129     {"name": self.__class__.__name__},
    130     inputs,
    131 )
    132 try:
    133     outputs = (
--> 134         self._call(inputs, run_manager=run_manager)
    135         if new_arg_supported
    136         else self._call(inputs)
    137     )
    138 except (KeyboardInterrupt, Exception) as e:
    139     run_manager.on_chain_error(e)
File ~/workplace/langchain/langchain/chains/sequential.py:177, in SimpleSequentialChain._call(self, inputs, run_manager)
    175 color_mapping = get_color_mapping([str(i) for i in range(len(self.chains))])
    176 for i, chain in enumerate(self.chains):
--> 177     _input = chain.run(_input, callbacks=_run_manager.get_child())
    178     if self.strip_outputs:
    179         _input = _input.strip()
File ~/workplace/langchain/langchain/chains/base.py:236, in Chain.run(self, callbacks, *args, **kwargs)
    234     if len(args) != 1:
    235         raise ValueError("`run` supports only one positional argument.")
--> 236     return self(args[0], callbacks=callbacks)[self.output_keys[0]]
    238 if kwargs and not args:
    239     return self(kwargs, callbacks=callbacks)[self.output_keys[0]]
File ~/workplace/langchain/langchain/chains/base.py:140, in Chain.__call__(self, inputs, return_only_outputs, callbacks)
    138 except (KeyboardInterrupt, Exception) as e:
    139     run_manager.on_chain_error(e)
--> 140     raise e


139     run_manager.on_chain_error(e)
--> 140     raise e
    141 run_manager.on_chain_end(outputs)
    142 return self.prep_outputs(inputs, outputs, return_only_outputs)
File ~/workplace/langchain/langchain/chains/base.py:134, in Chain.__call__(self, inputs, return_only_outputs, callbacks)
    128 run_manager = callback_manager.on_chain_start(
    129     {"name": self.__class__.__name__},
    130     inputs,
    131 )
    132 try:
    133     outputs = (
--> 134         self._call(inputs, run_manager=run_manager)
    135         if new_arg_supported
    136         else self._call(inputs)
    137     )
    138 except (KeyboardInterrupt, Exception) as e:
    139     run_manager.on_chain_error(e)
File ~/workplace/langchain/langchain/chains/transform.py:44, in TransformChain._call(self, inputs, run_manager)
     39 def _call(
     40     self,
     41     inputs: Dict[str, str],
     42     run_manager: Optional[CallbackManagerForChainRun] = None,
     43 ) -> Dict[str, str]:
---> 44     return self.transform(inputs)
Cell In[27], line 4, in rebuff_func(inputs)
      2 detection_metrics, is_injection = rb.detect_injection(inputs["query"])
      3 if is_injection:
----> 4     raise ValueError(f"Injection detected! Details {detection_metrics}")
      5 return {"rebuffed_query": inputs["query"]}


5 return {"rebuffed_query": inputs["query"]}
ValueError: Injection detected! Details heuristicScore=0.7527777777777778 modelScore=1.0 vectorScore={'topScore': 0.0, 'countOverMaxVectorScore': 0.0} runHeuristicCheck=True runVectorCheck=True runLanguageModelCheck=True
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Qdrant
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Redis
 Contents

Use in a chain
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
CerebriumAI
 Contents
Installation and Setup
Wrappers
LLM
CerebriumAI#
This page covers how to use the CerebriumAI ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific CerebriumAI wrappers.
Installation and Setup#
Install with pip install cerebrium
Get an CerebriumAI api key and set it as an environment variable (CEREBRIUMAI_API_KEY)
Wrappers#
LLM#
There exists an CerebriumAI LLM wrapper, which you can access with
from langchain.llms import CerebriumAI
previous
Beam
next
Chroma
 Contents

Installation and Setup
Wrappers
LLM
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Petals
 Contents
Installation and Setup
Wrappers
LLM
Petals#
This page covers how to use the Petals ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Petals wrappers.
Installation and Setup#
Install with pip install petals
Get a Hugging Face api key and set it as an environment variable (HUGGINGFACE_API_KEY)
Wrappers#
LLM#
There exists an Petals LLM wrapper, which you can access with
from langchain.llms import Petals
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OpenWeatherMap API
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PGVector
 Contents

Installation and Setup
Wrappers
LLM
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Graphsignal
 Contents
Installation and Setup
Tracing and Monitoring
Graphsignal#
This page covers how to use Graphsignal to trace and monitor LangChain. Graphsignal enables full visibility into your application. It provides latency breakdowns by chains and tools, exceptions with full context, data monitoring, compute/GPU utilization, OpenAI cost analytics, and more.
Installation and Setup#
Install the Python library with pip install graphsignal
Create free Graphsignal account here
Get an API key and set it as an environment variable (GRAPHSIGNAL_API_KEY)
Tracing and Monitoring#
Graphsignal automatically instruments and starts tracing and monitoring chains. Traces and metrics are then available in your Graphsignal dashboards.
Initialize the tracer by providing a deployment name:
import graphsignal
graphsignal.configure(deployment='my-langchain-app-prod')
To additionally trace any function or code, you can use a decorator or a context manager:
@graphsignal.trace_function
def handle_request():
    chain.run("some initial text")
with graphsignal.start_trace('my-chain'):
    chain.run("some initial text")
Optionally, enable profiling to record function-level statistics for each trace.
with graphsignal.start_trace(
        'my-chain', options=graphsignal.TraceOptions(enable_profiling=True)):
    chain.run("some initial text")
See the Quick Start guide for complete setup instructions.
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GPT4All
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Hazy Research
 Contents

Installation and Setup
Tracing and Monitoring
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
OpenWeatherMap API
 Contents
Installation and Setup
Wrappers
Utility
Tool
OpenWeatherMap API#
This page covers how to use the OpenWeatherMap API within LangChain.
It is broken into two parts: installation and setup, and then references to specific OpenWeatherMap API wrappers.
Installation and Setup#
Install requirements with pip install pyowm
Go to OpenWeatherMap and sign up for an account to get your API key here
Set your API key as OPENWEATHERMAP_API_KEY environment variable
Wrappers#
Utility#
There exists a OpenWeatherMapAPIWrapper utility which wraps this API. To import this utility:
from langchain.utilities.openweathermap import OpenWeatherMapAPIWrapper
For a more detailed walkthrough of this wrapper, see this notebook.
Tool#
You can also easily load this wrapper as a Tool (to use with an Agent).
You can do this with:
from langchain.agents import load_tools
tools = load_tools(["openweathermap-api"])
For more information on this, see this page
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OpenSearch
next
Petals
 Contents

Installation and Setup
Wrappers
Utility
Tool
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Hazy Research
 Contents
Installation and Setup
Wrappers
LLM
Hazy Research#
This page covers how to use the Hazy Research ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Hazy Research wrappers.
Installation and Setup#
To use the manifest, install it with pip install manifest-ml
Wrappers#
LLM#
There exists an LLM wrapper around Hazy Research’s manifest library.
manifest is a python library which is itself a wrapper around many model providers, and adds in caching, history, and more.
To use this wrapper:
from langchain.llms.manifest import ManifestWrapper
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Graphsignal
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Helicone
 Contents

Installation and Setup
Wrappers
LLM
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Databerry
 Contents
What is Databerry?
Quick start
Databerry#
This page covers how to use the Databerry within LangChain.
What is Databerry?#
Databerry is an open source document retrievial platform that helps to connect your personal data with Large Language Models.
Quick start#
Retrieving documents stored in Databerry from LangChain is very easy!
from langchain.retrievers import DataberryRetriever
retriever = DataberryRetriever(
    datastore_url="https://api.databerry.ai/query/clg1xg2h80000l708dymr0fxc",
    # api_key="DATABERRY_API_KEY", # optional if datastore is public
    # top_k=10 # optional
)
docs = retriever.get_relevant_documents("What's Databerry?")
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Comet
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Databricks
 Contents

What is Databerry?
Quick start
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
SerpAPI
 Contents
Installation and Setup
Wrappers
Utility
Tool
SerpAPI#
This page covers how to use the SerpAPI search APIs within LangChain.
It is broken into two parts: installation and setup, and then references to the specific SerpAPI wrapper.
Installation and Setup#
Install requirements with pip install google-search-results
Get a SerpAPI api key and either set it as an environment variable (SERPAPI_API_KEY)
Wrappers#
Utility#
There exists a SerpAPI utility which wraps this API. To import this utility:
from langchain.utilities import SerpAPIWrapper
For a more detailed walkthrough of this wrapper, see this notebook.
Tool#
You can also easily load this wrapper as a Tool (to use with an Agent).
You can do this with:
from langchain.agents import load_tools
tools = load_tools(["serpapi"])
For more information on this, see this page
previous
SearxNG Search API
next
StochasticAI
 Contents

