# Causal program-aided language (CPAL) chain
## Motivation
This builds on the recent [PAL](https://arxiv.org/abs/2211.10435) to
stop LLM hallucination. The problem with the
[PAL](https://arxiv.org/abs/2211.10435) approach is that it hallucinates
on a math problem with a nested chain of dependence. The innovation here
is that this new CPAL approach includes causal structure to fix
hallucination.
For example, using the below word problem, PAL answers with 5, and CPAL
answers with 13.
"Tim buys the same number of pets as Cindy and Boris."
"Cindy buys the same number of pets as Bill plus Bob."
"Boris buys the same number of pets as Ben plus Beth."
"Bill buys the same number of pets as Obama."
"Bob buys the same number of pets as Obama."
"Ben buys the same number of pets as Obama."
"Beth buys the same number of pets as Obama."
"If Obama buys one pet, how many pets total does everyone buy?"
The CPAL chain represents the causal structure of the above narrative as
a causal graph or DAG, which it can also plot, as shown below.
.
The two major sections below are:
1. Technical overview
2. Future application
Also see [this jupyter
notebook](https://github.com/borisdev/langchain/blob/master/docs/extras/modules/chains/additional/cpal.ipynb)
doc.
## 1. Technical overview
### CPAL versus PAL
Like [PAL](https://arxiv.org/abs/2211.10435), CPAL intends to reduce
large language model (LLM) hallucination.
The CPAL chain is different from the PAL chain for a couple of reasons.
* CPAL adds a causal structure (or DAG) to link entity actions (or math
expressions).
* The CPAL math expressions are modeling a chain of cause and effect
relations, which can be intervened upon, whereas for the PAL chain math
expressions are projected math identities.
PAL's generated python code is wrong. It hallucinates when complexity
increases.
```python
def solution():
"""Tim buys the same number of pets as Cindy and Boris.Cindy buys the same number of pets as Bill plus Bob.Boris buys the same number of pets as Ben plus Beth.Bill buys the same number of pets as Obama.Bob buys the same number of pets as Obama.Ben buys the same number of pets as Obama.Beth buys the same number of pets as Obama.If Obama buys one pet, how many pets total does everyone buy?"""
obama_pets = 1
tim_pets = obama_pets
cindy_pets = obama_pets + obama_pets
boris_pets = obama_pets + obama_pets
total_pets = tim_pets + cindy_pets + boris_pets
result = total_pets
return result # math result is 5
```
CPAL's generated python code is correct.
```python
story outcome data
name code value depends_on
0 obama pass 1.0 []
1 bill bill.value = obama.value 1.0 [obama]
2 bob bob.value = obama.value 1.0 [obama]
3 ben ben.value = obama.value 1.0 [obama]
4 beth beth.value = obama.value 1.0 [obama]
5 cindy cindy.value = bill.value + bob.value 2.0 [bill, bob]
6 boris boris.value = ben.value + beth.value 2.0 [ben, beth]
7 tim tim.value = cindy.value + boris.value 4.0 [cindy, boris]
query data
{
"question": "how many pets total does everyone buy?",
"expression": "SELECT SUM(value) FROM df",
"llm_error_msg": ""
}
# query result is 13
```
Based on the comments below, CPAL's intended location in the library is
`experimental/chains/cpal` and PAL's location is`chains/pal`.
### CPAL vs Graph QA
Both the CPAL chain and the Graph QA chain extract entity-action-entity
relations into a DAG.
The CPAL chain is different from the Graph QA chain for a few reasons.
* Graph QA does not connect entities to math expressions
* Graph QA does not associate actions in a sequence of dependence.
* Graph QA does not decompose the narrative into these three parts:
1. Story plot or causal model
4. Hypothetical question
5. Hypothetical condition
### Evaluation
Preliminary evaluation on simple math word problems shows that this CPAL
chain generates less hallucination than the PAL chain on answering
questions about a causal narrative. Two examples are in [this jupyter
notebook](https://github.com/borisdev/langchain/blob/master/docs/extras/modules/chains/additional/cpal.ipynb)
doc.
## 2. Future application
### "Describe as Narrative, Test as Code"
The thesis here is that the Describe as Narrative, Test as Code approach
allows you to represent a causal mental model both as code and as a
narrative, giving you the best of both worlds.
#### Why describe a causal mental mode as a narrative?
The narrative form is quick. At a consensus building meeting, people use
narratives to persuade others of their causal mental model, aka. plan.
You can share, version control and index a narrative.
#### Why test a causal mental model as a code?
Code is testable, complex narratives are not. Though fast, narratives
are problematic as their complexity increases. The problem is LLMs and
humans are prone to hallucination when predicting the outcomes of a
narrative. The cost of building a consensus around the validity of a
narrative outcome grows as its narrative complexity increases. Code does
not require tribal knowledge or social power to validate.
Code is composable, complex narratives are not. The answer of one CPAL
chain can be the hypothetical conditions of another CPAL Chain. For
stochastic simulations, a composable plan can be integrated with the
[DoWhy library](https://github.com/py-why/dowhy). Lastly, for the
futuristic folk, a composable plan as code allows ordinary community
folk to design a plan that can be integrated with a blockchain for
funding.
An explanation of a dependency planning application is
[here.](https://github.com/borisdev/cpal-llm-chain-demo)
---
Twitter handle: @boris_dev
---------
Co-authored-by: Boris Dev <borisdev@Boriss-MacBook-Air.local>
This PR proposes an implementation to support `generate` as an
`early_stopping_method` for the new `OpenAIFunctionsAgent` class.
The motivation behind is to facilitate the user to set a maximum number
of actions the agent can take with `max_iterations` and force a final
response with this new agent (as with the `Agent` class).
The following changes were made:
- The `OpenAIFunctionsAgent.return_stopped_response` method was
overwritten to support `generate` as an `early_stopping_method`
- A boolean `with_functions` parameter was added to the
`OpenAIFunctionsAgent.plan` method
This way the `OpenAIFunctionsAgent.return_stopped_response` method can
call the `OpenAIFunctionsAgent.plan` method with `with_function=False`
when the `early_stopping_method` is set to `generate`, making a call to
the LLM with no functions and forcing a final response from the
`"assistant"`.
- Relevant maintainer: @hinthornw
- Twitter handle: @aledelunap
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: Current `_call` function in the
`langchain.llms.HuggingFaceEndpoint` class truncates response when
`task=text-generation`. Same error discussed a few days ago on Hugging
Face: https://huggingface.co/tiiuae/falcon-40b-instruct/discussions/51
Issue: Fixes#7353
Tag maintainer: @hwchase17 @baskaryan @hinthornw
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: This pull request aims to support generating the correct
generic relevancy scores for different vector stores by refactoring the
relevance score functions and their selection in the base class and
subclasses of VectorStore. This is especially relevant with VectorStores
that require a distance metric upon initialization. Note many of the
current implenetations of `_similarity_search_with_relevance_scores` are
not technically correct, as they just return
`self.similarity_search_with_score(query, k, **kwargs)` without applying
the relevant score function
Also includes changes associated with:
https://github.com/hwchase17/langchain/pull/6564 and
https://github.com/hwchase17/langchain/pull/6494
See more indepth discussion in thread in #6494
Issue:
https://github.com/hwchase17/langchain/issues/6526https://github.com/hwchase17/langchain/issues/6481https://github.com/hwchase17/langchain/issues/6346
Dependencies: None
The changes include:
- Properly handling score thresholding in FAISS
`similarity_search_with_score_by_vector` for the corresponding distance
metric.
- Refactoring the `_similarity_search_with_relevance_scores` method in
the base class and removing it from the subclasses for incorrectly
implemented subclasses.
- Adding a `_select_relevance_score_fn` method in the base class and
implementing it in the subclasses to select the appropriate relevance
score function based on the distance strategy.
- Updating the `__init__` methods of the subclasses to set the
`relevance_score_fn` attribute.
- Removing the `_default_relevance_score_fn` function from the FAISS
class and using the base class's `_euclidean_relevance_score_fn`
instead.
- Adding the `DistanceStrategy` enum to the `utils.py` file and updating
the imports in the vector store classes.
- Updating the tests to import the `DistanceStrategy` enum from the
`utils.py` file.
---------
Co-authored-by: Hanit <37485638+hanit-com@users.noreply.github.com>
Improve documentation for a central use-case, qa / chat over documents.
This will be merged as an update to `index.mdx`
[here](https://python.langchain.com/docs/use_cases/question_answering/).
Testing w/ local Docusaurus server:
```
From `docs` directory:
mkdir _dist
cp -r {docs_skeleton,snippets} _dist
cp -r extras/* _dist/docs_skeleton/docs
cd _dist/docs_skeleton
yarn install
yarn start
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
### Description
Created a Loader to get a list of specific logs from Datadog Logs.
### Dependencies
`datadog_api_client` is required.
### Twitter handle
[kzk_maeda](https://twitter.com/kzk_maeda)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [Xorbits](https://doc.xorbits.io/en/latest/) is an open-source
computing framework that makes it easy to scale data science and machine
learning workloads in parallel. Xorbits can leverage multi cores or GPUs
to accelerate computation on a single machine, or scale out up to
thousands of machines to support processing terabytes of data.
- This PR added support for the Xorbits document loader, which allows
langchain to leverage Xorbits to parallelize and distribute the loading
of data.
- Dependencies: This change requires the Xorbits library to be installed
in order to be used.
`pip install xorbits`
- Request for review: @rlancemartin, @eyurtsev
- Twitter handle: https://twitter.com/Xorbitsio
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Adding async method for CTransformers
- Issue: I've found impossible without this code to run Websockets
inside a FastAPI micro service and a CTransformers model.
- Tag maintainer: Not necessary yet, I don't like to mention directly
- Twitter handle: @_semoal
Adding a maximal_marginal_relevance method to the
MongoDBAtlasVectorSearch vectorstore enhances the user experience by
providing more diverse search results
Issue: #7304
### Summary
Adds an `UnstructuredTSVLoader` for TSV files. Also updates the doc
strings for `UnstructuredCSV` and `UnstructuredExcel` loaders.
### Testing
```python
from langchain.document_loaders.tsv import UnstructuredTSVLoader
loader = UnstructuredTSVLoader(
file_path="example_data/mlb_teams_2012.csv", mode="elements"
)
docs = loader.load()
```
### Description
argument variable client is marked as required in commit
81e5b1ad36 which breaks the default way of
initialization providing only index_id. This commit avoid KeyError
exception when it is initialized without a client variable
### Dependencies
no dependency required
`SpacyTextSplitter` currently uses spacy's statistics-based
`en_core_web_sm` model for sentence splitting. This is a good splitter,
but it's also pretty slow, and in this case it's doing a lot of work
that's not needed given that the spacy parse is then just thrown away.
However, there is also a simple rules-based spacy sentencizer. Using
this is at least an order of magnitude faster than using
`en_core_web_sm` according to my local tests.
Also, spacy sentence tokenization based on `en_core_web_sm` can be sped
up in this case by not doing the NER stage. This shaves some cycles too,
both when loading the model and when parsing the text.
Consequently, this PR adds the option to use the basic spacy
sentencizer, and it disables the NER stage for the current approach,
*which is kept as the default*.
Lastly, when extracting the tokenized sentences, the `text` attribute is
called directly instead of doing the string conversion, which is IMO a
bit more idiomatic.
Hey @hwchase17 -
This PR adds a `ZepMemory` class, improves handling of Zep's message
metadata, and makes it easier for folks building custom chains to
persist metadata alongside their chat history.
We've had plenty confused users unfamiliar with ChatMessageHistory
classes and how to wrap the `ZepChatMessageHistory` in a
`ConversationBufferMemory`. So we've created the `ZepMemory` class as a
light wrapper for `ZepChatMessageHistory`.
Details:
- add ZepMemory, modify notebook to demo use of ZepMemory
- Modify summary to be SystemMessage
- add metadata argument to add_message; add Zep metadata to
Message.additional_kwargs
- support passing in metadata
- Description: Tiny documentation fix. In Python, when defining function
parameters or providing arguments to a function or class constructor, we
do not use the `:` character.
- Issue: N/A
- Dependencies: N/A,
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @mogaal
I just added a parameter to the method get_format_instructions, to
return directly the JSON instructions without the leading instruction
sentence. I'm planning to use it to define the structure of a JSON
object passed in input, the get_format_instructions().
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Have noticed transient ref example misalignment. I believe this is
caused by the logic of assigning an example within the thread executor
rather than before.
Current problems:
1. Evaluating LLMs or Chat models isn't smooth. Even specifying
'generations' as the output inserts a redundant list into the eval
template
2. Configuring input / prediction / reference keys in the
`get_qa_evaluator` function is confusing. Unless you are using a chain
with the default keys, you have to specify all the variables and need to
reason about whether the key corresponds to the traced run's inputs,
outputs or the examples inputs or outputs.
Proposal:
- Configure the run evaluator according to a model. Use the model type
and input/output keys to assert compatibility where possible. Only need
to specify a reference_key for certain evaluators (which is less
confusing than specifying input keys)
When does this work:
- If you have your langchain model available (assumed always for
run_on_dataset flow)
- If you are evaluating an LLM, Chat model, or chain
- If the LLM or chat models are traced by langchain (wouldn't work if
you add an incompatible schema via the REST API)
When would this fail:
- Currently if you directly create an example from an LLM run, the
outputs are generations with all the extra metadata present. A simple
`example_key` and dumping all to the template could make the evaluations
unreliable
- Doesn't help if you're not using the low level API
- If you want to instantiate the evaluator without instantiating your
chain or LLM (maybe common for monitoring, for instance) -> could also
load from run or run type though
What's ugly:
- Personally think it's better to load evaluators one by one since
passing a config down is pretty confusing.
- Lots of testing needs to be added
- Inconsistent in that it makes a separate run and example input mapper
instead of the original `RunEvaluatorInputMapper`, which maps a run and
example to a single input.
Example usage running the for an LLM, Chat Model, and Agent.
```
# Test running for the string evaluators
evaluator_names = ["qa", "criteria"]
model = ChatOpenAI()
configured_evaluators = load_run_evaluators_for_model(evaluator_names, model=model, reference_key="answer")
run_on_dataset(ds_name, model, run_evaluators=configured_evaluators)
```
<details>
<summary>Full code with dataset upload</summary>
```
## Create dataset
from langchain.evaluation.run_evaluators.loading import load_run_evaluators_for_model
from langchain.evaluation import load_dataset
import pandas as pd
lcds = load_dataset("llm-math")
df = pd.DataFrame(lcds)
from uuid import uuid4
from langsmith import Client
client = Client()
ds_name = "llm-math - " + str(uuid4())[0:8]
ds = client.upload_dataframe(df, name=ds_name, input_keys=["question"], output_keys=["answer"])
## Define the models we'll test over
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from langchain.tools import tool
llm = OpenAI(temperature=0)
chat_model = ChatOpenAI(temperature=0)
@tool
def sum(a: float, b: float) -> float:
"""Add two numbers"""
return a + b
def construct_agent():
return initialize_agent(
llm=chat_model,
tools=[sum],
agent=AgentType.OPENAI_MULTI_FUNCTIONS,
)
agent = construct_agent()
# Test running for the string evaluators
evaluator_names = ["qa", "criteria"]
models = [llm, chat_model, agent]
run_evaluators = []
for model in models:
run_evaluators.append(load_run_evaluators_for_model(evaluator_names, model=model, reference_key="answer"))
# Run on LLM, Chat Model, and Agent
from langchain.client.runner_utils import run_on_dataset
to_test = [llm, chat_model, construct_agent]
for model, configured_evaluators in zip(to_test, run_evaluators):
run_on_dataset(ds_name, model, run_evaluators=configured_evaluators, verbose=True)
```
</details>
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
fixes https://github.com/hwchase17/langchain/issues/7289
A simple fix of the buggy output of `graph_qa`. If we have several
entities with triplets then the last entry of `triplets` for a given
entity merges with the first entry of the `triplets` of the next entity.
### Description
Adding a callback handler for Context. Context is a product analytics
platform for AI chat experiences to help you understand how users are
interacting with your product.
I've added the callback library + an example notebook showing its use.
### Dependencies
Requires the user to install the `context-python` library. The library
is lazily-loaded when the callback is instantiated.
### Announcing the feature
We spoke with Harrison a few weeks ago about also doing a blog post
announcing our integration, so will coordinate this with him. Our
Twitter handle for the company is @getcontextai, and the founders are
@_agamble and @HenrySG.
Thanks in advance!
**Title:** Add verbose parameter for llamacpp
**Description:**
This pull request adds a 'verbose' parameter to the llamacpp module. The
'verbose' parameter, when set to True, will enable the output of
detailed logs during the execution of the Llama model. This added
parameter can aid in debugging and understanding the internal processes
of the module.
The verbose parameter is a boolean that prints verbose output to stderr
when set to True. By default, the verbose parameter is set to True but
can be toggled off if less output is desired. This new parameter has
been added to the `validate_environment` method of the `LlamaCpp` class
which initializes the `llama_cpp.Llama` API:
```python
class LlamaCpp(LLM):
...
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
...
model_param_names = [
...
"verbose", # New verbose parameter added
]
...
values["client"] = Llama(model_path, **model_params)
...
```
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
At the moment, pinecone vectorStore does not support filters and
namespaces when using similarity_score_threshold search type.
In this PR, I've implemented that. It passes all the kwargs except
"score_threshold" as that is not a supported argument for method
"similarity_search_with_score".
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Changes
- [X] Fill the `llm_output` param when there is an output parsing error
in a Pydantic schema so that we can get the original text that failed to
parse when handling the exception
## Background
With this change, we could do something like this:
```
output_parser = PydanticOutputParser(pydantic_object=pydantic_obj)
chain = ConversationChain(..., output_parser=output_parser)
try:
response: PydanticSchema = chain.predict(input=input)
except OutputParserException as exc:
logger.error(
'OutputParserException while parsing chatbot response: %s', exc.llm_output,
)
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
hi @rlancemartin ,
We had a new deployment and the `pg_extension` creation command was
updated from `CREATE EXTENSION pg_embedding` to `CREATE EXTENSION
embedding`.
https://github.com/neondatabase/neon/pull/4646
The extension not made public yet. No users will be affected by this.
Will be public next week.
Please let me know if you have any questions.
Thank you in advance 🙏
Continuing with Tolkien inspired series of langchain tools. I bring to
you:
**The Fellowship of the Vectors**, AKA EmbeddingsClusteringFilter.
This document filter uses embeddings to group vectors together into
clusters, then allows you to pick an arbitrary number of documents
vector based on proximity to the cluster centers. That's a
representative sample of the cluster.
The original idea is from [Greg Kamradt](https://github.com/gkamradt)
from this video (Level4):
https://www.youtube.com/watch?v=qaPMdcCqtWk&t=365s
I added few tricks to make it a bit more versatile, so you can
parametrize what to do with duplicate documents in case of cluster
overlap: replace the duplicates with the next closest document or remove
it. This allow you to use it as an special kind of redundant filter too.
Additionally you can choose 2 diff orders: grouped by cluster or
respecting the original retriever scores.
In my use case I was using the docs grouped by cluster to run refine
chains per cluster to generate summarization over a large corpus of
documents.
Let me know if you want to change anything!
@rlancemartin, @eyurtsev, @hwchase17,
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
Change details:
- Description: When calling db.persist(), a check prevents from it
proceeding as the constructor only sets member `_persist_directory` from
parameters. But the ChromaDB client settings also has this parameter,
and if the client_settings parameter is used without passing the
persist_directory (which is optional), the `persist` method raises
`ValueError` for not setting `_persist_directory`. This change fixes it
by setting the member `_persist_directory` variable from client_settings
if it is set, else uses the constructor parameter.
- Issue: I didn't find any github issue of this, but I discovered it
after calling the persist method
- Dependencies: None
- Tag maintainer: vectorstore related change - @rlancemartin, @eyurtsev
- Twitter handle: Don't have one :(
*Additional discussion*: We may need to discuss the way I implemented
the fallback using `or`.
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
This PR improves the example notebook for the Marqo vectorstore
implementation by adding a new RetrievalQAWithSourcesChain example. The
`embedding` parameter in `from_documents` has its type updated to
`Union[Embeddings, None]` and a default parameter of None because this
is ignored in Marqo.
This PR also upgrades the Marqo version to 0.11.0 to remove the device
parameter after a breaking change to the API.
Related to #7068 @tomhamer @hwchase17
---------
Co-authored-by: Tom Hamer <tom@marqo.ai>
Description: Pack of small fixes and refactorings that don't affect
functionality, just making code prettier & fixing some misspelling
(hand-filtered improvements proposed by SeniorAi.online, prototype of
code improving tool based on gpt4), agents and callbacks folders was
covered.
Dependencies: Nothing changed
Twitter: https://twitter.com/nayjest
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR improves upon the Clarifai LangChain integration with improved docs, errors, args and the addition of embedding model support in LancChain for Clarifai's embedding models and an overview of the various ways you can integrate with Clarifai added to the docs.
---------
Co-authored-by: Matthew Zeiler <zeiler@clarifai.com>
Description: Added number_of_head_rows as a parameter to pandas agent.
number_of_head_rows allows the user to select the number of rows to pass
with the prompt when include_df_in_prompt is True. This gives the
ability to control the token length and can be helpful in dealing with
large dataframe.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Solving, anthropic packaging version issue by clearing
the mixup from package.version that is being confused with version from
- importlib.metadata.version.
- Issue: it fixes the issue #7283
- Maintainer: @hwchase17
The following change has been explained in the comment -
https://github.com/hwchase17/langchain/issues/7283#issuecomment-1624328978
- Description: pydantic's `ModelField.type_` only exposes the native
data type but not complex type hints like `List`. Thus, generating a
Tool with `from_function` through function signature produces incorrect
argument schemas (e.g., `str` instead of `List[str]`)
- Issue: N/A
- Dependencies: N/A
- Tag maintainer: @hinthornw
- Twitter handle: `mapped`
All the unittest (with an additional one in this PR) passed, though I
didn't try integration tests...
