Query Operations#

Execute queries across tables using full-text search, vector similarity search, or hybrid approaches combining both with Reciprocal Rank Fusion (RRF).

Query Types#

Full-Text Search#

Uses Bleve query syntax for powerful text searching:

  • Field-specific queries: body:computer
  • Boolean operators: AND, OR, NOT
  • Range queries: year:>2020
  • Phrase queries: "exact phrase"

Natural language queries using vector similarity:

  • Searches across specified embedding indexes
  • Returns semantically similar documents
  • Supports multiple embedding models

Hybrid Search (RRF)#

Combines full-text and semantic search using Reciprocal Rank Fusion:

{
  "full_text_search": {"query": "body:computer"},
  "semantic_search": "artificial intelligence",
  "indexes": ["title_body_nomic"]
}

Filtering and Exclusion#

Refine results with powerful filtering:

Filter Prefix#

Only return documents whose keys start with a specific string:

{"filter_prefix": "user:"}

Returns only keys like "user:123", "user:456"

Filter Query#

Apply a Bleve query as an AND condition:

{"filter_query": {"query": "+category:technology +year:>2020"}}

Documents must match both the main query and filter query.

Exclusion Query#

Exclude documents matching a Bleve query (NOT condition):

{"exclusion_query": {"query": "category:deprecated OR status:archived"}}

Performance Note: All filters are applied before scoring and ranking, improving query performance.

RAG (Retrieval-Augmented Generation)#

Execute queries and generate summaries using LLMs:

  • Supports multiple retrieval queries
  • Streams results as Server-Sent Events (SSE)
  • Returns markdown summaries with inline citations
  • Combines results from multiple tables

Retrieval Agent#

Intelligent query routing with automatic query generation:

  • Classifies queries as "question" or "search"
  • Generates optimal search queries across tables
  • Returns generated responses (for questions) or document IDs (for searches)
  • Streams classification, keywords, queries, and results

Improving Search Relevance#

Antfly provides several techniques to improve search result quality:

1. Hybrid Search (Combine Full-Text + Semantic)#

Use both full_text_search and semantic_search together. Results are merged using Reciprocal Rank Fusion (RRF) to balance keyword matching and semantic similarity.

2. Reranking#

Apply a cross-encoder model to re-score results based on query-document relevance. Rerankers are more accurate than embedding models but slower, so use them on already-filtered results (e.g., top 50-100).

{
  "semantic_search": "gaming laptops",
  "limit": 100,
  "reranker": {
    "provider": "ollama",
    "model": "dengcao/Qwen3-Reranker-0.6B:F16",
    "field": "description"
  }
}

3. Result Pruning#

Filter out low-quality results by detecting score gaps or setting minimum thresholds:

{
  "semantic_search": "machine learning",
  "pruner": {
    "min_score_ratio": 0.5,
    "max_score_gap_percent": 30.0
  }
}
  • min_score_ratio: Keep only results scoring at least X% of the top result
  • max_score_gap_percent: Stop when a consecutive score gap exceeds X% of the total score range

4. Merge Strategies#

Choose how full-text and semantic results are combined:

  • rrf (default): Reciprocal Rank Fusion - balanced, works well in most cases
  • rsf: Relative Score Fusion - normalizes and weights scores differently
  • failover: Use full-text if embedding generation fails

5. Query Filtering#

Use filter_query and filter_prefix to pre-filter before scoring, improving both performance and relevance by removing irrelevant documents early.

Best Practice: Combine techniques for optimal results:

  1. Start with hybrid search (full-text + semantic)
  2. Apply filters to narrow the domain
  3. Use reranker on top results
  4. Apply pruner to remove outliers
Common questions about this section
  • What's the difference between full-text and semantic search?
  • How does hybrid search with RRF work?
  • How do I improve search relevance?
  • What's the difference between RAG and Retrieval Agent?

Perform a global query#

POST/query

Executes a query across all relevant tables and shards based on the query content.

Query Examples#

Full-text search:

{
  "table": "wikipedia",
  "full_text_search": {"query": "body:computer"},
  "limit": 10
}

Semantic search:

{
  "table": "articles",
  "semantic_search": "artificial intelligence applications",
  "indexes": ["title_body_embedding"],
  "limit": 20
}

Hybrid search (RRF):

{
  "table": "products",
  "full_text_search": {"query": "laptop gaming"},
  "semantic_search": "high performance gaming computers",
  "indexes": ["product_embedding"],
  "filter_query": {"query": "+price:<2000 +in_stock:true"},
  "fields": ["name", "price", "description"],
  "limit": 15
}

With filtering:

{
  "table": "users",
  "filter_prefix": "tenant:acme:",
  "full_text_search": {"query": "active:true"},
  "exclusion_query": {"query": "status:deleted"},
  "limit": 50
}

NDJSON format: For bulk queries, send multiple queries as NDJSON with Content-Type: application/x-ndjson. Each line must end with \n:

{"table":"wiki","semantic_search":"AI","indexes":["emb"],"limit":5}
{"table":"docs","full_text_search":{"query":"tutorial"},"limit":10}

Security#

Provide your bearer token in the Authorization header when making requests to protected resources.

