Templates (#12294)

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Jacob Lee <jacoblee93@gmail.com>

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"

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",
 ]

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"]

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(

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,
+    )

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

templates/.gitignore

@@ -0,0 +1 @@
+__pycache__

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.

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`.

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.

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.

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
+}

templates/anthropic-functions/anthropic_functions/__init__.py

@@ -0,0 +1,3 @@
+from anthropic_functions.chain import chain
+
+__all__ = ["chain"]

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)

templates/anthropic-functions/pyproject.toml

@@ -0,0 +1,21 @@
+[tool.poetry]
+name = "anthropic-function"
+version = "0.1.0"
+description = ""
+authors = ["Lance Martin <lance@langchain.dev>"]
+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"

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)

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,
+)

templates/anthropic-iterative-search/anthropic_iterative_search/agent_scratchpad.py

@@ -0,0 +1,15 @@
+def _format_docs(docs):
+    result = "\n".join(
+        [
+            f'<item index="{i+1}">\n<page_content>\n{r}\n</page_content>\n</item>'
+            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 += '</search_query>' + _format_docs(observation)
+    return thoughts

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()

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?>(.+?)</{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 + '</search_query>') 
+    if search_query is None:
+        docs = []
+        str_output = ""
+        for action, observation in steps:
+            docs.extend(observation)
+            str_output += action.log
+            str_output += '</search_query>' + _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)

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 <scratchpad> 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 <search_quality></search_quality> 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 <information></information> tags, WITHOUT actually answering the question. Otherwise, issue a new search.
+
+Here is the user's question: <question>{query}</question> Remind yourself to make short queries in your scratchpad as you plan out your strategy."""
+
+answer_prompt = "Here is a user query: <query>{query}</query>. Here is some relevant information: <information>{information}</information>. Please answer the question using the relevant information."

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. <tool_description> 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: <search_query>query_word</search_query>. * You'll then get results back in <search_result> tags.</tool_description>"""
+
+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)

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=['</search_query>']) | 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)

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}))

templates/anthropic-iterative-search/pyproject.toml

@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "anthropic_iterative_search"
+version = "0.0.1"
+description = ""
+authors = []
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = ">=0.0.313"
+anthropic = "^0.5.0"
+wikipedia = "^1.4.0"
+
+[tool.langserve]
+export_module = "anthropic_iterative_search"
+export_attr = "final_chain"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

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

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).

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()
+)

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)

templates/cassandra-entomology-rag/pyproject.toml

@@ -0,0 +1,22 @@
+[tool.poetry]
+name = "cassandra_entomology_rag"
+version = "0.0.1"
+description = ""
+authors = ["Stefano Lottini <stefano.lottini@datastax.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.11"
+langchain = ">=0.0.313"
+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"

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).")

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.

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

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).

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)

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)

templates/cassandra-synonym-caching/pyproject.toml

@@ -0,0 +1,22 @@
+[tool.poetry]
+name = "cassandra_synonym_caching"
+version = "0.0.1"
+description = ""
+authors = ["Stefano Lottini <stefano.lottini@datastax.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.11"
+langchain = ">=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"

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.

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()`
+
+<df>
+{dhead}
+</df>
+
+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:
+
+<question>How old is Jane?</question>
+<logic>Use `person_name_search` since you can use the query `Jane`</logic>
+
+<question>Who has id 320</question>
+<logic>Use `python_repl` since even though the question is about a person, you don't know their name so you can't include it.</logic>
+"""
+
+
+
+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")

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")

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?"
+    print(agent_executor.invoke({"input": question}))

templates/csv-agent/pyproject.toml

@@ -0,0 +1,27 @@
+[tool.poetry]
+name = "csv_agent"
+version = "0.0.1"
+description = ""
+authors = []
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.9.1,<3.13"
+langchain = ">=0.0.313"
+openai = "^0.28.1"
+tiktoken = "^0.5.1"
+faiss-cpu = "^1.7.4"
+pandas = "^2.1.1"
+setuptools = "^68.2.2"
+tabulate = "^0.9.0"
+pydantic = "<2"
+langchain-experimental = "^0.0.32"
+
+[tool.langserve]
+export_module = "csv_agent.chain"
+export_attr = "chain"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

templates/csv-agent/titanic.csv

@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
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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:<password>@<es-url>"
+
+If you want to populate the DB with some example info, you can run `python ingest.py`.

