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Oracle AI Database supports AI workloads where you query data by meaning (semantics), not just keywords. It combines semantic search over unstructured content with relational filtering over business data in a single system—so you can build retrieval workflows (like RAG) without introducing a separate vector database and fragmenting data across multiple platforms. This guide demonstrates how to use OracleVS (the LangChain vector store integration for Oracle AI Vector Search) to:
  • Ingest documents and embeddings into Oracle
  • Run similarity search
  • Create HNSW and IVF indexes
  • Apply metadata filters for advanced retrieval
  • Enable hybrid search (keyword + semantic) in Oracle Database 26ai
  • Run full-text search using Oracle Text

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Prerequisites

Install langchain-oracledb. The python-oracledb driver will be installed automatically as a dependency. 
pip install -qU langchain-oracledb

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Connect to Oracle Database

The following sample code will show how to connect to Oracle Database. By default, python-oracledb runs in a ‘Thin’ mode which connects directly to Oracle Database. This mode does not need Oracle Client libraries. However, some additional functionality is available when python-oracledb uses them. Python-oracledb is said to be in ‘Thick’ mode when Oracle Client libraries are used. Both modes have comprehensive functionality supporting the Python Database API v2.0 Specification. See the following guide that talks about features supported in each mode. You might want to switch to thick-mode if you are unable to use thin-mode. 
import oracledb

# Please update with your username, password, hostname, port and service_name
username = "<username>"
password = "<password>"
dsn = "<hostname>:<port>/<service_name>"

connection = oracledb.connect(user=username, password=password, dsn=dsn)
print("Connection successful!")

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Import the required dependencies

from langchain_oracledb.vectorstores import oraclevs
from langchain_oracledb.vectorstores.oraclevs import OracleVS
from langchain_community.vectorstores.utils import DistanceStrategy
from langchain_core.documents import Document
from langchain_huggingface import HuggingFaceEmbeddings

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Load documents

# Define a list of documents (The examples below are 5 random documents from Oracle Concepts Manual )

documents_json_list = [
    {
        "id": "cncpt_15.5.3.2.2_P4",
        "text": "If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.",
        "link": "https://docs.oracle.com/en/database/oracle/oracle-database/23/cncpt/logical-storage-structures.html#GUID-5387D7B2-C0CA-4C1E-811B-C7EB9B636442",
    },
    {
        "id": "cncpt_15.5.5_P1",
        "text": "A tablespace can be online (accessible) or offline (not accessible) whenever the database is open.\nA tablespace is usually online so that its data is available to users. The SYSTEM tablespace and temporary tablespaces cannot be taken offline.",
        "link": "https://docs.oracle.com/en/database/oracle/oracle-database/23/cncpt/logical-storage-structures.html#GUID-D02B2220-E6F5-40D9-AFB5-BC69BCEF6CD4",
    },
    {
        "id": "cncpt_22.3.4.3.1_P2",
        "text": "The database stores LOBs differently from other data types. Creating a LOB column implicitly creates a LOB segment and a LOB index. The tablespace containing the LOB segment and LOB index, which are always stored together, may be different from the tablespace containing the table.\nSometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.",
        "link": "https://docs.oracle.com/en/database/oracle/oracle-database/23/cncpt/concepts-for-database-developers.html#GUID-3C50EAB8-FC39-4BB3-B680-4EACCE49E866",
    },
    {
        "id": "cncpt_22.3.4.3.1_P3",
        "text": "The LOB segment stores data in pieces called chunks. A chunk is a logically contiguous set of data blocks and is the smallest unit of allocation for a LOB. A row in the table stores a pointer called a LOB locator, which points to the LOB index. When the table is queried, the database uses the LOB index to quickly locate the LOB chunks.",
        "link": "https://docs.oracle.com/en/database/oracle/oracle-database/23/cncpt/concepts-for-database-developers.html#GUID-3C50EAB8-FC39-4BB3-B680-4EACCE49E866",
    },
]

# Create LangChain Documents

documents_langchain = []

for doc in documents_json_list:
    metadata = {"id": doc["id"], "link": doc["link"]}
    doc_langchain = Document(page_content=doc["text"], metadata=metadata)
    documents_langchain.append(doc_langchain)

