create-knowledgebase

Create Bolna Knowledgebase

Endpoints

• Create: POST https://api.bolna.ai/knowledgebase
• Get: GET https://api.bolna.ai/knowledgebase/{rag_id}
• List: GET https://api.bolna.ai/knowledgebase/all
• Delete: DELETE https://api.bolna.ai/knowledgebase/{rag_id}

Use Authorization: Bearer $BOLNA_API_KEY.

Create from PDF

curl --request POST \
  --url https://api.bolna.ai/knowledgebase \
  --header "Authorization: Bearer $BOLNA_API_KEY" \
  --form 'file=@"/path/to/file.pdf"' \
  --form 'chunk_size=512' \
  --form 'similarity_top_k=15' \
  --form 'overlapping=128' \
  --form 'language_support=multilingual'

PDF max size is documented as 20 MB. Use language_support=multilingual for non-English documents or cross-lingual retrieval.

Create from URL

curl --request POST \
  --url https://api.bolna.ai/knowledgebase \
  --header "Authorization: Bearer $BOLNA_API_KEY" \
  --form 'url=https://example.com/docs' \
  --form 'chunk_size=512' \
  --form 'similarity_top_k=15' \
  --form 'overlapping=128'

Response fields

• rag_id: knowledgebase ID used for get/delete operations.
• file_name: PDF name or URL source name.
• status: processing, processed, or error.
• source_type: pdf or url.
• language_support: multilingual or null.

List responses include vector_id, which is what an agent uses for RAG wiring.

Poll until processed

curl --request GET \
  --url "https://api.bolna.ai/knowledgebase/$RAG_ID" \
  --header "Authorization: Bearer $BOLNA_API_KEY"

Do not attach a knowledgebase to an agent until it is processed.

Wire into an agent

When creating or updating an agent, configure the LLM as a knowledgebase agent and include the processed vector IDs in the vector store provider config. Use create-agent for the full agent shape.

Delete

curl --request DELETE \
  --url "https://api.bolna.ai/knowledgebase/$RAG_ID" \
  --header "Authorization: Bearer $BOLNA_API_KEY"

Confirm deletion first; agents using the vector may stop answering from that source.

Wire into a Bolna agent

In the agent config, use agent_type: "knowledgebase_agent" and attach the vector_id (one or more):

{
  "llm_agent": {
    "agent_type": "knowledgebase_agent",
    "agent_flow_type": "streaming",
    "llm_config": { "provider": "openai", "model": "gpt-4o", "max_tokens": 200, "temperature": 0.3 },
    "vector_store": {
      "provider": "lancedb",
      "provider_config": {
        "vector_ids": ["<vector_id_1>", "<vector_id_2>"],
        "similarity_top_k": 8
      }
    }
  }
}

For graph agents, attach per-node via rag_config on individual nodes — see bolna-graph-agents/references/edges-and-routing.md and the example in bolna-graph-agents/assets/full-example.json.

Script

python3 create-knowledgebase/scripts/create_knowledgebase.py \
  --file /path/to/manual.pdf \
  --multilingual

Tuning

Knob	Default	Effect
chunk_size	512	Larger chunks = more context per retrieval, less precise matching.
overlapping	128	Overlap between chunks. Higher prevents missing answers that straddle boundaries.
similarity_top_k	15	How many chunks to retrieve. Lower for tight answers, higher for broader synthesis.
language_support	null	Set to multilingual for non-English docs or cross-lingual retrieval.

See also

• create-agent — knowledgebase_agent example with vector wiring.
• bolna-graph-agents — per-node RAG via rag_config.
• https://raw.githubusercontent.com/bolna-ai/skills/main/references/prompting-tips.md — anti-hallucination guidance when answering from retrieved chunks.