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Threads, gRPC: MessageService.Get

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Improved by
Updated at April 24, 2025

Retrieve details of a specific message by its ID.

gRPC request

rpc Get (GetMessageRequest) returns (Message)

GetMessageRequest

{
  "thread_id": "string",
  "message_id": "string"
}

Request message for retrieving a message from a thread.

Field

Description

thread_id

string

Required field. ID of the thread that contains the message.

message_id

string

Required field. ID of the message to retrieve.

Message

{
  "id": "string",
  "thread_id": "string",
  "created_by": "string",
  "created_at": "google.protobuf.Timestamp",
  "author": {
    "id": "string",
    "role": "string"
  },
  "labels": "map<string, string>",
  "content": {
    "content": [
      {
        // Includes only one of the fields `text`
        "text": {
          "content": "string"
        }
        // end of the list of possible fields
      }
    ]
  },
  "status": "MessageStatus",
  "citations": [
    {
      "sources": [
        {
          // Includes only one of the fields `chunk`
          "chunk": {
            "search_index": {
              "id": "string",
              "folder_id": "string",
              "name": "string",
              "description": "string",
              "created_by": "string",
              "created_at": "google.protobuf.Timestamp",
              "updated_by": "string",
              "updated_at": "google.protobuf.Timestamp",
              "expiration_config": {
                "expiration_policy": "ExpirationPolicy",
                "ttl_days": "int64"
              },
              "expires_at": "google.protobuf.Timestamp",
              "labels": "map<string, string>",
              // Includes only one of the fields `text_search_index`, `vector_search_index`, `hybrid_search_index`
              "text_search_index": {
                "chunking_strategy": {
                  // Includes only one of the fields `static_strategy`
                  "static_strategy": {
                    "max_chunk_size_tokens": "int64",
                    "chunk_overlap_tokens": "int64"
                  }
                  // end of the list of possible fields
                },
                // Includes only one of the fields `ngram_tokenizer`, `standard_tokenizer`
                "ngram_tokenizer": {
                  "min_gram": "google.protobuf.Int64Value",
                  "max_gram": "google.protobuf.Int64Value"
                },
                "standard_tokenizer": "StandardTokenizer",
                // end of the list of possible fields
                // Includes only one of the fields `standard_analyzer`, `yandex_lemmer_analyzer`
                "standard_analyzer": "StandardAnalyzer",
                "yandex_lemmer_analyzer": "YandexLemmerAnalyzer"
                // end of the list of possible fields
              },
              "vector_search_index": {
                "doc_embedder_uri": "string",
                "query_embedder_uri": "string",
                "chunking_strategy": {
                  // Includes only one of the fields `static_strategy`
                  "static_strategy": {
                    "max_chunk_size_tokens": "int64",
                    "chunk_overlap_tokens": "int64"
                  }
                  // end of the list of possible fields
                }
              },
              "hybrid_search_index": {
                "text_search_index": {
                  "chunking_strategy": {
                    // Includes only one of the fields `static_strategy`
                    "static_strategy": {
                      "max_chunk_size_tokens": "int64",
                      "chunk_overlap_tokens": "int64"
                    }
                    // end of the list of possible fields
                  },
                  // Includes only one of the fields `ngram_tokenizer`, `standard_tokenizer`
                  "ngram_tokenizer": {
                    "min_gram": "google.protobuf.Int64Value",
                    "max_gram": "google.protobuf.Int64Value"
                  },
                  "standard_tokenizer": "StandardTokenizer",
                  // end of the list of possible fields
                  // Includes only one of the fields `standard_analyzer`, `yandex_lemmer_analyzer`
                  "standard_analyzer": "StandardAnalyzer",
                  "yandex_lemmer_analyzer": "YandexLemmerAnalyzer"
                  // end of the list of possible fields
                },
                "vector_search_index": {
                  "doc_embedder_uri": "string",
                  "query_embedder_uri": "string",
                  "chunking_strategy": {
                    // Includes only one of the fields `static_strategy`
                    "static_strategy": {
                      "max_chunk_size_tokens": "int64",
                      "chunk_overlap_tokens": "int64"
                    }
                    // end of the list of possible fields
                  }
                },
                "chunking_strategy": {
                  // Includes only one of the fields `static_strategy`
                  "static_strategy": {
                    "max_chunk_size_tokens": "int64",
                    "chunk_overlap_tokens": "int64"
                  }
                  // end of the list of possible fields
                },
                "normalization_strategy": "NormalizationStrategy",
                "combination_strategy": {
                  // Includes only one of the fields `mean_combination`, `rrf_combination`
                  "mean_combination": {
                    "mean_evaluation_technique": "MeanEvaluationTechnique",
                    "weights": [
                      "double"
                    ]
                  },
                  "rrf_combination": {
                    "k": "google.protobuf.Int64Value"
                  }
                  // end of the list of possible fields
                }
              }
              // end of the list of possible fields
            },
            "source_file": {
              "id": "string",
              "folder_id": "string",
              "name": "string",
              "description": "string",
              "mime_type": "string",
              "created_by": "string",
              "created_at": "google.protobuf.Timestamp",
              "updated_by": "string",
              "updated_at": "google.protobuf.Timestamp",
              "expiration_config": {
                "expiration_policy": "ExpirationPolicy",
                "ttl_days": "int64"
              },
              "expires_at": "google.protobuf.Timestamp",
              "labels": "map<string, string>"
            },
            "content": {
              "content": [
                {
                  // Includes only one of the fields `text`
                  "text": {
                    "content": "string"
                  }
                  // end of the list of possible fields
                }
              ]
            }
          }
          // end of the list of possible fields
        }
      ]
    }
  ]
}

