wip

backend/open_webui/retrieval/loaders/main.py

@@ -0,0 +1,190 @@
+import requests
+import logging
+import ftfy
+
+from langchain_community.document_loaders import (
+    BSHTMLLoader,
+    CSVLoader,
+    Docx2txtLoader,
+    OutlookMessageLoader,
+    PyPDFLoader,
+    TextLoader,
+    UnstructuredEPubLoader,
+    UnstructuredExcelLoader,
+    UnstructuredMarkdownLoader,
+    UnstructuredPowerPointLoader,
+    UnstructuredRSTLoader,
+    UnstructuredXMLLoader,
+    YoutubeLoader,
+)
+from langchain_core.documents import Document
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+known_source_ext = [
+    "go",
+    "py",
+    "java",
+    "sh",
+    "bat",
+    "ps1",
+    "cmd",
+    "js",
+    "ts",
+    "css",
+    "cpp",
+    "hpp",
+    "h",
+    "c",
+    "cs",
+    "sql",
+    "log",
+    "ini",
+    "pl",
+    "pm",
+    "r",
+    "dart",
+    "dockerfile",
+    "env",
+    "php",
+    "hs",
+    "hsc",
+    "lua",
+    "nginxconf",
+    "conf",
+    "m",
+    "mm",
+    "plsql",
+    "perl",
+    "rb",
+    "rs",
+    "db2",
+    "scala",
+    "bash",
+    "swift",
+    "vue",
+    "svelte",
+    "msg",
+    "ex",
+    "exs",
+    "erl",
+    "tsx",
+    "jsx",
+    "hs",
+    "lhs",
+]
+
+
+class TikaLoader:
+    def __init__(self, url, file_path, mime_type=None):
+        self.url = url
+        self.file_path = file_path
+        self.mime_type = mime_type
+
+    def load(self) -> list[Document]:
+        with open(self.file_path, "rb") as f:
+            data = f.read()
+
+        if self.mime_type is not None:
+            headers = {"Content-Type": self.mime_type}
+        else:
+            headers = {}
+
+        endpoint = self.url
+        if not endpoint.endswith("/"):
+            endpoint += "/"
+        endpoint += "tika/text"
+
+        r = requests.put(endpoint, data=data, headers=headers)
+
+        if r.ok:
+            raw_metadata = r.json()
+            text = raw_metadata.get("X-TIKA:content", "<No text content found>")
+
+            if "Content-Type" in raw_metadata:
+                headers["Content-Type"] = raw_metadata["Content-Type"]
+
+            log.info("Tika extracted text: %s", text)
+
+            return [Document(page_content=text, metadata=headers)]
+        else:
+            raise Exception(f"Error calling Tika: {r.reason}")
+
+
+class Loader:
+    def __init__(self, engine: str = "", **kwargs):
+        self.engine = engine
+        self.kwargs = kwargs
+
+    def load(
+        self, filename: str, file_content_type: str, file_path: str
+    ) -> list[Document]:
+        loader = self._get_loader(filename, file_content_type, file_path)
+        docs = loader.load()
+
+        return [
+            Document(
+                page_content=ftfy.fix_text(doc.page_content), metadata=doc.metadata
+            )
+            for doc in docs
+        ]
+
+    def _get_loader(self, filename: str, file_content_type: str, file_path: str):
+        file_ext = filename.split(".")[-1].lower()
+
+        if self.engine == "tika" and self.kwargs.get("TIKA_SERVER_URL"):
+            if file_ext in known_source_ext or (
+                file_content_type and file_content_type.find("text/") >= 0
+            ):
+                loader = TextLoader(file_path, autodetect_encoding=True)
+            else:
+                loader = TikaLoader(
+                    url=self.kwargs.get("TIKA_SERVER_URL"),
+                    file_path=file_path,
+                    mime_type=file_content_type,
+                )
+        else:
+            if file_ext == "pdf":
+                loader = PyPDFLoader(
+                    file_path, extract_images=self.kwargs.get("PDF_EXTRACT_IMAGES")
+                )
+            elif file_ext == "csv":
+                loader = CSVLoader(file_path)
+            elif file_ext == "rst":
+                loader = UnstructuredRSTLoader(file_path, mode="elements")
+            elif file_ext == "xml":
+                loader = UnstructuredXMLLoader(file_path)
+            elif file_ext in ["htm", "html"]:
+                loader = BSHTMLLoader(file_path, open_encoding="unicode_escape")
+            elif file_ext == "md":
+                loader = TextLoader(file_path, autodetect_encoding=True)
+            elif file_content_type == "application/epub+zip":
+                loader = UnstructuredEPubLoader(file_path)
+            elif (
+                file_content_type
+                == "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+                or file_ext == "docx"
+            ):
+                loader = Docx2txtLoader(file_path)
+            elif file_content_type in [
+                "application/vnd.ms-excel",
+                "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+            ] or file_ext in ["xls", "xlsx"]:
+                loader = UnstructuredExcelLoader(file_path)
+            elif file_content_type in [
+                "application/vnd.ms-powerpoint",
+                "application/vnd.openxmlformats-officedocument.presentationml.presentation",
+            ] or file_ext in ["ppt", "pptx"]:
+                loader = UnstructuredPowerPointLoader(file_path)
+            elif file_ext == "msg":
+                loader = OutlookMessageLoader(file_path)
+            elif file_ext in known_source_ext or (
+                file_content_type and file_content_type.find("text/") >= 0
+            ):
+                loader = TextLoader(file_path, autodetect_encoding=True)
+            else:
+                loader = TextLoader(file_path, autodetect_encoding=True)
+
+        return loader

backend/open_webui/retrieval/loaders/youtube.py

@@ -0,0 +1,117 @@
+import logging
+
+from typing import Any, Dict, Generator, List, Optional, Sequence, Union
+from urllib.parse import parse_qs, urlparse
+from langchain_core.documents import Document
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+ALLOWED_SCHEMES = {"http", "https"}
+ALLOWED_NETLOCS = {
+    "youtu.be",
+    "m.youtube.com",
+    "youtube.com",
+    "www.youtube.com",
+    "www.youtube-nocookie.com",
+    "vid.plus",
+}
+
+
+def _parse_video_id(url: str) -> Optional[str]:
+    """Parse a YouTube URL and return the video ID if valid, otherwise None."""
+    parsed_url = urlparse(url)
+
+    if parsed_url.scheme not in ALLOWED_SCHEMES:
+        return None
+
+    if parsed_url.netloc not in ALLOWED_NETLOCS:
+        return None
+
+    path = parsed_url.path
+
+    if path.endswith("/watch"):
+        query = parsed_url.query
+        parsed_query = parse_qs(query)
+        if "v" in parsed_query:
+            ids = parsed_query["v"]
+            video_id = ids if isinstance(ids, str) else ids[0]
+        else:
+            return None
+    else:
+        path = parsed_url.path.lstrip("/")
+        video_id = path.split("/")[-1]
+
+    if len(video_id) != 11:  # Video IDs are 11 characters long
+        return None
+
+    return video_id
+
+
+class YoutubeLoader:
+    """Load `YouTube` video transcripts."""
+
+    def __init__(
+        self,
+        video_id: str,
+        language: Union[str, Sequence[str]] = "en",
+        proxy_url: Optional[str] = None,
+    ):
+        """Initialize with YouTube video ID."""
+        _video_id = _parse_video_id(video_id)
+        self.video_id = _video_id if _video_id is not None else video_id
+        self._metadata = {"source": video_id}
+        self.language = language
+        self.proxy_url = proxy_url
+        if isinstance(language, str):
+            self.language = [language]
+        else:
+            self.language = language
+
+    def load(self) -> List[Document]:
+        """Load YouTube transcripts into `Document` objects."""
+        try:
+            from youtube_transcript_api import (
+                NoTranscriptFound,
+                TranscriptsDisabled,
+                YouTubeTranscriptApi,
+            )
+        except ImportError:
+            raise ImportError(
+                'Could not import "youtube_transcript_api" Python package. '
+                "Please install it with `pip install youtube-transcript-api`."
+            )
+
+        if self.proxy_url:
+            youtube_proxies = {
+                "http": self.proxy_url,
+                "https": self.proxy_url,
+            }
+            # Don't log complete URL because it might contain secrets
+            log.debug(f"Using proxy URL: {self.proxy_url[:14]}...")
+        else:
+            youtube_proxies = None
+
+        try:
+            transcript_list = YouTubeTranscriptApi.list_transcripts(
+                self.video_id, proxies=youtube_proxies
+            )
+        except Exception as e:
+            log.exception("Loading YouTube transcript failed")
+            return []
+
+        try:
+            transcript = transcript_list.find_transcript(self.language)
+        except NoTranscriptFound:
+            transcript = transcript_list.find_transcript(["en"])
+
+        transcript_pieces: List[Dict[str, Any]] = transcript.fetch()
+
+        transcript = " ".join(
+            map(
+                lambda transcript_piece: transcript_piece["text"].strip(" "),
+                transcript_pieces,
+            )
+        )
+        return [Document(page_content=transcript, metadata=self._metadata)]

backend/open_webui/retrieval/models/colbert.py

@@ -0,0 +1,81 @@
+import os
+import torch
+import numpy as np
+from colbert.infra import ColBERTConfig
+from colbert.modeling.checkpoint import Checkpoint
+
+
+class ColBERT:
+    def __init__(self, name, **kwargs) -> None:
+        print("ColBERT: Loading model", name)
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        DOCKER = kwargs.get("env") == "docker"
+        if DOCKER:
+            # This is a workaround for the issue with the docker container
+            # where the torch extension is not loaded properly
+            # and the following error is thrown:
+            # /root/.cache/torch_extensions/py311_cpu/segmented_maxsim_cpp/segmented_maxsim_cpp.so: cannot open shared object file: No such file or directory
+
+            lock_file = (
+                "/root/.cache/torch_extensions/py311_cpu/segmented_maxsim_cpp/lock"
+            )
+            if os.path.exists(lock_file):
+                os.remove(lock_file)
+
+        self.ckpt = Checkpoint(
+            name,
+            colbert_config=ColBERTConfig(model_name=name),
+        ).to(self.device)
+        pass
+
+    def calculate_similarity_scores(self, query_embeddings, document_embeddings):
+
+        query_embeddings = query_embeddings.to(self.device)
+        document_embeddings = document_embeddings.to(self.device)
+
+        # Validate dimensions to ensure compatibility
+        if query_embeddings.dim() != 3:
+            raise ValueError(
+                f"Expected query embeddings to have 3 dimensions, but got {query_embeddings.dim()}."
+            )
+        if document_embeddings.dim() != 3:
+            raise ValueError(
+                f"Expected document embeddings to have 3 dimensions, but got {document_embeddings.dim()}."
+            )
+        if query_embeddings.size(0) not in [1, document_embeddings.size(0)]:
+            raise ValueError(
+                "There should be either one query or queries equal to the number of documents."
+            )
+
+        # Transpose the query embeddings to align for matrix multiplication
+        transposed_query_embeddings = query_embeddings.permute(0, 2, 1)
+        # Compute similarity scores using batch matrix multiplication
+        computed_scores = torch.matmul(document_embeddings, transposed_query_embeddings)
+        # Apply max pooling to extract the highest semantic similarity across each document's sequence
+        maximum_scores = torch.max(computed_scores, dim=1).values
+
+        # Sum up the maximum scores across features to get the overall document relevance scores
+        final_scores = maximum_scores.sum(dim=1)
+
+        normalized_scores = torch.softmax(final_scores, dim=0)
+
+        return normalized_scores.detach().cpu().numpy().astype(np.float32)
+
+    def predict(self, sentences):
+
+        query = sentences[0][0]
+        docs = [i[1] for i in sentences]
+
+        # Embedding the documents
+        embedded_docs = self.ckpt.docFromText(docs, bsize=32)[0]
+        # Embedding the queries
+        embedded_queries = self.ckpt.queryFromText([query], bsize=32)
+        embedded_query = embedded_queries[0]
+
+        # Calculate retrieval scores for the query against all documents
+        scores = self.calculate_similarity_scores(
+            embedded_query.unsqueeze(0), embedded_docs
+        )
+
+        return scores

