How to Build Production‑Ready Chatbots with OpenSearch
How to Build Production‑Ready Chatbots with OpenSearch
Why OpenSearch for chatbots?
OpenSearch gives you the pieces you need to power modern chatbots: fast vector search for semantic retrieval, neural sparse + hybrid search to blend keyword and semantic signals, search pipelines for query orchestration and reranking, and built‑in conversation memory plus RAG processors to talk to an LLM. That means you can keep your knowledge base in OpenSearch, retrieve the right context in milliseconds, and generate grounded answers with your LLM of choice.
Vector search
OpenSearch implements k‑nearest neighbors (k‑NN) over knn_vector fields with approximate (ANN) and exact methods, so you can store document embeddings and retrieve the most similar chunks at low latency.
Neural sparse + hybrid search
You can generate sparse embeddings (token:weight pairs) and combine them with dense vectors and keyword BM25 in a hybrid query—often improving relevance over any single technique.
Pipelines & reranking
Search pipelines orchestrate steps like query rewriting, normalization, and reranking (e.g., via Cohere/Amazon Bedrock models or by-document fields) without moving data out of OpenSearch.
RAG & conversation memory
OpenSearch provides conversation memory APIs and a RAG processor that fetches prior messages + retrieved docs and sends them to an LLM—then stores the LLM reply back into memory.
Vector search
Neural sparse + hybrid search
Pipelines & reranking
RAG & conversation memory
OpenSearch implements k‑nearest neighbors (k‑NN) over knn_vector fields with approximate (ANN) and exact methods, so you can store document embeddings and retrieve the most similar chunks at low latency.
You can generate sparse embeddings (token:weight pairs) and combine them with dense vectors and keyword BM25 in a hybrid query—often improving relevance over any single technique.
Search pipelines orchestrate steps like query rewriting, normalization, and reranking (e.g., via Cohere/Amazon Bedrock models or by-document fields) without moving data out of OpenSearch.
OpenSearch provides conversation memory APIs and a RAG processor that fetches prior messages + retrieved docs and sends them to an LLM—then stores the LLM reply back into memory.
A reference architecture for an OpenSearch‑powered chatbot
Finally, the RAG step sends the retrieved chunks and the conversation history to your LLM to compose the final answer.
Want help building a chatbot with OpenSearch?
Dattell designs, deploys, and operates OpenSearch‑backed chatbots end‑to‑end—embedding pipelines, hybrid retrieval, search pipelines, conversation memory/RAG, and production SLAs across AWS/Azure/GCP or on‑prem.
Want help building a chatbot with OpenSearch?
Dattell designs, deploys, and operates OpenSearch‑backed chatbots end‑to‑end—embedding pipelines, hybrid retrieval, search pipelines, conversation memory/RAG, and production SLAs across AWS/Azure/GCP or on‑prem.
Step 1 — Create a vector index for your knowledge base
PUT kb_docs
{
"settings": {
"index.knn": true
},
"mappings": {
"properties": {
"doc_id": { "type": "keyword" },
"title": { "type": "text" },
"content": { "type": "text" },
"embedding": {
"type": "knn_vector",
"dimension": 768,
"method": {
"name": "hnsw",
"engine": "lucene",
"space_type": "cosinesimil",
"parameters": {
"ef_construction": 128,
"m": 24
}
}
}
}
}
}
Step 2 — Generate embeddings automatically at ingest
PUT _ingest/pipeline/kb_embed
{
"description": "Create embeddings for content -> embedding",
"processors": [
{
"text_embedding": {
"model_id": "<your_embedding_model_id>",
"field_map": { "content": "embedding" }
}
}
]
}
POST kb_docs/_doc?pipeline=kb_embed
{ "doc_id":"kb-001", "title":"Reset VPN", "content":"Steps to reset the VPN on macOS..." }
Step 3 — Retrieval: dense, sparse, and hybrid
Dense k‑NN
POST kb_docs/_search
{
"size": 5,
"query": {
"knn": {
"embedding": {
"vector": [/* your query embedding */],
"k": 50
}
}
}
}
Neural sparse + hybrid
OpenSearch can generate sparse embeddings and combine them with BM25 and/or dense vectors in a hybrid query—often a strong default for chatbots that must respect keywords, acronyms, and product names while capturing semantics.