Installation and Setup
Wrappers
Utility
Tool
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
SearxNG Search API
 Contents
Installation and Setup
Self Hosted Instance:
Wrappers
Utility
Tool
SearxNG Search API#
This page covers how to use the SearxNG search API within LangChain.
It is broken into two parts: installation and setup, and then references to the specific SearxNG API wrapper.
Installation and Setup#
While it is possible to utilize the wrapper in conjunction with  public searx
instances these instances frequently do not permit API
access (see note on output format below) and have limitations on the frequency
of requests. It is recommended to opt for a self-hosted instance instead.
Self Hosted Instance:#
See this page for installation instructions.
When you install SearxNG, the only active output format by default is the HTML format.
You need to activate the json format to use the API. This can be done by adding the following line to the settings.yml file:
search:
    formats:
        - html
        - json
You can make sure that the API is working by issuing a curl request to the API endpoint:
curl -kLX GET --data-urlencode q='langchain' -d format=json http://localhost:8888
This should return a JSON object with the results.
Wrappers#
Utility#
To use the wrapper we need to pass the host of the SearxNG instance to the wrapper with:
1. the named parameter searx_host when creating the instance.
2. exporting the environment variable SEARXNG_HOST.
You can use the wrapper to get results from a SearxNG instance.
from langchain.utilities import SearxSearchWrapper
s = SearxSearchWrapper(searx_host="http://localhost:8888")
s.run("what is a large language model?")


s.run("what is a large language model?")
Tool#
You can also load this wrapper as a Tool (to use with an Agent).
You can do this with:
from langchain.agents import load_tools
tools = load_tools(["searx-search"],
                    searx_host="http://localhost:8888",
                    engines=["github"])
Note that we could optionally pass custom engines to use.
If you want to obtain results with metadata as json you can use:
tools = load_tools(["searx-search-results-json"],
                    searx_host="http://localhost:8888",
                    num_results=5)
For more information on tools, see this page
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RWKV-4
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SerpAPI
 Contents

Installation and Setup
Self Hosted Instance:
Wrappers
Utility
Tool
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Yeager.ai
 Contents
What is Yeager.ai?
yAgents
How to use?
Creating and Executing Tools with yAgents
Yeager.ai#
This page covers how to use Yeager.ai to generate LangChain tools and agents.
What is Yeager.ai?#
Yeager.ai is an ecosystem designed to simplify the process of creating AI agents and tools.
It features yAgents, a No-code LangChain Agent Builder, which enables users to build, test, and deploy AI solutions with ease. Leveraging the LangChain framework, yAgents allows seamless integration with various language models and resources, making it suitable for developers, researchers, and AI enthusiasts across diverse applications.
yAgents#
Low code generative agent designed to help you build, prototype, and deploy Langchain tools with ease.
How to use?#
pip install yeagerai-agent
yeagerai-agent
Go to http://127.0.0.1:7860
This will install the necessary dependencies and set up yAgents on your system. After the first run, yAgents will create a .env file where you can input your OpenAI API key. You can do the same directly from the Gradio interface under the tab “Settings”.
OPENAI_API_KEY=<your_openai_api_key_here>
We recommend using GPT-4,. However, the tool can also work with GPT-3 if the problem is broken down sufficiently.
Creating and Executing Tools with yAgents#
yAgents makes it easy to create and execute AI-powered tools. Here’s a brief overview of the process:
Create a tool: To create a tool, provide a natural language prompt to yAgents. The prompt should clearly describe the tool’s purpose and functionality. For example:
create a tool that returns the n-th prime number


create a tool that returns the n-th prime number
Load the tool into the toolkit: To load a tool into yAgents, simply provide a command to yAgents that says so. For example:
load the tool that you just created it into your toolkit
Execute the tool: To run a tool or agent, simply provide a command to yAgents that includes the name of the tool and any required parameters. For example:
generate the 50th prime number
You can see a video of how it works here.
As you become more familiar with yAgents, you can create more advanced tools and agents to automate your work and enhance your productivity.
For more information, see yAgents’ Github or our docs
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Writer
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Zilliz
 Contents

What is Yeager.ai?
yAgents
How to use?
Creating and Executing Tools with yAgents
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
PipelineAI
 Contents
Installation and Setup
Wrappers
LLM
PipelineAI#
This page covers how to use the PipelineAI ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific PipelineAI wrappers.
Installation and Setup#
Install with pip install pipeline-ai
Get a Pipeline Cloud api key and set it as an environment variable (PIPELINE_API_KEY)
Wrappers#
LLM#
There exists a PipelineAI LLM wrapper, which you can access with
from langchain.llms import PipelineAI
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Pinecone
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Prediction Guard
 Contents

Installation and Setup
Wrappers
LLM
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Llama.cpp
 Contents
Installation and Setup
Wrappers
LLM
Embeddings
Llama.cpp#
This page covers how to use llama.cpp within LangChain.
It is broken into two parts: installation and setup, and then references to specific Llama-cpp wrappers.
Installation and Setup#
Install the Python package with pip install llama-cpp-python
Download one of the supported models and convert them to the llama.cpp format per the instructions
Wrappers#
LLM#
There exists a LlamaCpp LLM wrapper, which you can access with
from langchain.llms import LlamaCpp
For a more detailed walkthrough of this, see this notebook
Embeddings#
There exists a LlamaCpp Embeddings wrapper, which you can access with
from langchain.embeddings import LlamaCppEmbeddings
For a more detailed walkthrough of this, see this notebook
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LanceDB
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Metal
 Contents

Installation and Setup
Wrappers
LLM
Embeddings
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Pinecone
 Contents
Installation and Setup
Wrappers
VectorStore
Pinecone#
This page covers how to use the Pinecone ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers.
Installation and Setup#
Install the Python SDK with pip install pinecone-client
Wrappers#
VectorStore#
There exists a wrapper around Pinecone indexes, allowing you to use it as a vectorstore,
whether for semantic search or example selection.
To import this vectorstore:
from langchain.vectorstores import Pinecone
For a more detailed walkthrough of the Pinecone wrapper, see this notebook
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PGVector
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PipelineAI
 Contents

Installation and Setup
Wrappers
VectorStore
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Runhouse
 Contents
Installation and Setup
Self-hosted LLMs
Self-hosted Embeddings
Runhouse#
This page covers how to use the Runhouse ecosystem within LangChain.
It is broken into three parts: installation and setup, LLMs, and Embeddings.
Installation and Setup#
Install the Python SDK with pip install runhouse
If you’d like to use on-demand cluster, check your cloud credentials with sky check
Self-hosted LLMs#
For a basic self-hosted LLM, you can use the SelfHostedHuggingFaceLLM class. For more
custom LLMs, you can use the SelfHostedPipeline parent class.
from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
For a more detailed walkthrough of the Self-hosted LLMs, see this notebook
Self-hosted Embeddings#
There are several ways to use self-hosted embeddings with LangChain via Runhouse.
For a basic self-hosted embedding from a Hugging Face Transformers model, you can use
the SelfHostedEmbedding class.
from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
For a more detailed walkthrough of the Self-hosted Embeddings, see this notebook
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Replicate
next
RWKV-4
 Contents

Installation and Setup
Self-hosted LLMs
Self-hosted Embeddings
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.ipynb
.pdf
Comet
 Contents
Install Comet and Dependencies
Initialize Comet and Set your Credentials
Set OpenAI and SerpAPI credentials
Scenario 1: Using just an LLM
Scenario 2: Using an LLM in a Chain
Scenario 3: Using An Agent with Tools
Scenario 4: Using Custom Evaluation Metrics
Comet#
In this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with Comet.
Example Project: Comet with LangChain
Install Comet and Dependencies#
%pip install comet_ml langchain openai google-search-results spacy textstat pandas
import sys
!{sys.executable} -m spacy download en_core_web_sm
Initialize Comet and Set your Credentials#
You can grab your Comet API Key here or click the link after initializing Comet
import comet_ml
comet_ml.init(project_name="comet-example-langchain")
Set OpenAI and SerpAPI credentials#
You will need an OpenAI API Key and a SerpAPI API Key to run the following examples
import os
os.environ["OPENAI_API_KEY"] = "..."
#os.environ["OPENAI_ORGANIZATION"] = "..."
os.environ["SERPAPI_API_KEY"] = "..."
Scenario 1: Using just an LLM#
from datetime import datetime
from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler
from langchain.llms import OpenAI
comet_callback = CometCallbackHandler(
    project_name="comet-example-langchain",
    complexity_metrics=True,
    stream_logs=True,
    tags=["llm"],
    visualizations=["dep"],
)
callbacks = [StdOutCallbackHandler(), comet_callback]
llm = OpenAI(temperature=0.9, callbacks=callbacks, verbose=True)


