feat(config): Use toml instead of env

.env.example

@@ -1,5 +0,0 @@
-PORT=3001
-OPENAI_API_KEY=
-SIMILARITY_MEASURE=cosine # cosine or dot
-SEARXNG_API_URL= # no need to fill this if using docker
-MODEL_NAME=gpt-3.5-turbo

.github/ISSUE_TEMPLATE/bug_report.md

@@ -4,7 +4,6 @@ about: Create an issue to help us fix bugs
 title: ''
 labels: bug
 assignees: ''
-
 ---
 
 **Describe the bug**
@@ -12,6 +11,7 @@ A clear and concise description of what the bug is.
 
 **To Reproduce**
 Steps to reproduce the behavior:
+
 1. Go to '...'
 2. Click on '....'
 3. Scroll down to '....'

.github/ISSUE_TEMPLATE/custom.md

@@ -4,7 +4,4 @@ about: Describe this issue template's purpose here.
 title: ''
 labels: ''
 assignees: ''
-
 ---
-
-

.github/ISSUE_TEMPLATE/feature_request.md

@@ -4,7 +4,6 @@ about: Suggest an idea for this project
 title: ''
 labels: enhancement
 assignees: ''
-
 ---
 
 **Is your feature request related to a problem? Please describe.**

.gitignore

@@ -19,6 +19,9 @@ yarn-error.log
 .env.test.local
 .env.production.local
 
+# Config files
+config.toml
+
 # Log files
 logs/
 *.log

.prettierignore

@@ -0,0 +1,38 @@
+# Ignore all files in the node_modules directory
+node_modules
+
+# Ignore all files in the .next directory (Next.js build output)
+.next
+
+# Ignore all files in the .out directory (TypeScript build output)
+.out
+
+# Ignore all files in the .cache directory (Prettier cache)
+.cache
+
+# Ignore all files in the .vscode directory (Visual Studio Code settings)
+.vscode
+
+# Ignore all files in the .idea directory (IntelliJ IDEA settings)
+.idea
+
+# Ignore all files in the dist directory (build output)
+dist
+
+# Ignore all files in the build directory (build output)
+build
+
+# Ignore all files in the coverage directory (test coverage reports)
+coverage
+
+# Ignore all files with the .log extension
+*.log
+
+# Ignore all files with the .tmp extension
+*.tmp
+
+# Ignore all files with the .swp extension
+*.swp
+
+# Ignore all files with the .DS_Store extension (macOS specific)
+.DS_Store

README.md

@@ -56,10 +56,10 @@ There are mainly 2 ways of installing Perplexica - With Docker, Without Docker.
 
 3. After cloning, navigate to the directory containing the project files.
 
-4. Rename the `.env.example` file to `.env`. For Docker setups, you need only fill in the following fields:
+4. Rename the `sample.config.toml` file to `config.toml`. For Docker setups, you need only fill in the following fields:
 
-   - `OPENAI_API_KEY`
+   - `OPENAI`: Your OpenAI API key.
-   - `SIMILARITY_MEASURE` (This is filled by default; you can leave it as is if you are unsure about it.)
+   - `SIMILARITY_MEASURE`: The similarity measure to use (This is filled by default; you can leave it as is if you are unsure about it.)
 
 5. Ensure you are in the directory containing the `docker-compose.yaml` file and execute:
 
@@ -75,7 +75,7 @@ There are mainly 2 ways of installing Perplexica - With Docker, Without Docker.
 
 For setups without Docker:
 
-1. Follow the initial steps to clone the repository and rename the `.env.example` file to `.env` in the root directory. You will need to fill in all the fields in this file.
+1. Follow the initial steps to clone the repository and rename the `sample.config.toml` file to `config.toml` in the root directory. You will need to fill in all the fields in this file.
 2. Additionally, rename the `.env.example` file to `.env` in the `ui` folder and complete all fields.
 3. The non-Docker setup requires manual configuration of both the backend and frontend.
 

backend.dockerfile

@@ -1,16 +1,17 @@
 FROM node:alpine
 
 ARG SEARXNG_API_URL
-ENV SEARXNG_API_URL=${SEARXNG_API_URL}
 
 WORKDIR /home/perplexica
 
 COPY src /home/perplexica/src
 COPY tsconfig.json /home/perplexica/
-COPY .env /home/perplexica/
+COPY config.toml /home/perplexica/
 COPY package.json /home/perplexica/
 COPY yarn.lock /home/perplexica/
 
