Integration: IBM Db2
A Document Store for storing and retrieving documents from IBM Db2
Table of Contents
- IBM Db2 Document Store for Haystack
Installation
IBM Db2 (version 12.1.2 and later) provides a native VECTOR data type that adds support for vector similarity search directly inside the database, allowing Db2 to act as a fully featured vector store while keeping documents, embeddings, and metadata within your existing enterprise database.
For more information on Db2 vector capabilities, visit the IBM Db2 product page.
Use pip to install ibm-db-haystack:
pip install ibm-db-haystack
Usage
Set your IBM Db2 credentials as environment variables:
export DB2_USERNAME="db2inst1"
export DB2_PASSWORD="your_password"
Once installed, initialize IBMDb2DocumentStore.
from haystack.utils import Secret
from haystack_integrations.document_stores.ibm_db import IBMDb2DocumentStore
document_store = IBMDb2DocumentStore(
database="BLUDB",
hostname="your-db2-host",
port=50000,
username=Secret.from_env_var("DB2_USERNAME"),
password=Secret.from_env_var("DB2_PASSWORD"),
table_name="haystack_docs",
embedding_dim=768,
distance_metric="COSINE",
)
Supported distance metrics are COSINE, EUCLIDEAN, and MANHATTAN.
Writing Documents to IBMDb2DocumentStore
To write documents to IBMDb2DocumentStore, create an indexing pipeline.
from haystack import Pipeline
from haystack.components.converters import TextFileToDocument
from haystack.components.writers import DocumentWriter
from haystack.components.embedders import SentenceTransformersDocumentEmbedder
indexing = Pipeline()
indexing.add_component("converter", TextFileToDocument())
indexing.add_component("embedder", SentenceTransformersDocumentEmbedder())
indexing.add_component("writer", DocumentWriter(document_store))
indexing.connect("converter", "embedder")
indexing.connect("embedder", "writer")
indexing.run({"converter": {"sources": file_paths}})
Retrieval from IBMDb2DocumentStore
You can retrieve semantically similar documents to a given query using a simple pipeline that includes the IBMDb2EmbeddingRetriever.
from haystack import Pipeline
from haystack.components.embedders import SentenceTransformersTextEmbedder
from haystack_integrations.components.retrievers.ibm_db import IBMDb2EmbeddingRetriever
querying = Pipeline()
querying.add_component("embedder", SentenceTransformersTextEmbedder())
querying.add_component("retriever", IBMDb2EmbeddingRetriever(document_store=document_store, top_k=3))
querying.connect("embedder.embedding", "retriever.query_embedding")
results = querying.run({"embedder": {"text": "my query"}})
You can also combine vector similarity search with metadata filtering, including compound AND/OR conditions, executed in the same query as the vector search.
from haystack_integrations.components.retrievers.ibm_db import IBMDb2EmbeddingRetriever
retriever = IBMDb2EmbeddingRetriever(document_store=document_store, top_k=3)
results = retriever.run(
query_embedding=query_embedding,
filters={
"operator": "AND",
"conditions": [
{"field": "meta.category", "operator": "==", "value": "security"},
{"field": "meta.priority", "operator": "==", "value": "high"},
],
},
)
License
ibm-db-haystack is distributed under the terms of the
Apache-2.0 license.