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+components.agent.react +====================== + +.. automodule:: components.agent.react + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + ReActAgent + + + + + + + + + diff --git a/_sources/apis/components/None/components.data_process.data_components.rst.txt b/_sources/apis/components/None/components.data_process.data_components.rst.txt new file mode 100644 index 00000000..aeab577a --- /dev/null +++ b/_sources/apis/components/None/components.data_process.data_components.rst.txt @@ -0,0 +1,36 @@ +components.data\_process.data\_components +========================================= + +.. automodule:: components.data_process.data_components + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + retriever_output_to_context_str + + + + + + .. rubric:: Classes + + .. autosummary:: + + RetrieverOutputToContextStr + ToEmbeddings + + + + + + + + + diff --git a/_sources/apis/components/None/components.data_process.text_splitter.rst.txt b/_sources/apis/components/None/components.data_process.text_splitter.rst.txt new file mode 100644 index 00000000..94ca9d37 --- /dev/null +++ b/_sources/apis/components/None/components.data_process.text_splitter.rst.txt @@ -0,0 +1,29 @@ +components.data\_process.text\_splitter +======================================= + +.. automodule:: components.data_process.text_splitter + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + TextSplitter + + + + + + + + + diff --git a/_sources/apis/components/None/components.memory.memory.rst.txt b/_sources/apis/components/None/components.memory.memory.rst.txt new file mode 100644 index 00000000..434128e2 --- /dev/null +++ b/_sources/apis/components/None/components.memory.memory.rst.txt @@ -0,0 +1,29 @@ +components.memory.memory +======================== + +.. automodule:: components.memory.memory + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + Memory + + + + + + + + + diff --git a/_sources/apis/components/None/components.model_client.anthropic_client.rst.txt b/_sources/apis/components/None/components.model_client.anthropic_client.rst.txt new file mode 100644 index 00000000..d33a3ba1 --- /dev/null +++ b/_sources/apis/components/None/components.model_client.anthropic_client.rst.txt @@ -0,0 +1,29 @@ +components.model\_client.anthropic\_client +========================================== + +.. automodule:: components.model_client.anthropic_client + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + AnthropicAPIClient + + + + + + + + + diff --git a/_sources/apis/components/None/components.model_client.cohere_client.rst.txt b/_sources/apis/components/None/components.model_client.cohere_client.rst.txt new file mode 100644 index 00000000..fae4369e --- /dev/null +++ b/_sources/apis/components/None/components.model_client.cohere_client.rst.txt @@ -0,0 +1,29 @@ +components.model\_client.cohere\_client +======================================= + +.. automodule:: components.model_client.cohere_client + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + CohereAPIClient + + + + + + + + + diff --git a/_sources/apis/components/None/components.model_client.groq_client.rst.txt b/_sources/apis/components/None/components.model_client.groq_client.rst.txt new file mode 100644 index 00000000..306101a9 --- /dev/null +++ b/_sources/apis/components/None/components.model_client.groq_client.rst.txt @@ -0,0 +1,29 @@ +components.model\_client.groq\_client +===================================== + +.. automodule:: components.model_client.groq_client + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + GroqAPIClient + + + + + + + + + diff --git a/_sources/apis/components/None/components.model_client.openai_client.rst.txt b/_sources/apis/components/None/components.model_client.openai_client.rst.txt new file mode 100644 index 00000000..9ea23544 --- /dev/null +++ b/_sources/apis/components/None/components.model_client.openai_client.rst.txt @@ -0,0 +1,37 @@ +components.model\_client.openai\_client +======================================= + +.. automodule:: components.model_client.openai_client + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + get_all_messages_content + get_first_message_content + get_probabilities + + + + + + .. rubric:: Classes + + .. autosummary:: + + OpenAIClient + + + + + + + + + diff --git a/_sources/apis/components/None/components.model_client.transformers_client.rst.txt b/_sources/apis/components/None/components.model_client.transformers_client.rst.txt new file mode 100644 index 00000000..45779062 --- /dev/null +++ b/_sources/apis/components/None/components.model_client.transformers_client.rst.txt @@ -0,0 +1,38 @@ +components.model\_client.transformers\_client +============================================= + +.. automodule:: components.model_client.transformers_client + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + average_pool + + + + + + .. rubric:: Classes + + .. autosummary:: + + TransformerEmbedder + TransformerLLM + TransformerReranker + TransformersClient + + + + + + + + + diff --git a/_sources/apis/components/None/components.output_parsers.outputs.rst.txt b/_sources/apis/components/None/components.output_parsers.outputs.rst.txt new file mode 100644 index 00000000..618ad0fd --- /dev/null +++ b/_sources/apis/components/None/components.output_parsers.outputs.rst.txt @@ -0,0 +1,33 @@ +components.output\_parsers.outputs +================================== + +.. automodule:: components.output_parsers.outputs + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + BooleanOutputParser + JsonOutputParser + ListOutputParser + OutputParser + YamlOutputParser + + + + + + + + + diff --git a/_sources/apis/components/None/components.reasoning.chain_of_thought.rst.txt b/_sources/apis/components/None/components.reasoning.chain_of_thought.rst.txt new file mode 100644 index 00000000..cd5d2af4 --- /dev/null +++ b/_sources/apis/components/None/components.reasoning.chain_of_thought.rst.txt @@ -0,0 +1,23 @@ +components.reasoning.chain\_of\_thought +======================================= + +.. automodule:: components.reasoning.chain_of_thought + + + + + + + + + + + + + + + + + + + diff --git a/_sources/apis/components/None/components.retriever.bm25_retriever.rst.txt b/_sources/apis/components/None/components.retriever.bm25_retriever.rst.txt new file mode 100644 index 00000000..e6de69ed --- /dev/null +++ b/_sources/apis/components/None/components.retriever.bm25_retriever.rst.txt @@ -0,0 +1,37 @@ +components.retriever.bm25\_retriever +==================================== + +.. automodule:: components.retriever.bm25_retriever + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + split_text_by_word_fn + split_text_by_word_fn_then_lower_tokenized + split_text_tokenized + + + + + + .. rubric:: Classes + + .. autosummary:: + + BM25Retriever + + + + + + + + + diff --git a/_sources/apis/components/None/components.retriever.llm_retriever.rst.txt b/_sources/apis/components/None/components.retriever.llm_retriever.rst.txt new file mode 100644 index 00000000..d34209d5 --- /dev/null +++ b/_sources/apis/components/None/components.retriever.llm_retriever.rst.txt @@ -0,0 +1,29 @@ +components.retriever.llm\_retriever +=================================== + +.. automodule:: components.retriever.llm_retriever + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + LLMRetriever + + + + + + + + + diff --git a/_sources/apis/components/None/components.retriever.reranker_retriever.rst.txt b/_sources/apis/components/None/components.retriever.reranker_retriever.rst.txt new file mode 100644 index 00000000..24370bff --- /dev/null +++ b/_sources/apis/components/None/components.retriever.reranker_retriever.rst.txt @@ -0,0 +1,29 @@ +components.retriever.reranker\_retriever +======================================== + +.. automodule:: components.retriever.reranker_retriever + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + RerankerRetriever + + + + + + + + + diff --git a/_sources/apis/components/index.rst.txt b/_sources/apis/components/index.rst.txt index 4c1762fd..25ba56d9 100644 --- a/_sources/apis/components/index.rst.txt +++ b/_sources/apis/components/index.rst.txt @@ -3,81 +3,89 @@ Components ============== -The components section of the LightRAG API documentation outlines the detailed specifications and functionalities of various API components. Each component plays a crucial role in the LightRAG framework, providing specialized capabilities and interactions. +.. The components section of the LightRAG API documentation outlines the detailed specifications and functionalities of various API components. Each component plays a crucial role in the LightRAG framework, providing specialized capabilities and interactions. + + +ModelClient +~~~~~~~~~~~~~~~~~~~~ -Overview ----------- .. autosummary:: + :toctree: None - components.agent.react + components.model_client.groq_client components.model_client.anthropic_client + components.model_client.openai_client components.model_client.cohere_client components.model_client.google_client - components.model_client.groq_client - components.model_client.openai_client components.model_client.transformers_client - components.model_client.utils - components.data_process.data_components - components.data_process.text_splitter +Retriever +~~~~~~~~~~~~~~~~~~~~ - components.reasoning.chain_of_thought +.. autosummary:: + :toctree: None components.retriever.bm25_retriever components.retriever.faiss_retriever components.retriever.llm_retriever + components.retriever.postgres_retriever - components.retriever.reranker_retriever - components.output_parsers.outputs + components.retriever.reranker_retriever Output Parsers --------------- -.. toctree:: - :maxdepth: 1 +~~~~~~~~~~~~~~~~~~~~ - components.output_parsers +.. autosummary:: + :toctree: None -Agents ------- -.. toctree:: - :maxdepth: 1 + components.output_parsers.outputs - components.agent +Agent +~~~~~~~~~~~~~~~~~~~~ -Model Clients ------------------ -.. toctree:: - :maxdepth: 1 +.. autosummary:: + :toctree: None - components.model_client + components.agent.react Data Process ----------------- -.. toctree:: - :maxdepth: 1 +~~~~~~~~~~~~~~~~~~~~ - components.data_process +.. autosummary:: + :toctree: None -.. Embedders -.. --------- -.. .. toctree:: -.. :maxdepth: 1 -.. components.embedder + components.data_process.text_splitter + + components.data_process.data_components -.. Reasoners -.. --------- -.. .. toctree:: -.. :maxdepth: 1 +Memory +~~~~~~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: None + + components.memory.memory + +Reasoning + +.. autosummary:: + :toctree: None + + components.reasoning.chain_of_thought -.. components.reasoning -Retrievers ----------- .. toctree:: - :maxdepth: 1 + :maxdepth: 2 + :hidden: + components.model_client components.retriever + components.output_parsers + components.agent + components.data_process + components.reasoning + components.memory \ No newline at end of file diff --git a/apis/components/None/components.agent.react.html b/apis/components/None/components.agent.react.html new file mode 100644 index 00000000..2cf82678 --- /dev/null +++ b/apis/components/None/components.agent.react.html @@ -0,0 +1,701 @@ + + + + + + + + + + + components.agent.react — LightRAG documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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    components.agent.react#