Field

Description

id

string

Unique identifier of the message.

thread_id

string

ID of the thread that this message belongs to.

created_by

string

Identifier of the subject who created this message.

created_at

google.protobuf.Timestamp

Timestamp representing when the message was created.

author

Author

Author of the message, containing details about the message's creator.

labels

object (map<string, string>)

Set of key-value pairs that can be used to organize and categorize the message.

content

MessageContent

Content of the message.

status

enum MessageStatus

Status of the message.

  • MESSAGE_STATUS_UNSPECIFIED: Unspecified message status.
  • COMPLETED: Message was successfully created by a user or generated by an assistant.
  • TRUNCATED: Message generation was truncated due to reaching the maximum allowed number of tokens.
  • FILTERED_CONTENT: Message generation was stopped because potentially sensitive content was detected either in the prompt or in the generated response.

citations[]

Citation

List of citations used to generate the message.

Author

Author of the message, containing details about the message's creator.

Field

Description

id

string

Unique identifier of the author. This could be either the user's ID or the assistant's ID, depending on the role.

role

string

Role of the author, indicating whether the message was created by a "user" or an "assistant".

MessageContent

Represents the content of a message, which can consist of multiple parts.

Field

Description

content[]

ContentPart

A list of content parts that make up the message.

ContentPart

ContentPart represents an individual part of the message content, which can be of various types.

Field

Description

text

Text

Text content of the message part.

Includes only one of the fields text.

Specifies the type of content that the part contains.

Text

Text represents a textual content part of a message.

Field

Description

content

string

Text content of the message.

Citation

Represents a citation used for generating a message.

Field

Description

sources[]

Source

List of sources for citation.

Source

Represents a source used for generating a message citation.

Field

Description

chunk

FileChunk

File chunk source.

Includes only one of the fields chunk.

Specifies the type of source type.

FileChunk

FileChunk represents a chunk of a file used as a source.

Field

Description

search_index

SearchIndex

Search index associated with the file chunk.

source_file

File

The original file from which the chunk is derived.

content

ChunkContent

Content of the file chunk.

SearchIndex

Represents a search index used to store and query data, either using traditional keyword-based text search or vector-based search mechanisms.

Field

Description

id

string

Unique identifier of the search index.

folder_id

string

ID of the folder that the search index belongs to.

name

string

Name of the search index.

description

string

Description of the search index.

created_by

string

Identifier of the subject who created this search index.

created_at

google.protobuf.Timestamp

Timestamp representing when the search index was created.

updated_by

string

Identifier of the subject who last updated this search index.

updated_at

google.protobuf.Timestamp

Timestamp representing the last time this search index was updated.

expiration_config

ExpirationConfig

Configuration for the expiration of the search index, defining when and how the search index will expire.

expires_at

google.protobuf.Timestamp

Timestamp representing when the search index will expire.

labels

object (map<string, string>)

Set of key-value pairs that can be used to organize and categorize the search index.

text_search_index

TextSearchIndex

Keyword-based text search index configuration.
This type of index is used for traditional text search, where documents are indexed based on their keywords.

