Knowledge Base

Give your agents access to documents for retrieval-augmented generation. TurnCall supports three retrieval modes so you can choose the right strategy for your use case.

Retrieval Modes

Mode	Behavior	Best For
`prompt`	Full document text injected into system prompt at call/chat start	Small docs (<5KB): FAQs, company info
`auto`	Per-turn semantic search via pgvector, context injected before LLM	Product catalogs, policies
`tool`	LLM calls `query_knowledge` tool when it decides to search	Large tech docs, archives

Setup

Create a knowledge base

curl -X POST http://localhost:8090/v1/knowledge-bases \
  -H "Authorization: Bearer tc_xxx" \
  -H "Content-Type: application/json" \
  -d '{"name": "product-docs", "description": "Product documentation"}'

Upload a document

curl -X POST http://localhost:8090/v1/knowledge-bases/KB_ID/documents \
  -H "Authorization: Bearer tc_xxx" \
  -F "file=@product-catalog.pdf"

Supported file types: PDF, TXT, Markdown, DOCX, CSV, JSON, YAML, XML, TSV. Max 10 MB.

Link to an agent

Choose the retrieval mode that fits your content:

curl -X POST http://localhost:8090/v1/agents/AGENT_ID/knowledge-bases \
  -H "Authorization: Bearer tc_xxx" \
  -H "Content-Type: application/json" \
  -d '{"knowledge_base_id": "KB_ID", "mode": "prompt"}'

curl -X POST http://localhost:8090/v1/agents/AGENT_ID/knowledge-bases \
  -H "Authorization: Bearer tc_xxx" \
  -H "Content-Type: application/json" \
  -d '{"knowledge_base_id": "KB_ID", "mode": "auto", "top_k": 5}'

curl -X POST http://localhost:8090/v1/agents/AGENT_ID/knowledge-bases \
  -H "Authorization: Bearer tc_xxx" \
  -H "Content-Type: application/json" \
  -d '{
    "knowledge_base_id": "KB_ID",
    "mode": "tool",
    "tool_description": "Search product docs for specifications and pricing"
  }'

Test Search

Debug your knowledge base by running a test search:

curl -X POST http://localhost:8090/v1/knowledge-bases/KB_ID/search \
  -H "Authorization: Bearer tc_xxx" \
  -H "Content-Type: application/json" \
  -d '{"query": "What is the return policy?", "top_k": 3}'

Configuration

Knowledge Base Settings

Field	Default	Description
`embedding_model`	`text-embedding-3-small`	OpenAI embedding model
`chunk_size`	512	Tokens per chunk
`chunk_overlap`	64	Overlap between chunks

Link Settings

Field	Default	Description
`mode`	required	`prompt`, `auto`, or `tool`
`priority`	0	Ordering when multiple KBs are linked
`top_k`	5	Number of chunks to retrieve (auto/tool)
`similarity_threshold`	0.3	Minimum cosine similarity (vector leg; calibrated for `text-embedding-3-*`)
`tool_description`	—	Required for tool mode

Architecture

Upload → Extract + Clean (PDF/TXT/DOCX) → Chunk (token-based) → Contextual Enrichment (LLM, best-effort) → Embed (OpenAI) → Store (pgvector + tsvector)

Search: hybrid — vector KNN + Postgres full-text, fused by rank (RRF)

Voice: transport.input → STT → user_agg → [KnowledgeRetrievalProcessor] → LLM → TTS
Chat:  inbound message → session → [KB retrieval] → LLM completion → reply

Requires PostgreSQL with the pgvector extension: CREATE EXTENSION IF NOT EXISTS vector;

Getting Started

Guides

Retrieval Modes

Setup

Test Search

Configuration

Knowledge Base Settings

Link Settings

Architecture

​Retrieval Modes

​Setup

​Test Search

​Configuration

​Knowledge Base Settings

​Link Settings

​Architecture

Retrieval Modes

Setup

Test Search

Configuration

Knowledge Base Settings

Link Settings

Architecture