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    Implementation of ReAct.

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    Classes

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    ReActAgent([tools, max_steps, ...])

    ReActAgent uses generator as a planner that runs multiple and sequential functional call steps to generate the final response.

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    +class ReActAgent(tools: List[Callable | Callable[[...], Awaitable[Any]] | FunctionTool] = [], max_steps: int = 10, add_llm_as_fallback: bool = True, *, model_client: ModelClient, model_kwargs: Dict = {})[source]#
    +

    Bases: Component

    +

    ReActAgent uses generator as a planner that runs multiple and sequential functional call steps to generate the final response.

    +

    Users need to set up: +- tools: a list of tools to use to complete the task. Each tool is a function or a function tool. +- max_steps: the maximum number of steps the agent can take to complete the task. +- use_llm_as_fallback: a boolean to decide whether to use an additional LLM model as a fallback tool to answer the query. +- model_client: the model client to use to generate the response. +- model_kwargs: the model kwargs to use to generate the response.

    +

    For the generator, the default arguments are: +(1) default prompt: DEFAULT_REACT_AGENT_SYSTEM_PROMPT +(2) default output_processors: JsonParser

    +

    There are examples which is optional, a list of string examples in the prompt.

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    Example:

    +

    Reference: +[1] https://arxiv.org/abs/2210.03629, published in Mar, 2023.

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    +reset()[source]#
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    Reset the agent to start a new query.

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    +call(input: str, promt_kwargs: Dict | None = {}, model_kwargs: Dict | None = {}) Any[source]#
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    prompt_kwargs: additional prompt kwargs to either replace or add to the preset prompt kwargs.

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    components.data_process.data_components#

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    Helper components for data transformation such as embeddings and document splitting.

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    Functions

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    retriever_output_to_context_str(retriever_output)

    The retrieved documents from one or mulitple queries.

    +
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    Classes

    +
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    RetrieverOutputToContextStr([deduplicate])

    Wrap on functional F.retriever_output_to_context_str

    ToEmbeddings(embedder[, batch_size])

    It transforms a Sequence of Chunks or Documents to a List of Embeddings.

    +
    +
    +
    +class ToEmbeddings(embedder: Embedder, batch_size: int = 50)[source]#
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    Bases: Component

    +

    It transforms a Sequence of Chunks or Documents to a List of Embeddings.

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    It operates on a copy of the input data, and does not modify the input data.

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    + +
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    +class RetrieverOutputToContextStr(deduplicate: bool = False)[source]#
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    Bases: Component

    +

    Wrap on functional F.retriever_output_to_context_str

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    +retriever_output_to_context_str(retriever_output: RetrieverOutput | List[RetrieverOutput], deduplicate: bool = False) str[source]#
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    The retrieved documents from one or mulitple queries. +Deduplicate is especially helpful when you used query expansion.

