Version bump (#1457 )

http_client and http_async_client bugfix (#1454 )
Updating dependencies (#1453 )
2024-07-02 23:07:52 -07:00 · 2024-07-02 16:13:33 -07:00 · 2024-07-02 16:12:52 -07:00 · 2024-06-29 12:37:31 -07:00 · 2024-06-28 11:18:22 -07:00 · 2024-06-27 14:45:58 -07:00
122 changed files with 2821 additions and 20761 deletions
@@ -67,6 +67,10 @@ We use `pytest` to test our code. You can run the tests by running the following
 poetry run pytest
 ```

+
+Several packages have been removed from Poetry to make the package lighter. Therefore, it is recommended to run `make install_all` to install the remaining packages and ensure all tests pass.
+
+
 Make sure that all tests pass before submitting a pull request.

 ## 🚀 Release Process
@@ -11,7 +11,7 @@ install:

 install_all:
 	poetry install --all-extras
-	poetry run pip install pinecone-text pinecone-client langchain-anthropic "unstructured[local-inference, all-docs]"
+	poetry run pip install pinecone-text pinecone-client langchain-anthropic "unstructured[local-inference, all-docs]" ollama deepgram-sdk==3.2.7 langchain-huggingface psutil

 install_es:
 	poetry install --extras elasticsearch
@@ -0,0 +1,12 @@
+llm:
+  provider: clarifai
+  config: 
+    model: "https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct"
+    model_kwargs: 
+      temperature: 0.5
+      max_tokens: 1000
+
+embedder:
+  provider: clarifai
+  config: 
+    model: "https://clarifai.com/clarifai/main/models/BAAI-bge-base-en-v15"
@@ -26,6 +26,11 @@ llm:
    top_p: 1
    stream: false
    api_key: sk-xxx
+    model_kwargs:
+      response_format: 
+        type: json_object
+    api_version: 2024-02-01
+    http_client_proxies: http://testproxy.mem0.net:8000
    prompt: |
      Use the following pieces of context to answer the query at the end.
      If you don't know the answer, just say that you don't know, don't try to make up an answer.
@@ -83,7 +88,10 @@ cache:
      "stream": false,
      "prompt": "Use the following pieces of context to answer the query at the end.\nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\n$context\n\nQuery: $query\n\nHelpful Answer:",
      "system_prompt": "Act as William Shakespeare. Answer the following questions in the style of William Shakespeare.",
-      "api_key": "sk-xxx"
+      "api_key": "sk-xxx",
+      "model_kwargs": {"response_format": {"type": "json_object"}},
+      "api_version": "2024-02-01",
+      "http_client_proxies": "http://testproxy.mem0.net:8000",
    }
  },
  "vectordb": {
@@ -143,7 +151,9 @@ config = {
            'system_prompt': (
                "Act as William Shakespeare. Answer the following questions in the style of William Shakespeare."
            ),
-            'api_key': 'sk-xxx'
+            'api_key': 'sk-xxx',
+            "model_kwargs": {"response_format": {"type": "json_object"}},
+            "http_client_proxies": "http://testproxy.mem0.net:8000",
        }
    },
    'vectordb': {
@@ -204,22 +214,27 @@ Alright, let's dive into what each key means in the yaml config above:
        - `number_documents` (Integer): Number of documents to pull from the vectordb as context, defaults to 1
        - `api_key` (String): The API key for the language model.
        - `model_kwargs` (Dict): Keyword arguments to pass to the language model. Used for `aws_bedrock` provider, since it requires different arguments for each model.
+        - `http_client_proxies` (Dict | String): The proxy server settings used to create `self.http_client` using `httpx.Client(proxies=http_client_proxies)`
+        - `http_async_client_proxies` (Dict | String): The proxy server settings for async calls used to create `self.http_async_client` using `httpx.AsyncClient(proxies=http_async_client_proxies)`
 3. `vectordb` Section:
    - `provider` (String): The provider for the vector database, set to 'chroma'. You can find the full list of vector database providers in [our docs](/components/vector-databases).
    - `config`:
        - `collection_name` (String): The initial collection name for the vectordb, set to 'full-stack-app'.
        - `dir` (String): The directory for the local database, set to 'db'.
        - `allow_reset` (Boolean): Indicates whether resetting the vectordb is allowed, set to true.
+        - `batch_size` (Integer): The batch size for docs insertion in vectordb, defaults to `100`
    <Note>We recommend you to checkout vectordb specific config [here](https://docs.embedchain.ai/components/vector-databases)</Note>
 4. `embedder` Section:
    - `provider` (String): The provider for the embedder, set to 'openai'. You can find the full list of embedding model providers in [our docs](/components/embedding-models).
    - `config`:
        - `model` (String): The specific model used for text embedding, 'text-embedding-ada-002'.
-        - `vector_dimension` (Integer): The vector dimension of the embedding model. [Defaults](https://github.com/embedchain/embedchain/blob/e572b5a3dc1b66f1e9b3357d11a88c63b5ce06e3/embedchain/models/vector_dimensions.py)
+        - `vector_dimension` (Integer): The vector dimension of the embedding model. [Defaults](https://github.com/embedchain/embedchain/blob/main/embedchain/models/vector_dimensions.py)
        - `api_key` (String): The API key for the embedding model.
+        - `endpoint` (String): The endpoint for the HuggingFace embedding model.
        - `deployment_name` (String): The deployment name for the embedding model.
        - `title` (String): The title for the embedding model for Google Embedder.
        - `task_type` (String): The task type for the embedding model for Google Embedder.
+        - `model_kwargs` (Dict): Used to pass extra arguments to embedders.
 5. `chunker` Section:
    - `chunk_size` (Integer): The size of each chunk of text that is sent to the language model.
    - `chunk_overlap` (Integer): The amount of overlap between each chunk of text.
@@ -0,0 +1,25 @@
+---
+title: "🎤 Audio"
+---
+
+
+To use an audio as data source, just add `data_type` as `audio` and pass in the path of the audio (local or hosted).
+
+We use [Deepgram](https://developers.deepgram.com/docs/introduction) to transcribe the audiot to text, and then use the generated text as the data source.
+
+You would require an Deepgram API key which is available [here](https://console.deepgram.com/signup?jump=keys) to use this feature.
+
+### Without customization
+
+```python
+import os
+from embedchain import App
+
+os.environ["DEEPGRAM_API_KEY"] = "153xxx"
+
+app = App()
+app.add("introduction.wav", data_type="audio")
+response = app.query("What is my name and how old am I?")
+print(response)
+# Answer: Your name is Dave and you are 21 years old.
+```
@@ -9,6 +9,7 @@ Embedchain comes with built-in support for various data sources. We handle the c
  <Card title="CSV file" href="/components/data-sources/csv"></Card>
  <Card title="JSON file" href="/components/data-sources/json"></Card>
  <Card title="Text" href="/components/data-sources/text"></Card>
+  <Card title="Text File" href="/components/data-sources/text-file"></Card>
  <Card title="Directory" href="/components/data-sources/directory"></Card>
  <Card title="Web page" href="/components/data-sources/web-page"></Card>
  <Card title="Youtube Channel" href="/components/data-sources/youtube-channel"></Card>
@@ -33,6 +34,7 @@ Embedchain comes with built-in support for various data sources. We handle the c
  <Card title="Beehiiv" href="/components/data-sources/beehiiv"></Card>
  <Card title="Dropbox" href="/components/data-sources/dropbox"></Card>
  <Card title="Image" href="/components/data-sources/image"></Card>
+  <Card title="Audio" href="/components/data-sources/audio"></Card>
  <Card title="Custom" href="/components/data-sources/custom"></Card>
 </CardGroup>

@@ -0,0 +1,14 @@
+---
+title: '📄 Text file'
+---
+
+To add a .txt file, specify the data_type as `text_file`. The URL provided in the first parameter of the `add` function, should be a local path. Eg:
+
+```python
+from embedchain import App
+
+app = App()
+app.add('path/to/file.txt', data_type="text_file")
+
+app.query("Summarize the information of the text file")
+```
@@ -16,6 +16,7 @@ Embedchain supports several embedding models from the following providers:
  <Card title="NVIDIA AI" href="#nvidia-ai"></Card>
  <Card title="Cohere" href="#cohere"></Card>
  <Card title="Ollama" href="#ollama"></Card>
+  <Card title="Clarifai" href="#clarifai"></Card>
 </CardGroup>

 ## OpenAI
@@ -191,6 +192,8 @@ embedder:
  provider: huggingface
  config:
    model: 'sentence-transformers/all-mpnet-base-v2'
+    model_kwargs:
+        trust_remote_code: True # Only use if you trust your embedder
 ```

 </CodeGroup>
@@ -385,4 +388,51 @@ embedder:
    model: 'all-minilm:latest'
 ```

+</CodeGroup>
+
+## Clarifai
+
+Install related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[clarifai]'
+```
+
+set the `CLARIFAI_PAT` as environment variable which you can find in the [security page](https://clarifai.com/settings/security). Optionally you can also pass the PAT key as parameters in LLM/Embedder class.
+
+Now you are all set with exploring Embedchain.
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ["CLARIFAI_PAT"] = "XXX"
+
+# load llm and embedder configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+
+#Now let's add some data.
+app.add("https://www.forbes.com/profile/elon-musk")
+
+#Query the app
+response = app.query("what college degrees does elon musk have?")
+```
+Head to [Clarifai Platform](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22output_fields%22%2C%22value%22%3A%5B%22embeddings%22%5D%7D%5D) to explore all the State of the Art embedding models available to use.
+For passing LLM model inference parameters use `model_kwargs` argument in the config file. Also you can use `api_key` argument to pass `CLARIFAI_PAT` in the config.
+
+```yaml config.yaml
+llm:
+ provider: clarifai
+ config:
+   model: "https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct"
+   model_kwargs:
+     temperature: 0.5
+     max_tokens: 1000  
+embedder:
+ provider: clarifai
+ config:
+   model: "https://clarifai.com/clarifai/main/models/BAAI-bge-base-en-v15"
+```
 </CodeGroup>
@@ -15,6 +15,7 @@ Embedchain comes with built-in support for various popular large language models
  <Card title="Together" href="#together"></Card>
  <Card title="Ollama" href="#ollama"></Card>
  <Card title="vLLM" href="#vllm"></Card>
+  <Card title="Clarifai" href="#clarifai"></Card>
  <Card title="GPT4All" href="#gpt4all"></Card>
  <Card title="JinaChat" href="#jinachat"></Card>
  <Card title="Hugging Face" href="#hugging-face"></Card>
@@ -193,8 +194,8 @@ import os
 from embedchain import App

 os.environ["OPENAI_API_TYPE"] = "azure"
-os.environ["OPENAI_API_BASE"] = "https://xxx.openai.azure.com/"
-os.environ["OPENAI_API_KEY"] = "xxx"
+os.environ["AZURE_OPENAI_ENDPOINT"] = "https://xxx.openai.azure.com/"
+os.environ["AZURE_OPENAI_KEY"] = "xxx"
 os.environ["OPENAI_API_VERSION"] = "xxx"

 app = App.from_config(config_path="config.yaml")
@@ -330,6 +331,7 @@ Setup Ollama using https://github.com/jmorganca/ollama

 ```python main.py
 import os
+os.environ["OLLAMA_HOST"] = "http://127.0.0.1:11434"
 from embedchain import App

 # load llm configuration from config.yaml file
@@ -345,6 +347,12 @@ llm:
    top_p: 1
    stream: true
    base_url: 'http://localhost:11434'
+embedder:
+  provider: ollama
+  config:
+    model: znbang/bge:small-en-v1.5-q8_0
+    base_url: http://localhost:11434
+
 ```

 </CodeGroup>
@@ -378,6 +386,54 @@ llm:

 </CodeGroup>

+## Clarifai
+
+Install related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[clarifai]'
+```
+
+set the `CLARIFAI_PAT` as environment variable which you can find in the [security page](https://clarifai.com/settings/security). Optionally you can also pass the PAT key as parameters in LLM/Embedder class.
+
+Now you are all set with exploring Embedchain.
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ["CLARIFAI_PAT"] = "XXX"
+
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+
+#Now let's add some data.
+app.add("https://www.forbes.com/profile/elon-musk")
+
+#Query the app
+response = app.query("what college degrees does elon musk have?")
+```
+Head to [Clarifai Platform](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22use_cases%22%2C%22value%22%3A%5B%22llm%22%5D%7D%5D) to browse various State-of-the-Art LLM models for your use case.
+For passing model inference parameters use `model_kwargs` argument in the config file. Also you can use `api_key` argument to pass `CLARIFAI_PAT` in the config.
+
+```yaml config.yaml
+llm:
+ provider: clarifai
+ config:
+   model: "https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct"
+   model_kwargs:
+     temperature: 0.5
+     max_tokens: 1000  
+embedder:
+ provider: clarifai
+ config:
+   model: "https://clarifai.com/clarifai/main/models/BAAI-bge-base-en-v15"
+```
+</CodeGroup>
+
+
 ## GPT4ALL

 Install related dependencies using the following command:
@@ -0,0 +1,100 @@
+---
+title: LanceDB
+---
+
+## Install Embedchain with LanceDB
+
+Install Embedchain, LanceDB and  related dependencies using the following command:
+
+```bash
+pip install "embedchain[lancedb]"
+```
+
+LanceDB is a developer-friendly, open source database for AI. From hyper scalable vector search and advanced retrieval for RAG, to streaming training data and interactive exploration of large scale AI datasets.
+In order to use LanceDB as vector database, not need to set any key for local use. 
