e75354d410
* Prepend all Langchain-based embeddings with LC * Provide vanilla OpenAI embeddings * Add test for AzureOpenAIEmbeddings and OpenAIEmbeddings * Incorporate fastembed --------- Co-authored-by: ian_Cin <ian@cinnamon.is>
105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
import os
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from typing import List
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from kotaemon.base import BaseComponent, Document, LLMInterface, Node, Param, lazy
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from kotaemon.contribs.promptui.logs import ResultLog
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from kotaemon.embeddings import LCAzureOpenAIEmbeddings
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from kotaemon.indices import VectorIndexing, VectorRetrieval
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from kotaemon.llms import LCAzureChatOpenAI
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from kotaemon.storages import ChromaVectorStore, SimpleFileDocumentStore
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class QAResultLog(ResultLog):
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@staticmethod
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def _get_prompt(obj):
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return obj["prompt"]
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class QuestionAnsweringPipeline(BaseComponent):
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_promptui_resultlog = QAResultLog
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_promptui_outputs: list = [
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{
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"step": ".prompt",
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"getter": "_get_prompt",
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"component": "text",
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"params": {"label": "Constructed prompt to LLM"},
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},
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{
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"step": ".",
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"getter": "_get_output",
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"component": "text",
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"params": {"label": "Answer"},
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},
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]
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retrieval_top_k: int = 1
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llm: LCAzureChatOpenAI = LCAzureChatOpenAI.withx(
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azure_endpoint="https://bleh-dummy-2.openai.azure.com/",
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openai_api_key=os.environ.get("OPENAI_API_KEY", "default-key"),
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openai_api_version="2023-03-15-preview",
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deployment_name="dummy-q2-gpt35",
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temperature=0,
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request_timeout=60,
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)
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retrieving_pipeline: VectorRetrieval = Node(
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VectorRetrieval.withx(
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vector_store=lazy(ChromaVectorStore).withx(path="./tmp"),
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doc_store=lazy(SimpleFileDocumentStore).withx(path="docstore.json"),
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embedding=LCAzureOpenAIEmbeddings.withx(
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model="text-embedding-ada-002",
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deployment="dummy-q2-text-embedding",
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azure_endpoint="https://bleh-dummy-2.openai.azure.com/",
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openai_api_key=os.environ.get("OPENAI_API_KEY", "default-key"),
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),
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),
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ignore_ui=True,
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)
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def run(self, text: str) -> LLMInterface:
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# retrieve relevant documents as context
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matched_texts: List[str] = [
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_.text
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for _ in self.retrieving_pipeline(text, top_k=int(self.retrieval_top_k))
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]
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context = "\n".join(matched_texts)
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# generate the answer
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prompt = f'Answer the following question: "{text}". The context is: \n{context}'
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self.log_progress(".prompt", prompt=prompt)
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return self.llm(prompt).text
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class IndexingPipeline(VectorIndexing):
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vector_store: ChromaVectorStore = Param(
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lazy(ChromaVectorStore).withx(path="./tmp"),
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ignore_ui=True,
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)
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doc_store: SimpleFileDocumentStore = Param(
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lazy(SimpleFileDocumentStore).withx(path="docstore.json"),
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ignore_ui=True,
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)
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embedding: LCAzureOpenAIEmbeddings = LCAzureOpenAIEmbeddings.withx(
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model="text-embedding-ada-002",
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deployment="dummy-q2-text-embedding",
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azure_endpoint="https://bleh-dummy-2.openai.azure.com/",
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openai_api_key=os.environ.get("OPENAI_API_KEY", "default-key"),
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)
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def run(self, text: str) -> Document:
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"""Normally, this indexing pipeline returns nothing. For demonstration,
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we want it to return something, so let's return the number of documents
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in the vector store
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"""
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super().run(text)
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if self.doc_store is not None:
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# persist to local anytime an indexing is created
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# this can be bypassed when we have a FileDocumentStore
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self.doc_store.save("docstore.json")
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return Document(self.vector_store._collection.count())
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