mirror of
https://github.com/hwchase17/langchain.git
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docs: notebook linting (#14366)
Many jupyter notebooks didn't pass linting. List of these files are presented in the [tool.ruff.lint.per-file-ignores] section of the pyproject.toml . Addressed these bugs: - fixed bugs; added missed imports; updated pyproject.toml Only the `document_loaders/tensorflow_datasets.ipyn`, `cookbook/gymnasium_agent_simulation.ipynb` are not completely fixed. I'm not sure about imports. --------- Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Leonid Ganeline
GitHub
parent
52052cc7b9
commit
18aba7fdef
@ -31,7 +31,7 @@
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"source": [
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"import re\n",
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"\n",
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"from IPython.display import Image\n",
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"from IPython.display import Image, display\n",
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"from steamship import Block, Steamship"
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]
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},
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@ -180,7 +180,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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@ -37,7 +37,8 @@
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"source": [
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"#!pip install qianfan\n",
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"#!pip install bce-python-sdk\n",
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"#!pip install elasticsearch == 7.11.0"
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"#!pip install elasticsearch == 7.11.0\n",
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"#!pip install sentence-transformers"
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]
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},
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{
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@ -54,8 +55,10 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import sentence_transformers\n",
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"from baidubce.auth.bce_credentials import BceCredentials\n",
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"from baidubce.bce_client_configuration import BceClientConfiguration\n",
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"from langchain.chains.retrieval_qa import RetrievalQA\n",
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"from langchain.document_loaders.baiducloud_bos_directory import BaiduBOSDirectoryLoader\n",
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"from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
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"from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\n",
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@ -161,15 +164,22 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"version": "3.9.17"
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
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@ -177,5 +187,5 @@
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}
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@ -133,7 +133,7 @@
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"from tqdm import tqdm\n",
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"\n",
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"for i in tqdm(range(len(title_embeddings))):\n",
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" title = titles[i].replace(\"'\", \"''\")\n",
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" title = song_titles[i].replace(\"'\", \"''\")\n",
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" embedding = title_embeddings[i]\n",
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" sql_command = (\n",
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" f'UPDATE \"Track\" SET \"embeddings\" = ARRAY{embedding} WHERE \"Name\" ='\n",
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@ -681,9 +681,9 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.18"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}
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@ -187,7 +187,7 @@
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" for key in path:\n",
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" try:\n",
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" current = current[key]\n",
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" except:\n",
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" except KeyError:\n",
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" return None\n",
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" return current\n",
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"\n",
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@ -91,7 +91,7 @@
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"with patch(\"openai.resources.chat.completions.Completions.create\", side_effect=error):\n",
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" try:\n",
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" print(openai_llm.invoke(\"Why did the chicken cross the road?\"))\n",
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" except:\n",
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" except RateLimitError:\n",
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" print(\"Hit error\")"
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]
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},
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@ -114,7 +114,7 @@
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"with patch(\"openai.resources.chat.completions.Completions.create\", side_effect=error):\n",
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" try:\n",
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" print(llm.invoke(\"Why did the chicken cross the road?\"))\n",
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" except:\n",
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" except RateLimitError:\n",
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" print(\"Hit error\")"
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]
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},
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@ -156,7 +156,7 @@
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"with patch(\"openai.resources.chat.completions.Completions.create\", side_effect=error):\n",
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" try:\n",
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" print(chain.invoke({\"animal\": \"kangaroo\"}))\n",
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" except:\n",
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" except RateLimitError:\n",
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" print(\"Hit error\")"
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]
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},
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@ -190,10 +190,10 @@
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")\n",
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"\n",
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"chain = prompt | llm\n",
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"with patch(\"openai.ChatCompletion.create\", side_effect=error):\n",
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"with patch(\"openai.resources.chat.completions.Completions.create\", side_effect=error):\n",
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" try:\n",
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" print(chain.invoke({\"animal\": \"kangaroo\"}))\n",
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" except:\n",
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" except RateLimitError:\n",
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" print(\"Hit error\")"
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]
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},
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@ -291,7 +291,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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@ -93,7 +93,7 @@
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"with patch(\"openai.resources.chat.completions.Completions.create\", side_effect=error):\n",
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" try:\n",
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" print(openai_llm.invoke(\"Why did the chicken cross the road?\"))\n",
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" except:\n",
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" except RateLimitError:\n",
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" print(\"Hit error\")"
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]
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},
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@ -116,7 +116,7 @@
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"with patch(\"openai.resources.chat.completions.Completions.create\", side_effect=error):\n",