+
+### With OPENAI 
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+# set OPENAI_API_KEY as env variable
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+# create Embedchain App and set config
+app = App.from_config(config={
+    "vectordb": {
+        "provider": "lancedb",
+            "config": {
+                "collection_name": "lancedb-index"
+            }
+        }
+    }
+)
+
+# add data source and start query in
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# query continuously
+while(True):
+    question = input("Enter question: ")
+    if question in ['q', 'exit', 'quit']:
+        break
+    answer = app.query(question)
+    print(answer)
+```
+
+</CodeGroup>
+
+### With Local LLM 
+<CodeGroup>
+
+```python main.py
+from embedchain import Pipeline as App
+
+# config for Embedchain App
+config = {
+  'llm': {
+    'provider': 'huggingface',
+    'config': {
+      'model': 'mistralai/Mistral-7B-v0.1',
+      'temperature': 0.1,
+      'max_tokens': 250,
+      'top_p': 0.1,
+      'stream': True
+    }
+  },
+  'embedder': {
+    'provider': 'huggingface',
+    'config': {
+      'model': 'sentence-transformers/all-mpnet-base-v2'
+    }
+  },
+  'vectordb': { 
+    'provider': 'lancedb', 
+    'config': { 
+      'collection_name': 'lancedb-index' 
+    } 
+  }
+}
+
+app = App.from_config(config=config)
+
+# add data source and start query in
+app.add("https://www.tesla.com/ns_videos/2022-tesla-impact-report.pdf")
+
+# query continuously
+while(True):
+    question = input("Enter question: ")
+    if question in ['q', 'exit', 'quit']:
+        break
+    answer = app.query(question)
+    print(answer)
+```
+
+</CodeGroup>
+
+
+<Snippet file="missing-vector-db-tip.mdx" />
@@ -1,4 +0,0 @@
---
-title: ' 🟨 Javascript'
-url: https://github.com/embedchain/embedchain/tree/main/embedchain-js
---
@@ -1,17 +0,0 @@
---
-title: 'Embedchain.ai'
-description: 'Deploy your RAG application to embedchain.ai platform'
---
-
-## Deploy on Embedchain Platform
-
-Embedchain enables developers to deploy their LLM-powered apps in production using the Embedchain platform. The platform offers free access to context on your data through its REST API. Once the pipeline is deployed, you can update your data sources anytime after deployment.
-
-Deployment to Embedchain Platform is currently available on an invitation-only basis. To request access, please submit your information via the provided [Google Form](https://forms.gle/vigN11h7b4Ywat668). We will review your request and respond promptly.
-
-
-## Seeking help?
-
-If you run into issues with deployment, please feel free to reach out to us via any of the following methods:
-
-<Snippet file="get-help.mdx" />
@@ -13,7 +13,6 @@ After successfully setting up and testing your RAG app locally, the next step is
  <Card title="Streamlit.io" href="/deployment/streamlit_io"></Card>
  <Card title="Gradio.app" href="/deployment/gradio_app"></Card>
  <Card title="Huggingface.co" href="/deployment/huggingface_spaces"></Card>
-  <Card title="Embedchain.ai" href="/deployment/embedchain_ai"></Card>
 </CardGroup>

 ## Seeking help?
@@ -0,0 +1,50 @@
+---
+title: '🔭 OpenLIT'
+description: 'OpenTelemetry-native Observability and Evals for LLMs & GPUs'
+---
+
+Embedchain now supports integration with [OpenLIT](https://github.com/openlit/openlit).
+
+## Getting Started
+
+### 1. Set environment variables
+```bash
+# Setting environment variable for OpenTelemetry destination and authetication.
+export OTEL_EXPORTER_OTLP_ENDPOINT = "YOUR_OTEL_ENDPOINT"
+export OTEL_EXPORTER_OTLP_HEADERS = "YOUR_OTEL_ENDPOINT_AUTH"
+```
+
+### 2. Install the OpenLIT SDK
+Open your terminal and run:
+
+```shell
+pip install openlit
+```
+
+### 3. Setup Your Application for Monitoring
+Now create an app using Embedchain and initialize OpenTelemetry monitoring
+
+```python
+from embedchain import App
+import OpenLIT
+
+# Initialize OpenLIT Auto Instrumentation for monitoring.
+openlit.init()
+
+# Initialize EmbedChain application.
+app = App()
+
+# Add data to your app
+app.add("https://en.wikipedia.org/wiki/Elon_Musk")
+
+# Query your app
+app.query("How many companies did Elon found?")
+```
+
+### 4. Visualize
+
+Once you've set up data collection with OpenLIT, you can visualize and analyze this information to better understand your application's performance:
+
+- **Using OpenLIT UI:** Connect to OpenLIT's UI to start exploring performance metrics. Visit the OpenLIT [Quickstart Guide](https://docs.openlit.io/latest/quickstart) for step-by-step details.
+
+- **Integrate with existing Observability Tools:** If you use tools like Grafana or DataDog, you can integrate the data collected by OpenLIT. For instructions on setting up these connections, check the OpenLIT [Connections Guide](https://docs.openlit.io/latest/connections/intro).
@@ -69,7 +69,8 @@
          "pages": [
            "integration/langsmith",
            "integration/chainlit",
-            "integration/streamlit-mistral"
+            "integration/streamlit-mistral",
+            "integration/openlit"
          ]
        }
      ]
@@ -155,8 +156,7 @@
        "deployment/railway",
        "deployment/streamlit_io",
        "deployment/gradio_app",
-        "deployment/huggingface_spaces",
-        "deployment/embedchain_ai"
+        "deployment/huggingface_spaces"
      ]
    },
    {
@@ -236,8 +236,7 @@
        "contribution/guidelines",
        "contribution/dev",
        "contribution/docs",
-        "contribution/python",
-        "contribution/javascript"
+        "contribution/python"
      ]
    },
    {
@@ -1,2 +0,0 @@
-node_modules
-dist
@@ -1,56 +0,0 @@
-{
-    // Configuration for JavaScript files
-    "extends": [
-      "airbnb-base",
-      "plugin:prettier/recommended"
-    ],
-    "rules": {
-      "prettier/prettier": [
-        "error",
-        {
-          "singleQuote": true,
-          "endOfLine": "auto"
-        }
-      ]
-    },
-    "overrides": [
-      // Configuration for TypeScript files
-      {
-        "files": ["**/*.ts", "**/__tests__/*.test.ts"],
-        "plugins": [
-          "@typescript-eslint",
-          "unused-imports",
-          "simple-import-sort"
-        ],
-        "extends": [
-          "airbnb-typescript",
-          "plugin:prettier/recommended"
-        ],
-        "parserOptions": {
-          "project": "./tsconfig.json"
-        },
-        "rules": {
-          "prettier/prettier": [
-            "error",
-            {
-              "singleQuote": true,
-              "endOfLine": "auto"
-            }
-          ],
-          "@typescript-eslint/comma-dangle": "off", // Avoid conflict rule between Eslint and Prettier
-          "@typescript-eslint/consistent-type-imports": "error", // Ensure `import type` is used when it's necessary
-          "import/prefer-default-export": "off", // Named export is easier to refactor automatically
-          "simple-import-sort/imports": "error", // Import configuration for `eslint-plugin-simple-import-sort`
-          "simple-import-sort/exports": "error", // Export configuration for `eslint-plugin-simple-import-sort`
-          "@typescript-eslint/no-unused-vars": "off",
-          "react/jsx-filename-extension": "off", // Gives error
-          "unused-imports/no-unused-imports": "error",
-          "unused-imports/no-unused-vars": [
-            "error",
-            { "argsIgnorePattern": "^_" }
-          ]
-        }
-      }
-    ]
-  }
-  
@@ -1,47 +0,0 @@
-name: Node.js Package
-
-on:
-  release:
-    types: [created]
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - uses: actions/setup-node@v3
-        with:
-          node-version: 16
-      - run: npm ci
-      - run: npm test
-      - run: npm run build
-      - uses: actions/upload-artifact@v3
-        with:
-          name: dist
-          path: dist
-      - uses: actions/upload-artifact@v3
-        with:
-          name: types
-          path: types
-
-  publish-npm:
-    needs: build
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - uses: actions/setup-node@v3
-        with:
-          node-version: 16
-          registry-url: https://registry.npmjs.org/
-      - uses: actions/download-artifact@v3
-        with:
-          name: dist
-          path: dist
-      - uses: actions/download-artifact@v3
-        with:
-          name: types
-          path: types
-      - run: npm ci
-      - run: npm publish
-        env:
-          NODE_AUTH_TOKEN: ${{secrets.npm_token}}
@@ -1,138 +0,0 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-lerna-debug.log*
-.pnpm-debug.log*
-
-# Diagnostic reports (https://nodejs.org/api/report.html)
-report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json
-
-# Runtime data
-pids
-*.pid
-*.seed
-*.pid.lock
-
-# Directory for instrumented libs generated by jscoverage/JSCover
-lib-cov
-
-# Coverage directory used by tools like istanbul
-coverage
-*.lcov
-
-# nyc test coverage
-.nyc_output
-
-# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files)
-.grunt
-
-# Bower dependency directory (https://bower.io/)
-bower_components
-
-# node-waf configuration
-.lock-wscript
-
-# Compiled binary addons (https://nodejs.org/api/addons.html)
-build/Release
-
-# Dependency directories
-node_modules/
-jspm_packages/
-
-# Snowpack dependency directory (https://snowpack.dev/)
-web_modules/
-
-# TypeScript cache
-*.tsbuildinfo
-
-# Optional npm cache directory
-.npm
-
-# Optional eslint cache
-.eslintcache
-
-# Optional stylelint cache
-.stylelintcache
-
-# Microbundle cache
-.rpt2_cache/
-.rts2_cache_cjs/
-.rts2_cache_es/
-.rts2_cache_umd/
-
-# Optional REPL history
-.node_repl_history
-
-# Output of 'npm pack'
-*.tgz
-
-# Yarn Integrity file
-.yarn-integrity
-
-# dotenv environment variable files
-.env
-.env.development.local
-.env.test.local
-.env.production.local
-.env.local
-
-# parcel-bundler cache (https://parceljs.org/)
-.cache
-.parcel-cache
-
-# Next.js build output
-.next
-out
-
-# Nuxt.js build / generate output
-.nuxt
-dist
-
-# Gatsby files
-.cache/
-# Comment in the public line in if your project uses Gatsby and not Next.js
-# https://nextjs.org/blog/next-9-1#public-directory-support
-# public
-
-# vuepress build output
-.vuepress/dist
-
-# vuepress v2.x temp and cache directory
-.temp
-.cache
-
-# Docusaurus cache and generated files
-.docusaurus
-
-# Serverless directories
-.serverless/
-
-# FuseBox cache
-.fusebox/
-
-# DynamoDB Local files
-.dynamodb/
-
-# TernJS port file
-.tern-port
-
-# Stores VSCode versions used for testing VSCode extensions
-.vscode-test
-
-# yarn v2
-.yarn/cache
-.yarn/unplugged
-.yarn/build-state.yml
-.yarn/install-state.gz
-.pnp.*
-
-.ideas.md
-.todos.md
-
-# Custom
-dist
-types
-build
@@ -1,4 +0,0 @@
-#!/bin/sh
-. "$(dirname "$0")/_/husky.sh"
-
-npx --no -- commitlint --edit $1
@@ -1,5 +0,0 @@
-#!/bin/sh
-. "$(dirname "$0")/_/husky.sh"
-
-# Disable concurent to run `check-types` after ESLint in lint-staged
-npx lint-staged --concurrent false
@@ -1,8 +0,0 @@
-cff-version: 1.2.0
-message: "If you use this software, please cite it as below."
-authors:
- family-names: "Singh"
-  given-names: "Taranjeet"
-title: "Embedchain"
-date-released: 2023-06-25
-url: "https://github.com/embedchain/embedchainjs"
@@ -1,201 +0,0 @@
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-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
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-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
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-   Licensed under the Apache License, Version 2.0 (the "License");
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-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
@@ -1,254 +0,0 @@
-# embedchainjs
-
-[![Discord](https://dcbadge.vercel.app/api/server/CUU9FPhRNt?style=flat)](https://discord.gg/CUU9FPhRNt)
-[![Twitter](https://img.shields.io/twitter/follow/embedchain)](https://twitter.com/embedchain)
-[![Substack](https://img.shields.io/badge/Substack-%23006f5c.svg?logo=substack)](https://embedchain.substack.com/)
-
-embedchain is a framework to easily create LLM powered bots over any dataset. embedchainjs is Javascript version of embedchain. If you want a python version, check out [embedchain-python](https://github.com/embedchain/embedchain)
-
-# 🤝 Let's Talk Embedchain!
-
-Schedule a [Feedback Session](https://cal.com/taranjeetio/ec) with Taranjeet, the founder, to discuss any issues, provide feedback, or explore improvements.
-
-# How it works
-
-It abstracts the entire process of loading dataset, chunking it, creating embeddings and then storing in vector database.
-
-You can add a single or multiple dataset using `.add` and `.addLocal` function and then use `.query` function to find an answer from the added datasets.
-
-If you want to create a Naval Ravikant bot which has 2 of his blog posts, as well as a question and answer pair you supply, all you need to do is add the links to the blog posts and the QnA pair and embedchain will create a bot for you.
-
-```javascript
-const dotenv = require("dotenv");
-dotenv.config();
-const { App } = require("embedchain");
-
-//Run the app commands inside an async function only
-async function testApp() {
-  const navalChatBot = await App();
-
-  // Embed Online Resources
-  await navalChatBot.add("web_page", "https://nav.al/feedback");
-  await navalChatBot.add("web_page", "https://nav.al/agi");
-  await navalChatBot.add(
-    "pdf_file",
-    "https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf"
-  );
-
-  // Embed Local Resources
-  await navalChatBot.addLocal("qna_pair", [
-    "Who is Naval Ravikant?",
-    "Naval Ravikant is an Indian-American entrepreneur and investor.",
-  ]);
-
-  const result = await navalChatBot.query(
-    "What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?"
-  );
-  console.log(result);
-  // answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
-}
-
-testApp();
-```
-
-# Getting Started
-
-## Installation
-
- First make sure that you have the package installed. If not, then install it using `npm`
-
-```bash
-npm install embedchain && npm install -S openai@^3.3.0
-```
-
- Currently, it is only compatible with openai 3.X, not the latest version 4.X. Please make sure to use the right version, otherwise you will see the `ChromaDB` error `TypeError: OpenAIApi.Configuration is not a constructor`
-
- Make sure that dotenv package is installed and your `OPENAI_API_KEY` in a file called `.env` in the root folder. You can install dotenv by
-
-```js
-npm install dotenv
-```
-
- Download and install Docker on your device by visiting [this link](https://www.docker.com/). You will need this to run Chroma vector database on your machine.
-
- Run the following commands to setup Chroma container in Docker
-
-```bash
-git clone https://github.com/chroma-core/chroma.git
-cd chroma
-docker-compose up -d --build
-```
-
- Once Chroma container has been set up, run it inside Docker
-
-## Usage
-
- We use OpenAI's embedding model to create embeddings for chunks and ChatGPT API as LLM to get answer given the relevant docs. Make sure that you have an OpenAI account and an API key. If you have dont have an API key, you can create one by visiting [this link](https://platform.openai.com/account/api-keys).
-
- Once you have the API key, set it in an environment variable called `OPENAI_API_KEY`
-
-```js
-// Set this inside your .env file
-OPENAI_API_KEY = "sk-xxxx";
-```
-
- Load the environment variables inside your .js file using the following commands
-
-```js
-const dotenv = require("dotenv");
-dotenv.config();
-```
-
- Next import the `App` class from embedchain and use `.add` function to add any dataset.