Example: Authorization: Bearer YOUR_API_KEY

Request Body#

Example:

{
    "table": "wikipedia",
    "full_text_search": {
        "query": "+body:computer +category:technology",
        "boost": 1
    },
    "semantic_search": "artificial intelligence and machine learning applications",
    "embedding_template": "{{remotePDF url=this}}",
    "indexes": [
        "title_body_nomic",
        "description_embedding"
    ],
    "filter_prefix": "string",
    "filter_query": {
        "query": "+category:technology +year:>2020",
        "boost": 1
    },
    "exclusion_query": {
        "query": "category:deprecated OR status:archived",
        "boost": 1
    },
    "aggregations": {},
    "embeddings": {},
    "fields": [
        "title",
        "url",
        "summary",
        "created_at"
    ],
    "limit": 20,
    "offset": 0,
    "order_by": [
        {
            "field": "created_at",
            "desc": true
        },
        {
            "field": "score",
            "desc": true
        }
    ],
    "search_after": [
        "string"
    ],
    "search_before": [
        "string"
    ],
    "distance_under": 0.5,
    "distance_over": 0.1,
    "merge_config": {
        "strategy": "rrf",
        "weights": {
            "full_text": 0.3,
            "title_embedding": 1
        },
        "window_size": 1,
        "rank_constant": 0
    },
    "count": false,
    "profile": false,
    "reranker": {
        "provider": "ollama",
        "model": "dengcao/Qwen3-Reranker-0.6B:F16",
        "field": "content"
    },
    "analyses": {
        "pca": true,
        "tsne": true
    },
    "graph_searches": {},
    "expand_strategy": "union",
    "document_renderer": "{{encodeToon this.fields}}",
    "pruner": {
        "min_score_ratio": 0.5,
        "max_score_gap_percent": 30,
        "min_absolute_score": 0.01,
        "require_multi_index": true,
        "std_dev_threshold": 1.5
    },
    "join": {
        "right_table": "customers",
        "join_type": "inner",
        "on": {
            "left_field": "customer_id",
            "right_field": "id",
            "operator": "eq"
        },
        "right_filters": {
            "filter_query": {
                "term": "string",
                "field": "string",
                "boost": 0
            },
            "filter_prefix": "string",
            "limit": 0
        },
        "right_fields": [
            "name",
            "email",
            "tier"
        ],
        "strategy_hint": "broadcast",
        "nested_join": "..."
    },
    "foreign_sources": {}
}

Code Examples#

curl -X POST "/api/v1/query" \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
    "table": "wikipedia",
    "full_text_search": {
        "query": "+body:computer +category:technology",
        "boost": 1
    },
    "semantic_search": "artificial intelligence and machine learning applications",
    "embedding_template": "{{remotePDF url=this}}",
    "indexes": [
        "title_body_nomic",
        "description_embedding"
    ],
    "filter_prefix": "string",
    "filter_query": {
        "query": "+category:technology +year:>2020",
        "boost": 1
    },
    "exclusion_query": {
        "query": "category:deprecated OR status:archived",
        "boost": 1
    },
    "aggregations": {},
    "embeddings": {},
    "fields": [
        "title",
        "url",
        "summary",
        "created_at"
    ],
    "limit": 20,
    "offset": 0,
    "order_by": [
        {
            "field": "created_at",
            "desc": true
        },
        {
            "field": "score",
            "desc": true
        }
    ],
    "search_after": [
        "string"
    ],
    "search_before": [
        "string"
    ],
    "distance_under": 0.5,
    "distance_over": 0.1,
    "merge_config": {
        "strategy": "rrf",
        "weights": {
            "full_text": 0.3,
            "title_embedding": 1
        },
        "window_size": 1,
        "rank_constant": 0
    },
    "count": false,
    "profile": false,
    "reranker": {
        "provider": "ollama",
        "model": "dengcao/Qwen3-Reranker-0.6B:F16",
        "field": "content"
    },
    "analyses": {
        "pca": true,
        "tsne": true
    },
    "graph_searches": {},
    "expand_strategy": "union",
    "document_renderer": "{{encodeToon this.fields}}",
    "pruner": {
        "min_score_ratio": 0.5,
        "max_score_gap_percent": 30,
        "min_absolute_score": 0.01,
        "require_multi_index": true,
        "std_dev_threshold": 1.5
    },
    "join": {
        "right_table": "customers",
        "join_type": "inner",
        "on": {
            "left_field": "customer_id",
            "right_field": "id",
            "operator": "eq"
        },
        "right_filters": {
            "filter_query": {
                "term": "string",
                "field": "string",
                "boost": 0
            },
            "filter_prefix": "string",
            "limit": 0
        },
        "right_fields": [
            "name",
            "email",
            "tier"
        ],
        "strategy_hint": "broadcast",
        "nested_join": "..."
    },
    "foreign_sources": {}
}'

