[DOC] Add Contextual AI to Chroma Integration

[Jinash-Rouniyar]

Description of changes

• Showcases Complete RAG Pipeline example with Chroma + Contextual AI RAG Tools

[github-actions]

Reviewer Checklist

Please leverage this checklist to ensure your code review is thorough before approving

Testing, Bugs, Errors, Logs, Documentation

• Can you think of any use case in which the code does not behave as intended? Have they been tested?
• Can you think of any inputs or external events that could break the code? Is user input validated and safe? Have they been tested?
• If appropriate, are there adequate property based tests?
• If appropriate, are there adequate unit tests?
• Should any logging, debugging, tracing information be added or removed?
• Are error messages user-friendly?
• Have all documentation changes needed been made?
• Have all non-obvious changes been commented?

System Compatibility

• Are there any potential impacts on other parts of the system or backward compatibility?
• Does this change intersect with any items on our roadmap, and if so, is there a plan for fitting them together?

Quality

• Is this code of a unexpectedly high quality (Readability, Modularity, Intuitiveness)

[propel-code-bot]

Documentation: Add Contextual AI RAG integration guide

Adds a new Markdoc page that demonstrates a full Retrieval-Augmented Generation (RAG) workflow using Chroma together with Contextual AI’s Parse, Rerank, Generate, and LMUnit APIs. The guide is provided in both Python and TypeScript, covers document parsing, async job polling, vector storage in Chroma, reranking with custom instructions, grounded response generation, and quality evaluation. In addition, the global integrations index table is updated to list Contextual AI under framework integrations.

Key Changes

• New file docs/docs.trychroma.com/markdoc/content/integrations/frameworks/contextual-ai.md (≈395 LOC) containing step-by-step code examples, API explanations, and links to external resources.
• Updated docs/docs.trychroma.com/markdoc/content/integrations/chroma-integrations.md to include Contextual AI entry in the frameworks matrix.

Affected Areas

• Documentation site (Markdoc) – framework integrations section
• No application/runtime code touched

This summary was automatically generated by @propel-code-bot

[propel-code-bot]

[CriticalError]

Potential index out of bounds error: The code assumes results['documents'][0] and results['metadatas'][0] exist, but if the Chroma query returns no results, this will raise IndexError. ChromaDB query results are structured as arrays that may be empty. Add validation:

if not results['documents'] or not results['documents'][0]:
    raise Exception("No documents found for the query")

if not results['metadatas'] or not results['metadatas'][0]:
    raise Exception("No metadata found for the query")

rerank_response = contextual_client.rerank.create(
    query=query,
    documents=results['documents'][0],
    metadata=[str(m) for m in results['metadatas'][0]],
    # ... rest of parameters
)

Context for Agents

[**CriticalError**]

Potential index out of bounds error: The code assumes `results['documents'][0]` and `results['metadatas'][0]` exist, but if the Chroma query returns no results, this will raise `IndexError`. ChromaDB query results are structured as arrays that may be empty. Add validation:

```python
if not results['documents'] or not results['documents'][0]:
    raise Exception("No documents found for the query")

if not results['metadatas'] or not results['metadatas'][0]:
    raise Exception("No metadata found for the query")

rerank_response = contextual_client.rerank.create(
    query=query,
    documents=results['documents'][0],
    metadata=[str(m) for m in results['metadatas'][0]],
    # ... rest of parameters
)
```

File: docs/docs.trychroma.com/markdoc/content/integrations/frameworks/contextual-ai.md
Line: 110

[propel-code-bot]

[CriticalError]

Potential index out of bounds error: The code assumes rerank_response.results has items and that each result has a valid index attribute. If there are no rerank results or if r.index is out of bounds for the original results, this could cause IndexError. The Contextual AI rerank API returns results with index references that may not align with the original query results. Add bounds checking:

if not hasattr(rerank_response, 'results') or not rerank_response.results:
    raise Exception("No rerank results returned")

top_docs = []
for r in rerank_response.results[:5]:
    if hasattr(r, 'index') and r.index < len(results['documents'][0]):
        top_docs.append(results['documents'][0][r.index])
    else:
        print(f"Warning: Invalid index {r.index} in rerank results")

if not top_docs:
    raise Exception("No valid documents found after reranking")

Context for Agents

[**CriticalError**]

Potential index out of bounds error: The code assumes `rerank_response.results` has items and that each result has a valid `index` attribute. If there are no rerank results or if `r.index` is out of bounds for the original results, this could cause `IndexError`. The Contextual AI rerank API returns results with index references that may not align with the original query results. Add bounds checking:

```python
if not hasattr(rerank_response, 'results') or not rerank_response.results:
    raise Exception("No rerank results returned")

top_docs = []
for r in rerank_response.results[:5]:
    if hasattr(r, 'index') and r.index < len(results['documents'][0]):
        top_docs.append(results['documents'][0][r.index])
    else:
        print(f"Warning: Invalid index {r.index} in rerank results")

if not top_docs:
    raise Exception("No valid documents found after reranking")
```

File: docs/docs.trychroma.com/markdoc/content/integrations/frameworks/contextual-ai.md
Line: 120