NeuralDBRetriever

class langchain_community.retrievers.thirdai_neuraldb.NeuralDBRetriever

Bases: BaseRetriever

Document retriever that uses ThirdAI’s NeuralDB.

Note

NeuralDBRetriever implements the standard Runnable Interface. 🏃

The Runnable Interface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more.

param db: Any = None

NeuralDB instance

param metadata: Dict[str, Any] | None = None

Optional metadata associated with the retriever. Defaults to None. This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a retriever with its use case.

param tags: List[str] | None = None

Optional list of tags associated with the retriever. Defaults to None. These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a retriever with its use case.

param thirdai_key: SecretStr [Required]

ThirdAI API Key

Constraints:

• type = string

• writeOnly = True

• format = password

async abatch(inputs: List[Input], config: RunnableConfig | List[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) → List[Output]

Default implementation runs ainvoke in parallel using asyncio.gather.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

Parameters:

• inputs (List[Input]) – A list of inputs to the Runnable.

• config (RunnableConfig | List[RunnableConfig] | None) – A config to use when invoking the Runnable. The config supports standard keys like ‘tags’, ‘metadata’ for tracing purposes, ‘max_concurrency’ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.

• return_exceptions (bool) – Whether to return exceptions instead of raising them. Defaults to False.

• kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.

Returns:

A list of outputs from the Runnable.

Return type:

List[Output]

async abatch_as_completed(inputs: Sequence[Input], config: RunnableConfig | Sequence[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) → AsyncIterator[Tuple[int, Output | Exception]]

Run ainvoke in parallel on a list of inputs, yielding results as they complete.

Parameters:

• inputs (Sequence[Input]) – A list of inputs to the Runnable.

• config (RunnableConfig | Sequence[RunnableConfig] | None) – A config to use when invoking the Runnable. The config supports standard keys like ‘tags’, ‘metadata’ for tracing purposes, ‘max_concurrency’ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None. Defaults to None.

• return_exceptions (bool) – Whether to return exceptions instead of raising them. Defaults to False.

• kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.

Yields:

A tuple of the index of the input and the output from the Runnable.

Return type:

AsyncIterator[Tuple[int, Output | Exception]]

async aget_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: List[str] | None = None, metadata: Dict[str, Any] | None = None, run_name: str | None = None, **kwargs: Any) → List[Document]

Deprecated since version langchain-core==0.1.46: Use ainvoke instead.

Asynchronously get documents relevant to a query.

Users should favor using .ainvoke or .abatch rather than aget_relevant_documents directly.

Parameters:

• query (str) – string to find relevant documents for.

• callbacks (Callbacks) – Callback manager or list of callbacks.

• tags (Optional[List[str]]) – Optional list of tags associated with the retriever. These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Defaults to None.

• metadata (Optional[Dict[str, Any]]) – Optional metadata associated with the retriever. This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Defaults to None.

• run_name (Optional[str]) – Optional name for the run. Defaults to None.

• kwargs (Any) – Additional arguments to pass to the retriever.

Returns:

List of relevant documents.

Return type:

List[Document]

async ainvoke(input: str, config: RunnableConfig | None = None, **kwargs: Any) → List[Document]

Asynchronously invoke the retriever to get relevant documents.

Main entry point for asynchronous retriever invocations.

Parameters:

• input (str) – The query string.

• config (RunnableConfig | None) – Configuration for the retriever. Defaults to None.

• kwargs (Any) – Additional arguments to pass to the retriever.

Returns:

List of relevant documents.

Return type:

List[Document]

Examples:

await retriever.ainvoke("query")

associate(source: str, target: str) → None

The retriever associates a source phrase with a target phrase. When the retriever sees the source phrase, it will also consider results that are relevant to the target phrase.

Parameters:

• source (str) – text to associate to target.

• target (str) – text to associate source to.

Return type:

None

associate_batch(text_pairs: List[Tuple[str, str]]) → None

Given a batch of (source, target) pairs, the retriever associates each source phrase with the corresponding target phrase.

Parameters:

• text_pairs (List[Tuple[str, str]]) – list of (source, target) text pairs. For each pair in

• list (this) –

• target. (the source will be associated with the) –

Return type:

None

async astream(input: Input, config: RunnableConfig | None = None, **kwargs: Any | None) → AsyncIterator[Output]

Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.

Parameters:

• input (Input) – The input to the Runnable.

