## Search Pipelines `$ llamacloud-prod pipelines list` **get** `/api/v1/pipelines` Search for pipelines by name, type, or project. ### Parameters - `--organization-id: optional string` - `--pipeline-name: optional string` - `--pipeline-type: optional "PLAYGROUND" or "MANAGED"` Enum for representing the type of a pipeline - `--project-id: optional string` - `--project-name: optional string` ### Returns - `Response Search Pipelines Api V1 Pipelines Get: array of Pipeline` - `id: string` Unique identifier - `embedding_config: object { component, type } or AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or 5 more` - `MANAGED_OPENAI_EMBEDDING: object { component, type }` - `component: optional object { class_name, embed_batch_size, model_name, num_workers }` Configuration for the Managed OpenAI embedding model. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional "openai-text-embedding-3-small"` The name of the OpenAI embedding model. - `"openai-text-embedding-3-small"` - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "MANAGED_OPENAI_EMBEDDING"` Type of the embedding model. - `"MANAGED_OPENAI_EMBEDDING"` - `azure_openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 12 more }` Configuration for the Azure OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for Azure deployment. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for Azure OpenAI API. - `azure_deployment: optional string` The Azure deployment to use. - `azure_endpoint: optional string` The Azure endpoint to use. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "AZURE_EMBEDDING"` Type of the embedding model. - `"AZURE_EMBEDDING"` - `cohere_embedding_config: object { component, type }` - `component: optional object { api_key, class_name, embed_batch_size, 5 more }` Configuration for the Cohere embedding model. - `api_key: string` The Cohere API key. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embedding_type: optional string` Embedding type. If not provided float embedding_type is used when needed. - `input_type: optional string` Model Input type. If not provided, search_document and search_query are used when needed. - `model_name: optional string` The modelId of the Cohere model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `truncate: optional string` Truncation type - START/ END/ NONE - `type: optional "COHERE_EMBEDDING"` Type of the embedding model. - `"COHERE_EMBEDDING"` - `gemini_embedding_config: object { component, type }` - `component: optional object { api_base, api_key, class_name, 7 more }` Configuration for the Gemini embedding model. - `api_base: optional string` API base to access the model. Defaults to None. - `api_key: optional string` API key to access the model. Defaults to None. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `model_name: optional string` The modelId of the Gemini model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `output_dimensionality: optional number` Optional reduced dimension for output embeddings. Supported by models/text-embedding-004 and newer (e.g. gemini-embedding-001). Not supported by models/embedding-001. - `task_type: optional string` The task for embedding model. - `title: optional string` Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid. - `transport: optional string` Transport to access the model. Defaults to None. - `type: optional "GEMINI_EMBEDDING"` Type of the embedding model. - `"GEMINI_EMBEDDING"` - `hugging_face_inference_api_embedding_config: object { component, type }` - `component: optional object { token, class_name, cookies, 9 more }` Configuration for the HuggingFace Inference API embedding model. - `token: optional string or boolean` Hugging Face token. Will default to the locally saved token. Pass token=False if you don’t want to send your token to the server. - `union_member_0: string` - `union_member_1: boolean` - `class_name: optional string` - `cookies: optional map[string]` Additional cookies to send to the server. - `embed_batch_size: optional number` The batch size for embedding calls. - `headers: optional map[string]` Additional headers to send to the server. By default only the authorization and user-agent headers are sent. Values in this dictionary will override the default values. - `model_name: optional string` Hugging Face model name. If None, the task will be used. - `num_workers: optional number` The number of workers to use for async embedding calls. - `pooling: optional "cls" or "mean" or "last"` Enum of possible pooling choices with pooling behaviors. - `"cls"` - `"mean"` - `"last"` - `query_instruction: optional string` Instruction to prepend during query embedding. - `task: optional string` Optional task to pick Hugging Face's