from __future__ import annotations
import logging
import os
import traceback
import uuid
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Iterable, List, Optional, Tuple
import requests
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
logger = logging.getLogger(__name__)
[docs]class Clarifai(VectorStore):
"""`Clarifai AI` vector store.
To use, you should have the ``clarifai`` python SDK package installed.
Example:
.. code-block:: python
from langchain_community.vectorstores import Clarifai
from langchain_community.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Clarifai("langchain_store", embeddings.embed_query)
"""
[docs] def __init__(
self,
user_id: Optional[str] = None,
app_id: Optional[str] = None,
number_of_docs: Optional[int] = None,
pat: Optional[str] = None,
) -> None:
"""Initialize with Clarifai client.
Args:
user_id (Optional[str], optional): User ID. Defaults to None.
app_id (Optional[str], optional): App ID. Defaults to None.
pat (Optional[str], optional): Personal access token. Defaults to None.
number_of_docs (Optional[int], optional): Number of documents to return
during vector search. Defaults to None.
api_base (Optional[str], optional): API base. Defaults to None.
Raises:
ValueError: If user ID, app ID or personal access token is not provided.
"""
self._user_id = user_id or os.environ.get("CLARIFAI_USER_ID")
self._app_id = app_id or os.environ.get("CLARIFAI_APP_ID")
if pat:
os.environ["CLARIFAI_PAT"] = pat
self._pat = os.environ.get("CLARIFAI_PAT")
if self._user_id is None or self._app_id is None or self._pat is None:
raise ValueError(
"Could not find CLARIFAI_USER_ID, CLARIFAI_APP_ID or\
CLARIFAI_PAT in your environment. "
"Please set those env variables with a valid user ID, \
app ID and personal access token \
from https://clarifai.com/settings/security."
)
self._number_of_docs = number_of_docs
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Add texts to the Clarifai vectorstore. This will push the text
to a Clarifai application.
Application use a base workflow that create and store embedding for each text.
Make sure you are using a base workflow that is compatible with text
(such as Language Understanding).
Args:
texts (Iterable[str]): Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]], optional): Optional list of IDs.
"""
try:
from clarifai.client.input import Inputs
from google.protobuf.struct_pb2 import Struct
except ImportError as e:
raise ImportError(
"Could not import clarifai python package. "
"Please install it with `pip install clarifai`."
) from e
ltexts = list(texts)
length = len(ltexts)
assert length > 0, "No texts provided to add to the vectorstore."
if metadatas is not None:
assert length == len(
metadatas
), "Number of texts and metadatas should be the same."
if ids is not None:
assert len(ltexts) == len(
ids
), "Number of text inputs and input ids should be the same."
input_obj = Inputs(app_id=self._app_id, user_id=self._user_id)
batch_size = 32
input_job_ids = []
for idx in range(0, length, batch_size):
try:
batch_texts = ltexts[idx : idx + batch_size]
batch_metadatas = (
metadatas[idx : idx + batch_size] if metadatas else None
)
if batch_metadatas is not None:
meta_list = []
for meta in batch_metadatas:
meta_struct = Struct()
meta_struct.update(meta)
meta_list.append(meta_struct)
if ids is None:
ids = [uuid.uuid4().hex for _ in range(len(batch_texts))]
input_batch = [
input_obj.get_text_input(
input_id=ids[id],
raw_text=inp,
metadata=meta_list[id] if batch_metadatas else None,
)
for id, inp in enumerate(batch_texts)
]
result_id = input_obj.upload_inputs(inputs=input_batch)
input_job_ids.extend(result_id)
logger.debug("Input posted successfully.")
except Exception as error:
logger.warning(f"Post inputs failed: {error}")
traceback.print_exc()
return input_job_ids
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 4,
filters: Optional[dict] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Run similarity search with score using Clarifai.
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata.
Defaults to None.
Returns:
List[Document]: List of documents most similar to the query text.
"""
try:
from clarifai.client.search import Search
from clarifai_grpc.grpc.api import resources_pb2
from google.protobuf import json_format # type: ignore
except ImportError as e:
raise ImportError(
"Could not import clarifai python package. "
"Please install it with `pip install clarifai`."
) from e
# Get number of docs to return
if self._number_of_docs is not None:
k = self._number_of_docs
search_obj = Search(user_id=self._user_id, app_id=self._app_id, top_k=k)
rank = [{"text_raw": query}]
# Add filter by metadata if provided.
if filters is not None:
search_metadata = {"metadata": filters}
search_response = search_obj.query(ranks=rank, filters=[search_metadata])
else:
search_response = search_obj.query(ranks=rank)
# Retrieve hits
hits = [hit for data in search_response for hit in data.hits]
executor = ThreadPoolExecutor(max_workers=10)
def hit_to_document(hit: resources_pb2.Hit) -> Tuple[Document, float]:
metadata = json_format.MessageToDict(hit.input.data.metadata)
h = {"Authorization": f"Key {self._pat}"}
request = requests.get(hit.input.data.text.url, headers=h)
# override encoding by real educated guess as provided by chardet
request.encoding = request.apparent_encoding
requested_text = request.text
logger.debug(
f"\tScore {hit.score:.2f} for annotation: {hit.annotation.id}\
off input: {hit.input.id}, text: {requested_text[:125]}"
)
return (Document(page_content=requested_text, metadata=metadata), hit.score)
# Iterate over hits and retrieve metadata and text
futures = [executor.submit(hit_to_document, hit) for hit in hits]
docs_and_scores = [future.result() for future in futures]
return docs_and_scores
[docs] def similarity_search(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search using Clarifai.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
docs_and_scores = self.similarity_search_with_score(query, **kwargs)
return [doc for doc, _ in docs_and_scores]
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
user_id: Optional[str] = None,
app_id: Optional[str] = None,
number_of_docs: Optional[int] = None,
pat: Optional[str] = None,
**kwargs: Any,
) -> Clarifai:
"""Create a Clarifai vectorstore from a list of texts.
Args:
user_id (str): User ID.
app_id (str): App ID.
texts (List[str]): List of texts to add.
number_of_docs (Optional[int]): Number of documents to return
during vector search. Defaults to None.
metadatas (Optional[List[dict]]): Optional list of metadatas.
Defaults to None.
Returns:
Clarifai: Clarifai vectorstore.
"""
clarifai_vector_db = cls(
user_id=user_id,
app_id=app_id,
number_of_docs=number_of_docs,
pat=pat,
)
clarifai_vector_db.add_texts(texts=texts, metadatas=metadatas)
return clarifai_vector_db
[docs] @classmethod
def from_documents(
cls,
documents: List[Document],
embedding: Optional[Embeddings] = None,
user_id: Optional[str] = None,
app_id: Optional[str] = None,
number_of_docs: Optional[int] = None,
pat: Optional[str] = None,
**kwargs: Any,
) -> Clarifai:
"""Create a Clarifai vectorstore from a list of documents.
Args:
user_id (str): User ID.
app_id (str): App ID.
documents (List[Document]): List of documents to add.
number_of_docs (Optional[int]): Number of documents to return
during vector search. Defaults to None.
Returns:
Clarifai: Clarifai vectorstore.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return cls.from_texts(
user_id=user_id,
app_id=app_id,
texts=texts,
number_of_docs=number_of_docs,
pat=pat,
metadatas=metadatas,
)