backend/open_webui/retrieval/utils.py

@@ -0,0 +1,532 @@
+import logging
+import os
+import uuid
+from typing import Optional, Union
+
+import asyncio
+import requests
+
+from huggingface_hub import snapshot_download
+from langchain.retrievers import ContextualCompressionRetriever, EnsembleRetriever
+from langchain_community.retrievers import BM25Retriever
+from langchain_core.documents import Document
+
+from open_webui.apps.retrieval.vector.connector import VECTOR_DB_CLIENT
+from open_webui.utils.misc import get_last_user_message
+
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+from typing import Any
+
+from langchain_core.callbacks import CallbackManagerForRetrieverRun
+from langchain_core.retrievers import BaseRetriever
+
+
+class VectorSearchRetriever(BaseRetriever):
+    collection_name: Any
+    embedding_function: Any
+    top_k: int
+
+    def _get_relevant_documents(
+        self,
+        query: str,
+        *,
+        run_manager: CallbackManagerForRetrieverRun,
+    ) -> list[Document]:
+        result = VECTOR_DB_CLIENT.search(
+            collection_name=self.collection_name,
+            vectors=[self.embedding_function(query)],
+            limit=self.top_k,
+        )
+
+        ids = result.ids[0]
+        metadatas = result.metadatas[0]
+        documents = result.documents[0]
+
+        results = []
+        for idx in range(len(ids)):
+            results.append(
+                Document(
+                    metadata=metadatas[idx],
+                    page_content=documents[idx],
+                )
+            )
+        return results
+
+
+def query_doc(
+    collection_name: str,
+    query_embedding: list[float],
+    k: int,
+):
+    try:
+        result = VECTOR_DB_CLIENT.search(
+            collection_name=collection_name,
+            vectors=[query_embedding],
+            limit=k,
+        )
+
+        log.info(f"query_doc:result {result.ids} {result.metadatas}")
+        return result
+    except Exception as e:
+        print(e)
+        raise e
+
+
+def query_doc_with_hybrid_search(
+    collection_name: str,
+    query: str,
+    embedding_function,
+    k: int,
+    reranking_function,
+    r: float,
+) -> dict:
+    try:
+        result = VECTOR_DB_CLIENT.get(collection_name=collection_name)
+
+        bm25_retriever = BM25Retriever.from_texts(
+            texts=result.documents[0],
+            metadatas=result.metadatas[0],
+        )
+        bm25_retriever.k = k
+
+        vector_search_retriever = VectorSearchRetriever(
+            collection_name=collection_name,
+            embedding_function=embedding_function,
+            top_k=k,
+        )
+
+        ensemble_retriever = EnsembleRetriever(
+            retrievers=[bm25_retriever, vector_search_retriever], weights=[0.5, 0.5]
+        )
+        compressor = RerankCompressor(
+            embedding_function=embedding_function,
+            top_n=k,
+            reranking_function=reranking_function,
+            r_score=r,
+        )
+
+        compression_retriever = ContextualCompressionRetriever(
+            base_compressor=compressor, base_retriever=ensemble_retriever
+        )
+
+        result = compression_retriever.invoke(query)
+        result = {
+            "distances": [[d.metadata.get("score") for d in result]],
+            "documents": [[d.page_content for d in result]],
+            "metadatas": [[d.metadata for d in result]],
+        }
+
+        log.info(
+            "query_doc_with_hybrid_search:result "
+            + f'{result["metadatas"]} {result["distances"]}'
+        )
+        return result
+    except Exception as e:
+        raise e
+
+
+def merge_and_sort_query_results(
+    query_results: list[dict], k: int, reverse: bool = False
+) -> list[dict]:
+    # Initialize lists to store combined data
+    combined_distances = []
+    combined_documents = []
+    combined_metadatas = []
+
+    for data in query_results:
+        combined_distances.extend(data["distances"][0])
+        combined_documents.extend(data["documents"][0])
+        combined_metadatas.extend(data["metadatas"][0])
+
+    # Create a list of tuples (distance, document, metadata)
+    combined = list(zip(combined_distances, combined_documents, combined_metadatas))
+
+    # Sort the list based on distances
+    combined.sort(key=lambda x: x[0], reverse=reverse)
+
+    # We don't have anything :-(
+    if not combined:
+        sorted_distances = []
+        sorted_documents = []
+        sorted_metadatas = []
+    else:
+        # Unzip the sorted list
+        sorted_distances, sorted_documents, sorted_metadatas = zip(*combined)
+
+        # Slicing the lists to include only k elements
+        sorted_distances = list(sorted_distances)[:k]
+        sorted_documents = list(sorted_documents)[:k]
+        sorted_metadatas = list(sorted_metadatas)[:k]
+
+    # Create the output dictionary
+    result = {
+        "distances": [sorted_distances],
+        "documents": [sorted_documents],
+        "metadatas": [sorted_metadatas],
+    }
+
+    return result
+
+
+def query_collection(
+    collection_names: list[str],
+    queries: list[str],
+    embedding_function,
+    k: int,
+) -> dict:
+    results = []
+    for query in queries:
+        query_embedding = embedding_function(query)
+        for collection_name in collection_names:
+            if collection_name:
+                try:
+                    result = query_doc(
+                        collection_name=collection_name,
+                        k=k,
+                        query_embedding=query_embedding,
+                    )
+                    if result is not None:
+                        results.append(result.model_dump())
+                except Exception as e:
+                    log.exception(f"Error when querying the collection: {e}")
+            else:
+                pass
+
+    return merge_and_sort_query_results(results, k=k)
+
+
+def query_collection_with_hybrid_search(
+    collection_names: list[str],
+    queries: list[str],
+    embedding_function,
+    k: int,
+    reranking_function,
+    r: float,
+) -> dict:
+    results = []
+    error = False
+    for collection_name in collection_names:
+        try:
+            for query in queries:
+                result = query_doc_with_hybrid_search(
+                    collection_name=collection_name,
+                    query=query,
+                    embedding_function=embedding_function,
+                    k=k,
+                    reranking_function=reranking_function,
+                    r=r,
+                )
+                results.append(result)
+        except Exception as e:
+            log.exception(
+                "Error when querying the collection with " f"hybrid_search: {e}"
+            )
+            error = True
+
+    if error:
+        raise Exception(
+            "Hybrid search failed for all collections. Using Non hybrid search as fallback."
+        )
+
+    return merge_and_sort_query_results(results, k=k, reverse=True)
+
+
+def get_embedding_function(
+    embedding_engine,
+    embedding_model,
+    embedding_function,
+    url,
+    key,
+    embedding_batch_size,
+):
+    if embedding_engine == "":
+        return lambda query: embedding_function.encode(query).tolist()
+    elif embedding_engine in ["ollama", "openai"]:
+        func = lambda query: generate_embeddings(
+            engine=embedding_engine,
+            model=embedding_model,
+            text=query,
+            url=url,
+            key=key,
+        )
+
+        def generate_multiple(query, func):
+            if isinstance(query, list):
+                embeddings = []
+                for i in range(0, len(query), embedding_batch_size):
+                    embeddings.extend(func(query[i : i + embedding_batch_size]))
+                return embeddings
+            else:
+                return func(query)
+
+        return lambda query: generate_multiple(query, func)
+
+
+def get_sources_from_files(
+    files,
+    queries,
+    embedding_function,
+    k,
+    reranking_function,
+    r,
+    hybrid_search,
+):
+    log.debug(f"files: {files} {queries} {embedding_function} {reranking_function}")
+
+    extracted_collections = []
+    relevant_contexts = []
+
+    for file in files:
+        if file.get("context") == "full":
+            context = {
+                "documents": [[file.get("file").get("data", {}).get("content")]],
+                "metadatas": [[{"file_id": file.get("id"), "name": file.get("name")}]],
+            }
+        else:
+            context = None
+
+            collection_names = []
+            if file.get("type") == "collection":
+                if file.get("legacy"):
+                    collection_names = file.get("collection_names", [])
+                else:
+                    collection_names.append(file["id"])
+            elif file.get("collection_name"):
+                collection_names.append(file["collection_name"])
+            elif file.get("id"):
+                if file.get("legacy"):
+                    collection_names.append(f"{file['id']}")
+                else:
+                    collection_names.append(f"file-{file['id']}")
+
+            collection_names = set(collection_names).difference(extracted_collections)
+            if not collection_names:
+                log.debug(f"skipping {file} as it has already been extracted")
+                continue
+
+            try:
+                context = None
+                if file.get("type") == "text":
+                    context = file["content"]
+                else:
+                    if hybrid_search:
+                        try:
+                            context = query_collection_with_hybrid_search(
+                                collection_names=collection_names,
+                                queries=queries,
+                                embedding_function=embedding_function,
+                                k=k,
+                                reranking_function=reranking_function,
+                                r=r,
+                            )
+                        except Exception as e:
+                            log.debug(
+                                "Error when using hybrid search, using"
+                                " non hybrid search as fallback."
+                            )
+
+                    if (not hybrid_search) or (context is None):
+                        context = query_collection(
+                            collection_names=collection_names,
+                            queries=queries,
+                            embedding_function=embedding_function,
+                            k=k,
+                        )
+            except Exception as e:
+                log.exception(e)
+
+            extracted_collections.extend(collection_names)
+
+        if context:
+            if "data" in file:
+                del file["data"]
+            relevant_contexts.append({**context, "file": file})
+
+    sources = []
+    for context in relevant_contexts:
+        try:
+            if "documents" in context:
+                if "metadatas" in context:
+                    source = {
+                        "source": context["file"],
+                        "document": context["documents"][0],
+                        "metadata": context["metadatas"][0],
+                    }
+                    if "distances" in context and context["distances"]:
+                        source["distances"] = context["distances"][0]
+
+                    sources.append(source)
+        except Exception as e:
+            log.exception(e)
+
+    return sources
+
+
+def get_model_path(model: str, update_model: bool = False):
+    # Construct huggingface_hub kwargs with local_files_only to return the snapshot path
+    cache_dir = os.getenv("SENTENCE_TRANSFORMERS_HOME")
+
+    local_files_only = not update_model
+
+    snapshot_kwargs = {
+        "cache_dir": cache_dir,
+        "local_files_only": local_files_only,
+    }
+
+    log.debug(f"model: {model}")
+    log.debug(f"snapshot_kwargs: {snapshot_kwargs}")
+
+    # Inspiration from upstream sentence_transformers
+    if (
+        os.path.exists(model)
+        or ("\\" in model or model.count("/") > 1)
+        and local_files_only
+    ):
+        # If fully qualified path exists, return input, else set repo_id
+        return model
+    elif "/" not in model:
+        # Set valid repo_id for model short-name
+        model = "sentence-transformers" + "/" + model
+
+    snapshot_kwargs["repo_id"] = model
+
+    # Attempt to query the huggingface_hub library to determine the local path and/or to update
+    try:
+        model_repo_path = snapshot_download(**snapshot_kwargs)
+        log.debug(f"model_repo_path: {model_repo_path}")
+        return model_repo_path
+    except Exception as e:
+        log.exception(f"Cannot determine model snapshot path: {e}")
+        return model
+
+
+def generate_openai_batch_embeddings(
+    model: str, texts: list[str], url: str = "https://api.openai.com/v1", key: str = ""
+) -> Optional[list[list[float]]]:
+    try:
+        r = requests.post(
+            f"{url}/embeddings",
+            headers={
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {key}",
+            },
+            json={"input": texts, "model": model},
+        )
+        r.raise_for_status()
+        data = r.json()
+        if "data" in data:
+            return [elem["embedding"] for elem in data["data"]]
+        else:
+            raise "Something went wrong :/"
+    except Exception as e:
+        print(e)
+        return None
+
+
+def generate_ollama_batch_embeddings(
+    model: str, texts: list[str], url: str, key: str = ""
+) -> Optional[list[list[float]]]:
+    try:
+        r = requests.post(
+            f"{url}/api/embed",
+            headers={
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {key}",
+            },
+            json={"input": texts, "model": model},
+        )
+        r.raise_for_status()
+        data = r.json()
+
+        if "embeddings" in data:
+            return data["embeddings"]
+        else:
+            raise "Something went wrong :/"
+    except Exception as e:
+        print(e)
+        return None
+
+
+def generate_embeddings(engine: str, model: str, text: Union[str, list[str]], **kwargs):
+    url = kwargs.get("url", "")
+    key = kwargs.get("key", "")
+
+    if engine == "ollama":
+        if isinstance(text, list):
+            embeddings = generate_ollama_batch_embeddings(
+                **{"model": model, "texts": text, "url": url, "key": key}
+            )
+        else:
+            embeddings = generate_ollama_batch_embeddings(
+                **{"model": model, "texts": [text], "url": url, "key": key}
+            )
+        return embeddings[0] if isinstance(text, str) else embeddings
+    elif engine == "openai":
+        if isinstance(text, list):
+            embeddings = generate_openai_batch_embeddings(model, text, url, key)
+        else:
+            embeddings = generate_openai_batch_embeddings(model, [text], url, key)
+
+        return embeddings[0] if isinstance(text, str) else embeddings
+
+
+import operator
+from typing import Optional, Sequence
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.documents import BaseDocumentCompressor, Document
+
+
+class RerankCompressor(BaseDocumentCompressor):
+    embedding_function: Any
+    top_n: int
+    reranking_function: Any
+    r_score: float
+
+    class Config:
+        extra = "forbid"
+        arbitrary_types_allowed = True
+
+    def compress_documents(
+        self,
+        documents: Sequence[Document],
+        query: str,
+        callbacks: Optional[Callbacks] = None,
+    ) -> Sequence[Document]:
+        reranking = self.reranking_function is not None
+
+        if reranking:
+            scores = self.reranking_function.predict(
+                [(query, doc.page_content) for doc in documents]
+            )
+        else:
+            from sentence_transformers import util
+
+            query_embedding = self.embedding_function(query)
+            document_embedding = self.embedding_function(
+                [doc.page_content for doc in documents]
+            )
+            scores = util.cos_sim(query_embedding, document_embedding)[0]
+
+        docs_with_scores = list(zip(documents, scores.tolist()))
+        if self.r_score:
+            docs_with_scores = [
+                (d, s) for d, s in docs_with_scores if s >= self.r_score
+            ]
+
+        result = sorted(docs_with_scores, key=operator.itemgetter(1), reverse=True)
+        final_results = []
+        for doc, doc_score in result[: self.top_n]:
+            metadata = doc.metadata
+            metadata["score"] = doc_score
+            doc = Document(
+                page_content=doc.page_content,
+                metadata=metadata,
+            )
+            final_results.append(doc)
+        return final_results

backend/open_webui/retrieval/vector/connector.py

@@ -0,0 +1,22 @@
+from open_webui.config import VECTOR_DB
+
+if VECTOR_DB == "milvus":
+    from open_webui.apps.retrieval.vector.dbs.milvus import MilvusClient
+
+    VECTOR_DB_CLIENT = MilvusClient()
+elif VECTOR_DB == "qdrant":
+    from open_webui.apps.retrieval.vector.dbs.qdrant import QdrantClient
+
+    VECTOR_DB_CLIENT = QdrantClient()
+elif VECTOR_DB == "opensearch":
+    from open_webui.apps.retrieval.vector.dbs.opensearch import OpenSearchClient
+
+    VECTOR_DB_CLIENT = OpenSearchClient()
+elif VECTOR_DB == "pgvector":
+    from open_webui.apps.retrieval.vector.dbs.pgvector import PgvectorClient
+
+    VECTOR_DB_CLIENT = PgvectorClient()
+else:
+    from open_webui.apps.retrieval.vector.dbs.chroma import ChromaClient
+
+    VECTOR_DB_CLIENT = ChromaClient()

backend/open_webui/retrieval/vector/dbs/chroma.py

@@ -0,0 +1,174 @@
+import chromadb
+from chromadb import Settings
+from chromadb.utils.batch_utils import create_batches
+
+from typing import Optional
+
+from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
+from open_webui.config import (
+    CHROMA_DATA_PATH,
+    CHROMA_HTTP_HOST,
+    CHROMA_HTTP_PORT,
+    CHROMA_HTTP_HEADERS,
+    CHROMA_HTTP_SSL,
+    CHROMA_TENANT,
+    CHROMA_DATABASE,
+    CHROMA_CLIENT_AUTH_PROVIDER,
+    CHROMA_CLIENT_AUTH_CREDENTIALS,
+)
+
+
+class ChromaClient:
+    def __init__(self):
+        settings_dict = {
+            "allow_reset": True,
+            "anonymized_telemetry": False,
+        }
+        if CHROMA_CLIENT_AUTH_PROVIDER is not None:
+            settings_dict["chroma_client_auth_provider"] = CHROMA_CLIENT_AUTH_PROVIDER
+        if CHROMA_CLIENT_AUTH_CREDENTIALS is not None:
+            settings_dict["chroma_client_auth_credentials"] = (
+                CHROMA_CLIENT_AUTH_CREDENTIALS
+            )
+
+        if CHROMA_HTTP_HOST != "":
+            self.client = chromadb.HttpClient(
+                host=CHROMA_HTTP_HOST,
+                port=CHROMA_HTTP_PORT,
+                headers=CHROMA_HTTP_HEADERS,
+                ssl=CHROMA_HTTP_SSL,
+                tenant=CHROMA_TENANT,
+                database=CHROMA_DATABASE,
+                settings=Settings(**settings_dict),
+            )
+        else:
+            self.client = chromadb.PersistentClient(
+                path=CHROMA_DATA_PATH,
+                settings=Settings(**settings_dict),
+                tenant=CHROMA_TENANT,
+                database=CHROMA_DATABASE,
+            )
+
+    def has_collection(self, collection_name: str) -> bool:
+        # Check if the collection exists based on the collection name.
+        collections = self.client.list_collections()
+        return collection_name in [collection.name for collection in collections]
+
+    def delete_collection(self, collection_name: str):
+        # Delete the collection based on the collection name.
+        return self.client.delete_collection(name=collection_name)
+
+    def search(
+        self, collection_name: str, vectors: list[list[float | int]], limit: int
+    ) -> Optional[SearchResult]:
+        # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
+        try:
+            collection = self.client.get_collection(name=collection_name)
+            if collection:
+                result = collection.query(
+                    query_embeddings=vectors,
+                    n_results=limit,
+                )
+
+                return SearchResult(
+                    **{
+                        "ids": result["ids"],
+                        "distances": result["distances"],
+                        "documents": result["documents"],
+                        "metadatas": result["metadatas"],
+                    }
+                )
+            return None
+        except Exception as e:
+            return None
+
+    def query(
+        self, collection_name: str, filter: dict, limit: Optional[int] = None
+    ) -> Optional[GetResult]:
+        # Query the items from the collection based on the filter.
+        try:
+            collection = self.client.get_collection(name=collection_name)
+            if collection:
+                result = collection.get(
+                    where=filter,
+                    limit=limit,
+                )
+
+                return GetResult(
+                    **{
+                        "ids": [result["ids"]],
+                        "documents": [result["documents"]],
+                        "metadatas": [result["metadatas"]],
+                    }
+                )
+            return None
+        except Exception as e:
+            print(e)
+            return None
+
+    def get(self, collection_name: str) -> Optional[GetResult]:
+        # Get all the items in the collection.
+        collection = self.client.get_collection(name=collection_name)
+        if collection:
+            result = collection.get()
+            return GetResult(
+                **{
+                    "ids": [result["ids"]],
+                    "documents": [result["documents"]],
+                    "metadatas": [result["metadatas"]],
+                }
+            )
+        return None
+
+    def insert(self, collection_name: str, items: list[VectorItem]):
+        # Insert the items into the collection, if the collection does not exist, it will be created.
+        collection = self.client.get_or_create_collection(
+            name=collection_name, metadata={"hnsw:space": "cosine"}
+        )
+
+        ids = [item["id"] for item in items]
+        documents = [item["text"] for item in items]
+        embeddings = [item["vector"] for item in items]
+        metadatas = [item["metadata"] for item in items]
+
+        for batch in create_batches(
+            api=self.client,
+            documents=documents,
+            embeddings=embeddings,
+            ids=ids,
+            metadatas=metadatas,
+        ):
+            collection.add(*batch)
+
+    def upsert(self, collection_name: str, items: list[VectorItem]):
+        # Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
+        collection = self.client.get_or_create_collection(
+            name=collection_name, metadata={"hnsw:space": "cosine"}
+        )
+
+        ids = [item["id"] for item in items]
+        documents = [item["text"] for item in items]
+        embeddings = [item["vector"] for item in items]
+        metadatas = [item["metadata"] for item in items]
+
+        collection.upsert(
+            ids=ids, documents=documents, embeddings=embeddings, metadatas=metadatas
+        )
+
+    def delete(
+        self,
+        collection_name: str,
+        ids: Optional[list[str]] = None,
+        filter: Optional[dict] = None,
+    ):
+        # Delete the items from the collection based on the ids.
+        collection = self.client.get_collection(name=collection_name)
+        if collection:
+            if ids:
+                collection.delete(ids=ids)
+            elif filter:
+                collection.delete(where=filter)
+
+    def reset(self):
+        # Resets the database. This will delete all collections and item entries.
+        return self.client.reset()