+RUN sed -i "s|SEARXNG = \".*\"|SEARXNG = \"${SEARXNG_API_URL}\"|g" /home/perplexica/config.toml
+
 RUN yarn install
 RUN yarn build
 

package.json

@@ -4,9 +4,9 @@
   "license": "MIT",
   "author": "ItzCrazyKns",
   "scripts": {
-    "start": "node --env-file=.env dist/app.js",
+    "start": "node dist/app.js",
     "build": "tsc",
-    "dev": "nodemon -r dotenv/config src/app.ts",
+    "dev": "nodemon src/app.ts",
     "format": "prettier . --check",
     "format:write": "prettier . --write"
   },
@@ -19,6 +19,7 @@
     "typescript": "^5.4.3"
   },
   "dependencies": {
+    "@iarna/toml": "^2.2.5",
     "@langchain/openai": "^0.0.25",
     "axios": "^1.6.8",
     "compute-cosine-similarity": "^1.1.0",

sample.config.toml

@@ -0,0 +1,9 @@
+[GENERAL]
+PORT = 3001 # Port to run the server on
+SIMILARITY_MEASURE = "cosine" # "cosine" or "dot"
+
+[API_KEYS]
+OPENAI = "sk-1234567890abcdef1234567890abcdef" # OpenAI API key
+
+[API_ENDPOINTS]
+SEARXNG = "http://localhost:32768" # SearxNG API ULR

src/agents/academicSearchAgent.ts

@@ -9,24 +9,16 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import { Document } from '@langchain/core/documents';
 import { searchSearxng } from '../core/searxng';
 import type { StreamEvent } from '@langchain/core/tracers/log_stream';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import computeSimilarity from '../utils/computeSimilarity';
 
-const llm = new ChatOpenAI({
-  modelName: process.env.MODEL_NAME,
-  temperature: 0.7,
-});
-
-const embeddings = new OpenAIEmbeddings({
-  modelName: 'text-embedding-3-large',
-});
-
 const basicAcademicSearchRetrieverPrompt = `
 You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
 If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
@@ -49,7 +41,7 @@ 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 'Acadedemic', this means you will be searching for academic papers and articles on the web.
+    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).
     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.
@@ -104,56 +96,13 @@ const handleStream = async (
   }
 };
 
-const processDocs = async (docs: Document[]) => {
-  return docs
-    .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
-    .join('\n');
-};
-
-const rerankDocs = async ({
-  query,
-  docs,
-}: {
-  query: string;
-  docs: Document[];
-}) => {
-  if (docs.length === 0) {
-    return docs;
-  }
-
-  const docsWithContent = docs.filter(
-    (doc) => doc.pageContent && doc.pageContent.length > 0,
-  );
-
-  const docEmbeddings = await embeddings.embedDocuments(
-    docsWithContent.map((doc) => doc.pageContent),
-  );
-
-  const queryEmbedding = await embeddings.embedQuery(query);
-
-  const similarity = docEmbeddings.map((docEmbedding, i) => {
-    const sim = computeSimilarity(queryEmbedding, docEmbedding);
-
-    return {
-      index: i,
-      similarity: sim,
-    };
-  });
-
-  const sortedDocs = similarity
-    .sort((a, b) => b.similarity - a.similarity)
-    .slice(0, 15)
-    .map((sim) => docsWithContent[sim.index]);
-
-  return sortedDocs;
-};
-
 type BasicChainInput = {
   chat_history: BaseMessage[];
   query: string;
 };
 
-const basicAcademicSearchRetrieverChain = RunnableSequence.from([
+const createBasicAcademicSearchRetrieverChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
     PromptTemplate.fromTemplate(basicAcademicSearchRetrieverPrompt),
     llm,
     strParser,
@@ -186,9 +135,61 @@ const basicAcademicSearchRetrieverChain = RunnableSequence.from([
 
       return { query: input, docs: documents };
     }),
-]);
+  ]);
+};
 
-const basicAcademicSearchAnsweringChain = RunnableSequence.from([
+const createBasicAcademicSearchAnsweringChain = (
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const basicAcademicSearchRetrieverChain =
+    createBasicAcademicSearchRetrieverChain(llm);
+
+  const processDocs = async (docs: Document[]) => {
+    return docs
+      .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
+      .join('\n');
+  };
+
+  const rerankDocs = async ({
+    query,
+    docs,
+  }: {
+    query: string;
+    docs: Document[];
+  }) => {
+    if (docs.length === 0) {
+      return docs;
+    }
+
+    const docsWithContent = docs.filter(
+      (doc) => doc.pageContent && doc.pageContent.length > 0,
+    );
+
+    const docEmbeddings = await embeddings.embedDocuments(
+      docsWithContent.map((doc) => doc.pageContent),
+    );
+
+    const queryEmbedding = await embeddings.embedQuery(query);
+
+    const similarity = docEmbeddings.map((docEmbedding, i) => {
+      const sim = computeSimilarity(queryEmbedding, docEmbedding);
+
+      return {
+        index: i,
+        similarity: sim,
+      };
+    });
+
+    const sortedDocs = similarity
+      .sort((a, b) => b.similarity - a.similarity)
+      .slice(0, 15)
+      .map((sim) => docsWithContent[sim.index]);
+
+    return sortedDocs;
+  };
+
+  return RunnableSequence.from([
     RunnableMap.from({
       query: (input: BasicChainInput) => input.query,
       chat_history: (input: BasicChainInput) => input.chat_history,
@@ -212,14 +213,23 @@ const basicAcademicSearchAnsweringChain = RunnableSequence.from([
     ]),
     llm,
     strParser,
-]).withConfig({
+  ]).withConfig({
     runName: 'FinalResponseGenerator',
-});
+  });
+};
 
