointing to prod env would work. However, to be able to return valid results from client's side (e.g, Claude for Desktop), your personal key that is fetched from `https://platform.unstructured.io/app/account/api-keys` is needed.\n\n## Running the Server\nUsing the MCP CLI:\n```bash\nmcp run uns_mcp/server.py\n```\n\nor:\n```bash\nuv run uns_mcp/server.py\n```\n\n## Available Tools\n\n| Tool | Description |\n|------|-------------|\n| `list_sources` | Lists available sources from the Unstructured API. |\n| `get_source_info` | Get detailed information about a specific source connector. |\n| `create_[connector]_source` | Create a source connector. Currently, we have s3/google drive/azure connectors (more to come!) |\n| `update_[connector]_source` | Update an existing source connector by params. |\n| `delete_[connector]_source` | Delete a source connector by source id. |\n| `list_destinations` | Lists available destinations from the Unstructured API. |\n| `get_destination_info` | Get detailed info about a specific destination connector. Currently, we have s3/weaviate/astra/neo4j/mongo DB (more to come!) |\n| `create_[connector]_destination` | Create a destination connector by params. |\n| `update_[connector]_destination` | Update an existing destination connector by destination id. |\n| `delete_[connector]_destination` | Delete a destination connector by destination id. |\n| `list_workflows` | Lists workflows from the Unstructured API. |\n| `get_workflow_info` | Get detailed information about a specific workflow. |\n| `create_workflow` | Create a new workflow with source, destination id, etc. |\n| `run_workflow` | Run a specific workflow with workflow id |\n| `update_workflow` | Update an existing workflow by params. |\n| `delete_workflow` | Delete a specific workflow by id. |\n| `list_jobs` | Lists jobs for a specific workflow from the Unstructured API. |\n| `get_job_info` | Get detailed information about a specific job by job id. |\n| `cancel_job` |Delete a specific job by id. |\n\nTo use the tool that creates/updates/deletes a connector, the credentials for that specific connector must be defined in your .env file. Below is the list of `credentials` for the connectors we support:\n\n| Credential Name | Description |\n|------|-------------|\n| `ANTHROPIC_API_KEY` | required to run the `minimal_client` to interact with our server. |\n| `AWS_KEY`, `AWS_SECRET`| required to create S3 connector via `uns-mcp` server, see how in [documentation](https://docs.unstructured.io/api-reference/workflow/sources/s3) and [here](https://docs.unstructured.io/api-reference/workflow/destinations/s3) |\n| `WEAVIATE_CLOUD_API_KEY` | required to create Weaviate vector db connector, see how in [documentation](https://docs.unstructured.io/api-reference/workflow/destinations/weaviate) |\n| `FIRECRAWL_API_KEY` | required to use Firecrawl tools in `external/firecrawl.py`, sign up on [Firecrawl](https://www.firecrawl.dev/) and get an API key. |\n| `ASTRA_DB_APPLICATION_TOKEN`, `ASTRA_DB_API_ENDPOINT` | required to create Astradb connector via `uns-mcp` server, see how in [documentation](https://docs.unstructured.io/ui/destinations/astradb)|\n| `AZURE_CONNECTION_STRING`| required option 1 to create Azure connector via ``uns-mcp`` server, see how in [documentation](https://docs.unstructured.io/ui/sources/azure-blob-storage) |\n| `AZURE_ACCOUNT_NAME`+`AZURE_ACCOUNT_KEY`| required option 2 to create Azure connector via `uns-mcp` server, see how in [documentation](https://docs.unstructured.io/ui/sources/azure-blob-storage)|\n| `AZURE_ACCOUNT_NAME`+`AZURE_SAS_TOKEN` | required option 3 to create Azure connector via `uns-mcp` server, see how in [documentation](https://docs.unstructured.io/ui/sources/azure-blob-storage) |\n| `NEO4J_PASSWORD` | required to create Neo4j connector via `uns-mcp` server, see how in [documentation](https://docs.unstructured.io/ui/destinations/neo4j) |\n| `MONGO_DB_CONNECTION_STRING` | required to create Mongodb connector via `uns-mcp` server, see how in [documentation](https://docs.unstructured.io/ui/destinations/mongodb) |\n| `GOOGLEDRIVE_SERVICE_ACCOUNT_KEY` | a string value. The original server account key (follow [documentation](https://docs.unstructured.io/ui/sources/google-drive)) is in json file, run `cat /path/to/google_service_account_key.json | base64` in terminal to get the string value |\n| `DATABRICKS_CLIENT_ID`,`DATABRICKS_CLIENT_SECRET` | required