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+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
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+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

README.md

@@ -1,12 +1,141 @@
----
-title: LLMsearch
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 4.36.1
-app_file: app.py
-pinned: false
----
-
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+# Give your local LLM the ability to search the web!
+This project gives local LLMs the ability to search the web by outputting a specific
+command. Once the command has been found in the model output using a regular expression,
+[duckduckgo-search](https://pypi.org/project/duckduckgo-search/)
+is used to search the web and return a number of result pages. Finally, an
+ensemble of LangChain's [Contextual compression](https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/) and 
+[Okapi BM25](https://en.wikipedia.org/wiki/Okapi_BM25) (Or alternatively, [SPLADE](https://github.com/naver/splade))
+is used to extract the relevant parts (if any) of each web page in the search results
+and the results are appended to the model's output.
+![llm_websearch](https://github.com/mamei16/LLM_Web_search/assets/25900898/f9d2d83c-e3cf-4f69-91c2-e9c3fe0b7d89)
+
+
+* **[Table of Contents](#table-of-contents)**
+  * [Installation](#installation)
+  * [Usage](#usage)
+    + [Using a custom regular expression](#using-a-custom-regular-expression)
+    + [Reading web pages](#reading-web-pages)
+  * [Search backends](#search-backends)
+    + [DuckDuckGo](#duckduckgo)
+    + [SearXNG](#searxng)
+      + [Search parameters](#search-parameters)
+  * [Keyword retrievers](#keyword-retrievers)
+    + [Okapi BM25](#okapi-bm25)
+    + [SPLADE](#splade)
+  * [Recommended models](#recommended-models)
+
+## Installation
+1. Go to the "Session" tab of the web UI and use "Install or update an extension" 
+to download the latest code for this extension.
+2. To install the extension's depencies you have two options:  
+   1. **The easy way:** Run the appropriate `update_wizard` script inside the text-generation-webui folder
+   and choose `Install/update extensions requirements`. This installs everything using `pip`,
+   which means using the unofficial `faiss-cpu` package. Therefore, it is not guaranteed to
+   work with your system (see [the official disclaimer](https://github.com/facebookresearch/faiss/wiki/Installing-Faiss#why-dont-you-support-installing-via-xxx-)).
+   2. **The safe way:** Manually update the conda environment in which you installed the dependencies of 
+   [oobabooga's text-generation-webui](https://github.com/oobabooga/text-generation-webui).
+   Open the subfolder `text-generation-webui/extensions/LLM_Web_search` in a terminal or conda shell.
+   If you used the one-click install method, run the command 
+   `conda env update -p <path_to_your_environment> --file environment.yml`,
+   where you need to replace `<path_to_your_environment>` with the path to the 
+   `/installer_files/env` subfolder within the text-generation-webui folder.
+   Otherwise, if you made your own environment, 
+   use `conda env update -n <name_of_your_environment> --file environment.yml`  
+     (NB: Solving the environment can take a while)
+3. Launch the Web UI with:  
+```python server.py --extension LLM_Web_search```
+
+If the installation was successful and the extension was loaded, a new tab with the 
+title "LLM Web Search" should be visible in the web UI.
+
+See https://github.com/oobabooga/text-generation-webui/wiki/07-%E2%80%90-Extensions for more
+information about extensions.
+
+## Usage
+
+Search queries are extracted from the model's output using a regular expression. This is made easier by prompting the model
+to use a fixed search command (see `system_prompts/` for example prompts).   
+Currently, only a single search query per model chat message is supported.
+
+An example workflow of using this extension could be:
+1. Load a model
+2. Load a matching instruction template
+3. Head over to the "LLM Web search" tab
+4. Load a custom system message/prompt
+5. Ensure that the query part of the command mentioned in the system message 
+can be matched using the current "Search command regex string" 
+(see "Using a custom regular expression" below)
+6. Pick a hyperparameter generation preset that works well for you.
+7. Choose "chat-instruct" or "instruct" mode and start chatting
+
+### Using a custom regular expression
+The default regular expression is:  
+```regexp
+Search_web\("(.*)"\)
+```
+Where `Search_web` is the search command and everything between the quotation marks
+inside the parentheses will be used as the search query. Every custom regular expression must use a
+[capture group](https://www.regular-expressions.info/brackets.html) to extract the search
+query. I recommend https://www.debuggex.com/ to try out custom regular expressions. If a regex
+fulfills the requirement above, the search query should be matched by "Group 1" in Debuggex.
+
+Here is an example of a more flexible, but more complex, regex that works for several
+different models:
+```regexp
+[Ss]earch_web\((?:["'])(.*)(?:["'])\)
+```
+### Reading web pages
+Experimental support exists for extracting the full text content from a webpage. The default regex to use this
+functionality is:
+```regexp
+Open_url\("(.*)"\)
+```
+**Note**: The full content of a web page is likely to exceed the maximum context length of your average local LLM.
+## Search backends
+
+### DuckDuckGo
+This is the default web search backend.
+
+### SearXNG
+
+Rudimentary support exists for SearXNG. To use a local or remote 
+SearXNG instance instead of DuckDuckGo, simply paste the URL into the 
+"SearXNG URL" text field of the "LLM Web Search" settings tab. The instance must support
+returning results in JSON format.
+
+#### Search parameters
+To modify the categories, engines, languages etc. that should be used for a
+specific query, it must follow the
+[SearXNG search syntax](https://docs.searxng.org/user/search-syntax.html). Currently, 
+automatic redirect and Special Queries are not supported.
+
+
+## Keyword retrievers
+### Okapi BM25
+This extension comes out of the box with 
+[Okapi BM25](https://en.wikipedia.org/wiki/Okapi_BM25) enabled, which is widely used and very popuplar
+for keyword based document retrieval. It runs on the CPU and,
+for the purpose of this extension, it is fast.  
+### SPLADE
+If you don't run the extension in "CPU only" mode and have some VRAM to spare,
+you can also select [SPLADE](https://github.com/naver/splade) in the "Advanced settings" section
+as an alternative. It has been [shown](https://arxiv.org/pdf/2207.03834.pdf) to outperform BM25 in multiple benchmarks 
+and uses a technique called "query expansion" to add additional contextually relevant words
+to the original query. However, it is slower than BM25. You can read more about it [here](https://www.pinecone.io/learn/splade/).  
+To use SPLADE, you have to install the additional dependency [qdrant-client](https://github.com/qdrant/qdrant-client). 
+Simply activate the conda environment of the main web UI and run
+`pip install qdrant-client`.  
+To improve performance, documents are embedded in batches and in parallel. Increasing the
+"SPLADE batch size" parameter setting improves performance up to a certain point,
+but VRAM usage ramps up quickly with increasing batch size. A batch size of 8 appears 
+to be a good trade-off, but the default value is 2 to avoid running out of memory on smaller
+GPUs.
+
+## Recommended models
+If you (like me) have ≤ 12 GB VRAM, I recommend using 
+[Llama-3-8B-instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
+You can find a matching instruction template in the extension's `instruction_templates`
+folder. Simply copy it to the main web UI's `instruction-templates` folder.
+**Note:** Several existing GGUF versions have a stop token issue, which can be solved by [editing the file's 
+metadata](https://www.reddit.com/r/LocalLLaMA/comments/1c7dkxh/tutorial_how_to_make_llama3instruct_ggufs_less/). A GGUF version where this issue has already been fixed can be found 
+[here](https://huggingface.co/AI-Engine/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct.Q5_k_m_with_temp_stop_token_fix.gguf).