Includes only one of the fields text_search_index, vector_search_index, hybrid_search_index.

Type of the search index. It can be either a traditional keyword-based text search or a vector-based search.

vector_search_index

VectorSearchIndex

Vector-based search index configuration.
This type is used for vector search, where documents are indexed using vector embeddings.

Includes only one of the fields text_search_index, vector_search_index, hybrid_search_index.

Type of the search index. It can be either a traditional keyword-based text search or a vector-based search.

hybrid_search_index

HybridSearchIndex

Hybrid (vector-based + keyword-based) search index configuration
This type is used for hybrid search, where documents are indexed using both keyword-based and vector-based search mechanisms.

Includes only one of the fields text_search_index, vector_search_index, hybrid_search_index.

Type of the search index. It can be either a traditional keyword-based text search or a vector-based search.

ExpirationConfig

Field

Description

expiration_policy

enum ExpirationPolicy

  • EXPIRATION_POLICY_UNSPECIFIED
  • STATIC
  • SINCE_LAST_ACTIVE

ttl_days

int64

TextSearchIndex

Defines the configuration for a traditional keyword-based text search index.

Field

Description

chunking_strategy

ChunkingStrategy

Chunking strategy used to split text into smaller chunks before indexing.
In the case of text search, tokens are individual text characters.

ngram_tokenizer

NgramTokenizer

Tokenizer that generates n-grams.

Includes only one of the fields ngram_tokenizer, standard_tokenizer.

Tokenizer type used for text search. The tokenizer determines how the
input text is broken down into tokens before indexing.
If not specified, the default tokenizer configuration is applied.

standard_tokenizer

StandardTokenizer

Tokenizer that generates words.

Includes only one of the fields ngram_tokenizer, standard_tokenizer.

Tokenizer type used for text search. The tokenizer determines how the
input text is broken down into tokens before indexing.
If not specified, the default tokenizer configuration is applied.

standard_analyzer

StandardAnalyzer

Standard analyzer that performs common text processing operations to normalize text.

Includes only one of the fields standard_analyzer, yandex_lemmer_analyzer.

Analyzer type used for text search. The analyzer determines how the
tokenized text is further processed before indexing.
If not specified, the default analyzer configuration is applied.

yandex_lemmer_analyzer

YandexLemmerAnalyzer

Specialized analyzer that uses Yandex's lemmatization technology,
particularly effective for Russian and other Slavic languages.

Includes only one of the fields standard_analyzer, yandex_lemmer_analyzer.

Analyzer type used for text search. The analyzer determines how the
tokenized text is further processed before indexing.
If not specified, the default analyzer configuration is applied.

ChunkingStrategy

Defines a general strategy for chunking text into smaller segments.
Currently, only StaticChunkingStrategy is supported.

Field

Description

static_strategy

StaticChunkingStrategy

Includes only one of the fields static_strategy.

StaticChunkingStrategy

Defines a chunking strategy where chunks are created with a fixed maximum chunk size and an overlap between consecutive chunks.

Field

Description

max_chunk_size_tokens

int64

The maximum number of tokens allowed in a single chunk.
Constraints: must be within the range [100, 2048].
Default value: 800

chunk_overlap_tokens

int64

The number of tokens that should overlap between consecutive chunks.
This allows for some context from the previous chunk to be included in the next chunk.
Constraints: must be less than or equal to half of max_chunk_size_tokens.
Default value: 400

NgramTokenizer

Configuration for the NgramTokenizer, which splits text into overlapping character sequences (n-grams) of specified lengths.

Example:
Input text: hello
min_gram = 2, max_gram = 3

Generated tokens:

  • For n = 2 (2-character n-grams): he, el, ll, lo
  • For n = 3 (3-character n-grams): hel, ell, llo

Final tokens: [he, el, ll, lo, hel, ell, llo]

Field

Description

min_gram

google.protobuf.Int64Value

Minimum length of characters in a gram. Defaults to 3

max_gram

google.protobuf.Int64Value

Maximum length of characters in a gram. Defaults to 4

StandardTokenizer

A standard tokenizer that splits text on word boundaries and removes punctuation.
It follows the Unicode Text Segmentation rules as specified in Unicode Standard Annex #29.