​
Create vector stores with different distance metrics

First we will create three vector stores each with different distance functions. Since we have not created indices in them yet, they will just create tables for now. Later we will use these vector stores to create HNSW indicies. To understand more about the different types of indices Oracle AI Vector Search supports, refer to the following guide. You can manually connect to the Oracle Database and will see three tables : Documents_DOT, Documents_COSINE and Documents_EUCLIDEAN. We will then create three additional tables Documents_DOT_IVF, Documents_COSINE_IVF and Documents_EUCLIDEAN_IVF which will be used to create IVF indicies on the tables instead of HNSW indices. 
# Ingest documents into Oracle Vector Store using different distance strategies

# When using our API calls, start by initializing your vector store with a subset of your documents
# through from_documents(), then incrementally add more documents using add_texts().
# This approach prevents system overload and ensures efficient document processing.

model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

vector_store_dot = OracleVS.from_documents(
    documents_langchain,
    model,
    client=connection,
    table_name="Documents_DOT",
    distance_strategy=DistanceStrategy.DOT_PRODUCT,
)
vector_store_max = OracleVS.from_documents(
    documents_langchain,
    model,
    client=connection,
    table_name="Documents_COSINE",
    distance_strategy=DistanceStrategy.COSINE,
)
vector_store_euclidean = OracleVS.from_documents(
    documents_langchain,
    model,
    client=connection,
    table_name="Documents_EUCLIDEAN",
    distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,
)

# Ingest documents into Oracle Vector Store using different distance strategies
vector_store_dot_ivf = OracleVS.from_documents(
    documents_langchain,
    model,
    client=connection,
    table_name="Documents_DOT_IVF",
    distance_strategy=DistanceStrategy.DOT_PRODUCT,
)
vector_store_max_ivf = OracleVS.from_documents(
    documents_langchain,
    model,
    client=connection,
    table_name="Documents_COSINE_IVF",
    distance_strategy=DistanceStrategy.COSINE,
)
vector_store_euclidean_ivf = OracleVS.from_documents(
    documents_langchain,
    model,
    client=connection,
    table_name="Documents_EUCLIDEAN_IVF",
    distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,
)

def manage_texts(vector_stores):
    """
    Adds texts to each vector store, demonstrates error handling for duplicate additions,
    and performs deletion of texts. Showcases similarity searches and index creation for each vector store.

    Args:
    - vector_stores (list): A list of OracleVS instances.
    """
    texts = ["Rohan", "Shailendra"]
    metadata = [
        {"id": "100", "link": "Document Example Test 1"},
        {"id": "101", "link": "Document Example Test 2"},
    ]

    for i, vs in enumerate(vector_stores, start=1):
        # Adding texts
        try:
            vs.add_texts(texts, metadata)
            print(f"\n\n\nAdd texts complete for vector store {i}\n\n\n")
        except Exception as ex:
            print(f"\n\n\nExpected error on duplicate add for vector store {i}\n\n\n")

        # Deleting texts using the value of 'id'
        vs.delete([metadata[0]["id"]])
        print(f"\n\n\nDelete texts complete for vector store {i}\n\n\n")

        # Similarity search
        results = vs.similarity_search("How are LOBS stored in Oracle Database", 2)
        print(f"\n\n\nSimilarity search results for vector store {i}: {results}\n\n\n")


vector_store_list = [
    vector_store_dot,
    vector_store_max,
    vector_store_euclidean,
    vector_store_dot_ivf,
    vector_store_max_ivf,
    vector_store_euclidean_ivf,
]
manage_texts(vector_store_list)

​
Index creation with specific parameters

def create_search_indices(connection):
    """
    Creates search indices for the vector stores, each with specific parameters tailored to their distance strategy.
    """
    # Index for DOT_PRODUCT strategy
    # Notice we are creating a HNSW index with default parameters
    # This will default to creating a HNSW index with 8 Parallel Workers and use the Default Accuracy used by Oracle AI Vector Search
    oraclevs.create_index(
        connection,
        vector_store_dot,
        params={"idx_name": "hnsw_idx1", "idx_type": "HNSW"},
    )

    # Index for COSINE strategy with specific parameters
    # Notice we are creating a HNSW index with parallel 16 and Target Accuracy Specification as 97 percent
    oraclevs.create_index(
        connection,
        vector_store_max,
        params={
            "idx_name": "hnsw_idx2",
            "idx_type": "HNSW",
            "accuracy": 97,
            "parallel": 16,
        },
    )