backend/open_webui/retrieval/vector/dbs/milvus.py

@@ -0,0 +1,286 @@
+from pymilvus import MilvusClient as Client
+from pymilvus import FieldSchema, DataType
+import json
+
+from typing import Optional
+
+from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
+from open_webui.config import (
+    MILVUS_URI,
+)
+
+
+class MilvusClient:
+    def __init__(self):
+        self.collection_prefix = "open_webui"
+        self.client = Client(uri=MILVUS_URI)
+
+    def _result_to_get_result(self, result) -> GetResult:
+        ids = []
+        documents = []
+        metadatas = []
+
+        for match in result:
+            _ids = []
+            _documents = []
+            _metadatas = []
+            for item in match:
+                _ids.append(item.get("id"))
+                _documents.append(item.get("data", {}).get("text"))
+                _metadatas.append(item.get("metadata"))
+
+            ids.append(_ids)
+            documents.append(_documents)
+            metadatas.append(_metadatas)
+
+        return GetResult(
+            **{
+                "ids": ids,
+                "documents": documents,
+                "metadatas": metadatas,
+            }
+        )
+
+    def _result_to_search_result(self, result) -> SearchResult:
+        ids = []
+        distances = []
+        documents = []
+        metadatas = []
+
+        for match in result:
+            _ids = []
+            _distances = []
+            _documents = []
+            _metadatas = []
+
+            for item in match:
+                _ids.append(item.get("id"))
+                _distances.append(item.get("distance"))
+                _documents.append(item.get("entity", {}).get("data", {}).get("text"))
+                _metadatas.append(item.get("entity", {}).get("metadata"))
+
+            ids.append(_ids)
+            distances.append(_distances)
+            documents.append(_documents)
+            metadatas.append(_metadatas)
+
+        return SearchResult(
+            **{
+                "ids": ids,
+                "distances": distances,
+                "documents": documents,
+                "metadatas": metadatas,
+            }
+        )
+
+    def _create_collection(self, collection_name: str, dimension: int):
+        schema = self.client.create_schema(
+            auto_id=False,
+            enable_dynamic_field=True,
+        )
+        schema.add_field(
+            field_name="id",
+            datatype=DataType.VARCHAR,
+            is_primary=True,
+            max_length=65535,
+        )
+        schema.add_field(
+            field_name="vector",
+            datatype=DataType.FLOAT_VECTOR,
+            dim=dimension,
+            description="vector",
+        )
+        schema.add_field(field_name="data", datatype=DataType.JSON, description="data")
+        schema.add_field(
+            field_name="metadata", datatype=DataType.JSON, description="metadata"
+        )
+
+        index_params = self.client.prepare_index_params()
+        index_params.add_index(
+            field_name="vector",
+            index_type="HNSW",
+            metric_type="COSINE",
+            params={"M": 16, "efConstruction": 100},
+        )
+
+        self.client.create_collection(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            schema=schema,
+            index_params=index_params,
+        )
+
+    def has_collection(self, collection_name: str) -> bool:
+        # Check if the collection exists based on the collection name.
+        collection_name = collection_name.replace("-", "_")
+        return self.client.has_collection(
+            collection_name=f"{self.collection_prefix}_{collection_name}"
+        )
+
+    def delete_collection(self, collection_name: str):
+        # Delete the collection based on the collection name.
+        collection_name = collection_name.replace("-", "_")
+        return self.client.drop_collection(
+            collection_name=f"{self.collection_prefix}_{collection_name}"
+        )
+
+    def search(
+        self, collection_name: str, vectors: list[list[float | int]], limit: int
+    ) -> Optional[SearchResult]:
+        # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
+        collection_name = collection_name.replace("-", "_")
+        result = self.client.search(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            data=vectors,
+            limit=limit,
+            output_fields=["data", "metadata"],
+        )
+
+        return self._result_to_search_result(result)
+
+    def query(self, collection_name: str, filter: dict, limit: Optional[int] = None):
+        # Construct the filter string for querying
+        collection_name = collection_name.replace("-", "_")
+        if not self.has_collection(collection_name):
+            return None
+
+        filter_string = " && ".join(
+            [
+                f'metadata["{key}"] == {json.dumps(value)}'
+                for key, value in filter.items()
+            ]
+        )
+
+        max_limit = 16383  # The maximum number of records per request
+        all_results = []
+
+        if limit is None:
+            limit = float("inf")  # Use infinity as a placeholder for no limit
+
+        # Initialize offset and remaining to handle pagination
+        offset = 0
+        remaining = limit
+
+        try:
+            # Loop until there are no more items to fetch or the desired limit is reached
+            while remaining > 0:
+                print("remaining", remaining)
+                current_fetch = min(
+                    max_limit, remaining
+                )  # Determine how many items to fetch in this iteration
+
+                results = self.client.query(
+                    collection_name=f"{self.collection_prefix}_{collection_name}",
+                    filter=filter_string,
+                    output_fields=["*"],
+                    limit=current_fetch,
+                    offset=offset,
+                )
+
+                if not results:
+                    break
+
+                all_results.extend(results)
+                results_count = len(results)
+                remaining -= (
+                    results_count  # Decrease remaining by the number of items fetched
+                )
+                offset += results_count
+
+                # Break the loop if the results returned are less than the requested fetch count
+                if results_count < current_fetch:
+                    break
+
+            print(all_results)
+            return self._result_to_get_result([all_results])
+        except Exception as e:
+            print(e)
+            return None
+
+    def get(self, collection_name: str) -> Optional[GetResult]:
+        # Get all the items in the collection.
+        collection_name = collection_name.replace("-", "_")
+        result = self.client.query(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            filter='id != ""',
+        )
+        return self._result_to_get_result([result])
+
+    def insert(self, collection_name: str, items: list[VectorItem]):
+        # Insert the items into the collection, if the collection does not exist, it will be created.
+        collection_name = collection_name.replace("-", "_")
+        if not self.client.has_collection(
+            collection_name=f"{self.collection_prefix}_{collection_name}"
+        ):
+            self._create_collection(
+                collection_name=collection_name, dimension=len(items[0]["vector"])
+            )
+
+        return self.client.insert(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            data=[
+                {
+                    "id": item["id"],
+                    "vector": item["vector"],
+                    "data": {"text": item["text"]},
+                    "metadata": item["metadata"],
+                }
+                for item in items
+            ],
+        )
+
+    def upsert(self, collection_name: str, items: list[VectorItem]):
+        # Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
+        collection_name = collection_name.replace("-", "_")
+        if not self.client.has_collection(
+            collection_name=f"{self.collection_prefix}_{collection_name}"
+        ):
+            self._create_collection(
+                collection_name=collection_name, dimension=len(items[0]["vector"])
+            )
+
+        return self.client.upsert(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            data=[
+                {
+                    "id": item["id"],
+                    "vector": item["vector"],
+                    "data": {"text": item["text"]},
+                    "metadata": item["metadata"],
+                }
+                for item in items
+            ],
+        )
+
+    def delete(
+        self,
+        collection_name: str,
+        ids: Optional[list[str]] = None,
+        filter: Optional[dict] = None,
+    ):
+        # Delete the items from the collection based on the ids.
+        collection_name = collection_name.replace("-", "_")
+        if ids:
+            return self.client.delete(
+                collection_name=f"{self.collection_prefix}_{collection_name}",
+                ids=ids,
+            )
+        elif filter:
+            # Convert the filter dictionary to a string using JSON_CONTAINS.
+            filter_string = " && ".join(
+                [
+                    f'metadata["{key}"] == {json.dumps(value)}'
+                    for key, value in filter.items()
+                ]
+            )
+
+            return self.client.delete(
+                collection_name=f"{self.collection_prefix}_{collection_name}",
+                filter=filter_string,
+            )
+
+    def reset(self):
+        # Resets the database. This will delete all collections and item entries.
+        collection_names = self.client.list_collections()
+        for collection_name in collection_names:
+            if collection_name.startswith(self.collection_prefix):
+                self.client.drop_collection(collection_name=collection_name)

backend/open_webui/retrieval/vector/dbs/opensearch.py

@@ -0,0 +1,178 @@
+from opensearchpy import OpenSearch
+from typing import Optional
+
+from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
+from open_webui.config import (
+    OPENSEARCH_URI,
+    OPENSEARCH_SSL,
+    OPENSEARCH_CERT_VERIFY,
+    OPENSEARCH_USERNAME,
+    OPENSEARCH_PASSWORD,
+)
+
+
+class OpenSearchClient:
+    def __init__(self):
+        self.index_prefix = "open_webui"
+        self.client = OpenSearch(
+            hosts=[OPENSEARCH_URI],
+            use_ssl=OPENSEARCH_SSL,
+            verify_certs=OPENSEARCH_CERT_VERIFY,
+            http_auth=(OPENSEARCH_USERNAME, OPENSEARCH_PASSWORD),
+        )
+
+    def _result_to_get_result(self, result) -> GetResult:
+        ids = []
+        documents = []
+        metadatas = []
+
+        for hit in result["hits"]["hits"]:
+            ids.append(hit["_id"])
+            documents.append(hit["_source"].get("text"))
+            metadatas.append(hit["_source"].get("metadata"))
+
+        return GetResult(ids=ids, documents=documents, metadatas=metadatas)
+
+    def _result_to_search_result(self, result) -> SearchResult:
+        ids = []
+        distances = []
+        documents = []
+        metadatas = []
+
+        for hit in result["hits"]["hits"]:
+            ids.append(hit["_id"])
+            distances.append(hit["_score"])
+            documents.append(hit["_source"].get("text"))
+            metadatas.append(hit["_source"].get("metadata"))
+
+        return SearchResult(
+            ids=ids, distances=distances, documents=documents, metadatas=metadatas
+        )
+
+    def _create_index(self, index_name: str, dimension: int):
+        body = {
+            "mappings": {
+                "properties": {
+                    "id": {"type": "keyword"},
+                    "vector": {
+                        "type": "dense_vector",
+                        "dims": dimension,  # Adjust based on your vector dimensions
+                        "index": true,
+                        "similarity": "faiss",
+                        "method": {
+                            "name": "hnsw",
+                            "space_type": "ip",  # Use inner product to approximate cosine similarity
+                            "engine": "faiss",
+                            "ef_construction": 128,
+                            "m": 16,
+                        },
+                    },
+                    "text": {"type": "text"},
+                    "metadata": {"type": "object"},
+                }
+            }
+        }
+        self.client.indices.create(index=f"{self.index_prefix}_{index_name}", body=body)
+
+    def _create_batches(self, items: list[VectorItem], batch_size=100):
+        for i in range(0, len(items), batch_size):
+            yield items[i : i + batch_size]
+
+    def has_collection(self, index_name: str) -> bool:
+        # has_collection here means has index.
+        # We are simply adapting to the norms of the other DBs.
+        return self.client.indices.exists(index=f"{self.index_prefix}_{index_name}")
+
+    def delete_colleciton(self, index_name: str):
+        # delete_collection here means delete index.
+        # We are simply adapting to the norms of the other DBs.
+        self.client.indices.delete(index=f"{self.index_prefix}_{index_name}")
+
+    def search(
+        self, index_name: str, vectors: list[list[float]], limit: int
+    ) -> Optional[SearchResult]:
+        query = {
+            "size": limit,
+            "_source": ["text", "metadata"],
+            "query": {
+                "script_score": {
+                    "query": {"match_all": {}},
+                    "script": {
+                        "source": "cosineSimilarity(params.vector, 'vector') + 1.0",
+                        "params": {
+                            "vector": vectors[0]
+                        },  # Assuming single query vector
+                    },
+                }
+            },
+        }
+
+        result = self.client.search(
+            index=f"{self.index_prefix}_{index_name}", body=query
+        )
+
+        return self._result_to_search_result(result)
+
+    def get_or_create_index(self, index_name: str, dimension: int):
+        if not self.has_index(index_name):
+            self._create_index(index_name, dimension)
+
+    def get(self, index_name: str) -> Optional[GetResult]:
+        query = {"query": {"match_all": {}}, "_source": ["text", "metadata"]}
+
+        result = self.client.search(
+            index=f"{self.index_prefix}_{index_name}", body=query
+        )
+        return self._result_to_get_result(result)
+
+    def insert(self, index_name: str, items: list[VectorItem]):
+        if not self.has_index(index_name):
+            self._create_index(index_name, dimension=len(items[0]["vector"]))
+
+        for batch in self._create_batches(items):
+            actions = [
+                {
+                    "index": {
+                        "_id": item["id"],
+                        "_source": {
+                            "vector": item["vector"],
+                            "text": item["text"],
+                            "metadata": item["metadata"],
+                        },
+                    }
+                }
+                for item in batch
+            ]
+            self.client.bulk(actions)
+
+    def upsert(self, index_name: str, items: list[VectorItem]):
+        if not self.has_index(index_name):
+            self._create_index(index_name, dimension=len(items[0]["vector"]))
+
+        for batch in self._create_batches(items):
+            actions = [
+                {
+                    "index": {
+                        "_id": item["id"],
+                        "_source": {
+                            "vector": item["vector"],
+                            "text": item["text"],
+                            "metadata": item["metadata"],
+                        },
+                    }
+                }
+                for item in batch
+            ]
+            self.client.bulk(actions)
+
+    def delete(self, index_name: str, ids: list[str]):
+        actions = [
+            {"delete": {"_index": f"{self.index_prefix}_{index_name}", "_id": id}}
+            for id in ids
+        ]
+        self.client.bulk(body=actions)
+
+    def reset(self):
+        indices = self.client.indices.get(index=f"{self.index_prefix}_*")
+        for index in indices:
+            self.client.indices.delete(index=index)