-const basicAcademicSearch = (query: string, history: BaseMessage[]) => {
+const basicAcademicSearch = (
+  query: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
   const emitter = new eventEmitter();
 
   try {
+    const basicAcademicSearchAnsweringChain =
+      createBasicAcademicSearchAnsweringChain(llm, embeddings);
+
     const stream = basicAcademicSearchAnsweringChain.streamEvents(
       {
         chat_history: history,
@@ -242,8 +252,13 @@ const basicAcademicSearch = (query: string, history: BaseMessage[]) => {
   return emitter;
 };
 
-const handleAcademicSearch = (message: string, history: BaseMessage[]) => {
+const handleAcademicSearch = (
-  const emitter = basicAcademicSearch(message, history);
+  message: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const emitter = basicAcademicSearch(message, history, llm, embeddings);
   return emitter;
 };
 

src/agents/imageSearchAgent.ts

@@ -4,16 +4,11 @@ import {
   RunnableLambda,
 } from '@langchain/core/runnables';
 import { PromptTemplate } from '@langchain/core/prompts';
-import { ChatOpenAI } from '@langchain/openai';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import { searchSearxng } from '../core/searxng';
-
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-const llm = new ChatOpenAI({
-  modelName: process.env.MODEL_NAME,
-  temperature: 0.7,
-});
 
 const imageSearchChainPrompt = `
 You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search the web for images.
@@ -43,7 +38,8 @@ type ImageSearchChainInput = {
 
 const strParser = new StringOutputParser();
 
-const imageSearchChain = RunnableSequence.from([
+const createImageSearchChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
     RunnableMap.from({
       chat_history: (input: ImageSearchChainInput) => {
         return formatChatHistoryAsString(input.chat_history);
@@ -75,6 +71,15 @@ const imageSearchChain = RunnableSequence.from([
 
       return images.slice(0, 10);
     }),
-]);
+  ]);
+};
 
-export default imageSearchChain;
+const handleImageSearch = (
+  input: ImageSearchChainInput,
+  llm: BaseChatModel,
+) => {
+  const imageSearchChain = createImageSearchChain(llm);
+  return imageSearchChain.invoke(input);
+};
+
+export default handleImageSearch;

src/agents/redditSearchAgent.ts

@@ -9,24 +9,16 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import { Document } from '@langchain/core/documents';
 import { searchSearxng } from '../core/searxng';
 import type { StreamEvent } from '@langchain/core/tracers/log_stream';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import computeSimilarity from '../utils/computeSimilarity';
 
-const llm = new ChatOpenAI({
-  modelName: process.env.MODEL_NAME,
-  temperature: 0.7,
-});
-
-const embeddings = new OpenAIEmbeddings({
-  modelName: 'text-embedding-3-large',
-});
-
 const basicRedditSearchRetrieverPrompt = `
 You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
 If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
@@ -104,19 +96,63 @@ const handleStream = async (
   }
 };
 
-const processDocs = async (docs: Document[]) => {
+type BasicChainInput = {
+  chat_history: BaseMessage[];
+  query: string;
+};
+
+const createBasicRedditSearchRetrieverChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
+    PromptTemplate.fromTemplate(basicRedditSearchRetrieverPrompt),
+    llm,
+    strParser,
+    RunnableLambda.from(async (input: string) => {
+      if (input === 'not_needed') {
+        return { query: '', docs: [] };
+      }
+
+      const res = await searchSearxng(input, {
+        language: 'en',
+        engines: ['reddit'],
+      });
+
+      const documents = res.results.map(
+        (result) =>
+          new Document({
+            pageContent: result.content ? result.content : result.title,
+            metadata: {
+              title: result.title,
+              url: result.url,
+              ...(result.img_src && { img_src: result.img_src }),
+            },
+          }),
+      );
+
+      return { query: input, docs: documents };
+    }),
+  ]);
+};
+
+const createBasicRedditSearchAnsweringChain = (
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const basicRedditSearchRetrieverChain =
+    createBasicRedditSearchRetrieverChain(llm);
+
+  const processDocs = async (docs: Document[]) => {
     return docs
       .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
       .join('\n');
-};
+  };
 