    # Index for EUCLIDEAN_DISTANCE strategy with specific parameters
    # Notice we are creating a HNSW index by specifying Power User Parameters which are neighbors = 64 and efConstruction = 100
    oraclevs.create_index(
        connection,
        vector_store_euclidean,
        params={
            "idx_name": "hnsw_idx3",
            "idx_type": "HNSW",
            "neighbors": 64,
            "efConstruction": 100,
        },
    )

    # Index for DOT_PRODUCT strategy with specific parameters
    # Notice we are creating an IVF index with default parameters
    # This will default to creating an IVF index with 8 Parallel Workers and use the Default Accuracy used by Oracle AI Vector Search
    oraclevs.create_index(
        connection,
        vector_store_dot_ivf,
        params={
            "idx_name": "ivf_idx1",
            "idx_type": "IVF",
        },
    )

    # Index for COSINE strategy with specific parameters
    # Notice we are creating an IVF index with parallel 32 and Target Accuracy Specification as 90 percent
    oraclevs.create_index(
        connection,
        vector_store_max_ivf,
        params={
            "idx_name": "ivf_idx2",
            "idx_type": "IVF",
            "accuracy": 90,
            "parallel": 32,
        },
    )

    # Index for EUCLIDEAN_DISTANCE strategy with specific parameters
    # Notice we are creating an IVF index by specifying Power User Parameters which is neighbor_part = 64
    oraclevs.create_index(
        connection,
        vector_store_euclidean_ivf,
        params={"idx_name": "ivf_idx3", "idx_type": "IVF", "neighbor_part": 64},
    )

    print("Index creation complete.")


create_search_indices(connection)
Oracle Database 23ai supports pre-filtering, in-filtering, and post-filtering to enhance AI Vector Search capabilities. These filtering mechanisms allow users to apply constraints before, during, and after performing vector similarity searches, improving search performance and accuracy. Key Points about Filtering in Oracle 23ai:
  1. Pre-filtering Applies traditional SQL filters to reduce the dataset before performing the vector similarity search. Helps improve efficiency by limiting the amount of data processed by AI algorithms.
  2. In-filtering Utilizes AI Vector Search to perform similarity searches directly on vector embeddings, using optimized indexes and algorithms. Efficiently filters results based on vector similarity without requiring full dataset scans.
  3. Post-filtering Applies additional SQL filtering to refine the results after the vector similarity search. Allows further refinement based on business logic or additional metadata conditions.
Why is this Important?
  • Performance Optimization: Pre-filtering significantly reduces query execution time, making searches on massive datasets more efficient.
  • Accuracy Enhancement: In-filtering ensures that vector searches are semantically meaningful, improving the quality of search results.

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Filter details

OracleVS supports a set of filters that can be applied to metadata fields using filter parameter. These filters allow you to select and refine data based on various criteria. Available Filter Operators:
OperatorDescription
\$existsField exists.
\$eqField value equals the operand value (=).
\$neField exists and value does not equal the operand value (!=).
\$gtField value is greater than the operand value (>).
\$ltField value is less than the operand value (<).
\$gteField value is greater than or equal to the operand value (>=).
\$lteField value is less than or equal to the operand value (<=).
\$betweenField value is between (or equal to) two values in the operand array.
\$startsWithField value starts with the operand value.
\$hasSubstringField value contains the operand as a substring.
\$instrField value contains the operand as a substring.
\$regexField value matches the given regular expression pattern.
\$likeField value matches the operand pattern (using SQL-like syntax).
\$inField value equals at least one value in the operand array.
\$ninField exists, but its value is not equal to any in the operand array, or the field does not exist.
\$allField value is an array containing all items from the operand array, or a scalar matching a single operand.
  • You can combine these filters using logical operators:
Logical OperatorDescription
\$andLogical AND
\$orLogical OR
\$norLogical NOR
Example Filter: 
{
  "age": 65,
  "name": {"$regex": "*rk"},
  "$or": [
    {
      "$and": [
        {"name": "Jason"},
        {"drinks": {"$in": ["tea", "soda"]}}
      ]
    },
    {
      "$nor": [
        {"age": {"$lt": 65}},
        {"name": "Jason"}
      ]
    }
  ]
}
Additional Usage Tips:
  • You can omit $and when all filters in an object must be satisfied. These two are equivalent:
{ "$and": [
    { "name": { "$startsWith": "Fred" } },
    { "salary": { "$gt": 10000, "$lte": 20000 } }
]}