backend/open_webui/retrieval/vector/dbs/pgvector.py

@@ -0,0 +1,354 @@
+from typing import Optional, List, Dict, Any
+from sqlalchemy import (
+    cast,
+    column,
+    create_engine,
+    Column,
+    Integer,
+    select,
+    text,
+    Text,
+    values,
+)
+from sqlalchemy.sql import true
+from sqlalchemy.pool import NullPool
+
+from sqlalchemy.orm import declarative_base, scoped_session, sessionmaker
+from sqlalchemy.dialects.postgresql import JSONB, array
+from pgvector.sqlalchemy import Vector
+from sqlalchemy.ext.mutable import MutableDict
+
+from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
+from open_webui.config import PGVECTOR_DB_URL
+
+VECTOR_LENGTH = 1536
+Base = declarative_base()
+
+
+class DocumentChunk(Base):
+    __tablename__ = "document_chunk"
+
+    id = Column(Text, primary_key=True)
+    vector = Column(Vector(dim=VECTOR_LENGTH), nullable=True)
+    collection_name = Column(Text, nullable=False)
+    text = Column(Text, nullable=True)
+    vmetadata = Column(MutableDict.as_mutable(JSONB), nullable=True)
+
+
+class PgvectorClient:
+    def __init__(self) -> None:
+
+        # if no pgvector uri, use the existing database connection
+        if not PGVECTOR_DB_URL:
+            from open_webui.internal.db import Session
+
+            self.session = Session
+        else:
+            engine = create_engine(
+                PGVECTOR_DB_URL, pool_pre_ping=True, poolclass=NullPool
+            )
+            SessionLocal = sessionmaker(
+                autocommit=False, autoflush=False, bind=engine, expire_on_commit=False
+            )
+            self.session = scoped_session(SessionLocal)
+
+        try:
+            # Ensure the pgvector extension is available
+            self.session.execute(text("CREATE EXTENSION IF NOT EXISTS vector;"))
+
+            # Create the tables if they do not exist
+            # Base.metadata.create_all requires a bind (engine or connection)
+            # Get the connection from the session
+            connection = self.session.connection()
+            Base.metadata.create_all(bind=connection)
+
+            # Create an index on the vector column if it doesn't exist
+            self.session.execute(
+                text(
+                    "CREATE INDEX IF NOT EXISTS idx_document_chunk_vector "
+                    "ON document_chunk USING ivfflat (vector vector_cosine_ops) WITH (lists = 100);"
+                )
+            )
+            self.session.execute(
+                text(
+                    "CREATE INDEX IF NOT EXISTS idx_document_chunk_collection_name "
+                    "ON document_chunk (collection_name);"
+                )
+            )
+            self.session.commit()
+            print("Initialization complete.")
+        except Exception as e:
+            self.session.rollback()
+            print(f"Error during initialization: {e}")
+            raise
+
+    def adjust_vector_length(self, vector: List[float]) -> List[float]:
+        # Adjust vector to have length VECTOR_LENGTH
+        current_length = len(vector)
+        if current_length < VECTOR_LENGTH:
+            # Pad the vector with zeros
+            vector += [0.0] * (VECTOR_LENGTH - current_length)
+        elif current_length > VECTOR_LENGTH:
+            raise Exception(
+                f"Vector length {current_length} not supported. Max length must be <= {VECTOR_LENGTH}"
+            )
+        return vector
+
+    def insert(self, collection_name: str, items: List[VectorItem]) -> None:
+        try:
+            new_items = []
+            for item in items:
+                vector = self.adjust_vector_length(item["vector"])
+                new_chunk = DocumentChunk(
+                    id=item["id"],
+                    vector=vector,
+                    collection_name=collection_name,
+                    text=item["text"],
+                    vmetadata=item["metadata"],
+                )
+                new_items.append(new_chunk)
+            self.session.bulk_save_objects(new_items)
+            self.session.commit()
+            print(
+                f"Inserted {len(new_items)} items into collection '{collection_name}'."
+            )
+        except Exception as e:
+            self.session.rollback()
+            print(f"Error during insert: {e}")
+            raise
+
+    def upsert(self, collection_name: str, items: List[VectorItem]) -> None:
+        try:
+            for item in items:
+                vector = self.adjust_vector_length(item["vector"])
+                existing = (
+                    self.session.query(DocumentChunk)
+                    .filter(DocumentChunk.id == item["id"])
+                    .first()
+                )
+                if existing:
+                    existing.vector = vector
+                    existing.text = item["text"]
+                    existing.vmetadata = item["metadata"]
+                    existing.collection_name = (
+                        collection_name  # Update collection_name if necessary
+                    )
+                else:
+                    new_chunk = DocumentChunk(
+                        id=item["id"],
+                        vector=vector,
+                        collection_name=collection_name,
+                        text=item["text"],
+                        vmetadata=item["metadata"],
+                    )
+                    self.session.add(new_chunk)
+            self.session.commit()
+            print(f"Upserted {len(items)} items into collection '{collection_name}'.")
+        except Exception as e:
+            self.session.rollback()
+            print(f"Error during upsert: {e}")
+            raise
+
+    def search(
+        self,
+        collection_name: str,
+        vectors: List[List[float]],
+        limit: Optional[int] = None,
+    ) -> Optional[SearchResult]:
+        try:
+            if not vectors:
+                return None
+
+            # Adjust query vectors to VECTOR_LENGTH
+            vectors = [self.adjust_vector_length(vector) for vector in vectors]
+            num_queries = len(vectors)
+
+            def vector_expr(vector):
+                return cast(array(vector), Vector(VECTOR_LENGTH))
+
+            # Create the values for query vectors
+            qid_col = column("qid", Integer)
+            q_vector_col = column("q_vector", Vector(VECTOR_LENGTH))
+            query_vectors = (
+                values(qid_col, q_vector_col)
+                .data(
+                    [(idx, vector_expr(vector)) for idx, vector in enumerate(vectors)]
+                )
+                .alias("query_vectors")
+            )
+
+            # Build the lateral subquery for each query vector
+            subq = (
+                select(
+                    DocumentChunk.id,
+                    DocumentChunk.text,
+                    DocumentChunk.vmetadata,
+                    (
+                        DocumentChunk.vector.cosine_distance(query_vectors.c.q_vector)
+                    ).label("distance"),
+                )
+                .where(DocumentChunk.collection_name == collection_name)
+                .order_by(
+                    (DocumentChunk.vector.cosine_distance(query_vectors.c.q_vector))
+                )
+            )
+            if limit is not None:
+                subq = subq.limit(limit)
+            subq = subq.lateral("result")
+
+            # Build the main query by joining query_vectors and the lateral subquery
+            stmt = (
+                select(
+                    query_vectors.c.qid,
+                    subq.c.id,
+                    subq.c.text,
+                    subq.c.vmetadata,
+                    subq.c.distance,
+                )
+                .select_from(query_vectors)
+                .join(subq, true())
+                .order_by(query_vectors.c.qid, subq.c.distance)
+            )
+
+            result_proxy = self.session.execute(stmt)
+            results = result_proxy.all()
+
+            ids = [[] for _ in range(num_queries)]
+            distances = [[] for _ in range(num_queries)]
+            documents = [[] for _ in range(num_queries)]
+            metadatas = [[] for _ in range(num_queries)]
+
+            if not results:
+                return SearchResult(
+                    ids=ids,
+                    distances=distances,
+                    documents=documents,
+                    metadatas=metadatas,
+                )
+
+            for row in results:
+                qid = int(row.qid)
+                ids[qid].append(row.id)
+                distances[qid].append(row.distance)
+                documents[qid].append(row.text)
+                metadatas[qid].append(row.vmetadata)
+
+            return SearchResult(
+                ids=ids, distances=distances, documents=documents, metadatas=metadatas
+            )
+        except Exception as e:
+            print(f"Error during search: {e}")
+            return None
+
+    def query(
+        self, collection_name: str, filter: Dict[str, Any], limit: Optional[int] = None
+    ) -> Optional[GetResult]:
+        try:
+            query = self.session.query(DocumentChunk).filter(
+                DocumentChunk.collection_name == collection_name
+            )
+
+            for key, value in filter.items():
+                query = query.filter(DocumentChunk.vmetadata[key].astext == str(value))
+
+            if limit is not None:
+                query = query.limit(limit)
+
+            results = query.all()
+
+            if not results:
+                return None
+
+            ids = [[result.id for result in results]]
+            documents = [[result.text for result in results]]
+            metadatas = [[result.vmetadata for result in results]]
+
+            return GetResult(
+                ids=ids,
+                documents=documents,
+                metadatas=metadatas,
+            )
+        except Exception as e:
+            print(f"Error during query: {e}")
+            return None
+
+    def get(
+        self, collection_name: str, limit: Optional[int] = None
+    ) -> Optional[GetResult]:
+        try:
+            query = self.session.query(DocumentChunk).filter(
+                DocumentChunk.collection_name == collection_name
+            )
+            if limit is not None:
+                query = query.limit(limit)
+
+            results = query.all()
+
+            if not results:
+                return None
+
+            ids = [[result.id for result in results]]
+            documents = [[result.text for result in results]]
+            metadatas = [[result.vmetadata for result in results]]
+
+            return GetResult(ids=ids, documents=documents, metadatas=metadatas)
+        except Exception as e:
+            print(f"Error during get: {e}")
+            return None
+
+    def delete(
+        self,
+        collection_name: str,
+        ids: Optional[List[str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        try:
+            query = self.session.query(DocumentChunk).filter(
+                DocumentChunk.collection_name == collection_name
+            )
+            if ids:
+                query = query.filter(DocumentChunk.id.in_(ids))
+            if filter:
+                for key, value in filter.items():
+                    query = query.filter(
+                        DocumentChunk.vmetadata[key].astext == str(value)
+                    )
+            deleted = query.delete(synchronize_session=False)
+            self.session.commit()
+            print(f"Deleted {deleted} items from collection '{collection_name}'.")
+        except Exception as e:
+            self.session.rollback()
+            print(f"Error during delete: {e}")
+            raise
+
+    def reset(self) -> None:
+        try:
+            deleted = self.session.query(DocumentChunk).delete()
+            self.session.commit()
+            print(
+                f"Reset complete. Deleted {deleted} items from 'document_chunk' table."
+            )
+        except Exception as e:
+            self.session.rollback()
+            print(f"Error during reset: {e}")
+            raise
+
+    def close(self) -> None:
+        pass
+
+    def has_collection(self, collection_name: str) -> bool:
+        try:
+            exists = (
+                self.session.query(DocumentChunk)
+                .filter(DocumentChunk.collection_name == collection_name)
+                .first()
+                is not None
+            )
+            return exists
+        except Exception as e:
+            print(f"Error checking collection existence: {e}")
+            return False
+
+    def delete_collection(self, collection_name: str) -> None:
+        self.delete(collection_name)
+        print(f"Collection '{collection_name}' deleted.")

backend/open_webui/retrieval/vector/dbs/qdrant.py

@@ -0,0 +1,184 @@
+from typing import Optional
+
+from qdrant_client import QdrantClient as Qclient
+from qdrant_client.http.models import PointStruct
+from qdrant_client.models import models
+
+from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
+from open_webui.config import QDRANT_URI, QDRANT_API_KEY
+
+NO_LIMIT = 999999999
+
+
+class QdrantClient:
+    def __init__(self):
+        self.collection_prefix = "open-webui"
+        self.QDRANT_URI = QDRANT_URI
+        self.QDRANT_API_KEY = QDRANT_API_KEY
+        self.client = (
+            Qclient(url=self.QDRANT_URI, api_key=self.QDRANT_API_KEY)
+            if self.QDRANT_URI
+            else None
+        )
+
+    def _result_to_get_result(self, points) -> GetResult:
+        ids = []
+        documents = []
+        metadatas = []
+
+        for point in points:
+            payload = point.payload
+            ids.append(point.id)
+            documents.append(payload["text"])
+            metadatas.append(payload["metadata"])
+
+        return GetResult(
+            **{
+                "ids": [ids],
+                "documents": [documents],
+                "metadatas": [metadatas],
+            }
+        )
+
+    def _create_collection(self, collection_name: str, dimension: int):
+        collection_name_with_prefix = f"{self.collection_prefix}_{collection_name}"
+        self.client.create_collection(
+            collection_name=collection_name_with_prefix,
+            vectors_config=models.VectorParams(
+                size=dimension, distance=models.Distance.COSINE
+            ),
+        )
+
+        print(f"collection {collection_name_with_prefix} successfully created!")
+
+    def _create_collection_if_not_exists(self, collection_name, dimension):
+        if not self.has_collection(collection_name=collection_name):
+            self._create_collection(
+                collection_name=collection_name, dimension=dimension
+            )
+
+    def _create_points(self, items: list[VectorItem]):
+        return [
+            PointStruct(
+                id=item["id"],
+                vector=item["vector"],
+                payload={"text": item["text"], "metadata": item["metadata"]},
+            )
+            for item in items
+        ]
+
+    def has_collection(self, collection_name: str) -> bool:
+        return self.client.collection_exists(
+            f"{self.collection_prefix}_{collection_name}"
+        )
+
+    def delete_collection(self, collection_name: str):
+        return self.client.delete_collection(
+            collection_name=f"{self.collection_prefix}_{collection_name}"
+        )
+
+    def search(
+        self, collection_name: str, vectors: list[list[float | int]], limit: int
+    ) -> Optional[SearchResult]:
+        # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
+        if limit is None:
+            limit = NO_LIMIT  # otherwise qdrant would set limit to 10!
+
+        query_response = self.client.query_points(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            query=vectors[0],
+            limit=limit,
+        )
+        get_result = self._result_to_get_result(query_response.points)
+        return SearchResult(
+            ids=get_result.ids,
+            documents=get_result.documents,
+            metadatas=get_result.metadatas,
+            distances=[[point.score for point in query_response.points]],
+        )
+
+    def query(self, collection_name: str, filter: dict, limit: Optional[int] = None):
+        # Construct the filter string for querying
+        if not self.has_collection(collection_name):
+            return None
+        try:
+            if limit is None:
+                limit = NO_LIMIT  # otherwise qdrant would set limit to 10!
+
+            field_conditions = []
+            for key, value in filter.items():
+                field_conditions.append(
+                    models.FieldCondition(
+                        key=f"metadata.{key}", match=models.MatchValue(value=value)
+                    )
+                )
+
+            points = self.client.query_points(
+                collection_name=f"{self.collection_prefix}_{collection_name}",
+                query_filter=models.Filter(should=field_conditions),
+                limit=limit,
+            )
+            return self._result_to_get_result(points.points)
+        except Exception as e:
+            print(e)
+            return None
+
+    def get(self, collection_name: str) -> Optional[GetResult]:
+        # Get all the items in the collection.
+        points = self.client.query_points(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            limit=NO_LIMIT,  # otherwise qdrant would set limit to 10!
+        )
+        return self._result_to_get_result(points.points)
+
+    def insert(self, collection_name: str, items: list[VectorItem]):
+        # Insert the items into the collection, if the collection does not exist, it will be created.
+        self._create_collection_if_not_exists(collection_name, len(items[0]["vector"]))
+        points = self._create_points(items)
+        self.client.upload_points(f"{self.collection_prefix}_{collection_name}", points)
+
+    def upsert(self, collection_name: str, items: list[VectorItem]):
+        # Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
+        self._create_collection_if_not_exists(collection_name, len(items[0]["vector"]))
+        points = self._create_points(items)
+        return self.client.upsert(f"{self.collection_prefix}_{collection_name}", points)
+
+    def delete(
+        self,
+        collection_name: str,
+        ids: Optional[list[str]] = None,
+        filter: Optional[dict] = None,
+    ):
+        # Delete the items from the collection based on the ids.
+        field_conditions = []
+
+        if ids:
+            for id_value in ids:
+                field_conditions.append(
+                    models.FieldCondition(
+                        key="metadata.id",
+                        match=models.MatchValue(value=id_value),
+                    ),
+                ),
+        elif filter:
+            for key, value in filter.items():
+                field_conditions.append(
+                    models.FieldCondition(
+                        key=f"metadata.{key}",
+                        match=models.MatchValue(value=value),
+                    ),
+                ),
+
+        return self.client.delete(
+            collection_name=f"{self.collection_prefix}_{collection_name}",
+            points_selector=models.FilterSelector(
+                filter=models.Filter(must=field_conditions)
+            ),
+        )
+
+    def reset(self):
+        # Resets the database. This will delete all collections and item entries.
+        collection_names = self.client.get_collections().collections
+        for collection_name in collection_names:
+            if collection_name.name.startswith(self.collection_prefix):
+                self.client.delete_collection(collection_name=collection_name.name)

backend/open_webui/retrieval/vector/main.py

@@ -0,0 +1,19 @@
+from pydantic import BaseModel
+from typing import Optional, List, Any
+
+
+class VectorItem(BaseModel):
+    id: str
+    text: str
+    vector: List[float | int]
+    metadata: Any
+
+
+class GetResult(BaseModel):
+    ids: Optional[List[List[str]]]
+    documents: Optional[List[List[str]]]
+    metadatas: Optional[List[List[Any]]]
+
+
+class SearchResult(GetResult):
+    distances: Optional[List[List[float | int]]]