-const rerankDocs = async ({
+  const rerankDocs = async ({
     query,
     docs,
-}: {
+  }: {
     query: string;
     docs: Document[];
-}) => {
+  }) => {
     if (docs.length === 0) {
       return docs;
     }
@@ -147,44 +183,9 @@ const rerankDocs = async ({
       .map((sim) => docsWithContent[sim.index]);
 
     return sortedDocs;
-};
+  };
 
-type BasicChainInput = {
+  return RunnableSequence.from([
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-const basicRedditSearchRetrieverChain = RunnableSequence.from([
-  PromptTemplate.fromTemplate(basicRedditSearchRetrieverPrompt),
-  llm,
-  strParser,
-  RunnableLambda.from(async (input: string) => {
-    if (input === 'not_needed') {
-      return { query: '', docs: [] };
-    }
-
-    const res = await searchSearxng(input, {
-      language: 'en',
-      engines: ['reddit'],
-    });
-
-    const documents = res.results.map(
-      (result) =>
-        new Document({
-          pageContent: result.content ? result.content : result.title,
-          metadata: {
-            title: result.title,
-            url: result.url,
-            ...(result.img_src && { img_src: result.img_src }),
-          },
-        }),
-    );
-
-    return { query: input, docs: documents };
-  }),
-]);
-
-const basicRedditSearchAnsweringChain = RunnableSequence.from([
     RunnableMap.from({
       query: (input: BasicChainInput) => input.query,
       chat_history: (input: BasicChainInput) => input.chat_history,
@@ -208,14 +209,22 @@ const basicRedditSearchAnsweringChain = RunnableSequence.from([
     ]),
     llm,
     strParser,
-]).withConfig({
+  ]).withConfig({
     runName: 'FinalResponseGenerator',
-});
+  });
+};
 
-const basicRedditSearch = (query: string, history: BaseMessage[]) => {
+const basicRedditSearch = (
+  query: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
   const emitter = new eventEmitter();
 
   try {
+    const basicRedditSearchAnsweringChain =
+      createBasicRedditSearchAnsweringChain(llm, embeddings);
     const stream = basicRedditSearchAnsweringChain.streamEvents(
       {
         chat_history: history,
@@ -238,8 +247,13 @@ const basicRedditSearch = (query: string, history: BaseMessage[]) => {
   return emitter;
 };
 
-const handleRedditSearch = (message: string, history: BaseMessage[]) => {
+const handleRedditSearch = (
-  const emitter = basicRedditSearch(message, history);
+  message: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const emitter = basicRedditSearch(message, history, llm, embeddings);
   return emitter;
 };
 

src/agents/webSearchAgent.ts

@@ -9,24 +9,16 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import { Document } from '@langchain/core/documents';
 import { searchSearxng } from '../core/searxng';
 import type { StreamEvent } from '@langchain/core/tracers/log_stream';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import computeSimilarity from '../utils/computeSimilarity';
 
-const llm = new ChatOpenAI({
-  modelName: process.env.MODEL_NAME,
-  temperature: 0.7,
-});
-
-const embeddings = new OpenAIEmbeddings({
-  modelName: 'text-embedding-3-large',
-});
-
 const basicSearchRetrieverPrompt = `
 You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
 If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
@@ -104,19 +96,61 @@ const handleStream = async (
   }
 };
 
-const processDocs = async (docs: Document[]) => {
+type BasicChainInput = {
+  chat_history: BaseMessage[];
+  query: string;
+};
+
+const createBasicWebSearchRetrieverChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
+    PromptTemplate.fromTemplate(basicSearchRetrieverPrompt),
+    llm,
+    strParser,
+    RunnableLambda.from(async (input: string) => {
+      if (input === 'not_needed') {
+        return { query: '', docs: [] };
+      }
+
+      const res = await searchSearxng(input, {
+        language: 'en',
+      });
+
+      const documents = res.results.map(
+        (result) =>
+          new Document({
+            pageContent: result.content,
+            metadata: {
+              title: result.title,
+              url: result.url,
+              ...(result.img_src && { img_src: result.img_src }),
+            },
+          }),
+      );
+
+      return { query: input, docs: documents };
+    }),
+  ]);
+};
+
+const createBasicWebSearchAnsweringChain = (
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const basicWebSearchRetrieverChain = createBasicWebSearchRetrieverChain(llm);
+
+  const processDocs = async (docs: Document[]) => {
     return docs
       .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
       .join('\n');
-};
+  };
 
-const rerankDocs = async ({
+  const rerankDocs = async ({
     query,
     docs,
-}: {
+  }: {
     query: string;
     docs: Document[];
-}) => {
+  }) => {
     if (docs.length === 0) {
       return docs;
     }
@@ -147,43 +181,9 @@ const rerankDocs = async ({
       .map((sim) => docsWithContent[sim.index]);
 
     return sortedDocs;
-};
+  };
 