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" try:\n",
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" print(llm.invoke(\"Why did the chicken cross the road?\"))\n",
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" except:\n",
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" except RateLimitError:\n",
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" print(\"Hit error\")"
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]
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},
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@ -158,7 +158,7 @@
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"with patch(\"openai.resources.chat.completions.Completions.create\", side_effect=error):\n",
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" try:\n",
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" print(chain.invoke({\"animal\": \"kangaroo\"}))\n",
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" except:\n",
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" except RateLimitError:\n",
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" print(\"Hit error\")"
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]
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},
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@ -45,7 +45,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -55,7 +55,7 @@
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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": 2,
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"metadata": {},
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"outputs": [
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{
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@ -64,7 +64,7 @@
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"AIMessage(content=\" J'aime la programmation.\")"
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]
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},
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"execution_count": 3,
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -89,7 +89,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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@ -98,7 +98,7 @@
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"AIMessage(content=' プログラミングが大好きです')"
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]
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},
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"execution_count": 4,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -142,7 +142,7 @@
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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": 4,
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"metadata": {
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"tags": []
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},
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@ -198,7 +198,7 @@
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -212,7 +212,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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@ -221,12 +221,17 @@
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"AIMessage(content=' Why do you love programming?')"
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]
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},
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"execution_count": 7,
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"execution_count": 6,
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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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"source": [
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"system = (\n",
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" \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
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")\n",
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"human = \"{text}\"\n",
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"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
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"chain = prompt | chat\n",
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"\n",
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"asyncio.run(\n",
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@ -251,7 +256,7 @@
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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": 7,
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"metadata": {},
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"outputs": [
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{
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@ -129,12 +129,6 @@
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"**The above request should now appear on your [PromptLayer dashboard](https://www.promptlayer.com).**"
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]
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},
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{
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"cell_type": "markdown",
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"id": "05e9e2fe",
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"metadata": {},
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"source": []
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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@ -152,6 +146,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import promptlayer\n",
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"\n",
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"chat = PromptLayerChatOpenAI(return_pl_id=True)\n",
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"chat_results = chat.generate([[HumanMessage(content=\"I am a cat and I want\")]])\n",
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"\n",
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@ -172,7 +168,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@ -186,7 +182,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8 (default, Apr 13 2021, 12:59:45) \n[Clang 10.0.0 ]"
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"version": "3.10.12"
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},
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"vscode": {
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"interpreter": {
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@ -153,7 +153,7 @@
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"source": [
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"# Now all of the Tortoise's messages will take the AI message class\n",
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"# which maps to the 'assistant' role in OpenAI's training format\n",
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"alternating_sessions[0][\"messages\"][:3]"
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"chat_sessions[0][\"messages\"][:3]"
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]
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},
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{
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@ -191,7 +191,7 @@
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}
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],
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"source": [
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"training_data = convert_messages_for_finetuning(alternating_sessions)\n",
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"training_data = convert_messages_for_finetuning(chat_sessions)\n",
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"print(f\"Prepared {len(training_data)} dialogues for training\")"
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]
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},
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@ -416,7 +416,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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@ -23,8 +23,18 @@
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"source": [
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"from langchain.document_loaders import ArcGISLoader\n",
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"\n",
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"url = \"https://maps1.vcgov.org/arcgis/rest/services/Beaches/MapServer/7\"\n",
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"loader = ArcGISLoader(url)"
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"URL = \"https://maps1.vcgov.org/arcgis/rest/services/Beaches/MapServer/7\"\n",
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"loader = ArcGISLoader(URL)\n",
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"\n",
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"docs = loader.load()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1e174ebd-bbbd-4a66-a644-51e0df12982d",
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"metadata": {},
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"source": [
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"Let's measure loader latency."