recommended model, used when model_name is left as default of None. - `text_instruction: optional string` Instruction to prepend during text embedding. - `timeout: optional number` The maximum number of seconds to wait for a response from the server. Loading a new model in Inference API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - `type: optional "HUGGINGFACE_API_EMBEDDING"` Type of the embedding model. - `"HUGGINGFACE_API_EMBEDDING"` - `openai_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, api_base, api_key, 10 more }` Configuration for the OpenAI embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the OpenAI API. - `api_base: optional string` The base URL for OpenAI API. - `api_key: optional string` The OpenAI API key. - `api_version: optional string` The version for OpenAI API. - `class_name: optional string` - `default_headers: optional map[string]` The default headers for API requests. - `dimensions: optional number` The number of dimensions on the output embedding vectors. Works only with v3 embedding models. - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` Maximum number of retries. - `model_name: optional string` The name of the OpenAI embedding model. - `num_workers: optional number` The number of workers to use for async embedding calls. - `reuse_client: optional boolean` Reuse the OpenAI client between requests. When doing anything with large volumes of async API calls, setting this to false can improve stability. - `timeout: optional number` Timeout for each request. - `type: optional "OPENAI_EMBEDDING"` Type of the embedding model. - `"OPENAI_EMBEDDING"` - `vertex_ai_embedding_config: object { component, type }` - `component: optional object { client_email, location, private_key, 9 more }` Configuration for the VertexAI embedding model. - `client_email: string` The client email for the VertexAI credentials. - `location: string` The default location to use when making API calls. - `private_key: string` The private key for the VertexAI credentials. - `private_key_id: string` The private key ID for the VertexAI credentials. - `project: string` The default GCP project to use when making Vertex API calls. - `token_uri: string` The token URI for the VertexAI credentials. - `additional_kwargs: optional map[unknown]` Additional kwargs for the Vertex. - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `embed_mode: optional "default" or "classification" or "clustering" or 2 more` The embedding mode to use. - `"default"` - `"classification"` - `"clustering"` - `"similarity"` - `"retrieval"` - `model_name: optional string` The modelId of the VertexAI model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `type: optional "VERTEXAI_EMBEDDING"` Type of the embedding model. - `"VERTEXAI_EMBEDDING"` - `bedrock_embedding_config: object { component, type }` - `component: optional object { additional_kwargs, aws_access_key_id, aws_secret_access_key, 9 more }` Configuration for the Bedrock embedding model. - `additional_kwargs: optional map[unknown]` Additional kwargs for the bedrock client. - `aws_access_key_id: optional string` AWS Access Key ID to use - `aws_secret_access_key: optional string` AWS Secret Access Key to use - `aws_session_token: optional string` AWS Session Token to use - `class_name: optional string` - `embed_batch_size: optional number` The batch size for embedding calls. - `max_retries: optional number` The maximum number of API retries. - `model_name: optional string` The modelId of the Bedrock model to use. - `num_workers: optional number` The number of workers to use for async embedding calls. - `profile_name: optional string` The name of aws profile to use. If not given, then the default profile is used. - `region_name: optional string` AWS region name to use. Uses region configured in AWS CLI if not passed - `timeout: optional number` The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts. - `type: optional "BEDROCK_EMBEDDING"` Type of the embedding model. - `"BEDROCK_EMBEDDING"` - `name: string` - `project_id: string` - `config_hash: optional object { embedding_config_hash, parsing_config_hash, transform_config_hash }` Hashes for the configuration of a pipeline. - `embedding_config_hash: optional string` Hash of the embedding config. - `parsing_config_hash: optional string` Hash of the llama parse parameters. - `transform_config_hash: optional string` Hash of the transform config. - `created_at: optional string` Creation datetime - `data_sink: optional object { id, component, name, 4 more }` Schema for a data sink. - `id: string` Unique identifier - `component: map[unknown] or CloudPineconeVectorStore or CloudPostgresVectorStore or 5 more` Component that implements the