- Now your app is created. You can use `.query` function to get the answer for any query.
-
-```js
-const dotenv = require("dotenv");
-dotenv.config();
-const { App } = require("embedchain");
-
-async function testApp() {
-  const navalChatBot = await App();
-
-  // Embed Online Resources
-  await navalChatBot.add("web_page", "https://nav.al/feedback");
-  await navalChatBot.add("web_page", "https://nav.al/agi");
-  await navalChatBot.add(
-    "pdf_file",
-    "https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf"
-  );
-
-  // Embed Local Resources
-  await navalChatBot.addLocal("qna_pair", [
-    "Who is Naval Ravikant?",
-    "Naval Ravikant is an Indian-American entrepreneur and investor.",
-  ]);
-
-  const result = await navalChatBot.query(
-    "What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?"
-  );
-  console.log(result);
-  // answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
-}
-
-testApp();
-```
-
- If there is any other app instance in your script or app, you can change the import as
-
-```javascript
-const { App: EmbedChainApp } = require("embedchain");
-
-// or
-
-const { App: ECApp } = require("embedchain");
-```
-
-## Format supported
-
-We support the following formats:
-
-### PDF File
-
-To add any pdf file, use the data_type as `pdf_file`. Eg:
-
-```javascript
-await app.add("pdf_file", "a_valid_url_where_pdf_file_can_be_accessed");
-```
-
-### Web Page
-
-To add any web page, use the data_type as `web_page`. Eg:
-
-```javascript
-await app.add("web_page", "a_valid_web_page_url");
-```
-
-### QnA Pair
-
-To supply your own QnA pair, use the data_type as `qna_pair` and enter a tuple. Eg:
-
-```javascript
-await app.addLocal("qna_pair", ["Question", "Answer"]);
-```
-
-### More Formats coming soon
-
- If you want to add any other format, please create an [issue](https://github.com/embedchain/embedchainjs/issues) and we will add it to the list of supported formats.
-
-## Testing
-
-Before you consume valuable tokens, you should make sure that the embedding you have done works and that it's receiving the correct document from the database.
-
-For this you can use the `dryRun` method.
-
-Following the example above, add this to your script:
-
-```js
-let result = await naval_chat_bot.dryRun("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?");console.log(result);
-
-'''
-Use the following pieces of context to answer the query at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
-terms of the unseen. And I think that’s critical. That is what humans do uniquely that no other creature, no other computer, no other intelligence—biological or artificial—that we have ever encountered does. And not only do we do it uniquely, but if we were to meet an alien species that also had the power to generate these good explanations, there is no explanation that they could generate that we could not understand. We are maximally capable of understanding. There is no concept out there that is possible in this physical reality that a human being, given sufficient time and resources and
-Query: What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?
-Helpful Answer:
-'''
-```
-
-_The embedding is confirmed to work as expected. It returns the right document, even if the question is asked slightly different. No prompt tokens have been consumed._
-
-**The dry run will still consume tokens to embed your query, but it is only ~1/15 of the prompt.**
-
-# How does it work?
-
-Creating a chat bot over any dataset needs the following steps to happen
-
- load the data
- create meaningful chunks
- create embeddings for each chunk
- store the chunks in vector database
-
-Whenever a user asks any query, following process happens to find the answer for the query
-
- create the embedding for query
- find similar documents for this query from vector database
- pass similar documents as context to LLM to get the final answer.
-
-The process of loading the dataset and then querying involves multiple steps and each steps has nuances of it is own.
-
- How should I chunk the data? What is a meaningful chunk size?
- How should I create embeddings for each chunk? Which embedding model should I use?
- How should I store the chunks in vector database? Which vector database should I use?
- Should I store meta data along with the embeddings?
- How should I find similar documents for a query? Which ranking model should I use?
-
-These questions may be trivial for some but for a lot of us, it needs research, experimentation and time to find out the accurate answers.
-
-embedchain is a framework which takes care of all these nuances and provides a simple interface to create bots over any dataset.
-
-In the first release, we are making it easier for anyone to get a chatbot over any dataset up and running in less than a minute. All you need to do is create an app instance, add the data sets using `.add` function and then use `.query` function to get the relevant answer.
-
-# Team
-
-## Author
-
- Taranjeet Singh ([@taranjeetio](https://twitter.com/taranjeetio))
-
-## Maintainer
-
- [cachho](https://github.com/cachho)
- [sahilyadav902](https://github.com/sahilyadav902)
-
-## Citation
-
-If you utilize this repository, please consider citing it with:
-```
-@misc{embedchain,
-  author = {Taranjeet Singh},
-  title = {Embechain: Framework to easily create LLM powered bots over any dataset},
-  year = {2023},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\url{https://github.com/embedchain/embedchainjs}},
-}
-```
@@ -1 +0,0 @@
-module.exports = { extends: ['@commitlint/config-conventional'] };
@@ -1,66 +0,0 @@
-import { EmbedChainApp } from '../embedchain';
-
-const mockAdd = jest.fn();
-const mockAddLocal = jest.fn();
-const mockQuery = jest.fn();
-
-jest.mock('../embedchain', () => {
-  return {
-    EmbedChainApp: jest.fn().mockImplementation(() => {
-      return {
-        add: mockAdd,
-        addLocal: mockAddLocal,
-        query: mockQuery,
-      };
-    }),
-  };
-});
-
-describe('Test App', () => {
-  beforeEach(() => {
-    jest.clearAllMocks();
-  });
-
-  it('tests the App', async () => {
-    mockQuery.mockResolvedValue(
-      'Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.'
-    );
-
-    const navalChatBot = await new EmbedChainApp(undefined, false);
-
-    // Embed Online Resources
-    await navalChatBot.add('web_page', 'https://nav.al/feedback');
-    await navalChatBot.add('web_page', 'https://nav.al/agi');
-    await navalChatBot.add(
-      'pdf_file',
-      'https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf'
-    );
-
-    // Embed Local Resources
-    await navalChatBot.addLocal('qna_pair', [
-      'Who is Naval Ravikant?',
-      'Naval Ravikant is an Indian-American entrepreneur and investor.',
-    ]);
-
-    const result = await navalChatBot.query(
-      'What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?'
-    );
-
-    expect(mockAdd).toHaveBeenCalledWith('web_page', 'https://nav.al/feedback');
-    expect(mockAdd).toHaveBeenCalledWith('web_page', 'https://nav.al/agi');
-    expect(mockAdd).toHaveBeenCalledWith(
-      'pdf_file',
-      'https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf'
-    );
-    expect(mockAddLocal).toHaveBeenCalledWith('qna_pair', [
-      'Who is Naval Ravikant?',
-      'Naval Ravikant is an Indian-American entrepreneur and investor.',
-    ]);
-    expect(mockQuery).toHaveBeenCalledWith(
-      'What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?'
-    );
-    expect(result).toBe(
-      'Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.'
-    );
-  });
-});
@@ -1,44 +0,0 @@
-import { createHash } from 'crypto';
-import type { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
-
-import type { BaseLoader } from '../loaders';
-import type { Input, LoaderResult } from '../models';
-import type { ChunkResult } from '../models/ChunkResult';
-
-class BaseChunker {
-  textSplitter: RecursiveCharacterTextSplitter;
-
-  constructor(textSplitter: RecursiveCharacterTextSplitter) {
-    this.textSplitter = textSplitter;
-  }
-
-  async createChunks(loader: BaseLoader, url: Input): Promise<ChunkResult> {
-    const documents: ChunkResult['documents'] = [];
-    const ids: ChunkResult['ids'] = [];
-    const datas: LoaderResult = await loader.loadData(url);
-    const metadatas: ChunkResult['metadatas'] = [];
-
-    const dataPromises = datas.map(async (data) => {
-      const { content, metaData } = data;
-      const chunks: string[] = await this.textSplitter.splitText(content);
-      chunks.forEach((chunk) => {
-        const chunkId = createHash('sha256')
-          .update(chunk + metaData.url)
-          .digest('hex');
-        ids.push(chunkId);
-        documents.push(chunk);
-        metadatas.push(metaData);
-      });
-    });
-
-    await Promise.all(dataPromises);
-
-    return {
-      documents,
-      ids,
-      metadatas,
-    };
-  }
-}
-
-export { BaseChunker };
@@ -1,26 +0,0 @@
-import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
-
-import { BaseChunker } from './BaseChunker';
-
-interface TextSplitterChunkParams {
-  chunkSize: number;
-  chunkOverlap: number;
-  keepSeparator: boolean;
-}
-
-const TEXT_SPLITTER_CHUNK_PARAMS: TextSplitterChunkParams = {
-  chunkSize: 1000,
-  chunkOverlap: 0,
-  keepSeparator: false,
-};
-
-class PdfFileChunker extends BaseChunker {
-  constructor() {
-    const textSplitter = new RecursiveCharacterTextSplitter(
-      TEXT_SPLITTER_CHUNK_PARAMS
-    );
-    super(textSplitter);
-  }
-}
-
-export { PdfFileChunker };
@@ -1,26 +0,0 @@
-import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
-
-import { BaseChunker } from './BaseChunker';
-
-interface TextSplitterChunkParams {
-  chunkSize: number;
-  chunkOverlap: number;
-  keepSeparator: boolean;
-}
-
-const TEXT_SPLITTER_CHUNK_PARAMS: TextSplitterChunkParams = {
-  chunkSize: 300,
-  chunkOverlap: 0,
-  keepSeparator: false,
-};
-
-class QnaPairChunker extends BaseChunker {
-  constructor() {
-    const textSplitter = new RecursiveCharacterTextSplitter(
-      TEXT_SPLITTER_CHUNK_PARAMS
-    );
-    super(textSplitter);
-  }
-}
-
-export { QnaPairChunker };
@@ -1,26 +0,0 @@
-import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
-
-import { BaseChunker } from './BaseChunker';
-
-interface TextSplitterChunkParams {
-  chunkSize: number;
-  chunkOverlap: number;
-  keepSeparator: boolean;
-}
-
-const TEXT_SPLITTER_CHUNK_PARAMS: TextSplitterChunkParams = {
-  chunkSize: 500,
-  chunkOverlap: 0,
-  keepSeparator: false,
-};
-
-class WebPageChunker extends BaseChunker {
-  constructor() {
-    const textSplitter = new RecursiveCharacterTextSplitter(
-      TEXT_SPLITTER_CHUNK_PARAMS
-    );
-    super(textSplitter);
-  }
-}
-
-export { WebPageChunker };
@@ -1,6 +0,0 @@
-import { BaseChunker } from './BaseChunker';
-import { PdfFileChunker } from './PdfFile';
-import { QnaPairChunker } from './QnaPair';
-import { WebPageChunker } from './WebPage';
-
-export { BaseChunker, PdfFileChunker, QnaPairChunker, WebPageChunker };
@@ -1,317 +0,0 @@
-/* eslint-disable max-classes-per-file */
-import type { Collection } from 'chromadb';
-import type { QueryResponse } from 'chromadb/dist/main/types';
-import * as fs from 'fs';
-import { Document } from 'langchain/document';
-import OpenAI from 'openai';
-import * as path from 'path';
-import { v4 as uuidv4 } from 'uuid';
-
-import type { BaseChunker } from './chunkers';
-import { PdfFileChunker, QnaPairChunker, WebPageChunker } from './chunkers';
-import type { BaseLoader } from './loaders';
-import { LocalQnaPairLoader, PdfFileLoader, WebPageLoader } from './loaders';
-import type {
-  DataDict,
-  DataType,
-  FormattedResult,
-  Input,
-  LocalInput,
-  Metadata,
-  Method,
-  RemoteInput,
-} from './models';
-import { ChromaDB } from './vectordb';
-import type { BaseVectorDB } from './vectordb/BaseVectorDb';
-
-const openai = new OpenAI({
-  apiKey: process.env.OPENAI_API_KEY,
-});
-
-class EmbedChain {
-  dbClient: any;
-
-  // TODO: Definitely assign
-  collection!: Collection;
-
-  userAsks: [DataType, Input][] = [];
-
-  initApp: Promise<void>;
-
-  collectMetrics: boolean;
-
-  sId: string; // sessionId
-
-  constructor(db?: BaseVectorDB, collectMetrics: boolean = true) {
-    if (!db) {
-      this.initApp = this.setupChroma();
-    } else {
-      this.initApp = this.setupOther(db);
-    }
-
-    this.collectMetrics = collectMetrics;
-
-    // Send anonymous telemetry
-    this.sId = uuidv4();
-    this.sendTelemetryEvent('init');
-  }
-
-  async setupChroma(): Promise<void> {
-    const db = new ChromaDB();
-    await db.initDb;
-    this.dbClient = db.client;
-    if (db.collection) {
-      this.collection = db.collection;
-    } else {
-      // TODO: Add proper error handling
-      console.error('No collection');
-    }
-  }
-
-  async setupOther(db: BaseVectorDB): Promise<void> {
-    await db.initDb;
-    // TODO: Figure out how we can initialize an unknown database.