to create Databricks volume/delta table connector via `uns-mcp` server, see how in [documentation](https://docs.unstructured.io/ui/destinations/databricks-volumes) and [here](https://docs.unstructured.io/ui/destinations/databricks-delta-table) |\n| `ONEDRIVE_CLIENT_ID`, `ONEDRIVE_CLIENT_CRED`,`ONEDRIVE_TENANT_ID`| required to create One Drive connector via `uns-mcp` server, see how in [documentation](https://docs.unstructured.io/ui/destinations/onedrive) |\n| `LOG_LEVEL` | Used to set logging level for our `minimal_client`, e.g. set to ERROR to get everything |\n| `CONFIRM_TOOL_USE` | set to true so that `minimal_client` can confirm execution before each tool call |\n| `DEBUG_API_REQUESTS` | set to true so that `uns_mcp/server.py` can output request parameters for better debugging |\n\n\n### Firecrawl Source\n\n[Firecrawl](https://www.firecrawl.dev/) is a web crawling API that provides two main capabilities in our MCP:\n\n1. **HTML Content Retrieval**: Using `invoke_firecrawl_crawlhtml` to start crawl jobs and `check_crawlhtml_status` to monitor them\n2. **LLM-Optimized Text Generation**: Using `invoke_firecrawl_llmtxt` to generate text and `check_llmtxt_status` to retrieve results\n\nHow Firecrawl works:\n\n**Web Crawling Process:**\n- Starts with a specified URL and analyzes it to identify links\n- Uses the sitemap if available; otherwise follows links found on the website\n- Recursively traverses each link to discover all subpages\n- Gathers content from every visited page, handling JavaScript rendering and rate limits\n- Jobs can be cancelled with `cancel_crawlhtml_job` if needed\n- Use this if you require all the info extracted into raw HTML, Unstructured's workflow cleans it up really well :smile:\n\n**LLM Text Generation:**\n- After crawling, extracts clean, meaningful text content from the crawled pages\n- Generates optimized text formats specifically formatted for large language models\n- Results are automatically uploaded to the specified S3 location\n- Note: LLM text generation jobs cannot be cancelled once started. The `cancel_llmtxt_job` function is provided for consistency but is not currently supported by the Firecrawl API.\n\nNote: A `FIRECRAWL_API_KEY` environment variable must be set to use these functions.\n\n\n## Claude Desktop Integration\n\nTo install in Claude Desktop:\n\n1. Go to `~/Library/Application Support/Claude/` and create a `claude_desktop_config.json`.\n2. In that file add:\n```bash\n{\n \"mcpServers\":\n {\n \"UNS_MCP\":\n {\n \"command\": \"ABSOLUTE/PATH/TO/.local/bin/uv\",\n \"args\":\n [\n \"--directory\",\n \"ABSOLUTE/PATH/TO/YOUR-UNS-MCP-REPO/uns_mcp\",\n \"run\",\n \"server.py\"\n ],\n \"env\":\n [\n \"UNSTRUCTURED_API_KEY\":\"\"\n ],\n \"disabled\": false\n }\n }\n}\n```\n3. Restart Claude Desktop.\n\n4. Example Issues seen from Claude Desktop.\n - You will see `No destinations found` when you query for a list of destination connectors. Check your API key in `.env` or in your config json, it needs to be your personal key in `https://platform.unstructured.io/app/account/api-keys`.\n\n## Debugging tools\n\nAnthropic provides `MCP Inspector` tool to debug/test your MCP server. Run the following command to spin up a debugging UI. From there, you will be able to add environment variables (pointing to your local env) on the left pane. Include your personal API key there as env var. Go to `tools`, you can test out the capabilities you add to the MCP server.\n```\nmcp dev uns_mcp/server.py\n```\n\nIf you need to log request call parameters to `UnstructuredClient`, set the environment variable `DEBUG_API_REQUESTS=false`.\nThe logs are stored in a file with the format `unstructured-client-{date}.log`, which can be examined to debug request call parameters to `UnstructuredClient` functions.\n\n## Running locally minimal client\n```\nuv run python minimal_client/client.py uns_mcp/server.py\n```\n\nor\n\n```\nmake local-client\n```\n\nEnv variables to configure behavior of the client:\n- `LOG_LEVEL=\"ERROR\"` # If you would like to hide outputs from the LLM and present clear messages for the user\n- `CONFIRM_TOOL_USE='false'` If you would like to disable the tool use confirmation before running it (True by default). **BE MINDFUL** about that option, as LLM can decide to purge all data from your account or run some expensive workflows; use only for development purposes.