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]
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},
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{
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@ -261,7 +271,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"loader_geom = ArcGISLoader(url, return_geometry=True)"
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"loader_geom = ArcGISLoader(URL, return_geometry=True)"
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]
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},
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{
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@ -30,6 +30,16 @@
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"#!pip install datadog-api-client"
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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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"outputs": [],
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"source": [
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"DD_API_KEY = \"...\"\n",
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"DD_APP_KEY = \"...\""
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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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@ -73,7 +83,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@ -87,10 +97,9 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.11"
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},
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"orig_nbformat": 4
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}
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@ -65,6 +65,16 @@
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"%pip install langchain -q"
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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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"id": "2ab73cc1-d8e0-4b6d-bb03-9522b112fce5",
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"metadata": {},
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"outputs": [],
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"source": [
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"etherscanAPIKey = \"...\""
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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": 1,
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@ -74,7 +74,9 @@
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"source": [
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"# see https://python.langchain.com/docs/use_cases/summarization for more details\n",
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"from langchain.chains.summarize import load_summarize_chain\n",
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"from langchain.llms.fake import FakeListLLM\n",
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"\n",
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"llm = FakeListLLM()\n",
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"chain = load_summarize_chain(llm, chain_type=\"map_reduce\")\n",
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"chain.run(docs)"
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]
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@ -96,7 +98,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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@ -166,6 +166,9 @@
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}
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],
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"source": [
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"from langchain_core.documents import Document\n",
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"\n",
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"\n",
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"def decode_to_str(item: tf.Tensor) -> str:\n",
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" return item.numpy().decode(\"utf-8\")\n",
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"\n",
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@ -12,6 +12,18 @@
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"This example goes over how to use LangChain to interact with [Anyscale Endpoint](https://app.endpoints.anyscale.com/). "
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "515070aa-e241-480e-8d9a-afdf52f35322",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ANYSCALE_API_BASE = \"...\"\n",
|
||||
"ANYSCALE_API_KEY = \"...\"\n",
|
||||
"ANYSCALE_MODEL_NAME = \"...\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -160,7 +172,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.8"
|
||||
"version": "3.10.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@ -112,6 +112,24 @@
|
||||
"## Using NIBittensorLLM with Conversational Agent and Google Search Tool"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.tools import Tool\n",
|
||||
"from langchain.utilities import GoogleSearchAPIWrapper\n",
|
||||
"\n",
|
||||
"search = GoogleSearchAPIWrapper()\n",
|
||||
"\n",
|
||||
"tool = Tool(\n",
|
||||
" name=\"Google Search\",\n",
|
||||
" description=\"Search Google for recent results.\",\n",
|
||||
" func=search.run,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -129,7 +147,7 @@
|
||||
"\n",
|
||||
"memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"tools = [tool]\n",
|
||||
