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Thank you for contributing to LangChain! - [X] **PR title**: "package: description" - Where "package" is whichever of langchain, community, core, etc. is being modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI changes. - Example: "community: add foobar LLM" - [x] **PR message**: This PR adds top_k as a param to the Needle Retriever. By default we use top 10. - [X] **Add tests and docs**: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. - [X] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
102 lines
3.5 KiB
Python
102 lines
3.5 KiB
Python
from typing import Any, List, Optional # noqa: I001
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from langchain_core.callbacks import CallbackManagerForRetrieverRun
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from langchain_core.documents import Document
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from langchain_core.retrievers import BaseRetriever
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from pydantic import BaseModel, Field
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class NeedleRetriever(BaseRetriever, BaseModel):
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"""
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NeedleRetriever retrieves relevant documents or context from a Needle collection
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based on a search query.
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Setup:
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Install the `needle-python` library and set your Needle API key.
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.. code-block:: bash
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pip install needle-python
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export NEEDLE_API_KEY="your-api-key"
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Key init args:
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- `needle_api_key` (Optional[str]): The API key for authenticating with Needle.
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- `collection_id` (str): The ID of the Needle collection to search in.
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- `client` (Optional[NeedleClient]): An optional instance of the NeedleClient.
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- `top_k` (Optional[int]): Maximum number of results to return.
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Usage:
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.. code-block:: python
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from langchain_community.retrievers.needle import NeedleRetriever
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retriever = NeedleRetriever(
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needle_api_key="your-api-key",
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collection_id="your-collection-id",
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top_k=10 # optional
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)
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results = retriever.retrieve("example query")
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for doc in results:
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print(doc.page_content)
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"""
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client: Optional[Any] = None
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"""Optional instance of NeedleClient."""
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needle_api_key: Optional[str] = Field(None, description="Needle API Key")
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collection_id: Optional[str] = Field(
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..., description="The ID of the Needle collection to search in"
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)
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top_k: Optional[int] = Field(
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default=None, description="Maximum number of search results to return"
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)
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def _initialize_client(self) -> None:
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"""
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Initialize the NeedleClient with the provided API key.
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If a client instance is already provided, this method does nothing.
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"""
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try:
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from needle.v1 import NeedleClient
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except ImportError:
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raise ImportError("Please install with `pip install needle-python`.")
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if not self.client:
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self.client = NeedleClient(api_key=self.needle_api_key)
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def _search_collection(self, query: str) -> List[Document]:
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"""
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Search the Needle collection for relevant documents.
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Args:
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query (str): The search query used to find relevant documents.
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Returns:
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List[Document]: A list of documents matching the search query.
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"""
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self._initialize_client()
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if self.client is None:
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raise ValueError("NeedleClient is not initialized. Provide an API key.")
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results = self.client.collections.search(
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collection_id=self.collection_id, text=query, top_k=self.top_k
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)
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docs = [Document(page_content=result.content) for result in results]
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return docs
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def _get_relevant_documents(
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self, query: str, *, run_manager: CallbackManagerForRetrieverRun
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) -> List[Document]:
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"""
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Retrieve relevant documents based on the query.
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Args:
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query (str): The query string used to search the collection.
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Returns:
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List[Document]: A list of documents relevant to the query.
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"""
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# The `run_manager` parameter is included to match the superclass signature,
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# but it is not used in this implementation.
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return self._search_collection(query)
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