Responses#

{
  "responses": [
    {
      "hits": {
        "total": 0,
        "hits": [
          {
            "_id": "string",
            "_score": 0,
            "_index_scores": {},
            "_source": {},
            "_sort": [
              "string"
            ]
          }
        ],
        "max_score": 0
      },
      "aggregations": {},
      "analyses": {},
      "graph_results": {},
      "profile": {
        "shards": {
          "total": 0,
          "successful": 0,
          "failed": 0
        },
        "join": {
          "strategy_used": "broadcast",
          "left_rows_scanned": 0,
          "right_rows_scanned": 0,
          "rows_matched": 0,
          "rows_unmatched_left": 0,
          "rows_unmatched_right": 0,
          "duration_ms": 0
        },
        "reranker": {
          "model": "string",
          "documents_reranked": 0,
          "duration_ms": 0
        },
        "merge": {
          "strategy": "rrf",
          "full_text_hits": 0,
          "semantic_hits": 0,
          "duration_ms": 0
        }
      },
      "took": 0,
      "status": 0,
      "error": "string",
      "table": "string"
    }
  ]
}

Standalone evaluation endpoint#

POST/eval

Run evaluators on provided data without executing a query. Useful for testing evaluators, evaluating cached results, or batch evaluation.

Retrieval metrics (require ground_truth.relevant_ids and retrieved_ids):

  • recall, precision, ndcg, mrr, map

LLM-as-judge metrics (require judge config):

  • relevance, faithfulness, completeness, coherence, safety, helpfulness, correctness, citation_quality

Security#

Provide your bearer token in the Authorization header when making requests to protected resources.

Example: Authorization: Bearer YOUR_API_KEY

Request Body#

Example:

{
    "evaluators": [
        "recall"
    ],
    "judge": {
        "provider": "openai",
        "model": "gpt-4.1",
        "temperature": 0.7,
        "max_tokens": 2048
    },
    "ground_truth": {
        "relevant_ids": [
            "string"
        ],
        "expectations": "string"
    },
    "options": {
        "k": 1,
        "pass_threshold": 0,
        "timeout_seconds": 1
    },
    "query": "string",
    "output": "string",
    "context": [
        {}
    ],
    "retrieved_ids": [
        "string"
    ]
}

Code Examples#

curl -X POST "/api/v1/eval" \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
    "evaluators": [
        "recall"
    ],
    "judge": {
        "provider": "openai",
        "model": "gpt-4.1",
        "temperature": 0.7,
        "max_tokens": 2048
    },
    "ground_truth": {
        "relevant_ids": [
            "string"
        ],
        "expectations": "string"
    },
    "options": {
        "k": 1,
        "pass_threshold": 0,
        "timeout_seconds": 1
    },
    "query": "string",
    "output": "string",
    "context": [
        {}
    ],
    "retrieved_ids": [
        "string"
    ]
}'

Responses#

{
  "scores": {
    "retrieval": {},
    "generation": {}
  },
  "summary": {
    "average_score": 0,
    "passed": 0,
    "failed": 0,
    "total": 0
  },
  "duration_ms": 0
}

Build a search query from natural language#

POST/agents/query-builder

Uses an LLM to translate natural language search intent into a structured Bleve query. The generated query can be used directly in the QueryRequest.full_text_search or filter_query fields.

This endpoint is useful for:

  • Building queries from user descriptions
  • Generating example queries for a table's schema
  • Agentic retrieval in RAG pipelines

Security#

Provide your bearer token in the Authorization header when making requests to protected resources.

