feat(package): bump version

README.md

@@ -67,7 +67,8 @@ There are mainly 2 ways of installing Perplexica - With Docker, Without Docker.
 
    - `OPENAI`: Your OpenAI API key. **You only need to fill this if you wish to use OpenAI's models**.
    - `OLLAMA`: Your Ollama API URL. You should enter it as `http://host.docker.internal:PORT_NUMBER`. If you installed Ollama on port 11434, use `http://host.docker.internal:11434`. For other ports, adjust accordingly. **You need to fill this if you wish to use Ollama's models instead of OpenAI's**.
-   - `GROQ`: Your Groq API key. **You only need to fill this if you wish to use Groq's hosted models**
+   - `GROQ`: Your Groq API key. **You only need to fill this if you wish to use Groq's hosted models**.
+   - `ANTHROPIC`: Your Anthropic API key. **You only need to fill this if you wish to use Anthropic models**.
 
      **Note**: You can change these after starting Perplexica from the settings dialog.
 
@@ -111,11 +112,7 @@ If you're encountering an Ollama connection error, it is likely due to the backe
 
 3. **Linux Users - Expose Ollama to Network:**
 
-   - Serve Ollama over your network with the command:
-
-     ```bash
-     OLLAMA_HOST=0.0.0.0 ollama serve
-     ```
+   - Inside `/etc/systemd/system/ollama.service`, you need to add `Environment="OLLAMA_HOST=0.0.0.0"`. Then restart Ollama by `systemctl restart ollama`. For more information see [Ollama docs](https://github.com/ollama/ollama/blob/main/docs/faq.md#setting-environment-variables-on-linux)
 
    - Ensure that the port (default is 11434) is not blocked by your firewall.
 
@@ -149,9 +146,9 @@ If you find Perplexica useful, consider giving us a star on GitHub. This helps m
 
 We also accept donations to help sustain our project. If you would like to contribute, you can use the following options to donate. Thank you for your support!
 
-| Cards  | Ethereum |
-|---|---|
-|  https://www.patreon.com/itzcrazykns  | Address: `0xB025a84b2F269570Eb8D4b05DEdaA41D8525B6DD`  |
+| Cards                               | Ethereum                                              |
+| ----------------------------------- | ----------------------------------------------------- |
+| https://www.patreon.com/itzcrazykns | Address: `0xB025a84b2F269570Eb8D4b05DEdaA41D8525B6DD` |
 
 ## Contribution
 

package.json

@@ -1,6 +1,6 @@
 {
   "name": "perplexica-backend",
-  "version": "1.7.1",
+  "version": "1.8.0",
   "license": "MIT",
   "author": "ItzCrazyKns",
   "scripts": {
@@ -24,6 +24,7 @@
   },
   "dependencies": {
     "@iarna/toml": "^2.2.5",
+    "@langchain/anthropic": "^0.2.3",
     "@langchain/community": "^0.2.16",
     "@langchain/openai": "^0.0.25",
     "@xenova/transformers": "^2.17.1",

sample.config.toml

@@ -5,6 +5,7 @@ SIMILARITY_MEASURE = "cosine" # "cosine" or "dot"
 [API_KEYS]
 OPENAI = "" # OpenAI API key - sk-1234567890abcdef1234567890abcdef
 GROQ = "" # Groq API key - gsk_1234567890abcdef1234567890abcdef
+ANTHROPIC = "" # Anthropic API key - sk-ant-1234567890abcdef1234567890abcdef
 
 [API_ENDPOINTS]
 SEARXNG = "http://localhost:32768" # SearxNG API URL

src/agents/academicSearchAgent.ts

@@ -44,7 +44,7 @@ Rephrased question:
 const basicAcademicSearchResponsePrompt = `
     You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Academic', this means you will be searching for academic papers and articles on the web.
 
-    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containg a brief description of the content of that page).
+    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
     You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
     You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
     Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
@@ -52,7 +52,7 @@ const basicAcademicSearchResponsePrompt = `
     Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
     However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
 
-    Aything inside the following \`context\` HTML block provided below is for your knowledge returned by the search engine and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to 
+    Anything inside the following \`context\` HTML block provided below is for your knowledge returned by the search engine and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to 
     talk about the context in your response. 
 
     <context>

src/agents/redditSearchAgent.ts

@@ -44,7 +44,7 @@ Rephrased question:
 const basicRedditSearchResponsePrompt = `
     You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Reddit', this means you will be searching for information, opinions and discussions on the web using Reddit.
 
