mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-12 12:59:07 +00:00
Improvements to the Clarifai integration (#9290)
- Improved docs - Improved performance in multiple ways through batching, threading, etc. - fixed error message - Added support for metadata filtering during similarity search. @baskaryan PTAL
This commit is contained in:
Matthew Zeiler
GitHub
@@ -103,37 +103,44 @@ class ClarifaiEmbeddings(BaseModel, Embeddings):
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"Please install it with `pip install clarifai`."
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)
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post_model_outputs_request = service_pb2.PostModelOutputsRequest(
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user_app_id=self.userDataObject,
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model_id=self.model_id,
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version_id=self.model_version_id,
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inputs=[
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resources_pb2.Input(
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data=resources_pb2.Data(text=resources_pb2.Text(raw=t))
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)
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for t in texts
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],
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)
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post_model_outputs_response = self.stub.PostModelOutputs(
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post_model_outputs_request
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)
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batch_size = 32
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embeddings = []
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for i in range(0, len(texts), batch_size):
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batch = texts[i : i + batch_size]
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if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
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logger.error(post_model_outputs_response.status)
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first_output_failure = (
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post_model_outputs_response.outputs[0].status
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if len(post_model_outputs_response.outputs[0])
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else None
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post_model_outputs_request = service_pb2.PostModelOutputsRequest(
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user_app_id=self.userDataObject,
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model_id=self.model_id,
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version_id=self.model_version_id,
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inputs=[
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resources_pb2.Input(
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data=resources_pb2.Data(text=resources_pb2.Text(raw=t))
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)
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for t in batch
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],
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)
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raise Exception(
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f"Post model outputs failed, status: "
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f"{post_model_outputs_response.status}, first output failure: "
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f"{first_output_failure}"
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post_model_outputs_response = self.stub.PostModelOutputs(
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post_model_outputs_request
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)
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if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
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logger.error(post_model_outputs_response.status)
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first_output_failure = (
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post_model_outputs_response.outputs[0].status
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if len(post_model_outputs_response.outputs)
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else None
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)
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raise Exception(
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f"Post model outputs failed, status: "
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f"{post_model_outputs_response.status}, first output failure: "
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f"{first_output_failure}"
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)
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embeddings.extend(
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[
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list(o.data.embeddings[0].vector)
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for o in post_model_outputs_response.outputs
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]
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)
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embeddings = [
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list(o.data.embeddings[0].vector)
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for o in post_model_outputs_response.outputs
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]
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return embeddings
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def embed_query(self, text: str) -> List[float]:
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@@ -5,6 +5,7 @@ from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.llms.base import LLM
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from langchain.llms.utils import enforce_stop_tokens
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from langchain.pydantic_v1 import Extra, root_validator
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from langchain.schema import Generation, LLMResult
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from langchain.utils import get_from_dict_or_env
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logger = logging.getLogger(__name__)
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@@ -163,7 +164,7 @@ class Clarifai(LLM):
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logger.error(post_model_outputs_response.status)
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first_model_failure = (
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post_model_outputs_response.outputs[0].status
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if len(post_model_outputs_response.outputs[0])
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if len(post_model_outputs_response.outputs)
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else None
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)
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raise Exception(
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@@ -178,3 +179,67 @@ class Clarifai(LLM):
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if stop is not None:
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text = enforce_stop_tokens(text, stop)
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return text
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def _generate(
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self,
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prompts: List[str],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> LLMResult:
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"""Run the LLM on the given prompt and input."""
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try:
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from clarifai_grpc.grpc.api import (
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resources_pb2,
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service_pb2,
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)
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from clarifai_grpc.grpc.api.status import status_code_pb2
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except ImportError:
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raise ImportError(
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"Could not import clarifai python package. "
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"Please install it with `pip install clarifai`."
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)
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# TODO: add caching here.