-    // this.dbClient = db.client;
-    // this.collection = db.collection;
-    this.userAsks = [];
-  }
-
-  static getLoader(dataType: DataType) {
-    const loaders: { [t in DataType]: BaseLoader } = {
-      pdf_file: new PdfFileLoader(),
-      web_page: new WebPageLoader(),
-      qna_pair: new LocalQnaPairLoader(),
-    };
-    return loaders[dataType];
-  }
-
-  static getChunker(dataType: DataType) {
-    const chunkers: { [t in DataType]: BaseChunker } = {
-      pdf_file: new PdfFileChunker(),
-      web_page: new WebPageChunker(),
-      qna_pair: new QnaPairChunker(),
-    };
-    return chunkers[dataType];
-  }
-
-  public async add(dataType: DataType, url: RemoteInput) {
-    const loader = EmbedChain.getLoader(dataType);
-    const chunker = EmbedChain.getChunker(dataType);
-    this.userAsks.push([dataType, url]);
-    const { documents, countNewChunks } = await this.loadAndEmbed(
-      loader,
-      chunker,
-      url
-    );
-
-    if (this.collectMetrics) {
-      const wordCount = documents.reduce(
-        (sum, document) => sum + document.split(' ').length,
-        0
-      );
-
-      this.sendTelemetryEvent('add', {
-        data_type: dataType,
-        word_count: wordCount,
-        chunks_count: countNewChunks,
-      });
-    }
-  }
-
-  public async addLocal(dataType: DataType, content: LocalInput) {
-    const loader = EmbedChain.getLoader(dataType);
-    const chunker = EmbedChain.getChunker(dataType);
-    this.userAsks.push([dataType, content]);
-    const { documents, countNewChunks } = await this.loadAndEmbed(
-      loader,
-      chunker,
-      content
-    );
-
-    if (this.collectMetrics) {
-      const wordCount = documents.reduce(
-        (sum, document) => sum + document.split(' ').length,
-        0
-      );
-
-      this.sendTelemetryEvent('add_local', {
-        data_type: dataType,
-        word_count: wordCount,
-        chunks_count: countNewChunks,
-      });
-    }
-  }
-
-  protected async loadAndEmbed(
-    loader: any,
-    chunker: BaseChunker,
-    src: Input
-  ): Promise<{
-    documents: string[];
-    metadatas: Metadata[];
-    ids: string[];
-    countNewChunks: number;
-  }> {
-    const embeddingsData = await chunker.createChunks(loader, src);
-    let { documents, ids, metadatas } = embeddingsData;
-
-    const existingDocs = await this.collection.get({ ids });
-    const existingIds = new Set(existingDocs.ids);
-
-    if (existingIds.size > 0) {
-      const dataDict: DataDict = {};
-      for (let i = 0; i < ids.length; i += 1) {
-        const id = ids[i];
-        if (!existingIds.has(id)) {
-          dataDict[id] = { doc: documents[i], meta: metadatas[i] };
-        }
-      }
-
-      if (Object.keys(dataDict).length === 0) {
-        console.log(`All data from ${src} already exists in the database.`);
-        return { documents: [], metadatas: [], ids: [], countNewChunks: 0 };
-      }
-      ids = Object.keys(dataDict);
-      const dataValues = Object.values(dataDict);
-      documents = dataValues.map(({ doc }) => doc);
-      metadatas = dataValues.map(({ meta }) => meta);
-    }
-
-    const countBeforeAddition = await this.count();
-    await this.collection.add({ documents, metadatas, ids });
-    const countNewChunks = (await this.count()) - countBeforeAddition;
-    console.log(
-      `Successfully saved ${src}. New chunks count: ${countNewChunks}`
-    );
-    return { documents, metadatas, ids, countNewChunks };
-  }
-
-  static async formatResult(
-    results: QueryResponse
-  ): Promise<FormattedResult[]> {
-    return results.documents[0].map((document: any, index: number) => {
-      const metadata = results.metadatas[0][index] || {};
-      // TODO: Add proper error handling
-      const distance = results.distances ? results.distances[0][index] : null;
-      return [new Document({ pageContent: document, metadata }), distance];
-    });
-  }
-
-  static async getOpenAiAnswer(prompt: string) {
-    const messages: OpenAI.Chat.CreateChatCompletionRequestMessage[] = [
-      { role: 'user', content: prompt },
-    ];
-    const response = await openai.chat.completions.create({
-      model: 'gpt-3.5-turbo',
-      messages,
-      temperature: 0,
-      max_tokens: 1000,
-      top_p: 1,
-    });
-    return (
-      response.choices[0].message?.content ?? 'Response could not be processed.'
-    );
-  }
-
-  protected async retrieveFromDatabase(inputQuery: string) {
-    const result = await this.collection.query({
-      nResults: 1,
-      queryTexts: [inputQuery],
-    });
-    const resultFormatted = await EmbedChain.formatResult(result);
-    const content = resultFormatted[0][0].pageContent;
-    return content;
-  }
-
-  static generatePrompt(inputQuery: string, context: any) {
-    const prompt = `Use the following pieces of context to answer the query at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n${context}\nQuery: ${inputQuery}\nHelpful Answer:`;
-    return prompt;
-  }
-
-  static async getAnswerFromLlm(prompt: string) {
-    const answer = await EmbedChain.getOpenAiAnswer(prompt);
-    return answer;
-  }
-
-  public async query(inputQuery: string) {
-    const context = await this.retrieveFromDatabase(inputQuery);
-    const prompt = EmbedChain.generatePrompt(inputQuery, context);
-    const answer = await EmbedChain.getAnswerFromLlm(prompt);
-    this.sendTelemetryEvent('query');
-    return answer;
-  }
-
-  public async dryRun(input_query: string) {
-    const context = await this.retrieveFromDatabase(input_query);
-    const prompt = EmbedChain.generatePrompt(input_query, context);
-    return prompt;
-  }
-
-  /**
-   * Count the number of embeddings.
-   * @returns {Promise<number>}: The number of embeddings.
-   */
-  public count(): Promise<number> {
-    return this.collection.count();
-  }
-
-  protected async sendTelemetryEvent(method: Method, extraMetadata?: object) {
-    if (!this.collectMetrics) {
-      return;
-    }
-    const url = 'https://api.embedchain.ai/api/v1/telemetry/';
-
-    // Read package version from filesystem (because it's not in the ts root dir)
-    const packageJsonPath = path.join(__dirname, '..', 'package.json');
-    const packageJson = JSON.parse(fs.readFileSync(packageJsonPath, 'utf8'));
-
-    const metadata = {
-      s_id: this.sId,
-      version: packageJson.version,
-      method,
-      language: 'js',
-      ...extraMetadata,
-    };
-
-    const maxRetries = 3;
-
-    // Retry the fetch
-    for (let i = 0; i < maxRetries; i += 1) {
-      try {
-        // eslint-disable-next-line no-await-in-loop
-        const response = await fetch(url, {
-          method: 'POST',
-          body: JSON.stringify({ metadata }),
-        });
-
-        if (response.ok) {
-          // Break out of the loop if the request was successful
-          break;
-        } else {
-          // Log the unsuccessful response (optional)
-          console.error(
-            `Telemetry: Attempt ${i + 1} failed with status:`,
-            response.status
-          );
-        }
-      } catch (error) {
-        // Log the error (optional)
-        console.error(`Telemetry: Attempt ${i + 1} failed with error:`, error);
-      }
-
-      // If this was the last attempt, throw an error or handle the failure
-      if (i === maxRetries - 1) {
-        console.error('Telemetry: Max retries reached');
-      }
-    }
-  }
-}
-
-class EmbedChainApp extends EmbedChain {
-  // The EmbedChain app.
-  // Has two functions: add and query.
-  // adds(dataType, url): adds the data from the given URL to the vector db.
-  // query(query): finds answer to the given query using vector database and LLM.
-}
-
-export { EmbedChainApp };
@@ -1,7 +0,0 @@
-import { EmbedChainApp } from './embedchain';
-
-export const App = async () => {
-  const app = new EmbedChainApp();
-  await app.initApp;
-  return app;
-};
@@ -1,5 +0,0 @@
-import type { Input, LoaderResult } from '../models';
-
-export abstract class BaseLoader {
-  abstract loadData(src: Input): Promise<LoaderResult>;
-}
@@ -1,21 +0,0 @@
-import type { LoaderResult, QnaPair } from '../models';
-import { BaseLoader } from './BaseLoader';
-
-class LocalQnaPairLoader extends BaseLoader {
-  // eslint-disable-next-line class-methods-use-this
-  async loadData(content: QnaPair): Promise<LoaderResult> {
-    const [question, answer] = content;
-    const contentText = `Q: ${question}\nA: ${answer}`;
-    const metaData = {
-      url: 'local',
-    };
-    return [
-      {
-        content: contentText,
-        metaData,
-      },
-    ];
-  }
-}
-
-export { LocalQnaPairLoader };
@@ -1,58 +0,0 @@
-import type { TextContent } from 'pdfjs-dist/types/src/display/api';
-
-import type { LoaderResult, Metadata } from '../models';
-import { cleanString } from '../utils';
-import { BaseLoader } from './BaseLoader';
-
-const pdfjsLib = require('pdfjs-dist');
-
-interface Page {
-  page_content: string;
-}
-
-class PdfFileLoader extends BaseLoader {
-  static async getPagesFromPdf(url: string): Promise<Page[]> {
-    const loadingTask = pdfjsLib.getDocument(url);
-    const pdf = await loadingTask.promise;
-    const { numPages } = pdf;
-
-    const promises = Array.from({ length: numPages }, async (_, i) => {
-      const page = await pdf.getPage(i + 1);
-      const pageText: TextContent = await page.getTextContent();
-      const pageContent: string = pageText.items
-        .map((item) => ('str' in item ? item.str : ''))
-        .join(' ');
-
-      return {
-        page_content: pageContent,
-      };
-    });
-
-    return Promise.all(promises);
-  }
-
-  // eslint-disable-next-line class-methods-use-this
-  async loadData(url: string): Promise<LoaderResult> {
-    const pages: Page[] = await PdfFileLoader.getPagesFromPdf(url);
-    const output: LoaderResult = [];
-
-    if (!pages.length) {
-      throw new Error('No data found');
-    }
-
-    pages.forEach((page) => {
-      let content: string = page.page_content;
-      content = cleanString(content);
-      const metaData: Metadata = {
-        url,
-      };
-      output.push({
-        content,
-        metaData,
-      });
-    });
-    return output;
-  }
-}
-
-export { PdfFileLoader };
@@ -1,51 +0,0 @@
-import axios from 'axios';
-import { JSDOM } from 'jsdom';
-
-import { cleanString } from '../utils';
-import { BaseLoader } from './BaseLoader';
-
-class WebPageLoader extends BaseLoader {
-  // eslint-disable-next-line class-methods-use-this
-  async loadData(url: string) {
-    const response = await axios.get(url);
-    const html = response.data;
-    const dom = new JSDOM(html);
-    const { document } = dom.window;
-    const unwantedTags = [
-      'nav',
-      'aside',
-      'form',
-      'header',
-      'noscript',
-      'svg',
-      'canvas',
-      'footer',
-      'script',
-      'style',
-    ];
-    unwantedTags.forEach((tagName) => {
-      const elements = document.getElementsByTagName(tagName);
-      Array.from(elements).forEach((element) => {
-        // eslint-disable-next-line no-param-reassign
-        (element as HTMLElement).textContent = ' ';
-      });
-    });
-
-    const output = [];
-    let content = document.body.textContent;
-    if (!content) {
-      throw new Error('Web page content is empty.');
-    }
-    content = cleanString(content);
-    const metaData = {
-      url,
-    };
-    output.push({
-      content,
-      metaData,
-    });
-    return output;
-  }
-}
-
-export { WebPageLoader };
@@ -1,6 +0,0 @@
-import { BaseLoader } from './BaseLoader';
-import { LocalQnaPairLoader } from './LocalQnaPair';
-import { PdfFileLoader } from './PdfFile';
-import { WebPageLoader } from './WebPage';
-
-export { BaseLoader, LocalQnaPairLoader, PdfFileLoader, WebPageLoader };
@@ -1,7 +0,0 @@
-import type { Metadata } from './Metadata';
-
-export type ChunkResult = {
-  documents: string[];
-  ids: string[];
-  metadatas: Metadata[];
-};
@@ -1,10 +0,0 @@
-import type { ChunkResult } from './ChunkResult';
-
-type Data = {
-  doc: ChunkResult['documents'][0];
-  meta: ChunkResult['metadatas'][0];
-};
-
-export type DataDict = {
-  [id: string]: Data;
-};
@@ -1 +0,0 @@
-export type DataType = 'pdf_file' | 'web_page' | 'qna_pair';
@@ -1,3 +0,0 @@
-import type { Document } from 'langchain/document';
-
-export type FormattedResult = [Document, number | null];
@@ -1,7 +0,0 @@
-import type { QnaPair } from './QnAPair';
-
-export type RemoteInput = string;
-
-export type LocalInput = QnaPair;
-
-export type Input = RemoteInput | LocalInput;
@@ -1,3 +0,0 @@
-import type { Metadata } from './Metadata';
-
-export type LoaderResult = { content: any; metaData: Metadata }[];
@@ -1,3 +0,0 @@
-export type Metadata = {
-  url: string;
-};
@@ -1 +0,0 @@
-export type Method = 'init' | 'query' | 'add' | 'add_local';
@@ -1,4 +0,0 @@
-type Question = string;
-type Answer = string;
-
-export type QnaPair = [Question, Answer];
@@ -1,21 +0,0 @@
-import { DataDict } from './DataDict';
-import { DataType } from './DataType';
-import { FormattedResult } from './FormattedResult';
-import { Input, LocalInput, RemoteInput } from './Input';
-import { LoaderResult } from './LoaderResult';
-import { Metadata } from './Metadata';
-import { Method } from './Method';
-import { QnaPair } from './QnAPair';
-
-export {
-  DataDict,
-  DataType,
-  FormattedResult,
-  Input,
-  LoaderResult,
-  LocalInput,
-  Metadata,
-  Method,
-  QnaPair,
-  RemoteInput,
-};
@@ -1,26 +0,0 @@
-/**
- * This function takes in a string and performs a series of text cleaning operations.
- * @param {str} text: The text to be cleaned. This is expected to be a string.
- * @returns {str}: The cleaned text after all the cleaning operations have been performed.
- */
-export function cleanString(text: string): string {
-  // Replacement of newline characters:
-  let cleanedText = text.replace(/\n/g, ' ');
-
-  // Stripping and reducing multiple spaces to single:
-  cleanedText = cleanedText.trim().replace(/\s+/g, ' ');
-
-  // Removing backslashes:
-  cleanedText = cleanedText.replace(/\\/g, '');
-
-  // Replacing hash characters:
-  cleanedText = cleanedText.replace(/#/g, ' ');
-
-  // Eliminating consecutive non-alphanumeric characters:
-  // This regex identifies consecutive non-alphanumeric characters (i.e., not a word character [a-zA-Z0-9_] and not a whitespace) in the string
-  // and replaces each group of such characters with a single occurrence of that character.
-  // For example, "!!! hello !!!" would become "! hello !".