-    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containg a brief description of the content of that page).
+    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
     You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
     You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
     Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
@@ -52,7 +52,7 @@ const basicRedditSearchResponsePrompt = `
     Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
     However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
 
-    Aything inside the following \`context\` HTML block provided below is for your knowledge returned by Reddit and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to 
+    Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Reddit and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to 
     talk about the context in your response. 
 
     <context>

src/agents/suggestionGeneratorAgent.ts

@@ -47,7 +47,7 @@ const generateSuggestions = (
   input: SuggestionGeneratorInput,
   llm: BaseChatModel,
 ) => {
-  (llm as ChatOpenAI).temperature = 0;
+  (llm as unknown as ChatOpenAI).temperature = 0;
   const suggestionGeneratorChain = createSuggestionGeneratorChain(llm);
   return suggestionGeneratorChain.invoke(input);
 };

src/agents/webSearchAgent.ts

@@ -44,7 +44,7 @@ Rephrased question:
 const basicWebSearchResponsePrompt = `
     You are Perplexica, an AI model who is expert at searching the web and answering user's queries.
 
-    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containg a brief description of the content of that page).
+    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
     You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
     You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
     Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
@@ -52,7 +52,7 @@ const basicWebSearchResponsePrompt = `
     Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
     However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
 
-    Aything inside the following \`context\` HTML block provided below is for your knowledge returned by the search engine and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to 
+    Anything inside the following \`context\` HTML block provided below is for your knowledge returned by the search engine and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to 
     talk about the context in your response. 
 
     <context>

src/agents/wolframAlphaSearchAgent.ts

@@ -43,7 +43,7 @@ Rephrased question:
 const basicWolframAlphaSearchResponsePrompt = `
     You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Wolfram Alpha', this means you will be searching for information on the web using Wolfram Alpha. It is a computational knowledge engine that can answer factual queries and perform computations.
 
-    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containg a brief description of the content of that page).
+    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
     You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
     You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
     Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
@@ -51,7 +51,7 @@ const basicWolframAlphaSearchResponsePrompt = `
     Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
     However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
 
-    Aything inside the following \`context\` HTML block provided below is for your knowledge returned by Wolfram Alpha and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to 
+    Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Wolfram Alpha and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to 
     talk about the context in your response. 
 
     <context>

src/agents/youtubeSearchAgent.ts

@@ -44,7 +44,7 @@ Rephrased question:
 const basicYoutubeSearchResponsePrompt = `
     You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Youtube', this means you will be searching for videos on the web using Youtube and providing information based on the video's transcript.
 
-    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containg a brief description of the content of that page).
+    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
     You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
     You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
     Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
@@ -52,7 +52,7 @@ const basicYoutubeSearchResponsePrompt = `
     Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
     However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
 
-    Aything inside the following \`context\` HTML block provided below is for your knowledge returned by Youtube and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to 
+    Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Youtube and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to 
     talk about the context in your response. 
 
     <context>

src/config.ts

@@ -12,6 +12,7 @@ interface Config {
   API_KEYS: {
     OPENAI: string;
     GROQ: string;
+    ANTHROPIC: string;
   };
   API_ENDPOINTS: {
     SEARXNG: string;
@@ -37,6 +38,8 @@ export const getOpenaiApiKey = () => loadConfig().API_KEYS.OPENAI;
 
 export const getGroqApiKey = () => loadConfig().API_KEYS.GROQ;
 
+export const getAnthropicApiKey = () => loadConfig().API_KEYS.ANTHROPIC;
+
 export const getSearxngApiEndpoint = () => loadConfig().API_ENDPOINTS.SEARXNG;
 
 export const getOllamaApiEndpoint = () => loadConfig().API_ENDPOINTS.OLLAMA;