• config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.

• kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.

Yields:

The output of the Runnable.

Return type:

AsyncIterator[Output]

astream_events(input: Any, config: RunnableConfig | None = None, *, version: Literal['v1', 'v2'], include_names: Sequence[str] | None = None, include_types: Sequence[str] | None = None, include_tags: Sequence[str] | None = None, exclude_names: Sequence[str] | None = None, exclude_types: Sequence[str] | None = None, exclude_tags: Sequence[str] | None = None, **kwargs: Any) → AsyncIterator[StandardStreamEvent | CustomStreamEvent]

Beta

This API is in beta and may change in the future.

Generate a stream of events.

Use to create an iterator over StreamEvents that provide real-time information about the progress of the Runnable, including StreamEvents from intermediate results.

A StreamEvent is a dictionary with the following schema:

• event: str - Event names are of the

  format: on_[runnable_type]_(start|stream|end).

• name: str - The name of the Runnable that generated the event.

• run_id: str - randomly generated ID associated with the given execution of

  the Runnable that emitted the event. A child Runnable that gets invoked as part of the execution of a parent Runnable is assigned its own unique ID.

• parent_ids: List[str] - The IDs of the parent runnables that

  generated the event. The root Runnable will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list.

• tags: Optional[List[str]] - The tags of the Runnable that generated

  the event.

• metadata: Optional[Dict[str, Any]] - The metadata of the Runnable

  that generated the event.

• data: Dict[str, Any]

Below is a table that illustrates some evens that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.

ATTENTION This reference table is for the V2 version of the schema.

event	name	chunk	input	output
on_chat_model_start	[model name]		{“messages”: [[SystemMessage, HumanMessage]]}
on_chat_model_stream	[model name]	AIMessageChunk(content=”hello”)
on_chat_model_end	[model name]		{“messages”: [[SystemMessage, HumanMessage]]}	AIMessageChunk(content=”hello world”)
on_llm_start	[model name]		{‘input’: ‘hello’}
on_llm_stream	[model name]	‘Hello’
on_llm_end	[model name]		‘Hello human!’
on_chain_start	format_docs
on_chain_stream	format_docs	“hello world!, goodbye world!”
on_chain_end	format_docs		[Document(…)]	“hello world!, goodbye world!”
on_tool_start	some_tool		{“x”: 1, “y”: “2”}
on_tool_end	some_tool			{“x”: 1, “y”: “2”}
on_retriever_start	[retriever name]		{“query”: “hello”}
on_retriever_end	[retriever name]		{“query”: “hello”}	[Document(…), ..]
on_prompt_start	[template_name]		{“question”: “hello”}
on_prompt_end	[template_name]		{“question”: “hello”}	ChatPromptValue(messages: [SystemMessage, …])

In addition to the standard events, users can also dispatch custom events (see example below).

Custom events will be only be surfaced with in the v2 version of the API!

A custom event has following format:

Attribute	Type	Description
name	str	A user defined name for the event.
data	Any	The data associated with the event. This can be anything, though we suggest making it JSON serializable.

Here are declarations associated with the standard events shown above:

format_docs:

def format_docs(docs: List[Document]) -> str:
    '''Format the docs.'''
    return ", ".join([doc.page_content for doc in docs])

format_docs = RunnableLambda(format_docs)

some_tool:

@tool
def some_tool(x: int, y: str) -> dict:
    '''Some_tool.'''
    return {"x": x, "y": y}

prompt:

template = ChatPromptTemplate.from_messages(
    [("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})

Example:

from langchain_core.runnables import RunnableLambda

async def reverse(s: str) -> str:
    return s[::-1]

chain = RunnableLambda(func=reverse)

events = [
    event async for event in chain.astream_events("hello", version="v2")
]

# will produce the following events (run_id, and parent_ids
# has been omitted for brevity):
[
    {
        "data": {"input": "hello"},
        "event": "on_chain_start",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"chunk": "olleh"},
        "event": "on_chain_stream",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"output": "olleh"},
        "event": "on_chain_end",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
]

Example: Dispatch Custom Event

from langchain_core.callbacks.manager import (
    adispatch_custom_event,
)
from langchain_core.runnables import RunnableLambda, RunnableConfig
import asyncio


async def slow_thing(some_input: str, config: RunnableConfig) -> str:
    """Do something that takes a long time."""
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 1 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 2 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    return "Done"

slow_thing = RunnableLambda(slow_thing)

async for event in slow_thing.astream_events("some_input", version="v2"):
    print(event)

Parameters:

• input (Any) – The input to the Runnable.

