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templates/stepback-qa-prompting/stepback_qa_prompting/chain.py create mode 100644 templates/stepback-qa-prompting/tests/__init__.py create mode 100644 templates/summarize-anthropic/LICENSE create mode 100644 templates/summarize-anthropic/README.md create mode 100644 templates/summarize-anthropic/docs/LLaVA.pdf create mode 100644 templates/summarize-anthropic/poetry.lock create mode 100644 templates/summarize-anthropic/pyproject.toml create mode 100644 templates/summarize-anthropic/summarize_anthropic.ipynb create mode 100644 templates/summarize-anthropic/summarize_anthropic/__init__.py create mode 100644 templates/summarize-anthropic/summarize_anthropic/chain.py create mode 100644 templates/summarize-anthropic/tests/__init__.py create mode 100644 templates/xml-agent/LICENSE create mode 100644 templates/xml-agent/README.md create mode 100644 templates/xml-agent/main.py create mode 100644 templates/xml-agent/poetry.lock create mode 100644 templates/xml-agent/pyproject.toml create mode 100644 templates/xml-agent/tests/__init__.py create mode 100644 templates/xml-agent/xml_agent/__init__.py create mode 100644 templates/xml-agent/xml_agent/agent.py create mode 100644 templates/xml-agent/xml_agent/prompts.py diff --git a/libs/cli/langchain_cli/constants.py b/libs/cli/langchain_cli/constants.py index 92d4c3ece5d..7a1f7593929 100644 --- a/libs/cli/langchain_cli/constants.py +++ b/libs/cli/langchain_cli/constants.py @@ -1,3 +1,3 @@ DEFAULT_GIT_REPO = "https://github.com/langchain-ai/langchain.git" DEFAULT_GIT_SUBDIRECTORY = "templates" -DEFAULT_GIT_REF = "langserve-templates" +DEFAULT_GIT_REF = "master" diff --git a/libs/langchain/langchain/agents/agent_toolkits/__init__.py b/libs/langchain/langchain/agents/agent_toolkits/__init__.py index 537d5b6c250..b2e97b6f01d 100644 --- a/libs/langchain/langchain/agents/agent_toolkits/__init__.py +++ b/libs/langchain/langchain/agents/agent_toolkits/__init__.py @@ -21,9 +21,6 @@ from langchain.agents.agent_toolkits.azure_cognitive_services import ( from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import ( create_conversational_retrieval_agent, ) -from langchain.agents.agent_toolkits.conversational_retrieval.tool import ( - create_retriever_tool, -) from langchain.agents.agent_toolkits.csv.base import create_csv_agent from langchain.agents.agent_toolkits.file_management.toolkit import ( FileManagementToolkit, @@ -59,6 +56,7 @@ from langchain.agents.agent_toolkits.vectorstore.toolkit import ( ) from langchain.agents.agent_toolkits.xorbits.base import create_xorbits_agent from langchain.agents.agent_toolkits.zapier.toolkit import ZapierToolkit +from langchain.tools.retriever import create_retriever_tool __all__ = [ "AINetworkToolkit", @@ -87,7 +85,6 @@ __all__ = [ "create_pbi_agent", "create_pbi_chat_agent", "create_python_agent", - "create_retriever_tool", "create_spark_dataframe_agent", "create_spark_sql_agent", "create_sql_agent", @@ -95,4 +92,5 @@ __all__ = [ "create_vectorstore_router_agent", "create_xorbits_agent", "create_conversational_retrieval_agent", + "create_retriever_tool", ] diff --git a/libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/tool.py b/libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/tool.py index 43f0a2105d2..f7a09af8918 100644 --- a/libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/tool.py +++ b/libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/tool.py @@ -1,25 +1,3 @@ -from langchain.schema import BaseRetriever -from langchain.tools import Tool +from langchain.tools.retriever import create_retriever_tool - -def create_retriever_tool( - retriever: BaseRetriever, name: str, description: str -) -> Tool: - """Create a tool to do retrieval of documents. - - Args: - retriever: The retriever to use for the retrieval - name: The name for the tool. This will be passed to the language model, - so should be unique and somewhat descriptive. - description: The description for the tool. This will be passed to the language - model, so should be descriptive. - - Returns: - Tool class to pass to an agent - """ - return Tool( - name=name, - description=description, - func=retriever.get_relevant_documents, - coroutine=retriever.aget_relevant_documents, - ) +__all__ = ["create_retriever_tool"] diff --git a/libs/langchain/langchain/retrievers/you.py b/libs/langchain/langchain/retrievers/you.py index 2fe0e90a3e8..c1fe6946252 100644 --- a/libs/langchain/langchain/retrievers/you.py +++ b/libs/langchain/langchain/retrievers/you.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List +from typing import Any, Dict, List, Optional from langchain.callbacks.manager import CallbackManagerForRetrieverRun from langchain.pydantic_v1 import root_validator @@ -17,6 +17,7 @@ class YouRetriever(BaseRetriever): """ ydc_api_key: str + k: Optional[int] = None endpoint_type: str = "web" @root_validator(pre=True) @@ -45,6 +46,8 @@ class YouRetriever(BaseRetriever): for hit in results["hits"]: for snippet in hit["snippets"]: docs.append(Document(page_content=snippet)) + if self.k is not None and len(docs) >= self.k: + return docs return docs elif self.endpoint_type == "snippet": results = requests.get( diff --git a/libs/langchain/langchain/tools/retriever.py b/libs/langchain/langchain/tools/retriever.py new file mode 100644 index 00000000000..43f0a2105d2 --- /dev/null +++ b/libs/langchain/langchain/tools/retriever.py @@ -0,0 +1,25 @@ +from langchain.schema import BaseRetriever +from langchain.tools import Tool + + +def create_retriever_tool( + retriever: BaseRetriever, name: str, description: str +) -> Tool: + """Create a tool to do retrieval of documents. + + Args: + retriever: The retriever to use for the retrieval + name: The name for the tool. This will be passed to the language model, + so should be unique and somewhat descriptive. + description: The description for the tool. This will be passed to the language + model, so should be descriptive. + + Returns: + Tool class to pass to an agent + """ + return Tool( + name=name, + description=description, + func=retriever.get_relevant_documents, + coroutine=retriever.aget_relevant_documents, + ) diff --git a/pyproject.toml b/pyproject.toml index 071c1a2605e..be315d42e66 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,7 @@ codespell = "^2.2.0" [tool.codespell] -skip = '.git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package-lock.json,example_data,_dist,examples' +skip = '.git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package-lock.json,example_data,_dist,examples,templates' # Ignore latin etc ignore-regex = '.*(Stati Uniti|Tense=Pres).*' # whats is a typo but used frequently in queries so kept as is diff --git a/templates/.gitignore b/templates/.gitignore new file mode 100644 index 00000000000..bee8a64b79a --- /dev/null +++ b/templates/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/templates/INDEX.md b/templates/INDEX.md new file mode 100644 index 00000000000..ae70ec85f99 --- /dev/null +++ b/templates/INDEX.md @@ -0,0 +1,9 @@ +# Templates + +A list of all template repos + +⭐Retrieval Augmented Generation Chatbot: Build a chatbot over your data. Uses OpenAI and Pinecone. +⭐Extraction with OpenAI Functions: Do extraction of structured data from unstructured data. Uses OpenAI function calling. +⭐Local Retrieval Augmented Generation: Build a chatbot over your data. Uses only local tooling: Ollama, GPT4all, Chroma. +⭐OpenAI Functions Agent: Build a chatbot that can take actions. Uses OpenAI function calling and Tavily. +⭐XML Agent: Build a chatbot that can take actions. Uses Anthropic and You.com. diff --git a/templates/README.md b/templates/README.md new file mode 100644 index 00000000000..9404a538128 --- /dev/null +++ b/templates/README.md @@ -0,0 +1,71 @@ +# LangServe Hub + +Packages that can be easily hosted by LangServe using the `langserve` cli. + +## Using LangServe Hub + +You can install the `langservehub` CLI and use it as follows: +```bash +# install langservehub CLI +pip install --upgrade langservehub + +langservehub new my-app +cd my-app + +poetry install + +# if you have problems with poe, use `poetry run poe ...` instead + +# add the simple-pirate package +poe add --repo=pingpong-templates/hub simple-pirate + +# adding other GitHub repo packages, defaults to repo root +poe add --repo=hwchase17/chain-of-verification + +# with a custom api mount point (defaults to `/{package_name}`) +poe add --repo=pingpong-templates/hub simple-translator --api_path=/my/custom/path/translator + +poe list + +poe start +^C + +# remove packages by their api path: +poe remove my/custom/path/translator +``` + +## Creating New Packages + +You can also create new packages with the `langservehub package new` command + +```bash +# starting from this directory in langserve-hub +langservehub package new simple-newpackage +``` + +Now you can edit the chain in `simple-newpackage/simple_newpackage/chain.py` and put up a PR! + +Your package will be usable as `poe add --repo=pingpong-templates/hub simple-newpackage` when it's merged in. + +## Data Format + +What makes these packages work? + +- Poetry +- pyproject.toml files + +### Installable Packages + +Everything is a Poetry package currently. This allows poetry to manage our dependencies for us :). + +In addition to normal keys in the `pyproject.toml` file, you'll notice an additional `tool.langserve` key ([link](https://github.com/langchain-ai/langserve-hub/blob/main/simple/pirate/pyproject.toml#L13-L15)). + +This allows us to identify which module and attribute to import as the chain/runnable for the langserve `add_routes` call. + +### Apps (with installed langserve packages) + +Let's say you add the pirate package with `poe add --repo=pingpong-templates/hub simple-pirate`. + +First this downloads the simple-pirate package to pirate + +Then this adds a `poetry` path dependency, which gets picked up from `add_package_routes`. \ No newline at end of file diff --git a/templates/anthropic-functions/LICENSE b/templates/anthropic-functions/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/anthropic-functions/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/anthropic-functions/README.md b/templates/anthropic-functions/README.md new file mode 100644 index 00000000000..bc0f29e8c24 --- /dev/null +++ b/templates/anthropic-functions/README.md @@ -0,0 +1,39 @@ +# Function calling with Anthropic + +This template enables [Anthropic function calling](https://python.langchain.com/docs/integrations/chat/anthropic_functions). + +Function calling can be used for various tasks, such as extraction or tagging. + +Specify the function you want to use in `chain.py` + +By default, it will tag the input text using the following fields: + +* sentiment +* aggressiveness +* language + +## LLM + +This template will use `Claude2` by default. + +Be sure that `ANTHROPIC_API_KEY` is set in your enviorment. + +## Adding the template + +Create your LangServe app: +``` +langchain serve new my-app +cd my-app +``` + +Add template: +``` +langchain serve add anthropic-functions +``` + +Start server: +``` +langchain start +``` + +See Jupyter notebook `anthropic_functions` for various way to connect to the template. \ No newline at end of file diff --git a/templates/anthropic-functions/anthropic_functions.ipynb b/templates/anthropic-functions/anthropic_functions.ipynb new file mode 100644 index 00000000000..938a22a19d9 --- /dev/null +++ b/templates/anthropic-functions/anthropic_functions.ipynb @@ -0,0 +1,83 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4ae4b789", + "metadata": {}, + "source": [ + "## Document Loading\n", + "\n", + "Load a blog post on agents." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5d6bd62e", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders import WebBaseLoader\n", + "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n", + "text = loader.load()" + ] + }, + { + "cell_type": "markdown", + "id": "8e21575d", + "metadata": {}, + "source": [ + "## Run Template\n", + "\n", + "As shown in the README, add template and start server:\n", + "```\n", + "langchain serve add anthropic-functions\n", + "langchain start\n", + "```\n", + "\n", + "We can now look at the endpoints:\n", + "\n", + "http://127.0.0.1:8000/docs#\n", + "\n", + "And specifically at our loaded template:\n", + "\n", + "http://127.0.0.1:8000/docs#/default/invoke_anthropic-functions_invoke_post\n", + " \n", + "We can also use remote runnable to call it:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "92edba86", + "metadata": {}, + "outputs": [], + "source": [ + "from langserve.client import RemoteRunnable\n", + "anthropic_function_model = RemoteRunnable('http://localhost:8000/anthropic-functions')\n", + "anthropic_function_model.invoke(text[0].page_content[0:1500])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "langserve", + "language": "python", + "name": "langserve" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/templates/anthropic-functions/anthropic_functions/__init__.py b/templates/anthropic-functions/anthropic_functions/__init__.py new file mode 100644 index 00000000000..e3c87e24a8d --- /dev/null +++ b/templates/anthropic-functions/anthropic_functions/__init__.py @@ -0,0 +1,3 @@ +from anthropic_functions.chain import chain + +__all__ = ["chain"] diff --git a/templates/anthropic-functions/anthropic_functions/chain.py b/templates/anthropic-functions/anthropic_functions/chain.py new file mode 100644 index 00000000000..3d51bed0603 --- /dev/null +++ b/templates/anthropic-functions/anthropic_functions/chain.py @@ -0,0 +1,15 @@ +from langchain.chains import create_tagging_chain +from langchain_experimental.llms.anthropic_functions import AnthropicFunctions + +model = AnthropicFunctions(model='claude-2') + +schema = { + "properties": { + "sentiment": {"type": "string"}, + "aggressiveness": {"type": "integer"}, + "language": {"type": "string"}, + } +} + +# This is LLMChain, which implements invoke +chain = create_tagging_chain(schema, model) \ No newline at end of file diff --git a/templates/anthropic-functions/poetry.lock b/templates/anthropic-functions/poetry.lock new file mode 100644 index 00000000000..c342ab9a71a --- /dev/null +++ b/templates/anthropic-functions/poetry.lock @@ -0,0 +1,1548 @@ +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server 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"sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571"}, +] + +[package.dependencies] +idna = ">=2.0" +multidict = ">=4.0" + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8.1,<4.0" +content-hash = "d1074c3acdaddfb907c425c9efe19686a995038b6ed8e7ea7b1543c5b9df7082" diff --git a/templates/anthropic-functions/pyproject.toml b/templates/anthropic-functions/pyproject.toml new file mode 100644 index 00000000000..e37a4f0004c --- /dev/null +++ b/templates/anthropic-functions/pyproject.toml @@ -0,0 +1,21 @@ +[tool.poetry] +name = "anthropic-function" +version = "0.1.0" +description = "" +authors = ["Lance Martin "] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.322" +anthropic = ">=0.5.0" +langchainhub = ">=0.1.13" +langchain-experimental = "^0.0.33" + +[tool.langserve] +export_module = "anthropic_functions" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" \ No newline at end of file diff --git a/templates/anthropic-functions/tests/__init__.py b/templates/anthropic-functions/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/anthropic-iterative-search/README.md b/templates/anthropic-iterative-search/README.md new file mode 100644 index 00000000000..7670579e2d6 --- /dev/null +++ b/templates/anthropic-iterative-search/README.md @@ -0,0 +1,3 @@ +# anthropic-iterative-search + +Heavily inspired by [this notebook](https://github.com/anthropics/anthropic-cookbook/blob/main/long_context/wikipedia-search-cookbook.ipynb) diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/__init__.py b/templates/anthropic-iterative-search/anthropic_iterative_search/__init__.py new file mode 100644 index 00000000000..81cc8c187fa --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/__init__.py @@ -0,0 +1,11 @@ +from langchain.schema.runnable import ConfigurableField + +from .retriever_agent import executor +from .chain import chain + +final_chain = chain.configurable_alternatives( + ConfigurableField(id="chain"), + default_key="response", + # This adds a new option, with name `openai` that is equal to `ChatOpenAI()` + retrieve=executor, +) diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/agent_scratchpad.py b/templates/anthropic-iterative-search/anthropic_iterative_search/agent_scratchpad.py new file mode 100644 index 00000000000..209576943f1 --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/agent_scratchpad.py @@ -0,0 +1,15 @@ +def _format_docs(docs): + result = "\n".join( + [ + f'\n\n{r}\n\n' + for i, r in enumerate(docs) + ] + ) + return result + +def format_agent_scratchpad(intermediate_steps): + thoughts = "" + for action, observation in intermediate_steps: + thoughts += action.log + thoughts += '' + _format_docs(observation) + return thoughts diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/chain.py b/templates/anthropic-iterative-search/anthropic_iterative_search/chain.py new file mode 100644 index 00000000000..6ccbbda9343 --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/chain.py @@ -0,0 +1,15 @@ +from langchain.prompts import ChatPromptTemplate +from langchain.chat_models import ChatAnthropic +from langchain.schema.output_parser import StrOutputParser + +from .prompts import answer_prompt +from .retriever_agent import executor + +prompt = ChatPromptTemplate.from_template(answer_prompt) + +model = ChatAnthropic(model="claude-2", temperature=0, max_tokens_to_sample=1000) + +chain = { + "query": lambda x: x["query"], + "information": executor | (lambda x: x["output"]) +} | prompt | model | StrOutputParser() diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/output_parser.py b/templates/anthropic-iterative-search/anthropic_iterative_search/output_parser.py new file mode 100644 index 00000000000..f04837c9288 --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/output_parser.py @@ -0,0 +1,31 @@ +from langchain.schema.agent import AgentAction, AgentFinish +import re + +from .agent_scratchpad import _format_docs + + +def extract_between_tags(tag: str, string: str, strip: bool = True) -> str: + ext_list = re.findall(f"<{tag}\s?>(.+?)", string, re.DOTALL) + if strip: + ext_list = [e.strip() for e in ext_list] + if ext_list: + if len(ext_list) != 1: + raise ValueError + # Only return the first one + return ext_list[0] + +def parse_output(outputs): + partial_completion = outputs["partial_completion"] + steps = outputs["intermediate_steps"] + search_query = extract_between_tags('search_query', partial_completion + '') + if search_query is None: + docs = [] + str_output = "" + for action, observation in steps: + docs.extend(observation) + str_output += action.log + str_output += '' + _format_docs(observation) + str_output += partial_completion + return AgentFinish({"docs": docs, "output": str_output}, log=partial_completion) + else: + return AgentAction(tool="search", tool_input=search_query, log=partial_completion) diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/prompts.py b/templates/anthropic-iterative-search/anthropic_iterative_search/prompts.py new file mode 100644 index 00000000000..e58cd4bf4d7 --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/prompts.py @@ -0,0 +1,7 @@ +retrieval_prompt = """{retriever_description} Before beginning to research the user's question, first think for a moment inside tags about what information is necessary for a well-informed answer. If the user's question is complex, you may need to decompose the query into multiple subqueries and execute them individually. Sometimes the search engine will return empty search results, or the search results may not contain the information you need. In such cases, feel free to try again with a different query. + +After each call to the Search Engine Tool, reflect briefly inside tags about whether you now have enough information to answer, or whether more information is needed. If you have all the relevant information, write it in tags, WITHOUT actually answering the question. Otherwise, issue a new search. + +Here is the user's question: {query} Remind yourself to make short queries in your scratchpad as you plan out your strategy.""" + +answer_prompt = "Here is a user query: {query}. Here is some relevant information: {information}. Please answer the question using the relevant information." diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/retriever.py b/templates/anthropic-iterative-search/anthropic_iterative_search/retriever.py new file mode 100644 index 00000000000..d565dbbd9e2 --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/retriever.py @@ -0,0 +1,16 @@ +from langchain.retrievers import WikipediaRetriever +from langchain.tools import tool + +# This is used to tell the model how to best use the retriever. + +retriever_description = """You will be asked a question by a human user. You have access to the following tool to help answer the question. Search Engine Tool * The search engine will exclusively search over Wikipedia for pages similar to your query. It returns for each page its title and full page content. Use this tool if you want to get up-to-date and comprehensive information on a topic to help answer queries. Queries should be as atomic as possible -- they only need to address one part of the user's question. For example, if the user's query is "what is the color of a basketball?", your search query should be "basketball". Here's another example: if the user's question is "Who created the first neural network?", your first query should be "neural network". As you can see, these queries are quite short. Think keywords, not phrases. * At any time, you can make a call to the search engine using the following syntax: query_word. * You'll then get results back in tags.""" + +retriever = WikipediaRetriever() + +# This should be the same as the function name below +RETRIEVER_TOOL_NAME = "search" + +@tool +def search(query): + """Search with the retriever.""" + return retriever.get_relevant_documents(query) diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/retriever_agent.py b/templates/anthropic-iterative-search/anthropic_iterative_search/retriever_agent.py new file mode 100644 index 00000000000..139c631aabe --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/retriever_agent.py @@ -0,0 +1,29 @@ +from langchain.chat_models import ChatAnthropic +from langchain.prompts import ChatPromptTemplate +from langchain.schema.runnable import RunnablePassthrough, RunnableMap +from langchain.schema.output_parser import StrOutputParser +from langchain.agents import AgentExecutor + +from .retriever import search, RETRIEVER_TOOL_NAME, retriever_description +from .prompts import retrieval_prompt +from .agent_scratchpad import format_agent_scratchpad +from .output_parser import parse_output + +prompt = ChatPromptTemplate.from_messages([ + ("user", retrieval_prompt), + ("ai", "{agent_scratchpad}"), +]) +prompt = prompt.partial(retriever_description=retriever_description) + +model = ChatAnthropic(model="claude-2", temperature=0, max_tokens_to_sample=1000) + +chain = RunnablePassthrough.assign( + agent_scratchpad=lambda x: format_agent_scratchpad(x['intermediate_steps']) +) | prompt | model.bind( stop_sequences=['']) | StrOutputParser() + +agent_chain = RunnableMap({ + "partial_completion": chain, + "intermediate_steps": lambda x: x['intermediate_steps'] +}) | parse_output + +executor = AgentExecutor(agent=agent_chain, tools = [search], verbose=True) diff --git a/templates/anthropic-iterative-search/main.py b/templates/anthropic-iterative-search/main.py new file mode 100644 index 00000000000..5bb812a465c --- /dev/null +++ b/templates/anthropic-iterative-search/main.py @@ -0,0 +1,6 @@ +from anthropic_iterative_search import final_chain + + +if __name__ == "__main__": + query = "Which movie came out first: Oppenheimer, or Are You There God It's Me Margaret?" + print(final_chain.with_config(configurable={"chain": "retrieve"}).invoke({"query": query})) diff --git a/templates/anthropic-iterative-search/poetry.lock b/templates/anthropic-iterative-search/poetry.lock new file mode 100644 index 00000000000..56713fb41c8 --- /dev/null +++ b/templates/anthropic-iterative-search/poetry.lock @@ -0,0 +1,1581 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + {file = 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a/templates/anthropic-iterative-search/tests/__init__.py b/templates/anthropic-iterative-search/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/cassandra-entomology-rag/.env.template b/templates/cassandra-entomology-rag/.env.template new file mode 100644 index 00000000000..251125528ba --- /dev/null +++ b/templates/cassandra-entomology-rag/.env.template @@ -0,0 +1,13 @@ +export OPENAI_API_KEY="..." + +export ASTRA_DB_APPLICATION_TOKEN="AstraCS:..." +export ASTRA_DB_KEYSPACE="..." +export ASTRA_DB_ID="12345678-" + +# UNCOMMENT THE FOLLOWING FOR A CASSANDRA CLUSTER ... +# export USE_CASSANDRA_CLUSTER="1" +# ... then provide these parameters as well: +# export CASSANDRA_KEYSPACE="..." +# export CASSANDRA_CONTACT_POINTS="127.0.0.1" # optional +# export CASSANDRA_USERNAME="cassandra" # optional +# export CASSANDRA_PASSWORD="cassandra" # optional diff --git a/templates/cassandra-entomology-rag/README.md b/templates/cassandra-entomology-rag/README.md new file mode 100644 index 00000000000..88bc8fb03da --- /dev/null +++ b/templates/cassandra-entomology-rag/README.md @@ -0,0 +1,47 @@ +# RAG LangServe chain template + +A basic chain template showing the RAG pattern using +a vector store on Astra DB / Apache Cassandra®. + +## Setup: + +You need: + +- an [Astra](https://astra.datastax.com) Vector Database (free tier is fine!). **You need a [Database Administrator token](https://awesome-astra.github.io/docs/pages/astra/create-token/#c-procedure)**, in particular the string starting with `AstraCS:...`; +- likewise, get your [Database ID](https://awesome-astra.github.io/docs/pages/astra/faq/#where-should-i-find-a-database-identifier) ready, you will have to enter it below; +- an **OpenAI API Key**. (More info [here](https://cassio.org/start_here/#llm-access), note that out-of-the-box this demo supports OpenAI unless you tinker with the code.) + +_Note:_ you can alternatively use a regular Cassandra cluster: to do so, make sure you provide the `USE_CASSANDRA_CLUSTER` entry as shown in `.env.template` and the subsequent environment variables to specify how to connect to it. + +You need to provide the connection parameters and secrets through environment variables. Please refer to `.env.template` for what variables are required. + +### Populate the vector store + +Make sure you have the environment variables all set (see previous section), +then, from this directory, launch the following just once: + +``` +poetry run bash -c "cd [...]/cassandra_entomology_rag; python setup.py" +``` + +The output will be something like `Done (29 lines inserted).`. + +> **Note**: In a full application, the vector store might be populated in other ways: +> this step is to pre-populate the vector store with some rows for the +> demo RAG chains to sensibly work. + +### Sample inputs + +The chain's prompt is engineered to stay on topic and only use the provided context. + +To put this to test, experiment with these example questions: + +``` +"Are there more coleoptera or bugs?" +"Do Odonata have wings?" +"Do birds have wings?" <-- no entomology here! +``` + +## Reference + +Stand-alone repo with LangServe chain: [here](https://github.com/hemidactylus/langserve_cassandra_entomology_rag). diff --git a/templates/cassandra-entomology-rag/cassandra_entomology_rag/__init__.py b/templates/cassandra-entomology-rag/cassandra_entomology_rag/__init__.py new file mode 100644 index 00000000000..590c9465a8e --- /dev/null +++ b/templates/cassandra-entomology-rag/cassandra_entomology_rag/__init__.py @@ -0,0 +1,62 @@ +import os + +import cassio + +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.vectorstores import Cassandra +from langchain.prompts import ChatPromptTemplate +from langchain.schema.runnable import RunnablePassthrough +from langchain.schema.output_parser import StrOutputParser + + +use_cassandra = int(os.environ.get("USE_CASSANDRA_CLUSTER", "0")) +if use_cassandra: + from .cassandra_cluster_init import get_cassandra_connection + session, keyspace = get_cassandra_connection() + cassio.init( + session=session, + keyspace=keyspace, + ) +else: + cassio.init( + token=os.environ["ASTRA_DB_APPLICATION_TOKEN"], + database_id=os.environ["ASTRA_DB_ID"], + keyspace=os.environ.get("ASTRA_DB_KEYSPACE"), + ) + + +# inits +llm = ChatOpenAI() +embeddings = OpenAIEmbeddings() +vector_store = Cassandra( + session=None, + keyspace=None, + embedding=embeddings, + table_name="langserve_rag_demo", +) +retriever = vector_store.as_retriever(search_kwargs={'k': 3}) + +entomology_template = """ +You are an expert entomologist, tasked with answering enthusiast biologists' questions. +You must answer based only on the provided context, do not make up any fact. +Your answers must be concise and to the point, but strive to provide scientific details +(such as family, order, Latin names, and so on when appropriate). +You MUST refuse to answer questions on other topics than entomology, +as well as questions whose answer is not found in the provided context. + +CONTEXT: +{context} + +QUESTION: {question} + +YOUR ANSWER:""" + +entomology_prompt = ChatPromptTemplate.from_template(entomology_template) + +chain = ( + {"context": retriever, "question": RunnablePassthrough()} + | entomology_prompt + | llm + | StrOutputParser() +) \ No newline at end of file diff --git a/templates/cassandra-entomology-rag/cassandra_entomology_rag/cassandra_cluster_init.py b/templates/cassandra-entomology-rag/cassandra_entomology_rag/cassandra_cluster_init.py new file mode 100644 index 00000000000..e59e5bf6dfc --- /dev/null +++ b/templates/cassandra-entomology-rag/cassandra_entomology_rag/cassandra_cluster_init.py @@ -0,0 +1,27 @@ +import os + +from cassandra.cluster import Cluster +from cassandra.auth import PlainTextAuthProvider + + +def get_cassandra_connection(): + contact_points = [ + cp.strip() + for cp in os.environ.get("CASSANDRA_CONTACT_POINTS", "").split(',') + if cp.strip() + ] + CASSANDRA_KEYSPACE = os.environ["CASSANDRA_KEYSPACE"] + CASSANDRA_USERNAME = os.environ.get("CASSANDRA_USERNAME") + CASSANDRA_PASSWORD = os.environ.get("CASSANDRA_PASSWORD") + # + if CASSANDRA_USERNAME and CASSANDRA_PASSWORD: + auth_provider = PlainTextAuthProvider( + CASSANDRA_USERNAME, + CASSANDRA_PASSWORD, + ) + else: + auth_provider = None + + c_cluster = Cluster(contact_points if contact_points else None, auth_provider=auth_provider) + session = c_cluster.connect() + return (session, CASSANDRA_KEYSPACE) diff --git a/templates/cassandra-entomology-rag/poetry.lock b/templates/cassandra-entomology-rag/poetry.lock new file mode 100644 index 00000000000..fbc24e33295 --- /dev/null +++ b/templates/cassandra-entomology-rag/poetry.lock @@ -0,0 +1,1456 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = 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+openai = "^0.28.1" +tiktoken = "^0.5.1" +cassio = "^0.1.3" + +[tool.langserve] +export_module = "cassandra_entomology_rag" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/cassandra-entomology-rag/setup.py b/templates/cassandra-entomology-rag/setup.py new file mode 100644 index 00000000000..a84ce59a2f2 --- /dev/null +++ b/templates/cassandra-entomology-rag/setup.py @@ -0,0 +1,47 @@ +import os + +import cassio + +from langchain.vectorstores import Cassandra +from langchain.embeddings import OpenAIEmbeddings + + +use_cassandra = int(os.environ.get("USE_CASSANDRA_CLUSTER", "0")) +if use_cassandra: + from cassandra_entomology_rag.cassandra_cluster_init import get_cassandra_connection + session, keyspace = get_cassandra_connection() + cassio.init( + session=session, + keyspace=keyspace, + ) +else: + cassio.init( + token=os.environ["ASTRA_DB_APPLICATION_TOKEN"], + database_id=os.environ["ASTRA_DB_ID"], + keyspace=os.environ.get("ASTRA_DB_KEYSPACE"), + ) + + +if __name__ == '__main__': + embeddings = OpenAIEmbeddings() + vector_store = Cassandra( + session=None, + keyspace=None, + embedding=embeddings, + table_name="langserve_rag_demo", + ) + # + lines = [ + l.strip() + for l in open("sources.txt").readlines() + if l.strip() + if l[0] != "#" + ] + # deterministic IDs to prevent duplicates on multiple runs + ids = [ + "_".join(l.split(" ")[:2]).lower().replace(":", "") + for l in lines + ] + # + vector_store.add_texts(texts=lines, ids=ids) + print(f"Done ({len(lines)} lines inserted).") diff --git a/templates/cassandra-entomology-rag/sources.txt b/templates/cassandra-entomology-rag/sources.txt new file mode 100644 index 00000000000..c03c144753b --- /dev/null +++ b/templates/cassandra-entomology-rag/sources.txt @@ -0,0 +1,31 @@ +# source: https://www.thoughtco.com/a-guide-to-the-twenty-nine-insect-orders-1968419 + +Order Thysanura: The silverfish and firebrats are found in the order Thysanura. They are wingless insects often found in people's attics, and have a lifespan of several years. There are about 600 species worldwide. +Order Diplura: Diplurans are the most primitive insect species, with no eyes or wings. They have the unusual ability among insects to regenerate body parts. There are over 400 members of the order Diplura in the world. +Order Protura: Another very primitive group, the proturans have no eyes, no antennae, and no wings. They are uncommon, with perhaps less than 100 species known. +Order Collembola: The order Collembola includes the springtails, primitive insects without wings. There are approximately 2,000 species of Collembola worldwide. +Order Ephemeroptera: The mayflies of order Ephemeroptera are short-lived, and undergo incomplete metamorphosis. The larvae are aquatic, feeding on algae and other plant life. Entomologists have described about 2,100 species worldwide. +Order Odonata: The order Odonata includes dragonflies and damselflies, which undergo incomplete metamorphosis. They are predators of other insects, even in their immature stage. There are about 5,000 species in the order Odonata. +Order Plecoptera: The stoneflies of order Plecoptera are aquatic and undergo incomplete metamorphosis. The nymphs live under rocks in well flowing streams. Adults are usually seen on the ground along stream and river banks. There are roughly 3,000 species in this group. +Order Grylloblatodea: Sometimes referred to as "living fossils," the insects of the order Grylloblatodea have changed little from their ancient ancestors. This order is the smallest of all the insect orders, with perhaps only 25 known species living today. Grylloblatodea live at elevations above 1500 ft., and are commonly named ice bugs or rock crawlers. +Order Orthoptera: These are familiar insects (grasshoppers, locusts, katydids, and crickets) and one of the largest orders of herbivorous insects. Many species in the order Orthoptera can produce and detect sounds. Approximately 20,000 species exist in this group. +Order Phasmida: The order Phasmida are masters of camouflage, the stick and leaf insects. They undergo incomplete metamorphosis and feed on leaves. There are some 3,000 insects in this group, but only a small fraction of this number is leaf insects. Stick insects are the longest insects in the world. +Order Dermaptera: This order contains the earwigs, an easily recognized insect that often has pincers at the end of the abdomen. Many earwigs are scavengers, eating both plant and animal matter. The order Dermaptera includes less than 2,000 species. +Order Embiidina: The order Embioptera is another ancient order with few species, perhaps only 200 worldwide. The web spinners have silk glands in their front legs and weave nests under leaf litter and in tunnels where they live. Webspinners live in tropical or subtropical climates. +Order Dictyoptera: The order Dictyoptera includes roaches and mantids. Both groups have long, segmented antennae and leathery forewings held tightly against their backs. They undergo incomplete metamorphosis. Worldwide, there approximately 6,000 species in this order, most living in tropical regions. +Order Isoptera: Termites feed on wood and are important decomposers in forest ecosystems. They also feed on wood products and are thought of as pests for the destruction they cause to man-made structures. There are between 2,000 and 3,000 species in this order. +Order Zoraptera: Little is know about the angel insects, which belong to the order Zoraptera. Though they are grouped with winged insects, many are actually wingless. Members of this group are blind, small, and often found in decaying wood. There are only about 30 described species worldwide. +Order Psocoptera: Bark lice forage on algae, lichen, and fungus in moist, dark places. Booklice frequent human dwellings, where they feed on book paste and grains. They undergo incomplete metamorphosis. Entomologists have named about 3,200 species in the order Psocoptera. +Order Mallophaga: Biting lice are ectoparasites that feed on birds and some mammals. There are an estimated 3,000 species in the order Mallophaga, all of which undergo incomplete metamorphosis. +Order Siphunculata: The order Siphunculata are the sucking lice, which feed on the fresh blood of mammals. Their mouthparts are adapted for sucking or siphoning blood. There are only about 500 species of sucking lice. +Order Hemiptera: Most people use the term "bugs" to mean insects; an entomologist uses the term to refer to the order Hemiptera. The Hemiptera are the true bugs, and include cicadas, aphids, and spittlebugs, and others. This is a large group of over 70,000 species worldwide. +Order Thysanoptera: The thrips of order Thysanoptera are small insects that feed on plant tissue. Many are considered agricultural pests for this reason. Some thrips prey on other small insects as well. This order contains about 5,000 species. +Order Neuroptera: Commonly called the order of lacewings, this group actually includes a variety of other insects, too: dobsonflies, owlflies, mantidflies, antlions, snakeflies, and alderflies. Insects in the order Neuroptera undergo complete metamorphosis. Worldwide, there are over 5,500 species in this group. +Order Mecoptera: This order includes the scorpionflies, which live in moist, wooded habitats. Scorpionflies are omnivorous in both their larval and adult forms. The larva are caterpillar-like. There are less than 500 described species in the order Mecoptera. +Order Siphonaptera: Pet lovers fear insects in the order Siphonaptera - the fleas. Fleas are blood-sucking ectoparasites that feed on mammals, and rarely, birds. There are well over 2,000 species of fleas in the world. +Order Coleoptera: This group, the beetles and weevils, is the largest order in the insect world, with over 300,000 distinct species known. The order Coleoptera includes well-known families: june beetles, lady beetles, click beetles, and fireflies. All have hardened forewings that fold over the abdomen to protect the delicate hindwings used for flight. +Order Strepsiptera: Insects in this group are parasites of other insects, particularly bees, grasshoppers, and the true bugs. The immature Strepsiptera lies in wait on a flower and quickly burrows into any host insect that comes along. Strepsiptera undergo complete metamorphosis and pupate within the host insect's body. +Order Diptera: Diptera is one of the largest orders, with nearly 100,000 insects named to the order. These are the true flies, mosquitoes, and gnats. Insects in this group have modified hindwings which are used for balance during flight. The forewings function as the propellers for flying. +Order Lepidoptera: The butterflies and moths of the order Lepidoptera comprise the second largest group in the class Insecta. These well-known insects have scaly wings with interesting colors and patterns. You can often identify an insect in this order just by the wing shape and color. +Order Trichoptera: Caddisflies are nocturnal as adults and aquatic when immature. The caddisfly adults have silky hairs on their wings and body, which is key to identifying a Trichoptera member. The larvae spin traps for prey with silk. They also make cases from the silk and other materials that they carry and use for protection. +Order Hymenoptera: The order Hymenoptera includes many of the most common insects - ants, bees, and wasps. The larvae of some wasps cause trees to form galls, which then provides food for the immature wasps. Other wasps are parasitic, living in caterpillars, beetles, or even aphids. This is the third-largest insect order with just over 100,000 species. diff --git a/templates/cassandra-synonym-caching/.env.template b/templates/cassandra-synonym-caching/.env.template new file mode 100644 index 00000000000..251125528ba --- /dev/null +++ b/templates/cassandra-synonym-caching/.env.template @@ -0,0 +1,13 @@ +export OPENAI_API_KEY="..." + +export ASTRA_DB_APPLICATION_TOKEN="AstraCS:..." +export ASTRA_DB_KEYSPACE="..." +export ASTRA_DB_ID="12345678-" + +# UNCOMMENT THE FOLLOWING FOR A CASSANDRA CLUSTER ... +# export USE_CASSANDRA_CLUSTER="1" +# ... then provide these parameters as well: +# export CASSANDRA_KEYSPACE="..." +# export CASSANDRA_CONTACT_POINTS="127.0.0.1" # optional +# export CASSANDRA_USERNAME="cassandra" # optional +# export CASSANDRA_PASSWORD="cassandra" # optional diff --git a/templates/cassandra-synonym-caching/README.md b/templates/cassandra-synonym-caching/README.md new file mode 100644 index 00000000000..3a5ecc8fbeb --- /dev/null +++ b/templates/cassandra-synonym-caching/README.md @@ -0,0 +1,20 @@ +# LLM-cache LangServe chain template + +A simple chain template showcasing usage of LLM Caching +backed by Astra DB / Apache Cassandra®. + +## Setup: + +You need: + +- an [Astra](https://astra.datastax.com) Vector Database (free tier is fine!). **You need a [Database Administrator token](https://awesome-astra.github.io/docs/pages/astra/create-token/#c-procedure)**, in particular the string starting with `AstraCS:...