API Basic Operations

This cookbook walks through the core Morphik workflow using the REST API—multimodal ingestion for high-accuracy retrieval, text ingestion for OCR-driven chunks, and integrating with your own LLM.

Prerequisites

Morphik API endpoint (e.g., http://localhost:8000 or https://api.morphik.ai)

API credentials via MORPHIK_API_KEY (if authentication is enabled)

For the optional OpenAI example, set OPENAI_API_KEY

1. Environment setup

export MORPHIK_API_URL="https://api.morphik.ai"
export MORPHIK_API_KEY="your-api-key"
export QUESTION="What are the key takeaways from the uploaded document?"

2. Multimodal ingestion (ColPali)

This path indexes the original file contents directly, yielding higher accuracy for scanned pages, tables, and images.

# Ingest a document with ColPali (multimodal)
curl -X POST "${MORPHIK_API_URL}/ingest/file" \
  -H "Authorization: Bearer ${MORPHIK_API_KEY}" \
  -F "file=@/path/to/document.pdf" \
  -F 'metadata={"demo_variant": "multimodal"}' \
  -F "use_colpali=true"

Response:

{
  "external_id": "doc-123abc",
  "filename": "document.pdf",
  "status": "processing",
  "metadata": {"demo_variant": "multimodal"}
}

Wait for processing to complete

# Check document status
curl -X GET "${MORPHIK_API_URL}/documents/doc-123abc/status" \
  -H "Authorization: Bearer ${MORPHIK_API_KEY}"

Response when ready:

{
  "status": "completed",
  "document_id": "doc-123abc",
  "chunks_created": 15
}

3. Text ingestion (OCR + chunking)

This path OCRs the document before chunking it, making the text immediately available for retrieval.

# Ingest without ColPali (standard text extraction)
curl -X POST "${MORPHIK_API_URL}/ingest/file" \
  -H "Authorization: Bearer ${MORPHIK_API_KEY}" \
  -F "file=@/path/to/document.pdf" \
  -F 'metadata={"demo_variant": "standard"}' \
  -F "use_colpali=false"

4. Query with Morphik completion

Generate a completion directly using Morphik’s configured LLM.

Multimodal query (ColPali)

curl -X POST "${MORPHIK_API_URL}/query" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${MORPHIK_API_KEY}" \
  -d '{
    "query": "What are the key takeaways from the uploaded document?",
    "use_colpali": true,
    "k": 4,
    "filters": {"demo_variant": "multimodal"}
  }'

Response:

{
  "completion": "The key takeaways from the document are...",
  "usage": {
    "prompt_tokens": 1250,
    "completion_tokens": 150,
    "total_tokens": 1400
  },
  "sources": [
    {
      "document_id": "doc-123abc",
      "chunk_number": 3,
      "score": 0.89
    }
  ]
}

Text query (standard)

curl -X POST "${MORPHIK_API_URL}/query" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${MORPHIK_API_KEY}" \
  -d '{
    "query": "What are the key takeaways?",
    "use_colpali": false,
    "k": 4,
    "filters": {"demo_variant": "standard"}
  }'

5. Query with system prompt override

Override the default system prompt to customize the LLM’s behavior and response style.

curl -X POST "${MORPHIK_API_URL}/query" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${MORPHIK_API_KEY}" \
  -d '{
    "query": "What are the key takeaways?",
    "k": 4,
    "filters": {"demo_variant": "multimodal"},
    "prompt_overrides": {
      "query": {
        "system_prompt": "You are a legal expert. Analyze the document and provide insights in formal legal language. Always cite specific sections when making claims."
      }
    }
  }'

Example with different persona:

# Pirate assistant example
curl -X POST "${MORPHIK_API_URL}/query" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What is this document about?",
    "k": 3,
    "prompt_overrides": {
      "query": {
        "system_prompt": "You are a pirate assistant. Always respond in pirate speak with arrr and matey! Be entertaining while staying accurate to the document content."
      }
    }
  }'

Response:

{
  "completion": "Arrr, me hearty! This here document be talkin' about...",
  "usage": {"prompt_tokens": 1100, "completion_tokens": 85, "total_tokens": 1185}
}

6. Retrieve chunks for your own LLM

For production workloads, retrieve Morphik’s curated chunks and forward them to your preferred LLM. This gives you full control over prompts, orchestration, and rate limits.

Step 1: Retrieve relevant chunks

curl -X POST "${MORPHIK_API_URL}/retrieve/chunks" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${MORPHIK_API_KEY}" \
  -d '{
    "query": "What are the key takeaways?",
    "use_colpali": true,
    "k": 4,
    "padding": 1,
    "filters": {"demo_variant": "multimodal"}
  }'

Response:

[
  {
    "document_id": "doc-123abc",
    "chunk_number": 3,
    "content": "The key findings indicate...",
    "score": 0.89,
    "download_url": "https://storage.morphik.ai/chunks/doc-123abc-3.png",
    "content_type": "image/png"
  },
  {
    "document_id": "doc-123abc",
    "chunk_number": 7,
    "content": "Additional analysis shows...",
    "score": 0.82,
    "download_url": null,
    "content_type": "text/plain"
  }
]

Step 2: Forward to your LLM (OpenAI example)

Text-only chunks with OpenAI

# Extract text content from chunks
TEXT_CONTEXT=$(curl -s -X POST "${MORPHIK_API_URL}/retrieve/chunks" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What are the key takeaways?",
    "use_colpali": false,
    "k": 4,
    "filters": {"demo_variant": "standard"}
  }' | jq -r '.[] | "Source #\(.chunk_number):\n\(.content)"' | tr '\n' ' ')