backend/open_webui/retrieval/web/bing.py

@@ -0,0 +1,73 @@
+import logging
+import os
+from pprint import pprint
+from typing import Optional
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+import argparse
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+"""
+Documentation: https://docs.microsoft.com/en-us/bing/search-apis/bing-web-search/overview
+"""
+
+
+def search_bing(
+    subscription_key: str,
+    endpoint: str,
+    locale: str,
+    query: str,
+    count: int,
+    filter_list: Optional[list[str]] = None,
+) -> list[SearchResult]:
+    mkt = locale
+    params = {"q": query, "mkt": mkt, "answerCount": count}
+    headers = {"Ocp-Apim-Subscription-Key": subscription_key}
+
+    try:
+        response = requests.get(endpoint, headers=headers, params=params)
+        response.raise_for_status()
+        json_response = response.json()
+        results = json_response.get("webPages", {}).get("value", [])
+        if filter_list:
+            results = get_filtered_results(results, filter_list)
+        return [
+            SearchResult(
+                link=result["url"],
+                title=result.get("name"),
+                snippet=result.get("snippet"),
+            )
+            for result in results
+        ]
+    except Exception as ex:
+        log.error(f"Error: {ex}")
+        raise ex
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Search Bing from the command line.")
+    parser.add_argument(
+        "query",
+        type=str,
+        default="Top 10 international news today",
+        help="The search query.",
+    )
+    parser.add_argument(
+        "--count", type=int, default=10, help="Number of search results to return."
+    )
+    parser.add_argument(
+        "--filter", nargs="*", help="List of filters to apply to the search results."
+    )
+    parser.add_argument(
+        "--locale",
+        type=str,
+        default="en-US",
+        help="The locale to use for the search, maps to market in api",
+    )
+
+    args = parser.parse_args()
+
+    results = search_bing(args.locale, args.query, args.count, args.filter)
+    pprint(results)

backend/open_webui/retrieval/web/brave.py

@@ -0,0 +1,42 @@
+import logging
+from typing import Optional
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_brave(
+    api_key: str, query: str, count: int, filter_list: Optional[list[str]] = None
+) -> list[SearchResult]:
+    """Search using Brave's Search API and return the results as a list of SearchResult objects.
+
+    Args:
+        api_key (str): A Brave Search API key
+        query (str): The query to search for
+    """
+    url = "https://api.search.brave.com/res/v1/web/search"
+    headers = {
+        "Accept": "application/json",
+        "Accept-Encoding": "gzip",
+        "X-Subscription-Token": api_key,
+    }
+    params = {"q": query, "count": count}
+
+    response = requests.get(url, headers=headers, params=params)
+    response.raise_for_status()
+
+    json_response = response.json()
+    results = json_response.get("web", {}).get("results", [])
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+
+    return [
+        SearchResult(
+            link=result["url"], title=result.get("title"), snippet=result.get("snippet")
+        )
+        for result in results[:count]
+    ]

backend/open_webui/retrieval/web/duckduckgo.py

@@ -0,0 +1,50 @@
+import logging
+from typing import Optional
+
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from duckduckgo_search import DDGS
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_duckduckgo(
+    query: str, count: int, filter_list: Optional[list[str]] = None
+) -> list[SearchResult]:
+    """
+    Search using DuckDuckGo's Search API and return the results as a list of SearchResult objects.
+    Args:
+        query (str): The query to search for
+        count (int): The number of results to return
+
+    Returns:
+        list[SearchResult]: A list of search results
+    """
+    # Use the DDGS context manager to create a DDGS object
+    with DDGS() as ddgs:
+        # Use the ddgs.text() method to perform the search
+        ddgs_gen = ddgs.text(
+            query, safesearch="moderate", max_results=count, backend="api"
+        )
+        # Check if there are search results
+        if ddgs_gen:
+            # Convert the search results into a list
+            search_results = [r for r in ddgs_gen]
+
+    # Create an empty list to store the SearchResult objects
+    results = []
+    # Iterate over each search result
+    for result in search_results:
+        # Create a SearchResult object and append it to the results list
+        results.append(
+            SearchResult(
+                link=result["href"],
+                title=result.get("title"),
+                snippet=result.get("body"),
+            )
+        )
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+    # Return the list of search results
+    return results

backend/open_webui/retrieval/web/google_pse.py

@@ -0,0 +1,50 @@
+import logging
+from typing import Optional
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_google_pse(
+    api_key: str,
+    search_engine_id: str,
+    query: str,
+    count: int,
+    filter_list: Optional[list[str]] = None,
+) -> list[SearchResult]:
+    """Search using Google's Programmable Search Engine API and return the results as a list of SearchResult objects.
+
+    Args:
+        api_key (str): A Programmable Search Engine API key
+        search_engine_id (str): A Programmable Search Engine ID
+        query (str): The query to search for
+    """
+    url = "https://www.googleapis.com/customsearch/v1"
+
+    headers = {"Content-Type": "application/json"}
+    params = {
+        "cx": search_engine_id,
+        "q": query,
+        "key": api_key,
+        "num": count,
+    }
+
+    response = requests.request("GET", url, headers=headers, params=params)
+    response.raise_for_status()
+
+    json_response = response.json()
+    results = json_response.get("items", [])
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+    return [
+        SearchResult(
+            link=result["link"],
+            title=result.get("title"),
+            snippet=result.get("snippet"),
+        )
+        for result in results
+    ]

backend/open_webui/retrieval/web/jina_search.py

@@ -0,0 +1,39 @@
+import logging
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult
+from open_webui.env import SRC_LOG_LEVELS
+from yarl import URL
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_jina(api_key: str, query: str, count: int) -> list[SearchResult]:
+    """
+    Search using Jina's Search API and return the results as a list of SearchResult objects.
+    Args:
+        query (str): The query to search for
+        count (int): The number of results to return
+
+    Returns:
+        list[SearchResult]: A list of search results
+    """
+    jina_search_endpoint = "https://s.jina.ai/"
+    headers = {"Accept": "application/json", "Authorization": f"Bearer {api_key}"}
+    url = str(URL(jina_search_endpoint + query))
+    response = requests.get(url, headers=headers)
+    response.raise_for_status()
+    data = response.json()
+
+    results = []
+    for result in data["data"][:count]:
+        results.append(
+            SearchResult(
+                link=result["url"],
+                title=result.get("title"),
+                snippet=result.get("content"),
+            )
+        )
+
+    return results

backend/open_webui/retrieval/web/kagi.py

@@ -0,0 +1,50 @@
+import logging
+from typing import Optional
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_kagi(
+    api_key: str, query: str, count: int, filter_list: Optional[list[str]] = None
+) -> list[SearchResult]:
+    """Search using Kagi's Search API and return the results as a list of SearchResult objects.
+
+    The Search API will inherit the settings in your account, including results personalization and snippet length.
+
+    Args:
+        api_key (str): A Kagi Search API key
+        query (str): The query to search for
+        count (int): The number of results to return
+    """
+    url = "https://kagi.com/api/v0/search"
+    headers = {
+        "Authorization": f"Bot {api_key}",
+    }
+    params = {"q": query, "limit": count}
+
+    response = requests.get(url, headers=headers, params=params)
+    response.raise_for_status()
+    json_response = response.json()
+    search_results = json_response.get("data", [])
+    
+    results = [
+        SearchResult(
+            link=result["url"],
+            title=result["title"],
+            snippet=result.get("snippet")
+        )
+        for result in search_results
+        if result["t"] == 0
+    ]
+    
+    print(results)
+
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+
+    return results

backend/open_webui/retrieval/web/main.py

@@ -0,0 +1,22 @@
+from typing import Optional
+from urllib.parse import urlparse
+
+from pydantic import BaseModel
+
+
+def get_filtered_results(results, filter_list):
+    if not filter_list:
+        return results
+    filtered_results = []
+    for result in results:
+        url = result.get("url") or result.get("link", "")
+        domain = urlparse(url).netloc
+        if any(domain.endswith(filtered_domain) for filtered_domain in filter_list):
+            filtered_results.append(result)
+    return filtered_results
+
+
+class SearchResult(BaseModel):
+    link: str
+    title: Optional[str]
+    snippet: Optional[str]

backend/open_webui/retrieval/web/mojeek.py

@@ -0,0 +1,40 @@
+import logging
+from typing import Optional
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_mojeek(
+    api_key: str, query: str, count: int, filter_list: Optional[list[str]] = None
+) -> list[SearchResult]:
+    """Search using Mojeek's Search API and return the results as a list of SearchResult objects.
+
+    Args:
+        api_key (str): A Mojeek Search API key
+        query (str): The query to search for
+    """
+    url = "https://api.mojeek.com/search"
+    headers = {
+        "Accept": "application/json",
+    }
+    params = {"q": query, "api_key": api_key, "fmt": "json", "t": count}
+
+    response = requests.get(url, headers=headers, params=params)
+    response.raise_for_status()
+    json_response = response.json()
+    results = json_response.get("response", {}).get("results", [])
+    print(results)
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+
+    return [
+        SearchResult(
+            link=result["url"], title=result.get("title"), snippet=result.get("desc")
+        )
+        for result in results
+    ]

backend/open_webui/retrieval/web/searchapi.py

@@ -0,0 +1,48 @@
+import logging
+from typing import Optional
+from urllib.parse import urlencode
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_searchapi(
+    api_key: str,
+    engine: str,
+    query: str,
+    count: int,
+    filter_list: Optional[list[str]] = None,
+) -> list[SearchResult]:
+    """Search using searchapi.io's API and return the results as a list of SearchResult objects.
+
+    Args:
+      api_key (str): A searchapi.io API key
+      query (str): The query to search for
+    """
+    url = "https://www.searchapi.io/api/v1/search"
+
+    engine = engine or "google"
+
+    payload = {"engine": engine, "q": query, "api_key": api_key}
+
+    url = f"{url}?{urlencode(payload)}"
+    response = requests.request("GET", url)
+
+    json_response = response.json()
+    log.info(f"results from searchapi search: {json_response}")
+
+    results = sorted(
+        json_response.get("organic_results", []), key=lambda x: x.get("position", 0)
+    )
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+    return [
+        SearchResult(
+            link=result["link"], title=result["title"], snippet=result["snippet"]
+        )
+        for result in results[:count]
+    ]

backend/open_webui/retrieval/web/searxng.py

@@ -0,0 +1,91 @@
+import logging
+from typing import Optional
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_searxng(
+    query_url: str,
+    query: str,
+    count: int,
+    filter_list: Optional[list[str]] = None,
+    **kwargs,
+) -> list[SearchResult]:
+    """
+    Search a SearXNG instance for a given query and return the results as a list of SearchResult objects.
+
+    The function allows passing additional parameters such as language or time_range to tailor the search result.
+
+    Args:
+        query_url (str): The base URL of the SearXNG server.
+        query (str): The search term or question to find in the SearXNG database.
+        count (int): The maximum number of results to retrieve from the search.
+
+    Keyword Args:
+        language (str): Language filter for the search results; e.g., "en-US". Defaults to an empty string.
+        safesearch (int): Safe search filter for safer web results; 0 = off, 1 = moderate, 2 = strict. Defaults to 1 (moderate).
+        time_range (str): Time range for filtering results by date; e.g., "2023-04-05..today" or "all-time". Defaults to ''.
+        categories: (Optional[list[str]]): Specific categories within which the search should be performed, defaulting to an empty string if not provided.
+
+    Returns:
+        list[SearchResult]: A list of SearchResults sorted by relevance score in descending order.
+
+    Raise:
+        requests.exceptions.RequestException: If a request error occurs during the search process.
+    """
+
+    # Default values for optional parameters are provided as empty strings or None when not specified.
+    language = kwargs.get("language", "en-US")
+    safesearch = kwargs.get("safesearch", "1")
+    time_range = kwargs.get("time_range", "")
+    categories = "".join(kwargs.get("categories", []))
+
+    params = {
+        "q": query,
+        "format": "json",
+        "pageno": 1,
+        "safesearch": safesearch,
+        "language": language,
+        "time_range": time_range,
+        "categories": categories,
+        "theme": "simple",
+        "image_proxy": 0,
+    }
+
+    # Legacy query format
+    if "<query>" in query_url:
+        # Strip all query parameters from the URL
+        query_url = query_url.split("?")[0]
+
+    log.debug(f"searching {query_url}")
+
+    response = requests.get(
+        query_url,
+        headers={
+            "User-Agent": "Open WebUI (https://github.com/open-webui/open-webui) RAG Bot",
+            "Accept": "text/html",
+            "Accept-Encoding": "gzip, deflate",
+            "Accept-Language": "en-US,en;q=0.5",
+            "Connection": "keep-alive",
+        },
+        params=params,
+    )
+
+    response.raise_for_status()  # Raise an exception for HTTP errors.
+
+    json_response = response.json()
+    results = json_response.get("results", [])
+    sorted_results = sorted(results, key=lambda x: x.get("score", 0), reverse=True)
+    if filter_list:
+        sorted_results = get_filtered_results(sorted_results, filter_list)
+    return [
+        SearchResult(
+            link=result["url"], title=result.get("title"), snippet=result.get("content")
+        )
+        for result in sorted_results[:count]
+    ]

backend/open_webui/retrieval/web/serper.py

@@ -0,0 +1,43 @@
+import json
+import logging
+from typing import Optional
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_serper(
+    api_key: str, query: str, count: int, filter_list: Optional[list[str]] = None
+) -> list[SearchResult]:
+    """Search using serper.dev's API and return the results as a list of SearchResult objects.
+
+    Args:
+        api_key (str): A serper.dev API key
+        query (str): The query to search for
+    """
+    url = "https://google.serper.dev/search"
+
+    payload = json.dumps({"q": query})
+    headers = {"X-API-KEY": api_key, "Content-Type": "application/json"}
+
+    response = requests.request("POST", url, headers=headers, data=payload)
+    response.raise_for_status()
+
+    json_response = response.json()
+    results = sorted(
+        json_response.get("organic", []), key=lambda x: x.get("position", 0)
+    )
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+    return [
+        SearchResult(
+            link=result["link"],
+            title=result.get("title"),
+            snippet=result.get("description"),
+        )
+        for result in results[:count]
+    ]

backend/open_webui/retrieval/web/serply.py

@@ -0,0 +1,69 @@
+import logging
+from typing import Optional
+from urllib.parse import urlencode
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_serply(
+    api_key: str,
+    query: str,
+    count: int,
+    hl: str = "us",
+    limit: int = 10,
+    device_type: str = "desktop",
+    proxy_location: str = "US",
+    filter_list: Optional[list[str]] = None,
+) -> list[SearchResult]:
+    """Search using serper.dev's API and return the results as a list of SearchResult objects.
+
+    Args:
+        api_key (str): A serply.io API key
+        query (str): The query to search for
+        hl (str): Host Language code to display results in (reference https://developers.google.com/custom-search/docs/xml_results?hl=en#wsInterfaceLanguages)
+        limit (int): The maximum number of results to return [10-100, defaults to 10]
+    """
+    log.info("Searching with Serply")
+
+    url = "https://api.serply.io/v1/search/"
+
+    query_payload = {
+        "q": query,
+        "language": "en",
+        "num": limit,
+        "gl": proxy_location.upper(),
+        "hl": hl.lower(),
+    }
+
+    url = f"{url}{urlencode(query_payload)}"
+    headers = {
+        "X-API-KEY": api_key,
+        "X-User-Agent": device_type,
+        "User-Agent": "open-webui",
+        "X-Proxy-Location": proxy_location,
+    }
+
+    response = requests.request("GET", url, headers=headers)
+    response.raise_for_status()
+
+    json_response = response.json()
+    log.info(f"results from serply search: {json_response}")
+
+    results = sorted(
+        json_response.get("results", []), key=lambda x: x.get("realPosition", 0)
+    )
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+    return [
+        SearchResult(
+            link=result["link"],
+            title=result.get("title"),
+            snippet=result.get("description"),
+        )
+        for result in results[:count]
+    ]