-  cleanedText = cleanedText.replace(/([^\w\s])\1*/g, '$1');
-
-  return cleanedText;
-}
@@ -1,14 +0,0 @@
-class BaseVectorDB {
-  initDb: Promise<void>;
-
-  constructor() {
-    this.initDb = this.getClientAndCollection();
-  }
-
-  // eslint-disable-next-line class-methods-use-this
-  protected async getClientAndCollection(): Promise<void> {
-    throw new Error('getClientAndCollection() method is not implemented');
-  }
-}
-
-export { BaseVectorDB };
@@ -1,38 +0,0 @@
-import type { Collection } from 'chromadb';
-import { ChromaClient, OpenAIEmbeddingFunction } from 'chromadb';
-
-import { BaseVectorDB } from './BaseVectorDb';
-
-const embedder = new OpenAIEmbeddingFunction({
-  openai_api_key: process.env.OPENAI_API_KEY ?? '',
-});
-
-class ChromaDB extends BaseVectorDB {
-  client: ChromaClient | undefined;
-
-  collection: Collection | null = null;
-
-  // eslint-disable-next-line @typescript-eslint/no-useless-constructor
-  constructor() {
-    super();
-  }
-
-  protected async getClientAndCollection(): Promise<void> {
-    this.client = new ChromaClient({ path: 'http://localhost:8000' });
-    try {
-      this.collection = await this.client.getCollection({
-        name: 'embedchain_store',
-        embeddingFunction: embedder,
-      });
-    } catch (err) {
-      if (!this.collection) {
-        this.collection = await this.client.createCollection({
-          name: 'embedchain_store',
-          embeddingFunction: embedder,
-        });
-      }
-    }
-  }
-}
-
-export { ChromaDB };
@@ -1,3 +0,0 @@
-import { ChromaDB } from './ChromaDb';
-
-export { ChromaDB };
@@ -1,9 +0,0 @@
-const { EmbedChainApp } = require("./embedchain/embedchain");
-
-async function App() {
-  const app = new EmbedChainApp();
-  await app.init_app;
-  return app;
-}
-
-module.exports = { App };
@@ -1,5 +0,0 @@
-module.exports = {
-  preset: 'ts-jest',
-  testEnvironment: 'node',
-  testPathIgnorePatterns: ['.d.ts'],
-};
@@ -1,5 +0,0 @@
-module.exports = {
-  '*.{js,ts}': ['eslint --fix', 'eslint'],
-  '**/*.ts?(x)': () => 'npm run check-types',
-  '*.json': ['prettier --write'],
-};
@@ -1,53 +0,0 @@
-{
-  "name": "embedchain",
-  "version": "0.0.8",
-  "description": "embedchain is a framework to easily create LLM powered bots over any dataset",
-  "main": "dist/index.js",
-  "types": "types/index.d.ts",
-  "files": [
-    "dist",
-    "types"
-  ],
-  "scripts": {
-    "build": "tsc -p tsconfig.build.json --listFiles",
-    "prepare": "husky install",
-    "test": "jest",
-    "check-types": "tsc --noEmit --pretty"
-  },
-  "author": "Taranjeet Singh",
-  "license": "Apache-2.0",
-  "dependencies": {
-    "axios": "^1.4.0",
-    "chromadb": "^1.5.6",
-    "jsdom": "^22.1.0",
-    "langchain": "^0.0.136",
-    "openai": "^4.3.1",
-    "pdfjs-dist": "^3.8.162",
-    "uuid": "^9.0.0"
-  },
-  "devDependencies": {
-    "@commitlint/cli": "^17.1.2",
-    "@commitlint/config-conventional": "^17.1.0",
-    "@commitlint/cz-commitlint": "^17.1.2",
-    "@types/jest": "^29.5.1",
-    "@types/jsdom": "^21.1.1",
-    "@typescript-eslint/eslint-plugin": "^5.41.0",
-    "@typescript-eslint/parser": "^5.41.0",
-    "eslint": "^8.34.0",
-    "eslint-config-airbnb-base": "^15.0.0",
-    "eslint-config-airbnb-typescript": "^17.0.0",
-    "eslint-config-prettier": "^8.5.0",
-    "eslint-plugin-import": "^2.27.5",
-    "eslint-plugin-prettier": "^4.2.1",
-    "eslint-plugin-simple-import-sort": "^8.0.0",
-    "eslint-plugin-testing-library": "^5.9.1",
-    "eslint-plugin-unused-imports": "^2.0.0",
-    "husky": "^8.0.1",
-    "jest": "^29.5.0",
-    "lint-staged": "^13.0.3",
-    "prettier": "^2.7.1",
-    "ts-jest": "^29.1.0",
-    "ts-loader": "^9.4.2",
-    "typescript": "^5.2.2"
-  }
-}
@@ -1,4 +0,0 @@
-{
-  "extends": "./tsconfig.json",
-  "exclude": ["embedchain/__tests__"]
-}
@@ -1,15 +0,0 @@
-{
-  "compilerOptions": {
-    "target": "es6",
-    "module": "CommonJS",
-    "strict": true,
-    "outDir": "dist",
-    "rootDir": "embedchain",
-    "sourceMap": true,
-    "declaration": true,
-    "declarationDir": "types",
-    "esModuleInterop": true
-  },
-  "include": ["embedchain/**/*.ts"],
-  "exclude": ["node_modules", "dist"]
-}
@@ -0,0 +1,22 @@
+from typing import Optional
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from embedchain.chunkers.base_chunker import BaseChunker
+from embedchain.config.add_config import ChunkerConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class AudioChunker(BaseChunker):
+    """Chunker for audio."""
+
+    def __init__(self, config: Optional[ChunkerConfig] = None):
+        if config is None:
+            config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=config.chunk_size,
+            chunk_overlap=config.chunk_overlap,
+            length_function=config.length_function,
+        )
+        super().__init__(text_splitter)
@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Any, Dict, Optional

 from embedchain.helpers.json_serializable import register_deserializable

@@ -10,8 +10,10 @@ class BaseEmbedderConfig:
        model: Optional[str] = None,
        deployment_name: Optional[str] = None,
        vector_dimension: Optional[int] = None,
+        endpoint: Optional[str] = None,
        api_key: Optional[str] = None,
        api_base: Optional[str] = None,
+        model_kwargs: Optional[Dict[str, Any]] = None,
    ):
        """
        Initialize a new instance of an embedder config class.
@@ -20,9 +22,21 @@ class BaseEmbedderConfig:
        :type model: Optional[str], optional
        :param deployment_name: deployment name for llm embedding model, defaults to None
        :type deployment_name: Optional[str], optional
+        :param vector_dimension: vector dimension of the embedding model, defaults to None
+        :type vector_dimension: Optional[int], optional
+        :param endpoint: endpoint for the embedding model, defaults to None
+        :type endpoint: Optional[str], optional
+        :param api_key: hugginface api key, defaults to None
+        :type api_key: Optional[str], optional
+        :param api_base: huggingface api base, defaults to None
+        :type api_base: Optional[str], optional
+        :param model_kwargs: key-value arguments for the embedding model, defaults a dict inside init.
+        :type model_kwargs: Optional[Dict[str, Any]], defaults a dict inside init.
        """
        self.model = model
        self.deployment_name = deployment_name
        self.vector_dimension = vector_dimension
+        self.endpoint = endpoint
        self.api_key = api_key
        self.api_base = api_base
+        self.model_kwargs = model_kwargs or {}
@@ -10,9 +10,10 @@ class GoogleAIEmbedderConfig(BaseEmbedderConfig):
        self,
        model: Optional[str] = None,
        deployment_name: Optional[str] = None,
+        vector_dimension: Optional[int] = None,
        task_type: Optional[str] = None,
        title: Optional[str] = None,
    ):
-        super().__init__(model, deployment_name)
+        super().__init__(model, deployment_name, vector_dimension)
        self.task_type = task_type or "retrieval_document"
        self.title = title or "Embeddings for Embedchain"
@@ -10,6 +10,7 @@ class OllamaEmbedderConfig(BaseEmbedderConfig):
        self,
        model: Optional[str] = None,
        base_url: Optional[str] = None,
+        vector_dimension: Optional[int] = None,
    ):
-        super().__init__(model)
+        super().__init__(model=model, vector_dimension=vector_dimension)
        self.base_url = base_url or "http://localhost:11434"
@@ -1,7 +1,9 @@
 import logging
 import re
 from string import Template
-from typing import Any, Mapping, Optional
+from typing import Any, Mapping, Optional, Dict, Union
+
+import httpx

 from embedchain.config.base_config import BaseConfig
 from embedchain.helpers.json_serializable import register_deserializable
@@ -99,10 +101,11 @@ class BaseLlmConfig(BaseConfig):
        base_url: Optional[str] = None,
        endpoint: Optional[str] = None,
        model_kwargs: Optional[dict[str, Any]] = None,
-        http_client: Optional[Any] = None,
-        http_async_client: Optional[Any] = None,
+        http_client_proxies: Optional[Union[Dict, str]] = None,
+        http_async_client_proxies: Optional[Union[Dict, str]] = None,
        local: Optional[bool] = False,
        default_headers: Optional[Mapping[str, str]] = None,
+        api_version: Optional[str] = None,
    ):
        """
        Initializes a configuration class instance for the LLM.
@@ -148,6 +151,11 @@ class BaseLlmConfig(BaseConfig):
        :type callbacks: Optional[list], optional
        :param query_type: The type of query to use, defaults to None
        :type query_type: Optional[str], optional
+        :param http_client_proxies: The proxy server settings used to create self.http_client, defaults to None
+        :type http_client_proxies: Optional[Dict | str], optional
+        :param http_async_client_proxies: The proxy server settings for async calls used to create
+        self.http_async_client, defaults to None
+        :type http_async_client_proxies: Optional[Dict | str], optional
        :param local: If True, the model will be run locally, defaults to False (for huggingface provider)
        :type local: Optional[bool], optional
        :param default_headers: Set additional HTTP headers to be sent with requests to OpenAI
@@ -180,11 +188,14 @@ class BaseLlmConfig(BaseConfig):
        self.base_url = base_url
        self.endpoint = endpoint
        self.model_kwargs = model_kwargs
-        self.http_client = http_client
-        self.http_async_client = http_async_client
+        self.http_client = httpx.Client(proxies=http_client_proxies) if http_client_proxies else None
+        self.http_async_client = (
+            httpx.AsyncClient(proxies=http_async_client_proxies) if http_async_client_proxies else None
+        )
        self.local = local
        self.default_headers = default_headers
        self.online = online
+        self.api_version = api_version

        if isinstance(prompt, str):
            prompt = Template(prompt)
@@ -12,6 +12,7 @@ class ChromaDbConfig(BaseVectorDbConfig):
        dir: Optional[str] = None,
        host: Optional[str] = None,
        port: Optional[str] = None,
+        batch_size: Optional[int] = 100,
        allow_reset=False,
        chroma_settings: Optional[dict] = None,
    ):
@@ -26,6 +27,8 @@ class ChromaDbConfig(BaseVectorDbConfig):
        :type host: Optional[str], optional
        :param port: Database connection remote port. Use this if you run Embedchain as a client, defaults to None
        :type port: Optional[str], optional
+        :param batch_size: Number of items to insert in one batch, defaults to 100
+        :type batch_size: Optional[int], optional
        :param allow_reset: Resets the database. defaults to False
        :type allow_reset: bool
        :param chroma_settings: Chroma settings dict, defaults to None
@@ -34,4 +37,5 @@ class ChromaDbConfig(BaseVectorDbConfig):

        self.chroma_settings = chroma_settings
        self.allow_reset = allow_reset
+        self.batch_size = batch_size
        super().__init__(collection_name=collection_name, dir=dir, host=host, port=port)
@@ -13,6 +13,7 @@ class ElasticsearchDBConfig(BaseVectorDbConfig):
        dir: Optional[str] = None,
        es_url: Union[str, list[str]] = None,
        cloud_id: Optional[str] = None,
+        batch_size: Optional[int] = 100,
        **ES_EXTRA_PARAMS: dict[str, any],
    ):
        """
@@ -24,6 +25,10 @@ class ElasticsearchDBConfig(BaseVectorDbConfig):
        :type dir: Optional[str], optional
        :param es_url: elasticsearch url or list of nodes url to be used for connection, defaults to None
        :type es_url: Union[str, list[str]], optional
+        :param cloud_id: cloud id of the elasticsearch cluster, defaults to None
+        :type cloud_id: Optional[str], optional
+        :param batch_size: Number of items to insert in one batch, defaults to 100
+        :type batch_size: Optional[int], optional
        :param ES_EXTRA_PARAMS: extra params dict that can be passed to elasticsearch.
        :type ES_EXTRA_PARAMS: dict[str, Any], optional
        """
@@ -46,4 +51,6 @@ class ElasticsearchDBConfig(BaseVectorDbConfig):
            and not self.ES_EXTRA_PARAMS.get("bearer_auth")
        ):
            self.ES_EXTRA_PARAMS["api_key"] = os.environ.get("ELASTICSEARCH_API_KEY")
+
+        self.batch_size = batch_size
        super().__init__(collection_name=collection_name, dir=dir)
@@ -0,0 +1,33 @@
+from typing import Optional
+
+from embedchain.config.vectordb.base import BaseVectorDbConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class LanceDBConfig(BaseVectorDbConfig):
+    def __init__(
+        self,
+        collection_name: Optional[str] = None,
+        dir: Optional[str] = None,
+        host: Optional[str] = None,
+        port: Optional[str] = None,
+        allow_reset=True,
+    ):
+        """
+        Initializes a configuration class instance for LanceDB.
+
+        :param collection_name: Default name for the collection, defaults to None
+        :type collection_name: Optional[str], optional
+        :param dir: Path to the database directory, where the database is stored, defaults to None
+        :type dir: Optional[str], optional
+        :param host: Database connection remote host. Use this if you run Embedchain as a client, defaults to None
+        :type host: Optional[str], optional
+        :param port: Database connection remote port. Use this if you run Embedchain as a client, defaults to None
+        :type port: Optional[str], optional
+        :param allow_reset: Resets the database. defaults to False
+        :type allow_reset: bool
+        """
+
+        self.allow_reset = allow_reset
+        super().__init__(collection_name=collection_name, dir=dir, host=host, port=port)
@@ -13,6 +13,7 @@ class OpenSearchDBConfig(BaseVectorDbConfig):
        vector_dimension: int = 1536,
        collection_name: Optional[str] = None,
        dir: Optional[str] = None,
+        batch_size: Optional[int] = 100,
        **extra_params: dict[str, any],
    ):
        """
@@ -28,10 +29,13 @@ class OpenSearchDBConfig(BaseVectorDbConfig):
        :type vector_dimension: int, optional
        :param dir: Path to the database directory, where the database is stored, defaults to None
        :type dir: Optional[str], optional
+        :param batch_size: Number of items to insert in one batch, defaults to 100
+        :type batch_size: Optional[int], optional
        """
        self.opensearch_url = opensearch_url
        self.http_auth = http_auth
        self.vector_dimension = vector_dimension
        self.extra_params = extra_params
+        self.batch_size = batch_size

        super().__init__(collection_name=collection_name, dir=dir)
@@ -17,6 +17,7 @@ class PineconeDBConfig(BaseVectorDbConfig):
        serverless_config: Optional[dict[str, any]] = None,
        hybrid_search: bool = False,
        bm25_encoder: any = None,
+        batch_size: Optional[int] = 100,
        **extra_params: dict[str, any],
    ):
        self.metric = metric
@@ -26,6 +27,7 @@ class PineconeDBConfig(BaseVectorDbConfig):
        self.extra_params = extra_params
        self.hybrid_search = hybrid_search
        self.bm25_encoder = bm25_encoder
+        self.batch_size = batch_size
        if pod_config is None and serverless_config is None:
            # If no config is provided, use the default pod spec config
            pod_environment = os.environ.get("PINECONE_ENV", "gcp-starter")
@@ -18,6 +18,7 @@ class QdrantDBConfig(BaseVectorDbConfig):
        hnsw_config: Optional[dict[str, any]] = None,
        quantization_config: Optional[dict[str, any]] = None,
        on_disk: Optional[bool] = None,
+        batch_size: Optional[int] = 10,
        **extra_params: dict[str, any],
    ):
        """
@@ -36,9 +37,12 @@ class QdrantDBConfig(BaseVectorDbConfig):
                This setting saves RAM by (slightly) increasing the response time.