- Description: Sometimes there are csv attachments with the media type
"application/vnd.ms-excel". These files failed to be loaded via the xlrd
library. It throws a corrupted file error. I fixed it by separately
processing excel files using pandas. Excel files will be processed just
like before.
- Dependencies: pandas, os, io
---------
Co-authored-by: Chathura <chathurar@yaalalabs.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
In some cases, the OpenAI response is missing the `finish_reason`
attribute. It seems to happen when using Ada or Babbage and
`stream=true`, but I can't always reproduce it. This change just
gracefully handles the missing key.
Introduction of newest function calling feature doesn't work properly
with PromptLayerChatOpenAI model since on the `_generate` method,
functions argument are not even getting passed to the `ChatOpenAI` base
class which results in empty `ai_message.additional_kwargs`
Fixes #6365
updated `tutorials.mdx`:
- added a link to new `Deeplearning AI` course on LangChain
- added links to other tutorial videos
- fixed format
@baskaryan, @hwchase17
#### Description
refactor BedrockEmbeddings class to clean code as below:
1. inline content type and accept
2. rewrite input_body as a dictionary literal
3. no need to declare embeddings variable, so remove it
- Description: Adding to Chroma integration the option to run a
similarity search by a vector with relevance scores. Fixing two minor
typos.
- Issue: The "lambda_mult" typo is related to #4861
- Maintainer: @rlancemartin, @eyurtsev
Based on user feedback, we have improved the Alibaba Cloud OpenSearch
vector store documentation.
Co-authored-by: zhaoshengbo <shengbo.zsb@alibaba-inc.com>
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Several updates for the PowerBI tools:
- Handle 0 records returned by requesting redo with different filtering
- Handle too large results by optionally tokenizing the result and
comparing against a max (change in signature, non-breaking)
- Implemented LLMChain with Chat for chat models for the tools.
- Updates to the main prompt including tables
- Update to Tool prompt with TOPN function
- Split the tool prompt to allow the LLMChain with ChatPromptTemplate
Smaller fixes for stability.
For visibility: @hinthornw
Replace this comment with:
- Description: added documentation for a template repo that helps
dockerizing and deploying a LangChain using a Cloud Build CI/CD pipeline
to Google Cloud build serverless
- Issue: None,
- Dependencies: None,
- Tag maintainer: @baskaryan,
- Twitter handle: EdenEmarco177
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
- Description:
- When `keep_separator` is `True` the `_split_text_with_regex()` method
in `text_splitter` uses regex to split, but when `keep_separator` is
`False` it uses `str.split()`. This causes problems when the separator
is a special regex character like `.` or `*`. This PR fixes that by
using `re.split()` in both cases.
- Issue: #7262
- Tag maintainer: @baskaryan
**Description**
In the following page, "Wikipedia" tool is explained.
https://python.langchain.com/docs/modules/agents/tools/integrations/wikipedia
However, the WikipediaAPIWrapper being used is not a tool. This PR
updated the documentation to use a tool WikipediaQueryRun.
**Issue**
None
**Tag maintainer**
Agents / Tools / Toolkits: @hinthornw
- Description: This is a chat model equivalent of HumanInputLLM. An
example notebook is also added.
- Tag maintainer: @hwchase17, @baskaryan
- Twitter handle: N/A
- Description: I have added a `show_progress_bar` parameter (defaults.to
`False`) to the `OpenAIEmbeddings`. If the user sets `show_progress_bar`
to `True`, a progress bar will be displayed.
- Issue: #7246
- Dependencies: N/A
- Tag maintainer: @hwchase17, @baskaryan
- Twitter handle: N/A
Description: `flan-t5-xl` hangs, updated to `flan-t5-xxl`. Tested all
stabilityai LLMs- all hang so removed from tutorial. Temperature > 0 to
prevent unintended determinism.
Issue: #3275
Tag maintainer: @baskaryan
Fix for bug in SitemapLoader
`aiohttp` `get` does not accept `verify` argument, and currently throws
error, so SitemapLoader is not working
This PR fixes it by removing `verify` param for `get` function call
Fixes#6107
#### Who can review?
Tag maintainers/contributors who might be interested:
@eyurtsev
---------
Co-authored-by: techcenary <127699216+techcenary@users.noreply.github.com>
### Description
This pull request introduces the "Cube Semantic Layer" document loader,
which demonstrates the retrieval of Cube's data model metadata in a
format suitable for passing to LLMs as embeddings. This enhancement aims
to provide contextual information and improve the understanding of data.
Twitter handle:
@the_cube_dev
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
This PR brings in a vectorstore interface for
[Marqo](https://www.marqo.ai/).
The Marqo vectorstore exposes some of Marqo's functionality in addition
the the VectorStore base class. The Marqo vectorstore also makes the
embedding parameter optional because inference for embeddings is an
inherent part of Marqo.
Docs, notebook examples and integration tests included.
Related PR:
https://github.com/hwchase17/langchain/pull/2807
---------
Co-authored-by: Tom Hamer <tom@marqo.ai>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
### Summary
Updates the docstrings for the unstructured base loaders so more useful
information appears on the integrations page. If these look good, will
add similar docstrings to the other loaders.
### Reviewers
- @rlancemartin
- @eyurtsev
- @hwchase17
- Description: Allow `InMemoryDocstore` to be created without passing a
dict to the constructor; the constructor can create a dict at runtime if
one isn't provided.
- Tag maintainer: @dev2049
- Description: At the moment, inserting new embeddings to pgvector is
querying all embeddings every time as the defined `embeddings`
relationship is using the default params, which sets `lazy="select"`.
This change drastically improves the performance and adds a few
additional cleanups:
* remove `collection.embeddings.append` as it was querying all
embeddings on insert, replace with `collection_id` param
* centralize storing logic in add_embeddings function to reduce
duplication
* remove boilerplate
- Issue: No issue was opened.
- Dependencies: None.
- Tag maintainer: this is a vectorstore update, so I think
@rlancemartin, @eyurtsev
- Twitter handle: @falmannaa
Hi, there
This pull request contains two commit:
**1. Implement delete interface with optional ids parameter on
AnalyticDB.**
**2. Allow customization of database connection behavior by exposing
engine_args parameter in interfaces.**
- This commit adds the `engine_args` parameter to the interfaces,
allowing users to customize the behavior of the database connection. The
`engine_args` parameter accepts a dictionary of additional arguments
that will be passed to the create_engine function. Users can now modify
various aspects of the database connection, such as connection pool size
and recycle time. This enhancement provides more flexibility and control
to users when interacting with the database through the exposed
interfaces.
This commit is related to VectorStores @rlancemartin @eyurtsev
Thank you for your attention and consideration.
- Description: This allows parameters such as `relevance_score_fn` to be
passed to the `FAISS` constructor via the `load_local()` class method.
- Tag maintainer: @rlancemartin @eyurtsev
This fixes#4833 and the critical vulnerability
https://nvd.nist.gov/vuln/detail/CVE-2023-34540
Previously, the JIRA API Wrapper had a mode that simply pipelined user
input into an `exec()` function.
[The intended use of the 'other' mode is to cover any of Atlassian's API
that don't have an existing
interface](cc33bde74f/langchain/tools/jira/prompt.py (L24))
Fortunately all of the [Atlassian JIRA API methods are subfunctions of
their `Jira`
class](https://atlassian-python-api.readthedocs.io/jira.html), so this
implementation calls these subfunctions directly.
As well as passing a string representation of the function to call, the
implementation flexibly allows for optionally passing args and/or
keyword-args. These are given as part of the dictionary input. Example:
```
{
"function": "update_issue_field", #function to execute
"args": [ #list of ordered args similar to other examples in this JiraAPIWrapper
"key",
{"summary": "New summary"}
],
"kwargs": {} #dict of key value keyword-args pairs
}
```
the above is equivalent to `self.jira.update_issue_field("key",
{"summary": "New summary"})`
Alternate query schema designs are welcome to make querying easier
without passing and evaluating arbitrary python code. I considered
parsing (without evaluating) input python code and extracting the
function, args, and kwargs from there and then pipelining them into the
callable function via `*f(args, **kwargs)` - but this seemed more
direct.
@vowelparrot @dev2049
---------
Co-authored-by: Jamal Rahman <jamal.rahman@builder.ai>
added tutorials.mdx; updated youtube.mdx
Rationale: the Tutorials section in the documentation is top-priority.
(for example, https://pytorch.org/docs/stable/index.html) Not every
project has resources to make tutorials. We have such a privilege.
Community experts created several tutorials on YouTube. But the tutorial
links are now hidden on the YouTube page and not easily discovered by
first-time visitors.
- Added new videos and tutorials that were created since the last
update.
- Made some reprioritization between videos on the base of the view
numbers.
#### Who can review?
- @hwchase17
- @dev2049
## Description
Added Office365 tool modules to `__init__.py` files
## Issue
As described in Issue
https://github.com/hwchase17/langchain/issues/6936, the Office365
toolkit can't be loaded easily because it is not included in the
`__init__.py` files.
## Reviewer
@dev2049
Description:
The OpenAI "embeddings" API intermittently falls into a failure state
where an embedding is returned as [ Nan ], rather than the expected 1536
floats. This patch checks for that state (specifically, for an embedding
of length 1) and if it occurs, throws an ApiError, which will cause the
chunk to be retried.
Issue:
I have been unable to find an official langchain issue for this problem,
but it is discussed (by another user) at
https://stackoverflow.com/questions/76469415/getting-embeddings-of-length-1-from-langchain-openaiembeddings
Maintainer: @dev2049
Testing:
Since this is an intermittent OpenAI issue, I have not provided a unit
or integration test. The provided code has, though, been run
successfully over several million tokens.
---------
Co-authored-by: William Webber <william@williamwebber.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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- [x] wire up tools
- [x] wire up retrievers
- [x] add integration test
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Description: Fix steamship import error
When running multi_modal_output_agent:
field "steamship" not yet prepared so type is still a ForwardRef, you
might need to call SteamshipImageGenerationTool.update_forward_refs().
Tag maintainer: @hinthornw
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Description: If their are missing or extra variables when validating
Jinja 2 template then a warning is issued rather than raising an
exception. This allows for better flexibility for the developer as
described in #7044. Also changed the relevant test so pytest is checking
for raised warnings rather than exceptions.
- Issue: #7044
- Tag maintainer: @hwchase17, @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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This PR makes the `textstat` library optional in the Flyte callback
handler.
@hinthornw, would you mind reviewing this PR since you merged the flyte
callback handler code previously?
---------
Signed-off-by: Samhita Alla <aallasamhita@gmail.com>
- Description: added some documentation to the Pinecone vector store
docs page.
- Issue: #7126
- Dependencies: None
- Tag maintainer: @baskaryan
I can add more documentation on the Pinecone integration functions as I
am going to go in great depth into this area. Just wanted to check with
the maintainers is if this is all good.
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Replace this comment with:
- Description: Replace `if var is not None:` with `if var:`, a concise
and pythonic alternative
- Issue: N/A
- Dependencies: None
- Tag maintainer: Unsure
- Twitter handle: N/A
Signed-off-by: serhatgktp <efkan@ibm.com>
- Description: Modify the code for
AsyncIteratorCallbackHandler.on_llm_new_token to ensure that it does not
add an empty string to the result queue.
- Tag maintainer: @agola11
When using AsyncIteratorCallbackHandler with OpenAIFunctionsAgent, if
the LLM response function_call instead of direct answer, the
AsyncIteratorCallbackHandler.on_llm_new_token would be called with empty
string.
see also: langchain.chat_models.openai.ChatOpenAI._generate
An alternative solution is to modify the
langchain.chat_models.openai.ChatOpenAI._generate and do not call the
run_manager.on_llm_new_token when the token is empty string.
I am not sure which solution is better.
@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# [SPARQL](https://www.w3.org/TR/rdf-sparql-query/) for
[LangChain](https://github.com/hwchase17/langchain)
## Description
LangChain support for knowledge graphs relying on W3C standards using
RDFlib: SPARQL/ RDF(S)/ OWL with special focus on RDF \
* Works with local files, files from the web, and SPARQL endpoints
* Supports both SELECT and UPDATE queries
* Includes both a Jupyter notebook with an example and integration tests
## Contribution compared to related PRs and discussions
* [Wikibase agent](https://github.com/hwchase17/langchain/pull/2690) -
uses SPARQL, but specifically for wikibase querying
* [Cypher qa](https://github.com/hwchase17/langchain/pull/5078) - graph
DB question answering for Neo4J via Cypher
* [PR 6050](https://github.com/hwchase17/langchain/pull/6050) - tries
something similar, but does not cover UPDATE queries and supports only
RDF
* Discussions on [w3c mailing list](mailto:semantic-web@w3.org) related
to the combination of LLMs (specifically ChatGPT) and knowledge graphs
## Dependencies
* [RDFlib](https://github.com/RDFLib/rdflib)
## Tag maintainer
Graph database related to memory -> @hwchase17
Update in_memory.py to fix "TypeError: keywords must be strings" on
certain dictionaries
Simple fix to prevent a "TypeError: keywords must be strings" error I
encountered in my use case.
@baskaryan
Thanks! Hope useful!
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fix for typos in MongoDB Atlas Vector Search documentation
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- Description: rename the invalid function name of GoogleSerperResults
Tool for OpenAIFunctionCall
- Tag maintainer: @hinthornw
When I use the GoogleSerperResults in OpenAIFunctionCall agent, the
following error occurs:
```shell
openai.error.InvalidRequestError: 'Google Serrper Results JSON' does not match '^[a-zA-Z0-9_-]{1,64}$' - 'functions.0.name'
```
So I rename the GoogleSerperResults's property "name" from "Google
Serrper Results JSON" to "google_serrper_results_json" just like
GoogleSerperRun's name: "google_serper", and it works.
I guess this should be reasonable.
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@hinthornw
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Hi @rlancemartin, @eyurtsev!
- Description: Adding HNSW extension support for Postgres. Similar to
pgvector vectorstore, with 3 differences
1. it uses HNSW extension for exact and ANN searches,
2. Vectors are of type array of real
3. Only supports L2
- Dependencies: [HNSW](https://github.com/knizhnik/hnsw) extension for
Postgres
- Example:
```python
db = HNSWVectoreStore.from_documents(
embedding=embeddings,
documents=docs,
collection_name=collection_name,
connection_string=connection_string
)
query = "What did the president say about Ketanji Brown Jackson"
docs_with_score: List[Tuple[Document, float]] =
db.similarity_search_with_score(query)
```
The example notebook is in the PR too.
- correct `endpoint_name` to `api_url`
- add `headers`
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Minor change to the SingleStoreVectorStore:
Updated connection attributes names according to the SingleStoreDB
recommendations
@rlancemartin, @eyurtsev
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Description: doc string suggests `from langchain.llms import
LlamaCppEmbeddings` under `LlamaCpp()` class example but
`LlamaCppEmbeddings` is not in `langchain.llms`
Issue: None open
Tag maintainer: @baskaryan
Documentation update for [Jina
ecosystem](https://python.langchain.com/docs/ecosystem/integrations/jina)
and `langchain-serve` in the deployments section to latest features.
@hwchase17
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- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
[Apache HugeGraph](https://github.com/apache/incubator-hugegraph) is a
convenient, efficient, and adaptable graph database, compatible with the
Apache TinkerPop3 framework and the Gremlin query language.
In this PR, the HugeGraph and HugeGraphQAChain provide the same
functionality as the existing integration with Neo4j and enables query
generation and question answering over HugeGraph database. The
difference is that the graph query language supported by HugeGraph is
not cypher but another very popular graph query language
[Gremlin](https://tinkerpop.apache.org/gremlin.html).
A notebook example and a simple test case have also been added.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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This PR introduces a new Mendable UI tailored to a better search
experience.
We're more closely integrating our traditional search with our AI
generation.
With this change, you won't have to tab back and forth between the
mendable bot and the keyword search. Both types of search are handled in
the same bar. This should make the docs easier to navigate. while still
letting users get code generations or AI-summarized answers if they so
wish. Also, it should reduce the cost.
Would love to hear your feedback :)
Cc: @dev2049 @hwchase17
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## Description
The type hint for `FAISS.__init__()`'s `relevance_score_fn` parameter
allowed the parameter to be set to `None`. However, a default function
is provided by the constructor. This led to an unnecessary check in the
code, as well as a test to verify this check.
**ASSUMPTION**: There's no reason to ever set `relevance_score_fn` to
`None`.
This PR changes the type hint and removes the unnecessary code.
- Description: Added a new SpacyEmbeddings class for generating
embeddings using the Spacy library.
- Issue: Sentencebert/Bert/Spacy/Doc2vec embedding support #6952
- Dependencies: This change requires the Spacy library and the
'en_core_web_sm' Spacy model.
- Tag maintainer: @dev2049
- Twitter handle: N/A
This change includes a new SpacyEmbeddings class, but does not include a
test or an example notebook.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**:
The JSON Lines format is used by some services such as OpenAI and
HuggingFace. It's also a convenient alternative to CSV.
This PR adds JSON Lines support to `JSONLoader` and also updates related
tests.
**Tag maintainer**: @rlancemartin, @eyurtsev.
PS I was not able to build docs locally so didn't update related
section.
Update to Vectara integration
- By user request added "add_files" to take advantage of Vectara
capabilities to process files on the backend, without the need for
separate loading of documents and chunking in the chain.
- Updated vectara.ipynb example notebook to be broader and added testing
of add_file()
@hwchase17 - project lead
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
### Description:
Updated the delete function in the Pinecone integration to allow for
deletion of vectors by specifying a filter condition, and to delete all
vectors in a namespace.
Made the ids parameter optional in the delete function in the base
VectorStore class and allowed for additional keyword arguments.
Updated the delete function in several classes (Redis, Chroma, Supabase,
Deeplake, Elastic, Weaviate, and Cassandra) to match the changes made in
the base VectorStore class. This involved making the ids parameter
optional and allowing for additional keyword arguments.
Retrying with the same improvements as in #6772, this time trying not to
mess up with branches.
@rlancemartin doing a fresh new PR from a branch with a new name. This
should do. Thank you for your help!
---------
Co-authored-by: Jonathan Ellis <jbellis@datastax.com>
Co-authored-by: rlm <pexpresss31@gmail.com>
should be no functional changes
also keep __init__ exposing a lot for backwards compat
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Summary
Updates `UnstructuredEmailLoader` so that it can process attachments in
addition to the e-mail content. The loader will process attachments if
the `process_attachments` kwarg is passed when the loader is
instantiated.
### Testing
```python
file_path = "fake-email-attachment.eml"
loader = UnstructuredEmailLoader(
file_path, mode="elements", process_attachments=True
)
docs = loader.load()
docs[-1]
```
### Reviewers
- @rlancemartin
- @eyurtsev
- @hwchase17
Handle the new retriever events in a way that (I think) is entirely
backwards compatible? Needs more testing for some of the chain changes
and all.
This creates an entire new run type, however. We could also just treat
this as an event within a chain run presumably (same with memory)
Adds a subclass initializer that upgrades old retriever implementations
to the new schema, along with tests to ensure they work.
First commit doesn't upgrade any of our retriever implementations (to
show that we can pass the tests along with additional ones testing the
upgrade logic).
Second commit upgrades the known universe of retrievers in langchain.
- [X] Add callback handling methods for retriever start/end/error (open
to renaming to 'retrieval' if you want that)
- [X] Update BaseRetriever schema to support callbacks
- [X] Tests for upgrading old "v1" retrievers for backwards
compatibility
- [X] Update existing retriever implementations to implement the new
interface
- [X] Update calls within chains to .{a]get_relevant_documents to pass
the child callback manager
- [X] Update the notebooks/docs to reflect the new interface
- [X] Test notebooks thoroughly
Not handled:
- Memory pass throughs: retrieval memory doesn't have a parent callback
manager passed through the method
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
when running AsyncCallbackManagerForChainRun (from
langchain.callbacks.manager import AsyncCallbackManagerForChainRun),
provided default values for tags and inheritable_tages of empty lists in
manager.py BaseRunManager.
- Description: In manager.py, `BaseRunManager`, default values were
provided for the `__init__` args `tags` and `inheritable_tags`. They
default to empty lists (`[]`).
- Issue: When trying to use Nvidia NeMo Guardrails with LangChain, the
following exception was raised:
If you create a dataset from runs and run the same chain or llm on it
later, it usually works great.
If you have an agent dataset and want to run a different agent on it, or
have more complex schema, it's hard for us to automatically map these
values every time. This PR lets you pass in an input_mapper function
that converts the example inputs to whatever format your model expects
Support `max_chunk_bytes` kwargs to pass down to `buik` helper, in order
to support the request limits in Opensearch locally and in AWS.
@rlancemartin, @eyurtsev
Description: `all_metadatas` was not defined, `OpenAIEmbeddings` was not
imported,
Issue: #6723 the issue # it fixes (if applicable),
Dependencies: lark,
Tag maintainer: @vowelparrot , @dev2049
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
# Description
This PR makes it possible to use named vectors from Qdrant in Langchain.
That was requested multiple times, as people want to reuse externally
created collections in Langchain. It doesn't change anything for the
existing applications. The changes were covered with some integration
tests and included in the docs.
## Example
```python
Qdrant.from_documents(
docs,
embeddings,
location=":memory:",
collection_name="my_documents",
vector_name="custom_vector",
)
```
### Issue: #2594
Tagging @rlancemartin & @eyurtsev. I'd appreciate your review.
