What Carbon has been offering is essential: It Enhances LLM Applications

Automation and Workflows: Facilitates task automation, such as summarizing reports or analyzing trends across datasets.

Enterprise AI Search: Enables organizations to query their internal documents, emails, and reports through LLMs.

Knowledge Management: Helps LLMs access company-specific data to answer questions accurately.

How does Carbon accomplish these goals? here is the Step-by-Step Integration Process

1. Establishing Data Connectivity

• APIs and SDKs:
  • Carbon leverages the APIs or SDKs provided by third-party applications (e.g., Google Drive, SharePoint) to access data.
  • Authentication tokens or OAuth protocols are used to ensure secure and authorized access.
• Custom Connectors:
  • For proprietary or legacy systems, Carbon creates custom adapters or middleware to connect databases, file systems, or cloud services.

2. Data Extraction and Transformation

• Format Conversion:
  • Documents, spreadsheets, or images are parsed into machine-readable formats like JSON or text.
  • Optical Character Recognition (OCR) may be applied for extracting text from scanned documents.
• Data Cleansing:
  • Redundant or irrelevant data is removed to improve processing efficiency.
  • Metadata (e.g., file type, timestamp, ownership) is tagged for context.
• Chunking and Indexing:
  • Large datasets are split into manageable chunks and indexed for quick retrieval.
  • Embedding vectors may be generated using LLM-compatible models for semantic search.

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3. Integrating with the LLM

• Middleware Role:
  • Carbon serves as an intermediary, managing requests between the user and the data sources.
  • The middleware translates user queries into API calls or database queries.
• Dynamic Context Provision:
  • Based on the query, Carbon fetches only the relevant data and presents it to the LLM as context.
  • Context is provided in a prompt format to ensure the LLM understands the query and data relationship.

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4. Query Processing Workflow

1. User Query:
   • The user submits a query to the LLM-powered application.
2. Data Retrieval:
   • Carbon interprets the query, identifies relevant data sources, and retrieves the necessary data.
3. Data Enrichment:
   • If needed, the retrieved data is summarized or reformatted for better alignment with the LLM’s input structure.
4. Response Generation:
   • The LLM uses the provided context to generate a response.

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5. Security and Compliance

• Access Control:
  • Data access is restricted based on roles, permissions, and organizational policies.
• Encryption:
  • Data is encrypted both in transit and at rest to ensure security.
• Audit Trails:
  • Logs of data access and usage are maintained for compliance and debugging.

My specific goal is Training an LLM to work with a proprietary suite of formulas. It be fine-tuning or pre-training. in both cases, preparation of data

{
    "formula": "E = mc^2",
    "description": "Energy-mass equivalence formula in physics.",
    "variables": {
        "E": "Energy",
        "m": "Mass",
        "c": "Speed of light"
    },
    "examples": [
        {
            "input": "m = 10 kg, c = 3x10^8 m/s",
            "output": "E = 9x10^16 J"
        }
    ]
}

{"prompt": "What is the formula for energy-mass equivalence?", "completion": "E = mc^2"}

from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments

# Load pre-trained model and tokenizer
model = AutoModelForCausalLM.from_pretrained("gpt-neo")
tokenizer = AutoTokenizer.from_pretrained("gpt-neo")

# Prepare dataset
train_dataset = tokenizer("your_training_data.jsonl", return_tensors="pt", max_length=512)

# Fine-tuning setup
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=4,
    num_train_epochs=3,
)

# Train the model
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)
trainer.train()

If fine-tuning like above is not feasible, we can use prompt engineering to provide context during inference.

1. Build a Knowledge Base

• Store formulas and explanations in a database or a structured file (e.g., JSON, CSV).

2. Query Pipeline

• Retrieve Relevant Context: Build a retrieval system to fetch the most relevant formulas based on user queries. Example tools:
  • Vector Search Libraries: Use FAISS or Weaviate to index your formulas and find the closest match.
  • Semantic Search Models: Use pre-trained embeddings to rank context relevance.
• Construct Dynamic Prompts: Feed the retrieved formula into the LLM as context:pythonCopy code

prompt = f"""
Based on the following formula:
Formula: E = mc^2
Description: Energy-mass equivalence formula in physics.
Variables:
  - E: Energy
  - m: Mass
  - c: Speed of light

Question: What is the energy if mass is 5kg and speed of light is 3x10^8 m/s?
"""
response = openai.Completion.create(
    model="gpt-4",
    prompt=prompt,
    max_tokens=100
)

To be more concrete, here is the full sample codes:

from sentence_transformers import SentenceTransformer
import faiss
import openai
import numpy as np

# Load and encode formulas
formulas = [
    {"formula": "E = mc^2", 
     "description": "Energy-mass equivalence formula.", 
     "variables": {"E": "Energy", "m": "Mass", "c": "Speed of light"}
    },
    {"formula": "F = ma", 
     "description": "Newton's Second Law of Motion.", 
     "variables": {"F": "Force", "m": "Mass", "a": "Acceleration"}
    }
]

descriptions = [f"{f['formula']}: {f['description']}" for f in formulas]

model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(descriptions)

index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(np.array(embeddings))

# User query
query = "What is the energy of a 5kg object moving at the speed of light?"
query_embedding = model.encode([query])
D, I = index.search(np.array(query_embedding), k=1)
relevant_formula = formulas[I[0][0]]

# Dynamic Prompt
prompt = f"""
Based on the following formula:
Formula: {relevant_formula['formula']}
Description: {relevant_formula['description']}
Variables:
{', '.join([f"{key}: {value}" for key, value in relevant_formula['variables'].items()])}

Question: {query}
"""

# LLM API call
response = openai.Completion.create(
    model="gpt-4",
    prompt=prompt,
    max_tokens=100
)

# Print the response
print(response["choices"][0]["text"].strip())

Further, applying this dynamic prompting with large formula of your own in Jupyter AI is also easy, also use SentenceTransformers and FAISS to encode and index the formulas: