From 1ecefa80cefd8d2b555b612142b43f1cbb4fc721 Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Mon, 25 Dec 2023 17:20:55 +0800 Subject: [PATCH 01/97] refactor: sagemaker model type and add use titan embedding to third party apis --- .../src/components/LanguageModelStrategy.jsx | 68 ++++++++++--------- 1 file changed, 37 insertions(+), 31 deletions(-) diff --git a/ui-search/src/components/LanguageModelStrategy.jsx b/ui-search/src/components/LanguageModelStrategy.jsx index 7c0a82e8..fb132b74 100644 --- a/ui-search/src/components/LanguageModelStrategy.jsx +++ b/ui-search/src/components/LanguageModelStrategy.jsx @@ -23,8 +23,8 @@ const SIZE = 'l'; const TYPE = { sagemaker: 'sagemaker_endpoint', thirdParty: 'third_party_api' }; const SAGEMAKER_MODEL_TYPE = [ - { label: 'llama2', value: 'llama2' }, { label: 'non-llama2', value: 'non_llama2' }, + { label: 'llama2', value: 'llama2' }, ]; const THIRD_PARTY_API_MODEL_TYPES = [ { label: 'Bedrock', value: 'bedrock' }, @@ -110,6 +110,17 @@ const LanguageModelStrategy = () => { resetTitanEmbedding, ] = useToggle(false); + const [isCheckedLlama2Switch, bindLlama2Switch, resetLlama2Switch] = + useToggle(false); + + useEffect(() => { + if (isCheckedLlama2Switch) { + setSagemakerModelType('llama2'); + } else { + setSagemakerModelType('non_llama2'); + } + }, [isCheckedLlama2Switch]); + const resetForm = useCallback(() => { resetStrategyName(); resetTitanEmbedding(); @@ -117,6 +128,7 @@ const LanguageModelStrategy = () => { resetEmbeddingEndpoint(); resetThirdPartyEmbeddingEmbeddingEndpoint(); setThirdPartyModelType(THIRD_PARTY_API_MODEL_TYPES[0].value); + resetLlama2Switch(); setSagemakerModelType(SAGEMAKER_MODEL_TYPE[0].value); resetThirdPartyApiUrl(); resetThirdPartyApiKey(); @@ -191,7 +203,9 @@ const LanguageModelStrategy = () => { cell: (item) => item.embeddingEndpoint || item.thirdPartyEmbeddingEndpoint || - 'n/a', + item.isCheckedTitanEmbedding + ? 'Titan Embedding' + : 'n/a', }, { id: 'apiUrl', @@ -271,9 +285,9 @@ const LanguageModelStrategy = () => { embeddingEndpoint, modelType: sagemakerModelType, recordId: `${sagemakerEndpoint}-${genRandomNum()}`, + isCheckedTitanEmbedding, // *** different items sagemakerEndpoint, - isCheckedTitanEmbedding, }; } else { // Third Party APIs @@ -284,6 +298,7 @@ const LanguageModelStrategy = () => { modelType: thirdPartyModelType, modelName: thirdPartyModelName, recordId: `${thirdPartyModelName}-${genRandomNum()}`, + isCheckedTitanEmbedding, // *** different items apiUrl: thirdPartyApiUrl, apiKey: thirdPartyApiKey, @@ -339,7 +354,12 @@ const LanguageModelStrategy = () => { {type === TYPE.sagemaker ? ( <> - + + + LLama2 model deployed through Jumpstart + + + {/* @@ -348,14 +368,7 @@ const LanguageModelStrategy = () => { value={sagemakerModelType} items={SAGEMAKER_MODEL_TYPE} /> - {/* { value={thirdPartyModelType} items={THIRD_PARTY_API_MODEL_TYPES} /> - {/* - setThirdPartyModelName(detail.selectedOption.value) - } - options={thirdPartyModelNameOpts} - /> */} - - + + + Use Titan Embedding + + {isCheckedTitanEmbedding ? null : ( + + + + )} {thirdPartyModelType === 'bedrock' ? null : thirdPartyModelType === 'bedrock_api' ? ( <> From fbd08606f8505fc6d2e0bb5a4609ec9317cc8da2 Mon Sep 17 00:00:00 2001 From: paulboho Date: Thu, 4 Jan 2024 07:17:39 +0000 Subject: [PATCH 02/97] fix opensearch import file --- lambda/langchain_processor_dataload/smart_search_dataload.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/lambda/langchain_processor_dataload/smart_search_dataload.py b/lambda/langchain_processor_dataload/smart_search_dataload.py index dae71401..5ead41f6 100644 --- a/lambda/langchain_processor_dataload/smart_search_dataload.py +++ b/lambda/langchain_processor_dataload/smart_search_dataload.py @@ -9,7 +9,8 @@ from langchain.prompts.prompt import PromptTemplate from langchain.embeddings import SagemakerEndpointEmbeddings from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler -from langchain.vectorstores import OpenSearchVectorSearch +# from langchain.vectorstores import OpenSearchVectorSearch +from opensearch_vector_search import OpenSearchVectorSearch from langchain.docstore.document import Document from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.text_splitter import CharacterTextSplitter From 8e6ce00ad51a375f5a501524f1f7a0a35fd6dc17 Mon Sep 17 00:00:00 2001 From: paulboho Date: Thu, 4 Jan 2024 13:01:56 +0000 Subject: [PATCH 03/97] add isCheckedTitanEmbedding parameter --- lambda/langchain_processor_qa/lambda_function.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/lambda/langchain_processor_qa/lambda_function.py b/lambda/langchain_processor_qa/lambda_function.py index 0b8a8947..35b32e95 100644 --- a/lambda/langchain_processor_qa/lambda_function.py +++ b/lambda/langchain_processor_qa/lambda_function.py @@ -137,6 +137,15 @@ def lambda_handler(event, context): if "sagemakerEndpoint" in evt_body.keys(): sagemakerEndpoint = evt_body['sagemakerEndpoint'] + + isCheckedTitanEmbedding = False + if "isCheckedTitanEmbedding" in evt_body.keys(): + isCheckedTitanEmbedding = ast.literal_eval(str(evt_body['isCheckedTitanEmbedding']).title()) + print('isCheckedTitanEmbedding:', isCheckedTitanEmbedding) + + if isCheckedTitanEmbedding: + embeddingEndpoint = "bedrock-titan-embed" + print('embeddingEndpoint:', embeddingEndpoint) modelType = 'normal' if "modelType" in evt_body.keys(): From aa6283fe02db81c5a0c398f49b1ca3f9874c6d0d Mon Sep 17 00:00:00 2001 From: paulboho Date: Fri, 5 Jan 2024 02:32:06 +0000 Subject: [PATCH 04/97] fix dataload --- .../lambda_function.py | 37 +- .../opensearch_vector_search.py | 1059 +++++++++++++++++ .../smart_search_dataload.py | 308 +++-- .../opensearch_vector_search.py | 46 +- 4 files changed, 1361 insertions(+), 89 deletions(-) create mode 100644 lambda/langchain_processor_dataload/opensearch_vector_search.py diff --git a/lambda/langchain_processor_dataload/lambda_function.py b/lambda/langchain_processor_dataload/lambda_function.py index 2b3f02e1..82eefd24 100644 --- a/lambda/langchain_processor_dataload/lambda_function.py +++ b/lambda/langchain_processor_dataload/lambda_function.py @@ -97,8 +97,8 @@ def lambda_handler(event, context): password, host, port, - EMBEDDING_ENDPOINT_NAME, region, + EMBEDDING_ENDPOINT_NAME, searchEngine, zilliz_endpoint, zilliz_token, @@ -113,9 +113,42 @@ def lambda_handler(event, context): Filename=local_file ) print("finish download file") + + + #目前主要在英语文档拆分时用到，拆分后文档的最大token数量 + chunk_size = 1500 + #目前主要在英语文档拆分时用到，拆分后相邻文档的头尾最大重合token数量 + chunk_overlap = 10 + #目前主要在拆分csv文档时用到，一行的内容如果token数超过该参数，就会对一行内容进行拆分 + sep_word_len = 2000 + #目前主要在拆分QA格式的csv文档时用到，如果指定了title_name，在拆分时会确保单独对title列的文本进行向量化，在split_to_sentence_paragraph=True时使用 + qa_title_name = '' + #文档是否拆分为sentence、paragraph的格式，使用sentence文本进行向量化，使用多条sentence组合为paragraph，给到大模型推理 + split_to_sentence_paragraph = True + #使用多少条sentence组装为paragraph + paragraph_include_sentence_num = 3 + #需要向量化文本的最大字数，使用SageMaker部署的向量模型时使用 + text_max_length = 350 + #PDF格式的文件使用，设置为true时会先将PDF文件转换为HTML文件进行逻辑段落的拆分,在split_to_sentence_paragraph=True时使用 + pdf_to_html = False + #写入AOS的文本字段名称，langchain默认为text + text_field = 'paragraph' + #写入AOS的向量字段名称，langchain默认为vector_field + vector_field = 'sentence_vector' now1 = datetime.now()#begin time - loaded_files = dataload.init_knowledge_vector(local_file,bulk_size) + loaded_files = dataload.init_knowledge_vector(local_file, + chunk_size, + chunk_overlap, + sep_word_len, + qa_title_name, + split_to_sentence_paragraph, + paragraph_include_sentence_num, + text_max_length, + pdf_to_html, + text_field, + vector_field + ) now2 = datetime.now()#endtime print("File import takes time:",now2-now1) print("Complete the import of the following documents:", str(loaded_files)) diff --git a/lambda/langchain_processor_dataload/opensearch_vector_search.py b/lambda/langchain_processor_dataload/opensearch_vector_search.py new file mode 100644 index 00000000..614058ee --- /dev/null +++ b/lambda/langchain_processor_dataload/opensearch_vector_search.py @@ -0,0 +1,1059 @@ +from __future__ import annotations + +import uuid +import warnings +from typing import Any, Dict, Iterable, List, Optional, Tuple + +import numpy as np +from langchain.schema import Document +from langchain.schema.embeddings import Embeddings +from langchain.schema.vectorstore import VectorStore +from langchain.utils import get_from_dict_or_env +from langchain.vectorstores.utils import maximal_marginal_relevance + +IMPORT_OPENSEARCH_PY_ERROR = ( + "Could not import OpenSearch. Please install it with `pip install opensearch-py`." +) +SCRIPT_SCORING_SEARCH = "script_scoring" +PAINLESS_SCRIPTING_SEARCH = "painless_scripting" +MATCH_ALL_QUERY = {"match_all": {}} # type: Dict + + +def _import_opensearch() -> Any: + """Import OpenSearch if available, otherwise raise error.""" + try: + from opensearchpy import OpenSearch + except ImportError: + raise ImportError(IMPORT_OPENSEARCH_PY_ERROR) + return OpenSearch + + +def _import_bulk() -> Any: + """Import bulk if available, otherwise raise error.""" + try: + from opensearchpy.helpers import bulk + except ImportError: + raise ImportError(IMPORT_OPENSEARCH_PY_ERROR) + return bulk + + +def _import_not_found_error() -> Any: + """Import not found error if available, otherwise raise error.""" + try: + from opensearchpy.exceptions import NotFoundError + except ImportError: + raise ImportError(IMPORT_OPENSEARCH_PY_ERROR) + return NotFoundError + +def _get_opensearch_client(opensearch_url: str, **kwargs: Any) -> Any: + """Get OpenSearch client from the opensearch_url, otherwise raise error.""" + try: + opensearch = _import_opensearch() + hosts = kwargs.get("hosts",[]) + http_auth = kwargs.get("http_auth",[]) + client = opensearch(hosts=hosts,http_auth=http_auth,use_ssl = True,timeout=600) + except ValueError as e: + raise ImportError( + f"OpenSearch client string provided is not in proper format. " + f"Got error: {e} " + ) + return client + + +def _validate_embeddings_and_bulk_size(embeddings_length: int, bulk_size: int) -> None: + """Validate Embeddings Length and Bulk Size.""" + if embeddings_length == 0: + raise RuntimeError("Embeddings size is zero") + if bulk_size < embeddings_length: + raise RuntimeError( + f"The embeddings count, {embeddings_length} is more than the " + f"[bulk_size], {bulk_size}. Increase the value of [bulk_size]." + ) + + +def _validate_aoss_with_engines(is_aoss: bool, engine: str) -> None: + """Validate AOSS with the engine.""" + if is_aoss and engine != "nmslib" and engine != "faiss": + raise ValueError( + "Amazon OpenSearch Service Serverless only " + "supports `nmslib` or `faiss` engines" + ) + + +def _is_aoss_enabled(http_auth: Any) -> bool: + """Check if the service is http_auth is set as `aoss`.""" + if ( + http_auth is not None + and hasattr(http_auth, "service") + and http_auth.service == "aoss" + ): + return True + return False + + +def _bulk_ingest_embeddings( + client: Any, + index_name: str, + embeddings: List[List[float]], + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + vector_field: str = "vector_field", + text_field: str = "text", + mapping: Optional[Dict] = None, + max_chunk_bytes: Optional[int] = 1 * 1024 * 1024, + is_aoss: bool = False, +) -> List[str]: + """Bulk Ingest Embeddings into given index.""" + if not mapping: + mapping = dict() + + bulk = _import_bulk() + not_found_error = _import_not_found_error() + requests = [] + return_ids = [] + mapping = mapping + + try: + client.indices.get(index=index_name) + except not_found_error: + client.indices.create(index=index_name, body=mapping) + + for i, text in enumerate(texts): + metadata = metadatas[i] if metadatas else {} + _id = ids[i] if ids else str(uuid.uuid4()) + request = { + "_op_type": "index", + "_index": index_name, + vector_field: embeddings[i], + text_field: text, + "metadata": metadata, + } + if is_aoss: + request["id"] = _id + else: + request["_id"] = _id + requests.append(request) + return_ids.append(_id) + bulk(client, requests, max_chunk_bytes=max_chunk_bytes) + if not is_aoss: + client.indices.refresh(index=index_name) + return return_ids + + +def _default_scripting_text_mapping( + dim: int, + vector_field: str = "vector_field", +) -> Dict: + """For Painless Scripting or Script Scoring,the default mapping to create index.""" + return { + "mappings": { + "properties": { + vector_field: {"type": "knn_vector", "dimension": dim}, + } + } + } + + +def _default_text_mapping( + dim: int, + engine: str = "nmslib", + space_type: str = "l2", + ef_search: int = 512, + ef_construction: int = 512, + m: int = 16, + vector_field: str = "vector_field", +) -> Dict: + """For Approximate k-NN Search, this is the default mapping to create index.""" + return { + "settings": {"index": {"knn": True, "knn.algo_param.ef_search": ef_search}}, + "mappings": { + "properties": { + vector_field: { + "type": "knn_vector", + "dimension": dim, + "method": { + "name": "hnsw", + "space_type": space_type, + "engine": engine, + "parameters": {"ef_construction": ef_construction, "m": m}, + }, + } + } + }, + } + + +def _default_approximate_search_query( + query_vector: List[float], + k: int = 4, + vector_field: str = "vector_field", +) -> Dict: + """For Approximate k-NN Search, this is the default query.""" + return { + "size": k, + "query": {"knn": {vector_field: {"vector": query_vector, "k": k}}}, + } + + +def _approximate_search_query_with_boolean_filter( + query_vector: List[float], + boolean_filter: Dict, + k: int = 4, + vector_field: str = "vector_field", + subquery_clause: str = "must", +) -> Dict: + """For Approximate k-NN Search, with Boolean Filter.""" + return { + "size": k, + "query": { + "bool": { + "filter": boolean_filter, + subquery_clause: [ + {"knn": {vector_field: {"vector": query_vector, "k": k}}} + ], + } + }, + } + + +def _approximate_search_query_with_efficient_filter( + query_vector: List[float], + efficient_filter: Dict, + k: int = 4, + vector_field: str = "vector_field", +) -> Dict: + """For Approximate k-NN Search, with Efficient Filter for Lucene and + Faiss Engines.""" + search_query = _default_approximate_search_query( + query_vector, k=k, vector_field=vector_field + ) + search_query["query"]["knn"][vector_field]["filter"] = efficient_filter + return search_query + + +def _default_script_query( + query_vector: List[float], + k: int = 4, + space_type: str = "l2", + pre_filter: Optional[Dict] = None, + vector_field: str = "vector_field", +) -> Dict: + """For Script Scoring Search, this is the default query.""" + + if not pre_filter: + pre_filter = MATCH_ALL_QUERY + + return { + "size": k, + "query": { + "script_score": { + "query": pre_filter, + "script": { + "source": "knn_score", + "lang": "knn", + "params": { + "field": vector_field, + "query_value": query_vector, + "space_type": space_type, + }, + }, + } + }, + } + + +def __get_painless_scripting_source( + space_type: str, vector_field: str = "vector_field" +) -> str: + """For Painless Scripting, it returns the script source based on space type.""" + source_value = ( + "(1.0 + " + space_type + "(params.query_value, doc['" + vector_field + "']))" + ) + if space_type == "cosineSimilarity": + return source_value + else: + return "1/" + source_value + + +def _default_painless_scripting_query( + query_vector: List[float], + k: int = 4, + space_type: str = "l2Squared", + pre_filter: Optional[Dict] = None, + vector_field: str = "vector_field", +) -> Dict: + """For Painless Scripting Search, this is the default query.""" + + if not pre_filter: + pre_filter = MATCH_ALL_QUERY + + source = __get_painless_scripting_source(space_type, vector_field=vector_field) + return { + "size": k, + "query": { + "script_score": { + "query": pre_filter, + "script": { + "source": source, + "params": { + "field": vector_field, + "query_value": query_vector, + }, + }, + } + }, + } + + +#add get aos search docs +def _get_aos_docs(question, + index, + http_auth, + host, + docs_num, + text_field, + metadata_field + ) -> List[Document]: + import json + import requests + + num_output = 10 + source_includes = [text_field,metadata_field] + fields = [text_field] + headers = { "Content-Type": "application/json" } + url = f'https://{host}/{index}/_search' + print("url:",url) + query = { + "size": num_output, + "_source": { + "includes": source_includes + }, + 'query': { + "multi_match": { + "query": question, + "fields": fields + } + } + } + #r = requests.post(host + index + '/_search', auth=awsauth, headers=headers, json=query) + r = requests.post(url, auth=http_auth, headers=headers, json=query) + r = json.loads(r.text) + clean = [] + aos_docs = [] + for hit in r['hits']['hits']: + document_score = float(hit['_score'])/10 + document_paragraph = hit['_source'][text_field] + document_metadata = hit['_source'][metadata_field] + if document_paragraph not in clean: + #Remove duplicate paragraph + clean.append(document_paragraph) + document_paragraph = "\n".join(document_paragraph) + aos_docs.append( + (Document(page_content=document_paragraph,metadata=document_metadata), + document_score + ) + ) + aos_docs = sorted(aos_docs, key=lambda x: x[1], reverse=True) + aos_docs = aos_docs[:docs_num] + return aos_docs + + +class OpenSearchVectorSearch(VectorStore): + """`Amazon OpenSearch Vector Engine` vector store. + + Example: + .. code-block:: python + + from langchain_community.vectorstores import OpenSearchVectorSearch + opensearch_vector_search = OpenSearchVectorSearch( + "http://localhost:9200", + "embeddings", + embedding_function + ) + + """ + + def __init__( + self, + opensearch_url: str, + index_name: str, + embedding_function: Embeddings, + **kwargs: Any, + ): + """Initialize with necessary components.""" + self.embedding_function = embedding_function + self.index_name = index_name + self.http_auth = kwargs.get("http_auth") + self.is_aoss = _is_aoss_enabled(http_auth=self.http_auth) + self.client = _get_opensearch_client(opensearch_url, **kwargs) + self.engine = kwargs.get("engine") + self.hosts = kwargs.get("hosts",[]) + + + @property + def embeddings(self) -> Embeddings: + return self.embedding_function + + def __add( + self, + texts: Iterable[str], + embeddings: List[List[float]], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + bulk_size: int = 500, + **kwargs: Any, + ) -> List[str]: + _validate_embeddings_and_bulk_size(len(embeddings), bulk_size) + index_name = kwargs.get("index_name", self.index_name) + text_field = kwargs.get("text_field", "text") + dim = len(embeddings[0]) + engine = kwargs.get("engine", "nmslib") + space_type = kwargs.get("space_type", "l2") + ef_search = kwargs.get("ef_search", 512) + ef_construction = kwargs.get("ef_construction", 512) + m = kwargs.get("m", 16) + vector_field = kwargs.get("vector_field", "vector_field") + max_chunk_bytes = kwargs.get("max_chunk_bytes", 1 * 1024 * 1024) + + _validate_aoss_with_engines(self.is_aoss, engine) + + mapping = _default_text_mapping( + dim, engine, space_type, ef_search, ef_construction, m, vector_field + ) + + return _bulk_ingest_embeddings( + self.client, + index_name, + embeddings, + texts, + metadatas=metadatas, + ids=ids, + vector_field=vector_field, + text_field=text_field, + mapping=mapping, + max_chunk_bytes=max_chunk_bytes, + is_aoss=self.is_aoss, + ) + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + bulk_size: int = 500, + **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. + ids: Optional list of ids to associate with the texts. + bulk_size: Bulk API request count; Default: 500 + + Returns: + List of ids from adding the texts into the vectorstore. + + Optional Args: + vector_field: Document field embeddings are stored in. Defaults to + "vector_field". + + text_field: Document field the text of the document is stored in. Defaults + to "text". + """ + embedding_type = kwargs.get("embedding_type", "sagemaker") + if embedding_type == 'bedrock': + embeddings = self.embedding_function.embed_documents(list(texts)) + else: + embeddings = self.embedding_function.embed_documents(list(texts),chunk_size=10) + return self.__add( + texts, + embeddings, + metadatas=metadatas, + ids=ids, + bulk_size=bulk_size, + **kwargs, + ) + + + def add_texts_sentence_in_metadata( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + bulk_size: int = 500, + **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. + ids: Optional list of ids to associate with the texts. + bulk_size: Bulk API request count; Default: 500 + + Returns: + List of ids from adding the texts into the vectorstore. + + Optional Args: + vector_field: Document field embeddings are stored in. Defaults to + "vector_field". + + text_field: Document field the text of the document is stored in. Defaults + to "text". + """ + embedding_type = kwargs.get("embedding_type", "sagemaker") + if embedding_type == 'bedrock': + embeddings = self.embedding_function.embed_documents(list([metadata["sentence"] for metadata in metadatas])) + else: + embeddings = self.embedding_function.embed_documents(list([metadata["sentence"] for metadata in metadatas]),chunk_size=10) + return self.__add( + texts, + embeddings, + metadatas=metadatas, + ids=ids, + bulk_size=bulk_size, + **kwargs, + ) + + + def add_embeddings( + self, + text_embeddings: Iterable[Tuple[str, List[float]]], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + bulk_size: int = 500, + **kwargs: Any, + ) -> List[str]: + """Add the given texts and embeddings to the vectorstore. + + Args: + text_embeddings: Iterable pairs of string and embedding to + add to the vectorstore. + metadatas: Optional list of metadatas associated with the texts. + ids: Optional list of ids to associate with the texts. + bulk_size: Bulk API request count; Default: 500 + + Returns: + List of ids from adding the texts into the vectorstore. + + Optional Args: + vector_field: Document field embeddings are stored in. Defaults to + "vector_field". + + text_field: Document field the text of the document is stored in. Defaults + to "text". + """ + texts, embeddings = zip(*text_embeddings) + return self.__add( + list(texts), + list(embeddings), + metadatas=metadatas, + ids=ids, + bulk_size=bulk_size, + **kwargs, + ) + + def similarity_search( + self, query: str, k: int = 4, **kwargs: Any + ) -> List[Document]: + """Return docs most similar to query. + + By default, supports Approximate Search. + Also supports Script Scoring and Painless Scripting. + + 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. + + Optional Args: + vector_field: Document field embeddings are stored in. Defaults to + "vector_field". + + text_field: Document field the text of the document is stored in. Defaults + to "text". + + metadata_field: Document field that metadata is stored in. Defaults to + "metadata". + Can be set to a special value "*" to include the entire document. + + Optional Args for Approximate Search: + search_type: "approximate_search"; default: "approximate_search" + + boolean_filter: A Boolean filter is a post filter consists of a Boolean + query that contains a k-NN query and a filter. + + subquery_clause: Query clause on the knn vector field; default: "must" + + lucene_filter: the Lucene algorithm decides whether to perform an exact + k-NN search with pre-filtering or an approximate search with modified + post-filtering. (deprecated, use `efficient_filter`) + + efficient_filter: the Lucene Engine or Faiss Engine decides whether to + perform an exact k-NN search with pre-filtering or an approximate search + with modified post-filtering. + + Optional Args for Script Scoring Search: + search_type: "script_scoring"; default: "approximate_search" + + space_type: "l2", "l1", "linf", "cosinesimil", "innerproduct", + "hammingbit"; default: "l2" + + pre_filter: script_score query to pre-filter documents before identifying + nearest neighbors; default: {"match_all": {}} + + Optional Args for Painless Scripting Search: + search_type: "painless_scripting"; default: "approximate_search" + + space_type: "l2Squared", "l1Norm", "cosineSimilarity"; default: "l2Squared" + + pre_filter: script_score query to pre-filter documents before identifying + nearest neighbors; default: {"match_all": {}} + """ + search_method = kwargs.get("search_method", "vector") + txt_docs_num = int(kwargs.get("txt_docs_num", "0")) + vec_docs_score_thresholds = float(kwargs.get("vec_docs_score_thresholds", "0")) + txt_docs_score_thresholds = float(kwargs.get("txt_docs_score_thresholds", "0")) + + vec_docs = [] + aos_docs = [] + docs_with_scores = [] + if search_method != "text": + vec_docs = self.similarity_search_with_score(query, k, **kwargs) + + if search_method != "vector" and txt_docs_num > 0: + aos_host = self.hosts[0]['host'] + text_field = kwargs.get("text_field", "text") + metadata_field = kwargs.get("metadata_field", "metadata") + aos_docs = _get_aos_docs(query,self.index_name,self.http_auth,aos_host,txt_docs_num,text_field,metadata_field) + + if len(vec_docs+aos_docs) > 0: + new_vec_docs = [] + new_aos_docs = [] + if search_method != "text" and len(vec_docs) > 0 and vec_docs_score_thresholds > 0: + for doc in vec_docs: + if float(doc[1]) >= vec_docs_score_thresholds: + new_vec_docs.append(doc) + else: + new_vec_docs = vec_docs + + if search_method != "vector" and len(aos_docs) > 0 and txt_docs_score_thresholds > 0: + for doc in aos_docs: + if float(doc[1]) >= txt_docs_score_thresholds: + new_aos_docs.append(doc) + else: + new_aos_docs = aos_docs + + if search_method == "text": + docs_with_scores = new_aos_docs + elif search_method == "mix": + docs_with_scores = new_vec_docs + new_aos_docs + else: + docs_with_scores = new_vec_docs + + return docs_with_scores + # return [doc[0] for doc in docs_with_scores] + + + def similarity_search_with_score( + self, query: str, k: int = 4, **kwargs: Any + ) -> List[Tuple[Document, float]]: + """Return docs and it's scores most similar to query. + + By default, supports Approximate Search. + Also supports Script Scoring and Painless Scripting. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + + Returns: + List of Documents along with its scores most similar to the query. + + Optional Args: + same as `similarity_search` + """ + + text_field = kwargs.get("text_field", "text") + metadata_field = kwargs.get("metadata_field", "metadata") + embedding_type = kwargs.get("embedding_type", "sagemaker") + + hits = self._raw_similarity_search_with_score(query=query, k=k, **kwargs) + + documents_with_scores = [ + ( + Document( + page_content=hit["_source"][text_field][0] if isinstance(hit["_source"][text_field],list) else hit["_source"][text_field], + metadata=hit["_source"] + if metadata_field == "*" or metadata_field not in hit["_source"] + else hit["_source"][metadata_field], + ), + hit["_score"] * 100 if embedding_type == 'bedrock' else hit["_score"], + ) + for hit in hits + ] + return documents_with_scores + + def _raw_similarity_search_with_score( + self, query: str, k: int = 4, **kwargs: Any + ) -> List[dict]: + """Return raw opensearch documents (dict) including vectors, + scores most similar to query. + + By default, supports Approximate Search. + Also supports Script Scoring and Painless Scripting. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + + Returns: + List of dict with its scores most similar to the query. + + Optional Args: + same as `similarity_search` + """ + embedding = self.embedding_function.embed_query(query) + search_type = kwargs.get("search_type", "approximate_search") + vector_field = kwargs.get("vector_field", "vector_field") + index_name = kwargs.get("index_name", self.index_name) + filter = kwargs.get("filter", {}) + + if ( + self.is_aoss + and search_type != "approximate_search" + and search_type != SCRIPT_SCORING_SEARCH + ): + raise ValueError( + "Amazon OpenSearch Service Serverless only " + "supports `approximate_search` and `script_scoring`" + ) + + if search_type == "approximate_search": + boolean_filter = kwargs.get("boolean_filter", {}) + subquery_clause = kwargs.get("subquery_clause", "must") + efficient_filter = kwargs.get("efficient_filter", {}) + # `lucene_filter` is deprecated, added for Backwards Compatibility + lucene_filter = kwargs.get("lucene_filter", {}) + + if boolean_filter != {} and efficient_filter != {}: + raise ValueError( + "Both `boolean_filter` and `efficient_filter` are provided which " + "is invalid" + ) + + if lucene_filter != {} and efficient_filter != {}: + raise ValueError( + "Both `lucene_filter` and `efficient_filter` are provided which " + "is invalid. `lucene_filter` is deprecated" + ) + + if lucene_filter != {} and boolean_filter != {}: + raise ValueError( + "Both `lucene_filter` and `boolean_filter` are provided which " + "is invalid. `lucene_filter` is deprecated" + ) + + if ( + efficient_filter == {} + and boolean_filter == {} + and lucene_filter == {} + and filter != {} + ): + if self.engine in ["faiss", "lucene"]: + efficient_filter = filter + else: + boolean_filter = filter + + if boolean_filter != {}: + search_query = _approximate_search_query_with_boolean_filter( + embedding, + boolean_filter, + k=k, + vector_field=vector_field, + subquery_clause=subquery_clause, + ) + elif efficient_filter != {}: + search_query = _approximate_search_query_with_efficient_filter( + embedding, efficient_filter, k=k, vector_field=vector_field + ) + elif lucene_filter != {}: + warnings.warn( + "`lucene_filter` is deprecated. Please use the keyword argument" + " `efficient_filter`" + ) + search_query = _approximate_search_query_with_efficient_filter( + embedding, lucene_filter, k=k, vector_field=vector_field + ) + else: + search_query = _default_approximate_search_query( + embedding, k=k, vector_field=vector_field + ) + elif search_type == SCRIPT_SCORING_SEARCH: + space_type = kwargs.get("space_type", "l2") + pre_filter = kwargs.get("pre_filter", MATCH_ALL_QUERY) + search_query = _default_script_query( + embedding, k, space_type, pre_filter, vector_field + ) + elif search_type == PAINLESS_SCRIPTING_SEARCH: + space_type = kwargs.get("space_type", "l2Squared") + pre_filter = kwargs.get("pre_filter", MATCH_ALL_QUERY) + search_query = _default_painless_scripting_query( + embedding, k, space_type, pre_filter, vector_field + ) + else: + raise ValueError("Invalid `search_type` provided as an argument") + + response = self.client.search(index=index_name, body=search_query) + + return [hit for hit in response["hits"]["hits"]] + + def max_marginal_relevance_search( + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + **kwargs: Any, + ) -> list[Document]: + """Return docs selected using the maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + Defaults to 20. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + Returns: + List of Documents selected by maximal marginal relevance. + """ + + vector_field = kwargs.get("vector_field", "vector_field") + text_field = kwargs.get("text_field", "text") + metadata_field = kwargs.get("metadata_field", "metadata") + + # Get embedding of the user query + embedding = self.embedding_function.embed_query(query) + + # Do ANN/KNN search to get top fetch_k results where fetch_k >= k + results = self._raw_similarity_search_with_score(query, fetch_k, **kwargs) + + embeddings = [result["_source"][vector_field] for result in results] + + # Rerank top k results using MMR, (mmr_selected is a list of indices) + mmr_selected = maximal_marginal_relevance( + np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult + ) + + return [ + Document( + page_content=results[i]["_source"][text_field], + metadata=results[i]["_source"][metadata_field], + ) + for i in mmr_selected + ] + + @classmethod + def from_texts( + cls, + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + bulk_size: int = 500, + ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> OpenSearchVectorSearch: + """Construct OpenSearchVectorSearch wrapper from raw texts. + + Example: + .. code-block:: python + + from langchain_community.vectorstores import OpenSearchVectorSearch + from langchain_community.embeddings import OpenAIEmbeddings + embeddings = OpenAIEmbeddings() + opensearch_vector_search = OpenSearchVectorSearch.from_texts( + texts, + embeddings, + opensearch_url="http://localhost:9200" + ) + + OpenSearch by default supports Approximate Search powered by nmslib, faiss + and lucene engines recommended for large datasets. Also supports brute force + search through Script Scoring and Painless Scripting. + + Optional Args: + vector_field: Document field embeddings are stored in. Defaults to + "vector_field". + + text_field: Document field the text of the document is stored in. Defaults + to "text". + + Optional Keyword Args for Approximate Search: + engine: "nmslib", "faiss", "lucene"; default: "nmslib" + + space_type: "l2", "l1", "cosinesimil", "linf", "innerproduct"; default: "l2" + + ef_search: Size of the dynamic list used during k-NN searches. Higher values + lead to more accurate but slower searches; default: 512 + + ef_construction: Size of the dynamic list used during k-NN graph creation. + Higher values lead to more accurate graph but slower indexing speed; + default: 512 + + m: Number of bidirectional links created for each new element. Large impact + on memory consumption. Between 2 and 100; default: 16 + + Keyword Args for Script Scoring or Painless Scripting: + is_appx_search: False + + """ + embeddings = embedding.embed_documents(texts) + return cls.from_embeddings( + embeddings, + texts, + embedding, + metadatas=metadatas, + bulk_size=bulk_size, + ids=ids, + **kwargs, + ) + + @classmethod + def from_embeddings( + cls, + embeddings: List[List[float]], + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + bulk_size: int = 500, + ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> OpenSearchVectorSearch: + """Construct OpenSearchVectorSearch wrapper from pre-vectorized embeddings. + + Example: + .. code-block:: python + + from langchain_community.vectorstores import OpenSearchVectorSearch + from langchain_community.embeddings import OpenAIEmbeddings + embedder = OpenAIEmbeddings() + embeddings = embedder.embed_documents(["foo", "bar"]) + opensearch_vector_search = OpenSearchVectorSearch.from_embeddings( + embeddings, + texts, + embedder, + opensearch_url="http://localhost:9200" + ) + + OpenSearch by default supports Approximate Search powered by nmslib, faiss + and lucene engines recommended for large datasets. Also supports brute force + search through Script Scoring and Painless Scripting. + + Optional Args: + vector_field: Document field embeddings are stored in. Defaults to + "vector_field". + + text_field: Document field the text of the document is stored in. Defaults + to "text". + + Optional Keyword Args for Approximate Search: + engine: "nmslib", "faiss", "lucene"; default: "nmslib" + + space_type: "l2", "l1", "cosinesimil", "linf", "innerproduct"; default: "l2" + + ef_search: Size of the dynamic list used during k-NN searches. Higher values + lead to more accurate but slower searches; default: 512 + + ef_construction: Size of the dynamic list used during k-NN graph creation. + Higher values lead to more accurate graph but slower indexing speed; + default: 512 + + m: Number of bidirectional links created for each new element. Large impact + on memory consumption. Between 2 and 100; default: 16 + + Keyword Args for Script Scoring or Painless Scripting: + is_appx_search: False + + """ + opensearch_url = get_from_dict_or_env( + kwargs, "opensearch_url", "OPENSEARCH_URL" + ) + # List of arguments that needs to be removed from kwargs + # before passing kwargs to get opensearch client + keys_list = [ + "opensearch_url", + "index_name", + "is_appx_search", + "vector_field", + "text_field", + "engine", + "space_type", + "ef_search", + "ef_construction", + "m", + "max_chunk_bytes", + "is_aoss", + ] + _validate_embeddings_and_bulk_size(len(embeddings), bulk_size) + dim = len(embeddings[0]) + # Get the index name from either from kwargs or ENV Variable + # before falling back to random generation + index_name = get_from_dict_or_env( + kwargs, "index_name", "OPENSEARCH_INDEX_NAME", default=uuid.uuid4().hex + ) + is_appx_search = kwargs.get("is_appx_search", True) + vector_field = kwargs.get("vector_field", "vector_field") + text_field = kwargs.get("text_field", "text") + max_chunk_bytes = kwargs.get("max_chunk_bytes", 1 * 1024 * 1024) + http_auth = kwargs.get("http_auth") + is_aoss = _is_aoss_enabled(http_auth=http_auth) + engine = None + + if is_aoss and not is_appx_search: + raise ValueError( + "Amazon OpenSearch Service Serverless only " + "supports `approximate_search`" + ) + + if is_appx_search: + engine = kwargs.get("engine", "nmslib") + space_type = kwargs.get("space_type", "l2") + ef_search = kwargs.get("ef_search", 512) + ef_construction = kwargs.get("ef_construction", 512) + m = kwargs.get("m", 16) + + _validate_aoss_with_engines(is_aoss, engine) + + mapping = _default_text_mapping( + dim, engine, space_type, ef_search, ef_construction, m, vector_field + ) + else: + mapping = _default_scripting_text_mapping(dim) + + [kwargs.pop(key, None) for key in keys_list] + client = _get_opensearch_client(opensearch_url, **kwargs) + _bulk_ingest_embeddings( + client, + index_name, + embeddings, + texts, + ids=ids, + metadatas=metadatas, + vector_field=vector_field, + text_field=text_field, + mapping=mapping, + max_chunk_bytes=max_chunk_bytes, + is_aoss=is_aoss, + ) + kwargs["engine"] = engine + return cls(opensearch_url, index_name, embedding, **kwargs) \ No newline at end of file diff --git a/lambda/langchain_processor_dataload/smart_search_dataload.py b/lambda/langchain_processor_dataload/smart_search_dataload.py index 5ead41f6..9e33459b 100644 --- a/lambda/langchain_processor_dataload/smart_search_dataload.py +++ b/lambda/langchain_processor_dataload/smart_search_dataload.py @@ -4,12 +4,13 @@ from langchain.document_loaders import PyPDFLoader from langchain.document_loaders import Docx2txtLoader from langchain.document_loaders import UnstructuredHTMLLoader +from langchain.document_loaders import PDFMinerPDFasHTMLLoader from langchain.document_loaders.csv_loader import CSVLoader from langchain.embeddings.huggingface import HuggingFaceEmbeddings from langchain.prompts.prompt import PromptTemplate from langchain.embeddings import SagemakerEndpointEmbeddings from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler -# from langchain.vectorstores import OpenSearchVectorSearch +# from opensearch_vector_search import OpenSearchVectorSearch from opensearch_vector_search import OpenSearchVectorSearch from langchain.docstore.document import Document from langchain.text_splitter import RecursiveCharacterTextSplitter @@ -17,8 +18,8 @@ from langchain import SagemakerEndpoint from langchain.llms.sagemaker_endpoint import ContentHandlerBase from langchain.llms.sagemaker_endpoint import LLMContentHandler -from langchain.embeddings import BedrockEmbeddings from langchain.vectorstores import Zilliz +from langchain.embeddings import BedrockEmbeddings from chinese_text_splitter import ChineseTextSplitter import json from typing import Dict, List, Tuple, Optional,Any @@ -27,12 +28,16 @@ import boto3 import numpy as np +bulk_size = 10000 -def load_file(filepath,language,chunk_size: int=100, chunk_overlap: int=10): +def load_file(filepath,language,pdf_to_html: bool=False, chunk_size: int=100, chunk_overlap: int=10): print('begin to load ' + filepath + ' file') if filepath.lower().endswith(".pdf"): - loader = PyPDFLoader(filepath) + if pdf_to_html: + loader = PDFMinerPDFasHTMLLoader(filepath) + else: + loader = PyPDFLoader(filepath) elif filepath.lower().endswith(".docx"): loader = Docx2txtLoader(filepath) elif filepath.lower().endswith(".csv"): @@ -46,7 +51,8 @@ def load_file(filepath,language,chunk_size: int=100, chunk_overlap: int=10): if language.find("chinese")>=0: if filepath.lower().endswith(".pdf"): - textsplitter = ChineseTextSplitter(pdf=True) + if not pdf_to_html: + textsplitter = ChineseTextSplitter(pdf=True) else: textsplitter = ChineseTextSplitter() elif language == "english": @@ -55,7 +61,10 @@ def load_file(filepath,language,chunk_size: int=100, chunk_overlap: int=10): ) print('begin load and split') - docs = loader.load_and_split(textsplitter) + if filepath.lower().endswith(".pdf") and pdf_to_html: + docs = [loader.load()[0]] + else: + docs = loader.load_and_split(textsplitter) return docs @@ -83,6 +92,7 @@ def transform_output(self, output: bytes) -> List[List[float]]: ) return embeddings + def init_embeddings_bedrock(model_id: str = 'amazon.titan-embed-text-v1'): embeddings = BedrockEmbeddings(model_id=model_id) return embeddings @@ -111,7 +121,171 @@ def init_zilliz_vector_store(embeddings, zilliz_endpoint, zilliz_token, collecti connection_args={'uri': zilliz_endpoint, 'token': zilliz_token, 'secure': True}) return vector_store +#对需要进行向量化的文档进行截断 +def truncate_text(text,text_max_length): + return text[:text_max_length] + +#构建sentence对应的paragraph,默认为使用当前sentence的后面3条sentence组合为paragrapth +def assemble_paragraph(texts,i,paragraph_include_sentence_num): + append_num = 1 if paragraph_include_sentence_num < 1 else paragraph_include_sentence_num + paragraph = ",".join([t for t in texts[i: i + append_num]]) + return paragraph + +def insert_data(pre_title,sen_texts,phase_text,metadata,new_texts,new_metadatas,embedding_type: str='sagemaker',text_max_length: int=350): + if len(pre_title) > 0: + new_texts.append(phase_text) + metadata['sentence'] = truncate_text(pre_title,text_max_length) if embedding_type=='sagemaker' else pre_title + new_metadatas.append(metadata) + for sen_text in sen_texts: + if len(sen_text.strip()) > 0: + new_texts.append(phase_text) + metadata['sentence'] = truncate_text(sen_text,text_max_length) if embedding_type=='sagemaker' else sen_text + new_metadatas.append(metadata) + return new_texts,new_metadatas + +#定义CSV格式文件，在split_to_sentence_paragraph=Ture模式下的处理逻辑 +def csv_processor(texts,metadatas,language: str='chinese',qa_title_name: str='',sep_word_len: int=2000,embedding_type: str='sagemaker',text_max_length: int=350): + new_texts = [] + new_metadatas = [] + sep = '。' + if language == "english": + sep = '.' + pre_row = 0 + phase_text = "" + sen_texts = [] + pre_metadata = "" + pre_title = "" + for i in range(len(metadatas)): + text = texts[i] + metadata = dict(metadatas[i]) + row = int(metadata['row']) + title= qa_title_name if text.find(qa_title_name) >= 0 else '' + + if i == 0: + pre_metadata = metadata + pre_title = title + + if row == pre_row: + phase_text += (text + sep) + sen_texts.append(text) + word_len = 0 + for sen in sen_texts: + word_len += len(sen) + if word_len > sep_word_len: + new_texts,new_metadatas = insert_data(pre_title,sen_texts,phase_text,pre_metadata,new_texts,new_metadatas,embedding_type,text_max_length) + sen_texts = [] + phase_text = '' + else: + new_texts,new_metadatas = insert_data(pre_title,sen_texts,phase_text,pre_metadata,new_texts,new_metadatas,embedding_type,text_max_length) + phase_text = text + pre_row = row + pre_metadata = metadata + pre_title = title + sen_texts = [] + sen_texts.append(text) + if(len(sen_texts)>0): + new_texts,new_metadatas = insert_data(pre_title,sen_texts,phase_text,pre_metadata,new_texts,new_metadatas,embedding_type,text_max_length) + + return new_texts,new_metadatas + +#定义HTML文件的逻辑段落拆分方式，默认使用字体大小的信息进行拆分 +def html_file_processor(document,language,embedding_type,text_max_length: int=350): + from bs4 import BeautifulSoup + soup = BeautifulSoup(document.page_content,'html.parser') + content = soup.find_all('div') + + import re + cur_fs = None + cur_text = '' + snippets = [] # first collect all snippets that have the same font size + for c in content: + sp = c.find('span') + if not sp: + continue + st = sp.get('style') + if not st: + continue + fs = re.findall('font-size:(\d+)px',st) + if not fs: + continue + fs = int(fs[0]) + if not cur_fs: + cur_fs = fs + if fs == cur_fs: + cur_text += c.text + else: + cur_text = cur_text.replace('...','') + snippets.append((cur_text,cur_fs)) + cur_fs = fs + cur_text = c.text + snippets.append((cur_text,cur_fs)) + # print('snippets:',snippets) + cur_idx = -1 + semantic_snippets = [] + + for s in snippets: + if not semantic_snippets or s[1] > semantic_snippets[cur_idx].metadata['heading_font']: + metadata={'heading':s[0].strip(), 'content_font': 0, 'heading_font': s[1]} + metadata.update(document.metadata) + semantic_snippets.append(Document(page_content='',metadata=metadata)) + cur_idx += 1 + continue + + if not semantic_snippets[cur_idx].metadata['content_font'] or s[1] <= semantic_snippets[cur_idx].metadata['content_font']: + semantic_snippets[cur_idx].page_content += s[0].strip() + semantic_snippets[cur_idx].metadata['content_font'] = max(s[1], semantic_snippets[cur_idx].metadata['content_font']) + continue + + metadata={'heading':s[0].strip(), 'content_font': 0, 'heading_font': s[1]} + metadata.update(document.metadata) + semantic_snippets.append(Document(page_content='',metadata=metadata)) + cur_idx += 1 + + #print('semantic_snippets:',semantic_snippets) + texts = [d.page_content for d in semantic_snippets] + metadatas = [d.metadata for d in semantic_snippets] + + print('semantic_snippets texts len:',len(texts)) + html_texts = [] + html_metadatas = [] + if len(texts) >= 1: + extend_index = len(texts) + row = extend_index + for i in range(len(texts)): + if len(texts[i]) > 0: + row += 1 + if len(texts[i]) > 300: + new_text = '' + paragraph_len = 0 + if language == "english": + text_list = re.split('.|\n|;|!',texts[i]) + else: + text_list = re.split('。|\n|；|！',texts[i]) + new_text = metadatas[i]['heading'].strip() + ':' + for text in text_list: + if len(text) > 0: + new_text += (text + ' ') + paragraph_len += len(text) + if len(new_text) > 200: + html_texts.append(new_text) + html_metadatas.append({'source':metadatas[i]['source'],'row':row}) + new_text = '' + if paragraph_len > 600: + row += 1 + paragraph_len = 0 + if len(new_text) > 0: + html_texts.append(new_text) + html_metadatas.append({'source':metadatas[i]['source'],'row':row}) + else: + if len(texts[i]) > 0: + html_texts.append(metadatas[i]['heading'].strip() + ':' + texts[i].replace('\n',' ').strip()) + html_metadatas.append({'source':metadatas[i]['source'],'row':i}) + new_texts,new_metadatas = csv_processor(html_texts,html_metadatas,language,embedding_type=embedding_type,text_max_length=text_max_length) + return new_texts,new_metadatas + else: + return html_texts,html_metadatas + class SmartSearchDataload: def init_cfg(self, @@ -120,16 +294,19 @@ def init_cfg(self, opensearch_user_password, opensearch_host, opensearch_port, - embedding_endpoint_name, region, + embedding_endpoint_name, searchEngine: str = "opensearch", zilliz_endpoint: str = "", zilliz_token: str = "", language: str = "chinese", ): self.language = language + self.embedding_type = 'sagemaker' + if embedding_endpoint_name == 'bedrock-titan-embed': self.embeddings = init_embeddings_bedrock() + self.embedding_type = 'bedrock' else: self.embeddings = init_embeddings(embedding_endpoint_name,region,self.language) if searchEngine == "opensearch": @@ -148,12 +325,22 @@ def init_cfg(self, def init_knowledge_vector(self,filepath: str or List[str], - bulk_size: int = 10000, chunk_size: int=100, chunk_overlap: int=10, - sep_word_len: int=2000): + sep_word_len: int=2000, + qa_title_name: str= '', + split_to_sentence_paragraph: bool=True, + paragraph_include_sentence_num: int= 3, + text_max_length: int=350, + pdf_to_html: bool=False, + text_field: str= 'paragraph', + vector_field: str= 'sentence_vector', + ): loaded_files = [] failed_files = [] + pdf_to_html = False if not split_to_sentence_paragraph else pdf_to_html + print('split_to_sentence_paragraph:',split_to_sentence_paragraph) + print('pdf_to_html:',pdf_to_html) if isinstance(filepath, str): if not os.path.exists(filepath): print("Path does not exist") @@ -161,7 +348,7 @@ def init_knowledge_vector(self,filepath: str or List[str], elif os.path.isfile(filepath): file = os.path.split(filepath)[-1] try: - docs = load_file(filepath,self.language,chunk_size) + docs = load_file(filepath,self.language,pdf_to_html,chunk_size) print(f"{file} Loaded successfully") loaded_files.append(filepath) except Exception as e: @@ -173,8 +360,8 @@ def init_knowledge_vector(self,filepath: str or List[str], for file in tqdm(os.listdir(filepath), desc="Load the file"): fullfilepath = os.path.join(filepath, file) try: - doc = load_file(fullfilepath,self.language,chunk_size) - docs += doc + doc = load_file(fullfilepath,self.language,pdf_to_html,chunk_size) + docs += doc loaded_files.append(fullfilepath) except Exception as e: failed_files.append(file) @@ -188,90 +375,39 @@ def init_knowledge_vector(self,filepath: str or List[str], for file in filepath: try: print("begin to load file, file:",file,self.language) - docs = load_file(file,self.language,chunk_size) + docs = load_file(file,self.language,pdf_to_html,chunk_size) print(f"{file} Loaded successfully") loaded_files.append(file) except Exception as e: print(e) print(f"{file} Failed to load") if len(docs) > 0: - print("The file is loaded and the vector library is being generated") + print("The file is loaded and the vector library is being generated") if self.vector_store is not None: - new_texts = [] - new_metadatas = [] texts = [d.page_content for d in docs] metadatas = [d.metadata for d in docs] - sep = '。' - if self.language == "english": - sep = '.' - - if len(metadatas) > 0 and 'row' in metadatas[0].keys(): - pre_row = 0 - phase_text = "" - sen_texts = [] - pre_metadata = "" - pre_title = "" - for i in range(len(metadatas)): - text = texts[i] - metadata = dict(metadatas[i]) - row = int(metadata['row']) - title='' - # if text.find('question') >= 0 and text.find('answer') >= 0: - # title = text.split('question:')[1].split('answer:')[0].strip() - - if i == 0: - pre_metadata = metadata - pre_title = title - - if row == pre_row: - phase_text += (text + sep) - sen_texts.append(text) - word_len = 0 - for sen in sen_texts: - word_len += len(sen) - if word_len > sep_word_len: - if len(pre_title) > 0: - new_text = pre_title + "@@@" + phase_text - new_texts.append(new_text) - new_metadatas.append(pre_metadata) - for sen_text in sen_texts: - new_text = sen_text + "@@@" + phase_text - new_texts.append(new_text) - new_metadatas.append(pre_metadata) - sen_texts = [] - phase_text = '' - - else: - if len(pre_title) > 0: - new_text = pre_title + "@@@" + phase_text - new_texts.append(new_text) - new_metadatas.append(pre_metadata) - for sen_text in sen_texts: - new_text = sen_text + "@@@" + phase_text - new_texts.append(new_text) - new_metadatas.append(pre_metadata) - phase_text = text - pre_row = row - pre_metadata = metadata - pre_title = title - sen_texts = [] - sen_texts.append(text) - - if(len(sen_texts)>0): - if len(pre_title) > 0: - new_text = pre_title + "@@@" + phase_text - new_texts.append(new_text) - new_metadatas.append(pre_metadata) - for sen_text in sen_texts: - new_text = sen_text + "@@@" + phase_text - new_texts.append(new_text) - new_metadatas.append(pre_metadata) - + + if split_to_sentence_paragraph: + new_texts = [] + new_metadatas = [] + if len(metadatas) > 0 and 'row' in metadatas[0].keys(): + new_texts,new_metadatas = csv_processor(texts,metadatas,self.language,qa_title_name,sep_word_len,self.embedding_type,text_max_length=text_max_length) + elif len(texts) > 0 and texts[0].find('') >=0: + new_texts,new_metadatas = html_file_processor(docs[0],self.language,self.embedding_type,text_max_length) + # print('new_texts:',new_texts) + # print('new_metadatas:',new_metadatas) + else: + texts_length = len(texts) + for i in range(0, texts_length): + paragraph = assemble_paragraph(texts,i,paragraph_include_sentence_num) + new_texts.append(paragraph) + metadata = metadatas[i] + metadata['sentence'] = truncate_text(texts[i],text_max_length) if self.embedding_type=='sagemaker' else texts[i] + new_metadatas.append(metadata) + ids = self.vector_store.add_texts_sentence_in_metadata(new_texts, new_metadatas, bulk_size=bulk_size, text_field=text_field,vector_field=vector_field,embedding_type=self.embedding_type) else: - new_texts = texts - new_metadatas = metadatas - - ids = self.vector_store.add_texts(new_texts, new_metadatas, bulk_size=bulk_size, language=self.language) + new_texts = [truncate_text(text,text_max_length) for text in texts] if self.embedding_type=='sagemaker' else texts + ids = self.vector_store.add_texts(texts, metadatas, bulk_size=bulk_size,text_field=text_field,vector_field=vector_field,embedding_type=self.embedding_type) return loaded_files else: print("Vector library is not specified, please specify the vector database") diff --git a/lambda/langchain_processor_qa/opensearch_vector_search.py b/lambda/langchain_processor_qa/opensearch_vector_search.py index b88602d1..a76e82ab 100644 --- a/lambda/langchain_processor_qa/opensearch_vector_search.py +++ b/lambda/langchain_processor_qa/opensearch_vector_search.py @@ -462,7 +462,11 @@ def add_texts( text_field: Document field the text of the document is stored in. Defaults to "text". """ - embeddings = self.embedding_function.embed_documents(list(texts)) + embedding_type = kwargs.get("embedding_type", "sagemaker") + if embedding_type == 'bedrock': + embeddings = self.embedding_function.embed_documents(list(texts)) + else: + embeddings = self.embedding_function.embed_documents(list(texts),chunk_size=10) return self.__add( texts, embeddings, @@ -472,6 +476,46 @@ def add_texts( **kwargs, ) + def add_texts_sentence_in_metadata( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + bulk_size: int = 500, + **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. + ids: Optional list of ids to associate with the texts. + bulk_size: Bulk API request count; Default: 500 + + Returns: + List of ids from adding the texts into the vectorstore. + + Optional Args: + vector_field: Document field embeddings are stored in. Defaults to + "vector_field". + + text_field: Document field the text of the document is stored in. Defaults + to "text". + """ + embedding_type = kwargs.get("embedding_type", "sagemaker") + if embedding_type == 'bedrock': + embeddings = self.embedding_function.embed_documents(list([metadata["sentence"] for metadata in metadatas])) + else: + embeddings = self.embedding_function.embed_documents(list([metadata["sentence"] for metadata in metadatas]),chunk_size=10) + return self.__add( + texts, + embeddings, + metadatas=metadatas, + ids=ids, + bulk_size=bulk_size, + **kwargs, + ) + def add_embeddings( self, text_embeddings: Iterable[Tuple[str, List[float]]], From 1e9f0eeafadf41c1e03da0dac4cbb46464b32d26 Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Mon, 8 Jan 2024 11:41:10 +0800 Subject: [PATCH 05/97] improve: change Display Scores to Confidence Scores --- ui-search/src/components/ModalCreateSession.jsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ui-search/src/components/ModalCreateSession.jsx b/ui-search/src/components/ModalCreateSession.jsx index 99d5871e..837d0691 100644 --- a/ui-search/src/components/ModalCreateSession.jsx +++ b/ui-search/src/components/ModalCreateSession.jsx @@ -618,7 +618,7 @@ export default function ModalCreateSession({ dismissModal, modalVisible }) { ) ) : null} {isKendra ? null : ( - + Query-Answer score Query-Doc scores From 6d283721c8214ef590f3506f80e480a471cce8c8 Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Mon, 8 Jan 2024 11:41:37 +0800 Subject: [PATCH 06/97] fix: language model table embedding endpoint display --- ui-search/src/components/LanguageModelStrategy.jsx | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/ui-search/src/components/LanguageModelStrategy.jsx b/ui-search/src/components/LanguageModelStrategy.jsx index fb132b74..3127676e 100644 --- a/ui-search/src/components/LanguageModelStrategy.jsx +++ b/ui-search/src/components/LanguageModelStrategy.jsx @@ -200,12 +200,11 @@ const LanguageModelStrategy = () => { id: 'embeddingEndpoint', header: 'EmbeddingEndpoint', width: 200, - cell: (item) => - item.embeddingEndpoint || - item.thirdPartyEmbeddingEndpoint || - item.isCheckedTitanEmbedding - ? 'Titan Embedding' - : 'n/a', + cell: (item) => { + if (item.isCheckedTitanEmbedding) + return 'Use Titan Embedding'; + return item.embeddingEndpoint || 'n/a'; + }, }, { id: 'apiUrl', From f6f4448690ae435f24044ab2c26e87545db2ea88 Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Mon, 8 Jan 2024 12:02:44 +0800 Subject: [PATCH 07/97] fix: streaming_end bug --- ui-search/src/components/Session/SessionInput.jsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ui-search/src/components/Session/SessionInput.jsx b/ui-search/src/components/Session/SessionInput.jsx index 12ce7cfd..d75d6449 100644 --- a/ui-search/src/components/Session/SessionInput.jsx +++ b/ui-search/src/components/Session/SessionInput.jsx @@ -89,7 +89,7 @@ const SessionInput = ({ data }) => { return; case 'streaming_end': // do this when streaming ends - onStreaming(data); + onStreaming(data, firstStream); setLoading(false); firstStream = true; resetQuery(); From cad49c4eae0ee754b742a0fde2599c99a56fa7b6 Mon Sep 17 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a/ui-search/public/index.html b/ui-search/public/index.html index 762322c3..2872dffd 100644 --- a/ui-search/public/index.html +++ b/ui-search/public/index.html @@ -2,14 +2,14 @@ - + - + - Smart Search + %REACT_APP_WEBSITE_NAME% %REACT_APP_UI_VERSION% diff --git a/ui-search/src/components/NavSide.jsx b/ui-search/src/components/NavSide.jsx index 52400ab8..2199f247 100644 --- a/ui-search/src/components/NavSide.jsx +++ b/ui-search/src/components/NavSide.jsx @@ -24,7 +24,10 @@ export default function NavSide() { return ( { if (!event.detail.external) { From 8d497faed892fb9e076ca2acbb5881ff8a6d0f5e Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Tue, 27 Feb 2024 12:48:29 +0800 Subject: [PATCH 26/97] feat: add cohere embedding --- ui-search/src/components/LanguageModelStrategy.jsx | 1 + 1 file changed, 1 insertion(+) diff --git a/ui-search/src/components/LanguageModelStrategy.jsx b/ui-search/src/components/LanguageModelStrategy.jsx index 5f570181..622429a7 100644 --- a/ui-search/src/components/LanguageModelStrategy.jsx +++ b/ui-search/src/components/LanguageModelStrategy.jsx @@ -59,6 +59,7 @@ const THIRD_PARTY_API_MODEL_NAMES = [ ]; const EMBEDDING_ENDPOINTS = [ { label: 'Titan Embedding', value: 'bedrock-titan-embed' }, + { label: 'Cohere Embedding', value: 'cohere.embed-multilingual-v3' }, ]; const LanguageModelStrategy = () => { From cbcf3f0e7956524c3be631fa18021e2bbe00e2ad Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Tue, 27 Feb 2024 12:51:35 +0800 Subject: [PATCH 27/97] feat: add anthropic.claude-v2:1 --- ui-search/src/components/LanguageModelStrategy.jsx | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/ui-search/src/components/LanguageModelStrategy.jsx b/ui-search/src/components/LanguageModelStrategy.jsx index 622429a7..581acfe7 100644 --- a/ui-search/src/components/LanguageModelStrategy.jsx +++ b/ui-search/src/components/LanguageModelStrategy.jsx @@ -41,6 +41,11 @@ const THIRD_PARTY_API_MODEL_NAMES = [ value: 'anthropic.claude-v2', modelType: ['bedrock', 'bedrock_api'], }, + { + label: 'anthropic.claude-v2:1', + value: 'anthropic.claude-v2:1', + modelType: ['bedrock', 'bedrock_api'], + }, { label: 'anthropic.claude-v1', value: 'anthropic.claude-v1', From bd92dac1f7204f303c9182320fea33694826dc9d Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Tue, 27 Feb 2024 13:37:20 +0800 Subject: [PATCH 28/97] feat: add endpoint list fetcher --- .../src/components/LanguageModelStrategy.jsx | 36 ++++++++---- .../src/components/ModalCreateSession.jsx | 3 +- ui-search/src/components/UploadFiles.jsx | 31 +++++++---- ui-search/src/hooks/useEndpointList.js | 55 +++++++++++++++++++ ui-search/src/hooks/useIndexNameList.js | 20 ++++--- 5 files changed, 115 insertions(+), 30 deletions(-) create mode 100644 ui-search/src/hooks/useEndpointList.js diff --git a/ui-search/src/components/LanguageModelStrategy.jsx b/ui-search/src/components/LanguageModelStrategy.jsx index 581acfe7..19665c84 100644 --- a/ui-search/src/components/LanguageModelStrategy.jsx +++ b/ui-search/src/components/LanguageModelStrategy.jsx @@ -1,4 +1,3 @@ -// @ts-nocheck import { Autosuggest, Box, @@ -20,6 +19,7 @@ import useInput from 'src/hooks/useInput'; import useLsLanguageModelList from 'src/hooks/useLsLanguageModelList'; import useToggle from 'src/hooks/useToggle'; import { genRandomNum } from 'src/utils/genUID'; +import useEndpointList from 'src/hooks/useEndpointList'; const SIZE = 'l'; @@ -62,14 +62,15 @@ const THIRD_PARTY_API_MODEL_NAMES = [ modelType: ['bedrock', 'bedrock_api'], }, ]; -const EMBEDDING_ENDPOINTS = [ - { label: 'Titan Embedding', value: 'bedrock-titan-embed' }, - { label: 'Cohere Embedding', value: 'cohere.embed-multilingual-v3' }, -]; const LanguageModelStrategy = () => { const [strategyName, bindStrategyName, resetStrategyName] = useInput(''); const [type, setType] = useState(TYPE.sagemaker); + const [ + OptionsSagemakerEndpoint, + OptionsEmbeddingEndpoint, + loadingEndpointList, + ] = useEndpointList(); const [sagemakerEndpoint, bindSagemakerEndpoint, resetSagemakerEndpoint] = useInput(''); const [ @@ -361,6 +362,7 @@ const LanguageModelStrategy = () => { {type === TYPE.sagemaker ? ( + // **** SageMaker ************************************************************* <> @@ -369,9 +371,16 @@ const LanguageModelStrategy = () => { - `Use: "${v}"`} {...bindSagemakerEndpoint} - placeholder="Please provide SageMaker Endpoint" + loadingText="loading endpoint list" + statusType={ + loadingEndpointList ? 'loading' : 'finished' + } + options={OptionsSagemakerEndpoint} + placeholder="Search or enter value" + empty="No matches found" /> @@ -383,13 +392,16 @@ const LanguageModelStrategy = () => { `Use: "${v}"`} {...bindEmbeddingEndpoint} - options={EMBEDDING_ENDPOINTS} - placeholder="Please enter your embedding endpoint" + loadingText="loading endpoint list" + statusType={loadingEndpointList ? 'loading' : 'finished'} + options={OptionsEmbeddingEndpoint} + placeholder="Search or enter value" empty="No matches found" /> ) : ( + // **** Third Party *********************************************************** <> { `Use: "${v}"`} {...bindThirdPartyEmbeddingEndpoint} - options={EMBEDDING_ENDPOINTS} - placeholder="Please enter your embedding endpoint" + loadingText="loading endpoint list" + statusType={loadingEndpointList ? 'loading' : 'finished'} + options={OptionsEmbeddingEndpoint} + placeholder="Search or enter value" empty="No matches found" /> diff --git a/ui-search/src/components/ModalCreateSession.jsx b/ui-search/src/components/ModalCreateSession.jsx index d4521992..0f2aa81d 100644 --- a/ui-search/src/components/ModalCreateSession.jsx +++ b/ui-search/src/components/ModalCreateSession.jsx @@ -107,8 +107,7 @@ export default function ModalCreateSession({ dismissModal, modalVisible }) { const [indexName, setIndexName] = useState(''); const [kendraIndexId, setKendraIndexId] = useState(''); - const { indexNameList, loading: loadingIndexNameList } = - useIndexNameList(modalVisible); + const [indexNameList, loadingIndexNameList] = useIndexNameList(modalVisible); const [searchMethod, setSearchMethod] = useState(SEARCH_METHOD[0].value); const [txtDocsNum, setTxtDocsNum] = useState(0); const [vecDocsScoreThresholds, setVecDocsScoreThresholds] = useState(0); diff --git a/ui-search/src/components/UploadFiles.jsx b/ui-search/src/components/UploadFiles.jsx index 315cf1ff..32a863b4 100644 --- a/ui-search/src/components/UploadFiles.jsx +++ b/ui-search/src/components/UploadFiles.jsx @@ -18,6 +18,7 @@ import useIndexNameList from 'src/hooks/useIndexNameList'; import useInput from 'src/hooks/useInput'; import useLsAppConfigs from 'src/hooks/useLsAppConfigs'; import { OPTIONS_SEARCH_ENGINE } from './ModalCreateSession'; +import useEndpointList from 'src/hooks/useEndpointList'; const SIZE = 'l'; const OPTIONS_FILE_LANG = [ @@ -32,14 +33,18 @@ const indexNameIsInvalid = (indexName) => /^\.|.*[A-Z|\s].*/.test(indexName); const UploadFiles = () => { const { urlApiGateway, s3FileUpload } = useLsAppConfigs(); - const { indexNameList, loading: loadingIndexNameList } = useIndexNameList(); + const [indexNameList, loadingIndexNameList] = useIndexNameList(); + const [ + OptionsSagemakerEndpoint, + OptionsEmbeddingEndpoint, + loadingEndpointList, + ] = useEndpointList(); const [searchEngine, bindSearchEngine, resetSearchEngine] = useInput( OPTIONS_SEARCH_ENGINE[0].value ); const [indexName, setIndexName] = useState(''); - const [embeddingEndpointName, bindEmbeddingEndpointName, resetEmbEpN] = - useInput(''); + const [embeddingEndpoint, setEmbeddingEndpoint] = useState(''); const [chunkSize, setChunkSize] = useState(200); const [selectedFiles, setSelectedFiles] = useState([]); const [fileLang, bindFileLang, resetFileLang] = useInput( @@ -49,7 +54,7 @@ const UploadFiles = () => { const resetForm = useCallback(() => { resetSearchEngine(); setIndexName(''); - resetEmbEpN(); + setEmbeddingEndpoint(''); setChunkSize(200); setSelectedFiles([]); resetFileLang(); @@ -61,7 +66,7 @@ const UploadFiles = () => { !indexName || indexNameIsInvalid(indexName) || !selectedFiles.length || - !embeddingEndpointName || + !embeddingEndpoint || !chunkSize; // const [processing file] @@ -115,7 +120,7 @@ const UploadFiles = () => { object_key: `source_data/${indexName}/${file.name}`, language: fileLang, search_engine: searchEngine, - embedding_endpoint_name: embeddingEndpointName, + embedding_endpoint_name: embeddingEndpoint, chunk_size: chunkSize, index: indexName, }; @@ -183,16 +188,22 @@ const UploadFiles = () => { loadingText="loading index names" statusType={loadingIndexNameList ? 'loading' : 'finished'} options={indexNameList.map((name) => ({ value: name }))} - placeholder="Enter value" + placeholder="Search or enter value" empty="No matches found" /> - `Use: "${v}"`} + onChange={({ detail }) => setEmbeddingEndpoint(detail.value)} + value={embeddingEndpoint} + loadingText="loading endpoint list" + statusType={loadingEndpointList ? 'loading' : 'finished'} + options={OptionsEmbeddingEndpoint} + placeholder="Search or enter value" + empty="No matches found" /> diff --git a/ui-search/src/hooks/useEndpointList.js b/ui-search/src/hooks/useEndpointList.js new file mode 100644 index 00000000..91261c69 --- /dev/null +++ b/ui-search/src/hooks/useEndpointList.js @@ -0,0 +1,55 @@ +import { useEffect, useState } from 'react'; +import useLsAppConfigs from './useLsAppConfigs'; +import toast from 'react-hot-toast'; + +const EMBEDDING_ENDPOINTS_FIXED = [ + { label: 'Titan Embedding', value: 'bedrock-titan-embed' }, + { label: 'Cohere Embedding', value: 'cohere.embed-multilingual-v3' }, +]; + +/** + * @param fetchNow {boolean} + * if fetchNow is true, the endpoint list will be fetched immediately. + * if fetchNow is false, the hook will return old list + * @returns {Object} + */ +const useEndpointList = (fetchNow = true) => { + const { urlApiGateway } = useLsAppConfigs(); + const [optionsEndpoint, setOptionsEndpoint] = useState([]); + const [loading, setLoading] = useState(false); + + useEffect(() => { + setLoading(true); + if (!urlApiGateway) return setLoading(false); + let timer = setTimeout(() => { + if (fetchNow) { + fetch(`${urlApiGateway}/endpoint_list`) + .then((res) => { + if (res.ok) return res.json(); + throw new Error('Network response was not ok.'); + }) + .then((data) => { + if (Array.isArray(data)) { + setOptionsEndpoint(data.map((d) => ({ value: d }))); + } else { + throw new Error('Endpoint list is not an array'); + } + }) + .catch((error) => { + toast.error('Error fetching endpoint list'); + setOptionsEndpoint([]); + }) + .finally(() => setLoading(false)); + } + }, 1000); + return () => clearTimeout(timer); + }, [urlApiGateway, fetchNow]); + + return [ + optionsEndpoint, + [...optionsEndpoint, ...EMBEDDING_ENDPOINTS_FIXED], + loading, + ]; +}; + +export default useEndpointList; diff --git a/ui-search/src/hooks/useIndexNameList.js b/ui-search/src/hooks/useIndexNameList.js index ac675bb9..46df804e 100644 --- a/ui-search/src/hooks/useIndexNameList.js +++ b/ui-search/src/hooks/useIndexNameList.js @@ -2,33 +2,39 @@ import { useEffect, useState } from 'react'; import useLsAppConfigs from './useLsAppConfigs'; import toast from 'react-hot-toast'; -const useIndexNameList = (bool = true) => { +/** + * @param fetchNow boolean + * if fetchNow is true, the endpoint list will be fetched immediately. + * if fetchNow is false, the hook will return old list + * @returns {Object} + */ +const useIndexNameList = (fetchNow = true) => { const { urlApiGateway } = useLsAppConfigs(); - const [indexNameList, setIndexNameList] = useState([]); + const [list, setList] = useState([]); const [loading, setLoading] = useState(false); useEffect(() => { setLoading(true); if (!urlApiGateway) return setLoading(false); let timer = setTimeout(() => { - if (bool) { + if (fetchNow) { fetch(`${urlApiGateway}/knowledge_base_handler`) .then((res) => { if (res.ok) return res.json(); throw new Error('Network response was not ok.'); }) - .then((data) => setIndexNameList(data.map((item) => item.name))) + .then((data) => setList(data.map((item) => item.name))) .catch((error) => { toast.error('Error fetching knowledge base index names'); - setIndexNameList([]); + setList([]); }) .finally(() => setLoading(false)); } }, 1000); return () => clearTimeout(timer); - }, [urlApiGateway, bool]); + }, [urlApiGateway, fetchNow]); - return { indexNameList, loading }; + return [list, loading]; }; export default useIndexNameList; From 09903e414287004f88a99ab54452e25c6aefb639 Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Wed, 28 Feb 2024 20:35:58 +0800 Subject: [PATCH 29/97] fix: session brief strategy name display --- ui-search/src/components/Session/SessionBrief.jsx | 2 +- ui-search/src/hooks/useEndpointList.js | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ui-search/src/components/Session/SessionBrief.jsx b/ui-search/src/components/Session/SessionBrief.jsx index fb6d61bb..a1f040d7 100644 --- a/ui-search/src/components/Session/SessionBrief.jsx +++ b/ui-search/src/components/Session/SessionBrief.jsx @@ -110,7 +110,7 @@ const SessionBrief = ({ configs, expanded }) => { {searchEngine} - {llmData?.modelName} + {llmData?.strategyName} {language} {isKendra ? ( diff --git a/ui-search/src/hooks/useEndpointList.js b/ui-search/src/hooks/useEndpointList.js index 91261c69..c2f76e07 100644 --- a/ui-search/src/hooks/useEndpointList.js +++ b/ui-search/src/hooks/useEndpointList.js @@ -47,7 +47,7 @@ const useEndpointList = (fetchNow = true) => { return [ optionsEndpoint, - [...optionsEndpoint, ...EMBEDDING_ENDPOINTS_FIXED], + [...EMBEDDING_ENDPOINTS_FIXED, ...optionsEndpoint], loading, ]; }; From 3b9411f1f1ebe64edb127c846024fcae8f516233 Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Thu, 29 Feb 2024 16:54:35 +0800 Subject: [PATCH 30/97] chore: do not display unused vars in session brief --- ui-search/src/components/Session/SessionBrief.jsx | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ui-search/src/components/Session/SessionBrief.jsx b/ui-search/src/components/Session/SessionBrief.jsx index a1f040d7..4d2e61f1 100644 --- a/ui-search/src/components/Session/SessionBrief.jsx +++ b/ui-search/src/components/Session/SessionBrief.jsx @@ -161,10 +161,10 @@ const SessionBrief = ({ configs, expanded }) => { )} - - + {/* */} + {/* */} - + {/* */} {!isKendra && ( <> Date: Fri, 1 Mar 2024 15:04:44 +0800 Subject: [PATCH 31/97] ui-improve: search time display --- ui-search/src/components/Session/SessionChats.jsx | 4 ++-- ui-search/src/components/Session/SessionInput.jsx | 8 +++++--- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/ui-search/src/components/Session/SessionChats.jsx b/ui-search/src/components/Session/SessionChats.jsx index 0c5110f4..737a3c7c 100644 --- a/ui-search/src/components/Session/SessionChats.jsx +++ b/ui-search/src/components/Session/SessionChats.jsx @@ -17,7 +17,7 @@ const SessionChats = ({ conversations }) => { ) : ( conversations.map(({ type, content }, i) => type === 'customer' ? ( - // customer chat + // customer search query record