Example: Authorization: Bearer YOUR_API_KEY

Request Body#

Example:

{
    "table": "articles",
    "intent": "Find all published articles about machine learning from the last year",
    "schema_fields": [
        "title",
        "content",
        "status",
        "published_at"
    ],
    "generator": {
        "provider": "openai",
        "model": "gpt-4.1",
        "temperature": 0.7,
        "max_tokens": 2048
    }
}

Code Examples#

curl -X POST "/api/v1/agents/query-builder" \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
    "table": "articles",
    "intent": "Find all published articles about machine learning from the last year",
    "schema_fields": [
        "title",
        "content",
        "status",
        "published_at"
    ],
    "generator": {
        "provider": "openai",
        "model": "gpt-4.1",
        "temperature": 0.7,
        "max_tokens": 2048
    }
}'

Responses#

{
  "query": {
    "and": [
      {
        "match": "machine learning",
        "field": "content"
      },
      {
        "term": "published",
        "field": "status"
      }
    ]
  },
  "explanation": "Searches for 'machine learning' in content field AND requires status to be exactly 'published'",
  "confidence": 0.85,
  "warnings": [
    "Field 'category' not found in schema, using content field instead"
  ]
}

Retrieval Agent - Agentic document retrieval with tool calling#

POST/agents/retrieval

Uses a DFA-based approach to retrieve documents: clarify → select_strategy → refine_query → execute

Key Features:

  • Multi-strategy: Semantic, BM25, tree, graph, metadata, or hybrid
  • Query Pipeline: Chain queries with references (e.g., tree search starting from semantic results)
  • Clarification: Optional multi-turn for query disambiguation
  • Reasoning Chain: Returns steps taken during retrieval

Strategies:

  • semantic: Vector similarity search using embeddings
  • bm25: Full-text search with BM25 scoring
  • metadata: Structured field queries
  • tree: Iterative tree navigation with summarization (PageIndex-style)
  • graph: Relationship-based traversal
  • hybrid: Combine strategies with RRF or rerank

SSE Event Types:

  • step_started: Pipeline step began (see SSEStepStarted schema)
  • step_progress: Progress within a step (see SSEStepProgress schema)
  • step_completed: Pipeline step finished (see SSEStepCompleted schema)
  • classification: Query classification result (see ClassificationTransformationResult)
  • reasoning: Streamed reasoning text chunk (string)
  • followup: Generated follow-up question (string)
  • hit: Individual document result (see QueryHit)
  • tool_mode: Tool calling mode selected (see SSEToolMode)
  • eval: Evaluation metrics (see EvalResult)
  • done: Retrieval complete (see RetrievalAgentResult)
  • error: Error occurred (see SSEError)

Security#

Provide your bearer token in the Authorization header when making requests to protected resources.

Example: Authorization: Bearer YOUR_API_KEY

Request Body#

Example:

