Source code for langchain_community.vectorstores.vectara

from __future__ import annotations

import json
import logging
import os
from dataclasses import dataclass, field
from hashlib import md5
from typing import Any, Iterable, List, Optional, Tuple, Type

import requests
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import Field
from langchain_core.vectorstores import VectorStore, VectorStoreRetriever

logger = logging.getLogger(__name__)


[docs]@dataclass class SummaryConfig: """Configuration for summary generation. is_enabled: True if summary is enabled, False otherwise max_results: maximum number of results to summarize response_lang: requested language for the summary prompt_name: name of the prompt to use for summarization (see https://docs.vectara.com/docs/learn/grounded-generation/select-a-summarizer) """ is_enabled: bool = False max_results: int = 7 response_lang: str = "eng" prompt_name: str = "vectara-summary-ext-v1.2.0"
[docs]@dataclass class MMRConfig: """Configuration for Maximal Marginal Relevance (MMR) search. is_enabled: True if MMR is enabled, False otherwise mmr_k: number of results to fetch for MMR, defaults to 50 diversity_bias: number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to minimum diversity and 1 to maximum diversity. Defaults to 0.3. Note: diversity_bias is equivalent 1-lambda_mult where lambda_mult is the value often used in max_marginal_relevance_search() We chose to use that since we believe it's more intuitive to the user. """ is_enabled: bool = False mmr_k: int = 50 diversity_bias: float = 0.3
[docs]@dataclass class VectaraQueryConfig: """Configuration for Vectara query. k: Number of Documents to return. Defaults to 10. lambda_val: lexical match parameter for hybrid search. filter Dictionary of argument(s) to filter on metadata. For example a filter can be "doc.rating > 3.0 and part.lang = 'deu'"} see https://docs.vectara.com/docs/search-apis/sql/filter-overview for more details. score_threshold: minimal score threshold for the result. If defined, results with score less than this value will be filtered out. n_sentence_context: number of sentences before/after the matching segment to add, defaults to 2 mmr_config: MMRConfig configuration dataclass summary_config: SummaryConfig configuration dataclass """ k: int = 10 lambda_val: float = 0.0 filter: str = "" score_threshold: Optional[float] = None n_sentence_context: int = 2 mmr_config: MMRConfig = field(default_factory=MMRConfig) summary_config: SummaryConfig = field(default_factory=SummaryConfig)
[docs]class Vectara(VectorStore): """`Vectara API` vector store. See (https://vectara.com). Example: .. code-block:: python from langchain_community.vectorstores import Vectara vectorstore = Vectara( vectara_customer_id=vectara_customer_id, vectara_corpus_id=vectara_corpus_id, vectara_api_key=vectara_api_key ) """
[docs] def __init__( self, vectara_customer_id: Optional[str] = None, vectara_corpus_id: Optional[str] = None, vectara_api_key: Optional[str] = None, vectara_api_timeout: int = 120, source: str = "langchain", ): """Initialize with Vectara API.""" self._vectara_customer_id = vectara_customer_id or os.environ.get( "VECTARA_CUSTOMER_ID" ) self._vectara_corpus_id = vectara_corpus_id or os.environ.get( "VECTARA_CORPUS_ID" ) self._vectara_api_key = vectara_api_key or os.environ.get("VECTARA_API_KEY") if ( self._vectara_customer_id is None or self._vectara_corpus_id is None or self._vectara_api_key is None ): logger.warning( "Can't find Vectara credentials, customer_id or corpus_id in " "environment." ) else: logger.debug(f"Using corpus id {self._vectara_corpus_id}") self._source = source self._session = requests.Session() # to reuse connections adapter = requests.adapters.HTTPAdapter(max_retries=3) self._session.mount("http://", adapter) self.vectara_api_timeout = vectara_api_timeout
@property def embeddings(self) -> Optional[Embeddings]: return None def _get_post_headers(self) -> dict: """Returns headers that should be attached to each post request.""" return { "x-api-key": self._vectara_api_key, "customer-id": self._vectara_customer_id, "Content-Type": "application/json", "X-Source": self._source, } def _delete_doc(self, doc_id: str) -> bool: """ Delete a document from the Vectara corpus. Args: url (str): URL of the page to delete. doc_id (str): ID of the document to delete. Returns: bool: True if deletion was successful, False otherwise. """ body = { "customer_id": self._vectara_customer_id, "corpus_id": self._vectara_corpus_id, "document_id": doc_id, } response = self._session.post( "https://api.vectara.io/v1/delete-doc", data=json.dumps(body), verify=True, headers=self._get_post_headers(), timeout=self.vectara_api_timeout, ) if response.status_code != 200: logger.error( f"Delete request failed for doc_id = {doc_id} with status code " f"{response.status_code}, reason {response.reason}, text " f"{response.text}" ) return False