• config (RunnableConfig | None) – The config to use for the Runnable.

• version (Literal['v1', 'v2']) – The version of the schema to use either v2 or v1. Users should use v2. v1 is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in v2.

• include_names (Sequence[str] | None) – Only include events from runnables with matching names.

• include_types (Sequence[str] | None) – Only include events from runnables with matching types.

• include_tags (Sequence[str] | None) – Only include events from runnables with matching tags.

• exclude_names (Sequence[str] | None) – Exclude events from runnables with matching names.

• exclude_types (Sequence[str] | None) – Exclude events from runnables with matching types.

• exclude_tags (Sequence[str] | None) – Exclude events from runnables with matching tags.

• kwargs (Any) – Additional keyword arguments to pass to the Runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.

Yields:

An async stream of StreamEvents.

Raises:

NotImplementedError – If the version is not v1 or v2.

Return type:

AsyncIterator[StandardStreamEvent | CustomStreamEvent]

batch(inputs: List[Input], config: RunnableConfig | List[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) → List[Output]

Default implementation runs invoke in parallel using a thread pool executor.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

Parameters:

• inputs (List[Input]) –

• config (RunnableConfig | List[RunnableConfig] | None) –

• return_exceptions (bool) –

• kwargs (Any | None) –

Return type:

List[Output]

batch_as_completed(inputs: Sequence[Input], config: RunnableConfig | Sequence[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) → Iterator[Tuple[int, Output | Exception]]

Run invoke in parallel on a list of inputs, yielding results as they complete.

Parameters:

• inputs (Sequence[Input]) –

• config (RunnableConfig | Sequence[RunnableConfig] | None) –

• return_exceptions (bool) –

• kwargs (Any | None) –

Return type:

Iterator[Tuple[int, Output | Exception]]

configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) → RunnableSerializable[Input, Output]

Configure alternatives for Runnables that can be set at runtime.

Parameters:

• which (ConfigurableField) – The ConfigurableField instance that will be used to select the alternative.

• default_key (str) – The default key to use if no alternative is selected. Defaults to “default”.

• prefix_keys (bool) – Whether to prefix the keys with the ConfigurableField id. Defaults to False.

• **kwargs (Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) – A dictionary of keys to Runnable instances or callables that return Runnable instances.

Returns:

A new Runnable with the alternatives configured.

Return type:

RunnableSerializable[Input, Output]

from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatAnthropic(
    model_name="claude-3-sonnet-20240229"
).configurable_alternatives(
    ConfigurableField(id="llm"),
    default_key="anthropic",
    openai=ChatOpenAI()
)

# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)

# uses ChatOpenAI
print(
    model.with_config(
        configurable={"llm": "openai"}
    ).invoke("which organization created you?").content
)

configurable_fields(**kwargs: ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) → RunnableSerializable[Input, Output]

Configure particular Runnable fields at runtime.

Parameters:

**kwargs (ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) – A dictionary of ConfigurableField instances to configure.

Returns:

A new Runnable with the fields configured.

Return type:

RunnableSerializable[Input, Output]

from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatOpenAI(max_tokens=20).configurable_fields(
    max_tokens=ConfigurableField(
        id="output_token_number",
        name="Max tokens in the output",
        description="The maximum number of tokens in the output",
    )
)

# max_tokens = 20
print(
    "max_tokens_20: ",
    model.invoke("tell me something about chess").content
)

# max_tokens = 200
print("max_tokens_200: ", model.with_config(
    configurable={"output_token_number": 200}
    ).invoke("tell me something about chess").content
)

classmethod from_checkpoint(checkpoint: str | Path, thirdai_key: str | None = None) → NeuralDBRetriever

Create a NeuralDBRetriever with a base model from a saved checkpoint

To use, set the THIRDAI_KEY environment variable with your ThirdAI API key, or pass thirdai_key as a named parameter.