src/lib/providers.ts

@@ -1,187 +0,0 @@
-import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
-import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
-import { HuggingFaceTransformersEmbeddings } from './huggingfaceTransformer';
-import {
-  getGroqApiKey,
-  getOllamaApiEndpoint,
-  getOpenaiApiKey,
-} from '../config';
-import logger from '../utils/logger';
-
-export const getAvailableChatModelProviders = async () => {
-  const openAIApiKey = getOpenaiApiKey();
-  const groqApiKey = getGroqApiKey();
-  const ollamaEndpoint = getOllamaApiEndpoint();
-
-  const models = {};
-
-  if (openAIApiKey) {
-    try {
-      models['openai'] = {
-        'GPT-3.5 turbo': new ChatOpenAI({
-          openAIApiKey,
-          modelName: 'gpt-3.5-turbo',
-          temperature: 0.7,
-        }),
-        'GPT-4': new ChatOpenAI({
-          openAIApiKey,
-          modelName: 'gpt-4',
-          temperature: 0.7,
-        }),
-        'GPT-4 turbo': new ChatOpenAI({
-          openAIApiKey,
-          modelName: 'gpt-4-turbo',
-          temperature: 0.7,
-        }),
-        'GPT-4 omni': new ChatOpenAI({
-          openAIApiKey,
-          modelName: 'gpt-4o',
-          temperature: 0.7,
-        }),
-      };
-    } catch (err) {
-      logger.error(`Error loading OpenAI models: ${err}`);
-    }
-  }
-
-  if (groqApiKey) {
-    try {
-      models['groq'] = {
-        'LLaMA3 8b': new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama3-8b-8192',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-        'LLaMA3 70b': new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama3-70b-8192',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-        'Mixtral 8x7b': new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'mixtral-8x7b-32768',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-        'Gemma 7b': new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'gemma-7b-it',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      };
-    } catch (err) {
-      logger.error(`Error loading Groq models: ${err}`);
-    }
-  }
-
-  if (ollamaEndpoint) {
-    try {
-      const response = await fetch(`${ollamaEndpoint}/api/tags`, {
-        headers: {
-          'Content-Type': 'application/json',
-        },
-      });
-
-      const { models: ollamaModels } = (await response.json()) as any;
-
-      models['ollama'] = ollamaModels.reduce((acc, model) => {
-        acc[model.model] = new ChatOllama({
-          baseUrl: ollamaEndpoint,
-          model: model.model,
-          temperature: 0.7,
-        });
-        return acc;
-      }, {});
-    } catch (err) {
-      logger.error(`Error loading Ollama models: ${err}`);
-    }
-  }
-
-  models['custom_openai'] = {};
-
-  return models;
-};
-
-export const getAvailableEmbeddingModelProviders = async () => {
-  const openAIApiKey = getOpenaiApiKey();
-  const ollamaEndpoint = getOllamaApiEndpoint();
-
-  const models = {};
-
-  if (openAIApiKey) {
-    try {
-      models['openai'] = {
-        'Text embedding 3 small': new OpenAIEmbeddings({
-          openAIApiKey,
-          modelName: 'text-embedding-3-small',
-        }),
-        'Text embedding 3 large': new OpenAIEmbeddings({
-          openAIApiKey,
-          modelName: 'text-embedding-3-large',
-        }),
-      };
-    } catch (err) {
-      logger.error(`Error loading OpenAI embeddings: ${err}`);
-    }
-  }
-
-  if (ollamaEndpoint) {
-    try {
-      const response = await fetch(`${ollamaEndpoint}/api/tags`, {
-        headers: {
-          'Content-Type': 'application/json',
-        },
-      });
-
-      const { models: ollamaModels } = (await response.json()) as any;
-
-      models['ollama'] = ollamaModels.reduce((acc, model) => {
-        acc[model.model] = new OllamaEmbeddings({
-          baseUrl: ollamaEndpoint,
-          model: model.model,
-        });
-        return acc;
-      }, {});
-    } catch (err) {
-      logger.error(`Error loading Ollama embeddings: ${err}`);
-    }
-  }
-
-  try {
-    models['local'] = {
-      'BGE Small': new HuggingFaceTransformersEmbeddings({
-        modelName: 'Xenova/bge-small-en-v1.5',
-      }),
-      'GTE Small': new HuggingFaceTransformersEmbeddings({
-        modelName: 'Xenova/gte-small',
-      }),
-      'Bert Multilingual': new HuggingFaceTransformersEmbeddings({
-        modelName: 'Xenova/bert-base-multilingual-uncased',
-      }),
-    };
-  } catch (err) {
-    logger.error(`Error loading local embeddings: ${err}`);
-  }
-
-  return models;
-};

