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chore!: BREAKING CHANGE: vector_db_id -> vector_store_id (#3923)
# What does this PR do? ## Test Plan CI vector_io tests will fail until next client sync passed with llamastack/llama-stack-client-python#286 checked out locally
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29 files changed

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-175
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‎client-sdks/stainless/openapi.yml‎

Lines changed: 10 additions & 10 deletions
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@@ -9862,7 +9862,7 @@ components:
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$ref: '#/components/schemas/RAGDocument'
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description: >-
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List of documents to index in the RAG system
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vector_db_id:
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vector_store_id:
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type: string
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description: >-
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ID of the vector database to store the document embeddings
@@ -9873,7 +9873,7 @@ components:
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additionalProperties: false
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required:
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- documents
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- vector_db_id
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- vector_store_id
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- chunk_size_in_tokens
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title: InsertRequest
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DefaultRAGQueryGeneratorConfig:
@@ -10044,7 +10044,7 @@ components:
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$ref: '#/components/schemas/InterleavedContent'
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description: >-
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The query content to search for in the indexed documents
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vector_db_ids:
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vector_store_ids:
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type: array
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items:
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type: string
@@ -10057,7 +10057,7 @@ components:
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additionalProperties: false
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required:
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- content
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- vector_db_ids
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- vector_store_ids
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title: QueryRequest
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RAGQueryResult:
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type: object
@@ -10281,7 +10281,7 @@ components:
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InsertChunksRequest:
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type: object
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properties:
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vector_db_id:
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vector_store_id:
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type: string
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description: >-
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The identifier of the vector database to insert the chunks into.
@@ -10300,13 +10300,13 @@ components:
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description: The time to live of the chunks.
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additionalProperties: false
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required:
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- vector_db_id
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- vector_store_id
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- chunks
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title: InsertChunksRequest
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QueryChunksRequest:
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type: object
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properties:
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vector_db_id:
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vector_store_id:
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type: string
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description: >-
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The identifier of the vector database to query.
@@ -10326,7 +10326,7 @@ components:
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description: The parameters of the query.
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additionalProperties: false
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required:
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- vector_db_id
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- vector_store_id
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- query
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title: QueryChunksRequest
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QueryChunksResponse:
@@ -11844,7 +11844,7 @@ components:
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description: Type of the step in an agent turn.
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const: memory_retrieval
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default: memory_retrieval
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vector_db_ids:
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vector_store_ids:
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type: string
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description: >-
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The IDs of the vector databases to retrieve context from.
@@ -11857,7 +11857,7 @@ components:
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- turn_id
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- step_id
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- step_type
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- vector_db_ids
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- vector_store_ids
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- inserted_context
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title: MemoryRetrievalStep
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description: >-

‎docs/docs/providers/vector_io/inline_sqlite-vec.mdx‎

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Original file line numberDiff line numberDiff line change
@@ -72,14 +72,14 @@ description: |
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Example with hybrid search:
7373
```python
7474
response = await vector_io.query_chunks(
75-
vector_db_id="my_db",
75+
vector_store_id="my_db",
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query="your query here",
7777
params={"mode": "hybrid", "max_chunks": 3, "score_threshold": 0.7},
7878
)
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8080
# Using RRF ranker
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response = await vector_io.query_chunks(
82-
vector_db_id="my_db",
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vector_store_id="my_db",
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query="your query here",
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params={
8585
"mode": "hybrid",
@@ -91,7 +91,7 @@ description: |
9191
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# Using weighted ranker
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response = await vector_io.query_chunks(
94-
vector_db_id="my_db",
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vector_store_id="my_db",
9595
query="your query here",
9696
params={
9797
"mode": "hybrid",
@@ -105,7 +105,7 @@ description: |
105105
Example with explicit vector search:
106106
```python
107107
response = await vector_io.query_chunks(
108-
vector_db_id="my_db",
108+
vector_store_id="my_db",
109109
query="your query here",
110110
params={"mode": "vector", "max_chunks": 3, "score_threshold": 0.7},
111111
)
@@ -114,7 +114,7 @@ description: |
114114
Example with keyword search:
115115
```python
116116
response = await vector_io.query_chunks(
117-
vector_db_id="my_db",
117+
vector_store_id="my_db",
118118
query="your query here",
119119
params={"mode": "keyword", "max_chunks": 3, "score_threshold": 0.7},
120120
)
@@ -277,14 +277,14 @@ The SQLite-vec provider supports three search modes:
277277
Example with hybrid search:
278278
```python
279279
response = await vector_io.query_chunks(
280-
vector_db_id="my_db",
280+
vector_store_id="my_db",
281281
query="your query here",
282282
params={"mode": "hybrid", "max_chunks": 3, "score_threshold": 0.7},
283283
)
284284

285285
# Using RRF ranker
286286
response = await vector_io.query_chunks(
287-
vector_db_id="my_db",
287+
vector_store_id="my_db",
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query="your query here",
289289
params={
290290
"mode": "hybrid",
@@ -296,7 +296,7 @@ response = await vector_io.query_chunks(
296296

297297
# Using weighted ranker
298298
response = await vector_io.query_chunks(
299-
vector_db_id="my_db",
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vector_store_id="my_db",
300300
query="your query here",
301301
params={
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"mode": "hybrid",
@@ -310,7 +310,7 @@ response = await vector_io.query_chunks(
310310
Example with explicit vector search:
311311
```python
312312
response = await vector_io.query_chunks(
313-
vector_db_id="my_db",
313+
vector_store_id="my_db",
314314
query="your query here",
315315
params={"mode": "vector", "max_chunks": 3, "score_threshold": 0.7},
316316
)
@@ -319,7 +319,7 @@ response = await vector_io.query_chunks(
319319
Example with keyword search:
320320
```python
321321
response = await vector_io.query_chunks(
322-
vector_db_id="my_db",
322+
vector_store_id="my_db",
323323
query="your query here",
324324
params={"mode": "keyword", "max_chunks": 3, "score_threshold": 0.7},
325325
)