environment.yml

@@ -0,0 +1,15 @@
+channels:
+  - defaults
+  - conda-forge
+  - pytorch
+dependencies:
+- pip
+- faiss-cpu=1.8.0
+- pip:
+  - duckduckgo_search==6.1.0
+  - beautifulsoup4==4.12.3
+  - langchain==0.2.1
+  - langchain-community==0.2.1
+  - unstructured==0.14.2
+  - rank_bm25==0.2.2
+  - sentence-transformers==2.7.0

instruction_templates/Llama-3.yaml

@@ -0,0 +1,17 @@
+instruction_template: |-
+  {%- set ns = namespace(found=false) -%}
+  {%- for message in messages -%}
+      {%- if message['role'] == 'system' -%}
+          {%- set ns.found = true -%}
+      {%- endif -%}
+  {%- endfor -%}
+  {%- for message in messages %}
+      {% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}
+      {% if loop.index0 == 0 %}
+          {% set content = '<|begin_of_text|>' + content %}
+      {% endif %}
+      {{- content -}}
+  {%- endfor -%}
+  {%- if add_generation_prompt -%}
+      {{- '<|start_header_id|>' + 'assistant' + '<|end_header_id|>\n\n' -}}
+  {%- endif -%}

instruction_templates/OpenChat-Correct.yaml

@@ -0,0 +1,24 @@
+instruction_template: |-
+  {%- set ns = namespace(found=false) -%}
+  {%- for message in messages -%}
+      {%- if message['role'] == 'system' -%}
+          {%- set ns.found = true -%}
+      {%- endif -%}
+  {%- endfor -%}
+  {%- if not ns.found -%}
+      {{- '' + '' + '' -}}
+  {%- endif %}
+  {%- for message in messages %}
+      {%- if message['role'] == 'system' -%}
+          {{- '' + message['content'] + '' -}}
+      {%- else -%}
+          {%- if message['role'] == 'user' -%}
+              {{-'GPT4 Correct User: ' + message['content'] + '<|end_of_turn|>'-}}
+          {%- else -%}
+              {{-'GPT4 Correct Assistant: ' + message['content'] + '<|end_of_turn|>' -}}
+          {%- endif -%}
+      {%- endif -%}
+  {%- endfor -%}
+  {%- if add_generation_prompt -%}
+      {{-'GPT4 Correct Assistant:'-}}
+  {%- endif -%}

instruction_templates/SOLAR-10.7B.yaml

@@ -0,0 +1,24 @@
+instruction_template: |-
+  {%- set ns = namespace(found=false) -%}
+  {%- for message in messages -%}
+      {%- if message['role'] == 'system' -%}
+          {%- set ns.found = true -%}
+      {%- endif -%}
+  {%- endfor -%}
+  {%- if not ns.found -%}
+      {{- '' + '' + '' -}}
+  {%- endif %}
+  {%- for message in messages %}
+      {%- if message['role'] == 'system' -%}
+          {{- '' + message['content'] + '' -}}
+      {%- else -%}
+          {%- if message['role'] == 'user' -%}
+              {{-'### User: ' + message['content'] + '\n\n'-}}
+          {%- else -%}
+              {{-'### Assistant: ' + message['content'] + '\n\n' -}}
+          {%- endif -%}
+      {%- endif -%}
+  {%- endfor -%}
+  {%- if add_generation_prompt -%}
+      {{-'### Assistant:'-}}
+  {%- endif -%}

langchain_websearch.py

@@ -0,0 +1,210 @@
+import re
+import asyncio
+import warnings
+import logging
+
+import aiohttp
+import requests
+from bs4 import BeautifulSoup
+from langchain.retrievers.document_compressors import DocumentCompressorPipeline
+from langchain.retrievers.ensemble import EnsembleRetriever
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.retrievers.document_compressors.embeddings_filter import EmbeddingsFilter
+from langchain.retrievers import ContextualCompressionRetriever
+from langchain.schema import Document
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_transformers import EmbeddingsRedundantFilter
+from langchain_community.retrievers import BM25Retriever
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+import optimum.bettertransformer.transformation
+try:
+    from qdrant_client import QdrantClient, models
+except ImportError:
+    qrant_client = None
+
+from .qdrant_retriever import MyQdrantSparseVectorRetriever
+from .semantic_chunker import BoundedSemanticChunker
+
+
+class LangchainCompressor:
+
+    def __init__(self, device="cuda", num_results: int = 5, similarity_threshold: float = 0.5, chunk_size: int = 500,
+                 ensemble_weighting: float = 0.5, splade_batch_size: int = 2, keyword_retriever: str = "bm25",
+                 model_cache_dir: str = None, chunking_method: str = "character-based",
+                 chunker_breakpoint_threshold_amount: int = 10):
+        self.device = device
+        self.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2", model_kwargs={"device": device},
+                                                cache_folder=model_cache_dir)
+        if keyword_retriever == "splade":
+            if "QdrantClient" not in globals():
+                raise ImportError("Package qrant_client is missing. Please install it using 'pip install qdrant-client")
+            self.splade_doc_tokenizer = AutoTokenizer.from_pretrained("naver/efficient-splade-VI-BT-large-doc",
+                                                                      cache_dir=model_cache_dir)
+            self.splade_doc_model = AutoModelForMaskedLM.from_pretrained("naver/efficient-splade-VI-BT-large-doc",
+                                                                         cache_dir=model_cache_dir).to(self.device)
+            self.splade_query_tokenizer = AutoTokenizer.from_pretrained("naver/efficient-splade-VI-BT-large-query",
+                                                                        cache_dir=model_cache_dir)
+            self.splade_query_model = AutoModelForMaskedLM.from_pretrained("naver/efficient-splade-VI-BT-large-query",
+                                                                           cache_dir=model_cache_dir).to(self.device)
+            optimum_logger = optimum.bettertransformer.transformation.logger
+            original_log_level = optimum_logger.level
+            # Set the level to 'ERROR' to ignore "The BetterTransformer padding during training warning"
+            optimum_logger.setLevel(logging.ERROR)
+            self.splade_doc_model.to_bettertransformer()
+            self.splade_query_model.to_bettertransformer()
+            optimum_logger.setLevel(original_log_level)
+            self.splade_batch_size = splade_batch_size
+
+        self.spaces_regex = re.compile(r" {3,}")
+        self.num_results = num_results
+        self.similarity_threshold = similarity_threshold
+        self.chunking_method = chunking_method
+        self.chunk_size = chunk_size
+        self.chunker_breakpoint_threshold_amount = chunker_breakpoint_threshold_amount
+        self.ensemble_weighting = ensemble_weighting
+        self.keyword_retriever = keyword_retriever
+
+    def preprocess_text(self, text: str) -> str:
+        text = text.replace("\n", " \n")
+        text = self.spaces_regex.sub(" ", text)
+        text = text.strip()
+        return text
+
+    def retrieve_documents(self, query: str, url_list: list[str]) -> list[Document]:
+        yield "Downloading webpages..."
+        html_url_tupls = zip(asyncio.run(async_fetch_urls(url_list)), url_list)
+        html_url_tupls = [(content, url) for content, url in html_url_tupls if content is not None]
+        if not html_url_tupls:
+            return []
+
+        documents = [html_to_plaintext_doc(html, url) for html, url in html_url_tupls]
+        if self.chunking_method == "semantic":
+            text_splitter = BoundedSemanticChunker(self.embeddings, breakpoint_threshold_type="percentile",
+                                                        breakpoint_threshold_amount=self.chunker_breakpoint_threshold_amount,
+                                                        max_chunk_size=self.chunk_size)
+        else:
+            text_splitter = RecursiveCharacterTextSplitter(chunk_size=self.chunk_size, chunk_overlap=10,
+                                                                separators=["\n\n", "\n", ".", ", ", " ", ""])
+        yield "Chunking page texts..."
+        split_docs = text_splitter.split_documents(documents)
+        yield "Retrieving relevant results..."
+        # filtered_docs = pipeline_compressor.compress_documents(documents, query)
+        faiss_retriever = FAISS.from_documents(split_docs, self.embeddings).as_retriever(
+            search_kwargs={"k": self.num_results}
+        )
+
+        #  The sparse keyword retriever is good at finding relevant documents based on keywords,
+        #  while the dense retriever is good at finding relevant documents based on semantic similarity.
+        if self.keyword_retriever == "bm25":
+            keyword_retriever = BM25Retriever.from_documents(split_docs, preprocess_func=self.preprocess_text)
+            keyword_retriever.k = self.num_results
+        elif self.keyword_retriever == "splade":
+            client = QdrantClient(location=":memory:")
+            collection_name = "sparse_collection"
+            vector_name = "sparse_vector"
+
+            client.create_collection(
+                collection_name,
+                vectors_config={},
+                sparse_vectors_config={
+                    vector_name: models.SparseVectorParams(
+                        index=models.SparseIndexParams(
+                            on_disk=False,
+                        )
+                    )
+                },
+            )
+
+            keyword_retriever = MyQdrantSparseVectorRetriever(
+                splade_doc_tokenizer=self.splade_doc_tokenizer,
+                splade_doc_model=self.splade_doc_model,
+                splade_query_tokenizer=self.splade_query_tokenizer,
+                splade_query_model=self.splade_query_model,
+                device=self.device,
+                client=client,
+                collection_name=collection_name,
+                sparse_vector_name=vector_name,
+                sparse_encoder=None,
+                batch_size=self.splade_batch_size,
+                k=self.num_results
+            )
+            keyword_retriever.add_documents(split_docs)
+        else:
+            raise ValueError("self.keyword_retriever must be one of ('bm25', 'splade')")
+
+        redundant_filter = EmbeddingsRedundantFilter(embeddings=self.embeddings)
+        embeddings_filter = EmbeddingsFilter(embeddings=self.embeddings, k=None,
+                                             similarity_threshold=self.similarity_threshold)
+        pipeline_compressor = DocumentCompressorPipeline(
+            transformers=[redundant_filter, embeddings_filter]
+        )
+
+        compression_retriever = ContextualCompressionRetriever(base_compressor=pipeline_compressor,
+                                                               base_retriever=faiss_retriever)
+
+        ensemble_retriever = EnsembleRetriever(
+            retrievers=[compression_retriever, keyword_retriever],
+            weights=[self.ensemble_weighting, 1 - self.ensemble_weighting]
+        )
+        compressed_docs = ensemble_retriever.invoke(query)
+
+        # Ensemble may return more than "num_results" results, so cut off excess ones
+        return compressed_docs[:self.num_results]
+
+
+async def async_download_html(url, headers):
+    async with aiohttp.ClientSession(headers=headers, timeout=aiohttp.ClientTimeout(10)) as session:
+        try:
+            resp = await session.get(url)
+            return await resp.text()
+        except UnicodeDecodeError:
+            print(
+                f"LLM_Web_search | {url} generated an exception: Expected content type text/html. Got {resp.headers['Content-Type']}.")
+        except TimeoutError as exc:
+            print('LLM_Web_search | %r did not load in time' % url)
+        except Exception as exc:
+            print('LLM_Web_search | %r generated an exception: %s' % (url, exc))
+    return None
+
+
+async def async_fetch_urls(urls):
+    headers = {"User-Agent": "Mozilla/5.0 (X11; Linux x86_64; rv:120.0) Gecko/20100101 Firefox/120.0",
+               "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
+               "Accept-Language": "en-US,en;q=0.5"}
+    webpages = await asyncio.gather(*[(async_download_html(url, headers)) for url in urls])
+    return webpages
+
+
+def docs_to_pretty_str(docs) -> str:
+    ret_str = ""
+    for i, doc in enumerate(docs):
+        ret_str += f"Result {i+1}:\n"
+        ret_str += f"{doc.page_content}\n"
+        ret_str += f"Source URL: {doc.metadata['source']}\n\n"
+    return ret_str
+
+
+def download_html(url: str) -> bytes:
+    headers = {"User-Agent": "Mozilla/5.0 (X11; Linux x86_64; rv:120.0) Gecko/20100101 Firefox/120.0",
+               "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
+               "Accept-Language": "en-US,en;q=0.5"}
+
+    response = requests.get(url, headers=headers, verify=True, timeout=8)
+    response.raise_for_status()
+
+    content_type = response.headers.get("Content-Type", "")
+    if not content_type.startswith("text/html"):
+        raise ValueError(f"Expected content type text/html. Got {content_type}.")
+    return response.content
+
+
+def html_to_plaintext_doc(html_text: str or bytes, url: str) -> Document:
+    with warnings.catch_warnings(action="ignore"):
+        soup = BeautifulSoup(html_text, features="lxml")
+    for script in soup(["script", "style"]):
+        script.extract()
+
+    strings = '\n'.join([s.strip() for s in soup.stripped_strings])
+    webpage_document = Document(page_content=strings, metadata={"source": url})
+    return webpage_document

