mirror of
https://github.com/hwchase17/langchain.git
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Added deeplake use case examples of the new features (#6528)
<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @hwchase17 VectorStores / Retrievers / Memory - @dev2049 --> 1. Added use cases of the new features 2. Done some code refactoring --------- Co-authored-by: Ivo Stranic <istranic@gmail.com>
This commit is contained in:
Adilkhan Sarsen
GitHub
@@ -5,8 +5,8 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Use LangChain, GPT and Deep Lake to work with code base\n",
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"In this tutorial, we are going to use Langchain + Deep Lake with GPT to analyze the code base of the LangChain itself. "
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"# Use LangChain, GPT and Activeloop's Deep Lake to work with code base\n",
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"In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT to analyze the code base of the LangChain itself. "
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]
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},
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{
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@@ -60,7 +60,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {
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"tags": []
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@@ -81,19 +81,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 2,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" ········\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import os\n",
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"from getpass import getpass\n",
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@@ -112,21 +104,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 3,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" ········\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"os.environ[\"ACTIVELOOP_TOKEN\"] = getpass.getpass(\"Activeloop Token:\")"
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"activeloop_token = getpass(\"Activeloop Token:\")\n",
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"os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
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]
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},
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@@ -149,19 +134,20 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!ls \"../../../..\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1147\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"\n",
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@@ -189,180 +175,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": null,
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"metadata": {
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"tags": []
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"outputs": [
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"text": [
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"3477\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"\n",
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@@ -383,22 +200,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', document_model_name='text-embedding-ada-002', query_model_name='text-embedding-ada-002', embedding_ctx_length=8191, openai_api_key=None, openai_organization=None, allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6)"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"\n",
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@@ -417,11 +223,33 @@
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"from langchain.vectorstores import DeepLake\n",
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"\n",
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"db = DeepLake.from_documents(\n",
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" texts, embeddings, dataset_path=f\"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code\"\n",
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" texts, embeddings, dataset_path=f\"hub://{<org_id>}/langchain-code\"\n",
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")\n",
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"db"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# from langchain.vectorstores import DeepLake\n",
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"\n",
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"# db = DeepLake.from_documents(\n",
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"# texts, embeddings, dataset_path=f\"hub://{<org_id>}/langchain-code\", runtime={\"tensor_db\": True}\n",
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"# )\n",
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"# db"
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]
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},
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"execution_count": 16,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stderr",
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"text": [
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"-"
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"text": [
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"This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/user_name/langchain-code\n",
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"\n"
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]
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},
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"name": "stderr",
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"output_type": "stream",
|
||||
"text": [
|
||||
"/"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"hub://user_name/langchain-code loaded successfully.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Deep Lake Dataset in hub://user_name/langchain-code already exists, loading from the storage\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Dataset(path='hub://user_name/langchain-code', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])\n",
|
||||
"\n",
|
||||
" tensor htype shape dtype compression\n",
|
||||
" ------- ------- ------- ------- ------- \n",
|
||||
" embedding generic (3477, 1536) float32 None \n",
|
||||
" ids text (3477, 1) str None \n",
|
||||
" metadata json (3477, 1) str None \n",
|
||||
" text text (3477, 1) str None \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"db = DeepLake(\n",
|
||||
" dataset_path=f\"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code\",\n",
|
||||
" dataset_path=f\"hub://{<org_id>}/langchain-code\",\n",
|
||||
" read_only=True,\n",
|
||||
" embedding_function=embeddings,\n",
|
||||
")"
|
||||
@@ -500,7 +276,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
@@ -523,7 +299,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
@@ -545,7 +321,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
@@ -658,7 +434,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.6"
|
||||
"version": "3.9.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -5,8 +5,8 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Deep Lake\n",
|
||||
"In this tutorial, we are going to use Langchain + Deep Lake with GPT4 to analyze the code base of the twitter algorithm. "
|
||||
"# Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop's Deep Lake\n",
|
||||
"In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT4 to analyze the code base of the twitter algorithm. "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -15,7 +15,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!python3 -m pip install --upgrade langchain deeplake openai tiktoken"
|
||||
"!python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -41,7 +41,8 @@
|
||||
"from langchain.vectorstores import DeepLake\n",
|
||||
"\n",
|
||||
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
|
||||
"os.environ[\"ACTIVELOOP_TOKEN\"] = getpass.getpass(\"Activeloop Token:\")"
|
||||
"activeloop_token = getpass.getpass(\"Activeloop Token:\")\n",
|
||||
"os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -149,6 +150,29 @@
|
||||
"db.add_documents(texts)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# username = \"davitbun\" # replace with your username from app.activeloop.ai\n",
|
||||
"# db = DeepLake(\n",
|
||||
"# dataset_path=f\"hub://{username}/twitter-algorithm\",\n",
|
||||
"# embedding_function=embeddings,\n",
|
||||
"# runtime={\"tensor_db\": True}\n",
|
||||
"# )\n",
|
||||
"# db.add_documents(texts)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
@@ -176,6 +200,7 @@
|
||||
" dataset_path=\"hub://davitbun/twitter-algorithm\",\n",
|
||||
" read_only=True,\n",
|
||||
" embedding_function=embeddings,\n",
|
||||
" \n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
|
||||
Reference in New Issue
Block a user