Connect your vector databases to Arcbeam to see which documents influenced each trace
Connecting your to Arcbeam creates a complete picture of your AI system. You’ll see not just what your model said, but exactly which documents from your knowledge base influenced each response.
Why Connect Data Sources?
When you only see traces without data sources, you know your AI system produced an answer but you don’t know where it came from. Was it hallucinating? Did it retrieve the right documents? Which parts of your knowledge base are actually being used?With data sources connected, every trace shows:
Document Attribution
Which specific documents were retrieved and their exact content
Usage Analytics
How often each document is used across all queries
Quality Insights
Which documents need updating or improvement
Actionable Data
Turn observability into concrete improvements
How It Works
1
Choose your source
Navigate to Data Platform → Data Sources and choose your data source.
Navigate to the data source pages
2
Connect your vector database
Tell Arcbeam how to access your .
Connect your vector database
3
Map your schema
Type which fields contain your document content, IDs, and metadata.
For nested columns type them out with a . in between each level.Example: If the field you want to reference is col3 in {'col1' : {'col2': {'col3': 'value'}}}, then type in col1.col2.col3
Specify the schema mapping of your vector database.
4
Sync once
Arcbeam pulls metadata about your documents (not the vectors themselves).
5
See the connections
When traces come in, Arcbeam automatically links retrieved documents to each query.
Your data sources are now connected and enriching your traces!
What Gets Synced
Arcbeam syncs metadata about your documents, not the documents themselves.
Data Type
Synced?
Description
Document IDs
✓ Yes
To match retrieved docs in traces
Source attribution
✓ Yes
Which file/URL each document came from
Basic metadata
✓ Yes
Timestamps, document names, etc.
Document content
✓ Yes
Text content of documents (stays in your DB, referenced in traces)
Vector embeddings
✗ Never
Your are never synced
Unmapped fields
✗ Never
Only the metadata fields you explicitly specify are copied
The actual document content stays in your vector database. Arcbeam only stores what’s needed to show you which documents were used in each trace.
Supported Vector Databases
pgvector
PostgreSQL with pgvector extension (fully supported)
Coming Soon
Pinecone, Weaviate, Chroma, and others
Privacy and Security
What Data is Accessed
Arcbeam only reads the fields you explicitly map:
Field Type
Accessed?
Description
Document IDs
✓ Yes
Required to match documents in traces
Document content (text)
✓ Yes
Document text content for display
Source attribution fields
✓ Yes
Track which file/URL documents came from
Metadata fields
✓ Optional
Only fields you explicitly map in configuration
Timestamp fields
✓ Optional
Only if you configure last updated tracking
Vector embeddings
✗ Never
Your embeddings are never accessed or synced
Unmapped fields
✗ Never
Only explicitly mapped fields are read
Connection Security
Connection strings are encrypted at rest
Database credentials are never logged or exposed
All connections use SSL/TLS when available
Read-only access is recommended
Create a dedicated read-only database user for Arcbeam to minimize security risk.
Track which documents are retrieved and their impact
Track which documents are most useful
✓ Yes
See usage analytics and document performance
Debug why certain answers were given
✓ Yes
Trace answers back to source documents
Measure knowledge base quality
✓ Yes
Identify gaps and improvement opportunities
Only using direct LLM calls (no retrieval)
✗ Skip
No document retrieval to track
Just want to track costs and errors
✗ Skip
Traces alone provide this information
Using function calling without RAG
✗ Skip
No vector database retrieval involved
Quick Example
Here’s what connecting a pgvector database looks like:
# Your vector database has a table with this structure:# CREATE TABLE knowledge_base (# id TEXT PRIMARY KEY,# content TEXT,# source TEXT,# updated_at TIMESTAMP# );# In Arcbeam dashboard:# 1. Add pgvector integration# 2. Provide connection string# 3. Map schema:# - ID field: "id"# - Document field: "content"# - Source field: "source"# - Last updated: "updated_at" (optional)
Once connected, traces automatically show which documents were retrieved:
Retrieved Source Data
What You Can Do With Connected Data
Track Document Usage
See which documents are retrieved most often, which are never used, and which ones lead to good vs bad responses.
Debug RAG Pipelines
When a trace shows wrong information, see exactly which documents were retrieved and whether they contained the right content.
Measure Knowledge Base Quality
Find gaps in your knowledge base by seeing which queries don’t retrieve useful documents.