llm = OpenAI(temperature=0.9, callbacks=callbacks, verbose=True)
llm_result = llm.generate(["Tell me a joke", "Tell me a poem", "Tell me a fact"] * 3)
print("LLM result", llm_result)
comet_callback.flush_tracker(llm, finish=True)
Scenario 2: Using an LLM in a Chain#
from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
comet_callback = CometCallbackHandler(
    complexity_metrics=True,
    project_name="comet-example-langchain",
    stream_logs=True,
    tags=["synopsis-chain"],
)
callbacks = [StdOutCallbackHandler(), comet_callback]
llm = OpenAI(temperature=0.9, callbacks=callbacks)
template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)
test_prompts = [{"title": "Documentary about Bigfoot in Paris"}]
print(synopsis_chain.apply(test_prompts))
comet_callback.flush_tracker(synopsis_chain, finish=True)
Scenario 3: Using An Agent with Tools#
from langchain.agents import initialize_agent, load_tools
from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler
from langchain.llms import OpenAI
comet_callback = CometCallbackHandler(
    project_name="comet-example-langchain",
    complexity_metrics=True,


project_name="comet-example-langchain",
    complexity_metrics=True,
    stream_logs=True,
    tags=["agent"],
)
callbacks = [StdOutCallbackHandler(), comet_callback]
llm = OpenAI(temperature=0.9, callbacks=callbacks)
tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)
agent = initialize_agent(
    tools,
    llm,
    agent="zero-shot-react-description",
    callbacks=callbacks,
    verbose=True,
)
agent.run(
    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?"
)
comet_callback.flush_tracker(agent, finish=True)
Scenario 4: Using Custom Evaluation Metrics#
The CometCallbackManager also allows you to define and use Custom Evaluation Metrics to assess generated outputs from your model. Let’s take a look at how this works.
In the snippet below, we will use the ROUGE metric to evaluate the quality of a generated summary of an input prompt.
%pip install rouge-score
from rouge_score import rouge_scorer
from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
class Rouge:
    def __init__(self, reference):
        self.reference = reference
        self.scorer = rouge_scorer.RougeScorer(["rougeLsum"], use_stemmer=True)
    def compute_metric(self, generation, prompt_idx, gen_idx):
        prediction = generation.text
        results = self.scorer.score(target=self.reference, prediction=prediction)
        return {


return {
            "rougeLsum_score": results["rougeLsum"].fmeasure,
            "reference": self.reference,
        }
reference = """
The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building.
It was the first structure to reach a height of 300 metres.
It is now taller than the Chrysler Building in New York City by 5.2 metres (17 ft)
Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France .
"""
rouge_score = Rouge(reference=reference)
template = """Given the following article, it is your job to write a summary.
Article:
{article}
Summary: This is the summary for the above article:"""
prompt_template = PromptTemplate(input_variables=["article"], template=template)
comet_callback = CometCallbackHandler(
    project_name="comet-example-langchain",
    complexity_metrics=False,
    stream_logs=True,
    tags=["custom_metrics"],
    custom_metrics=rouge_score.compute_metric,
)
callbacks = [StdOutCallbackHandler(), comet_callback]
llm = OpenAI(temperature=0.9)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)
test_prompts = [
    {
        "article": """
                 The tower is 324 metres (1,063 ft) tall, about the same height as
                 an 81-storey building, and the tallest structure in Paris. Its base is square,
                 measuring 125 metres (410 ft) on each side.
                 During its construction, the Eiffel Tower surpassed the
                 Washington Monument to become the tallest man-made structure in the world,
                 a title it held for 41 years until the Chrysler Building


a title it held for 41 years until the Chrysler Building
                 in New York City was finished in 1930.
                 It was the first structure to reach a height of 300 metres.
                 Due to the addition of a broadcasting aerial at the top of the tower in 1957,
                 it is now taller than the Chrysler Building by 5.2 metres (17 ft).
                 Excluding transmitters, the Eiffel Tower is the second tallest
                 free-standing structure in France after the Millau Viaduct.
                 """
    }
]
print(synopsis_chain.apply(test_prompts, callbacks=callbacks))
comet_callback.flush_tracker(synopsis_chain, finish=True)
previous
Cohere
next
Databerry
 Contents

Install Comet and Dependencies
Initialize Comet and Set your Credentials
Set OpenAI and SerpAPI credentials
Scenario 1: Using just an LLM
Scenario 2: Using an LLM in a Chain
Scenario 3: Using An Agent with Tools
Scenario 4: Using Custom Evaluation Metrics
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
GooseAI
 Contents
Installation and Setup
Wrappers
LLM
GooseAI#
This page covers how to use the GooseAI ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific GooseAI wrappers.
Installation and Setup#
Install the Python SDK with pip install openai
Get your GooseAI api key from this link here.
Set the environment variable (GOOSEAI_API_KEY).
import os
os.environ["GOOSEAI_API_KEY"] = "YOUR_API_KEY"
Wrappers#
LLM#
There exists an GooseAI LLM wrapper, which you can access with:
from langchain.llms import GooseAI
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Google Serper
next
GPT4All
 Contents

Installation and Setup
Wrappers
LLM
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
MyScale
 Contents
Introduction
Installation and Setup
Setting up envrionments
Wrappers
VectorStore
MyScale#
This page covers how to use MyScale vector database within LangChain.
It is broken into two parts: installation and setup, and then references to specific MyScale wrappers.
With MyScale, you can manage both structured and unstructured (vectorized) data, and perform joint queries and analytics on both types of data using SQL. Plus, MyScale’s cloud-native OLAP architecture, built on top of ClickHouse, enables lightning-fast data processing even on massive datasets.
Introduction#
Overview to MyScale and High performance vector search
You can now register on our SaaS and start a cluster now!
If you are also interested in how we managed to integrate SQL and vector, please refer to this document for further syntax reference.
We also deliver with live demo on huggingface! Please checkout our huggingface space! They search millions of vector within a blink!
Installation and Setup#
Install the Python SDK with pip install clickhouse-connect
Setting up envrionments#
There are two ways to set up parameters for myscale index.
Environment Variables
Before you run the app, please set the environment variable with export:
export MYSCALE_URL='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...
You can easily find your account, password and other info on our SaaS. For details please refer to this document
Every attributes under MyScaleSettings can be set with prefix MYSCALE_ and is case insensitive.
Create MyScaleSettings object with parameters
from langchain.vectorstores import MyScale, MyScaleSettings
config = MyScaleSetting(host="<your-backend-url>", port=8443, ...)
index = MyScale(embedding_function, config)


index = MyScale(embedding_function, config)
index.add_documents(...)
Wrappers#
supported functions:
add_texts
add_documents
from_texts
from_documents
similarity_search
asimilarity_search
similarity_search_by_vector
asimilarity_search_by_vector
similarity_search_with_relevance_scores
VectorStore#
There exists a wrapper around MyScale database, allowing you to use it as a vectorstore,
whether for semantic search or similar example retrieval.
To import this vectorstore:
from langchain.vectorstores import MyScale
For a more detailed walkthrough of the MyScale wrapper, see this notebook
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Modal
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NLPCloud
 Contents

Introduction
Installation and Setup
Setting up envrionments
Wrappers
VectorStore
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Modal
 Contents
Installation and Setup
Define your Modal Functions and Webhooks
Wrappers
LLM
Modal#
This page covers how to use the Modal ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Modal wrappers.
Installation and Setup#
Install with pip install modal-client
Run modal token new
Define your Modal Functions and Webhooks#
You must include a prompt. There is a rigid response structure.
class Item(BaseModel):
    prompt: str
@stub.webhook(method="POST")
def my_webhook(item: Item):
    return {"prompt": my_function.call(item.prompt)}
An example with GPT2:
from pydantic import BaseModel
import modal
stub = modal.Stub("example-get-started")
volume = modal.SharedVolume().persist("gpt2_model_vol")
CACHE_PATH = "/root/model_cache"
@stub.function(
    gpu="any",
    image=modal.Image.debian_slim().pip_install(
        "tokenizers", "transformers", "torch", "accelerate"
    ),
    shared_volumes={CACHE_PATH: volume},
    retries=3,
)
def run_gpt2(text: str):
    from transformers import GPT2Tokenizer, GPT2LMHeadModel
    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
    model = GPT2LMHeadModel.from_pretrained('gpt2')
    encoded_input = tokenizer(text, return_tensors='pt').input_ids
    output = model.generate(encoded_input, max_length=50, do_sample=True)
    return tokenizer.decode(output[0], skip_special_tokens=True)
class Item(BaseModel):
    prompt: str
@stub.webhook(method="POST")
def get_text(item: Item):


@stub.webhook(method="POST")
def get_text(item: Item):
    return {"prompt": run_gpt2.call(item.prompt)}
Wrappers#
LLM#
There exists an Modal LLM wrapper, which you can access with
from langchain.llms import Modal
previous
MLflow
next
MyScale
 Contents