Step 4 — Orchestrate with a search pipeline (+ optional reranking)
Search pipelines let you modularize retrieval steps server‑side: normalize scores, enrich queries, call ML for inference, and rerank results—so your app code stays simple.
PUT /_search/pipeline/hybrid_pipeline
{
"request_processors": [
{ "filter_query": { "query": { "term": { "is_public": true } } } }
],
"response_processors": [
{ "normalization": { "technique": "min_max" } },
{ "rerank": { "type": "by_field", "field": "rerank_score", "size": 50 } }
]
}
Attach it per‑request:
GET kb_docs/_search?search_pipeline=hybrid_pipeline
OpenSearch documents the primitives (request/response processors) and shows end‑to‑end examples for reranking (by field or with cross‑encoders/hosted models).
Step 5 — Add conversation memory and RAG
OpenSearch ships conversation memory (stores message history) and a RAG processor that sends retrieved docs + prior turns to your LLM, then stores the answer back in memory.
Create memory:
POST /_plugins/_ml/memory/
{ "name": "customer-support-chat" }
Use RAG at query time:
GET /kb_docs/_search
{
"query": { "match": { "content": "How do I reset my VPN?" } },
"ext": {
"generative_qa_parameters": {
"llm_model": "gpt-3.5-turbo",
"llm_question": "User asks: How to reset VPN on macOS?",
"memory_id": "<your_memory_id>",
"context_size": 5,
"message_size": 5
}
}
}
Under the hood, the pipeline fetches search results and recent messages, then calls the LLM via your connector. It persists the reply and returns both the documents and the generated answer.
Putting it together from an app (Python)
from opensearchpy import OpenSearch
client = OpenSearch(
hosts=[{"host": "localhost", "port": 9200}],
http_auth=("admin", "admin"),
use_ssl=False
)
# 1) Embed the user question with the same model used at ingest
# query_vec = embed("How do I reset my VPN on macOS?") # your embedding fn
# 2) Retrieve candidates (dense example shown; for hybrid, call your pipeline)
resp = client.search(
index="kb_docs",
body={
"size": 8,
"query": {
"knn": { "embedding": { "vector": query_vec, "k": 64 } }
}
},
params={"search_pipeline": "hybrid_pipeline"} # optional
)
# 3) (Option A) Do your own RAG call in app code
# 4) (Option B) Let OpenSearch do RAG + memory; see the REST example above
Security & governance checklist
Here are a few basic security topics that are applicable to most use cases. Many use cases will have unique needs, and this list should not be considered exhaustive. Firstly, enforce index-level and field-level security for confidential data. Secondly, use document-level security and per-tenant routing if the chatbot serves multiple teams or customers. Thirdly, log all prompts, retrieved chunks, and model outputs for auditability. And lastly, add guardrails such as prompt templates, allow-list tools, and timeouts in your pipeline and application.
Want help building a chatbot with OpenSearch?
Dattell designs, deploys, and operates OpenSearch‑backed chatbots end‑to‑end—embedding pipelines, hybrid retrieval, search pipelines, conversation memory/RAG, and production SLAs across AWS/Azure/GCP or on‑prem.
Check out our OpenSearch Support Services page for more information on how we support teams with their AI workflows.
Want help building a chatbot with OpenSearch?
Dattell designs, deploys, and operates OpenSearch‑backed chatbots end‑to‑end—embedding pipelines, hybrid retrieval, search pipelines, conversation memory/RAG, and production SLAs across AWS/Azure/GCP or on‑prem.
Check out our OpenSearch Support Services page for more information on how we support teams with their AI workflows.
Want help building a chatbot with OpenSearch?
Check out our OpenSearch Support Services page for more information on how we support teams with their AI workflows.