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    components.data_process.text_splitter#

    +

    Splitting texts is commonly used as a preprocessing step before embedding and retrieving texts.

    +

    We encourage you to process your data here and define your own embedding and retrieval methods. These methods can highly depend on the product environment and may extend beyond the scope of this library.

    +

    However, the following approaches are commonly shared:

    +
      +
    • Document Storage: Define how to store the documents, both raw and chunked. For example, LlamaIndex uses Document Stores to manage ingested document chunks.

    • +
    • Document Chunking: Segment documents into manageable chunks suitable for further processing.

    • +
    • Vectorization: Embed each chunk and store the resulting vectors in Vector Stores. For example, LLama index utilizes Vector Stores.

    • +
    • Retrieval: Leverage vectors for context retrieval.

    • +
    +

    Classes

    +
    + + + + + +

    TextSplitter([split_by, chunk_size, ...])

    Text Splitter for Chunking Documents

    +
    +
    +
    +class TextSplitter(split_by: Literal['word', 'sentence', 'page', 'passage', 'token'] = 'word', chunk_size: int = 800, chunk_overlap: int = 200, batch_size: int = 1000)[source]#
    +

    Bases: Component

    +

    Text Splitter for Chunking Documents

    +

    TextSplitter first utilizes split_by to specify the text-splitting criterion and breaks the long text into smaller texts. +Then we create a sliding window with length= chunk_size. It moves at step= chunk_size - chunk_overlap. +The texts inside each window will get merged to a smaller chunk. The generated chunks from the splitted text will be returned.

    +

    Splitting Types

    +

    TextSplitter supports 2 types of splitting.

    +
      +
    • Type 1: Specify the exact text splitting point such as space<” “> and periods<”.”>. It is intuitive, for example, split_by “word”:

    • +
    +
    "Hello, world!" -> ["Hello, " ,"world!"]
    +
    +
    +
      +
    • Type 2: Use tokenizer. It works as:

    • +
    +
    "Hello, world!" -> ['Hello', ',', ' world', '!']
    +
    +
    +

    This aligns with how models see text in the form of tokens (Reference), +Tokenizer reflects the real token numbers the models take in and helps the developers control budgets.

    +

    Definitions

    +
      +
    • split_by specifies the split rule, i.e. the smallest unit during splitting. We support "word", "sentence", "page", "passage", and "token". The splitter utilizes the corresponding separator from the SEPARATORS dictionary.

    • +
    +

    For Type 1 splitting, we apply Python str.split() to break the text.

    +
      +
    • SEPARATORS: Maps split_by criterions to their exact text separators, e.g., spaces <” “> for “word” or periods <”.”> for “sentence”.

    • +
    +
    +

    Note

    +

    For option token, its separator is “” because we directly split by a tokenizer, instead of text point.

    +
    +
      +
    • chunk_size is the the maximum number of units in each chunk.

    • +
    • chunk_overlap is the number of units that each chunk should overlap. Including context at the borders prevents sudden meaning shift in text between sentences/context, especially in sentiment analysis.

    • +
    • Splitting Details

    • +
    +

    Type 1: +The TextSplitter utilizes Python’s str.split(separator) method. +Developers can refer to

    +
    {
    +    "page": "\f",
    +    "passage": "\n",
    +    "word": " ",
    +    "sentence": "."
    +}
    +
    +
    +

    for exact points of text division.

    +
    +

    Note

    +

    Developers need to determine how to assign text to each data chunk for the embedding and retrieval tasks.

    +
    +

    Type 2: +We implement a tokenizer using cl100k_base encoding that aligns with how models see text in the form of tokens. +E.g. “tiktoken is great!” -> [“t”, “ik”, “token”, “ is”, “ great”, “!”] This helps developers control the token usage and budget better.

    +
      +
    • Merge Details

    • +
    +

    Type 1/Type 2 create a list of split texts. TextSplitter then reattaches the specified separator to each piece of the split text, except for the last segment. +This approach maintains the original spacing and punctuation, which is critical in contexts like natural language processing where text formatting can impact interpretations and outcomes. +E.g. “hello world!” split by “word” will be kept as “hello “ and “world!”

    +
      +
    • Customization

    • +
    +

    You can also customize the SEPARATORS. For example, by defining SEPARATORS = {“question”: “?”} and setting split_by = “question”, the document will be split at each ?, ideal for processing text structured +as a series of questions. If you need to customize tokenizer, please check Reference.

    +
      +
    • Integration with Other Document Types

    • +
    +

    This functionality is ideal for segmenting texts into sentences, words, pages, or passages, which can then be processed further for NLP applications. +For PDFs, developers will need to extract the text before using the splitter. Libraries like PyPDF2 or PDFMiner can be utilized for this purpose. +LightRAG’s future implementations will introduce splitters for JSON, HTML, markdown, and code.

    +

    Example:

    +
    from lightrag.components.data_process.text_splitter import TextSplitter
    +from lightrag.core.types import Document
    +
    +# Configure the splitter settings
    +text_splitter = TextSplitter(
    +    split_by="word",
    +    chunk_size=5,
    +    chunk_overlap=1
    +)
    +
    +# Example document
    +doc = Document(
    +    text="Example text. More example text. Even more text to illustrate.",
    +    id="doc1"
    +)
    +
    +# Execute the splitting
    +splitted_docs = text_splitter.call(documents=[doc])
    +
    +for doc in splitted_docs:
    +    print(doc)
    +
    +# Output:
    +# Document(id=44a8aa37-0d16-40f0-9ca4-2e25ae5336c8, text='Example text. More example text. ', meta_data=None, vector=[], parent_doc_id=doc1, order=0, score=None)
    +# Document(id=ca0af45b-4f88-49b5-97db-163da9868ea4, text='text. Even more text to ', meta_data=None, vector=[], parent_doc_id=doc1, order=1, score=None)
    +# Document(id=e7b617b2-3927-4248-afce-ec0fc247ac8b, text='to illustrate.', meta_data=None, vector=[], parent_doc_id=doc1, order=2, score=None)
    +
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    +split_text(text: str) List[str][source]#
    +

    Splits the provided text into chunks.

    +

    Splits based on the specified split_by, chunk size, and chunk overlap settings.

    +
    +
    Parameters:
    +

    text (str) – The text to split.

    +
    +
    Returns:
    +

    A list of text chunks.

    +
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    Return type:
    +

    List[str]

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    +call(documents: List[Document]) List[Document][source]#
    +

    Process the splitting task on a list of documents in batch.