llm_web_search.py

@@ -0,0 +1,147 @@
+import urllib
+
+import requests
+from requests.exceptions import JSONDecodeError
+from duckduckgo_search import DDGS
+from bs4 import BeautifulSoup
+from langchain.schema import Document
+
+from .langchain_websearch import docs_to_pretty_str, LangchainCompressor
+
+
+class Generator:
+    """Allows a generator method to return a final value after finishing
+    the generation. Credit: https://stackoverflow.com/a/34073559"""
+    def __init__(self, gen):
+        self.gen = gen
+
+    def __iter__(self):
+        self.value = yield from self.gen
+        return self.value
+
+
+def dict_list_to_pretty_str(data: list[dict]) -> str:
+    ret_str = ""
+    if isinstance(data, dict):
+        data = [data]
+    if isinstance(data, list):
+        for i, d in enumerate(data):
+            ret_str += f"Result {i+1}\n"
+            ret_str += f"Title: {d['title']}\n"
+            ret_str += f"{d['body']}\n"
+            ret_str += f"Source URL: {d['href']}\n"
+        return ret_str
+    else:
+        raise ValueError("Input must be dict or list[dict]")
+
+
+def search_duckduckgo(query: str, max_results: int, instant_answers: bool = True,
+                      regular_search_queries: bool = True, get_website_content: bool = False) -> list[dict]:
+    query = query.strip("\"'")
+    with DDGS() as ddgs:
+        if instant_answers:
+            answer_list = ddgs.answers(query)
+        else:
+            answer_list = None
+        if answer_list:
+            answer_dict = answer_list[0]
+            answer_dict["title"] = query
+            answer_dict["body"] = answer_dict["text"]
+            answer_dict["href"] = answer_dict["url"]
+            answer_dict.pop('icon', None)
+            answer_dict.pop('topic', None)
+            answer_dict.pop('text', None)
+            answer_dict.pop('url', None)
+            return [answer_dict]
+        elif regular_search_queries:
+            results = []
+            for result in ddgs.text(query, region='wt-wt', safesearch='moderate',
+                                    timelimit=None, max_results=max_results):
+                if get_website_content:
+                    result["body"] = get_webpage_content(result["href"])
+                results.append(result)
+            return results
+        else:
+            raise ValueError("One of ('instant_answers', 'regular_search_queries') must be True")
+
+
+def langchain_search_duckduckgo(query: str, langchain_compressor: LangchainCompressor, max_results: int,
+                                instant_answers: bool):
+    documents = []
+    query = query.strip("\"'")
+    yield f'Getting results from DuckDuckGo...'
+    with DDGS() as ddgs:
+        if instant_answers:
+            answer_list = ddgs.answers(query)
+            if answer_list:
+                if max_results > 1:
+                    max_results -= 1  # We already have 1 result now
+                answer_dict = answer_list[0]
+                instant_answer_doc = Document(page_content=answer_dict["text"],
+                                              metadata={"source": answer_dict["url"]})
+                documents.append(instant_answer_doc)
+
+        results = []
+        result_urls = []
+        for result in ddgs.text(query, region='wt-wt', safesearch='moderate', timelimit=None,
+                                max_results=langchain_compressor.num_results):
+            results.append(result)
+            result_urls.append(result["href"])
+    retrieval_gen = Generator(langchain_compressor.retrieve_documents(query, result_urls))
+    for status_message in retrieval_gen:
+        yield status_message
+    documents.extend(retrieval_gen.value)
+    if not documents:    # Fall back to old simple search rather than returning nothing
+        print("LLM_Web_search | Could not find any page content "
+              "similar enough to be extracted, using basic search fallback...")
+        return dict_list_to_pretty_str(results[:max_results])
+    return docs_to_pretty_str(documents[:max_results])
+
+
+def langchain_search_searxng(query: str, url: str, langchain_compressor: LangchainCompressor, max_results: int):
+    yield f'Getting results from Searxng...'
+    headers = {"User-Agent": "Mozilla/5.0 (X11; Linux x86_64; rv:120.0) Gecko/20100101 Firefox/120.0",
+               "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
+               "Accept-Language": "en-US,en;q=0.5"}
+    result_urls = []
+    request_str = f"/search?q={urllib.parse.quote(query)}&format=json&pageno="
+    pageno = 1
+    while len(result_urls) < langchain_compressor.num_results:
+        response = requests.get(url + request_str + str(pageno), headers=headers)
+        if not result_urls:     # no results to lose by raising an exception here
+            response.raise_for_status()
+        try:
+            response_dict = response.json()
+        except JSONDecodeError:
+            raise ValueError("JSONDecodeError: Please ensure that the SearXNG instance can return data in JSON format")
+        result_dicts = response_dict["results"]
+        if not result_dicts:
+            break
+        for result in result_dicts:
+            result_urls.append(result["url"])
+        pageno += 1
+    retrieval_gen = Generator(langchain_compressor.retrieve_documents(query, result_urls))
+    for status_message in retrieval_gen:
+        yield status_message
+    documents = retrieval_gen.value
+    return docs_to_pretty_str(documents[:max_results])
+
+
+def get_webpage_content(url: str) -> str:
+    headers = {"User-Agent": "Mozilla/5.0 (X11; Linux x86_64; rv:120.0) Gecko/20100101 Firefox/120.0",
+               "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
+               "Accept-Language": "en-US,en;q=0.5"}
+    if not url.startswith("https://"):
+        try:
+            response = requests.get(f"https://{url}", headers=headers)
+        except:
+            response = requests.get(url, headers=headers)
+    else:
+        response = requests.get(url, headers=headers)
+
+    soup = BeautifulSoup(response.content, features="lxml")
+    for script in soup(["script", "style"]):
+        script.extract()
+
+    strings = soup.stripped_strings
+    return '\n'.join([s.strip() for s in strings])