"prefix = \"\"\"Answer prompt based on LLM if there is need to search something then use internet and observe internet result and give accurate reply of user questions also try to use authenticated sources\"\"\"\n",
|
||||
"suffix = \"\"\"Begin!\n",
|
||||
" {chat_history}\n",
|
||||
@ -137,14 +155,14 @@
|
||||
" {agent_scratchpad}\"\"\"\n",
|
||||
"\n",
|
||||
"prompt = ZeroShotAgent.create_prompt(\n",
|
||||
" tools,\n",
|
||||
" tools=tools,\n",
|
||||
" prefix=prefix,\n",
|
||||
" suffix=suffix,\n",
|
||||
" input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"],\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"llm = NIBittensorLLM(\n",
|
||||
" system_prompt=\"Your task is to determine response based on user prompt\"\n",
|
||||
" system_prompt=\"Your task is to determine a response based on user prompt\"\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
|
||||
@ -176,7 +194,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.1"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -33,7 +33,13 @@
|
||||
"\n",
|
||||
"```\n",
|
||||
"pip install mlflow>=2.9\n",
|
||||
"```"
|
||||
"```\n",
|
||||
"\n",
|
||||
"Also, we need `dbutils` for this example.\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"pip install dbutils\n",
|
||||
"```\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -269,6 +275,8 @@
|
||||
"\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"import dbutils\n",
|
||||
"\n",
|
||||
"os.environ[\"DATABRICKS_TOKEN\"] = dbutils.secrets.get(\"myworkspace\", \"api_token\")\n",
|
||||
"\n",
|
||||
"llm = Databricks(host=\"myworkspace.cloud.databricks.com\", endpoint_name=\"dolly\")\n",
|
||||
@ -606,7 +614,7 @@
|
||||
"widgets": {}
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "llm",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -620,10 +628,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.18"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
@ -28,7 +28,6 @@
|
||||
"cell_type": "markdown",
|
||||
"id": "b50f0598",
|
||||
"metadata": {
|
||||
"jp-MarkdownHeadingCollapsed": true,
|
||||
"tags": []
|
||||
},
|
||||
"source": [
|
||||
@ -113,7 +112,6 @@
|
||||
"cell_type": "markdown",
|
||||
"id": "4bf59c12",
|
||||
"metadata": {
|
||||
"jp-MarkdownHeadingCollapsed": true,
|
||||
"tags": []
|
||||
},
|
||||
"source": [
|
||||
@ -231,6 +229,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import langchain\n",
|
||||
"from langchain.cache import UpstashRedisCache\n",
|
||||
"from upstash_redis import Redis\n",
|
||||
"\n",
|
||||
@ -1589,7 +1588,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.1"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -289,7 +289,7 @@
|
||||
"source": [
|
||||
"pipeline = load_pipeline()\n",
|
||||
"llm = SelfHostedPipeline.from_pipeline(\n",
|
||||
" pipeline=pipeline, hardware=gpu, model_reqs=model_reqs\n",
|
||||
" pipeline=pipeline, hardware=gpu, model_reqs=[\"pip:./\", \"transformers\", \"torch\"]\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
@ -308,6 +308,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pickle\n",
|
||||
"\n",
|
||||
"rh.blob(pickle.dumps(pipeline), path=\"models/pipeline.pkl\").save().to(\n",
|
||||
" gpu, path=\"models\"\n",
|
||||
")\n",
|
||||
@ -332,7 +334,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.6"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -54,6 +54,15 @@
|
||||
"Also you'll need to create a [Activeloop]((https://activeloop.ai/)) account."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ORG_ID = \"...\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
||||
@ -160,6 +160,8 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Create Elasticsearch connection\n",
|
||||
"from elasticsearch import Elasticsearch\n",
|
||||
"\n",
|
||||
"es_connection = Elasticsearch(\n",
|
||||
" hosts=[\"https://es_cluster_url:port\"], basic_auth=(\"user\", \"password\")\n",
|
||||
")"
|
||||
@ -259,9 +261,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.3"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
}
|
||||
|
||||
@ -20,6 +20,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# Set API key\n",
|
||||
"embaas_api_key = \"YOUR_API_KEY\"\n",
|
||||
"# or set environment variable\n",
|
||||
@ -139,9 +141,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
@ -131,6 +131,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# if you are behind an explicit proxy, you can use the OPENAI_PROXY environment variable to pass through\n",
|
||||
"os.environ[\"OPENAI_PROXY\"] = \"http://proxy.yourcompany.com:8080\""
|
||||
]
|
||||
@ -138,7 +140,7 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.11.1 64-bit",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -152,7 +154,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.1"
|
||||
"version": "3.10.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@ -225,6 +225,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# if you are behind an explicit proxy, you can use the OPENAI_PROXY environment variable to pass through\n",
|
||||
"os.environ[\"OPENAI_PROXY\"] = \"http://proxy.yourcompany.com:8080\""
|
||||
]
|
||||
@ -246,7 +248,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
"version": "3.10.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@ -101,8 +101,10 @@
|
||||
"# Or you can try the options below to display the image inline in this notebook\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" import google.colab\n",
|
||||
"\n",
|
||||
" IN_COLAB = True\n",
|
||||
"except:\n",
|
||||
"except ImportError:\n",
|
||||
" IN_COLAB = False\n",
|
||||
"\n",
|