{
    "query": "How do I configure OAuth?",
    "queries": [
        {
            "table": "docs",
            "semantic_search": "How do I configure OAuth?",
            "indexes": [
                "doc_embeddings"
            ],
            "limit": 10
        }
    ],
    "messages": [
        {
            "role": "user",
            "content": "string",
            "tool_calls": [
                {
                    "id": "string",
                    "name": "string",
                    "arguments": {}
                }
            ],
            "tool_results": [
                {
                    "tool_call_id": "string",
                    "result": {},
                    "error": "string"
                }
            ]
        }
    ],
    "context": [
        {
            "role": "user",
            "content": "string",
            "tool_calls": [
                {
                    "id": "string",
                    "name": "string",
                    "arguments": {}
                }
            ],
            "tool_results": [
                {
                    "tool_call_id": "string",
                    "result": {},
                    "error": "string"
                }
            ]
        }
    ],
    "agent_knowledge": "This collection contains API documentation for the Acme product suite.",
    "accumulated_filters": [
        {
            "field": "string",
            "operator": "eq",
            "value": null
        }
    ],
    "max_iterations": 0,
    "max_context_tokens": 0,
    "reserve_tokens": 4000,
    "stream": true,
    "generator": {
        "provider": "openai",
        "model": "gpt-4.1",
        "temperature": 0.7,
        "max_tokens": 2048
    },
    "chain": [
        {
            "generator": {
                "provider": "openai",
                "model": "gpt-4.1",
                "temperature": 0.7,
                "max_tokens": 2048
            },
            "retry": {
                "max_attempts": 1,
                "initial_backoff_ms": 100,
                "backoff_multiplier": 1,
                "max_backoff_ms": 0
            },
            "condition": "always"
        }
    ],
    "steps": {
        "tools": {
            "enabled_tools": [
                "add_filter",
                "search",
                "websearch"
            ],
            "websearch_config": {
                "provider": "google",
                "max_results": 1,
                "timeout_ms": 0,
                "safe_search": true,
                "language": "en",
                "region": "us"
            },
            "fetch_config": {
                "s3_credentials": {
                    "endpoint": "s3.amazonaws.com",
                    "use_ssl": true,
                    "access_key_id": "your-access-key-id",
                    "secret_access_key": "your-secret-access-key",
                    "session_token": "your-session-token"
                },
                "max_content_length": 0,
                "allowed_hosts": [
                    "string"
                ],
                "block_private_ips": true,
                "max_download_size_bytes": 0,
                "timeout_seconds": 0
            },
            "max_tool_iterations": 1
        },
        "classification": {
            "enabled": true,
            "generator": {
                "provider": "openai",
                "model": "gpt-4.1",
                "temperature": 0.7,
                "max_tokens": 2048
            },
            "chain": [
                {
                    "generator": {
                        "provider": "openai",
                        "model": "gpt-4.1",
                        "temperature": 0.7,
                        "max_tokens": 2048
                    },
                    "retry": {
                        "max_attempts": 1,
                        "initial_backoff_ms": 100,
                        "backoff_multiplier": 1,
                        "max_backoff_ms": 0
                    },
                    "condition": "always"
                }
            ],
            "with_reasoning": true,
            "force_strategy": "simple",
            "force_semantic_mode": "rewrite",
            "multi_phrase_count": 1
        },
        "generation": {
            "enabled": true,
            "generator": {
                "provider": "openai",
                "model": "gpt-4.1",
                "temperature": 0.7,
                "max_tokens": 2048
            },
            "chain": [
                {
                    "generator": {
                        "provider": "openai",
                        "model": "gpt-4.1",
                        "temperature": 0.7,
                        "max_tokens": 2048
                    },
                    "retry": {
                        "max_attempts": 1,
                        "initial_backoff_ms": 100,
                        "backoff_multiplier": 1,
                        "max_backoff_ms": 0
                    },
                    "condition": "always"
                }
            ],
            "system_prompt": "string",
            "generation_context": "Be concise and technical. Include code examples where relevant."
        },
        "followup": {
            "enabled": true,
            "generator": {
                "provider": "openai",
                "model": "gpt-4.1",
                "temperature": 0.7,
                "max_tokens": 2048
            },
            "chain": [
                {
                    "generator": {
                        "provider": "openai",
                        "model": "gpt-4.1",
                        "temperature": 0.7,
                        "max_tokens": 2048
                    },
                    "retry": {
                        "max_attempts": 1,
                        "initial_backoff_ms": 100,
                        "backoff_multiplier": 1,
                        "max_backoff_ms": 0
                    },
                    "condition": "always"
                }
            ],
            "count": 1,
            "context": "Focus on implementation details and edge cases"
        },
        "confidence": {
            "enabled": true,
            "generator": {
                "provider": "openai",
                "model": "gpt-4.1",
                "temperature": 0.7,
                "max_tokens": 2048
            },
            "chain": [
                {
                    "generator": {
                        "provider": "openai",
                        "model": "gpt-4.1",
                        "temperature": 0.7,
                        "max_tokens": 2048
                    },
                    "retry": {
                        "max_attempts": 1,
                        "initial_backoff_ms": 100,
                        "backoff_multiplier": 1,
                        "max_backoff_ms": 0
                    },
                    "condition": "always"
                }
            ],
            "context": "Be conservative - only give high confidence if resources directly address the question"
        },
        "eval": {
            "evaluators": [
                "recall"
            ],
            "judge": {
                "provider": "openai",
                "model": "gpt-4.1",
                "temperature": 0.7,
                "max_tokens": 2048
            },
            "ground_truth": {
                "relevant_ids": [
                    "string"
                ],
                "expectations": "string"
            },
            "options": {
                "k": 1,
                "pass_threshold": 0,
                "timeout_seconds": 1
            }
        }
    },
    "document_renderer": "{{encodeToon this.fields}}"
}