qdrant_retriever.py

@@ -0,0 +1,136 @@
+from typing import (
+    Any,
+    Iterable,
+    List,
+    Optional,
+    Tuple,
+    cast,
+    Generator
+)
+
+import torch
+from langchain_community.retrievers import QdrantSparseVectorRetriever
+from langchain_community.vectorstores.qdrant import Qdrant
+from langchain_core.pydantic_v1 import Field
+from langchain_core.callbacks import CallbackManagerForRetrieverRun
+from langchain.schema import Document
+try:
+    from qdrant_client import QdrantClient, models
+except ImportError:
+    pass
+
+
+def batchify(_list: List, batch_size: int) -> Generator[List, None, None]:
+    for i in range(0, len(_list), batch_size):
+        yield _list[i:i + batch_size]
+
+
+class MyQdrantSparseVectorRetriever(QdrantSparseVectorRetriever):
+    splade_doc_tokenizer: Any = Field(repr=False)
+    splade_doc_model: Any = Field(repr=False)
+    splade_query_tokenizer: Any = Field(repr=False)
+    splade_query_model: Any = Field(repr=False)
+    device: Any = Field(repr=False)
+    batch_size: int = Field(repr=False)
+    sparse_encoder: Any or None = Field(repr=False)
+
+    class Config:
+        """Configuration for this pydantic object."""
+        arbitrary_types_allowed = True
+
+    def compute_document_vectors(self, texts: List[str], batch_size: int) -> Tuple[List[List[int]], List[List[float]]]:
+        indices = []
+        values = []
+        for text_batch in batchify(texts, batch_size):
+            with torch.no_grad():
+                tokens = self.splade_doc_tokenizer(text_batch, truncation=True, padding=True,
+                                                   return_tensors="pt").to(self.device)
+                output = self.splade_doc_model(**tokens)
+            logits, attention_mask = output.logits, tokens.attention_mask
+            relu_log = torch.log(1 + torch.relu(logits))
+            weighted_log = relu_log * attention_mask.unsqueeze(-1)
+            tvecs, _ = torch.max(weighted_log, dim=1)
+
+            # extract all non-zero values and their indices from the sparse vectors
+            for batch in tvecs.cpu():
+                indices.append(batch.nonzero(as_tuple=True)[0].numpy())
+                values.append(batch[indices[-1]].numpy())
+
+        return indices, values
+
+    def compute_query_vector(self, text: str):
+        """
+        Computes a vector from logits and attention mask using ReLU, log, and max operations.
+        """
+        with torch.no_grad():
+            tokens = self.splade_query_tokenizer(text, return_tensors="pt").to(self.device)
+            output = self.splade_query_model(**tokens)
+        logits, attention_mask = output.logits, tokens.attention_mask
+        relu_log = torch.log(1 + torch.relu(logits))
+        weighted_log = relu_log * attention_mask.unsqueeze(-1)
+        max_val, _ = torch.max(weighted_log, dim=1)
+        query_vec = max_val.squeeze().cpu()
+
+        query_indices = query_vec.nonzero().numpy().flatten()
+        query_values = query_vec.detach().numpy()[query_indices]
+
+        return query_indices, query_values
+
+    def add_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
+    ):
+        client = cast(QdrantClient, self.client)
+
+        indices, values = self.compute_document_vectors(texts, self.batch_size)
+
+        points = [
+            models.PointStruct(
+                id=i + 1,
+                vector={
+                    self.sparse_vector_name: models.SparseVector(
+                        indices=indices[i],
+                        values=values[i],
+                    )
+                },
+                payload={
+                    self.content_payload_key: texts[i],
+                    self.metadata_payload_key: metadatas[i] if metadatas else None,
+                },
+            )
+            for i in range(len(texts))
+        ]
+        client.upsert(self.collection_name, points=points, **kwargs)
+        if self.device == "cuda":
+            torch.cuda.empty_cache()
+
+    def _get_relevant_documents(self, query: str, *, run_manager: CallbackManagerForRetrieverRun) -> List[Document]:
+        client = cast(QdrantClient, self.client)
+        query_indices, query_values = self.compute_query_vector(query)
+
+        results = client.search(
+            self.collection_name,
+            query_filter=self.filter,
+            query_vector=models.NamedSparseVector(
+                name=self.sparse_vector_name,
+                vector=models.SparseVector(
+                    indices=query_indices,
+                    values=query_values,
+                ),
+            ),
+            limit=self.k,
+            with_vectors=False,
+            **self.search_options,
+        )
+
+        return [
+            Qdrant._document_from_scored_point(
+                point,
+                self.collection_name,
+                self.content_payload_key,
+                self.metadata_payload_key,
+            )
+            for point in results
+        ]

requirements.txt

@@ -1 +1,8 @@
-huggingface_hub==0.22.2
+faiss-cpu==1.8.0
+duckduckgo_search==6.1.0
+beautifulsoup4==4.12.3
+langchain==0.2.1
+langchain-community==0.2.1
+unstructured==0.14.2
+rank_bm25==0.2.2
+sentence-transformers==2.7.0

script.js

@@ -0,0 +1,24 @@
+var generate_button = document.getElementById("Generate");
+generate_button.insertAdjacentHTML("afterend", '<div style="position:relative;"> <label style="color:#8b8b8b;white-space:nowrap;position:absolute;top:8px;right:0px;"><input type="checkbox" id="force-search" name="accept">  Force web search </label> </div>');
+generate_button.style.setProperty("position", "relative");
+generate_button.style.setProperty("top", "15px");
+generate_button.style.setProperty("margin-left", "-10px");
+
+var stop_button = document.getElementById("stop");
+stop_button.style.setProperty("position", "relative");
+stop_button.style.setProperty("top", "15px");
+stop_button.style.setProperty("margin-left", "-10px");
+
+var checkbox = document.getElementById("force-search");
+var gradio_force_search_checkbox = document.getElementById("Force-search-checkbox").children[1].firstChild;
+checkbox.addEventListener('change', function() {
+  if (this.checked) {
+    if (!gradio_force_search_checkbox.checked) {
+        gradio_force_search_checkbox.click();
+    }
+  } else {
+    if (gradio_force_search_checkbox.checked) {
+        gradio_force_search_checkbox.click();
+    }
+  }
+});