backend/open_webui/retrieval/web/serpstack.py

@@ -0,0 +1,48 @@
+import logging
+from typing import Optional
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult, get_filtered_results
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_serpstack(
+    api_key: str,
+    query: str,
+    count: int,
+    filter_list: Optional[list[str]] = None,
+    https_enabled: bool = True,
+) -> list[SearchResult]:
+    """Search using serpstack.com's and return the results as a list of SearchResult objects.
+
+    Args:
+        api_key (str): A serpstack.com API key
+        query (str): The query to search for
+        https_enabled (bool): Whether to use HTTPS or HTTP for the API request
+    """
+    url = f"{'https' if https_enabled else 'http'}://api.serpstack.com/search"
+
+    headers = {"Content-Type": "application/json"}
+    params = {
+        "access_key": api_key,
+        "query": query,
+    }
+
+    response = requests.request("POST", url, headers=headers, params=params)
+    response.raise_for_status()
+
+    json_response = response.json()
+    results = sorted(
+        json_response.get("organic_results", []), key=lambda x: x.get("position", 0)
+    )
+    if filter_list:
+        results = get_filtered_results(results, filter_list)
+    return [
+        SearchResult(
+            link=result["url"], title=result.get("title"), snippet=result.get("snippet")
+        )
+        for result in results[:count]
+    ]

backend/open_webui/retrieval/web/tavily.py

@@ -0,0 +1,38 @@
+import logging
+
+import requests
+from open_webui.apps.retrieval.web.main import SearchResult
+from open_webui.env import SRC_LOG_LEVELS
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def search_tavily(api_key: str, query: str, count: int) -> list[SearchResult]:
+    """Search using Tavily's Search API and return the results as a list of SearchResult objects.
+
+    Args:
+        api_key (str): A Tavily Search API key
+        query (str): The query to search for
+
+    Returns:
+        list[SearchResult]: A list of search results
+    """
+    url = "https://api.tavily.com/search"
+    data = {"query": query, "api_key": api_key}
+
+    response = requests.post(url, json=data)
+    response.raise_for_status()
+
+    json_response = response.json()
+
+    raw_search_results = json_response.get("results", [])
+
+    return [
+        SearchResult(
+            link=result["url"],
+            title=result.get("title", ""),
+            snippet=result.get("content"),
+        )
+        for result in raw_search_results[:count]
+    ]

backend/open_webui/retrieval/web/testdata/bing.json

@@ -0,0 +1,58 @@
+{
+	"_type": "SearchResponse",
+	"queryContext": {
+		"originalQuery": "Top 10 international results"
+	},
+	"webPages": {
+		"webSearchUrl": "https://www.bing.com/search?q=Top+10+international+results",
+		"totalEstimatedMatches": 687,
+		"value": [
+			{
+				"id": "https://api.bing.microsoft.com/api/v7/#WebPages.0",
+				"name": "2024 Mexican Grand Prix - F1 results and latest standings ... - PlanetF1",
+				"url": "https://www.planetf1.com/news/f1-results-2024-mexican-grand-prix-race-standings",
+				"datePublished": "2024-10-27T00:00:00.0000000",
+				"datePublishedFreshnessText": "1 day ago",
+				"isFamilyFriendly": true,
+				"displayUrl": "https://www.planetf1.com/news/f1-results-2024-mexican-grand-prix-race-standings",
+				"snippet": "Nico Hulkenberg and Pierre Gasly completed the top 10. A full report of the Mexican Grand Prix is available at the bottom of this article. F1 results – 2024 Mexican Grand Prix",
+				"dateLastCrawled": "2024-10-28T07:15:00.0000000Z",
+				"cachedPageUrl": "https://cc.bingj.com/cache.aspx?q=Top+10+international+results&d=916492551782&mkt=en-US&setlang=en-US&w=zBsfaAPyF2tUrHFHr_vFFdUm8sng4g34",
+				"language": "en",
+				"isNavigational": false,
+				"noCache": false
+			},
+			{
+				"id": "https://api.bing.microsoft.com/api/v7/#WebPages.1",
+				"name": "F1 Results Today: HUGE Verstappen penalties cause major title change",
+				"url": "https://www.gpfans.com/en/f1-news/1033512/f1-results-today-mexican-grand-prix-huge-max-verstappen-penalties-cause-major-title-change/",
+				"datePublished": "2024-10-27T00:00:00.0000000",
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+				"language": "en",
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+			{
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+				"displayUrl": "https://www.loverugbyleague.com/post/international-power-rankings-england-flying...",
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+		"someResultsRemoved": true
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+}

backend/open_webui/retrieval/web/testdata/brave.json

@@ -0,0 +1,998 @@
+{
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+		"is_navigational": true,
+		"is_news_breaking": false,
+		"spellcheck_off": true,
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+		"more_results_available": true,
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+			},
+			{
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+			{
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+					"Meanwhile, others shared on Y Combinator’s Hacker News, where a Python team member detailed their specific duties on the technical front and noted that, for years, much of the work was done with fewer than 10 people. Another Hacker News commenter said their early years on the Python team were spent paying down internal technical debt accumulated from not having a strong Python strategy.",
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+					"Calculations are simple with Python, and expression syntax is straightforward: the operators +, -, * and / work as expected; parentheses () can be used for grouping. More about simple math functions in Python 3.",
+					"The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. More about defining functions in Python 3",
+					"Lists (known as arrays in other languages) are one of the compound data types that Python understands. Lists can be indexed, sliced and manipulated with other built-in functions. More about lists in Python 3",
+					"# Python 3: Simple output (with Unicode) >>> print(\"Hello, I'm Python!\") Hello, I'm Python! # Input, assignment >>> name = input('What is your name?\\n') >>> print('Hi, %s.' % name) What is your name? Python Hi, Python."
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+					"Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language and first released it in 1991 as Python 0.9.0. Python 2.0 was released in 2000. Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2.",
+					"Python was invented in the late 1980s by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands as a successor to the ABC programming language, which was inspired by SETL, capable of exception handling and interfacing with the Amoeba operating system.",
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+					"It is one of the most efficient, dependable, and potent online compilers for the Python programming language. It is not necessary for you to bother about establishing a Python environment in your local. Now You can immediately execute the Python code in the web browser of your choice.",
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+					"Now You can immediately execute the Python code in the web browser of your choice. Using this Python editor is simple and quick to get up and running with. Simply type in the programme, and then press the RUN button! The code can be saved online by choosing the SHARE option, which also gives you the ability to access your code from any location providing you have internet access."
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+					"# Online Python compiler (interpreter) to run Python online. # Write Python 3 code in this online editor and run it. print(\"Try programiz.pro\")"
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+					"Difference between / vs. // operator in Python",
+					"Double Star or (**) is one of the Arithmetic Operator (Like +, -, *, **, /, //, %) in Python Language. It is also known as Power Operator.",
+					"The time complexity of the given Python program is O(n), where n is the number of key-value pairs in the input dictionary.",
+					"Inplace Operators in Python | Set 2 (ixor(), iand(), ipow(),…)"
+				]
+			},
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+					"r/Python: The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python…",
+					"By default, Python allows you to import and use anything, anywhere. Over time, this results in modules that were intended to be separate getting tightly coupled together, and domain boundaries breaking down. We experienced this first-hand at a unicorn startup, where the eng team paused development for over a year in an attempt to split up packages into independent services.",
+					"Hello r/Python! It's time to share what you've been working on! Whether it's a work-in-progress, a completed masterpiece, or just a rough idea, let us know what you're up to!",
+					"Whether it's your job, your hobby, or your passion project, all Python-related work is welcome here."
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+					"You can pass many options to the configure script; run ./configure --help to find out more. On macOS case-insensitive file systems and on Cygwin, the executable is called python.exe; elsewhere it's just python.",
+					"Building a complete Python installation requires the use of various additional third-party libraries, depending on your build platform and configure options. Not all standard library modules are buildable or useable on all platforms. Refer to the Install dependencies section of the Developer Guide for current detailed information on dependencies for various Linux distributions and macOS.",
+					"To get an optimized build of Python, configure --enable-optimizations before you run make. This sets the default make targets up to enable Profile Guided Optimization (PGO) and may be used to auto-enable Link Time Optimization (LTO) on some platforms. For more details, see the sections below.",
+					"Copyright © 2001-2024 Python Software Foundation. All rights reserved."
+				]
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+					"You might have noticed that methods like insert, remove or sort that only modify the list have no return value printed – they return the default None. [1] This is a design principle for all mutable data structures in Python.",
+					"We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types (see Sequence Types — list, tuple, range). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple.",
+					"Python also includes a data type for sets. A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.",
+					"Another useful data type built into Python is the dictionary (see Mapping Types — dict). Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys."
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+					"Traceback (most recent call last): File \"/usr/lib/python3.12/importlib/metadata/__init__.py\", line 397, in from_name return next(cls.discover(name=name)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ StopIteration During handling of the above exception, another exception occurred: Traceback (most recent call last): File \"/usr/bin/informant\", line 33, in <module> sys.exit(load_entry_point('informant==0.5.0', 'console_scripts', 'informant')()) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File \"/usr/bin/informant\", line 22, in importlib_load_entry_point for entry_point in distribution(dis"
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+					"Well organized and easy to understand Web building tutorials with lots of examples of how to use HTML, CSS, JavaScript, SQL, Python, PHP, Bootstrap, Java, XML and more.",
+					"HTML CSS JAVASCRIPT SQL PYTHON JAVA PHP HOW TO W3.CSS C C++ C# BOOTSTRAP REACT MYSQL JQUERY EXCEL XML DJANGO NUMPY PANDAS NODEJS R TYPESCRIPT ANGULAR GIT POSTGRESQL MONGODB ASP AI GO KOTLIN SASS VUE DSA GEN AI SCIPY AWS CYBERSECURITY DATA SCIENCE",
+					"Python Variables Variable Names Assign Multiple Values Output Variables Global Variables Variable Exercises Python Data Types Python Numbers Python Casting Python Strings",
+					"Python Strings Slicing Strings Modify Strings Concatenate Strings Format Strings Escape Characters String Methods String Exercises Python Booleans Python Operators Python Lists"
+				]
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+			{
+				"title": "bug: AUR package wants to use python but does not find any preset version · Issue #1740 · asdf-vm/asdf",
+				"url": "https://github.com/asdf-vm/asdf/issues/1740",
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+				"description": "Describe the Bug I am not sure why this is happening, I am trying to install tlpui from AUR and it fails, here are some logs to help: ==&gt; Making package: tlpui 2:1.6.5-1 (Mi 10 apr 2024 23:19:15 +0...",
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+					"==> Starting build()... No preset version installed for command python Please install a version by running one of the following: asdf install python 3.8 or add one of the following versions in your config file at /home/ferret/.tool-versions python 3.11.0 python 3.12.1 python 3.12.3 ==> ERROR: A failure occurred in build(). Aborting...",
+					"-> error making: tlpui-exit status 4 -> Failed to install the following packages. Manual intervention is required: tlpui - exit status 4 ferret@FX505DT in ~ $ cat /home/ferret/.tool-versions nodejs 21.6.0 python 3.12.3 ferret@FX505DT in ~ $ python -V Python 3.12.3 ferret@FX505DT in ~ $ which python /home/ferret/.asdf/shims/python",
+					"Describe the Bug I am not sure why this is happening, I am trying to install tlpui from AUR and it fails, here are some logs to help: ==> Making package: tlpui 2:1.6.5-1 (Mi 10 apr 2024 23:19:15 +0300) ==> Retrieving sources... -> Found ..."
+				]
+			},
+			{
+				"title": "What are python.exe and python3.exe, and why do they appear to point to App Installer? | Windows 11 Forum",
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+					"Why are python.exe and python3.exe listed as App Installer? I assume that App Installer refers to installation of Microsoft Store / UWP apps, but if that's the case, then why are they called python.exe and python3.exe? Or are python.exe and python3.exe simply serving as aliases / pointers pointing to App Installer, which is itself a Microsoft Store App?",
+					"Or are python.exe and python3.exe simply serving as aliases / pointers pointing to App Installer, which is itself a Microsoft Store App? I wish to soon install Python, along with an integrated development editor (IDE), on my machine, so that I can code in Python.",
+					"I wish to soon install Python, along with an integrated development editor (IDE), on my machine, so that I can code in Python. But is a Python interpreter already on my computer as suggested, if obliquely, by the presence of python.exe and python3.exe? I kind of doubt it."
+				]
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+					"Increment and Decrement Operators in Python - Python does not have unary increment/decrement operator (++/--). Instead to increment a value, usea += 1to decrement a value, use −a -= 1Example>>> a = 0 >>> >>> #Increment >>> a +=1 >>> >>> #Decrement >>> a -= 1 >>> >>> #value of a >>> a 0Python does not provide multiple ways to do the same thing",
+					"So what above statement means in python is: create an object of type int having value 1 and give the name a to it. The object is an instance of int having value 1 and the name a refers to it. The assigned name a and the object to which it refers are distinct.",
+					"Python does not provide multiple ways to do the same thing .",
+					"However, be careful if you are coming from a language like C, Python doesn’t have \"variables\" in the sense that C does, instead python uses names and objects and in python integers (int’s) are immutable."
+				]
+			},
+			{
+				"title": "Gumroad – How not to suck at Python / SideFX Houdini | CG Persia",
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+					"Posted in: 2D, CG Releases, Downloads, Learning, Tutorials, Videos. Tagged: Gumroad, Python, Sidefx. Leave a Comment",
+					"01 – Python – Fundamentals Get the Fundamentals of python before starting the fun stuff ! 02 – Python Construction Part02 digging further into python concepts 03 – Houdini – Python Basics Applying some basic python in Houdini and starting to make tools !",
+					"02 – Python Construction Part02 digging further into python concepts 03 – Houdini – Python Basics Applying some basic python in Houdini and starting to make tools ! 04 – Houdini – Python Intermediate Applying some more advanced python in Houdini to make tools ! 05 – Houdini – Python Expert Using QtDesigner in combinaison with Houdini Python/Pyside to create advanced tools."
+				]
+			},
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+					"Whether it’s your first time programming or you’re a seasoned programmer, you’ll have to install or update Python every now and then --- or if necessary, uninstall it. In this article, you'll learn how to do just that.",
+					"Some systems come with Python, so to start off, we’ll first check to see if it’s installed on your system before we proceed. To do that, we’ll need to open a terminal. Since you might be new to programming, let’s go over how to open a terminal for Linux, Windows, and macOS.",
+					"Before we dive into setting up your system so you can program in Python, let’s talk terminal basics and benefits.",
+					"However, let’s focus on why we need it for working with Python. We use a terminal, or command line, to:"
+				]
+			}
+		],
+		"family_friendly": true
+	}
+}

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backend/open_webui/retrieval/web/testdata/serper.json