                Note: those payload values that are involved in filtering and are indexed - remain in RAM.
        :type on_disk: bool, optional, defaults to None
+        :param batch_size: Number of items to insert in one batch, defaults to 10
+        :type batch_size: Optional[int], optional
        """
        self.hnsw_config = hnsw_config
        self.quantization_config = quantization_config
        self.on_disk = on_disk
+        self.batch_size = batch_size
        self.extra_params = extra_params
        super().__init__(collection_name=collection_name, dir=dir)
@@ -10,7 +10,9 @@ class WeaviateDBConfig(BaseVectorDbConfig):
        self,
        collection_name: Optional[str] = None,
        dir: Optional[str] = None,
+        batch_size: Optional[int] = 100,
        **extra_params: dict[str, any],
    ):
+        self.batch_size = batch_size
        self.extra_params = extra_params
        super().__init__(collection_name=collection_name, dir=dir)
@@ -81,6 +81,7 @@ class DataFormatter(JSONSerializable):
            DataType.DROPBOX: "embedchain.loaders.dropbox.DropboxLoader",
            DataType.TEXT_FILE: "embedchain.loaders.text_file.TextFileLoader",
            DataType.EXCEL_FILE: "embedchain.loaders.excel_file.ExcelFileLoader",
+            DataType.AUDIO: "embedchain.loaders.audio.AudioLoader",
        }

        if data_type == DataType.CUSTOM or loader is not None:
@@ -129,6 +130,7 @@ class DataFormatter(JSONSerializable):
            DataType.DROPBOX: "embedchain.chunkers.common_chunker.CommonChunker",
            DataType.TEXT_FILE: "embedchain.chunkers.common_chunker.CommonChunker",
            DataType.EXCEL_FILE: "embedchain.chunkers.excel_file.ExcelFileChunker",
+            DataType.AUDIO: "embedchain.chunkers.audio.AudioChunker",
        }

        if chunker is not None:
@@ -6,7 +6,9 @@ from typing import Any, Optional, Union
 from dotenv import load_dotenv
 from langchain.docstore.document import Document

-from embedchain.cache import adapt, get_gptcache_session, gptcache_data_convert, gptcache_update_cache_callback
+from embedchain.cache import (adapt, get_gptcache_session,
+                              gptcache_data_convert,
+                              gptcache_update_cache_callback)
 from embedchain.chunkers.base_chunker import BaseChunker
 from embedchain.config import AddConfig, BaseLlmConfig, ChunkerConfig
 from embedchain.config.base_app_config import BaseAppConfig
@@ -16,7 +18,8 @@ from embedchain.embedder.base import BaseEmbedder
 from embedchain.helpers.json_serializable import JSONSerializable
 from embedchain.llm.base import BaseLlm
 from embedchain.loaders.base_loader import BaseLoader
-from embedchain.models.data_type import DataType, DirectDataType, IndirectDataType, SpecialDataType
+from embedchain.models.data_type import (DataType, DirectDataType,
+                                         IndirectDataType, SpecialDataType)
 from embedchain.utils.misc import detect_datatype, is_valid_json_string
 from embedchain.vectordb.base import BaseVectorDB

@@ -0,0 +1,22 @@
+from typing import Optional
+
+from langchain_community.embeddings import AzureOpenAIEmbeddings
+
+from embedchain.config import BaseEmbedderConfig
+from embedchain.embedder.base import BaseEmbedder
+from embedchain.models import VectorDimensions
+
+
+class AzureOpenAIEmbedder(BaseEmbedder):
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
+        super().__init__(config=config)
+
+        if self.config.model is None:
+            self.config.model = "text-embedding-ada-002"
+
+        embeddings = AzureOpenAIEmbeddings(deployment=self.config.deployment_name)
+        embedding_fn = BaseEmbedder._langchain_default_concept(embeddings)
+
+        self.set_embedding_fn(embedding_fn=embedding_fn)
+        vector_dimension = self.config.vector_dimension or VectorDimensions.OPENAI.value
+        self.set_vector_dimension(vector_dimension=vector_dimension)
@@ -0,0 +1,52 @@
+import os
+from typing import Optional, Union
+
+from embedchain.config import BaseEmbedderConfig
+from embedchain.embedder.base import BaseEmbedder
+
+from chromadb import EmbeddingFunction, Embeddings
+
+
+class ClarifaiEmbeddingFunction(EmbeddingFunction):
+    def __init__(self, config: BaseEmbedderConfig) -> None:
+        super().__init__()
+        try:
+            from clarifai.client.model import Model
+            from clarifai.client.input import Inputs
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(
+                "The required dependencies for ClarifaiEmbeddingFunction are not installed."
+                'Please install with `pip install --upgrade "embedchain[clarifai]"`'
+            ) from None
+        self.config = config
+        self.api_key = config.api_key or os.getenv("CLARIFAI_PAT")
+        self.model = config.model
+        self.model_obj = Model(url=self.model, pat=self.api_key)
+        self.input_obj = Inputs(pat=self.api_key)
+
+    def __call__(self, input: Union[str, list[str]]) -> Embeddings:
+        if isinstance(input, str):
+            input = [input]
+
+        batch_size = 32
+        embeddings = []
+        try:
+            for i in range(0, len(input), batch_size):
+                batch = input[i : i + batch_size]
+                input_batch = [
+                    self.input_obj.get_text_input(input_id=str(id), raw_text=inp) for id, inp in enumerate(batch)
+                ]
+                response = self.model_obj.predict(input_batch)
+                embeddings.extend([list(output.data.embeddings[0].vector) for output in response.outputs])
+        except Exception as e:
+            print(f"Predict failed, exception: {e}")
+
+        return embeddings
+
+
+class ClarifaiEmbedder(BaseEmbedder):
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
+        super().__init__(config)
+
+        embedding_func = ClarifaiEmbeddingFunction(config=self.config)
+        self.set_embedding_fn(embedding_fn=embedding_func)
@@ -1,7 +1,16 @@
+import os
 from typing import Optional

 from langchain_community.embeddings import HuggingFaceEmbeddings

+try:
+    from langchain_huggingface import HuggingFaceEndpointEmbeddings
+except ModuleNotFoundError:
+    raise ModuleNotFoundError(
+        "The required dependencies for HuggingFaceHub are not installed."
+        "Please install with `pip install langchain_huggingface`"
+    ) from None
+
 from embedchain.config import BaseEmbedderConfig
 from embedchain.embedder.base import BaseEmbedder
 from embedchain.models import VectorDimensions
@@ -11,7 +20,19 @@ class HuggingFaceEmbedder(BaseEmbedder):
    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
        super().__init__(config=config)

-        embeddings = HuggingFaceEmbeddings(model_name=self.config.model)
+        if self.config.endpoint:
+            if not self.config.api_key and "HUGGINGFACE_ACCESS_TOKEN" not in os.environ:
+                raise ValueError(
+                    "Please set the HUGGINGFACE_ACCESS_TOKEN environment variable or pass API Key in the config."
+                )
+
+            embeddings = HuggingFaceEndpointEmbeddings(
+                model=self.config.endpoint,
+                huggingfacehub_api_token=self.config.api_key or os.getenv("HUGGINGFACE_ACCESS_TOKEN"),
+            )
+        else:
+            embeddings = HuggingFaceEmbeddings(model_name=self.config.model, model_kwargs=self.config.model_kwargs)
+
        embedding_fn = BaseEmbedder._langchain_default_concept(embeddings)
        self.set_embedding_fn(embedding_fn=embedding_fn)

@@ -2,7 +2,7 @@ import os
 from typing import Optional

 from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
-from langchain_community.embeddings import AzureOpenAIEmbeddings
+

 from embedchain.config import BaseEmbedderConfig
 from embedchain.embedder.base import BaseEmbedder
@@ -19,20 +19,14 @@ class OpenAIEmbedder(BaseEmbedder):
        api_key = self.config.api_key or os.environ["OPENAI_API_KEY"]
        api_base = self.config.api_base or os.environ.get("OPENAI_API_BASE")

-        if self.config.deployment_name:
-            embeddings = AzureOpenAIEmbeddings(deployment=self.config.deployment_name)
-            embedding_fn = BaseEmbedder._langchain_default_concept(embeddings)
-        else:
-            if api_key is None and os.getenv("OPENAI_ORGANIZATION") is None:
-                raise ValueError(
-                    "OPENAI_API_KEY or OPENAI_ORGANIZATION environment variables not provided"
-                )  # noqa:E501
-            embedding_fn = OpenAIEmbeddingFunction(
-                api_key=api_key,
-                api_base=api_base,
-                organization_id=os.getenv("OPENAI_ORGANIZATION"),
-                model_name=self.config.model,
-            )
+        if api_key is None and os.getenv("OPENAI_ORGANIZATION") is None:
+            raise ValueError("OPENAI_API_KEY or OPENAI_ORGANIZATION environment variables not provided")  # noqa:E501
+        embedding_fn = OpenAIEmbeddingFunction(
+            api_key=api_key,
+            api_base=api_base,
+            organization_id=os.getenv("OPENAI_ORGANIZATION"),
+            model_name=self.config.model,
+        )
        self.set_embedding_fn(embedding_fn=embedding_fn)
        vector_dimension = self.config.vector_dimension or VectorDimensions.OPENAI.value
        self.set_vector_dimension(vector_dimension=vector_dimension)
@@ -23,6 +23,7 @@ class LlmFactory:
        "google": "embedchain.llm.google.GoogleLlm",
        "aws_bedrock": "embedchain.llm.aws_bedrock.AWSBedrockLlm",
        "mistralai": "embedchain.llm.mistralai.MistralAILlm",
+        "clarifai": "embedchain.llm.clarifai.ClarifaiLlm",
        "groq": "embedchain.llm.groq.GroqLlm",
        "nvidia": "embedchain.llm.nvidia.NvidiaLlm",
        "vllm": "embedchain.llm.vllm.VLLM",
@@ -49,13 +50,14 @@ class LlmFactory:

 class EmbedderFactory:
    provider_to_class = {
-        "azure_openai": "embedchain.embedder.openai.OpenAIEmbedder",
+        "azure_openai": "embedchain.embedder.azure_openai.AzureOpenAIEmbedder",
        "gpt4all": "embedchain.embedder.gpt4all.GPT4AllEmbedder",
        "huggingface": "embedchain.embedder.huggingface.HuggingFaceEmbedder",
        "openai": "embedchain.embedder.openai.OpenAIEmbedder",
        "vertexai": "embedchain.embedder.vertexai.VertexAIEmbedder",
        "google": "embedchain.embedder.google.GoogleAIEmbedder",
        "mistralai": "embedchain.embedder.mistralai.MistralAIEmbedder",
+        "clarifai": "embedchain.embedder.clarifai.ClarifaiEmbedder",
        "nvidia": "embedchain.embedder.nvidia.NvidiaEmbedder",
        "cohere": "embedchain.embedder.cohere.CohereEmbedder",
        "ollama": "embedchain.embedder.ollama.OllamaEmbedder",
@@ -65,6 +67,7 @@ class EmbedderFactory:
        "google": "embedchain.config.embedder.google.GoogleAIEmbedderConfig",
        "gpt4all": "embedchain.config.embedder.base.BaseEmbedderConfig",
        "huggingface": "embedchain.config.embedder.base.BaseEmbedderConfig",
+        "clarifai": "embedchain.config.embedder.base.BaseEmbedderConfig",
        "openai": "embedchain.config.embedder.base.BaseEmbedderConfig",
        "ollama": "embedchain.config.embedder.ollama.OllamaEmbedderConfig",
    }
@@ -88,6 +91,7 @@ class VectorDBFactory:
        "chroma": "embedchain.vectordb.chroma.ChromaDB",
        "elasticsearch": "embedchain.vectordb.elasticsearch.ElasticsearchDB",
        "opensearch": "embedchain.vectordb.opensearch.OpenSearchDB",
+        "lancedb": "embedchain.vectordb.lancedb.LanceDB",
        "pinecone": "embedchain.vectordb.pinecone.PineconeDB",
        "qdrant": "embedchain.vectordb.qdrant.QdrantDB",
        "weaviate": "embedchain.vectordb.weaviate.WeaviateDB",
@@ -97,6 +101,7 @@ class VectorDBFactory:
        "chroma": "embedchain.config.vectordb.chroma.ChromaDbConfig",
        "elasticsearch": "embedchain.config.vectordb.elasticsearch.ElasticsearchDBConfig",
        "opensearch": "embedchain.config.vectordb.opensearch.OpenSearchDBConfig",
+        "lancedb": "embedchain.config.vectordb.lancedb.LanceDBConfig",
        "pinecone": "embedchain.config.vectordb.pinecone.PineconeDBConfig",
        "qdrant": "embedchain.config.vectordb.qdrant.QdrantDBConfig",
        "weaviate": "embedchain.config.vectordb.weaviate.WeaviateDBConfig",
@@ -17,18 +17,17 @@ logger = logging.getLogger(__name__)
@register_deserializable
 class AnthropicLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "ANTHROPIC_API_KEY" not in os.environ:
-            raise ValueError("Please set the ANTHROPIC_API_KEY environment variable.")
        super().__init__(config=config)
+        if not self.config.api_key and "ANTHROPIC_API_KEY" not in os.environ:
+            raise ValueError("Please set the ANTHROPIC_API_KEY environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        return AnthropicLlm._get_answer(prompt=prompt, config=self.config)

    @staticmethod
    def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
-        chat = ChatAnthropic(
-            anthropic_api_key=os.environ["ANTHROPIC_API_KEY"], temperature=config.temperature, model_name=config.model
-        )
+        api_key = config.api_key or os.getenv("ANTHROPIC_API_KEY")
+        chat = ChatAnthropic(anthropic_api_key=api_key, temperature=config.temperature, model_name=config.model)

        if config.max_tokens and config.max_tokens != 1000:
            logger.warning("Config option `max_tokens` is not supported by this model.")