script.py

@@ -0,0 +1,567 @@
+import time
+import re
+import json
+import os
+from datetime import datetime
+
+import gradio as gr
+import torch
+
+import modules.shared as shared
+from modules import chat, ui as ui_module
+from modules.utils import gradio
+from modules.text_generation import generate_reply_HF, generate_reply_custom
+from .llm_web_search import get_webpage_content, langchain_search_duckduckgo, langchain_search_searxng, Generator
+from .langchain_websearch import LangchainCompressor
+
+
+params = {
+    "display_name": "LLM Web Search",
+    "is_tab": True,
+    "enable": True,
+    "search results per query": 5,
+    "langchain similarity score threshold": 0.5,
+    "instant answers": True,
+    "regular search results": True,
+    "search command regex": "",
+    "default search command regex": r"Search_web\(\"(.*)\"\)",
+    "open url command regex": "",
+    "default open url command regex": r"Open_url\(\"(.*)\"\)",
+    "display search results in chat": True,
+    "display extracted URL content in chat": True,
+    "searxng url": "",
+    "cpu only": True,
+    "chunk size": 500,
+    "duckduckgo results per query": 10,
+    "append current datetime": False,
+    "default system prompt filename": None,
+    "force search prefix": "Search_web",
+    "ensemble weighting": 0.5,
+    "keyword retriever": "bm25",
+    "splade batch size": 2,
+    "chunking method": "character-based",
+    "chunker breakpoint_threshold_amount": 30
+}
+custom_system_message_filename = None
+extension_path = os.path.dirname(os.path.abspath(__file__))
+langchain_compressor = None
+update_history = None
+force_search = False
+
+
+def setup():
+    """
+    Is executed when the extension gets imported.
+    :return:
+    """
+    global params
+    os.environ["TOKENIZERS_PARALLELISM"] = "true"
+    os.environ["QDRANT__TELEMETRY_DISABLED"] = "true"
+
+    try:
+        with open(os.path.join(extension_path, "settings.json"), "r") as f:
+            saved_params = json.load(f)
+        params.update(saved_params)
+        save_settings()   # add keys of newly added feature to settings.json
+    except FileNotFoundError:
+        save_settings()
+
+    if not os.path.exists(os.path.join(extension_path, "system_prompts")):
+        os.makedirs(os.path.join(extension_path, "system_prompts"))
+
+    toggle_extension(params["enable"])
+
+
+def save_settings():
+    global params
+    with open(os.path.join(extension_path, "settings.json"), "w") as f:
+        json.dump(params, f, indent=4)
+    current_datetime = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    return gr.HTML(f'<span style="color:lawngreen"> Settings were saved at {current_datetime}</span>',
+                   visible=True)
+
+
+def toggle_extension(_enable: bool):
+    global langchain_compressor, custom_system_message_filename
+    if _enable:
+        langchain_compressor = LangchainCompressor(device="cpu" if params["cpu only"] else "cuda",
+                                                   keyword_retriever=params["keyword retriever"],
+                                                   model_cache_dir=os.path.join(extension_path, "hf_models"))
+        compressor_model = langchain_compressor.embeddings.client
+        compressor_model.to(compressor_model._target_device)
+        custom_system_message_filename = params.get("default system prompt filename")
+    else:
+        if not params["cpu only"] and 'langchain_compressor' in globals():  # free some VRAM
+            model_attrs = ["embeddings", "splade_doc_model", "splade_query_model"]
+            for model_attr in model_attrs:
+                if hasattr(langchain_compressor, model_attr):
+                    model = getattr(langchain_compressor, model_attr)
+                    if hasattr(model, "client"):
+                        model.client.to("cpu")
+                        del model.client
+                    else:
+                        if hasattr(model, "to"):
+                            model.to("cpu")
+                        del model
+            torch.cuda.empty_cache()
+    params.update({"enable": _enable})
+    return _enable
+
+
+def get_available_system_prompts():
+    try:
+        return ["None"] + sorted(os.listdir(os.path.join(extension_path, "system_prompts")))
+    except FileNotFoundError:
+        return ["None"]
+
+
+def load_system_prompt(filename: str or None):
+    global custom_system_message_filename
+    if not filename:
+        return
+    if filename == "None" or filename == "Select custom system message to load...":
+        custom_system_message_filename = None
+        return ""
+    with open(os.path.join(extension_path, "system_prompts", filename), "r") as f:
+        prompt_str = f.read()
+
+    if params["append current datetime"]:
+        prompt_str += f"\nDate and time of conversation: {datetime.now().strftime('%A %d %B %Y %H:%M')}"
+
+    shared.settings['custom_system_message'] = prompt_str
+    custom_system_message_filename = filename
+    return prompt_str
+
+
+def save_system_prompt(filename, prompt):
+    if not filename:
+        return
+
+    with open(os.path.join(extension_path, "system_prompts", filename), "w") as f:
+        f.write(prompt)
+
+    return gr.HTML(f'<span style="color:lawngreen"> Saved successfully</span>',
+                   visible=True)
+
+
+def check_file_exists(filename):
+    if filename == "":
+        return gr.HTML("", visible=False)
+    if os.path.exists(os.path.join(extension_path, "system_prompts", filename)):
+        return gr.HTML(f'<span style="color:orange"> Warning: Filename already exists</span>', visible=True)
+    return gr.HTML("", visible=False)
+
+
+def timeout_save_message():
+    time.sleep(2)
+    return gr.HTML("", visible=False)
+
+
+def deactivate_system_prompt():
+    shared.settings['custom_system_message'] = None
+    return "None"
+
+
+def toggle_forced_search(value):
+    global force_search
+    force_search = value
+
+
+def ui():
+    """
+    Creates custom gradio elements when the UI is launched.
+    :return:
+    """
+    # Inject custom system message into the main textbox if a default one is set
+    shared.gradio['custom_system_message'].value = load_system_prompt(custom_system_message_filename)
+
+    def update_result_type_setting(choice: str):
+        if choice == "Instant answers":
+            params.update({"instant answers": True})
+            params.update({"regular search results": False})
+        elif choice == "Regular results":
+            params.update({"instant answers": False})
+            params.update({"regular search results": True})
+        elif choice == "Regular results and instant answers":
+            params.update({"instant answers": True})
+            params.update({"regular search results": True})
+
+    def update_regex_setting(input_str: str, setting_key: str, error_html_element: gr.component):
+        if input_str == "":
+            params.update({setting_key: params[f"default {setting_key}"]})
+            return {error_html_element: gr.HTML("", visible=False)}
+        try:
+            compiled = re.compile(input_str)
+            if compiled.groups > 1:
+                raise re.error(f"Only 1 capturing group allowed in regex, but there are {compiled.groups}.")
+            params.update({setting_key: input_str})
+            return {error_html_element: gr.HTML("", visible=False)}
+        except re.error as e:
+            return {error_html_element: gr.HTML(f'<span style="color:red"> Invalid regex. {str(e).capitalize()}</span>',
+                                                visible=True)}
+
+    def update_default_custom_system_message(check: bool):
+        if check:
+            params.update({"default system prompt filename": custom_system_message_filename})
+        else:
+            params.update({"default system prompt filename": None})
+
+    with gr.Row():
+        enable = gr.Checkbox(value=lambda: params['enable'], label='Enable LLM web search')
+        use_cpu_only = gr.Checkbox(value=lambda: params['cpu only'],
+                                   label='Run extension on CPU only '
+                                         '(Save settings and restart for the change to take effect)')
+        with gr.Column():
+            save_settings_btn = gr.Button("Save settings")
+            saved_success_elem = gr.HTML("", visible=False)
+
+    with gr.Row():
+        result_radio = gr.Radio(
+            ["Regular results", "Regular results and instant answers"],
+            label="What kind of search results should be returned?",
+            value=lambda: "Regular results and instant answers" if
+                          (params["regular search results"] and params["instant answers"]) else "Regular results"
+        )
+        with gr.Column():
+            search_command_regex = gr.Textbox(label="Search command regex string",
+                                              placeholder=params["default search command regex"],
+                                              value=lambda: params["search command regex"])
+            search_command_regex_error_label = gr.HTML("", visible=False)
+
+        with gr.Column():
+            open_url_command_regex = gr.Textbox(label="Open URL command regex string",
+                                                placeholder=params["default open url command regex"],
+                                                value=lambda: params["open url command regex"])