    +

    Batch processes a list of documents, splitting each document’s text according to the configured +split_by, chunk size, and chunk overlap.

    +
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    Parameters:
    +

    documents (List[Document]) – A list of Document objects to process.

    +
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    Returns:
    +

    A list of new Document objects, each containing a chunk of text from the original documents.

    +
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    Return type:
    +

    List[Document]

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    Raises:
    +
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    • TypeError – If ‘documents’ is not a list or contains non-Document objects.

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    • ValueError – If any document’s text is None.

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    components.memory.memory#

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    Memory for user-assistant conversations. [Not completed]

    +

    Memory can include data modeling, in-memory data storage, local file data storage, cloud data persistence, data pipeline, data retriever. +It is itself an LLM application and different use cases can do it differently.

    +

    This implementation covers the minimal and local memory experience for the user-assistant conversation.

    +

    Classes

    +
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    Memory([turn_db])

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    +class Memory(turn_db: LocalDB = None)[source]#
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    components.model_client.anthropic_client#

    +

    Anthropic ModelClient integration.

    +

    Classes

    +
    + + + + + +

    AnthropicAPIClient([api_key])

    A component wrapper for the Anthropic API client.

    +
    +
    +
    +class AnthropicAPIClient(api_key: str | None = None)[source]#
    +

    Bases: ModelClient

    +

    A component wrapper for the Anthropic API client.

    +

    Visit https://docs.anthropic.com/en/docs/intro-to-claude for more api details.

    +
    +
    +init_sync_client()[source]#
    +
    + +
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    +init_async_client()[source]#
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    +parse_chat_completion(completion: Message) str[source]#
    +

    Parse the chat completion to str.

    +
    + +
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    +convert_inputs_to_api_kwargs(input: Any | None = None, model_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED) dict[source]#
    +

    Bridge the Component’s standard input and model_kwargs into API-specific format, the api_kwargs that will be used in _call and _acall methods.

    +
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    Parameters:
    +
      +
    • input (Optional[Any], optional) – input to the model. Defaults to None.

    • +
    • model_kwargs (Dict) – model kwargs

    • +
    • model_type (ModelType) – model type

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    +call(api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED)[source]#
    +

    kwargs is the combined input and model_kwargs

    +
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    components.model_client.cohere_client#

    +

    Cohere ModelClient integration.

    +

    Classes

    +
    + + + + + +

    CohereAPIClient([api_key])

    A component wrapper for the Cohere API.

    +
    +
    +
    +class CohereAPIClient(api_key: str | None = None)[source]#
    +

    Bases: ModelClient

    +

    A component wrapper for the Cohere API.

    +

    Visit https://docs.cohere.com/ for more api details.

    +

    References: +- Cohere reranker: https://docs.cohere.com/reference/rerank

    +

    Tested Cohere models: 6/16/2024 +- rerank-english-v3.0, rerank-multilingual-v3.0, rerank-english-v2.0, rerank-multilingual-v2.0

    +
    +
    +init_sync_client()[source]#
    +
    + +
    +
    +init_async_client()[source]#
    +
    + +
    +
    +convert_inputs_to_api_kwargs(input: Any | None = None, model_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED) Dict[source]#
    +
    +
    For rerank model, expect model_kwargs to have the following keys:

    model: str, +query: str, +documents: List[str], +top_n: int,

    +
    +
    +
    + +
    +
    +call(api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED)[source]#
    +

    Subclass use this to call the API with the sync client. +model_type: this decides which API, such as chat.completions or embeddings for OpenAI. +api_kwargs: all the arguments that the API call needs, subclass should implement this method.

    +

    Additionally in subclass you can implement the error handling and retry logic here. See OpenAIClient for example.

    +
    + +
    +
    +async acall(api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED)[source]#
    +

    Subclass use this to call the API with the async client.

    +
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    components.model_client.groq_client#

    +

    Groq ModelClient integration.

    +

    Classes

    +
    + + + + + +

    GroqAPIClient([api_key])

    A component wrapper for the Groq API client.

    +
    +
    +
    +class GroqAPIClient(api_key: str | None = None)[source]#
    +

    Bases: ModelClient

    +

    A component wrapper for the Groq API client.

    +

    Visit https://console.groq.com/docs/ for more api details. +Check https://console.groq.com/docs/models for the available models.

    +

    Tested Groq models: 4/22/2024 +- llama3-8b-8192 +- llama3-70b-8192 +- mixtral-8x7b-32768 +- gemma-7b-it

    +
    +
    +init_sync_client()[source]#
    +
    + +
    +
    +init_async_client()[source]#
    +
    + +
    +
    +parse_chat_completion(completion: Any) str[source]#
    +

    Parse the completion to a string output.

    +
    + +
    +
    +convert_inputs_to_api_kwargs(input: Any | None = None, model_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED) Dict[source]#
    +

    Bridge the Component’s standard input and model_kwargs into API-specific format, the api_kwargs that will be used in _call and _acall methods.

    +
    +
    Parameters:
    +
      +
    • input (Optional[Any], optional) – input to the model. Defaults to None.

    • +
    • model_kwargs (Dict) – model kwargs

    • +
    • model_type (ModelType) – model type

    • +
    +
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    +
    + +
    +
    +call(api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED)[source]#
    +

    Subclass use this to call the API with the sync client. +model_type: this decides which API, such as chat.completions or embeddings for OpenAI. +api_kwargs: all the arguments that the API call needs, subclass should implement this method.

    +

    Additionally in subclass you can implement the error handling and retry logic here. See OpenAIClient for example.

    +
    + +
    +
    +async acall(api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED)[source]#
    +

    Subclass use this to call the API with the async client.

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    components.model_client.openai_client#

    +

    OpenAI ModelClient integration.

    +

    Functions

    +
    + + + + + + + + + + + +

    get_all_messages_content(completion)

    When the n > 1, get all the messages content.

    get_first_message_content(completion)

    When we only need the content of the first message.

    get_probabilities(completion)

    Get the probabilities of each token in the completion.

    +
    +

    Classes

    +
    + + + + + +

    OpenAIClient([api_key, chat_completion_parser])

    A component wrapper for the OpenAI API client.

    +
    +
    +
    +get_first_message_content(completion: Completion) str[source]#
    +

    When we only need the content of the first message. +It is the default parser for chat completion.

    +
    + +
    +
    +get_all_messages_content(completion: Completion) List[str][source]#
    +

    When the n > 1, get all the messages content.