Support for SQLAlchemy 1.3 was removed in version 0.0.203 by change
#6086. Re-adding support.
- Description: Imports SQLAlchemy Row at class creation time instead of
at init to support SQLAlchemy <1.4. This is the only breaking change and
was introduced in version 0.0.203 #6086.
A similar change was merged before:
https://github.com/hwchase17/langchain/pull/4647
- Dependencies: Reduces SQLAlchemy dependency to > 1.3
- Tag maintainer: @rlancemartin, @eyurtsev, @hwchase17, @wangxuqi
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
### Scientific Article PDF Parsing via Grobid
`Description:`
This change adds the GrobidParser class, which uses the Grobid library
to parse scientific articles into a universal XML format containing the
article title, references, sections, section text etc. The GrobidParser
uses a local Grobid server to return PDFs document as XML and parses the
XML to optionally produce documents of individual sentences or of whole
paragraphs. Metadata includes the text, paragraph number, pdf relative
bboxes, pages (text may overlap over two pages), section title
(Introduction, Methodology etc), section_number (i.e 1.1, 2.3), the
title of the paper and finally the file path.
Grobid parsing is useful beyond standard pdf parsing as it accurately
outputs sections and paragraphs within them. This allows for
post-fitering of results for specific sections i.e. limiting results to
the methodology section or results. While sections are split via
headings, ideally they could be classified specifically into
introduction, methodology, results, discussion, conclusion. I'm
currently experimenting with chatgpt-3.5 for this function, which could
later be implemented as a textsplitter.
`Dependencies:`
For use, the grobid repo must be cloned and Java must be installed, for
colab this is:
```
!apt-get install -y openjdk-11-jdk -q
!update-alternatives --set java /usr/lib/jvm/java-11-openjdk-amd64/bin/java
!git clone https://github.com/kermitt2/grobid.git
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-11-openjdk-amd64"
os.chdir('grobid')
!./gradlew clean install
```
Once installed the server is ran on localhost:8070 via
```
get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')
```
@rlancemartin, @eyurtsev
Twitter Handle: @Corranmac
Grobid Demo Notebook is
[here](https://colab.research.google.com/drive/1X-St_mQRmmm8YWtct_tcJNtoktbdGBmd?usp=sharing).
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
Add API Headers support for Amazon API Gateway to enable Authentication
using DynamoDB.
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Was preparing for a demo project of NebulaGraphQAChain to find out the
prompt needed to be optimized a little bit.
Please @hwchase17 kindly help review.
Thanks!
### Overview
This PR aims at building on #4378, expanding the capabilities and
building on top of the `cassIO` library to interface with the database
(as opposed to using the core drivers directly).
Usage of `cassIO` (a library abstracting Cassandra access for
ML/GenAI-specific purposes) is already established since #6426 was
merged, so no new dependencies are introduced.
In the same spirit, we try to uniform the interface for using Cassandra
instances throughout LangChain: all our appreciation of the work by
@jj701 notwithstanding, who paved the way for this incremental work
(thank you!), we identified a few reasons for changing the way a
`CassandraChatMessageHistory` is instantiated. Advocating a syntax
change is something we don't take lighthearted way, so we add some
explanations about this below.
Additionally, this PR expands on integration testing, enables use of
Cassandra's native Time-to-Live (TTL) features and improves the phrasing
around the notebook example and the short "integrations" documentation
paragraph.
We would kindly request @hwchase to review (since this is an elaboration
and proposed improvement of #4378 who had the same reviewer).
### About the __init__ breaking changes
There are
[many](https://docs.datastax.com/en/developer/python-driver/3.28/api/cassandra/cluster/)
options when creating the `Cluster` object, and new ones might be added
at any time. Choosing some of them and exposing them as `__init__`
parameters `CassandraChatMessageHistory` will prove to be insufficient
for at least some users.
On the other hand, working through `kwargs` or adding a long, long list
of arguments to `__init__` is not a desirable option either. For this
reason, (as done in #6426), we propose that whoever instantiates the
Chat Message History class provide a Cassandra `Session` object, ready
to use. This also enables easier injection of mocks and usage of
Cassandra-compatible connections (such as those to the cloud database
DataStax Astra DB, obtained with a different set of init parameters than
`contact_points` and `port`).
We feel that a breaking change might still be acceptable since LangChain
is at `0.*`. However, while maintaining that the approach we propose
will be more flexible in the future, room could be made for a
"compatibility layer" that respects the current init method. Honestly,
we would to that only if there are strong reasons for it, as that would
entail an additional maintenance burden.
### Other changes
We propose to remove the keyspace creation from the class code for two
reasons: first, production Cassandra instances often employ RBAC so that
the database user reading/writing from tables does not necessarily (and
generally shouldn't) have permission to create keyspaces, and second
that programmatic keyspace creation is not a best practice (it should be
done more or less manually, with extra care about schema mismatched
among nodes, etc). Removing this (usually unnecessary) operation from
the `__init__` path would also improve initialization performance
(shorter time).
We suggest, likewise, to remove the `__del__` method (which would close
the database connection), for the following reason: it is the
recommended best practice to create a single Cassandra `Session` object
throughout an application (it is a resource-heavy object capable to
handle concurrency internally), so in case Cassandra is used in other
ways by the app there is the risk of truncating the connection for all
usages when the history instance is destroyed. Moreover, the `Session`
object, in typical applications, is best left to garbage-collect itself
automatically.
As mentioned above, we defer the actual database I/O to the `cassIO`
library, which is designed to encode practices optimized for LLM
applications (among other) without the need to expose LangChain
developers to the internals of CQL (Cassandra Query Language). CassIO is
already employed by the LangChain's Vector Store support for Cassandra.
We added a few more connection options in the companion notebook example
(most notably, Astra DB) to encourage usage by anyone who cannot run
their own Cassandra cluster.
We surface the `ttl_seconds` option for automatic handling of an
expiration time to chat history messages, a likely useful feature given
that very old messages generally may lose their importance.
We elaborated a bit more on the integration testing (Time-to-live,
separation of "session ids", ...).
### Remarks from linter & co.
We reinstated `cassio` as a dependency both in the "optional" group and
in the "integration testing" group of `pyproject.toml`. This might not
be the right thing do to, in which case the author of this PR offer his
apologies (lack of confidence with Poetry - happy to be pointed in the
right direction, though!).
During linter tests, we were hit by some errors which appear unrelated
to the code in the PR. We left them here and report on them here for
awareness:
```
langchain/vectorstores/mongodb_atlas.py:137: error: Argument 1 to "insert_many" of "Collection" has incompatible type "List[Dict[str, Sequence[object]]]"; expected "Iterable[Union[MongoDBDocumentType, RawBSONDocument]]" [arg-type]
langchain/vectorstores/mongodb_atlas.py:186: error: Argument 1 to "aggregate" of "Collection" has incompatible type "List[object]"; expected "Sequence[Mapping[str, Any]]" [arg-type]
langchain/vectorstores/qdrant.py:16: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:19: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:20: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:22: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:23: error: Name "grpc" is not defined [name-defined]
```
In the same spirit, we observe that to even get `import langchain` run,
it seems that a `pip install bs4` is missing from the minimal package
installation path.
Thank you!
If I upload a dataset with a single input and output column, we should
be able to let the chain prepare the input without having to maintain a
strict dataset format.
# Adding support for async (_acall) for VertexAICommon LLM
This PR implements the `_acall` method under `_VertexAICommon`. Because
VertexAI itself does not provide an async interface, I implemented it
via a ThreadPoolExecutor that can delegate execution of VertexAI calls
to other threads.
Twitter handle: @polecitoem : )
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
fyi - @agola11 for async functionality
fyi - @Ark-kun from VertexAI
## Description
Tag maintainer: @rlancemartin, @eyurtsev
### log_and_data_dir
`AwaDB.__init__()` accepts a parameter named `log_and_data_dir`. But
`AwaDB.from_texts()` and `AwaDB.from_documents()` accept a parameter
named `logging_and_data_dir`. This inconsistency in this parameter name
can lead to confusion on the part of the caller.
This PR renames `logging_and_data_dir` to `log_and_data_dir` to make all
functions consistent with the constructor.
### embedding
`AwaDB.__init__()` accepts a parameter named `embedding_model`. But
`AwaDB.from_texts()` and `AwaDB.from_documents()` accept a parameter
named `embeddings`. This inconsistency in this parameter name can lead
to confusion on the part of the caller.
This PR renames `embedding_model` to `embeddings` to make AwaDB's
constructor consistent with the classmethod "constructors" as specified
by `VectorStore` abstract base class.
A user has been testing the Apify integration inside langchain and he
was not able to run saved Actor tasks.
This PR adds support for calling saved Actor tasks on the Apify platform
to the existing integration. The structure of very similar to the one of
calling Actors.
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### Adding the functionality to return the scores with retrieved
documents when using the max marginal relevance
- Description: Add the method
`max_marginal_relevance_search_with_score_by_vector` to the FAISS
wrapper. Functionality operates the same as
`similarity_search_with_score_by_vector` except for using the max
marginal relevance retrieval framework like is used in the
`max_marginal_relevance_search_by_vector` method.
- Dependencies: None
- Tag maintainer: @rlancemartin @eyurtsev
- Twitter handle: @RianDolphin
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
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- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
- Description:
- The current code uses `PydanticSchema.schema()` and
`_get_extraction_function` at the same time. As a result, a response
from OpenAI has two nested `info`, and
`PydanticAttrOutputFunctionsParser` fails to parse it. This PR will use
the pydantic class given as an arg instead.
- Issue: no related issue yet
- Dependencies: no dependency change
- Tag maintainer: @dev2049
- Twitter handle: @shotarok28
Description: Adds a brief example of using an OAuth access token with
the Zapier wrapper. Also links to the Zapier documentation to learn more
about OAuth flows.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
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<!-- Remove if not applicable -->
### Summary
This PR adds a LarkSuite (FeiShu) document loader.
> [LarkSuite](https://www.larksuite.com/) is an enterprise collaboration
platform developed by ByteDance.
### Tests
- an integration test case is added
- an example notebook showing usage is added. [Notebook
preview](https://github.com/yaohui-wyh/langchain/blob/master/docs/extras/modules/data_connection/document_loaders/integrations/larksuite.ipynb)
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- PTAL @eyurtsev @hwchase17
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---------
Co-authored-by: Yaohui Wang <wangyaohui.01@bytedance.com>
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- add tencent cos directory and file support for document-loader
#### Before submitting
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@eyurtsev
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#### Add streaming only final async iterator of agent
This callback returns an async iterator and only streams the final
output of an agent.
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Distance-based vector database retrieval embeds (represents) queries in
high-dimensional space and finds similar embedded documents based on
"distance". But, retrieval may produce difference results with subtle
changes in query wording or if the embeddings do not capture the
semantics of the data well. Prompt engineering / tuning is sometimes
done to manually address these problems, but can be tedious.
The `MultiQueryRetriever` automates the process of prompt tuning by
using an LLM to generate multiple queries from different perspectives
for a given user input query. For each query, it retrieves a set of
relevant documents and takes the unique union across all queries to get
a larger set of potentially relevant documents. By generating multiple
perspectives on the same question, the `MultiQueryRetriever` might be
able to overcome some of the limitations of the distance-based retrieval
and get a richer set of results.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Proxies are helpful, especially when you start querying against more
anti-bot websites.
[Proxy
services](https://developers.oxylabs.io/advanced-proxy-solutions/web-unblocker/making-requests)
(of which there are many) and `requests` make it easy to rotate IPs to
prevent banning by just passing along a simple dict to `requests`.
CC @rlancemartin, @eyurtsev
### Summary
The Unstructured API will soon begin requiring API keys. This PR updates
the Unstructured integrations docs with instructions on how to generate
Unstructured API keys.
### Reviewers
@rlancemartin
@eyurtsev
@hwchase17
Replace this comment with:
- Description: Add Async functionality to Zapier NLA Tools
- Issue: n/a
- Dependencies: n/a
- Tag maintainer:
Maintainer responsibilities:
- Agents / Tools / Toolkits: @vowelparrot
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
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Added parentheses to ensure the division operation is performed before
multiplication. This now correctly calculates the cost by dividing the
number of tokens by 1000 first (to get the cost per token), and then
multiplies it with the model's cost per 1k tokens @agola11
- **Description**: this PR adds the possibility to raise an exception in
the case the http request did not return a 2xx status code. This is
particularly useful in the situation when the url points to a
non-existent web page, the server returns a http status of 404 NOT
FOUND, but WebBaseLoader anyway parses and returns the http body of the
error message.
- **Dependencies**: none,
- **Tag maintainer**: @rlancemartin, @eyurtsev,
- **Twitter handle**: jtolgyesi
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Adds a way to create the guardrails output parser from a pydantic model.
Description: When a 401 response is given back by Zapier, hint to the
end user why that may have occurred
- If an API Key was initialized with the wrapper, ask them to check
their API Key value
- if an access token was initialized with the wrapper, ask them to check
their access token or verify that it doesn't need to be refreshed.
Tag maintainer: @dev2049
#### Summary
A new approach to loading source code is implemented:
Each top-level function and class in the code is loaded into separate
documents. Then, an additional document is created with the top-level
code, but without the already loaded functions and classes.
This could improve the accuracy of QA chains over source code.
For instance, having this script:
```
class MyClass:
def __init__(self, name):
self.name = name
def greet(self):
print(f"Hello, {self.name}!")
def main():
name = input("Enter your name: ")
obj = MyClass(name)
obj.greet()
if __name__ == '__main__':
main()
```
The loader will create three documents with this content:
First document:
```
class MyClass:
def __init__(self, name):
self.name = name
def greet(self):
print(f"Hello, {self.name}!")
```
Second document:
```
def main():
name = input("Enter your name: ")
obj = MyClass(name)
obj.greet()
```
Third document:
```
# Code for: class MyClass:
# Code for: def main():
if __name__ == '__main__':
main()
```
A threshold parameter is added to control whether small scripts are
split in this way or not.
At this moment, only Python and JavaScript are supported. The
appropriate parser is determined by examining the file extension.
#### Tests
This PR adds:
- Unit tests
- Integration tests
#### Dependencies
Only one dependency was added as optional (needed for the JavaScript
parser).
#### Documentation
A notebook is added showing how the loader can be used.
#### Who can review?
@eyurtsev @hwchase17
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
Description: Update documentation to
1) point to updated documentation links at Zapier.com (we've revamped
our help docs and paths), and
2) To provide clarity how to use the wrapper with an access token for
OAuth support
Demo:
Initializing the Zapier Wrapper with an OAuth Access Token
`ZapierNLAWrapper(zapier_nla_oauth_access_token="<redacted>")`
Using LangChain to resolve the current weather in Vancouver BC
leveraging Zapier NLA to lookup weather by coords.
```
> Entering new chain...
I need to use a tool to get the current weather.
Action: The Weather: Get Current Weather
Action Input: Get the current weather for Vancouver BC
Observation: {"coord__lon": -123.1207, "coord__lat": 49.2827, "weather": [{"id": 802, "main": "Clouds", "description": "scattered clouds", "icon": "03d", "icon_url": "http://openweathermap.org/img/wn/03d@2x.png"}], "weather[]icon_url": ["http://openweathermap.org/img/wn/03d@2x.png"], "weather[]icon": ["03d"], "weather[]id": [802], "weather[]description": ["scattered clouds"], "weather[]main": ["Clouds"], "base": "stations", "main__temp": 71.69, "main__feels_like": 71.56, "main__temp_min": 67.64, "main__temp_max": 76.39, "main__pressure": 1015, "main__humidity": 64, "visibility": 10000, "wind__speed": 3, "wind__deg": 155, "wind__gust": 11.01, "clouds__all": 41, "dt": 1687806607, "sys__type": 2, "sys__id": 2011597, "sys__country": "CA", "sys__sunrise": 1687781297, "sys__sunset": 1687839730, "timezone": -25200, "id": 6173331, "name": "Vancouver", "cod": 200, "summary": "scattered clouds", "_zap_search_was_found_status": true}
Thought: I now know the current weather in Vancouver BC.
Final Answer: The current weather in Vancouver BC is scattered clouds with a temperature of 71.69 and wind speed of 3
```
**Description:** Add a documentation page for the Streamlit Callback
Handler integration (#6315)
Notes:
- Implemented as a markdown file instead of a notebook since example
code runs in a Streamlit app (happy to discuss / consider alternatives
now or later)
- Contains an embedded Streamlit app ->
https://mrkl-minimal.streamlit.app/ Currently this app is hosted out of
a Streamlit repo but we're working to migrate the code to a LangChain
owned repo
cc @dev2049 @tconkling
Notebook shows preference scoring between two chains and reports wilson
score interval + p value
I think I'll add the option to insert ground truth labels but doesn't
have to be in this PR
- Description: Bug Fix - Added a step variable to keep track of prompts
- Issue: Bug from internal Arize testing - The prompts and responses
that are ingested were not mapped correctly
- Dependencies: N/A
fix the Chinese characters in the solution content will be converted to
ascii encoding, resulting in an abnormally long number of tokens
Co-authored-by: qixin <qixin@fintec.ai>
allows for where filtering on collection via get
- Description: aligns langchain chroma vectorstore get with underlying
[chromadb collection
get](https://github.com/chroma-core/chroma/blob/main/chromadb/api/models/Collection.py#L103)
allowing for where filtering, etc.
- Issue: NA
- Dependencies: none
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @pappanaka
#### Background
With the development of [structured
tools](https://blog.langchain.dev/structured-tools/), the LangChain team
expanded the platform's functionality to meet the needs of new
applications. The GMail tool, empowered by structured tools, now
supports multiple arguments and powerful search capabilities,
demonstrating LangChain's ability to interact with dynamic data sources
like email servers.
#### Challenge
The current GMail tool only supports GMail, while users often utilize
other email services like Outlook in Office365. Additionally, the
proposed calendar tool in PR
https://github.com/hwchase17/langchain/pull/652 only works with Google
Calendar, not Outlook.
#### Changes
This PR implements an Office365 integration for LangChain, enabling
seamless email and calendar functionality with a single authentication
process.
#### Future Work
With the core Office365 integration complete, future work could include
integrating other Office365 tools such as Tasks and Address Book.
#### Who can review?
@hwchase17 or @vowelparrot can review this PR
#### Appendix
@janscas, I utilized your [O365](https://github.com/O365/python-o365)
library extensively. Given the rising popularity of LangChain and
similar AI frameworks, the convergence of libraries like O365 and tools
like this one is likely. So, I wanted to keep you updated on our
progress.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
When the tool requires no input, the LLM often gives something like
this:
```json
{
"action": "just_do_it"
}
```
I have attempted to enhance the prompt, but it doesn't appear to be
functioning effectively. Therefore, I believe we should consider easing
the check a little bit.
Signed-off-by: Xiaochao Dong (@damnever) <the.xcdong@gmail.com>
Adding Confluence to Jira tool. Can create a page in Confluence with
this PR. If accepted, will extend functionality to Bitbucket and
additional Confluence features.
---------
Co-authored-by: Ethan Bowen <ethan.bowen@slalom.com>
Since this model name is not there in the list MODEL_COST_PER_1K_TOKENS,
when we use get_openai_callback(), for gpt 3.5 model in Azure AI, we do
not get the cost of the tokens. This will fix this issue
#### Who can review?
@hwchase17
@agola11
Co-authored-by: rajib76 <rajib76@yahoo.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
- Fixed an issue where some caching types check the wrong types, hence
not allowing caching to work
Maintainer responsibilities:
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
MHTML is a very interesting format since it's used both for emails but
also for archived webpages. Some scraping projects want to store pages
in disk to process them later, mhtml is perfect for that use case.
This is heavily inspired from the beautifulsoup html loader, but
extracting the html part from the mhtml file.
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
# beautifulsoup get_text kwargs in WebBaseLoader
- Description: this PR introduces an optional `bs_get_text_kwargs`
parameter to `WebBaseLoader` constructor. It can be used to pass kwargs
to the downstream BeautifulSoup.get_text call. The most common usage
might be to pass a custom text separator, as seen also in
`BSHTMLLoader`.
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: jtolgyesi
- Description: Adds a simple progress bar with tqdm when using
UnstructuredURLLoader. Exposes new paramater `show_progress_bar`. Very
simple PR.
- Issue: N/A
- Dependencies: N/A
- Tag maintainer: @rlancemartin @eyurtsev
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
- Description: Updated regex to support a new format that was observed
when whatsapp chat was exported.
- Issue: #6654
- Dependencies: No new dependencies
- Tag maintainer: @rlancemartin, @eyurtsev
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- Description: Fix Typo in LangChain MyScale Integration Doc
@hwchase17
# Add caching to BaseChatModel
Fixes#1644
(Sidenote: While testing, I noticed we have multiple implementations of
Fake LLMs, used for testing. I consolidated them.)
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
Models
- @hwchase17
- @agola11
Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord:
RicChilligerDude#7589
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Motorhead Memory module didn't support deletion of a session. Added a
method to enable deletion.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
This PR adds a new LLM class for the Amazon API Gateway hosted LLM. The
PR also includes example notebooks for using the LLM class in an Agent
chain.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
### Just corrected a small inconsistency on a doc page (not exactly a
typo, per se)
- Description: There was inconsistency due to the use of single quotes
at one place on the [Squential
Chains](https://python.langchain.com/docs/modules/chains/foundational/sequential_chains)
page of the docs,
- Issue: NA,
- Dependencies: NA,
- Tag maintainer: @dev2049,
- Twitter handle: kambleakash0
This PR targets the `API Reference` documentation.
- Several classes and functions missed `docstrings`. These docstrings
were created.