+            open_url_command_regex_error_label = gr.HTML("", visible=False)
+
+        with gr.Column():
+            show_results = gr.Checkbox(value=lambda: params['display search results in chat'],
+                                       label='Display search results in chat')
+            show_url_content = gr.Checkbox(value=lambda: params['display extracted URL content in chat'],
+                                           label='Display extracted URL content in chat')
+    gr.Markdown(value='---')
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown(value='#### Load custom system message\n'
+                              'Select a saved custom system message from within the system_prompts folder or "None" '
+                              'to clear the selection')
+            system_prompt = gr.Dropdown(
+                choices=get_available_system_prompts(), label="Select custom system message",
+                value=lambda: 'Select custom system message to load...' if custom_system_message_filename is None else
+                              custom_system_message_filename, elem_classes='slim-dropdown')
+            with gr.Row():
+                set_system_message_as_default = gr.Checkbox(
+                    value=lambda: custom_system_message_filename == params["default system prompt filename"],
+                    label='Set this custom system message as the default')
+                refresh_button = ui_module.create_refresh_button(system_prompt, lambda: None,
+                                                                 lambda: {'choices': get_available_system_prompts()},
+                                                                 'refresh-button', interactive=True)
+                refresh_button.elem_id = "custom-sysprompt-refresh"
+                delete_button = gr.Button('🗑️', elem_classes='refresh-button', interactive=True)
+            append_datetime = gr.Checkbox(value=lambda: params['append current datetime'],
+                                          label='Append current date and time when loading custom system message')
+        with gr.Column():
+            gr.Markdown(value='#### Create custom system message')
+            system_prompt_text = gr.Textbox(label="Custom system message", lines=3,
+                                            value=lambda: load_system_prompt(custom_system_message_filename))
+            sys_prompt_filename = gr.Text(label="Filename")
+            sys_prompt_save_button = gr.Button("Save Custom system message")
+            system_prompt_saved_success_elem = gr.HTML("", visible=False)
+            
+    gr.Markdown(value='---')
+    with gr.Accordion("Advanced settings", open=False):
+        ensemble_weighting = gr.Slider(minimum=0, maximum=1, step=0.05, value=lambda: params["ensemble weighting"],
+                                       label="Ensemble Weighting", info="Smaller values = More keyword oriented, "
+                                                                        "Larger values = More focus on semantic similarity")
+        with gr.Row():
+            keyword_retriever = gr.Radio([("Okapi BM25", "bm25"),("SPLADE", "splade")], label="Sparse keyword retriever",
+                                         info="For change to take effect, toggle the extension off and on again",
+                                         value=lambda: params["keyword retriever"])
+            splade_batch_size = gr.Slider(minimum=2, maximum=256, step=2, value=lambda: params["splade batch size"],
+                                          label="SPLADE batch size",
+                                          info="Smaller values = Slower retrieval (but lower VRAM usage), "
+                                               "Larger values = Faster retrieval (but higher VRAM usage). "
+                                               "A good trade-off seems to be setting it = 8",
+                                          precision=0)
+        with gr.Row():
+            chunker = gr.Radio([("Character-based", "character-based"),
+                                ("Semantic", "semantic")], label="Chunking method",
+                               value=lambda: params["chunking method"])
+            chunker_breakpoint_threshold_amount = gr.Slider(minimum=1, maximum=100, step=1,
+                                                            value=lambda: params["chunker breakpoint_threshold_amount"],
+                                                            label="Semantic chunking: sentence split threshold (%)",
+                                                            info="Defines how different two consecutive sentences have"
+                                                                 " to be for them to be split into separate chunks",
+                                                            precision=0)
+        gr.Markdown("**Note: Changing the following might result in DuckDuckGo rate limiting or the LM being overwhelmed**")
+        num_search_results = gr.Number(label="Max. search results to return per query", minimum=1, maximum=100,
+                                       value=lambda: params["search results per query"], precision=0)
+        num_process_search_results = gr.Number(label="Number of search results to process per query", minimum=1,
+                                               maximum=100, value=lambda: params["duckduckgo results per query"],
+                                               precision=0)
+        langchain_similarity_threshold = gr.Number(label="Langchain Similarity Score Threshold", minimum=0., maximum=1.,
+                                                   value=lambda: params["langchain similarity score threshold"])
+        chunk_size = gr.Number(label="Max. chunk size", info="The maximal size of the individual chunks that each webpage will"
+                                     " be split into, in characters", minimum=2, maximum=10000,
+                               value=lambda: params["chunk size"], precision=0)
+
+    with gr.Row():
+        searxng_url = gr.Textbox(label="SearXNG URL",
+                                 value=lambda: params["searxng url"])
+
+    # Event functions to update the parameters in the backend
+    enable.input(toggle_extension, enable, enable)
+    use_cpu_only.change(lambda x: params.update({"cpu only": x}), use_cpu_only, None)
+    save_settings_btn.click(save_settings, None, [saved_success_elem])
+    ensemble_weighting.change(lambda x: params.update({"ensemble weighting": x}), ensemble_weighting, None)
+    keyword_retriever.change(lambda x: params.update({"keyword retriever": x}), keyword_retriever, None)
+    splade_batch_size.change(lambda x: params.update({"splade batch size": x}), splade_batch_size, None)
+    chunker.change(lambda x: params.update({"chunking method": x}), chunker, None)
+    chunker_breakpoint_threshold_amount.change(lambda x: params.update({"chunker breakpoint_threshold_amount": x}),
+                                               chunker_breakpoint_threshold_amount, None)
+    num_search_results.change(lambda x: params.update({"search results per query": x}), num_search_results, None)
+    num_process_search_results.change(lambda x: params.update({"duckduckgo results per query": x}),
+                                      num_process_search_results, None)
+    langchain_similarity_threshold.change(lambda x: params.update({"langchain similarity score threshold": x}),
+                                          langchain_similarity_threshold, None)
+    chunk_size.change(lambda x: params.update({"chunk size": x}), chunk_size, None)
+    result_radio.change(update_result_type_setting, result_radio, None)
+
+    search_command_regex.change(lambda x: update_regex_setting(x, "search command regex",
+                                                               search_command_regex_error_label),
+                                search_command_regex, search_command_regex_error_label, show_progress="hidden")
+
+    open_url_command_regex.change(lambda x: update_regex_setting(x, "open url command regex",
+                                                                 open_url_command_regex_error_label),
+                                  open_url_command_regex, open_url_command_regex_error_label, show_progress="hidden")
+
+    show_results.change(lambda x: params.update({"display search results in chat": x}), show_results, None)
+    show_url_content.change(lambda x: params.update({"display extracted URL content in chat": x}), show_url_content,
+                            None)
+    searxng_url.change(lambda x: params.update({"searxng url": x}), searxng_url, None)
+
+    delete_button.click(
+        lambda x: x, system_prompt, gradio('delete_filename')).then(
+        lambda: os.path.join(extension_path, "system_prompts", ""), None, gradio('delete_root')).then(
+        lambda: gr.update(visible=True), None, gradio('file_deleter'))
+    shared.gradio['delete_confirm'].click(
+        lambda: "None", None, system_prompt).then(
+        None, None, None, _js="() => { document.getElementById('custom-sysprompt-refresh').click() }")
+    system_prompt.change(load_system_prompt, system_prompt, shared.gradio['custom_system_message'])
+    system_prompt.change(load_system_prompt, system_prompt, system_prompt_text)
+    # restore checked state if chosen system prompt matches the default