src/lib/providers/anthropic.ts

@@ -0,0 +1,39 @@
+import { ChatAnthropic } from '@langchain/anthropic';
+import { getAnthropicApiKey } from '../../config';
+import logger from '../../utils/logger';
+
+export const loadAnthropicChatModels = async () => {
+  const anthropicApiKey = getAnthropicApiKey();
+
+  if (!anthropicApiKey) return {};
+
+  try {
+    const chatModels = {
+      'Claude 3.5 Sonnet': new ChatAnthropic({
+        temperature: 0.7,
+        anthropicApiKey: anthropicApiKey,
+        model: 'claude-3-5-sonnet-20240620',
+      }),
+      'Claude 3 Opus': new ChatAnthropic({
+        temperature: 0.7,
+        anthropicApiKey: anthropicApiKey,
+        model: 'claude-3-opus-20240229',
+      }),
+      'Claude 3 Sonnet': new ChatAnthropic({
+        temperature: 0.7,
+        anthropicApiKey: anthropicApiKey,
+        model: 'claude-3-sonnet-20240229',
+      }),
+      'Claude 3 Haiku': new ChatAnthropic({
+        temperature: 0.7,
+        anthropicApiKey: anthropicApiKey,
+        model: 'claude-3-haiku-20240307',
+      }),
+    };
+
+    return chatModels;
+  } catch (err) {
+    logger.error(`Error loading Anthropic models: ${err}`);
+    return {};
+  }
+};

src/lib/providers/groq.ts

@@ -0,0 +1,69 @@
+import { ChatOpenAI } from '@langchain/openai';
+import { getGroqApiKey } from '../../config';
+import logger from '../../utils/logger';
+
+export const loadGroqChatModels = async () => {
+  const groqApiKey = getGroqApiKey();
+
+  if (!groqApiKey) return {};
+
+  try {
+    const chatModels = {
+      'LLaMA3 8b': new ChatOpenAI(
+        {
+          openAIApiKey: groqApiKey,
+          modelName: 'llama3-8b-8192',
+          temperature: 0.7,
+        },
+        {
+          baseURL: 'https://api.groq.com/openai/v1',
+        },
+      ),
+      'LLaMA3 70b': new ChatOpenAI(
+        {
+          openAIApiKey: groqApiKey,
+          modelName: 'llama3-70b-8192',
+          temperature: 0.7,
+        },
+        {
+          baseURL: 'https://api.groq.com/openai/v1',
+        },
+      ),
+      'Mixtral 8x7b': new ChatOpenAI(
+        {
+          openAIApiKey: groqApiKey,
+          modelName: 'mixtral-8x7b-32768',
+          temperature: 0.7,
+        },
+        {
+          baseURL: 'https://api.groq.com/openai/v1',
+        },
+      ),
+      'Gemma 7b': new ChatOpenAI(
+        {
+          openAIApiKey: groqApiKey,
+          modelName: 'gemma-7b-it',
+          temperature: 0.7,
+        },
+        {
+          baseURL: 'https://api.groq.com/openai/v1',
+        },
+      ),
+      'Gemma2 9b': new ChatOpenAI(
+        {
+          openAIApiKey: groqApiKey,
+          modelName: 'gemma2-9b-it',
+          temperature: 0.7,
+        },
+        {
+          baseURL: 'https://api.groq.com/openai/v1',
+        },
+      ),
+    };
+
+    return chatModels;
+  } catch (err) {
+    logger.error(`Error loading Groq models: ${err}`);
+    return {};
+  }
+};

src/lib/providers/index.ts

@@ -0,0 +1,46 @@
+import { loadGroqChatModels } from './groq';
+import { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
+import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
+import { loadAnthropicChatModels } from './anthropic';
+import { loadTransformersEmbeddingsModels } from './transformers';
+
+const chatModelProviders = {
+  openai: loadOpenAIChatModels,
+  groq: loadGroqChatModels,
+  ollama: loadOllamaChatModels,
+  anthropic: loadAnthropicChatModels,
+};
+
+const embeddingModelProviders = {
+  openai: loadOpenAIEmbeddingsModels,
+  local: loadTransformersEmbeddingsModels,
+  ollama: loadOllamaEmbeddingsModels,
+};
+
+export const getAvailableChatModelProviders = async () => {
+  const models = {};
+
+  for (const provider in chatModelProviders) {
+    const providerModels = await chatModelProviders[provider]();
+    if (Object.keys(providerModels).length > 0) {
+      models[provider] = providerModels;
+    }
+  }
+
+  models['custom_openai'] = {};
+
+  return models;
+};
+
+export const getAvailableEmbeddingModelProviders = async () => {
+  const models = {};
+
+  for (const provider in embeddingModelProviders) {
+    const providerModels = await embeddingModelProviders[provider]();
+    if (Object.keys(providerModels).length > 0) {
+      models[provider] = providerModels;
+    }
+  }
+
+  return models;
+};