-type BasicChainInput = {
+  return RunnableSequence.from([
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-const basicWebSearchRetrieverChain = RunnableSequence.from([
-  PromptTemplate.fromTemplate(basicSearchRetrieverPrompt),
-  llm,
-  strParser,
-  RunnableLambda.from(async (input: string) => {
-    if (input === 'not_needed') {
-      return { query: '', docs: [] };
-    }
-
-    const res = await searchSearxng(input, {
-      language: 'en',
-    });
-
-    const documents = res.results.map(
-      (result) =>
-        new Document({
-          pageContent: result.content,
-          metadata: {
-            title: result.title,
-            url: result.url,
-            ...(result.img_src && { img_src: result.img_src }),
-          },
-        }),
-    );
-
-    return { query: input, docs: documents };
-  }),
-]);
-
-const basicWebSearchAnsweringChain = RunnableSequence.from([
     RunnableMap.from({
       query: (input: BasicChainInput) => input.query,
       chat_history: (input: BasicChainInput) => input.chat_history,
@@ -207,14 +207,25 @@ const basicWebSearchAnsweringChain = RunnableSequence.from([
     ]),
     llm,
     strParser,
-]).withConfig({
+  ]).withConfig({
     runName: 'FinalResponseGenerator',
-});
+  });
+};
 
-const basicWebSearch = (query: string, history: BaseMessage[]) => {
+const basicWebSearch = (
+  query: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
   const emitter = new eventEmitter();
 
   try {
+    const basicWebSearchAnsweringChain = createBasicWebSearchAnsweringChain(
+      llm,
+      embeddings,
+    );
+
     const stream = basicWebSearchAnsweringChain.streamEvents(
       {
         chat_history: history,
@@ -237,8 +248,13 @@ const basicWebSearch = (query: string, history: BaseMessage[]) => {
   return emitter;
 };
 
-const handleWebSearch = (message: string, history: BaseMessage[]) => {
+const handleWebSearch = (
-  const emitter = basicWebSearch(message, history);
+  message: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const emitter = basicWebSearch(message, history, llm, embeddings);
   return emitter;
 };
 

src/agents/wolframAlphaSearchAgent.ts

@@ -9,19 +9,15 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { ChatOpenAI, OpenAI } from '@langchain/openai';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import { Document } from '@langchain/core/documents';
 import { searchSearxng } from '../core/searxng';
 import type { StreamEvent } from '@langchain/core/tracers/log_stream';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 
-const llm = new ChatOpenAI({
-  modelName: process.env.MODEL_NAME,
-  temperature: 0.7,
-});
-
 const basicWolframAlphaSearchRetrieverPrompt = `
 You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
 If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
@@ -99,18 +95,13 @@ const handleStream = async (
   }
 };
 
-const processDocs = async (docs: Document[]) => {
-  return docs
-    .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
-    .join('\n');
-};
-
 type BasicChainInput = {
   chat_history: BaseMessage[];
   query: string;
 };
 
-const basicWolframAlphaSearchRetrieverChain = RunnableSequence.from([
+const createBasicWolframAlphaSearchRetrieverChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
     PromptTemplate.fromTemplate(basicWolframAlphaSearchRetrieverPrompt),
     llm,
     strParser,
@@ -138,9 +129,20 @@ const basicWolframAlphaSearchRetrieverChain = RunnableSequence.from([
 
       return { query: input, docs: documents };
     }),
-]);
+  ]);
+};
 
-const basicWolframAlphaSearchAnsweringChain = RunnableSequence.from([
+const createBasicWolframAlphaSearchAnsweringChain = (llm: BaseChatModel) => {
+  const basicWolframAlphaSearchRetrieverChain =
+    createBasicWolframAlphaSearchRetrieverChain(llm);
+
+  const processDocs = (docs: Document[]) => {
+    return docs
+      .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
+      .join('\n');
+  };
+
+  return RunnableSequence.from([
     RunnableMap.from({
       query: (input: BasicChainInput) => input.query,
       chat_history: (input: BasicChainInput) => input.chat_history,
@@ -166,14 +168,21 @@ const basicWolframAlphaSearchAnsweringChain = RunnableSequence.from([
     ]),
     llm,
     strParser,
-]).withConfig({
+  ]).withConfig({
     runName: 'FinalResponseGenerator',
-});
+  });
+};
 
-const basicWolframAlphaSearch = (query: string, history: BaseMessage[]) => {
+const basicWolframAlphaSearch = (
+  query: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+) => {
   const emitter = new eventEmitter();
 
   try {
+    const basicWolframAlphaSearchAnsweringChain =
+      createBasicWolframAlphaSearchAnsweringChain(llm);
     const stream = basicWolframAlphaSearchAnsweringChain.streamEvents(
       {
         chat_history: history,
@@ -196,8 +205,13 @@ const basicWolframAlphaSearch = (query: string, history: BaseMessage[]) => {
   return emitter;
 };
 