{
  "name": { "$startsWith": "Fred" },
  "salary": { "$gt": 10000, "$lte": 20000 }
}
  • The $not clause can negate a comparison operator:
{ "address.zip": { "$not": { "$eq": "90001" } } }
  • Using field: scalar is equivalent to field: { "$eq": scalar }:
{ "animal": "cat" }
For more filter examples, refer to the test specification. 
# Conduct advanced searches after creating the indices
def conduct_advanced_searches(vector_stores):
    query = "How are LOBS stored in Oracle Database"
    # Constructing a filter for direct comparison against document metadata
    # This filter aims to include documents whose metadata 'id' is exactly '2'
    db_filter = {
        "$and": [
            {"id": "101"},  # FilterCondition
            {
                "$or": [  # FilterGroup
                    {"status": "approved"},
                    {"link": "Document Example Test 2"},
                    {
                        "$and": [  # Nested FilterGroup
                            {"status": "approved"},
                            {"link": "Document Example Test 2"},
                        ]
                    },
                ]
            },
        ]
    }

    for i, vs in enumerate(vector_stores, start=1):
        print(f"\n--- Vector Store {i} Advanced Searches ---")
        # Similarity search without a filter
        print("\nSimilarity search results without filter:")
        print(vs.similarity_search(query, 2))

        # Similarity search with a filter
        print("\nSimilarity search results with filter:")
        print(vs.similarity_search(query, 2, filter=db_filter))

        # Similarity search with relevance score
        print("\nSimilarity search with relevance score:")
        print(vs.similarity_search_with_score(query, 2))

        # Similarity search with relevance score with filter
        print("\nSimilarity search with relevance score with filter:")
        print(vs.similarity_search_with_score(query, 2, filter=db_filter))

        # Max marginal relevance search
        print("\nMax marginal relevance search results:")
        print(vs.max_marginal_relevance_search(query, 2, fetch_k=20, lambda_mult=0.5))

        # Max marginal relevance search with filter
        print("\nMax marginal relevance search results with filter:")
        print(
            vs.max_marginal_relevance_search(
                query, 2, fetch_k=20, lambda_mult=0.5, filter=db_filter
            )
        )


conduct_advanced_searches(vector_store_list)
Oracle Database 26ai supports hybrid search, combining keyword (full-text) and semantic (vector) search into a single retrieval capability. The langchain-oracledb integration exposes:
  • OracleVectorizerPreference: creates a DB-side vectorizer preference used by hybrid indexes.
  • create_hybrid_index / acreate_hybrid_index: creates a HYBRID VECTOR INDEX.
  • OracleHybridSearchRetriever: executes keyword, semantic, or hybrid retrieval over an OracleVS table.

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Prerequisites and model configuration

When using hybrid search, configure your OracleVS with OracleEmbeddings so the vectorizer preference exactly matches the embedding configuration. You can further tune the hybrid vector index by supplying additional parameters via OracleVectorizerPreference. For details, see the documentation. 
from langchain_core.documents import Document
from langchain_oracledb.embeddings import OracleEmbeddings
from langchain_oracledb.vectorstores.oraclevs import OracleVS
from langchain_oracledb.retrievers.hybrid_search import (
    OracleVectorizerPreference,
    create_hybrid_index,
    OracleHybridSearchRetriever,
)

# Use OracleEmbeddings (database-resident model shown)
embeddings = OracleEmbeddings(conn=connection, params={"provider": "database", "model": "DB_MODEL"})

# Create/load your vector store
vs = OracleVS(connection, table_name="DOCS", embedding_function=embeddings)

# Create a vectorizer preference
pref = OracleVectorizerPreference.create_preference(
    vector_store=vs, preference_name="PREF_DOCS"
)

# Create a HYBRID VECTOR INDEX
create_hybrid_index(
    connection,
    idx_name="IDX_DOCS_HYB",
    vectorizer_preference=pref
)