\n\n## Running locally minimal client, accessing local the MCP server over HTTP + SSE\n\nThe main difference here is it becomes easier to set breakpoints on the server side during development -- the client and server are decoupled.\n\n```\n# in one terminal, run the server:\nuv run python uns_mcp/server.py --host 127.0.0.1 --port 8080\n\nor\nmake sse-server\n\n# in another terminal, run the client:\nuv run python minimal_client/client.py \"http://127.0.0.1:8080/sse\"\n\nor\nmake sse-client\n```\n\nHint: `ctrl+c` out of the client first, then the server. Otherwise the server appears to hang.\n\n## CHANGELOG.md\n\nAny new developed features/fixes/enhancements will be added to CHANGELOG.md. 0.x.x-dev pre-release format is preferred before we bump to a stable version.\n","url","https://github.com/Unstructured-IO/UNS-MCP","githubRepository",{"_69":70,"_71":72,"_73":74,"_75":76,"_77":78,"_79":80,"_84":85,"_86":-5,"_87":88},"defaultBranch","main","fullName","Unstructured-IO/UNS-MCP","githubTreeId",104004,"language","Python","name","UNS-MCP","owner",{"_81":82,"_17":83},"login","Unstructured-IO","nbOQfB4KnN","recentStargazers",6,"spdxLicense","stargazers",13,"glamaJson","id",1828,"inspectable",true,"integrations",[96,106],{"_97":98,"_39":104,"_17":105},"brand",{"_99":100,"_77":101,"_102":103},"logoSvgPath","M24 0v24H0V0h24ZM10.933 15.89H6.84v5.52h4.198v-.93H7.955v-1.503h2.77v-.93h-2.77v-1.224h2.978v-.934Zm2.146 0h-1.084v5.52h1.035v-3.6l2.226 3.6h1.118v-5.52h-1.036v3.686l-2.259-3.687Zm5.117 0h-1.208l1.973 5.52h1.19l1.976-5.52h-1.182l-1.352 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","inputSchema",{"_41":48,"_145":146,"_46":147,"_35":149},"title","create_s3_sourceArguments",[77,148],"remote_url",{"_77":150,"_152":153,"_148":157},{"_41":42,"_145":151},"Name","recursive",{"_41":154,"_145":155,"_156":28},"boolean","Recursive","default",{"_41":42,"_145":158},"Remote Url",{"_77":160,"_39":161,"_143":162},"update_s3_source","Update an S3 source connector.\n\n Args:\n source_id: ID of the source connector to update\n remote_url: The S3 URI to the bucket or folder\n recursive: Whether to access subfolders within the bucket\n\n Returns:\n String containing the updated source connector information\n ",{"_41":48,"_145":163,"_46":164,"_35":166},"update_s3_sourceArguments",[165],"source_id",{"_152":167,"_165":173,"_148":175},{"_168":169,"_145":155,"_156":-5},"anyOf",[170,171],{"_41":154},{"_41":172},"null",{"_41":42,"_145":174},"Source Id",{"_168":176,"_145":158,"_156":-5},[177,178],{"_41":42},{"_41":172},{"_77":180,"_39":181,"_143":182},"delete_s3_source","Delete an S3 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",{"_41":48,"_145":306,"_46":307,"_35":310},"create_weaviate_destinationArguments",[77,308,309],"cluster_url","collection",{"_77":311,"_309":312,"_308":314},{"_41":42,"_145":151},{"_41":42,"_145":313},"Collection",{"_41":42,"_145":315},"Cluster Url",{"_77":317,"_39":318,"_143":319},"update_weaviate_destination","Update an weaviate destination connector.\n\n Args:\n destination_id: ID of the destination connector to update\n cluster_url (optional): URL of the weaviate cluster\n collection (optional): Name of the collection(like a file) to use in the weaviate cluster\n\n Returns:\n String containing the updated destination connector information\n ",{"_41":48,"_145":320,"_46":321,"_35":322},"update_weaviate_destinationArguments",[282],{"_309":323,"_308":327,"_282":331},{"_168":324,"_145":313,"_156":-5},[325,326],{"_41":42},{"_41":172},{"_168":328,"_145":315,"_156":-5},[329,330],{"_41":42},{"_41":172},{"_41":42,"_145":293},{"_77":333,"_39":334,"_143":335},"delete_weaviate_destination","Delete an weaviate destination connector.\n\n Args:\n destination_id: ID of the destination connector to delete\n\n Returns:\n String containing the result of the deletion\n ",{"_41":48,"_145":336,"_46":337,"_35":338},"delete_weaviate_destinationArguments",[282],{"_282":339},{"_41":42,"_145":293},{"_77":341,"_39":342,"_143":343},"create_astradb_destination","Create an AstraDB destination connector.\n\n Args:\n name: A unique name for this connector\n collection_name: The name of the collection to use \n keyspace: The AstraDB keyspace \n batch_size: The batch size for inserting documents, must be positive (default: 20)\n \n Note: A collection in AstraDB is a schemaless document store optimized for NoSQL workloads, \n equivalent to a table in traditional databases.