data sink - `union_member_0: map[unknown]` - `cloud_pinecone_vector_store: object { api_key, index_name, class_name, 3 more }` Cloud Pinecone Vector Store. This class is used to store the configuration for a Pinecone vector store, so that it can be created and used in LlamaCloud. Args: api_key (str): API key for authenticating with Pinecone index_name (str): name of the Pinecone index namespace (optional[str]): namespace to use in the Pinecone index insert_kwargs (optional[dict]): additional kwargs to pass during insertion - `api_key: string` The API key for authenticating with Pinecone - `index_name: string` - `class_name: optional string` - `insert_kwargs: optional map[unknown]` - `namespace: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_postgres_vector_store: object { database, embed_dim, host, 10 more }` - `database: string` - `embed_dim: number` - `host: string` - `password: string` - `port: number` - `schema_name: string` - `table_name: string` - `user: string` - `class_name: optional string` - `hnsw_settings: optional object { distance_method, ef_construction, ef_search, 2 more }` HNSW settings for PGVector. - `distance_method: optional "l2" or "ip" or "cosine" or 3 more` The distance method to use. - `"l2"` - `"ip"` - `"cosine"` - `"l1"` - `"hamming"` - `"jaccard"` - `ef_construction: optional number` The number of edges to use during the construction phase. - `ef_search: optional number` The number of edges to use during the search phase. - `m: optional number` The number of bi-directional links created for each new element. - `vector_type: optional "vector" or "half_vec" or "bit" or "sparse_vec"` The type of vector to use. - `"vector"` - `"half_vec"` - `"bit"` - `"sparse_vec"` - `hybrid_search: optional boolean` - `perform_setup: optional boolean` - `supports_nested_metadata_filters: optional boolean` - `cloud_qdrant_vector_store: object { api_key, collection_name, url, 4 more }` Cloud Qdrant Vector Store. This class is used to store the configuration for a Qdrant vector store, so that it can be created and used in LlamaCloud. Args: collection_name (str): name of the Qdrant collection url (str): url of the Qdrant instance api_key (str): API key for authenticating with Qdrant max_retries (int): maximum number of retries in case of a failure. Defaults to 3 client_kwargs (dict): additional kwargs to pass to the Qdrant client - `api_key: string` - `collection_name: string` - `url: string` - `class_name: optional string` - `client_kwargs: optional map[unknown]` - `max_retries: optional number` - `supports_nested_metadata_filters: optional true` - `true` - `cloud_azure_ai_search_vector_store: object { search_service_api_key, search_service_endpoint, class_name, 8 more }` Cloud Azure AI Search Vector Store. - `search_service_api_key: string` - `search_service_endpoint: string` - `class_name: optional string` - `client_id: optional string` - `client_secret: optional string` - `embedding_dimension: optional number` - `filterable_metadata_field_keys: optional map[unknown]` - `index_name: optional string` - `search_service_api_version: optional string` - `supports_nested_metadata_filters: optional true` - `true` - `tenant_id: optional string` - `cloud_mongodb_atlas_vector_search: object { collection_name, db_name, mongodb_uri, 5 more }` Cloud MongoDB Atlas Vector Store. This class is used to store the configuration for a MongoDB Atlas vector store, so that it can be created and used in LlamaCloud. Args: mongodb_uri (str): URI for connecting to MongoDB Atlas db_name (str): name of the MongoDB database collection_name (str): name of the MongoDB collection vector_index_name (str): name of the MongoDB Atlas vector index fulltext_index_name (str): name of the MongoDB Atlas full-text index - `collection_name: string` - `db_name: string` - `mongodb_uri: string` - `class_name: optional string` - `embedding_dimension: optional number` - `fulltext_index_name: optional string` - `supports_nested_metadata_filters: optional boolean` - `vector_index_name: optional string` - `cloud_milvus_vector_store: object { uri, token, class_name, 3 more }` Cloud Milvus Vector Store. - `uri: string` - `token: optional string` - `class_name: optional string` - `collection_name: optional string` - `embedding_dimension: optional number` - `supports_nested_metadata_filters: optional boolean` - `cloud_astra_db_vector_store: object { token, api_endpoint, collection_name, 4 more }` Cloud AstraDB Vector Store. This class is used to store the configuration for an AstraDB vector store, so that it can be created and used in LlamaCloud. Args: token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. collection_name (str): Collection name to use. If not existing, it will be created. embedding_dimension (int): Length of the embedding vectors in use. keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace' - `token: string` The Astra DB Application Token to use - `api_endpoint: string` The Astra DB JSON API endpoint for your database - `collection_name: string` Collection name to use. If not existing, it will be created - `embedding_dimension: number` Length of the embedding vectors in use - `class_name: optional string` - `keyspace: optional string` The keyspace to use. If not provided, 'default_keyspace' - `supports_nested_metadata_filters: optional true` - `true` - `name: string` The name of the data sink. - `project_id: string` - `sink_type: "PINECONE" or "POSTGRES" or "QDRANT" or 4 more` - `"PINECONE"` - `"POSTGRES"` - `"QDRANT"` - `"AZUREAI_SEARCH"` - `"MONGODB_ATLAS"` - `"MILVUS"` - `"ASTRA_DB"` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config: optional object { id, embedding_config, name, 3 more }` Schema for an embedding model config. - `id: string` Unique identifier - `embedding_config: AzureOpenAIEmbeddingConfig or CohereEmbeddingConfig or GeminiEmbeddingConfig or 4 more` The embedding configuration for the embedding model config. - `azure_openai_embedding_config: object { component, type }` - `cohere_embedding_config: object { component, type }` - `gemini_embedding_config: object { component, type }` - `hugging_face_inference_api_embedding_config: object { component, type }` - `openai_embedding_config: object { component, type }` - `vertex_ai_embedding_config: object { component, type }` - `bedrock_embedding_config: object { component, type }` - `name: string` The name of the embedding model config. - `project_id: string` - `created_at: optional string` Creation datetime - `updated_at: optional string` Update datetime - `embedding_model_config_id: optional string` The ID of the EmbeddingModelConfig this pipeline is using. - `llama_parse_parameters: optional object { adaptive_long_table, aggressive_table_extraction, annotate_links, 116 more }` Settings that can be configured for how to use LlamaParse to parse files within a LlamaCloud pipeline. - `adaptive_long_table: optional boolean` - `aggressive_table_extraction: optional boolean` - `annotate_links: optional boolean` - `auto_mode: optional boolean` - `auto_mode_configuration_json: optional string` - `auto_mode_trigger_on_image_in_page: optional boolean` - `auto_mode_trigger_on_regexp_in_page: optional string` - `auto_mode_trigger_on_table_in_page: optional boolean` - `auto_mode_trigger_on_text_in_page: optional string` - `azure_openai_api_version: optional string` - `azure_openai_deployment_name: optional string` - `azure_openai_endpoint: optional string` - `azure_openai_key: optional string` - `bbox_bottom: optional number` - `bbox_left: optional number` - `bbox_right: optional number` - `bbox_top: optional number` - `bounding_box: optional string` - `compact_markdown_table: optional boolean` - `complemental_formatting_instruction: optional string` - `content_guideline_instruction: optional string` - `continuous_mode: optional boolean` - `disable_image_extraction: optional boolean` - `disable_ocr: optional boolean` - `disable_reconstruction: optional boolean` - `do_not_cache: optional boolean` - `do_not_unroll_columns: optional boolean` - `enable_cost_optimizer: optional boolean` - `extract_charts: optional boolean` - `extract_layout: optional boolean` - `extract_printed_page_number: optional boolean` - `fast_mode: optional boolean` - `formatting_instruction: optional string` - `gpt4o_api_key: optional string` - `gpt4o_mode: optional boolean` - `guess_xlsx_sheet_name: optional boolean` - `hide_footers: optional boolean` - `hide_headers: optional boolean` - `high_res_ocr: optional boolean` - `html_make_all_elements_visible: optional boolean` - `html_remove_fixed_elements: optional boolean` - `html_remove_navigation_elements: optional boolean` - `http_proxy: optional string` - `ignore_document_elements_for_layout_detection: optional boolean` - `images_to_save: optional array of "screenshot" or "embedded" or "layout"` - `"screenshot"` - `"embedded"` - `"layout"` - `inline_images_in_markdown: optional boolean` - `input_s3_path: optional string` - `input_s3_region: optional string` - `input_url: optional string` - `internal_is_screenshot_job: optional boolean` - `invalidate_cache: optional boolean` - `is_formatting_instruction: optional boolean` - `job_timeout_extra_time_per_page_in_seconds: optional number` - `job_timeout_in_seconds: optional number` - `keep_page_separator_when_merging_tables: optional boolean` - `languages: optional array of