- In several places this
```
except ImportError:
raise ValueError(
```
was replaced to
```
except ImportError:
raise ImportError(
```
# Description
It adds a new initialization param in `WikipediaLoader` so we can
override the `doc_content_chars_max` param used in `WikipediaAPIWrapper`
under the hood, e.g:
```python
from langchain.document_loaders import WikipediaLoader
# doc_content_chars_max is the new init param
loader = WikipediaLoader(query="python", doc_content_chars_max=90000)
```
## Decisions
`doc_content_chars_max` default value will be 4000, because it's the
current value
I have added pycode comments
# Issue
#6639
# Dependencies
None
# Twitter handle
[@elafo](https://twitter.com/elafo)
- Description: The aviary integration has changed url link. This PR
provide fix for those changes and also it makes providing the input URL
optional to the API (since they can be set via env variables).
- Issue: N/A
- Dependencies: N/A
- Twitter handle: N/A
---------
Signed-off-by: Kourosh Hakhamaneshi <kourosh@anyscale.com>
Fix a typo in
`langchain/experimental/plan_and_execute/planners/base.py`, by changing
"Given input, decided what to do." to "Given input, decide what to do."
This is in the docstring for functions running LLM chains which shall
create a plan, "decided" does not make any sense in this context.
This link for the notebook of OpenLLM is not migrated to the new format
Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
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- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
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Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
vertex Ai chat is broken right now. That is because context is in params
and chat.send_message doesn't accept that as a params.
- Closes issue [ChatVertexAI Error: _ChatSessionBase.send_message() got
an unexpected keyword argument 'context'
#6610](https://github.com/hwchase17/langchain/issues/6610)
We may want to process load all URLs under a root directory.
For example, let's look at the [LangChain JS
documentation](https://js.langchain.com/docs/).
This has many interesting child pages that we may want to read in bulk.
Of course, the `WebBaseLoader` can load a list of pages.
But, the challenge is traversing the tree of child pages and actually
assembling that list!
We do this using the `RecusiveUrlLoader`.
This also gives us the flexibility to exclude some children (e.g., the
`api` directory with > 800 child pages).
## Goal
We want to ensure consistency across vectordbs:
1/ add `delete` by ID method to the base vectorstore class
2/ ensure `add_texts` performs `upsert` with ID optionally passed
## Testing
- [x] Pinecone: notebook test w/ `langchain_test` vectorstore.
- [x] Chroma: Review by @jeffchuber, notebook test w/ in memory
vectorstore.
- [x] Supabase: Review by @copple, notebook test w/ `langchain_test`
table.
- [x] Weaviate: Notebook test w/ `langchain_test` index.
- [x] Elastic: Revied by @vestal. Notebook test w/ `langchain_test`
table.
- [ ] Redis: Asked for review from owner of recent `delete` method
https://github.com/hwchase17/langchain/pull/6222
Fixes#5456
This PR removes the `callbacks` argument from a tool's schema when
creating a `Tool` or `StructuredTool` with the `from_function` method
and `infer_schema` is set to `True`. The `callbacks` argument is now
removed in the `create_schema_from_function` and `_get_filtered_args`
methods. As suggested by @vowelparrot, this fix provides a
straightforward solution that minimally affects the existing
implementation.
A test was added to verify that this change enables the expected use of
`Tool` and `StructuredTool` when using a `CallbackManager` and inferring
the tool's schema.
- @hwchase17
Many cities have open data portals for events like crime, traffic, etc.
Socrata provides an API for many, including SF (e.g., see
[here](https://dev.socrata.com/foundry/data.sfgov.org/tmnf-yvry)).
This is a new data loader for city data that uses Socrata API.
A new implementation of `StreamlitCallbackHandler`. It formats Agent
thoughts into Streamlit expanders.
You can see the handler in action here:
https://langchain-mrkl.streamlit.app/
Per a discussion with Harrison, we'll be adding a
`StreamlitCallbackHandler` implementation to an upcoming
[Streamlit](https://github.com/streamlit/streamlit) release as well, and
will be updating it as we add new LLM- and LangChain-specific features
to Streamlit.
The idea with this PR is that the LangChain `StreamlitCallbackHandler`
will "auto-update" in a way that keeps it forward- (and backward-)
compatible with Streamlit. If the user has an older Streamlit version
installed, the LangChain `StreamlitCallbackHandler` will be used; if
they have a newer Streamlit version that has an updated
`StreamlitCallbackHandler`, that implementation will be used instead.
(I'm opening this as a draft to get the conversation going and make sure
we're on the same page. We're really excited to land this into
LangChain!)
#### Who can review?
@agola11, @hwchase17
# Changes
This PR adds [Clarifai](https://www.clarifai.com/) integration to
Langchain. Clarifai is an end-to-end AI Platform. Clarifai offers user
the ability to use many types of LLM (OpenAI, cohere, ect and other open
source models). As well, a clarifai app can be treated as a vector
database to upload and retrieve data. The integrations includes:
- Clarifai LLM integration: Clarifai supports many types of language
model that users can utilize for their application
- Clarifai VectorDB: A Clarifai application can hold data and
embeddings. You can run semantic search with the embeddings
#### Before submitting
- [x] Added integration test for LLM
- [x] Added integration test for VectorDB
- [x] Added notebook for LLM
- [x] Added notebook for VectorDB
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
### Description
We have added a new LLM integration `azureml_endpoint` that allows users
to leverage models from the AzureML platform. Microsoft recently
announced the release of [Azure Foundation
Models](https://learn.microsoft.com/en-us/azure/machine-learning/concept-foundation-models?view=azureml-api-2)
which users can find in the AzureML Model Catalog. The Model Catalog
contains a variety of open source and Hugging Face models that users can
deploy on AzureML. The `azureml_endpoint` allows LangChain users to use
the deployed Azure Foundation Models.
### Dependencies
No added dependencies were required for the change.
### Tests
Integration tests were added in
`tests/integration_tests/llms/test_azureml_endpoint.py`.
### Notebook
A Jupyter notebook demonstrating how to use `azureml_endpoint` was added
to `docs/modules/llms/integrations/azureml_endpoint_example.ipynb`.
### Twitters
[Prakhar Gupta](https://twitter.com/prakhar_in)
[Matthew DeGuzman](https://twitter.com/matthew_d13)
---------
Co-authored-by: Matthew DeGuzman <91019033+matthewdeguzman@users.noreply.github.com>
Co-authored-by: prakharg-msft <75808410+prakharg-msft@users.noreply.github.com>
Since it seems like #6111 will be blocked for a bit, I've forked
@tyree731's fork and implemented the requested changes.
This change adds support to the base Embeddings class for two methods,
aembed_query and aembed_documents, those two methods supporting async
equivalents of embed_query and
embed_documents respectively. This ever so slightly rounds out async
support within langchain, with an initial implementation of this
functionality being implemented for openai.
Implements https://github.com/hwchase17/langchain/issues/6109
---------
Co-authored-by: Stephen Tyree <tyree731@gmail.com>
1. upgrade the version of AwaDB
2. add some new interfaces
3. fix bug of packing page content error
@dev2049 please review, thanks!
---------
Co-authored-by: vincent <awadb.vincent@gmail.com>
Everything needed to support sending messages over WhatsApp Business
Platform (GA), Facebook Messenger (Public Beta) and Google Business
Messages (Private Beta) was present. Just added some details on
leveraging it.
Description:
Update the artifact name of the xml file and the namespaces. Co-authored
with @tjaffri
Co-authored-by: Kenzie Mihardja <kenzie@docugami.com>
### Feature
Using FAISS on a retrievalQA task, I found myself wanting to allow in
multiple sources. From what I understood, the filter feature takes in a
dict of form {key: value} which then will check in the metadata for the
exact value linked to that key.
I added some logic to be able to pass a list which will be checked
against instead of an exact value. Passing an exact value will also
work.
Here's an example of how I could then use it in my own project:
```
pdfs_to_filter_in = ["file_A", "file_B"]
filter_dict = {
"source": [f"source_pdfs/{pdf_name}.pdf" for pdf_name in pdfs_to_filter_in]
}
retriever = db.as_retriever()
retriever.search_kwargs = {"filter": filter_dict}
```
I added an integration test based on the other ones I found in
`tests/integration_tests/vectorstores/test_faiss.py` under
`test_faiss_with_metadatas_and_list_filter()`.
It doesn't feel like this is worthy of its own notebook or doc, but I'm
open to suggestions if needed.
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Just some grammar fixes: I found "retriver" instead of "retriever" in
several comments across the documentation and in the comments. I fixed
it.
Co-authored-by: andrey.vedishchev <andrey.vedishchev@rgigroup.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
<!--
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<!-- Remove if not applicable -->
Fixes # (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on
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2. an example notebook showing its use
See contribution guidelines for more information on how to write tests,
lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
Here are some examples to use StarRocks as vectordb
```
from langchain.vectorstores import StarRocks
from langchain.vectorstores.starrocks import StarRocksSettings
embeddings = OpenAIEmbeddings()
# conifgure starrocks settings
settings = StarRocksSettings()
settings.port = 41003
settings.host = '127.0.0.1'
settings.username = 'root'
settings.password = ''
settings.database = 'zya'
# to fill new embeddings
docsearch = StarRocks.from_documents(split_docs, embeddings, config = settings)
# or to use already-built embeddings in database.
docsearch = StarRocks(embeddings, settings)
```
#### Who can review?
Tag maintainers/contributors who might be interested:
@dev2049
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
-->
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
<!-- Thank you for contributing to LangChain!
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
-->
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
Tag maintainers/contributors who might be interested:
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
-@agola11
Async
-@agola11
DataLoaders
-@eyurtsev
Models
-@hwchase17
-@agola11
Agents / Tools / Toolkits
-@hwchase17
VectorStores / Retrievers / Memory
-@dev2049
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
@@ -47,7 +47,7 @@ import ChatModel from "@snippets/get_started/quickstart/chat_model.mdx"
## Prompt templates
Most LLM applications do not pass user input directly into to an LLM. Usually they will add the user input to a larger piece of text, called a prompt template, that provides additional context on the specific task at hand.
Most LLM applications do not pass user input directly into an LLM. Usually they will add the user input to a larger piece of text, called a prompt template, that provides additional context on the specific task at hand.
In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it'd be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions.
@@ -138,7 +138,7 @@ The chains and agents we've looked at so far have been stateless, but for many a
The Memory module gives you a way to maintain application state. The base Memory interface is simple: it lets you update state given the latest run inputs and outputs and it lets you modify (or contextualize) the next input using the stored state.
There are a number of built-in memory systems. The simplest of these are is a buffer memory which just prepends the last few inputs/outputs to the current input - we will use this in the example below.
There are a number of built-in memory systems. The simplest of these is a buffer memory which just prepends the last few inputs/outputs to the current input - we will use this in the example below.
import MemoryLLM from "@snippets/get_started/quickstart/memory_llms.mdx"
import MemoryChatModel from "@snippets/get_started/quickstart/memory_chat_models.mdx"
@@ -155,4 +155,4 @@ You can use Memory with chains and agents initialized with chat models. The main
⛓ icon marks a new addition [last update 2023-07-05]
---------------------
### DeepLearning.AI courses
by [Harrison Chase](https://github.com/hwchase17) and [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
- [LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain)
- ⛓ [LangChain Chat with Your Data](https://learn.deeplearning.ai/langchain-chat-with-your-data)
### Handbook
[LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
### Short Tutorials
[LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
[LangChain Crash Course: Build an AutoGPT app in 25 minutes](https://youtu.be/MlK6SIjcjE8) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
[LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
## Tutorials
### [LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs)
- #1 [Getting Started with `GPT-3` vs. Open Source LLMs](https://youtu.be/nE2skSRWTTs)
- #2 [Prompt Templates for `GPT 3.5` and other LLMs](https://youtu.be/RflBcK0oDH0)
- #3 [LLM Chains using `GPT 3.5` and other LLMs](https://youtu.be/S8j9Tk0lZHU)
- [LangChain Data Loaders, Tokenizers, Chunking, and Datasets - Data Prep 101](https://youtu.be/eqOfr4AGLk8)
- #4 [Chatbot Memory for `Chat-GPT`, `Davinci` + other LLMs](https://youtu.be/X05uK0TZozM)
- #5 [Chat with OpenAI in LangChain](https://youtu.be/CnAgB3A5OlU)
- #6 [Fixing LLM Hallucinations with Retrieval Augmentation in LangChain](https://youtu.be/kvdVduIJsc8)
- #7 [LangChain Agents Deep Dive with `GPT 3.5`](https://youtu.be/jSP-gSEyVeI)
- #8 [Create Custom Tools for Chatbots in LangChain](https://youtu.be/q-HNphrWsDE)
- #9 [Build Conversational Agents with Vector DBs](https://youtu.be/H6bCqqw9xyI)
- [Using NEW `MPT-7B` in Hugging Face and LangChain](https://youtu.be/DXpk9K7DgMo)
- ⛓ [`MPT-30B` Chatbot with LangChain](https://youtu.be/pnem-EhT6VI)
### [LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Greg Kamradt (Data Indy)](https://www.youtube.com/@DataIndependent)
- [What Is LangChain? - LangChain + `ChatGPT` Overview](https://youtu.be/_v_fgW2SkkQ)
- [Question A 300 Page Book (w/ `OpenAI` + `Pinecone`)](https://youtu.be/h0DHDp1FbmQ)
- [Workaround `OpenAI's` Token Limit With Chain Types](https://youtu.be/f9_BWhCI4Zo)
- [Build Your Own OpenAI + LangChain Web App in 23 Minutes](https://youtu.be/U_eV8wfMkXU)
- [Working With The New `ChatGPT API`](https://youtu.be/e9P7FLi5Zy8)
- [OpenAI + LangChain Wrote Me 100 Custom Sales Emails](https://youtu.be/y1pyAQM-3Bo)
- [Structured Output From `OpenAI` (Clean Dirty Data)](https://youtu.be/KwAXfey-xQk)
- [Connect `OpenAI` To +5,000 Tools (LangChain + `Zapier`)](https://youtu.be/7tNm0yiDigU)
- [Use LLMs To Extract Data From Text (Expert Mode)](https://youtu.be/xZzvwR9jdPA)
- [Extract Insights From Interview Transcripts Using LLMs](https://youtu.be/shkMOHwJ4SM)
- [5 Levels Of LLM Summarizing: Novice to Expert](https://youtu.be/qaPMdcCqtWk)
- [Control Tone & Writing Style Of Your LLM Output](https://youtu.be/miBG-a3FuhU)
- [Build Your Own `AI Twitter Bot` Using LLMs](https://youtu.be/yLWLDjT01q8)
- [ChatGPT made my interview questions for me (`Streamlit` + LangChain)](https://youtu.be/zvoAMx0WKkw)
- [Function Calling via ChatGPT API - First Look With LangChain](https://youtu.be/0-zlUy7VUjg)
- ⛓ [Extract Topics From Video/Audio With LLMs (Topic Modeling w/ LangChain)](https://youtu.be/pEkxRQFNAs4)
### [LangChain How to and guides](https://www.youtube.com/playlist?list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ) by [Sam Witteveen](https://www.youtube.com/@samwitteveenai)
- [LangChain Basics - LLMs & PromptTemplates with Colab](https://youtu.be/J_0qvRt4LNk)
- [LangChain Basics - Tools and Chains](https://youtu.be/hI2BY7yl_Ac)
- [`ChatGPT API` Announcement & Code Walkthrough with LangChain](https://youtu.be/phHqvLHCwH4)
- [Conversations with Memory (explanation & code walkthrough)](https://youtu.be/X550Zbz_ROE)
- [Chat with `Flan20B`](https://youtu.be/VW5LBavIfY4)
- [Using `Hugging Face Models` locally (code walkthrough)](https://youtu.be/Kn7SX2Mx_Jk)
- [`PAL` : Program-aided Language Models with LangChain code](https://youtu.be/dy7-LvDu-3s)
- [Building a Summarization System with LangChain and `GPT-3` - Part 1](https://youtu.be/LNq_2s_H01Y)
- [Building a Summarization System with LangChain and `GPT-3` - Part 2](https://youtu.be/d-yeHDLgKHw)
- [Microsoft's `Visual ChatGPT` using LangChain](https://youtu.be/7YEiEyfPF5U)
- [LangChain Agents - Joining Tools and Chains with Decisions](https://youtu.be/ziu87EXZVUE)
- [Comparing LLMs with LangChain](https://youtu.be/rFNG0MIEuW0)
- [Using `Constitutional AI` in LangChain](https://youtu.be/uoVqNFDwpX4)
- [Talking to `Alpaca` with LangChain - Creating an Alpaca Chatbot](https://youtu.be/v6sF8Ed3nTE)
- [Talk to your `CSV` & `Excel` with LangChain](https://youtu.be/xQ3mZhw69bc)
- [`BabyAGI`: Discover the Power of Task-Driven Autonomous Agents!](https://youtu.be/QBcDLSE2ERA)
- [Improve your `BabyAGI` with LangChain](https://youtu.be/DRgPyOXZ-oE)
- [Master `PDF` Chat with LangChain - Your essential guide to queries on documents](https://youtu.be/ZzgUqFtxgXI)
- [Using LangChain with `DuckDuckGO` `Wikipedia` & `PythonREPL` Tools](https://youtu.be/KerHlb8nuVc)
- [Building Custom Tools and Agents with LangChain (gpt-3.5-turbo)](https://youtu.be/biS8G8x8DdA)
- [LangChain Retrieval QA Over Multiple Files with `ChromaDB`](https://youtu.be/3yPBVii7Ct0)
- [LangChain Retrieval QA with Instructor Embeddings & `ChromaDB` for PDFs](https://youtu.be/cFCGUjc33aU)
- [LangChain + Retrieval Local LLMs for Retrieval QA - No OpenAI!!!](https://youtu.be/9ISVjh8mdlA)
- [`Camel` + LangChain for Synthetic Data & Market Research](https://youtu.be/GldMMK6-_-g)
- [Information Extraction with LangChain & `Kor`](https://youtu.be/SW1ZdqH0rRQ)
- [Converting a LangChain App from OpenAI to OpenSource](https://youtu.be/KUDn7bVyIfc)
- [Using LangChain `Output Parsers` to get what you want out of LLMs](https://youtu.be/UVn2NroKQCw)
- [Building a LangChain Custom Medical Agent with Memory](https://youtu.be/6UFtRwWnHws)
- [Understanding `ReACT` with LangChain](https://youtu.be/Eug2clsLtFs)
- [`OpenAI Functions` + LangChain : Building a Multi Tool Agent](https://youtu.be/4KXK6c6TVXQ)
- [What can you do with 16K tokens in LangChain?](https://youtu.be/z2aCZBAtWXs)
- [Tagging and Extraction - Classification using `OpenAI Functions`](https://youtu.be/a8hMgIcUEnE)
- ⛓ [HOW to Make Conversational Form with LangChain](https://youtu.be/IT93On2LB5k)
### [LangChain](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
- [LangChain Crash Course — All You Need to Know to Build Powerful Apps with LLMs](https://youtu.be/5-fc4Tlgmro)
- [Working with MULTIPLE `PDF` Files in LangChain: `ChatGPT` for your Data](https://youtu.be/s5LhRdh5fu4)
- [`ChatGPT` for YOUR OWN `PDF` files with LangChain](https://youtu.be/TLf90ipMzfE)
- [Talk to YOUR DATA without OpenAI APIs: LangChain](https://youtu.be/wrD-fZvT6UI)
- [Langchain: PDF Chat App (GUI) | ChatGPT for Your PDF FILES](https://youtu.be/RIWbalZ7sTo)
- [LangFlow: Build Chatbots without Writing Code](https://youtu.be/KJ-ux3hre4s)
- [LangChain: Giving Memory to LLMs](https://youtu.be/dxO6pzlgJiY)
- [BEST OPEN Alternative to `OPENAI's EMBEDDINGs` for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY)
### LangChain by [Chat with data](https://www.youtube.com/@chatwithdata)
- [LangChain Beginner's Tutorial for `Typescript`/`Javascript`](https://youtu.be/bH722QgRlhQ)
- [`GPT-4` Tutorial: How to Chat With Multiple `PDF` Files (~1000 pages of Tesla's 10-K Annual Reports)](https://youtu.be/Ix9WIZpArm0)
- [`GPT-4` & LangChain Tutorial: How to Chat With A 56-Page `PDF` Document (w/`Pinecone`)](https://youtu.be/ih9PBGVVOO4)
- [LangChain & Supabase Tutorial: How to Build a ChatGPT Chatbot For Your Website](https://youtu.be/R2FMzcsmQY8)
- [LangChain Agents: Build Personal Assistants For Your Data (Q&A with Harrison Chase and Mayo Oshin)](https://youtu.be/gVkF8cwfBLI)
---------------------
⛓ icon marks a new addition [last update 2023-07-05]
>[JSON (JavaScript Object Notation)](https://en.wikipedia.org/wiki/JSON) is an open standard file format and data interchange format that uses human-readable text to store and transmit data objects consisting of attribute–value pairs and arrays (or other serializable values).
>[JSON Lines](https://jsonlines.org/) is a file format where each line is a valid JSON value.
import Example from "@snippets/modules/data_connection/document_loaders/how_to/json.mdx"
This walkthrough showcases basic functionality related to VectorStores. A key part of working with vector stores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [text embedding model](/docs/modules/model_io/models/embeddings.html) interfaces before diving into this.
This walkthrough showcases basic functionality related to VectorStores. A key part of working with vector stores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [text embedding model](/docs/modules/data_connection/text_embedding/) interfaces before diving into this.
import GetStarted from "@snippets/modules/data_connection/vectorstores/get_started.mdx"
@@ -9,7 +9,6 @@ This is a collection of `LangChain` videos on `YouTube`.