`; +- likewise, get your [Database ID](https://awesome-astra.github.io/docs/pages/astra/faq/#where-should-i-find-a-database-identifier) ready, you will have to enter it below; +- an **OpenAI API Key**. (More info [here](https://cassio.org/start_here/#llm-access), note that out-of-the-box this demo supports OpenAI unless you tinker with the code.) + +_Note:_ you can alternatively use a regular Cassandra cluster: to do so, make sure you provide the `USE_CASSANDRA_CLUSTER` entry as shown in `.env.template` and the subsequent environment variables to specify how to connect to it. + +You need to provide the connection parameters and secrets through environment variables. Please refer to `.env.template` for what variables are required. + +## Reference + +Stand-alone LangServe template repo: [here](https://github.com/hemidactylus/langserve_cassandra_synonym_caching). diff --git a/templates/cassandra-synonym-caching/cassandra_synonym_caching/__init__.py b/templates/cassandra-synonym-caching/cassandra_synonym_caching/__init__.py new file mode 100644 index 00000000000..412d5a4e427 --- /dev/null +++ b/templates/cassandra-synonym-caching/cassandra_synonym_caching/__init__.py @@ -0,0 +1,44 @@ +import os + +import cassio + +import langchain +from langchain.schema import BaseMessage +from langchain.prompts import ChatPromptTemplate +from langchain.chat_models import ChatOpenAI +from langchain.schema.runnable import RunnableLambda +from langchain.cache import CassandraCache + +use_cassandra = int(os.environ.get("USE_CASSANDRA_CLUSTER", "0")) +if use_cassandra: + from .cassandra_cluster_init import get_cassandra_connection + session, keyspace = get_cassandra_connection() + cassio.init( + session=session, + keyspace=keyspace, + ) +else: + cassio.init( + token=os.environ["ASTRA_DB_APPLICATION_TOKEN"], + database_id=os.environ["ASTRA_DB_ID"], + keyspace=os.environ.get("ASTRA_DB_KEYSPACE"), + ) + +# inits +langchain.llm_cache = CassandraCache(session=None, keyspace=None) +llm = ChatOpenAI() + +# custom runnables +def msg_splitter(msg: BaseMessage): + return [ + w.strip() + for w in msg.content.split(",") + if w.strip() + ] + +# synonym-route preparation +synonym_prompt = ChatPromptTemplate.from_template( + "List up to five comma-separated synonyms of this word: {word}" +) + +chain = synonym_prompt | llm | RunnableLambda(msg_splitter) diff --git a/templates/cassandra-synonym-caching/cassandra_synonym_caching/cassandra_cluster_init.py b/templates/cassandra-synonym-caching/cassandra_synonym_caching/cassandra_cluster_init.py new file mode 100644 index 00000000000..e59e5bf6dfc --- /dev/null +++ b/templates/cassandra-synonym-caching/cassandra_synonym_caching/cassandra_cluster_init.py @@ -0,0 +1,27 @@ +import os + +from cassandra.cluster import Cluster +from cassandra.auth import PlainTextAuthProvider + + +def get_cassandra_connection(): + contact_points = [ + cp.strip() + for cp in os.environ.get("CASSANDRA_CONTACT_POINTS", "").split(',') + if cp.strip() + ] + CASSANDRA_KEYSPACE = os.environ["CASSANDRA_KEYSPACE"] + CASSANDRA_USERNAME = os.environ.get("CASSANDRA_USERNAME") + CASSANDRA_PASSWORD = os.environ.get("CASSANDRA_PASSWORD") + # + if CASSANDRA_USERNAME and CASSANDRA_PASSWORD: + auth_provider = PlainTextAuthProvider( + CASSANDRA_USERNAME, + CASSANDRA_PASSWORD, + ) + else: + auth_provider = None + + c_cluster = Cluster(contact_points if contact_points else None, auth_provider=auth_provider) + session = c_cluster.connect() + return (session, CASSANDRA_KEYSPACE) diff --git a/templates/cassandra-synonym-caching/poetry.lock b/templates/cassandra-synonym-caching/poetry.lock new 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">=0.0.313" +openai = "^0.28.1" +tiktoken = "^0.5.1" +cassio = "^0.1.3" + +[tool.langserve] +export_module = "cassandra_synonym_caching" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/csv-agent/README.md b/templates/csv-agent/README.md new file mode 100644 index 00000000000..ef45bc6c467 --- /dev/null +++ b/templates/csv-agent/README.md @@ -0,0 +1,5 @@ +# csv-agent + +This is a csv agent that uses both a Python REPL as well as a vectorstore to allow for interaction with text data. + +Set up that is required is running `ingest.py` to do the ingestion into a vectorstore. diff --git a/templates/csv-agent/csv_agent/__init__.py b/templates/csv-agent/csv_agent/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/csv-agent/csv_agent/agent.py b/templates/csv-agent/csv_agent/agent.py new file mode 100644 index 00000000000..9badaaeee0a --- /dev/null +++ b/templates/csv-agent/csv_agent/agent.py @@ -0,0 +1,66 @@ +from langchain.agents import OpenAIFunctionsAgent, AgentExecutor +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain_experimental.tools import PythonAstREPLTool +import pandas as pd +from langchain.chat_models import ChatOpenAI +from langsmith import Client +from langchain.smith import RunEvalConfig, run_on_dataset +from pydantic import BaseModel, Field +from langchain.embeddings import OpenAIEmbeddings +from langchain.vectorstores import FAISS +from langchain.tools.retriever import create_retriever_tool +from pathlib import Path + +MAIN_DIR = Path(__file__).parents[1] + +pd.set_option('display.max_rows', 20) +pd.set_option('display.max_columns', 20) + +embedding_model = OpenAIEmbeddings() +vectorstore = FAISS.load_local(MAIN_DIR / "titanic_data", embedding_model) +retriever_tool = create_retriever_tool(vectorstore.as_retriever(), "person_name_search", "Search for a person by name") + + +TEMPLATE = """You are working with a pandas dataframe in Python. The name of the dataframe is `df`. +It is important to understand the attributes of the dataframe before working with it. This is the result of running `df.head().to_markdown()` + + +{dhead} + + +You are not meant to use only these rows to answer questions - they are meant as a way of telling you about the shape and schema of the dataframe. +You also do not have use only the information here to answer questions - you can run intermediate queries to do exporatory data analysis to give you more information as needed. + +You have a tool called `person_name_search` through which you can lookup a person by name and find the records corresponding to people with similar name as the query. +You should only really use this if your search term contains a persons name. Otherwise, try to solve it with code. + +For example: + +How old is Jane? +Use `person_name_search` since you can use the query `Jane` + +Who has id 320 +Use `python_repl` since even though the question is about a person, you don't know their name so you can't include it. +""" + + + +class PythonInputs(BaseModel): + query: str = Field(description="code snippet to run") + + +df = pd.read_csv("titanic.csv") +template = TEMPLATE.format(dhead=df.head().to_markdown()) + +prompt = ChatPromptTemplate.from_messages([ + ("system", template), + MessagesPlaceholder(variable_name="agent_scratchpad"), + ("human", "{input}") +]) + +repl = PythonAstREPLTool(locals={"df": df}, name="python_repl", + description="Runs code and returns the output of the final line", + args_schema=PythonInputs) +tools = [repl, retriever_tool] +agent = OpenAIFunctionsAgent(llm=ChatOpenAI(temperature=0, model="gpt-4"), prompt=prompt, tools=tools) +agent_executor = AgentExecutor(agent=agent, tools=tools, max_iterations=5, early_stopping_method="generate") diff --git a/templates/csv-agent/ingest.py b/templates/csv-agent/ingest.py new file mode 100644 index 00000000000..5bb6784ea8a --- /dev/null +++ b/templates/csv-agent/ingest.py @@ -0,0 +1,13 @@ +from langchain.document_loaders import CSVLoader +from langchain.tools.retriever import create_retriever_tool +from langchain.indexes import VectorstoreIndexCreator +from langchain.vectorstores import FAISS + +loader = CSVLoader('/Users/harrisonchase/Downloads/titanic.csv') + +docs = loader.load() +index_creator = VectorstoreIndexCreator(vectorstore_cls=FAISS) + +index = index_creator.from_documents(docs) + +index.vectorstore.save_local("titanic_data") diff --git a/templates/csv-agent/main.py b/templates/csv-agent/main.py new file mode 100644 index 00000000000..8814b924787 --- /dev/null +++ b/templates/csv-agent/main.py @@ -0,0 +1,5 @@ +from csv_agent.agent import agent_executor + +if __name__ == "__main__": + question = "who was in cabin c28?" + 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a/templates/elastic-query-generator/README.md b/templates/elastic-query-generator/README.md new file mode 100644 index 00000000000..de5fedcc4b5 --- /dev/null +++ b/templates/elastic-query-generator/README.md @@ -0,0 +1,28 @@ +# elastic-query-generator + +We can use LLMs to interact with Elasticsearch analytics databases in natural language. + +This chain builds search queries via the Elasticsearch DSL API (filters and aggregations). + +The Elasticsearch client must have permissions for index listing, mapping description and search queries. + + + +## Setup + +## Installing Elasticsearch + +There are a number of ways to run Elasticsearch. + +### Elastic Cloud + +Create a free trial account on [Elastic Cloud](https://cloud.elastic.co/registration?utm_source=langchain&utm_content=langserve). + +With a deployment, update the connection string. + +Password and connection (elasticsearch url) can be found on the deployment console. + +```bash +> export ELASTIC_SEARCH_SERVER="https://elastic:@" + +If you want to populate the DB with some example info, you can run `python ingest.py`. diff --git a/templates/elastic-query-generator/elastic_query_generator/__init__.py b/templates/elastic-query-generator/elastic_query_generator/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/elastic-query-generator/elastic_query_generator/chain.py b/templates/elastic-query-generator/elastic_query_generator/chain.py new file mode 100644 index 00000000000..917dbd71da3 --- /dev/null +++ b/templates/elastic-query-generator/elastic_query_generator/chain.py @@ -0,0 +1,25 @@ +import os +from langchain.chat_models import ChatOpenAI +from langchain.output_parsers.json import SimpleJsonOutputParser +from elasticsearch import Elasticsearch +from pathlib import Path + +from .prompts import DSL_PROMPT +from .elastic_index_info import get_indices_infos + +es_host = os.environ["ELASTIC_SEARCH_SERVER"] +es_password = os.environ["ELASTIC_PASSWORD"] + +db = Elasticsearch( + es_host, + http_auth=('elastic', es_password), + ca_certs=Path(__file__).parents[1] / 'http_ca.crt' # Replace with your actual path +) + +_model = ChatOpenAI(temperature=0, model="gpt-4") + +chain = { + "input": lambda x: x["input"], + "indices_info": lambda _: get_indices_infos(db), + "top_k": lambda x: x.get("top_k", 5), +} | DSL_PROMPT | _model | SimpleJsonOutputParser() diff --git a/templates/elastic-query-generator/elastic_query_generator/elastic_index_info.py b/templates/elastic-query-generator/elastic_query_generator/elastic_index_info.py new file mode 100644 index 00000000000..8d059afdd8f --- /dev/null +++ b/templates/elastic-query-generator/elastic_query_generator/elastic_index_info.py @@ -0,0 +1,32 @@ +from typing import List + +def _list_indices(database, include_indices=None, ignore_indices=None) -> List[str]: + all_indices = [ + index["index"] for index in database.cat.indices(format="json") + ] + + if include_indices: + all_indices = [i for i in all_indices if i in include_indices] + if ignore_indices: + all_indices = [i for i in all_indices if i not in ignore_indices] + + return all_indices + +def get_indices_infos(database, sample_documents_in_index_info=5) -> str: + indices = _list_indices(database) + mappings = database.indices.get_mapping(index=",".join(indices)) + if sample_documents_in_index_info > 0: + for k, v in mappings.items(): + hits = database.search( + index=k, + query={"match_all": {}}, + size=sample_documents_in_index_info, + )["hits"]["hits"] + hits = [str(hit["_source"]) for hit in hits] + mappings[k]["mappings"] = str(v) + "\n\n/*\n" + "\n".join(hits) + "\n*/" + return "\n\n".join( + [ + "Mapping for index {}:\n{}".format(index, mappings[index]["mappings"]) + for index in mappings + ] + ) \ No newline at end of file diff --git a/templates/elastic-query-generator/elastic_query_generator/prompts.py b/templates/elastic-query-generator/elastic_query_generator/prompts.py new file mode 100644 index 00000000000..1df5c1e78b3 --- /dev/null +++ b/templates/elastic-query-generator/elastic_query_generator/prompts.py @@ -0,0 +1,21 @@ +from langchain.prompts.prompt import PromptTemplate + +PROMPT_SUFFIX = """Only use the following Elasticsearch indices: +{indices_info} + +Question: {input} +ESQuery:""" + +DEFAULT_DSL_TEMPLATE = """Given an input question, create a syntactically correct Elasticsearch query to run. Always limit your query to at most {top_k} results, unless the user specifies in their question a specific number of examples they wish to obtain, or unless its implied that they want to see all. You can order the results by a relevant column to return the most interesting examples in the database. + +Unless told to do not query for all the columns from a specific index, only ask for a the few relevant columns given the question. + +Pay attention to use only the column names that you can see in the mapping description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which index. Return the query as valid json. + +Use the following format: + +Question: Question here +ESQuery: Elasticsearch Query formatted as json +""" + +DSL_PROMPT = PromptTemplate.from_template(DEFAULT_DSL_TEMPLATE + PROMPT_SUFFIX) diff --git a/templates/elastic-query-generator/ingest.py b/templates/elastic-query-generator/ingest.py new file mode 100644 index 00000000000..528e5324909 --- /dev/null +++ b/templates/elastic-query-generator/ingest.py @@ -0,0 +1,21 @@ +import os +from elasticsearch import Elasticsearch + +es_host = os.environ["ELASTIC_SEARCH_SERVER"] +es_password = os.environ["ELASTIC_PASSWORD"] + +db = Elasticsearch( + es_host, + http_auth=('elastic', es_password), + ca_certs='http_ca.crt' # Replace with your actual path +) + +customers = [ + {"firstname": "Jennifer", "lastname": "Walters"}, + {"firstname": "Monica","lastname":"Rambeau"}, + {"firstname": "Carol","lastname":"Danvers"}, + {"firstname": "Wanda","lastname":"Maximoff"}, + {"firstname": "Jennifer","lastname":"Takeda"}, +] +for i, customer in enumerate(customers): + db.create(index="customers", document=customer, id=i) diff --git a/templates/elastic-query-generator/main.py b/templates/elastic-query-generator/main.py new file mode 100644 index 00000000000..30025fe8841 --- /dev/null +++ b/templates/elastic-query-generator/main.py @@ -0,0 +1,5 @@ +from elastic_query_generator.chain import chain + + +if __name__ == "__main__": + print(chain.invoke({"input": "how many customers named Carol"})) diff --git a/templates/elastic-query-generator/poetry.lock b/templates/elastic-query-generator/poetry.lock new file mode 100644 index 00000000000..3affe0a7c90 --- /dev/null +++ b/templates/elastic-query-generator/poetry.lock @@ -0,0 +1,1246 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + 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">=3.8.1,<4.0" +langchain = ">=0.0.313" +elasticsearch = "^8.10.1" + +[tool.langserve] +export_module = "elastic_query_generator.chain" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/elastic-query-generator/tests/__init__.py b/templates/elastic-query-generator/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/extraction-openai-functions/LICENSE b/templates/extraction-openai-functions/LICENSE new file mode 100644 index 00000000000..578fc414ad4 --- /dev/null +++ b/templates/extraction-openai-functions/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 pingpong-templates + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/extraction-openai-functions/README.md b/templates/extraction-openai-functions/README.md new file mode 100644 index 00000000000..d98c465c6d4 --- /dev/null +++ b/templates/extraction-openai-functions/README.md @@ -0,0 +1,41 @@ +# Extraction with OpenAI Function Calling + +This template shows how to do extraction of structured data from unstructured data, using OpenAI [function calling](https://python.langchain.com/docs/modules/chains/how_to/openai_functions). + +Specify the information you want to extract in `chain.py` + +By default, it will extract the title and author of papers. + +## LLM + +This template will use `OpenAI` by default. + +Be sure that `OPENAI_API_KEY` is set in your environment. + +## Adding the template + +Install the langchain package +``` +pip install -e packages/extraction_openai_functions +``` + +Edit app/server.py to add that package to the routes +``` +from fastapi import FastAPI +from langserve import add_routes +from extraction_openai_functions.chain import chain + +app = FastAPI() +add_routes(app, chain) +``` + +Run the app +``` +python app/server.py +``` + +You can use this template in the Playground: + +http://127.0.0.1:8000/extraction-openai-functions/playground/ + +Also, see Jupyter notebook `openai_functions` for various other ways to connect to the template. \ No newline at end of file diff --git a/templates/extraction-openai-functions/extraction_openai_functions/__init__.py b/templates/extraction-openai-functions/extraction_openai_functions/__init__.py new file mode 100644 index 00000000000..d2350f5de2c --- /dev/null +++ b/templates/extraction-openai-functions/extraction_openai_functions/__init__.py @@ -0,0 +1,3 @@ +from extraction_openai_functions.chain import chain + +__all__ = ["chain"] diff --git a/templates/extraction-openai-functions/extraction_openai_functions/chain.py b/templates/extraction-openai-functions/extraction_openai_functions/chain.py new file mode 100644 index 00000000000..6e9dcc530ce --- /dev/null +++ b/templates/extraction-openai-functions/extraction_openai_functions/chain.py @@ -0,0 +1,41 @@ +from langchain.pydantic_v1 import BaseModel +from typing import List, Optional +from langchain.chat_models import ChatOpenAI +from langchain.prompts import ChatPromptTemplate +from langchain.utils.openai_functions import convert_pydantic_to_openai_function +from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser +import json + + +template = """A article will be passed to you. Extract from it all papers that are mentioned by this article. + +Do not extract the name of the article itself. If no papers are mentioned that's fine - you don't need to extract any! Just return an empty list. + +Do not make up or guess ANY extra information. Only extract what exactly is in the text.""" + +prompt = ChatPromptTemplate.from_messages([ + ("system", template), + ("human", "{input}") +]) + +# Function output schema +class Paper(BaseModel): + """Information about papers mentioned.""" + title: str + author: Optional[str] + + +class Info(BaseModel): + """Information to extract""" + papers: List[Paper] + +# Function definition +model = ChatOpenAI() +function = [convert_pydantic_to_openai_function(Info)] +chain = prompt | model.bind( + functions=function, function_call={"name": "Info"} +) | (lambda x: json.loads(x.additional_kwargs['function_call']['arguments'])['papers']) + +# chain = prompt | model.bind( +# functions=function, function_call={"name": "Info"} +# ) | JsonKeyOutputFunctionsParser(key_name="papers") diff --git a/templates/extraction-openai-functions/openai_functions.ipynb b/templates/extraction-openai-functions/openai_functions.ipynb new file mode 100644 index 00000000000..2f172569889 --- /dev/null +++ b/templates/extraction-openai-functions/openai_functions.ipynb @@ -0,0 +1,116 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "16f2c32e", + "metadata": {}, + "source": [ + "## Document Loading\n", + "\n", + "Load a blog post on agents." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c9fadce0", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders import WebBaseLoader\n", + "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n", + "text = loader.load()" + ] + }, + { + "cell_type": "markdown", + "id": "4086be03", + "metadata": {}, + "source": [ + "## Run Template\n", + "\n", + "\n", + "As shown in the README, add template and start server:\n", + "```\n", + "langchain serve add openai-functions\n", + "langchain start\n", + "```\n", + "\n", + "We can now look at the endpoints:\n", + "\n", + "http://127.0.0.1:8000/docs#\n", + "\n", + "And specifically at our loaded template:\n", + "\n", + "http://127.0.0.1:8000/docs#/default/invoke_openai_functions_invoke_post\n", + " \n", + "We can also use remote runnable to call it." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ed507784", + "metadata": {}, + "outputs": [], + "source": [ + "from langserve.client import RemoteRunnable\n", + "oai_function = RemoteRunnable('http://localhost:8000/openai-functions')" + ] + }, + { + "cell_type": "markdown", + "id": "68046695", + "metadata": {}, + "source": [ + "The function call will perform tagging:\n", + "\n", + "* summarize\n", + "* provide keywords\n", + "* provide language" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6dace748", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='', additional_kwargs={'function_call': {'name': 'Overview', 'arguments': '{\\n \"summary\": \"This article discusses the concept of building agents with LLM (large language model) as their core controller. It explores the potentiality of LLM as a general problem solver and describes the key components of an LLM-powered autonomous agent system, including planning, memory, and tool use. The article also presents case studies and challenges related to building LLM-powered agents.\",\\n \"language\": \"English\",\\n \"keywords\": \"LLM, autonomous agents, planning, memory, tool use, case studies, challenges\"\\n}'}})" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oai_function.invoke(text[0].page_content[0:1500])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "langserve", + "language": "python", + "name": "langserve" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/templates/extraction-openai-functions/poetry.lock b/templates/extraction-openai-functions/poetry.lock new file mode 100644 index 00000000000..9f0d0140142 --- 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+[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.323" +openai = ">=0.27.9" + +[tool.langserve] +export_module = "exctraction_openai_functions" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/extraction-openai-functions/tests/__init__.py b/templates/extraction-openai-functions/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/neo4j-cypher-ft/README.md b/templates/neo4j-cypher-ft/README.md new file mode 100644 index 00000000000..cfbf9a1227b --- /dev/null +++ b/templates/neo4j-cypher-ft/README.md @@ -0,0 +1,22 @@ +# Neo4j Knowledge Graph: Enhanced mapping from text to database using a full-text index + +This template allows you to chat with Neo4j graph database in natural language, using an OpenAI LLM. +The addition of the full-text index ensures efficient mapping of values from text to database for more precise Cypher statement generation. + +## Set up Environment + +You need to define the following environment variables + +``` +OPENAI_API_KEY= +NEO4J_URI= +NEO4J_USERNAME= +NEO4J_PASSWORD= +``` + +## Installation + +```bash +# from inside your LangServe instance +poe add neo4j-cypher +``` diff --git a/templates/neo4j-cypher-ft/ingest.py b/templates/neo4j-cypher-ft/ingest.py new file mode 100644 index 00000000000..98f43aff40f --- /dev/null +++ b/templates/neo4j-cypher-ft/ingest.py @@ -0,0 +1,21 @@ +from langchain.graphs import Neo4jGraph + +graph = Neo4jGraph() + +# Import sample data +graph.query( + """ +MERGE (m:Movie {name:"Top Gun"}) +WITH m +UNWIND ["Tom Cruise", "Val Kilmer", "Anthony Edwards", "Meg Ryan"] AS actor +MERGE (a:Person {name:actor}) +MERGE (a)-[:ACTED_IN]->(m) +""" +) + +# Create full text index for entity matching +# on Person and Movie nodes +graph.query( + "CREATE FULLTEXT INDEX entity IF NOT EXISTS" + " FOR (m:Movie|Person) ON EACH [m.title, m.name]" +) diff --git a/templates/neo4j-cypher-ft/main.py b/templates/neo4j-cypher-ft/main.py new file mode 100644 index 00000000000..490d4542536 --- /dev/null +++ b/templates/neo4j-cypher-ft/main.py @@ -0,0 +1,5 @@ +from neo4j_cypher_ft.chain import chain + +if __name__ == "__main__": + original_query = "Did tom cruis act in top gun?" + print(chain.invoke({"question": original_query})) diff --git a/templates/neo4j-cypher-ft/neo4j_cypher_ft/__init__.py b/templates/neo4j-cypher-ft/neo4j_cypher_ft/__init__.py new file mode 100644 index 00000000000..56cf88fe137 --- /dev/null +++ b/templates/neo4j-cypher-ft/neo4j_cypher_ft/__init__.py @@ -0,0 +1,3 @@ +from neo4j_cypher_ft.chain import chain + +__all__ = ["chain"] diff --git a/templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py b/templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py new file mode 100644 index 00000000000..acedfe4aac0 --- /dev/null +++ b/templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py @@ -0,0 +1,123 @@ + +from typing import List, Optional + +from langchain.chat_models import ChatOpenAI +from langchain.graphs import Neo4jGraph +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough +from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema +from langchain.chains.openai_functions import create_structured_output_chain +try: + from pydantic.v1.main import BaseModel, Field +except ImportError: + from pydantic.main import BaseModel, Field + +# Connection to Neo4j +graph = Neo4jGraph() + +# Cypher validation tool for relationship directions +corrector_schema = [ + Schema(el["start"], el["type"], el["end"]) + for el in graph.structured_schema.get("relationships") +] +cypher_validation = CypherQueryCorrector(corrector_schema) + +# LLMs +cypher_llm = ChatOpenAI(model_name="gpt-4", temperature=0.0) +qa_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0) + +# Extract entities from text +class Entities(BaseModel): + """Identifying information about entities.""" + + names: List[str] = Field( + ..., + description="All the person, organization, or business entities that appear in the text", + ) + +prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are extracting organization and person entities from the text.", + ), + ( + "human", + "Use the given format to extract information from the following input: {question}", + ), + ] +) + +# Fulltext index query +def map_to_database(entities: Entities) -> Optional[str]: + result = "" + for entity in entities.names: + response = graph.query( + "CALL db.index.fulltext.queryNodes('entity', $entity + '*', {limit:1})" + " YIELD node,score RETURN node.name AS result", + {"entity":entity}) + try: + result += f"{entity} maps to {response[0]['result']} in database\n" + except IndexError: + pass + return result + +entity_chain = create_structured_output_chain( + Entities, qa_llm, prompt +) + +# Generate Cypher statement based on natural language input +cypher_template = """Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question: +{schema} +Entities in the question map to the following database values: +{entities_list} +Question: {question} +Cypher query:""" + +cypher_prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "Given an input question, convert it to a Cypher query. No pre-amble.", + ), + ("human", cypher_template), + ] +) + +cypher_response = ( + RunnablePassthrough.assign(names=entity_chain) + | RunnablePassthrough.assign( + entities_list=lambda x: map_to_database(x['names']['function']), + schema=lambda _: graph.get_schema, + ) + | cypher_prompt + | cypher_llm.bind(stop=["\nCypherResult:"]) + | StrOutputParser() +) + +# Generate natural language response based on database results +response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response: +Question: {question} +Cypher query: {query} +Cypher Response: {response}""" + +response_prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "Given an input question and Cypher response, convert it to a natural language answer. No pre-amble.", + ), + ("human", response_template), + ] +) + +chain = ( + RunnablePassthrough.assign(query=cypher_response) + | RunnablePassthrough.assign( + response=lambda x: graph.query(cypher_validation(x["query"])), + ) + | response_prompt + | qa_llm + | StrOutputParser() +) diff --git a/templates/neo4j-cypher-ft/poetry.lock b/templates/neo4j-cypher-ft/poetry.lock new file mode 100644 index 00000000000..496f0a491ba --- /dev/null +++ b/templates/neo4j-cypher-ft/poetry.lock @@ -0,0 +1,1296 @@ +# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = 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b/templates/neo4j-cypher-ft/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/neo4j-cypher/README.md b/templates/neo4j-cypher/README.md new file mode 100644 index 00000000000..731a25a69f4 --- /dev/null +++ b/templates/neo4j-cypher/README.md @@ -0,0 +1,21 @@ +# Neo4j Knowledge Graph with OpenAI LLMs + +This template allows you to chat with Neo4j graph database in natural language, using an OpenAI LLM. + +## Set up Environment + +You need to define the following environment variables + +``` +OPENAI_API_KEY= +NEO4J_URI= +NEO4J_USERNAME= +NEO4J_PASSWORD= +``` + +## Installation + +```bash +# from inside your LangServe instance +poe add neo4j-cypher +``` diff --git a/templates/neo4j-cypher/ingest.py b/templates/neo4j-cypher/ingest.py new file mode 100644 index 00000000000..61adaa5135d --- /dev/null +++ b/templates/neo4j-cypher/ingest.py @@ -0,0 +1,13 @@ +from langchain.graphs import Neo4jGraph + +graph = Neo4jGraph() + +graph.query( + """ +MERGE (m:Movie {name:"Top Gun"}) +WITH m +UNWIND ["Tom Cruise", "Val Kilmer", "Anthony Edwards", "Meg Ryan"] AS actor +MERGE (a:Actor {name:actor}) +MERGE (a)-[:ACTED_IN]->(m) +""" +) diff --git a/templates/neo4j-cypher/main.py b/templates/neo4j-cypher/main.py new file mode 100644 index 00000000000..611cbe2aff9 --- /dev/null +++ b/templates/neo4j-cypher/main.py @@ -0,0 +1,5 @@ +from neo4j_cypher.chain import chain + +if __name__ == "__main__": + original_query = "Who played in Top Gun?" + print(chain.invoke({"question": original_query})) diff --git a/templates/neo4j-cypher/neo4j_cypher/__init__.py b/templates/neo4j-cypher/neo4j_cypher/__init__.py new file mode 100644 index 00000000000..c18e08325b6 --- /dev/null +++ b/templates/neo4j-cypher/neo4j_cypher/__init__.py @@ -0,0 +1,3 @@ +from neo4j_cypher.chain import chain + +__all__ = ["chain"] diff --git a/templates/neo4j-cypher/neo4j_cypher/chain.py b/templates/neo4j-cypher/neo4j_cypher/chain.py new file mode 100644 index 00000000000..befbb9ebb30 --- /dev/null +++ b/templates/neo4j-cypher/neo4j_cypher/chain.py @@ -0,0 +1,72 @@ +from langchain.chat_models import ChatOpenAI +from langchain.graphs import Neo4jGraph +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough +from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema + +# Connection to Neo4j +graph = Neo4jGraph() + +# Cypher validation tool for relationship directions +corrector_schema = [ + Schema(el["start"], el["type"], el["end"]) + for el in graph.structured_schema.get("relationships") +] +cypher_validation = CypherQueryCorrector(corrector_schema) + +# LLMs +cypher_llm = ChatOpenAI(model_name="gpt-4", temperature=0.0) +qa_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0) + +# Generate Cypher statement based on natural language input +cypher_template = """Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question: +{schema} + +Question: {question} +Cypher query:""" + +cypher_prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "Given an input question, convert it to a Cypher query. No pre-amble.", + ), + ("human", cypher_template), + ] +) + +cypher_response = ( + RunnablePassthrough.assign( + schema=lambda _: graph.get_schema, + ) + | cypher_prompt + | cypher_llm.bind(stop=["\nCypherResult:"]) + | StrOutputParser() +) + +# Generate natural language response based on database results +response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response: +Question: {question} +Cypher query: {query} +Cypher Response: {response}""" + +response_prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "Given an input question and Cypher response, convert it to a natural language answer. No pre-amble.", + ), + ("human", response_template), + ] +) + +chain = ( + RunnablePassthrough.assign(query=cypher_response) + | RunnablePassthrough.assign( + response=lambda x: graph.query(cypher_validation(x["query"])), + ) + | response_prompt + | qa_llm + | StrOutputParser() +) diff --git a/templates/neo4j-cypher/poetry.lock b/templates/neo4j-cypher/poetry.lock new file mode 100644 index 00000000000..496f0a491ba --- /dev/null +++ b/templates/neo4j-cypher/poetry.lock @@ -0,0 +1,1296 @@ +# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = 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b/templates/neo4j-cypher/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/neo4j-generation/README.md b/templates/neo4j-generation/README.md new file mode 100644 index 00000000000..9689e11ddf4 --- /dev/null +++ b/templates/neo4j-generation/README.md @@ -0,0 +1,25 @@ +# Graph Generation Chain for Neo4j Knowledge Graph + +Harness the power of natural language understanding and convert plain text into structured knowledge graphs with the Graph Generation Chain. +This system integrates with the Neo4j graph database using OpenAI's