# Send to OpenAI
curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${OPENAI_API_KEY}" \
  -d "{
    \"model\": \"gpt-4o-mini\",
    \"messages\": [
      {
        \"role\": \"system\",
        \"content\": \"You are a helpful assistant. Use only the provided context to answer questions.\"
      },
      {
        \"role\": \"user\",
        \"content\": \"Context: ${TEXT_CONTEXT}\n\nQuestion: ${QUESTION}\"
      }
    ]
  }"

Multimodal chunks with OpenAI (vision)

# Retrieve multimodal chunks with images
CHUNKS=$(curl -s -X POST "${MORPHIK_API_URL}/retrieve/chunks" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What are the key takeaways?",
    "use_colpali": true,
    "k": 4,
    "filters": {"demo_variant": "multimodal"}
  }')

# Build multimodal content (requires jq processing for proper JSON)
# For simplicity, here's a Python example:

cat > query_openai.py << 'EOF'
import os
import json
import requests

chunks = requests.post(
    f"{os.environ['MORPHIK_API_URL']}/retrieve/chunks",
    json={
        "query": os.environ["QUESTION"],
        "use_colpali": True,
        "k": 4,
        "filters": {"demo_variant": "multimodal"}
    }
).json()

# Build multimodal message content
content = [{"type": "text", "text": f"Answer using these sources.\n\nQuestion: {os.environ['QUESTION']}"}]

for chunk in chunks:
    if chunk.get("download_url") and chunk.get("content_type", "").startswith("image/"):
        content.append({
            "type": "image_url",
            "image_url": {"url": chunk["download_url"]}
        })

# Send to OpenAI
response = requests.post(
    "https://api.openai.com/v1/chat/completions",
    headers={"Authorization": f"Bearer {os.environ['OPENAI_API_KEY']}"},
    json={
        "model": "gpt-4o",
        "messages": [{"role": "user", "content": content}]
    }
)

print(response.json()["choices"][0]["message"]["content"])
EOF

python query_openai.py

Step 3: Using other LLM providers

The same pattern works with any LLM provider. Morphik handles retrieval and chunking; you control the completion: Anthropic Claude:

curl https://api.anthropic.com/v1/messages \
  -H "x-api-key: ${ANTHROPIC_API_KEY}" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d "{
    \"model\": \"claude-3-5-sonnet-20241022\",
    \"max_tokens\": 1024,
    \"messages\": [{
      \"role\": \"user\",
      \"content\": \"Context: ${TEXT_CONTEXT}\n\nQuestion: ${QUESTION}\"
    }]
  }"

Google Gemini:

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key=${GOOGLE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "{
    \"contents\": [{
      \"parts\": [{
        \"text\": \"Context: ${TEXT_CONTEXT}\n\nQuestion: ${QUESTION}\"
      }]
    }]
  }"

7. Advanced: Custom prompt template

You can also override the prompt template that formats the context and question:

curl -X POST "${MORPHIK_API_URL}/query" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What are the revenue figures?",
    "k": 4,
    "prompt_overrides": {
      "query": {
        "system_prompt": "You are a financial analyst. Provide precise numerical answers.",
        "prompt_template": "Based on the following financial data:\n\n{context}\n\nAnalyze and answer: {question}\n\nProvide specific numbers and percentages where available."
      }
    }
  }'

Summary

Morphik’s REST API provides flexible integration options:

Managed completions: Use Morphik’s configured LLM with optional system prompt overrides
Bring your own LLM: Retrieve curated chunks and forward to any LLM provider
Multimodal support: Handle both text and visual content seamlessly
Full control: Override system prompts, prompt templates, and completion parameters

This separation of concerns lets you focus on your application logic while Morphik handles high-quality retrieval and chunking.

Get Started

Core Functions

Concepts

Using Morphik

Cookbooks

Creating Apps

Self-Hosting

Morphik Community

1. Environment setup

2. Multimodal ingestion (ColPali)

Wait for processing to complete

3. Text ingestion (OCR + chunking)

4. Query with Morphik completion

Multimodal query (ColPali)

Text query (standard)

5. Query with system prompt override

6. Retrieve chunks for your own LLM

Step 1: Retrieve relevant chunks

Step 2: Forward to your LLM (OpenAI example)

Text-only chunks with OpenAI

Multimodal chunks with OpenAI (vision)

Step 3: Using other LLM providers

7. Advanced: Custom prompt template

Summary

​1. Environment setup

​2. Multimodal ingestion (ColPali)

​Wait for processing to complete

​3. Text ingestion (OCR + chunking)

​4. Query with Morphik completion

​Multimodal query (ColPali)

​Text query (standard)

​5. Query with system prompt override

​6. Retrieve chunks for your own LLM

​Step 1: Retrieve relevant chunks

​Step 2: Forward to your LLM (OpenAI example)

​Text-only chunks with OpenAI

​Multimodal chunks with OpenAI (vision)

​Step 3: Using other LLM providers

​7. Advanced: Custom prompt template

​Summary

1. Environment setup

2. Multimodal ingestion (ColPali)

Wait for processing to complete

3. Text ingestion (OCR + chunking)

4. Query with Morphik completion

Multimodal query (ColPali)

Text query (standard)

5. Query with system prompt override

6. Retrieve chunks for your own LLM

Step 1: Retrieve relevant chunks

Step 2: Forward to your LLM (OpenAI example)

Text-only chunks with OpenAI

Multimodal chunks with OpenAI (vision)

Step 3: Using other LLM providers

7. Advanced: Custom prompt template

Summary