Tengxun cloud real name number: using tengxun cloud vector database (VectorDB) large model, zero step pit to build enterprise-level private knowledge base

In this era of large models (LLM) "flying all over the sky", many companies and developers will encounter an extremely troublesome problem when landing AI applications:

Large models have hallucinations, do not know the private data within the enterprise, or are not timely enough to answer

.

Fine-tuning is not only expensive, but data updates are extremely cumbersome. At present, the most mature and cost-effective solution in the industry is

RAG (Retrieval Enhancement Generation, Retrievers-Augmented Generation)

. The core "heart" of the RAG architecture is

Vector database (Vector Database)

.

Today, we will use tengxunyun to research

TencentDB for VectorDB (Tencent Cloud Vector Database)

, with LangChain and large models, take you from scratch to build an efficient and accurate enterprise-level private knowledge base!

Why did 1. choose Tengxun Cloud VectorDB?

Before writing the code, let's talk about why we chose the VectorDB of Tengxun Cloud this time instead of using open source components (such as Milvus or Faiss) to toss around ourselves:

Out of the box, no operation and maintenance: open source vector database is easy to build, but high availability, fragmentation, expansion and index optimization are all pits. Tencent Cloud encapsulates all these underlying complexities.

Ultra-high concurrency and single-machine performance: The single-machine supports up to millions of QPS, and the delay is controlled in milliseconds, which is very suitable for seamless expansion after the business scale increases.

Native support for RAG scenarios: Built-in Embedding vectorization model and scalar filtering function, directly eliminating the cost of building your own Embedding services.

Pre-preparation tip: Before you start, you need to open Tengxun cloud service. If you are an enterprise team or need to purchase resources in bulk, it is recommended to complete tengxunyun account purchase and real-name authentication through formal channels, not only to obtain professional access guidance from architects, but also to enjoy more cost-effective exclusive discounts.

2. overall architecture design (RAG workflow)

The core process of our private knowledge base is only 3 steps:

Plaintext

[Original Document (.pdf/.md)]

│

▼

[1. Text slicing (Chunking) & vectorization (Embedding)]

│

▼

[2. Deposit Tengxun Cloud VectorDB]-(User Questions)-[3. Vector Similarity Retrieval]

│

▼

[4. Assembled Prompt Submitted to Large Model]-[Output Accurate Answer]

3. Practical Steps: Building a Knowledge Base from 0 to 1

Step 1: Create a Tencent Cloud VectorDB instance

Log on to the Tengxun cloud console and search to enter the vector database TencentDB for VectorDB page.

Click "New Instance" and select the billing mode (it is recommended to select pay-as-you-go billing for the test and package years and months for the production environment).

Configure Network (VPC): Attention! The vector database must be located in the same VPC (private network) as your cloud server (CVM) or function compute, so that you can access it quickly through the intranet.

After the instance is created, record the intranet IP address, port number (usually 80), user name (root), and key (Key) of the instance.

Step 2: Environment Preparation and SDK Installation

In your Python development environment (or Tengxun Cloud CVM), install the official SDK and necessary dependency libraries:

Bash

pip install tcvectordb langchain langchain-community pypdf

Step 3: Write code-document processing and vector warehousing.

Suppose we have a copy of the internal

company_policy.pdf

(Employee Handbook). We need to read it, slice it, convert it into a vector, and write it to the Tencent cloud VectorDB.

Step 4: Implement retrieval and large model question answering (RAG closed loop)

After the document is put into storage, when the user raises a question, we go to the VectorDB to retrieve the text paragraph with the highest degree of correlation, and finally feed it to the large model (such as DeepSeek or mixed-element large model):

def ask_knowledge_base(query_text):# 1. Convert the user's question into a vector (use the same Embedding model as the storage)# Tengxun cloud VectorDB supports Embedding Pipeline, or it can be generated by local models.

query_vector = get_embedding(query_text)# Suppose this is your vectorization function #2. Do similarity Top-K retrieval in VectorDB

res = coll.search (

vectors=[query_vector]

Limit = 3# takes the most similar 3 paragraphs of text

&nbs

p;)

#3. Extract the retrieved context

context_list = [doc['text_content'] for doc in res[0]]

context = "\n---\n".join(context_list)

#4. Build a Prompt with Context

prompt = f "" "You are a professional business assistant. Please answer user questions based only on the [References] provided below. If it is not mentioned in the information, please answer "Sorry, the relevant content was not found in the internal information". [Reference]: {context} [User Question]: {query_text} "" "# 5. Call the large model to generate an answer

response = call_llm# Call your LLM APIreturn response

# Test call

answer = ask_knowledge_base("How many days of paid annual leave does the company have?")

print(answer)

4. Pit Avoidance Guidelines and Best Practices

In the actual landing process, there are several "blood and tears lessons" worthy of your attention:

The size of the chunk is critical: if the chunk is too small (e. g. 50 words), it is easy to lose context information; If the chunk is too large (e. g. 2000 words), too much noise will be retrieved. Suggestion: 300~500 words per block, and the overlap (overlap) is set at 10% ~ 15%.

Scalar filtering (Filter) make good use of: If your knowledge base contains data from multiple departments (such as HR, finance, technology), be sure to add category scalar fields when building an index. Filter the department first when searching, which can greatly improve the accuracy and save computing power.

Hybrid Search: Vector search alone (semantic matching) is sometimes insensitive to proprietary words (e. g. product model, person name). Tengxun cloud VectorDB combined with full-text search/keyword filtering hybrid search mode, the effect will be much better.

Summary

Through

Tencent Cloud VectorDB + Big Model

With only a few dozen lines of code, we avoided the complex underlying environment to build a highly concurrent, low-latency, and extremely accurate corporate private knowledge base.

If you're planning to deploy your own AI knowledge base system within your team, try it out. In the early stage of construction, it is recommended to do a good job of computing power and storage resources planning, choose compliance and cost-effective.

Purchase a Tencent Cloud account

and service guarantee channels

Let your AI project land efficiently and stably!