Integrating Documentum with an Amazon Bedrock Chatbot API for Product Manuals AI Notes

This article outlines the process of building a product manual chatbot API using Amazon Bedrock, with a specific focus on integrating content sourced from a Documentum repository. By leveraging the power of vector embeddings and Large Language Models (LLMs) within Bedrock, we can create an intelligent and accessible way for users to find information within extensive product documentation managed by Documentum.

The Need for Integration:

Many organizations manage their critical product documentation within enterprise content management systems like Documentum. To make this valuable information readily available to users through modern conversational interfaces, a seamless integration with AI-powered platforms like Amazon Bedrock is essential. This allows users to ask natural language questions and receive accurate, contextually relevant answers derived from the product manuals.

Architecture Overview:

The proposed architecture involves the following key components:

Documentum Repository: The central content management system storing the product manuals.
Document Extraction Service: A custom-built service responsible for accessing Documentum, retrieving relevant product manuals and their content, and potentially extracting associated metadata.
Amazon S3: An AWS object storage service used as an intermediary staging area for the extracted documents. Bedrock’s Knowledge Base can directly ingest data from S3.
Amazon Bedrock Knowledge Base: A managed service that ingests and processes the documents from S3, creates vector embeddings, and enables efficient semantic search.
Chatbot API (FastAPI): A Python-based API built using FastAPI, providing endpoints for users to query the product manuals. This API interacts with the Bedrock Knowledge Base for retrieval and an LLM for answer generation.
Bedrock LLM: A Large Language Model (e.g., Anthropic Claude) within Amazon Bedrock used to generate human-like answers based on the retrieved context.

Step-by-Step Implementation:

1. Documentum Extraction Service:

This is a crucial custom component. The implementation will depend on your Documentum environment and preferred programming language.

Accessing Documentum: Utilize the Documentum Content Server API (DFC) or the Documentum REST API to establish a connection. This will involve handling authentication and session management.
Document Retrieval: Implement logic to query and retrieve the specific product manuals intended for the chatbot. You might filter based on document types, metadata (e.g., product name, version), or other relevant criteria.
Content Extraction: Extract the actual textual content from the retrieved documents. This might involve handling various file formats (PDF, DOCX, etc.) and ensuring clean text extraction.
Metadata Extraction (Optional): Extract relevant metadata associated with the documents. While Bedrock primarily uses content for embeddings, this metadata could be useful for future enhancements or filtering within the extraction process.
Data Preparation: Structure the extracted content and potentially metadata. You can save each document as a separate file or create structured JSON files.
Uploading to S3: Use the AWS SDK for Python (boto3) to upload the prepared files to a designated S3 bucket in your AWS account. Organize the files logically within the bucket (e.g., by product).

Conceptual Python Snippet (Illustrative – Replace with actual Documentum interaction):

Python

import boto3
# Assuming you have a library or logic to interact with Documentum

# AWS Configuration
REGION_NAME = "us-east-1"
S3_BUCKET_NAME = "your-bedrock-ingestion-bucket"
s3_client = boto3.client('s3', region_name=REGION_NAME)

def extract_and_upload_document(documentum_document_id, s3_prefix="documentum/"):
    """
    Conceptual function to extract content from Documentum and upload to S3.
    Replace with your actual Documentum interaction.
    """
    # --- Replace this with your actual Documentum API calls ---
    content = f"Content of Document {documentum_document_id} from Documentum."
    filename = f"{documentum_document_id}.txt"
    # --- End of Documentum interaction ---

    s3_key = os.path.join(s3_prefix, filename)
    try:
        s3_client.put_object(Bucket=S3_BUCKET_NAME, Key=s3_key, Body=content.encode('utf-8'))
        print(f"Uploaded {filename} to s3://{S3_BUCKET_NAME}/{s3_key}")
        return True
    except Exception as e:
        print(f"Error uploading {filename} to S3: {e}")
        return False

if __name__ == "__main__":
    documentum_ids_to_ingest = &lsqb;"product_manual_123", "installation_guide_456"]
    for doc_id in documentum_ids_to_ingest:
        extract_and_upload_document(doc_id)

2. Amazon S3 Configuration:

Ensure you have an S3 bucket created in your AWS account where the Documentum extraction service will upload the product manuals.

3. Amazon Bedrock Knowledge Base Setup:

Navigate to the Amazon Bedrock service in the AWS Management Console.
Create a new Knowledge Base.
When configuring the data source, select “Amazon S3” as the source type.
Specify the S3 bucket and the prefix (e.g., documentum/) where the Documentum extraction service uploads the files.
Configure the synchronization settings for the data source. You can choose on-demand synchronization or set up a schedule for periodic updates.
Bedrock will then process the documents in the S3 bucket, chunk them, generate vector embeddings, and build an index for efficient retrieval.

4. Chatbot API (FastAPI):

Create a Python-based API using FastAPI to handle user queries and interact with the Bedrock Knowledge Base.