ParsingLanguages` - `"af"` - `"az"` - `"bs"` - `"cs"` - `"cy"` - `"da"` - `"de"` - `"en"` - `"es"` - `"et"` - `"fr"` - `"ga"` - `"hr"` - `"hu"` - `"id"` - `"is"` - `"it"` - `"ku"` - `"la"` - `"lt"` - `"lv"` - `"mi"` - `"ms"` - `"mt"` - `"nl"` - `"no"` - `"oc"` - `"pi"` - `"pl"` - `"pt"` - `"ro"` - `"rs_latin"` - `"sk"` - `"sl"` - `"sq"` - `"sv"` - `"sw"` - `"tl"` - `"tr"` - `"uz"` - `"vi"` - `"ar"` - `"fa"` - `"ug"` - `"ur"` - `"bn"` - `"as"` - `"mni"` - `"ru"` - `"rs_cyrillic"` - `"be"` - `"bg"` - `"uk"` - `"mn"` - `"abq"` - `"ady"` - `"kbd"` - `"ava"` - `"dar"` - `"inh"` - `"che"` - `"lbe"` - `"lez"` - `"tab"` - `"tjk"` - `"hi"` - `"mr"` - `"ne"` - `"bh"` - `"mai"` - `"ang"` - `"bho"` - `"mah"` - `"sck"` - `"new"` - `"gom"` - `"sa"` - `"bgc"` - `"th"` - `"ch_sim"` - `"ch_tra"` - `"ja"` - `"ko"` - `"ta"` - `"te"` - `"kn"` - `layout_aware: optional boolean` - `line_level_bounding_box: optional boolean` - `markdown_table_multiline_header_separator: optional string` - `max_pages: optional number` - `max_pages_enforced: optional number` - `merge_tables_across_pages_in_markdown: optional boolean` - `model: optional string` - `outlined_table_extraction: optional boolean` - `output_pdf_of_document: optional boolean` - `output_s3_path_prefix: optional string` - `output_s3_region: optional string` - `output_tables_as_HTML: optional boolean` - `page_error_tolerance: optional number` - `page_footer_prefix: optional string` - `page_footer_suffix: optional string` - `page_header_prefix: optional string` - `page_header_suffix: optional string` - `page_prefix: optional string` - `page_separator: optional string` - `page_suffix: optional string` - `parse_mode: optional "parse_page_without_llm" or "parse_page_with_llm" or "parse_page_with_lvm" or 5 more` Enum for representing the mode of parsing to be used. - `"parse_page_without_llm"` - `"parse_page_with_llm"` - `"parse_page_with_lvm"` - `"parse_page_with_agent"` - `"parse_page_with_layout_agent"` - `"parse_document_with_llm"` - `"parse_document_with_lvm"` - `"parse_document_with_agent"` - `parsing_instruction: optional string` - `precise_bounding_box: optional boolean` - `premium_mode: optional boolean` - `presentation_out_of_bounds_content: optional boolean` - `presentation_skip_embedded_data: optional boolean` - `preserve_layout_alignment_across_pages: optional boolean` - `preserve_very_small_text: optional boolean` - `preset: optional string` - `priority: optional "low" or "medium" or "high" or "critical"` The priority for the request. This field may be ignored or overwritten depending on the organization tier. - `"low"` - `"medium"` - `"high"` - `"critical"` - `project_id: optional string` - `remove_hidden_text: optional boolean` - `replace_failed_page_mode: optional "raw_text" or "blank_page" or "error_message"` Enum for representing the different available page error handling modes. - `"raw_text"` - `"blank_page"` - `"error_message"` - `replace_failed_page_with_error_message_prefix: optional string` - `replace_failed_page_with_error_message_suffix: optional string` - `save_images: optional boolean` - `skip_diagonal_text: optional boolean` - `specialized_chart_parsing_agentic: optional boolean` - `specialized_chart_parsing_efficient: optional boolean` - `specialized_chart_parsing_plus: optional boolean` - `specialized_image_parsing: optional boolean` - `spreadsheet_extract_sub_tables: optional boolean` - `spreadsheet_force_formula_computation: optional boolean` - `spreadsheet_include_hidden_sheets: optional boolean` - `strict_mode_buggy_font: optional boolean` - `strict_mode_image_extraction: optional boolean` - `strict_mode_image_ocr: optional boolean` - `strict_mode_reconstruction: optional boolean` - `structured_output: optional boolean` - `structured_output_json_schema: optional string` - `structured_output_json_schema_name: optional string` - `system_prompt: optional string` - `system_prompt_append: optional string` - `take_screenshot: optional boolean` - `target_pages: optional string` - `tier: optional string` - `use_vendor_multimodal_model: optional boolean` - `user_prompt: optional string` - `vendor_multimodal_api_key: optional string` - `vendor_multimodal_model_name: optional string` - `version: optional string` - `webhook_configurations: optional array of object { webhook_events, webhook_headers, webhook_output_format, webhook_url }` Outbound webhook endpoints to notify on job status changes - `webhook_events: optional array of "extract.pending" or "extract.success" or "extract.error" or 14 more` Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered. - `"extract.pending"` - `"extract.success"` - `"extract.error"` - `"extract.partial_success"` - `"extract.cancelled"` - `"parse.pending"` - `"parse.running"` - `"parse.success"` - `"parse.error"` - `"parse.partial_success"` - `"parse.cancelled"` - `"classify.pending"` - `"classify.success"` - `"classify.error"` - `"classify.partial_success"` - `"classify.cancelled"` - `"unmapped_event"` - `webhook_headers: optional map[string]` Custom HTTP headers sent with each webhook request (e.g. auth tokens) - `webhook_output_format: optional string` Response format sent to the webhook: 'string' (default) or 'json' - `webhook_url: optional string` URL to receive webhook POST notifications - `webhook_url: optional string` - `managed_pipeline_id: optional string` The ID of the ManagedPipeline this playground pipeline is linked to. - `metadata_config: optional object { excluded_embed_metadata_keys, excluded_llm_metadata_keys }` Metadata configuration for the pipeline. - `excluded_embed_metadata_keys: optional array of string` List of metadata keys to exclude from embeddings - `excluded_llm_metadata_keys: optional array of string` List of metadata keys to exclude from LLM during retrieval - `pipeline_type: optional "PLAYGROUND" or "MANAGED"` Type of pipeline. Either PLAYGROUND or MANAGED. - `"PLAYGROUND"` - `"MANAGED"` - `preset_retrieval_parameters: optional object { alpha, class_name, dense_similarity_cutoff, 11 more }` Preset retrieval parameters for the pipeline. - `alpha: optional number` Alpha value for hybrid retrieval to determine the weights between dense and sparse retrieval. 0 is sparse retrieval and 1 is dense retrieval. - `class_name: optional string` - `dense_similarity_cutoff: optional number` Minimum similarity score wrt query for retrieval - `dense_similarity_top_k: optional number` Number of nodes for dense retrieval. - `enable_reranking: optional boolean` Enable reranking for retrieval - `files_top_k: optional number` Number of files to retrieve (only for retrieval mode files_via_metadata and files_via_content). - `rerank_top_n: optional number` Number of reranked nodes for returning. - `retrieval_mode: optional "chunks" or "files_via_metadata" or "files_via_content" or "auto_routed"` The retrieval mode for the query. - `"chunks"` - `"files_via_metadata"` - `"files_via_content"` - `"auto_routed"` - `retrieve_image_nodes: optional boolean` Whether to retrieve image nodes. - `retrieve_page_figure_nodes: optional boolean` Whether to retrieve page figure nodes. - `retrieve_page_screenshot_nodes: optional boolean` Whether to retrieve page screenshot nodes. - `search_filters: optional object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `MetadataFilter: object { key, value, operator }` Comprehensive metadata filter for vector stores to support more operators. Value uses Strict types, as int, float and str are compatible types and were all converted to string before. See: https://docs.pydantic.dev/latest/usage/types/#strict-types - `key: string` - `value: number or string or array of string or 2 more` - `union_member_0: number` - `union_member_1: string` - `union_member_2: array of string` - `union_member_3: array of number` - `union_member_4: array of number` - `operator: optional "==" or ">" or "<" or 11 more` Vector store filter operator. - `"=="` - `">"` - `"<"` - `"!="` - `">="` - `"<="` - `"in"` - `"nin"` - `"any"` - `"all"` - `"text_match"` - `"text_match_insensitive"` - `"contains"` - `"is_empty"` - `metadata_filters: object { filters, condition }` Metadata filters for vector stores. - `filters: array of object { key, value, operator } or MetadataFilters` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `"and"` - `"or"` - `"not"` - `condition: optional "and" or "or" or "not"` Vector store filter conditions to combine different filters. - `search_filters_inference_schema: optional map[map[unknown] or array of unknown or string or 2 more]` JSON Schema that will be used to infer search_filters. Omit or leave as null to skip inference. - `union_member_0: map[unknown]` - `union_member_1: array of unknown` - `union_member_2: string` - `union_member_3: number` - `union_member_4: boolean` - `sparse_similarity_top_k: optional number` Number of nodes for sparse retrieval. - `sparse_model_config: optional object { class_name, model_type }` Configuration for sparse embedding models used in hybrid search. This allows users to choose between Splade and BM25 models for sparse retrieval in managed data sinks. - `class_name: optional string` - `model_type: optional "splade" or "bm25" or "auto"` The sparse model type to use. 