in_memory

Return a dict representation of an object.

Calculate maximal marginal relevance.

Class for storing a piece of text and associated metadata.

Interface for vector store.

Interface for embedding models.

This is an interface meant for implementing text embedding models.

Text embedding models are used to map text to a vector (a point in n-dimensional space).

Texts that are similar will usually be mapped to points that are close to each other in this space. The exact details of what's considered "similar" and how "distance" is measured in this space are dependent on the specific embedding model.

This abstraction contains a method for embedding a list of documents and a method for embedding a query text. The embedding of a query text is expected to be a single vector, while the embedding of a list of documents is expected to be a list of vectors.

Usually the query embedding is identical to the document embedding, but the abstraction allows treating them independently.

In addition to the synchronous methods, this interface also provides asynchronous versions of the methods.

By default, the asynchronous methods are implemented using the synchronous methods; however, implementations may choose to override the asynchronous methods with an async native implementation for performance reasons.

In-memory vector store implementation.

Uses a dictionary, and computes cosine similarity for search using numpy.

Load LangChain objects from JSON strings or objects.

How it works

Each Serializable LangChain object has a unique identifier (its "class path"), which is a list of strings representing the module path and class name. For example:

• AIMessage -> ["langchain_core", "messages", "ai", "AIMessage"]
• ChatPromptTemplate -> ["langchain_core", "prompts", "chat", "ChatPromptTemplate"]

When deserializing, the class path from the JSON 'id' field is checked against an allowlist. If the class is not in the allowlist, deserialization raises a ValueError.

Security model

The allowed_objects parameter controls which classes can be deserialized:

• 'core' (default): Allow classes defined in the serialization mappings for langchain_core.
• 'all': Allow classes defined in the serialization mappings. This includes core LangChain types (messages, prompts, documents, etc.) and trusted partner integrations. See langchain_core.load.mapping for the full list.
• Explicit list of classes: Only those specific classes are allowed.

For simple data types like messages and documents, the default allowlist is safe to use. These classes do not perform side effects during initialization.

Import paths are also validated against trusted namespaces before any module is imported.

Best practices

• Use the most restrictive allowed_objects possible. Prefer an explicit list of classes over 'core' or 'all'.
• Keep secrets_from_env set to False (the default). If you must use it, ensure the serialized data comes from a fully trusted source, as a crafted payload can read arbitrary environment variables.
• When using secrets_map, include only the specific secrets that the serialized object requires.

Injection protection (escape-based)

During serialization, plain dicts that contain an 'lc' key are escaped by wrapping them: {"__lc_escaped__": {...}}. During deserialization, escaped dicts are unwrapped and returned as plain dicts, NOT instantiated as LC objects.

This is an allowlist approach: only dicts explicitly produced by Serializable.to_json() (which are NOT escaped) are treated as LC objects; everything else is user data.

Even if an attacker's payload includes __lc_escaped__ wrappers, it will be unwrapped to plain dicts and NOT instantiated as malicious objects.

Examples

from langchain_core.load import load
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.messages import AIMessage, HumanMessage

# Use default allowlist (classes from mappings) - recommended
obj = load(data)

# Allow only specific classes (most restrictive)
obj = load(
    data,
    allowed_objects=[
        ChatPromptTemplate,
        AIMessage,
        HumanMessage,
    ],
)