LLM. +By leveraging OpenAI Functions capabilities, the Graph Generation Chain efficiently extracts graph structure from text. + +## Set up Environment + +You need to define the following environment variables + +``` +OPENAI_API_KEY= +NEO4J_URI= +NEO4J_USERNAME= +NEO4J_PASSWORD= +``` + +## Installation + +To get started with the Graph Generation Chain: + +```bash +# from inside your LangServe instance +poe add neo4j-generation +``` \ No newline at end of file diff --git a/templates/neo4j-generation/main.py b/templates/neo4j-generation/main.py new file mode 100644 index 00000000000..df2298613e0 --- /dev/null +++ b/templates/neo4j-generation/main.py @@ -0,0 +1,14 @@ +from neo4j_generation.chain import chain + + +if __name__ == "__main__": + text = "Harrison works at LangChain, which is located in San Francisco" + allowed_nodes = ["Person", "Organization", "Location"] + allowed_relationships = ["WORKS_AT", "LOCATED_IN"] + print( + chain( + text, + allowed_nodes=allowed_nodes, + allowed_relationships=allowed_relationships, + ) + ) diff --git a/templates/neo4j-generation/neo4j_generation/__init__.py b/templates/neo4j-generation/neo4j_generation/__init__.py new file mode 100644 index 00000000000..87f97d321fe --- /dev/null +++ b/templates/neo4j-generation/neo4j_generation/__init__.py @@ -0,0 +1,3 @@ +from neo4j_generation.chain import chain + +__all__ = ["chain"] diff --git a/templates/neo4j-generation/neo4j_generation/chain.py b/templates/neo4j-generation/neo4j_generation/chain.py new file mode 100644 index 00000000000..8f7aa835879 --- /dev/null +++ b/templates/neo4j-generation/neo4j_generation/chain.py @@ -0,0 +1,109 @@ +from typing import Optional, List +from langchain.chains.openai_functions import ( + create_structured_output_chain, +) +from langchain.chat_models import ChatOpenAI +from langchain.prompts import ChatPromptTemplate +from langchain.graphs import Neo4jGraph +from langchain.graphs.graph_document import GraphDocument +from langchain.schema import Document + +from neo4j_generation.utils import ( + KnowledgeGraph, + map_to_base_node, + map_to_base_relationship, +) + +graph = Neo4jGraph() + + +llm = ChatOpenAI(model="gpt-3.5-turbo-16k", temperature=0) + + +def get_extraction_chain( + allowed_nodes: Optional[List[str]] = None, allowed_rels: Optional[List[str]] = None +): + """ + Constructs and returns an extraction chain for building a knowledge graph based on specified parameters. + + The function generates a chat prompt template, outlining the instructions for an LLM to extract information + and construct a knowledge graph. It primarily focuses on consistency in labeling nodes, handling numerical data + and dates, coreference resolution, and strict compliance with the provided rules. + + Parameters: + - allowed_nodes (Optional[List[str]]): A list of node labels that are allowed to be used in the knowledge graph. + If not provided, there won't be any specific restriction on node labels. + - allowed_rels (Optional[List[str]]): A list of relationship types that are allowed in the knowledge graph. + If not provided, there won't be any specific restriction on relationship types. + """ + prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + f"""# Knowledge Graph Instructions for GPT-4 +## 1. Overview +You are a top-tier algorithm designed for extracting information in structured formats to build a knowledge graph. +- **Nodes** represent entities and concepts. They're akin to Wikipedia nodes. +- The aim is to achieve simplicity and clarity in the knowledge graph, making it accessible for a vast audience. +## 2. Labeling Nodes +- **Consistency**: Ensure you use basic or elementary types for node labels. + - For example, when you identify an entity representing a person, always label it as **"person"**. Avoid using more specific terms like "mathematician" or "scientist". +- **Node IDs**: Never utilize integers as node IDs. Node IDs should be names or human-readable identifiers found in the text. +{'- **Allowed Node Labels:**' + ", ".join(allowed_nodes) if allowed_nodes else ""} +{'- **Allowed Relationship Types**:' + ", ".join(allowed_rels) if allowed_rels else ""} +## 3. Handling Numerical Data and Dates +- Numerical data, like age or other related information, should be incorporated as attributes or properties of the respective nodes. +- **No Separate Nodes for Dates/Numbers**: Do not create separate nodes for dates or numerical values. Always attach them as attributes or properties of nodes. +- **Property Format**: Properties must be in a key-value format. +- **Quotation Marks**: Never use escaped single or double quotes within property values. +- **Naming Convention**: Use camelCase for property keys, e.g., `birthDate`. +## 4. Coreference Resolution +- **Maintain Entity Consistency**: When extracting entities, it's vital to ensure consistency. +If an entity, such as "John Doe", is mentioned multiple times in the text but is referred to by different names or pronouns (e.g., "Joe", "he"), +always use the most complete identifier for that entity throughout the knowledge graph. In this example, use "John Doe" as the entity ID. +Remember, the knowledge graph should be coherent and easily understandable, so maintaining consistency in entity references is crucial. +## 5. Strict Compliance +Adhere to the rules strictly. Non-compliance will result in termination. + """, + ), + ( + "human", + "Use the given format to extract information from the following input: {input}", + ), + ("human", "Tip: Make sure to answer in the correct format"), + ] + ) + return create_structured_output_chain(KnowledgeGraph, llm, prompt, verbose=False) + + +def chain( + text: str, + allowed_nodes: Optional[List[str]] = None, + allowed_relationships: Optional[List[str]] = None, +) -> str: + """ + Process the given text to extract graph data and constructs a graph document from the extracted information. + The constructed graph document is then added to the graph. + + Parameters: + - text (str): The input text from which the information will be extracted to construct the graph. + - allowed_nodes (Optional[List[str]]): A list of node labels to guide the extraction process. + If not provided, extraction won't have specific restriction on node labels. + - allowed_relationships (Optional[List[str]]): A list of relationship types to guide the extraction process. + If not provided, extraction won't have specific restriction on relationship types. + + Returns: + str: A confirmation message indicating the completion of the graph construction. + """ + # Extract graph data using OpenAI functions + extract_chain = get_extraction_chain(allowed_nodes, allowed_relationships) + data = extract_chain.run(text) + # Construct a graph document + graph_document = GraphDocument( + nodes=[map_to_base_node(node) for node in data.nodes], + relationships=[map_to_base_relationship(rel) for rel in data.rels], + source=Document(page_content=text), + ) + # Store information into a graph + graph.add_graph_documents([graph_document]) + return "Graph construction finished" diff --git a/templates/neo4j-generation/neo4j_generation/utils.py b/templates/neo4j-generation/neo4j_generation/utils.py new file mode 100644 index 00000000000..00b086116b6 --- /dev/null +++ b/templates/neo4j-generation/neo4j_generation/utils.py @@ -0,0 +1,73 @@ +from langchain.graphs.graph_document import ( + Node as BaseNode, + Relationship as BaseRelationship, +) +from typing import List, Optional +from langchain.pydantic_v1 import Field, BaseModel + + +class Property(BaseModel): + """A single property consisting of key and value""" + + key: str = Field(..., description="key") + value: str = Field(..., description="value") + + +class Node(BaseNode): + properties: Optional[List[Property]] = Field( + None, description="List of node properties" + ) + + +class Relationship(BaseRelationship): + properties: Optional[List[Property]] = Field( + None, description="List of relationship properties" + ) + + +class KnowledgeGraph(BaseModel): + """Generate a knowledge graph with entities and relationships.""" + + nodes: List[Node] = Field(..., description="List of nodes in the knowledge graph") + rels: List[Relationship] = Field( + ..., description="List of relationships in the knowledge graph" + ) + + +def format_property_key(s: str) -> str: + words = s.split() + if not words: + return s + first_word = words[0].lower() + capitalized_words = [word.capitalize() for word in words[1:]] + return "".join([first_word] + capitalized_words) + + +def props_to_dict(props) -> dict: + """Convert properties to a dictionary.""" + properties = {} + if not props: + return properties + for p in props: + properties[format_property_key(p.key)] = p.value + return properties + + +def map_to_base_node(node: Node) -> BaseNode: + """Map the KnowledgeGraph Node to the base Node.""" + properties = props_to_dict(node.properties) if node.properties else {} + # Add name property for better Cypher statement generation + properties["name"] = node.id.title() + return BaseNode( + id=node.id.title(), type=node.type.capitalize(), properties=properties + ) + + +def map_to_base_relationship(rel: Relationship) -> BaseRelationship: + """Map the KnowledgeGraph Relationship to the base Relationship.""" + source = map_to_base_node(rel.source) + target = map_to_base_node(rel.target) + properties = props_to_dict(rel.properties) if rel.properties else {} + return BaseRelationship( + source=source, target=target, type=rel.type, properties=properties + ) diff --git a/templates/neo4j-generation/poetry.lock b/templates/neo4j-generation/poetry.lock new file mode 100644 index 00000000000..126b6b5101b --- /dev/null +++ b/templates/neo4j-generation/poetry.lock @@ -0,0 +1,1296 @@ +# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1d84166673694841d8953f0a8d0c90e1087739d24632fe86b1a08819168b4566"}, + 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"sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571"}, +] + +[package.dependencies] +idna = ">=2.0" +multidict = ">=4.0" + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8.1,<4.0" +content-hash = "64cec7b74299cce8ae31e1c43db21da46e11c0177a6e65a942df7a9707283e76" diff --git a/templates/neo4j-generation/pyproject.toml b/templates/neo4j-generation/pyproject.toml new file mode 100644 index 00000000000..f49693f37dc --- /dev/null +++ b/templates/neo4j-generation/pyproject.toml @@ -0,0 +1,21 @@ +[tool.poetry] +name = "neo4j_generation" +version = "0.0.1" +description = "" +authors = ["Tomaz Bratanic "] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.320" +openai = "^0.28.1" +neo4j = "^5.12.0" + +[tool.langserve] +export_module = "neo4j_generation.chain" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/neo4j-generation/tests/__init__.py b/templates/neo4j-generation/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/neo4j-parent/README.md b/templates/neo4j-parent/README.md new file mode 100644 index 00000000000..032f96aa1c6 --- /dev/null +++ b/templates/neo4j-parent/README.md @@ -0,0 +1,21 @@ +# Parent Document Retriever with Neo4j Vector Index + +This template allows you to balance precise embeddings and context retention by splitting documents into smaller chunks and retrieving their original or larger text information. + +## Set up Environment + +You need to define the following environment variables + +``` +OPENAI_API_KEY= +NEO4J_URI= +NEO4J_USERNAME= +NEO4J_PASSWORD= +``` + +## Installation + +```bash +# from inside your LangServe instance +poe add neo4j-parent +``` diff --git a/templates/neo4j-parent/dune.txt b/templates/neo4j-parent/dune.txt new file mode 100644 index 00000000000..3417aaac3fc --- /dev/null +++ b/templates/neo4j-parent/dune.txt @@ -0,0 +1,228 @@ +Dune is a 1965 epic science fiction novel by American author Frank Herbert, originally published as two separate serials in Analog magazine. It tied with Roger Zelazny's This Immortal for the Hugo Award in 1966 and it won the inaugural Nebula Award for Best Novel. It is the first installment of the Dune Chronicles. It is one of the world's best-selling science fiction novels.Dune is set in the distant future in a feudal interstellar society in which various noble houses control planetary fiefs. It tells the story of young Paul Atreides, whose family accepts the stewardship of the planet Arrakis. While the planet is an inhospitable and sparsely populated desert wasteland, it is the only source of melange, or "spice", a drug that extends life and enhances mental abilities. Melange is also necessary for space navigation, which requires a kind of multidimensional awareness and foresight that only the drug provides. As melange can only be produced on Arrakis, control of the planet is a coveted and dangerous undertaking. The story explores the multilayered interactions of politics, religion, ecology, technology, and human emotion, as the factions of the empire confront each other in a struggle for the control of Arrakis and its spice. +Herbert wrote five sequels: Dune Messiah, Children of Dune, God Emperor of Dune, Heretics of Dune, and Chapterhouse: Dune. Following Herbert's death in 1986, his son Brian Herbert and author Kevin J. Anderson continued the series in over a dozen additional novels since 1999. +Adaptations of the novel to cinema have been notoriously difficult and complicated. In the 1970s, cult filmmaker Alejandro Jodorowsky attempted to make a film based on the novel. After three years of development, the project was canceled due to a constantly growing budget. In 1984, a film adaptation directed by David Lynch was released to mostly negative responses from critics and failure at the box office, although it later developed a cult following. The book was also adapted into the 2000 Sci-Fi Channel miniseries Frank Herbert's Dune and its 2003 sequel Frank Herbert's Children of Dune (the latter of which combines the events of Dune Messiah and Children of Dune). A second film adaptation directed by Denis Villeneuve was released on October 21, 2021, to positive reviews. It grossed $401 million worldwide and went on to be nominated for ten Academy Awards, winning six. Villeneuve's film covers roughly the first half of the original novel; a sequel, which will cover the remaining story, will be released in March 2024. +The series has also been used as the basis for several board, role-playing, and video games. +Since 2009, the names of planets from the Dune novels have been adopted for the real-life nomenclature of plains and other features on Saturn's moon Titan. + + +== Origins == +After his novel The Dragon in the Sea was published in 1957, Herbert traveled to Florence, Oregon, at the north end of the Oregon Dunes. Here, the United States Department of Agriculture was attempting to use poverty grasses to stabilize the sand dunes. Herbert claimed in a letter to his literary agent, Lurton Blassingame, that the moving dunes could "swallow whole cities, lakes, rivers, highways." Herbert's article on the dunes, "They Stopped the Moving Sands", was never completed (and only published decades later in The Road to Dune), but its research sparked Herbert's interest in ecology and deserts.Herbert further drew inspiration from Native American mentors like "Indian Henry" (as Herbert referred to the man to his son; likely a Henry Martin of the Hoh tribe) and Howard Hansen. Both Martin and Hansen grew up on the Quileute reservation near Herbert's hometown. According to historian Daniel Immerwahr, Hansen regularly shared his writing with Herbert. "White men are eating the earth," Hansen told Herbert in 1958, after sharing a piece on the effect of logging on the Quileute reservation. "They're gonna turn this whole planet into a wasteland, just like North Africa." The world could become a "big dune," Herbert responded in agreement.Herbert was also interested in the idea of the superhero mystique and messiahs. He believed that feudalism was a natural condition humans fell into, where some led and others gave up the responsibility of making decisions and just followed orders. He found that desert environments have historically given birth to several major religions with messianic impulses. He decided to join his interests together so he could play religious and ecological ideas against each other. In addition, he was influenced by the story of T. E. Lawrence and the "messianic overtones" in Lawrence's involvement in the Arab Revolt during World War I. In an early version of Dune, the hero was actually very similar to Lawrence of Arabia, but Herbert decided the plot was too straightforward and added more layers to his story.Herbert drew heavy inspiration also from Lesley Blanch's The Sabres of Paradise (1960), a narrative history recounting a mid-19th century conflict in the Caucasus between rugged Islamized caucasian tribes and the expansive Russian Empire. Language used on both sides of that conflict become terms in Herbert's world—chakobsa, a Caucasian hunting language, becomes a battle language of humans spread across the galaxy; kanly, a word for blood feud in the 19th century Caucasus, represents a feud between Dune's noble Houses; sietch and tabir are both words for camp borrowed from Ukrainian Cossacks (of the Pontic–Caspian steppe).Herbert also borrowed some lines which Blanch stated were Caucasian proverbs. "To kill with the point lacked artistry", used by Blanch to describe the Caucasus peoples' love of swordsmanship, becomes in Dune "Killing with the tip lacks artistry", a piece of advice given to a young Paul during his training. "Polish comes from the city, wisdom from the hills", a Caucasian aphorism, turns into a desert expression: "Polish comes from the cities, wisdom from the desert". + +Another significant source of inspiration for Dune was Herbert's experiences with psilocybin and his hobby of cultivating mushrooms, according to mycologist Paul Stamets's account of meeting Herbert in the 1980s:Frank went on to tell me that much of the premise of Dune—the magic spice (spores) that allowed the bending of space (tripping), the giant sand worms (maggots digesting mushrooms), the eyes of the Fremen (the cerulean blue of Psilocybe mushrooms), the mysticism of the female spiritual warriors, the Bene Gesserits (influenced by the tales of Maria Sabina and the sacred mushroom cults of Mexico)—came from his perception of the fungal life cycle, and his imagination was stimulated through his experiences with the use of magic mushrooms.Herbert spent the next five years researching, writing, and revising. He published a three-part serial Dune World in the monthly Analog, from December 1963 to February 1964. The serial was accompanied by several illustrations that were not published again. After an interval of a year, he published the much slower-paced five-part The Prophet of Dune in the January–May 1965 issues. The first serial became "Book 1: Dune" in the final published Dune novel, and the second serial was divided into "Book Two: Muad'dib" and "Book Three: The Prophet". The serialized version was expanded, reworked, and submitted to more than twenty publishers, each of whom rejected it. The novel, Dune, was finally accepted and published in August 1965 by Chilton Books, a printing house better known for publishing auto repair manuals. Sterling Lanier, an editor at Chilton, had seen Herbert's manuscript and had urged his company to take a risk in publishing the book. However, the first printing, priced at $5.95 (equivalent to $55.25 in 2022), did not sell well and was poorly received by critics as being atypical of science fiction at the time. Chilton considered the publication of Dune a write-off and Lanier was fired. Over the course of time, the book gained critical acclaim, and its popularity spread by word-of-mouth to allow Herbert to start working full time on developing the sequels to Dune, elements of which were already written alongside Dune.At first Herbert considered using Mars as setting for his novel, but eventually decided to use a fictional planet instead. His son Brian said that "Readers would have too many preconceived ideas about that planet, due to the number of stories that had been written about it."Herbert dedicated his work "to the people whose labors go beyond ideas into the realm of 'real materials'—to the dry-land ecologists, wherever they may be, in whatever time they work, this effort at prediction is dedicated in humility and admiration." + + +== Plot == +Duke Leto Atreides of House Atreides, ruler of the ocean planet Caladan, is assigned by the Padishah Emperor Shaddam IV to serve as fief ruler of the planet Arrakis. Although Arrakis is a harsh and inhospitable desert planet, it is of enormous importance because it is the only planetary source of melange, or the "spice", a unique and incredibly valuable substance that extends human youth, vitality and lifespan. It is also through the consumption of spice that Spacing Guild Navigators are able to effect safe interstellar travel. Shaddam, jealous of Duke Leto Atreides's rising popularity in the Landsraad, sees House Atreides as a potential future rival and threat, so conspires with House Harkonnen, the former stewards of Arrakis and the longstanding enemies of House Atreides, to destroy Leto and his family after their arrival. Leto is aware his assignment is a trap of some kind, but is compelled to obey the Emperor's orders anyway. +Leto's concubine Lady Jessica is an acolyte of the Bene Gesserit, an exclusively female group that pursues mysterious political aims and wields seemingly superhuman physical and mental abilities, such as the ability to control their bodies down to the cellular level, and also decide the sex of their children. Though Jessica was instructed by the Bene Gesserit to bear a daughter as part of their breeding program, out of love for Leto she bore a son, Paul. From a young age, Paul has been trained in warfare by Leto's aides, the elite soldiers Duncan Idaho and Gurney Halleck. Thufir Hawat, the Duke's Mentat (human computers, able to store vast amounts of data and perform advanced calculations on demand), has instructed Paul in the ways of political intrigue. Jessica has also trained her son in Bene Gesserit disciplines. +Paul's prophetic dreams interest Jessica's superior, the Reverend Mother Gaius Helen Mohiam, who subjects Paul to the deadly gom jabbar test. Holding a poisonous needle to his neck ready to strike should he be unable to resist the impulse to withdraw his hand from the nerve induction box, she tests Paul's self-control to overcome the extreme psychological pain he is being subjected to through the box. +Leto, Jessica, and Paul travel with their household to occupy Arrakeen, the capital on Arrakis formerly held by House Harkonnen. Leto learns of the dangers involved in harvesting the spice, which is protected by giant sandworms, and seeks to negotiate with the planet's native Fremen people, seeing them as a valuable ally rather than foes. Soon after the Atreides's arrival, Harkonnen forces attack, joined by the Emperor's ferocious Sardaukar troops in disguise. Leto is betrayed by his personal physician, the Suk doctor Wellington Yueh, who delivers a drugged Leto to the Baron Vladimir Harkonnen and his twisted Mentat, Piter De Vries. Yueh, however, arranges for Jessica and Paul to escape into the desert, where they are presumed dead by the Harkonnens. Yueh replaces one of Leto's teeth with a poison gas capsule, hoping Leto can kill the Baron during their encounter. The Baron narrowly avoids the gas due to his shield, which kills Leto, De Vries, and the others in the room. The Baron forces Hawat to take over De Vries's position by dosing him with a long-lasting, fatal poison and threatening to withhold the regular antidote doses unless he obeys. While he follows the Baron's orders, Hawat works secretly to undermine the Harkonnens. +Having fled into the desert, Paul is exposed to high concentrations of spice and has visions through which he realizes he has significant powers (as a result of the Bene Gesserit breeding scheme). He foresees potential futures in which he lives among the planet's native Fremen before leading them on a Holy Jihad across the known universe. +It is revealed Jessica is the daughter of Baron Harkonnen, a secret kept from her by the Bene Gesserit. After being captured by Fremen, Paul and Jessica are accepted into the Fremen community of Sietch Tabr, and teach the Fremen the Bene Gesserit fighting technique known as the "weirding way". Paul proves his manhood by killing a Fremen named Jamis in a ritualistic crysknife fight and chooses the Fremen name Muad'Dib, while Jessica opts to undergo a ritual to become a Reverend Mother by drinking the poisonous Water of Life. Pregnant with Leto's daughter, she inadvertently causes the unborn child, Alia, to become infused with the same powers in the womb. Paul takes a Fremen lover, Chani, and has a son with her, Leto II. +Two years pass and Paul's powerful prescience manifests, which confirms for the Fremen that he is their prophesied messiah, a legend planted by the Bene Gesserit's Missionaria Protectiva. Paul embraces his father's belief that the Fremen could be a powerful fighting force to take back Arrakis, but also sees that if he does not control them, their jihad could consume the entire universe. Word of the new Fremen leader reaches both Baron Harkonnen and the Emperor as spice production falls due to their increasingly destructive raids. The Baron encourages his brutish nephew Glossu Rabban to rule with an iron fist, hoping the contrast with his shrewder nephew Feyd-Rautha will make the latter popular among the people of Arrakis when he eventually replaces Rabban. The Emperor, suspecting the Baron of trying to create troops more powerful than the Sardaukar to seize power, sends spies to monitor activity on Arrakis. Hawat uses the opportunity to sow seeds of doubt in the Baron about the Emperor's true plans, putting further strain on their alliance. +Gurney, having survived the Harkonnen coup becomes a smuggler, reuniting with Paul and Jessica after a Fremen raid on his harvester. Believing Jessica to be the traitor, Gurney threatens to kill her, but is stopped by Paul. Paul did not foresee Gurney's attack, and concludes he must increase his prescience by drinking the Water of Life, which is traditionally fatal to males. Paul falls into unconsciousness for three weeks after drinking the poison, but when he wakes, he has clairvoyance across time and space: he is the Kwisatz Haderach, the ultimate goal of the Bene Gesserit breeding program. +Paul senses the Emperor and Baron are amassing fleets around Arrakis to quell the Fremen rebellion, and prepares the Fremen for a major offensive against the Harkonnen troops. The Emperor arrives with the Baron on Arrakis. The Emperor's troops seize a Fremen outpost, killing many including young Leto II, while Alia is captured and taken to the Emperor. Under cover of an electric storm, which shorts out the Emperor's troops' defensive shields, Paul and the Fremen, riding giant sandworms, assault the capital while Alia assassinates the Baron and escapes. The Fremen quickly defeat both the Harkonnen and Sardaukar troops. +Paul faces the Emperor, threatening to destroy spice production forever unless Shaddam abdicates the throne. Feyd-Rautha attempts to stop Paul by challenging him to a ritualistic knife fight, during which he attempts to cheat and kill Paul with a poison spur in his belt. Paul gains the upper hand and kills him. The Emperor reluctantly cedes the throne to Paul and promises his daughter Princess Irulan's hand in marriage. As Paul takes control of the Empire, he realizes that while he has achieved his goal, he is no longer able to stop the Fremen jihad, as their belief in him is too powerful to restrain. + + +== Characters == +House AtreidesPaul Atreides, the Duke's son, and main character of the novel +Duke Leto Atreides, head of House Atreides +Lady Jessica, Bene Gesserit and concubine of the Duke, mother of Paul and Alia +Alia Atreides, Paul's younger sister +Thufir Hawat, Mentat and Master of Assassins to House Atreides +Gurney Halleck, staunchly loyal troubadour warrior of the Atreides +Duncan Idaho, Swordmaster for House Atreides, graduate of the Ginaz School +Wellington Yueh, Suk doctor for the Atreides who is secretly working for House HarkonnenHouse HarkonnenBaron Vladimir Harkonnen, head of House Harkonnen +Piter De Vries, twisted Mentat +Feyd-Rautha, nephew and heir-presumptive of the Baron +Glossu "Beast" Rabban, also called Rabban Harkonnen, older nephew of the Baron +Iakin Nefud, Captain of the GuardHouse CorrinoShaddam IV, Padishah Emperor of the Known Universe (the Imperium) +Princess Irulan, Shaddam's eldest daughter and heir, also a historian +Count Fenring, the Emperor's closest friend, advisor, and "errand boy"Bene GesseritReverend Mother Gaius Helen Mohiam, Proctor Superior of the Bene Gesserit school and the Emperor's Truthsayer +Lady Margot Fenring, Bene Gesserit wife of Count FenringFremenThe Fremen, native inhabitants of Arrakis +Stilgar, Fremen leader of Sietch Tabr +Chani, Paul's Fremen concubine and a Sayyadina (female acolyte) of Sietch Tabr +Dr. Liet-Kynes, the Imperial Planetologist on Arrakis and father of Chani, as well as a revered figure among the Fremen +The Shadout Mapes, head housekeeper of imperial residence on Arrakis +Jamis, Fremen killed by Paul in ritual duel +Harah, wife of Jamis and later servant to Paul who helps raise Alia among the Fremen +Reverend Mother Ramallo, religious leader of Sietch TabrSmugglersEsmar Tuek, a powerful smuggler and the father of Staban Tuek +Staban Tuek, the son of Esmar Tuek and a powerful smuggler who befriends and takes in Gurney Halleck and his surviving men after the attack on the Atreides + + +== Themes and influences == +The Dune series is a landmark of science fiction. Herbert deliberately suppressed technology in his Dune universe so he could address the politics of humanity, rather than the future of humanity's technology. For example, a key pre-history event to the novel's present is the "Butlerian Jihad", in which all robots and computers were destroyed, eliminating these common elements to science fiction from the novel as to allow focus on humanity. Dune considers the way humans and their institutions might change over time. Director John Harrison, who adapted Dune for Syfy's 2000 miniseries, called the novel a universal and timeless reflection of "the human condition and its moral dilemmas", and said: + +A lot of people refer to Dune as science fiction. I never do. I consider it an epic adventure in the classic storytelling tradition, a story of myth and legend not unlike the Morte d'Arthur or any messiah story. It just happens to be set in the future ... The story is actually more relevant today than when Herbert wrote it. In the 1960s, there were just these two colossal superpowers duking it out. Today we're living in a more feudal, corporatized world more akin to Herbert's universe of separate families, power centers and business interests, all interrelated and kept together by the one commodity necessary to all. +But Dune has also been called a mix of soft and hard science fiction since "the attention to ecology is hard, the anthropology and the psychic abilities are soft." Hard elements include the ecology of Arrakis, suspensor technology, weapon systems, and ornithopters, while soft elements include issues relating to religion, physical and mental training, cultures, politics, and psychology.Herbert said Paul's messiah figure was inspired by the Arthurian legend, and that the scarcity of water on Arrakis was a metaphor for oil, as well as air and water itself, and for the shortages of resources caused by overpopulation. Novelist Brian Herbert, his son and biographer, wrote: + +Dune is a modern-day conglomeration of familiar myths, a tale in which great sandworms guard a precious treasure of melange, the geriatric spice that represents, among other things, the finite resource of oil. The planet Arrakis features immense, ferocious worms that are like dragons of lore, with "great teeth" and a "bellows breath of cinnamon." This resembles the myth described by an unknown English poet in Beowulf, the compelling tale of a fearsome fire dragon who guarded a great treasure hoard in a lair under cliffs, at the edge of the sea. The desert of Frank Herbert's classic novel is a vast ocean of sand, with giant worms diving into the depths, the mysterious and unrevealed domain of Shai-hulud. Dune tops are like the crests of waves, and there are powerful sandstorms out there, creating extreme danger. On Arrakis, life is said to emanate from the Maker (Shai-hulud) in the desert-sea; similarly all life on Earth is believed to have evolved from our oceans. Frank Herbert drew parallels, used spectacular metaphors, and extrapolated present conditions into world systems that seem entirely alien at first blush. But close examination reveals they aren't so different from systems we know … and the book characters of his imagination are not so different from people familiar to us. +Each chapter of Dune begins with an epigraph excerpted from the fictional writings of the character Princess Irulan. In forms such as diary entries, historical commentary, biography, quotations and philosophy, these writings set tone and provide exposition, context and other details intended to enhance understanding of Herbert's complex fictional universe and themes. They act as foreshadowing and invite the reader to keep reading to close the gap between what the epigraph says and what is happening in the main narrative. The epigraphs also give the reader the feeling that the world they are reading about is epically distanced, since Irulan writes about an idealized image of Paul as if he had already passed into memory. Brian Herbert wrote: "Dad told me that you could follow any of the novel's layers as you read it, and then start the book all over again, focusing on an entirely different layer. At the end of the book, he intentionally left loose ends and said he did this to send the readers spinning out of the story with bits and pieces of it still clinging to them, so that they would want to go back and read it again." + + +=== Middle-Eastern and Islamic references === +Due to the similarities between some of Herbert's terms and ideas and actual words and concepts in the Arabic language, as well as the series' "Islamic undertones" and themes, a Middle-Eastern influence on Herbert's works has been noted repeatedly. In his descriptions of the Fremen culture and language, Herbert uses both authentic Arabic words and Arabic-sounding words. For example, one of the names for the sandworm, Shai-hulud, is derived from Arabic: شيء خلود, romanized: šayʾ ḫulūd, lit. 'immortal thing' or Arabic: شيخ خلود, romanized: šayḫ ḫulūd, lit. 'old man of eternity'. The title of the Fremen housekeeper, the Shadout Mapes, is borrowed from the Arabic: شادوف‎, romanized: šādūf, the Egyptian term for a device used to raise water. In particular, words related to the messianic religion of the Fremen, first implanted by the Bene Gesserit, are taken from Arabic, including Muad'Dib (from Arabic: مؤدب, romanized: muʾaddib, lit. 'educator'), Usul (from Arabic: أصول, romanized: ʾuṣūl, lit. 'fundamental principles'), Shari-a (from Arabic: شريعة, romanized: šarīʿa, lit. 'sharia; path'), Shaitan (from Arabic: شيطان, romanized: šayṭān, lit. 'Shaitan; devil; fiend', and jinn (from Arabic: جن, romanized: ǧinn, lit. 