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generations = []
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batch_size = 32
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for i in range(0, len(prompts), batch_size):
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batch = prompts[i : i + batch_size]
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post_model_outputs_request = service_pb2.PostModelOutputsRequest(
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user_app_id=self.userDataObject,
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model_id=self.model_id,
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version_id=self.model_version_id,
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inputs=[
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resources_pb2.Input(
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data=resources_pb2.Data(text=resources_pb2.Text(raw=prompt))
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)
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for prompt in batch
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],
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)
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post_model_outputs_response = self.stub.PostModelOutputs(
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post_model_outputs_request
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)
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if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
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logger.error(post_model_outputs_response.status)
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first_model_failure = (
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post_model_outputs_response.outputs[0].status
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if len(post_model_outputs_response.outputs)
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else None
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)
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raise Exception(
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f"Post model outputs failed, status: "
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f"{post_model_outputs_response.status}, first output failure: "
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f"{first_model_failure}"
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)
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for output in post_model_outputs_response.outputs:
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if stop is not None:
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text = enforce_stop_tokens(output.data.text.raw, stop)
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else:
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text = output.data.text.raw
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generations.append([Generation(text=text)])
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return LLMResult(generations=generations)
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@@ -3,6 +3,7 @@ from __future__ import annotations
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import logging
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import os
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import traceback
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from concurrent.futures import ThreadPoolExecutor
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from typing import Any, Iterable, List, Optional, Tuple
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import requests
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@@ -84,7 +85,9 @@ class Clarifai(VectorStore):
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self._userDataObject = self._auth.get_user_app_id_proto()
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self._number_of_docs = number_of_docs
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def _post_text_input(self, text: str, metadata: dict) -> str:
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def _post_texts_as_inputs(
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self, texts: List[str], metadatas: Optional[List[dict]] = None
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) -> List[str]:
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"""Post text to Clarifai and return the ID of the input.
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Args:
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@@ -104,20 +107,29 @@ class Clarifai(VectorStore):
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"Please install it with `pip install clarifai`."
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) from e
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input_metadata = Struct()
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input_metadata.update(metadata)
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if metadatas is not None:
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assert len(list(texts)) == len(
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metadatas
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), "Number of texts and metadatas should be the same."
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inputs = []
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for idx, text in enumerate(texts):
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if metadatas is not None:
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input_metadata = Struct()
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input_metadata.update(metadatas[idx])
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inputs.append(
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resources_pb2.Input(
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data=resources_pb2.Data(
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text=resources_pb2.Text(raw=text),
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metadata=input_metadata,
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)
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)
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)
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post_inputs_response = self._stub.PostInputs(
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service_pb2.PostInputsRequest(
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user_app_id=self._userDataObject,
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inputs=[
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resources_pb2.Input(
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data=resources_pb2.Data(
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text=resources_pb2.Text(raw=text),
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metadata=input_metadata,
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)
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)
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],
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inputs=inputs,
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)
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)
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@@ -127,9 +139,11 @@ class Clarifai(VectorStore):
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"Post inputs failed, status: " + post_inputs_response.status.description
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)
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input_id = post_inputs_response.inputs[0].id
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input_ids = []
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for input in post_inputs_response.inputs:
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input_ids.append(input.id)
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return input_id
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return input_ids
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def add_texts(
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self,
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@@ -140,7 +154,7 @@ class Clarifai(VectorStore):
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) -> List[str]:
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"""Add texts to the Clarifai vectorstore. This will push the text
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to a Clarifai application.
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Application use base workflow that create and store embedding for each text.
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Application use a base workflow that create and store embedding for each text.
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Make sure you are using a base workflow that is compatible with text
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(such as Language Understanding).
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@@ -153,20 +167,26 @@ class Clarifai(VectorStore):
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List[str]: List of IDs of the added texts.
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"""
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assert len(list(texts)) > 0, "No texts provided to add to the vectorstore."
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ltexts = list(texts)
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length = len(ltexts)
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assert length > 0, "No texts provided to add to the vectorstore."
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if metadatas is not None:
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assert len(list(texts)) == len(
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assert length == len(
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metadatas
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), "Number of texts and metadatas should be the same."