@@ -26,7 +26,7 @@ const SessionChats = ({ conversations }) => {
{readTimestamp(content.timestamp)}
) : ( - // robot chat + // robot response record ) ) diff --git a/ui-search/src/components/Session/SessionInput.jsx b/ui-search/src/components/Session/SessionInput.jsx index 2bad5582..43ee0dca 100644 --- a/ui-search/src/components/Session/SessionInput.jsx +++ b/ui-search/src/components/Session/SessionInput.jsx @@ -204,8 +204,8 @@ const SessionInput = ({ data }) => { useEffect(() => { if (loading) { const interval = setInterval( - () => setSecondsTaken((prev) => prev + 10), - 10 + () => setSecondsTaken((prev) => prev + 100), + 100 ); return () => clearInterval(interval); } @@ -243,7 +243,9 @@ const SessionInput = ({ data }) => {
{loading ? ( -
{secondsTaken / 1000} s
+
+ {Number(secondsTaken / 1000).toFixed(1)} s +
) : (
From e7f08eb6d174281d023081c429d483c113c695d7 Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Fri, 1 Mar 2024 17:10:43 +0800 Subject: [PATCH 32/97] ui-feat: add session prompty templates --- ui-search/src/components/AppConfigs.jsx | 28 +- .../src/components/ModalCreateSession.jsx | 32 +- ui-search/src/components/NavSide.jsx | 1 + ui-search/src/hooks/useLsSessionList.js | 5 +- ui-search/src/utils/PROMPT_TEMPLATES.js | 155 ++ ui-search/src/utils/backup.js | 1914 +++++++++++++++++ 6 files changed, 2107 insertions(+), 28 deletions(-) create mode 100644 ui-search/src/utils/PROMPT_TEMPLATES.js create mode 100644 ui-search/src/utils/backup.js diff --git a/ui-search/src/components/AppConfigs.jsx b/ui-search/src/components/AppConfigs.jsx index 2b505d92..b46ece4c 100644 --- a/ui-search/src/components/AppConfigs.jsx +++ b/ui-search/src/components/AppConfigs.jsx @@ -19,7 +19,7 @@ import { useEffect, useState } from 'react'; import toast from 'react-hot-toast'; import useLsAppConfigs, { INIT_APP_CONFIGS } from 'src/hooks/useLsAppConfigs'; import useLsSessionList from 'src/hooks/useLsSessionList'; -import genDefaultSessions from 'src/utils/genDefaultSessions'; +import PROMPT_TEMPLATES from 'src/utils/PROMPT_TEMPLATES'; function FormInputWithDebounceAndToast({ label, @@ -180,21 +180,24 @@ const AppConfigs = () => { + + } + > + + + + i === 1 ? { ...item, disabled: true } : item + )} + /> + + - Confirm - - - } - > - - - - i === 1 ? { ...item, disabled: true } : item - )} - /> - - - `Use: "${v}"`} - onChange={({ detail }) => setIndexName(detail.value)} - value={indexName} - loadingText="loading index names" - statusType={loadingIndexNameList ? 'loading' : 'finished'} - options={indexNameList.map((name) => ({ value: name }))} - placeholder="Search or enter value" - empty="No matches found" - /> - - - - `Use: "${v}"`} - onChange={({ detail }) => setEmbeddingEndpoint(detail.value)} - value={embeddingEndpoint} - loadingText="loading endpoint list" - statusType={loadingEndpointList ? 'loading' : 'finished'} - options={OptionsEmbeddingEndpoint} + onChange={({ detail }) => setIndexName(detail.value)} + value={indexName} + loadingText="loading index names" + statusType={loadingIndexNameList ? 'loading' : 'finished'} + options={indexNameList.map((name) => ({ value: name }))} placeholder="Search or enter value" empty="No matches found" /> - - setChunkSize(detail.value)} - /> - - - - + + `Use: "${v}"`} + onChange={({ detail }) => + setEmbeddingEndpoint(detail.value) + } + value={embeddingEndpoint} + loadingText="loading endpoint list" + statusType={loadingEndpointList ? 'loading' : 'finished'} + options={OptionsEmbeddingEndpoint} + placeholder="Search or enter value" + empty="No matches found" + /> + + + setChunkSize(detail.value)} + /> + + + + + + setSelectedFiles(detail.value)} + value={selectedFiles} + accept=".docx,.doc,.pdf" + // accept=".txt,.csv,.docx,.doc,.pdf" + i18nStrings={{ + uploadButtonText: (e) => + e ? 'Choose files' : 'Choose file', + dropzoneText: (e) => + e ? 'Drop files to upload' : 'Drop file to upload', + removeFileAriaLabel: (e) => `Remove file ${e + 1}`, + limitShowFewer: 'Show fewer files', + limitShowMore: 'Show more files', + errorIconAriaLabel: 'Error', + }} + // multiple + showFileLastModified + showFileSize + showFileThumbnail + tokenLimit={3} + // constraintText="File format: txt, csv" + /> + + + + + + + + + + + + } + > + Job List + + } + > + - setSelectedFiles(detail.value)} - value={selectedFiles} - accept=".docx,.doc,.pdf" - // accept=".txt,.csv,.docx,.doc,.pdf" - i18nStrings={{ - uploadButtonText: (e) => - e ? 'Choose files' : 'Choose file', - dropzoneText: (e) => - e ? 'Drop files to upload' : 'Drop file to upload', - removeFileAriaLabel: (e) => `Remove file ${e + 1}`, - limitShowFewer: 'Show fewer files', - limitShowMore: 'Show more files', - errorIconAriaLabel: 'Error', - }} - // multiple - showFileLastModified - showFileSize - showFileThumbnail - tokenLimit={3} - // constraintText="File format: txt, csv" - /> - - - - - - - - - + + No resources + + + } + columnDefinitions={[ + { + id: 'fileName', + header: 'Name', + width: 90, + cell: (item) => item.fileName, + }, + { + id: 'contentType', + header: 'Type', + isRowHeader: true, + width: 90, + cell: (item) => item.contentType || 'n/a', + }, + { + id: 'jobStatus', + header: 'Job Status', + width: 60, + cell: ({ jobStatus }) => { + switch (jobStatus) { + default: + return ( + + Uploaded + + ); + case FILE_STATUS.processing: + return ( + + Processing + + ); + case FILE_STATUS.completed: + return ( + + Completed + + ); + case FILE_STATUS.failed: + return ( + Failed + ); + } + }, + }, + { + id: 'createdAt', + header: 'Created Time', + width: 90, + cell: (item) => { + const date = new Date(item.createdAt * 1000); + return date.toLocaleString(); + }, + }, + { + id: 'index', + header: 'Index Name', + width: 90, + cell: (item) => item.index, + }, + { + id: 'language', + header: 'File Language', + width: 90, + cell: (item) => item.language, + }, + { + id: 'searchEngine', + header: 'Search Engine', + width: 90, + cell: (item) => item.searchEngine, + }, + { + id: 'embeddingEndpoint', + header: 'Embedding Endpoint Name', + width: 90, + cell: (item) => item.embeddingEndpoint, + }, + { + id: 'chunkSize', + header: 'Chunk Size', + width: 90, + cell: (item) => item.chunkSize, + }, + { + id: 'sourceKey', + header: 'Source Key', + width: 90, + cell: (item) => item.sourceKey, + }, + { + id: 'id', + header: 'Job Id', + width: 90, + cell: (item) => item.id, + }, + ]} + /> + + + ); }; diff --git a/ui-search/src/hooks/useIndexNameList.js b/ui-search/src/hooks/useIndexNameList.js index 46df804e..0fec0866 100644 --- a/ui-search/src/hooks/useIndexNameList.js +++ b/ui-search/src/hooks/useIndexNameList.js @@ -18,7 +18,7 @@ const useIndexNameList = (fetchNow = true) => { if (!urlApiGateway) return setLoading(false); let timer = setTimeout(() => { if (fetchNow) { - fetch(`${urlApiGateway}/knowledge_base_handler`) + fetch(`${urlApiGateway}/knowledge_base_handler/indices`) .then((res) => { if (res.ok) return res.json(); throw new Error('Network response was not ok.'); diff --git a/ui-search/src/utils/genUID.js b/ui-search/src/utils/genUID.js index 7dddf3cf..4b123c2b 100644 --- a/ui-search/src/utils/genUID.js +++ b/ui-search/src/utils/genUID.js @@ -10,3 +10,15 @@ export function genRandomNum(n = 100000) { export default function genUID(n = 100000) { return `${Date.now()}-${genRandomNum(n)}`; } + +export function genUIDWithPrefix(prefix, n = 100000) { + return `${prefix}-${genUID(n)}`; +} + +export function genUUID() { + return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function (c) { + var r = (Math.random() * 16) | 0, + v = c == 'x' ? r : (r & 0x3) | 0x8; + return v.toString(16); + }); +} From d16573247be8393665f4361327f37418f93a5f44 Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Fri, 8 Mar 2024 18:37:47 +0800 Subject: [PATCH 71/97] chore: cleanup code --- ui-search/src/components/UploadFiles.jsx | 12 ++++-------- ui-search/src/utils/genUID.js | 2 +- 2 files changed, 5 insertions(+), 9 deletions(-) diff --git a/ui-search/src/components/UploadFiles.jsx b/ui-search/src/components/UploadFiles.jsx index 29802c8d..7989f0c6 100644 --- a/ui-search/src/components/UploadFiles.jsx +++ b/ui-search/src/components/UploadFiles.jsx @@ -42,13 +42,9 @@ const FILE_STATUS = { const indexNameIsInvalid = (indexName) => /^\.|.*[A-Z|\s].*/.test(indexName); const UploadFiles = () => { - const { urlApiGateway, s3FileUpload } = useLsAppConfigs(); + const { urlApiGateway } = useLsAppConfigs(); const [indexNameList, loadingIndexNameList] = useIndexNameList(); - const [ - OptionsSagemakerEndpoint, - OptionsEmbeddingEndpoint, - loadingEndpointList, - ] = useEndpointList(); + const [OptionsEmbeddingEndpoint, loadingEndpointList] = useEndpointList(); const [searchEngine, bindSearchEngine, resetSearchEngine] = useInput( OPTIONS_SEARCH_ENGINE[0].value @@ -149,8 +145,8 @@ const UploadFiles = () => { const resUploadFileToS3 = await fetch(presignedUrl, { method: 'PUT', - headers: { 'Content-Type': 'multipart/form-data' }, - body: formData, + // headers: { 'Content-Type': 'multipart/form-data' }, + body: file, }); if (!resUploadFileToS3.ok) diff --git a/ui-search/src/utils/genUID.js b/ui-search/src/utils/genUID.js index 4b123c2b..f32ebb16 100644 --- a/ui-search/src/utils/genUID.js +++ b/ui-search/src/utils/genUID.js @@ -18,7 +18,7 @@ export function genUIDWithPrefix(prefix, n = 100000) { export function genUUID() { return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function (c) { var r = (Math.random() * 16) | 0, - v = c == 'x' ? r : (r & 0x3) | 0x8; + v = c === 'x' ? r : (r & 0x3) | 0x8; return v.toString(16); }); } From 612147b26f132edf3108cdf5bb7fc8cc3c0c3ab7 Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Mon, 11 Mar 2024 13:27:57 +0800 Subject: [PATCH 72/97] ui-fix: add default embedding models in upload file module --- ui-search/src/components/UploadFiles.jsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ui-search/src/components/UploadFiles.jsx b/ui-search/src/components/UploadFiles.jsx index 7989f0c6..ddaa1c41 100644 --- a/ui-search/src/components/UploadFiles.jsx +++ b/ui-search/src/components/UploadFiles.jsx @@ -44,7 +44,7 @@ const indexNameIsInvalid = (indexName) => /^\.|.*[A-Z|\s].*/.test(indexName); const UploadFiles = () => { const { urlApiGateway } = useLsAppConfigs(); const [indexNameList, loadingIndexNameList] = useIndexNameList(); - const [OptionsEmbeddingEndpoint, loadingEndpointList] = useEndpointList(); + const [_, OptionsEmbeddingEndpoint, loadingEndpointList] = useEndpointList(); const [searchEngine, bindSearchEngine, resetSearchEngine] = useInput( OPTIONS_SEARCH_ENGINE[0].value From 5bfbb047f057886df9649fe33dc0d9fb8c941068 Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Mon, 11 Mar 2024 13:31:36 +0800 Subject: [PATCH 73/97] ui-feat: upload file table sorting --- ui-search/src/components/UploadFiles.jsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ui-search/src/components/UploadFiles.jsx b/ui-search/src/components/UploadFiles.jsx index ddaa1c41..9b54f78b 100644 --- a/ui-search/src/components/UploadFiles.jsx +++ b/ui-search/src/components/UploadFiles.jsx @@ -83,7 +83,7 @@ const UploadFiles = () => { const res = await fetch(`${urlApiGateway}/knowledge_base_handler/jobs`); if (!res.ok) throw new Error('Failed to get job list'); const data = await res.json(); - setJobList(data); + setJobList(data.sort((a, b) => b.createdAt - a.createdAt)); } catch (error) { console.error(error); toast.error('Failed to get job list'); From a81bc03184c99c5b5ee10d1f8d6bf56766461ae2 Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Mon, 11 Mar 2024 13:42:50 +0800 Subject: [PATCH 74/97] ui-feat: enable chunksize in upload file module --- ui-search/src/components/UploadFiles.jsx | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/ui-search/src/components/UploadFiles.jsx b/ui-search/src/components/UploadFiles.jsx index 9b54f78b..f7768661 100644 --- a/ui-search/src/components/UploadFiles.jsx +++ b/ui-search/src/components/UploadFiles.jsx @@ -255,11 +255,18 @@ const UploadFiles = () => { empty="No matches found" /> - + 200) && + 'Value should be (0-200]' + } + > setChunkSize(detail.value)} /> From 953d29b752c21db8340784333dcf1fd51105086f Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Mon, 11 Mar 2024 13:47:09 +0800 Subject: [PATCH 75/97] ui-feat: chunksize 100-200 --- ui-search/src/components/Session/SessionInput.jsx | 1 - 1 file changed, 1 deletion(-) diff --git a/ui-search/src/components/Session/SessionInput.jsx b/ui-search/src/components/Session/SessionInput.jsx index 43ee0dca..73c5ac4c 100644 --- a/ui-search/src/components/Session/SessionInput.jsx +++ b/ui-search/src/components/Session/SessionInput.jsx @@ -210,7 +210,6 @@ const SessionInput = ({ data }) => { return () => clearInterval(interval); } setSecondsTaken(0); - return () => {}; }, [loading]); return ( From 8e773713098141d6e111bf733be2baac54cb6273 Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Mon, 11 Mar 2024 16:34:57 +0800 Subject: [PATCH 76/97] ui-fix: content undefined bug --- ui-search/src/components/Session/index.jsx | 3 +-- ui-search/src/components/UploadFiles.jsx | 6 +++--- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/ui-search/src/components/Session/index.jsx b/ui-search/src/components/Session/index.jsx index fd900dbd..6df0227c 100644 --- a/ui-search/src/components/Session/index.jsx +++ b/ui-search/src/components/Session/index.jsx @@ -19,10 +19,9 @@ const Session = () => { (item) => item.sessionId === sessionId ); if (sessionData) { - // simple verification that the data is created by the user (already in the localStorage) + // console.log(sessionData.conversations.at(-1).content.text); setData(sessionData); } else { - // try to visit other sessions in the database (e.g. not created by the user) // navigate back to landing page navigate('/'); } diff --git a/ui-search/src/components/UploadFiles.jsx b/ui-search/src/components/UploadFiles.jsx index f7768661..d468a41c 100644 --- a/ui-search/src/components/UploadFiles.jsx +++ b/ui-search/src/components/UploadFiles.jsx @@ -258,10 +258,10 @@ const UploadFiles = () => { 200) && - 'Value should be (0-200]' + (chunkSize < 100 || chunkSize > 200) && + 'Value should be [100-200]' } > Date: Mon, 11 Mar 2024 21:32:00 +0800 Subject: [PATCH 77/97] ui-feat: refresh index name list on focus --- .../src/components/ModalCreateSession.jsx | 4 +- ui-search/src/components/UploadFiles.jsx | 4 +- ui-search/src/hooks/useEndpointList.js | 45 ++++++++++--------- ui-search/src/hooks/useIndexNameList.js | 34 +++++++------- 4 files changed, 48 insertions(+), 39 deletions(-) diff --git a/ui-search/src/components/ModalCreateSession.jsx b/ui-search/src/components/ModalCreateSession.jsx index 5fbcaea3..0f7f5715 100644 --- a/ui-search/src/components/ModalCreateSession.jsx +++ b/ui-search/src/components/ModalCreateSession.jsx @@ -107,7 +107,8 @@ export default function ModalCreateSession({ dismissModal, modalVisible }) { const [indexName, setIndexName] = useState(''); const [kendraIndexId, setKendraIndexId] = useState(''); - const [indexNameList, loadingIndexNameList] = useIndexNameList(modalVisible); + const [indexNameList, loadingIndexNameList, refreshIndexNameList] = + useIndexNameList(modalVisible); const [searchMethod, setSearchMethod] = useState(SEARCH_METHOD[0].value); const [txtDocsNum, setTxtDocsNum] = useState(0); const [vecDocsScoreThresholds, setVecDocsScoreThresholds] = useState(0); @@ -485,6 +486,7 @@ export default function ModalCreateSession({ dismissModal, modalVisible }) { } > = 0 && txtDocsNum <= 10 + txtDocsNum >= 1 && txtDocsNum <= 200 ? '' - : 'A number between 0 and 10' + : 'A number between 1 and 200' } > Date: Fri, 15 Mar 2024 16:48:42 +0800 Subject: [PATCH 86/97] ui-fix: num of doc constrain --- ui-search/src/components/ModalCreateSession.jsx | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ui-search/src/components/ModalCreateSession.jsx b/ui-search/src/components/ModalCreateSession.jsx index 38cf2fc2..4bca007b 100644 --- a/ui-search/src/components/ModalCreateSession.jsx +++ b/ui-search/src/components/ModalCreateSession.jsx @@ -543,9 +543,9 @@ export default function ModalCreateSession({ dismissModal, modalVisible }) { label="Number of doc for vector search" constraintText="Integer between 1 and 200" errorText={ - topK >= 1 && topK <= 10 + topK >= 0 && topK <= 10 ? '' - : 'A number between 1 and 200' + : 'A number between 0 and 200' } > = 1 && txtDocsNum <= 200 + txtDocsNum >= 0 && txtDocsNum <= 200 ? '' - : 'A number between 1 and 200' + : 'A number between 0 and 200' } > Date: Fri, 15 Mar 2024 16:50:01 +0800 Subject: [PATCH 87/97] ui-fix: num of doc constrain --- ui-search/src/components/ModalCreateSession.jsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ui-search/src/components/ModalCreateSession.jsx b/ui-search/src/components/ModalCreateSession.jsx index 4bca007b..5a79328f 100644 --- a/ui-search/src/components/ModalCreateSession.jsx +++ b/ui-search/src/components/ModalCreateSession.jsx @@ -541,7 +541,7 @@ export default function ModalCreateSession({ dismissModal, modalVisible }) { = 0 && topK <= 10 ? '' @@ -564,7 +564,7 @@ export default function ModalCreateSession({ dismissModal, modalVisible }) { = 0 && txtDocsNum <= 200 ? '' From a672728f038aed4a9644a7f83ac24a12cba233bf Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Mon, 