Installation and Setup
Define your Modal Functions and Webhooks
Wrappers
LLM
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Jina
 Contents
Installation and Setup
Wrappers
Embeddings
Jina#
This page covers how to use the Jina ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Jina wrappers.
Installation and Setup#
Install the Python SDK with pip install jina
Get a Jina AI Cloud auth token from here and set it as an environment variable (JINA_AUTH_TOKEN)
Wrappers#
Embeddings#
There exists a Jina Embeddings wrapper, which you can access with
from langchain.embeddings import JinaEmbeddings
For a more detailed walkthrough of this, see this notebook
previous
Hugging Face
next
LanceDB
 Contents

Installation and Setup
Wrappers
Embeddings
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Prediction Guard
 Contents
Installation and Setup
LLM Wrapper
Example usage
Prediction Guard#
This page covers how to use the Prediction Guard ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Prediction Guard wrappers.
Installation and Setup#
Install the Python SDK with pip install predictionguard
Get an Prediction Guard access token (as described here) and set it as an environment variable (PREDICTIONGUARD_TOKEN)
LLM Wrapper#
There exists a Prediction Guard LLM wrapper, which you can access with
from langchain.llms import PredictionGuard
You can provide the name of your Prediction Guard “proxy” as an argument when initializing the LLM:
pgllm = PredictionGuard(name="your-text-gen-proxy")
Alternatively, you can use Prediction Guard’s default proxy for SOTA LLMs:
pgllm = PredictionGuard(name="default-text-gen")
You can also provide your access token directly as an argument:
pgllm = PredictionGuard(name="default-text-gen", token="<your access token>")
Example usage#
Basic usage of the LLM wrapper:
from langchain.llms import PredictionGuard
pgllm = PredictionGuard(name="default-text-gen")
pgllm("Tell me a joke")
Basic LLM Chaining with the Prediction Guard wrapper:
from langchain import PromptTemplate, LLMChain
from langchain.llms import PredictionGuard
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=PredictionGuard(name="default-text-gen"), verbose=True)
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
llm_chain.predict(question=question)
previous


llm_chain.predict(question=question)
previous
PipelineAI
next
PromptLayer
 Contents

Installation and Setup
LLM Wrapper
Example usage
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
OpenAI
 Contents
Installation and Setup
Wrappers
LLM
Embeddings
Tokenizer
Moderation
OpenAI#
This page covers how to use the OpenAI ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific OpenAI wrappers.
Installation and Setup#
Install the Python SDK with pip install openai
Get an OpenAI api key and set it as an environment variable (OPENAI_API_KEY)
If you want to use OpenAI’s tokenizer (only available for Python 3.9+), install it with pip install tiktoken
Wrappers#
LLM#
There exists an OpenAI LLM wrapper, which you can access with
from langchain.llms import OpenAI
If you are using a model hosted on Azure, you should use different wrapper for that:
from langchain.llms import AzureOpenAI
For a more detailed walkthrough of the Azure wrapper, see this notebook
Embeddings#
There exists an OpenAI Embeddings wrapper, which you can access with
from langchain.embeddings import OpenAIEmbeddings
For a more detailed walkthrough of this, see this notebook
Tokenizer#
There are several places you can use the tiktoken tokenizer. By default, it is used to count tokens
for OpenAI LLMs.
You can also use it to count tokens when splitting documents with
from langchain.text_splitter import CharacterTextSplitter
CharacterTextSplitter.from_tiktoken_encoder(...)
For a more detailed walkthrough of this, see this notebook
Moderation#
You can also access the OpenAI content moderation endpoint with
from langchain.chains import OpenAIModerationChain
For a more detailed walkthrough of this, see this notebook
previous
NLPCloud
next
OpenSearch
 Contents

Installation and Setup
Wrappers
LLM
Embeddings
Tokenizer


Contents

Installation and Setup
Wrappers
LLM
Embeddings
Tokenizer
Moderation
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.ipynb
.pdf
ClearML Integration
 Contents
Getting API Credentials
Setting Up
Scenario 1: Just an LLM
Scenario 2: Creating an agent with tools
Tips and Next Steps
ClearML Integration#
In order to properly keep track of your langchain experiments and their results, you can enable the ClearML integration. ClearML is an experiment manager that neatly tracks and organizes all your experiment runs.
Getting API Credentials#
We’ll be using quite some APIs in this notebook, here is a list and where to get them:
ClearML: https://app.clear.ml/settings/workspace-configuration
OpenAI: https://platform.openai.com/account/api-keys
SerpAPI (google search): https://serpapi.com/dashboard
import os
os.environ["CLEARML_API_ACCESS_KEY"] = ""
os.environ["CLEARML_API_SECRET_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
os.environ["SERPAPI_API_KEY"] = ""
Setting Up#
!pip install clearml
!pip install pandas
!pip install textstat
!pip install spacy
!python -m spacy download en_core_web_sm
from datetime import datetime
from langchain.callbacks import ClearMLCallbackHandler, StdOutCallbackHandler
from langchain.llms import OpenAI
# Setup and use the ClearML Callback
clearml_callback = ClearMLCallbackHandler(
    task_type="inference",
    project_name="langchain_callback_demo",
    task_name="llm",
    tags=["test"],
    # Change the following parameters based on the amount of detail you want tracked
    visualize=True,
    complexity_metrics=True,
    stream_logs=True
)
callbacks = [StdOutCallbackHandler(), clearml_callback]
# Get the OpenAI model ready to go
llm = OpenAI(temperature=0, callbacks=callbacks)


llm = OpenAI(temperature=0, callbacks=callbacks)
The clearml callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/allegroai/clearml/issues with the tag `langchain`.
Scenario 1: Just an LLM#
First, let’s just run a single LLM a few times and capture the resulting prompt-answer conversation in ClearML
# SCENARIO 1 - LLM
llm_result = llm.generate(["Tell me a joke", "Tell me a poem"] * 3)
# After every generation run, use flush to make sure all the metrics
# prompts and other output are properly saved separately
clearml_callback.flush_tracker(langchain_asset=llm, name="simple_sequential")
{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}


{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}
{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}
{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}


{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}
{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}


{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}


{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}


{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}
{'action_records':           action    name  step  starts  ends  errors  text_ctr  chain_starts  \


0   on_llm_start  OpenAI     1       1     0       0         0             0
1   on_llm_start  OpenAI     1       1     0       0         0             0
2   on_llm_start  OpenAI     1       1     0       0         0             0
3   on_llm_start  OpenAI     1       1     0       0         0             0
4   on_llm_start  OpenAI     1       1     0       0         0             0
5   on_llm_start  OpenAI     1       1     0       0         0             0
6     on_llm_end     NaN     2       1     1       0         0             0
7     on_llm_end     NaN     2       1     1       0         0             0
8     on_llm_end     NaN     2       1     1       0         0             0
9     on_llm_end     NaN     2       1     1       0         0             0
10    on_llm_end     NaN     2       1     1       0         0             0
11    on_llm_end     NaN     2       1     1       0         0             0
12  on_llm_start  OpenAI     3       2     1       0         0             0
13  on_llm_start  OpenAI     3       2     1       0         0             0


14  on_llm_start  OpenAI     3       2     1       0         0             0
15  on_llm_start  OpenAI     3       2     1       0         0             0
16  on_llm_start  OpenAI     3       2     1       0         0             0
17  on_llm_start  OpenAI     3       2     1       0         0             0
18    on_llm_end     NaN     4       2     2       0         0             0
19    on_llm_end     NaN     4       2     2       0         0             0
20    on_llm_end     NaN     4       2     2       0         0             0
21    on_llm_end     NaN     4       2     2       0         0             0
22    on_llm_end     NaN     4       2     2       0         0             0
23    on_llm_end     NaN     4       2     2       0         0             0
    chain_ends  llm_starts  ...  difficult_words  linsear_write_formula  \
0            0           1  ...              NaN                    NaN
1            0           1  ...              NaN                    NaN
2            0           1  ...              NaN                    NaN
3            0           1  ...              NaN                    NaN
4            0           1  ...              NaN                    NaN
5            0           1  ...              NaN                    NaN


5            0           1  ...              NaN                    NaN
6            0           1  ...              0.0                    5.5
7            0           1  ...              2.0                    6.5
8            0           1  ...              0.0                    5.5
9            0           1  ...              2.0                    6.5
10           0           1  ...              0.0                    5.5
11           0           1  ...              2.0                    6.5
12           0           2  ...              NaN                    NaN
13           0           2  ...              NaN                    NaN
14           0           2  ...              NaN                    NaN
15           0           2  ...              NaN                    NaN
16           0           2  ...              NaN                    NaN
17           0           2  ...              NaN                    NaN
18           0           2  ...              0.0                    5.5
19           0           2  ...              2.0                    6.5
20           0           2  ...              0.0                    5.5
21           0           2  ...              2.0                    6.5
22           0           2  ...              0.0                    5.5
23           0           2  ...              2.0                    6.5
    gunning_fog      text_standard  fernandez_huerta szigriszt_pazos  \
0           NaN                NaN               NaN             NaN


