APIClaw Rag API
The rag API from APIClaw — 4 operation(s) for rag.
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OpenAPI Specification
openapi: 3.2.0
info:
title: LiteLLM Rag API
description: 'Proxy Server to call 100+ LLMs in the OpenAI format. **Customize Swagger Docs**
👉 ```LiteLLM Admin Panel on /ui```. Create, Edit Keys with SSO. Having issues? Try ```Fallback Login```
💸 ```LiteLLM Model Cost Map```.
🔎 ```LiteLLM Model Hub```. See available models on the proxy. **Docs**'
version: 1.102.1
tags:
- name: Rag
paths:
/rag/ingest:
post:
tags:
- Rag
summary: Rag Ingest
description: 'RAG Ingest endpoint - all-in-one document ingestion pipeline.
Supports form upload (for files) or JSON body (for URLs).
## Form upload (for files):
```bash
curl -X POST "http://localhost:4000/v1/rag/ingest" \
-H "Authorization: Bearer sk-1234" \
-F file="@document.pdf" \
-F ''ingest_options={"vector_store": {"custom_llm_provider": "openai"}}''
```
## JSON body (for URLs):
```bash
curl -X POST "http://localhost:4000/v1/rag/ingest" \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
-d ''{
"file_url": "https://example.com/document.pdf",
"ingest_options": {"vector_store": {"custom_llm_provider": "openai"}}
}''
```
## Bedrock:
```bash
curl -X POST "http://localhost:4000/v1/rag/ingest" \
-H "Authorization: Bearer sk-1234" \
-F file="@document.pdf" \
-F ''ingest_options={"vector_store": {"custom_llm_provider": "bedrock"}}''
```'
operationId: rag_ingest_rag_ingest_post
responses:
'200':
description: Successful Response
content:
application/json:
schema: {}
security:
- APIKeyHeader: []
/v1/rag/ingest:
post:
tags:
- Rag
summary: Rag Ingest
description: 'RAG Ingest endpoint - all-in-one document ingestion pipeline.
Supports form upload (for files) or JSON body (for URLs).
## Form upload (for files):
```bash
curl -X POST "http://localhost:4000/v1/rag/ingest" \
-H "Authorization: Bearer sk-1234" \
-F file="@document.pdf" \
-F ''ingest_options={"vector_store": {"custom_llm_provider": "openai"}}''
```
## JSON body (for URLs):
```bash
curl -X POST "http://localhost:4000/v1/rag/ingest" \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
-d ''{
"file_url": "https://example.com/document.pdf",
"ingest_options": {"vector_store": {"custom_llm_provider": "openai"}}
}''
```
## Bedrock:
```bash
curl -X POST "http://localhost:4000/v1/rag/ingest" \
-H "Authorization: Bearer sk-1234" \
-F file="@document.pdf" \
-F ''ingest_options={"vector_store": {"custom_llm_provider": "bedrock"}}''
```'
operationId: rag_ingest_v1_rag_ingest_post
responses:
'200':
description: Successful Response
content:
application/json:
schema: {}
security:
- APIKeyHeader: []
/rag/query:
post:
tags:
- Rag
summary: Rag Query
description: 'RAG Query endpoint - search vector store, optionally rerank, and generate LLM response.
This endpoint:
1. Extracts the query from the last user message
2. Searches the vector store for relevant context
3. Optionally reranks the results
4. Generates an LLM response with the retrieved context
## Example Request:
```bash
curl -X POST "http://localhost:4000/v1/rag/query" \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
-d ''{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "What is LiteLLM?"}],
"retrieval_config": {
"vector_store_id": "vs_abc123",
"custom_llm_provider": "openai",
"top_k": 5
}
}''
```
## With Reranking:
```bash
curl -X POST "http://localhost:4000/v1/rag/query" \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
-d ''{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "What is LiteLLM?"}],
"retrieval_config": {
"vector_store_id": "vs_abc123",
"custom_llm_provider": "openai",
"top_k": 10
},
"rerank": {
"enabled": true,
"model": "cohere/rerank-english-v3.0",
"top_n": 3
}
}''
```'
operationId: rag_query_rag_query_post
responses:
'200':
description: Successful Response
content:
application/json:
schema: {}
security:
- APIKeyHeader: []
/v1/rag/query:
post:
tags:
- Rag
summary: Rag Query
description: 'RAG Query endpoint - search vector store, optionally rerank, and generate LLM response.
This endpoint:
1. Extracts the query from the last user message
2. Searches the vector store for relevant context
3. Optionally reranks the results
4. Generates an LLM response with the retrieved context
## Example Request:
```bash
curl -X POST "http://localhost:4000/v1/rag/query" \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
-d ''{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "What is LiteLLM?"}],
"retrieval_config": {
"vector_store_id": "vs_abc123",
"custom_llm_provider": "openai",
"top_k": 5
}
}''
```
## With Reranking:
```bash
curl -X POST "http://localhost:4000/v1/rag/query" \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
-d ''{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "What is LiteLLM?"}],
"retrieval_config": {
"vector_store_id": "vs_abc123",
"custom_llm_provider": "openai",
"top_k": 10
},
"rerank": {
"enabled": true,
"model": "cohere/rerank-english-v3.0",
"top_n": 3
}
}''
```'
operationId: rag_query_v1_rag_query_post
responses:
'200':
description: Successful Response
content:
application/json:
schema: {}
security:
- APIKeyHeader: []
components:
securitySchemes:
APIKeyHeader:
type: apiKey
description: Bearer token
in: header
name: x-litellm-api-key