'jinn; spirit; demon; mythical being'). It is likely Herbert relied on second-hand resources such as phrasebooks and desert adventure stories to find these Arabic words and phrases for the Fremen. They are meaningful and carefully chosen, and help create an "imagined desert culture that resonates with exotic sounds, enigmas, and pseudo-Islamic references" and has a distinctly Bedouin aesthetic.As a foreigner who adopts the ways of a desert-dwelling people and then leads them in a military capacity, Paul Atreides bears many similarities to the historical T. E. Lawrence. His 1962 biopic Lawrence of Arabia has also been identified as a potential influence. The Sabres of Paradise (1960) has also been identified as a potential influence upon Dune, with its depiction of Imam Shamil and the Islamic culture of the Caucasus inspiring some of the themes, characters, events and terminology of Dune.The environment of the desert planet Arrakis was primarily inspired by the environments of the Middle East. Similarly Arrakis as a bioregion is presented as a particular kind of political site. Herbert has made it resemble a desertified petrostate area. The Fremen people of Arrakis were influenced by the Bedouin tribes of Arabia, and the Mahdi prophecy originates from Islamic eschatology. Inspiration is also adopted from medieval historian Ibn Khaldun's cyclical history and his dynastic concept in North Africa, hinted at by Herbert's reference to Khaldun's book Kitāb al-ʿibar ("The Book of Lessons"). The fictionalized version of the "Kitab al-ibar" in Dune is a combination of a Fremen religious manual and a desert survival book. + + +==== Additional language and historic influences ==== +In addition to Arabic, Dune derives words and names from a variety of other languages, including Hebrew, Navajo, Latin, Dutch ("Landsraad"), Chakobsa, the Nahuatl language of the Aztecs, Greek, Persian, Sanskrit ("prana bindu", "prajna"), Russian, Turkish, Finnish, and Old English. Bene Gesserit is simply the Latin for "It will have been well fought", also carrying the sense of "It will have been well managed", which stands as a statement of the order's goal and as a pledge of faithfulness to that goal. Critics tend to miss the literal meaning of the phrase, some positing that the term is derived from the Latin meaning "it will have been well borne", which interpretation is not well supported by their doctrine in the story.Through the inspiration from The Sabres of Paradise, there are also allusions to the tsarist-era Russian nobility and Cossacks. Frank Herbert stated that bureaucracy that lasted long enough would become a hereditary nobility, and a significant theme behind the aristocratic families in Dune was "aristocratic bureaucracy" which he saw as analogous to the Soviet Union. + + +=== Environmentalism and ecology === +Dune has been called the "first planetary ecology novel on a grand scale". Herbert hoped it would be seen as an "environmental awareness handbook" and said the title was meant to "echo the sound of 'doom'". It was reviewed in the best selling countercultural Whole Earth Catalog in 1968 as a "rich re-readable fantasy with clear portrayal of the fierce environment it takes to cohere a community".After the publication of Silent Spring by Rachel Carson in 1962, science fiction writers began treating the subject of ecological change and its consequences. Dune responded in 1965 with its complex descriptions of Arrakis life, from giant sandworms (for whom water is deadly) to smaller, mouse-like life forms adapted to live with limited water. Dune was followed in its creation of complex and unique ecologies by other science fiction books such as A Door into Ocean (1986) and Red Mars (1992). Environmentalists have pointed out that Dune's popularity as a novel depicting a planet as a complex—almost living—thing, in combination with the first images of Earth from space being published in the same time period, strongly influenced environmental movements such as the establishment of the international Earth Day.While the genre of climate fiction was popularized in the 2010s in response to real global climate change, Dune as well as other early science fiction works from authors like J. G. Ballard (The Drowned World) and Kim Stanley Robinson (the Mars trilogy) have retroactively been considered pioneering examples of the genre. + + +=== Declining empires === +The Imperium in Dune contains features of various empires in Europe and the Near East, including the Roman Empire, Holy Roman Empire, and Ottoman Empire. Lorenzo DiTommaso compared Dune's portrayal of the downfall of a galactic empire to Edward Gibbon's Decline and Fall of the Roman Empire, which argues that Christianity allied with the profligacy of the Roman elite led to the fall of Ancient Rome. In "The Articulation of Imperial Decadence and Decline in Epic Science Fiction" (2007), DiTommaso outlines similarities between the two works by highlighting the excesses of the Emperor on his home planet of Kaitain and of the Baron Harkonnen in his palace. The Emperor loses his effectiveness as a ruler through an excess of ceremony and pomp. The hairdressers and attendants he brings with him to Arrakis are even referred to as "parasites". The Baron Harkonnen is similarly corrupt and materially indulgent. Gibbon's Decline and Fall partly blames the fall of Rome on the rise of Christianity. Gibbon claimed that this exotic import from a conquered province weakened the soldiers of Rome and left it open to attack. The Emperor's Sardaukar fighters are little match for the Fremen of Dune not only because of the Sardaukar's overconfidence and the fact that Jessica and Paul have trained the Fremen in their battle tactics, but because of the Fremen's capacity for self-sacrifice. The Fremen put the community before themselves in every instance, while the world outside wallows in luxury at the expense of others.The decline and long peace of the Empire sets the stage for revolution and renewal by genetic mixing of successful and unsuccessful groups through war, a process culminating in the Jihad led by Paul Atreides, described by Frank Herbert as depicting "war as a collective orgasm" (drawing on Norman Walter's 1950 The Sexual Cycle of Human Warfare), themes that would reappear in God Emperor of Dune's Scattering and Leto II's all-female Fish Speaker army. + + +=== Gender dynamics === +Gender dynamics are complex in Dune. Within the Fremen sietch communities, women have almost full equality. They carry weapons and travel in raiding parties with men, fighting when necessary alongside the men. They can take positions of leadership as a Sayyadina or as a Reverend Mother (if she can survive the ritual of ingesting the Water of Life.) Both of these sietch religious leaders are routinely consulted by the all-male Council and can have a decisive voice in all matters of sietch life, security and internal politics. They are also protected by the entire community. Due to the high mortality rate among their men, women outnumber men in most sietches. Polygamy is common, and sexual relationships are voluntary and consensual; as Stilgar says to Jessica, "women among us are not taken against their will." +In contrast, the Imperial aristocracy leaves young women of noble birth very little agency. Frequently trained by the Bene Gesserit, they are raised to eventually marry other aristocrats. Marriages between Major and Minor Houses are political tools to forge alliances or heal old feuds; women are given very little say in the matter. Many such marriages are quietly maneuvered by the Bene Gesserit to produce offspring with some genetic characteristics needed by the sisterhood's human-breeding program. In addition, such highly-placed sisters were in a position to subtly influence their husbands' actions in ways that could move the politics of the Imperium toward Bene Gesserit goals. +The gom jabbar test of humanity is administered by the female Bene Gesserit order but rarely to males. The Bene Gesserit have seemingly mastered the unconscious and can play on the unconscious weaknesses of others using the Voice, yet their breeding program seeks after a male Kwisatz Haderach. Their plan is to produce a male who can "possess complete racial memory, both male and female," and look into the black hole in the collective unconscious that they fear. A central theme of the book is the connection, in Jessica's son, of this female aspect with his male aspect. This aligns with concepts in Jungian psychology, which features conscious/unconscious and taking/giving roles associated with males and females, as well as the idea of the collective unconscious. Paul's approach to power consistently requires his upbringing under the matriarchal Bene Gesserit, who operate as a long-dominating shadow government behind all of the great houses and their marriages or divisions. He is trained by Jessica in the Bene Gesserit Way, which includes prana-bindu training in nerve and muscle control and precise perception. Paul also receives Mentat training, thus helping prepare him to be a type of androgynous Kwisatz Haderach, a male Reverend Mother.In a Bene Gesserit test early in the book, it is implied that people are generally "inhuman" in that they irrationally place desire over self-interest and reason. This applies Herbert's philosophy that humans are not created equal, while equal justice and equal opportunity are higher ideals than mental, physical, or moral equality. + + +=== Heroism === +I am showing you the superhero syndrome and your own participation in it. +Throughout Paul's rise to superhuman status, he follows a plotline common to many stories describing the birth of a hero. He has unfortunate circumstances forced onto him. After a long period of hardship and exile, he confronts and defeats the source of evil in his tale. As such, Dune is representative of a general trend beginning in 1960s American science fiction in that it features a character who attains godlike status through scientific means. Eventually, Paul Atreides gains a level of omniscience which allows him to take over the planet and the galaxy, and causes the Fremen of Arrakis to worship him like a god. Author Frank Herbert said in 1979, "The bottom line of the Dune trilogy is: beware of heroes. Much better [to] rely on your own judgment, and your own mistakes." He wrote in 1985, "Dune was aimed at this whole idea of the infallible leader because my view of history says that mistakes made by a leader (or made in a leader's name) are amplified by the numbers who follow without question."Juan A. Prieto-Pablos says Herbert achieves a new typology with Paul's superpowers, differentiating the heroes of Dune from earlier heroes such as Superman, van Vogt's Gilbert Gosseyn and Henry Kuttner's telepaths. Unlike previous superheroes who acquire their powers suddenly and accidentally, Paul's are the result of "painful and slow personal progress." And unlike other superheroes of the 1960s—who are the exception among ordinary people in their respective worlds—Herbert's characters grow their powers through "the application of mystical philosophies and techniques." For Herbert, the ordinary person can develop incredible fighting skills (Fremen, Ginaz swordsmen and Sardaukar) or mental abilities (Bene Gesserit, Mentats, Spacing Guild Navigators). + + +=== Zen and religion === + +Early in his newspaper career, Herbert was introduced to Zen by two Jungian psychologists, Ralph and Irene Slattery, who "gave a crucial boost to his thinking". Zen teachings ultimately had "a profound and continuing influence on [Herbert's] work". Throughout the Dune series and particularly in Dune, Herbert employs concepts and forms borrowed from Zen Buddhism. The Fremen are referred to as Zensunni adherents, and many of Herbert's epigraphs are Zen-spirited. In "Dune Genesis", Frank Herbert wrote: + +What especially pleases me is to see the interwoven themes, the fugue like relationships of images that exactly replay the way Dune took shape. As in an Escher lithograph, I involved myself with recurrent themes that turn into paradox. The central paradox concerns the human vision of time. What about Paul's gift of prescience - the Presbyterian fixation? For the Delphic Oracle to perform, it must tangle itself in a web of predestination. Yet predestination negates surprises and, in fact, sets up a mathematically enclosed universe whose limits are always inconsistent, always encountering the unprovable. It's like a koan, a Zen mind breaker. It's like the Cretan Epimenides saying, "All Cretans are liars." +Brian Herbert called the Dune universe "a spiritual melting pot", noting that his father incorporated elements of a variety of religions, including Buddhism, Sufi mysticism and other Islamic belief systems, Catholicism, Protestantism, Judaism, and Hinduism. He added that Frank Herbert's fictional future in which "religious beliefs have combined into interesting forms" represents the author's solution to eliminating arguments between religions, each of which claimed to have "the one and only revelation." + + +=== Asimov's Foundation === +Tim O'Reilly suggests that Herbert also wrote Dune as a counterpoint to Isaac Asimov's Foundation series. In his monograph on Frank Herbert, O'Reilly wrote that "Dune is clearly a commentary on the Foundation trilogy. Herbert has taken a look at the same imaginative situation that provoked Asimov's classic—the decay of a galactic empire—and restated it in a way that draws on different assumptions and suggests radically different conclusions. The twist he has introduced into Dune is that the Mule, not the Foundation, is his hero." According to O'Reilly, Herbert bases the Bene Gesserit on the scientific shamans of the Foundation, though they use biological rather than statistical science. In contrast to the Foundation series and its praise of science and rationality, Dune proposes that the unconscious and unexpected are actually what are needed for humanity.Both Herbert and Asimov explore the implications of prescience (i.e., visions of the future) both psychologically and socially. The Foundation series deploys a broadly determinist approach to prescient vision rooted in mathematical reasoning on a macroscopic social level. Dune, by contrast, invents a biologically rooted power of prescience that becomes determinist when the user actively relies on it to navigate past an undefined threshold of detail. Herbert’s eugenically produced and spice-enhanced prescience is also personalized to individual actors whose roles in later books constrain each other's visions, rendering the future more or less mutable as time progresses. In what might be a comment on Foundation, Herbert's most powerfully prescient being in God Emperor of Dune laments the boredom engendered by prescience, and values surprises, especially regarding one's death, as a psychological necessity.However, both works contain a similar theme of the restoration of civilization and seem to make the fundamental assumption that "political maneuvering, the need to control material resources, and friendship or mating bonds will be fundamentally the same in the future as they are now." + + +== Critical reception == +Dune tied with Roger Zelazny's This Immortal for the Hugo Award in 1966 and won the inaugural Nebula Award for Best Novel. Reviews of the novel have been largely positive, and Dune is considered by some critics to be the best science fiction book ever written. The novel has been translated into dozens of languages, and has sold almost 20 million copies. Dune has been regularly cited as one of the world's best-selling science fiction novels.Arthur C. Clarke described Dune as "unique" and wrote, "I know nothing comparable to it except The Lord of the Rings." Robert A. Heinlein described the novel as "powerful, convincing, and most ingenious." It was described as "one of the monuments of modern science fiction" by the Chicago Tribune, and P. Schuyler Miller called Dune "one of the landmarks of modern science fiction ... an amazing feat of creation." The Washington Post described it as "a portrayal of an alien society more complete and deeply detailed than any other author in the field has managed ... a story absorbing equally for its action and philosophical vistas ... An astonishing science fiction phenomenon." Algis Budrys praised Dune for the vividness of its imagined setting, saying "The time lives. It breathes, it speaks, and Herbert has smelt it in his nostrils". He found that the novel, however, "turns flat and tails off at the end. ... [T]ruly effective villains simply simper and melt; fierce men and cunning statesmen and seeresses all bend before this new Messiah". Budrys faulted in particular Herbert's decision to kill Paul's infant son offstage, with no apparent emotional impact, saying "you cannot be so busy saving a world that you cannot hear an infant shriek". After criticizing unrealistic science fiction, Carl Sagan in 1978 listed Dune as among stories "that are so tautly constructed, so rich in the accommodating details of an unfamiliar society that they sweep me along before I have even a chance to be critical".The Louisville Times wrote, "Herbert's creation of this universe, with its intricate development and analysis of ecology, religion, politics, and philosophy, remains one of the supreme and seminal achievements in science fiction." Writing for The New Yorker, Jon Michaud praised Herbert's "clever authorial decision" to exclude robots and computers ("two staples of the genre") from his fictional universe, but suggested that this may be one explanation why Dune lacks "true fandom among science-fiction fans" to the extent that it "has not penetrated popular culture in the way that The Lord of the Rings and Star Wars have". Tamara I. Hladik wrote that the story "crafts a universe where lesser novels promulgate excuses for sequels. All its rich elements are in balance and plausible—not the patchwork confederacy of made-up languages, contrived customs, and meaningless histories that are the hallmark of so many other, lesser novels."On November 5, 2019, the BBC News listed Dune on its list of the 100 most influential novels.J. R. R. Tolkien refused to review Dune, on the grounds that he disliked it "with some intensity" and thus felt it would be unfair to Herbert, another working author, if he gave an honest review of the book. + + +== First edition prints and manuscripts == +The first edition of Dune is one of the most valuable in science fiction book collecting. Copies have been sold for more than $10,000 at auction. The Chilton first edition of the novel is 9+1⁄4 inches (235 mm) tall, with bluish green boards and a price of $5.95 on the dust jacket, and notes Toronto as the Canadian publisher on the copyright page. Up to this point, Chilton had been publishing only automobile repair manuals.California State University, Fullerton's Pollak Library has several of Herbert's draft manuscripts of Dune and other works, with the author's notes, in their Frank Herbert Archives. + + +== Sequels and prequels == + +After Dune proved to be a critical and financial success for Herbert, he was able to devote himself full time to writing additional novels in the series. He had already drafted parts of the second and third while writing Dune. The series included Dune Messiah (1969), Children of Dune (1976), God Emperor of Dune (1981), Heretics of Dune (1984), and Chapterhouse: Dune (1985), each sequentially continuing on the narrative from Dune. Herbert died on February 11, 1986.Herbert's son, Brian Herbert, had found several thousand pages of notes left by his father that outlined ideas for other narratives related to Dune. Brian Herbert enlisted author Kevin J. Anderson to help build out prequel novels to the events of Dune. Brian Herbert's and Anderson's Dune prequels first started publication in 1999, and have led to additional stories that take place between those of Frank Herbert's books. The notes for what would have been Dune 7 also enabled them to publish Hunters of Dune (2006) and Sandworms of Dune (2007), sequels to Frank Herbert's final novel Chapterhouse: Dune, which complete the chronological progression of his original series, and wrap up storylines that began in Heretics of Dune. + + +== Adaptations == + +Dune has been considered as an "unfilmable" and "uncontainable" work to adapt from novel to film or other visual medium. Described by Wired, "It has four appendices and a glossary of its own gibberish, and its action takes place on two planets, one of which is a desert overrun by worms the size of airport runways. Lots of important people die or try to kill each other, and they're all tethered to about eight entangled subplots." There have been several attempts to achieve this difficult conversion with various degrees of success. + + +=== Early stalled attempts === +In 1971, the production company Apjac International (APJ) (headed by Arthur P. Jacobs) optioned the rights to film Dune. As Jacobs was busy with other projects, such as the sequel to Planet of the Apes, Dune was delayed for another year. Jacobs' first choice for director was David Lean, but he turned down the offer. Charles Jarrott was also considered to direct. Work was also under way on a script while the hunt for a director continued. Initially, the first treatment had been handled by Robert Greenhut, the producer who had lobbied Jacobs to make the movie in the first place, but subsequently Rospo Pallenberg was approached to write the script, with shooting scheduled to begin in 1974. However, Jacobs died in 1973. +In December 1974, a French consortium led by Jean-Paul Gibon purchased the film rights from APJ, with Alejandro Jodorowsky set to direct. In 1975, Jodorowsky planned to film the story as a 14-hour feature, set to star his own son Brontis Jodorowsky in the lead role of Paul Atreides, Salvador Dalí as Shaddam IV, Padishah Emperor, Amanda Lear as Princess Irulan, Orson Welles as Baron Vladimir Harkonnen, Gloria Swanson as Reverend Mother Gaius Helen Mohiam, David Carradine as Duke Leto Atreides, Geraldine Chaplin as Lady Jessica, Alain Delon as Duncan Idaho, Hervé Villechaize as Gurney Halleck, Udo Kier as Piter De Vries, and Mick Jagger as Feyd-Rautha. It was at first proposed to score the film with original music by Karlheinz Stockhausen, Henry Cow, and Magma; later on, the soundtrack was to be provided by Pink Floyd. Jodorowsky set up a pre-production unit in Paris consisting of Chris Foss, a British artist who designed covers for science fiction periodicals, Jean Giraud (Moebius), a French illustrator who created and also wrote and drew for Metal Hurlant magazine, and H. R. Giger. Moebius began designing creatures and characters for the film, while Foss was brought in to design the film's space ships and hardware. Giger began designing the Harkonnen Castle based on Moebius's storyboards. Dan O'Bannon was to head the special effects department.Dalí was cast as the Emperor. Dalí later demanded to be paid $100,000 per hour; Jodorowsky agreed, but tailored Dalí's part to be filmed in one hour, drafting plans for other scenes of the emperor to use a mechanical mannequin as substitute for Dalí. According to Giger, Dalí was "later invited to leave the film because of his pro-Franco statements". Just as the storyboards, designs, and script were finished, the financial backing dried up. Frank Herbert traveled to Europe in 1976 to find that $2 million of the $9.5 million budget had already been spent in pre-production, and that Jodorowsky's script would result in a 14-hour movie ("It was the size of a phone book", Herbert later recalled). Jodorowsky took creative liberties with the source material, but Herbert said that he and Jodorowsky had an amicable relationship. Jodorowsky said in 1985 that he found the Dune story mythical and had intended to recreate it rather than adapt the novel; though he had an "enthusiastic admiration" for Herbert, Jodorowsky said he had done everything possible to distance the author and his input from the project. Although Jodorowsky was embittered by the experience, he said the Dune project changed his life, and some of the ideas were used in his and Moebius's The Incal. O'Bannon entered a psychiatric hospital after the production failed, then worked on 13 scripts, the last of which became Alien. A 2013 documentary, Jodorowsky's Dune, was made about Jodorowsky's failed attempt at an adaptation. +In 1976, Dino De Laurentiis acquired the rights from Gibon's consortium. De Laurentiis commissioned Herbert to write a new screenplay in 1978; the script Herbert turned in was 175 pages long, the equivalent of nearly three hours of screen time. De Laurentiis then hired director Ridley Scott in 1979, with Rudy Wurlitzer writing the screenplay and H. R. Giger retained from the Jodorowsky production; Scott and Giger had also just worked together on the film Alien, after O'Bannon recommended the artist. Scott intended to split the novel into two movies. He worked on three drafts of the script, using The Battle of Algiers as a point of reference, before moving on to direct another science fiction film, Blade Runner (1982). As he recalls, the pre-production process was slow, and finishing the project would have been even more time-intensive: + +But after seven months I dropped out of Dune, by then Rudy Wurlitzer had come up with a first-draft script which I felt was a decent distillation of Frank Herbert's. But I also realised Dune was going to take a lot more work—at least two and a half years' worth. And I didn't have the heart to attack that because my older brother Frank unexpectedly died of cancer while I was prepping the De Laurentiis picture. Frankly, that freaked me out. So I went to Dino and told him the Dune script was his. +—From Ridley Scott: The Making of his Movies by Paul M. Sammon + + +=== 1984 film by David Lynch === + +In 1981, the nine-year film rights were set to expire. De Laurentiis re-negotiated the rights from the author, adding to them the rights to the Dune sequels (written and unwritten). After seeing The Elephant Man, De Laurentiis' daughter Raffaella decided that David Lynch should direct the movie. Around that time Lynch received several other directing offers, including Return of the Jedi. He agreed to direct Dune and write the screenplay even though he had not read the book, was not familiar with the story, or even been interested in science fiction. Lynch worked on the script for six months with Eric Bergren and Christopher De Vore. The team yielded two drafts of the script before it split over creative differences. Lynch would subsequently work on five more drafts. Production of the work was troubled by problems at the Mexican studio and hampering the film's timeline. Lynch ended up producing a nearly three-hour long film, but at demands from Universal Pictures, the film's distributor, he cut it back to about two hours, hastily filming additional scenes to make up for some of the cut footage.This first film of Dune, directed by Lynch, was released in 1984, nearly 20 years after the book's publication. Though Herbert said the book's depth and symbolism seemed to intimidate many filmmakers, he was pleased with the film, saying that "They've got it. It begins as Dune does. And I hear my dialogue all the way through. There are some interpretations and liberties, but you're gonna come out knowing you've seen Dune." Reviews of the film were negative, saying that it was incomprehensible to those unfamiliar with the book, and that fans would be disappointed by the way it strayed from the book's plot. Upon release for television and other forms of home media, Universal opted to reintroduce much of the footage that Lynch had cut, creating an over-three-hour long version with extensive monologue exposition. Lynch was extremely displeased with this move, and demanded that Universal replace his name on these cuts with the pseudonym "Alan Smithee", and has generally distanced himself from the film since. + + +=== 2000 miniseries by John Harrison === + +In 2000, John Harrison adapted the novel into Frank Herbert's Dune, a miniseries which premiered on American Sci-Fi Channel. As of 2004, the miniseries was one of the three highest-rated programs broadcast on the Sci-Fi Channel. + + +=== Further film attempts === +In 2008, Paramount Pictures announced that they would produce a new film based on the book, with Peter Berg attached to direct. Producer Kevin Misher, who spent a year securing the rights from the Herbert estate, was to be joined by Richard Rubinstein and John Harrison (of both Sci-Fi Channel miniseries) as well as Sarah Aubrey and Mike Messina. The producers stated that they were going for a "faithful adaptation" of the novel, and considered "its theme of finite ecological resources particularly timely." Science fiction author Kevin J. Anderson and Frank Herbert's son Brian Herbert, who had together written multiple Dune sequels and prequels since 1999, were attached to the project as technical advisors. In October 2009, Berg dropped out of the project, later saying that it "for a variety of reasons wasn't the right thing" for him. Subsequently, with a script draft by Joshua Zetumer, Paramount reportedly sought a new director who could do the film for under $175 million. In 2010, Pierre Morel was signed on to direct, with screenwriter Chase Palmer incorporating Morel's vision of the project into Zetumer's original draft. By November 2010, Morel left the project. Paramount finally dropped plans for a remake in March 2011. + + +=== Films by Denis Villeneuve === + +In November 2016, Legendary Entertainment acquired the film and TV rights for Dune. Variety reported in December 2016 that Denis Villeneuve was in negotiations to direct the project, which was confirmed in February 2017. In April 2017, Legendary announced that Eric Roth would write the screenplay. Villeneuve explained in March 2018 that his adaptation will be split into two films, with the first installment scheduled to begin production in 2019. Casting includes Timothée Chalamet as Paul Atreides, Dave Bautista as Rabban, Stellan Skarsgård as Baron Harkonnen, Rebecca Ferguson as Lady Jessica, Charlotte Rampling as Reverend Mother Mohiam, Oscar Isaac as Duke Leto Atreides, Zendaya as Chani, Javier Bardem as Stilgar, Josh Brolin as Gurney Halleck, Jason Momoa as Duncan Idaho, David Dastmalchian as Piter De Vries, Chang Chen as Dr. Yueh, and Stephen Henderson as Thufir Hawat. Warner Bros. Pictures distributed the film, which had its initial premiere on September 3, 2021, at the Venice Film Festival, and wide release in both theaters and streaming on HBO Max on October 21, 2021, as part of Warner Bros.'s approach to handling the impact of the COVID-19 pandemic on the film industry. The film received "generally favorable reviews" on Metacritic. It has gone on to win multiple awards and was named by the National Board of Review as one of the 10 best films of 2021, as well as the American Film Institute in their annual top 10 list. The film went on to be nominated for ten Academy Awards, winning six, the most wins of the night for any film in contention.A sequel, Dune: Part Two, was scheduled for release on November 3, 2023, but will now instead be released on March 15th 2024 amid the 2023 SAG-AFTRA strike. + + +=== Audiobooks === +In 1993, Recorded Books Inc. released a 20-disc audiobook narrated by George Guidall. In 2007, Audio Renaissance released an audio book narrated by Simon Vance with some parts performed by Scott Brick, Orlagh Cassidy, Euan Morton, and other performers. + + +== Cultural influence == +Dune has been widely influential, inspiring numerous novels, music, films, television, games, and comic books. It is considered one of the greatest and most influential science fiction novels of all time, with numerous modern science fiction works such as Star Wars owing their existence to Dune. Dune has also been referenced in numerous other works of popular culture, including Star Trek, Chronicles of Riddick, The Kingkiller Chronicle and Futurama. Dune was cited as a source of inspiration for Hayao Miyazaki's anime film Nausicaä of the Valley of the Wind (1984) for its post-apocalyptic world.Dune was parodied in 1984's National Lampoon's Doon by Ellis Weiner, which William F. Touponce called "something of a tribute to Herbert's success on college campuses", noting that "the only other book to have been so honored is Tolkien's The Lord of the Rings," which was parodied by The Harvard Lampoon in 1969. + + +=== Music === +In 1978, French electronic musician Richard Pinhas released the nine-track Dune-inspired album Chronolyse, which includes the seven-part Variations sur le thème des Bene Gesserit. +In 1979, German electronic music pioneer Klaus Schulze released an LP titled Dune featuring motifs and lyrics inspired by the novel. +A similar musical project, Visions of Dune, was released also in 1979 by Zed (a pseudonym of French electronic musician Bernard Sjazner). +Heavy metal band Iron Maiden wrote the song "To Tame a Land" based on the Dune story. It appears as the closing track to their 1983 album Piece of Mind. The original working title of the song was "Dune"; however, the band was denied permission to use it, with Frank Herbert's agents stating "Frank Herbert doesn't like rock bands, particularly heavy rock bands, and especially bands like Iron Maiden". +Dune inspired the German happy hardcore band Dune, who have released several albums with space travel-themed songs. +The progressive hardcore band Shai Hulud took their name from Dune. +"Traveller in Time", from the 1991 Blind Guardian album Tales from the Twilight World, is based mostly on Paul Atreides' visions of future and past. +The title of the 1993 Fear Factory album Fear is The Mindkiller is a quote from the "litany against fear". +The song "Near Fantastica", from the Matthew Good album Avalanche, makes reference to the "litany against fear", repeating "can't feel fear, fear's the mind killer" through a section of the song. +In the Fatboy Slim song "Weapon of Choice", the line "If you walk without rhythm/You won't attract the worm" is a near quotation from the sections of novel in which Stilgar teaches Paul to ride sandworms. +Dune also inspired the 1999 album The 2nd Moon by the German death metal band Golem, which is a concept album about the series. +Dune influenced Thirty Seconds to Mars on their self-titled debut album. +The Youngblood Brass Band's song "Is an Elegy" on Center:Level:Roar references "Muad'Dib", "Arrakis" and other elements from the novel. +The debut album of Canadian musician Grimes, called Geidi Primes, is a concept album based on Dune. +Japanese singer Kenshi Yonezu, released a song titled "Dune", also known as "Sand Planet". The song was released on 2017, and it was created using the voice synthesizer Hatsune Miku for her 10th anniversary. +"Fear is the Mind Killer", a song released in 2018 by Zheani (an Australian rapper) uses a quote from Dune. +"Litany Against Fear" is a spoken track released in 2018 under the 'Eight' album by Zheani. She recites an extract from Dune. +Sleep's 2018 album The Sciences features a song, Giza Butler, that references several aspects of Dune. +Tool's 2019 album Fear Inoculum has a song entitled "Litanie contre la peur (Litany against fear)". +"Rare to Wake", from Shannon Lay's album Geist (2019), is inspired by Dune. +Heavy Metal band Diamond Head based the song "The Sleeper" and its prelude, both off the album The Coffin Train, on the series. + + +=== Games === + +There have been a number of games based on the book, starting with the strategy–adventure game Dune (1992). The most important game adaptation is Dune II (1992), which established the conventions of modern real-time strategy games and is considered to be among the most influential video games of all time.The online game Lost Souls includes Dune-derived elements, including sandworms and melange—addiction to which can produce psychic talents. The 2016 game Enter the Gungeon features the spice melange as a random item which gives the player progressively stronger abilities and penalties with repeated uses, mirroring the long-term effects melange has on users.Rick Priestley cites Dune as a major influence on his 1987 wargame, Warhammer 40,000.In 2023, Funcom announced Dune: Awakening, an upcoming massively multiplayer online game set in the universe of Dune. + + +=== Space exploration === +The Apollo 15 astronauts named a small crater on Earth's Moon after the novel during the 1971 mission, and the name was formally adopted by the International Astronomical Union in 1973. Since 2009, the names of planets from the Dune novels have been adopted for the real-world nomenclature of plains and other features on Saturn's moon Titan, like Arrakis Planitia. + + +== See also == +Soft science fiction – Sub-genre of science fiction emphasizing "soft" sciences or human emotions +Hydraulic empire – Government by control of access to water + + +== References == + + +== Further reading == +Clute, John; Nicholls, Peter (1995). The Encyclopedia of Science Fiction. New York: St. Martin's Press. p. 1386. ISBN 978-0-312-13486-0. +Clute, John; Nicholls, Peter (1995). The Multimedia Encyclopedia of Science Fiction (CD-ROM). Danbury, CT: Grolier. ISBN 978-0-7172-3999-3. +Huddleston, Tom. The Worlds of Dune: The Places and Cultures That Inspired Frank Herbert. Minneapolis: Quarto Publishing Group UK, 2023. +Jakubowski, Maxim; Edwards, Malcolm (1983). The Complete Book of Science Fiction and Fantasy Lists. St Albans, Herts, UK: Granada Publishing Ltd. p. 350. ISBN 978-0-586-05678-3. +Kennedy, Kara. Frank Herbert's Dune: A Critical Companion. Cham, Switzerland: Palgrave Macmillan, 2022. +Kennedy, Kara. Women's Agency in the Dune Universe: Tracing Women's Liberation through Science Fiction. Cham, Switzerland: Palgrave Macmillan, 2020. +Nardi, Dominic J. & N. Trevor Brierly, eds. Discovering Dune: Essays on Frank Herbert's Epic Saga. Jefferson, NC: McFarland & Co., 2022. +Nicholas, Jeffery, ed. Dune and Philosophy: Weirding Way of Mentat. Chicago: Open Court, 2011. +Nicholls, Peter (1979). The Encyclopedia of Science Fiction. St Albans, Herts, UK: Granada Publishing Ltd. p. 672. ISBN 978-0-586-05380-5. +O’Reilly, Timothy. Frank Herbert. New York: Frederick Ungar, 1981. +Pringle, David (1990). The Ultimate Guide to Science Fiction. London: Grafton Books Ltd. p. 407. ISBN 978-0-246-13635-0. +Tuck, Donald H. (1974). The Encyclopedia of Science Fiction and Fantasy. Chicago: Advent. p. 136. ISBN 978-0-911682-20-5. +Williams, Kevin C. The Wisdom of the Sand: Philosophy and Frank Herbert's Dune. New York: Hampton Press, 2013. + + +== External links == + +Official website for Dune and its sequels +Dune title listing at the Internet Speculative Fiction Database +Turner, Paul (October 1973). "Vertex Interviews Frank Herbert" (Interview). Vol. 1, no. 4. Archived from the original on May 19, 2009. +Spark Notes: Dune, detailed study guide +DuneQuotes.com – Collection of quotes from the Dune series +Dune by Frank Herbert, reviewed by Ted Gioia (Conceptual Fiction) +"Frank Herbert Biography and Bibliography at LitWeb.net". www.litweb.net. Archived from the original on April 2, 2009. Retrieved January 2, 2009. +Works of Frank Herbert at Curlie +Timberg, Scott (April 18, 2010). "Frank Herbert's Dune holds timely – and timeless – appeal". Los Angeles Times. Archived from the original on December 3, 2013. Retrieved November 27, 2013. +Walton, Jo (January 12, 2011). "In league with the future: Frank Herbert's Dune (Review)". Tor.com. Retrieved November 27, 2013. +Leonard, Andrew (June 4, 2015). "To Save California, Read Dune". Nautilus. Archived from the original on November 4, 2017. Retrieved June 15, 2015. +Dune by Frank Herbert – Foreshadowing & Dedication at Fact Behind Fiction +Frank Herbert by Tim O'Reilly +DuneScholar.com – Collection