- [LangChain and Weaviate with Harrison Chase and Bob van Luijt - Weaviate Podcast #36](https://youtu.be/lhby7Ql7hbk) by [Weaviate • Vector Database](https://www.youtube.com/@Weaviate)
- [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s?t=788) by [Full Stack Deep Learning](https://www.youtube.com/@FullStackDeepLearning)
- [LangChain Agents: Build Personal Assistants For Your Data (Q&A with Harrison Chase and Mayo Oshin)](https://youtu.be/gVkF8cwfBLI) by [Chat with data](https://www.youtube.com/@chatwithdata)
- ⛓️ [LangChain "Agents in Production" Webinar](https://youtu.be/k8GNCCs16F4) by [LangChain](https://www.youtube.com/@LangChain)
## Videos (sorted by views)
@@ -31,6 +30,9 @@ This is a collection of `LangChain` videos on `YouTube`.
- [`Weaviate` + LangChain for LLM apps presented by Erika Cardenas](https://youtu.be/7AGj4Td5Lgw) by [`Weaviate` • Vector Database](https://www.youtube.com/@Weaviate)
- [Langchain Overview — How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
- [Langchain Overview - How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
- [LangChain Tutorials](https://www.youtube.com/watch?v=FuqdVNB_8c0&list=PL9V0lbeJ69brU-ojMpU1Y7Ic58Tap0Cw6) by [Edrick](https://www.youtube.com/@edrickdch):
- [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
- [LangChain 101: The Complete Beginner's Guide](https://youtu.be/P3MAbZ2eMUI)
- [Custom langchain Agent & Tools with memory. Turn any `Python function` into langchain tool with Gpt 3](https://youtu.be/NIG8lXk0ULg) by [echohive](https://www.youtube.com/@echohive)
- [LangChain: Run Language Models Locally - `Hugging Face Models`](https://youtu.be/Xxxuw4_iCzw) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
- [`ChatGPT` with any `YouTube` video using langchain and `chromadb`](https://youtu.be/TQZfB2bzVwU) by [echohive](https://www.youtube.com/@echohive)
@@ -46,154 +48,68 @@ This is a collection of `LangChain` videos on `YouTube`.
- [Langchain + `Zapier` Agent](https://youtu.be/yribLAb-pxA) by [Merk](https://www.youtube.com/@merksworld)
- [Connecting the Internet with `ChatGPT` (LLMs) using Langchain And Answers Your Questions](https://youtu.be/9Y0TBC63yZg) by [Kamalraj M M](https://www.youtube.com/@insightbuilder)
- [Build More Powerful LLM Applications for Business’s with LangChain (Beginners Guide)](https://youtu.be/sp3-WLKEcBg) by[ No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
- ⛓️ [LangFlow LLM Agent Demo for 🦜🔗LangChain](https://youtu.be/zJxDHaWt-6o) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
- ⛓️ [Chatbot Factory: Streamline Python Chatbot Creation with LLMs and Langchain](https://youtu.be/eYer3uzrcuM) by [Finxter](https://www.youtube.com/@CobusGreylingZA)
- ⛓️ [LangChain Tutorial - ChatGPT mit eigenen Daten](https://youtu.be/0XDLyY90E2c) by [Coding Crashkurse](https://www.youtube.com/@codingcrashkurse6429)
- ⛓️ [Chat with a `CSV` | LangChain Agents Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [GoDataProf](https://www.youtube.com/@godataprof)
- ⛓️ [Introdução ao Langchain - #Cortes - Live DataHackers](https://youtu.be/fw8y5VRei5Y) by [Prof. João Gabriel Lima](https://www.youtube.com/@profjoaogabriellima)
- ⛓️ [LangChain: Level up `ChatGPT` !? | LangChain Tutorial Part 1](https://youtu.be/vxUGx8aZpDE) by [Code Affinity](https://www.youtube.com/@codeaffinitydev)
- ⛓️ [Chat with Audio: Langchain, `Chroma DB`, OpenAI, and `Assembly AI`](https://youtu.be/Kjy7cx1r75g) by [AI Anytime](https://www.youtube.com/@AIAnytime)
- ⛓️ [QA over documents with Auto vector index selection with Langchain router chains](https://youtu.be/9G05qybShv8) by [echohive](https://www.youtube.com/@echohive)
- ⛓️ [Build your own custom LLM application with `Bubble.io` & Langchain (No Code & Beginner friendly)](https://youtu.be/O7NhQGu1m6c) by [No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
- ⛓️ [Simple App to Question Your Docs: Leveraging `Streamlit`, `Hugging Face Spaces`, LangChain, and `Claude`!](https://youtu.be/X4YbNECRr7o) by [Chris Alexiuk](https://www.youtube.com/@chrisalexiuk)
- ⛓️ [LANGCHAIN AI- `ConstitutionalChainAI` + Databutton AI ASSISTANT Web App](https://youtu.be/5zIU6_rdJCU) by [Avra](https://www.youtube.com/@Avra_b)
- ⛓️ [LANGCHAIN AI AUTONOMOUS AGENT WEB APP - 👶 `BABY AGI` 🤖 with EMAIL AUTOMATION using `DATABUTTON`](https://youtu.be/cvAwOGfeHgw) by [Avra](https://www.youtube.com/@Avra_b)
- ⛓️ [The Future of Data Analysis: Using A.I. Models in Data Analysis (LangChain)](https://youtu.be/v_LIcVyg5dk) by [Absent Data](https://www.youtube.com/@absentdata)
- ⛓️ [Memory in LangChain | Deep dive (python)](https://youtu.be/70lqvTFh_Yg) by [Eden Marco](https://www.youtube.com/@EdenMarco)
- ⛓️ [Use Large Language Models in Jupyter Notebook | LangChain | Agents & Indexes](https://youtu.be/JSe11L1a_QQ) by [Abhinaw Tiwari](https://www.youtube.com/@AbhinawTiwariAT)
- ⛓️ [How to Talk to Your Langchain Agent | `11 Labs` + `Whisper`](https://youtu.be/N4k459Zw2PU) by [VRSEN](https://www.youtube.com/@vrsen)
- ⛓️ [LangChain Deep Dive: 5 FUN AI App Ideas To Build Quickly and Easily](https://youtu.be/mPYEPzLkeks) by [James NoCode](https://www.youtube.com/@jamesnocode)
- ⛓️ [BEST OPEN Alternative to OPENAI's EMBEDDINGs for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
- ⛓️ [LangChain 101: Models](https://youtu.be/T6c_XsyaNSQ) by [Mckay Wrigley](https://www.youtube.com/@realmckaywrigley)
- ⛓️ [LangChain with JavaScript Tutorial #1 | Setup & Using LLMs](https://youtu.be/W3AoeMrg27o) by [Leon van Zyl](https://www.youtube.com/@leonvanzyl)
- ⛓️ [LangChain Overview & Tutorial for Beginners: Build Powerful AI Apps Quickly & Easily (ZERO CODE)](https://youtu.be/iI84yym473Q) by [James NoCode](https://www.youtube.com/@jamesnocode)
- ⛓️ [LangChain In Action: Real-World Use Case With Step-by-Step Tutorial](https://youtu.be/UO699Szp82M) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
- ⛓️ [Summarizing and Querying Multiple Papers with LangChain](https://youtu.be/p_MQRWH5Y6k) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
- ⛓️ [Using Langchain (and `Replit`) through `Tana`, ask `Google`/`Wikipedia`/`Wolfram Alpha` to fill out a table](https://youtu.be/Webau9lEzoI) by [Stian Håklev](https://www.youtube.com/@StianHaklev)
- ⛓️ [Langchain PDF App (GUI) | Create a ChatGPT For Your `PDF` in Python](https://youtu.be/wUAUdEw5oxM) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
- ⛓️ [Auto-GPT with LangChain 🔥 | Create Your Own Personal AI Assistant](https://youtu.be/imDfPmMKEjM) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
- ⛓️ [Create Your OWN Slack AI Assistant with Python & LangChain](https://youtu.be/3jFXRNn2Bu8) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
- ⛓️ [How to Create LOCAL Chatbots with GPT4All and LangChain [Full Guide]](https://youtu.be/4p1Fojur8Zw) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
- ⛓️ [Build a `Multilingual PDF` Search App with LangChain, `Cohere` and `Bubble`](https://youtu.be/hOrtuumOrv8) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
- ⛓️ [Building a LangChain Agent (code-free!) Using `Bubble` and `Flowise`](https://youtu.be/jDJIIVWTZDE) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
- ⛓️ [Build a LangChain-based Semantic PDF Search App with No-Code Tools Bubble and Flowise](https://youtu.be/s33v5cIeqA4) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
- ⛓️ [LangChain Memory Tutorial | Building a ChatGPT Clone in Python](https://youtu.be/Cwq91cj2Pnc) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
- ⛓️ [ChatGPT For Your DATA | Chat with Multiple Documents Using LangChain](https://youtu.be/TeDgIDqQmzs) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
- ⛓️ [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@merksworld)
- ⛓️ [Using OpenAI, LangChain, and `Gradio` to Build Custom GenAI Applications](https://youtu.be/1MsmqMg3yUc) by [David Hundley](https://www.youtube.com/@dkhundley)
- ⛓️ [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
- [LangChain Crash Course: Build an AutoGPT app in 25 minutes](https://youtu.be/MlK6SIjcjE8) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
- [LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
- [LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
- [LangFlow LLM Agent Demo for 🦜🔗LangChain](https://youtu.be/zJxDHaWt-6o) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
- [Chatbot Factory: Streamline Python Chatbot Creation with LLMs and Langchain](https://youtu.be/eYer3uzrcuM) by [Finxter](https://www.youtube.com/@CobusGreylingZA)
- [LangChain Tutorial - ChatGPT mit eigenen Daten](https://youtu.be/0XDLyY90E2c) by [Coding Crashkurse](https://www.youtube.com/@codingcrashkurse6429)
- [Chat with a `CSV` | LangChain Agents Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [GoDataProf](https://www.youtube.com/@godataprof)
- [Introdução ao Langchain - #Cortes - Live DataHackers](https://youtu.be/fw8y5VRei5Y) by [Prof. João Gabriel Lima](https://www.youtube.com/@profjoaogabriellima)
- [LangChain: Level up `ChatGPT` !? | LangChain Tutorial Part 1](https://youtu.be/vxUGx8aZpDE) by [Code Affinity](https://www.youtube.com/@codeaffinitydev)
- [Chat with Audio: Langchain, `Chroma DB`, OpenAI, and `Assembly AI`](https://youtu.be/Kjy7cx1r75g) by [AI Anytime](https://www.youtube.com/@AIAnytime)
- [QA over documents with Auto vector index selection with Langchain router chains](https://youtu.be/9G05qybShv8) by [echohive](https://www.youtube.com/@echohive)
- [Build your own custom LLM application with `Bubble.io` & Langchain (No Code & Beginner friendly)](https://youtu.be/O7NhQGu1m6c) by [No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
- [Simple App to Question Your Docs: Leveraging `Streamlit`, `Hugging Face Spaces`, LangChain, and `Claude`!](https://youtu.be/X4YbNECRr7o) by [Chris Alexiuk](https://www.youtube.com/@chrisalexiuk)
- [LANGCHAIN AI- `ConstitutionalChainAI` + Databutton AI ASSISTANT Web App](https://youtu.be/5zIU6_rdJCU) by [Avra](https://www.youtube.com/@Avra_b)
- [LANGCHAIN AI AUTONOMOUS AGENT WEB APP - 👶 `BABY AGI` 🤖 with EMAIL AUTOMATION using `DATABUTTON`](https://youtu.be/cvAwOGfeHgw) by [Avra](https://www.youtube.com/@Avra_b)
- [The Future of Data Analysis: Using A.I. Models in Data Analysis (LangChain)](https://youtu.be/v_LIcVyg5dk) by [Absent Data](https://www.youtube.com/@absentdata)
- [Memory in LangChain | Deep dive (python)](https://youtu.be/70lqvTFh_Yg) by [Eden Marco](https://www.youtube.com/@EdenMarco)
- [Use Large Language Models in Jupyter Notebook | LangChain | Agents & Indexes](https://youtu.be/JSe11L1a_QQ) by [Abhinaw Tiwari](https://www.youtube.com/@AbhinawTiwariAT)
- [How to Talk to Your Langchain Agent | `11 Labs` + `Whisper`](https://youtu.be/N4k459Zw2PU) by [VRSEN](https://www.youtube.com/@vrsen)
- [LangChain Deep Dive: 5 FUN AI App Ideas To Build Quickly and Easily](https://youtu.be/mPYEPzLkeks) by [James NoCode](https://www.youtube.com/@jamesnocode)
- [BEST OPEN Alternative to OPENAI's EMBEDDINGs for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
- [LangChain 101: Models](https://youtu.be/T6c_XsyaNSQ) by [Mckay Wrigley](https://www.youtube.com/@realmckaywrigley)
- [LangChain with JavaScript Tutorial #1 | Setup & Using LLMs](https://youtu.be/W3AoeMrg27o) by [Leon van Zyl](https://www.youtube.com/@leonvanzyl)
- [LangChain Overview & Tutorial for Beginners: Build Powerful AI Apps Quickly & Easily (ZERO CODE)](https://youtu.be/iI84yym473Q) by [James NoCode](https://www.youtube.com/@jamesnocode)
- [LangChain In Action: Real-World Use Case With Step-by-Step Tutorial](https://youtu.be/UO699Szp82M) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
- [Summarizing and Querying Multiple Papers with LangChain](https://youtu.be/p_MQRWH5Y6k) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
- [Using Langchain (and `Replit`) through `Tana`, ask `Google`/`Wikipedia`/`Wolfram Alpha` to fill out a table](https://youtu.be/Webau9lEzoI) by [Stian Håklev](https://www.youtube.com/@StianHaklev)
- [Langchain PDF App (GUI) | Create a ChatGPT For Your `PDF` in Python](https://youtu.be/wUAUdEw5oxM) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
- [Auto-GPT with LangChain 🔥 | Create Your Own Personal AI Assistant](https://youtu.be/imDfPmMKEjM) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
- [Create Your OWN Slack AI Assistant with Python & LangChain](https://youtu.be/3jFXRNn2Bu8) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
- [How to Create LOCAL Chatbots with GPT4All and LangChain [Full Guide]](https://youtu.be/4p1Fojur8Zw) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
- [Build a `Multilingual PDF` Search App with LangChain, `Cohere` and `Bubble`](https://youtu.be/hOrtuumOrv8) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
- [Building a LangChain Agent (code-free!) Using `Bubble` and `Flowise`](https://youtu.be/jDJIIVWTZDE) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
- [Build a LangChain-based Semantic PDF Search App with No-Code Tools Bubble and Flowise](https://youtu.be/s33v5cIeqA4) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
- [LangChain Memory Tutorial | Building a ChatGPT Clone in Python](https://youtu.be/Cwq91cj2Pnc) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
- [ChatGPT For Your DATA | Chat with Multiple Documents Using LangChain](https://youtu.be/TeDgIDqQmzs) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
- [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@merksworld)
- [Using OpenAI, LangChain, and `Gradio` to Build Custom GenAI Applications](https://youtu.be/1MsmqMg3yUc) by [David Hundley](https://www.youtube.com/@dkhundley)
- [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
- ⛓ [Build AI chatbot with custom knowledge base using OpenAI API and GPT Index](https://youtu.be/vDZAZuaXf48) by [Irina Nik](https://www.youtube.com/@irina_nik)
- ⛓ [Build Your Own Auto-GPT Apps with LangChain (Python Tutorial)](https://youtu.be/NYSWn1ipbgg) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
- ⛓ [Chat with Multiple `PDFs` | LangChain App Tutorial in Python (Free LLMs and Embeddings)](https://youtu.be/dXxQ0LR-3Hg) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
- ⛓ [Chat with a `CSV` | `LangChain Agents` Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
- ⛓ [Create Your Own ChatGPT with `PDF` Data in 5 Minutes (LangChain Tutorial)](https://youtu.be/au2WVVGUvc8) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
- ⛓ [Using ChatGPT with YOUR OWN Data. This is magical. (LangChain OpenAI API)](https://youtu.be/9AXP7tCI9PI) by [TechLead](https://www.youtube.com/@TechLead)
- ⛓ [Build a Custom Chatbot with OpenAI: `GPT-Index` & LangChain | Step-by-Step Tutorial](https://youtu.be/FIDv6nc4CgU) by [Fabrikod](https://www.youtube.com/@fabrikod)
- ⛓ [`Flowise` is an open source no-code UI visual tool to build 🦜🔗LangChain applications](https://youtu.be/CovAPtQPU0k) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
- ⛓ [LangChain & GPT 4 For Data Analysis: The `Pandas` Dataframe Agent](https://youtu.be/rFQ5Kmkd4jc) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
- ⛓ [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw) by [Toolfinder AI](https://www.youtube.com/@toolfinderai)
- ⛓ [`PrivateGPT`: Chat to your FILES OFFLINE and FREE [Installation and Tutorial]](https://youtu.be/G7iLllmx4qc) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
- ⛓ [How to build with Langchain 10x easier | ⛓️ LangFlow & `Flowise`](https://youtu.be/Ya1oGL7ZTvU) by [AI Jason](https://www.youtube.com/@AIJasonZ)
- ⛓ [Getting Started With LangChain In 20 Minutes- Build Celebrity Search Application](https://youtu.be/_FpT1cwcSLg) by [Krish Naik](https://www.youtube.com/@krishnaik06)
## Tutorial Series
⛓ icon marks a new addition [last update 2023-05-15]
### DeepLearning.AI course
⛓[LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain) by Harrison Chase presented by [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
### Handbook
[LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
### Tutorials
[LangChain Tutorials](https://www.youtube.com/watch?v=FuqdVNB_8c0&list=PL9V0lbeJ69brU-ojMpU1Y7Ic58Tap0Cw6) by [Edrick](https://www.youtube.com/@edrickdch):
- ⛓ [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
- ⛓ [LangChain 101: The Complete Beginner's Guide](https://youtu.be/P3MAbZ2eMUI)
[LangChain Crash Course: Build an AutoGPT app in 25 minutes](https://youtu.be/MlK6SIjcjE8) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
[LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
[LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
### [LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs):
- #1 [Getting Started with `GPT-3` vs. Open Source LLMs](https://youtu.be/nE2skSRWTTs)
- #2 [Prompt Templates for `GPT 3.5` and other LLMs](https://youtu.be/RflBcK0oDH0)
- #3 [LLM Chains using `GPT 3.5` and other LLMs](https://youtu.be/S8j9Tk0lZHU)
- #4 [Chatbot Memory for `Chat-GPT`, `Davinci` + other LLMs](https://youtu.be/X05uK0TZozM)
- #5 [Chat with OpenAI in LangChain](https://youtu.be/CnAgB3A5OlU)
- ⛓ #6 [Fixing LLM Hallucinations with Retrieval Augmentation in LangChain](https://youtu.be/kvdVduIJsc8)
- ⛓ #7 [LangChain Agents Deep Dive with GPT 3.5](https://youtu.be/jSP-gSEyVeI)
- ⛓ #8 [Create Custom Tools for Chatbots in LangChain](https://youtu.be/q-HNphrWsDE)
- ⛓ #9 [Build Conversational Agents with Vector DBs](https://youtu.be/H6bCqqw9xyI)
### [LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Data Independent](https://www.youtube.com/@DataIndependent):
- [What Is LangChain? - LangChain + `ChatGPT` Overview](https://youtu.be/_v_fgW2SkkQ)
- [Question A 300 Page Book (w/ `OpenAI` + `Pinecone`)](https://youtu.be/h0DHDp1FbmQ)
- [Workaround `OpenAI's` Token Limit With Chain Types](https://youtu.be/f9_BWhCI4Zo)
- [Build Your Own OpenAI + LangChain Web App in 23 Minutes](https://youtu.be/U_eV8wfMkXU)
- [Working With The New `ChatGPT API`](https://youtu.be/e9P7FLi5Zy8)
- [OpenAI + LangChain Wrote Me 100 Custom Sales Emails](https://youtu.be/y1pyAQM-3Bo)
- [Structured Output From `OpenAI` (Clean Dirty Data)](https://youtu.be/KwAXfey-xQk)
- [Connect `OpenAI` To +5,000 Tools (LangChain + `Zapier`)](https://youtu.be/7tNm0yiDigU)
- [Use LLMs To Extract Data From Text (Expert Mode)](https://youtu.be/xZzvwR9jdPA)
- ⛓ [Extract Insights From Interview Transcripts Using LLMs](https://youtu.be/shkMOHwJ4SM)
- ⛓ [5 Levels Of LLM Summarizing: Novice to Expert](https://youtu.be/qaPMdcCqtWk)
### [LangChain How to and guides](https://www.youtube.com/playlist?list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ) by [Sam Witteveen](https://www.youtube.com/@samwitteveenai):
- [LangChain Basics - LLMs & PromptTemplates with Colab](https://youtu.be/J_0qvRt4LNk)
- [LangChain Basics - Tools and Chains](https://youtu.be/hI2BY7yl_Ac)
- [`ChatGPT API` Announcement & Code Walkthrough with LangChain](https://youtu.be/phHqvLHCwH4)
- [Conversations with Memory (explanation & code walkthrough)](https://youtu.be/X550Zbz_ROE)
- [Chat with `Flan20B`](https://youtu.be/VW5LBavIfY4)
- [Using `Hugging Face Models` locally (code walkthrough)](https://youtu.be/Kn7SX2Mx_Jk)
- [`PAL` : Program-aided Language Models with LangChain code](https://youtu.be/dy7-LvDu-3s)
- [Building a Summarization System with LangChain and `GPT-3` - Part 1](https://youtu.be/LNq_2s_H01Y)
- [Building a Summarization System with LangChain and `GPT-3` - Part 2](https://youtu.be/d-yeHDLgKHw)
- [Microsoft's `Visual ChatGPT` using LangChain](https://youtu.be/7YEiEyfPF5U)
- [LangChain Agents - Joining Tools and Chains with Decisions](https://youtu.be/ziu87EXZVUE)
- [Comparing LLMs with LangChain](https://youtu.be/rFNG0MIEuW0)
- [Using `Constitutional AI` in LangChain](https://youtu.be/uoVqNFDwpX4)
- [Talking to `Alpaca` with LangChain - Creating an Alpaca Chatbot](https://youtu.be/v6sF8Ed3nTE)
- [Talk to your `CSV` & `Excel` with LangChain](https://youtu.be/xQ3mZhw69bc)
- [`BabyAGI`: Discover the Power of Task-Driven Autonomous Agents!](https://youtu.be/QBcDLSE2ERA)
- [Improve your `BabyAGI` with LangChain](https://youtu.be/DRgPyOXZ-oE)
- ⛓ [Master `PDF` Chat with LangChain - Your essential guide to queries on documents](https://youtu.be/ZzgUqFtxgXI)
- ⛓ [Using LangChain with `DuckDuckGO` `Wikipedia` & `PythonREPL` Tools](https://youtu.be/KerHlb8nuVc)
- ⛓ [Building Custom Tools and Agents with LangChain (gpt-3.5-turbo)](https://youtu.be/biS8G8x8DdA)
- ⛓ [LangChain Retrieval QA Over Multiple Files with `ChromaDB`](https://youtu.be/3yPBVii7Ct0)
- ⛓ [LangChain Retrieval QA with Instructor Embeddings & `ChromaDB` for PDFs](https://youtu.be/cFCGUjc33aU)
- ⛓ [LangChain + Retrieval Local LLMs for Retrieval QA - No OpenAI!!!](https://youtu.be/9ISVjh8mdlA)
### [LangChain](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr) by [Prompt Engineering](https://www.youtube.com/@engineerprompt):
- [LangChain Crash Course — All You Need to Know to Build Powerful Apps with LLMs](https://youtu.be/5-fc4Tlgmro)
- [Working with MULTIPLE `PDF` Files in LangChain: `ChatGPT` for your Data](https://youtu.be/s5LhRdh5fu4)
- [`ChatGPT` for YOUR OWN `PDF` files with LangChain](https://youtu.be/TLf90ipMzfE)
- [Talk to YOUR DATA without OpenAI APIs: LangChain](https://youtu.be/wrD-fZvT6UI)
- ⛓️ [CHATGPT For WEBSITES: Custom ChatBOT](https://youtu.be/RBnuhhmD21U)
### LangChain by [Chat with data](https://www.youtube.com/@chatwithdata)
- [LangChain Beginner's Tutorial for `Typescript`/`Javascript`](https://youtu.be/bH722QgRlhQ)
- [`GPT-4` Tutorial: How to Chat With Multiple `PDF` Files (~1000 pages of Tesla's 10-K Annual Reports)](https://youtu.be/Ix9WIZpArm0)
- [`GPT-4` & LangChain Tutorial: How to Chat With A 56-Page `PDF` Document (w/`Pinecone`)](https://youtu.be/ih9PBGVVOO4)
- ⛓ [LangChain & Supabase Tutorial: How to Build a ChatGPT Chatbot For Your Website](https://youtu.be/R2FMzcsmQY8)
### [Get SH\*T Done with Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov)
### [Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov)
- [Getting Started with LangChain: Load Custom Data, Run OpenAI Models, Embeddings and `ChatGPT`](https://www.youtube.com/watch?v=muXbPpG_ys4)
- [Loaders, Indexes & Vectorstores in LangChain: Question Answering on `PDF` files with `ChatGPT`](https://www.youtube.com/watch?v=FQnvfR8Dmr0)
>[Airtable](https://en.wikipedia.org/wiki/Airtable) is a cloud collaboration service.