    +
    + +
    +
    +get_probabilities(completion: Completion) List[List[TokenLogProb]][source]#
    +

    Get the probabilities of each token in the completion.

    +
    + +
    +
    +class OpenAIClient(api_key: str | None = None, chat_completion_parser: Callable[[Completion], Any] = None)[source]#
    +

    Bases: ModelClient

    +

    A component wrapper for the OpenAI API client.

    +

    Support both embedding and chat completion API.

    +

    Users (1) simplify use Embedder and Generator components by passing OpenAIClient() as the model_client. +(2) can use this as an example to create their own API client or extend this class(copying and modifing the code) in their own project.

    +
    +

    Note

    +

    We suggest users not to use response_format to enforce output data type or tools and tool_choice in your model_kwargs when calling the API. +We do not know how OpenAI is doing the formating or what prompt they have added. +Instead +- use OutputParser for response parsing and formating.

    +
    +
    +
    Parameters:
    +
      +
    • api_key (Optional[str], optional) – OpenAI API key. Defaults to None.

    • +
    • chat_completion_parser (Callable[[Completion], Any], optional) – A function to parse the chat completion to a str. Defaults to None. +Default is get_first_message_content.

    • +
    +
    +
    +

    References

    + +
    +
    +init_sync_client()[source]#
    +
    + +
    +
    +init_async_client()[source]#
    +
    + +
    +
    +parse_chat_completion(completion: Completion) Any[source]#
    +

    Parse the completion to a str.

    +
    + +
    +
    +parse_embedding_response(response: CreateEmbeddingResponse) EmbedderOutput[source]#
    +

    Parse the embedding response to a structure LightRAG components can understand.

    +

    Should be called in Embedder.

    +
    + +
    +
    +convert_inputs_to_api_kwargs(input: Any | None = None, model_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED) Dict[source]#
    +

    Specify the API input type and output api_kwargs that will be used in _call and _acall methods. +Convert the Component’s standard input, and system_input(chat model) and model_kwargs into API-specific format

    +
    + +
    +
    +call(api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED)[source]#
    +

    kwargs is the combined input and model_kwargs

    +
    + +
    +
    +async acall(api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED)[source]#
    +

    kwargs is the combined input and model_kwargs

    +
    + +
    +
    +classmethod from_dict(data: Dict[str, Any]) T[source]#
    +

    Create an instance from previously serialized data +using to_dict() method.

    +
    + +
    +
    +to_dict() Dict[str, Any][source]#
    +

    Convert the component to a dictionary.

    +
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    components.model_client.transformers_client#

    +

    Huggingface transformers ModelClient integration.

    +

    Functions

    +
    + + + + + +

    average_pool(last_hidden_states, attention_mask)

    +
    +

    Classes

    +
    + + + + + + + + + + + + + + +

    TransformerEmbedder([model_name])

    Local model SDK for transformers.

    TransformerLLM([model_name])

    TransformerReranker([model_name])

    Local model SDK for a reranker model using transformers.

    TransformersClient([model_name])

    LightRAG API client for transformers.

    +
    +
    +
    +average_pool(last_hidden_states: Tensor, attention_mask: Tensor) Tensor[source]#
    +
    + +
    +
    +class TransformerEmbedder(model_name: str | None = 'thenlper/gte-base')[source]#
    +

    Bases: object

    +

    Local model SDK for transformers.

    +

    There are two ways to run transformers: +(1) model and then run model inference +(2) Pipeline and then run pipeline inference

    +

    This file demonstrates how to +(1) create a torch model inference component: TransformerEmbedder which equalize to OpenAI(), the SyncAPIClient +(2) Convert this model inference component to LightRAG API client: TransformersClient

    +

    The is now just an exmplary component that initialize a certain model from transformers and run inference on it. +It is not tested on all transformer models yet. It might be necessary to write one for each model.

    +

    References: +- transformers: https://huggingface.co/docs/transformers/en/index +- thenlper/gte-base model:https://huggingface.co/thenlper/gte-base

    +
    +
    +models: Dict[str, type] = {}#
    +
    + +
    +
    +init_model(model_name: str)[source]#
    +
    + +
    +
    +infer_gte_base_embedding(input=typing.Union[str, typing.List[str]], tolist: bool = True)[source]#
    +
    + +
    + +
    +
    +class TransformerReranker(model_name: str | None = 'BAAI/bge-reranker-base')[source]#
    +

    Bases: object

    +

    Local model SDK for a reranker model using transformers.

    +

    References: +- model: https://huggingface.co/BAAI/bge-reranker-base +- paper: https://arxiv.org/abs/2309.07597

    +

    note: +If you are using Macbook M1 series chips, you need to ensure torch.device("mps") is set.

    +
    +
    +models: Dict[str, type] = {}#
    +
    + +
    +
    +init_model(model_name: str)[source]#
    +
    + +
    +
    +infer_bge_reranker_base(query: str, documents: List[str]) List[float][source]#
    +
    + +
    + +
    +
    +class TransformerLLM(model_name: str | None = 'HuggingFaceH4/zephyr-7b-beta')[source]#
    +

    Bases: object

    +
    +
    +models: Dict[str, type] = {}#
    +
    + +
    +
    +init_model(model_name: str)[source]#
    +
    + +
    +
    +parse_chat_completion(input_text: str, response: str)[source]#
    +
    + +
    +
    +call(input_text: str, skip_special_tokens: bool = True, clean_up_tokenization_spaces: bool = False, max_length: int = 150)[source]#
    +
    + +
    + +
    +
    +class TransformersClient(model_name: str | None = None)[source]#
    +

    Bases: ModelClient

    +

    LightRAG API client for transformers.

    +

    Use: ``ls ~/.cache/huggingface/hub `` to see the cached models.

    +
    +
    +support_models = {'BAAI/bge-reranker-base': {'type': ModelType.RERANKER}, 'HuggingFaceH4/zephyr-7b-beta': {'type': ModelType.LLM}, 'thenlper/gte-base': {'type': ModelType.EMBEDDER}}#
    +
    + +
    +
    +init_sync_client()[source]#
    +
    + +
    +
    +init_reranker_client()[source]#
    +
    + +
    +
    +init_llm_client()[source]#
    +
    + +
    +
    +parse_embedding_response(response: Any) EmbedderOutput[source]#
    +

    Parse the embedding response to a structure LightRAG components can understand.

    +
    + +
    +
    +call(api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED)[source]#
    +

    Subclass use this to call the API with the sync client. +model_type: this decides which API, such as chat.completions or embeddings for OpenAI. +api_kwargs: all the arguments that the API call needs, subclass should implement this method.

