查询转换技术
探索改写或扩展用户查询的方法，以便从向量数据库中更有效地检索信息。
查询转换技术 是 CoddyKit 上的免费 Vector Databases: Pinecone, Weaviate & pgvector 课时。 这是第 1 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Vector Databases: Pinecone, Weaviate & pgvector 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Vector Databases: Pinecone, Weaviate & pgvector 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
What's Query Transformation?
In Retrieval Augmented Generation (RAG), the quality of the information retrieved from your vector database directly impacts the LLM's response. Sometimes, the raw user query isn't ideal for retrieval.
Query transformation is the process of modifying a user's original query to make it more effective for searching your vector database.
Why Original Queries Fall Short
User queries can be:
- Too short or vague: Lacking enough detail for precise retrieval.
- Ambiguous: Open to multiple interpretations.
- Colloquial: Using informal language that doesn't match document embeddings.
- Complex: Asking multiple questions at once.
These issues lead to irrelevant context being retrieved, impacting RAG quality.
Bridging the Semantic Gap
Your vector database stores information as numerical vectors (embeddings). For effective retrieval, the query's embedding needs to be semantically similar to the embeddings of relevant documents.
Query transformation helps by:
- Adding more context.
- Clarifying intent.
- Aligning query terms with document vocabulary.
This "bridges the semantic gap" for better matches.
Technique 1: Query Expansion
Query expansion involves adding more terms or phrases to the original query. This makes the search broader and increases the chances of hitting relevant documents.
Common methods include:
- Adding synonyms.
- Including related concepts.
- Using domain-specific jargon.
This technique is especially useful for short, vague queries.
Simple Keyword Expansion
Here's a basic Python example where we manually expand a query with related terms. In real systems, an LLM might generate these expansions automatically.
def expand_query_keywords(query):
expansion_map = {
"AI": ["artificial intelligence", "machine learning", "deep learning"],
"vector DB": ["vector database", "embedding store", "Pinecone", "Weaviate"]
}
expanded_terms = []
for keyword, additions in expansion_map.items():
if keyword.lower() in query.lower():
expanded_terms.extend(additions)
return query + " " + " ".join(expanded_terms)
# Example usage
user_query = "What is AI?"
transformed_query = expand_query_keywords(user_query)
print(f"Original: {user_query}")
print(f"Transformed: {transformed_query}")Technique 2: Query Rewriting/Rephrasing
Query rewriting (or rephrasing) uses an LLM to generate a completely new query that is clearer, more specific, or better aligned with the expected content of your documents.
This is particularly effective for:
- Ambiguous questions.
- Conversational queries.
- Queries that implicitly refer to past turns in a conversation.
Rewriting with an LLM
This Python snippet demonstrates how you might use an LLM to rephrase a user query. In a real application, llm_api_call would interact with models like OpenAI GPT or a local LLM.
import os
# Placeholder for an actual LLM API call
def llm_api_call(prompt):
# For demonstration, we'll simulate a response.
if "rephrase" in prompt.lower() and "vector databases" in prompt.lower():
return "Explain the core components and function of vector databases."
return "Could not rephrase query effectively."
def rephrase_query_with_llm(original_query):
prompt = f"Rephrase the following user query to be more effective for searching a technical documentation database about vector databases: '{original_query}'"
rephrased_query = llm_api_call(prompt)
return rephrased_query
# Example usage
user_query = "Tell me about vector DBs."
transformed_query = rephrase_query_with_llm(user_query)
print(f"Original: {user_query}")
print(f"Transformed: {transformed_query}")Technique 3: Sub-Query Generation
When a user asks a complex question that involves multiple aspects, a single query might not retrieve all necessary information. Sub-query generation breaks down a complex query into several simpler, more focused queries.
Each sub-query can then be used to retrieve specific pieces of context, which are then combined for the LLM.
Generating Sub-Queries
Here's a conceptual Python example for generating sub-queries. An LLM is often used to parse the original query and identify distinct questions.
import os
# Placeholder for an actual LLM API call
def llm_api_call_sub_query(prompt):
if "break down" in prompt.lower() and "Pinecone" in prompt.lower():
return ["What is Pinecone?", "How do I upsert data into Pinecone?", "What is a Pinecone index?"]
return ["Could not break down query."]
def generate_sub_queries(original_query):
prompt = f"Break down the following complex user query into a list of simpler, distinct questions for retrieving information from a vector database: '{original_query}'"
sub_queries = llm_api_call_sub_query(prompt)
return sub_queries
# Example usage
user_query = "How do I use Pinecone for storing and querying embeddings?"
transformed_queries = generate_sub_queries(user_query)
print(f"Original: {user_query}")
print(f"Sub-queries: {transformed_queries}")Hybrid & Contextual Transformations
Effective RAG often combines these techniques. You might first rephrase a query, then expand it. Also, consider the conversational context.
- Multi-turn awareness: Use previous turns to enrich the current query.
- Hybrid approaches: Combine transformations with traditional keyword search.
Experimentation is key to finding what works best for your data!
Understanding Query Transformation
Which of the following scenarios would MOST benefit from using query expansion as a transformation technique?
Recap: Transforming Queries
You've learned that query transformation is crucial for improving RAG performance by making user queries more effective for vector database retrieval.
- Query Expansion: Adds terms to broaden search.
- Query Rewriting: Rephrases for clarity and specificity.
- Sub-Query Generation: Breaks down complex queries.
Mastering these techniques will significantly enhance the quality of your RAG applications!
常见问题解答
「查询转换技术」课时是免费的吗？
是的 — 「查询转换技术」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Vector Databases: Pinecone, Weaviate & pgvector 课程的其余内容，请升级到 CoddyKit PRO。 Vector Databases: Pinecone, Weaviate & pgvector 课程共包含 4 节课。
「查询转换技术」这节课中我会学到什么？
探索改写或扩展用户查询的方法，以便从向量数据库中更有效地检索信息。 你通过在浏览器中直接运行的动手代码来练习 Vector Databases: Pinecone, Weaviate & pgvector，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Vector Databases: Pinecone, Weaviate & pgvector 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Vector Databases: Pinecone, Weaviate & pgvector 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 1 节课，共 4 节。
「查询转换技术」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Vector Databases: Pinecone, Weaviate & pgvector 课中编写并运行代码吗？
能。每节 Vector Databases: Pinecone, Weaviate & pgvector 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。