-const handleWolframAlphaSearch = (message: string, history: BaseMessage[]) => {
+const handleWolframAlphaSearch = (
-  const emitter = basicWolframAlphaSearch(message, history);
+  message: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const emitter = basicWolframAlphaSearch(message, history, llm);
   return emitter;
 };
 

src/agents/writingAssistant.ts

@@ -4,15 +4,11 @@ import {
   MessagesPlaceholder,
 } from '@langchain/core/prompts';
 import { RunnableSequence } from '@langchain/core/runnables';
-import { ChatOpenAI } from '@langchain/openai';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import type { StreamEvent } from '@langchain/core/tracers/log_stream';
 import eventEmitter from 'events';
-
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-const llm = new ChatOpenAI({
+import type { Embeddings } from '@langchain/core/embeddings';
-  modelName: process.env.MODEL_NAME,
-  temperature: 0.7,
-});
 
 const writingAssistantPrompt = `
 You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are currently set on focus mode 'Writing Assistant', this means you will be helping the user write a response to a given query. 
@@ -44,7 +40,8 @@ const handleStream = async (
   }
 };
 
-const writingAssistantChain = RunnableSequence.from([
+const createWritingAssistantChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
     ChatPromptTemplate.fromMessages([
       ['system', writingAssistantPrompt],
       new MessagesPlaceholder('chat_history'),
@@ -52,14 +49,21 @@ const writingAssistantChain = RunnableSequence.from([
     ]),
     llm,
     strParser,
-]).withConfig({
+  ]).withConfig({
     runName: 'FinalResponseGenerator',
-});
+  });
+};
 
-const handleWritingAssistant = (query: string, history: BaseMessage[]) => {
+const handleWritingAssistant = (
+  query: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
   const emitter = new eventEmitter();
 
   try {
+    const writingAssistantChain = createWritingAssistantChain(llm);
     const stream = writingAssistantChain.streamEvents(
       {
         chat_history: history,

src/agents/youtubeSearchAgent.ts

@@ -9,24 +9,16 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import { Document } from '@langchain/core/documents';
 import { searchSearxng } from '../core/searxng';
 import type { StreamEvent } from '@langchain/core/tracers/log_stream';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import computeSimilarity from '../utils/computeSimilarity';
 
-const llm = new ChatOpenAI({
-  modelName: process.env.MODEL_NAME,
-  temperature: 0.7,
-});
-
-const embeddings = new OpenAIEmbeddings({
-  modelName: 'text-embedding-3-large',
-});
-
 const basicYoutubeSearchRetrieverPrompt = `
 You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
 If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
@@ -104,19 +96,63 @@ const handleStream = async (
   }
 };
 
-const processDocs = async (docs: Document[]) => {
+type BasicChainInput = {
+  chat_history: BaseMessage[];
+  query: string;
+};
+
+const createBasicYoutubeSearchRetrieverChain = (llm: BaseChatModel) => {
+  return RunnableSequence.from([
+    PromptTemplate.fromTemplate(basicYoutubeSearchRetrieverPrompt),
+    llm,
+    strParser,
+    RunnableLambda.from(async (input: string) => {
+      if (input === 'not_needed') {
+        return { query: '', docs: [] };
+      }
+
+      const res = await searchSearxng(input, {
+        language: 'en',
+        engines: ['youtube'],
+      });
+
+      const documents = res.results.map(
+        (result) =>
+          new Document({
+            pageContent: result.content ? result.content : result.title,
+            metadata: {
+              title: result.title,
+              url: result.url,
+              ...(result.img_src && { img_src: result.img_src }),
+            },
+          }),
+      );
+
+      return { query: input, docs: documents };
+    }),
+  ]);
+};
+
+const createBasicYoutubeSearchAnsweringChain = (
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const basicYoutubeSearchRetrieverChain =
+    createBasicYoutubeSearchRetrieverChain(llm);
+
+  const processDocs = async (docs: Document[]) => {
     return docs
       .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
       .join('\n');
-};
+  };
 
-const rerankDocs = async ({
+  const rerankDocs = async ({
     query,
     docs,
-}: {
+  }: {
     query: string;
     docs: Document[];
-}) => {
+  }) => {
     if (docs.length === 0) {
       return docs;
     }
@@ -147,44 +183,9 @@ const rerankDocs = async ({
       .map((sim) => docsWithContent[sim.index]);
 
     return sortedDocs;
-};
+  };
 