||||
"if IN_COLAB:\n",
|
||||
|
||||
@ -187,7 +187,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
"version": "3.10.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@ -104,6 +104,8 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from IPython.display import display\n",
|
||||
"\n",
|
||||
"display(im)"
|
||||
]
|
||||
},
|
||||
@ -232,7 +234,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.3"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -145,6 +145,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import awadb\n",
|
||||
"\n",
|
||||
"awadb_client = awadb.Client()\n",
|
||||
"ret = awadb_client.Load(\"langchain_awadb\")\n",
|
||||
"if ret:\n",
|
||||
@ -178,7 +180,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.6"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -78,6 +78,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.document_loaders import TextLoader\n",
|
||||
"from langchain.text_splitter import CharacterTextSplitter\n",
|
||||
"\n",
|
||||
"loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
|
||||
"documents = loader.load()\n",
|
||||
@ -145,15 +146,22 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"version": "3.9.17"
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.12"
|
||||
},
|
||||
"orig_nbformat": 4,
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
|
||||
@ -161,5 +169,5 @@
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
@ -52,6 +52,9 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
|
||||
]
|
||||
},
|
||||
|
||||
@ -99,7 +99,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"results = ndb.similarity_search(\"Who was inspired by Ada Lovelace?\")\n",
|
||||
"print(res.page_content)"
|
||||
"print(results[0].page_content)"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -119,7 +119,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -344,7 +344,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!pip install requests requests-aws4auth"
|
||||
"#!pip install boto3 requests requests-aws4auth"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -362,6 +362,8 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import boto3\n",
|
||||
"from opensearchpy import RequestsHttpConnection\n",
|
||||
"from requests_aws4auth import AWS4Auth\n",
|
||||
"\n",
|
||||
"service = \"aoss\" # must set the service as 'aoss'\n",
|
||||
@ -404,6 +406,16 @@
|
||||
"## Using AOS (Amazon OpenSearch Service)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "4b02cd8d-f182-476b-935a-737f9c05d8e4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!pip install boto3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -419,7 +431,9 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# This is just an example to show how to use AOS , you need to set proper values.\n",
|
||||
"# This is just an example to show how to use Amazon OpenSearch Service, you need to set proper values.\n",
|
||||
"import boto3\n",
|
||||
"from opensearchpy import RequestsHttpConnection\n",
|
||||
"\n",
|
||||
"service = \"es\" # must set the service as 'es'\n",
|
||||
"region = \"us-east-2\"\n",
|
||||
|
||||
@ -13,6 +13,16 @@
|
||||
"We want it to be much more conversational."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7b9e9ef1-dc3c-4253-bd8b-5e95637bfe33",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"OPENAI_API_KEY = \"...\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
@ -575,7 +585,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.1"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -27,6 +27,10 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain.chains.llm import LLMChain\n",
|
||||
"from langchain.chat_models.openai import ChatOpenAI\n",
|
||||
"from langchain_core.prompts import PromptTemplate\n",
|
||||
"\n",
|
||||
"chat = ChatOpenAI(temperature=0)\n",
|
||||
"prompt_template = \"Tell me a {adjective} joke\"\n",
|
||||
"llm_chain = LLMChain(llm=chat, prompt=PromptTemplate.from_template(prompt_template))\n",
|
||||
@ -174,7 +178,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.3"
|
||||
"version": "3.10.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@ -19,6 +19,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.embeddings import OpenAIEmbeddings\n",
|
||||
"from langchain.retrievers import BM25Retriever, EnsembleRetriever\n",
|
||||
"from langchain.vectorstores import FAISS"
|
||||
]
|
||||
@ -81,7 +82,7 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -95,10 +96,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.8"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
@ -55,8 +55,8 @@
|
||||
" TextLoader(\"../../state_of_the_union.txt\"),\n",
|
||||
"]\n",
|
||||
"docs = []\n",
|
||||
"for l in loaders:\n",
|
||||
" docs.extend(l.load())\n",
|
||||
"for loader in loaders:\n",
|
||||
" docs.extend(loader.load())\n",
|
||||
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000)\n",
|
||||
"docs = text_splitter.split_documents(docs)"
|
||||
]
|
||||
@ -601,7 +601,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.5"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -61,8 +61,8 @@
|
||||
" TextLoader(\"../../state_of_the_union.txt\"),\n",
|
||||
"]\n",
|
||||
"docs = []\n",
|
||||
"for l in loaders:\n",
|
||||
" docs.extend(l.load())"
|
||||
"for loader in loaders:\n",
|
||||
" docs.extend(loader.load())"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -432,7 +432,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.5"
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -74,48 +74,5 @@ extend-exclude = [
