[DOC] Add Contextual AI to Chroma Integration

docs/docs.trychroma.com/markdoc/content/integrations/chroma-integrations.md

@@ -48,6 +48,7 @@ We welcome pull requests to add new Integrations to the community.
 | [Langchain](./frameworks/langchain)         | ✓      | ✓            |
 | [LlamaIndex](./frameworks/llamaindex)       | ✓      | ✓            |
 | [Braintrust](./frameworks/braintrust)       | ✓      | ✓            |
+| [Contextual AI](./frameworks/contextual-ai) | ✓      | -            |
 | [OpenLLMetry](./frameworks/openllmetry)     | ✓      | Coming Soon! |
 | [Streamlit](./frameworks/streamlit)         | ✓      | -            |
 | [Haystack](./frameworks/haystack)           | ✓      | -            |

docs/docs.trychroma.com/markdoc/content/integrations/frameworks/contextual-ai.md

@@ -0,0 +1,222 @@
+---
+id: contextual-ai
+name: Contextual AI
+---
+
+# Contextual AI
+
+[Contextual AI](https://contextual.ai/?utm_campaign=Standalone-api-integration&utm_source=chroma&utm_medium=github&utm_content=repo) provides enterprise-grade components for building production RAG agents. It offers state-of-the-art document parsing, reranking, generation, and evaluation capabilities that integrate seamlessly with Chroma as the vector database. Contextual AI's tools enable developers to build document intelligence applications with advanced parsing, instruction-following reranking, grounded generation with minimal hallucinations, and natural language testing for response quality.
+
+![](https://img.shields.io/badge/License-Commercial-blue.svg)
+
+| [Docs](https://docs.contextual.ai/user-guides/beginner-guide?utm_campaign=Standalone-api-integration&utm_source=chroma&utm_medium=github&utm_content=repo) | [GitHub](https://github.com/ContextualAI?utm_campaign=Standalone-api-integration&utm_source=chroma&utm_medium=github&utm_content=repo) | [Examples](https://github.com/ContextualAI/examples) | [Blog](https://contextual.ai/blog/?utm_campaign=Standalone-api-integration&utm_source=chroma&utm_medium=github&utm_content=repo) |
+
+You can use Chroma together with Contextual AI's Parse, Rerank, Generate, and LMUnit APIs to build and evaluate comprehensive RAG pipelines.
+
+## Installation
+
+```terminal
+pip install chromadb contextual-client
+```
+
+### Complete RAG Pipeline
+
+#### Parse documents and store in Chroma
+
+```python
+from contextual import ContextualAI
+import chromadb
+from chromadb.utils import embedding_functions
+from time import sleep, time
+
+# Initialize clients
+contextual_client = ContextualAI(api_key="your-contextual-api-key")
+chroma_client = chromadb.Client()
+
+# Parse document
+with open("document.pdf", "rb") as f:
+    parse_response = contextual_client.parse.create(
+        raw_file=f,
+        parse_mode="standard",
+        enable_document_hierarchy=True
+    )
+
+# Monitor job status (Parse API is asynchronous)
+start_time = time()
+timeout_seconds = 600  # 10 minutes
+while time() - start_time < timeout_seconds:
+    status = contextual_client.parse.job_status(parse_response.job_id)
+    if status.status == "completed":
+        break
+    elif status.status == "failed":
+        raise Exception("Parse job failed")
+    sleep(30)
+else:
+    raise Exception("Parse job timed out")
+
+# Get results after job completion
+results = contextual_client.parse.job_results(
+    parse_response.job_id,
+    output_types=['blocks-per-page']
+)
+
+# Create Chroma collection
+openai_ef = embedding_functions.OpenAIEmbeddingFunction(
+    api_key="your-openai-api-key",
+    model_name="text-embedding-3-small"
+)
+
+# Create or get existing collection
+collection = chroma_client.create_collection(
+    name="documents",
+    embedding_function=openai_ef,
+    get_or_create=True
+)
+
+# Add parsed content to Chroma
+texts, metadatas, ids = [], [], []
+
+for page in results.pages:
+    for block in page.blocks:
+        if block.type in ['text', 'heading', 'table']:
+            texts.append(block.markdown)
+            metadatas.append({
+                "page": page.index + 1,
+                "block_type": block.type
+            })
+            ids.append(f"block_{block.id}")
+
+collection.add(
+    documents=texts,
+    metadatas=metadatas,
+    ids=ids
+)
+```
+
+#### Query Chroma and rerank results with custom instructions
+
+```python
+# Query Chroma
+query = "What are the key findings?"