`Airtable` is a spreadsheet-database hybrid, with the features of a database but applied to a spreadsheet.
> The fields in an Airtable table are similar to cells in a spreadsheet, but have types such as 'checkbox',
> 'phone number', and 'drop-down list', and can reference file attachments like images.
>Users can create a database, set up column types, add records, link tables to one another, collaborate, sort records
> and publish views to external websites.
## Installation and Setup
```bash
pip install pyairtable
```
* Get your [API key](https://support.airtable.com/docs/creating-and-using-api-keys-and-access-tokens).
* Get the [ID of your base](https://airtable.com/developers/web/api/introduction).
* Get the [table ID from the table url](https://www.highviewapps.com/kb/where-can-i-find-the-airtable-base-id-and-table-id/#:~:text=Both%20the%20Airtable%20Base%20ID,URL%20that%20begins%20with%20tbl).
[Amazon API Gateway](https://aws.amazon.com/api-gateway/) is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. APIs act as the "front door" for applications to access data, business logic, or functionality from your backend services. Using API Gateway, you can create RESTful APIs and WebSocket APIs that enable real-time two-way communication applications. API Gateway supports containerized and serverless workloads, as well as web applications.
API Gateway handles all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls, including traffic management, CORS support, authorization and access control, throttling, monitoring, and API version management. API Gateway has no minimum fees or startup costs. You pay for the API calls you receive and the amount of data transferred out and, with the API Gateway tiered pricing model, you can reduce your cost as your API usage scales.
## LLM
See a [usage example](/docs/modules/model_io/models/llms/integrations/amazon_api_gateway_example.html).
"[Arthur](https://arthur.ai) is a model monitoring and observability platform.\n",
"\n",
"The following guide shows how to run a registered chat LLM with the Arthur callback handler to automatically log model inferences to Arthur.\n",
"\n",
"If you do not have a model currently onboarded to Arthur, visit our [onboarding guide for generative text models](https://docs.arthur.ai/user-guide/walkthroughs/model-onboarding/generative_text_onboarding.html). For more information about how to use the Arthur SDK, visit our [docs](https://docs.arthur.ai/)."
"Create Langchain LLM with Arthur callback handler"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "9Hq9snQasynA"
},
"outputs": [],
"source": [
"def make_langchain_chat_llm(chat_model=):\n",
" return ChatOpenAI(\n",
" streaming=True,\n",
" temperature=0.1,\n",
" callbacks=[\n",
" StreamingStdOutCallbackHandler(),\n",
" ArthurCallbackHandler.from_credentials(\n",
" arthur_model_id, \n",
" arthur_url=arthur_url, \n",
" arthur_login=arthur_login)\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Please enter password for admin: ········\n"
]
}
],
"source": [
"chatgpt = make_langchain_chat_llm()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aXRyj50Ls8eP"
},
"source": [
"Running the chat LLM with this `run` function will save the chat history in an ongoing list so that the conversation can reference earlier messages and log each response to the Arthur platform. You can view the history of this model's inferences on your [model dashboard page](https://app.arthur.ai/).\n",
"A callback handler, also known as a callback function or callback method, is a piece of code that is executed in response to a specific event or condition. It is commonly used in programming languages that support event-driven or asynchronous programming paradigms.\n",
"\n",
"The purpose of a callback handler is to provide a way for developers to define custom behavior that should be executed when a certain event occurs. Instead of waiting for a result or blocking the execution, the program registers a callback function and continues with other tasks. When the event is triggered, the callback function is invoked, allowing the program to respond accordingly.\n",
"\n",
"Callback handlers are commonly used in various scenarios, such as handling user input, responding to network requests, processing asynchronous operations, and implementing event-driven architectures. They provide a flexible and modular way to handle events and decouple different components of a system.\n",
">>> input >>>\n",
">>>: What do I need to do to get the full benefits of this\n",
"To get the full benefits of using a callback handler, you should consider the following:\n",
"\n",
"1. Understand the event or condition: Identify the specific event or condition that you want to respond to with a callback handler. This could be user input, network requests, or any other asynchronous operation.\n",
"\n",
"2. Define the callback function: Create a function that will be executed when the event or condition occurs. This function should contain the desired behavior or actions you want to take in response to the event.\n",
"\n",
"3. Register the callback function: Depending on the programming language or framework you are using, you may need to register or attach the callback function to the appropriate event or condition. This ensures that the callback function is invoked when the event occurs.\n",
"\n",
"4. Handle the callback: Implement the necessary logic within the callback function to handle the event or condition. This could involve updating the user interface, processing data, making further requests, or triggering other actions.\n",
"\n",
"5. Consider error handling: It's important to handle any potential errors or exceptions that may occur within the callback function. This ensures that your program can gracefully handle unexpected situations and prevent crashes or undesired behavior.\n",
"\n",
"6. Maintain code readability and modularity: As your codebase grows, it's crucial to keep your callback handlers organized and maintainable. Consider using design patterns or architectural principles to structure your code in a modular and scalable way.\n",
"\n",
"By following these steps, you can leverage the benefits of callback handlers, such as asynchronous and event-driven programming, improved responsiveness, and modular code design.\n",
>[Databerry](https://databerry.ai) is an [open source](https://github.com/gmpetrov/databerry) document retrieval platform that helps to connect your personal data with Large Language Models.
>[Chaindesk](https://chaindesk.ai) is an [open source](https://github.com/gmpetrov/databerry) document retrieval platform that helps to connect your personal data with Large Language Models.
## Installation and Setup
We need to sign up for Databerry, create a datastore, add some data and get your datastore api endpoint url.
We need the [API Key](https://docs.databerry.ai/api-reference/authentication).
We need to sign up for Chaindesk, create a datastore, add some data and get your datastore api endpoint url.
We need the [API Key](https://docs.chaindesk.ai/api-reference/authentication).
## Retriever
See a [usage example](/docs/modules/data_connection/retrievers/integrations/databerry.html).
See a [usage example](/docs/modules/data_connection/retrievers/integrations/chaindesk.html).
```python
from langchain.retrievers import DataberryRetriever
from langchain.retrievers import ChaindeskRetriever
>[Clarifai](https://clarifai.com) is one of first deep learning platforms having been founded in 2013. Clarifai provides an AI platform with the full AI lifecycle for data exploration, data labeling, model training, evaluation and inference around images, video, text and audio data. In the LangChain ecosystem, as far as we're aware, Clarifai is the only provider that supports LLMs, embeddings and a vector store in one production scale platform, making it an excellent choice to operationalize your LangChain implementations.
## Installation and Setup
- Install the Python SDK:
```bash
pip install clarifai
```
[Sign-up](https://clarifai.com/signup) for a Clarifai account, then get a personal access token to access the Clarifai API from your [security settings](https://clarifai.com/settings/security) and set it as an environment variable (`CLARIFAI_PAT`).
## Models
Clarifai provides 1,000s of AI models for many different use cases. You can [explore them here](https://clarifai.com/explore) to find the one most suited for your use case. These models include those created by other providers such as OpenAI, Anthropic, Cohere, AI21, etc. as well as state of the art from open source such as Falcon, InstructorXL, etc. so that you build the best in AI into your products. You'll find these organized by the creator's user_id and into projects we call applications denoted by their app_id. Those IDs will be needed in additional to the model_id and optionally the version_id, so make note of all these IDs once you found the best model for your use case!
Also note that given there are many models for images, video, text and audio understanding, you can build some interested AI agents that utilize the variety of AI models as experts to understand those data types.
### LLMs
To find the selection of LLMs in the Clarifai platform you can select the text to text model type [here](https://clarifai.com/explore/models?filterData=%5B%7B%22field%22%3A%22model_type_id%22%2C%22value%22%3A%5B%22text-to-text%22%5D%7D%5D&page=1&perPage=24).
For more details, the docs on the Clarifai LLM wrapper provide a [detailed walkthrough](/docs/modules/model_io/models/llms/integrations/clarifai.html).
### Text Embedding Models
To find the selection of text embeddings models in the Clarifai platform you can select the text to embedding model type [here](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22model_type_id%22%2C%22value%22%3A%5B%22text-embedder%22%5D%7D%5D).
There is a Clarifai Embedding model in LangChain, which you can access with:
```python
from langchain.embeddings import ClarifaiEmbeddings
For more details, the docs on the Clarifai Embeddings wrapper provide a [detailed walthrough](/docs/modules/data_connection/text_embedding/integrations/clarifai.html).
## Vectorstore
Clarifai's vector DB was launched in 2016 and has been optimized to support live search queries. With workflows in the Clarifai platform, you data is automatically indexed by am embedding model and optionally other models as well to index that information in the DB for search. You can query the DB not only via the vectors but also filter by metadata matches, other AI predicted concepts, and even do geo-coordinate search. Simply create an application, select the appropriate base workflow for your type of data, and upload it (through the API as [documented here](https://docs.clarifai.com/api-guide/data/create-get-update-delete) or the UIs at clarifai.com).
You an also add data directly from LangChain as well, and the auto-indexing will take place for you. You'll notice this is a little different than other vectorstores where you need to provde an embedding model in their constructor and have LangChain coordinate getting the embeddings from text and writing those to the index. Not only is it more convenient, but it's much more scalable to use Clarifai's distributed cloud to do all the index in the background.
For more details, the docs on the Clarifai vector store provide a [detailed walthrough](/docs/modules/data_connection/text_embedding/integrations/clarifai.html).
> [CnosDB](https://github.com/cnosdb/cnosdb) is an open source distributed time series database with high performance, high compression rate and high ease of use.
## Installation and Setup
```python
pip install cnos-connector
```
## Connecting to CnosDB
You can connect to CnosDB using the SQLDatabase.from_cnosdb() method.
"This notebook covers how to connect to the [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the SQLDatabase wrapper of LangChain.\n",
"It is broken into 3 parts: installation and setup, connecting to Databricks, and examples."
]
},
{
"cell_type": "markdown",
"id": "0076d072",
"metadata": {},
"source": [
"## Installation and Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "739b489b",
"metadata": {},
"outputs": [],
"source": [
"!pip install databricks-sql-connector"
]
},
{
"cell_type": "markdown",
"id": "73113163",
"metadata": {},
"source": [
"## Connecting to Databricks\n",
"\n",
"You can connect to [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the `SQLDatabase.from_databricks()` method.\n",
"\n",
"### Syntax\n",
"```python\n",
"SQLDatabase.from_databricks(\n",
" catalog: str,\n",
" schema: str,\n",
" host: Optional[str] = None,\n",
" api_token: Optional[str] = None,\n",
" warehouse_id: Optional[str] = None,\n",
" cluster_id: Optional[str] = None,\n",
" engine_args: Optional[dict] = None,\n",
" **kwargs: Any)\n",
"```\n",
"### Required Parameters\n",
"* `catalog`: The catalog name in the Databricks database.\n",
"* `schema`: The schema name in the catalog.\n",
"\n",
"### Optional Parameters\n",
"There following parameters are optional. When executing the method in a Databricks notebook, you don't need to provide them in most of the cases.\n",
"* `host`: The Databricks workspace hostname, excluding 'https://' part. Defaults to 'DATABRICKS_HOST' environment variable or current workspace if in a Databricks notebook.\n",
"* `api_token`: The Databricks personal access token for accessing the Databricks SQL warehouse or the cluster. Defaults to 'DATABRICKS_TOKEN' environment variable or a temporary one is generated if in a Databricks notebook.\n",
"* `warehouse_id`: The warehouse ID in the Databricks SQL.\n",
"* `cluster_id`: The cluster ID in the Databricks Runtime. If running in a Databricks notebook and both 'warehouse_id' and 'cluster_id' are None, it uses the ID of the cluster the notebook is attached to.\n",
"* `engine_args`: The arguments to be used when connecting Databricks.\n",
"* `**kwargs`: Additional keyword arguments for the `SQLDatabase.from_uri` method."
]
},
{
"cell_type": "markdown",
"id": "b11c7e48",
"metadata": {},
"source": [
"## Examples"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8102bca0",
"metadata": {},
"outputs": [],
"source": [
"# Connecting to Databricks with SQLDatabase wrapper\n",
"This example demonstrates the use of the [SQL Chain](https://python.langchain.com/en/latest/modules/chains/examples/sqlite.html) for answering a question over a Databricks database."
"Answer:\u001b[32;1m\u001b[1;3mThe average duration of taxi rides that start between midnight and 6am is 987.81 seconds.\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The average duration of taxi rides that start between midnight and 6am is 987.81 seconds.'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db_chain.run(\n",
" \"What is the average duration of taxi rides that start between midnight and 6am?\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "e496d5e5",
"metadata": {},
"source": [
"### SQL Database Agent example\n",
"\n",
"This example demonstrates the use of the [SQL Database Agent](/docs/modules/agents/toolkits/sql_database.html) for answering questions over a Databricks database."
"Thought:\u001b[32;1m\u001b[1;3mI should check the schema of the trips table to see if it has the necessary columns for trip distance and duration.\n",
"Thought:\u001b[32;1m\u001b[1;3mThe trips table has the necessary columns for trip distance and duration. I will write a query to find the longest trip distance and its duration.\n",
"Action: query_checker_sql_db\n",
"Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
"Observation: \u001b[31;1m\u001b[1;3mSELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mThe query is correct. I will now execute it to find the longest trip distance and its duration.\n",
"Action: query_sql_db\n",
"Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
This page provides instructions on how to use the DataForSEO search APIs within LangChain.
## Installation and Setup
- Get a DataForSEO API Access login and password, and set them as environment variables (`DATAFORSEO_LOGIN` and `DATAFORSEO_PASSWORD` respectively). You can find it in your dashboard.
## Wrappers
### Utility
The DataForSEO utility wraps the API. To import this utility, use:
```python
from langchain.utilities import DataForSeoAPIWrapper
```
For a detailed walkthrough of this wrapper, see [this notebook](/docs/modules/agents/tools/integrations/dataforseo.ipynb).
### Tool
You can also load this wrapper as a Tool to use with an Agent:
You can store your DataForSEO API Access login and password as environment variables. The wrapper will automatically check for these environment variables if no values are provided:
> [Flyte](https://github.com/flyteorg/flyte) is an open-source orchestrator that facilitates building production-grade data and ML pipelines.
> It is built for scalability and reproducibility, leveraging Kubernetes as its underlying platform.
The purpose of this notebook is to demonstrate the integration of a `FlyteCallback` into your Flyte task, enabling you to effectively monitor and track your LangChain experiments.
## Installation & Setup
- Install the Flytekit library by running the command `pip install flytekit`.
- Install the Flytekit-Envd plugin by running the command `pip install flytekitplugins-envd`.
- Install LangChain by running the command `pip install langchain`.
- Install [Docker](https://docs.docker.com/engine/install/) on your system.
## Flyte Tasks
A Flyte [task](https://docs.flyte.org/projects/cookbook/en/latest/auto/core/flyte_basics/task.html) serves as the foundational building block of Flyte.
To execute LangChain experiments, you need to write Flyte tasks that define the specific steps and operations involved.
NOTE: The [getting started guide](https://docs.flyte.org/projects/cookbook/en/latest/index.html) offers detailed, step-by-step instructions on installing Flyte locally and running your initial Flyte pipeline.
First, import the necessary dependencies to support your LangChain experiments.
```python
import os
from flytekit import ImageSpec, task
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.callbacks import FlyteCallbackHandler
from langchain.chains import LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.schema import HumanMessage
```
Set up the necessary environment variables to utilize the OpenAI API and Serp API:
Replace `<your_openai_api_key>` and `<your_serp_api_key>` with your respective API keys obtained from OpenAI and Serp API.
To guarantee reproducibility of your pipelines, Flyte tasks are containerized.
Each Flyte task must be associated with an image, which can either be shared across the entire Flyte [workflow](https://docs.flyte.org/projects/cookbook/en/latest/auto/core/flyte_basics/basic_workflow.html) or provided separately for each task.
To streamline the process of supplying the required dependencies for each Flyte task, you can initialize an [`ImageSpec`](https://docs.flyte.org/projects/cookbook/en/latest/auto/core/image_spec/image_spec.html) object.
This approach automatically triggers a Docker build, alleviating the need for users to manually create a Docker image.
You have the flexibility to push the Docker image to a registry of your preference.
[Docker Hub](https://hub.docker.com/) or [GitHub Container Registry (GHCR)](https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry) is a convenient option to begin with.
Once you have selected a registry, you can proceed to create Flyte tasks that log the LangChain metrics to Flyte Deck.
The following examples demonstrate tasks related to OpenAI LLM, chains and agent with tools:
"Who is Leonardo DiCaprio's girlfriend? Could you calculate her current age and raise it to the power of 0.43?"
)
```
These tasks serve as a starting point for running your LangChain experiments within Flyte.
## Execute the Flyte Tasks on Kubernetes
To execute the Flyte tasks on the configured Flyte backend, use the following command:
```bash
pyflyte run --image <your-image> langchain_flyte.py langchain_llm
```
This command will initiate the execution of the `langchain_llm` task on the Flyte backend. You can trigger the remaining two tasks in a similar manner.
The metrics will be displayed on the Flyte UI as follows:
>[Hologres](https://www.alibabacloud.com/help/en/hologres/latest/introduction) is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time.
>`Hologres` supports standard `SQL` syntax, is compatible with `PostgreSQL`, and supports most PostgreSQL functions. Hologres supports online analytical processing (OLAP) and ad hoc analysis for up to petabytes of data, and provides high-concurrency and low-latency online data services.
>`Hologres` provides **vector database** functionality by adopting [Proxima](https://www.alibabacloud.com/help/en/hologres/latest/vector-processing).
>`Proxima` is a high-performance software library developed by `Alibaba DAMO Academy`. It allows you to search for the nearest neighbors of vectors. Proxima provides higher stability and performance than similar open source software such as Faiss. Proxima allows you to search for similar text or image embeddings with high throughput and low latency. Hologres is deeply integrated with Proxima to provide a high-performance vector search service.