\n A keyspace is the top-level namespace in AstraDB that groups multiple collections.\n We require the users to create their own collection and keyspace before creating the connector.\n\n Returns:\n String containing the created destination connector information\n ",{"_41":48,"_145":344,"_46":345,"_35":348},"create_astradb_destinationArguments",[77,346,347],"collection_name","keyspace",{"_77":349,"_347":350,"_352":353,"_346":357},{"_41":42,"_145":151},{"_41":42,"_145":351},"Keyspace","batch_size",{"_41":354,"_145":355,"_156":356},"integer","Batch Size",20,{"_41":42,"_145":358},"Collection Name",{"_77":360,"_39":361,"_143":362},"update_astradb_destination","Update an AstraDB destination connector.\n\n Args:\n destination_id: ID of the destination connector to update\n collection_name: The name of the collection to use (optional)\n keyspace: The AstraDB keyspace (optional)\n batch_size: The batch size for inserting documents (optional)\n \n Note: We require the users to create their own collection and keyspace before creating the connector.\n\n Returns:\n String containing the updated destination connector information\n ",{"_41":48,"_145":363,"_46":364,"_35":365},"update_astradb_destinationArguments",[282],{"_347":366,"_352":370,"_282":374,"_346":375},{"_168":367,"_145":351,"_156":-5},[368,369],{"_41":42},{"_41":172},{"_168":371,"_145":355,"_156":-5},[372,373],{"_41":354},{"_41":172},{"_41":42,"_145":293},{"_168":376,"_145":358,"_156":-5},[377,378],{"_41":42},{"_41":172},{"_77":380,"_39":381,"_143":382},"delete_astradb_destination","Delete an AstraDB destination connector.\n\n Args:\n destination_id: ID of the destination connector to delete\n\n Returns:\n String containing the result of the deletion\n ",{"_41":48,"_145":383,"_46":384,"_35":385},"delete_astradb_destinationArguments",[282],{"_282":386},{"_41":42,"_145":293},{"_77":388,"_39":389,"_143":390},"create_neo4j_destination","Create an neo4j destination connector.\n\n Args:\n name: A unique name for this connector\n database: The neo4j database, e.g. \"neo4j\"\n uri: The neo4j URI, e.g. neo4j+s://.databases.neo4j.io\n username: The neo4j username\n\n\n Returns:\n String containing the created destination connector information\n ",{"_41":48,"_145":391,"_46":392,"_35":396},"create_neo4j_destinationArguments",[77,393,394,395],"database","uri","username",{"_394":397,"_77":399,"_393":400,"_395":402,"_352":404},{"_41":42,"_145":398},"Uri",{"_41":42,"_145":151},{"_41":42,"_145":401},"Database",{"_41":42,"_145":403},"Username",{"_168":405,"_145":355,"_156":132},[406,407],{"_41":354},{"_41":172},{"_77":409,"_39":410,"_143":411},"update_neo4j_destination","Update an neo4j destination connector.\n\n Args:\n destination_id: ID of the destination connector to update\n database: The neo4j database, e.g. \"neo4j\"\n uri: The neo4j URI, e.g. neo4j+s://.databases.neo4j.io\n username: The neo4j username\n\n\n Returns:\n String containing the updated destination connector information\n ",{"_41":48,"_145":412,"_46":413,"_35":414},"update_neo4j_destinationArguments",[282],{"_394":415,"_393":419,"_395":423,"_352":427,"_282":431},{"_168":416,"_145":398,"_156":-5},[417,418],{"_41":42},{"_41":172},{"_168":420,"_145":401,"_156":-5},[421,422],{"_41":42},{"_41":172},{"_168":424,"_145":403,"_156":-5},[425,426],{"_41":42},{"_41":172},{"_168":428,"_145":355,"_156":-5},[429,430],{"_41":354},{"_41":172},{"_41":42,"_145":293},{"_77":433,"_39":434,"_143":435},"delete_neo4j_destination","Delete an neo4j destination connector.\n\n Args:\n destination_id: ID of the destination connector to delete\n\n Returns:\n String containing the result of the deletion\n ",{"_41":48,"_145":436,"_46":437,"_35":438},"delete_neo4j_destinationArguments",[282],{"_282":439},{"_41":42,"_145":293},{"_77":441,"_39":442,"_143":443},"invoke_firecrawl_crawlhtml","Start an asynchronous web crawl job using Firecrawl to retrieve HTML content.\n\n Args:\n url: URL to crawl\n s3_uri: S3 URI where results will be uploaded \n limit: Maximum number of pages to crawl (default: 100)\n\n Returns:\n Dictionary with crawl job information including the job ID\n ",{"_41":48,"_145":444,"_46":445,"_35":447},"invoke_firecrawl_crawlhtmlArguments",[65,446],"s3_uri",{"_65":448,"_450":451,"_446":453},{"_41":42,"_145":449},"Url","limit",{"_41":354,"_145":452,"_156":132},"Limit",{"_41":42,"_145":454},"S3 Uri",{"_77":456,"_39":457,"_143":458},"check_crawlhtml_status","Check the status of an existing Firecrawl HTML crawl job.