    +

    Additionally in subclass you can implement the error handling and retry logic here. See OpenAIClient for example.

    +
    + +
    +
    +convert_inputs_to_api_kwargs(input: Any, model_kwargs: dict = {}, model_type: ModelType = ModelType.UNDEFINED) dict[source]#
    +

    Bridge the Component’s standard input and model_kwargs into API-specific format, the api_kwargs that will be used in _call and _acall methods.

    +
    +
    Parameters:
    +
      +
    • input (Optional[Any], optional) – input to the model. Defaults to None.

    • +
    • model_kwargs (Dict) – model kwargs

    • +
    • model_type (ModelType) – model type

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    components.output_parsers.outputs#

    +

    The most commonly used output parsers for the Generator.

    +

    Classes

    +
    + + + + + + + + + + + + + + + + + +

    BooleanOutputParser()

    Boolean output parser to parse boolean values from the string.

    JsonOutputParser(data_class[, examples, ...])

    ListOutputParser([list_output_format_template])

    List output parser to parse list of objects from the string.

    OutputParser(*args, **kwargs)

    The abstract class for all output parsers.

    YamlOutputParser(data_class[, examples, ...])

    YAML output parser using dataclass for schema extraction.

    +
    +
    +
    +class OutputParser(*args, **kwargs)[source]#
    +

    Bases: Component

    +

    The abstract class for all output parsers.

    +

    This interface helps users customize output parsers with consistent interfaces for the Generator. +Even though you don’t always need to subclass it.

    +

    LightRAG uses two core components: +1. the Prompt to format output instruction +2. A string parser component from core.string_parser for response parsing.

    +
    +
    +format_instructions() str[source]#
    +

    Return the formatted instructions to use in prompt for the output format.

    +
    + +
    +
    +call(input: str) Any[source]#
    +

    Parse the output string to the desired format and return the parsed output.

    +
    + +
    + +
    +
    +class YamlOutputParser(data_class: DataClass, examples: List[DataClass] = None, exclude_fields: List[str] | Dict[str, List[str]] | None = None)[source]#
    +

    Bases: OutputParser

    +

    YAML output parser using dataclass for schema extraction.

    +
    +

    Note

    +

    Only use yaml for simple dataclass objects. For complex objects, use JSON.

    +
    +
    +
    Parameters:
    +
      +
    • data_class (Type) – The dataclass to extract the schema for the YAML output.

    • +
    • example (Type, optional) – The example dataclass object to show in the prompt. Defaults to None.

    • +
    • yaml_output_format_template (str, optional) – The template for the YAML output format. Defaults to YAML_OUTPUT_FORMAT.

    • +
    • output_processors (Component, optional) – The output processors to parse the YAML string to JSON object. Defaults to YamlParser().

    • +
    +
    +
    +

    Examples:

    +
    >>> from prompts.outputs import YamlOutputParser
    +>>> from dataclasses import dataclass, field
    +>>> from typing import List
    +>>>
    +>>> @dataclass
    +>>> class ThoughtAction:
    +>>>     thought: str = field(metadata={"description": "Reasoning behind the answer"}) # required field
    +>>>     answer: str = field(metadata={"description": "Your answer to the question"}, default=None) # optional field
    +>>>
    +>>> # If you want to parse it back to the dataclass, you can add a from_dict method to the dataclass
    +>>> # def from_dict(self, d: Dict[str, Any]) -> "ThoughtAction":
    +>>> #     return ThoughtAction(**d)
    +>>>
    +>>> yaml_parser = YamlOutputParser(data_class_for_yaml=ThoughtAction)
    +>>> yaml_format_instructions = yaml_parser.format_instructions()
    +>>> print(yaml_format_instructions)
    +>>> yaml_str = '''The output should be formatted as a standard YAML instance with the following JSON schema:
    +>>> ```
    +>>> 'thought': {'type': 'str', 'description': 'Reasoning behind the answer', 'required': True}, 'answer': {'type': 'str', 'description': '
    +>>> Your answer to the question', 'required': False, 'default': None}
    +>>> ```
    +>>> -Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!
    +>>> -Follow the YAML formatting conventions with an indent of 2 spaces.
    +>>> '''
    +>>> # use it in the generator
    +>>> task_desc_str = "You are a helpful assistant who answers user query. "+yaml_format_instructions
    +>>> generator = Generator(output_processors=yaml_parser, ..., preset_prompt_kwargs={"task_desc_str": task_desc_str})
    +>>> generator("Should i be a doctor?")
    +
    +
    +
    +
    +format_instructions(format_type: DataClassFormatType | None = None) str[source]#
    +

    Return the formatted instructions to use in prompt for the YAML output format.

    +
    +
    Parameters:
    +
      +
    • format_type (DataClassFormatType, optional) – The format type to show in the prompt. +Defaults to DataClassFormatType.SIGNATURE_YAML for less token usage. +Options: DataClassFormatType.SIGNATURE_YAML, DataClassFormatType.SIGNATURE_JSON, DataClassFormatType.SCHEMA.

    • +
    • exclude (List[str], optional) – The fields to exclude from the schema of the data class.

    • +
    +
    +
    +
    + +
    +
    +call(input: str) Dict[str, Any][source]#
    +

    Parse the YAML string to JSON object and return the JSON object.

    +
    + +
    + +
    +
    +class JsonOutputParser(data_class: DataClass, examples: List[DataClass] = None, exclude_fields: List[str] | Dict[str, List[str]] | None = None)[source]#
    +

    Bases: OutputParser

    +
    +
    +format_instructions(format_type: DataClassFormatType | None = None) str[source]#
    +

    Return the formatted instructions to use in prompt for the JSON output format.

    +
    +
    Parameters:
    +

    format_type (DataClassFormatType, optional) – The format type to show in the prompt. +Defaults to DataClassFormatType.SIGNATURE_JSON for less token usage compared with DataClassFormatType.SCHEMA. +Options: DataClassFormatType.SIGNATURE_YAML, DataClassFormatType.SIGNATURE_JSON, DataClassFormatType.SCHEMA.

    +
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    + +
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    +call(input: str) Any[source]#
    +

    Parse the output string to the desired format and return the parsed output.

    +
    + +
    + +
    +
    +class ListOutputParser(list_output_format_template: str = "Your output should be formatted as a standard Python list.\n- Start the list with '[' and end with ']'")[source]#
    +

    Bases: OutputParser

    +

    List output parser to parse list of objects from the string.