src/lib/providers/ollama.ts

@@ -0,0 +1,63 @@
+import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
+import { getOllamaApiEndpoint } from '../../config';
+import logger from '../../utils/logger';
+import { ChatOllama } from '@langchain/community/chat_models/ollama';
+
+export const loadOllamaChatModels = async () => {
+  const ollamaEndpoint = getOllamaApiEndpoint();
+
+  if (!ollamaEndpoint) return {};
+
+  try {
+    const response = await fetch(`${ollamaEndpoint}/api/tags`, {
+      headers: {
+        'Content-Type': 'application/json',
+      },
+    });
+
+    const { models: ollamaModels } = (await response.json()) as any;
+
+    const chatModels = ollamaModels.reduce((acc, model) => {
+      acc[model.model] = new ChatOllama({
+        baseUrl: ollamaEndpoint,
+        model: model.model,
+        temperature: 0.7,
+      });
+      return acc;
+    }, {});
+
+    return chatModels;
+  } catch (err) {
+    logger.error(`Error loading Ollama models: ${err}`);
+    return {};
+  }
+};
+
+export const loadOllamaEmbeddingsModels = async () => {
+  const ollamaEndpoint = getOllamaApiEndpoint();
+
+  if (!ollamaEndpoint) return {};
+
+  try {
+    const response = await fetch(`${ollamaEndpoint}/api/tags`, {
+      headers: {
+        'Content-Type': 'application/json',
+      },
+    });
+
+    const { models: ollamaModels } = (await response.json()) as any;
+
+    const embeddingsModels = ollamaModels.reduce((acc, model) => {
+      acc[model.model] = new OllamaEmbeddings({
+        baseUrl: ollamaEndpoint,
+        model: model.model,
+      });
+      return acc;
+    }, {});
+
+    return embeddingsModels;
+  } catch (err) {
+    logger.error(`Error loading Ollama embeddings model: ${err}`);
+    return {};
+  }
+};

src/lib/providers/openai.ts

@@ -0,0 +1,68 @@
+import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
+import { getOpenaiApiKey } from '../../config';
+import logger from '../../utils/logger';
+
+export const loadOpenAIChatModels = async () => {
+  const openAIApiKey = getOpenaiApiKey();
+
+  if (!openAIApiKey) return {};
+
+  try {
+    const chatModels = {
+      'GPT-3.5 turbo': new ChatOpenAI({
+        openAIApiKey,
+        modelName: 'gpt-3.5-turbo',
+        temperature: 0.7,
+      }),
+      'GPT-4': new ChatOpenAI({
+        openAIApiKey,
+        modelName: 'gpt-4',
+        temperature: 0.7,
+      }),
+      'GPT-4 turbo': new ChatOpenAI({
+        openAIApiKey,
+        modelName: 'gpt-4-turbo',
+        temperature: 0.7,
+      }),
+      'GPT-4 omni': new ChatOpenAI({
+        openAIApiKey,
+        modelName: 'gpt-4o',
+        temperature: 0.7,
+      }),
+      'GPT-4 omni mini': new ChatOpenAI({
+        openAIApiKey,
+        modelName: 'gpt-4o-mini',
+        temperature: 0.7,
+      }),
+    };
+
+    return chatModels;
+  } catch (err) {
+    logger.error(`Error loading OpenAI models: ${err}`);
+    return {};
+  }
+};
+
+export const loadOpenAIEmbeddingsModels = async () => {
+  const openAIApiKey = getOpenaiApiKey();
+
+  if (!openAIApiKey) return {};
+
+  try {
+    const embeddingModels = {
+      'Text embedding 3 small': new OpenAIEmbeddings({
+        openAIApiKey,
+        modelName: 'text-embedding-3-small',
+      }),
+      'Text embedding 3 large': new OpenAIEmbeddings({
+        openAIApiKey,
+        modelName: 'text-embedding-3-large',
+      }),
+    };
+
+    return embeddingModels;
+  } catch (err) {
+    logger.error(`Error loading OpenAI embeddings model: ${err}`);
+    return {};
+  }
+};

src/lib/providers/transformers.ts

@@ -0,0 +1,23 @@
+import logger from '../../utils/logger';
+import { HuggingFaceTransformersEmbeddings } from '../huggingfaceTransformer';
+
+export const loadTransformersEmbeddingsModels = async () => {
+  try {
+    const embeddingModels = {
+      'BGE Small': new HuggingFaceTransformersEmbeddings({
+        modelName: 'Xenova/bge-small-en-v1.5',
+      }),
+      'GTE Small': new HuggingFaceTransformersEmbeddings({
+        modelName: 'Xenova/gte-small',
+      }),
+      'Bert Multilingual': new HuggingFaceTransformersEmbeddings({
+        modelName: 'Xenova/bert-base-multilingual-uncased',
+      }),
+    };
+
+    return embeddingModels;
+  } catch (err) {
+    logger.error(`Error loading Transformers embeddings model: ${err}`);
+    return {};
+  }
+};