\n\n Args:\n crawl_id: ID of the crawl job to check\n\n Returns:\n Dictionary containing the current status of the crawl job\n ",{"_41":48,"_145":459,"_46":460,"_35":462},"check_crawlhtml_statusArguments",[461],"crawl_id",{"_461":463},{"_41":42,"_145":464},"Crawl Id",{"_77":466,"_39":467,"_143":468},"invoke_firecrawl_llmtxt","Start an asynchronous llmfull.txt generation job using Firecrawl.\n This file is a standardized markdown file containing information to help LLMs use a website at inference time. \n The llmstxt endpoint leverages Firecrawl to crawl your website and extracts data using gpt-4o-mini\n Args:\n url: URL to crawl\n s3_uri: S3 URI where results will be uploaded\n max_urls: Maximum number of pages to crawl (1-100, default: 10)\n\n Returns:\n Dictionary with job information including the job ID\n ",{"_41":48,"_145":469,"_46":470,"_35":471},"invoke_firecrawl_llmtxtArguments",[65,446],{"_65":472,"_446":473,"_474":475},{"_41":42,"_145":449},{"_41":42,"_145":454},"max_urls",{"_41":354,"_145":476,"_156":477},"Max Urls",10,{"_77":479,"_39":480,"_143":481},"check_llmtxt_status","Check the status of an existing llmfull.txt generation job.\n\n Args:\n job_id: ID of the llmfull.txt generation job to check\n\n Returns:\n Dictionary containing the current status of the job and text content if completed\n ",{"_41":48,"_145":482,"_46":483,"_35":485},"check_llmtxt_statusArguments",[484],"job_id",{"_484":486},{"_41":42,"_145":487},"Job Id",{"_77":489,"_39":490,"_143":491},"cancel_crawlhtml_job","Cancel an in-progress Firecrawl HTML crawl job.\n\n Args:\n crawl_id: ID of the crawl job to cancel\n\n Returns:\n Dictionary containing the result of the cancellation\n ",{"_41":48,"_145":492,"_46":493,"_35":494},"cancel_crawlhtml_jobArguments",[461],{"_461":495},{"_41":42,"_145":464},{"_77":497,"_39":498,"_143":499},"list_sources","\n List available sources from the Unstructured API.\n\n Args:\n source_type: Optional source connector type to filter by\n\n Returns:\n String containing the list of sources\n ",{"_41":48,"_145":500,"_35":501},"list_sourcesArguments",{"_502":503},"source_type",{"_168":504,"_145":507,"_156":-5},[505,506],{"_41":42},{"_41":172},"Source Type",{"_77":509,"_39":510,"_143":511},"get_source_info","Get detailed information about a specific source connector.\n\n Args:\n source_id: ID of the source connector to get information for, should be valid UUID\n\n Returns:\n String containing the source connector information\n ",{"_41":48,"_145":512,"_46":513,"_35":514},"get_source_infoArguments",[165],{"_165":515},{"_41":42,"_145":174},{"_77":517,"_39":518,"_143":519},"list_destinations","List available destinations from the Unstructured API.\n\n Args:\n destination_type: Optional destination connector type to filter by\n\n Returns:\n String containing the list of destinations\n ",{"_41":48,"_145":520,"_35":521},"list_destinationsArguments",{"_522":523},"destination_type",{"_168":524,"_145":527,"_156":-5},[525,526],{"_41":42},{"_41":172},"Destination Type",{"_77":529,"_39":530,"_143":531},"get_destination_info","Get detailed information about a specific destination connector.\n\n Args:\n destination_id: ID of the destination connector to get information for\n\n Returns:\n String containing the destination connector information\n ",{"_41":48,"_145":532,"_46":533,"_35":534},"get_destination_infoArguments",[282],{"_282":535},{"_41":42,"_145":293},{"_77":537,"_39":538,"_143":539},"list_workflows","\n List workflows from the Unstructured API.\n\n Args:\n destination_id: Optional destination connector ID to filter by\n source_id: Optional source connector ID to filter by\n status: Optional workflow status to filter by\n\n Returns:\n String containing the list of workflows\n ",{"_41":48,"_145":540,"_35":541},"list_workflowsArguments",{"_542":543,"_165":548,"_282":552},"status",{"_168":544,"_145":547,"_156":-5},[545,546],{"_41":42},{"_41":172},"Status",{"_168":549,"_145":174,"_156":-5},[550,551],{"_41":42},{"_41":172},{"_168":553,"_145":293,"_156":-5},[554,555],{"_41":42},{"_41":172},{"_77":557,"_39":558,"_143":559},"get_workflow_info","Get detailed information about a specific workflow.\n\n Args:\n workflow_id: ID of the workflow to get information for\n\n Returns:\n String containing the workflow information\n ",{"_41":48,"_145":560,"_46":561,"_35":563},"get_workflow_infoArguments",[562],"workflow_id",{"_562":564},{"_41":42,"_145":565},"Workflow Id",{"_77":567,"_39":568,"_143":569},"create_workflow","Create a new workflow.