I'm building a multi-source retrieval pipeline; recommend the right stack

Here’s a practical stack recommendation for a multi-source retrieval pipeline. I’ll optimize for relevance, scalability, and maintainability rather than “most trendy.”

1) Core architecture I’d recommend

Use a hybrid retrieval stack:

• Ingestion/connectors: pull from APIs, docs, DBs, object stores, web, etc.
• Normalization layer: convert everything into a canonical document schema.
• Chunking + metadata enrichment: split content into retrievable units with source/time/permission metadata.
• Indexing:
  • BM25 / keyword search for exact matches
  • Vector embeddings for semantic search
  • optionally reranking for final ordering
• Fusion / retrieval orchestration: combine results from multiple sources and modalities.
• Governance layer: deduplication, freshness, ACLs, provenance, auditability.

This pattern usually beats “vector-only.”

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2) Recommended stack by layer

A. Ingestion / connectors

Pick based on source diversity:

• Airbyte or Fivetran for standard SaaS/DB ingestion
• Custom Python connectors for APIs, files, and web sources
• Dagster or Airflow for orchestration

Recommendation:

• If you want flexibility and engineering control: Dagster + custom connectors
• If you want managed data movement for common sources: Airbyte

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B. Document processing / normalization

• Python
• Pydantic for schema validation
• Unstructured for PDFs, HTML, DOCX, email, etc.
• Apache Tika if you need broad file parsing
• OCR: Tesseract, AWS Textract, or Google Document AI

Canonical schema should include:

• doc_id
• source
• source_type
• title
• text
• chunks
• metadata
• created_at
• updated_at
• acl / permissions
• provenance

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C. Chunking and enrichment

• LlamaIndex or LangChain for chunking utilities and document loaders
• Or build your own simple chunker if you want tight control

Best practice:

• chunk by semantic boundaries where possible
• keep chunk sizes moderate
• preserve parent document references
• attach metadata to every chunk

Add enrichments like:

• language detection
• entity extraction
• timestamps
• source confidence
• access scope

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D. Indexing / storage

For multi-source retrieval, I’d usually use two indexes:

1. Keyword index

• OpenSearch / Elasticsearch
• Great for exact terms, filters, faceting, auditability

2. Vector index

Options:

• Pinecone: managed, simple
• Weaviate: flexible, good hybrid features
• Milvus: strong open-source choice at scale
• pgvector: great if you want to stay inside Postgres and your scale is moderate

Recommendation by scale:

• Small to medium: Postgres + pgvector + OpenSearch
• Medium to large: OpenSearch + Pinecone/Weaviate/Milvus
• If you want simplest managed setup: Pinecone + OpenSearch

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E. Embeddings

Use a strong general-purpose embedding model:

• OpenAI text-embedding-3-large / small
• Cohere embeddings
• BGE-M3 or e5 family if you want open-source/self-hosted

Recommendation:

• Fastest to production: hosted embeddings
• Highest control / lower cost at scale: BGE-M3 or e5-large

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F. Retrieval orchestration

Use hybrid retrieval:

• BM25 top-k
• vector top-k
• merge using weighted score fusion
• rerank top candidates with a cross-encoder / reranker

Useful tools:

• Elastic/OpenSearch hybrid search
• RRF (Reciprocal Rank Fusion) for merging results
• Cohere Rerank or open-source rerankers
• LlamaIndex if you want retrieval abstractions quickly

Best practice: rerank after fusion, not before.

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G. Reranking

This is often the biggest relevance boost.

Options:

• Cohere Rerank
• bge-reranker
• cross-encoder/ms-marco variants

Use reranking on:

• top 20–100 fused candidates
• with query + chunk text + metadata

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H. Serving / API layer

• FastAPI for retrieval service
• Redis for caching
• Postgres for metadata, ACLs, jobs, lineage
• Kafka / RabbitMQ / SQS if you need event-driven ingestion

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I. Observability / evaluation

Don’t skip this.

• OpenTelemetry for traces
• Prometheus + Grafana for metrics
• LangSmith or Arize Phoenix for retrieval/debugging evals
• Build offline evaluation with:
  • recall@k
  • MRR
  • nDCG
  • answer grounding metrics

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3) A good default stack

If you want a strong “just build it” recommendation:

Best balanced stack

• Orchestration: Dagster
• Parsing: Unstructured + custom Python
• Metadata store: Postgres
• Keyword search: OpenSearch
• Vector store: pgvector (or Pinecone if managed)
• Embeddings: OpenAI or BGE-M3
• Reranker: Cohere Rerank or bge-reranker
• API: FastAPI
• Cache: Redis
• Observability: OpenTelemetry + Grafana
• Retrieval framework: custom orchestration, with optional LlamaIndex/LangChain helpers

This gives you:

• good hybrid retrieval
• easy metadata filtering
• source provenance
• scalability without overcomplicating the system

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4) If you want fewer moving parts

Lean stack

• Postgres + pgvector
• OpenSearch (or Postgres full-text if smaller)
• FastAPI
• Dagster
• Python + Unstructured

Use this if:

• your corpus is under a few million chunks
• you want operational simplicity
• you can tolerate moderate search sophistication

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5) If you want maximum scale

Scalable stack

• Kafka for ingestion events
• Spark / Ray for processing
• OpenSearch for text search
• Milvus / Weaviate / Pinecone for vectors
• Postgres for metadata/ACLs
• Dagster for orchestration
• FastAPI serving layer

Use this if:

• you have many sources
• frequent updates
• high QPS
• multiple teams consuming the pipeline

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6) My strongest recommendation on retrieval strategy

For multi-source retrieval, use:

1. Source-aware filtering
   • filter by freshness, permissions, source trust
2. Hybrid search
   • BM25 + vectors
3. Deduplication
   • same document from multiple sources
4. Reranking
   • to improve final ordering
5. Provenance returned with every result
   • crucial for debugging and trust

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7) Common mistakes to avoid

• Going vector-only
• Ignoring ACLs and provenance
• Chunking too aggressively
• No reranking
• Mixing all sources into one undifferentiated index
• Not separating metadata from content
• No offline evaluation set
• No freshness strategy for updates/deletions

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8) If you tell me these 5 things, I can give a much sharper stack

1. Source types: SaaS, PDFs, web, DBs, tickets, code, etc.
2. Corpus size: docs/chunks and update frequency
3. Query patterns: exact lookup vs semantic Q&A vs filtering
4. Latency target: sub-second, a few seconds, batch
5. Deployment preference: managed cloud vs self-hosted

If you want, I can also give you:

• a reference architecture diagram
• a cloud-native stack on AWS/GCP/Azure
• or a minimal MVP stack vs production stack comparison.