‎docs/static/deprecated-llama-stack-spec.html‎

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@@ -4390,7 +4390,7 @@
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"const": "memory_retrieval",
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"default": "memory_retrieval"
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},
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"vector_db_ids": {
4393+
"vector_store_ids": {
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"type": "string",
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"description": "The IDs of the vector databases to retrieve context from."
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},
@@ -4404,7 +4404,7 @@
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"turn_id",
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"step_id",
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"step_type",
4407-
"vector_db_ids",
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"vector_store_ids",
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"inserted_context"
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],
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"title": "MemoryRetrievalStep",

‎docs/static/deprecated-llama-stack-spec.yaml‎

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@@ -3252,7 +3252,7 @@ components:
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description: Type of the step in an agent turn.
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const: memory_retrieval
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default: memory_retrieval
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vector_db_ids:
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vector_store_ids:
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type: string
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description: >-
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The IDs of the vector databases to retrieve context from.
@@ -3265,7 +3265,7 @@ components:
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- turn_id
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- step_id
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- step_type
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- vector_db_ids
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- vector_store_ids
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- inserted_context
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title: MemoryRetrievalStep
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description: >-

‎docs/static/experimental-llama-stack-spec.html‎

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"const": "memory_retrieval",
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"default": "memory_retrieval"
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},
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"vector_db_ids": {
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"vector_store_ids": {
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"type": "string",
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"description": "The IDs of the vector databases to retrieve context from."
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},
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"turn_id",
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"step_id",
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"step_type",
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"vector_db_ids",
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"vector_store_ids",
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"inserted_context"
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],
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"title": "MemoryRetrievalStep",

‎docs/static/experimental-llama-stack-spec.yaml‎

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@@ -2085,7 +2085,7 @@ components:
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description: Type of the step in an agent turn.
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const: memory_retrieval
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default: memory_retrieval
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vector_db_ids:
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vector_store_ids:
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type: string
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description: >-
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The IDs of the vector databases to retrieve context from.
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- turn_id
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- step_id
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- step_type
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- vector_db_ids
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- vector_store_ids
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- inserted_context
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title: MemoryRetrievalStep
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description: >-

‎docs/static/llama-stack-spec.html‎

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},
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"description": "List of documents to index in the RAG system"
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},
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"vector_db_id": {
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"vector_store_id": {
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"type": "string",
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"description": "ID of the vector database to store the document embeddings"
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},
@@ -11424,7 +11424,7 @@
1142411424
"additionalProperties": false,
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"required": [
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"documents",
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"vector_db_id",
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"vector_store_id",
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"chunk_size_in_tokens"
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],
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"title": "InsertRequest"
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"$ref": "#/components/schemas/InterleavedContent",
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"description": "The query content to search for in the indexed documents"
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},
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"vector_db_ids": {
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"vector_store_ids": {
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"type": "array",
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"items": {
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"type": "string"
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"additionalProperties": false,
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"required": [
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"content",
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"vector_db_ids"
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"vector_store_ids"
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],
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"title": "QueryRequest"
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},
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"InsertChunksRequest": {
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"type": "object",
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"properties": {
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"vector_db_id": {
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"vector_store_id": {
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"type": "string",
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"description": "The identifier of the vector database to insert the chunks into."
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},
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},
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"additionalProperties": false,
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"required": [
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"vector_db_id",
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"vector_store_id",
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"chunks"
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],
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"title": "InsertChunksRequest"
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},
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"QueryChunksRequest": {
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"type": "object",
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"properties": {
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"vector_db_id": {
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"vector_store_id": {
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"type": "string",
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"description": "The identifier of the vector database to query."
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},
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},
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"additionalProperties": false,
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"required": [
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"vector_db_id",
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"vector_store_id",
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"query"
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],
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"title": "QueryChunksRequest"

‎docs/static/llama-stack-spec.yaml‎

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$ref: '#/components/schemas/RAGDocument'
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description: >-
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List of documents to index in the RAG system
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vector_db_id:
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vector_store_id:
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type: string
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description: >-
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ID of the vector database to store the document embeddings
@@ -8660,7 +8660,7 @@ components:
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additionalProperties: false
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required:
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- documents
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- vector_db_id
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- vector_store_id
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- chunk_size_in_tokens
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title: InsertRequest
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DefaultRAGQueryGeneratorConfig:
@@ -8831,7 +8831,7 @@ components:
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$ref: '#/components/schemas/InterleavedContent'
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description: >-
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The query content to search for in the indexed documents
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vector_db_ids:
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vector_store_ids:
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type: array
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items:
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type: string
@@ -8844,7 +8844,7 @@ components:
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additionalProperties: false
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required:
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- content
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- vector_db_ids
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- vector_store_ids
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title: QueryRequest
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RAGQueryResult:
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type: object
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InsertChunksRequest:
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type: object
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properties:
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vector_db_id:
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vector_store_id:
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type: string
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description: >-
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The identifier of the vector database to insert the chunks into.
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description: The time to live of the chunks.
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additionalProperties: false
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required:
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- vector_db_id
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- vector_store_id
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- chunks
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title: InsertChunksRequest
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QueryChunksRequest:
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type: object
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properties:
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vector_db_id:
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vector_store_id:
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type: string
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description: >-
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The identifier of the vector database to query.
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description: The parameters of the query.
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additionalProperties: false
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required:
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- vector_db_id
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- vector_store_id
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- query
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title: QueryChunksRequest
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QueryChunksResponse:

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