18 Mar 2024 13:20:33 +0800 Subject: [PATCH 88/97] ui-fix: txtDocsNum 200 --- ui-search/src/components/ModalCreateSession.jsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ui-search/src/components/ModalCreateSession.jsx b/ui-search/src/components/ModalCreateSession.jsx index 5a79328f..11fed38a 100644 --- a/ui-search/src/components/ModalCreateSession.jsx +++ b/ui-search/src/components/ModalCreateSession.jsx @@ -543,7 +543,7 @@ export default function ModalCreateSession({ dismissModal, modalVisible }) { label="Number of doc for vector search" constraintText="Integer between 0 and 200" errorText={ - topK >= 0 && topK <= 10 + topK >= 0 && topK <= 200 ? '' : 'A number between 0 and 200' } From c23fcc7d4d3c2c69ffe96321f067c8577f2cd278 Mon Sep 17 00:00:00 2001 From: Peiwen Li Date: Mon, 18 Mar 2024 13:45:19 +0800 Subject: [PATCH 89/97] ui-refactor: chunck size opt --- ui-search/src/components/UploadFiles.jsx | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/ui-search/src/components/UploadFiles.jsx b/ui-search/src/components/UploadFiles.jsx index 7b433957..adb91ed5 100644 --- a/ui-search/src/components/UploadFiles.jsx +++ b/ui-search/src/components/UploadFiles.jsx @@ -260,10 +260,9 @@ const UploadFiles = () => { 200) && - 'Value should be [100-200]' + chunkSize < 100 && 'Value should be larger than 100' } > Date: Mon, 18 Mar 2024 23:33:38 +0800 Subject: [PATCH 90/97] remove legacy code --- deployment/lib/ss_lambdastack.py | 191 ----------------- deployment/lib/ss_lambdavpcstack.py | 196 ------------------ lambda/.DS_Store | Bin 18436 -> 10244 bytes .../{ => legacy}/lex_gbt/lambda_function.py | 0 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lambda/opensearch-search-knn/urllib3/util/__pycache__/url.cpython-37.pyc delete mode 100644 lambda/opensearch-search-knn/urllib3/util/__pycache__/wait.cpython-37.pyc diff --git a/deployment/lib/ss_lambdastack.py b/deployment/lib/ss_lambdastack.py index 073c7187..5d00f450 100644 --- a/deployment/lib/ss_lambdastack.py +++ b/deployment/lib/ss_lambdastack.py @@ -53,82 +53,6 @@ def __init__(self, scope: Construct, construct_id: str, search_engine_key: str, ) print("These functions are selected (configuration is in cdk.json context 'selection'): ", func_selection) - # role and policy (smartsearch knn doc,opensearch-search-knn,knn_faq),all three function using same policy. - if 'knn' in func_selection or 'knn_faq' in func_selection or 'knn_doc' in func_selection: - knn_policy_statement = _iam.PolicyStatement( - actions=[ - 'sagemaker:InvokeEndpointAsync', - 'sagemaker:InvokeEndpoint', - 'es:ESHttpPost', - 'secretsmanager:GetSecretValue', - ], - resources=['*'] # 可根据需求进行更改 - ) - knn_lambda_role = _iam.Role( - self, 'knn_lambda_role_Role', - assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') - ) - knn_lambda_role.add_to_policy(knn_policy_statement) - - # role and policy (insert feedback) - if 'feedback' in func_selection: - fb_policy_statement = _iam.PolicyStatement( - actions=[ - 'dynamodb:*', - ], - resources=['*'] # 可根据需求进行更改 - ) - fb_lambda_role = _iam.Role( - self, 'fb_lambda_role_Role', - assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') - ) - fb_lambda_role.add_to_policy(fb_policy_statement) - - # create dynamo table to collect feedback - table_name = self.node.try_get_context("table_name") - feedback_table = dynamodb.Table( - self, table_name, - partition_key=dynamodb.Attribute( - name="SearchInputs", - type=dynamodb.AttributeType.STRING - ), - sort_key=dynamodb.Attribute( - name="_id", - type=dynamodb.AttributeType.STRING - ), - # read_capacity=50, - # write_capacity=50, - removal_policy=RemovalPolicy.DESTROY - ) - # role and policy (opensearch-lambda-post_selection) - if 'post_selection' in func_selection: - ps_policy_statement = _iam.PolicyStatement( - actions=[ - 'lambda:InvokeFunctionUrl', - 'lambda:InvokeFunction', - 'lambda:InvokeAsync' - ], - resources=['*'] # 可根据需求进行更改 - ) - ps_lambda_role = _iam.Role( - self, 'ps_lambda_role_Role', - assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') - ) - ps_lambda_role.add_to_policy(ps_policy_statement) - - # role and policy (open-search_xgb_train_deploy) - if 'xgb_train' in func_selection: - xgb_policy_statement = _iam.PolicyStatement( - actions=[ - 'ecr:*', - ], - resources=['*'] # 可根据需求进行更改 - ) - xgb_lambda_role = _iam.Role( - self, 'xgb_lambda_role_Role', - assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') - ) - xgb_lambda_role.add_to_policy(xgb_policy_statement) if 'langchain_processor_qa' in func_selection: langchain_qa_func = self.create_langchain_qa_func(search_engine_key=search_engine_key) @@ -245,121 +169,6 @@ def __init__(self, scope: Construct, construct_id: str, search_engine_key: str, web_socket_url = f"wss://{web_socket_api.api_id}.execute-api.{os.getenv('AWS_REGION')}.amazonaws.com{CN_SUFFIX}/prod" cdk.CfnOutput(self, 'web_socket_api', value=web_socket_url, export_name='WebSocketApi') - if 'knn_faq' in func_selection: - self.opensearch_search_knn_faq_lambda = self.define_lambda_function('opensearch-search-knn-faq', - knn_lambda_role) - self.opensearch_search_knn_faq_lambda.add_environment("host", host) - # search_knn_faq_resource - search_faq_resource = api.root.add_resource( - 'search_faq', - default_cors_preflight_options=apigw.CorsOptions( - allow_methods=['GET', 'OPTIONS'], - allow_origins=apigw.Cors.ALL_ORIGINS) - ) - - search_faq_integration = apigw.LambdaIntegration( - self.opensearch_search_knn_faq_lambda, - proxy=True, - integration_responses=[ - apigw.IntegrationResponse( - status_code="200", - response_parameters={ - 'method.response.header.Access-Control-Allow-Origin': "'*'" - } - ) - ] - ) - - user_model = api.add_model("UserModel", - schema=apigw.JsonSchema( - type=apigw.JsonSchemaType.OBJECT, - properties={ - "ind": apigw.JsonSchema( - type=apigw.JsonSchemaType.STRING - ), - "knn": apigw.JsonSchema( - type=apigw.JsonSchemaType.STRING - ), - "ml": apigw.JsonSchema( - type=apigw.JsonSchemaType.STRING - ), - "q": apigw.JsonSchema( - type=apigw.JsonSchemaType.STRING - ) - }, - required=["ind", "knn", "q"] - ) - ) - - search_faq_resource.add_method( - 'GET', - search_faq_integration, - request_models={ - "application/json": user_model - }, - method_responses=[ - apigw.MethodResponse( - status_code="200", - response_parameters={ - 'method.response.header.Access-Control-Allow-Origin': True - } - ) - ] - ) - - if 'feedback' in func_selection: - self.opensearch_lambda_insert_feedback_lambda = self.define_lambda_function( - 'opensearch-lambda-insert_feedback', fb_lambda_role) - if 'post_selection' in func_selection: - self.opensearch_lambda_post_selection_lambda = self.define_lambda_function( - 'opensearch-lambda-post_selection', ps_lambda_role) - # if 'bot' in func_selection: - # self.opensearch_lambda_post_selection_lambda = self.define_lambda_function('ai_bot_qa',ps_lambda_role,timeout= 60) - if 'xgb_train' in func_selection: - self.open_search_xgb_train_deploy_lambda = self.define_lambda_function('open-search_xgb_train_deploy', - xgb_lambda_role) - if 'knn_doc' in func_selection: - self.opensearch_search_knn_doc_lambda = self.define_lambda_function('opensearch-search-knn-doc', - knn_lambda_role, timeout=60) - self.opensearch_search_knn_doc_lambda.add_environment("host", host) - ################################################# - self.opensearch_search_knn_doc_lambda.add_environment("domain_name", web_socket_api.api_id) - self.opensearch_search_knn_doc_lambda.add_environment("region", os.getenv('AWS_REGION', 'us-west-2')) - - # search_knn_doc_resource - search_knn_doc_resource = api.root.add_resource( - 'search_knn_doc', - default_cors_preflight_options=apigw.CorsOptions( - allow_methods=['GET', 'OPTIONS'], - allow_origins=apigw.Cors.ALL_ORIGINS) - ) - - search_knn_doc_integration = apigw.LambdaIntegration( - self.opensearch_search_knn_doc_lambda, - proxy=True, - integration_responses=[ - apigw.IntegrationResponse( - status_code="200", - response_parameters={ - 'method.response.header.Access-Control-Allow-Origin': "'*'" - } - ) - ] - ) - - search_knn_doc_resource.add_method( - 'GET', - search_knn_doc_integration, - method_responses=[ - apigw.MethodResponse( - status_code="200", - response_parameters={ - 'method.response.header.Access-Control-Allow-Origin': True - } - ) - ] - ) - if 'langchain_processor_qa' in func_selection and langchain_qa_func is not None: self.create_apigw_resource_method_for_langchain_qa( api=api, diff --git a/deployment/lib/ss_lambdavpcstack.py b/deployment/lib/ss_lambdavpcstack.py index ec5cfe7b..c0a6b6a2 100644 --- a/deployment/lib/ss_lambdavpcstack.py +++ b/deployment/lib/ss_lambdavpcstack.py @@ -63,85 +63,6 @@ def __init__(self, scope: Construct, construct_id: str, search_engine_key: str, ) print("These functions are slected( configuration is in cdk.json context 'selection'): ", func_selection) - # role and policy (smartsearch knn doc,opensearch-search-knn,knn_faq),all three function using same policy. - if ('knn' in func_selection or 'knn_faq' in func_selection or 'knn_doc' in func_selection): - knn_policy_statement = _iam.PolicyStatement( - actions=[ - 'sagemaker:InvokeEndpointAsync', - 'sagemaker:InvokeEndpoint', - 'es:ESHttpPost', - 'ec2:CreateNetworkInterface', - 'ec2:DescribeNetworkInterfaces', - 'ec2:DeleteNetworkInterface', - 'secretsmanager:GetSecretValue', - ], - resources=['*'] # 可根据需求进行更改 - ) - knn_lambda_role = _iam.Role( - self, 'knn_lambda_role_Role', - assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') - ) - knn_lambda_role.add_to_policy(knn_policy_statement) - - # role and policy (insert feedback) - if ('feedback' in func_selection): - fb_policy_statement = _iam.PolicyStatement( - actions=[ - 'dynamodb:*', - ], - resources=['*'] # 可根据需求进行更改 - ) - fb_lambda_role = _iam.Role( - self, 'fb_lambda_role_Role', - assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') - ) - fb_lambda_role.add_to_policy(fb_policy_statement) - # create dynamo table - table_name = self.node.try_get_context("table_name") - feedback_table = dynamodb.Table( - self, table_name, - partition_key=dynamodb.Attribute( - name="SearchInputs", - type=dynamodb.AttributeType.STRING - ), - sort_key=dynamodb.Attribute( - name="_id", - type=dynamodb.AttributeType.STRING - ), - removal_policy=RemovalPolicy.DESTROY - ) - # role and policy (opensearch-lambda-post_selection) - if ('post_selection' in func_selection): - ps_policy_statement = _iam.PolicyStatement( - actions=[ - 'lambda:InvokeFunctionUrl', - 'lambda:InvokeFunction', - 'lambda:InvokeAsync', - 'ec2:CreateNetworkInterface', - 'ec2:DescribeNetworkInterfaces', - 'ec2:DeleteNetworkInterface' - ], - resources=['*'] # 可根据需求进行更改 - ) - ps_lambda_role = _iam.Role( - self, 'ps_lambda_role_Role', - assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') - ) - ps_lambda_role.add_to_policy(ps_policy_statement) - - # role and policy (open-search_xgb_train_deploy) - if ('xgb_train' in func_selection): - xgb_policy_statement = _iam.PolicyStatement( - actions=[ - 'ecr:*', - ], - resources=['*'] # 可根据需求进行更改 - ) - xgb_lambda_role = _iam.Role( - self, 'xgb_lambda_role_Role', - assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') - ) - xgb_lambda_role.add_to_policy(xgb_policy_statement) if 'langchain_processor_qa' in func_selection: langchain_qa_func = self.create_langchain_qa_func(search_engine_key=search_engine_key, vpc=vpc, @@ -306,123 +227,6 @@ def __init__(self, scope: Construct, construct_id: str, search_engine_key: str, cdk.CfnOutput(self, 'web_socket_api', value=web_socket_url, export_name='WebSocketApi') - if ('knn_faq' in func_selection): - self.opensearch_search_knn_faq_lambda = self.define_lambda_function('opensearch-search-knn-faq', - knn_lambda_role) - self.opensearch_search_knn_faq_lambda.add_environment("host", host) - # search_knn_faq_resource - search_faq_resource = api.root.add_resource( - 'search_faq', - default_cors_preflight_options=apigw.CorsOptions( - allow_methods=['GET', 'OPTIONS'], - allow_origins=apigw.Cors.ALL_ORIGINS) - ) - - search_faq_integration = apigw.LambdaIntegration( - self.opensearch_search_knn_faq_lambda, - proxy=True, - integration_responses=[ - apigw.IntegrationResponse( - status_code="200", - response_parameters={ - 'method.response.header.Access-Control-Allow-Origin': "'*'" - } - ) - ] - ) - - user_model = api.add_model("UserModel", - schema=apigw.JsonSchema( - type=apigw.JsonSchemaType.OBJECT, - properties={ - "ind": apigw.JsonSchema( - type=apigw.JsonSchemaType.STRING - ), - "knn": apigw.JsonSchema( - type=apigw.JsonSchemaType.STRING - ), - "ml": apigw.JsonSchema( - type=apigw.JsonSchemaType.STRING - ), - "q": apigw.JsonSchema( - type=apigw.JsonSchemaType.STRING - ) - }, - required=["ind", "knn", "q"] - ) - ) - - search_faq_resource.add_method( - 'GET', - search_faq_integration, - request_models={ - "application/json": user_model - }, - method_responses=[ - apigw.MethodResponse( - status_code="200", - response_parameters={ - 'method.response.header.Access-Control-Allow-Origin': True - } - ) - ] - ) - - if ('feedback' in func_selection): - self.opensearch_lambda_insert_feedback_lambda = self.define_lambda_function( - 'opensearch-lambda-insert_feedback', fb_lambda_role) - if ('post_selection' in func_selection): - self.opensearch_lambda_post_selection_lambda = self.define_lambda_function( - 'opensearch-lambda-post_selection', ps_lambda_role) - # if('bot' in func_selection): - # self.opensearch_lambda_post_selection_lambda = self.define_lambda_function('ai_bot_qa',ps_lambda_role,timeout= 60) - if ('xgb_train' in func_selection): - self.open_search_xgb_train_deploy_lambda = self.define_lambda_function('open-search_xgb_train_deploy', - xgb_lambda_role) - if ('knn_doc' in func_selection): - self.opensearch_search_knn_doc_lambda = self.define_lambda_function('opensearch-search-knn-doc', - knn_lambda_role, vpc=vpc, - vpc_subnets=vpc_subnets_selection, - timeout=60) - self.opensearch_search_knn_doc_lambda.add_environment("host", host) - ################################################# - self.opensearch_search_knn_doc_lambda.add_environment("domain_name", web_socket_api.api_id) - self.opensearch_search_knn_doc_lambda.add_environment("region", os.getenv('AWS_REGION', 'us-west-2')) - - # search_knn_doc_resource - search_knn_doc_resource = api.root.add_resource( - 'search_knn_doc', - default_cors_preflight_options=apigw.CorsOptions( - allow_methods=['GET', 'OPTIONS'], - allow_origins=apigw.Cors.ALL_ORIGINS) - ) - - search_knn_doc_integration = apigw.LambdaIntegration( - self.opensearch_search_knn_doc_lambda, - proxy=True, - integration_responses=[ - apigw.IntegrationResponse( - status_code="200", - response_parameters={ - 'method.response.header.Access-Control-Allow-Origin': "'*'" - } - ) - ] - ) - - search_knn_doc_resource.add_method( - 'GET', - search_knn_doc_integration, - method_responses=[ - apigw.MethodResponse( - status_code="200", - response_parameters={ - 'method.response.header.Access-Control-Allow-Origin': True - } - ) - ] - ) - if 'langchain_processor_qa' in func_selection and langchain_qa_func is not None: self.create_apigw_resource_method_for_langchain_qa( api=api, diff --git a/lambda/.DS_Store b/lambda/.DS_Store index 0e075cbccc04be33bfccb374fe1733d84334c421..54a7b1dfe68b2cbfed053f639cd795fcf2af695a 100644 GIT binary patch delta 350 zcmZpfz}ONX!N9=4=v10w$iScgWO4v8h!)_PSSU78Pja$>3yUO>&maWEj6iV)hGd2e zhD3%;hCGJ&&4NtdST`#$t!3QI&cVXLD75*H!W&LrHijIAREBh*s$_=B$tg+=B9Ih_`sYxNIrXT}mj~L7zR6{nel1^lq*x)M243Ssh2GXvePyzb*JM(0I7t6`^ bJQ5;IkO0#FsR9O$0f?TQ&NE^07gc5eu7yeW literal 18436 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z@>)6K=lC?AIi$@gexBF)Nf=G@S$+z3&G0#X8u~gv!{-l4c#>b>3*3esv-~1I%N-b< z;&hexi?6^c((q~F^g^kkFot2JnDCqAm#nHx6Z=kl) zWT`J>kghNFRTM9wf>(M2{l#h2hC$;}pS?0*@Cp@a;S?6^vEGH6Ar*3x(*x9)HuU#> zRs6FsP<;5a|i8K*j+V(IuK3$W2ammwhp~t>m)E@Q|NvCXF(rt$Y;6fxvG;VZ;Vo>5`vx#M>i2a&0djGs z^j9zrTl|Jl%kc5+3^a&4qKto4tahE&0XkS2V{U_h_#+dtZ=jjP9GGST-f!ooqT6f* zJOFrK)slgdqA$%O@+0|yytIJX<`JXhd(x)d;O3z_w3MwJ@CA54qiwwr+M2d4=A@VJ3JZyAmncXn!=#-=N^QrW4O{+QgZ|XdTRsIM2{eNRMPAY)@TOup*`|<7fmv`TFF@Wj zRGBrej_zf)O5iRwR?A#xHZ)(B*L$Al~F6_VC5Ly@x@f)JR59< zyCT?2WhY8DH8XZlIdlRsA(KHY%~G4m>mZNqK-&zc8e65g;@wjl$K#EO9XP{40#nN{%MK40dvowma5w} zwILnY%%pl{e37B*nKhUQ+{drWbmf z(XL3mG87c0G+i!`Uiqb@9 zf}*x7WCjwvlz<$V*z_%2DR-vo`~`aOv&R4c From a09ee190e9c1ba014c10a0681765cbcb8ba734d5 Mon Sep 17 00:00:00 2001 From: kris Date: Mon, 18 Mar 2024 23:36:36 +0800 Subject: [PATCH 91/97] fix bug --- lambda/.DS_Store | Bin 10244 -> 10244 bytes .../langchain_processor_qa/lambda_function.py | 6 +++--- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/lambda/.DS_Store b/lambda/.DS_Store index 54a7b1dfe68b2cbfed053f639cd795fcf2af695a..140ba4a31cac33db702edadfd66ea8603a67d1c6 100644 GIT binary patch delta 78 zcmZn(XbIR*FUYuavY=F?Pj)IAqNo_4Bhp4i?bx?eE iPHtX)_vDpQ@{B!`KS~&E&Jdc%yO~|#FUw+PQDy+0su#up delta 56 zcmZn(XbIR*FF09Hs#L_(#8OAW!lG73q1w>Uz(7aA+`xG9EGc6~vB?4gnw!