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+			"snippet": "Apple Inc. is an American multinational technology company specializing in consumer electronics, software and online services headquartered in Cupertino, ...",
+			"attributes": {
+				"Products": "AirPods; Apple Watch; iPad; iPhone; Mac",
+				"Founders": "Steve Jobs; Steve Wozniak; Ronald Wayne",
+				"Founded": "April 1, 1976; 46 years ago in Los Altos, California, U.S",
+				"Industry": "Consumer electronics; Software services; Online services"
+			},
+			"sitelinks": [
+				{
+					"title": "History",
+					"link": "https://en.wikipedia.org/wiki/History_of_Apple_Inc."
+				},
+				{
+					"title": "Timeline of Apple Inc. products",
+					"link": "https://en.wikipedia.org/wiki/Timeline_of_Apple_Inc._products"
+				},
+				{
+					"title": "List of software by Apple Inc.",
+					"link": "https://en.wikipedia.org/wiki/List_of_software_by_Apple_Inc."
+				},
+				{
+					"title": "Apple Store",
+					"link": "https://en.wikipedia.org/wiki/Apple_Store"
+				}
+			],
+			"position": 2
+		},
+		{
+			"title": "Apple Inc. | History, Products, Headquarters, & Facts | Britannica",
+			"link": "https://www.britannica.com/topic/Apple-Inc",
+			"snippet": "Apple Inc., formerly Apple Computer, Inc., American manufacturer of personal computers, smartphones, tablet computers, computer peripherals, ...",
+			"date": "Aug 31, 2022",
+			"attributes": {
+				"Related People": "Steve Jobs Steve Wozniak Jony Ive Tim Cook Angela Ahrendts",
+				"Date": "1976 - present",
+				"Areas Of Involvement": "peripheral device"
+			},
+			"position": 3
+		},
+		{
+			"title": "AAPL: Apple Inc Stock Price Quote - NASDAQ GS - Bloomberg.com",
+			"link": "https://www.bloomberg.com/quote/AAPL:US",
+			"snippet": "Stock analysis for Apple Inc (AAPL:NASDAQ GS) including stock price, stock chart, company news, key statistics, fundamentals and company profile.",
+			"position": 4
+		},
+		{
+			"title": "Apple Inc. (AAPL) Company Profile & Facts - Yahoo Finance",
+			"link": "https://finance.yahoo.com/quote/AAPL/profile/",
+			"snippet": "Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. It also sells various related ...",
+			"position": 5
+		},
+		{
+			"title": "AAPL | Apple Inc. Stock Price & News - WSJ",
+			"link": "https://www.wsj.com/market-data/quotes/AAPL",
+			"snippet": "Apple, Inc. engages in the design, manufacture, and sale of smartphones, personal computers, tablets, wearables and accessories, and other varieties of ...",
+			"position": 6
+		},
+		{
+			"title": "Apple Inc Company Profile - Apple Inc Overview - GlobalData",
+			"link": "https://www.globaldata.com/company-profile/apple-inc/",
+			"snippet": "Apple Inc (Apple) designs, manufactures, and markets smartphones, tablets, personal computers (PCs), portable and wearable devices. The company also offers ...",
+			"position": 7
+		},
+		{
+			"title": "Apple Inc (AAPL) Stock Price & News - Google Finance",
+			"link": "https://www.google.com/finance/quote/AAPL:NASDAQ?hl=en",
+			"snippet": "Get the latest Apple Inc (AAPL) real-time quote, historical performance, charts, and other financial information to help you make more informed trading and ...",
+			"position": 8
+		}
+	],
+	"peopleAlsoAsk": [
+		{
+			"question": "What does Apple Inc mean?",
+			"snippet": "Apple Inc., formerly Apple Computer, Inc., American manufacturer of personal\ncomputers, smartphones, tablet computers, computer peripherals, and computer\nsoftware. It was the first successful personal computer company and the\npopularizer of the graphical user interface.\nAug 31, 2022",
+			"title": "Apple Inc. | History, Products, Headquarters, & Facts | Britannica",
+			"link": "https://www.britannica.com/topic/Apple-Inc"
+		},
+		{
+			"question": "Is Apple and Apple Inc same?",
+			"snippet": "Apple was founded as Apple Computer Company on April 1, 1976, by Steve Jobs,\nSteve Wozniak and Ronald Wayne to develop and sell Wozniak's Apple I personal\ncomputer. It was incorporated by Jobs and Wozniak as Apple Computer, Inc.",
+			"title": "Apple Inc. - Wikipedia",
+			"link": "https://en.wikipedia.org/wiki/Apple_Inc."
+		},
+		{
+			"question": "Who owns Apple Inc?",
+			"snippet": "Apple Inc. is owned by two main institutional investors (Vanguard Group and\nBlackRock, Inc). While its major individual shareholders comprise people like\nArt Levinson, Tim Cook, Bruce Sewell, Al Gore, Johny Sroujli, and others.",
+			"title": "Who Owns Apple In 2022? - FourWeekMBA",
+			"link": "https://fourweekmba.com/who-owns-apple/"
+		},
+		{
+			"question": "What products does Apple Inc offer?",
+			"snippet": "APPLE FOOTER\nStore.\nMac.\niPad.\niPhone.\nWatch.\nAirPods.\nTV & Home.\nAirTag.",
+			"title": "More items...",
+			"link": "https://www.apple.com/business/"
+		}
+	],
+	"relatedSearches": [
+		{
+			"query": "Who invented the iPhone"
+		},
+		{
+			"query": "Apple Inc competitors"
+		},
+		{
+			"query": "Apple iPad"
+		},
+		{
+			"query": "iPhones"
+		},
+		{
+			"query": "Apple Inc us"
+		},
+		{
+			"query": "Apple company history"
+		},
+		{
+			"query": "Apple Store"
+		},
+		{
+			"query": "Apple customer service"
+		},
+		{
+			"query": "Apple Watch"
+		},
+		{
+			"query": "Apple Inc Industry"
+		},
+		{
+			"query": "Apple Inc registered address"
+		},
+		{
+			"query": "Apple Inc Bloomberg"
+		}
+	]
+}

backend/open_webui/retrieval/web/testdata/serply.json

@@ -0,0 +1,206 @@
+{
+	"ads": [],
+	"ads_count": 0,
+	"answers": [],
+	"results": [
+		{
+			"title": "Apple",
+			"link": "https://www.apple.com/",
+			"description": "Discover the innovative world of Apple and shop everything iPhone, iPad, Apple Watch, Mac, and Apple TV, plus explore accessories, entertainment, ...",
+			"additional_links": [
+				{
+					"text": "AppleApplehttps://www.apple.com",
+					"href": "https://www.apple.com/"
+				}
+			],
+			"cite": {},
+			"subdomains": [
+				{
+					"title": "Support",
+					"link": "https://support.apple.com/",
+					"description": "SupportContact - iPhone Support - Billing and Subscriptions - Apple Repair"
+				},
+				{
+					"title": "Store",
+					"link": "https://www.apple.com/store",
+					"description": "StoreShop iPhone - Shop iPad - App Store - Shop Mac - ..."
+				},
+				{
+					"title": "Mac",
+					"link": "https://www.apple.com/mac/",
+					"description": "MacMacBook Air - MacBook Pro - iMac - Compare Mac models - Mac mini"
+				},
+				{
+					"title": "iPad",
+					"link": "https://www.apple.com/ipad/",
+					"description": "iPadShop iPad - iPad Pro - iPad Air - Compare iPad models - ..."
+				},
+				{
+					"title": "Watch",
+					"link": "https://www.apple.com/watch/",
+					"description": "WatchShop Apple Watch - Series 9 - SE - Ultra 2 - Nike - Hermès - ..."
+				}
+			],
+			"realPosition": 1
+		},
+		{
+			"title": "Apple",
+			"link": "https://www.apple.com/",
+			"description": "Discover the innovative world of Apple and shop everything iPhone, iPad, Apple Watch, Mac, and Apple TV, plus explore accessories, entertainment, ...",
+			"additional_links": [
+				{
+					"text": "AppleApplehttps://www.apple.com",
+					"href": "https://www.apple.com/"
+				}
+			],
+			"cite": {},
+			"realPosition": 2
+		},
+		{
+			"title": "Apple Inc.",
+			"link": "https://en.wikipedia.org/wiki/Apple_Inc.",
+			"description": "Apple Inc. (formerly Apple Computer, Inc.) is an American multinational corporation and technology company headquartered in Cupertino, California, ...",
+			"additional_links": [
+				{
+					"text": "Apple Inc.Wikipediahttps://en.wikipedia.org › wiki › Apple_Inc",
+					"href": "https://en.wikipedia.org/wiki/Apple_Inc."
+				},
+				{
+					"text": "",
+					"href": "https://en.wikipedia.org/wiki/Apple_Inc."
+				},
+				{
+					"text": "History",
+					"href": "https://en.wikipedia.org/wiki/History_of_Apple_Inc."
+				},
+				{
+					"text": "List of Apple products",
+					"href": "https://en.wikipedia.org/wiki/List_of_Apple_products"
+				},
+				{
+					"text": "Litigation involving Apple Inc.",
+					"href": "https://en.wikipedia.org/wiki/Litigation_involving_Apple_Inc."
+				},
+				{
+					"text": "Apple Park",
+					"href": "https://en.wikipedia.org/wiki/Apple_Park"
+				}
+			],
+			"cite": {
+				"domain": "https://en.wikipedia.org › wiki › Apple_Inc",
+				"span": " › wiki › Apple_Inc"
+			},
+			"realPosition": 3
+		},
+		{
+			"title": "Apple Inc. (AAPL) Company Profile & Facts",
+			"link": "https://finance.yahoo.com/quote/AAPL/profile/",
+			"description": "Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line ...",
+			"additional_links": [
+				{
+					"text": "Apple Inc. (AAPL) Company Profile & FactsYahoo Financehttps://finance.yahoo.com › quote › AAPL › profile",
+					"href": "https://finance.yahoo.com/quote/AAPL/profile/"
+				}
+			],
+			"cite": {
+				"domain": "https://finance.yahoo.com › quote › AAPL › profile",
+				"span": " › quote › AAPL › profile"
+			},
+			"realPosition": 4
+		},
+		{
+			"title": "Apple Inc - Company Profile and News",
+			"link": "https://www.bloomberg.com/profile/company/AAPL:US",
+			"description": "Apple Inc. Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables and accessories, and sells a variety of related ...",
+			"additional_links": [
+				{
+					"text": "Apple Inc - Company Profile and NewsBloomberghttps://www.bloomberg.com › company › AAPL:US",
+					"href": "https://www.bloomberg.com/profile/company/AAPL:US"
+				},
+				{
+					"text": "",
+					"href": "https://www.bloomberg.com/profile/company/AAPL:US"
+				}
+			],
+			"cite": {
+				"domain": "https://www.bloomberg.com › company › AAPL:US",
+				"span": " › company › AAPL:US"
+			},
+			"realPosition": 5
+		},
+		{
+			"title": "Apple Inc. | History, Products, Headquarters, & Facts",
+			"link": "https://www.britannica.com/money/Apple-Inc",
+			"description": "May 22, 2024 — Apple Inc. is an American multinational technology company that revolutionized the technology sector through its innovation of computer ...",
+			"additional_links": [
+				{
+					"text": "Apple Inc. | History, Products, Headquarters, & FactsBritannicahttps://www.britannica.com › money › Apple-Inc",
+					"href": "https://www.britannica.com/money/Apple-Inc"
+				},
+				{
+					"text": "",
+					"href": "https://www.britannica.com/money/Apple-Inc"
+				}
+			],
+			"cite": {
+				"domain": "https://www.britannica.com › money › Apple-Inc",
+				"span": " › money › Apple-Inc"
+			},
+			"realPosition": 6
+		}
+	],
+	"shopping_ads": [],
+	"places": [
+		{
+			"title": "Apple Inc."
+		},
+		{
+			"title": "Apple Inc"
+		},
+		{
+			"title": "Apple Inc"
+		}
+	],
+	"related_searches": {
+		"images": [],
+		"text": [
+			{
+				"title": "apple inc full form",
+				"link": "https://www.google.com/search?sca_esv=6b6df170a5c9891b&sca_upv=1&q=Apple+Inc+full+form&sa=X&ved=2ahUKEwjLxuSJwM-GAxUHODQIHYuJBhgQ1QJ6BAhPEAE"
+			},
+			{
+				"title": "apple company history",
+				"link": "https://www.google.com/search?sca_esv=6b6df170a5c9891b&sca_upv=1&q=Apple+company+history&sa=X&ved=2ahUKEwjLxuSJwM-GAxUHODQIHYuJBhgQ1QJ6BAhOEAE"
+			},
+			{
+				"title": "apple store",
+				"link": "https://www.google.com/search?sca_esv=6b6df170a5c9891b&sca_upv=1&q=Apple+Store&sa=X&ved=2ahUKEwjLxuSJwM-GAxUHODQIHYuJBhgQ1QJ6BAhQEAE"
+			},
+			{
+				"title": "apple id",
+				"link": "https://www.google.com/search?sca_esv=6b6df170a5c9891b&sca_upv=1&q=Apple+id&sa=X&ved=2ahUKEwjLxuSJwM-GAxUHODQIHYuJBhgQ1QJ6BAhSEAE"
+			},
+			{
+				"title": "apple inc industry",
+				"link": "https://www.google.com/search?sca_esv=6b6df170a5c9891b&sca_upv=1&q=Apple+Inc+industry&sa=X&ved=2ahUKEwjLxuSJwM-GAxUHODQIHYuJBhgQ1QJ6BAhREAE"
+			},
+			{
+				"title": "apple login",
+				"link": "https://www.google.com/search?sca_esv=6b6df170a5c9891b&sca_upv=1&q=Apple+login&sa=X&ved=2ahUKEwjLxuSJwM-GAxUHODQIHYuJBhgQ1QJ6BAhTEAE"
+			}
+		]
+	},
+	"image_results": [],
+	"carousel": [],
+	"total": 2450000000,
+	"knowledge_graph": "",
+	"related_questions": [
+		"What does the Apple Inc do?",
+		"Why did Apple change to Apple Inc?",
+		"Who owns Apple Inc.?",
+		"What is Apple Inc best known for?"
+	],
+	"carousel_count": 0,
+	"ts": 2.491065263748169,
+	"device_type": null
+}