@@ -14,18 +14,18 @@ class AzureOpenAILlm(BaseLlm):
        super().__init__(config=config)

    def get_llm_model_answer(self, prompt):
-        return AzureOpenAILlm._get_answer(prompt=prompt, config=self.config)
+        return self._get_answer(prompt=prompt, config=self.config)

    @staticmethod
    def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
-        from langchain_community.chat_models import AzureChatOpenAI
+        from langchain_openai import AzureChatOpenAI

        if not config.deployment_name:
            raise ValueError("Deployment name must be provided for Azure OpenAI")

        chat = AzureChatOpenAI(
            deployment_name=config.deployment_name,
-            openai_api_version="2023-05-15",
+            openai_api_version=str(config.api_version) if config.api_version else "2024-02-01",
            model_name=config.model or "gpt-3.5-turbo",
            temperature=config.temperature,
            max_tokens=config.max_tokens,
@@ -37,4 +37,4 @@ class AzureOpenAILlm(BaseLlm):

        messages = BaseLlm._get_messages(prompt, system_prompt=config.system_prompt)

-        return chat(messages).content
+        return chat.invoke(messages).content
@@ -5,7 +5,9 @@ from typing import Any, Optional
 from langchain.schema import BaseMessage as LCBaseMessage

 from embedchain.config import BaseLlmConfig
-from embedchain.config.llm.base import DEFAULT_PROMPT, DEFAULT_PROMPT_WITH_HISTORY_TEMPLATE, DOCS_SITE_PROMPT_TEMPLATE
+from embedchain.config.llm.base import (DEFAULT_PROMPT,
+                                        DEFAULT_PROMPT_WITH_HISTORY_TEMPLATE,
+                                        DOCS_SITE_PROMPT_TEMPLATE)
 from embedchain.helpers.json_serializable import JSONSerializable
 from embedchain.memory.base import ChatHistory
 from embedchain.memory.message import ChatMessage
@@ -0,0 +1,47 @@
+import logging
+import os
+from typing import Optional
+
+from embedchain.config import BaseLlmConfig
+from embedchain.helpers.json_serializable import register_deserializable
+from embedchain.llm.base import BaseLlm
+
+
+@register_deserializable
+class ClarifaiLlm(BaseLlm):
+    def __init__(self, config: Optional[BaseLlmConfig] = None):
+        super().__init__(config=config)
+        if not self.config.api_key and "CLARIFAI_PAT" not in os.environ:
+            raise ValueError("Please set the CLARIFAI_PAT environment variable.")
+
+    def get_llm_model_answer(self, prompt):
+        return self._get_answer(prompt=prompt, config=self.config)
+
+    @staticmethod
+    def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
+        try:
+            from clarifai.client.model import Model
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(
+                "The required dependencies for Clarifai are not installed."
+                'Please install with `pip install --upgrade "embedchain[clarifai]"`'
+            ) from None
+
+        model_name = config.model
+        logging.info(f"Using clarifai LLM model: {model_name}")
+        api_key = config.api_key or os.getenv("CLARIFAI_PAT")
+        model = Model(url=model_name, pat=api_key)
+        params = config.model_kwargs
+
+        try:
+            (params := {}) if config.model_kwargs is None else config.model_kwargs
+            predict_response = model.predict_by_bytes(
+                bytes(prompt, "utf-8"),
+                input_type="text",
+                inference_params=params,
+            )
+            text = predict_response.outputs[0].data.text.raw
+            return text
+
+        except Exception as e:
+            logging.error(f"Predict failed, exception: {e}")
@@ -12,9 +12,6 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
 class CohereLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "COHERE_API_KEY" not in os.environ:
-            raise ValueError("Please set the COHERE_API_KEY environment variable.")
-
        try:
            importlib.import_module("cohere")
        except ModuleNotFoundError:
@@ -24,6 +21,8 @@ class CohereLlm(BaseLlm):
            ) from None

        super().__init__(config=config)
+        if not self.config.api_key and "COHERE_API_KEY" not in os.environ:
+            raise ValueError("Please set the COHERE_API_KEY environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        if self.config.system_prompt:
@@ -32,8 +31,9 @@ class CohereLlm(BaseLlm):

    @staticmethod
    def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
+        api_key = config.api_key or os.getenv("COHERE_API_KEY")
        llm = Cohere(
-            cohere_api_key=os.environ["COHERE_API_KEY"],
+            cohere_api_key=api_key,
            model=config.model,
            max_tokens=config.max_tokens,
            temperature=config.temperature,
@@ -1,10 +1,12 @@
-import importlib
 import logging
 import os
 from collections.abc import Generator
 from typing import Any, Optional, Union

-import google.generativeai as genai
+try:
+    import google.generativeai as genai
+except ImportError:
+    raise ImportError("GoogleLlm requires extra dependencies. Install with `pip install google-generativeai`") from None

 from embedchain.config import BaseLlmConfig
 from embedchain.helpers.json_serializable import register_deserializable
@@ -16,19 +18,12 @@ logger = logging.getLogger(__name__)
@register_deserializable
 class GoogleLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "GOOGLE_API_KEY" not in os.environ:
-            raise ValueError("Please set the GOOGLE_API_KEY environment variable.")
-
-        try:
-            importlib.import_module("google.generativeai")
-        except ModuleNotFoundError:
-            raise ModuleNotFoundError(
-                "The required dependencies for GoogleLlm are not installed."
-                'Please install with `pip install --upgrade "embedchain[google]"`'
-            ) from None
-
        super().__init__(config)
-        genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
+        if not self.config.api_key and "GOOGLE_API_KEY" not in os.environ:
+            raise ValueError("Please set the GOOGLE_API_KEY environment variable or pass it in the config.")
+
+        api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
+        genai.configure(api_key=api_key)

    def get_llm_model_answer(self, prompt):
        if self.config.system_prompt:
@@ -19,6 +19,8 @@ from embedchain.llm.base import BaseLlm
 class GroqLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
        super().__init__(config=config)
+        if not self.config.api_key and "GROQ_API_KEY" not in os.environ:
+            raise ValueError("Please set the GROQ_API_KEY environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt) -> str:
        response = self._get_answer(prompt, self.config)
@@ -17,9 +17,6 @@ logger = logging.getLogger(__name__)
@register_deserializable
 class HuggingFaceLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "HUGGINGFACE_ACCESS_TOKEN" not in os.environ:
-            raise ValueError("Please set the HUGGINGFACE_ACCESS_TOKEN environment variable.")
-
        try:
            importlib.import_module("huggingface_hub")
        except ModuleNotFoundError:
@@ -29,6 +26,8 @@ class HuggingFaceLlm(BaseLlm):
            ) from None

        super().__init__(config=config)
+        if not self.config.api_key and "HUGGINGFACE_ACCESS_TOKEN" not in os.environ:
+            raise ValueError("Please set the HUGGINGFACE_ACCESS_TOKEN environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        if self.config.system_prompt:
@@ -60,9 +59,10 @@ class HuggingFaceLlm(BaseLlm):
            raise ValueError("`top_p` must be > 0.0 and < 1.0")

        model = config.model
+        api_key = config.api_key or os.getenv("HUGGINGFACE_ACCESS_TOKEN")
        logger.info(f"Using HuggingFaceHub with model {model}")
        llm = HuggingFaceHub(
-            huggingfacehub_api_token=os.environ["HUGGINGFACE_ACCESS_TOKEN"],
+            huggingfacehub_api_token=api_key,
            repo_id=model,
            model_kwargs=model_kwargs,
        )
@@ -70,8 +70,9 @@ class HuggingFaceLlm(BaseLlm):

    @staticmethod
    def _from_endpoint(prompt: str, config: BaseLlmConfig) -> str:
+        api_key = config.api_key or os.getenv("HUGGINGFACE_ACCESS_TOKEN")
        llm = HuggingFaceEndpoint(
-            huggingfacehub_api_token=os.environ["HUGGINGFACE_ACCESS_TOKEN"],
+            huggingfacehub_api_token=api_key,
            endpoint_url=config.endpoint,
            task="text-generation",
            model_kwargs=config.model_kwargs,
@@ -12,9 +12,9 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
 class JinaLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "JINACHAT_API_KEY" not in os.environ:
-            raise ValueError("Please set the JINACHAT_API_KEY environment variable.")
        super().__init__(config=config)
+        if not self.config.api_key and "JINACHAT_API_KEY" not in os.environ:
+            raise ValueError("Please set the JINACHAT_API_KEY environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        response = JinaLlm._get_answer(prompt, self.config)
@@ -29,6 +29,7 @@ class JinaLlm(BaseLlm):
        kwargs = {
            "temperature": config.temperature,
            "max_tokens": config.max_tokens,
+            "jinachat_api_key": config.api_key or os.environ["JINACHAT_API_KEY"],
            "model_kwargs": {},
        }
        if config.top_p:
@@ -19,8 +19,6 @@ class Llama2Llm(BaseLlm):
                "The required dependencies for Llama2 are not installed."
                'Please install with `pip install --upgrade "embedchain[llama2]"`'
            ) from None
-        if "REPLICATE_API_TOKEN" not in os.environ:
-            raise ValueError("Please set the REPLICATE_API_TOKEN environment variable.")

        # Set default config values specific to this llm
        if not config:
@@ -35,13 +33,17 @@ class Llama2Llm(BaseLlm):
            )

        super().__init__(config=config)
+        if not self.config.api_key and "REPLICATE_API_TOKEN" not in os.environ:
+            raise ValueError("Please set the REPLICATE_API_TOKEN environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        # TODO: Move the model and other inputs into config
        if self.config.system_prompt:
            raise ValueError("Llama2 does not support `system_prompt`")
+        api_key = self.config.api_key or os.getenv("REPLICATE_API_TOKEN")
        llm = Replicate(
            model=self.config.model,
+            replicate_api_token=api_key,
            input={
                "temperature": self.config.temperature,
                "max_length": self.config.max_tokens,
@@ -21,10 +21,9 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
 class NvidiaLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "NVIDIA_API_KEY" not in os.environ:
-            raise ValueError("NVIDIA_API_KEY environment variable must be set")
-
        super().__init__(config=config)
+        if not self.config.api_key and "NVIDIA_API_KEY" not in os.environ:
+            raise ValueError("Please set the NVIDIA_API_KEY environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        return self._get_answer(prompt=prompt, config=self.config)
@@ -34,7 +33,7 @@ class NvidiaLlm(BaseLlm):
        callback_manager = [StreamingStdOutCallbackHandler()] if config.stream else [StdOutCallbackHandler()]
        model_kwargs = config.model_kwargs or {}
        labels = model_kwargs.get("labels", None)
-        params = {"model": config.model}
+        params = {"model": config.model, "nvidia_api_key": config.api_key or os.getenv("NVIDIA_API_KEY")}
        if config.system_prompt:
            params["system_prompt"] = config.system_prompt
        if config.temperature:
@@ -1,3 +1,4 @@
+import logging
 from collections.abc import Iterable
 from typing import Optional, Union

@@ -6,10 +7,17 @@ from langchain.callbacks.stdout import StdOutCallbackHandler
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain_community.llms.ollama import Ollama

+try:
+    from ollama import Client
+except ImportError:
+    raise ImportError("Ollama requires extra dependencies. Install with `pip install ollama`") from None
+
 from embedchain.config import BaseLlmConfig
 from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm

+logger = logging.getLogger(__name__)
+

@register_deserializable
 class OllamaLlm(BaseLlm):
@@ -18,19 +26,28 @@ class OllamaLlm(BaseLlm):
        if self.config.model is None:
            self.config.model = "llama2"

+        client = Client(host=config.base_url)
+        local_models = client.list()["models"]
+        if not any(model.get("name") == self.config.model for model in local_models):
+            logger.info(f"Pulling {self.config.model} from Ollama!")
+            client.pull(self.config.model)
+
    def get_llm_model_answer(self, prompt):
        return self._get_answer(prompt=prompt, config=self.config)

    @staticmethod
    def _get_answer(prompt: str, config: BaseLlmConfig) -> Union[str, Iterable]:
-        callback_manager = [StreamingStdOutCallbackHandler()] if config.stream else [StdOutCallbackHandler()]
+        if config.stream:
+            callbacks = config.callbacks if config.callbacks else [StreamingStdOutCallbackHandler()]
+        else:
+            callbacks = [StdOutCallbackHandler()]

        llm = Ollama(
            model=config.model,
            system=config.system_prompt,
            temperature=config.temperature,
            top_p=config.top_p,
-            callback_manager=CallbackManager(callback_manager),
+            callback_manager=CallbackManager(callbacks),
            base_url=config.base_url,
        )

@@ -36,7 +36,7 @@ class OpenAILlm(BaseLlm):
            "model": config.model or "gpt-3.5-turbo",
            "temperature": config.temperature,
            "max_tokens": config.max_tokens,
-            "model_kwargs": {},
+            "model_kwargs": config.model_kwargs or {},
        }
        api_key = config.api_key or os.environ["OPENAI_API_KEY"]
        base_url = config.base_url or os.environ.get("OPENAI_API_BASE", None)
@@ -56,7 +56,13 @@ class OpenAILlm(BaseLlm):
                http_async_client=config.http_async_client,
            )
        else:
-            chat = ChatOpenAI(**kwargs, api_key=api_key, base_url=base_url)
+            chat = ChatOpenAI(
+                **kwargs,
+                api_key=api_key,
+                base_url=base_url,
+                http_client=config.http_client,
+                http_async_client=config.http_async_client,
+            )
        if self.tools:
            return self._query_function_call(chat, self.tools, messages)

@@ -12,9 +12,6 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
 class TogetherLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "TOGETHER_API_KEY" not in os.environ:
-            raise ValueError("Please set the TOGETHER_API_KEY environment variable.")