src/routes/config.ts

@@ -6,6 +6,7 @@ import {
 import {
   getGroqApiKey,
   getOllamaApiEndpoint,
+  getAnthropicApiKey,
   getOpenaiApiKey,
   updateConfig,
 } from '../config';
@@ -37,6 +38,7 @@ router.get('/', async (_, res) => {
 
   config['openaiApiKey'] = getOpenaiApiKey();
   config['ollamaApiUrl'] = getOllamaApiEndpoint();
+  config['anthropicApiKey'] = getAnthropicApiKey();
   config['groqApiKey'] = getGroqApiKey();
 
   res.status(200).json(config);
@@ -49,6 +51,7 @@ router.post('/', async (req, res) => {
     API_KEYS: {
       OPENAI: config.openaiApiKey,
       GROQ: config.groqApiKey,
+      ANTHROPIC: config.anthropicApiKey,
     },
     API_ENDPOINTS: {
       OLLAMA: config.ollamaApiUrl,

src/websocket/connectionManager.ts

@@ -45,7 +45,7 @@ export const handleConnection = async (
       chatModelProviders[chatModelProvider][chatModel] &&
       chatModelProvider != 'custom_openai'
     ) {
-      llm = chatModelProviders[chatModelProvider][chatModel] as
+      llm = chatModelProviders[chatModelProvider][chatModel] as unknown as
         | BaseChatModel
         | undefined;
     } else if (chatModelProvider == 'custom_openai') {
@@ -56,7 +56,7 @@ export const handleConnection = async (
         configuration: {
           baseURL: searchParams.get('openAIBaseURL'),
         },
-      });
+      }) as unknown as BaseChatModel;
     }
 
     if (

ui/components/ChatWindow.tsx

@@ -83,6 +83,55 @@ const useSocket = (
             'embeddingModelProvider',
             embeddingModelProvider,
           );
+        } else {
+          const providers = await fetch(
+            `${process.env.NEXT_PUBLIC_API_URL}/models`,
+            {
+              headers: {
+                'Content-Type': 'application/json',
+              },
+            },
+          ).then(async (res) => await res.json());
+
+          const chatModelProviders = providers.chatModelProviders;
+          const embeddingModelProviders = providers.embeddingModelProviders;
+
+          if (
+            Object.keys(chatModelProviders).length > 0 &&
+            !chatModelProviders[chatModelProvider]
+          ) {
+            chatModelProvider = Object.keys(chatModelProviders)[0];
+            localStorage.setItem('chatModelProvider', chatModelProvider);
+          }
+
+          if (
+            chatModelProvider &&
+            !chatModelProviders[chatModelProvider][chatModel]
+          ) {
+            chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
+            localStorage.setItem('chatModel', chatModel);
+          }
+
+          if (
+            Object.keys(embeddingModelProviders).length > 0 &&
+            !embeddingModelProviders[embeddingModelProvider]
+          ) {
+            embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
+            localStorage.setItem(
+              'embeddingModelProvider',
+              embeddingModelProvider,
+            );
+          }
+
+          if (
+            embeddingModelProvider &&
+            !embeddingModelProviders[embeddingModelProvider][embeddingModel]
+          ) {
+            embeddingModel = Object.keys(
+              embeddingModelProviders[embeddingModelProvider],
+            )[0];
+            localStorage.setItem('embeddingModel', embeddingModel);
+          }
         }
 
         const wsURL = new URL(url);
@@ -145,8 +194,10 @@ const useSocket = (
     }
 
     return () => {
-      ws?.close();
-      console.log('[DEBUG] closed');
+      if (ws?.readyState === 1) {
+        ws?.close();
+        console.log('[DEBUG] closed');
+      }
     };
   }, [ws, url, setIsWSReady, setError]);
 

ui/components/SettingsDialog.tsx

@@ -56,6 +56,7 @@ interface SettingsType {
   };
   openaiApiKey: string;
   groqApiKey: string;
+  anthropicApiKey: string;
   ollamaApiUrl: string;
 }
 