    +
    +
    +format_instructions() str[source]#
    +

    Return the formatted instructions to use in prompt for the output format.

    +
    + +
    +
    +call(input: str) list[source]#
    +

    Parse the output string to the desired format and return the parsed output.

    +
    + +
    + +
    +
    +class BooleanOutputParser[source]#
    +

    Bases: OutputParser

    +

    Boolean output parser to parse boolean values from the string.

    +
    +
    +format_instructions() str[source]#
    +

    Return the formatted instructions to use in prompt for the output format.

    +
    + +
    +
    +call(input: str) bool[source]#
    +

    Parse the output string to the desired format and return the parsed output.

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    components.reasoning.chain_of_thought#

    +

    Chain of the thought(CoT) is to mimic a step-by-step thought process for arriving at the answer.

    +

    https://arxiv.org/abs/2201.11903, published in Jan, 2023

    +

    Chain of the thought(CoT) is to mimic a step-by-step thought process for arriving at the answer. You can achieve it in two ways: +1. Add instructions such as “Let’s think step-by-step to answer this question”. +2. Add few-shot examples such as +‘ +Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now? +A: Roger started with 5 balls. 2 cansof 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11. +‘

    +

    NOTE: CoT can be helpful for more complicated task, it also varies from task to task and model to model. +For instance, CoT might already be supported in gpt3.5+ api calls.

    +

    Benchmark it with and without CoT to see if it helps.

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    + + \ No newline at end of file diff --git a/apis/components/None/components.retriever.bm25_retriever.html b/apis/components/None/components.retriever.bm25_retriever.html new file mode 100644 index 00000000..c8b002ec --- /dev/null +++ b/apis/components/None/components.retriever.bm25_retriever.html @@ -0,0 +1,824 @@ + + + + + + + + + + + components.retriever.bm25_retriever — LightRAG documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    components.retriever.bm25_retriever#

    +

    BM25 retriever implementation.

    +

    Functions

    + +

    Classes

    +
    + + + + + +

    BM25Retriever([top_k, k1, b, epsilon, ...])

    Fast Implementation of Best Matching 25 ranking function.

    +
    +
    +
    +split_text_by_word_fn(x: str) List[str][source]#
    +
    + +
    +
    +split_text_by_word_fn_then_lower_tokenized(x: str) List[str][source]#
    +
    + +
    +
    +split_text_tokenized(x: str) List[str][source]#
    +
    + +
    +
    +class BM25Retriever(top_k: int = 5, k1: float = 1.5, b: float = 0.75, epsilon: float = 0.25, documents: Sequence[Any] | None = None, document_map_func: Callable[[Any], str] | None = None, use_tokenizer: bool = True)[source]#
    +

    Bases: Retriever[str, str]

    +

    Fast Implementation of Best Matching 25 ranking function.

    +

    It expects str as the final document type after document_map_func if the given document is not already in the format of List[str]. +It expects Union[str, Sequence[str]] as the input in retrieve() method.

    +
    +\[ \begin{align}\begin{aligned}\text{idf}(q_i) = \log\left(\frac{N - n(q_i) + 0.5}{n(q_i) + 0.5}\right)\\\text{score}(q, d) = \sum_{i=1}^{n} \text{idf}(q_i) \cdot \frac{f(q_i, d) \cdot (k1 + 1)}{f(q_i, d) + k1 \cdot \left(1 - b + b \cdot \frac{|d|}{\text{avgdl}}\right)}\end{aligned}\end{align} \]
    +
    +
    Explanation:
      +
    • IDF(q_i) is the inverse document frequency of term q_i, which measures how important the term is. To avoid division by zero, 0.5 is added to the denominator, also for diminishing the weight of terms that occur very frequently in the document set and increase the weight of terms that occur rarely.

    • +
    • f(q_i, d) is the term frequency of term q_i in document d, which measures how often the term occurs in the document. The term frequency is normalized by dividing the raw term frequency by the document length.

    • +
    • |d| is the length of the document d in words or tokens.

    • +
    • avgdl is the average document length in the corpus.

    • +
    • N is the total number of documents in the corpus.

    • +
    • n(q_i) is the number of documents containing term q_i.

    • +
    +
    +
    +

    References

    +

    [1] https://en.wikipedia.org/wiki/Okapi_BM25 +[2] dorianbrown/rank_bm25 +[3] Improvements to BM25 and Language Models Examined: https://www.cs.otago.ac.nz/homepages/andrew/papers/2014-2.pdf

    +
    +
    Parameters:
    +
      +
    • top_k – (int): The number of documents to return

    • +
    • k1 – (float, optional): Constant used for influencing the term frequency saturation. After saturation is reached, additional +presence for the term adds a significantly less additional score. According to [1]_, experiments suggest +that 1.2 < k1 < 2 yields reasonably good results, although the optimal value depends on factors such as +the type of documents or queries.

    • +
    • b – (float, optional): Constant used for influencing the effects of different document lengths relative to average document length. +When b is bigger, lengthier documents (compared to average) have more impact on its effect. According to +[1]_, experiments suggest that 0.5 < b < 0.8 yields reasonably good results, although the optimal value +depends on factors such as the type of documents or queries.

    • +
    • epsilon – (float, optional): Used to adapt the negative idf score to epilon * average_idf. Default is 0.25

    • +
    • documents – (List[Any], optional): The list of documents to build the index from. Default is None.

    • +
    • document_map_func – (Callable, optional): The function to transform the document into List[str]. +You don’t need it if your documents are already in format List[str].

    • +
    • use_tokenizer – (bool, optional): Whether to use the default tokenizer to split the text into words. Default is True.

    • +
    +
    +
    +

    Examples:

    +
    from lightrag.components.retriever.bm25_retriever import BM25Retriever
    +
    +documents = ["hello world", "world is beautiful", "today is a good day"]
    +
    +
    +
      +
    1. Pass the documents from the __init__ method:

    2. +
    +
    retriever = BM25Retriever(top_k=1, documents=documents)
    +output = retriever("hello")
    +print(output)
    +# Output:
    +# [RetrieverOutput(doc_indices=[0], doc_scores=[0.6229580777634034], query=None, documents=None)]
    +
    +
    +
      +
    1. Pass the documents from the build_index_from_documents() method:

    2. +
    +
      +
    1. Save the index to file and load it back:

    2. +
    +
    retriever.save_to_file("bm25_index.json")
    +retriever2 = BM25Retriever.load_from_file("bm25_index.json")
    +output = retriever2("hello")
    +print(output)
    +
    +
    +

    note: +The retriever only fill in the doc_indices and doc_scores. The documents needs to be filled in by the user.