Code Examples#

curl -X POST "/api/v1/agents/retrieval" \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
    "query": "How do I configure OAuth?",
    "queries": [
        {
            "table": "docs",
            "semantic_search": "How do I configure OAuth?",
            "indexes": [
                "doc_embeddings"
            ],
            "limit": 10
        }
    ],
    "messages": [
        {
            "role": "user",
            "content": "string",
            "tool_calls": [
                {
                    "id": "string",
                    "name": "string",
                    "arguments": {}
                }
            ],
            "tool_results": [
                {
                    "tool_call_id": "string",
                    "result": {},
                    "error": "string"
                }
            ]
        }
    ],
    "context": [
        {
            "role": "user",
            "content": "string",
            "tool_calls": [
                {
                    "id": "string",
                    "name": "string",
                    "arguments": {}
                }
            ],
            "tool_results": [
                {
                    "tool_call_id": "string",
                    "result": {},
                    "error": "string"
                }
            ]
        }
    ],
    "agent_knowledge": "This collection contains API documentation for the Acme product suite.",
    "accumulated_filters": [
        {
            "field": "string",
            "operator": "eq",
            "value": null
        }
    ],
    "max_iterations": 0,
    "max_context_tokens": 0,
    "reserve_tokens": 4000,
    "stream": true,
    "generator": {
        "provider": "openai",
        "model": "gpt-4.1",
        "temperature": 0.7,
        "max_tokens": 2048
    },
    "chain": [
        {
            "generator": {
                "provider": "openai",
                "model": "gpt-4.1",
                "temperature": 0.7,
                "max_tokens": 2048
            },
            "retry": {
                "max_attempts": 1,
                "initial_backoff_ms": 100,
                "backoff_multiplier": 1,
                "max_backoff_ms": 0
            },
            "condition": "always"
        }
    ],
    "steps": {
        "tools": {
            "enabled_tools": [
                "add_filter",
                "search",
                "websearch"
            ],
            "websearch_config": {
                "provider": "google",
                "max_results": 1,
                "timeout_ms": 0,
                "safe_search": true,
                "language": "en",
                "region": "us"
            },
            "fetch_config": {
                "s3_credentials": {
                    "endpoint": "s3.amazonaws.com",
                    "use_ssl": true,
                    "access_key_id": "your-access-key-id",
                    "secret_access_key": "your-secret-access-key",
                    "session_token": "your-session-token"
                },
                "max_content_length": 0,
                "allowed_hosts": [
                    "string"
                ],
                "block_private_ips": true,
                "max_download_size_bytes": 0,
                "timeout_seconds": 0
            },
            "max_tool_iterations": 1
        },
        "classification": {
            "enabled": true,
            "generator": {
                "provider": "openai",
                "model": "gpt-4.1",
                "temperature": 0.7,
                "max_tokens": 2048
            },
            "chain": [
                {
                    "generator": {
                        "provider": "openai",
                        "model": "gpt-4.1",
                        "temperature": 0.7,
                        "max_tokens": 2048
                    },
                    "retry": {
                        "max_attempts": 1,
                        "initial_backoff_ms": 100,
                        "backoff_multiplier": 1,
                        "max_backoff_ms": 0
                    },
                    "condition": "always"
                }
            ],
            "with_reasoning": true,
            "force_strategy": "simple",
            "force_semantic_mode": "rewrite",
            "multi_phrase_count": 1
        },
        "generation": {
            "enabled": true,
            "generator": {
                "provider": "openai",
                "model": "gpt-4.1",
                "temperature": 0.7,
                "max_tokens": 2048
            },
            "chain": [
                {
                    "generator": {
                        "provider": "openai",
                        "model": "gpt-4.1",
                        "temperature": 0.7,
                        "max_tokens": 2048
                    },
                    "retry": {
                        "max_attempts": 1,
                        "initial_backoff_ms": 100,
                        "backoff_multiplier": 1,
                        "max_backoff_ms": 0
                    },
                    "condition": "always"
                }
            ],
            "system_prompt": "string",
            "generation_context": "Be concise and technical. Include code examples where relevant."
        },
        "followup": {
            "enabled": true,
            "generator": {
                "provider": "openai",
                "model": "gpt-4.1",
                "temperature": 0.7,
                "max_tokens": 2048
            },
            "chain": [
                {
                    "generator": {
                        "provider": "openai",
                        "model": "gpt-4.1",
                        "temperature": 0.7,
                        "max_tokens": 2048
                    },
                    "retry": {
                        "max_attempts": 1,
                        "initial_backoff_ms": 100,
                        "backoff_multiplier": 1,
                        "max_backoff_ms": 0
                    },
                    "condition": "always"
                }
            ],
            "count": 1,
            "context": "Focus on implementation details and edge cases"
        },
        "confidence": {
            "enabled": true,
            "generator": {
                "provider": "openai",
                "model": "gpt-4.1",
                "temperature": 0.7,
                "max_tokens": 2048
            },
            "chain": [
                {
                    "generator": {
                        "provider": "openai",
                        "model": "gpt-4.1",
                        "temperature": 0.7,
                        "max_tokens": 2048
                    },
                    "retry": {
                        "max_attempts": 1,
                        "initial_backoff_ms": 100,
                        "backoff_multiplier": 1,
                        "max_backoff_ms": 0
                    },
                    "condition": "always"
                }
            ],
            "context": "Be conservative - only give high confidence if resources directly address the question"
        },
        "eval": {
            "evaluators": [
                "recall"
            ],
            "judge": {
                "provider": "openai",
                "model": "gpt-4.1",
                "temperature": 0.7,
                "max_tokens": 2048
            },
            "ground_truth": {
                "relevant_ids": [
                    "string"
                ],
                "expectations": "string"
            },
            "options": {
                "k": 1,
                "pass_threshold": 0,
                "timeout_seconds": 1
            }
        }
    },
    "document_renderer": "{{encodeToon this.fields}}"
}'

