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LangChain integration provides retrieval chains and pipelines for building RAG applications with vector databases.

​
Chain Types

VectorDB provides several pre-built chain types for LangChain:
  • Semantic Search: Dense vector retrieval using embedding models
  • Hybrid Indexing: Combined dense and sparse vector indexing
  • Sparse Indexing: BM25-style keyword-based retrieval
  • MMR (Maximal Marginal Relevance): Diversity-optimized retrieval
  • Parent Document Retrieval: Hierarchical chunking with parent-child relationships
  • Query Enhancement: Multi-query and HyDE (Hypothetical Document Embeddings)
  • Reranking: Cross-encoder reranking of retrieved results
  • Contextual Compression: Token optimization through context compression
  • Agentic RAG: Self-reflective retrieval with routing decisions
  • Multi-tenancy: Namespace-based data isolation
  • Metadata Filtering: Advanced filtering on document metadata
  • JSON Indexing: Indexing and filtering on nested JSON fields
  • Diversity Filtering: MMR-based diversity in retrieval

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Supported Vector Databases

All LangChain chains support these vector databases:
  • Chroma: ChromaSemanticSearchPipeline, ChromaMmrSearchPipeline, etc.
  • Milvus: MilvusSemanticSearchPipeline, MilvusHybridSearchPipeline, etc.
  • Pinecone: PineconeSemanticSearchPipeline, PineconeHybridSearchPipeline, etc.
  • Qdrant: QdrantSemanticSearchPipeline, QdrantMmrSearchPipeline, etc.
  • Weaviate: WeaviateSemanticSearchPipeline, WeaviateHybridSearchPipeline, etc.

​
Common Chain Methods

All LangChain chains share common initialization patterns and methods:

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Constructor Pattern

Pipeline(
    config_path: str,
    collection_name: Optional[str] = None,
    embedding_model: Optional[str] = None,
    **kwargs
)
​
config_path
str
required
Path to YAML configuration file containing database credentials and settings
​
collection_name
str
Override collection name from config
​
embedding_model
str
Override embedding model from config (e.g., “sentence-transformers/all-MiniLM-L6-v2”)
​
**kwargs
Any
Additional chain-specific parameters
Perform retrieval search and return LangChain Documents. 
search(
    query: str,
    top_k: int = 10,
    filters: Optional[Dict[str, Any]] = None,
    **kwargs
) -> List[Document]
​
query
str
required
Query text to search for
​
top_k
int
default:"10"
Number of results to return
​
filters
Dict[str, Any]
Metadata filters to apply
​
**kwargs
Any
Chain-specific search parameters
​
documents
List[Document]
Retrieved LangChain Document objects ordered by relevance

​
as_retriever

Convert pipeline to LangChain Retriever interface. 
as_retriever(**kwargs) -> BaseRetriever
​
**kwargs
Any
Retriever configuration parameters
​
retriever
BaseRetriever
LangChain BaseRetriever instance for use in chains

​
Example Usage

from langchain_openai import OpenAIEmbeddings
from vectordb.langchain.semantic_search import ChromaSemanticSearchPipeline

# Initialize pipeline
pipeline = ChromaSemanticSearchPipeline(
    config_path="config.yaml",
    collection_name="my_docs",
    embedding_model="text-embedding-3-small"
)

# Search
results = pipeline.search(
    query="What is machine learning?",
    top_k=5
)

# Use as retriever in a chain
retriever = pipeline.as_retriever()
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4"),
    retriever=retriever
)
result = qa_chain.invoke({"query": "Explain quantum computing"})

from vectordb.langchain.hybrid_indexing import MilvusHybridSearchPipeline

pipeline = MilvusHybridSearchPipeline(
    config_path="config.yaml",
    collection_name="hybrid_docs"
)

# Hybrid search combines dense and sparse vectors
results = pipeline.search(
    query="quantum computing applications",
    top_k=10,
    ranker_type="rrf"  # Reciprocal Rank Fusion
)

​
Agentic RAG

from langchain_groq import ChatGroq
from vectordb.langchain.agentic_rag import ChromaAgenticRAGPipeline

llm = ChatGroq(model="llama-3.3-70b-versatile")
pipeline = ChromaAgenticRAGPipeline(
    config_path="config.yaml",
    llm=llm
)

# Agentic search with self-reflection
result = pipeline.search(
    query="Complex multi-hop question",
    max_iterations=3
)

​
Multi-tenancy

from vectordb.langchain.multi_tenancy import PineconeMultiTenancyPipeline

pipeline = PineconeMultiTenancyPipeline(config_path="config.yaml")

# Index documents for tenant A
from langchain_core.documents import Document

documents = [
    Document(page_content="Financial report Q1"),
    Document(page_content="Financial report Q2")
]
pipeline.index(documents, namespace="tenant_a")

# Search within tenant A only
results = pipeline.search(
    query="financial reports",
    namespace="tenant_a",
    top_k=5
)

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