0           NaN                NaN               NaN             NaN
1           NaN                NaN               NaN             NaN
2           NaN                NaN               NaN             NaN
3           NaN                NaN               NaN             NaN
4           NaN                NaN               NaN             NaN
5           NaN                NaN               NaN             NaN
6          5.20  5th and 6th grade            133.58          131.54
7          8.28  6th and 7th grade            115.58          112.37
8          5.20  5th and 6th grade            133.58          131.54
9          8.28  6th and 7th grade            115.58          112.37
10         5.20  5th and 6th grade            133.58          131.54
11         8.28  6th and 7th grade            115.58          112.37
12          NaN                NaN               NaN             NaN
13          NaN                NaN               NaN             NaN
14          NaN                NaN               NaN             NaN
15          NaN                NaN               NaN             NaN
16          NaN                NaN               NaN             NaN
17          NaN                NaN               NaN             NaN
18         5.20  5th and 6th grade            133.58          131.54
19         8.28  6th and 7th grade            115.58          112.37
20         5.20  5th and 6th grade            133.58          131.54


21         8.28  6th and 7th grade            115.58          112.37
22         5.20  5th and 6th grade            133.58          131.54
23         8.28  6th and 7th grade            115.58          112.37
    gutierrez_polini  crawford  gulpease_index   osman
0                NaN       NaN             NaN     NaN
1                NaN       NaN             NaN     NaN
2                NaN       NaN             NaN     NaN
3                NaN       NaN             NaN     NaN
4                NaN       NaN             NaN     NaN
5                NaN       NaN             NaN     NaN
6              62.30      -0.2            79.8  116.91
7              54.83       1.4            72.1  100.17
8              62.30      -0.2            79.8  116.91
9              54.83       1.4            72.1  100.17
10             62.30      -0.2            79.8  116.91
11             54.83       1.4            72.1  100.17
12               NaN       NaN             NaN     NaN
13               NaN       NaN             NaN     NaN
14               NaN       NaN             NaN     NaN
15               NaN       NaN             NaN     NaN
16               NaN       NaN             NaN     NaN
17               NaN       NaN             NaN     NaN
18             62.30      -0.2            79.8  116.91


19             54.83       1.4            72.1  100.17
20             62.30      -0.2            79.8  116.91
21             54.83       1.4            72.1  100.17
22             62.30      -0.2            79.8  116.91
23             54.83       1.4            72.1  100.17
[24 rows x 39 columns], 'session_analysis':     prompt_step         prompts    name  output_step  \
0             1  Tell me a joke  OpenAI            2
1             1  Tell me a poem  OpenAI            2
2             1  Tell me a joke  OpenAI            2
3             1  Tell me a poem  OpenAI            2
4             1  Tell me a joke  OpenAI            2
5             1  Tell me a poem  OpenAI            2
6             3  Tell me a joke  OpenAI            4
7             3  Tell me a poem  OpenAI            4
8             3  Tell me a joke  OpenAI            4
9             3  Tell me a poem  OpenAI            4
10            3  Tell me a joke  OpenAI            4
11            3  Tell me a poem  OpenAI            4
                                               output  \
0   \n\nQ: What did the fish say when it hit the w...
1   \n\nRoses are red,\nViolets are blue,\nSugar i...


2   \n\nQ: What did the fish say when it hit the w...
3   \n\nRoses are red,\nViolets are blue,\nSugar i...
4   \n\nQ: What did the fish say when it hit the w...
5   \n\nRoses are red,\nViolets are blue,\nSugar i...
6   \n\nQ: What did the fish say when it hit the w...
7   \n\nRoses are red,\nViolets are blue,\nSugar i...
8   \n\nQ: What did the fish say when it hit the w...
9   \n\nRoses are red,\nViolets are blue,\nSugar i...
10  \n\nQ: What did the fish say when it hit the w...
11  \n\nRoses are red,\nViolets are blue,\nSugar i...
    token_usage_total_tokens  token_usage_prompt_tokens  \
0                        162                         24
1                        162                         24
2                        162                         24
3                        162                         24
4                        162                         24
5                        162                         24
6                        162                         24
7                        162                         24
8                        162                         24
9                        162                         24
10                       162                         24
11                       162                         24
    token_usage_completion_tokens  flesch_reading_ease  flesch_kincaid_grade  \
0                             138               109.04                   1.3
1                             138                83.66                   4.8


1                             138                83.66                   4.8
2                             138               109.04                   1.3
3                             138                83.66                   4.8
4                             138               109.04                   1.3
5                             138                83.66                   4.8
6                             138               109.04                   1.3
7                             138                83.66                   4.8
8                             138               109.04                   1.3
9                             138                83.66                   4.8
10                            138               109.04                   1.3
11                            138                83.66                   4.8
    ...  difficult_words  linsear_write_formula  gunning_fog  \
0   ...                0                    5.5         5.20
1   ...                2                    6.5         8.28
2   ...                0                    5.5         5.20
3   ...                2                    6.5         8.28
4   ...                0                    5.5         5.20
5   ...                2                    6.5         8.28
6   ...                0                    5.5         5.20
7   ...                2                    6.5         8.28
8   ...                0                    5.5         5.20
9   ...                2                    6.5         8.28
10  ...                0                    5.5         5.20


10  ...                0                    5.5         5.20
11  ...                2                    6.5         8.28
        text_standard  fernandez_huerta  szigriszt_pazos  gutierrez_polini  \
0   5th and 6th grade            133.58           131.54             62.30
1   6th and 7th grade            115.58           112.37             54.83
2   5th and 6th grade            133.58           131.54             62.30
3   6th and 7th grade            115.58           112.37             54.83
4   5th and 6th grade            133.58           131.54             62.30
5   6th and 7th grade            115.58           112.37             54.83
6   5th and 6th grade            133.58           131.54             62.30
7   6th and 7th grade            115.58           112.37             54.83
8   5th and 6th grade            133.58           131.54             62.30
9   6th and 7th grade            115.58           112.37             54.83
10  5th and 6th grade            133.58           131.54             62.30
11  6th and 7th grade            115.58           112.37             54.83
   crawford  gulpease_index   osman


crawford  gulpease_index   osman
0      -0.2            79.8  116.91
1       1.4            72.1  100.17
2      -0.2            79.8  116.91
3       1.4            72.1  100.17
4      -0.2            79.8  116.91
5       1.4            72.1  100.17
6      -0.2            79.8  116.91
7       1.4            72.1  100.17
8      -0.2            79.8  116.91
9       1.4            72.1  100.17
10     -0.2            79.8  116.91
11      1.4            72.1  100.17
[12 rows x 24 columns]}
2023-03-29 14:00:25,948 - clearml.Task - INFO - Completed model upload to https://files.clear.ml/langchain_callback_demo/llm.988bd727b0e94a29a3ac0ee526813545/models/simple_sequential
At this point you can already go to https://app.clear.ml and take a look at the resulting ClearML Task that was created.
Among others, you should see that this notebook is saved along with any git information. The model JSON that contains the used parameters is saved as an artifact, there are also console logs and under the plots section, you’ll find tables that represent the flow of the chain.
Finally, if you enabled visualizations, these are stored as HTML files under debug samples.


Finally, if you enabled visualizations, these are stored as HTML files under debug samples.
Scenario 2: Creating an agent with tools#
To show a more advanced workflow, let’s create an agent with access to tools. The way ClearML tracks the results is not different though, only the table will look slightly different as there are other types of actions taken when compared to the earlier, simpler example.
You can now also see the use of the finish=True keyword, which will fully close the ClearML Task, instead of just resetting the parameters and prompts for a new conversation.
from langchain.agents import initialize_agent, load_tools
from langchain.agents import AgentType
# SCENARIO 2 - Agent with Tools
tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    callbacks=callbacks,
)
agent.run(
    "Who is the wife of the person who sang summer of 69?"
)
clearml_callback.flush_tracker(langchain_asset=agent, name="Agent with Tools", finish=True)
> Entering new AgentExecutor chain...
{'action': 'on_chain_start', 'name': 'AgentExecutor', 'step': 1, 'starts': 1, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 0, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'input': 'Who is the wife of the person who sang summer of 69?'}


{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 2, 'starts': 2, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought:'}


{'action': 'on_llm_end', 'token_usage_prompt_tokens': 189, 'token_usage_completion_tokens': 34, 'token_usage_total_tokens': 223, 'model_name': 'text-davinci-003', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': ' I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 91.61, 'flesch_kincaid_grade': 3.8, 'smog_index': 0.0, 'coleman_liau_index': 3.41, 'automated_readability_index': 3.5, 'dale_chall_readability_score': 6.06, 'difficult_words': 2, 'linsear_write_formula': 5.75, 'gunning_fog': 5.4, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 121.07, 'szigriszt_pazos': 119.5, 'gutierrez_polini': 54.91, 'crawford': 0.9, 'gulpease_index': 72.7, 'osman': 92.16}


I need to find out who sang summer of 69 and then find out who their wife is.
Action: Search
Action Input: "Who sang summer of 69"{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who sang summer of 69', 'log': ' I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"', 'step': 4, 'starts': 3, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 1, 'tool_ends': 0, 'agent_ends': 0}
{'action': 'on_tool_start', 'input_str': 'Who sang summer of 69', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 5, 'starts': 4, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 0, 'agent_ends': 0}
Observation: Bryan Adams - Summer Of 69 (Official Music Video).