Example:
Input text: Hello, world! How are you?
Output tokens: [Hello, world, How, are, you]

Field

Description

Empty

StandardAnalyzer

A standard analyzer that uses StandardTokenizer.

Field

Description

Empty

YandexLemmerAnalyzer

A specialized analyzer that uses Yandex's lemmatization technology to reduce words to their base forms.
Particularly effective for Russian and other Slavic languages, handling their complex morphology.
For more information, see:
https://yandex.cloud/en/docs/tutorials/dataplatform/opensearch-yandex-lemmer

Field

Description

Empty

VectorSearchIndex

Defines the configuration for a vector-based search index. This type uses embeddings to represent documents and queries.

Field

Description

doc_embedder_uri

string

The ID of the model to be used for obtaining document text embeddings.

query_embedder_uri

string

The ID of the model to be used for obtaining query text embeddings.

chunking_strategy

ChunkingStrategy

Chunking strategy used to split text into smaller chunks before indexing.
In the case of vector search, tokens are produced by the tokenizer from the embedding model.

HybridSearchIndex

Defines the configuration for a hybrid (vector-based + keyword-based) search index. This type uses both embeddings and keyword-based search to represent documents and queries.

Field

Description

text_search_index

TextSearchIndex

Configuration for a traditional keyword-based text search index.

vector_search_index

VectorSearchIndex

Configuration for a vector-based search index.

chunking_strategy

ChunkingStrategy

Common chunking strategy that applies to both text and vector search indexes.
If provided, it overrides the individual chunking strategies in both text_search_index and vector_search_index.
In this case, both text and vector search will use token-based chunking, where tokens are produced by the tokenizer of the embedding model.

normalization_strategy

enum NormalizationStrategy

Normalization strategy for relevance scores from different indices. Default is MIN_MAX_STRATEGY

  • NORMALIZATION_STRATEGY_UNSPECIFIED
  • MIN_MAX: https://en.wikipedia.org/wiki/Feature_scaling#Rescaling_(min-max_normalization)
  • L2: https://en.wikipedia.org/wiki/Cosine_similarity#L2-normalized_Euclidean_distance

combination_strategy

CombinationStrategy

Combination strategy for merging rankings from different indices. Default is arithmetic mean

CombinationStrategy

Combination strategy for merging rankings from different indices

Field

Description

mean_combination

MeanCombinationStrategy

Includes only one of the fields mean_combination, rrf_combination.

rrf_combination

ReciprocalRankFusionCombinationStrategy

Includes only one of the fields mean_combination, rrf_combination.

MeanCombinationStrategy

Field

Description

mean_evaluation_technique

enum MeanEvaluationTechnique

Technique for averaging relevance scores from different indices. Default is ARITHMETIC

  • MEAN_EVALUATION_TECHNIQUE_UNSPECIFIED
  • ARITHMETIC: https://en.wikipedia.org/wiki/Arithmetic_mean
  • GEOMETRIC: https://en.wikipedia.org/wiki/Geometric_mean
  • HARMONIC: https://en.wikipedia.org/wiki/Harmonic_mean

weights[]

double

Weights used for evaluating the weighted mean of relevance scores. The sum of the values must equal 1.0
If not provided, all scores are given equal weight

ReciprocalRankFusionCombinationStrategy

https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf

Field

Description

k

google.protobuf.Int64Value

The parameter k for RRFscore. Default is 60

File

Field

Description

id

string

Unique identifier of the file.

folder_id

string

ID of the folder that the file belongs to.

name

string

Name of the file.

description

string

Description of the file.

mime_type

string

MIME type of the file, indicating the file's format (e.g., "application/pdf")

created_by

string

Identifier of the subject who created this file.

created_at

google.protobuf.Timestamp

Timestamp representing when the file was created.

updated_by

string

Identifier of the subject who last updated this file.

updated_at

google.protobuf.Timestamp

Timestamp representing the last time this file was updated.

expiration_config

ExpirationConfig

Configuration for the expiration of the file, defining when and how the file will expire.

expires_at

google.protobuf.Timestamp

Timestamp representing when the file will expire.

labels

object (map<string, string>)

Set of key-value pairs that can be used to organize and categorize the file.

ChunkContent

Represents the content of a file chunk.

Field

Description

content[]

ContentPart

A list of content parts that make up the chunk.
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