of scholarly essays \ No newline at end of file diff --git a/templates/neo4j-parent/ingest.py b/templates/neo4j-parent/ingest.py new file mode 100644 index 00000000000..7c8111925d2 --- /dev/null +++ b/templates/neo4j-parent/ingest.py @@ -0,0 +1,46 @@ +from langchain.graphs import Neo4jGraph +from langchain.vectorstores import Neo4jVector +from langchain.document_loaders import TextLoader +from langchain.text_splitter import TokenTextSplitter +from langchain.embeddings.openai import OpenAIEmbeddings +from pathlib import Path + +txt_path = Path(__file__).parent / "dune.txt" + +graph = Neo4jGraph() + +# Load the text file +loader = TextLoader(txt_path) +documents = loader.load() + +# Define chunking strategy +parent_splitter = TokenTextSplitter(chunk_size=512, chunk_overlap=24) +child_splitter = TokenTextSplitter(chunk_size=100, chunk_overlap=24) + +# Store parent-child patterns into graph +parent_documents = parent_splitter.split_documents(documents) +for parent in parent_documents: + child_documents = child_splitter.split_documents([parent]) + params = { + "parent": parent.page_content, + "children": [c.page_content for c in child_documents], + } + graph.query( + """ + CREATE (p:Parent {text: $parent}) + WITH p + UNWIND $children AS child + CREATE (c:Child {text: child}) + CREATE (c)-[:HAS_PARENT]->(p) + """, + params, + ) + +# Calculate embedding values on the child nodes +Neo4jVector.from_existing_graph( + OpenAIEmbeddings(), + index_name="retrieval", + node_label="Child", + text_node_properties=["text"], + embedding_node_property="embedding", +) diff --git a/templates/neo4j-parent/main.py b/templates/neo4j-parent/main.py new file mode 100644 index 00000000000..990775d71cb --- /dev/null +++ b/templates/neo4j-parent/main.py @@ -0,0 +1,6 @@ +from neo4j_parent.chain import chain + + +if __name__ == "__main__": + original_query = "What is the plot of the Dune?" + print(chain.invoke(original_query)) diff --git a/templates/neo4j-parent/neo4j_parent/__init__.py b/templates/neo4j-parent/neo4j_parent/__init__.py new file mode 100644 index 00000000000..0e89831116e --- /dev/null +++ b/templates/neo4j-parent/neo4j_parent/__init__.py @@ -0,0 +1,3 @@ +from neo4j_parent.chain import chain + +__all__ = ["chain"] diff --git a/templates/neo4j-parent/neo4j_parent/chain.py b/templates/neo4j-parent/neo4j_parent/chain.py new file mode 100644 index 00000000000..703402b0728 --- /dev/null +++ b/templates/neo4j-parent/neo4j_parent/chain.py @@ -0,0 +1,37 @@ +from langchain.prompts import ChatPromptTemplate +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough, RunnableParallel +from langchain.vectorstores import Neo4jVector + +retrieval_query = """ +MATCH (node)-[:HAS_PARENT]->(parent) +RETURN parent.text AS text, score, {} AS metadata +""" + +vectorstore = Neo4jVector.from_existing_index( + OpenAIEmbeddings(), + index_name="retrieval", + node_label="Child", + embedding_node_property="embedding", + retrieval_query=retrieval_query + +) +retriever = vectorstore.as_retriever() + +template = """Answer the question based only on the following context: +{context} + +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +model = ChatOpenAI() + +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +) \ No newline at end of file diff --git a/templates/neo4j-parent/poetry.lock b/templates/neo4j-parent/poetry.lock new file mode 100644 index 00000000000..46acd442ce9 --- /dev/null +++ b/templates/neo4j-parent/poetry.lock @@ -0,0 +1,1354 @@ +# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. + 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">=0.0.320" +tiktoken = "^0.5.1" + +[tool.langserve] +export_module = "neo4j_parent" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/neo4j-parent/tests/__init__.py b/templates/neo4j-parent/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/openai-functions-agent/LICENSE b/templates/openai-functions-agent/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/openai-functions-agent/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/openai-functions-agent/README.md b/templates/openai-functions-agent/README.md new file mode 100644 index 00000000000..b757910b462 --- /dev/null +++ b/templates/openai-functions-agent/README.md @@ -0,0 +1,16 @@ +# OpenAI Functions Agent + +This template creates an agent that uses OpenAI function calling to communicate its decisions of what actions to take. +This example creates an agent that can optionally look up things on the internet using Tavily's search engine. + +## LLM + +This template will use `OpenAI` by default. + +Be sure that `OPENAI_API_KEY` is set in your environment. + +## Tools + +This template will use `Tavily` by default. + +Be sure that `TAVILY_API_KEY` is set in your environment. diff --git a/templates/openai-functions-agent/main.py b/templates/openai-functions-agent/main.py new file mode 100644 index 00000000000..f0e1f5963f9 --- /dev/null +++ b/templates/openai-functions-agent/main.py @@ -0,0 +1,5 @@ +from openai_functions_agent.agent import agent_executor + +if __name__ == "__main__": + question = "who won the womens world cup in 2023?" + print(agent_executor.invoke({"input": question, "chat_history": []})) diff --git a/templates/openai-functions-agent/openai_functions_agent/__init__.py b/templates/openai-functions-agent/openai_functions_agent/__init__.py new file mode 100644 index 00000000000..1d97eae2490 --- /dev/null +++ b/templates/openai-functions-agent/openai_functions_agent/__init__.py @@ -0,0 +1,3 @@ +from openai_functions_agent.agent import agent_executor + +__all__ = ["agent_executor"] diff --git a/templates/openai-functions-agent/openai_functions_agent/agent.py b/templates/openai-functions-agent/openai_functions_agent/agent.py new file mode 100644 index 00000000000..60cff54a1ef --- /dev/null +++ b/templates/openai-functions-agent/openai_functions_agent/agent.py @@ -0,0 +1,54 @@ +from typing import List, Tuple +from langchain.schema.messages import HumanMessage, AIMessage +from langchain.chat_models import ChatOpenAI +from langchain.agents import AgentExecutor +from langchain.utilities.tavily_search import TavilySearchAPIWrapper +from langchain.tools.tavily_search import TavilySearchResults +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain.tools.render import format_tool_to_openai_function +from langchain.agents.format_scratchpad import format_to_openai_functions +from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser +from langchain.pydantic_v1 import BaseModel + + +# Fake Tool +search = TavilySearchAPIWrapper() +tavily_tool = TavilySearchResults(api_wrapper=search) + +tools = [tavily_tool] + +llm = ChatOpenAI(temperature=0) +prompt = ChatPromptTemplate.from_messages([ + ("system", "You are very powerful assistant, but bad at calculating lengths of words."), + MessagesPlaceholder(variable_name="chat_history"), + ("user", "{input}"), + MessagesPlaceholder(variable_name="agent_scratchpad"), +]) + +llm_with_tools = llm.bind( + functions=[format_tool_to_openai_function(t) for t in tools] +) + +def _format_chat_history(chat_history: List[Tuple[str, str]]): + buffer = [] + for human, ai in chat_history: + buffer.append(HumanMessage(content=human)) + buffer.append(AIMessage(content=ai)) + return buffer + + +agent = { + "input": lambda x: x["input"], + "chat_history": lambda x: _format_chat_history(x['chat_history']), + "agent_scratchpad": lambda x: format_to_openai_functions(x['intermediate_steps']), +} | prompt | llm_with_tools | OpenAIFunctionsAgentOutputParser() + +class AgentInput(BaseModel): + input: str + chat_history: List[Tuple[str, str]] + +agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types( + input_type=AgentInput +) + 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+authors = ["Lance Martin "] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.322" +openai = ">=0.5.0" +tavily-python = "^0.1.9" + +[tool.langserve] +export_module = "openai_functions_agent" +export_attr = "agent_executor" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" \ No newline at end of file diff --git a/templates/openai-functions-agent/tests/__init__.py b/templates/openai-functions-agent/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-chroma-private/LICENSE b/templates/rag-chroma-private/LICENSE new file mode 100644 index 00000000000..578fc414ad4 --- /dev/null +++ b/templates/rag-chroma-private/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 pingpong-templates + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/rag-chroma-private/README.md b/templates/rag-chroma-private/README.md new file mode 100644 index 00000000000..d1e84307f29 --- /dev/null +++ b/templates/rag-chroma-private/README.md @@ -0,0 +1,49 @@ +# Private RAG + +This template performs privae RAG (no reliance on external APIs) using: + +* Ollama for the LLM +* GPT4All for embeddings + +## LLM + +Follow instructions [here](https://python.langchain.com/docs/integrations/chat/ollama) to download Ollama. + +The instructions also show how to download your LLM of interest with Ollama: + +* This template uses `llama2:13b-chat` +* But you can pick from many [here](https://ollama.ai/library) + +## Set up local embeddings + +This will use [GPT4All](https://python.langchain.com/docs/integrations/text_embedding/gpt4all) embeddings. + +## Chroma + +[Chroma](https://python.langchain.com/docs/integrations/vectorstores/chroma) is an open-source vector database. + +This template will create and add documents to the vector database in `chain.py`. + +By default, this will load a popular blog post on agents. + +However, you can choose from a large number of document loaders [here](https://python.langchain.com/docs/integrations/document_loaders). + +## Adding the template + +Create your LangServe app: +``` +langchain serve new my-app +cd my-app +``` + +Add template: +``` +langchain serve add rag-chroma-private +``` + +Start server: +``` +langchain start +``` + +See Jupyter notebook `rag_chroma_private` for various way to connect to the template. \ No newline at end of file diff --git a/templates/rag-chroma-private/docs/LLaVA.pdf b/templates/rag-chroma-private/docs/LLaVA.pdf new file mode 100644 index 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+langchain = ">=0.0.313, <0.1" +tiktoken = ">=0.5.1" +chromadb = ">=0.4.14" +gpt4all = ">=1.0.8" +beautifulsoup4 = ">=4.12.2" + +[tool.langserve] +export_module = "rag_chroma_private" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-chroma-private/rag_chroma_private/__init__.py b/templates/rag-chroma-private/rag_chroma_private/__init__.py new file mode 100644 index 00000000000..c06d4f844d6 --- /dev/null +++ b/templates/rag-chroma-private/rag_chroma_private/__init__.py @@ -0,0 +1,3 @@ +from rag_chroma_private.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-chroma-private/rag_chroma_private/chain.py b/templates/rag-chroma-private/rag_chroma_private/chain.py new file mode 100644 index 00000000000..0e6b659aa7c --- /dev/null +++ b/templates/rag-chroma-private/rag_chroma_private/chain.py @@ -0,0 +1,48 @@ +from langchain.vectorstores import Chroma +from langchain.chat_models import ChatOllama +from langchain.prompts import ChatPromptTemplate +from langchain.embeddings import GPT4AllEmbeddings +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough, RunnableParallel + +# Load +from langchain.document_loaders import WebBaseLoader +loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") +data = loader.load() + +# Split +from langchain.text_splitter import RecursiveCharacterTextSplitter +text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) +all_splits = text_splitter.split_documents(data) + +# Add to vectorDB +vectorstore = Chroma.from_documents(documents=all_splits, + collection_name="rag-private", + embedding=GPT4AllEmbeddings(), + ) +retriever = vectorstore.as_retriever() + +# Prompt +# Optionally, pull from the Hub +# from langchain import hub +# prompt = hub.pull("rlm/rag-prompt") +# Or, define your own: +template = """Answer the question based only on the following context: +{context} + +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +# LLM +# Select the LLM that you downloaded +ollama_llm = "llama2:13b-chat" +model = ChatOllama(model=ollama_llm) + +# RAG chain +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +) diff --git a/templates/rag-chroma-private/tests/__init__.py b/templates/rag-chroma-private/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-chroma/LICENSE b/templates/rag-chroma/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/rag-chroma/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/rag-chroma/README.md b/templates/rag-chroma/README.md new file mode 100644 index 00000000000..9511e3fcdc2 --- /dev/null +++ b/templates/rag-chroma/README.md @@ -0,0 +1,35 @@ +# RAG Chroma + +This template performs RAG using Chroma and OpenAI. + +## Chroma + +[Chroma](https://python.langchain.com/docs/integrations/vectorstores/chroma) is an open-source vector database. + +This template will create and add documents to the vector database in `chain.py`. + +These documents can be loaded from [many sources](https://python.langchain.com/docs/integrations/document_loaders). + +## LLM + +Be sure that `OPENAI_API_KEY` is set in order to the OpenAI models. + +## Adding the template + +Create your LangServe app: +``` +langchain serve new my-app +cd my-app +``` + +Add template: +``` +langchain serve add rag-chroma +``` + +Start server: +``` +langchain start +``` + +See Jupyter notebook `rag_chroma` for various way to connect to the template. diff --git a/templates/rag-chroma/poetry.lock b/templates/rag-chroma/poetry.lock new file mode 100644 index 00000000000..9f0d0140142 --- /dev/null +++ b/templates/rag-chroma/poetry.lock @@ -0,0 +1,2296 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1d84166673694841d8953f0a8d0c90e1087739d24632fe86b1a08819168b4566"}, + 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+openai = ">=0.28.1" +tiktoken = ">=0.5.1" +chromadb = ">=0.4.14" + +[tool.langserve] +export_module = "rag_chroma" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-chroma/rag_chroma.ipynb b/templates/rag-chroma/rag_chroma.ipynb new file mode 100644 index 00000000000..5e0b45d5d41 --- /dev/null +++ b/templates/rag-chroma/rag_chroma.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "681a5d1e", + "metadata": {}, + "source": [ + "## Run Template\n", + "\n", + "As shown in the README, add template and start server:\n", + "```\n", + "langchain serve add rag-chroma\n", + "langchain start\n", + "```\n", + "\n", + "We can now look at the endpoints:\n", + "\n", + "http://127.0.0.1:8000/docs#\n", + "\n", + "And specifically at our loaded template:\n", + "\n", + "http://127.0.0.1:8000/docs#/default/invoke_rag_chroma_invoke_post\n", + " \n", + "We can also use remote runnable to call it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d774be2a", + "metadata": {}, + "outputs": [], + "source": [ + "from langserve.client import RemoteRunnable\n", + "rag_app = RemoteRunnable('http://localhost:8000/rag-chroma')\n", + "rag_app.invoke(\"Where id Harrison work\")" + ] + } + ], + "metadata": { + "kernelspec": { + "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", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/templates/rag-chroma/rag_chroma/__init__.py b/templates/rag-chroma/rag_chroma/__init__.py new file mode 100644 index 00000000000..a625fc70997 --- /dev/null +++ b/templates/rag-chroma/rag_chroma/__init__.py @@ -0,0 +1,3 @@ +from rag_chroma.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-chroma/rag_chroma/chain.py b/templates/rag-chroma/rag_chroma/chain.py new file mode 100644 index 00000000000..ecf4a9ca5e1 --- /dev/null +++ b/templates/rag-chroma/rag_chroma/chain.py @@ -0,0 +1,55 @@ +from operator import itemgetter +from langchain.prompts import ChatPromptTemplate +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough, RunnableParallel +from langchain.vectorstores import Chroma + +# Example for document loading (from url), splitting, and creating vectostore + +''' +# Load +from langchain.document_loaders import WebBaseLoader +loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") +data = loader.load() + +# Split +from langchain.text_splitter import RecursiveCharacterTextSplitter +text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) +all_splits = text_splitter.split_documents(data) + +# Add to vectorDB +vectorstore = Chroma.from_documents(documents=all_splits, + collection_name="rag-chroma", + embedding=OpenAIEmbeddings(), + ) +retriever = vectorstore.as_retriever() +''' + +# Embed a single document as a test +vectorstore = Chroma.from_texts( + ["harrison worked at kensho"], + collection_name="rag-chroma", + embedding=OpenAIEmbeddings() +) +retriever = vectorstore.as_retriever() + +# RAG prompt +template = """Answer the question based only on the following context: +{context} + +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +# LLM +model = ChatOpenAI() + +# RAG chain +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +) diff --git a/templates/rag-chroma/tests/__init__.py b/templates/rag-chroma/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-conversation/LICENSE b/templates/rag-conversation/LICENSE new file mode 100644 index 00000000000..578fc414ad4 --- /dev/null +++ b/templates/rag-conversation/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 pingpong-templates + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/rag-conversation/README.md b/templates/rag-conversation/README.md new file mode 100644 index 00000000000..29e85483c48 --- /dev/null +++ b/templates/rag-conversation/README.md @@ -0,0 +1,13 @@ +# Conversational RAG + +This template performs [conversational](https://python.langchain.com/docs/expression_language/cookbook/retrieval#conversational-retrieval-chain) [retrieval](https://python.langchain.com/docs/use_cases/question_answering/), which is one of the most popular LLM use-cases. + +It passes both a conversation history and retrieved documents into an LLM for synthesis. + +## LLM + +Be sure that `OPENAI_API_KEY` is set in order to use the OpenAI models. + +## Pinecone + +Be sure that `PINECONE_API_KEY` is set in order to use Pinecone. diff --git a/templates/rag-conversation/poetry.lock b/templates/rag-conversation/poetry.lock new file mode 100644 index 00000000000..a41723c7830 --- /dev/null +++ b/templates/rag-conversation/poetry.lock @@ -0,0 +1,2379 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1d84166673694841d8953f0a8d0c90e1087739d24632fe86b1a08819168b4566"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:253bf92b744b3170eb4c4ca2fa58f9c4b87aeb1df42f71d4e78815e6e8b73c9e"}, + {file = 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(>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8.1,<4.0" +content-hash = "cb79694b764461126fd022edd644a2ef0ee437be5a42c00faa82d283615b5196" diff --git a/templates/rag-conversation/pyproject.toml b/templates/rag-conversation/pyproject.toml new file mode 100644 index 00000000000..c2b83537595 --- /dev/null +++ b/templates/rag-conversation/pyproject.toml @@ -0,0 +1,22 @@ +[tool.poetry] +name = "rag-conversation" +version = "0.1.0" +description = "" +authors = ["Lance Martin "] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.313, <0.1" +openai = ">=0.28.1" +tiktoken = ">=0.5.1" +pinecone-client = ">=2.2.4" +beautifulsoup4 = "^4.12.2" + +[tool.langserve] +export_module = "rag_conversation" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-conversation/rag_conversation.ipynb b/templates/rag-conversation/rag_conversation.ipynb new file mode 100644 index 00000000000..dcf63077bf1 --- /dev/null +++ b/templates/rag-conversation/rag_conversation.ipynb @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "424a9d8d", + "metadata": {}, + "source": [ + "## Run Template\n", + "\n", + "\n", + "As shown in the README, add template and start server:\n", + "```\n", + "langchain serve add rag-conversation\n", + "langchain start\n", + "```\n", + "\n", + "We can now look at the endpoints:\n", + "\n", + "http://127.0.0.1:8000/docs#\n", + "\n", + "And specifically at our loaded template:\n", + "\n", + "http://127.0.0.1:8000/docs#/default/invoke_rag_conversation_invoke_post\n", + " \n", + "We can also use remote runnable to call it." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "5f521923", + "metadata": {}, + "outputs": [], + "source": [ + "from langserve.client import RemoteRunnable\n", + "rag_app = RemoteRunnable('http://localhost:8000/rag-conversation')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "679bd83b", + "metadata": {}, + "outputs": [], + "source": [ + "question = \"How does agent memory work?\"\n", + "answer = rag_app.invoke({\n", + " \"question\": question,\n", + " \"chat_history\": [],\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "94a05616", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content=\"Agent memory works by utilizing both short-term memory and long-term memory mechanisms. \\n\\nShort-term memory allows the agent to learn and retain information within the current context or task. This in-context learning helps the agent handle complex tasks efficiently. \\n\\nOn the other hand, long-term memory enables the agent to retain and recall an unlimited amount of information over extended periods. This is achieved by leveraging an external vector store, such as a memory stream, which serves as a comprehensive database of the agent's past experiences in natural language. The memory stream records observations and events directly provided by the agent, and inter-agent communication can also trigger new natural language statements to be added to the memory.\\n\\nTo access and utilize the stored information, a retrieval model is employed. This model determines the context that is most relevant, recent, and important to inform the agent's behavior. By retrieving information from memory, the agent can reflect on past actions, learn from mistakes, and refine its behavior for future steps, ultimately improving the quality of its results.\")" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "answer" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ce206c8a", + "metadata": {}, + "outputs": [], + "source": [ + "chat_history = [(question, answer.content)]\n", + "answer = rag_app.invoke({\n", + " \"question\": \"What are the different types?\",\n", + " \"chat_history\": chat_history,\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "4626f167", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='The different types of memory utilized by the agent are sensory memory, short-term memory, and long-term memory.')" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "answer" + ] + } + ], + "metadata": { + "kernelspec": { + "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", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/templates/rag-conversation/rag_conversation/__init__.py b/templates/rag-conversation/rag_conversation/__init__.py new file mode 100644 index 00000000000..4535ca34e88 --- /dev/null +++ b/templates/rag-conversation/rag_conversation/__init__.py @@ -0,0 +1,3 @@ +from rag_conversation.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-conversation/rag_conversation/chain.py b/templates/rag-conversation/rag_conversation/chain.py new file mode 100644 index 00000000000..f6ad06164d2 --- /dev/null +++ b/templates/rag-conversation/rag_conversation/chain.py @@ -0,0 +1,92 @@ +from typing import Tuple, List +from pydantic import BaseModel +from operator import itemgetter +from langchain.vectorstores import Pinecone +from langchain.chat_models import ChatOpenAI +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain.embeddings import OpenAIEmbeddings +from langchain.schema import format_document, AIMessage, HumanMessage +from langchain.prompts.prompt import PromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough, RunnableBranch, RunnableLambda, RunnableMap + +### Ingest code - you may need to run this the first time +# Load +# from langchain.document_loaders import WebBaseLoader +# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") +# data = loader.load() + +# # Split +# from langchain.text_splitter import RecursiveCharacterTextSplitter +# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) +# all_splits = text_splitter.split_documents(data) +# +# # Add to vectorDB +# vectorstore = Pinecone.from_documents( +# documents=all_splits, embedding=OpenAIEmbeddings(), index_name='langchain-test' +# ) +# retriever = vectorstore.as_retriever() + +vectorstore = Pinecone.from_existing_index("langchain-test", OpenAIEmbeddings()) +retriever = vectorstore.as_retriever() + +# Condense a chat history and follow-up question into a standalone question +_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. +Chat History: +{chat_history} +Follow Up Input: {question} +Standalone question:""" +CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template) + +# RAG answer synthesis prompt +template = """Answer the question based only on the following context: + +{context} +""" +ANSWER_PROMPT = ChatPromptTemplate.from_messages([ + ("system",template), + MessagesPlaceholder(variable_name="chat_history"), + ("user", "{question}") +]) + +# Conversational Retrieval Chain +DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}") +def _combine_documents(docs, document_prompt = DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"): + doc_strings = [format_document(doc, document_prompt) for doc in docs] + return document_separator.join(doc_strings) + +def _format_chat_history(chat_history: List[Tuple[str, str]]) -> List: + buffer = [] + for human, ai in chat_history: + buffer.append(HumanMessage(content=human)) + buffer.append(AIMessage(content=ai)) + return buffer + +# User input +class ChatHistory(BaseModel): + chat_history: List[Tuple[str, str]] + question: str + + +_search_query = RunnableBranch( + # If input includes chat_history, we condense it with the follow-up question + ( + RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config( + run_name="HasChatHistoryCheck" + ), # Condense follow-up question and chat into a standalone_question + RunnablePassthrough.assign( + chat_history=lambda x: _format_chat_history(x['chat_history']) + ) | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0) | StrOutputParser(), + ), + # Else, we have no chat history, so just pass through the question + RunnableLambda(itemgetter("question")) + + ) + +_inputs = RunnableMap({ + "question": lambda x: x["question"], + "chat_history": lambda x: _format_chat_history(x['chat_history']), + "context": _search_query | retriever | _combine_documents +}).with_types(input_type=ChatHistory) + +chain = _inputs | ANSWER_PROMPT | ChatOpenAI() | StrOutputParser() diff --git a/templates/rag-conversation/tests/__init__.py b/templates/rag-conversation/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-elasticsearch/LICENSE b/templates/rag-elasticsearch/LICENSE new file mode 100644 index 00000000000..578fc414ad4 --- /dev/null +++ b/templates/rag-elasticsearch/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 pingpong-templates + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/rag-elasticsearch/README.md b/templates/rag-elasticsearch/README.md new file mode 100644 index 00000000000..e96631816a7 --- /dev/null +++ b/templates/rag-elasticsearch/README.md @@ -0,0 +1,59 @@ +# Elasticsearch RAG Example + +Using Langserve and ElasticSearch to build a RAG search example for answering questions on workplace documents. + +Relies on sentence transformer `MiniLM-L6-v2` for embedding passages and questions. + +## Running Elasticsearch + +There are a number of ways to run Elasticsearch. + +### Elastic Cloud + +Create a free trial account on [Elastic Cloud](https://cloud.elastic.co/registration?utm_source=langchain&utm_content=langserve). + +Once you have created an account, you can create a deployment. With a deployment, you can use these environment variables to connect to your Elasticsearch instance: + +```bash +export ELASTIC_CLOUD_ID = +export ELASTIC_USERNAME = +export ELASTIC_PASSWORD = +``` + +### Docker + +For local development, you can use Docker: + +```bash +docker run -p 9200:9200 \ + -e "discovery.type=single-node" \ + -e "xpack.security.enabled=false" \ + -e "xpack.security.http.ssl.enabled=false" \ + -e "xpack.license.self_generated.type=trial" \ + docker.elastic.co/elasticsearch/elasticsearch:8.10.0 +``` + +This will run Elasticsearch on port 9200. You can then check that it is running by visiting [http://localhost:9200](http://localhost:9200). + +With a deployment, you can use these environment variables to connect to your Elasticsearch instance: + +```bash +export ES_URL = "http://localhost:9200" +``` + +## Documents + +To load fictional workplace documents, run the following command from the root of this repository: + +```bash +python ./data/load_documents.py +``` + +However, you can choose from a large number of document loaders [here](https://python.langchain.com/docs/integrations/document_loaders). + +## Installation + +```bash +# from inside your LangServe instance +poe add rag-elasticsearch +``` diff --git a/templates/rag-elasticsearch/data/documents.json b/templates/rag-elasticsearch/data/documents.json new file mode 100644 index 00000000000..b81cf1647f2 --- /dev/null +++ b/templates/rag-elasticsearch/data/documents.json @@ -0,0 +1,161 @@ +[ + { + "content": "Effective: March 2020\nPurpose\n\nThe purpose of this full-time work-from-home policy is to provide guidelines and support for employees to conduct their work remotely, ensuring the continuity and productivity of business operations during the COVID-19 pandemic and beyond.\nScope\n\nThis policy applies to all employees who are eligible for remote work as determined by their role and responsibilities. It is designed to allow employees to work from home full time while maintaining the same level of performance and collaboration as they would in the office.\nEligibility\n\nEmployees who can perform their work duties remotely and have received approval from their direct supervisor and the HR department are eligible for this work-from-home arrangement.\nEquipment and Resources\n\nThe necessary equipment and resources will be provided to employees for remote work, including a company-issued laptop, software licenses, and access to secure communication tools. Employees are responsible for maintaining and protecting the company's equipment and data.\nWorkspace\n\nEmployees working from home are responsible for creating a comfortable and safe workspace that is conducive to productivity. This includes ensuring that their home office is ergonomically designed, well-lit, and free from distractions.\nCommunication\n\nEffective communication is vital for successful remote work. Employees are expected to maintain regular communication with their supervisors, colleagues, and team members through email, phone calls, video conferences, and other approved communication tools.\nWork Hours and Availability\n\nEmployees are expected to maintain their regular work hours and be available during normal business hours, unless otherwise agreed upon with their supervisor. Any changes to work hours or availability must be communicated to the employee's supervisor and the HR department.\nPerformance Expectations\n\nEmployees working from home are expected to maintain the same level of performance and productivity as if they were working in the office. Supervisors and team members will collaborate to establish clear expectations and goals for remote work.\nTime Tracking and Overtime\n\nEmployees are required to accurately track their work hours using the company's time tracking system. Non-exempt employees must obtain approval from their supervisor before working overtime.\nConfidentiality and Data Security\n\nEmployees must adhere to the company's confidentiality and data security policies while working from home. This includes safeguarding sensitive information, securing personal devices and internet connections, and reporting any security breaches to the IT department.\nHealth and Well-being\n\nThe company encourages employees to prioritize their health and well-being while working from home. This includes taking regular breaks, maintaining a work-life balance, and seeking support from supervisors and colleagues when needed.\nPolicy Review and Updates\n\nThis work-from-home policy will be reviewed periodically and updated as necessary, taking into account changes in public health guidance, business needs, and employee feedback.\nQuestions and Concerns\n\nEmployees are encouraged to direct any questions or concerns about this policy to their supervisor or the HR department.\n", + "summary": "This policy outlines the guidelines for full-time remote work, including eligibility, equipment and resources, workspace requirements, communication expectations, performance expectations, time tracking and overtime, confidentiality and data security, health and well-being, and policy reviews and updates. Employees are encouraged to direct any questions or concerns", + "name": "Work From Home Policy", + "url": "./sharepoint/Work from home policy.txt", + "created_on": "2020-03-01", + "updated_at": "2020-03-01", + "category": "teams", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Starting May 2022, the company will be implementing a two-day in-office work requirement per week for all eligible employees. Please coordinate with your supervisor and HR department to schedule your in-office workdays while continuing to follow all safety protocols.\n", + "summary": "Starting May 2022, employees will need to work two days a week in the office. Coordinate with your supervisor and HR department for these days while following safety protocols.", + "name": "April Work From Home Update", + "url": "./sharepoint/April work from home update.txt", + "created_on": "2022-04-29", + "updated_at": "2022-04-29", + "category": "teams", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "As we continue to prioritize the well-being of our employees, we are making a slight adjustment to our hybrid work policy. Starting May 1, 2023, employees will be required to work from the office three days a week, with two days designated for remote work. Please communicate with your supervisor and HR department to establish your updated in-office workdays.\n", + "summary": "Starting May 1, 2023, our hybrid work policy will require employees to work from the office three days a week and two days remotely.", + "name": "Wfh Policy Update May 2023", + "url": "./sharepoint/WFH policy update May 2023.txt", + "created_on": "2023-05-01", + "updated_at": "2023-05-01", + "category": "teams", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Executive Summary:\nThis sales strategy document outlines the key objectives, focus areas, and action plans for our tech company's sales operations in fiscal year 2024. Our primary goal is to increase revenue, expand market share, and strengthen customer relationships in our target markets.\n\nI. Objectives for Fiscal Year 2024\n\nIncrease revenue by 20% compared to fiscal year 2023.\nExpand market share in key segments by 15%.\nRetain 95% of existing customers and increase customer satisfaction ratings.\nLaunch at least two new products or services in high-demand market segments.\n\nII. Focus Areas\nA. Target Markets:\nContinue to serve existing markets with a focus on high-growth industries.\nIdentify and penetrate new markets with high potential for our products and services.\n\nB. Customer Segmentation:\nStrengthen relationships with key accounts and strategic partners.\nPursue new customers in underserved market segments.\nDevelop tailored offerings for different customer segments based on their needs and preferences.\n\nC. Product/Service Portfolio:\nOptimize the existing product/service portfolio by focusing on high-demand solutions.\nDevelop and launch innovative products/services in emerging technology areas.\nEnhance post-sales support and customer service to improve customer satisfaction.\n\nIII. Action Plans\nA. Sales Team Development:\nExpand the sales team to cover new markets and industries.\nProvide ongoing training to sales staff on product knowledge, sales techniques, and industry trends.\nImplement a performance-based incentive system to reward top performers.\n\nB. Marketing and Promotion:\nDevelop targeted marketing campaigns for different customer segments and industries.\nLeverage digital marketing channels to increase brand visibility and lead generation.\nParticipate in industry events and trade shows to showcase our products and services.\n\nC. Partner Ecosystem:\nStrengthen existing partnerships and establish new strategic alliances to expand market reach.\nCollaborate with partners on joint marketing and sales initiatives.\nProvide partner training and support to ensure they effectively represent our products and services.\n\nD. Customer Success:\nImplement a proactive customer success program to improve customer retention and satisfaction.\nDevelop a dedicated customer support team to address customer inquiries and concerns promptly.\nCollect and analyze customer feedback to identify areas for improvement in our products, services, and processes.\n\nIV. Monitoring and Evaluation\nEstablish key performance indicators (KPIs) to track progress toward our objectives.\nConduct regular sales team meetings to review performance, share best practices, and address challenges.\nConduct quarterly reviews of our sales strategy to ensure alignment with market trends and adjust as needed.\n\nBy following this sales strategy for fiscal year 2024, our tech company aims to achieve significant growth and success in our target markets, while also providing exceptional value and service to our customers.