'bm25' uses Qdrant's FastEmbed BM25 model (default for new pipelines), 'splade' uses HuggingFace Splade model, 'auto' selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade). - `"splade"` - `"bm25"` - `"auto"` - `status: optional "CREATED" or "DELETING"` Status of the pipeline. - `"CREATED"` - `"DELETING"` - `transform_config: optional AutoTransformConfig or AdvancedModeTransformConfig` Configuration for the transformation. - `auto_transform_config: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` Chunk overlap for the transformation. - `chunk_size: optional number` Chunk size for the transformation. - `mode: optional "auto"` - `"auto"` - `advanced_mode_transform_config: object { chunking_config, mode, segmentation_config }` - `chunking_config: optional object { mode } or object { chunk_overlap, chunk_size, mode } or object { chunk_overlap, chunk_size, mode, separator } or 2 more` Configuration for the chunking. - `NoneChunkingConfig: object { mode }` - `mode: optional "none"` - `"none"` - `CharacterChunkingConfig: object { chunk_overlap, chunk_size, mode }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "character"` - `"character"` - `TokenChunkingConfig: object { chunk_overlap, chunk_size, mode, separator }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "token"` - `"token"` - `separator: optional string` - `SentenceChunkingConfig: object { chunk_overlap, chunk_size, mode, 2 more }` - `chunk_overlap: optional number` - `chunk_size: optional number` - `mode: optional "sentence"` - `"sentence"` - `paragraph_separator: optional string` - `separator: optional string` - `SemanticChunkingConfig: object { breakpoint_percentile_threshold, buffer_size, mode }` - `breakpoint_percentile_threshold: optional number` - `buffer_size: optional number` - `mode: optional "semantic"` - `"semantic"` - `mode: optional "advanced"` - `"advanced"` - `segmentation_config: optional object { mode } or object { mode, page_separator } or object { mode }` Configuration for the segmentation. - `NoneSegmentationConfig: object { mode }` - `mode: optional "none"` - `"none"` - `PageSegmentationConfig: object { mode, page_separator }` - `mode: optional "page"` - `"page"` - `page_separator: optional string` - `ElementSegmentationConfig: object { mode }` - `mode: optional "element"` - `"element"` - `updated_at: optional string` Update datetime ### Example ```cli llamacloud-prod pipelines list \ --api-key 'My API Key' ``` #### Response ```json [ { "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "embedding_config": { "component": { "class_name": "class_name", "embed_batch_size": 1, "model_name": "openai-text-embedding-3-small", "num_workers": 0 }, "type": "MANAGED_OPENAI_EMBEDDING" }, "name": "name", "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "config_hash": { "embedding_config_hash": "embedding_config_hash", "parsing_config_hash": "parsing_config_hash", "transform_config_hash": "transform_config_hash" }, "created_at": "2019-12-27T18:11:19.117Z", "data_sink": { "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "component": { "foo": "bar" }, "name": "name", "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "sink_type": "PINECONE", "created_at": "2019-12-27T18:11:19.117Z", "updated_at": "2019-12-27T18:11:19.117Z" }, "embedding_model_config": { "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "embedding_config": { "component": { "additional_kwargs": { "foo": "bar" }, "api_base": "api_base", "api_key": "api_key", "api_version": "api_version", "azure_deployment": "azure_deployment", "azure_endpoint": "azure_endpoint", "class_name": "class_name", "default_headers": { "foo": "string" }, "dimensions": 0, "embed_batch_size": 1, "max_retries": 0, "model_name": "model_name", "num_workers": 0, "reuse_client": true, "timeout": 0 }, "type": "AZURE_EMBEDDING" }, "name": "name", "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "created_at": "2019-12-27T18:11:19.117Z", "updated_at": "2019-12-27T18:11:19.117Z" }, "embedding_model_config_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "llama_parse_parameters": { "adaptive_long_table": true, "aggressive_table_extraction": true, "annotate_links": true, "auto_mode": true, "auto_mode_configuration_json": "auto_mode_configuration_json", "auto_mode_trigger_on_image_in_page": true, "auto_mode_trigger_on_regexp_in_page": "auto_mode_trigger_on_regexp_in_page", "auto_mode_trigger_on_table_in_page": true, "auto_mode_trigger_on_text_in_page": "auto_mode_trigger_on_text_in_page", "azure_openai_api_version": "azure_openai_api_version", "azure_openai_deployment_name": "azure_openai_deployment_name", "azure_openai_endpoint": "azure_openai_endpoint", "azure_openai_key": "azure_openai_key", "bbox_bottom": 0, "bbox_left": 0, "bbox_right": 0, "bbox_top": 