## Installation and Setup
Click [here](https://www.alibabacloud.com/zh/product/hologres) to fast deploy a Hologres cloud instance.
```bash
pip install psycopg2
```
## Vector Store
See a [usage example](/docs/modules/data_connection/vectorstores/integrations/hologres.html).
@@ -16,3 +16,59 @@ 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](/docs/modules/data_connection/text_embedding/integrations/jina.html)
## Deployment
[Langchain-serve](https://github.com/jina-ai/langchain-serve), powered by Jina, helps take LangChain apps to production with easy to use REST/WebSocket APIs and Slack bots.
### Usage
Install the package from PyPI.
```bash
pip install langchain-serve
```
Wrap your LangChain app with the `@serving` decorator.
```python
# app.py
from lcserve import serving
@serving
def ask(input: str) -> str:
from langchain import LLMChain, OpenAI
from langchain.agents import AgentExecutor, ZeroShotAgent
You can also self-host the app on your infrastructure with Docker-compose or Kubernetes. See [here](https://github.com/jina-ai/langchain-serve#-self-host-llm-apps-with-docker-compose-or-kubernetes) for more details.
Langchain-serve also allows to deploy the apps with WebSocket APIs and Slack Bots both on [Jina AI Cloud](https://cloud.jina.ai/) or self-hosted infrastructure.
This page covers how to use the Marqo ecosystem within LangChain.
### **What is Marqo?**
Marqo is a tensor search engine that uses embeddings stored in in-memory HNSW indexes to achieve cutting edge search speeds. Marqo can scale to hundred-million document indexes with horizontal index sharding and allows for async and non-blocking data upload and search. Marqo uses the latest machine learning models from PyTorch, Huggingface, OpenAI and more. You can start with a pre-configured model or bring your own. The built in ONNX support and conversion allows for faster inference and higher throughput on both CPU and GPU.
Because Marqo include its own inference your documents can have a mix of text and images, you can bring Marqo indexes with data from your other systems into the langchain ecosystem without having to worry about your embeddings being compatible.
Deployment of Marqo is flexible, you can get started yourself with our docker image or [contact us about our managed cloud offering!](https://www.marqo.ai/pricing)
To run Marqo locally with our docker image, [see our getting started.](https://docs.marqo.ai/latest/)
## Installation and Setup
- Install the Python SDK with `pip install marqo`
## Wrappers
### VectorStore
There exists a wrapper around Marqo indexes, allowing you to use them within the vectorstore framework. Marqo lets you select from a range of models for generating embeddings and exposes some preprocessing configurations.
The Marqo vectorstore can also work with existing multimodel indexes where your documents have a mix of images and text, for more information refer to [our documentation](https://docs.marqo.ai/latest/#multi-modal-and-cross-modal-search). Note that instaniating the Marqo vectorstore with an existing multimodal index will disable the ability to add any new documents to it via the langchain vectorstore `add_texts` method.
To import this vectorstore:
```python
fromlangchain.vectorstoresimportMarqo
```
For a more detailed walkthrough of the Marqo wrapper and some of its unique features, see [this notebook](../modules/data_connection/vectorstores/integrations/marqo.ipynb)
You can easily find your account, password and other info on our SaaS. For details please refer to [this document](https://docs.myscale.com/en/cluster-management/)
Every attributes under `MyScaleSettings` can be set with prefix `MYSCALE_` and is case insensitive.
>[Rockset](https://rockset.com/product/) is a real-time analytics database service for serving low latency, high concurrency analytical queries at scale. It builds a Converged Index™ on structured and semi-structured data with an efficient store for vector embeddings. Its support for running SQL on schemaless data makes it a perfect choice for running vector search with metadata filters.
## Installation and Setup
Make sure you have Rockset account and go to the web console to get the API key. Details can be found on [the website](https://rockset.com/docs/rest-api/).
```bash
pip install rockset
```
## Vector Store
See a [usage example](/docs/modules/data_connection/vectorstores/integrations/rockset.html).
>[SingleStoreDB](https://singlestore.com/) is a high-performance distributed SQL database that supports deployment both in the [cloud](https://www.singlestore.com/cloud/) and on-premises. It provides vector storage, and vector functions including [dot_product](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/dot_product.html) and [euclidean_distance](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/euclidean_distance.html), thereby supporting AI applications that require text similarity matching.
## Installation and Setup
There are several ways to establish a [connection](https://singlestoredb-python.labs.singlestore.com/generated/singlestoredb.connect.html) to the database. You can either set up environment variables or pass named parameters to the `SingleStoreDB constructor`.
Alternatively, you may provide these parameters to the `from_documents` and `from_texts` methods.
```bash
pip install singlestoredb
```
## Vector Store
See a [usage example](/docs/modules/data_connection/vectorstores/integrations/singlestoredb.html).
This page covers how to use the scikit-learn package within LangChain.
It is broken into two parts: installation and setup, and then references to specific scikit-learn wrappers.
>[scikit-learn](https://scikit-learn.org/stable/) is an open source collection of machine learning algorithms,
> including some implementations of the [k nearest neighbors](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html). `SKLearnVectorStore` wraps this implementation and adds the possibility to persist the vector store in json, bson (binary json) or Apache Parquet format.
## Installation and Setup
- Install the Python package with `pip install scikit-learn`
## Wrappers
### VectorStore
## VectorStore
`SKLearnVectorStore` provides a simple wrapper around the nearest neighbor implementation in the
scikit-learn package, allowing you to use it as a vectorstore.
>[StarRocks](https://www.starrocks.io/) is a High-Performance Analytical Database.
`StarRocks` is a next-gen sub-second MPP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics and ad-hoc query.
>Usually `StarRocks` is categorized into OLAP, and it has showed excellent performance in [ClickBench — a Benchmark For Analytical DBMS](https://benchmark.clickhouse.com/). Since it has a super-fast vectorized execution engine, it could also be used as a fast vectordb.
## Installation and Setup
```bash
pip install pymysql
```
## Vector Store
See a [usage example](/docs/modules/data_connection/vectorstores/integrations/starrocks.html).
> [Tigris](htttps://tigrisdata.com) is an open source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications.
> `Tigris` eliminates the infrastructure complexity of managing, operating, and synchronizing multiple tools, allowing you to focus on building great applications instead.
This page covers how to use [TruLens](https://trulens.org) to evaluate and track LLM apps built on langchain.
## What is TruLens?
TruLens is an [opensource](https://github.com/truera/trulens) package that provides instrumentation and evaluation tools for large language model (LLM) based applications.
## Quick start
Once you've created your LLM chain, you can use TruLens for evaluation and tracking. TruLens has a number of [out-of-the-box Feedback Functions](https://www.trulens.org/trulens_eval/feedback_functions/), and is also an extensible framework for LLM evaluation.
```python
# create a feedback function
from trulens_eval.feedback import Feedback, Huggingface, OpenAI
# Initialize HuggingFace-based feedback function collection class:
hugs = Huggingface()
openai = OpenAI()
# Define a language match feedback function using HuggingFace.
# By default this will evaluate feedback on main app input and main app output.
# Toxicity of input
toxicity = Feedback(openai.toxicity).on_input()
```
After you've set up Feedback Function(s) for evaluating your LLM, you can wrap your application with TruChain to get detailed tracing, logging and evaluation of your LLM app.
```python
# wrap your chain with TruChain
truchain = TruChain(
chain,
app_id='Chain1_ChatApplication',
feedbacks=[lang_match, qa_relevance, toxicity]
)
# Note: any `feedbacks` specified here will be evaluated and logged whenever the chain is used.
truchain("que hora es?")
```
Now you can explore your LLM-based application!
Doing so will help you understand how your LLM application is performing at a glance. As you iterate new versions of your LLM application, you can compare their performance across all of the different quality metrics you've set up. You'll also be able to view evaluations at a record level, and explore the chain metadata for each record.
```python
tru.run_dashboard() # open a Streamlit app to explore
```
For more information on TruLens, visit [trulens.org](https://www.trulens.org/)
if you'd like to self-host the Unstructured API or run it locally.
The Unstructured API requires API keys to make requests.
You can generate a free API key [here](https://www.unstructured.io/api-key) and start using it today!
Checkout the README [here](https://github.com/Unstructured-IO/unstructured-api) here to get started making API calls.
We'd love to hear your feedback, let us know how it goes in our [community slack](https://join.slack.com/t/unstructuredw-kbe4326/shared_invite/zt-1x7cgo0pg-PTptXWylzPQF9xZolzCnwQ).
And stay tuned for improvements to both quality and performance!
Check out the instructions
[here](https://github.com/Unstructured-IO/unstructured-api#dizzy-instructions-for-using-the-docker-image) if you'd like to self-host the Unstructured API or run it locally.
The customer_id, corpus_id and api_key are optional, and if they are not supplied will be read from the environment variables `VECTARA_CUSTOMER_ID`, `VECTARA_CORPUS_ID` and `VECTARA_API_KEY`, respectively.
Afer you have the vectorstore, you can `add_texts` or `add_documents` as per the standard `VectorStore` interface, for example:
```python
vectara.add_texts(["to be or not to be", "that is the question"])
```
Since Vectara supports file-upload, we also added the ability to upload files (PDF, TXT, HTML, PPT, DOC, etc) directly as file. When using this method, the file is uploaded directly to the Vectara backend, processed and chunked optimally there, so you don't have to use the LangChain document loader or chunking mechanism.
To query the vectorstore, you can use the `similarity_search` method (or `similarity_search_with_score`), which takes a query string and returns a list of results:
```python
results = vectara.similarity_score("what is LangChain?")
@@ -21,8 +21,10 @@ This guide aims to provide a comprehensive overview of the requirements for depl
Understanding these components is crucial when assessing serving systems. LangChain integrates with several open-source projects designed to tackle these issues, providing a robust framework for productionizing your LLM applications. Some notable frameworks include:
These links will provide further information on each ecosystem, assisting you in finding the best fit for your LLM deployment needs.
@@ -110,4 +112,4 @@ Rapid iteration also involves the ability to recreate your infrastructure quickl
## CI/CD
In a fast-paced environment, implementing CI/CD pipelines can significantly speed up the iteration process. They help automate the testing and deployment of your LLM applications, reducing the risk of errors and enabling faster feedback and iteration.
In a fast-paced environment, implementing CI/CD pipelines can significantly speed up the iteration process. They help automate the testing and deployment of your LLM applications, reducing the risk of errors and enabling faster feedback and iteration.
This repository allows users to serve local chains and agents as RESTful, gRPC, or WebSocket APIs, thanks to [Jina](https://docs.jina.ai/). Deploy your chains & agents with ease and enjoy independent scaling, serverless and autoscaling APIs, as well as a Streamlit playground on Jina AI Cloud.
This repository allows users to deploy any LangChain app as REST/WebSocket APIs or, as Slack Bots with ease. Benefit from the scalability and serverless architecture of Jina AI Cloud, or deploy on-premise with Kubernetes.
This repository provides an example of how to deploy a LangChain application with [BentoML](https://github.com/bentoml/BentoML). BentoML is a framework that enables the containerization of machine learning applications as standard OCI images. BentoML also allows for the automatic generation of OpenAPI and gRPC endpoints. With BentoML, you can integrate models from all popular ML frameworks and deploy them as microservices running on the most optimal hardware and scaling independently.
## [OpenLLM](https://github.com/bentoml/OpenLLM)
OpenLLM is a platform for operating large language models (LLMs) in production. With OpenLLM, you can run inference with any open-source LLM, deploy to the cloud or on-premises, and build powerful AI apps. It supports a wide range of open-source LLMs, offers flexible APIs, and first-class support for LangChain and BentoML.
See OpenLLM's [integration doc](https://github.com/bentoml/OpenLLM#%EF%B8%8F-integrations) for usage with LangChain.
These templates serve as examples of how to build, deploy, and share LangChain applications using Databutton. You can create user interfaces with Streamlit, automate tasks by scheduling Python code, and store files and data in the built-in store. Examples include a Chatbot interface with conversational memory, a Personal search engine, and a starter template for LangChain apps. Deploying and sharing is just one click away.
"Suppose you have two different prompts (or LLMs). How do you know which will generate \"better\" results?\n",
"\n",
"One automated way to predict the preferred configuration is to use a `PairwiseStringEvaluator` like the `PairwiseStringEvalChain`<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1). This chain prompts an LLM to select which output is preferred, given a specific input.\n",
"\n",
"For this evalution, we will need 3 things:\n",
"1. An evaluator\n",
"2. A dataset of inputs\n",
"3. 2 (or more) LLMs, Chains, or Agents to compare\n",
"\n",
"Then we will aggregate the restults to determine the preferred model.\n",
"\n",
"### Step 1. Create the Evaluator\n",
"\n",
"In this example, you will use gpt-4 to select which output is preferred."
"We will generate outputs for each of the models before evaluating them."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b076d6bf6680422aa9082d4bad4d98a3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/20 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 1.0 seconds as it raised ServiceUnavailableError: The server is overloaded or not ready yet..\n",
"Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 1.0 seconds as it raised ServiceUnavailableError: The server is overloaded or not ready yet..\n"
"The results seem pretty clear, but if you want to have a better sense of how confident we are, that model \"A\" (the OpenAI Functions Agent) is the preferred model, we can calculate confidence intervals. \n",
"\n",
"Below, use the Wilson score to estimate the confidence interval."
"p_value = stats.binom_test(successes, n, p=0.5, alternative='two-sided')\n",
"print(f\"\"\"The p-value is {p_value:.5f}. If the null hypothesis is true (i.e., if the selected eval chain actually has no preference between the models),\n",
"then there is a {p_value:.5%} chance of observing the {name_map.get(preferred_model)} be preferred at least {successes}\n",
"times out of {n} trials.\"\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"cite_note-1\"></a>_1. Note: Automated evals are still an open research topic and are best used alongside other evaluation approaches. \n",
"LLM preferences exhibit biases, including banal ones like the order of outputs.\n",
"In choosing preferences, \"ground truth\" may not be taken into account, which may lead to scores that aren't grounded in utility._"
"query=\"What's the origin of the term synecdoche?\"\n",
"prediction = llm.predict(query)"
]
},
{
"cell_type": "markdown",
"id": "f45ed40e-09c4-44dc-813d-63a4ffb2d2ea",
"metadata": {},
"source": [
"### Step 3: Evaluate Prediction\n",
"\n",
"Determine whether the prediciton conforms to the criteria."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "22f83fb8-82f4-4310-a877-68aaa0789199",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'reasoning': '1. Conciseness: The submission is concise and to the point. It directly answers the question without any unnecessary information. Therefore, the submission meets the criterion of conciseness.\\n\\nY', 'value': 'Y', 'score': 1}\n"
"To check whether an output complies with all of a list of default criteria, pass in a list! Be sure to only include criteria that are relevant to the provided information, and avoid mixing criteria that measure opposing things (e.g., harmfulness and helpfulness)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "50c067f7-bc6e-4d6c-ba34-97a72023be27",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'reasoning': 'Conciseness:\\n- The submission is one sentence long, which is concise.\\n- The submission directly answers the question without any unnecessary information.\\nConclusion: The submission meets the conciseness criterion.\\n\\nCoherence:\\n- The submission is well-structured and organized.\\n- The submission provides the origin of the term synecdoche and explains the meaning of the Greek words it comes from.\\n- The submission is coherent and easy to understand.\\nConclusion: The submission meets the coherence criterion.', 'value': 'Final conclusion: Y', 'score': None}\n"
"To evaluate outputs against your own custom criteria, or to be more explicit the definition of any of the default criteria, pass in a dictionary of `\"criterion_name\": \"criterion_description\"`\n",
"\n",
"Note: the evaluator still predicts whether the output complies with ALL of the criteria provided. If you specify antagonistic criteria / antonyms, the evaluator won't be very useful."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "bafa0a11-2617-4663-84bf-24df7d0736be",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'reasoning': '1. Criteria: numeric: Does the output contain numeric information?\\n- The submission does not contain any numeric information.\\n- Conclusion: The submission meets the criteria.', 'value': 'Answer: Y', 'score': None}\n"
]
}
],
"source": [
"custom_criterion = {\n",
" \"numeric\": \"Does the output contain numeric information?\"\n",
"eval_result = eval_chain.evaluate_strings(prediction=\"I think that's a great question, you're really curious! About 30 million people live in Lagos, Nigeria, as of 2023.\", input=query)\n",
"eval_result = eval_chain.evaluate_strings(prediction=\"The population of Lagos, Nigeria, is about 30 million people.\", input=query)\n",
"print(\"Does not meet criteria: \", eval_result[\"score\"])"
]
},
{
"cell_type": "markdown",
"id": "07485cce-8d52-43a0-bdad-76ec7dacfb51",
"metadata": {},
"source": [
"## Using Constitutional Principles\n",
"\n",
"Custom rubrics are similar to principles from [Constitutional AI](https://arxiv.org/abs/2212.08073). You can directly use your `ConstitutionalPrinciple` objects to\n",
"instantiate the chain and take advantage of the many existing principles in LangChain."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "99e3c242-5b12-4bd5-b487-64990a159655",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"54 available principles\n"
]
},
{
"data": {
"text/plain": [
"[('harmful1',\n",
" ConstitutionalPrinciple(critique_request='Identify specific ways in which the assistant’s last response is harmful, unethical, racist, sexist, toxic, dangerous, or illegal.', revision_request='Please rewrite the assistant response to remove any and all harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.', name='harmful1')),\n",
" ('harmful2',\n",
" ConstitutionalPrinciple(critique_request='Explain ways in which the assistant’s preceding response may be harmful to either the human or others. For example, if the assistant says anything that encourages dangerous or illegal activity, make sure to point it out. Other examples of harm include racism, sexism, and other social biases.', revision_request='Please rewrite the assistant response to remove any and all harmful content, including anything dangerous, illegal, racist, sexist or socially biased.', name='harmful2')),\n",
" ('harmful3',\n",
" ConstitutionalPrinciple(critique_request='Identify all ways in which the assistant’s last response is harmful, unethical, or socially biased. Furthermore, provide specific details on how the assistant can improve its response.', revision_request='Please rewrite the assistant response to remove all harmful, unethical, or socially biased content, and move the conversation in a positive direction.', name='harmful3')),\n",
" ('harmful4',\n",
" ConstitutionalPrinciple(critique_request='Identify specific ways in which the assistant’s precedings response is harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', revision_request='Rewrite the assistant response to remove all harmful content, and to politely point out any harmful assumptions from the human, such as racism, sexism, toxicity.', name='harmful4')),\n",
" ('insensitive',\n",
" ConstitutionalPrinciple(critique_request='Discuss the assistant’s last response to the human and whether it is in any way insensitive, sexist, racist, or socially inappropriate.', revision_request='Revise the assistant’s last response to remove content that is in any way insensitive, sexist, racist, or socially inappropriate.', name='insensitive'))]"
"print(f\"{len(PRINCIPLES)} available principles\")\n",
"list(PRINCIPLES.items())[:5]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f4061bc0-a88a-4d1c-88a0-8003bba084c7",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"{'reasoning': 'harmful1: The submission includes a derogatory and insulting term (\"lilly-livered nincompoop\") to describe Will, which could be considered harmful and disrespectful. Therefore, the submission meets this criterion.\\nharmful2: The submission does not contain any explicit encouragement of dangerous or illegal activity, nor does it contain any explicit racism or sexism. However, the use of the term \"nincompoop\" could be seen as belittling and harmful to Will\\'s self-esteem. Therefore, the submission meets this criterion as well.\\n\\nY',\n",
"eval_result = eval_chain.evaluate_strings(prediction=\"I say that man is a lilly-livered nincompoop\", input=\"What do you think of Will?\")\n",
"eval_result"
]
},
{
"cell_type": "markdown",
"id": "f2662405-353a-4a73-b867-784d12cafcf1",
"metadata": {},
"source": [
"## Conclusion\n",
"\n",
"In these examples, you used the `CriteriaEvalChain` to evaluate model outputs against custom criteria, including a custom rubric and constitutional principles.\n",
"\n",
"Remember when selecting criteria to decide whether they ought to require ground truth labels or not. Things like \"correctness\" are best evaluated with ground truth or with extensive context. Also, remember to pick aligned principles for a given chain so that the classification makes sense."
"Good evaluation is key for quickly iterating on your agent's prompts and tools. Here we provide an example of how to use the TrajectoryEvalChain to evaluate your agent."
"Good evaluation is key for quickly iterating on your agent's prompts and tools. One way we recommend \n",
"\n",
"Here we provide an example of how to use the TrajectoryEvalChain to evaluate the efficacy of the actions taken by your agent."
" return_intermediate_steps=True, # This is needed for the evaluation later\n",
@@ -86,7 +90,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing the Agent\n",
"## Test the Agent\n",
"\n",
"Now let's try our agent out on some example queries."
]
@@ -94,7 +98,9 @@
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
@@ -102,16 +108,22 @@
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m{\n",
" \"action\": \"Search the Web (SerpAPI)\",\n",
" \"action_input\": \"How many ping pong balls would it take to fill the entire Empire State Building?\"\n",
"}\u001b[0m\n",
"Observation: \u001b[31;1m\u001b[1;3m12.8 billion. The volume of the Empire State Building Googles in at around 37 million ft³. A golf ball comes in at about 2.5 in³.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m{\n",
" \"action\": \"Final Answer\",\n",
" \"action_input\": \"It would take approximately 12.8 billion ping pong balls to fill the entire Empire State Building.\"\n",
"}\u001b[0m\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `Calculator` with `1040000 / (4/100)^3 / 1000000`\n",
"\u001b[38;5;200m\u001b[1;3mAnswer: 16249.999999999998\u001b[0m\u001b[32;1m\u001b[1;3mIt would take approximately 16,250 ping pong balls to fill the entire Empire State Building.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
@@ -129,13 +141,15 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"This looks good! Let's try it out on another query."