Python

# chatbot_api.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import boto3
import json
import os

# Configuration
REGION_NAME = "us-east-1"  # Replace with your AWS region
KNOWLEDGE_BASE_ID = "kb-your-knowledge-base-id"  # Replace with your Knowledge Base ID
LLM_MODEL_ID = "anthropic.claude-v3-opus-20240229"  # Replace with your desired LLM model ID

bedrock_runtime = boto3.client("bedrock-runtime", region_name=REGION_NAME)
bedrock_knowledge = boto3.client("bedrock-agent-runtime", region_name=REGION_NAME)

app = FastAPI(title="Product Manual Chatbot API")

class ChatRequest(BaseModel):
    product_name: str  # Optional: If you have product-specific manuals
    user_question: str

class ChatResponse(BaseModel):
    answer: str

def retrieve_pdf_context(knowledge_base_id, product_name, user_question, max_results=3):
    """Retrieves relevant document snippets from the Knowledge Base."""
    query = user_question # The Knowledge Base handles semantic search across all ingested data
    if product_name:
        query = f"Information about {product_name} related to: {user_question}"

    try:
        response = bedrock_knowledge.retrieve(
            knowledgeBaseId=knowledge_base_id,
            retrievalConfiguration={
                "vectorSearchConfiguration": {
                    "query": {
                        "text": query
                    }
                }
            },
            retrieveMaxResults=max_results
        )
        results = response.get("retrievalResults", &lsqb;])
        if results:
            context_texts = &lsqb;result.get("content", {}).get("text", "") for result in results]
            return "\n\n".join(context_texts)
        else:
            return None
    except Exception as e:
        print(f"Error during retrieval: {e}")
        raise HTTPException(status_code=500, detail="Error retrieving context")

def generate_answer(prompt, model_id=LLM_MODEL_ID):
    """Generates an answer using the specified Bedrock LLM."""
    try:
        if model_id.startswith("anthropic"):
            body = json.dumps({"prompt": prompt, "max_tokens_to_sample": 500, "temperature": 0.6, "top_p": 0.9})
            mime_type = "application/json"
        elif model_id.startswith("ai21"):
            body = json.dumps({"prompt": prompt, "maxTokens": 300, "temperature": 0.7, "topP": 1})
            mime_type = "application/json"
        elif model_id.startswith("cohere"):
            body = json.dumps({"prompt": prompt, "max_tokens": 300, "temperature": 0.7, "p": 0.7})
            mime_type = "application/json"
        else:
            raise HTTPException(status_code=400, detail=f"Model ID '{model_id}' not supported")

        response = bedrock_runtime.invoke_model(body=body, modelId=model_id, accept=mime_type, contentType=mime_type)
        response_body = json.loads(response.get("body").read())

        if model_id.startswith("anthropic"):
            return response_body.get("completion").strip()
        elif model_id.startswith("ai21"):
            return response_body.get("completions")&lsqb;0].get("data").get("text").strip()
        elif model_id.startswith("cohere"):
            return response_body.get("generations")&lsqb;0].get("text").strip()
        else:
            return None

    except Exception as e:
        print(f"Error generating answer with model '{model_id}': {e}")
        raise HTTPException(status_code=500, detail=f"Error generating answer with LLM")

@app.post("/chat/", response_model=ChatResponse)
async def chat_with_manual(request: ChatRequest):
    """Endpoint for querying the product manuals."""
    context = retrieve_pdf_context(KNOWLEDGE_BASE_ID, request.product_name, request.user_question)

    if context:
        prompt = f"""You are a helpful chatbot assistant for product manuals. Use the following information to answer the user's question. If the information doesn't directly answer, try to infer or provide related helpful information. Do not make up information.

        <context>
        {context}
        </context>

        User Question: {request.user_question}
        """
        answer = generate_answer(prompt)
        if answer:
            return {"answer": answer}
        else:
            raise HTTPException(status_code=500, detail="Could not generate an answer")
    else:
        raise HTTPException(status_code=404, detail="No relevant information found")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

5. Bedrock LLM for Answer Generation:

The generate_answer function in the API interacts with a chosen LLM within Bedrock (e.g., Anthropic Claude) to formulate a response based on the retrieved context from the Knowledge Base and the user’s question.

Deployment and Scheduling:

Document Extraction Service: This service can be deployed as a scheduled job (e.g., using AWS Lambda and CloudWatch Events) to periodically synchronize content from Documentum to S3, ensuring the Knowledge Base stays up-to-date.
Chatbot API: The FastAPI application can be deployed on various platforms like AWS ECS, AWS Lambda with API Gateway, or EC2 instances.

Conclusion:

Integrating Documentum with an Amazon Bedrock chatbot API for product manuals offers a powerful way to unlock valuable information and provide users with an intuitive and efficient self-service experience. By building a custom extraction service to bridge the gap between Documentum and Bedrock’s data source requirements, organizations can leverage the advanced AI capabilities of Bedrock to create intelligent conversational interfaces for their product documentation. This approach enhances accessibility, improves user satisfaction, and reduces the reliance on manual document searching. Remember to carefully plan the Documentum extraction process, considering factors like scalability, incremental updates, and error handling to ensure a robust and reliable solution.

AI Notes

Integrating Documentum with an Amazon Bedrock Chatbot API for Product Manuals

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