return True def _index_doc(self, doc: dict, use_core_api: bool = False) -> str: request: dict[str, Any] = {} request["customer_id"] = self._vectara_customer_id request["corpus_id"] = self._vectara_corpus_id request["document"] = doc api_endpoint = ( "https://api.vectara.io/v1/core/index" if use_core_api else "https://api.vectara.io/v1/index" ) response = self._session.post( headers=self._get_post_headers(), url=api_endpoint, data=json.dumps(request), timeout=self.vectara_api_timeout, verify=True, ) status_code = response.status_code result = response.json() status_str = result["status"]["code"] if "status" in result else None if status_code == 409 or status_str and (status_str == "ALREADY_EXISTS"): return "E_ALREADY_EXISTS" elif status_str and (status_str == "FORBIDDEN"): return "E_NO_PERMISSIONS" else: return "E_SUCCEEDED"
[docs] def add_files( self, files_list: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any, ) -> List[str]: """ Vectara provides a way to add documents directly via our API where pre-processing and chunking occurs internally in an optimal way This method provides a way to use that API in LangChain Args: files_list: Iterable of strings, each representing a local file path. Files could be text, HTML, PDF, markdown, doc/docx, ppt/pptx, etc. see API docs for full list metadatas: Optional list of metadatas associated with each file Returns: List of ids associated with each of the files indexed """ doc_ids = [] for inx, file in enumerate(files_list): if not os.path.exists(file): logger.error(f"File {file} does not exist, skipping") continue md = metadatas[inx] if metadatas else {} files: dict = { "file": (file, open(file, "rb")), "doc_metadata": json.dumps(md), } headers = self._get_post_headers() headers.pop("Content-Type") response = self._session.post( f"https://api.vectara.io/upload?c={self._vectara_customer_id}&o={self._vectara_corpus_id}&d=True", files=files, verify=True, headers=headers, timeout=self.vectara_api_timeout, ) if response.status_code == 409: doc_id = response.json()["document"]["documentId"] logger.info( f"File {file} already exists on Vectara (doc_id={doc_id}), skipping" ) elif response.status_code == 200: doc_id = response.json()["document"]["documentId"] doc_ids.append(doc_id) else: logger.info(f"Error indexing file {file}: {response.json()}") return doc_ids
[docs] def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, doc_metadata: Optional[dict] = None, **kwargs: Any, ) -> List[str]: """Run more texts through the embeddings and add to the vectorstore. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. doc_metadata: optional metadata for the document This function indexes all the input text strings in the Vectara corpus as a single Vectara document, where each input text is considered a "section" and the metadata are associated with each section. if 'doc_metadata' is provided, it is associated with the Vectara document. Returns: document ID of the document added """ doc_hash = md5() for t in texts: doc_hash.update(t.encode()) doc_id = doc_hash.hexdigest() if metadatas is None: metadatas = [{} for _ in texts] if doc_metadata: doc_metadata["source"] = "langchain" else: doc_metadata = {"source": "langchain"} use_core_api = kwargs.get("use_core_api", False) section_key = "parts" if use_core_api else "section" doc = { "document_id": doc_id, "metadataJson": json.dumps(doc_metadata), section_key: [ {"text": text, "metadataJson": json.dumps(md)} for text, md in zip(texts, metadatas) ], } success_str = self._index_doc(doc, use_core_api=use_core_api) if success_str == "E_ALREADY_EXISTS": self._delete_doc(doc_id) self._index_doc(doc) elif success_str == "E_NO_PERMISSIONS": print( # noqa: T201 """No permissions to add document to Vectara. Check your corpus ID, customer ID and API key""" ) return [doc_id]
[docs] def vectara_query( self, query: str, config: VectaraQueryConfig, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Run a Vectara query Args: query: Text to look up documents similar to. config: VectaraQueryConfig object Returns: A list of k Documents matching the given query If summary is enabled, last document is the summary text with 'summary'=True """ if isinstance(config.mmr_config, dict): config.mmr_config = MMRConfig(**config.mmr_config) if isinstance(config.summary_config, dict): config.summary_config = SummaryConfig(**config.summary_config) data = { "query": [ { "query": query, "start": 0, "numResults": ( config.mmr_config.mmr_k if config.mmr_config.is_enabled else config.k ), "contextConfig": { "sentencesBefore": config.n_sentence_context, "sentencesAfter": config.n_sentence_context, }, "corpusKey": [ { "customerId": self._vectara_customer_id, "corpusId": self._vectara_corpus_id, "metadataFilter": config.filter, "lexicalInterpolationConfig": {"lambda": config.lambda_val}, } ], } ] } if config.mmr_config.is_enabled: data["query"][0]["rerankingConfig"] = { "rerankerId": 