\n", + "summary": "This sales strategy document outlines objectives, focus areas, and action plans for our tech company's sales operations in fiscal year 2024. Our primary goal is to increase revenue, expand market share, and strengthen customer relationships in our target markets. Focus areas include targeting new markets, segmenting customers, enhancing", + "name": "Fy2024 Company Sales Strategy", + "url": "./sharepoint/FY2024 Company Sales Strategy.txt", + "category": "teams", + "created_on": "2023-04-15", + "updated_at": "2023-04-15", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Purpose\n\nThe purpose of this vacation policy is to outline the guidelines and procedures for requesting and taking time off from work for personal and leisure purposes. This policy aims to promote a healthy work-life balance and encourage employees to take time to rest and recharge.\nScope\n\nThis policy applies to all full-time and part-time employees who have completed their probationary period.\nVacation Accrual\n\nFull-time employees accrue vacation time at a rate of [X hours] per month, equivalent to [Y days] per year. Part-time employees accrue vacation time on a pro-rata basis, calculated according to their scheduled work hours.\n\nVacation time will begin to accrue from the first day of employment, but employees are eligible to take vacation time only after completing their probationary period. Unused vacation time will be carried over to the next year, up to a maximum of [Z days]. Any additional unused vacation time will be forfeited.\nVacation Scheduling\n\nEmployees are required to submit vacation requests to their supervisor at least [A weeks] in advance, specifying the start and end dates of their vacation. Supervisors will review and approve vacation requests based on business needs, ensuring adequate coverage during the employee's absence.\n\nEmployees are encouraged to plan their vacations around the company's peak and non-peak periods to minimize disruptions. Vacation requests during peak periods may be subject to limitations and require additional advance notice.\nVacation Pay\n\nEmployees will receive their regular pay during their approved vacation time. Vacation pay will be calculated based on the employee's average earnings over the [B weeks] preceding their vacation.\nUnplanned Absences and Vacation Time\n\nIn the event of an unplanned absence due to illness or personal emergencies, employees may use their accrued vacation time, subject to supervisor approval. Employees must inform their supervisor as soon as possible and provide any required documentation upon their return to work.\nVacation Time and Termination of Employment\n\nIf an employee's employment is terminated, they will be paid out for any unused vacation time, calculated based on their current rate of pay.\nPolicy Review and Updates\n\nThis vacation policy will be reviewed periodically and updated as necessary, taking into account changes in labor laws, business needs, and employee feedback.\nQuestions and Concerns\n\nEmployees are encouraged to direct any questions or concerns about this policy to their supervisor or the HR department.\n", + "summary": ": This policy outlines the guidelines and procedures for requesting and taking time off from work for personal and leisure purposes. Full-time employees accrue vacation time at a rate of [X hours] per month, equivalent to [Y days] per year. Vacation requests must be submitted to supervisors at least", + "name": "Company Vacation Policy", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/ES6rw9bKZxVBobG1WUoJpikBF9Bhx1pw_GvJWbsg-Z_HNA?e=faSHVt", + "created_on": "2018-04-15", + "updated_at": "2018-04-16", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + + { + "content": "This career leveling matrix provides a framework for understanding the various roles and responsibilities of Software Engineers, as well as the skills and experience required for each level. This matrix is intended to support employee development, facilitate performance evaluations, and provide a clear career progression path.\nJunior Software Engineer\n\nResponsibilities:\nCollaborate with team members to design, develop, and maintain software applications and components.\nWrite clean, well-structured, and efficient code following established coding standards.\nParticipate in code reviews, providing and receiving constructive feedback.\nTroubleshoot and resolve software defects and issues.\nAssist with the creation of technical documentation.\nContinuously learn and stay up-to-date with new technologies and best practices.\n\nSkills & Experience:\nBachelor\u2019s degree in Computer Science or a related field, or equivalent work experience.\nBasic understanding of software development principles and methodologies.\nProficiency in at least one programming language.\nStrong problem-solving and analytical skills.\nEffective communication and collaboration skills.\nEagerness to learn and grow within the field.\nSenior Software Engineer\n\nResponsibilities:\nDesign, develop, and maintain complex software applications and components.\nLead and mentor junior team members in software development best practices and techniques.\nConduct code reviews and ensure adherence to coding standards and best practices.\nCollaborate with cross-functional teams to define, design, and deliver software solutions.\nIdentify, troubleshoot, and resolve complex software defects and issues.\nContribute to the creation and maintenance of technical documentation.\nEvaluate and recommend new technologies, tools, and practices to improve software quality and efficiency.\n\nSkills & Experience:\nBachelor\u2019s degree in Computer Science or a related field, or equivalent work experience.\n5+ years of software development experience.\nProficiency in multiple programming languages and technologies.\nDemonstrated ability to design and implement complex software solutions.\nStrong leadership, mentoring, and collaboration skills.\nExcellent problem-solving, analytical, and communication skills.\nPrincipal Software Engineer\n\nResponsibilities:\nLead the design, development, and maintenance of large-scale, mission-critical software applications and components.\nProvide technical leadership and mentorship to software engineering teams.\nDrive the adoption of advanced software development practices and technologies.\nCollaborate with product management, architecture, and other stakeholders to define and deliver strategic software initiatives.\nIdentify, troubleshoot, and resolve the most complex software defects and issues.\nCreate and maintain technical documentation, including architectural designs and best practice guidelines.\nRepresent [Company Name] as a thought leader in the software engineering community, including speaking at conferences, publishing articles, and contributing to open-source projects.\n\nSkills & Experience:\nBachelor\u2019s degree in Computer Science or a related field, or equivalent work experience.\n10+ years of software development experience, with a focus on large-scale, mission-critical applications.\nExpertise in multiple programming languages, technologies, and software development methodologies.\nProven ability to lead and mentor high-performing software engineering teams.\nExceptional problem-solving, analytical, and communication skills.\nStrong business acumen and ability to influence decision-making at the executive level.\n\nBy following this career leveling matrix, we aim to support the growth and development of Software Engineers, enabling them to reach their full potential and contribute meaningfully to the success of the organization.\n", + "summary": "\nThis career leveling matrix provides a framework for understanding the various roles and responsibilities of Software Engineers, as well as the skills and experience required for each level. It is intended to support employee development, facilitate performance evaluations, and provide a clear career progression path.", + "name": "Swe Career Matrix", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/EVYuEyRhHh5Aqc3a39sqbGcBkqKIHRWtJBjjUjNs6snpMg?e=nv1mf4", + "created_on": "2018-04-15", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Title: Working with the Sales Team as an Engineer in a Tech Company\n\nIntroduction:\nAs an engineer in a tech company, collaboration with the sales team is essential to ensure the success of the company's products and services. This guidance document aims to provide an overview of how engineers can effectively work with the sales team, fostering a positive and productive working environment.\nUnderstanding the Sales Team's Role:\nThe sales team is responsible for promoting and selling the company's products and services to potential clients. Their role involves establishing relationships with customers, understanding their needs, and ensuring that the offered solutions align with their requirements.\n\nAs an engineer, it is important to understand the sales team's goals and objectives, as this will help you to provide them with the necessary information, tools, and support to successfully sell your company's products and services.\nCommunication:\nEffective communication is key to successfully working with the sales team. Make sure to maintain open lines of communication, and be responsive to their questions and concerns. This includes:\n\na. Attending sales meetings and conference calls when required.\nb. Providing regular product updates and training sessions to the sales team.\nc. Being available to answer technical questions and clarifications.\nCollaboration:\nCollaborate with the sales team in developing and refining sales materials, such as product presentations, demos, and technical documents. This will ensure that the sales team has accurate and up-to-date information to present to clients.\n\nAdditionally, work closely with the sales team on customer projects or product customizations, providing technical guidance, and ensuring that the solutions meet the customer's requirements.\nCustomer Engagement:\nAt times, engineers may be asked to join sales meetings or calls with potential clients to provide technical expertise. In these situations, it is important to:\n\na. Be prepared and understand the customer's needs and pain points.\nb. Clearly explain the technical aspects of the product or solution in a simple language that the customer can understand.\nc. Address any concerns or questions the customer may have.\nContinuous Improvement:\nActively seek feedback from the sales team regarding product performance, customer experiences, and market trends. Use this feedback to identify areas of improvement and collaborate with other engineers to enhance the product or service offerings.\nMutual Respect and Support:\nIt is essential to treat your colleagues in the sales team with respect and professionalism. Recognize and appreciate their efforts in promoting and selling the company's products and services. In turn, the sales team should also respect and appreciate the technical expertise and knowledge of the engineering team.\n\nBy working together, both the engineering and sales teams can contribute to the overall success of the company.\n\nConclusion:\nCollaboration between engineers and the sales team is crucial for a tech company's success. By understanding each other's roles, maintaining effective communication, collaborating on projects, and supporting one another, both teams can work together to achieve the company's goals and ensure customer satisfaction.\n", + "summary": ": This guide provides an overview of how engineers can effectively collaborate with the sales team to ensure the success of a tech company. It includes understanding the sales team's role, communicating and collaborating on projects, engaging customers, and providing mutual respect and support.", + "name": "Sales Engineering Collaboration", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/EW21-KJnfHBFoRiF49_uJMcBfHyPKimuPOFsCcJypQWaBQ?e=mGdIqe", + "created_on": "2019-04-15", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Purpose\nThe purpose of this Intellectual Property Policy is to establish guidelines and procedures for the ownership, protection, and utilization of intellectual property generated by employees during their employment. This policy aims to encourage creativity and innovation while ensuring that the interests of both the company and its employees are protected.\n\nScope\nThis policy applies to all employees, including full-time, part-time, temporary, and contract employees.\n\nDefinitions\na. Intellectual Property (IP): Refers to creations of the mind, such as inventions, literary and artistic works, designs, symbols, and images, that are protected by copyright, trademark, patent, or other forms of legal protection.\nb. Company Time: Refers to the time during which an employee is actively engaged in performing their job duties.\nc. Outside Company Time: Refers to the time during which an employee is not engaged in performing their job duties.\n\nOwnership of Intellectual Property\na. Work Generated on Company Time\ni. Any intellectual property created, conceived, or developed by an employee during company time or using company resources, equipment, or facilities shall be considered the property of the Company.\nii. Employees are required to promptly disclose any such intellectual property to their supervisor or the appropriate department head.\nb. Work Generated Outside Company Time\ni. Intellectual property created, conceived, or developed by an employee outside of company time and without the use of company resources, equipment, or facilities shall generally remain the property of the employee.\nii. However, if the intellectual property is directly related to the employee's job responsibilities, or if the employee has used company resources, equipment, or facilities in its creation, it may be considered the property of the Company.\nProtection and Utilization of Intellectual Property\na. The Company shall have the right to protect, license, and commercialize any intellectual property owned by the company as it deems appropriate.\nb. Employees are expected to cooperate with the Company in obtaining any necessary legal protection for intellectual property owned by the company, including by signing any documents or providing any necessary information or assistance.\nConfidentiality\nEmployees are expected to maintain the confidentiality of any intellectual property owned by the Company and not disclose it to any third parties without the express written consent of an authorized representative of the company.\nEmployee Acknowledgment\nAll employees are required to sign an acknowledgment of this Intellectual Property Policy as a condition of their employment with [Company Name]. By signing the acknowledgment, employees agree to abide by the terms of this policy and understand that any violations may result in disciplinary action, up to and including termination of employment.\nPolicy Review\nThis Intellectual Property Policy shall be reviewed periodically and may be amended as necessary to ensure its continued effectiveness and compliance with applicable laws and regulations. Employees will be notified of any significant changes to this policy.\n", + "summary": "This Intellectual Property Policy outlines guidelines and procedures for the ownership, protection, and utilization of intellectual property generated by employees during their employment. It establishes the company's ownership of work generated on company time, while recognizing employee ownership of work generated outside of company time without the use of company resources. The policy", + "name": "Intellectual Property Policy", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/EWz3cYEVdzBNsiHsYbKhms4BVYGhravyrUw3T3lzxL4pTg?e=mPIgbO", + "created_on": "2021-06-15", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + + { + "content": "Code of Conduct\nPurpose\n\nThe purpose of this code of conduct is to establish guidelines for professional and ethical behavior in the workplace. It outlines the principles and values that all employees are expected to uphold in their interactions with colleagues, customers, partners, and other stakeholders.\nScope\n\nThis code of conduct applies to all employees, contractors, and volunteers within the organization, regardless of their role or seniority.\nCore Values\n\nEmployees are expected to adhere to the following core values:\n\na. Integrity: Act honestly, ethically, and in the best interests of the organization at all times.\nb. Respect: Treat all individuals with dignity, courtesy, and fairness, regardless of their background, beliefs, or position.\nc. Accountability: Take responsibility for one's actions and decisions, and be willing to learn from mistakes.\nd. Collaboration: Work cooperatively with colleagues and partners to achieve shared goals and promote a positive work environment.\ne. Excellence: Strive for the highest standards of performance and continuously seek opportunities for improvement.\nCompliance with Laws and Regulations\n\nEmployees must comply with all applicable laws, regulations, and organizational policies in the course of their work. This includes, but is not limited to, employment laws, data protection regulations, and industry-specific guidelines.\nConflicts of Interest\n\nEmployees should avoid situations where their personal interests may conflict with or influence their professional judgment. If a potential conflict of interest arises, employees must disclose it to their supervisor or the appropriate authority within the organization.\nConfidentiality and Information Security\n\nEmployees are responsible for safeguarding the organization's confidential information, as well as any sensitive information entrusted to them by clients, partners, or other third parties. This includes adhering to data protection policies and using secure communication channels.\nHarassment and Discrimination\n\nThe organization is committed to providing a workplace free from harassment, discrimination, and bullying. Employees are expected to treat others with respect and report any incidents of inappropriate behavior to their supervisor or the human resources department.\nHealth and Safety\n\nEmployees must follow all health and safety guidelines and procedures to maintain a safe and healthy work environment. This includes reporting any hazards or unsafe conditions to the appropriate personnel.\nUse of Company Resources\n\nEmployees are expected to use company resources, including time, equipment, and funds, responsibly and for their intended purposes. Misuse or theft of company resources is strictly prohibited.\nReporting Violations\n\nEmployees have a responsibility to report any suspected violations of this code of conduct, as well as any illegal or unethical behavior, to their supervisor or the appropriate authority within the organization. The organization will protect the confidentiality of employees who report violations and will not tolerate retaliation against those who raise concerns.\nConsequences of Non-Compliance\n\nFailure to adhere to this code of conduct may result in disciplinary action, up to and including termination of employment. The organization reserves the right to take legal action against individuals who engage in illegal or unethical conduct.\nPolicy Review and Updates\n\nThis code of conduct will be reviewed periodically and updated as necessary to ensure it remains relevant and effective in promoting ethical behavior and professional standards within the organization.\nQuestions and Concerns\n\nEmployees are encouraged to seek guidance from their supervisor or the human resources department if they have questions or concerns about this code of conduct or its application to specific situations.\n", + "summary": "This code of conduct outlines the principles and values that all employees are expected to uphold in their interactions with colleagues, customers, partners, and other stakeholders. It sets out core values such as integrity, respect, accountability, collaboration and excellence. Employees must comply with all applicable laws, regulations, and organizational", + "name": "Code Of Conduct", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/ER3xmeKaZ_pAqPeJWyyNR0QBg6QmoWIGPhwfEyCABWHrPA?e=cvzrgV", + "created_on": "2018-01-12", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Content:\nThe purpose of this office pet policy is to outline the guidelines and procedures for bringing pets into the workplace. This policy aims to create a positive and inclusive work environment while ensuring the comfort, safety, and well-being of all employees, visitors, and pets.\nScope\n\nThis policy applies to all employees who wish to bring their pets to the office. Pets covered under this policy include dogs, cats, and other small, non-exotic animals, subject to approval by the HR department.\nPet Approval Process\n\nEmployees must obtain prior approval from their supervisor and the HR department before bringing their pets to the office. The approval process includes:\n\na. Submitting a written request, including a description of the pet, its breed, age, and temperament.\nb. Providing proof of up-to-date vaccinations and any required licenses or permits.\nc. Obtaining written consent from all employees who share the workspace with the pet owner.\n\nThe HR department reserves the right to deny or revoke pet approval based on the specific circumstances or concerns raised by other employees.\nPet Behavior and Supervision\n\nEmployees are responsible for the behavior and well-being of their pets while in the office. Pets must be:\n\na. Well-behaved, non-aggressive, and not disruptive to the work environment.\nb. House-trained and able to eliminate waste in designated areas outside the office.\nc. Kept on a leash or in a secure enclosure when not in the employee's immediate work area.\n\nEmployees must closely supervise their pets and promptly address any issues or concerns raised by other staff members.\nAllergies and Phobias\n\nEmployees with allergies or phobias related to pets must inform the HR department, which will work with the affected employees and pet owners to find a suitable solution. This may include adjusting workspaces, limiting the number or types of pets allowed, or implementing additional safety measures.\nCleanliness and Hygiene\n\nEmployees are responsible for maintaining a clean and hygienic work environment. This includes:\n\na. Cleaning up after their pets, both indoors and outdoors.\nb. Regularly grooming their pets to minimize shedding and odors.\nc. Ensuring their pets are free of pests, such as fleas and ticks.\nLiability\n\nPet owners are liable for any damage or injury caused by their pets. Employees are encouraged to obtain pet liability insurance to cover potential incidents.\nRestricted Areas\n\nPets are not allowed in certain areas of the office, including meeting rooms, restrooms, kitchen and dining areas, and any other designated spaces. Signage will be posted to indicate these restricted areas.\nPolicy Review and Updates\n\nThis office pet policy will be reviewed periodically and updated as necessary, taking into account employee feedback, changes in legislation, and best practices for maintaining a safe and inclusive work environment.\nQuestions and Concerns\n\nEmployees are encouraged to direct any questions or concerns about this policy to their supervisor or the HR department.\n", + "summary": "This policy outlines the guidelines and procedures for bringing pets into the workplace. It covers approval process, pet behavior and supervision, allergies and phobias, cleanliness and hygiene, liability, restricted areas, and policy review. Employees must obtain prior approval from their supervisor and the HR department before bringing their", + "name": "Office Pet Policy", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/ETf-69wBeaZJpAn3CY7ExRABQWvav-p24VOnB6C0A4l2pQ?e=X72WuK", + "created_on": "2018-01-12", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Performance Management Policy\nPurpose and Scope\nThe purpose of this Performance Management Policy is to establish a consistent and transparent process for evaluating, recognizing, and rewarding employee performance. This policy applies to all employees and aims to foster a culture of continuous improvement, professional growth, and open communication between employees and management.\nPerformance Planning and Goal Setting\nAt the beginning of each performance cycle, employees and their supervisors will collaborate to set clear, achievable, and measurable performance goals. These goals should align with the company\u2019s strategic objectives and take into account the employee\u2019s job responsibilities, professional development, and career aspirations.\nOngoing Feedback and Communication\nThroughout the performance cycle, employees and supervisors are encouraged to engage in regular, constructive feedback and open communication. This includes discussing progress towards goals, addressing challenges, and identifying opportunities for improvement or additional support. Regular check-ins and updates help ensure that employees stay on track and receive the guidance they need to succeed.\nPerformance Evaluation\nAt the end of each performance cycle, employees will participate in a formal performance evaluation with their supervisor. This evaluation will assess the employee\u2019s overall performance, including their achievements, areas for improvement, and progress towards goals. Both the employee and supervisor should come prepared to discuss specific examples, accomplishments, and challenges from the performance period.\nPerformance Ratings\nBased on the performance evaluation, employees will receive a performance rating that reflects their overall performance during the cycle. The rating system should be clearly defined and consistently applied across the organization. Performance ratings will be used to inform decisions regarding promotions, salary increases, and other rewards or recognition.\nPromotions and Advancements\nHigh-performing employees who consistently demonstrate strong performance, leadership, and a commitment to the company\u2019s values may be considered for promotions or other advancement opportunities. Promotions will be based on factors such as performance ratings, skills, experience, and the needs of the organization. Employees interested in pursuing a promotion should discuss their career goals and development plans with their supervisor.\nPerformance Improvement Plans\nEmployees who receive a low performance rating or are struggling to meet their performance goals may be placed on a Performance Improvement Plan (PIP). A PIP is a structured plan designed to help the employee address specific areas of concern, set achievable improvement goals, and receive additional support or resources as needed. Employees on a PIP will be closely monitored and re-evaluated at the end of the improvement period to determine if satisfactory progress has been made.\nRecognition and Rewards\nOur company believes in recognizing and rewarding employees for their hard work and dedication. In addition to promotions and salary increases, employees may be eligible for other forms of recognition or rewards based on their performance. This may include bonuses, awards, or other incentives designed to motivate and celebrate employee achievements. The specific criteria and eligibility for these rewards will be communicated by the HR department or management.\n", + "summary": "This Performance Management Policy outlines a consistent and transparent process for evaluating, recognizing, and rewarding employees. It includes goal setting, ongoing feedback, performance evaluations, ratings, promotions, and rewards. The policy applies to all employees and encourages open communication and professional growth.", + "name": "Performance Management Policy", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/ERsxt9p1uehJqeJu4JlxkakBavbKwcldrYv_hpv3xHikAw?e=pf5R2C", + "created_on": "2018-01-12", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + + { + "content": "Our sales organization is structured to effectively serve our customers and achieve our business objectives across multiple regions. The organization is divided into the following main regions:\n\nThe Americas: This region includes the United States, Canada, Mexico, as well as Central and South America. The North America South America region (NASA) has two Area Vice-Presidents: Laura Martinez is the Area Vice-President of North America, and Gary Johnson is the Area Vice-President of South America.\n\nEurope: Our European sales team covers the entire continent, including the United Kingdom, Germany, France, Spain, Italy, and other countries. The team is responsible for understanding the unique market dynamics and cultural nuances, enabling them to effectively target and engage with customers across the region. The Area Vice-President for Europe is Rajesh Patel.\nAsia-Pacific: This region encompasses countries such as China, Japan, South Korea, India, Australia, and New Zealand. Our sales team in the Asia-Pacific region works diligently to capitalize on growth opportunities and address the diverse needs of customers in this vast and rapidly evolving market. The Area Vice-President for Asia-Pacific is Mei Li.\nMiddle East & Africa: This region comprises countries across the Middle East and Africa, such as the United Arab Emirates, Saudi Arabia, South Africa, and Nigeria. Our sales team in this region is responsible for navigating the unique market challenges and identifying opportunities to expand our presence and better serve our customers. The Area Vice-President for Middle East & Africa is Jamal Abdi.\n\nEach regional sales team consists of dedicated account managers, sales representatives, and support staff, led by their respective Area Vice-Presidents. They are responsible for identifying and pursuing new business opportunities, nurturing existing client relationships, and ensuring customer satisfaction. The teams collaborate closely with other departments, such as marketing, product development, and customer support, to ensure we consistently deliver high-quality products and services to our clients.\n", + "summary": "\nOur sales organization is divided into four regions: The Americas, Europe, Asia-Pacific, and Middle East & Africa. Each region is led by an Area Vice-President and consists of dedicated account managers, sales representatives, and support staff. They collaborate with other departments to ensure the delivery of high", + "name": "Sales Organization Overview", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/EYsr1eqgn9hMslMJFLR-k54BBX-O3iC26bK7xNEBtYIBkg?e=xeAjiT", + "created_on": "2018-01-15", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Introduction:\nThis document outlines the compensation bands strategy for the various teams within our IT company. The goal is to establish a fair and competitive compensation structure that aligns with industry standards, rewards performance, and attracts top talent. By implementing this strategy, we aim to foster employee satisfaction and retention while ensuring the company's overall success.\n\nPurpose:\nThe purpose of this compensation bands strategy is to:\na. Define clear guidelines for salary ranges based on job levels and market benchmarks.\nb. Support equitable compensation practices across different teams.\nc. Encourage employee growth and performance.\nd. Enable effective budgeting and resource allocation.\n\nJob Levels:\nTo establish a comprehensive compensation structure, we have defined distinct job levels within each team. These levels reflect varying degrees of skills, experience, and responsibilities. The levels include:\na. Entry-Level: Employees with limited experience or early career professionals.\nb. Intermediate-Level: Employees with moderate experience and demonstrated competence.\nc. Senior-Level: Experienced employees with advanced skills and leadership capabilities.\nd. Leadership-Level: Managers and team leaders responsible for strategic decision-making.\n\nCompensation Bands:\nBased on the job levels, the following compensation bands have been established:\na. Entry-Level Band: This band encompasses salary ranges for employees in entry-level positions. It aims to provide competitive compensation for individuals starting their careers within the company.\n\nb. Intermediate-Level Band: This band covers salary ranges for employees who have gained moderate experience and expertise in their respective roles. It rewards employees for their growing skill set and contributions.\n\nc. Senior-Level Band: The senior-level band includes salary ranges for experienced employees who have attained advanced skills and have a proven track record of delivering results. It reflects the increased responsibilities and expectations placed upon these individuals.\n\nd. Leadership-Level Band: This band comprises salary ranges for managers and team leaders responsible for guiding and overseeing their respective teams. It considers their leadership abilities, strategic thinking, and the impact they have on the company's success.\n\nMarket Benchmarking:\nTo ensure our compensation remains competitive, regular market benchmarking will be conducted. This involves analyzing industry salary trends, regional compensation data, and market demand for specific roles. The findings will inform periodic adjustments to our compensation bands to maintain alignment with the market.\n\nPerformance-Based Compensation:\nIn addition to the defined compensation bands, we emphasize a performance-based compensation model. Performance evaluations will be conducted regularly, and employees exceeding performance expectations will be eligible for bonuses, incentives, and salary increases. This approach rewards high achievers and motivates employees to excel in their roles.\n\nConclusion:\nBy implementing this compensation bands strategy, our IT company aims to establish fair and competitive compensation practices that align with market standards and foster employee satisfaction. Regular evaluations and market benchmarking will enable us to adapt and refine the strategy to meet the evolving needs of our organization.", + "summary": "This document outlines a compensation framework for IT teams. It includes job levels, compensation bands, and performance-based incentives to ensure fair and competitive wages. Regular market benchmarking will be conducted to adjust the bands according to industry trends.", + "name": "Compensation Framework For It Teams", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/EaAFec6004tAg21g4i67rfgBBRqCm1yY7AZLLQyyaMtsEQ?e=wTMb4z", + "created_on": "2018-01-12", + "category": "sharepoint", + "restricted": true, + "_run_ml_inference": true, + "rolePermissions": ["manager"] + }, + { + "content": "As an employee in Canada, it's essential to understand how to update your tax elections forms to ensure accurate tax deductions from your pay. This guide will help you navigate the process of updating your TD1 Personal Tax Credits Return form.\n\nStep 1: Access the TD1 form\nThe TD1 form is available on the Canada Revenue Agency (CRA) website. Your employer might provide you with a paper copy or a link to the online form. You can access the form directly through the following link: https://www.canada.ca/en/revenue-agency/services/forms-publications/td1-personal-tax-credits-returns.html\n\nStep 2: Choose the correct form version\nYou'll need to fill out the federal TD1 form and, if applicable, the provincial or territorial TD1 form. Select the appropriate version based on your province or territory of residence.\n\nStep 3: Download and open the form\nFor the best experience, download and open the TD1 form in Adobe Reader. If you have visual impairments, consider using the large print version available on the CRA website.\n\nStep 4: Complete the form\nFill out the form by entering your personal information, such as your name, Social Insurance Number (SIN), and address. Then, go through each section to claim any personal tax credits that apply to you. These credits may include:\nBasic personal amount\nAmount for an eligible dependant\nAmount for infirm dependants age 18 or older\nCaregiver amount\nDisability amount\nTuition and education amounts\n\nRead the instructions carefully for each section to ensure you claim the correct amounts.\n\nStep 5: Sign and date the form\nOnce you've completed the form, sign and date it at the bottom.\n\nStep 6: Submit the form to your employer\nSubmit the completed and signed TD1 form to your employer. You can either scan and send it electronically, or provide a printed copy. Your employer will use the information on your TD1 form to calculate the correct amount of tax to be deducted from your pay.\n\nStep 7: Update your TD1 form as needed\nIt's essential to update your TD1 form whenever your personal circumstances change, such as getting married, having a child, or becoming eligible for a new tax credit. Inform your employer of these changes and submit an updated TD1 form to ensure accurate tax deductions.\n\nUpdating your tax elections forms is a crucial step in ensuring the correct tax deductions from your pay as a new employee in Canada. Follow this guide and keep your TD1 form up to date to avoid any discrepancies in your tax filings.