Example

from langchain_community.retrievers import NeuralDBRetriever

retriever = NeuralDBRetriever.from_checkpoint(
    checkpoint="/path/to/checkpoint.ndb",
    thirdai_key="your-thirdai-key",
)

retriever.insert([
    "/path/to/doc.pdf",
    "/path/to/doc.docx",
    "/path/to/doc.csv",
])

documents = retriever.invoke("AI-driven music therapy")

Parameters:

• checkpoint (str | Path) –

• thirdai_key (str | None) –

Return type:

NeuralDBRetriever

classmethod from_scratch(thirdai_key: str | None = None, **model_kwargs: dict) → NeuralDBRetriever

Create a NeuralDBRetriever from scratch.

To use, set the THIRDAI_KEY environment variable with your ThirdAI API key, or pass thirdai_key as a named parameter.

Example

from langchain_community.retrievers import NeuralDBRetriever

retriever = NeuralDBRetriever.from_scratch(
    thirdai_key="your-thirdai-key",
)

retriever.insert([
    "/path/to/doc.pdf",
    "/path/to/doc.docx",
    "/path/to/doc.csv",
])

documents = retriever.invoke("AI-driven music therapy")

Parameters:

• thirdai_key (str | None) –

• model_kwargs (dict) –

Return type:

NeuralDBRetriever

get_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: List[str] | None = None, metadata: Dict[str, Any] | None = None, run_name: str | None = None, **kwargs: Any) → List[Document]

Deprecated since version langchain-core==0.1.46: Use invoke instead.

Retrieve documents relevant to a query.

Users should favor using .invoke or .batch rather than get_relevant_documents directly.

Parameters:

• query (str) – string to find relevant documents for.

• callbacks (Callbacks) – Callback manager or list of callbacks. Defaults to None.

• tags (Optional[List[str]]) – Optional list of tags associated with the retriever. These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Defaults to None.

• metadata (Optional[Dict[str, Any]]) – Optional metadata associated with the retriever. This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Defaults to None.

• run_name (Optional[str]) – Optional name for the run. Defaults to None.

• kwargs (Any) – Additional arguments to pass to the retriever.

Returns:

List of relevant documents.

Return type:

List[Document]

insert(sources: List[Any], train: bool = True, fast_mode: bool = True, **kwargs: dict) → None

Inserts files / document sources into the retriever.

Parameters:

• train (bool) – When True this means that the underlying model in the

• files. (NeuralDB will undergo unsupervised pretraining on the inserted) –

• True. (Defaults to) –

• fast_mode (bool) – Much faster insertion with a slight drop in performance.

• True. –

• sources (List[Any]) –

• kwargs (dict) –

Return type:

None

invoke(input: str, config: RunnableConfig | None = None, **kwargs: Any) → List[Document]

Invoke the retriever to get relevant documents.

Main entry point for synchronous retriever invocations.

Parameters:

• input (str) – The query string.

• config (RunnableConfig | None) – Configuration for the retriever. Defaults to None.

• kwargs (Any) – Additional arguments to pass to the retriever.

Returns:

List of relevant documents.

Return type:

List[Document]

Examples:

retriever.invoke("query")

save(path: str) → None

Saves a NeuralDB instance to disk. Can be loaded into memory by calling NeuralDB.from_checkpoint(path)

Parameters:

path (str) – path on disk to save the NeuralDB instance to.

Return type:

None

stream(input: Input, config: RunnableConfig | None = None, **kwargs: Any | None) → Iterator[Output]

Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.

Parameters:

• input (Input) – The input to the Runnable.

• config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.

• kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.

Yields:

The output of the Runnable.

Return type:

Iterator[Output]

to_json() → SerializedConstructor | SerializedNotImplemented

Serialize the Runnable to JSON.

Returns:

A JSON-serializable representation of the Runnable.

Return type:

SerializedConstructor | SerializedNotImplemented

upvote(query: str, document_id: int) → None

The retriever upweights the score of a document for a specific query. This is useful for fine-tuning the retriever to user behavior.

Parameters:

• query (str) – text to associate with document_id

• document_id (int) – id of the document to associate query with.

Return type:

None

upvote_batch(query_id_pairs: List[Tuple[str, int]]) → None

Given a batch of (query, document id) pairs, the retriever upweights the scores of the document for the corresponding queries. This is useful for fine-tuning the retriever to user behavior.

Parameters:

• query_id_pairs (List[Tuple[str, int]]) – list of (query, document id) pairs. For each pair in

• list (this) –

• query. (the model will upweight the document id for the) –

Return type:

None

Examples using NeuralDBRetriever

• **NeuralDB**