272725718, "mmrConfig": {"diversityBias": config.mmr_config.diversity_bias}, } if config.summary_config.is_enabled: data["query"][0]["summary"] = [ { "maxSummarizedResults": config.summary_config.max_results, "responseLang": config.summary_config.response_lang, "summarizerPromptName": config.summary_config.prompt_name, } ] response = self._session.post( headers=self._get_post_headers(), url="https://api.vectara.io/v1/query", data=json.dumps(data), timeout=self.vectara_api_timeout, ) if response.status_code != 200: logger.error( "Query failed %s", f"(code {response.status_code}, reason {response.reason}, details " f"{response.text})", ) return [], "" # type: ignore[return-value] result = response.json() if config.score_threshold: responses = [ r for r in result["responseSet"][0]["response"] if r["score"] > config.score_threshold ] else: responses = result["responseSet"][0]["response"] documents = result["responseSet"][0]["document"] metadatas = [] for x in responses: md = {m["name"]: m["value"] for m in x["metadata"]} doc_num = x["documentIndex"] doc_md = {m["name"]: m["value"] for m in documents[doc_num]["metadata"]} if "source" not in doc_md: doc_md["source"] = "vectara" md.update(doc_md) metadatas.append(md) res = [ ( Document( page_content=x["text"], metadata=md, ), x["score"], ) for x, md in zip(responses, metadatas) ] if config.mmr_config.is_enabled: res = res[: config.k] if config.summary_config.is_enabled: summary = result["responseSet"][0]["summary"][0]["text"] res.append( (Document(page_content=summary, metadata={"summary": True}), 0.0) ) return res
[docs] def similarity_search_with_score( self, query: str, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return Vectara documents most similar to query, along with scores. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 10. any other querying variable in VectaraQueryConfig like: - lambda_val: lexical match parameter for hybrid search. - filter: filter string - score_threshold: minimal score threshold for the result. - n_sentence_context: number of sentences before/after the matching segment - mmr_config: optional configuration for MMR (see MMRConfig dataclass) - summary_config: optional configuration for summary (see SummaryConfig dataclass) Returns: List of Documents most similar to the query and score for each. """ config = VectaraQueryConfig(**kwargs) docs = self.vectara_query(query, config) return docs
[docs] @classmethod def from_texts( cls: Type[Vectara], texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, **kwargs: Any, ) -> Vectara: """Construct Vectara wrapper from raw documents. This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import Vectara vectara = Vectara.from_texts( texts, vectara_customer_id=customer_id, vectara_corpus_id=corpus_id, vectara_api_key=api_key, ) """ # Notes: # * Vectara generates its own embeddings, so we ignore the provided # embeddings (required by interface) # * when metadatas[] are provided they are associated with each "part" # in Vectara. doc_metadata can be used to provide additional metadata # for the document itself (applies to all "texts" in this call) doc_metadata = kwargs.pop("doc_metadata", {}) vectara = cls(**kwargs) vectara.add_texts(texts, metadatas, doc_metadata=doc_metadata, **kwargs) return vectara
[docs] @classmethod def from_files( cls: Type[Vectara], files: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, **kwargs: Any, ) -> Vectara: """Construct Vectara wrapper from raw documents. This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import Vectara vectara = Vectara.from_files( files_list, vectara_customer_id=customer_id, vectara_corpus_id=corpus_id, vectara_api_key=api_key, ) """ # Note: Vectara generates its own embeddings, so we ignore the provided # embeddings (required by interface) vectara = cls(**kwargs) vectara.add_files(files, metadatas) return vectara
[docs]class VectaraRetriever(VectorStoreRetriever): """Retriever for `Vectara`.""" vectorstore: Vectara """Vectara vectorstore.""" search_kwargs: dict = Field( default_factory=lambda: { "lambda_val": 0.0, "k": 5, "filter": "", "n_sentence_context": "2", "summary_config": SummaryConfig(), } ) """Search params. k: Number of Documents to return. Defaults to 5. lambda_val: lexical match parameter for hybrid search. filter: Dictionary of argument(s) to filter on metadata. For example a filter can be "doc.rating > 3.0 and part.lang = 'deu'"} see https://docs.vectara.com/docs/search-apis/sql/filter-overview for more details. n_sentence_context: number of sentences before/after the matching segment to add """
[docs] def add_texts( self, texts: List[str], metadatas: Optional[List[dict]] = None, doc_metadata: Optional[dict] = None, ) -> None: """Add text to the Vectara vectorstore. Args: texts (List[str]): The text metadatas (List[dict]): Metadata dicts, must line up with existing store """ self.vectorstore.add_texts(texts, metadatas, doc_metadata or {})