\n", + "summary": ": This guide gives a step-by-step explanation of how to update your TD1 Personal Tax Credits Return form. Access the form from the CRA website and choose the correct version based on your province or territory of residence. Download and open the form in Adobe Reader, fill out the form by entering", + "name": "Updating Your Tax Elections Forms", + "url": "./github/Updating Your Tax Elections Forms.txt", + "created_on": "2022-12-20", + "category": "github", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Welcome to our team! We are excited to have you on board and look forward to your valuable contributions. This onboarding guide is designed to help you get started by providing essential information about our policies, procedures, and resources. Please read through this guide carefully and reach out to the HR department if you have any questions.\nIntroduction to Our Company Culture and Values\nOur company is committed to creating a diverse, inclusive, and supportive work environment. We believe that our employees are our most valuable asset and strive to foster a culture of collaboration, innovation, and continuous learning. Our core values include:\nIntegrity: We act ethically and honestly in all our interactions.\nTeamwork: We work together to achieve common goals and support each other's growth.\nExcellence: We strive for the highest quality in our products, services, and relationships.\nInnovation: We encourage creativity and embrace change to stay ahead in the market.\nRespect: We treat each other with dignity and value the unique perspectives of all our colleagues.\nKey Onboarding Steps\nTo ensure a smooth onboarding process, please complete the following steps within your first week:\nAttend orientation: You will be invited to an orientation session to meet your colleagues and learn more about our company's history, mission, and values.\nReview policies and procedures: Familiarize yourself with our employee handbook, which contains important information about our policies and procedures. Please read it thoroughly and adhere to the guidelines.\nComplete required training: You may be required to complete mandatory training sessions, such as safety training or anti-harassment training. Ensure that you attend and complete these sessions as soon as possible.\nUpdating Tax Elections and Documents\nIt is crucial to ensure your tax information is accurate and up-to-date, regardless of the country you work in. Please follow these steps to update your tax elections and documents:\nComplete tax forms: Fill out the necessary tax forms for your country or region, which determine the amount of income tax withheld from your paycheck. You should complete new tax forms if your personal or financial situation changes, such as marriage, divorce, or a change in the number of dependents.\nSubmit regional tax forms: Depending on your location, you may be required to complete additional regional or local tax forms. Check with the HR department to determine which forms are necessary.\nUpdate your address: If you move, make sure to update your address with the HR department to ensure accurate tax reporting.\nBenefits Enrollment\nAs a new employee, you are eligible for various benefits, including health insurance, retirement plans, and paid time off. You will receive detailed information about our benefits package during orientation. To enroll in the benefits, please follow these steps:\nReview benefits options: Carefully review the benefits package and choose the options that best meet your needs.\nComplete enrollment forms: Fill out the necessary forms to enroll in your chosen benefits. Submit these forms to the HR department within 30 days of your start date.\nDesignate beneficiaries: If applicable, designate beneficiaries for your life insurance and retirement plans.\nGetting Settled in Your Workspace\nTo help you feel comfortable and productive in your new workspace, take the following steps:\nSet up your workstation: Organize your desk, chair, and computer according to your preferences. If you require any additional equipment or accommodations, please contact the HR department.\nObtain necessary supplies: Request any necessary office supplies, such as pens, notepads, or folders, from the designated supply area or by contacting the appropriate department.\nFamiliarize yourself with office resources: Locate common areas, such as break rooms, restrooms, and meeting rooms. Familiarize yourself with office equipment, including printers, scanners, and telephones.\n", + "summary": "\nThis onboarding guide provides essential information to new employees on our company culture and values, key onboarding steps, tax elections and documents, benefits enrollment, and setting up their workspace.", + "name": "New Employee Onboarding Guide", + "url": "./github/New Employee Onboarding guide.txt", + "created_on": "2018-01-12", + "category": "github", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + } +] diff --git a/templates/rag-elasticsearch/ingest.py b/templates/rag-elasticsearch/ingest.py new file mode 100644 index 00000000000..0ba72df0afa --- /dev/null +++ b/templates/rag-elasticsearch/ingest.py @@ -0,0 +1,52 @@ +from langchain.document_loaders import JSONLoader +from langchain.embeddings import HuggingFaceEmbeddings +from langchain.vectorstores.elasticsearch import ElasticsearchStore +from langchain.text_splitter import RecursiveCharacterTextSplitter +import os + +ELASTIC_CLOUD_ID = os.getenv("ELASTIC_CLOUD_ID") +ELASTIC_USERNAME = os.getenv("ELASTIC_USERNAME", "elastic") +ELASTIC_PASSWORD = os.getenv("ELASTIC_PASSWORD") +ES_URL = os.getenv("ES_URL", "http://localhost:9200") + +if ELASTIC_CLOUD_ID and ELASTIC_USERNAME and ELASTIC_PASSWORD: + es_connection_details = { + "es_cloud_id": ELASTIC_CLOUD_ID, + "es_user": ELASTIC_USERNAME, + "es_password": ELASTIC_PASSWORD, + } +else: + es_connection_details = {"es_url": ES_URL} + + +# Metadata extraction function +def metadata_func(record: dict, metadata: dict) -> dict: + metadata["name"] = record.get("name") + metadata["summary"] = record.get("summary") + metadata["url"] = record.get("url") + metadata["category"] = record.get("category") + metadata["updated_at"] = record.get("updated_at") + + return metadata + + +## Load Data +loader = JSONLoader( + file_path="./data/documents.json", + jq_schema=".[]", + content_key="content", + metadata_func=metadata_func, +) + +text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=250) +all_splits = text_splitter.split_documents(loader.load()) + +# Add to vectorDB +vectorstore = ElasticsearchStore.from_documents( + documents=all_splits, + embedding=HuggingFaceEmbeddings( + model_name="all-MiniLM-L6-v2", model_kwargs={"device": "cpu"} + ), + **es_connection_details, + index_name="workplace-search-example", +) diff --git a/templates/rag-elasticsearch/main.py b/templates/rag-elasticsearch/main.py new file mode 100644 index 00000000000..6ca4a83b79a --- /dev/null +++ b/templates/rag-elasticsearch/main.py @@ -0,0 +1,31 @@ +from rag_elasticsearch import chain + +if __name__ == "__main__": + questions = [ + "What is the nasa sales team?", + "What is our work from home policy?", + "Does the company own my personal project?", + "How does compensation work?", + ] + + response = chain.invoke( + { + "question": questions[0], + "chat_history": [], + } + ) + print(response) + + follow_up_question = "What are their objectives?" + + response = chain.invoke( + { + "question": follow_up_question, + "chat_history": [ + "What is the nasa sales team?", + "The sales team of NASA consists of Laura Martinez, the Area Vice-President of North America, and Gary Johnson, the Area Vice-President of South America. 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"poetry.core.masonry.api" diff --git a/templates/rag-elasticsearch/rag_elasticsearch/__init__.py b/templates/rag-elasticsearch/rag_elasticsearch/__init__.py new file mode 100644 index 00000000000..a30a18d8eb1 --- /dev/null +++ b/templates/rag-elasticsearch/rag_elasticsearch/__init__.py @@ -0,0 +1,3 @@ +from rag_elasticsearch.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-elasticsearch/rag_elasticsearch/chain.py b/templates/rag-elasticsearch/rag_elasticsearch/chain.py new file mode 100644 index 00000000000..220a546b5af --- /dev/null +++ b/templates/rag-elasticsearch/rag_elasticsearch/chain.py @@ -0,0 +1,56 @@ +from langchain.chat_models import ChatOpenAI +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough, RunnableMap +from langchain.embeddings import HuggingFaceEmbeddings +from langchain.vectorstores.elasticsearch import ElasticsearchStore +from langchain.schema import format_document +from typing import Tuple, List +from operator import itemgetter +from .prompts import CONDENSE_QUESTION_PROMPT, LLM_CONTEXT_PROMPT, DOCUMENT_PROMPT +from .connection import es_connection_details + +# Setup connecting to Elasticsearch +vectorstore = ElasticsearchStore( + **es_connection_details, + embedding=HuggingFaceEmbeddings( + model_name="all-MiniLM-L6-v2", model_kwargs={"device": "cpu"} + ), + index_name="workplace-search-example", +) +retriever = vectorstore.as_retriever() + +# Set up LLM to user +llm = ChatOpenAI(temperature=0) + + +def _combine_documents( + docs, document_prompt=DOCUMENT_PROMPT, document_separator="\n\n" +): + doc_strings = [format_document(doc, document_prompt) for doc in docs] + return document_separator.join(doc_strings) + + +def _format_chat_history(chat_history: List[Tuple]) -> str: + buffer = "" + for dialogue_turn in chat_history: + human = "Human: " + dialogue_turn[0] + ai = "Assistant: " + dialogue_turn[1] + buffer += "\n" + "\n".join([human, ai]) + return buffer + + +_inputs = RunnableMap( + standalone_question=RunnablePassthrough.assign( + chat_history=lambda x: _format_chat_history(x["chat_history"]) + ) + | CONDENSE_QUESTION_PROMPT + | llm + | StrOutputParser(), +) + +_context = { + "context": itemgetter("standalone_question") | retriever | _combine_documents, + "question": lambda x: x["standalone_question"], +} + +chain = _inputs | _context | LLM_CONTEXT_PROMPT | llm | StrOutputParser() diff --git a/templates/rag-elasticsearch/rag_elasticsearch/connection.py b/templates/rag-elasticsearch/rag_elasticsearch/connection.py new file mode 100644 index 00000000000..2e2ebd18148 --- /dev/null +++ b/templates/rag-elasticsearch/rag_elasticsearch/connection.py @@ -0,0 +1,15 @@ +import os + +ELASTIC_CLOUD_ID = os.getenv("ELASTIC_CLOUD_ID") +ELASTIC_USERNAME = os.getenv("ELASTIC_USERNAME", "elastic") +ELASTIC_PASSWORD = os.getenv("ELASTIC_PASSWORD") +ES_URL = os.getenv("ES_URL", "http://localhost:9200") + +if ELASTIC_CLOUD_ID and ELASTIC_USERNAME and ELASTIC_PASSWORD: + es_connection_details = { + "es_cloud_id": ELASTIC_CLOUD_ID, + "es_user": ELASTIC_USERNAME, + "es_password": ELASTIC_PASSWORD, + } +else: + es_connection_details = {"es_url": ES_URL} diff --git a/templates/rag-elasticsearch/rag_elasticsearch/prompts.py b/templates/rag-elasticsearch/rag_elasticsearch/prompts.py new file mode 100644 index 00000000000..431692fb4f8 --- /dev/null +++ b/templates/rag-elasticsearch/rag_elasticsearch/prompts.py @@ -0,0 +1,39 @@ +from langchain.prompts import ChatPromptTemplate, PromptTemplate + +# Used to condense a question and chat history into a single question +condense_question_prompt_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. If there is no chat history, just rephrase the question to be a standalone question. + +Chat History: +{chat_history} +Follow Up Input: {question} +""" +CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template( + condense_question_prompt_template +) + +# RAG Prompt to provide the context and question for LLM to answer +# We also ask the LLM to cite the source of the passage it is answering from +llm_context_prompt_template = """ +Use the following passages to answer the user's question. +Each passage has a SOURCE which is the title of the document. When answering, cite source name of the passages you are answering from below the answer in a unique bullet point list. + +If you don't know the answer, just say that you don't know, don't try to make up an answer. + +---- +{context} +---- +Question: {question} +""" + +LLM_CONTEXT_PROMPT = ChatPromptTemplate.from_template(llm_context_prompt_template) + +# Used to build a context window from passages retrieved +document_prompt_template = """ +--- +NAME: {name} +PASSAGE: +{page_content} +--- +""" + +DOCUMENT_PROMPT = PromptTemplate.from_template(document_prompt_template) diff --git a/templates/rag-fusion/README.md b/templates/rag-fusion/README.md new file mode 100644 index 00000000000..97c4de22910 --- /dev/null +++ b/templates/rag-fusion/README.md @@ -0,0 +1,5 @@ +# RAG Fusion + +Re-implemented from [this GitHub repo](https://github.com/Raudaschl/rag-fusion), all credit to original author + +> RAG-Fusion, a search methodology that aims to bridge the gap between traditional search paradigms and the multifaceted dimensions of human queries. Inspired by the capabilities of Retrieval Augmented Generation (RAG), this project goes a step further by employing multiple query generation and Reciprocal Rank Fusion to re-rank search results. diff --git a/templates/rag-fusion/ingest.py b/templates/rag-fusion/ingest.py new file mode 100644 index 00000000000..a21fff11572 --- /dev/null +++ b/templates/rag-fusion/ingest.py @@ -0,0 +1,20 @@ +import pinecone +from langchain.vectorstores import Pinecone +from langchain.embeddings import OpenAIEmbeddings + +pinecone.init(api_key="...",environment="...") + +all_documents = { + "doc1": "Climate change and economic impact.", + "doc2": "Public health concerns due to climate change.", + "doc3": "Climate change: A social perspective.", + "doc4": "Technological solutions to climate change.", + "doc5": "Policy changes needed to combat climate change.", + "doc6": "Climate change and its impact on biodiversity.", + "doc7": "Climate change: The science and models.", + "doc8": "Global warming: A subset of climate change.", + "doc9": "How climate change affects daily weather.", + "doc10": "The history of climate change activism." +} + +Pinecone.from_texts(list(all_documents.values()), OpenAIEmbeddings(), index_name='rag-fusion') diff --git a/templates/rag-fusion/main.py b/templates/rag-fusion/main.py new file mode 100644 index 00000000000..a283e8b2c1c --- /dev/null +++ b/templates/rag-fusion/main.py @@ -0,0 +1,6 @@ +from rag_fusion.chain import chain + + +if __name__ == "__main__": + original_query = "impact of climate change" + print(chain.invoke(original_query)) diff --git a/templates/rag-fusion/poetry.lock b/templates/rag-fusion/poetry.lock new file mode 100644 index 00000000000..fd12ee18c3a --- /dev/null +++ b/templates/rag-fusion/poetry.lock @@ -0,0 +1,1403 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +category = 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"^0.28.1" +pinecone-client = "^2.2.4" +langchainhub = "^0.1.13" + +[tool.langserve] +export_module = "rag_fusion.chain" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-fusion/rag_fusion/__init__.py b/templates/rag-fusion/rag_fusion/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-fusion/rag_fusion/chain.py b/templates/rag-fusion/rag_fusion/chain.py new file mode 100644 index 00000000000..e6cedfaa905 --- /dev/null +++ b/templates/rag-fusion/rag_fusion/chain.py @@ -0,0 +1,33 @@ +from langchain.chat_models import ChatOpenAI +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain import hub +import pinecone +from langchain.vectorstores import Pinecone +from langchain.embeddings import OpenAIEmbeddings +from langchain.load import dumps, loads + +def reciprocal_rank_fusion(results: list[list], k=60): + fused_scores = {} + for docs in results: + # Assumes the docs are returned in sorted order of relevance + for rank, doc in enumerate(docs): + doc_str = dumps(doc) + if doc_str not in fused_scores: + fused_scores[doc_str] = 0 + previous_score = fused_scores[doc_str] + fused_scores[doc_str] += 1 / (rank + k) + + reranked_results = [(loads(doc), score) for doc, score in sorted(fused_scores.items(), key=lambda x: x[1], reverse=True)] + return reranked_results + +pinecone.init(api_key="...", environment="...") + +prompt = hub.pull('langchain-ai/rag-fusion-query-generation') + +generate_queries = prompt | ChatOpenAI(temperature=0) | StrOutputParser() | (lambda x: x.split("\n")) + +vectorstore = Pinecone.from_existing_index("rag-fusion", OpenAIEmbeddings()) +retriever = vectorstore.as_retriever() + +chain = {"original_query": lambda x: x} | generate_queries | retriever.map() | reciprocal_rank_fusion diff --git a/templates/rag-fusion/tests/__init__.py b/templates/rag-fusion/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-pinecone/LICENSE b/templates/rag-pinecone/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/rag-pinecone/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/rag-pinecone/README.md b/templates/rag-pinecone/README.md new file mode 100644 index 00000000000..b45f023aa8e --- /dev/null +++ b/templates/rag-pinecone/README.md @@ -0,0 +1,37 @@ +# RAG Pinecone + +This template performs RAG using Pinecone and OpenAI. + +## Pinecone + +This connects to a hosted Pinecone vectorstore. + +Be sure that you have set a few env variables in `chain.py`: + +* `PINECONE_API_KEY` +* `PINECONE_ENV` +* `index_name` + +## LLM + +Be sure that `OPENAI_API_KEY` is set in order to the OpenAI models. + +## Installation + +Create your LangServe app: +``` +langchain serve new my-app +cd my-app +``` + +Add template: +``` +langchain serve add rag-pinecone +``` + +Start server: +``` +langchain start +``` + +See Jupyter notebook `rag_pinecone` for various way to connect to the template. diff --git a/templates/rag-pinecone/pinecone_rag.ipynb b/templates/rag-pinecone/pinecone_rag.ipynb new file mode 100644 index 00000000000..1a48d126d80 --- /dev/null +++ b/templates/rag-pinecone/pinecone_rag.ipynb @@ -0,0 +1,84 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "681a5d1e", + "metadata": {}, + "source": [ + "## Connect to template\n", + "\n", + "`Context`\n", + " \n", + "* LangServe apps gives you access to templates.\n", + "* Templates LLM pipeline (runnables or chains) end-points accessible via FastAPI.\n", + "* The environment for these templates is managed by Poetry.\n", + "\n", + "`Create app`\n", + "\n", + "* Install LangServe and create an app.\n", + "* This will create a new Poetry environment /\n", + "```\n", + "pip install < to add > \n", + "langchain serve new my-app\n", + "cd my-app\n", + "```\n", + "\n", + "`Add templates`\n", + "\n", + "* When we add a template, we update the Poetry config file with the necessary dependencies.\n", + "* It also automatically installed these template dependencies in your Poetry environment\n", + "```\n", + "langchain serve add rag-pinecone\n", + "```\n", + "\n", + "`Start FastAPI server`\n", + "\n", + "```\n", + "langchain start\n", + "```\n", + "\n", + "Note, we can now look at the endpoints:\n", + "\n", + "http://127.0.0.1:8000/docs#\n", + "\n", + "And look specifically at our loaded template:\n", + "\n", + "http://127.0.0.1:8000/docs#/default/invoke_rag_pinecone_invoke_post\n", + " \n", + "We can also use remote runnable to call it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d774be2a", + "metadata": {}, + "outputs": [], + "source": [ + "from langserve.client import RemoteRunnable\n", + "rag_app_pinecone = RemoteRunnable('http://localhost:8000/rag-pinecone')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", 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<0.1" +openai = ">=0.28.1" +tiktoken = ">=0.5.1" +pinecone-client = ">=2.2.4" + +[tool.langserve] +export_module = "rag_pinecone" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-pinecone/rag_pinecone/__init__.py b/templates/rag-pinecone/rag_pinecone/__init__.py new file mode 100644 index 00000000000..17d7773f46e --- /dev/null +++ b/templates/rag-pinecone/rag_pinecone/__init__.py @@ -0,0 +1,3 @@ +from rag_pinecone.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-pinecone/rag_pinecone/chain.py b/templates/rag-pinecone/rag_pinecone/chain.py new file mode 100644 index 00000000000..cf572251b60 --- /dev/null +++ b/templates/rag-pinecone/rag_pinecone/chain.py @@ -0,0 +1,46 @@ +import os +import pinecone +from operator import itemgetter +from langchain.vectorstores import Pinecone +from langchain.prompts import ChatPromptTemplate +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough, RunnableParallel + +# Pinecone init +# Find API key in console at app.pinecone.io +YOUR_API_KEY = os.getenv('PINECONE_API_KEY') or 'PINECONE_API_KEY' +# Find ENV (cloud region) next to API key in console +YOUR_ENV = os.getenv('PINECONE_ENVIRONMENT') or 'PINECONE_ENV' +# Init +pinecone.init( + api_key=YOUR_API_KEY, + environment=YOUR_ENV +) + +# Get vectorstore +text_field = "text" +index_name = "langchain-multi-query-demo" +index = pinecone.Index(index_name) +vectorstore = Pinecone(index, + OpenAIEmbeddings(), + text_field) + +retriever = vectorstore.as_retriever() + +# RAG prompt +template = """Answer the question based only on the following context: +{context} +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +# RAG +model = ChatOpenAI() +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +) diff --git a/templates/rag-pinecone/tests/__init__.py b/templates/rag-pinecone/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-semi-structured/LICENSE b/templates/rag-semi-structured/LICENSE new file mode 100644 index 00000000000..578fc414ad4 --- /dev/null +++ b/templates/rag-semi-structured/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 pingpong-templates + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/rag-semi-structured/README.md b/templates/rag-semi-structured/README.md new file mode 100644 index 00000000000..9aa3acd5f4b --- /dev/null +++ b/templates/rag-semi-structured/README.md @@ -0,0 +1,47 @@ +# Semi structured RAG + +This template performs RAG on semi-structured data (e.g., a PDF with text and tables). + +See this [blog post](https://langchain-blog.ghost.io/ghost/#/editor/post/652dc74e0633850001e977d4) for useful background context. + +## Data loading + +We use [partition_pdf](https://unstructured-io.github.io/unstructured/bricks/partition.html#partition-pdf) from Unstructured to extract both table and text elements. + +This will require some system-level package installations, e.g., on Mac: + +``` +brew install tesseract poppler +``` + +## Chroma + +[Chroma](https://python.langchain.com/docs/integrations/vectorstores/chroma) is an open-source vector database. + +This template will create and add documents to the vector database in `chain.py`. + +These documents can be loaded from [many sources](https://python.langchain.com/docs/integrations/document_loaders). + +## LLM + +Be sure that `OPENAI_API_KEY` is set in order to the OpenAI models. + +## Adding the template + +Create your LangServe app: +``` +langchain serve new my-app +cd my-app +``` + +Add template: +``` +langchain serve add rag-semi-structured +``` + +Start server: +``` +langchain start +``` + +See Jupyter notebook `rag_semi_structured` for various way to connect to the template. \ No newline at end of file diff --git a/templates/rag-semi-structured/docs/LLaVA.pdf b/templates/rag-semi-structured/docs/LLaVA.pdf new file mode 100644 index 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+langchain = ">=0.0.313, <0.1" +tiktoken = ">=0.5.1" +chromadb = ">=0.4.14" +openai = ">=0.27.9" +unstructured = ">=0.10.19" +pdf2image = ">=1.16.3" + +[tool.langserve] +export_module = "rag_semi_structured" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-semi-structured/rag_semi_structured.ipynb b/templates/rag-semi-structured/rag_semi_structured.ipynb new file mode 100644 index 00000000000..3d66ab75f81 --- /dev/null +++ b/templates/rag-semi-structured/rag_semi_structured.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "30fc2c27", + "metadata": {}, + "source": [ + "## Run Template\n", + "\n", + "As shown in the README, add template and start server:\n", + "```\n", + "langchain serve add rag-semi-structured\n", + "langchain start\n", + "```\n", + "\n", + "We can now look at the endpoints:\n", + "\n", + "http://127.0.0.1:8000/docs#\n", + "\n", + "And specifically at our loaded template:\n", + "\n", + "http://127.0.0.1:8000/docs#/default/invoke_rag_chroma_private_invoke_post\n", + " \n", + "We can also use remote runnable to call it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65f5b560", + "metadata": {}, + "outputs": [], + "source": [ + "from langserve.client import RemoteRunnable\n", + "rag_app = RemoteRunnable('http://localhost:8000/rag-chroma-private')\n", + "rag_app.invoke(\"How does agent memory work?\")" + ] + } + ], + "metadata": { + "kernelspec": { + "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", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/templates/rag-semi-structured/rag_semi_structured/__init__.py b/templates/rag-semi-structured/rag_semi_structured/__init__.py new file mode 100644 index 00000000000..55f823dde08 --- /dev/null +++ b/templates/rag-semi-structured/rag_semi_structured/__init__.py @@ -0,0 +1,3 @@ +from rag_semi_structured.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-semi-structured/rag_semi_structured/chain.py b/templates/rag-semi-structured/rag_semi_structured/chain.py new file mode 100644 index 00000000000..6622e83234e --- /dev/null +++ b/templates/rag-semi-structured/rag_semi_structured/chain.py @@ -0,0 +1,105 @@ +# Load +import uuid +from langchain.chat_models import ChatOpenAI +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.vectorstores import Chroma +from langchain.storage import InMemoryStore +from unstructured.partition.pdf import partition_pdf +from langchain.schema.document import Document +from langchain.embeddings import OpenAIEmbeddings +from langchain.retrievers.multi_vector import MultiVectorRetriever +from langchain.schema.runnable import RunnablePassthrough + +# Path to docs +path = "docs" +raw_pdf_elements = partition_pdf(filename=path+"LLaMA2.pdf", + # Unstructured first finds embedded image blocks + extract_images_in_pdf=False, + # Use layout model (YOLOX) to get bounding boxes (for tables) and find titles + # Titles are any sub-section of the document + infer_table_structure=True, + # Post processing to aggregate text once we have the title + chunking_strategy="by_title", + # Chunking params to aggregate text blocks + # Attempt to create a new chunk 3800 chars + # Attempt to keep chunks > 2000 chars + max_characters=4000, + new_after_n_chars=3800, + combine_text_under_n_chars=2000, + image_output_dir_path=path) + +# Categorize by type +tables = [] +texts = [] +for element in raw_pdf_elements: + if "unstructured.documents.elements.Table" in str(type(element)): + tables.append(str(element)) + elif "unstructured.documents.elements.CompositeElement" in str(type(element)): + texts.append(str(element)) + +# Summarize + +prompt_text="""You are an assistant tasked with summarizing tables and text. \ +Give a concise summary of the table or text. Table or text chunk: {element} """ +prompt = ChatPromptTemplate.from_template(prompt_text) +model = ChatOpenAI(temperature=0,model="gpt-4") +summarize_chain = {"element": lambda x:x} | prompt | model | StrOutputParser() + +# Apply +table_summaries = summarize_chain.batch(tables, {"max_concurrency": 5}) +# To save time / cost, only do text summaries if chunk sizes are large +# text_summaries = summarize_chain.batch(texts, {"max_concurrency": 5}) +# We can just assign text_summaries to the raw texts +text_summaries = texts + +# Use multi vector retriever + +# The vectorstore to use to index the child chunks +vectorstore = Chroma( + collection_name="summaries", + embedding_function=OpenAIEmbeddings() +) + +# The storage layer for the parent documents +store = InMemoryStore() +id_key = "doc_id" + +# The retriever (empty to start) +retriever = MultiVectorRetriever( + vectorstore=vectorstore, + docstore=store, + id_key=id_key, +) + +# Add texts +doc_ids = [str(uuid.uuid4()) for _ in texts] +summary_texts = [Document(page_content=s,metadata={id_key: doc_ids[i]}) for i, s in enumerate(text_summaries)] +retriever.vectorstore.add_documents(summary_texts) +retriever.docstore.mset(list(zip(doc_ids, texts))) + +# Add tables +table_ids = [str(uuid.uuid4()) for _ in tables] +summary_tables = [Document(page_content=s,metadata={id_key: table_ids[i]}) for i, s in enumerate(table_summaries)] +retriever.vectorstore.add_documents(summary_tables) +retriever.docstore.mset(list(zip(table_ids, tables))) + +# RAG + +# Prompt template +template = """Answer the question based only on the following context, which can include text and tables: +{context} +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +# LLM +model = ChatOpenAI(temperature=0,model="gpt-4") + +# RAG pipeline +chain = ( + {"context": retriever, "question": RunnablePassthrough()} + | prompt + | model + | StrOutputParser() +) \ No newline at end of file diff --git a/templates/rag-semi-structured/tests/__init__.py b/templates/rag-semi-structured/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-supabase/.gitignore b/templates/rag-supabase/.gitignore new file mode 100644 index 00000000000..4c49bd78f1d --- /dev/null +++ b/templates/rag-supabase/.gitignore @@ -0,0 +1 @@ +.env diff --git a/templates/rag-supabase/README.md b/templates/rag-supabase/README.md new file mode 100644 index 00000000000..7279f8284c4 --- /dev/null +++ b/templates/rag-supabase/README.md @@ -0,0 +1,103 @@ +# RAG with Supabase + +> [Supabase](https://supabase.com/docs) is an open-source Firebase alternative. It is built on top of [PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL), a free and open-source relational database management system (RDBMS) and uses [pgvector](https://github.com/pgvector/pgvector) to store embeddings within your tables. + +Use this package to host a retrieval augment generation (RAG) API using LangServe + Supabase. + +## Install Package + +From within your `langservehub` project run: + +```shell +poetry run poe add rag-supabase +``` + +## Setup Supabase Database + +Use these steps to setup your Supabase database if you haven't already. + +1. Head over to https://database.new to provision your Supabase database. +2. In the studio, jump to the [SQL editor](https://supabase.com/dashboard/project/_/sql/new) and run the following script to enable `pgvector` and setup your database as a vector store: + + ```sql + -- Enable the pgvector extension to work with embedding vectors + create extension if not exists vector; + + -- Create a table to store your documents + create table + documents ( + id uuid primary key, + content text, -- corresponds to Document.pageContent + metadata jsonb, -- corresponds to Document.metadata + embedding vector (1536) -- 1536 works for OpenAI embeddings, change as needed + ); + + -- Create a function to search for documents + create function match_documents ( + query_embedding vector (1536), + filter jsonb default '{}' + ) returns table ( + id uuid, + content text, + metadata jsonb, + similarity float + ) language plpgsql as $$ + #variable_conflict use_column + begin + return query + select + id, + content, + metadata, + 1 - (documents.embedding <=> query_embedding) as similarity + from documents + where metadata @> filter + order by documents.embedding <=> query_embedding; + end; + $$; + ``` + +## Setup Environment Variables + +Since we are using [`SupabaseVectorStore`](https://python.langchain.com/docs/integrations/vectorstores/supabase) and [`OpenAIEmbeddings`](https://python.langchain.com/docs/integrations/text_embedding/openai), we need to load their API keys. + +Create a `.env` file in the root of your project: + +_.env_ + +```shell +SUPABASE_URL= +SUPABASE_SERVICE_KEY= +OPENAI_API_KEY= +``` + +To find your `SUPABASE_URL` and `SUPABASE_SERVICE_KEY`, head to your Supabase project's [API settings](https://supabase.com/dashboard/project/_/settings/api). + +- `SUPABASE_URL` corresponds to the Project URL +- `SUPABASE_SERVICE_KEY` corresponds to the `service_role` API key + +To get your `OPENAI_API_KEY`, navigate to [API keys](https://platform.openai.com/account/api-keys) on your OpenAI account and create a new secret key. + +Add this file to your `.gitignore` if it isn't already there (so that we don't commit secrets): + +_.gitignore_ + +``` +.env +``` + +Install [`python-dotenv`](https://github.com/theskumar/python-dotenv) which we will use to load the environment variables into the app: + +```shell +poetry add python-dotenv +``` + +Finally, call `load_dotenv()` in `server.py`. + +_app/server.py_ + +```python +from dotenv import load_dotenv + +load_dotenv() +``` diff --git a/templates/rag-supabase/poetry.lock b/templates/rag-supabase/poetry.lock new file mode 100644 index 00000000000..935940528c1 --- /dev/null +++ b/templates/rag-supabase/poetry.lock @@ -0,0 +1,1698 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = 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">=0.0.313" +openai = "^0.28.1" +tiktoken = "^0.5.1" +supabase = "^1.2.0" + +[tool.poetry.group.dev.dependencies] +python-dotenv = { extras = ["cli"], version = "^1.0.0" } + +[tool.langserve] +export_module = "rag_supabase.chain" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-supabase/rag_supabase/__init__.py b/templates/rag-supabase/rag_supabase/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-supabase/rag_supabase/chain.py b/templates/rag-supabase/rag_supabase/chain.py new file mode 100644 index 00000000000..ef35319dcdb --- /dev/null +++ b/templates/rag-supabase/rag_supabase/chain.py @@ -0,0 +1,42 @@ +import os +from langchain.prompts import ChatPromptTemplate +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough, RunnableParallel + +from supabase.client import create_client +from langchain.embeddings.openai import OpenAIEmbeddings +from langchain.vectorstores.supabase import SupabaseVectorStore + +supabase_url = os.environ.get("SUPABASE_URL") +supabase_key = os.environ.get("SUPABASE_SERVICE_KEY") +supabase = create_client(supabase_url, supabase_key) + +embeddings = OpenAIEmbeddings() + +vectorstore = SupabaseVectorStore( + client=supabase, + embedding=embeddings, + table_name="documents", + query_name="match_documents" +) + +retriever = vectorstore.as_retriever() + +template = """Answer the question based only on the following context: +{context} + +Question: {question} +""" + +prompt = ChatPromptTemplate.from_template(template) + +model = ChatOpenAI() + +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +) diff --git a/templates/rag-supabase/tests/__init__.py b/templates/rag-supabase/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rewrite-retrieve-read/README.md b/templates/rewrite-retrieve-read/README.md new file mode 100644 index 00000000000..16f65645513 --- /dev/null +++ b/templates/rewrite-retrieve-read/README.md @@ -0,0 +1,7 @@ +# Rewrite-Retrieve-Read + +**Rewrite-Retrieve-Read** is a method proposed in the paper [Query Rewriting for Retrieval-Augmented Large Language Models](https://arxiv.org/pdf/2305.14283.pdf) + +> Because the original query can not be always optimal to retrieve for the LLM, especially in the real world... we first prompt an LLM to rewrite the queries, then conduct retrieval-augmented reading + +We show how you can easily do that with LangChain Expression Language diff --git a/templates/rewrite-retrieve-read/main.py b/templates/rewrite-retrieve-read/main.py new file mode 100644 index 00000000000..3b0ce289251 --- /dev/null +++ b/templates/rewrite-retrieve-read/main.py @@ 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+langchain = ">=0.0.313" +duckduckgo-search = "^3.9.3" + +[tool.langserve] +export_module = "rewrite_retrieve_read.chain" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rewrite-retrieve-read/rewrite_retrieve_read/__init__.py b/templates/rewrite-retrieve-read/rewrite_retrieve_read/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rewrite-retrieve-read/rewrite_retrieve_read/chain.py b/templates/rewrite-retrieve-read/rewrite_retrieve_read/chain.py new file mode 100644 index 00000000000..7094f9132c3 --- /dev/null +++ b/templates/rewrite-retrieve-read/rewrite_retrieve_read/chain.py @@ -0,0 +1,43 @@ +from operator import itemgetter + +from langchain.prompts import ChatPromptTemplate +from langchain.chat_models import ChatOpenAI +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough, RunnableLambda +from langchain.utilities import DuckDuckGoSearchAPIWrapper + +template = """Answer the users question based only on the following context: + + +{context} + + +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +model = ChatOpenAI(temperature=0) + +search = DuckDuckGoSearchAPIWrapper() + + +def retriever(query): + return search.run(query) + +template = """Provide a better search query for \ +web search engine to answer the given question, end \ +the queries with ’**’. Question: \ +{x} Answer:""" +rewrite_prompt = ChatPromptTemplate.from_template(template) + +# Parser to remove the `**` + +def _parse(text): + return text.strip("**") + +rewriter = rewrite_prompt | ChatOpenAI(temperature=0) | StrOutputParser() | _parse + +chain = { + "context": {"x": RunnablePassthrough()} | rewriter | retriever, + "question": RunnablePassthrough() + } | prompt | model | StrOutputParser() diff --git a/templates/rewrite-retrieve-read/tests/__init__.py b/templates/rewrite-retrieve-read/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/self-query-supabase/.gitignore b/templates/self-query-supabase/.gitignore new file mode 100644 index 00000000000..4c49bd78f1d --- /dev/null +++ b/templates/self-query-supabase/.gitignore @@ -0,0 +1 @@ +.env diff --git a/templates/self-query-supabase/README.md b/templates/self-query-supabase/README.md new file mode 100644 index 00000000000..d7382001d8f --- /dev/null +++ b/templates/self-query-supabase/README.md @@ -0,0 +1,103 @@ +# Self-querying with Supabase + +> [Supabase](https://supabase.com/docs) is an open-source Firebase alternative. It is built on top of [PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL), a free and open-source relational database