# Build a retriever and search
retriever = OracleHybridSearchRetriever(
    vector_store=vs,
    idx_name="IDX_DOCS_HYB",
    search_mode="hybrid",     # "hybrid" | "keyword" | "semantic"
    k=5,
    return_scores=True,       # includes score, text_score, vector_score in metadata
)

docs = retriever.invoke("refund policy for premium plan")
for d in docs:
    print(d.page_content, d.metadata.get("score"), d.metadata.get("text_score"), d.metadata.get("vector_score"))

# Optional cleanup when done with the preference:
# pref.drop_preference()
Alternative create the index without an explicit preference:
  • If you don’t want to manage a named preference, pass the vector_store instead. The function will create a temporary preference, build the index, then drop the preference automatically.
create_hybrid_index(
    connection,
    idx_name="IDX_DOCS_HYB2",
    vector_store=vs,          # mutually exclusive with vectorizer_preference
    params={"parallel": 8},
)
Notes and tips:
  • search_mode decides which signals are used:
    • “keyword”: keyword-only
    • “semantic”: vector-only
    • “hybrid” (default): both combined
  • Pass DBMS_HYBRID_VECTOR parameters via the retriever’s params argument.
  • return_scores=True adds overall score and component text_score and vector_score to Document.metadata.
  • Async usage is supported via acreate_hybrid_index and OracleHybridSearchRetriever.ainvoke.
More information:

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Full-text search (Oracle Text)

You can run high-quality keyword search directly against Oracle Database using Oracle Text. The langchain-oracledb integration exposes:
  • create_text_index / acreate_text_index: creates an Oracle Text SEARCH INDEX on a column.
  • OracleTextSearchRetriever: runs CONTAINS queries and returns LangChain Document objects.
Indexing options:
  • If you have an OracleVS vector store, you can index its built-in “text” column.
  • You can also index any other table/column by providing table_name + column_name directly.
from langchain_oracledb.retrievers.text_search import create_text_index, OracleTextSearchRetriever

# Using an OracleVS table (indexes the 'text' column)
retriever_text = None
create_text_index(
    connection,
    idx_name="IDX_DOCS_TEXT",
    vector_store=vs,
)

# Build a retriever. With OracleVS, returned_columns defaults to ["metadata"].
retriever_text = OracleTextSearchRetriever(
    vector_store=vs,
    k=5,
    fuzzy=True,          # applies Oracle Text FUZZY per token when operator_search=False
    return_scores=True,  # adds SCORE(1) as metadata["score"]
)
docs = retriever_text.invoke("refund policy")
for d in docs:
    print(d.page_content, d.metadata.get("score"))

# Alternatively, index an arbitrary table/column:
# create_text_index(connection, idx_name="IDX_MYDOCS_TEXT", table_name="MYDOCS", column_name="CONTENT")
# retriever_text = OracleTextSearchRetriever(client=connection, table_name="MYDOCS", column_name="CONTENT", k=5)
Operator mode and advanced queries:
  • Default behavior (operator_search=False):
    • Input is treated as literal text, tokenized on non-word characters, and rewritten as an ACCUM expression.
    • With fuzzy=True, each token is wrapped as FUZZY("token") to match misspellings.
  • Operator mode (operator_search=True):
    • Pass an Oracle Text expression unchanged (NEAR, ABOUT, AND, OR, NOT, WITHIN, etc.). In this mode, fuzzy is ignored.
Returned columns:
  • When targeting a raw table, include extra columns in results via returned_columns; they are attached to Document.metadata.
  • With OracleVS, returned_columns defaults to [“metadata”].
# Operator mode example
retriever_text_ops = OracleTextSearchRetriever(
    vector_store=vs,
    operator_search=True,   # pass Oracle Text expression directly
    return_scores=True,
)
docs = retriever_text_ops.invoke('NEAR((policy, refund), 2, TRUE)')
Notes and tips:
  • When using operator_search=True, fuzzy is ignored (by design).
  • Async usage is supported via acreate_text_index and OracleTextSearchRetriever.ainvoke.
More information:

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End to end demo

Please refer to our complete demo guide Oracle AI Vector Search End-to-End Demo Guide to build an end to end RAG pipeline with the help of Oracle AI Vector Search.
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