Responses#

{
  "id": "ragr_cr3ig20h5tbs73e3ahrg",
  "model": "gemini-2.0-flash",
  "created_at": 0,
  "status": "completed",
  "incomplete_details": {
    "reason": "max_iterations"
  },
  "usage": {
    "input_tokens": 0,
    "output_tokens": 0,
    "total_tokens": 0,
    "cached_input_tokens": 0,
    "llm_calls": 0,
    "resources_retrieved": 0,
    "prune_stats": {
      "resources_kept": 0,
      "resources_pruned": 0,
      "tokens_kept": 0,
      "tokens_pruned": 0
    }
  },
  "hits": [
    {
      "_id": "string",
      "_score": 0,
      "_index_scores": {},
      "_source": {},
      "_sort": [
        "string"
      ]
    }
  ],
  "reasoning_chain": [
    {
      "id": "step_cr3ig20h5tbs73e3ahrg",
      "step": "semantic_search",
      "action": "Searched for OAuth configuration in doc_embeddings index",
      "status": "success",
      "error_message": "string",
      "duration_ms": 0,
      "details": {}
    }
  ],
  "strategy_used": "semantic",
  "clarification_request": {
    "question": "Did you mean OAuth 1.0 or OAuth 2.0?",
    "options": [
      "OAuth 1.0",
      "OAuth 2.0",
      "Both"
    ],
    "reason": "The query mentions OAuth but doesn't specify which version"
  },
  "applied_filters": [
    {
      "field": "string",
      "operator": "eq",
      "value": null
    }
  ],
  "tool_calls_made": 0,
  "messages": [
    {
      "role": "user",
      "content": "string",
      "tool_calls": [
        {
          "id": "string",
          "name": "string",
          "arguments": {}
        }
      ],
      "tool_results": [
        {
          "tool_call_id": "string",
          "result": {},
          "error": "string"
        }
      ]
    }
  ],
  "classification": {
    "route_type": "question",
    "strategy": "simple",
    "semantic_mode": "rewrite",
    "improved_query": "string",
    "semantic_query": "string",
    "step_back_query": "string",
    "sub_questions": [
      "string"
    ],
    "multi_phrases": [
      "string"
    ],
    "reasoning": "string",
    "confidence": 0
  },
  "generation": "string",
  "generation_confidence": 0,
  "context_relevance": 0,
  "followup_questions": [
    "string"
  ],
  "eval_result": {
    "scores": {
      "retrieval": {},
      "generation": {}
    },
    "summary": {
      "average_score": 0,
      "passed": 0,
      "failed": 0,
      "total": 0
    },
    "duration_ms": 0
  }
}

Query a specific table#

POST/tables/{tableName}/query

Security#

Provide your bearer token in the Authorization header when making requests to protected resources.

Example: Authorization: Bearer YOUR_API_KEY

Request Body#

Example:

{
    "table": "wikipedia",
    "full_text_search": {
        "query": "+body:computer +category:technology",
        "boost": 1
    },
    "semantic_search": "artificial intelligence and machine learning applications",
    "embedding_template": "{{remotePDF url=this}}",
    "indexes": [
        "title_body_nomic",
        "description_embedding"
    ],
    "filter_prefix": "string",
    "filter_query": {
        "query": "+category:technology +year:>2020",
        "boost": 1
    },
    "exclusion_query": {
        "query": "category:deprecated OR status:archived",
        "boost": 1
    },
    "aggregations": {},
    "embeddings": {},
    "fields": [
        "title",
        "url",
        "summary",
        "created_at"
    ],
    "limit": 20,
    "offset": 0,
    "order_by": [
        {
            "field": "created_at",
            "desc": true
        },
        {
            "field": "score",
            "desc": true
        }
    ],
    "search_after": [
        "string"
    ],
    "search_before": [
        "string"
    ],
    "distance_under": 0.5,
    "distance_over": 0.1,
    "merge_config": {
        "strategy": "rrf",
        "weights": {
            "full_text": 0.3,
            "title_embedding": 1
        },
        "window_size": 1,
        "rank_constant": 0
    },
    "count": false,
    "profile": false,
    "reranker": {
        "provider": "ollama",
        "model": "dengcao/Qwen3-Reranker-0.6B:F16",
        "field": "content"
    },
    "analyses": {
        "pca": true,
        "tsne": true
    },
    "graph_searches": {},
    "expand_strategy": "union",
    "document_renderer": "{{encodeToon this.fields}}",
    "pruner": {
        "min_score_ratio": 0.5,
        "max_score_gap_percent": 30,
        "min_absolute_score": 0.01,
        "require_multi_index": true,
        "std_dev_threshold": 1.5
    },
    "join": {
        "right_table": "customers",
        "join_type": "inner",
        "on": {
            "left_field": "customer_id",
            "right_field": "id",
            "operator": "eq"
        },
        "right_filters": {
            "filter_query": {
                "term": "string",
                "field": "string",
                "boost": 0
            },
            "filter_prefix": "string",
            "limit": 0
        },
        "right_fields": [
            "name",
            "email",
            "tier"
        ],
        "strategy_hint": "broadcast",
        "nested_join": "..."
    },
    "foreign_sources": {}
}