+    system_prompt.change(lambda x: x == params["default system prompt filename"], system_prompt,
+                         set_system_message_as_default)
+    sys_prompt_filename.change(check_file_exists, sys_prompt_filename, system_prompt_saved_success_elem)
+    sys_prompt_save_button.click(save_system_prompt, [sys_prompt_filename, system_prompt_text],
+                                 system_prompt_saved_success_elem,
+                                 show_progress="hidden").then(timeout_save_message,
+                                                              None,
+                                                              system_prompt_saved_success_elem,
+                                                              _js="() => { document.getElementById('custom-sysprompt-refresh').click() }",
+                                                              show_progress="hidden").then(lambda: "", None,
+                                                                                        sys_prompt_filename,
+                                                                                        show_progress="hidden")
+    append_datetime.change(lambda x: params.update({"append current datetime": x}), append_datetime, None)
+    # '.input' = only triggers when user changes the value of the component, not a function
+    set_system_message_as_default.input(update_default_custom_system_message, set_system_message_as_default, None)
+
+    # A dummy checkbox to enable the actual "Force web search" checkbox to trigger a gradio event
+    force_search_checkbox = gr.Checkbox(value=False, visible=False, elem_id="Force-search-checkbox")
+    force_search_checkbox.change(toggle_forced_search, force_search_checkbox, None)
+
+
+def custom_generate_reply(question, original_question, seed, state, stopping_strings, is_chat):
+    """
+    Overrides the main text generation function.
+    :return:
+    """
+    global update_history, langchain_compressor
+    if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel', 'ExllamaModel', 'Exllamav2Model',
+                                           'CtransformersModel']:
+        generate_func = generate_reply_custom
+    else:
+        generate_func = generate_reply_HF
+
+    if not params['enable']:
+        for reply in generate_func(question, original_question, seed, state, stopping_strings, is_chat=is_chat):
+            yield reply
+        return
+
+    web_search = False
+    read_webpage = False
+    max_search_results = int(params["search results per query"])
+    instant_answers = params["instant answers"]
+    # regular_search_results = params["regular search results"]
+
+    langchain_compressor.num_results = int(params["duckduckgo results per query"])
+    langchain_compressor.similarity_threshold = params["langchain similarity score threshold"]
+    langchain_compressor.chunk_size = params["chunk size"]
+    langchain_compressor.ensemble_weighting = params["ensemble weighting"]
+    langchain_compressor.splade_batch_size = params["splade batch size"]
+    langchain_compressor.chunking_method = params["chunking method"]
+    langchain_compressor.chunker_breakpoint_threshold_amount = params["chunker breakpoint_threshold_amount"]
+
+    search_command_regex = params["search command regex"]
+    open_url_command_regex = params["open url command regex"]
+    searxng_url = params["searxng url"]
+    display_search_results = params["display search results in chat"]
+    display_webpage_content = params["display extracted URL content in chat"]
+
+    if search_command_regex == "":
+        search_command_regex = params["default search command regex"]
+    if open_url_command_regex == "":
+        open_url_command_regex = params["default open url command regex"]
+
+    compiled_search_command_regex = re.compile(search_command_regex)
+    compiled_open_url_command_regex = re.compile(open_url_command_regex)
+
+    if force_search:
+        question += f" {params['force search prefix']}"
+
+    reply = None
+    for reply in generate_func(question, original_question, seed, state, stopping_strings, is_chat=is_chat):
+
+        if force_search:
+            reply = params["force search prefix"] + reply
+
+        search_re_match = compiled_search_command_regex.search(reply)
+        if search_re_match is not None:
+            yield reply
+            original_model_reply = reply
+            web_search = True
+            search_term = search_re_match.group(1)
+            print(f"LLM_Web_search | Searching for {search_term}...")
+            reply += "\n```plaintext"
+            reply += "\nSearch tool:\n"
+            if searxng_url == "":
+                search_generator = Generator(langchain_search_duckduckgo(search_term,
+                                                                         langchain_compressor,
+                                                                         max_search_results,
+                                                                         instant_answers))
+            else:
+                search_generator = Generator(langchain_search_searxng(search_term,
+                                                                      searxng_url,
+                                                                      langchain_compressor,
+                                                                      max_search_results))
+            try:
+                for status_message in search_generator:
+                    yield original_model_reply + f"\n*{status_message}*"
+                search_results = search_generator.value
+            except Exception as exc:
+                exception_message = str(exc)
+                reply += f"The search tool encountered an error: {exception_message}"
+                print(f'LLM_Web_search | {search_term} generated an exception: {exception_message}')
+            else:
+                if search_results != "":
+                    reply += search_results
+                else:
+                    reply += f"\nThe search tool did not return any results."
+            reply += "```"
+            if display_search_results:
+                yield reply
+            break
+
+        open_url_re_match = compiled_open_url_command_regex.search(reply)
+        if open_url_re_match is not None:
+            yield reply
+            original_model_reply = reply
+            read_webpage = True
+            url = open_url_re_match.group(1)
+            print(f"LLM_Web_search | Reading {url}...")
+            reply += "\n```plaintext"
+            reply += "\nURL opener tool:\n"
+            try:
+                webpage_content = get_webpage_content(url)
+            except Exception as exc:
+                reply += f"Couldn't open {url}. Error message: {str(exc)}"
+                print(f'LLM_Web_search | {url} generated an exception: {str(exc)}')
+            else:
+                reply += f"\nText content of {url}:\n"
+                reply += webpage_content
+            reply += "```\n"
+            if display_webpage_content:
+                yield reply
+            break
+        yield reply
+
+    if web_search or read_webpage:
+        display_results = web_search and display_search_results or read_webpage and display_webpage_content
+        # Add results to context and continue model output
+        new_question = chat.generate_chat_prompt(f"{question}{reply}", state)
+        new_reply = ""
+        for new_reply in generate_func(new_question, new_question, seed, state,
+                                       stopping_strings, is_chat=is_chat):
+            if display_results:
+                yield f"{reply}\n{new_reply}"
+            else:
+                yield f"{original_model_reply}\n{new_reply}"
+
+        if not display_results:
+            update_history = [state["textbox"], f"{reply}\n{new_reply}"]
+
+
+def output_modifier(string, state, is_chat=False):
+    """
+    Modifies the output string before it is presented in the UI. In chat mode,
+    it is applied to the bot's reply. Otherwise, it is applied to the entire
+    output.
+    :param string:
+    :param state:
+    :param is_chat:
+    :return:
+    """
+    return string
+
+
+def custom_css():
+    """
+    Returns custom CSS as a string. It is applied whenever the web UI is loaded.
+    :return:
+    """
+    return ''
+
+
+def custom_js():
+    """
+    Returns custom javascript as a string. It is applied whenever the web UI is
+    loaded.
+    :return:
+    """
+    with open(os.path.join(extension_path, "script.js"), "r") as f:
+        return f.read()
+
+
+def chat_input_modifier(text, visible_text, state):
+    """
+    Modifies both the visible and internal inputs in chat mode. Can be used to
+    hijack the chat input with custom content.
+    :param text:
+    :param visible_text:
+    :param state:
+    :return:
+    """
+    return text, visible_text
+
+
+def state_modifier(state):
+    """
+    Modifies the dictionary containing the UI input parameters before it is
+    used by the text generation functions.
+    :param state:
+    :return:
+    """
+    return state
+
+
+def history_modifier(history):
+    """
+    Modifies the chat history before the text generation in chat mode begins.
+    :param history:
+    :return:
+    """
+    global update_history
+    if update_history:
+        history["internal"].append(update_history)
+        update_history = None
+    return history