\n\n Args:\n workflow_config: A Typed Dictionary containing required fields (destination_id - should be a\n valid UUID, name, source_id - should be a valid UUID, workflow_type) and non-required fields\n (schedule, and workflow_nodes). Note workflow_nodes is only enabled when workflow_type\n is `custom` and is a list of WorkflowNodeTypedDict: partition, prompter,chunk, embed\n Below is an example of a partition workflow node:\n {\n \"name\": \"vlm-partition\",\n \"type\": \"partition\",\n \"sub_type\": \"vlm\",\n \"settings\": {\n \"provider\": \"your favorite provider\",\n \"model\": \"your favorite model\"\n }\n }\n\n\n Returns:\n String containing the created workflow information\n \n\nCustom workflow DAG nodes\n- If WorkflowType is set to custom, you must also specify the settings for the workflow’s\ndirected acyclic graph (DAG) nodes. These nodes’ settings are specified in the workflow_nodes array.\n- A Source node is automatically created when you specify the source_id value outside of the\nworkflow_nodes array.\n- A Destination node is automatically created when you specify the destination_id value outside\nof the workflow_nodes array.\n- You can specify Partitioner, Chunker, Prompter, and Embedder nodes.\n- The order of the nodes in the workflow_nodes array will be the same order that these nodes appear\nin the DAG, with the first node in the array added directly after the Source node.\nThe Destination node follows the last node in the array.\n- Be sure to specify nodes in the allowed order. The following DAG placements are all allowed:\n - Source -> Partitioner -> Destination,\n - Source -> Partitioner -> Chunker -> Destination,\n - Source -> Partitioner -> Chunker -> Embedder -> Destination,\n - Source -> Partitioner -> Prompter -> Chunker -> Destination,\n - Source -> Partitioner -> Prompter -> Chunker -> Embedder -> Destination\n\nPartitioner node\nA Partitioner node has a type of partition and a subtype of auto, vlm, hi_res, or fast.\n\nExamples:\n- auto strategy:\n{\n \"name\": \"Partitioner\",\n \"type\": \"partition\",\n \"subtype\": \"vlm\",\n \"settings\": {\n \"provider\": \"anthropic\", (required)\n \"model\": \"claude-3-5-sonnet-20241022\", (required)\n \"output_format\": \"text/html\",\n \"user_prompt\": null,\n \"format_html\": true,\n \"unique_element_ids\": true,\n \"is_dynamic\": true,\n \"allow_fast\": true\n }\n}\n\n- vlm strategy:\n Allowed values are provider and model. Below are examples:\n - \"provider\": \"anthropic\" \"model\": \"claude-3-5-sonnet-20241022\",\n - \"provider\": \"openai\" \"model\": \"gpt-4o\"\n\n\n- hi_res strategy:\n{\n \"name\": \"Partitioner\",\n \"type\": \"partition\",\n \"subtype\": \"unstructured_api\",\n \"settings\": {\n \"strategy\": \"hi_res\",\n \"include_page_breaks\": ,\n \"pdf_infer_table_structure\": ,\n \"exclude_elements\": [\n \"\",\n \"\"\n ],\n \"xml_keep_tags\": ,\n \"encoding\": \"\",\n \"ocr_languages\": [\n \"\",\n \"\"\n ],\n \"extract_image_block_types\": [\n \"image\",\n \"table\"\n ],\n \"infer_table_structure\": \n }\n}\n- fast strategy\n{\n \"name\": \"Partitioner\",\n \"type\": \"partition\",\n \"subtype\": \"unstructured_api\",\n \"settings\": {\n \"strategy\": \"fast\",\n \"include_page_breaks\": ,\n \"pdf_infer_table_structure\": ,\n \"exclude_elements\": [\n \"\",\n \"\"\n ],\n \"xml_keep_tags\": ,\n \"encoding\": \"\",\n \"ocr_languages\": [\n \"\",\n \"\"\n ],\n \"extract_image_block_types\": [\n \"image\",\n \"table\"\n ],\n \"infer_table_structure\": \n }\n}\n\n\nChunker node\nA Chunker node has a type of chunk and subtype of chunk_by_character or chunk_by_title.\n\n- chunk_by_character\n{\n \"name\": \"Chunker\",\n \"type\": \"chunk\",\n \"subtype\": \"chunk_by_character\",\n \"settings\": {\n \"include_orig_elements\": ,\n \"new_after_n_chars\": , (required, if not provided\nset same as max_characters)\n \"max_characters\": , (required)\n \"overlap\": , (required, if not provided set default to 0)\n \"overlap_all\": ,\n \"contextual_chunking_strategy\": \"v1\"\n }\n}\n\n- chunk_by_title\n{\n \"name\": \"Chunker\",\n \"type\": \"chunk\",\n \"subtype\": \"chunk_by_title\",\n \"settings\": {\n \"multipage_sections\": ,\n \"combine_text_under_n_chars\": ,\n \"include_orig_elements\": ,\n \"new_after_n_chars\": , (required, if not provided\nset same as