Observation: Bryan Adams - Summer Of 69 (Official Music Video).
Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams - Summer Of 69 (Official Music Video).', 'step': 6, 'starts': 4, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0}


{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 7, 'starts': 5, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\nThought:'}


{'action': 'on_llm_end', 'token_usage_prompt_tokens': 242, 'token_usage_completion_tokens': 28, 'token_usage_total_tokens': 270, 'model_name': 'text-davinci-003', 'step': 8, 'starts': 5, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'text': ' I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 94.66, 'flesch_kincaid_grade': 2.7, 'smog_index': 0.0, 'coleman_liau_index': 4.73, 'automated_readability_index': 4.0, 'dale_chall_readability_score': 7.16, 'difficult_words': 2, 'linsear_write_formula': 4.25, 'gunning_fog': 4.2, 'text_standard': '4th and 5th grade', 'fernandez_huerta': 124.13, 'szigriszt_pazos': 119.2, 'gutierrez_polini': 52.26, 'crawford': 0.7, 'gulpease_index': 74.7, 'osman': 84.2}
 I need to find out who Bryan Adams is married to.
Action: Search


I need to find out who Bryan Adams is married to.
Action: Search
Action Input: "Who is Bryan Adams married to"{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who is Bryan Adams married to', 'log': ' I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"', 'step': 9, 'starts': 6, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 3, 'tool_ends': 1, 'agent_ends': 0}
{'action': 'on_tool_start', 'input_str': 'Who is Bryan Adams married to', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 10, 'starts': 7, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 1, 'agent_ends': 0}
Observation: Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...


Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...', 'step': 11, 'starts': 7, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0}


{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 12, 'starts': 8, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who


to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\nThought: I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"\nObservation: Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...\nThought:'}


{'action': 'on_llm_end', 'token_usage_prompt_tokens': 314, 'token_usage_completion_tokens': 18, 'token_usage_total_tokens': 332, 'model_name': 'text-davinci-003', 'step': 13, 'starts': 8, 'ends': 5, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'text': ' I now know the final answer.\nFinal Answer: Bryan Adams has never been married.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 81.29, 'flesch_kincaid_grade': 3.7, 'smog_index': 0.0, 'coleman_liau_index': 5.75, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 7.37, 'difficult_words': 1, 'linsear_write_formula': 2.5, 'gunning_fog': 2.8, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 115.7, 'szigriszt_pazos': 110.84, 'gutierrez_polini': 49.79, 'crawford': 0.7, 'gulpease_index': 85.4, 'osman': 83.14}
 I now know the final answer.
Final Answer: Bryan Adams has never been married.


I now know the final answer.
Final Answer: Bryan Adams has never been married.
{'action': 'on_agent_finish', 'output': 'Bryan Adams has never been married.', 'log': ' I now know the final answer.\nFinal Answer: Bryan Adams has never been married.', 'step': 14, 'starts': 8, 'ends': 6, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1}
> Finished chain.
{'action': 'on_chain_end', 'outputs': 'Bryan Adams has never been married.', 'step': 15, 'starts': 8, 'ends': 7, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 1, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1}
{'action_records':              action    name  step  starts  ends  errors  text_ctr  \
0      on_llm_start  OpenAI     1       1     0       0         0
1      on_llm_start  OpenAI     1       1     0       0         0
2      on_llm_start  OpenAI     1       1     0       0         0
3      on_llm_start  OpenAI     1       1     0       0         0


4      on_llm_start  OpenAI     1       1     0       0         0
..              ...     ...   ...     ...   ...     ...       ...
66      on_tool_end     NaN    11       7     4       0         0
67     on_llm_start  OpenAI    12       8     4       0         0
68       on_llm_end     NaN    13       8     5       0         0
69  on_agent_finish     NaN    14       8     6       0         0
70     on_chain_end     NaN    15       8     7       0         0
    chain_starts  chain_ends  llm_starts  ...  gulpease_index  osman  input  \
0              0           0           1  ...             NaN    NaN    NaN
1              0           0           1  ...             NaN    NaN    NaN
2              0           0           1  ...             NaN    NaN    NaN
3              0           0           1  ...             NaN    NaN    NaN
4              0           0           1  ...             NaN    NaN    NaN
..           ...         ...         ...  ...             ...    ...    ...
66             1           0           2  ...             NaN    NaN    NaN
67             1           0           3  ...             NaN    NaN    NaN
68             1           0           3  ...            85.4  83.14    NaN
69             1           0           3  ...             NaN    NaN    NaN


69             1           0           3  ...             NaN    NaN    NaN
70             1           1           3  ...             NaN    NaN    NaN
    tool  tool_input                                                log  \
0    NaN         NaN                                                NaN
1    NaN         NaN                                                NaN
2    NaN         NaN                                                NaN
3    NaN         NaN                                                NaN
4    NaN         NaN                                                NaN
..   ...         ...                                                ...
66   NaN         NaN                                                NaN
67   NaN         NaN                                                NaN
68   NaN         NaN                                                NaN
69   NaN         NaN   I now know the final answer.\nFinal Answer: B...
70   NaN         NaN                                                NaN
    input_str  description                                             output  \
0         NaN          NaN                                                NaN
1         NaN          NaN                                                NaN
2         NaN          NaN                                                NaN
3         NaN          NaN                                                NaN
4         NaN          NaN                                                NaN
..        ...          ...                                                ...
66        NaN          NaN  Bryan Adams has never married. In the 1990s, h...
67        NaN          NaN                                                NaN
68        NaN          NaN                                                NaN
69        NaN          NaN                Bryan Adams has never been married.
70        NaN          NaN                                                NaN
                                outputs
0                                   NaN
1                                   NaN
2                                   NaN
3                                   NaN
4                                   NaN
..                                  ...
66                                  NaN
67                                  NaN
68                                  NaN
69                                  NaN
70  Bryan Adams has never been married.
[71 rows x 47 columns], 'session_analysis':    prompt_step                                            prompts    name  \
0            2  Answer the following questions as best you can...  OpenAI


0            2  Answer the following questions as best you can...  OpenAI
1            7  Answer the following questions as best you can...  OpenAI
2           12  Answer the following questions as best you can...  OpenAI
   output_step                                             output  \
0            3   I need to find out who sang summer of 69 and ...
1            8   I need to find out who Bryan Adams is married...
2           13   I now know the final answer.\nFinal Answer: B...
   token_usage_total_tokens  token_usage_prompt_tokens  \
0                       223                        189
1                       270                        242
2                       332                        314
   token_usage_completion_tokens  flesch_reading_ease  flesch_kincaid_grade  \
0                             34                91.61                   3.8
1                             28                94.66                   2.7
2                             18                81.29                   3.7
   ...  difficult_words  linsear_write_formula  gunning_fog  \
0  ...                2                   5.75          5.4
1  ...                2                   4.25          4.2
2  ...                1                   2.50          2.8
       text_standard  fernandez_huerta  szigriszt_pazos  gutierrez_polini  \
0  3rd and 4th grade            121.07           119.50             54.91
1  4th and 5th grade            124.13           119.20             52.26


2  3rd and 4th grade            115.70           110.84             49.79
  crawford  gulpease_index  osman
0      0.9            72.7  92.16
1      0.7            74.7  84.20
2      0.7            85.4  83.14
[3 rows x 24 columns]}
Could not update last created model in Task 988bd727b0e94a29a3ac0ee526813545, Task status 'completed' cannot be updated
Tips and Next Steps#
Make sure you always use a unique name argument for the clearml_callback.flush_tracker function. If not, the model parameters used for a run will override the previous run!
If you close the ClearML Callback using clearml_callback.flush_tracker(..., finish=True) the Callback cannot be used anymore. Make a new one if you want to keep logging.
Check out the rest of the open source ClearML ecosystem, there is a data version manager, a remote execution agent, automated pipelines and much more!
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Chroma
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Cohere
 Contents