|
||||
# These files were failing the listed rules at the time ruff was adopted for notebooks.
|
||||
# Don't require them to change at once, though we should look into them eventually.
|
||||
"cookbook/gymnasium_agent_simulation.ipynb" = ["F821"]
|
||||
"cookbook/multi_modal_output_agent.ipynb" = ["F821"]
|
||||
"cookbook/multi_modal_RAG_chroma.ipynb" = ["F821"]
|
||||
"cookbook/qianfan_baidu_elasticesearch_RAG.ipynb" = ["F821"]
|
||||
"cookbook/retrieval_in_sql.ipynb" = ["F821"]
|
||||
"cookbook/wikibase_agent.ipynb" = ["E722"]
|
||||
"docs/docs/expression_language/how_to/configure.ipynb" = ["F821"]
|
||||
"docs/docs/expression_language/how_to/fallbacks.ipynb" = ["E722"]
|
||||
"docs/docs/guides/fallbacks.ipynb" = ["E722"]
|
||||
"docs/docs/integrations/chat_loaders/imessage.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/chat/google_vertex_ai_palm.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/chat/promptlayer_chatopenai.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/document_loaders/arcgis.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/document_loaders/datadog_logs.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/document_loaders/embaas.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/document_loaders/etherscan.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/document_loaders/larksuite.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/document_loaders/tensorflow_datasets.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/llms/anyscale.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/llms/bittensor.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/llms/databricks.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/llms/llm_caching.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/llms/runhouse.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/retrievers/Activeloop DeepMemory+LangChain.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/text_embedding/cohere.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/text_embedding/elasticsearch.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/text_embedding/embaas.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/text_embedding/jina.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/text_embedding/localai.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/text_embedding/openai.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/tools/dalle_image_generator.ipynb" = ["E722"]
|
||||
"docs/docs/integrations/tools/gradio_tools.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/vectorstores/async_faiss.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/vectorstores/awadb.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/vectorstores/baiducloud_vector_search.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/vectorstores/faiss.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/vectorstores/mongodb_atlas.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/vectorstores/nucliadb.ipynb" = ["F821"]
|
||||
"docs/docs/integrations/vectorstores/opensearch.ipynb" = ["F821"]
|
||||
"docs/docs/modules/agents/agent_types/chat_conversation_agent.ipynb" = ["F821"]
|
||||
"docs/docs/modules/chains/how_to/call_methods.ipynb" = ["F821"]
|
||||
"docs/docs/modules/data_connection/retrievers/ensemble.ipynb" = ["F821"]
|
||||
"docs/docs/modules/data_connection/retrievers/multi_vector.ipynb" = ["E741"]
|
||||
"docs/docs/modules/data_connection/retrievers/parent_document_retriever.ipynb" = ["E741"]
|
||||
"docs/docs/modules/data_connection/text_embedding/caching_embeddings.ipynb" = ["F821"]
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user