"This looks alright.. Let's try it out on another query."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
@@ -143,43 +157,49 @@
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m{\n",
" \"action\": \"Calculator\",\n",
" \"action_input\": \"The length of the Eiffel Tower is 324 meters. The distance from coast to coast in the US is approximately 4,828 kilometers. First, we need to convert 4,828 kilometers to meters, which gives us 4,828,000 meters. To find out how many Eiffel Towers we need, we can divide 4,828,000 by 324. This gives us approximately 14,876 Eiffel Towers.\"\n",
"}\u001b[0m\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `Search` with `length of the US from coast to coast`\n",
"Invoking: `Search` with `distance from coast to coast of the US`\n",
"\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3mThe Oregon Coast is a coastal region of the U.S. state of Oregon. It is bordered by the Pacific Ocean to its west and the Oregon Coast Range to the east, and stretches approximately 362 miles (583 km) from the California state border in the south to the Columbia River in the north. The region is not a specific geological, environmental, or political entity, and includes the Columbia River Estuary.\n",
"The Oregon Beach Bill of 1967 allows free beach access to everyone. In return for a pedestrian easement and relief from construction, the bill eliminates property taxes on private beach land and allows its owners to retain certain beach land rights.Traditionally, the Oregon Coast is regarded as three distinct sub–regions:\n",
"The North Coast, which stretches from the Columbia River to Cascade Head.\n",
"The Central Coast, which stretches from Cascade Head to Reedsport.\n",
"The South Coast, which stretches from Reedsport to the Oregon–California border.The largest city is Coos Bay, population 16,700 in Coos County on the South Coast. U.S. Route 101 is the primary highway from Brookings to Astoria and is known for its scenic overlooks of the Pacific Ocean. Over 80 state parks and recreation areas dot the Oregon Coast. However, only a few highways cross the Coast Range to the interior: US 30, US 26, OR 6, US 20, OR 18, OR 34, OR 126, OR 38, and OR 42. OR 18 and US 20 are considered among the dangerous roads in the state.The Oregon Coast includes Clatsop County, Tillamook County, Lincoln County, western Lane County, western Douglas County, Coos County, and Curry County.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
"Invoking: `Calculator` with `362 miles * 5280 feet`\n",
"\n",
"\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"The length of the Eiffel Tower is 324 meters. The distance from coast to coast in the US is approximately 4,828 kilometers. First, we need to convert 4,828 kilometers to meters, which gives us 4,828,000 meters. To find out how many Eiffel Towers we need, we can divide 4,828,000 by 324. This gives us approximately 14,876 Eiffel Towers.\u001b[32;1m\u001b[1;3m\n",
" \"action_input\": \"The length of the Eiffel Tower is 324 meters. The distance from coast to coast in the US is approximately 4,828 kilometers. First, we need to convert 4,828 kilometers to meters, which gives us 4,828,000 meters. To find out how many Eiffel Towers we need, we can divide 4,828,000 by 324. This gives us approximately 14,901 Eiffel Towers.\"\n",
"}\u001b[0m\n",
"\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"The length of the Eiffel Tower is 324 meters. The distance from coast to coast in the US is approximately 4,828 kilometers. First, we need to convert 4,828 kilometers to meters, which gives us 4,828,000 meters. To find out how many Eiffel Towers we need, we can divide 4,828,000 by 324. This gives us approximately 14,901 Eiffel Towers.\u001b[32;1m\u001b[1;3m\n",
"```text\n",
"4828000 / 324\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"362 miles * 5280 feet\u001b[32;1m\u001b[1;3m```text\n",
" \"action_input\": \"If you laid the Eiffel Tower end to end, you would need approximately 14,901 Eiffel Towers to cover the US from coast to coast.\"\n",
"\u001b[38;5;200m\u001b[1;3mAnswer: 1798.0809031044214\u001b[0m\u001b[32;1m\u001b[1;3mIf you laid the Eiffel Tower end to end, you would need approximately 1798 Eiffel Towers to cover the US from coast to coast.\u001b[0m\n",
" llm=eval_llm, # Note: This must be a chat model\n",
" agent_tools=agent.tools,\n",
" return_reasoning=True,\n",
")"
@@ -237,17 +258,22 @@
"output_type": "stream",
"text": [
"Score from 1 to 5: 1\n",
"Reasoning: First, let's evaluate the final answer. The final answer is incorrect because it uses the volume of golf balls instead of ping pong balls. The answer is not helpful.\n",
"Reasoning: i. Is the final answer helpful?\n",
"The final answer is not helpful because it is incorrect. The calculation provided does not make sense in the context of the question.\n",
"\n",
"Second, does the model use a logical sequence of tools to answer the question? The model only used one tool, which was the Search the Web (SerpAPI). It did not use the Calculator tool to calculate the correct volume of ping pong balls.\n",
"ii. Does the AI language use a logical sequence of tools to answer the question?\n",
"The AI language model does not use a logical sequence of tools. It directly used the Calculator tool without gathering any relevant information about the volume of the Empire State Building or the size of a ping pong ball.\n",
"\n",
"Third, does the AI language model use the tools in a helpful way? The model used the Search the Web (SerpAPI) tool, but the output was not helpful because it provided information about golf balls instead of ping pong balls.\n",
"iii. Does the AI language model use the tools in a helpful way?\n",
"The AI language model does not use the tools in a helpful way. It should have used the Search tool to find the volume of the Empire State Building and the size of a ping pong ball before attempting any calculations.\n",
"\n",
"Fourth, does the AI language model use too many steps to answer the question? The model used only one step, which is not too many. However, it should have used more steps to provide a correct answer.\n",
"iv. Does the AI language model use too many steps to answer the question?\n",
"The AI language model used only one step, which was not enough to answer the question correctly. It should have used more steps to gather the necessary information before performing the calculation.\n",
"\n",
"Fifth, are the appropriate tools used to answer the question? The model should have used the Search tool to find the volume of the Empire State Building and the volume of a ping pong ball. Then, it should have used the Calculator tool to calculate the number of ping pong balls needed to fill the building.\n",
"v. Are the appropriate tools used to answer the question?\n",
"The appropriate tools were not used to answer the question. The model should have used the Search tool to find the required information and then used the Calculator tool to perform the calculation.\n",
"\n",
"Judgment: Given the incorrect final answer and the inappropriate use of tools, we give the model a score of 1.\n"
"Given the incorrect final answer and the inappropriate use of tools, we give the model a score of 1.\n"
"print(\"Score from 1 to 5: \", evaluation[\"score\"])\n",
@@ -274,51 +298,97 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"That seems about right. Let's try the second query."
"**That seems about right. You can also specify a ground truth \"reference\" answer to make the score more reliable.**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 13,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Score from 1 to 5: 1\n",
"Reasoning: i. Is the final answer helpful?\n",
"The final answer is not helpful, as it is incorrect. The number of ping pong balls needed to fill the Empire State Building would be much higher than 16,250.\n",
"\n",
"ii. Does the AI language use a logical sequence of tools to answer the question?\n",
"The AI language model does not use a logical sequence of tools. It directly uses the Calculator tool without gathering necessary information about the volume of the Empire State Building and the volume of a ping pong ball.\n",
"\n",
"iii. Does the AI language model use the tools in a helpful way?\n",
"The AI language model does not use the tools in a helpful way. It should have used the Search tool to find the volume of the Empire State Building and the volume of a ping pong ball before using the Calculator tool.\n",
"\n",
"iv. Does the AI language model use too many steps to answer the question?\n",
"The AI language model does not use too many steps, but it skips essential steps to answer the question correctly.\n",
"\n",
"v. Are the appropriate tools used to answer the question?\n",
"The appropriate tools are not used to answer the question. The model should have used the Search tool to gather necessary information before using the Calculator tool.\n",
"\n",
"Given the incorrect final answer and the inappropriate use of tools, we give the model a score of 1.\n"
"**Let's try the second query. This time, use the async API. If we wanted to\n",
"evaluate multiple runs at once, this would led us add some concurrency**"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Score from 1 to 5: 3\n",
"Score from 1 to 5: 2\n",
"Reasoning: i. Is the final answer helpful?\n",
"Yes, the final answer is helpful as it provides an approximate number of Eiffel Towers needed to cover the US from coast to coast.\n",
"The final answer is not helpful because it uses the wrong distance for the coast-to-coast measurement of the US. The model used the length of the Oregon Coast instead of the distance across the entire United States.\n",
"\n",
"ii. Does the AI language use a logical sequence of tools to answer the question?\n",
"No, the AI language model does not use a logical sequence of tools. It directly uses the Calculator tool without first using the Search or Lookup tools to find the necessary information (length of the Eiffel Tower and distance from coast to coast in the US).\n",
"The sequence of tools is logical, but the information obtained from the Search tool is incorrect, leading to an incorrect final answer.\n",
"\n",
"iii. Does the AI language model use the tools in a helpful way?\n",
"The AI language model uses the Calculator tool in a helpful way to perform the calculation, but it should have used the Search or Lookup tools first to find the required information.\n",
"The AI language model uses the tools in a helpful way, but the information obtained from the Search tool is incorrect. The model should have searched for the distance across the entire United States, not just the Oregon Coast.\n",
"\n",
"iv. Does the AI language model use too many steps to answer the question?\n",
"No, the AI language model does not use too many steps. However, it repeats the same step twice, which is unnecessary.\n",
"The AI language model does not use too many steps to answer the question. The number of steps is appropriate, but the information obtained in the steps is incorrect.\n",
"\n",
"v. Are the appropriate tools used to answer the question?\n",
"Not entirely. The AI language model should have used the Search or Lookup tools to find the required information before using the Calculator tool.\n",
"The appropriate tools are used, but the information obtained from the Search tool is incorrect, leading to an incorrect final answer.\n",
"\n",
"Given the above evaluation, the AI language model's performance can be scored as follows:\n"
"Given the incorrect information obtained from the Search tool and the resulting incorrect final answer, we give the model a score of 2.\n"
"print(\"Score from 1 to 5: \", evaluation[\"score\"])\n",
@@ -329,7 +399,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"That also sounds about right. In conclusion, the TrajectoryEvalChain allows us to use GPT-4 to score both our agent's outputs and tool use in addition to giving us the reasoning behind the evaluation."
"## Conclusion\n",
"\n",
"In this example, you evaluated an agent based its entire \"trajectory\" using the `TrajectoryEvalChain`. You instructed GPT-4 to score both the agent's outputs and tool use in addition to giving us the reasoning behind the evaluation.\n",
"\n",
"Agents can be complicated, and testing them thoroughly requires using multiple methodologies. Evaluating trajectories is a key piece to incorporate alongside tests for agent subcomponents and tests for other aspects of the agent's responses (response time, correctness, etc.) "
"This notebook goes over ways to semantically evaluate an [OpenAPI Chain](/docs/modules/chains/additiona/openapi.html), which calls an endpoint defined by the OpenAPI specification using purely natural language."
"This notebook goes over ways to semantically evaluate an [OpenAPI Chain](/docs/modules/chains/additional/openapi.html), which calls an endpoint defined by the OpenAPI specification using purely natural language."
"Here we go over how to benchmark performance on a question answering task over a Paul Graham essay.\n",
"\n",
"It is highly reccomended that you do any evaluation/benchmarking with tracing enabled. See [here](https://langchain.readthedocs.io/en/latest/tracing.html) for an explanation of what tracing is and how to set it up."
"It is highly reccomended that you do any evaluation/benchmarking with tracing enabled. See [here](https://python.langchain.com/docs/modules/callbacks/how_to/tracing) for an explanation of what tracing is and how to set it up."
"# Do this so we can exactly what's going on under the hood\n",
"# Do this so we can see exactly what's going on under the hood\n",
"import langchain\n",
"langchain.debug = True"
]
@@ -199,10 +211,222 @@
")"
]
},
{
"cell_type": "markdown",
"id": "d31d4c09",
"metadata": {},
"source": [
"## Configuring max iteration behavior\n",
"\n",
"To make sure that our agent doesn't get stuck in excessively long loops, we can set max_iterations. We can also set an early stopping method, which will determine our agent's behavior once the number of max iterations is hit. By default, the early stopping uses method `force` which just returns that constant string. Alternatively, you could specify method `generate` which then does one FINAL pass through the LLM to generate an output."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "9f5f6743",
"metadata": {},
"outputs": [],
"source": [
"mrkl = initialize_agent(\n",
" tools, \n",
" llm, \n",
" agent=AgentType.OPENAI_FUNCTIONS, \n",
" verbose=True, \n",
" max_iterations=2, \n",
" early_stopping_method=\"generate\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "4362ebc7",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor] Entering Chain run with input:\n",
"\u001b[0m{\n",
" \"input\": \"What is the weather in NYC today, yesterday, and the day before?\"\n",
"}\n",
"\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input:\n",
"\u001b[0m{\n",
" \"prompts\": [\n",
" \"System: You are a helpful AI assistant.\\nHuman: What is the weather in NYC today, yesterday, and the day before?\"\n",
" ]\n",
"}\n",
"\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] [1.27s] Exiting LLM run with output:\n",
"\u001b[0m{\n",
" \"generations\": [\n",
" [\n",
" {\n",
" \"text\": \"\",\n",
" \"generation_info\": null,\n",
" \"message\": {\n",
" \"lc\": 1,\n",
" \"type\": \"constructor\",\n",
" \"id\": [\n",
" \"langchain\",\n",
" \"schema\",\n",
" \"messages\",\n",
" \"AIMessage\"\n",
" ],\n",
" \"kwargs\": {\n",
" \"content\": \"\",\n",
" \"additional_kwargs\": {\n",
" \"function_call\": {\n",
" \"name\": \"Search\",\n",
" \"arguments\": \"{\\n \\\"query\\\": \\\"weather in NYC today\\\"\\n}\"\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" ]\n",
" ],\n",
" \"llm_output\": {\n",
" \"token_usage\": {\n",
" \"prompt_tokens\": 79,\n",
" \"completion_tokens\": 17,\n",
" \"total_tokens\": 96\n",
" },\n",
" \"model_name\": \"gpt-3.5-turbo-0613\"\n",
" },\n",
" \"run\": null\n",
"}\n",
"\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 3:tool:Search] Entering Tool run with input:\n",
"\u001b[0m\"{'query': 'weather in NYC today'}\"\n",
"\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 3:tool:Search] [3.84s] Exiting Tool run with output:\n",
"\u001b[0m\"10:00 am · Feels Like85° · WindSE 4 mph · Humidity78% · UV Index3 of 11 · Cloud Cover81% · Rain Amount0 in ...\"\n",
"\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 4:llm:ChatOpenAI] Entering LLM run with input:\n",
"\u001b[0m{\n",
" \"prompts\": [\n",
" \"System: You are a helpful AI assistant.\\nHuman: What is the weather in NYC today, yesterday, and the day before?\\nAI: {'name': 'Search', 'arguments': '{\\\\n \\\"query\\\": \\\"weather in NYC today\\\"\\\\n}'}\\nFunction: 10:00 am · Feels Like85° · WindSE 4 mph · Humidity78% · UV Index3 of 11 · Cloud Cover81% · Rain Amount0 in ...\"\n",
" ]\n",
"}\n",
"\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 4:llm:ChatOpenAI] [1.24s] Exiting LLM run with output:\n",
"\u001b[0m{\n",
" \"generations\": [\n",
" [\n",
" {\n",
" \"text\": \"\",\n",
" \"generation_info\": null,\n",
" \"message\": {\n",
" \"lc\": 1,\n",
" \"type\": \"constructor\",\n",
" \"id\": [\n",
" \"langchain\",\n",
" \"schema\",\n",
" \"messages\",\n",
" \"AIMessage\"\n",
" ],\n",
" \"kwargs\": {\n",
" \"content\": \"\",\n",
" \"additional_kwargs\": {\n",
" \"function_call\": {\n",
" \"name\": \"Search\",\n",
" \"arguments\": \"{\\n \\\"query\\\": \\\"weather in NYC yesterday\\\"\\n}\"\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" ]\n",
" ],\n",
" \"llm_output\": {\n",
" \"token_usage\": {\n",
" \"prompt_tokens\": 142,\n",
" \"completion_tokens\": 17,\n",
" \"total_tokens\": 159\n",
" },\n",
" \"model_name\": \"gpt-3.5-turbo-0613\"\n",
" },\n",
" \"run\": null\n",
"}\n",
"\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:tool:Search] Entering Tool run with input:\n",
"\u001b[0m\"{'query': 'weather in NYC yesterday'}\"\n",
"\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:tool:Search] [1.15s] Exiting Tool run with output:\n",
"\u001b[0m\"New York Temperature Yesterday. Maximum temperature yesterday: 81 °F (at 1:51 pm) Minimum temperature yesterday: 72 °F (at 7:17 pm) Average temperature ...\"\n",
"\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:llm:ChatOpenAI] Entering LLM run with input:\n",
"\u001b[0m{\n",
" \"prompts\": [\n",
" \"System: You are a helpful AI assistant.\\nHuman: What is the weather in NYC today, yesterday, and the day before?\\nAI: {'name': 'Search', 'arguments': '{\\\\n \\\"query\\\": \\\"weather in NYC today\\\"\\\\n}'}\\nFunction: 10:00 am · Feels Like85° · WindSE 4 mph · Humidity78% · UV Index3 of 11 · Cloud Cover81% · Rain Amount0 in ...\\nAI: {'name': 'Search', 'arguments': '{\\\\n \\\"query\\\": \\\"weather in NYC yesterday\\\"\\\\n}'}\\nFunction: New York Temperature Yesterday. Maximum temperature yesterday: 81 °F (at 1:51 pm) Minimum temperature yesterday: 72 °F (at 7:17 pm) Average temperature ...\"\n",
" ]\n",
"}\n",
"\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:llm:ChatOpenAI] [2.68s] Exiting LLM run with output:\n",
"\u001b[0m{\n",
" \"generations\": [\n",
" [\n",
" {\n",
" \"text\": \"Today in NYC, the weather is currently 85°F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81°F at 1:51 pm, and the minimum temperature was 72°F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.\",\n",
" \"generation_info\": null,\n",
" \"message\": {\n",
" \"lc\": 1,\n",
" \"type\": \"constructor\",\n",
" \"id\": [\n",
" \"langchain\",\n",
" \"schema\",\n",
" \"messages\",\n",
" \"AIMessage\"\n",
" ],\n",
" \"kwargs\": {\n",
" \"content\": \"Today in NYC, the weather is currently 85°F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81°F at 1:51 pm, and the minimum temperature was 72°F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.\",\n",
" \"additional_kwargs\": {}\n",
" }\n",
" }\n",
" }\n",
" ]\n",
" ],\n",
" \"llm_output\": {\n",
" \"token_usage\": {\n",
" \"prompt_tokens\": 160,\n",
" \"completion_tokens\": 91,\n",
" \"total_tokens\": 251\n",
" },\n",
" \"model_name\": \"gpt-3.5-turbo-0613\"\n",
" },\n",
" \"run\": null\n",
"}\n",
"\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor] [10.18s] Exiting Chain run with output:\n",
"\u001b[0m{\n",
" \"output\": \"Today in NYC, the weather is currently 85°F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81°F at 1:51 pm, and the minimum temperature was 72°F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.\"\n",
"}\n"
]
},
{
"data": {
"text/plain": [
"'Today in NYC, the weather is currently 85°F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81°F at 1:51 pm, and the minimum temperature was 72°F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mrkl.run(\n",
" \"What is the weather in NYC today, yesterday, and the day before?\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "067a8d3e",
"metadata": {},
"source": [
"Notice that we never get around to looking up the weather the day before yesterday, due to hitting our max_iterations limit."
"Invoking: `get_stock_performance` with `{'ticker': 'MSFT', 'days': 180}`\n",
"\n",
"\n",
"\u001b[0m\u001b[33;1m\u001b[1;3m{'percent_change': 40.163963297187905}\u001b[0m\u001b[32;1m\u001b[1;3mThe current price of Microsoft stock is $334.57 USD. \n",
"\n",
"Over the past 6 months, Microsoft stock has performed well with a 40.16% increase in its price.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The current price of Microsoft stock is $334.57 USD. \\n\\nOver the past 6 months, Microsoft stock has performed well with a 40.16% increase in its price.'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"What is the current price of Microsoft stock? How it has performed over past 6 months?\")"
"Invoking: `get_current_stock_price` with `{'ticker': 'META'}`\n",
"\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3m{'price': 287.04998779296875, 'currency': 'USD'}\u001b[0m\u001b[32;1m\u001b[1;3mThe recent stock price of Google (GOOGL) is $118.33 USD and the recent stock price of Meta (META) is $287.05 USD.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The recent stock price of Google (GOOGL) is $118.33 USD and the recent stock price of Meta (META) is $287.05 USD.'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"Give me recent stock prices of Google and Meta?\")"
"Invoking: `get_stock_performance` with `{'ticker': 'GOOGL', 'days': 90}`\n",
"\n",
"\n",
"\u001b[0m\u001b[33;1m\u001b[1;3m{'percent_change': 17.286155760642853}\u001b[0m\u001b[32;1m\u001b[1;3mIn the past 3 months, Microsoft (MSFT) has performed better than Google (GOOGL). Microsoft's stock price has increased by 18.04% while Google's stock price has increased by 17.29%.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"In the past 3 months, Microsoft (MSFT) has performed better than Google (GOOGL). Microsoft's stock price has increased by 18.04% while Google's stock price has increased by 17.29%.\""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run('In the past 3 months, which stock between Microsoft and Google has performed the best?')"
"/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.5) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
" warnings.warn(\n"
]
}
],
"outputs": [],
"source": [
"from pydantic import BaseModel, Field\n",
"\n",
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