Improve with User Feedback
When users give thumbs down, see which documents were involved and update them accordingly.
Next Steps
Connect pgvector
Step-by-step guide to connecting a pgvector database
Data Lineage
Understand how data flows through your system
View Retrieved Documents
Explore retrieved documents in traces
Track Document Usage
Analyze which documents are being used
Common Questions
Does this slow down my database?
No. Syncing is a one-time operation that reads metadata. It doesn’t run queries during normal operation. Your application’s vector database queries are completely separate.
What if my schema changes?
Update the schema mapping in Arcbeam, then trigger a re-sync. Arcbeam will refresh the metadata.
Can I connect multiple databases?
Yes. You can connect multiple vector databases or different tables/indices within the same database. Each becomes a separate dataset in Arcbeam.
Do I need read-write access?
No. Arcbeam only needs read access to your vector database. Using a read-only user is recommended for security.
What if I'm using multiple vector databases?
Connect each one separately. Arcbeam will track documents across all of them and show you which database was used for each retrieval.
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errors`}),_jsx(_components.td,{children:`✗ Skip`}),_jsx(_components.td,{children:`Traces alone provide this information`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:`Using function calling without RAG`}),_jsx(_components.td,{children:`✗ Skip`}),_jsx(_components.td,{children:`No vector database retrieval involved`})]})]})]}),`\n`,_jsx(Heading,{level:`2`,id:`quick-example`,children:`Quick Example`}),`\n`,_jsx(_components.p,{children:`Here’s what connecting a pgvector database looks like:`}),`\n`,_jsx(CodeBlock,{filename:``,numberOfLines:`16`,language:`python`,children:_jsx(_components.pre,{className:`shiki shiki-themes github-light-default dark-plus`,style:{backgroundColor:`#ffffff`,\"--shiki-dark-bg\":`#0B0C0E`,color:`#1f2328`,\"--shiki-dark\":`#D4D4D4`},language:`python`,children:_jsxs(_components.code,{language:`python`,numberOfLines:`16`,children:[_jsx(_components.span,{className:`line`,children:_jsx(_components.span,{style:{color:`#6E7781`,\"--shiki-dark\":`#6A9955`},children:`# Your vector database has a table with this structure:`})}),`\n`,_jsx(_components.span,{className:`line`,children:_jsx(_components.span,{style:{color:`#6E7781`,\"--shiki-dark\":`#6A9955`},children:`# CREATE TABLE knowledge_base (`})}),`\n`,_jsx(_components.span,{className:`line`,children:_jsx(_components.span,{style:{color:`#6E7781`,\"--shiki-dark\":`#6A9955`},children:`# id TEXT PRIMARY KEY,`})}),`\n`,_jsx(_components.span,{className:`line`,children:_jsx(_components.span,{style:{color:`#6E7781`,\"--shiki-dark\":`#6A9955`},children:`# content TEXT,`})}),`\n`,_jsx(_components.span,{className:`line`,children:_jsx(_components.span,{style:{color:`#6E7781`,\"--shiki-dark\":`#6A9955`},children:`# source TEXT,`})}),`\n`,_jsx(_components.span,{className:`line`,children:_jsx(_components.span,{style:{color:`#6E7781`,\"--shiki-dark\":`#6A9955`},children:`# updated_at TIMESTAMP`})}),`\n`,_jsx(_components.span,{className:`line`,children:_jsx(_components.span,{style:{color:`#6E7781`,\"--shiki-dark\":`#6A9955`},children:`# );`})}),`\n`,_jsx(_components.span,{className:`line`}),`\n`,_jsx(_components.span,{className:`line`,children:_jsx(_components.span,{style:{color:`#6E7781`,\"--shiki-dark\":`#6A9955`},children:`# In Arcbeam dashboard:`})}),`\n`,_jsx(_components.span,{className:`line`,children:_jsx(_components.span,{style:{color:`#6E7781`,\"--shiki-dark\":`#6A9955`},children:`# 1. Add pgvector integration`})}),`\n`,_jsx(_components.span,{className:`line`,children:_jsx(_components.span,{style:{color:`#6E7781`,\"--shiki-dark\":`#6A9955`},children:`# 2. Provide connection string`})}),`\n`,_jsx(_components.span,{className:`line`,children:_jsx(_components.span,{style:{color:`#6E7781`,\"--shiki-dark\":`#6A9955`},children:`# 3. 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