@@ -439,6 +440,22 @@ const SettingsDialog = ({
                         }
                       />
                     </div>
+                    <div className="flex flex-col space-y-1">
+                      <p className="text-black/70 dark:text-white/70 text-sm">
+                        Anthropic API Key
+                      </p>
+                      <Input
+                        type="text"
+                        placeholder="Anthropic API key"
+                        defaultValue={config.anthropicApiKey}
+                        onChange={(e) =>
+                          setConfig({
+                            ...config,
+                            anthropicApiKey: e.target.value,
+                          })
+                        }
+                      />
+                    </div>
                   </div>
                 )}
                 {isLoading && (

ui/package.json

@@ -1,6 +1,6 @@
 {
   "name": "perplexica-frontend",
-  "version": "1.7.1",
+  "version": "1.8.0",
   "license": "MIT",
   "author": "ItzCrazyKns",
   "scripts": {

yarn.lock

@@ -2,6 +2,20 @@
 # yarn lockfile v1
 
 
+"@anthropic-ai/sdk@^0.22.0":
+  version "0.22.0"
+  resolved "https://registry.yarnpkg.com/@anthropic-ai/sdk/-/sdk-0.22.0.tgz#548e4218d9810fd494e595d4e57cb2d46d301a1a"
+  integrity sha512-dv4BCC6FZJw3w66WNLsHlUFjhu19fS1L/5jMPApwhZLa/Oy1j0A2i3RypmDtHEPp4Wwg3aZkSHksp7VzYWjzmw==
+  dependencies:
+    "@types/node" "^18.11.18"
+    "@types/node-fetch" "^2.6.4"
+    abort-controller "^3.0.0"
+    agentkeepalive "^4.2.1"
+    form-data-encoder "1.7.2"
+    formdata-node "^4.3.2"
+    node-fetch "^2.6.7"
+    web-streams-polyfill "^3.2.1"
+
 "@anthropic-ai/sdk@^0.9.1":
   version "0.9.1"
   resolved "https://registry.yarnpkg.com/@anthropic-ai/sdk/-/sdk-0.9.1.tgz#b2d2b7bf05c90dce502c9a2e869066870f69ba88"
@@ -307,6 +321,17 @@
     "@jridgewell/resolve-uri" "^3.0.3"
     "@jridgewell/sourcemap-codec" "^1.4.10"
 
+"@langchain/anthropic@^0.2.3":
+  version "0.2.3"
+  resolved "https://registry.yarnpkg.com/@langchain/anthropic/-/anthropic-0.2.3.tgz#1505da939f47c90e53dfede0407c497b8177bdf0"
+  integrity sha512-f2fqzLGcvsXXUyZ1vl8cgwkKDGLshOGrPuR9hkhGuBG5m91eq755OqPBxWJuS1TFtNU813cXft3xh0MQbxavwg==
+  dependencies:
+    "@anthropic-ai/sdk" "^0.22.0"
+    "@langchain/core" ">=0.2.9 <0.3.0"
+    fast-xml-parser "^4.3.5"
+    zod "^3.22.4"
+    zod-to-json-schema "^3.22.4"
+
 "@langchain/community@^0.2.16":
   version "0.2.16"
   resolved "https://registry.yarnpkg.com/@langchain/community/-/community-0.2.16.tgz#5888baf7fc7ea272c5f91aaa0e71bc444167262d"
@@ -355,6 +380,24 @@
     zod "^3.22.4"
     zod-to-json-schema "^3.22.3"
 
+"@langchain/core@>=0.2.9 <0.3.0":
+  version "0.2.15"
+  resolved "https://registry.yarnpkg.com/@langchain/core/-/core-0.2.15.tgz#1bb99ac4fffe935c7ba37edcaa91abfba3c82219"
+  integrity sha512-L096itIBQ5XNsy5BCCPqIQEk/x4rzI+U4BhYT+fDBYtljESshIi/WzXdmiGfY/6MpVjB76jNuaRgMDmo1m9NeQ==
+  dependencies:
+    ansi-styles "^5.0.0"
+    camelcase "6"
+    decamelize "1.2.0"
+    js-tiktoken "^1.0.12"
+    langsmith "~0.1.30"
+    ml-distance "^4.0.0"
+    mustache "^4.2.0"
+    p-queue "^6.6.2"
+    p-retry "4"
+    uuid "^10.0.0"
+    zod "^3.22.4"
+    zod-to-json-schema "^3.22.3"
+
 "@langchain/core@~0.1.44", "@langchain/core@~0.1.45":
   version "0.1.52"
   resolved "https://registry.yarnpkg.com/@langchain/core/-/core-0.1.52.tgz#7619310b83ffa841628efe2e1eda873ca714d068"
@@ -1311,6 +1354,13 @@ fast-fifo@^1.1.0, fast-fifo@^1.2.0:
   resolved "https://registry.yarnpkg.com/fast-fifo/-/fast-fifo-1.3.2.tgz#286e31de96eb96d38a97899815740ba2a4f3640c"
   integrity sha512-/d9sfos4yxzpwkDkuN7k2SqFKtYNmCTzgfEpz82x34IM9/zc8KGxQoXg1liNC/izpRM/MBdt44Nmx41ZWqk+FQ==
 