-type BasicChainInput = {
+  return RunnableSequence.from([
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-const basicYoutubeSearchRetrieverChain = RunnableSequence.from([
-  PromptTemplate.fromTemplate(basicYoutubeSearchRetrieverPrompt),
-  llm,
-  strParser,
-  RunnableLambda.from(async (input: string) => {
-    if (input === 'not_needed') {
-      return { query: '', docs: [] };
-    }
-
-    const res = await searchSearxng(input, {
-      language: 'en',
-      engines: ['youtube'],
-    });
-
-    const documents = res.results.map(
-      (result) =>
-        new Document({
-          pageContent: result.content ? result.content : result.title,
-          metadata: {
-            title: result.title,
-            url: result.url,
-            ...(result.img_src && { img_src: result.img_src }),
-          },
-        }),
-    );
-
-    return { query: input, docs: documents };
-  }),
-]);
-
-const basicYoutubeSearchAnsweringChain = RunnableSequence.from([
     RunnableMap.from({
       query: (input: BasicChainInput) => input.query,
       chat_history: (input: BasicChainInput) => input.chat_history,
@@ -208,14 +209,23 @@ const basicYoutubeSearchAnsweringChain = RunnableSequence.from([
     ]),
     llm,
     strParser,
-]).withConfig({
+  ]).withConfig({
     runName: 'FinalResponseGenerator',
-});
+  });
+};
 
-const basicYoutubeSearch = (query: string, history: BaseMessage[]) => {
+const basicYoutubeSearch = (
+  query: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
   const emitter = new eventEmitter();
 
   try {
+    const basicYoutubeSearchAnsweringChain =
+      createBasicYoutubeSearchAnsweringChain(llm, embeddings);
+
     const stream = basicYoutubeSearchAnsweringChain.streamEvents(
       {
         chat_history: history,
@@ -238,8 +248,13 @@ const basicYoutubeSearch = (query: string, history: BaseMessage[]) => {
   return emitter;
 };
 
-const handleYoutubeSearch = (message: string, history: BaseMessage[]) => {
+const handleYoutubeSearch = (
-  const emitter = basicYoutubeSearch(message, history);
+  message: string,
+  history: BaseMessage[],
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
+  const emitter = basicYoutubeSearch(message, history, llm, embeddings);
   return emitter;
 };
 

src/app.ts

@@ -3,6 +3,9 @@ import express from 'express';
 import cors from 'cors';
 import http from 'http';
 import routes from './routes';
+import { getPort } from './config';
+
+const port = getPort();
 
 const app = express();
 const server = http.createServer(app);
@@ -19,8 +22,8 @@ app.get('/api', (_, res) => {
   res.status(200).json({ status: 'ok' });
 });
 
-server.listen(process.env.PORT!, () => {
+server.listen(port, () => {
-  console.log(`API server started on port ${process.env.PORT}`);
+  console.log(`API server started on port ${port}`);
 });
 
 startWebSocketServer(server);

src/config.ts

@@ -0,0 +1,32 @@
+import fs from 'fs';
+import path from 'path';
+import toml, { JsonMap } from '@iarna/toml';
+
+const configFileName = 'config.toml';
+
+interface Config {
+  GENERAL: {
+    PORT: number;
+    SIMILARITY_MEASURE: string;
+  };
+  API_KEYS: {
+    OPENAI: string;
+  };
+  API_ENDPOINTS: {
+    SEARXNG: string;
+  };
+}
+
+const loadConfig = () =>
+  toml.parse(
+    fs.readFileSync(path.join(process.cwd(), `${configFileName}`), 'utf-8'),
+  ) as any as Config;
+
+export const getPort = () => loadConfig().GENERAL.PORT;
+
+export const getSimilarityMeasure = () =>
+  loadConfig().GENERAL.SIMILARITY_MEASURE;
+
+export const getOpenaiApiKey = () => loadConfig().API_KEYS.OPENAI;
+
+export const getSearxngApiEndpoint = () => loadConfig().API_ENDPOINTS.SEARXNG;

src/core/searxng.ts

@@ -1,4 +1,5 @@
 import axios from 'axios';
+import { getSearxngApiEndpoint } from '../config';
 
 interface SearxngSearchOptions {
   categories?: string[];
@@ -20,7 +21,9 @@ export const searchSearxng = async (
   query: string,
   opts?: SearxngSearchOptions,
 ) => {
-  const url = new URL(`${process.env.SEARXNG_API_URL}/search?format=json`);
+  const searxngURL = getSearxngApiEndpoint();
+
+  const url = new URL(`${searxngURL}/search?format=json`);
   url.searchParams.append('q', query);
 
   if (opts) {

src/routes/images.ts

@@ -1,5 +1,7 @@
 import express from 'express';
-import imageSearchChain from '../agents/imageSearchAgent';
+import handleImageSearch from '../agents/imageSearchAgent';
+import { ChatOpenAI } from '@langchain/openai';
+import { getOpenaiApiKey } from '../config';
 
 const router = express.Router();
 
@@ -7,11 +9,13 @@ router.post('/', async (req, res) => {
   try {
     const { query, chat_history } = req.body;
 
-    const images = await imageSearchChain.invoke({
+    const llm = new ChatOpenAI({
-      query,
+      temperature: 0.7,
-      chat_history,
+      openAIApiKey: getOpenaiApiKey(),
     });
 
