add skeleton of thought (#13883)

templates/skeleton-of-thought/.gitignore

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+__pycache__

templates/skeleton-of-thought/LICENSE

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+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/skeleton-of-thought/README.md

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+# skeleton-of-thought
+
+Implements "Skeleton of Thought" from [this](https://sites.google.com/view/sot-llm) paper.
+
+This technique makes it possible to generate longer generates more quickly by first generating a skeleton, then generating each point of the outline.
+
+## Environment Setup
+
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
+
+To get your `OPENAI_API_KEY`, navigate to [API keys](https://platform.openai.com/account/api-keys) on your OpenAI account and create a new secret key.
+
+## Usage
+
+To use this package, you should first have the LangChain CLI installed:
+
+```shell
+pip install -U langchain-cli
+```
+
+To create a new LangChain project and install this as the only package, you can do:
+
+```shell
+langchain app new my-app --package skeleton-of-thought
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add skeleton-of-thought
+```
+
+And add the following code to your `server.py` file:
+```python
+from skeleton_of_thought import chain as skeleton_of_thought_chain
+
+add_routes(app, skeleton_of_thought_chain, path="/skeleton-of-thought")
+```
+
+(Optional) Let's now configure LangSmith. 
+LangSmith will help us trace, monitor and debug LangChain applications. 
+LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). 
+If you don't have access, you can skip this section
+
+
+```shell
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
+
+If you are inside this directory, then you can spin up a LangServe instance directly by:
+
+```shell
+langchain serve
+```
+
+This will start the FastAPI app with a server is running locally at 
+[http://localhost:8000](http://localhost:8000)
+
+We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
+We can access the playground at [http://127.0.0.1:8000/skeleton-of-thought/playground](http://127.0.0.1:8000/skeleton-of-thought/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/skeleton-of-thought")
+```

templates/skeleton-of-thought/pyproject.toml

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+[tool.poetry]
+name = "skeleton-of-thought"
+version = "0.0.1"
+description = ""
+authors = []
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = ">=0.0.313, <0.1"
+openai = "^0.28.1"
+
+[tool.poetry.group.dev.dependencies]
+langchain-cli = ">=0.0.4"
+fastapi = "^0.104.0"
+sse-starlette = "^1.6.5"
+
+[tool.langserve]
+export_module = "skeleton_of_thought"
+export_attr = "chain"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

templates/skeleton-of-thought/skeleton_of_thought/__init__.py

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+from skeleton_of_thought.chain import chain
+
+__all__ = ["chain"]

templates/skeleton-of-thought/skeleton_of_thought/chain.py

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+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough
+
+skeleton_generator_template = """[User:] You’re an organizer responsible for only \
+giving the skeleton (not the full content) for answering the question.
+Provide the skeleton in a list of points (numbered 1., 2., 3., etc.) to answer \
+the question. \
+Instead of writing a full sentence, each skeleton point should be very short \
+with only 3∼5 words. \
+Generally, the skeleton should have 3∼10 points. Now, please provide the skeleton \
+for the following question.
+{question}
+Skeleton:
+[Assistant:] 1."""
+
+skeleton_generator_prompt = ChatPromptTemplate.from_template(
+    skeleton_generator_template
+)
+
+skeleton_generator_chain = (
+    skeleton_generator_prompt | ChatOpenAI() | StrOutputParser() | (lambda x: "1. " + x)
+)
+
+point_expander_template = """[User:] You’re responsible for continuing \
+the writing of one and only one point in the overall answer to the following question.
+{question}
+The skeleton of the answer is
+{skeleton}
+Continue and only continue the writing of point {point_index}. \
+Write it **very shortly** in 1∼2 sentence and do not continue with other points!
+[Assistant:] {point_index}. {point_skeleton}"""
+
+point_expander_prompt = ChatPromptTemplate.from_template(point_expander_template)
+
+point_expander_chain = RunnablePassthrough.assign(
+    continuation=point_expander_prompt | ChatOpenAI() | StrOutputParser()
+) | (lambda x: x["point_skeleton"].strip() + " " + x["continuation"])
+
+
+def parse_numbered_list(input_str):
+    """Parses a numbered list into a list of dictionaries
+
+    Each element having two keys:
+    'index' for the index in the numbered list, and 'point' for the content.
+    """
+    # Split the input string into lines
+    lines = input_str.split("\n")
+
+    # Initialize an empty list to store the parsed items
+    parsed_list = []
+
+    for line in lines:
+        # Split each line at the first period to separate the index from the content
+        parts = line.split(". ", 1)
+
+        if len(parts) == 2:
+            # Convert the index part to an integer
+            # and strip any whitespace from the content
+            index = int(parts[0])
+            point = parts[1].strip()
+
+            # Add a dictionary to the parsed list
+            parsed_list.append({"point_index": index, "point_skeleton": point})
+
+    return parsed_list
+
+
+def create_list_elements(_input):
+    skeleton = _input["skeleton"]
+    numbered_list = parse_numbered_list(skeleton)
+    for el in numbered_list:
+        el["skeleton"] = skeleton
+        el["question"] = _input["question"]
+    return numbered_list
+
+
+def get_final_answer(expanded_list):
+    final_answer_str = "Here's a comprehensive answer:\n\n"
+    for i, el in enumerate(expanded_list):
+        final_answer_str += f"{i+1}. {el}\n\n"
+    return final_answer_str
+
+
+class ChainInput(BaseModel):
+    question: str
+
+
+chain = (
+    RunnablePassthrough.assign(skeleton=skeleton_generator_chain)
+    | create_list_elements
+    | point_expander_chain.map()
+    | get_final_answer
+).with_types(input_type=ChainInput)