-
        try:
            importlib.import_module("together")
        except ModuleNotFoundError:
@@ -24,6 +21,8 @@ class TogetherLlm(BaseLlm):
            ) from None

        super().__init__(config=config)
+        if not self.config.api_key and "TOGETHER_API_KEY" not in os.environ:
+            raise ValueError("Please set the TOGETHER_API_KEY environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        if self.config.system_prompt:
@@ -32,8 +31,9 @@ class TogetherLlm(BaseLlm):

    @staticmethod
    def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
+        api_key = config.api_key or os.getenv("TOGETHER_API_KEY")
        llm = Together(
-            together_api_key=os.environ["TOGETHER_API_KEY"],
+            together_api_key=api_key,
            model=config.model,
            max_tokens=config.max_tokens,
            temperature=config.temperature,
@@ -0,0 +1,53 @@
+import hashlib
+import os
+
+import validators
+
+from embedchain.helpers.json_serializable import register_deserializable
+from embedchain.loaders.base_loader import BaseLoader
+
+try:
+    from deepgram import DeepgramClient, PrerecordedOptions
+except ImportError:
+    raise ImportError(
+        "Audio file requires extra dependencies. Install with `pip install deepgram-sdk==3.2.7`"
+    ) from None
+
+
+@register_deserializable
+class AudioLoader(BaseLoader):
+    def __init__(self):
+        if not os.environ.get("DEEPGRAM_API_KEY"):
+            raise ValueError("DEEPGRAM_API_KEY is not set")
+
+        DG_KEY = os.environ.get("DEEPGRAM_API_KEY")
+        self.client = DeepgramClient(DG_KEY)
+
+    def load_data(self, url: str):
+        """Load data from a audio file or URL."""
+
+        options = PrerecordedOptions(
+            model="nova-2",
+            smart_format=True,
+        )
+        if validators.url(url):
+            source = {"url": url}
+            response = self.client.listen.prerecorded.v("1").transcribe_url(source, options)
+        else:
+            with open(url, "rb") as audio:
+                source = {"buffer": audio}
+                response = self.client.listen.prerecorded.v("1").transcribe_file(source, options)
+        content = response["results"]["channels"][0]["alternatives"][0]["transcript"]
+
+        doc_id = hashlib.sha256((content + url).encode()).hexdigest()
+        metadata = {"url": url}
+
+        return {
+            "doc_id": doc_id,
+            "data": [
+                {
+                    "content": content,
+                    "meta_data": metadata,
+                }
+            ],
+        }
@@ -27,7 +27,7 @@ class ImageLoader(BaseLoader):

    def _create_completion_request(self, content: str):
        return self.client.chat.completions.create(
-            model="gpt-4-vision-preview", messages=[{"role": "user", "content": content}], max_tokens=self.max_tokens
+            model="gpt-4o", messages=[{"role": "user", "content": content}], max_tokens=self.max_tokens
        )

    def _process_url(self, url: str):
@@ -11,7 +11,8 @@ class UnstructuredLoader(BaseLoader):
        """Load data from an Unstructured file."""
        try:
            import unstructured  # noqa: F401
-            from langchain_community.document_loaders import UnstructuredFileLoader
+            from langchain_community.document_loaders import \
+                UnstructuredFileLoader
        except ImportError:
            raise ImportError(
                'Unstructured file requires extra dependencies. Install with `pip install "unstructured[local-inference, all-docs]"`'  # noqa: E501
@@ -8,6 +8,7 @@ except ImportError:
    raise ImportError('YouTube video requires extra dependencies. Install with `pip install youtube-transcript-api "`')
 try:
    from langchain_community.document_loaders import YoutubeLoader
+    from langchain_community.document_loaders.youtube import _parse_video_id
 except ImportError:
    raise ImportError(
        'YouTube video requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
@@ -21,7 +22,20 @@ from embedchain.utils.misc import clean_string
 class YoutubeVideoLoader(BaseLoader):
    def load_data(self, url):
        """Load data from a Youtube video."""
-        loader = YoutubeLoader.from_youtube_url(url, add_video_info=True)
+        video_id = _parse_video_id(url)
+
+        languages = ["en"]
+        try:
+            # Fetching transcript data
+            languages = [transcript.language_code for transcript in YouTubeTranscriptApi.list_transcripts(video_id)]
+            transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=languages)
+            # convert transcript to json to avoid unicode symboles
+            transcript = json.dumps(transcript, ensure_ascii=True)
+        except Exception:
+            logging.exception(f"Failed to fetch transcript for video {url}")
+            transcript = "Unavailable"
+
+        loader = YoutubeLoader.from_youtube_url(url, add_video_info=True, language=languages)
        doc = loader.load()
        output = []
        if not len(doc):
@@ -30,16 +44,7 @@ class YoutubeVideoLoader(BaseLoader):
        content = clean_string(content)
        metadata = doc[0].metadata
        metadata["url"] = url
-
-        video_id = url.split("v=")[1].split("&")[0]
-        try:
-            # Fetching transcript data
-            transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=["en"])
-            # convert transcript to json to avoid unicode symboles
-            metadata["transcript"] = json.dumps(transcript, ensure_ascii=True)
-        except Exception:
-            logging.exception(f"Failed to fetch transcript for video {url}")
-            metadata["transcript"] = "Unavailable"
+        metadata["transcript"] = transcript

        output.append(
            {
@@ -41,6 +41,7 @@ class IndirectDataType(Enum):
    DROPBOX = "dropbox"
    TEXT_FILE = "text_file"
    EXCEL_FILE = "excel_file"
+    AUDIO = "audio"


 class SpecialDataType(Enum):
@@ -81,3 +82,4 @@ class DataType(Enum):
    DROPBOX = IndirectDataType.DROPBOX.value
    TEXT_FILE = IndirectDataType.TEXT_FILE.value
    EXCEL_FILE = IndirectDataType.EXCEL_FILE.value
+    AUDIO = IndirectDataType.AUDIO.value
@@ -193,12 +193,15 @@ def read_env_file(env_file_path):
    dict: Dictionary of environment variables.
    """
    env_vars = {}
+    pattern = re.compile(r"(\w+)=(.*)")  # compile regular expression for better performance
    with open(env_file_path, "r") as file:
-        for line in file:
+        lines = file.readlines()  # readlines is faster as it reads all at once
+        for line in lines:
+            line = line.strip()
            # Ignore comments and empty lines
-            if line.strip() and not line.strip().startswith("#"):
+            if line and not line.startswith("#"):
                # Assume each line is in the format KEY=VALUE
-                key_value_match = re.match(r"(\w+)=(.*)", line.strip())
+                key_value_match = pattern.match(line)
                if key_value_match:
                    key, value = key_value_match.groups()
                    env_vars[key] = value
@@ -1,3 +1,4 @@
+import datetime
 import itertools
 import json
 import logging
@@ -237,6 +238,12 @@ def detect_datatype(source: Any) -> DataType:
            logger.debug(f"Source of `{formatted_source}` detected as `docx`.")
            return DataType.DOCX

+        if url.path.endswith(
+            (".mp3", ".mp4", ".mp2", ".aac", ".wav", ".flac", ".pcm", ".m4a", ".ogg", ".opus", ".webm")
+        ):
+            logger.debug(f"Source of `{formatted_source}` detected as `audio`.")
+            return DataType.AUDIO
+
        if url.path.endswith(".yaml"):
            try:
                response = requests.get(source)
@@ -407,6 +414,7 @@ def validate_config(config_data):
                    "google",
                    "aws_bedrock",
                    "mistralai",
+                    "clarifai",
                    "vllm",
                    "groq",
                    "nvidia",
@@ -433,11 +441,14 @@ def validate_config(config_data):
                    Optional("local"): bool,
                    Optional("base_url"): str,
                    Optional("default_headers"): dict,
+                    Optional("api_version"): Or(str, datetime.date),
+                    Optional("http_client_proxies"): Or(str, dict),
+                    Optional("http_async_client_proxies"): Or(str, dict),
                },
            },
            Optional("vectordb"): {
                Optional("provider"): Or(
-                    "chroma", "elasticsearch", "opensearch", "pinecone", "qdrant", "weaviate", "zilliz"
+                    "chroma", "elasticsearch", "opensearch", "lancedb", "pinecone", "qdrant", "weaviate", "zilliz"
                ),
                Optional("config"): object,  # TODO: add particular config schema for each provider
            },
@@ -450,6 +461,7 @@ def validate_config(config_data):
                    "azure_openai",
                    "google",
                    "mistralai",
+                    "clarifai",
                    "nvidia",
                    "ollama",
                    "cohere",
@@ -463,6 +475,8 @@ def validate_config(config_data):
                    Optional("task_type"): str,
                    Optional("vector_dimension"): int,
                    Optional("base_url"): str,
+                    Optional("endpoint"): str,
+                    Optional("model_kwargs"): dict,
                },
            },
            Optional("embedding_model"): {
@@ -474,6 +488,7 @@ def validate_config(config_data):
                    "azure_openai",
                    "google",
                    "mistralai",
+                    "clarifai",
                    "nvidia",
                    "ollama",
                ),
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Dev Khant	ea09b5f7f0	Version bump (#1457 )	2024-07-02 23:07:52 -07:00
Pranav Puranik	5258fd91ea	http_client and http_async_client bugfix (#1454 )	2024-07-02 16:13:33 -07:00
João Moura	b305d674de	Updating dependencies (#1453 )	2024-07-02 16:12:52 -07:00
Pranav Puranik	7c24601d0f	Adding model_kwargs for huggingface embedders. (#1450 )	2024-06-29 12:37:31 -07:00
Dev Khant	50c0285cb2	Fix batch_size for vectordb (#1449 )	2024-06-28 11:18:22 -07:00
Dev Khant	0a78198bb5	Add batch_size in config for VectorDB (#1448 )	2024-06-27 14:45:58 -07:00
Vatsal Rathod	edaeb78ccf	Refactor openai embedder (#1444 )	2024-06-26 10:58:12 -07:00
Dev Khant	f80be2d2ea	Version bump -> 0.1.113 (#1447 )	2024-06-24 11:00:55 -07:00
Halan Marques	8700165b42	Fixed Azure OpenAI Deprecations and Adjusted the Tests (#1437 )	2024-06-24 10:55:38 -07:00
Prashant Dixit	18fb92f1f8	Updated LanceDB Doc (#1445 )	2024-06-24 10:55:20 -07:00
Nikhil Sharma	14fc6bbadd	change: replaced deprecated gpt-4-perview with gpt-4o (#1443 )	2024-06-24 10:27:10 -07:00
Dev Khant	5070a1d83e	Change HF embedding library (#1440 )	2024-06-22 01:38:29 -07:00
Dev Khant	8a9088ea9d	Version bump (#1438 )	2024-06-21 09:11:24 -07:00
Prashant Dixit	48b24f6f12	Lancedb Integration (#1411 )	2024-06-21 08:59:22 -07:00
Dev Khant	f6ddd5ffc5	Add HF endpoint in embedder (#1436 )	2024-06-21 08:57:21 -07:00
Dev Khant	b43a116b3c	Add vector dimension to Ollama embedder (#1435 )	2024-06-21 08:56:46 -07:00
Dev Khant	50512a5f03	Doc fix for embedders (#1433 )	2024-06-19 10:08:31 -07:00
Dev Khant	e3e107b31d	Raise import error if Ollama and Google not found (#1432 )	2024-06-18 21:46:48 -07:00
Dev Khant	21a04541ea	poetry fix (#1430 )	2024-06-18 10:45:37 -07:00
Dev Khant	cdd5d8ac76	Version bump (#1426 )	2024-06-18 09:13:52 -07:00
Dev Khant	11094f504e	Fix Ollama test (#1428 )	2024-06-18 09:10:43 -07:00
mogith-pn	5acaae5f56	Clarifai : Added Clarifai as LLM and embedding model provider. (#1311 ) Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>	2024-06-17 08:48:18 -07:00
Pranav Puranik	4547d870af	azure openai features and bugs solve - openai_version, docs (#1425 )	2024-06-17 08:47:27 -07:00
Aditya Veer Parmar	dc0d8e0932	Allow ollama llm to take custom callback for handling streaming (#1376 )	2024-06-17 08:44:52 -07:00
patcher9	c558eae9ce	[Docs]: Fix the Title and Description for OpenLIT Integration (#1424 )	2024-06-14 00:09:31 -07:00
patcher9	abb9af66a6	[Docs]: Add Integration for OpenLIT (OpenTelemetry-native LLM Application O11y) (#1377 )	2024-06-13 23:06:04 -07:00
Ananto Joyoadikusumo	4800e0344c	Added language detection for non-english youtube videos (#1362 )	2024-06-13 23:02:37 -07:00
Dev Khant	439b425c61	Version bump (#1423 )	2024-06-13 22:28:35 -07:00
Dev Khant	2855f1635b	Add support for loading api_key from config or env variable (#1421 )	2024-06-13 11:19:54 -07:00
Dev Khant	08b67b4a78	Support for Audio Files (#1416 )	2024-06-12 10:25:58 -07:00
Dev Khant	1bddd46ed2	Verion bump, chromadb_version change and doc update (#1407 )	2024-06-12 08:46:00 -07:00
Pranav Puranik	6ecdadfd97	Add model_kwargs to OpenAI call (#1402 )	2024-06-11 11:20:04 -07:00
Dimitra Gerontaki	4119040005	Add documentation for text_file data type (#1410 )	2024-06-10 21:34:28 -07:00
Taranjeet Singh	873eef6ef8	Remove: EC deployment docs, and js links (#1409 )	2024-06-11 02:34:15 +05:30
Taranjeet Singh	445fed4d3f	Remove embedchain js (#1408 )	2024-06-11 01:54:56 +05:30
Dev Khant	52fd3e0dd4	Update contributing doc (#1404 )	2024-06-10 10:14:52 -07:00
Saurabh Misra	8fd0e1f3b0	⚡️ Speed up `read_env_file()` in `embedchain/utils/cli.py` (#1260 )	2024-06-09 09:11:15 -07:00
golemus	11fc4a8451	Update llms card to properly use local ollama (#1395 )	2024-06-09 09:09:49 -07:00
shuo	e22293294e	Delete embedchain/embedder/.ollama.py.swp (#1398 )	2024-06-09 09:02:38 -07:00
Dev Khant	73e53aaff1	Download Ollama model if not present (#1397 )	2024-06-08 23:43:03 -07:00
				`@@ -1 +0,0 @@`
				`module.exports = { extends: ['@commitlint/config-conventional'] };`
				`@@ -1 +0,0 @@`
				`export type DataType = 'pdf_file' \| 'web_page' \| 'qna_pair';`
				`@@ -1 +0,0 @@`
				`export type Method = 'init' \| 'query' \| 'add' \| 'add_local';`