management system (RDBMS) and uses [pgvector](https://github.com/pgvector/pgvector) to store embeddings within your tables. + +Use this package to host a LangServe API that can [self-query](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query) Supabase. You'll be able to use natural language to generate a structured query against the database. + +## Install Package + +From within your `langservehub` project run: + +```shell +poetry run poe add self-query-supabase +``` + +## Setup Supabase Database + +Use these steps to setup your Supabase database if you haven't already. + +1. Head over to https://database.new to provision your Supabase database. +2. In the studio, jump to the [SQL editor](https://supabase.com/dashboard/project/_/sql/new) and run the following script to enable `pgvector` and setup your database as a vector store: + + ```sql + -- Enable the pgvector extension to work with embedding vectors + create extension if not exists vector; + + -- Create a table to store your documents + create table + documents ( + id uuid primary key, + content text, -- corresponds to Document.pageContent + metadata jsonb, -- corresponds to Document.metadata + embedding vector (1536) -- 1536 works for OpenAI embeddings, change as needed + ); + + -- Create a function to search for documents + create function match_documents ( + query_embedding vector (1536), + filter jsonb default '{}' + ) returns table ( + id uuid, + content text, + metadata jsonb, + similarity float + ) language plpgsql as $$ + #variable_conflict use_column + begin + return query + select + id, + content, + metadata, + 1 - (documents.embedding <=> query_embedding) as similarity + from documents + where metadata @> filter + order by documents.embedding <=> query_embedding; + end; + $$; + ``` + +## Setup Environment Variables + +Since we are using [`SupabaseVectorStore`](https://python.langchain.com/docs/integrations/vectorstores/supabase) and [`OpenAIEmbeddings`](https://python.langchain.com/docs/integrations/text_embedding/openai), we need to load their API keys. + +Create a `.env` file in the root of your project: + +_.env_ + +```shell +SUPABASE_URL= +SUPABASE_SERVICE_KEY= +OPENAI_API_KEY= +``` + +To find your `SUPABASE_URL` and `SUPABASE_SERVICE_KEY`, head to your Supabase project's [API settings](https://supabase.com/dashboard/project/_/settings/api). + +- `SUPABASE_URL` corresponds to the Project URL +- `SUPABASE_SERVICE_KEY` corresponds to the `service_role` API key + +To get your `OPENAI_API_KEY`, navigate to [API keys](https://platform.openai.com/account/api-keys) on your OpenAI account and create a new secret key. + +Add this file to your `.gitignore` if it isn't already there (so that we don't commit secrets): + +_.gitignore_ + +``` +.env +``` + +Install [`python-dotenv`](https://github.com/theskumar/python-dotenv) which we will use to load the environment variables into the app: + +```shell +poetry add python-dotenv +``` + +Finally, call `load_dotenv()` in `server.py`. + +_app/server.py_ + +```python +from dotenv import load_dotenv + +load_dotenv() +``` diff --git a/templates/self-query-supabase/poetry.lock b/templates/self-query-supabase/poetry.lock new file mode 100644 index 00000000000..802f41eb1fb --- /dev/null +++ b/templates/self-query-supabase/poetry.lock @@ -0,0 +1,1710 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = 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">=3.8.1,<4.0" +langchain = ">=0.0.313" +openai = "^0.28.1" +tiktoken = "^0.5.1" +supabase = "^1.2.0" +lark = "^1.1.8" + +[tool.poetry.group.dev.dependencies] +python-dotenv = { extras = ["cli"], version = "^1.0.0" } + +[tool.langserve] +export_module = "self_query_supabase.chain" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/self-query-supabase/self_query_supabase/__init__.py b/templates/self-query-supabase/self_query_supabase/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/self-query-supabase/self_query_supabase/chain.py b/templates/self-query-supabase/self_query_supabase/chain.py new file mode 100644 index 00000000000..c32f5154a6a --- /dev/null +++ b/templates/self-query-supabase/self_query_supabase/chain.py @@ -0,0 +1,64 @@ +import os +from langchain.llms.openai import OpenAI +from langchain.retrievers.self_query.base import SelfQueryRetriever +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough, RunnableParallel +from langchain.chains.query_constructor.base import AttributeInfo + +from supabase.client import create_client +from langchain.embeddings.openai import OpenAIEmbeddings +from langchain.vectorstores.supabase import SupabaseVectorStore + +supabase_url = os.environ.get("SUPABASE_URL") +supabase_key = os.environ.get("SUPABASE_SERVICE_KEY") +supabase = create_client(supabase_url, supabase_key) + +embeddings = OpenAIEmbeddings() + +vectorstore = SupabaseVectorStore( + client=supabase, + embedding=embeddings, + table_name="documents", + query_name="match_documents" +) + +# Adjust this based on the metadata you store in the `metadata` JSON column +metadata_field_info = [ + AttributeInfo( + name="genre", + description="The genre of the movie", + type="string or list[string]", + ), + AttributeInfo( + name="year", + description="The year the movie was released", + type="integer", + ), + AttributeInfo( + name="director", + description="The name of the movie director", + type="string", + ), + AttributeInfo( + name="rating", description="A 1-10 rating for the movie", type="float" + ), +] + +# Adjust this based on the type of documents you store +document_content_description = "Brief summary of a movie" +llm = OpenAI(temperature=0) + +retriever = SelfQueryRetriever.from_llm( + llm, + vectorstore, + document_content_description, + metadata_field_info, + verbose=True +) + +chain = ( + RunnableParallel({"query": RunnablePassthrough()}) + | retriever +) diff --git a/templates/self-query-supabase/tests/__init__.py b/templates/self-query-supabase/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/sql-llama2/README.md b/templates/sql-llama2/README.md new file mode 100644 index 00000000000..c3574f066a4 --- /dev/null +++ b/templates/sql-llama2/README.md @@ -0,0 +1,15 @@ +# SQL with LLaMA2 + +This template allows you to chat with a SQL database in natural language using LLaMA2. + +It is configured to use [Replicate](https://python.langchain.com/docs/integrations/llms/replicate). + +But, it can be adapted to any API that support LLaMA2, including [Fireworks](https://python.langchain.com/docs/integrations/chat/fireworks) and others. + +See related templates `sql-ollama` and `sql-llamacpp` for private, local chat with SQL. + +## Installation +```bash +# from inside your LangServe instance +poe add sql-llama2 +``` diff --git a/templates/sql-llama2/nba_roster.db b/templates/sql-llama2/nba_roster.db new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/sql-llama2/poetry.lock b/templates/sql-llama2/poetry.lock new file mode 100644 index 00000000000..01f5778dc5b --- /dev/null +++ b/templates/sql-llama2/poetry.lock @@ -0,0 +1,1204 @@ +# This file is automatically @generated by 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b/templates/sql-llama2/sql_llama2/__init__.py new file mode 100644 index 00000000000..7e2706adcab --- /dev/null +++ b/templates/sql-llama2/sql_llama2/__init__.py @@ -0,0 +1,3 @@ +from llama2.chain import chain + +__all__ = ["chain"] \ No newline at end of file diff --git a/templates/sql-llama2/sql_llama2/chain.py b/templates/sql-llama2/sql_llama2/chain.py new file mode 100644 index 00000000000..235301252ba --- /dev/null +++ b/templates/sql-llama2/sql_llama2/chain.py @@ -0,0 +1,73 @@ +from langchain.llms import Replicate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough +from langchain.prompts import ChatPromptTemplate + +# make sure to set REPLICATE_API_TOKEN in your environment +# use llama-2-13b model in replicate +replicate_id = "meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d" +llm = Replicate( + model=replicate_id, + model_kwargs={"temperature": 0.01, "max_length": 500, "top_p": 1}, +) + +from pathlib import Path +from langchain.utilities import SQLDatabase +db_path = Path(__file__).parent / "nba_roster.db" +rel = db_path.relative_to(Path.cwd()) +db_string = f"sqlite:///{rel}" +db = SQLDatabase.from_uri(db_string, sample_rows_in_table_info=0) + +def get_schema(_): + return db.get_table_info() + + +def run_query(query): + return db.run(query) + + +template_query = """Based on the table schema below, write a SQL query that would answer the user's question: +{schema} + +Question: {question} +SQL Query:""" +prompt = ChatPromptTemplate.from_messages( + [ + ("system", "Given an input question, convert it to a SQL query. No pre-amble."), + ("human", template_query), + ] +) + +sql_response = ( + RunnablePassthrough.assign(schema=get_schema) + | prompt + | llm.bind(stop=["\nSQLResult:"]) + | StrOutputParser() +) + +template_response = """Based on the table schema below, question, sql query, and sql response, write a natural language response: +{schema} + +Question: {question} +SQL Query: {query} +SQL Response: {response}""" + +prompt_response = ChatPromptTemplate.from_messages( + [ + ( + "system", + "Given an input question and SQL response, convert it to a natural language answer. No pre-amble.", + ), + ("human", template_response), + ] +) + +chain = ( + RunnablePassthrough.assign(query=sql_response) + | RunnablePassthrough.assign( + schema=get_schema, + response=lambda x: db.run(x["query"]), + ) + | prompt_response + | llm +) diff --git a/templates/sql-llama2/sql_llama2/nba_roster.db b/templates/sql-llama2/sql_llama2/nba_roster.db new file mode 100644 index 0000000000000000000000000000000000000000..9ea4e20c367f04791176cf30a1ad7913e2b9594c GIT binary patch literal 57344 zcmeIbd30pybsvU(dDsZ{G@BR@wJ$T%GYwQ>E36HmyE(&U_yD{HYEY=x zQ%iTFNs+F_K2dTko05-2S(Xz!iga`wnaZ(vEXqnm$qE(bC^}J&O(_n;Sk`)!M3x;} zN=!S>@80`;uc}@ZhURik{y`s2lVT5Uf6M*u^1Hu#-`?GgbiwjGv~y=>!4qtIUt4>7 z+Z~Umt*xyC{|w_F{i_}SrAz-m_)q%(ztyjfwwn+CcnJSi`|yvo;r}jwU7o<@30$7Q zCp1|b^T%N$?30$7Q2kpJrrl#8e;z+?f$XM9}^X;6SFIc(S zf9pu>E-maYdG;3;HSbUtX$qYe~*7* zXM68G{&Znw$+Na~!C$?){~ka2;JyAZvAwae1ko3-dv9T5VfRh`7n-&`*Nsyj82K`t z{qV>)NB(T&Pe=ZECp1|b^T%N$?30$7Q|BojyR9(g2K3Khi-v=sh<9DO-Vf@}-evIGy$_f15Tc%2W 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[here](https://github.com/abetlen/llama-cpp-python/blob/main/docs/install/macos.md). + +``` +wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh +bash Miniforge3-MacOSX-arm64.sh +conda create -n llama python=3.9.16 +conda activate /Users/rlm/miniforge3/envs/llama +CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dir +``` + +### LLM + +It will download Mistral-7b model from [here](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF). + +You can select other files and specify their download path (browse [here](https://huggingface.co/TheBloke)). + +## Set up SQL DB + +This template includes an example DB of 2023 NBA rosters. + +You can see instructions to build this DB [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb). + +## Installation + +```bash +# from inside your LangServe instance +poe add sql/llama2-ollama +``` diff --git 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"README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.313" +llama-cpp-python = ">=0.1.79" + +[tool.langserve] +export_module = "sql_llamacpp" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/sql-llamacpp/sql_llamacpp/__init__.py b/templates/sql-llamacpp/sql_llamacpp/__init__.py new file mode 100644 index 00000000000..376ed218308 --- /dev/null +++ b/templates/sql-llamacpp/sql_llamacpp/__init__.py @@ -0,0 +1,3 @@ +from llamacpp.chain import chain + +__all__ = ["chain"] \ No newline at end of file diff --git a/templates/sql-llamacpp/sql_llamacpp/chain.py b/templates/sql-llamacpp/sql_llamacpp/chain.py new file mode 100644 index 00000000000..bf8e796d0e5 --- /dev/null +++ b/templates/sql-llamacpp/sql_llamacpp/chain.py @@ -0,0 +1,106 @@ +from langchain.llms import LlamaCpp +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough + +# Get LLM +import os +import requests +# File name and URL +file_name = "mistral-7b-instruct-v0.1.Q4_K_M.gguf" +url = "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf" +# Check if file is present in the current directory +if not os.path.exists(file_name): + print(f"'{file_name}' not found. Downloading...") + # Download the file + response = requests.get(url) + response.raise_for_status() # Raise an exception for HTTP errors + with open(file_name, 'wb') as f: + f.write(response.content) + print(f"'{file_name}' has been downloaded.") +else: + print(f"'{file_name}' already exists in the current directory.") + +# Add the LLM downloaded from HF +model_path = file_name +n_gpu_layers = 1 # Metal set to 1 is enough. +n_batch = 512 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip. +llm = LlamaCpp( + model_path=model_path, + n_gpu_layers=n_gpu_layers, + n_batch=n_batch, + n_ctx=2048, + f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls + verbose=True, +) + +from pathlib import Path +from langchain.utilities import SQLDatabase +db_path = Path(__file__).parent / "nba_roster.db" +rel = db_path.relative_to(Path.cwd()) +db_string = f"sqlite:///{rel}" +db = SQLDatabase.from_uri(db_string, sample_rows_in_table_info=0) + +def get_schema(_): + return db.get_table_info() + + +def run_query(query): + return db.run(query) + +# Prompt +from langchain.memory import ConversationBufferMemory +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +template = """Based on the table schema below, write a SQL query that would answer the user's question: +{schema} + +Question: {question} +SQL Query:""" +prompt = ChatPromptTemplate.from_messages([ + ("system", "Given an input question, convert it to a SQL query. No pre-amble."), + MessagesPlaceholder(variable_name="history"), + ("human", template) +]) + +memory = ConversationBufferMemory(return_messages=True) + +# Chain to query with memory +from langchain.schema.runnable import RunnableLambda + +sql_chain = ( + RunnablePassthrough.assign( + schema=get_schema, + history=RunnableLambda(lambda x: memory.load_memory_variables(x)["history"]) + )| prompt + | llm.bind(stop=["\nSQLResult:"]) + | StrOutputParser() +) + +def save(input_output): + output = {"output": input_output.pop("output")} + memory.save_context(input_output, output) + return output['output'] + +sql_response_memory = RunnablePassthrough.assign(output=sql_chain) | save + +# Chain to answer +template = """Based on the table schema below, question, sql query, and sql response, write a natural language response: +{schema} + +Question: {question} +SQL Query: {query} +SQL Response: {response}""" +prompt_response = ChatPromptTemplate.from_messages([ + ("system", "Given an input question and SQL response, convert it to a natural language answer. 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Ollama. + +Also follow instructions to download your LLM of interest: + +* This template uses `llama2:13b-chat` +* But you can pick from many LLMs [here](https://ollama.ai/library) + +## Set up SQL DB + +This template includes an example DB of 2023 NBA rosters. + +You can see instructions to build this DB [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb). + +## Installation + +```bash +# from inside your LangServe instance +poe add sql-ollama +``` diff --git a/templates/sql-ollama/nba_roster.db b/templates/sql-ollama/nba_roster.db new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/sql-ollama/poetry.lock b/templates/sql-ollama/poetry.lock new file mode 100644 index 00000000000..ca0dce847d9 --- /dev/null +++ b/templates/sql-ollama/poetry.lock @@ -0,0 +1,1182 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description 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+[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.313" + +[tool.langserve] +export_module = "sql_ollama" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/sql-ollama/sql_ollama/__init__.py b/templates/sql-ollama/sql_ollama/__init__.py new file mode 100644 index 00000000000..d92f8055226 --- /dev/null +++ b/templates/sql-ollama/sql_ollama/__init__.py @@ -0,0 +1,3 @@ +from ollama.chain import chain + +__all__ = ["chain"] \ No newline at end of file diff --git a/templates/sql-ollama/sql_ollama/chain.py b/templates/sql-ollama/sql_ollama/chain.py new file mode 100644 index 00000000000..248e5c8a5b4 --- /dev/null +++ b/templates/sql-ollama/sql_ollama/chain.py @@ -0,0 +1,78 @@ +from langchain.chat_models import ChatOllama +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough + +# Add the LLM downloaded from Ollama +ollama_llm = "llama2:13b-chat" +llm = ChatOllama(model=ollama_llm) + +from pathlib import Path +from langchain.utilities import SQLDatabase +db_path = Path(__file__).parent / "nba_roster.db" +rel = db_path.relative_to(Path.cwd()) +db_string = f"sqlite:///{rel}" +db = SQLDatabase.from_uri(db_string, sample_rows_in_table_info=0) + +def get_schema(_): + return db.get_table_info() + +def run_query(query): + return db.run(query) + +# Prompt +from langchain.memory import ConversationBufferMemory +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +template = """Based on the table schema below, write a SQL query that would answer the user's question: +{schema} + +Question: {question} +SQL Query:""" +prompt = ChatPromptTemplate.from_messages([ + ("system", "Given an input question, convert it to a SQL query. No pre-amble."), + MessagesPlaceholder(variable_name="history"), + ("human", template) +]) + +memory = ConversationBufferMemory(return_messages=True) + +# Chain to query with memory +from langchain.schema.runnable import RunnableLambda + +sql_chain = ( + RunnablePassthrough.assign( + schema=get_schema, + history=RunnableLambda(lambda x: memory.load_memory_variables(x)["history"]) + )| prompt + | llm.bind(stop=["\nSQLResult:"]) + | StrOutputParser() +) + +def save(input_output): + output = {"output": input_output.pop("output")} + memory.save_context(input_output, output) + return output['output'] + +sql_response_memory = RunnablePassthrough.assign(output=sql_chain) | save + +# Chain to answer +template = """Based on the table schema below, question, sql query, and sql response, write a natural language response: +{schema} + +Question: {question} +SQL Query: {query} +SQL Response: {response}""" +prompt_response = ChatPromptTemplate.from_messages([ + ("system", "Given an input question and SQL response, convert it to a natural language answer. No pre-amble."), + ("human", template) +]) + +chain = ( + RunnablePassthrough.assign(query=sql_response_memory) + | RunnablePassthrough.assign( + schema=get_schema, + response=lambda x: db.run(x["query"]), + ) + | prompt_response + | llm +) diff --git a/templates/sql-ollama/sql_ollama/nba_roster.db b/templates/sql-ollama/sql_ollama/nba_roster.db new file mode 100644 index 0000000000000000000000000000000000000000..9ea4e20c367f04791176cf30a1ad7913e2b9594c GIT binary patch literal 57344 zcmeIbd30pybsvU(dDsZ{G@BR@wJ$T%GYwQ>E36HmyE(&U_yD{HYEY=x zQ%iTFNs+F_K2dTko05-2S(Xz!iga`wnaZ(vEXqnm$qE(bC^}J&O(_n;Sk`)!M3x;} zN=!S>@80`;uc}@ZhURik{y`s2lVT5Uf6M*u^1Hu#-`?GgbiwjGv~y=>!4qtIUt4>7 z+Z~Umt*xyC{|w_F{i_}SrAz-m_)q%(ztyjfwwn+CcnJSi`|yvo;r}jwU7o<@30$7Q zCp1|b^T%N$?30$7Q2kpJrrl#8e;z+?f$XM9}^X;6SFIc(S zf9pu>E-maYdG;3;HSbUtX$qYe~*7* zXM68G{&Znw$+Na~!C$?){~ka2;JyAZvAwae1ko3-dv9T5VfRh`7n-&`*Nsyj82K`t z{qV>)NB(T&Pe=ZECp1|b^T%N$?30$7Q|BojyR9(g2K3Khi-v=sh<9DO-Vf@}-evIGy$_f15Tc%2W 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This can be combined with regular question-answering applications by then doing retrieval on both the original and step-back question. + +Read the paper [here](https://arxiv.org/abs/2310.06117) + +See an excelent blog post on this by Cobus Greyling [here](https://cobusgreyling.medium.com/a-new-prompt-engineering-technique-has-been-introduced-called-step-back-prompting-b00e8954cacb) + +In this template we will replicate this technique. 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+langchain = ">=0.0.313" +duckduckgo-search = "^3.9.3" + +[tool.langserve] +export_module = "stepback_qa_prompting.chain" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/stepback-qa-prompting/stepback_qa_prompting/__init__.py b/templates/stepback-qa-prompting/stepback_qa_prompting/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/stepback-qa-prompting/stepback_qa_prompting/chain.py b/templates/stepback-qa-prompting/stepback_qa_prompting/chain.py new file mode 100644 index 00000000000..6fd256e2fb8 --- /dev/null +++ b/templates/stepback-qa-prompting/stepback_qa_prompting/chain.py @@ -0,0 +1,65 @@ +from langchain.chat_models import ChatOpenAI +from langchain.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableLambda +from langchain.utilities import DuckDuckGoSearchAPIWrapper + + +search = DuckDuckGoSearchAPIWrapper(max_results=4) + +def retriever(query): + return search.run(query) + + +# Few Shot Examples +examples = [ + { + "input": "Could the members of The Police perform lawful arrests?", + "output": "what can the members of The Police do?" + }, + { + "input": "Jan Sindel’s was born in what country?", + "output": "what is Jan Sindel’s personal history?" + }, +] +# We now transform these to example messages +example_prompt = ChatPromptTemplate.from_messages( + [ + ("human", "{input}"), + ("ai", "{output}"), + ] +) +few_shot_prompt = FewShotChatMessagePromptTemplate( + example_prompt=example_prompt, + examples=examples, +) + +prompt = ChatPromptTemplate.from_messages([ + ("system", """You are an expert at world knowledge. Your task is to step back and paraphrase a question to a more generic step-back question, which is easier to answer. Here are a few examples:"""), + # Few shot examples + few_shot_prompt, + # New question + ("user", "{question}"), +]) + +question_gen = prompt | ChatOpenAI(temperature=0) | StrOutputParser() + +response_prompt_template = """You are an expert of world knowledge. I am going to ask you a question. Your response should be comprehensive and not contradicted with the following context if they are relevant. Otherwise, ignore them if they are not relevant. + +{normal_context} +{step_back_context} + +Original Question: {question} +Answer:""" +response_prompt = ChatPromptTemplate.from_template(response_prompt_template) + +chain = { + # Retrieve context using the normal question + "normal_context": RunnableLambda(lambda x: x['question']) | retriever, + # Retrieve context using the step-back question + "step_back_context": question_gen | retriever, + # Pass on the question + "question": lambda x: x["question"] +} | response_prompt | ChatOpenAI(temperature=0) | StrOutputParser() + + diff --git a/templates/stepback-qa-prompting/tests/__init__.py b/templates/stepback-qa-prompting/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/summarize-anthropic/LICENSE b/templates/summarize-anthropic/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/summarize-anthropic/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/summarize-anthropic/README.md b/templates/summarize-anthropic/README.md new file mode 100644 index 00000000000..d7d163ebfb0 --- /dev/null +++ b/templates/summarize-anthropic/README.md @@ -0,0 +1,36 @@ +# Summarize documents with Anthropic + +This template uses Anthropic's `Claude2` to summarize documents. + +To do this, we can use various prompts from LangChain hub, such as: + +* [This fun summarization prompt](https://smith.langchain.com/hub/hwchase17/anthropic-paper-qa) +* [Chain of density summarization prompt](https://smith.langchain.com/hub/lawwu/chain_of_density) + +`Claude2` has a large (100k token) context window, allowing us to summarize documents over 100 pages. + +## LLM + +This template will use `Claude2` by default. + +Be sure that `ANTHROPIC_API_KEY` is set in your enviorment. + +## Adding the template + +Create your LangServe app: +``` +langchain serve new my-app +cd my-app +``` + +Add template: +``` +langchain serve add summarize-anthropic +``` + +Start server: +``` +langchain start +``` + +See Jupyter notebook `summarize_anthropic` for various way to connect to the template. diff --git a/templates/summarize-anthropic/docs/LLaVA.pdf b/templates/summarize-anthropic/docs/LLaVA.pdf new file mode 100644 index 0000000000000000000000000000000000000000..88da76cecd550f8346a7cbea3b64cf756805eb28 GIT binary patch literal 5254150 zcmb?k2O!n!`?vQFmBdL#GSA>RM%i2TCi7U?n^IO3A|sn3$taQRG9tSqQ8p4bY=H_PWh8a`G*3HAr9sqW8ciqJc zkv4NTgUF#B(XM7c;fD(D{f>ye-x2lo9T9uIiI#_>t)um~yMgilGPZ~LSNkA%p?kgo z4F2s6c%cvpR|^|kcVJQvSM>K$5&zGqd)poMZ59!{Fo>3`neERw|7H>e_Jj!kHi-fd z8E$3hBCpDNsWWUjU*r9}U2eW`U6$M2mI-Z}{#IEgWs8m?Oj3@8)0NJH zz4Fn<^82b)UB2PBqUQRKlVUnI`!B)c`7@5Dy;I1L{&@b7U)aez()7ApdOi69y0)L@ 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+langchain = ">=0.0.313, <0.1" +anthropic = ">=0.5.0" +langchainhub = ">=0.1.13" + +[tool.langserve] +export_module = "summarize_anthropic" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/summarize-anthropic/summarize_anthropic.ipynb b/templates/summarize-anthropic/summarize_anthropic.ipynb new file mode 100644 index 00000000000..008b6f74b0e --- /dev/null +++ b/templates/summarize-anthropic/summarize_anthropic.ipynb @@ -0,0 +1,180 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b4ea3722", + "metadata": {}, + "source": [ + "## Document Loading\n", + "\n", + "Load a blog post on agents." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a65a2603", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting pdf2image\n", + " Using cached pdf2image-1.16.3-py3-none-any.whl (11 kB)\n", + "Requirement already satisfied: pillow in /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages (from pdf2image) (8.4.0)\n", + "Installing collected packages: pdf2image\n", + "Successfully installed pdf2image-1.16.3\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "! pip install pdf2image" + ] + }, + { + "cell_type": "markdown", + "id": "6ff363da", + "metadata": {}, + "source": [ + "Academic papers." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d79e191a", + "metadata": {}, + "outputs": [], + "source": [ + "import arxiv\n", + "from langchain.chat_models import ChatAnthropic\n", + "from langchain.document_loaders import ArxivLoader, UnstructuredPDFLoader\n", + "# Load a paper to use\n", + "paper = next(arxiv.Search(query=\"Visual Instruction Tuning\").results())\n", + "paper.download_pdf(filename=\"downloaded-paper.pdf\")\n", + "loader = UnstructuredPDFLoader(\"downloaded-paper.pdf\")\n", + "doc = loader.load()[0]" + ] + }, + { + "cell_type": "markdown", + "id": "db964a34", + "metadata": {}, + "source": [ + "Also try loading blog posts." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b2f10100", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders import WebBaseLoader\n", + "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n", + "text = loader.load()" + ] + }, + { + "cell_type": "markdown", + "id": "361fcf5c", + "metadata": {}, + "source": [ + "## Connect to template\n", + "\n", + "`Context`\n", + " \n", + "* LangServe apps gives you access to templates.\n", + "* Templates LLM pipeline (runnables or chains) end-points accessible via FastAPI.\n", + "* The environment for these templates is managed by Poetry.\n", + "\n", + "`Create app`\n", + "\n", + "* Install LangServe and create an app.\n", + "* This will create a new Poetry environment /\n", + "```\n", + "pip install < to add > \n", + "langchain serve new my-app\n", + "cd my-app\n", + "```\n", + "\n", + "`Add templates`\n", + "\n", + "* When we add a template, we update the Poetry config file with the necessary dependencies.\n", + "* It also automatically installed these template dependencies in your Poetry environment\n", + "```\n", + "langchain serve add summarize-anthropic\n", + "```\n", + "\n", + "`Start FastAPI server`\n", + "\n", + "```\n", + "langchain start\n", + "```\n", + "\n", + "Note, we can now look at the endpoints:\n", + "\n", + "http://127.0.0.1:8000/docs#\n", + "\n", + "And look specifically at our loaded template:\n", + "\n", + "http://127.0.0.1:8000/docs#/default/invoke_summarize_anthropic_invoke_post\n", + " \n", + "We can also use remote runnable to call it.\n", + "\n", + "## Summarization\n", + "\n", + "We will use [this](https://smith.langchain.com/hub/hwchase17/anthropic-paper-qa) prompt." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3c673b53", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\" Here is my attempt to complete the requested tasks:\\n\\n\\nThe scientists made a robot friend that can see pictures and talk about them. First they let a big talking robot make up fun conversations about pictures. Then they taught their robot friend to have conversations too. Their robot friend got pretty good at seeing pictures and talking about them!\\n\\n\\n\\nVisual Instruction Tuning\\nRecipe adapted from Haotian Liu et al.\\n\\nIngredients:\\n- 1 large language model (we used GPT-4)\\n- 158,000 images with text descriptions \\n- 1 vision model (we used CLIP)\\n- 1 large multimodal model (we used LLaMA)\\n\\nInstructions:\\n1. Have the GPT-4 model make up conversations, long descriptions, and complex stories about the images, using only the text descriptions as hints. \\n2. Connect the vision model and language model together into one big multimodal model. This will be your robot friend!\\n3. Show the robot friend examples of the conversations GPT-4 made up.\\n4. Let the robot friend practice having conversations about images, using what it learned from GPT-4.\\n5. Test how well it can see and talk about new images!\\n\\n\\n\\nOh Muse, sing of scientists wise, \\nWho built a robot with ears and eyes, \\nTaught by a speaker most eloquent, \\nThey molded silicon assistant.\\n\\nThis visual machine could see and tell \\nOf images with knowledge it knew well. \\nWhen humans speak in words precise, \\nThe robot answers concise and wise.\\n\\nThough simpler than human true creation, \\nIt showed the path for innovation. \\nMore data, bigger models in time, \\nMay lead to an assistant sublime.\\n\\n\\n\\nOh great, another computer vision model. What is it this time, some overhyped multimodal beast trained on mountains of compute? Bah! Back in my day we built models with only a handful of pixels and a Commodore 64. And they worked just fine, not like these newfangled neural nets that gobble up GPUs like candy.\\n\\nLet's see, they got GPT-4 to hallucinate training data from captions? Well that hardly seems rigorous. Those mechaturkeys will fabricate anything if you let them. And they didn't even collect real human annotations? Training multimodal models is hard enough without using made-up data. \\n\\nI'm sure their LaVa MuDdLe model does decently on narrow tasks, but don't be surprised when it fails catastrophically in the real world. These things may seem clever, but they're as dumb as a bag of rocks under the hood. Mark my words, we should keep them far away from any mission critical systems, or we'll regret it!\\n\\nBah! Call me when we build real intelligence, not these statistical hackjobs.\\n\"" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langserve.client import RemoteRunnable\n", + "summarization = RemoteRunnable('http://localhost:8000/summarize-anthropic')\n", + "summarization.invoke({\"text\":doc.page_content})" + ] + } + ], + "metadata": { + "kernelspec": { + "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", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/templates/summarize-anthropic/summarize_anthropic/__init__.py b/templates/summarize-anthropic/summarize_anthropic/__init__.py new file mode 100644 index 00000000000..84e4c58fed0 --- /dev/null +++ b/templates/summarize-anthropic/summarize_anthropic/__init__.py @@ -0,0 +1,3 @@ +from summarize_anthropic.chain import chain + +__all__ = ["chain"] diff --git a/templates/summarize-anthropic/summarize_anthropic/chain.py b/templates/summarize-anthropic/summarize_anthropic/chain.py new file mode 100644 index 00000000000..e2b6dae3859 --- /dev/null +++ b/templates/summarize-anthropic/summarize_anthropic/chain.py @@ -0,0 +1,8 @@ +from langchain import hub +from langchain.chat_models import ChatAnthropic +from langchain.schema.output_parser import StrOutputParser + +# Create chain +prompt = hub.pull("hwchase17/anthropic-paper-qa") +model = ChatAnthropic(model="claude-2", max_tokens=10000) +chain = prompt | model | StrOutputParser() \ No newline at end of file diff --git a/templates/summarize-anthropic/tests/__init__.py b/templates/summarize-anthropic/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/xml-agent/LICENSE b/templates/xml-agent/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/xml-agent/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/xml-agent/README.md b/templates/xml-agent/README.md new file mode 100644 index 00000000000..b5ec5962806 --- /dev/null +++ b/templates/xml-agent/README.md @@ -0,0 +1,17 @@ +# XML Agent + +This template creates an agent that uses XML syntax to communicate its decisions of what actions to take. +For this example, we use Anthropic since Anthropic's Claude models are particularly good at writing XML syntax. +This example creates an agent that can optionally look up things on the internet using You.com's retriever. + +## LLM + +This template will use `Anthropic` by default. + +Be sure that `ANTHROPIC_API_KEY` is set in your environment. + +## Tools + +This template will use `You.com` by default. + +Be sure that 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b/templates/xml-agent/pyproject.toml new file mode 100644 index 00000000000..13db08059e1 --- /dev/null +++ b/templates/xml-agent/pyproject.toml @@ -0,0 +1,20 @@ +[tool.poetry] +name = "xml-agent" +version = "0.1.0" +description = "" +authors = ["Lance Martin "] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.322" +anthropic = ">=0.5.0" +langchainhub = ">=0.1.13" + +[tool.langserve] +export_module = "xml_agent" +export_attr = "agent_executor" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" \ No newline at end of file diff --git a/templates/xml-agent/tests/__init__.py b/templates/xml-agent/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/xml-agent/xml_agent/__init__.py b/templates/xml-agent/xml_agent/__init__.py new file mode 100644 index 00000000000..0ddde78c9db --- /dev/null +++ b/templates/xml-agent/xml_agent/__init__.py @@ -0,0 +1,3 @@ +from xml_agent.agent import agent_executor + +__all__ = ["agent_executor"] diff --git a/templates/xml-agent/xml_agent/agent.py b/templates/xml-agent/xml_agent/agent.py new file mode 100644 index 00000000000..cf1d2031557 --- /dev/null +++ b/templates/xml-agent/xml_agent/agent.py @@ -0,0 +1,48 @@ +from langchain.chat_models import ChatAnthropic +from langchain.tools.render import render_text_description +from langchain.agents.format_scratchpad import format_xml +from langchain.agents import AgentExecutor +from langchain.retrievers.you import YouRetriever +from langchain.agents.agent_toolkits.conversational_retrieval.tool import create_retriever_tool +from langchain.pydantic_v1 import BaseModel +from xml_agent.prompts import conversational_prompt, parse_output +from langchain.schema import AIMessage, HumanMessage +from typing import List, Tuple + +def _format_chat_history(chat_history: List[Tuple[str, str]]): + buffer = [] + for human, ai in chat_history: + buffer.append(HumanMessage(content=human)) + buffer.append(AIMessage(content=ai)) + return buffer + + +model = ChatAnthropic(model="claude-2") + +# Fake Tool +retriever = YouRetriever(k=5) +retriever_tool = create_retriever_tool(retriever, "search", "Use this to search for current events.") + +tools = [retriever_tool] + +prompt = conversational_prompt.partial( + tools=render_text_description(tools), + tool_names=", ".join([t.name for t in tools]), +) +llm_with_stop = model.bind(stop=[""]) + +agent = { + "question": lambda x: x["question"], + "agent_scratchpad": lambda x: format_xml(x['intermediate_steps']), + "chat_history": lambda x: _format_chat_history(x["chat_history"]), +} | prompt | llm_with_stop | parse_output + +class AgentInput(BaseModel): + question: str + chat_history: List[Tuple[str, str]] + +agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True).with_types( + input_type=AgentInput +) + +agent_executor = agent_executor | (lambda x: x["output"]) diff --git a/templates/xml-agent/xml_agent/prompts.py b/templates/xml-agent/xml_agent/prompts.py new file mode 100644 index 00000000000..a9919010060 --- /dev/null +++ b/templates/xml-agent/xml_agent/prompts.py @@ -0,0 +1,50 @@ +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain.schema import AgentAction, AgentFinish + +template = """You are a helpful assistant. Help the user answer any questions. + +You have access to the following tools: + +{tools} + +In order to use a tool, you can use and tags. You will then get back a response in the form +For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond: + +searchweather in SF +64 degrees + +When you are done, you can respond as normal to the user. + +Example 1: + +Human: Hi! + +Assistant: Hi! How are you? + +Human: What is the weather in SF? +Assistant: searchweather in SF +64 degrees +It is 64 degress in SF + + +Begin!""" + +conversational_prompt = ChatPromptTemplate.from_messages([ + ("system", template), + MessagesPlaceholder(variable_name="chat_history"), + ("user", "{question}"), + ("ai", "{agent_scratchpad}") +]) + + +def parse_output(message): + text = message.content + if "" in text: + tool, tool_input = text.split("") + _tool = tool.split("")[1] + _tool_input = tool_input.split("")[1] + if "" in _tool_input: + _tool_input = _tool_input.split("")[0] + return AgentAction(tool=_tool, tool_input=_tool_input, log=text) + else: + return AgentFinish(return_values={"output": text}, log=text) \ No newline at end of file

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