Code Examples#

curl -X POST "/api/v1/tables/{tableName}/query" \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
    "table": "wikipedia",
    "full_text_search": {
        "query": "+body:computer +category:technology",
        "boost": 1
    },
    "semantic_search": "artificial intelligence and machine learning applications",
    "embedding_template": "{{remotePDF url=this}}",
    "indexes": [
        "title_body_nomic",
        "description_embedding"
    ],
    "filter_prefix": "string",
    "filter_query": {
        "query": "+category:technology +year:>2020",
        "boost": 1
    },
    "exclusion_query": {
        "query": "category:deprecated OR status:archived",
        "boost": 1
    },
    "aggregations": {},
    "embeddings": {},
    "fields": [
        "title",
        "url",
        "summary",
        "created_at"
    ],
    "limit": 20,
    "offset": 0,
    "order_by": [
        {
            "field": "created_at",
            "desc": true
        },
        {
            "field": "score",
            "desc": true
        }
    ],
    "search_after": [
        "string"
    ],
    "search_before": [
        "string"
    ],
    "distance_under": 0.5,
    "distance_over": 0.1,
    "merge_config": {
        "strategy": "rrf",
        "weights": {
            "full_text": 0.3,
            "title_embedding": 1
        },
        "window_size": 1,
        "rank_constant": 0
    },
    "count": false,
    "profile": false,
    "reranker": {
        "provider": "ollama",
        "model": "dengcao/Qwen3-Reranker-0.6B:F16",
        "field": "content"
    },
    "analyses": {
        "pca": true,
        "tsne": true
    },
    "graph_searches": {},
    "expand_strategy": "union",
    "document_renderer": "{{encodeToon this.fields}}",
    "pruner": {
        "min_score_ratio": 0.5,
        "max_score_gap_percent": 30,
        "min_absolute_score": 0.01,
        "require_multi_index": true,
        "std_dev_threshold": 1.5
    },
    "join": {
        "right_table": "customers",
        "join_type": "inner",
        "on": {
            "left_field": "customer_id",
            "right_field": "id",
            "operator": "eq"
        },
        "right_filters": {
            "filter_query": {
                "term": "string",
                "field": "string",
                "boost": 0
            },
            "filter_prefix": "string",
            "limit": 0
        },
        "right_fields": [
            "name",
            "email",
            "tier"
        ],
        "strategy_hint": "broadcast",
        "nested_join": "..."
    },
    "foreign_sources": {}
}'

Responses#

{
  "responses": [
    {
      "hits": {
        "total": 0,
        "hits": [
          {
            "_id": "string",
            "_score": 0,
            "_index_scores": {},
            "_source": {},
            "_sort": [
              "string"
            ]
          }
        ],
        "max_score": 0
      },
      "aggregations": {},
      "analyses": {},
      "graph_results": {},
      "profile": {
        "shards": {
          "total": 0,
          "successful": 0,
          "failed": 0
        },
        "join": {
          "strategy_used": "broadcast",
          "left_rows_scanned": 0,
          "right_rows_scanned": 0,
          "rows_matched": 0,
          "rows_unmatched_left": 0,
          "rows_unmatched_right": 0,
          "duration_ms": 0
        },
        "reranker": {
          "model": "string",
          "documents_reranked": 0,
          "duration_ms": 0
        },
        "merge": {
          "strategy": "rrf",
          "full_text_hits": 0,
          "semantic_hits": 0,
          "duration_ms": 0
        }
      },
      "took": 0,
      "status": 0,
      "error": "string",
      "table": "string"
    }
  ]
}