max_characters)\n \"max_characters\": , (required)\n \"overlap\": , (required, if not provided set default to 0)\n \"overlap_all\": ,\n \"contextual_chunking_strategy\": \"v1\"\n }\n}\n\n\nPrompter node\nAn Prompter node has a type of prompter and subtype of:\n- openai_image_description,\n- anthropic_image_description,\n- bedrock_image_description,\n- vertexai_image_description,\n- openai_table_description,\n- anthropic_table_description,\n- bedrock_table_description,\n- vertexai_table_description,\n- openai_table2html,\n- openai_ner\n\nExample:\n{\n \"name\": \"Prompter\",\n \"type\": \"prompter\",\n \"subtype\": \"\",\n \"settings\": {}\n}\n\n\nEmbedder node\nAn Embedder node has a type of embed\n\nAllowed values for subtype and model_name include:\n\n- \"subtype\": \"azure_openai\"\n - \"model_name\": \"text-embedding-3-small\"\n - \"model_name\": \"text-embedding-3-large\"\n - \"model_name\": \"text-embedding-ada-002\"\n- \"subtype\": \"bedrock\"\n - \"model_name\": \"amazon.titan-embed-text-v2:0\"\n - \"model_name\": \"amazon.titan-embed-text-v1\"\n - \"model_name\": \"amazon.titan-embed-image-v1\"\n - \"model_name\": \"cohere.embed-english-v3\"\n - \"model_name\": \"cohere.embed-multilingual-v3\"\n- \"subtype\": \"togetherai\":\n - \"model_name\": \"togethercomputer/m2-bert-80M-2k-retrieval\"\n - \"model_name\": \"togethercomputer/m2-bert-80M-8k-retrieval\"\n - \"model_name\": \"togethercomputer/m2-bert-80M-32k-retrieval\"\n\nExample:\n{\n \"name\": \"Embedder\",\n \"type\": \"embed\",\n \"subtype\": \"\",\n \"settings\": {\n \"model_name\": \"\"\n }\n}\n",{"_41":48,"_570":571,"_145":657,"_46":658,"_35":660},"$defs",{"_572":573,"_587":588,"_594":595,"_599":600,"_606":607,"_613":614,"_626":627,"_643":644,"_648":649},"Schedule",{"_574":575,"_41":42,"_145":572},"enum",[576,577,578,579,580,581,582,583,584,585,586],"every 15 minutes","every hour","every 2 hours","every 4 hours","every 6 hours","every 8 hours","every 10 hours","every 12 hours","daily","weekly","monthly","WorkflowType",{"_574":589,"_41":42,"_145":587},[590,591,592,593],"basic","advanced","platinum","custom","Nullable_str_",{"_168":596},[597,598],{"_41":42},{"_41":172},"WorkflowNodeType",{"_574":601,"_41":42,"_145":599},[602,603,604,605],"partition","prompter","chunk","embed","Nullable_Schedule_",{"_168":608},[609,612],{"_610":611},"$ref","#/$defs/Schedule",{"_41":172},"WorkflowNodeTypedDict",{"_41":48,"_145":613,"_46":615,"_35":617},[77,616,41],"subtype",{"_77":618,"_41":619,"_616":621,"_623":624},{"_41":42,"_145":151},{"_610":620},"#/$defs/WorkflowNodeType",{"_41":42,"_145":622},"Subtype","settings",{"_610":625},"#/$defs/Nullable_Dict_str__Any__","CreateWorkflowTypedDict",{"_41":48,"_145":626,"_46":628,"_35":630},[77,629],"workflow_type",{"_77":631,"_632":633,"_165":635,"_629":637,"_282":639,"_640":641},{"_41":42,"_145":151},"schedule",{"_610":634},"#/$defs/Nullable_Schedule_",{"_610":636},"#/$defs/Nullable_str_",{"_610":638},"#/$defs/WorkflowType",{"_610":636},"workflow_nodes",{"_610":642},"#/$defs/Nullable_List_WorkflowNodeTypedDict__","Nullable_Dict_str__Any__",{"_168":645},[646,647],{"_41":48},{"_41":172},"Nullable_List_WorkflowNodeTypedDict__",{"_168":650},[651,656],{"_41":652,"_653":654},"array","items",{"_610":655},"#/$defs/WorkflowNodeTypedDict",{"_41":172},"create_workflowArguments",[659],"workflow_config",{"_659":661},{"_610":662},"#/$defs/CreateWorkflowTypedDict",{"_77":664,"_39":665,"_143":666},"run_workflow","Run a specific workflow.\n\n Args:\n workflow_id: ID of the workflow to run\n\n Returns:\n String containing the response from the workflow execution\n ",{"_41":48,"_145":667,"_46":668,"_35":669},"run_workflowArguments",[562],{"_562":670},{"_41":42,"_145":565},{"_77":672,"_39":673,"_143":674},"update_workflow","Update an existing workflow.\n\n Args:\n workflow_id: ID of the workflow to update\n workflow_config: A Typed Dictionary containing required fields (destination_id,\n name, source_id, workflow_type) and non-required fields (schedule, and workflow_nodes)\n\n Returns:\n String containing the updated workflow information\n \n\nCustom workflow DAG nodes\n- If WorkflowType is set to custom, you must also specify the settings for the workflow’s\ndirected acyclic graph (DAG) nodes. These nodes’ settings are specified in the workflow_nodes array.\n- A Source node is automatically created when you specify the source_id value outside of the\nworkflow_nodes array.