backend/open_webui/retrieval/web/testdata/serpstack.json

@@ -0,0 +1,276 @@
+{
+	"request": {
+		"success": true,
+		"total_time_taken": 3.4,
+		"processed_timestamp": 1714968442,
+		"search_url": "http://www.google.com/search?q=mcdonalds\u0026gl=us\u0026hl=en\u0026safe=0\u0026num=10"
+	},
+	"search_parameters": {
+		"engine": "google",
+		"type": "web",
+		"device": "desktop",
+		"auto_location": "1",
+		"google_domain": "google.com",
+		"gl": "us",
+		"hl": "en",
+		"safe": "0",
+		"news_type": "all",
+		"exclude_autocorrected_results": "0",
+		"images_color": "any",
+		"page": "1",
+		"num": "10",
+		"output": "json",
+		"csv_fields": "search_parameters.query,organic_results.position,organic_results.title,organic_results.url,organic_results.domain",
+		"query": "mcdonalds",
+		"action": "search",
+		"access_key": "aac48e007e15c532bb94ffb34532a4b2",
+		"error": {}
+	},
+	"search_information": {
+		"total_results": 1170000000,
+		"time_taken_displayed": 0.49,
+		"detected_location": {},
+		"did_you_mean": {},
+		"no_results_for_original_query": false,
+		"showing_results_for": {}
+	},
+	"organic_results": [
+		{
+			"position": 1,
+			"title": "Our Full McDonald\u0027s Food Menu",
+			"snippet": "",
+			"prerender": false,
+			"cached_page_url": {},
+			"related_pages_url": {},
+			"url": "https://www.mcdonalds.com/us/en-us/full-menu.html",
+			"domain": "www.mcdonalds.com",
+			"displayed_url": "https://www.mcdonalds.com \u203a en-us \u203a full-menu"
+		},
+		{
+			"position": 2,
+			"title": "McDonald\u0027s",
+			"snippet": "McDonald\u0027s is the world\u0027s largest fast food restaurant chain, serving over 69 million customers daily in over 100 countries in more than 40,000 outlets as of\u00a0...",
+			"prerender": false,
+			"cached_page_url": {},
+			"related_pages_url": {},
+			"url": "https://en.wikipedia.org/wiki/McDonald%27s",
+			"domain": "en.wikipedia.org",
+			"displayed_url": "https://en.wikipedia.org \u203a wiki \u203a McDonald\u0027s"
+		},
+		{
+			"position": 3,
+			"title": "Restaurants Near Me: Nearby McDonald\u0027s Locations",
+			"snippet": "",
+			"prerender": false,
+			"cached_page_url": {},
+			"related_pages_url": {},
+			"url": "https://www.mcdonalds.com/us/en-us/restaurant-locator.html",
+			"domain": "www.mcdonalds.com",
+			"displayed_url": "https://www.mcdonalds.com \u203a en-us \u203a restaurant-locator"
+		},
+		{
+			"position": 4,
+			"title": "Download the McDonald\u0027s App: Deals, Promotions \u0026 ...",
+			"snippet": "Download the McDonald\u0027s app for Mobile Order \u0026 Pay, exclusive deals and coupons, menu information and special promotions.",
+			"prerender": false,
+			"cached_page_url": {},
+			"related_pages_url": {},
+			"url": "https://www.mcdonalds.com/us/en-us/download-app.html",
+			"domain": "www.mcdonalds.com",
+			"displayed_url": "https://www.mcdonalds.com \u203a en-us \u203a download-app"
+		},
+		{
+			"position": 5,
+			"title": "McDonald\u0027s Restaurant Careers in the US",
+			"snippet": "McDonald\u0027s restaurant jobs are one-of-a-kind \u2013 just like you. Restaurants are hiring across all levels, from Crew team to Management. Apply today!",
+			"prerender": false,
+			"cached_page_url": {},
+			"related_pages_url": {},
+			"url": "https://jobs.mchire.com/",
+			"domain": "jobs.mchire.com",
+			"displayed_url": "https://jobs.mchire.com"
+		}
+	],
+	"inline_images": [
+		{
+			"image_url": "https://serpstack-assets.apilayer.net/2418910010831954152.png",
+			"title": ""
+		}
+	],
+	"local_results": [
+		{
+			"position": 1,
+			"title": "McDonald\u0027s",
+			"coordinates": {
+				"latitude": 0,
+				"longitude": 0
+			},
+			"address": "",
+			"rating": 0,
+			"reviews": 0,
+			"type": "",
+			"price": {},
+			"url": 0
+		},
+		{
+			"position": 2,
+			"title": "McDonald\u0027s",
+			"coordinates": {
+				"latitude": 0,
+				"longitude": 0
+			},
+			"address": "",
+			"rating": 0,
+			"reviews": 0,
+			"type": "",
+			"price": {},
+			"url": 0
+		},
+		{
+			"position": 3,
+			"title": "McDonald\u0027s",
+			"coordinates": {
+				"latitude": 0,
+				"longitude": 0
+			},
+			"address": "",
+			"rating": 0,
+			"reviews": 0,
+			"type": "",
+			"price": {},
+			"url": 0
+		}
+	],
+	"top_stories": [
+		{
+			"block_position": 1,
+			"title": "Menu nutrition",
+			"url": "/search?safe=0\u0026sca_esv=c9c7fd42856085e2\u0026sca_upv=1\u0026gl=us\u0026hl=en\u0026q=mcdonald%27s+double+quarter+pounder+with+cheese\u0026stick=H4sIAAAAAAAAAONgFuLUz9U3ME-vLDBX4tVP1zc0TCsuNE0ytjTTUs5OttJPy89P0c9NzSuNLyjKL8tMSS2yAvNS80qKMlOLF7Hq5ian5Ocl5qSoFyuk5Jcm5aQqFJYmFpWkFikU5JfmATUolGeWZCgkZ6SmFqcCAM4ilJtxAAAA\u0026sa=X\u0026ved=2ahUKEwjF55alk_iFAxXlamwGHbqgAs4Qri56BAh0EAM",
+			"source": "",
+			"uploaded": "",
+			"uploaded_utc": "2024-05-06T04:07:22.082Z"
+		},
+		{
+			"block_position": 2,
+			"title": "Profiles",
+			"url": "https://www.instagram.com/McDonalds",
+			"source": "",
+			"uploaded": "",
+			"uploaded_utc": "2024-05-06T04:07:22.082Z"
+		},
+		{
+			"block_position": 3,
+			"title": "People also search for",
+			"url": "/search?safe=0\u0026sca_esv=c9c7fd42856085e2\u0026sca_upv=1\u0026gl=us\u0026hl=en\u0026si=ACC90nzx_D3_zUKRnpAjmO0UBLNxnt7EyN4YYdru6U3bxLI-L5Wg8IL2sxPFxxcDEhVbocy-LJPZIvZySijw0ho2hfZ-KtV-sSEEJ9lw7JuEkXHDnRK5y4Dm8aqbiLwugbLbslwjG3hO_gpDTFZK2VoUGZPy2nrmOBCy0G3PoOfoiEtct2GSZlUz0uufG-xP8emtNzQKQpvjkAm5Zmi57iVZueiD62upz7-x2N3dAbwtm6FkInAPRw1yR91zuT7F3lEaPblTW3LaRwCDC0bvaRCh9x4N9zHgY1OOQa_rzts2jf5WpXcuw4Y%3D\u0026q=Burger+King\u0026sa=X\u0026ved=2ahUKEwjF55alk_iFAxXlamwGHbqgAs4Qs9oBKAB6BAhzEAI",
+			"source": "",
+			"uploaded": "",
+			"uploaded_utc": "2024-05-06T04:07:22.082Z"
+		}
+	],
+	"related_questions": [
+		{
+			"question": "What\u0027s a number 7 at McDonald\u0027s?What\u0027s a number 7 at McDonald\u0027s?What\u0027s a number 7 at McDonald\u0027s?",
+			"answer": "",
+			"title": "",
+			"displayed_url": ""
+		},
+		{
+			"question": "Why is McDonald\u0027s changing their name?Why is McDonald\u0027s changing their name?Why is McDonald\u0027s changing their name?",
+			"answer": "",
+			"title": "",
+			"displayed_url": ""
+		},
+		{
+			"question": "What is the oldest still running Mcdonalds?What is the oldest still running Mcdonalds?What is the oldest still running Mcdonalds?",
+			"answer": "",
+			"title": "",
+			"displayed_url": ""
+		},
+		{
+			"question": "Why is McDonald\u0027s now WcDonald\u0027s?Why is McDonald\u0027s now WcDonald\u0027s?Why is McDonald\u0027s now WcDonald\u0027s?",
+			"answer": "",
+			"title": "",
+			"displayed_url": ""
+		}
+	],
+	"knowledge_graph": {
+		"title": "",
+		"type": "Fast-food restaurant company",
+		"image_urls": ["https://serpstack-assets.apilayer.net/2418910010831954152.png"],
+		"description": "McDonald\u0027s Corporation is an American multinational fast food chain, founded in 1940 as a restaurant operated by Richard and Maurice McDonald, in San Bernardino, California, United States.",
+		"source": {
+			"name": "Wikipedia",
+			"url": "https://en.wikipedia.org/wiki/McDonald\u0027s"
+		},
+		"people_also_search_for": [],
+		"known_attributes": [
+			{
+				"attribute": "kc:/business/business_operation:founder",
+				"link": "http://www.google.com/search?safe=0\u0026sca_esv=c9c7fd42856085e2\u0026sca_upv=1\u0026gl=us\u0026hl=en\u0026q=Ray+Kroc\u0026si=ACC90nzx_D3_zUKRnpAjmO0UBLNxnt7EyN4YYdru6U3bxLI-LxARWRdbk5SkoY2sDn5Qq7yOmqYGei6qZ7sfJhsjZXBPgjMlLbS7824rpJOm69GzqVWMdoNIZiFX2T4A2td14sZOn4a1BexZLtZXHU7NZdF6VsWbGMVuiSYtXdev7uaUjEJKumiwlqTAATTebOriYTEBuSzC\u0026sa=X\u0026ved=2ahUKEwjF55alk_iFAxXlamwGHbqgAs4QmxMoAHoECHgQAg",
+				"name": "Founder: ",
+				"value": "Ray Kroc"
+			},
+			{
+				"attribute": "kc:/organization/organization:ceo",
+				"link": "http://www.google.com/search?safe=0\u0026sca_esv=c9c7fd42856085e2\u0026sca_upv=1\u0026gl=us\u0026hl=en\u0026q=Chris+Kempczinski\u0026si=ACC90nwLLwns5sISZcdzuISy7t-NHozt8Cbt6G3WNQfC9ekAgKFbjdEFCDgxLbt57EDZGosYDGiZuq1AcBhA6IhTOSZxfVSySuGQ3VDwmmTA7Z93n3K3596jAuZH9VVv5h8PyvKJSuGuSsQWviJTl3eKj2UL1ZIWuDgkjyVMnC47rN7j0G9PlHRCCLdQF7VDQ1gubTiC4onXqLRBTbwAj6a--PD6Jv_NoA%3D%3D\u0026sa=X\u0026ved=2ahUKEwjF55alk_iFAxXlamwGHbqgAs4QmxMoAHoECHUQAg",
+				"name": "CEO: ",
+				"value": "Chris Kempczinski (Nov 1, 2019\u2013)"
+			},
+			{
+				"attribute": "kc:/business/employer:revenue",
+				"link": "",
+				"name": "Revenue: ",
+				"value": "25.49\u00a0billion USD (2023)"
+			},
+			{
+				"attribute": "kc:/organization/organization:founded",
+				"link": "http://www.google.com/search?safe=0\u0026sca_esv=c9c7fd42856085e2\u0026sca_upv=1\u0026gl=us\u0026hl=en\u0026q=Des+Plaines\u0026si=ACC90nyvvWro6QmnyY1IfSdgk5wwjB1r8BGd_IWRjXqmKPQqm_yqLtI_DBi5PXGOtg_Z3qrzzEP6mcih1nN7h5A7v6OefnEJiC7a8dBR-v9LxlRubfyR6vlMr3fZ3TmVKWwz9FRpvZb1eYNt-RM7KIDKQlwGEIgINvzhxjUrv6uxSmceduzxd8W7Pkz71XGwxF0F8OlSzHlx\u0026sa=X\u0026ved=2ahUKEwjF55alk_iFAxXlamwGHbqgAs4QmxMoAHoECG4QAg",
+				"name": "Founded: ",
+				"value": "April 15, 1955, Des Plaines, IL"
+			},
+			{
+				"attribute": "kc:/organization/organization:headquarters",
+				"link": "http://www.google.com/search?safe=0\u0026sca_esv=c9c7fd42856085e2\u0026sca_upv=1\u0026gl=us\u0026hl=en\u0026q=Chicago\u0026si=ACC90nyvvWro6QmnyY1IfSdgk5wwjB1r8BGd_IWRjXqmKPQqm-46AEJ_kJbUIEvsvEEZqteiYJvXVXs2ScRNDvFFpjfeAaW3dxtpTGCgcsf5RMdi6IdzOdtjJMN3ZaFwqZOmdi7tC6r0Mh1O9bnP3HrVDB9hH02m7aA6f70dCAfTdpOFnGxDU6wVMAI5MxWBE3wTugtUDOK-\u0026sa=X\u0026ved=2ahUKEwjF55alk_iFAxXlamwGHbqgAs4QmxMoAHoECHYQAg",
+				"name": "Headquarters: ",
+				"value": "Chicago, IL"
+			},
+			{
+				"attribute": "kc:/organization/organization:president",
+				"link": "http://www.google.com/search?safe=0\u0026sca_esv=c9c7fd42856085e2\u0026sca_upv=1\u0026gl=us\u0026hl=en\u0026q=Chris+Kempczinski\u0026si=ACC90nwLLwns5sISZcdzuISy7t-NHozt8Cbt6G3WNQfC9ekAgKFbjdEFCDgxLbt57EDZGosYDGiZuq1AcBhA6IhTOSZxfVSySuGQ3VDwmmTA7Z93n3K3596jAuZH9VVv5h8PyvKJSuGuSsQWviJTl3eKj2UL1ZIWuDgkjyVMnC47rN7j0G9PlHRCCLdQF7VDQ1gubTiC4onXqLRBTbwAj6a--PD6Jv_NoA%3D%3D\u0026sa=X\u0026ved=2ahUKEwjF55alk_iFAxXlamwGHbqgAs4QmxMoAHoECHEQAg",
+				"name": "President: ",
+				"value": "Chris Kempczinski"
+			}
+		],
+		"website": "https://www.mcdonalds.com/us/en-us.html",
+		"profiles": [
+			{
+				"name": "Instagram",
+				"url": "https://www.instagram.com/McDonalds"
+			},
+			{
+				"name": "X (Twitter)",
+				"url": "https://twitter.com/McDonalds"
+			},
+			{
+				"name": "Facebook",
+				"url": "https://www.facebook.com/McDonaldsUS"
+			},
+			{
+				"name": "YouTube",
+				"url": "https://www.youtube.com/user/McDonaldsUS"
+			},
+			{
+				"name": "Pinterest",
+				"url": "https://www.pinterest.com/mcdonalds"
+			}
+		],
+		"founded": "April 15, 1955, Des Plaines, IL",
+		"headquarters": "Chicago, IL",
+		"founders": [
+			{
+				"name": "Ray Kroc",
+				"link": "http://www.google.com/search?safe=0\u0026sca_esv=c9c7fd42856085e2\u0026sca_upv=1\u0026gl=us\u0026hl=en\u0026q=Ray+Kroc\u0026si=ACC90nzx_D3_zUKRnpAjmO0UBLNxnt7EyN4YYdru6U3bxLI-LxARWRdbk5SkoY2sDn5Qq7yOmqYGei6qZ7sfJhsjZXBPgjMlLbS7824rpJOm69GzqVWMdoNIZiFX2T4A2td14sZOn4a1BexZLtZXHU7NZdF6VsWbGMVuiSYtXdev7uaUjEJKumiwlqTAATTebOriYTEBuSzC\u0026sa=X\u0026ved=2ahUKEwjF55alk_iFAxXlamwGHbqgAs4QmxMoAHoECHgQAg"
+			}
+		]
+	}
+}

backend/open_webui/retrieval/web/utils.py

@@ -0,0 +1,97 @@
+import socket
+import urllib.parse
+import validators
+from typing import Union, Sequence, Iterator
+
+from langchain_community.document_loaders import (
+    WebBaseLoader,
+)
+from langchain_core.documents import Document
+
+
+from open_webui.constants import ERROR_MESSAGES
+from open_webui.config import ENABLE_RAG_LOCAL_WEB_FETCH
+from open_webui.env import SRC_LOG_LEVELS
+
+import logging
+
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["RAG"])
+
+
+def validate_url(url: Union[str, Sequence[str]]):
+    if isinstance(url, str):
+        if isinstance(validators.url(url), validators.ValidationError):
+            raise ValueError(ERROR_MESSAGES.INVALID_URL)
+        if not ENABLE_RAG_LOCAL_WEB_FETCH:
+            # Local web fetch is disabled, filter out any URLs that resolve to private IP addresses
+            parsed_url = urllib.parse.urlparse(url)
+            # Get IPv4 and IPv6 addresses
+            ipv4_addresses, ipv6_addresses = resolve_hostname(parsed_url.hostname)
+            # Check if any of the resolved addresses are private
+            # This is technically still vulnerable to DNS rebinding attacks, as we don't control WebBaseLoader
+            for ip in ipv4_addresses:
+                if validators.ipv4(ip, private=True):
+                    raise ValueError(ERROR_MESSAGES.INVALID_URL)
+            for ip in ipv6_addresses:
+                if validators.ipv6(ip, private=True):
+                    raise ValueError(ERROR_MESSAGES.INVALID_URL)
+        return True
+    elif isinstance(url, Sequence):
+        return all(validate_url(u) for u in url)
+    else:
+        return False
+
+
+def resolve_hostname(hostname):
+    # Get address information
+    addr_info = socket.getaddrinfo(hostname, None)
+
+    # Extract IP addresses from address information
+    ipv4_addresses = [info[4][0] for info in addr_info if info[0] == socket.AF_INET]
+    ipv6_addresses = [info[4][0] for info in addr_info if info[0] == socket.AF_INET6]
+
+    return ipv4_addresses, ipv6_addresses
+
+
+class SafeWebBaseLoader(WebBaseLoader):
+    """WebBaseLoader with enhanced error handling for URLs."""
+
+    def lazy_load(self) -> Iterator[Document]:
+        """Lazy load text from the url(s) in web_path with error handling."""
+        for path in self.web_paths:
+            try:
+                soup = self._scrape(path, bs_kwargs=self.bs_kwargs)
+                text = soup.get_text(**self.bs_get_text_kwargs)
+
+                # Build metadata
+                metadata = {"source": path}
+                if title := soup.find("title"):
+                    metadata["title"] = title.get_text()
+                if description := soup.find("meta", attrs={"name": "description"}):
+                    metadata["description"] = description.get(
+                        "content", "No description found."
+                    )
+                if html := soup.find("html"):
+                    metadata["language"] = html.get("lang", "No language found.")
+
+                yield Document(page_content=text, metadata=metadata)
+            except Exception as e:
+                # Log the error and continue with the next URL
+                log.error(f"Error loading {path}: {e}")
+
+
+def get_web_loader(
+    url: Union[str, Sequence[str]],
+    verify_ssl: bool = True,
+    requests_per_second: int = 2,
+):
+    # Check if the URL is valid
+    if not validate_url(url):
+        raise ValueError(ERROR_MESSAGES.INVALID_URL)
+    return SafeWebBaseLoader(
+        url,
+        verify_ssl=verify_ssl,
+        requests_per_second=requests_per_second,
+        continue_on_failure=True,
+    )