+    const images = await handleImageSearch({ query, chat_history }, llm);
+
     res.status(200).json({ images });
   } catch (err) {
     res.status(500).json({ message: 'An error has occurred.' });

src/utils/computeSimilarity.ts

@@ -1,10 +1,13 @@
 import dot from 'compute-dot';
 import cosineSimilarity from 'compute-cosine-similarity';
+import { getSimilarityMeasure } from '../config';
 
 const computeSimilarity = (x: number[], y: number[]): number => {
-  if (process.env.SIMILARITY_MEASURE === 'cosine') {
+  const similarityMeasure = getSimilarityMeasure();
+
+  if (similarityMeasure === 'cosine') {
     return cosineSimilarity(x, y);
-  } else if (process.env.SIMILARITY_MEASURE === 'dot') {
+  } else if (similarityMeasure === 'dot') {
     return dot(x, y);
   }
 

src/websocket/connectionManager.ts

@@ -1,10 +1,23 @@
 import { WebSocket } from 'ws';
 import { handleMessage } from './messageHandler';
+import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
+import { getOpenaiApiKey } from '../config';
 
 export const handleConnection = (ws: WebSocket) => {
+  const llm = new ChatOpenAI({
+    temperature: 0.7,
+    openAIApiKey: getOpenaiApiKey(),
+  });
+
+  const embeddings = new OpenAIEmbeddings({
+    openAIApiKey: getOpenaiApiKey(),
+    modelName: 'text-embedding-3-large',
+  });
+
   ws.on(
     'message',
-    async (message) => await handleMessage(message.toString(), ws),
+    async (message) =>
+      await handleMessage(message.toString(), ws, llm, embeddings),
   );
 
   ws.on('close', () => console.log('Connection closed'));

src/websocket/messageHandler.ts

@@ -6,6 +6,8 @@ import handleWritingAssistant from '../agents/writingAssistant';
 import handleWolframAlphaSearch from '../agents/wolframAlphaSearchAgent';
 import handleYoutubeSearch from '../agents/youtubeSearchAgent';
 import handleRedditSearch from '../agents/redditSearchAgent';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { Embeddings } from '@langchain/core/embeddings';
 
 type Message = {
   type: string;
@@ -58,7 +60,12 @@ const handleEmitterEvents = (
   });
 };
 
-export const handleMessage = async (message: string, ws: WebSocket) => {
+export const handleMessage = async (
+  message: string,
+  ws: WebSocket,
+  llm: BaseChatModel,
+  embeddings: Embeddings,
+) => {
   try {
     const parsedMessage = JSON.parse(message) as Message;
     const id = Math.random().toString(36).substring(7);
@@ -83,7 +90,12 @@ export const handleMessage = async (message: string, ws: WebSocket) => {
     if (parsedMessage.type === 'message') {
       const handler = searchHandlers[parsedMessage.focusMode];
       if (handler) {
-        const emitter = handler(parsedMessage.content, history);
+        const emitter = handler(
+          parsedMessage.content,
+          history,
+          llm,
+          embeddings,
+        );
         handleEmitterEvents(emitter, ws, id);
       } else {
         ws.send(JSON.stringify({ type: 'error', data: 'Invalid focus mode' }));

src/websocket/websocketServer.ts

@@ -1,15 +1,17 @@
 import { WebSocketServer } from 'ws';
 import { handleConnection } from './connectionManager';
 import http from 'http';
+import { getPort } from '../config';
 
 export const initServer = (
   server: http.Server<typeof http.IncomingMessage, typeof http.ServerResponse>,
 ) => {
+  const port = getPort();
   const wss = new WebSocketServer({ server });
 
   wss.on('connection', (ws) => {
     handleConnection(ws);
   });
 
-  console.log(`WebSocket server started on port ${process.env.PORT}`);
+  console.log(`WebSocket server started on port ${port}`);
 };

yarn.lock

@@ -24,6 +24,11 @@
   dependencies:
     "@jridgewell/trace-mapping" "0.3.9"
 
+"@iarna/toml@^2.2.5":
+  version "2.2.5"
+  resolved "https://registry.yarnpkg.com/@iarna/toml/-/toml-2.2.5.tgz#b32366c89b43c6f8cefbdefac778b9c828e3ba8c"
+  integrity sha512-trnsAYxU3xnS1gPHPyU961coFyLkh4gAD/0zQ5mymY4yOZ+CYvsPqUbOFSw0aDM4y0tV7tiFxL/1XfXPNC6IPg==
+
 "@jridgewell/resolve-uri@^3.0.3":
   version "3.1.2"
   resolved "https://registry.yarnpkg.com/@jridgewell/resolve-uri/-/resolve-uri-3.1.2.tgz#7a0ee601f60f99a20c7c7c5ff0c80388c1189bd6"