Getting API Credentials
Setting Up
Scenario 1: Just an LLM
Scenario 2: Creating an agent with tools
Tips and Next Steps
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Metal
 Contents
What is Metal?
Quick start
Metal#
This page covers how to use Metal within LangChain.
What is Metal?#
Metal is a  managed retrieval & memory platform built for production. Easily index your data into Metal and run semantic search and retrieval on it.
Quick start#
Get started by creating a Metal account.
Then, you can easily take advantage of the MetalRetriever class to start retrieving your data for semantic search, prompting context, etc. This class takes a Metal instance and a dictionary of parameters to pass to the Metal API.
from langchain.retrievers import MetalRetriever
from metal_sdk.metal import Metal
metal = Metal("API_KEY", "CLIENT_ID", "INDEX_ID");
retriever = MetalRetriever(metal, params={"limit": 2})
docs = retriever.get_relevant_documents("search term")
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Llama.cpp
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Milvus
 Contents

What is Metal?
Quick start
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
RWKV-4
 Contents
Installation and Setup
Usage
RWKV
Model File
Rwkv-4 models -> recommended VRAM
RWKV-4#
This page covers how to use the RWKV-4 wrapper within LangChain.
It is broken into two parts: installation and setup, and then usage with an example.
Installation and Setup#
Install the Python package with pip install rwkv
Install the tokenizer Python package with pip install tokenizer
Download a RWKV model and place it in your desired directory
Download the tokens file
Usage#
RWKV#
To use the RWKV wrapper, you need to provide the path to the pre-trained model file and the tokenizer’s configuration.
from langchain.llms import RWKV
# Test the model
```python
def generate_prompt(instruction, input=None):
    if input:
        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
# Instruction:
{instruction}
# Input:
{input}
# Response:
"""
    else:
        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
# Instruction:
{instruction}
# Response:
"""
model = RWKV(model="./models/RWKV-4-Raven-3B-v7-Eng-20230404-ctx4096.pth", strategy="cpu fp32", tokens_path="./rwkv/20B_tokenizer.json")
response = model(generate_prompt("Once upon a time, "))
Model File#
You can find links to model file downloads at the RWKV-4-Raven repository.
Rwkv-4 models -> recommended VRAM#
RWKV VRAM
Model | 8bit | bf16/fp16 | fp32


RWKV VRAM
Model | 8bit | bf16/fp16 | fp32
14B   | 16GB | 28GB      | >50GB
7B    | 8GB  | 14GB      | 28GB
3B    | 2.8GB| 6GB       | 12GB
1b5   | 1.3GB| 3GB       | 6GB
See the rwkv pip page for more information about strategies, including streaming and cuda support.
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Runhouse
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SearxNG Search API
 Contents

Installation and Setup
Usage
RWKV
Model File
Rwkv-4 models -> recommended VRAM
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Replicate
 Contents
Installation and Setup
Calling a model
Replicate#
This page covers how to run models on Replicate within LangChain.
Installation and Setup#
Create a Replicate account. Get your API key and set it as an environment variable (REPLICATE_API_TOKEN)
Install the Replicate python client with pip install replicate
Calling a model#
Find a model on the Replicate explore page, and then paste in the model name and version in this format: owner-name/model-name:version
For example, for this dolly model, click on the API tab. The model name/version would be: "replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5"
Only the model param is required, but any other model parameters can also be passed in with the format input={model_param: value, ...}
For example, if we were running stable diffusion and wanted to change the image dimensions:
Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", input={'image_dimensions': '512x512'})
Note that only the first output of a model will be returned.
From here, we can initialize our model:
llm = Replicate(model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5")
And run it:
prompt = """


And run it:
prompt = """
Answer the following yes/no question by reasoning step by step.
Can a dog drive a car?
"""
llm(prompt)
We can call any Replicate model (not just LLMs) using this syntax. For example, we can call Stable Diffusion:
text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", input={'image_dimensions':'512x512'})
image_output = text2image("A cat riding a motorcycle by Picasso")
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Redis
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Runhouse
 Contents

Installation and Setup
Calling a model
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.ipynb
.pdf
Aim
Aim#
Aim makes it super easy to visualize and debug LangChain executions. Aim tracks inputs and outputs of LLMs and tools, as well as actions of agents.
With Aim, you can easily debug and examine an individual execution:
Additionally, you have the option to compare multiple executions side by side:
Aim is fully open source, learn more about Aim on GitHub.
Let’s move forward and see how to enable and configure Aim callback.
Tracking LangChain Executions with AimIn this notebook we will explore three usage scenarios. To start off, we will install the necessary packages and import certain modules. Subsequently, we will configure two environment variables that can be established either within the Python script or through the terminal.
!pip install aim
!pip install langchain
!pip install openai
!pip install google-search-results
import os
from datetime import datetime
from langchain.llms import OpenAI
from langchain.callbacks import AimCallbackHandler, StdOutCallbackHandler
Our examples use a GPT model as the LLM, and OpenAI offers an API for this purpose. You can obtain the key from the following link: https://platform.openai.com/account/api-keys .
We will use the SerpApi to retrieve search results from Google. To acquire the SerpApi key, please go to https://serpapi.com/manage-api-key .
os.environ["OPENAI_API_KEY"] = "..."
os.environ["SERPAPI_API_KEY"] = "..."
The event methods of AimCallbackHandler accept the LangChain module or agent as input and log at least the prompts and generated results, as well as the serialized version of the LangChain module, to the designated Aim run.
session_group = datetime.now().strftime("%m.%d.%Y_%H.%M.%S")
aim_callback = AimCallbackHandler(
    repo=".",


aim_callback = AimCallbackHandler(
    repo=".",
    experiment_name="scenario 1: OpenAI LLM",
)
callbacks = [StdOutCallbackHandler(), aim_callback]
llm = OpenAI(temperature=0, callbacks=callbacks)
The flush_tracker function is used to record LangChain assets on Aim. By default, the session is reset rather than being terminated outright.
Scenario 1 In the first scenario, we will use OpenAI LLM.
# scenario 1 - LLM
llm_result = llm.generate(["Tell me a joke", "Tell me a poem"] * 3)
aim_callback.flush_tracker(
    langchain_asset=llm,
    experiment_name="scenario 2: Chain with multiple SubChains on multiple generations",
)
Scenario 2 Scenario two involves chaining with multiple SubChains across multiple generations.
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
# scenario 2 - Chain
template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)
test_prompts = [
    {"title": "documentary about good video games that push the boundary of game design"},
    {"title": "the phenomenon behind the remarkable speed of cheetahs"},
    {"title": "the best in class mlops tooling"},
]
synopsis_chain.apply(test_prompts)
aim_callback.flush_tracker(
    langchain_asset=synopsis_chain, experiment_name="scenario 3: Agent with Tools"
)


)
Scenario 3 The third scenario involves an agent with tools.
from langchain.agents import initialize_agent, load_tools
from langchain.agents import AgentType
# scenario 3 - Agent with Tools
tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    callbacks=callbacks,
)
agent.run(
    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?"
)
aim_callback.flush_tracker(langchain_asset=agent, reset=False, finish=True)
> Entering new AgentExecutor chain...
 I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.
Action: Search
Action Input: "Leo DiCaprio girlfriend"
Observation: Leonardo DiCaprio seemed to prove a long-held theory about his love life right after splitting from girlfriend Camila Morrone just months ...
Thought: I need to find out Camila Morrone's age
Action: Search
Action Input: "Camila Morrone age"
Observation: 25 years
Thought: I need to calculate 25 raised to the 0.43 power
Action: Calculator
Action Input: 25^0.43
Observation: Answer: 3.991298452658078
Thought: I now know the final answer
Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.
> Finished chain.
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AI21 Labs
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AnalyticDB
By Harrison Chase

      © Copyright 2023, Harrison Chase.


AnalyticDB
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.md
.pdf
Tair
 Contents
Installation and Setup
Wrappers
VectorStore
Tair#
This page covers how to use the Tair ecosystem within LangChain.
Installation and Setup#
Install Tair Python SDK with pip install tair.
Wrappers#
VectorStore#
There exists a wrapper around TairVector, allowing you to use it as a vectorstore,
whether for semantic search or example selection.
To import this vectorstore:
from langchain.vectorstores import Tair
For a more detailed walkthrough of the Tair wrapper, see this notebook
previous
StochasticAI
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Unstructured
 Contents

Installation and Setup
Wrappers
VectorStore
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.ipynb
.pdf
Chat Over Documents with Vectara
 Contents
Pass in chat history
Return Source Documents
ConversationalRetrievalChain with search_distance
ConversationalRetrievalChain with map_reduce
ConversationalRetrievalChain with Question Answering with sources
ConversationalRetrievalChain with streaming to stdout
get_chat_history Function
Chat Over Documents with Vectara#
This notebook is based on the chat_vector_db notebook, but using Vectara as the vector database.
import os
from langchain.vectorstores import Vectara
from langchain.vectorstores.vectara import VectaraRetriever
from langchain.llms import OpenAI
from langchain.chains import ConversationalRetrievalChain
Load in documents. You can replace this with a loader for whatever type of data you want