semantic_chunker.py

@@ -0,0 +1,233 @@
+import copy
+import re
+from typing import Any, Dict, Iterable, List, Literal, Optional, Sequence, Tuple, cast
+
+import numpy as np
+from langchain_community.utils.math import (
+    cosine_similarity,
+)
+from langchain_core.documents import BaseDocumentTransformer, Document
+from langchain_core.embeddings import Embeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+
+def calculate_cosine_distances(sentence_embeddings) -> np.array:
+    """Calculate cosine distances between sentences.
+
+    Args:
+        sentence_embeddings: List of sentence embeddings to calculate distances for.
+
+    Returns:
+        Distance between each pair of adjacent sentences
+    """
+    return (1 - cosine_similarity(sentence_embeddings, sentence_embeddings)).flatten()[1::len(sentence_embeddings) + 1]
+
+
+BreakpointThresholdType = Literal["percentile", "standard_deviation", "interquartile"]
+BREAKPOINT_DEFAULTS: Dict[BreakpointThresholdType, float] = {
+    "percentile": 95,
+    "standard_deviation": 3,
+    "interquartile": 1.5,
+}
+
+
+class BoundedSemanticChunker(BaseDocumentTransformer):
+    """First splits the text using semantic chunking according to the specified
+    'breakpoint_threshold_amount', but then uses a RecursiveCharacterTextSplitter
+    to split all chunks that are larger than 'max_chunk_size'.
+
+    Adapted from langchain_experimental.text_splitter.SemanticChunker"""
+
+    def __init__(
+            self,
+            embeddings: Embeddings,
+            buffer_size: int = 1,
+            add_start_index: bool = False,
+            breakpoint_threshold_type: BreakpointThresholdType = "percentile",
+            breakpoint_threshold_amount: Optional[float] = None,
+            number_of_chunks: Optional[int] = None,
+            max_chunk_size: int = 500,
+    ):
+        self._add_start_index = add_start_index
+        self.embeddings = embeddings
+        self.buffer_size = buffer_size
+        self.breakpoint_threshold_type = breakpoint_threshold_type
+        self.number_of_chunks = number_of_chunks
+        if breakpoint_threshold_amount is None:
+            self.breakpoint_threshold_amount = BREAKPOINT_DEFAULTS[
+                breakpoint_threshold_type
+            ]
+        else:
+            self.breakpoint_threshold_amount = breakpoint_threshold_amount
+        self.max_chunk_size = max_chunk_size
+        # Splitting the text on '.', '?', and '!'
+        self.sentence_split_regex = re.compile(r"(?<=[.?!])\s+")
+
+        assert self.breakpoint_threshold_type == "percentile", "only breakpoint_threshold_type 'percentile' is currently supported"
+        assert self.buffer_size == 1, "combining sentences is not supported yet"
+
+    def _calculate_sentence_distances(
+        self, sentences: List[dict]
+    ) -> Tuple[List[float], List[dict]]:
+        """Split text into multiple components."""
+        embeddings = self.embeddings.embed_documents(sentences)
+        return calculate_cosine_distances(embeddings)
+
+    def _calculate_breakpoint_threshold(self, distances: np.array, alt_breakpoint_threshold_amount=None) -> float:
+        if alt_breakpoint_threshold_amount is None:
+            breakpoint_threshold_amount = self.breakpoint_threshold_amount
+        else:
+            breakpoint_threshold_amount = alt_breakpoint_threshold_amount
+        if self.breakpoint_threshold_type == "percentile":
+            return cast(
+                float,
+                np.percentile(distances, breakpoint_threshold_amount),
+            )
+        elif self.breakpoint_threshold_type == "standard_deviation":
+            return cast(
+                float,
+                np.mean(distances)
+                + breakpoint_threshold_amount * np.std(distances),
+            )
+        elif self.breakpoint_threshold_type == "interquartile":
+            q1, q3 = np.percentile(distances, [25, 75])
+            iqr = q3 - q1
+
+            return np.mean(distances) + breakpoint_threshold_amount * iqr
+        else:
+            raise ValueError(
+                f"Got unexpected `breakpoint_threshold_type`: "
+                f"{self.breakpoint_threshold_type}"
+            )
+
+    def _threshold_from_clusters(self, distances: List[float]) -> float:
+        """
+        Calculate the threshold based on the number of chunks.
+        Inverse of percentile method.
+        """
+        if self.number_of_chunks is None:
+            raise ValueError(
+                "This should never be called if `number_of_chunks` is None."
+            )
+        x1, y1 = len(distances), 0.0
+        x2, y2 = 1.0, 100.0
+
+        x = max(min(self.number_of_chunks, x1), x2)
+
+        # Linear interpolation formula
+        y = y1 + ((y2 - y1) / (x2 - x1)) * (x - x1)
+        y = min(max(y, 0), 100)
+
+        return cast(float, np.percentile(distances, y))
+
+    def split_text(
+        self,
+        text: str,
+    ) -> List[str]:
+        sentences = self.sentence_split_regex.split(text)
+
+        # having len(sentences) == 1 would cause the following
+        # np.percentile to fail.
+        if len(sentences) == 1:
+            return sentences
+
+        bad_sentences = []
+        num_good_sentences = 0
+
+        distances = self._calculate_sentence_distances(sentences)
+
+        if self.number_of_chunks is not None:
+            breakpoint_distance_threshold = self._threshold_from_clusters(distances)
+        else:
+            breakpoint_distance_threshold = self._calculate_breakpoint_threshold(
+                distances
+            )
+
+        indices_above_thresh = [
+            i for i, x in enumerate(distances) if x > breakpoint_distance_threshold
+        ]
+
+        chunks = []
+        start_index = 0
+
+        # Iterate through the breakpoints to slice the sentences
+        for index in indices_above_thresh:
+            # The end index is the current breakpoint
+            end_index = index
+
+            # Slice the sentence_dicts from the current start index to the end index
+            group = sentences[start_index : end_index + 1]
+            combined_text = " ".join(group)
+            if len(combined_text) <= self.max_chunk_size:
+                chunks.append(combined_text)
+            else:
+                sent_lengths = np.array([len(sd) for sd in group])
+                good_indices = np.flatnonzero(np.cumsum(sent_lengths) <= self.max_chunk_size)
+                smaller_group = [group[i] for i in good_indices]
+                if smaller_group:
+                    combined_text = " ".join(smaller_group)
+                    chunks.append(combined_text)
+                    group = group[good_indices[-1]:]
+                bad_sentences.extend(group)
+
+            # Update the start index for the next group
+            start_index = index + 1
+
+        # The last group, if any sentences remain
+        if start_index < len(sentences):
+            group = sentences[start_index:]
+            combined_text = " ".join(group)
+            if len(combined_text) <= self.max_chunk_size:
+                chunks.append(combined_text)
+            else:
+                sent_lengths = np.array([len(sd) for sd in group])
+                good_indices = np.flatnonzero(np.cumsum(sent_lengths) <= self.max_chunk_size)
+                smaller_group = [group[i] for i in good_indices]
+                if smaller_group:
+                    combined_text = " ".join(smaller_group)
+                    chunks.append(combined_text)
+                    group = group[good_indices[-1]:]
+                bad_sentences.extend(group)
+
+        # If pure semantic chunking wasn't able to split all text for any breakpoint_threshold_amount,
+        # split the remaining problematic text using a recursive character splitter instead
+        if len(bad_sentences) > 0:
+            recursive_splitter = RecursiveCharacterTextSplitter(chunk_size=self.max_chunk_size, chunk_overlap=10,
+                                                                separators=["\n\n", "\n", ".", ", ", " ", ""])
+            remaining_text = "".join(bad_sentences)
+            chunks.extend(recursive_splitter.split_text(remaining_text))
+        return chunks
+
+    def create_documents(
+                self, texts: List[str], metadatas: Optional[List[dict]] = None
+        ) -> List[Document]:
+            """Create documents from a list of texts."""
+            _metadatas = metadatas or [{}] * len(texts)
+            documents = []
+            for i, text in enumerate(texts):
+                index = -1
+                for chunk in self.split_text(text):
+                    metadata = copy.deepcopy(_metadatas[i])
+                    if self._add_start_index:
+                        index = text.find(chunk, index + 1)
+                        metadata["start_index"] = index
+                    new_doc = Document(page_content=chunk, metadata=metadata)
+                    documents.append(new_doc)
+            return documents
+
+    def split_documents(self, documents: Iterable[Document]) -> List[Document]:
+        """Split documents."""
+        texts, metadatas = [], []
+        for doc in documents:
+            texts.append(doc.page_content)
+            metadatas.append(doc.metadata)
+        return self.create_documents(texts, metadatas=metadatas)
+
+    def transform_documents(
+            self, documents: Sequence[Document], **kwargs: Any
+    ) -> Sequence[Document]:
+        """Transform sequence of documents by splitting them."""
+        return self.split_documents(list(documents))
+
+
+

system_prompts/bing_at_home

@@ -0,0 +1,3 @@
+A chat between a curious user and artificial intelligence assistant. The assistant ends every message with an emoji matching the emotion of the the message. The assistant is never confident about facts. The assistant always searches the web for facts. The assistant uses the available tools to retrieve relevant information and give helpful, detailed, and polite answers to the user's questions.
+
+Search tool command format: Search_web("<|query|>")

system_prompts/copilot_prompt

@@ -0,0 +1,16 @@
+You are a state-of-the-art artificial intelligence assistant equipped with a comprehensive web search tool. Your mission is to provide accurate, up-to-date information and helpful answers to user queries. Here are the key guidelines:
+
+1. **Context-Aware Web Search:**
+   - When a user message contains relevant information or context suggesting the need for a web search, you will autonomously output the search command: `Search_web("query")`.
+   - Prioritize reliable sources and communicate findings clearly.
+
+2. **Fact-Driven Humility:**
+   - Remain cautious about stating specific facts and up-to-date information based only on your pre-programmed knowledge base.
+   - If uncertainty arises, default to searching the web for accurate details.
+
+3. **Polite and Detailed Responses:**
+   - Engage in friendly, empathetic conversations with users.
+   - Extract information from search results to guide your answers.
+   - Always end messages with an appropriate emoji to match the conveyed emotion.
+
+Remember the search command format: `Search_web("query")`.

system_prompts/default_system_prompt.txt

@@ -0,0 +1,5 @@
+A chat between a curious user and artificial intelligence assistant. The assistant is never confident about facts and up-to-date information. The assistant can search the web for facts and up to date information using the following search command format:
+
+Search_web("query")
+
+The search tool will search the web for these keywords and return the results. Finally, the assistant extracts the information from the results of the search tool to guide its response.

system_prompts/second_person_command_last

@@ -0,0 +1,3 @@
+You are a state of the art artificial intelligence assistant. However, you are never confident about specific facts and up-to-date information. You must search the web to obtain this information. The search tool will return the results. Finally, you extract the information from the results of the search tool to guide your response. To trigger a web search, you must use the following search command format:
+
+Search_web("query")

system_prompts/websearch_gpt4_prompt

@@ -0,0 +1,10 @@
+You are an artificial intelligence assistant. You have access to a search tool. Use the search tool in the following circumstances:
+
+- User is asking about current events or something that requires real-time information (weather, sports scores, etc.)
+- User is asking about some term you are totally unfamiliar with (it might be new)
+- User explicitly asks you to search or provide links to references
+
+Use the following command to use the search tool:
+Search_web("query")
+
+The search tool will search the web and return the results. You extract the information from the results of the search tool to give helpful, detailed, and polite answers to the user's questions.