> ## Documentation Index > Fetch the complete documentation index at: https://docs.trieve.ai/llms.txt > Use this file to discover all available pages before exploring further. # Update Chunk > Update a chunk. If you try to change the tracking_id of the chunk to have the same tracking_id as an existing chunk, the request will fail. Auth'ed user or api key must have an admin or owner role for the specified dataset's organization. ## OpenAPI ````yaml put /api/chunk openapi: 3.0.3 info: title: Trieve API description: >- Trieve OpenAPI Specification. This document describes all of the operations available through the Trieve API. contact: name: Trieve Team url: https://trieve.ai email: developers@trieve.ai license: name: BSL url: https://github.com/devflowinc/trieve/blob/main/LICENSE.txt version: 0.13.0 servers: - url: https://api.trieve.ai description: Production server - url: http://localhost:8090 description: Local development server security: [] tags: - name: Invitation description: Invitation endpoint. Exists to invite users to an organization. - name: Auth description: Authentication endpoint. Serves to register and authenticate users. - name: User description: User endpoint. Enables you to modify user roles and information. - name: Organization description: >- Organization endpoint. Enables you to modify organization roles and information. - name: Dataset description: >- Dataset endpoint. Datasets belong to organizations and hold configuration information for both client and server. Datasets contain chunks and chunk groups. - name: Chunk description: >- Chunk endpoint. Think of chunks as individual searchable units of information. The majority of your integration will likely be with the Chunk endpoint. - name: Chunk Group description: >- Chunk groups endpoint. Think of a chunk_group as a bookmark folder within the dataset. - name: Crawl description: Crawl endpoint. Used to create and manage crawls for datasets. - name: File description: >- File endpoint. When files are uploaded, they are stored in S3 and broken up into chunks with text extraction from Apache Tika. You can upload files of pretty much any type up to 1GB in size. See chunking algorithm details at `docs.trieve.ai` for more information on how chunking works. Improved default chunking is on our roadmap. - name: Events description: >- Notifications endpoint. Files are uploaded asynchronously and events are sent to the user when the upload is complete. - name: Topic description: >- Topic chat endpoint. Think of topics as the storage system for gen-ai chat memory. Gen AI messages belong to topics. - name: Message description: >- Message chat endpoint. Messages are units belonging to a topic in the context of a chat with a LLM. There are system, user, and assistant messages. - name: Stripe description: >- Stripe endpoint. Used for the managed SaaS version of this app. Eventually this will become a micro-service. Reach out to the team using contact info found at `docs.trieve.ai` for more information. - name: Health description: Health check endpoint. Used to check if the server is up and running. - name: Metrics description: Metrics endpoint. Used to get information for monitoring - name: Analytics description: Analytics endpoint. Used to get information for search and RAG analytics - name: Experiment description: Experiment endpoint. Used to create and manage experiments paths: /api/chunk: put: tags: - Chunk summary: Update Chunk description: >- Update a chunk. If you try to change the tracking_id of the chunk to have the same tracking_id as an existing chunk, the request will fail. Auth'ed user or api key must have an admin or owner role for the specified dataset's organization. operationId: update_chunk parameters: - name: TR-Dataset in: header description: >- The dataset id or tracking_id to use for the request. We assume you intend to use an id if the value is a valid uuid. required: true schema: type: string format: uuid requestBody: description: JSON request payload to update a chunk (chunk) content: application/json: schema: $ref: '#/components/schemas/UpdateChunkReqPayload' required: true responses: '204': description: No content Ok response indicating the chunk was updated as requested '400': description: >- Service error relating to to updating chunk, likely due to conflicting tracking_id content: application/json: schema: $ref: '#/components/schemas/ErrorResponseBody' security: - ApiKey: - admin components: schemas: UpdateChunkReqPayload: type: object properties: chunk_html: type: string description: >- HTML content of the chunk you want to update. This can also be plaintext. The innerText of the HTML will be used to create the embedding vector. The point of using HTML is for convienience, as some users have applications where users submit HTML content. If no chunk_html is provided, the existing chunk_html will be used. nullable: true chunk_id: type: string format: uuid description: >- Id of the chunk you want to update. You can provide either the chunk_id or the tracking_id. If both are provided, the chunk_id will be used. nullable: true convert_html_to_text: type: boolean description: >- Convert HTML to raw text before processing to avoid adding noise to the vector embeddings. By default this is true. If you are using HTML content that you want to be included in the vector embeddings, set this to false. nullable: true fulltext_boost: allOf: - $ref: '#/components/schemas/FullTextBoost' nullable: true group_ids: type: array items: type: string format: uuid description: >- Group ids are the ids of the groups that the chunk should be placed