&v7V>Up MQ~1ZSm{pV+0LI4;TL1t6 diff --git a/lambda/langchain_processor_qa/lambda_function.py b/lambda/langchain_processor_qa/lambda_function.py index d49e015d..b48732f8 100644 --- a/lambda/langchain_processor_qa/lambda_function.py +++ b/lambda/langchain_processor_qa/lambda_function.py @@ -195,9 +195,9 @@ def lambda_handler(event, context): if "isCheckedTitanEmbedding" in llmData.keys(): isCheckedTitanEmbedding = ast.literal_eval(str(llmData['isCheckedTitanEmbedding']).title()) print('isCheckedTitanEmbedding:', isCheckedTitanEmbedding) - if isCheckedTitanEmbedding or embeddingEndpoint == "bedrock-titan-embed": - embeddingEndpoint = "amazon.titan-embed-text-v1" - print('embeddingEndpoint:', embeddingEndpoint) + if isCheckedTitanEmbedding or embeddingEndpoint == "bedrock-titan-embed": + embeddingEndpoint = "amazon.titan-embed-text-v1" + print('embeddingEndpoint:', embeddingEndpoint) searchEngine = "opensearch" if not search_engine_opensearch and search_engine_kendra: From 684b80aebe67737bda3d31af138629e83579ea72 Mon Sep 17 00:00:00 2001 From: paulhebo Date: Tue, 19 Mar 2024 09:23:46 +0000 Subject: [PATCH 92/97] fix cohere embedding length problem --- lambda/knowledge_base_handler/job/glue-job-script.py | 3 ++- lambda/knowledge_base_handler/job/smart_search_dataload.py | 4 ++-- lambda/langchain_processor_dataload/lambda_function.py | 2 ++ lambda/langchain_processor_dataload/smart_search_dataload.py | 4 ++-- 4 files changed, 8 insertions(+), 5 deletions(-) diff --git a/lambda/knowledge_base_handler/job/glue-job-script.py b/lambda/knowledge_base_handler/job/glue-job-script.py index a4ec5dec..ce54f3d2 100644 --- a/lambda/knowledge_base_handler/job/glue-job-script.py +++ b/lambda/knowledge_base_handler/job/glue-job-script.py @@ -81,7 +81,8 @@ def main(): qa_title_name = body.get('qa_title_name', '标题') split_to_sentence_paragraph = body.get('split_to_sentence_paragraph', True) paragraph_include_sentence_num = body.get('paragraph_include_sentence_num', 3) - text_max_length = body.get('text_max_length', 350) + default_text_max_length = 1500 if EMBEDDING_ENDPOINT_NAME.find('cohere') >=0 or EMBEDDING_ENDPOINT_NAME.find('titan') >=0 else 350 + text_max_length = body.get('text_max_length', default_text_max_length) pdf_to_html = body.get('pdf_to_html', False) text_field = body.get('text_field', 'paragraph') vector_field = body.get('vector_field', 'sentence_vector') diff --git a/lambda/knowledge_base_handler/job/smart_search_dataload.py b/lambda/knowledge_base_handler/job/smart_search_dataload.py index 23f247ae..78acef7c 100644 --- a/lambda/knowledge_base_handler/job/smart_search_dataload.py +++ b/lambda/knowledge_base_handler/job/smart_search_dataload.py @@ -417,7 +417,7 @@ def init_knowledge_vector(self,filepath: str or List[str], texts_length = len(texts) for i in range(0, texts_length): metadata = metadatas[i] - metadata['sentence'] = truncate_text(texts[i],text_max_length) if self.embedding_type=='sagemaker' else texts[i] + metadata['sentence'] = truncate_text(texts[i],text_max_length) if len(texts[i]) > text_max_length else texts[i] if len(metadata['sentence']) > 0: paragraph = assemble_paragraph(texts,i,paragraph_include_sentence_num).strip() new_texts.append(paragraph) @@ -426,7 +426,7 @@ def init_knowledge_vector(self,filepath: str or List[str], print('new metadatas len:',len(new_metadatas)) ids = self.vector_store.add_texts_sentence_in_metadata(new_texts, new_metadatas, bulk_size=bulk_size, text_field=text_field,vector_field=vector_field,embedding_type=self.embedding_type) else: - new_texts = [truncate_text(text,text_max_length) for text in texts] if self.embedding_type=='sagemaker' else texts + new_texts = [truncate_text(text,text_max_length) for text in texts] ids = self.vector_store.add_texts(new_texts, metadatas, bulk_size=bulk_size,text_field=text_field,vector_field=vector_field,embedding_type=self.embedding_type) return loaded_files else: diff --git a/lambda/langchain_processor_dataload/lambda_function.py b/lambda/langchain_processor_dataload/lambda_function.py index 6296c412..dd8a1b68 100644 --- a/lambda/langchain_processor_dataload/lambda_function.py +++ b/lambda/langchain_processor_dataload/lambda_function.py @@ -129,6 +129,8 @@ def lambda_handler(event, context): paragraph_include_sentence_num = 3 #需要向量化文本的最大字数，使用SageMaker部署的向量模型时使用 text_max_length = 350 + if EMBEDDING_ENDPOINT_NAME.find('cohere') >=0 or EMBEDDING_ENDPOINT_NAME.find('titan') >=0: + text_max_length = 1500 #PDF格式的文件使用，设置为true时会先将PDF文件转换为HTML文件进行逻辑段落的拆分,在split_to_sentence_paragraph=True时使用 pdf_to_html = False #写入AOS的文本字段名称，langchain默认为text diff --git a/lambda/langchain_processor_dataload/smart_search_dataload.py b/lambda/langchain_processor_dataload/smart_search_dataload.py index 6e928ecb..5743ca4b 100644 --- a/lambda/langchain_processor_dataload/smart_search_dataload.py +++ b/lambda/langchain_processor_dataload/smart_search_dataload.py @@ -417,7 +417,7 @@ def init_knowledge_vector(self,filepath: str or List[str], texts_length = len(texts) for i in range(0, texts_length): metadata = metadatas[i] - metadata['sentence'] = truncate_text(texts[i],text_max_length) if self.embedding_type=='sagemaker' else texts[i] + metadata['sentence'] = truncate_text(texts[i],text_max_length) if len(texts[i]) > text_max_length else texts[i] if len(metadata['sentence']) > 0: paragraph = assemble_paragraph(texts,i,paragraph_include_sentence_num).strip() new_texts.append(paragraph) @@ -426,7 +426,7 @@ def init_knowledge_vector(self,filepath: str or List[str], print('new metadatas len:',len(new_metadatas)) ids = self.vector_store.add_texts_sentence_in_metadata(new_texts, new_metadatas, bulk_size=bulk_size, text_field=text_field,vector_field=vector_field,embedding_type=self.embedding_type) else: - new_texts = [truncate_text(text,text_max_length) for text in texts] if self.embedding_type=='sagemaker' else texts + new_texts = [truncate_text(text,text_max_length) for text in texts] ids = self.vector_store.add_texts(new_texts, metadatas, bulk_size=bulk_size,text_field=text_field,vector_field=vector_field,embedding_type=self.embedding_type) return loaded_files else: From 2c14ec0aaccc9b8dc9776a0190b2c3abe0f2f304 Mon Sep 17 00:00:00 2001 From: hebo Date: Thu, 21 Mar 2024 02:17:57 +0000 Subject: [PATCH 93/97] fix isCheckedTitanEmbedding --- lambda/langchain_processor_qa/lambda_function.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lambda/langchain_processor_qa/lambda_function.py b/lambda/langchain_processor_qa/lambda_function.py index b48732f8..62520106 100644 --- a/lambda/langchain_processor_qa/lambda_function.py +++ b/lambda/langchain_processor_qa/lambda_function.py @@ -172,6 +172,7 @@ def lambda_handler(event, context): streaming = ast.literal_eval(str(evt_body['streaming']).title()) print('streaming:',streaming) + isCheckedTitanEmbedding = False if "llmData" in evt_body.keys(): llmData = dict(evt_body['llmData']) if "embeddingEndpoint" in llmData.keys(): @@ -191,7 +192,6 @@ def lambda_handler(event, context): apiKey = llmData['apiKey'] if "secretKey" in llmData.keys(): secretKey = llmData['secretKey'] - isCheckedTitanEmbedding = False if "isCheckedTitanEmbedding" in llmData.keys(): isCheckedTitanEmbedding = ast.literal_eval(str(llmData['isCheckedTitanEmbedding']).title()) print('isCheckedTitanEmbedding:', isCheckedTitanEmbedding) From ca6c1aa88d364aa08a175b911d34c5e5de01b5c9 Mon Sep 17 00:00:00 2001 From: hebo Date: Thu, 21 Mar 2024 02:19:53 +0000 Subject: [PATCH 94/97] fix isCheckedTitanEmbedding 2 --- lambda/langchain_processor_qa/lambda_function.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/lambda/langchain_processor_qa/lambda_function.py b/lambda/langchain_processor_qa/lambda_function.py index 62520106..a386230b 100644 --- a/lambda/langchain_processor_qa/lambda_function.py +++ b/lambda/langchain_processor_qa/lambda_function.py @@ -195,9 +195,9 @@ def lambda_handler(event, context): if "isCheckedTitanEmbedding" in llmData.keys(): isCheckedTitanEmbedding = ast.literal_eval(str(llmData['isCheckedTitanEmbedding']).title()) print('isCheckedTitanEmbedding:', isCheckedTitanEmbedding) - if isCheckedTitanEmbedding or embeddingEndpoint == "bedrock-titan-embed": - embeddingEndpoint = "amazon.titan-embed-text-v1" - print('embeddingEndpoint:', embeddingEndpoint) + if isCheckedTitanEmbedding or embeddingEndpoint == "bedrock-titan-embed": + embeddingEndpoint = "amazon.titan-embed-text-v1" + print('embeddingEndpoint:', embeddingEndpoint) searchEngine = "opensearch" if not search_engine_opensearch and search_engine_kendra: From 04eda23e67420440c3aa97b2e0c5ac2577960e00 Mon Sep 17 00:00:00 2001 From: kris Date: Thu, 21 Mar 2024 16:50:11 +0800 Subject: [PATCH 95/97] update lambda vpc stask --- deployment/lib/ss_lambdavpcstack.py | 366 +++++++++++++++++++++++----- 1 file changed, 299 insertions(+), 67 deletions(-) diff --git a/deployment/lib/ss_lambdavpcstack.py b/deployment/lib/ss_lambdavpcstack.py index c0a6b6a2..1fb39f5e 100644 --- a/deployment/lib/ss_lambdavpcstack.py +++ b/deployment/lib/ss_lambdavpcstack.py @@ -9,18 +9,23 @@ CfnParameter, Aws, Duration, + aws_ec2, aws_lambda as _lambda, aws_apigateway as apigw, aws_dynamodb as dynamodb, aws_s3 as s3, aws_s3_notifications as s3n, aws_iam as _iam, - aws_ec2, + aws_sqs as sqs, + aws_glue as glue, + aws_s3_deployment as s3deploy, ContextProvider, RemovalPolicy ) from aws_cdk.aws_apigatewayv2_integrations_alpha import WebSocketLambdaIntegration from aws_cdk import aws_apigatewayv2_alpha as apigwv2 +from aws_cdk.aws_lambda_event_sources import DynamoEventSource +from aws_cdk.aws_lambda_event_sources import SqsEventSource import os binary_media_types = ["multipart/form-data"] @@ -101,22 +106,7 @@ def __init__(self, scope: Construct, construct_id: str, search_engine_key: str, required=["ind", "knn", "q"] ) ) - # - # search_knn_resource.add_method( - # 'GET', - # search_knn_integration, - # request_models={ - # "application/json": user_model - # }, - # method_responses=[ - # apigw.MethodResponse( - # status_code="200", - # response_parameters={ - # 'method.response.header.Access-Control-Allow-Origin': True - # } - # ) - # ] - # ) + websocket_table = dynamodb.Table(self, "websocket", partition_key=dynamodb.Attribute(name="id", type=dynamodb.AttributeType.STRING), @@ -693,56 +683,56 @@ def create_file_upload_prerequisites(self, api, search_engine_key, vpc=None, vpc "integration.request.path.sub_prefix": "method.request.path.sub_prefix", } - """ - 6. Create Lambda for list all indices from OpenSearch for front-end - """ - function_name = 'knowledge_base_handler' - - _knowledge_base_role_policy = _iam.PolicyStatement( - actions=[ - 's3:AmazonS3FullAccess', - 'lambda:AWSLambdaBasicExecutionRole', - 'secretsmanager:SecretsManagerReadWrite' - ], - resources=['*'] # 可根据需求进行更改 - ) - knowledge_base_handler_role = _iam.Role( - self, 'knowledge_base_handler_role', - assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') - ) - knowledge_base_handler_role.add_to_policy(_data_load_role_policy) - - knowledge_base_handler_role.add_managed_policy( - _iam.ManagedPolicy.from_aws_managed_policy_name("service-role/AWSLambdaBasicExecutionRole") - ) - - knowledge_base_handler_role.add_managed_policy( - _iam.ManagedPolicy.from_aws_managed_policy_name("SecretsManagerReadWrite") - ) - - knowledge_base_handler_role.add_managed_policy( - _iam.ManagedPolicy.from_aws_managed_policy_name("AmazonS3FullAccess") - ) - - knowledge_base_handler_function = _lambda.Function( - self, function_name, - function_name=function_name, - runtime=_lambda.Runtime.PYTHON_3_9, - role=knowledge_base_handler_role, - layers=[self.langchain_processor_qa_layer], - code=_lambda.Code.from_asset('../lambda/' + function_name), - handler='lambda_function' + '.lambda_handler', - timeout=Duration.minutes(10), - vpc=vpc, - vpc_subnets=vpc_subnets, - reserved_concurrent_executions=20 - ) - knowledge_base_handler_function.add_environment("host", search_engine_key) - - self.create_apigw_resource_method_for_knowledge_base_handler( - api=api, - knowledge_base_handler_function=knowledge_base_handler_function - ) + # """ + # 6. Create Lambda for list all indices from OpenSearch for front-end + # """ + # function_name = 'knowledge_base_handler' + + # _knowledge_base_role_policy = _iam.PolicyStatement( + # actions=[ + # 's3:AmazonS3FullAccess', + # 'lambda:AWSLambdaBasicExecutionRole', + # 'secretsmanager:SecretsManagerReadWrite' + # ], + # resources=['*'] # 可根据需求进行更改 + # ) + # knowledge_base_handler_role = _iam.Role( + # self, 'knowledge_base_handler_role', + # assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') + # ) + # knowledge_base_handler_role.add_to_policy(_data_load_role_policy) + + # knowledge_base_handler_role.add_managed_policy( + # _iam.ManagedPolicy.from_aws_managed_policy_name("service-role/AWSLambdaBasicExecutionRole") + # ) + + # knowledge_base_handler_role.add_managed_policy( + # _iam.ManagedPolicy.from_aws_managed_policy_name("SecretsManagerReadWrite") + # ) + + # knowledge_base_handler_role.add_managed_policy( + # _iam.ManagedPolicy.from_aws_managed_policy_name("AmazonS3FullAccess") + # ) + + # knowledge_base_handler_function = _lambda.Function( + # self, function_name, + # function_name=function_name, + # runtime=_lambda.Runtime.PYTHON_3_9, + # role=knowledge_base_handler_role, + # layers=[self.langchain_processor_qa_layer], + # code=_lambda.Code.from_asset('../lambda/' + function_name), + # handler='lambda_function' + '.lambda_handler', + # timeout=Duration.minutes(10), + # vpc=vpc, + # vpc_subnets=vpc_subnets, + # reserved_concurrent_executions=20 + # ) + # knowledge_base_handler_function.add_environment("host", search_engine_key) + + # self.create_apigw_resource_method_for_knowledge_base_handler( + # api=api, + # knowledge_base_handler_function=knowledge_base_handler_function + # ) """ 7. Create Lambda for list all sagemaker endpoint for front-end @@ -896,3 +886,245 @@ def create_content_moderation_func(self, vpc=None, vpc_subnets=None): content_moderation_func.add_environment("content_moderation_api", content_moderation_api) return content_moderation_func + + def create_apigw_resource_method_for_knowledge_base_handler(self, api, **kwargs): + knowledge_base_handler = api.root.add_resource('knowledge_base_handler') + + jobs = knowledge_base_handler.add_resource( + "jobs", + default_cors_preflight_options=apigw.CorsOptions( + allow_methods=['GET','POST'], + allow_origins=apigw.Cors.ALL_ORIGINS) + ) + jobs_id = jobs.add_resource( + "{id}", + default_cors_preflight_options=apigw.CorsOptions( + allow_methods=['PUT','DELETE','GET'], + allow_origins=apigw.Cors.ALL_ORIGINS) + ) + presignurl = knowledge_base_handler.add_resource( + "presignurl", + default_cors_preflight_options=apigw.CorsOptions( + allow_methods=['POST'], + allow_origins=apigw.Cors.ALL_ORIGINS) + ) + indices = knowledge_base_handler.add_resource( + "indices", + default_cors_preflight_options=apigw.CorsOptions( + allow_methods=['GET'], + allow_origins=apigw.Cors.ALL_ORIGINS) + ) + + get_presignurl_function = kwargs.get('get_presignurl_function') + create_job_function = kwargs.get('create_job_function') + + list_jobs_function = kwargs.get('list_jobs_function') + get_indice_list_function = kwargs.get('get_indice_list_function') + + def create_api_integration(function,resource,method): + + sub_resource_integration = apigw.LambdaIntegration( + function, + proxy=True, + integration_responses=[ + apigw.IntegrationResponse( + status_code="200", + response_parameters={ + 'method.response.header.Access-Control-Allow-Origin': "'*'" + } + ) + ] + ) + resource.add_method( + method, + sub_resource_integration, + method_responses=[ + apigw.MethodResponse( + status_code="200", + response_parameters={ + 'method.response.header.Access-Control-Allow-Origin': True + } + ) + ] + ) + + #POST knowledge_base_handler/jobs + create_api_integration(create_job_function,jobs,"POST") + #GET knowledge_base_handler/jobs + create_api_integration(list_jobs_function,jobs,"GET") + + #POST knowledge_base_handler/presignurl + create_api_integration(get_presignurl_function,presignurl,"POST") + #GET knowledge_base_handler/indices + create_api_integration(get_indice_list_function,indices,"GET") + + def create_knowledge_base_handler(self, api, search_engine_key): + + REGION = os.getenv('AWS_REGION', '') + EMBEDDING_ENDPOINT_NAME = "bedrock-titan-embed" + SEARCH_ENGINE = "opensearch" + PRIMARY_KEY = 'id' + + _knowledge_base_role_policy = _iam.PolicyStatement( + actions=[ + 'sagemaker:InvokeEndpointAsync', + 'sagemaker:InvokeEndpoint', + 's3:AmazonS3FullAccess', + 'lambda:AWSLambdaBasicExecutionRole', + 'secretsmanager:GetSecretValue', + 'bedrock:*', + 'dynamodb:AmazonDynamoDBFullAccess', + 'logs:*', + 'glue:*' + ], + resources=['*'] # 可根据需求进行更改 + ) + + knowledge_base_handler_role = _iam.Role( + self, 'knowledge_base_handler_rolev2', + assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') + ) + knowledge_base_handler_role.add_to_policy(_knowledge_base_role_policy) + + job_table = dynamodb.Table( + self, "job table", + partition_key=dynamodb.Attribute( + name="id", + type=dynamodb.AttributeType.STRING, + ), + removal_policy=RemovalPolicy.DESTROY, + stream=dynamodb.StreamViewType.NEW_IMAGE + ) + + deployment = s3deploy.BucketDeployment(self, "extraPythonFiles", + sources=[s3deploy.Source.asset("../lambda/knowledge_base_handler/job")], + destination_key_prefix="glue_job_assets", + destination_bucket=self.bucket + ) + + GlueS3Prefix = f"s3://{self.bucket.bucket_name}/glue_job_assets", + + knowledge_base_handler_glue_role = _iam.Role( + self, 'knowledge_base_handler_rolev3', + assumed_by=_iam.ServicePrincipal('glue.amazonaws.com') + ) + knowledge_base_handler_glue_role.add_to_policy(_knowledge_base_role_policy) + self.bucket.grant_read_write(knowledge_base_handler_glue_role) + job_table.grant_full_access(knowledge_base_handler_glue_role) + + glue_job = glue.CfnJob( + self, "MyGlueJob", + name="my-glue-job", + role=knowledge_base_handler_glue_role.role_name, + execution_property = glue.CfnJob.ExecutionPropertyProperty( + max_concurrent_runs=5 + ), + command=glue.CfnJob.JobCommandProperty( + name="pythonshell", + python_version="3.9", + script_location=f"s3://{self.bucket.bucket_name}/glue_job_assets/glue-job-script.py", + ), + default_arguments={ + "--TempDir": f"s3://{self.bucket.bucket_name}/temporary", + "--extra-py-files": f"s3://{self.bucket.bucket_name}/glue_job_assets/smart_search_dataload.py,s3://{self.bucket.bucket_name}/glue_job_assets/bedrock.py,s3://{self.bucket.bucket_name}/glue_job_assets/chinese_text_splitter.py,s3://{self.bucket.bucket_name}/glue_job_assets/opensearch_vector_search.py", + "--additional-python-modules": "tiktoken,tqdm==4.65.0,boto3==1.28.72,langchain==0.0.325,opensearch-py==2.3.2,docx2txt==0.8,pypdf==3.16.4,numexpr==2.8.4", + }, + glue_version="3.0", + max_capacity=1.0, + ) + + self.bucket.grant_read_write(knowledge_base_handler_role) + job_table.grant_full_access(knowledge_base_handler_role) + + function_name = 'createJob', + create_job_function = _lambda.Function( + self, 'createJob', + function_name='createJob', + runtime=_lambda.Runtime.PYTHON_3_9, + role=knowledge_base_handler_role, + layers=[self.langchain_processor_qa_layer], + code=_lambda.Code.from_asset(f"../lambda/knowledge_base_handler/job"), + handler='lambda_function' + '.lambda_handler', + timeout=Duration.minutes(5), + environment={ + "EMBEDDING_ENDPOINT_NAME": EMBEDDING_ENDPOINT_NAME, + "BUCKET": self.bucket.bucket_name, + "HOST": search_engine_key, + "REGION": REGION, + "SEARCH_ENGINE": SEARCH_ENGINE, + "TABLE_NAME": job_table.table_name, + "PRIMARY_KEY": PRIMARY_KEY, + } + ) + + function_name = 'listJobs', + list_jobs_function = _lambda.Function( + self, 'listJobs', + function_name='listJobs', + runtime=_lambda.Runtime.PYTHON_3_9, + role=knowledge_base_handler_role, + layers=[self.langchain_processor_qa_layer], + code=_lambda.Code.from_asset(f"../lambda/knowledge_base_handler/crud"), + handler='get-all' + '.handler', + timeout=Duration.minutes(5), + environment={ + "EMBEDDING_ENDPOINT_NAME": EMBEDDING_ENDPOINT_NAME, + "BUCKET": self.bucket.bucket_name, + "HOST": search_engine_key, + "REGION": REGION, + "SEARCH_ENGINE": SEARCH_ENGINE, + "TABLE_NAME": job_table.table_name, + "PRIMARY_KEY": PRIMARY_KEY + } + ) + + function_name = 'getPresignURL', + get_presignurl_function = _lambda.Function( + self, 'getPresignURL', + function_name='getPresignURL', + runtime=_lambda.Runtime.PYTHON_3_9, + role=knowledge_base_handler_role, + layers=[self.langchain_processor_qa_layer], + code=_lambda.Code.from_asset(f"../lambda/knowledge_base_handler/s3action"), + handler='get-presign-url' + '.handler', + timeout=Duration.minutes(5), + environment={ + "EMBEDDING_ENDPOINT_NAME": EMBEDDING_ENDPOINT_NAME, + "BUCKET": self.bucket.bucket_name, + "HOST": search_engine_key, + "REGION": REGION, + "SEARCH_ENGINE": SEARCH_ENGINE, + "TABLE_NAME": job_table.table_name, + "PRIMARY_KEY": PRIMARY_KEY + } + ) + + function_name = 'getIndexList', + get_indice_list_function = _lambda.Function( + self, 'getIndiceList', + function_name='getIndiceList', + runtime=_lambda.Runtime.PYTHON_3_9, + role=knowledge_base_handler_role, + layers=[self.langchain_processor_qa_layer], + code=_lambda.Code.from_asset('../lambda/knowledge_base_handler/indices'), + handler='lambda_function' + '.lambda_handler', + timeout=Duration.minutes(5), + reserved_concurrent_executions=20, + environment={ + "EMBEDDING_ENDPOINT_NAME": EMBEDDING_ENDPOINT_NAME, + "BUCKET": self.bucket.bucket_name, + "HOST": search_engine_key, + "REGION": REGION, + "SEARCH_ENGINE": SEARCH_ENGINE, + "TABLE_NAME": job_table.table_name, + "PRIMARY_KEY": PRIMARY_KEY + } + ) + + self.create_apigw_resource_method_for_knowledge_base_handler( + api=api, + create_job_function=create_job_function, + get_presignurl_function=get_presignurl_function, + list_jobs_function=list_jobs_function, + get_indice_list_function=get_indice_list_function, + ) \ No newline at end of file From 8b0198f502f78c27b906cdcf3be0cb49a9fbfc09 Mon Sep 17 00:00:00 2001 From: kris Date: Fri, 22 Mar 2024 11:50:48 +0800 Subject: [PATCH 96/97] add knowledge_base_handler --- deployment/cdk.json | 2 +- deployment/lib/ss_lambdavpcstack.py | 367 +++++++++++++++++++++++++++- lambda/.DS_Store | Bin 10244 -> 10244 bytes 3 files changed, 367 insertions(+), 2 deletions(-) diff --git a/deployment/cdk.json b/deployment/cdk.json index 6ea8a06f..d5b4e9ca 100644 --- a/deployment/cdk.json +++ b/deployment/cdk.json @@ -31,7 +31,7 @@ "aws", "aws-cn" ], - "vpc_deployment": false, + "vpc_deployment": true, "vpc_name": "smart_search-vpc", "subnet_name": "smart_search-subnet-private1-us-east-1a", "subnet_id": "subnet-1234", diff --git a/deployment/lib/ss_lambdavpcstack.py b/deployment/lib/ss_lambdavpcstack.py index 1fb39f5e..caaf1108 100644 --- a/deployment/lib/ss_lambdavpcstack.py +++ b/deployment/lib/ss_lambdavpcstack.py @@ -366,7 +366,7 @@ def create_langchain_qa_func(self, search_engine_key, vpc=None, vpc_subnets=None timeout=Duration.minutes(10), vpc=vpc, vpc_subnets=vpc_subnets, - reserved_concurrent_executions=100 + reserved_concurrent_executions=10 ) langchain_processor_qa_function.add_environment("host", search_engine_key) langchain_processor_qa_function.add_environment("index", index) @@ -441,6 +441,371 @@ def create_apigw_resource_method_for_langchain_qa(self, api, langchain_processor langchain_processor_qa_function.add_environment("dynamodb_table_name", chat_table.table_name) cdk.CfnOutput(self, 'chat_table_name', value=chat_table.table_name, export_name='ChatTableName') + def create_apigw_resource_method_for_endpoint_list(self, api, endpoint_list_function): + + endpoint_list_resource = api.root.add_resource( + 'endpoint_list', + default_cors_preflight_options=apigw.CorsOptions( + allow_methods=['GET', 'OPTIONS'], + allow_origins=apigw.Cors.ALL_ORIGINS) + ) + + endpoint_list_integration = apigw.LambdaIntegration( + endpoint_list_function, + proxy=True, + integration_responses=[ + apigw.IntegrationResponse( + status_code="200", + response_parameters={ + 'method.response.header.Access-Control-Allow-Origin': "'*'" + } + ) + ] + ) + + endpoint_list_resource.add_method( + 'GET', + endpoint_list_integration, + method_responses=[ + apigw.MethodResponse( + status_code="200", + response_parameters={ + 'method.response.header.Access-Control-Allow-Origin': True + } + ) + ] + ) + + def create_file_upload_prerequisites(self, api, search_engine_key, vpc=None, vpc_subnets=None): + # Now hardcode for testing first + ACCOUNT = os.getenv('AWS_ACCOUNT_ID', '') + REGION = os.getenv('AWS_REGION', '') + bucket_for_uploaded_files = "intelligent-search-data-bucket" + "-" + ACCOUNT + "-" + REGION + execution_role_name = self.node.try_get_context("execution_role_name") + REGION + index = self.node.try_get_context("index") + language = self.node.try_get_context("language") + embedding_endpoint_name = self.node.try_get_context("embedding_endpoint_name") + search_engine_opensearch = self.node.try_get_context("search_engine_opensearch") + search_engine_zilliz = self.node.try_get_context("search_engine_zilliz") + zilliz_endpoint = self.node.try_get_context("zilliz_endpoint") + zilliz_token = self.node.try_get_context("zilliz_token") + CN_SUFFIX = "-cn" if "cn-" in REGION else "" + + """ + 1. Create S3 bucket for storing uploaded files + """ + + _bucket_name = bucket_for_uploaded_files + + _bucket = s3.Bucket(self, + id=_bucket_name, + bucket_name=_bucket_name, + block_public_access=s3.BlockPublicAccess.BLOCK_ALL, + encryption=s3.BucketEncryption.S3_MANAGED, + enforce_ssl=True, + versioned=False, + removal_policy=RemovalPolicy.DESTROY + ) + + _bucket.add_cors_rule( + allowed_headers=["*"], + allowed_methods=[ + s3.HttpMethods.GET, + s3.HttpMethods.PUT, + s3.HttpMethods.POST + ], + allowed_origins=["*"] + ) + + self.bucket = _bucket + + """ + 2. Create Execution Role for Uploading file to S3 + IAM RoleName: custom-role-document-ai-upload-to-s3 + """ + _role_name = execution_role_name + _inline_policies = { + "AllowS3UploadPermission": _iam.PolicyDocument( + statements=[ + _iam.PolicyStatement( + actions=[ + "s3:PutObject", + ], + resources=[f"arn:aws{CN_SUFFIX}:s3:::{_bucket_name}/*"]), + ] + ), + "AllowLogCreation": _iam.PolicyDocument( + statements=[ + _iam.PolicyStatement( + actions=[ + "logs:CreateLogGroup", + "logs:CreateLogStream", + "logs:DescribeLogGroups", + "logs:DescribeLogStreams", + "logs:PutLogEvents", + "logs:GetLogEvents", + "logs:FilterLogEvents" + ], + resources=["*"]), + ]) + } + + _execution_role = _iam.Role(self, + id=_role_name, + role_name=_role_name, + assumed_by=_iam.ServicePrincipal("apigateway.amazonaws.com"), + description="Execution role for uploading file from APIGW to S3 directly.", + inline_policies=_inline_policies + ) + + """ + 3. Create Lambda for processing file and save to ES + """ + function_name = 'langchain_processor_dataload' + + _data_load_role_policy = _iam.PolicyStatement( + actions=[ + 'sagemaker:InvokeEndpointAsync', + 'sagemaker:InvokeEndpoint', + 's3:AmazonS3FullAccess', + 'lambda:AWSLambdaBasicExecutionRole', + 'secretsmanager:SecretsManagerReadWrite', + 'bedrock:*' + ], + resources=['*'] # 可根据需求进行更改 + ) + data_load_role = _iam.Role( + self, 'data_load_role', + assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') + ) + data_load_role.add_to_policy(_data_load_role_policy) + + data_load_role.add_managed_policy( + _iam.ManagedPolicy.from_aws_managed_policy_name("service-role/AWSLambdaBasicExecutionRole") + ) + + data_load_role.add_managed_policy( + _iam.ManagedPolicy.from_aws_managed_policy_name("SecretsManagerReadWrite") + ) + + data_load_role.add_managed_policy( + _iam.ManagedPolicy.from_aws_managed_policy_name("AmazonS3FullAccess") + ) + + data_load_function = _lambda.Function( + self, function_name, + function_name=function_name, + runtime=_lambda.Runtime.PYTHON_3_9, + role=data_load_role, + layers=[self.langchain_processor_qa_layer], + code=_lambda.Code.from_asset('../lambda/' + function_name), + handler='lambda_function' + '.lambda_handler', + timeout=Duration.minutes(10), + vpc=vpc, + vpc_subnets=vpc_subnets, + reserved_concurrent_executions=10 + ) + data_load_function.add_environment("host", search_engine_key) + data_load_function.add_environment("index", index) + data_load_function.add_environment("language", language) + data_load_function.add_environment("embedding_endpoint_name", embedding_endpoint_name) + data_load_function.add_environment("search_engine_opensearch", str(search_engine_opensearch)) + data_load_function.add_environment("search_engine_zilliz", str(search_engine_zilliz)) + data_load_function.add_environment("zilliz_endpoint", str(zilliz_endpoint)) + data_load_function.add_environment("zilliz_token", str(zilliz_token)) + + """ + 4. Update S3 file notification with Lambda + prefix is {BUCKET_NAME}/source_data/ + """ + _bucket.add_event_notification( + s3.EventType.OBJECT_CREATED, + s3n.LambdaDestination(data_load_function), + s3.NotificationKeyFilter( + prefix="source_data/", + ), + ) + + """ + 5. Create S3-based API Gateway + """ + # Create Resources in below structure + # /{bucket}/{prefix}/{sub_prefix}/{filename} + file_upload_root = api.root.add_resource(path_part="file_upload") + bucket_resource = file_upload_root.add_resource(path_part="{bucket}") + prefix_resource = bucket_resource.add_resource(path_part="{prefix}") + sub_prefix_resource = prefix_resource.add_resource(path_part="{sub_prefix}") + filename_resource = sub_prefix_resource.add_resource( + path_part="{filename}", + default_cors_preflight_options=apigw.CorsOptions( + allow_methods=['PUT', 'POST', 'OPTIONS'], + allow_origins=apigw.Cors.ALL_ORIGINS + ) + ) + + # Create S3 Integration for APIGw + request_parameters = { + "method.request.path.bucket": True, # True if param is mandatory + "method.request.path.filename": True, # True if param is mandatory + "method.request.path.prefix": True, # True if param is mandatory + "method.request.path.sub_prefix": True # True if param is mandatory + } + + request_parameters_in_integration_options = { + "integration.request.path.bucket": "method.request.path.bucket", + "integration.request.path.key": "method.request.path.filename", + "integration.request.path.prefix": "method.request.path.prefix", + "integration.request.path.sub_prefix": "method.request.path.sub_prefix", + } + + """ + 6. Create Lambda for list all sagemaker endpoint for front-end + """ + function_name = 'endpoint_list' + + _endpoint_list_role_policy = _iam.PolicyStatement( + actions=[ + 's3:AmazonS3FullAccess', + 'lambda:AWSLambdaBasicExecutionRole', + 'secretsmanager:SecretsManagerReadWrite' + ], + resources=['*'] # 可根据需求进行更改 + ) + endpoint_list_role = _iam.Role( + self, 'endpoint_list_role', + assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') + ) + endpoint_list_role.add_to_policy(_data_load_role_policy) + + endpoint_list_role.add_managed_policy( + _iam.ManagedPolicy.from_aws_managed_policy_name("service-role/AWSLambdaBasicExecutionRole") + ) + + endpoint_list_role.add_managed_policy( + _iam.ManagedPolicy.from_aws_managed_policy_name("AmazonSageMakerReadOnly") + ) + + + endpoint_list_function = _lambda.Function( + self, function_name, + function_name=function_name, + runtime=_lambda.Runtime.PYTHON_3_9, + role=endpoint_list_role, + #layers=[self.langchain_processor_qa_layer], + code=_lambda.Code.from_asset('../lambda/' + function_name), + handler='lambda_function' + '.lambda_handler', + timeout=Duration.minutes(10), + vpc=vpc, + vpc_subnets=vpc_subnets, + reserved_concurrent_executions=10 + ) + + self.create_apigw_resource_method_for_endpoint_list( + api=api, + endpoint_list_function=endpoint_list_function + ) + + # Create Integration Options + """ + Covering: + 1. Content Handling : Default passthrough, if not specify + 2. URL Path Parameters + 3. Credential Role + """ + _s3_apigw_put_integration_options = apigw.IntegrationOptions( + request_parameters=request_parameters_in_integration_options, + credentials_role=_execution_role, + integration_responses=[ + apigw.IntegrationResponse( + status_code="200", + response_parameters={ + "method.response.header.Access-Control-Allow-Headers": "'Content-Type,X-Amz-Date,Authorization,X-Api-Key,X-Amz-Security-Token'", + "method.response.header.Access-Control-Allow-Origin": "'*'", + "method.response.header.Access-Control-Allow-Methods": "'PUT,POST,OPTIONS'", + }, + response_templates={ + "application/json": "" + + } + ) + ] + ) + + s3_apigw_integration = apigw.AwsIntegration( + service="s3", + path="{bucket}/{prefix}/{sub_prefix}/{key}", + region=os.getenv('region'), + integration_http_method="PUT", + options=_s3_apigw_put_integration_options + ) + + filename_resource.add_method( + http_method='PUT', + integration=s3_apigw_integration, + request_parameters=request_parameters, + method_responses=[ + apigw.MethodResponse( + status_code="200", + response_parameters={ + 'method.response.header.Access-Control-Allow-Origin': True, + 'method.response.header.Access-Control-Allow-Headers': True, + 'method.response.header.Access-Control-Allow-Methods': True, + }, + ) + ] + ) + + def create_content_moderation_func(self, vpc=None, vpc_subnets=None): + _api_url_suffix = "_cn" if 'cn-' in os.getenv('AWS_REGION', '') else "" + content_moderation_api = self.node.try_get_context(f"content_moderation_api{_api_url_suffix}") + # content_moderation_token = self.node.try_get_context("content_moderation_account_token_in_base64") + content_moderation_result_table = self.node.try_get_context("content_moderation_result_table") + + _content_moderation_role_policy = _iam.PolicyStatement( + actions=[ + 'lambda:AWSLambdaBasicExecutionRole', + 'secretsmanager:GetSecretValue', + "logs:CreateLogGroup", + "logs:CreateLogStream", + "logs:DescribeLogGroups", + "logs:DescribeLogStreams", + "logs:PutLogEvents", + "logs:GetLogEvents", + "logs:FilterLogEvents" + ], + resources=['*'] + ) + + content_moderation_role = _iam.Role( + self, 'content_moderation_role', + assumed_by=_iam.ServicePrincipal('lambda.amazonaws.com') + ) + content_moderation_role.add_to_policy(_content_moderation_role_policy) + + content_moderation_role.add_managed_policy( + _iam.ManagedPolicy.from_aws_managed_policy_name("service-role/AWSLambdaBasicExecutionRole") + ) + + # add langchain processor for smart query and answer + function_name_content_moderation = 'content_moderation' + content_moderation_func = _lambda.Function( + self, function_name_content_moderation, + function_name=function_name_content_moderation, + runtime=_lambda.Runtime.PYTHON_3_9, + role=content_moderation_role, + layers=[self.langchain_processor_qa_layer], + code=_lambda.Code.from_asset('../lambda/' + function_name_content_moderation), + handler='lambda_function' + '.lambda_handler', + memory_size=256, + timeout=Duration.minutes(10), + vpc=vpc, + vpc_subnets=vpc_subnets, + reserved_concurrent_executions=10 + ) + content_moderation_func.add_environment("content_moderation_api", content_moderation_api) + + return content_moderation_func + def create_apigw_resource_method_for_knowledge_base_handler(self, api, knowledge_base_handler_function): knowledge_base_handler_resource = api.root.add_resource( diff --git a/lambda/.DS_Store b/lambda/.DS_Store index 140ba4a31cac33db702edadfd66ea8603a67d1c6..1e6d61ebfe4e89c2cd5241a628cacb68bdb8bb3e 100644 GIT binary patch delta 1349 zcmds#OGs2v7{|~5q;{@Oc}FK7ht#nt#K~%WU`9q-J~Bl`$C|RT8RtTm#u=SEj#$x{ zxrvCfp@^t9ElRA2jOaxz!d{COWt0^`5fSvTMYISPUFTkdNZYmPEYAPqJKyF0zaRg; z`o8*$-;6A;FfYfPn_mS z)wKs?pI2EY`4v^eFB7`04!PCmZIpLNCi44%aA9`x+{^{mB?Ze?tg2XB8}3akN)wIo zDXE-0I$d%{ryS@OEsTxCi^c>)IyZ;M5|ctaozG{i+3gHStNoICyS4W$o-K$@W>?gI zX`LLyVnI{{`*zu@&G1}9J`>$SlgH_jJdq(@XeeT0wXi;@dDfaHLopK^t-iK4$s16# z-b!A|#QQAVXEKHON?y*`MrE7RFV%HOP-R~PL|(T)xT5ke1ou^0Pr7-1a2QJlgVoW3+IW&o8P&1uJEwqp>r6shA*3vr7IEB8z23>Wb4z)-Mi(+Vl7I5#czLk@_K~zo%emruj!yNK21C^taXiO?X delta 1315 zcmchVS!_&E7{|Z=Debvj+)it><)E2bBGt~6P-+=VYip@=%Cv~gn5!<+JIuW;BVmYL zB*aDHNvw|zDnZ2hB(a4w9we4nfPWymngBbphDx4CR<^YewM z>sWePB%(%aQ+YYLE>Rn zP4|?|w*_Z$4(D+NS8*LTa5KfZ4NvhBuYMs{)s-CDt>^u{tsisVq_<`a{oEtj@%($D cx?{UrflB^4QU5JaQYRjku(_0VKSXKx0ivZXVgLXD From 13b1b5de17e19798be863ca435c3c981711675bb Mon Sep 17 00:00:00 2001 From: kris Date: Fri, 22 Mar 2024 11:51:50 +0800 Subject: [PATCH 97/97] vpc set to false as default --- deployment/cdk.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deployment/cdk.json b/deployment/cdk.json index d5b4e9ca..6ea8a06f 100644 --- a/deployment/cdk.json +++ b/deployment/cdk.json @@ -31,7 +31,7 @@ "aws", "aws-cn" ], - "vpc_deployment": true, + "vpc_deployment": false, "vpc_name": "smart_search-vpc", "subnet_name": "smart_search-subnet-private1-us-east-1a", "subnet_id": "subnet-1234",