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()

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
+        ]
+    )

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)

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)

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"}))

templates/elastic-query-generator/pyproject.toml

@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "elastic_query_generator"
+version = "0.0.1"
+description = ""
+authors = []
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=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"

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.

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.

templates/extraction-openai-functions/extraction_openai_functions/__init__.py

@@ -0,0 +1,3 @@
+from extraction_openai_functions.chain import chain
+
+__all__ = ["chain"]

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")

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
+}

templates/extraction-openai-functions/pyproject.toml

@@ -0,0 +1,19 @@
+[tool.poetry]
+name = "extraction-openai-functions"
+version = "0.1.0"
+description = ""
+authors = ["Lance Martin <lance@langchain.dev>"]
+readme = "README.md"
+
+[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"

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=<YOUR_OPENAI_API_KEY>
+NEO4J_URI=<YOUR_NEO4J_URI>
+NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
+NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
+```
+
+## Installation
+
+```bash
+# from inside your LangServe instance
+poe add neo4j-cypher
+```

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]"
+)

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}))

templates/neo4j-cypher-ft/neo4j_cypher_ft/__init__.py

@@ -0,0 +1,3 @@
+from neo4j_cypher_ft.chain import chain
+
+__all__ = ["chain"]

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()
+)

templates/neo4j-cypher-ft/pyproject.toml

@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "neo4j_cypher"
+version = "0.1.0"
+description = ""
+authors = ["Tomaz Bratanic <tomaz.bratanic@neo4j.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = ">=0.0.320"
+neo4j = ">5.12"
+openai = "^0.28.1"
+
+[tool.langserve]
+export_module = "neo4j.cypher"
+export_attr = "chain"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

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=<YOUR_OPENAI_API_KEY>
+NEO4J_URI=<YOUR_NEO4J_URI>
+NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
+NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
+```
+
+## Installation
+
+```bash
+# from inside your LangServe instance
+poe add neo4j-cypher
+```

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)
+"""
+)

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}))

templates/neo4j-cypher/neo4j_cypher/__init__.py

@@ -0,0 +1,3 @@
+from neo4j_cypher.chain import chain
+
+__all__ = ["chain"]

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()
+)

templates/neo4j-cypher/pyproject.toml

@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "neo4j_cypher"
+version = "0.1.0"
+description = ""
+authors = ["Tomaz Bratanic <tomaz.bratanic@neo4j.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = ">=0.0.320"
+neo4j = ">5.12"
+openai = "^0.28.1"
+
+[tool.langserve]
+export_module = "neo4j.cypher"
+export_attr = "chain"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

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=<YOUR_OPENAI_API_KEY>
+NEO4J_URI=<YOUR_NEO4J_URI>
+NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
+NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
+```
+
+## Installation
+
+To get started with the Graph Generation Chain:
+
+```bash
+# from inside your LangServe instance
+poe add neo4j-generation
+```

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,
+        )
+    )

templates/neo4j-generation/neo4j_generation/__init__.py

@@ -0,0 +1,3 @@
+from neo4j_generation.chain import chain
+
+__all__ = ["chain"]

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"

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
+    )

templates/neo4j-generation/pyproject.toml

@@ -0,0 +1,21 @@
+[tool.poetry]
+name = "neo4j_generation"
+version = "0.0.1"
+description = ""
+authors = ["Tomaz Bratanic <tomaz.bratanic@neo4j.com>"]
+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"

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=<YOUR_OPENAI_API_KEY>
+NEO4J_URI=<YOUR_NEO4J_URI>
+NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
+NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
+```
+
+## Installation
+
+```bash
+# from inside your LangServe instance
+poe add neo4j-parent
+```

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

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",
+)

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))

templates/neo4j-parent/neo4j_parent/__init__.py

@@ -0,0 +1,3 @@
+from neo4j_parent.chain import chain
+
+__all__ = ["chain"]