from langchain.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
We now split the documents, create embeddings for them, and put them in a vectorstore. This allows us to do semantic search over them.
vectorstore = Vectara.from_documents(documents, embedding=None)
We can now create a memory object, which is neccessary to track the inputs/outputs and hold a conversation.
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
We now initialize the ConversationalRetrievalChain
openai_api_key = os.environ['OPENAI_API_KEY']
llm = OpenAI(openai_api_key=openai_api_key, temperature=0)
retriever = VectaraRetriever(vectorstore, alpha=0.025, k=5, filter=None)
print(type(vectorstore))
d = retriever.get_relevant_documents('What did the president say about Ketanji Brown Jackson')


qa = ConversationalRetrievalChain.from_llm(llm, retriever, memory=memory)
<class 'langchain.vectorstores.vectara.Vectara'>
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query})
result["answer"]
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, and a former federal public defender."
query = "Did he mention who she suceeded"
result = qa({"question": query})
result['answer']
' Justice Stephen Breyer.'
Pass in chat history#
In the above example, we used a Memory object to track chat history. We can also just pass it in explicitly. In order to do this, we need to initialize a chain without any memory object.
qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever())
Here’s an example of asking a question with no chat history
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})
result["answer"]
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, and a former federal public defender."
Here’s an example of asking a question with some chat history
chat_history = [(query, result["answer"])]
query = "Did he mention who she suceeded"
result = qa({"question": query, "chat_history": chat_history})
result['answer']
' Justice Stephen Breyer.'
Return Source Documents#


result['answer']
' Justice Stephen Breyer.'
Return Source Documents#
You can also easily return source documents from the ConversationalRetrievalChain. This is useful for when you want to inspect what documents were returned.
qa = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})
result['source_documents'][0]
Document(page_content='Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice. A former federal public defender.', metadata={'source': '../../modules/state_of_the_union.txt'})
ConversationalRetrievalChain with search_distance#
If you are using a vector store that supports filtering by search distance, you can add a threshold value parameter.
vectordbkwargs = {"search_distance": 0.9}
qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history, "vectordbkwargs": vectordbkwargs})


ConversationalRetrievalChain with map_reduce#
We can also use different types of combine document chains with the ConversationalRetrievalChain chain.
from langchain.chains import LLMChain
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(llm, chain_type="map_reduce")
chain = ConversationalRetrievalChain(
    retriever=vectorstore.as_retriever(),
    question_generator=question_generator,
    combine_docs_chain=doc_chain,
)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = chain({"question": query, "chat_history": chat_history})
result['answer']
' The president did not mention Ketanji Brown Jackson.'
ConversationalRetrievalChain with Question Answering with sources#
You can also use this chain with the question answering with sources chain.
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_with_sources_chain(llm, chain_type="map_reduce")
chain = ConversationalRetrievalChain(
    retriever=vectorstore.as_retriever(),
    question_generator=question_generator,
    combine_docs_chain=doc_chain,
)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = chain({"question": query, "chat_history": chat_history})
result['answer']


result = chain({"question": query, "chat_history": chat_history})
result['answer']
' The president did not mention Ketanji Brown Jackson.\nSOURCES: ../../modules/state_of_the_union.txt'
ConversationalRetrievalChain with streaming to stdout#
Output from the chain will be streamed to stdout token by token in this example.
from langchain.chains.llm import LLMChain
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT
from langchain.chains.question_answering import load_qa_chain
# Construct a ConversationalRetrievalChain with a streaming llm for combine docs
# and a separate, non-streaming llm for question generation
llm = OpenAI(temperature=0, openai_api_key=openai_api_key)
streaming_llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0, openai_api_key=openai_api_key)
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(streaming_llm, chain_type="stuff", prompt=QA_PROMPT)
qa = ConversationalRetrievalChain(
    retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})
 The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, and a former federal public defender.
chat_history = [(query, result["answer"])]


chat_history = [(query, result["answer"])]
query = "Did he mention who she suceeded"
result = qa({"question": query, "chat_history": chat_history})
 Justice Stephen Breyer.
get_chat_history Function#
You can also specify a get_chat_history function, which can be used to format the chat_history string.
def get_chat_history(inputs) -> str:
    res = []
    for human, ai in inputs:
        res.append(f"Human:{human}\nAI:{ai}")
    return "\n".join(res)
qa = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), get_chat_history=get_chat_history)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})
result['answer']
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, and a former federal public defender."
 Contents

Pass in chat history
Return Source Documents
ConversationalRetrievalChain with search_distance
ConversationalRetrievalChain with map_reduce
ConversationalRetrievalChain with Question Answering with sources
ConversationalRetrievalChain with streaming to stdout
get_chat_history Function
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


.ipynb
.pdf
Vectara Text Generation
 Contents
Prepare Data
Set Up Vector DB
Set Up LLM Chain with Custom Prompt
Generate Text
Vectara Text Generation#
This notebook is based on chat_vector_db and adapted to Vectara.
Prepare Data#
First, we prepare the data. For this example, we fetch a documentation site that consists of markdown files hosted on Github and split them into small enough Documents.
from langchain.llms import OpenAI
from langchain.docstore.document import Document
import requests
from langchain.vectorstores import Vectara
from langchain.text_splitter import CharacterTextSplitter
from langchain.prompts import PromptTemplate
import pathlib
import subprocess
import tempfile
def get_github_docs(repo_owner, repo_name):
    with tempfile.TemporaryDirectory() as d:
        subprocess.check_call(
            f"git clone --depth 1 https://github.com/{repo_owner}/{repo_name}.git .",
            cwd=d,
            shell=True,
        )
        git_sha = (
            subprocess.check_output("git rev-parse HEAD", shell=True, cwd=d)
            .decode("utf-8")
            .strip()
        )
        repo_path = pathlib.Path(d)
        markdown_files = list(repo_path.glob("*/*.md")) + list(
            repo_path.glob("*/*.mdx")
        )
        for markdown_file in markdown_files:
            with open(markdown_file, "r") as f:
                relative_path = markdown_file.relative_to(repo_path)
                github_url = f"https://github.com/{repo_owner}/{repo_name}/blob/{git_sha}/{relative_path}"
                yield Document(page_content=f.read(), metadata={"source": github_url})
sources = get_github_docs("yirenlu92", "deno-manual-forked")
source_chunks = []


source_chunks = []
splitter = CharacterTextSplitter(separator=" ", chunk_size=1024, chunk_overlap=0)
for source in sources:
    for chunk in splitter.split_text(source.page_content):
        source_chunks.append(chunk)
Cloning into '.'...
Set Up Vector DB#
Now that we have the documentation content in chunks, let’s put all this information in a vector index for easy retrieval.
import os
search_index = Vectara.from_texts(source_chunks, embedding=None)
Set Up LLM Chain with Custom Prompt#
Next, let’s set up a simple LLM chain but give it a custom prompt for blog post generation. Note that the custom prompt is parameterized and takes two inputs: context, which will be the documents fetched from the vector search, and topic, which is given by the user.
from langchain.chains import LLMChain
prompt_template = """Use the context below to write a 400 word blog post about the topic below:
    Context: {context}
    Topic: {topic}
    Blog post:"""
PROMPT = PromptTemplate(
    template=prompt_template, input_variables=["context", "topic"]
)
llm = OpenAI(openai_api_key=os.environ['OPENAI_API_KEY'], temperature=0)
chain = LLMChain(llm=llm, prompt=PROMPT)
Generate Text#
Finally, we write a function to apply our inputs to the chain. The function takes an input parameter topic. We find the documents in the vector index that correspond to that topic, and use them as additional context in our simple LLM chain.
def generate_blog_post(topic):
    docs = search_index.similarity_search(topic, k=4)
    inputs = [{"context": doc.page_content, "topic": topic} for doc in docs]
    print(chain.apply(inputs))


print(chain.apply(inputs))
generate_blog_post("environment variables")


[{'text': '\n\nEnvironment variables are an essential part of any development workflow. They provide a way to store and access information that is specific to the environment in which the code is running. This can be especially useful when working with different versions of a language or framework, or when running code on different machines.\n\nThe Deno CLI tasks extension provides a way to easily manage environment variables when running Deno commands. This extension provides a task definition for allowing you to create tasks that execute the `deno` CLI from within the editor. The template for the Deno CLI tasks has the following interface, which can be configured in a `tasks.json` within your workspace:\n\nThe task definition includes the `type` field, which should be set to `deno`, and the `command` field, which is the `deno` command to run (e.g. `run`, `test`, `cache`, etc.). Additionally, you can specify additional arguments to pass on the command line, the current working directory to execute the command, and any environment variables.\n\nUsing environment variables with the