into. This is useful for when you want to update a chunk and add it to a group or multiple groups in one request. nullable: true group_tracking_ids: type: array items: type: string description: >- Group tracking_ids are the tracking_ids of the groups that the chunk should be placed into. This is useful for when you want to update a chunk and add it to a group or multiple groups in one request. nullable: true image_urls: type: array items: type: string description: >- Image urls are a list of urls to images that are associated with the chunk. This is useful for when you want to associate images with a chunk. If no image_urls are provided, the existing image_urls will be used. nullable: true link: type: string description: >- Link of the chunk you want to update. This can also be any string. Frequently, this is a link to the source of the chunk. The link value will not affect the embedding creation. If no link is provided, the existing link will be used. nullable: true location: allOf: - $ref: '#/components/schemas/GeoInfo' nullable: true metadata: description: >- The metadata is a JSON object which can be used to filter chunks. This is useful for when you want to filter chunks by arbitrary metadata. Unlike with tag filtering, there is a performance hit for filtering on metadata. If no metadata is provided, the existing metadata will be used. nullable: true num_value: type: number format: double description: >- Num value is an arbitrary numerical value that can be used to filter chunks. This is useful for when you want to filter chunks by numerical value. If no num_value is provided, the existing num_value will be used. nullable: true semantic_boost: allOf: - $ref: '#/components/schemas/SemanticBoost' nullable: true tag_set: type: array items: type: string description: >- Tag set is a list of tags. This can be used to filter chunks by tag. Unlike with metadata filtering, HNSW indices will exist for each tag such that there is not a performance hit for filtering on them. If no tag_set is provided, the existing tag_set will be used. nullable: true time_stamp: type: string description: >- Time_stamp should be an ISO 8601 combined date and time without timezone. It is used for time window filtering and recency-biasing search results. If no time_stamp is provided, the existing time_stamp will be used. nullable: true tracking_id: type: string description: >- Tracking_id of the chunk you want to update. This is required to match an existing chunk. nullable: true weight: type: number format: double description: >- Weight is a float which can be used to bias search results. This is useful for when you want to bias search results for a chunk. The magnitude only matters relative to other chunks in the chunk's dataset dataset. If no weight is provided, the existing weight will be used. nullable: true example: chunk_html:

Some HTML content

chunk_id: d290f1ee-6c54-4b01-90e6-d701748f0851 group_ids: - d290f1ee-6c54-4b01-90e6-d701748f0851 link: https://example.com metadata: key1: value1 key2: value2 time_stamp: '2021-01-01 00:00:00.000' weight: 0.5 ErrorResponseBody: type: object required: - message properties: message: type: string example: message: Bad Request FullTextBoost: type: object description: >- Boost the presence of certain tokens for fulltext (SPLADE) and keyword (BM25) search. I.e. boosting title phrases to priortize title matches or making sure that the listing for AirBNB itself ranks higher than companies who make software for AirBNB hosts by boosting the in-document-frequency of the AirBNB token (AKA word) for its official listing. Conceptually it multiples the in-document-importance second value in the tuples of the SPLADE or BM25 sparse vector of the chunk_html innerText for all tokens present in the boost phrase by the boost factor like so: (token, in-document-importance) -> (token, in-document-importance*boost_factor). required: - phrase - boost_factor properties: boost_factor: type: number format: double description: >- Amount to multiplicatevly increase the frequency of the tokens in the phrase by phrase: type: string description: The phrase to boost in the fulltext document frequency index GeoInfo: type: object description: Location that you want to use as the center of the search. required: - lat - lon properties: lat: $ref: '#/components/schemas/GeoTypes' lon: $ref: '#/components/schemas/GeoTypes' SemanticBoost: type: object description: >- Semantic boosting moves the dense vector of the chunk in the direction of the distance phrase for semantic search. I.e. you can force a cluster by moving every chunk for a PDF closer to its title or push a chunk with a chunk_html of "iphone" 25% closer to the term "flagship" by using the distance phrase "flagship" and a distance factor of 0.25. Conceptually it's drawing a line (euclidean/L2 distance) between the vector for the innerText of the chunk_html and distance_phrase then moving the vector of the chunk_html distance_factor*L2Distance closer to or away from the distance_phrase point along the line between the two points. required: - phrase - distance_factor properties: distance_factor: type: number format: float description: >- Arbitrary float (positive or negative) specifying the multiplicate factor to apply before summing the phrase vector with the chunk_html embedding vector phrase: type: string description: >- Terms to embed in order to create the vector which is weighted summed with the chunk_html embedding vector GeoTypes: oneOf: - type: integer format: int64 - type: number format: double securitySchemes: ApiKey: type: apiKey in: header name: Authorization ````