\n- A Destination node is automatically created when you specify the destination_id value outside\nof the workflow_nodes array.\n- You can specify Partitioner, Chunker, Prompter, and Embedder nodes.\n- The order of the nodes in the workflow_nodes array will be the same order that these nodes appear\nin the DAG, with the first node in the array added directly after the Source node.\nThe Destination node follows the last node in the array.\n- Be sure to specify nodes in the allowed order. The following DAG placements are all allowed:\n - Source -> Partitioner -> Destination,\n - Source -> Partitioner -> Chunker -> Destination,\n - Source -> Partitioner -> Chunker -> Embedder -> Destination,\n - Source -> Partitioner -> Prompter -> Chunker -> Destination,\n - Source -> Partitioner -> Prompter -> Chunker -> Embedder -> Destination\n\nPartitioner node\nA Partitioner node has a type of partition and a subtype of auto, vlm, hi_res, or fast.\n\nExamples:\n- auto strategy:\n{\n \"name\": \"Partitioner\",\n \"type\": \"partition\",\n \"subtype\": \"vlm\",\n \"settings\": {\n \"provider\": \"anthropic\", (required)\n \"model\": \"claude-3-5-sonnet-20241022\", (required)\n \"output_format\": \"text/html\",\n \"user_prompt\": null,\n \"format_html\": true,\n \"unique_element_ids\": true,\n \"is_dynamic\": true,\n \"allow_fast\": true\n }\n}\n\n- vlm strategy:\n Allowed values are provider and model. Below are examples:\n - \"provider\": \"anthropic\" \"model\": \"claude-3-5-sonnet-20241022\",\n - \"provider\": \"openai\" \"model\": \"gpt-4o\"\n\n\n- hi_res strategy:\n{\n \"name\": \"Partitioner\",\n \"type\": \"partition\",\n \"subtype\": \"unstructured_api\",\n \"settings\": {\n \"strategy\": \"hi_res\",\n \"include_page_breaks\": ,\n \"pdf_infer_table_structure\": ,\n \"exclude_elements\": [\n \"\",\n \"\"\n ],\n \"xml_keep_tags\": ,\n \"encoding\": \"\",\n \"ocr_languages\": [\n \"\",\n \"\"\n ],\n \"extract_image_block_types\": [\n \"image\",\n \"table\"\n ],\n \"infer_table_structure\": \n }\n}\n- fast strategy\n{\n \"name\": \"Partitioner\",\n \"type\": \"partition\",\n \"subtype\": \"unstructured_api\",\n \"settings\": {\n \"strategy\": \"fast\",\n \"include_page_breaks\": ,\n \"pdf_infer_table_structure\": ,\n \"exclude_elements\": [\n \"\",\n \"\"\n ],\n \"xml_keep_tags\": ,\n \"encoding\": \"\",\n \"ocr_languages\": [\n \"\",\n \"\"\n ],\n \"extract_image_block_types\": [\n \"image\",\n \"table\"\n ],\n \"infer_table_structure\": \n }\n}\n\n\nChunker node\nA Chunker node has a type of chunk and subtype of chunk_by_character or chunk_by_title.\n\n- chunk_by_character\n{\n \"name\": \"Chunker\",\n \"type\": \"chunk\",\n \"subtype\": \"chunk_by_character\",\n \"settings\": {\n \"include_orig_elements\": ,\n \"new_after_n_chars\": , (required, if not provided\nset same as max_characters)\n \"max_characters\": , (required)\n \"overlap\": , (required, if not provided set default to 0)\n \"overlap_all\": ,\n \"contextual_chunking_strategy\": \"v1\"\n }\n}\n\n- chunk_by_title\n{\n \"name\": \"Chunker\",\n \"type\": \"chunk\",\n \"subtype\": \"chunk_by_title\",\n \"settings\": {\n \"multipage_sections\": ,\n \"combine_text_under_n_chars\": ,\n \"include_orig_elements\": ,\n \"new_after_n_chars\": , (required, if not provided\nset same as max_characters)\n \"max_characters\": , (required)\n \"overlap\": , (required, if not provided set default to 0)\n \"overlap_all\": ,\n \"contextual_chunking_strategy\": \"v1\"\n }\n}\n\n\nPrompter node\nAn Prompter node has a type of prompter and subtype of:\n- openai_image_description,\n- anthropic_image_description,\n- bedrock_image_description,\n- vertexai_image_description,\n- openai_table_description,\n- anthropic_table_description,\n- bedrock_table_description,\n- vertexai_table_description,\n- openai_table2html,\n- openai_ner\n\nExample:\n{\n \"name\": \"Prompter\",\n \"type\": \"prompter\",\n \"subtype\": \"\",\n \"settings\": {}\n}\n\n\nEmbedder node\nAn Embedder node has a type of embed\n\nAllowed values for subtype and model_name include:\n\n- \"subtype\": \"azure_openai\"\n - \"model_name\": \"text-embedding-3-small\"\n - \"model_name\": \"text-embedding-3-large\"\n - \"model_name\": \"text-embedding-ada-002\"\n- \"subtype\": \"bedrock\"\n - \"model_name\": \"amazon.titan-embed-text-v2:0\"\n - \"model_name\": \"amazon.titan-embed-text-v1\"\n - \"model_name\": 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