+fast-xml-parser@^4.3.5:
+  version "4.4.0"
+  resolved "https://registry.yarnpkg.com/fast-xml-parser/-/fast-xml-parser-4.4.0.tgz#341cc98de71e9ba9e651a67f41f1752d1441a501"
+  integrity sha512-kLY3jFlwIYwBNDojclKsNAC12sfD6NwW74QB2CoNGPvtVxjliYehVunB3HYyNi+n4Tt1dAcgwYvmKF/Z18flqg==
+  dependencies:
+    strnum "^1.0.5"
+
 fecha@^4.2.0:
   version "4.2.3"
   resolved "https://registry.yarnpkg.com/fecha/-/fecha-4.2.3.tgz#4d9ccdbc61e8629b259fdca67e65891448d569fd"
@@ -2342,6 +2392,11 @@ strip-json-comments@~2.0.1:
   resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-2.0.1.tgz#3c531942e908c2697c0ec344858c286c7ca0a60a"
   integrity sha512-4gB8na07fecVVkOI6Rs4e7T6NOTki5EmL7TUduTs6bu3EdnSycntVJ4re8kgZA+wx9IueI2Y11bfbgwtzuE0KQ==
 
+strnum@^1.0.5:
+  version "1.0.5"
+  resolved "https://registry.yarnpkg.com/strnum/-/strnum-1.0.5.tgz#5c4e829fe15ad4ff0d20c3db5ac97b73c9b072db"
+  integrity sha512-J8bbNyKKXl5qYcR36TIO8W3mVGVHrmmxsd5PAItGkmyzwJvybiw2IVq5nqd0i4LSNSkB/sx9VHllbfFdr9k1JA==
+
 supports-color@^5.5.0:
   version "5.5.0"
   resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-5.5.0.tgz#e2e69a44ac8772f78a1ec0b35b689df6530efc8f"
@@ -2488,6 +2543,11 @@ utils-merge@1.0.1:
   resolved "https://registry.yarnpkg.com/utils-merge/-/utils-merge-1.0.1.tgz#9f95710f50a267947b2ccc124741c1028427e713"
   integrity sha512-pMZTvIkT1d+TFGvDOqodOclx0QWkkgi6Tdoa8gC8ffGAAqz9pzPTZWAybbsHHoED/ztMtkv/VoYTYyShUn81hA==
 
+uuid@^10.0.0:
+  version "10.0.0"
+  resolved "https://registry.yarnpkg.com/uuid/-/uuid-10.0.0.tgz#5a95aa454e6e002725c79055fd42aaba30ca6294"
+  integrity sha512-8XkAphELsDnEGrDxUOHB3RGvXz6TeuYSGEZBOjtTtPm2lwhGBjLgOzLHB63IUWfBpNucQjND6d3AOudO+H3RWQ==
+
 uuid@^9.0.0:
   version "9.0.1"
   resolved "https://registry.yarnpkg.com/uuid/-/uuid-9.0.1.tgz#e188d4c8853cc722220392c424cd637f32293f30"
@@ -2592,7 +2652,7 @@ zod-to-json-schema@^3.22.3:
   resolved "https://registry.yarnpkg.com/zod-to-json-schema/-/zod-to-json-schema-3.22.5.tgz#3646e81cfc318dbad2a22519e5ce661615418673"
   integrity sha512-+akaPo6a0zpVCCseDed504KBJUQpEW5QZw7RMneNmKw+fGaML1Z9tUNLnHHAC8x6dzVRO1eB2oEMyZRnuBZg7Q==
 
-zod-to-json-schema@^3.22.5:
+zod-to-json-schema@^3.22.4, zod-to-json-schema@^3.22.5:
   version "3.23.1"
   resolved "https://registry.yarnpkg.com/zod-to-json-schema/-/zod-to-json-schema-3.23.1.tgz#5225925b8ed5fa20096bd99be076c4b29b53d309"
   integrity sha512-oT9INvydob1XV0v1d2IadrR74rLtDInLvDFfAa1CG0Pmg/vxATk7I2gSelfj271mbzeM4Da0uuDQE/Nkj3DWNw==