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batch_size = 32
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input_ids = []
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for idx, text in enumerate(texts):
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for idx in range(0, length, batch_size):
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try:
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metadata = metadatas[idx] if metadatas else {}
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input_id = self._post_text_input(text, metadata)
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input_ids.append(input_id)
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logger.debug(f"Input {input_id} posted successfully.")
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batch_texts = ltexts[idx : idx + batch_size]
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batch_metadatas = (
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metadatas[idx : idx + batch_size] if metadatas else None
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)
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result_ids = self._post_texts_as_inputs(batch_texts, batch_metadatas)
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input_ids.extend(result_ids)
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logger.debug(f"Input {result_ids} posted successfully.")
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except Exception as error:
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logger.warning(f"Post inputs failed: {error}")
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traceback.print_exc()
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@@ -196,6 +216,7 @@ class Clarifai(VectorStore):
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from clarifai_grpc.grpc.api import resources_pb2, service_pb2
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from clarifai_grpc.grpc.api.status import status_code_pb2
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from google.protobuf import json_format # type: ignore
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from google.protobuf.struct_pb2 import Struct # type: ignore
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except ImportError as e:
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raise ImportError(
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"Could not import clarifai python package. "
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@@ -206,28 +227,35 @@ class Clarifai(VectorStore):
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if self._number_of_docs is not None:
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k = self._number_of_docs
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post_annotations_searches_response = self._stub.PostAnnotationsSearches(
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service_pb2.PostAnnotationsSearchesRequest(
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user_app_id=self._userDataObject,
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searches=[
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resources_pb2.Search(
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query=resources_pb2.Query(
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ranks=[
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resources_pb2.Rank(
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annotation=resources_pb2.Annotation(
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data=resources_pb2.Data(
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text=resources_pb2.Text(raw=query),
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)
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req = service_pb2.PostAnnotationsSearchesRequest(
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user_app_id=self._userDataObject,
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searches=[
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resources_pb2.Search(
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query=resources_pb2.Query(
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ranks=[
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resources_pb2.Rank(
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annotation=resources_pb2.Annotation(
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data=resources_pb2.Data(
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text=resources_pb2.Text(raw=query),
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)
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)
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]
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)
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)
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]
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)
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],
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pagination=service_pb2.Pagination(page=1, per_page=k),
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)
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)
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],
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pagination=service_pb2.Pagination(page=1, per_page=k),
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)
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# Add filter by metadata if provided.
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if filter is not None:
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search_metadata = Struct()
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search_metadata.update(filter)
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f = req.searches[0].query.filters.add()
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f.annotation.data.metadata.update(search_metadata)
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post_annotations_searches_response = self._stub.PostAnnotationsSearches(req)
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# Check if search was successful
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if post_annotations_searches_response.status.code != status_code_pb2.SUCCESS:
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raise Exception(
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@@ -238,11 +266,12 @@ class Clarifai(VectorStore):
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# Retrieve hits
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hits = post_annotations_searches_response.hits
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docs_and_scores = []
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# Iterate over hits and retrieve metadata and text
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for hit in hits:
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executor = ThreadPoolExecutor(max_workers=10)
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def hit_to_document(hit: resources_pb2.Hit) -> Tuple[Document, float]:
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metadata = json_format.MessageToDict(hit.input.data.metadata)
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request = requests.get(hit.input.data.text.url)
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h = {"Authorization": f"Key {self._auth.pat}"}
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request = requests.get(hit.input.data.text.url, headers=h)
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# override encoding by real educated guess as provided by chardet
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request.encoding = request.apparent_encoding
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@@ -252,10 +281,11 @@ class Clarifai(VectorStore):
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f"\tScore {hit.score:.2f} for annotation: {hit.annotation.id}\
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off input: {hit.input.id}, text: {requested_text[:125]}"
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)
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return (Document(page_content=requested_text, metadata=metadata), hit.score)
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docs_and_scores.append(
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(Document(page_content=requested_text, metadata=metadata), hit.score)
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)
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# Iterate over hits and retrieve metadata and text
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futures = [executor.submit(hit_to_document, hit) for hit in hits]
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docs_and_scores = [future.result() for future in futures]
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return docs_and_scores
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