0, "bounding_box": "bounding_box", "compact_markdown_table": true, "complemental_formatting_instruction": "complemental_formatting_instruction", "content_guideline_instruction": "content_guideline_instruction", "continuous_mode": true, "disable_image_extraction": true, "disable_ocr": true, "disable_reconstruction": true, "do_not_cache": true, "do_not_unroll_columns": true, "enable_cost_optimizer": true, "extract_charts": true, "extract_layout": true, "extract_printed_page_number": true, "fast_mode": true, "formatting_instruction": "formatting_instruction", "gpt4o_api_key": "gpt4o_api_key", "gpt4o_mode": true, "guess_xlsx_sheet_name": true, "hide_footers": true, "hide_headers": true, "high_res_ocr": true, "html_make_all_elements_visible": true, "html_remove_fixed_elements": true, "html_remove_navigation_elements": true, "http_proxy": "http_proxy", "ignore_document_elements_for_layout_detection": true, "images_to_save": [ "screenshot" ], "inline_images_in_markdown": true, "input_s3_path": "input_s3_path", "input_s3_region": "input_s3_region", "input_url": "input_url", "internal_is_screenshot_job": true, "invalidate_cache": true, "is_formatting_instruction": true, "job_timeout_extra_time_per_page_in_seconds": 0, "job_timeout_in_seconds": 0, "keep_page_separator_when_merging_tables": true, "languages": [ "af" ], "layout_aware": true, "line_level_bounding_box": true, "markdown_table_multiline_header_separator": "markdown_table_multiline_header_separator", "max_pages": 0, "max_pages_enforced": 0, "merge_tables_across_pages_in_markdown": true, "model": "model", "outlined_table_extraction": true, "output_pdf_of_document": true, "output_s3_path_prefix": "output_s3_path_prefix", "output_s3_region": "output_s3_region", "output_tables_as_HTML": true, "page_error_tolerance": 0, "page_footer_prefix": "page_footer_prefix", "page_footer_suffix": "page_footer_suffix", "page_header_prefix": "page_header_prefix", "page_header_suffix": "page_header_suffix", "page_prefix": "page_prefix", "page_separator": "page_separator", "page_suffix": "page_suffix", "parse_mode": "parse_page_without_llm", "parsing_instruction": "parsing_instruction", "precise_bounding_box": true, "premium_mode": true, "presentation_out_of_bounds_content": true, "presentation_skip_embedded_data": true, "preserve_layout_alignment_across_pages": true, "preserve_very_small_text": true, "preset": "preset", "priority": "low", "project_id": "project_id", "remove_hidden_text": true, "replace_failed_page_mode": "raw_text", "replace_failed_page_with_error_message_prefix": "replace_failed_page_with_error_message_prefix", "replace_failed_page_with_error_message_suffix": "replace_failed_page_with_error_message_suffix", "save_images": true, "skip_diagonal_text": true, "specialized_chart_parsing_agentic": true, "specialized_chart_parsing_efficient": true, "specialized_chart_parsing_plus": true, "specialized_image_parsing": true, "spreadsheet_extract_sub_tables": true, "spreadsheet_force_formula_computation": true, "spreadsheet_include_hidden_sheets": true, "strict_mode_buggy_font": true, "strict_mode_image_extraction": true, "strict_mode_image_ocr": true, "strict_mode_reconstruction": true, "structured_output": true, "structured_output_json_schema": "structured_output_json_schema", "structured_output_json_schema_name": "structured_output_json_schema_name", "system_prompt": "system_prompt", "system_prompt_append": "system_prompt_append", "take_screenshot": true, "target_pages": "target_pages", "tier": "tier", "use_vendor_multimodal_model": true, "user_prompt": "user_prompt", "vendor_multimodal_api_key": "vendor_multimodal_api_key", "vendor_multimodal_model_name": "vendor_multimodal_model_name", "version": "version", "webhook_configurations": [ { "webhook_events": [ "parse.success", "parse.error" ], "webhook_headers": { "Authorization": "Bearer sk-..." }, "webhook_output_format": "json", "webhook_url": "https://example.com/webhooks/llamacloud" } ], "webhook_url": "webhook_url" }, "managed_pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e", "metadata_config": { "excluded_embed_metadata_keys": [ "string" ], "excluded_llm_metadata_keys": [ "string" ] }, "pipeline_type": "PLAYGROUND", "preset_retrieval_parameters": { "alpha": 0, "class_name": "class_name", "dense_similarity_cutoff": 0, "dense_similarity_top_k": 1, "enable_reranking": true, "files_top_k": 1, "rerank_top_n": 1, "retrieval_mode": "chunks", "retrieve_image_nodes": true, "retrieve_page_figure_nodes": true, "retrieve_page_screenshot_nodes": true, "search_filters": { "filters": [ { "key": "key", "value": 0, "operator": "==" } ], "condition": "and" }, "search_filters_inference_schema": { "foo": { "foo": "bar" } }, "sparse_similarity_top_k": 1 }, "sparse_model_config": { "class_name": "class_name", "model_type": "splade" }, "status": "CREATED", "transform_config": { "chunk_overlap": 0, "chunk_size": 1, "mode": "auto" }, "updated_at": "2019-12-27T18:11:19.117Z" } ] ```