    +
    +
    +reset_index()[source]#
    +

    Used for both initializing and resetting the index.

    +
    + +
    +
    +build_index_from_documents(documents: Sequence[RetrieverDocumentType], document_map_func: Callable[[Any], str] | None = None, **kwargs)[source]#
    +

    Built index from the text field of each document in the list of documents

    +
    + +
    +
    +call(input: str | Sequence[str], top_k: int | None = None, **kwargs) List[RetrieverOutput][source]#
    +

    Retrieve the top n documents for the query and return only the indexes of the documents.

    +
    +
    Parameters:
    +
      +
    • input – Union[str, List[str]]: The query or list of queries

    • +
    • top_k – Optional[int]: The number of documents to return

    • +
    +
    +
    +
    + +
    +
    +save_to_file(path: str)[source]#
    +

    Save the state, including the index to a file.

    +

    Optional for subclass to implement a default persistence method. +Subclass can leverge component’s to_dict method to get the states and choose to save them in any file format.

    +
    + +
    +
    +classmethod load_from_file(path: str)[source]#
    +

    Load the state, including index from a file to restore the retriever.

    +

    Subclass can leverge component’s from_dict method to restore the states from the file.

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    + + \ No newline at end of file diff --git a/apis/components/None/components.retriever.llm_retriever.html b/apis/components/None/components.retriever.llm_retriever.html new file mode 100644 index 00000000..2bba204a --- /dev/null +++ b/apis/components/None/components.retriever.llm_retriever.html @@ -0,0 +1,726 @@ + + + + + + + + + + + components.retriever.llm_retriever — LightRAG documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    components.retriever.llm_retriever#

    +

    LLM as retriever module.

    +

    Classes

    +
    + + + + + +

    LLMRetriever(*[, top_k, model_kwargs, ...])

    Use LLM to access the query and the documents to retrieve the top k relevant indices of the documents.

    +
    +
    +
    +class LLMRetriever(*, top_k: int | None = 1, model_client: ModelClient, model_kwargs: Dict[str, Any] = {}, documents: Sequence[RetrieverDocumentType] | None = None, document_map_func: Callable[[Any], str] | None = None)[source]#
    +

    Bases: Retriever[str, str]

    +

    Use LLM to access the query and the documents to retrieve the top k relevant indices of the documents.

    +

    Users can follow this example and to customize the prompt or additionally ask it to output score along with the indices.

    +
    +
    Parameters:
    +
      +
    • top_k (Optional[int], optional) – top k documents to fetch. Defaults to 1.

    • +
    • model_client (ModelClient) – the model client to use.

    • +
    • model_kwargs (Dict[str, Any], optional) – the model kwargs. Defaults to {}.

    • +
    +
    +
    +
    +

    Note

    +

    There is chance some queries might fail, which will lead to empty response None for that query in the List of RetrieverOutput. Users should handle this case.

    +
    +
    +
    +reset_index()[source]#
    +

    Initialize/reset any attributes/states for the index.

    +
    + +
    +
    +build_index_from_documents(documents: Sequence[RetrieverDocumentType], document_map_func: Callable[[Any], str] | None = None)[source]#
    +

    prepare the user query input for the retriever

    +
    + +
    +
    +call(input: str | Sequence[str], top_k: int | None = None, model_kwargs: Dict[str, Any] = {}) List[RetrieverOutput][source]#
    +

    Retrieve the k relevant documents.

    +
    +
    Parameters:
    +
      +
    • query_or_queries (RetrieverStrQueriesType) – a string or a list of strings.

    • +
    • top_k (Optional[int], optional) – top k documents to fetch. Defaults to None.

    • +
    • model_kwargs (Dict[str, Any], optional) – the model kwargs. +You can switch to another model provided by the same model client without reinitializing the retriever. Defaults to {}.

    • +
    +
    +
    Returns:
    +

    the developers should be aware that the returned LLMRetrieverOutputType is actually a list of GeneratorOutput(GeneratorOutput), post processing is required depends on how you instruct the model to output in the prompt and what output_processors you set up. +E.g. If the prompt is to output a list of indices and the output_processors is ListParser(), then it return: GeneratorOutput(data=[indices], error=None, raw_response=’[indices]’)

    +
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    Return type:
    +

    RetrieverOutputType

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    components.retriever.reranker_retriever#

    +

    Reranking model using modelclient as a retriever.

    +

    Classes

    +
    + + + + + +

    RerankerRetriever(model_client[, ...])

    A retriever that uses a reranker model to rank the documents and retrieve the top-k documents.

    +
    +
    +
    +class RerankerRetriever(model_client: ModelClient, model_kwargs: Dict = {}, top_k: int = 5, documents: Sequence[RetrieverDocumentType] | None = None, document_map_func: Callable[[Any], str] | None = None)[source]#
    +

    Bases: Retriever[str, str | Sequence[str]]

    +

    A retriever that uses a reranker model to rank the documents and retrieve the top-k documents.

    +
    +
    Parameters:
    +
      +
    • top_k (int, optional) – The number of top documents to retrieve. Defaults to 5.

    • +
    • model_client (ModelClient) – The model client that has a reranker model, +such as CohereAPIClient or TransformersClient.

    • +
    • model_kwargs (Dict) – The model kwargs to pass to the model client.

    • +
    • documents (Optional[RetrieverDocumentsType], optional) – The documents to build the index from. Defaults to None.

    • +
    • document_map_func (Optional[Callable[[Any], str]], optional) – The function to map the document of Any type to the specific type RetrieverDocumentType that the retriever expects. Defaults to None.

    • +
    +
    +
    +

    Examples:

    +
    +
    +reset_index()[source]#
    +

    Initialize/reset any attributes/states for the index.

    +
    + +
    +
    +build_index_from_documents(documents: Sequence[RetrieverDocumentType], document_map_func: Callable[[Any], str] | None = None)[source]#
    +

    Built index from the [document_map_func(doc) for doc in documents].

    +
    + +
    +
    +call(input: str | Sequence[str], top_k: int | None = None) List[RetrieverOutput][source]#
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    + + \ No newline at end of file diff --git a/apis/components/_autosummary/components.agent.react.html b/apis/components/_autosummary/components.agent.react.html index 7f216f3d..ed9876c1 100644 --- a/apis/components/_autosummary/components.agent.react.html +++ b/apis/components/_autosummary/components.agent.react.html @@ -52,7 +52,7 @@ - + @@ -394,14 +394,20 @@

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