+results = collection.query(
+    query_texts=[query],
+    n_results=10
+)
+
+# Rerank with instruction-following
+rerank_response = contextual_client.rerank.create(
+    query=query,
+    documents=results['documents'][0],
+    metadata=[str(m) for m in results['metadatas'][0]],
+    model="ctxl-rerank-v2-instruct-multilingual",
+    instruction="Prioritize recent documents. Technical details and specific findings should rank higher than general information."
+)
+
+# Get top documents
+top_docs = [
+    results['documents'][0][r.index]
+    for r in rerank_response.results[:5]
+]
+```
+
+#### Generate grounded response
+
+```python
+# Generate grounded response
+generate_response = contextual_client.generate.create(
+    messages=[{
+        "role": "user",
+        "content": query
+    }],
+    knowledge=top_docs,
+    model="v1",  # Supported models: v1, v2
+    avoid_commentary=False,
+    temperature=0.7
+)
+
+print("Response:", generate_response.response)
+```
+
+#### Evaluate response quality with LMUnit
+
+```python
+# Evaluate generated response quality
+lmunit_response = contextual_client.lmunit.create(
+    query=query,
+    response=generate_response.response,
+    unit_test="The response should be technically accurate and cite specific findings"
+)
+
+print(f"Quality Score: {lmunit_response.score}")
+
+# Score interpretation (continuous scale 1-5):
+# 5 = Excellent - Fully satisfies criteria
+# 4 = Good - Minor issues
+# 3 = Acceptable - Some issues
+# 2 = Poor - Significant issues
+# 1 = Unacceptable - Fails criteria
+```
+
+## Advanced Usage
+
+For more advanced usage examples including table extraction, document hierarchy preservation, and multi-document RAG pipelines, please refer to the comprehensive examples in our Jupyter notebooks:
+
+- [Contextual AI + Chroma Examples](https://github.com/ContextualAI/examples/tree/main/18-contextualai-chroma?utm_campaign=Standalone-api-integration&utm_source=chroma&utm_medium=github&utm_content=repo)
+
+## Components
+
+### Parse API
+
+Advanced document parsing that handles PDFs, DOCX, and PPTX files with:
+
+- Document hierarchy preservation through parent-child relationships
+- Intelligent table extraction with automatic splitting for large tables
+- Multiple output formats: markdown-document, markdown-per-page, blocks-per-page
+- Figure and caption extraction
+
+[Parse API Documentation](https://docs.contextual.ai/api-reference/parse/parse-file?utm_campaign=Standalone-api-integration&utm_source=chroma&utm_medium=github&utm_content=repo)
+
+### Rerank API
+
+State-of-the-art reranker with instruction-following capabilities:
+
+- BEIR benchmark-leading accuracy
+- Custom reranking instructions for domain-specific requirements
+- Handles conflicting retrieval results
+- Multi-lingual support
+
+Models: `ctxl-rerank-v2-instruct-multilingual`, `ctxl-rerank-v2-instruct-multilingual-mini`, `ctxl-rerank-v1-instruct`
+
+[Rerank API Documentation](https://docs.contextual.ai/api-reference/rerank/rerank?utm_campaign=Standalone-api-integration&utm_source=chroma&utm_medium=github&utm_content=repo)
+
+### Generate API (GLM)
+
+Grounded Language Model optimized for minimal hallucinations:
+
+- Industry-leading groundedness for RAG applications, currently #1 on the [FACTS Grounding benchmark](https://www.kaggle.com/benchmarks/google/facts-grounding) from Google DeepMind
+- Knowledge attribution for source transparency
+- Conversational context support
+- Optimized for enterprise use cases
+
+**Supported Models:** `v1`, `v2`
+
+[Generate API Documentation](https://docs.contextual.ai/api-reference/generate/generate?utm_campaign=Standalone-api-integration&utm_source=chroma&utm_medium=github&utm_content=repo)
+
+### LMUnit API
+
+Natural language unit testing for LLM response evaluation:
+
+- State-of-the-art response quality assessment
+- Structured testing methodology
+- Domain-agnostic evaluation framework
+- API-based evaluation at scale
+
+**Scoring Scale (Continuous 1-5):**
+
+- **5**: Excellent - Fully satisfies criteria
+- **4**: Good - Minor issues
+- **3**: Acceptable - Some issues
+- **2**: Poor - Significant issues
+- **1**: Unacceptable - Fails criteria
+
+[LMUnit Documentation](https://docs.contextual.ai/api-reference/lmunit/lmunit?utm_campaign=Standalone-api-integration&utm_source=chroma&utm_medium=github&utm_content=repo)